diff --git a/.travis.yml b/.travis.yml index 405e6fa4b..8e9e20632 100644 --- a/.travis.yml +++ b/.travis.yml @@ -2,24 +2,13 @@ sudo: required dist: trusty language: cpp -cache: - bundler: true - directories: - - dealii - - build - -before_install: - - sudo apt-get install libcr-dev mpich2 mpich2-doc - - sudo apt-get install p4est -# - test -n $CC && unset CC -# - test -n $CXX && unset CXX +services: + - docker -#install: -# - git clone https://github.com/dealii/dealii -# - mkdir build -# - cd build -# - cmake -DCMAKE_INSTALL_PREFIX=. ../dealii -# - make -j2 install -# - make test +before_install: + #- docker pull stvdwtt/prisms_pf + #- docker run -i -t stvdwtt/prisms_pf + - docker pull dealii/dealii:v8.4.2-gcc-mpi-fulldepsmanual-debugrelease + - docker run dealii/dealii:v8.4.2-gcc-mpi-fulldepsmanual-debugrelease /bin/sh -c "git clone -b auto_r_test https://github.com/prisms-center/phaseField; python phaseField/tests/automatic_regression_test/run_auto_regression_tests.py" script: bash test_ci.sh diff --git a/README.md b/README.md index 1f19138c1..2f8ce6fca 100644 --- a/README.md +++ b/README.md @@ -2,12 +2,12 @@ PRISMS-PF ================= Code repository: https://github.com/prisms-center/phaseField
Code documentation: https://goo.gl/00y23N
-User registration link: http://goo.gl/forms/GXo7Im8p2Y +User registration link: http://goo.gl/forms/GXo7Im8p2Y
+User discussion group: https://groups.google.com/forum/#!forum/prisms-pf-users Version information: -This version of the code, 1.1, is an update the first release version of PRISMS-PF. For:q -information concerning the differences between versions, please consult version_changes.txt. +This version of the code, 1.1, is an update the first release version of PRISMS-PF. For information concerning the differences between versions, please consult version_changes.txt. What is PRISMS-PF? @@ -101,14 +101,11 @@ Since PRISMS-PF is still under active development, GNU Lesser General Public License (LGPL). Please see the file LICENSE for details. -Mailing Lists: - - + prismsphaseField.users@umich.edu - + prismsphaseField.dev@umich.edu - Further information, questions, issues and bugs: + + + prisms-pf-users@googlegroups.com (user forum) + + prismsphaseField.dev@umich.edu (developer email list) - Contact the developers at prismsphaseField.dev@umich.edu diff --git a/applications/CHAC_anisotropy/parameters.h b/applications/CHAC_anisotropy/parameters.h index 68cf6ae00..b0bc9dc5b 100644 --- a/applications/CHAC_anisotropy/parameters.h +++ b/applications/CHAC_anisotropy/parameters.h @@ -56,6 +56,8 @@ // outputCondition is "LIST" #define outputList {} +// Status is printed to the screen every skipPrintSteps +#define skipPrintSteps 10 // ================================================================================= // Set the flag determining if the total free energy is calculated for each output diff --git a/applications/CHAC_anisotropyRegularized/parameters.h b/applications/CHAC_anisotropyRegularized/parameters.h index c6ccfb9fe..58ebfdc59 100644 --- a/applications/CHAC_anisotropyRegularized/parameters.h +++ b/applications/CHAC_anisotropyRegularized/parameters.h @@ -56,6 +56,8 @@ // outputCondition is "LIST" #define outputList {} +// Status is printed to the screen every skipPrintSteps +#define skipPrintSteps 10 // ================================================================================= // Set the flag determining if the total free energy is calculated for each output diff --git a/applications/CHiMaD_benchmark1a/parameters.h b/applications/CHiMaD_benchmark1a/parameters.h index 78284c645..8158f8ee4 100644 --- a/applications/CHiMaD_benchmark1a/parameters.h +++ b/applications/CHiMaD_benchmark1a/parameters.h @@ -56,6 +56,9 @@ // outputCondition is "LIST" #define outputList {0,200,1000,2000,4000,20000,40000,100000,200000,400000,600000,2000000} +// Status is printed to the screen every skipPrintSteps +#define skipPrintSteps 10 + // ================================================================================= // Set the flag determining if the total free energy is calculated for each output // ================================================================================= diff --git a/applications/CHiMaD_benchmark1c/ICs_and_BCs.h b/applications/CHiMaD_benchmark1c/ICs_and_BCs.h deleted file mode 100644 index 779a46b42..000000000 --- a/applications/CHiMaD_benchmark1c/ICs_and_BCs.h +++ /dev/null @@ -1,86 +0,0 @@ -template -class InitialCondition : public Function -{ -public: - unsigned int index; - Vector values; - InitialCondition (const unsigned int _index) : Function(1), index(_index) { - std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1); - } - double value (const Point &p, const unsigned int component = 0) const - { - double scalar_IC = 0; - // ===================================================================== - // ENTER THE INITIAL CONDITIONS HERE FOR SCALAR FIELDS - // ===================================================================== - // Enter the function describing conditions for the fields at point "p". - // Use "if" statements to set the initial condition for each variable - // according to its variable index. - - if (index == 0){ - double epsilon=0.01; - double c0 = 0.5; - double dx=spanX/((double) subdivisionsX)/std::pow(2.0,refineFactor); - scalar_IC = c0+epsilon*(std::cos(0.105*p[0])*std::cos(0.11*p[1])+ - std::pow(std::cos(0.13*p[0])*std::cos(0.087*p[1]),2.0)+ - std::cos(0.025*p[0]-0.15*p[1])*std::cos(0.07*p[0]-0.02*p[1])); - - } - else { - scalar_IC = 0.0; - } - - // ===================================================================== - return scalar_IC; - } -}; - -template -class InitialConditionVec : public Function -{ -public: - unsigned int index; - //Vector values; - InitialConditionVec (const unsigned int _index) : Function(dim), index(_index) { - std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1); - } - void vector_value (const Point &p,Vector &vector_IC) const - { - // ===================================================================== - // ENTER THE INITIAL CONDITIONS HERE FOR VECTOR FIELDS - // ===================================================================== - // Enter the function describing conditions for the fields at point "p". - // Use "if" statements to set the initial condition for each variable - // according to its variable index. - - - // ===================================================================== - } -}; - - -template -void generalizedProblem::setBCs(){ - - // ===================================================================== - // ENTER THE BOUNDARY CONDITIONS HERE - // ===================================================================== - // This function sets the BCs for the problem variables - // The function "inputBCs" should be called for each component of - // each variable and should be in numerical order. Four input arguments - // set the same BC on the entire boundary. Two plus two times the - // number of dimensions inputs sets separate BCs on each face of the domain. - // Inputs to "inputBCs": - // First input: variable number - // Second input: component number - // Third input: BC type (options are "ZERO_DERIVATIVE", "DIRICHLET", and "PERIODIC") - // Fourth input: BC value (ignored unless the BC type is "DIRICHLET") - // Odd inputs after the third: BC type - // Even inputs after the third: BC value - // Face numbering: starts at zero with the minimum of the first direction, one for the maximum of the first direction - // two for the minimum of the second direction, etc. - - inputBCs(0,0,"ZERO_DERIVATIVE",0); - inputBCs(1,0,"ZERO_DERIVATIVE",0); - -} diff --git a/applications/allenCahn/output.txt b/applications/allenCahn/output.txt new file mode 100644 index 000000000..74556a9ee --- /dev/null +++ b/applications/allenCahn/output.txt @@ -0,0 +1,8169 @@ +rm: *vtu: No such file or directory +-- Autopilot invoked +### +# +# Project main set up with deal.II-8.4.1 found at +# /Applications/deal.II.app/Contents/Resources +# +# CMAKE_BUILD_TYPE: Debug +# +# You can now run +# $ make - to compile and link the program +# $ make run - to (compile, link and) run the program +# +# $ make sign - to sign the executable with the supplied OSX developer key +# +# $ make debug - to switch the build type to 'Debug' +# $ make release - to switch the build type to 'Release' +# +# $ make edit_cache - to change (cached) configuration variables +# and rerun the configure and generate phases of CMake +# +# $ make strip_comments - to strip the source files in this +# directory off the documentation comments +# $ make clean - to remove the generated executable as well as +# all intermediate compilation files +# $ make runclean - to remove all output generated by the program +# $ make distclean - to clean the directory from _all_ generated +# files (includes clean, runclean and the removal +# of the generated build system) +# $ make info - to view this message again +# +# Have a nice day! +# +### +-- Configuring done +-- Generating done +-- Build files have been written to: /Users/stephendewitt/Documents/workspace/PRISMS_workspace/phaseField_dev/applications/allenCahn +Scanning dependencies of target release +[100%] Switch CMAKE_BUILD_TYPE to Release +-- Autopilot invoked +-- Run $ make info to print a detailed help message +-- Configuring done +-- Generating done +-- Build files have been written to: /Users/stephendewitt/Documents/workspace/PRISMS_workspace/phaseField_dev/applications/allenCahn +Scanning dependencies of target main +[ 50%] Building CXX object CMakeFiles/main.dir/main.cc.o +[100%] Linking CXX executable main +[100%] Built target main +Built target release +creating problem mesh... +periodic facepairs: 0 +problem dimensions: 100x100x100 +number of elements: 16384 + +initializing matrix free object +initializing finite element space P^1 for PARABOLIC:SCALAR field 'n' +field ' n' DOF : 16641 (Constraint DOF : 0) +total DOF : 16641 +initializing parallel::distributed residual and solution vectors +computed mass matrix (using FE space for field: 0) + +solving... + +fields: 1 +fields: 1 +fields: 1 +fields: 1 +Output written to:solution-000000.pvtu + +Energy: 1035 +Energy Components: 0 1035 0 + +Time stepping parameters: timeStep: 0.001 timeFinal: 20 timeIncrements: 20000 + +time increment:10 time: 0.01 +field ' n' [explicit solve]: current solution: 1.015462e+02, current residual:6.145542e+01 +wall time: 0.000363111s + +time increment:20 time: 0.02 +field ' n' [explicit solve]: current solution: 1.011776e+02, current residual:6.122960e+01 +wall time: 0.000341892s + +time increment:30 time: 0.03 +field ' n' [explicit solve]: current solution: 1.008983e+02, current residual:6.105796e+01 +wall time: 0.000355005s + +time increment:40 time: 0.04 +field ' n' [explicit solve]: current solution: 1.006736e+02, current residual:6.091951e+01 +wall time: 0.000336885s + +time increment:50 time: 0.05 +field ' n' [explicit solve]: current solution: 1.004845e+02, current residual:6.080286e+01 +wall time: 0.000384092s + +time increment:60 time: 0.06 +field ' n' [explicit solve]: current solution: 1.003203e+02, current residual:6.070143e+01 +wall time: 0.000332832s + +time increment:70 time: 0.07 +field ' n' [explicit solve]: current solution: 1.001743e+02, current residual:6.061120e+01 +wall time: 0.000303984s + +time increment:80 time: 0.08 +field ' n' [explicit solve]: current solution: 1.000423e+02, current residual:6.052955e+01 +wall time: 0.000417948s + +time increment:90 time: 0.09 +field ' n' [explicit solve]: current solution: 9.992134e+01, current residual:6.045474e+01 +wall time: 0.00033617s + +time increment:100 time: 0.1 +field ' n' [explicit solve]: current solution: 9.980943e+01, current residual:6.038551e+01 +wall time: 0.000401974s + +time increment:110 time: 0.11 +field ' n' [explicit solve]: current solution: 9.970511e+01, current residual:6.032098e+01 +wall time: 0.000326157s + +time increment:120 time: 0.12 +field ' n' [explicit solve]: current solution: 9.960728e+01, current residual:6.026045e+01 +wall time: 0.000329971s + +time increment:130 time: 0.13 +field ' n' [explicit solve]: current solution: 9.951508e+01, current residual:6.020340e+01 +wall time: 0.000341892s + +time increment:140 time: 0.14 +field ' n' [explicit solve]: current solution: 9.942784e+01, current residual:6.014941e+01 +wall time: 0.000334024s + +time increment:150 time: 0.15 +field ' n' [explicit solve]: current solution: 9.934502e+01, current residual:6.009816e+01 +wall time: 0.000337124s + +time increment:160 time: 0.16 +field ' n' [explicit solve]: current solution: 9.926617e+01, current residual:6.004936e+01 +wall time: 0.000339985s + +time increment:170 time: 0.17 +field ' n' [explicit solve]: current solution: 9.919093e+01, current residual:6.000278e+01 +wall time: 0.000330925s + +time increment:180 time: 0.18 +field ' n' [explicit solve]: current solution: 9.911898e+01, current residual:5.995823e+01 +wall time: 0.000365973s + +time increment:190 time: 0.19 +field ' n' [explicit solve]: current solution: 9.905005e+01, current residual:5.991555e+01 +wall time: 0.000430822s + +time increment:200 time: 0.2 +field ' n' [explicit solve]: current solution: 9.898392e+01, current residual:5.987459e+01 +wall time: 0.00028801s + +time increment:210 time: 0.21 +field ' n' [explicit solve]: current solution: 9.892037e+01, current residual:5.983522e+01 +wall time: 0.00031805s + +time increment:220 time: 0.22 +field ' n' [explicit solve]: current solution: 9.885923e+01, current residual:5.979733e+01 +wall time: 0.000361919s + +time increment:230 time: 0.23 +field ' n' [explicit solve]: current solution: 9.880035e+01, current residual:5.976084e+01 +wall time: 0.000362158s + +time increment:240 time: 0.24 +field ' n' [explicit solve]: current solution: 9.874358e+01, current residual:5.972564e+01 +wall time: 0.000324011s + +time increment:250 time: 0.25 +field ' n' [explicit solve]: current solution: 9.868880e+01, current residual:5.969167e+01 +wall time: 0.000340939s + +time increment:260 time: 0.26 +field ' n' [explicit solve]: current solution: 9.863590e+01, current residual:5.965885e+01 +wall time: 0.000344992s + +time increment:270 time: 0.27 +field ' n' [explicit solve]: current solution: 9.858478e+01, current residual:5.962713e+01 +wall time: 0.000324965s + +time increment:280 time: 0.28 +field ' n' [explicit solve]: current solution: 9.853534e+01, current residual:5.959644e+01 +wall time: 0.000310898s + +time increment:290 time: 0.29 +field ' n' [explicit solve]: current solution: 9.848749e+01, current residual:5.956673e+01 +wall time: 0.000364065s + +time increment:300 time: 0.3 +field ' n' [explicit solve]: current solution: 9.844116e+01, current residual:5.953796e+01 +wall time: 0.000342131s + +time increment:310 time: 0.31 +field ' n' [explicit solve]: current solution: 9.839629e+01, current residual:5.951007e+01 +wall time: 0.00031805s + +time increment:320 time: 0.32 +field ' n' [explicit solve]: current solution: 9.835279e+01, current residual:5.948304e+01 +wall time: 0.000323057s + +time increment:330 time: 0.33 +field ' n' [explicit solve]: current solution: 9.831062e+01, current residual:5.945682e+01 +wall time: 0.000356913s + +time increment:340 time: 0.34 +field ' n' [explicit solve]: current solution: 9.826971e+01, current residual:5.943137e+01 +wall time: 0.000328064s + +time increment:350 time: 0.35 +field ' n' [explicit solve]: current solution: 9.823002e+01, current residual:5.940668e+01 +wall time: 0.000324011s + +time increment:360 time: 0.36 +field ' n' [explicit solve]: current solution: 9.819150e+01, current residual:5.938269e+01 +wall time: 0.000347137s + +time increment:370 time: 0.37 +field ' n' [explicit solve]: current solution: 9.815409e+01, current residual:5.935940e+01 +wall time: 0.000341892s + +time increment:380 time: 0.38 +field ' n' [explicit solve]: current solution: 9.811777e+01, current residual:5.933677e+01 +wall time: 0.000352144s + +time increment:390 time: 0.39 +field ' n' [explicit solve]: current solution: 9.808248e+01, current residual:5.931477e+01 +wall time: 0.000324965s + +time increment:400 time: 0.4 +field ' n' [explicit solve]: current solution: 9.804819e+01, current residual:5.929339e+01 +wall time: 0.000349998s + +time increment:410 time: 0.41 +field ' n' [explicit solve]: current solution: 9.801486e+01, current residual:5.927260e+01 +wall time: 0.000331879s + +time increment:420 time: 0.42 +field ' n' [explicit solve]: current solution: 9.798247e+01, current residual:5.925238e+01 +wall time: 0.000298977s + +time increment:430 time: 0.43 +field ' n' [explicit solve]: current solution: 9.795098e+01, current residual:5.923271e+01 +wall time: 0.000355005s + +time increment:440 time: 0.44 +field ' n' [explicit solve]: current solution: 9.792036e+01, current residual:5.921358e+01 +wall time: 0.000337124s + +time increment:450 time: 0.45 +field ' n' [explicit solve]: current solution: 9.789058e+01, current residual:5.919497e+01 +wall time: 0.000323057s + +time increment:460 time: 0.46 +field ' n' [explicit solve]: current solution: 9.786162e+01, current residual:5.917685e+01 +wall time: 0.000350952s + +time increment:470 time: 0.47 +field ' n' [explicit solve]: current solution: 9.783345e+01, current residual:5.915922e+01 +wall time: 0.00035882s + +time increment:480 time: 0.48 +field ' n' [explicit solve]: current solution: 9.780604e+01, current residual:5.914207e+01 +wall time: 0.000335932s + +time increment:490 time: 0.49 +field ' n' [explicit solve]: current solution: 9.777938e+01, current residual:5.912536e+01 +wall time: 0.000357151s + +time increment:500 time: 0.5 +field ' n' [explicit solve]: current solution: 9.775345e+01, current residual:5.910911e+01 +wall time: 0.000349998s + +time increment:510 time: 0.51 +field ' n' [explicit solve]: current solution: 9.772822e+01, current residual:5.909328e+01 +wall time: 0.000356913s + +time 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residual:5.899356e+01 +wall time: 0.000344038s + +time increment:590 time: 0.59 +field ' n' [explicit solve]: current solution: 9.754941e+01, current residual:5.898077e+01 +wall time: 0.000313997s + +time increment:600 time: 0.6 +field ' n' [explicit solve]: current solution: 9.752969e+01, current residual:5.896833e+01 +wall time: 0.000396967s + +time increment:610 time: 0.61 +field ' n' [explicit solve]: current solution: 9.751051e+01, current residual:5.895621e+01 +wall time: 0.000377893s + +time increment:620 time: 0.62 +field ' n' [explicit solve]: current solution: 9.749186e+01, current residual:5.894441e+01 +wall time: 0.000316858s + +time increment:630 time: 0.63 +field ' n' [explicit solve]: current solution: 9.747372e+01, current residual:5.893292e+01 +wall time: 0.000357151s + +time increment:640 time: 0.64 +field ' n' [explicit solve]: current solution: 9.745608e+01, current residual:5.892174e+01 +wall time: 0.000346184s + +time increment:650 time: 0.65 +field ' n' [explicit 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increment:720 time: 0.72 +field ' n' [explicit solve]: current solution: 9.733156e+01, current residual:5.884248e+01 +wall time: 0.000351906s + +time increment:730 time: 0.73 +field ' n' [explicit solve]: current solution: 9.731792e+01, current residual:5.883375e+01 +wall time: 0.000290871s + +time increment:740 time: 0.74 +field ' n' [explicit solve]: current solution: 9.730468e+01, current residual:5.882526e+01 +wall time: 0.000313997s + +time increment:750 time: 0.75 +field ' n' [explicit solve]: current solution: 9.729183e+01, current residual:5.881701e+01 +wall time: 0.000291109s + +time increment:760 time: 0.76 +field ' n' [explicit solve]: current solution: 9.727935e+01, current residual:5.880899e+01 +wall time: 0.000333071s + +time increment:770 time: 0.77 +field ' n' [explicit solve]: current solution: 9.726725e+01, current residual:5.880120e+01 +wall time: 0.00039506s + +time increment:780 time: 0.78 +field ' n' [explicit solve]: current solution: 9.725551e+01, current residual:5.879363e+01 +wall time: 0.000344038s + +time increment:790 time: 0.79 +field ' n' [explicit solve]: current solution: 9.724413e+01, current residual:5.878628e+01 +wall time: 0.000347137s + +time increment:800 time: 0.8 +field ' n' [explicit solve]: current solution: 9.723310e+01, current residual:5.877915e+01 +wall time: 0.000298023s + +time increment:810 time: 0.81 +field ' n' [explicit solve]: current solution: 9.722240e+01, current residual:5.877222e+01 +wall time: 0.000303984s + +time increment:820 time: 0.82 +field ' n' [explicit solve]: current solution: 9.721204e+01, current residual:5.876550e+01 +wall time: 0.000313997s + +time increment:830 time: 0.83 +field ' n' [explicit solve]: current solution: 9.720201e+01, current residual:5.875897e+01 +wall time: 0.000295877s + +time increment:840 time: 0.84 +field ' n' [explicit solve]: current solution: 9.719230e+01, current residual:5.875265e+01 +wall time: 0.000332832s + +time increment:850 time: 0.85 +field ' n' [explicit solve]: current solution: 9.718290e+01, current residual:5.874651e+01 +wall time: 0.000366926s + +time increment:860 time: 0.86 +field ' n' [explicit solve]: current solution: 9.717382e+01, current residual:5.874057e+01 +wall time: 0.000346899s + +time increment:870 time: 0.87 +field ' n' [explicit solve]: current solution: 9.716503e+01, current residual:5.873481e+01 +wall time: 0.00037384s + +time increment:880 time: 0.88 +field ' n' [explicit solve]: current solution: 9.715653e+01, current residual:5.872923e+01 +wall time: 0.000348091s + +time increment:890 time: 0.89 +field ' n' [explicit solve]: current solution: 9.714833e+01, current residual:5.872383e+01 +wall time: 0.000317097s + +time increment:900 time: 0.9 +field ' n' [explicit solve]: current solution: 9.714041e+01, current residual:5.871860e+01 +wall time: 0.00035882s + +time increment:910 time: 0.91 +field ' n' [explicit solve]: current solution: 9.713277e+01, current residual:5.871354e+01 +wall time: 0.000402927s + +time increment:920 time: 0.92 +field ' n' [explicit solve]: current solution: 9.712540e+01, current residual:5.870865e+01 +wall time: 0.000348091s + +time increment:930 time: 0.93 +field ' n' [explicit solve]: current solution: 9.711829e+01, current residual:5.870392e+01 +wall time: 0.000293016s + +time increment:940 time: 0.94 +field ' n' [explicit solve]: current solution: 9.711145e+01, current residual:5.869935e+01 +wall time: 0.000321865s + +time increment:950 time: 0.95 +field ' n' [explicit solve]: current solution: 9.710487e+01, current residual:5.869494e+01 +wall time: 0.000335932s + +time increment:960 time: 0.96 +field ' n' [explicit solve]: current solution: 9.709853e+01, current residual:5.869069e+01 +wall time: 0.000315905s + +time increment:970 time: 0.97 +field ' n' [explicit solve]: current solution: 9.709244e+01, current residual:5.868658e+01 +wall time: 0.000306129s + +time increment:980 time: 0.98 +field ' n' [explicit solve]: current solution: 9.708660e+01, current residual:5.868263e+01 +wall time: 0.000293016s + +time increment:990 time: 0.99 +field ' n' [explicit solve]: current solution: 9.708099e+01, current residual:5.867881e+01 +wall time: 0.000303984s + +time increment:1000 time: 1 +field ' n' [explicit solve]: current solution: 9.707561e+01, current residual:5.867515e+01 +wall time: 0.000296116s + +time increment:1010 time: 1.01 +field ' n' [explicit solve]: current solution: 9.707046e+01, current residual:5.867162e+01 +wall time: 0.000309944s + +time increment:1020 time: 1.02 +field ' n' [explicit solve]: current solution: 9.706553e+01, current residual:5.866823e+01 +wall time: 0.000311852s + +time increment:1030 time: 1.03 +field ' n' [explicit solve]: current solution: 9.706082e+01, current residual:5.866497e+01 +wall time: 0.000359058s + +time increment:1040 time: 1.04 +field ' n' [explicit solve]: current solution: 9.705633e+01, current residual:5.866185e+01 +wall time: 0.000296116s + +time increment:1050 time: 1.05 +field ' n' [explicit solve]: current solution: 9.705204e+01, current residual:5.865885e+01 +wall time: 0.000339031s + +time increment:1060 time: 1.06 +field ' n' [explicit solve]: current solution: 9.704796e+01, current residual:5.865598e+01 +wall time: 0.000354052s + +time increment:1070 time: 1.07 +field ' n' [explicit solve]: current solution: 9.704409e+01, current residual:5.865324e+01 +wall time: 0.000372171s + +time increment:1080 time: 1.08 +field ' n' [explicit solve]: current solution: 9.704041e+01, current residual:5.865062e+01 +wall time: 0.0003829s + +time increment:1090 time: 1.09 +field ' n' [explicit solve]: current solution: 9.703692e+01, current residual:5.864812e+01 +wall time: 0.000347137s + +time increment:1100 time: 1.1 +field ' n' [explicit solve]: current solution: 9.703363e+01, current residual:5.864574e+01 +wall time: 0.00029707s + +time increment:1110 time: 1.11 +field ' n' [explicit solve]: current solution: 9.703052e+01, current residual:5.864347e+01 +wall time: 0.000282049s + +time increment:1120 time: 1.12 +field ' n' [explicit solve]: current solution: 9.702760e+01, current residual:5.864132e+01 +wall time: 0.000348091s + +time increment:1130 time: 1.13 +field ' n' [explicit solve]: current solution: 9.702486e+01, current residual:5.863927e+01 +wall time: 0.000306129s + +time increment:1140 time: 1.14 +field ' n' [explicit solve]: current solution: 9.702229e+01, current residual:5.863734e+01 +wall time: 0.00028491s + +time increment:1150 time: 1.15 +field ' n' [explicit solve]: current solution: 9.701989e+01, current residual:5.863551e+01 +wall time: 0.000352859s + +time increment:1160 time: 1.16 +field ' n' [explicit solve]: current solution: 9.701767e+01, current residual:5.863379e+01 +wall time: 0.000301123s + +time increment:1170 time: 1.17 +field ' n' [explicit solve]: current solution: 9.701561e+01, current residual:5.863217e+01 +wall time: 0.000315905s + +time increment:1180 time: 1.18 +field ' n' [explicit solve]: current solution: 9.701371e+01, current residual:5.863065e+01 +wall time: 0.000329018s + +time increment:1190 time: 1.19 +field ' n' [explicit solve]: current solution: 9.701198e+01, current residual:5.862923e+01 +wall time: 0.000319958s + +time increment:1200 time: 1.2 +field ' n' [explicit solve]: current solution: 9.701040e+01, current residual:5.862791e+01 +wall time: 0.000323772s + +time increment:1210 time: 1.21 +field ' n' [explicit solve]: current solution: 9.700897e+01, current residual:5.862669e+01 +wall time: 0.000353813s + +time increment:1220 time: 1.22 +field ' n' [explicit solve]: current solution: 9.700770e+01, current residual:5.862556e+01 +wall time: 0.000374079s + +time increment:1230 time: 1.23 +field ' n' [explicit solve]: current solution: 9.700657e+01, current residual:5.862452e+01 +wall time: 0.000387907s + +time increment:1240 time: 1.24 +field ' n' [explicit solve]: current solution: 9.700559e+01, current residual:5.862357e+01 +wall time: 0.000365019s + +time increment:1250 time: 1.25 +field ' n' [explicit solve]: current solution: 9.700476e+01, current residual:5.862271e+01 +wall time: 0.000347853s + +time increment:1260 time: 1.26 +field ' n' [explicit solve]: current solution: 9.700406e+01, current residual:5.862194e+01 +wall time: 0.000355959s + +time increment:1270 time: 1.27 +field ' n' [explicit solve]: current solution: 9.700349e+01, current residual:5.862125e+01 +wall time: 0.000364065s + +time increment:1280 time: 1.28 +field ' n' [explicit solve]: current solution: 9.700307e+01, current residual:5.862065e+01 +wall time: 0.000409842s + +time increment:1290 time: 1.29 +field ' n' [explicit solve]: current solution: 9.700277e+01, current residual:5.862013e+01 +wall time: 0.000338793s + +time increment:1300 time: 1.3 +field ' n' [explicit solve]: current solution: 9.700261e+01, current residual:5.861969e+01 +wall time: 0.000347137s + +time increment:1310 time: 1.31 +field ' n' [explicit solve]: current solution: 9.700257e+01, current residual:5.861933e+01 +wall time: 0.000397921s + +time increment:1320 time: 1.32 +field ' n' [explicit solve]: current solution: 9.700265e+01, current residual:5.861905e+01 +wall time: 0.000343084s + +time increment:1330 time: 1.33 +field ' n' [explicit solve]: current solution: 9.700286e+01, current residual:5.861884e+01 +wall time: 0.000368834s + +time increment:1340 time: 1.34 +field ' n' [explicit solve]: current solution: 9.700319e+01, current residual:5.861871e+01 +wall time: 0.000342846s + +time increment:1350 time: 1.35 +field ' n' [explicit solve]: current solution: 9.700364e+01, current residual:5.861865e+01 +wall time: 0.000345945s + +time increment:1360 time: 1.36 +field ' n' [explicit solve]: current solution: 9.700420e+01, current residual:5.861867e+01 +wall time: 0.000386s + +time increment:1370 time: 1.37 +field ' n' [explicit solve]: current solution: 9.700487e+01, current residual:5.861876e+01 +wall time: 0.000375032s + +time increment:1380 time: 1.38 +field ' n' [explicit solve]: current solution: 9.700566e+01, current residual:5.861892e+01 +wall time: 0.000329971s + +time increment:1390 time: 1.39 +field ' n' [explicit solve]: current solution: 9.700656e+01, current residual:5.861914e+01 +wall time: 0.000316858s + +time increment:1400 time: 1.4 +field ' n' [explicit solve]: current solution: 9.700756e+01, current residual:5.861944e+01 +wall time: 0.000365019s + +time increment:1410 time: 1.41 +field ' n' [explicit solve]: current solution: 9.700867e+01, current residual:5.861980e+01 +wall time: 0.000349998s + +time increment:1420 time: 1.42 +field ' n' [explicit solve]: current solution: 9.700988e+01, current residual:5.862022e+01 +wall time: 0.000378132s + +time increment:1430 time: 1.43 +field ' n' [explicit solve]: current solution: 9.701119e+01, current residual:5.862071e+01 +wall time: 0.000374794s + +time increment:1440 time: 1.44 +field ' n' [explicit solve]: current solution: 9.701261e+01, current residual:5.862126e+01 +wall time: 0.000363827s + +time increment:1450 time: 1.45 +field ' n' [explicit solve]: current solution: 9.701412e+01, current residual:5.862188e+01 +wall time: 0.000365019s + +time increment:1460 time: 1.46 +field ' n' [explicit solve]: current solution: 9.701572e+01, current residual:5.862255e+01 +wall time: 0.000360966s + +time increment:1470 time: 1.47 +field ' n' [explicit solve]: current solution: 9.701742e+01, current residual:5.862329e+01 +wall time: 0.00031805s + +time increment:1480 time: 1.48 +field ' n' [explicit solve]: current solution: 9.701921e+01, current residual:5.862408e+01 +wall time: 0.000380993s + +time increment:1490 time: 1.49 +field ' n' [explicit solve]: current solution: 9.702110e+01, current residual:5.862493e+01 +wall time: 0.000910997s + +time increment:1500 time: 1.5 +field ' n' [explicit solve]: current solution: 9.702307e+01, current residual:5.862584e+01 +wall time: 0.000472069s + +time increment:1510 time: 1.51 +field ' n' [explicit solve]: current solution: 9.702513e+01, current residual:5.862680e+01 +wall time: 0.000386953s + +time increment:1520 time: 1.52 +field ' n' [explicit solve]: current solution: 9.702728e+01, current residual:5.862782e+01 +wall time: 0.000366926s + +time increment:1530 time: 1.53 +field ' n' [explicit solve]: current solution: 9.702951e+01, current residual:5.862889e+01 +wall time: 0.000363111s + +time increment:1540 time: 1.54 +field ' n' [explicit solve]: current solution: 9.703182e+01, current residual:5.863001e+01 +wall time: 0.000401974s + +time increment:1550 time: 1.55 +field ' n' [explicit solve]: current solution: 9.703421e+01, current residual:5.863118e+01 +wall time: 0.000346899s + +time increment:1560 time: 1.56 +field ' n' [explicit solve]: current solution: 9.703669e+01, current residual:5.863241e+01 +wall time: 0.000342131s + +time increment:1570 time: 1.57 +field ' n' [explicit solve]: current solution: 9.703924e+01, current residual:5.863368e+01 +wall time: 0.000475168s + +time increment:1580 time: 1.58 +field ' n' [explicit solve]: current solution: 9.704187e+01, current residual:5.863501e+01 +wall time: 0.000396013s + +time increment:1590 time: 1.59 +field ' n' [explicit solve]: current solution: 9.704457e+01, current residual:5.863638e+01 +wall time: 0.000402927s + +time increment:1600 time: 1.6 +field ' n' [explicit solve]: current solution: 9.704735e+01, current residual:5.863780e+01 +wall time: 0.000414848s + +time increment:1610 time: 1.61 +field ' n' [explicit solve]: current solution: 9.705021e+01, current residual:5.863927e+01 +wall time: 0.000421047s + +time increment:1620 time: 1.62 +field ' n' [explicit solve]: current solution: 9.705313e+01, current residual:5.864078e+01 +wall time: 0.000365019s + +time increment:1630 time: 1.63 +field ' n' [explicit solve]: current solution: 9.705612e+01, current residual:5.864234e+01 +wall time: 0.000374079s + +time increment:1640 time: 1.64 +field ' n' [explicit solve]: current solution: 9.705918e+01, current residual:5.864394e+01 +wall time: 0.000382185s + +time increment:1650 time: 1.65 +field ' n' [explicit solve]: current solution: 9.706231e+01, current residual:5.864558e+01 +wall time: 0.000403166s + +time increment:1660 time: 1.66 +field ' n' [explicit solve]: current solution: 9.706551e+01, current residual:5.864727e+01 +wall time: 0.000419855s + +time increment:1670 time: 1.67 +field ' n' [explicit solve]: current solution: 9.706877e+01, current residual:5.864899e+01 +wall time: 0.000380039s + +time increment:1680 time: 1.68 +field ' n' [explicit solve]: current solution: 9.707210e+01, current residual:5.865076e+01 +wall time: 0.000337124s + +time increment:1690 time: 1.69 +field ' n' [explicit solve]: current solution: 9.707549e+01, current residual:5.865257e+01 +wall time: 0.000320196s + +time increment:1700 time: 1.7 +field ' n' [explicit solve]: current solution: 9.707894e+01, current residual:5.865442e+01 +wall time: 0.000349998s + +time increment:1710 time: 1.71 +field ' n' [explicit solve]: current solution: 9.708245e+01, current residual:5.865631e+01 +wall time: 0.000335932s + +time increment:1720 time: 1.72 +field ' n' [explicit solve]: current solution: 9.708602e+01, current residual:5.865824e+01 +wall time: 0.000347853s + +time increment:1730 time: 1.73 +field ' n' [explicit solve]: current solution: 9.708965e+01, current residual:5.866020e+01 +wall time: 0.000379086s + +time increment:1740 time: 1.74 +field ' n' [explicit solve]: current solution: 9.709333e+01, current residual:5.866220e+01 +wall time: 0.000339985s + +time increment:1750 time: 1.75 +field ' n' [explicit solve]: current solution: 9.709707e+01, current residual:5.866424e+01 +wall time: 0.000418901s + +time increment:1760 time: 1.76 +field ' n' [explicit solve]: current solution: 9.710087e+01, current residual:5.866631e+01 +wall time: 0.000319004s + +time increment:1770 time: 1.77 +field ' n' [explicit solve]: current solution: 9.710472e+01, current residual:5.866842e+01 +wall time: 0.000354052s + +time increment:1780 time: 1.78 +field ' n' [explicit solve]: current solution: 9.710862e+01, current residual:5.867056e+01 +wall time: 0.000384808s + +time increment:1790 time: 1.79 +field ' n' [explicit solve]: current solution: 9.711258e+01, current residual:5.867273e+01 +wall time: 0.000379086s + +time increment:1800 time: 1.8 +field ' n' [explicit solve]: current solution: 9.711658e+01, current residual:5.867494e+01 +wall time: 0.000341177s + +time increment:1810 time: 1.81 +field ' n' [explicit solve]: current solution: 9.712064e+01, current residual:5.867718e+01 +wall time: 0.000365973s + +time increment:1820 time: 1.82 +field ' n' [explicit solve]: current solution: 9.712475e+01, current residual:5.867945e+01 +wall time: 0.000328064s + +time increment:1830 time: 1.83 +field ' n' [explicit solve]: current solution: 9.712890e+01, current residual:5.868175e+01 +wall time: 0.000368834s + +time increment:1840 time: 1.84 +field ' n' [explicit solve]: current solution: 9.713310e+01, current residual:5.868408e+01 +wall time: 0.000391006s + +time increment:1850 time: 1.85 +field ' n' [explicit solve]: current solution: 9.713734e+01, current residual:5.868644e+01 +wall time: 0.000384092s + +time increment:1860 time: 1.86 +field ' n' [explicit solve]: current solution: 9.714163e+01, current residual:5.868883e+01 +wall time: 0.000319958s + +time increment:1870 time: 1.87 +field ' n' [explicit solve]: current solution: 9.714597e+01, current residual:5.869125e+01 +wall time: 0.000333071s + +time increment:1880 time: 1.88 +field ' n' [explicit solve]: current solution: 9.715034e+01, current residual:5.869370e+01 +wall time: 0.000360012s + +time increment:1890 time: 1.89 +field ' n' [explicit solve]: current solution: 9.715476e+01, current residual:5.869617e+01 +wall time: 0.000349998s + +time increment:1900 time: 1.9 +field ' n' [explicit solve]: current solution: 9.715922e+01, current residual:5.869867e+01 +wall time: 0.000313997s + +time increment:1910 time: 1.91 +field ' n' [explicit solve]: current solution: 9.716373e+01, current residual:5.870120e+01 +wall time: 0.000346899s + +time increment:1920 time: 1.92 +field ' n' [explicit solve]: current solution: 9.716827e+01, current residual:5.870375e+01 +wall time: 0.000386s + +time increment:1930 time: 1.93 +field ' n' [explicit solve]: current solution: 9.717284e+01, current residual:5.870633e+01 +wall time: 0.000324011s + +time increment:1940 time: 1.94 +field ' n' [explicit solve]: current solution: 9.717746e+01, current residual:5.870893e+01 +wall time: 0.000329971s + +time increment:1950 time: 1.95 +field ' n' [explicit solve]: current solution: 9.718211e+01, current residual:5.871156e+01 +wall time: 0.000389814s + +time increment:1960 time: 1.96 +field ' n' [explicit solve]: current solution: 9.718680e+01, current residual:5.871421e+01 +wall time: 0.00032115s + +time increment:1970 time: 1.97 +field ' n' [explicit solve]: current solution: 9.719153e+01, current residual:5.871688e+01 +wall time: 0.000371933s + +time increment:1980 time: 1.98 +field ' n' [explicit solve]: current solution: 9.719629e+01, current residual:5.871958e+01 +wall time: 0.000335932s + +time increment:1990 time: 1.99 +field ' n' [explicit solve]: current solution: 9.720108e+01, current residual:5.872229e+01 +wall time: 0.00031209s + +time increment:2000 time: 2 +field ' n' [explicit solve]: current solution: 9.720590e+01, current residual:5.872503e+01 +wall time: 0.000302076s +fields: 1 +fields: 1 +fields: 1 +fields: 1 +Output written to:solution-002000.pvtu + +Energy: 246.134 +Energy Components: 113.318 132.816 0 + +time increment:2010 time: 2.01 +field ' n' [explicit solve]: current solution: 9.721076e+01, current residual:5.872779e+01 +wall time: 0.000313044s + +time increment:2020 time: 2.02 +field ' n' [explicit solve]: current solution: 9.721564e+01, current residual:5.873057e+01 +wall time: 0.000305891s + +time increment:2030 time: 2.03 +field ' n' [explicit solve]: current solution: 9.722056e+01, current residual:5.873337e+01 +wall time: 0.000439167s + +time increment:2040 time: 2.04 +field ' n' [explicit solve]: current solution: 9.722550e+01, current residual:5.873618e+01 +wall time: 0.000303984s + +time increment:2050 time: 2.05 +field ' n' [explicit solve]: current solution: 9.723048e+01, current residual:5.873902e+01 +wall time: 0.00037694s + +time increment:2060 time: 2.06 +field ' n' [explicit solve]: current solution: 9.723548e+01, current residual:5.874187e+01 +wall time: 0.000366926s + +time increment:2070 time: 2.07 +field ' n' [explicit solve]: current solution: 9.724050e+01, current residual:5.874474e+01 +wall time: 0.000396013s + +time increment:2080 time: 2.08 +field ' n' [explicit solve]: current solution: 9.724556e+01, current residual:5.874763e+01 +wall time: 0.000321865s + +time increment:2090 time: 2.09 +field ' n' [explicit solve]: current solution: 9.725063e+01, current residual:5.875054e+01 +wall time: 0.000339985s + +time increment:2100 time: 2.1 +field ' n' [explicit solve]: current solution: 9.725574e+01, current residual:5.875346e+01 +wall time: 0.000324011s + +time increment:2110 time: 2.11 +field ' n' [explicit solve]: current solution: 9.726086e+01, current residual:5.875640e+01 +wall time: 0.000340939s + +time increment:2120 time: 2.12 +field ' n' [explicit solve]: current solution: 9.726601e+01, current residual:5.875935e+01 +wall time: 0.00030899s + +time increment:2130 time: 2.13 +field ' n' [explicit solve]: current solution: 9.727118e+01, current residual:5.876231e+01 +wall time: 0.000317097s + +time increment:2140 time: 2.14 +field ' n' [explicit solve]: current solution: 9.727637e+01, current residual:5.876529e+01 +wall time: 0.000357866s + +time increment:2150 time: 2.15 +field ' n' [explicit solve]: current solution: 9.728158e+01, current residual:5.876829e+01 +wall time: 0.000359058s + +time increment:2160 time: 2.16 +field ' n' [explicit solve]: current solution: 9.728681e+01, current residual:5.877129e+01 +wall time: 0.000382185s + +time increment:2170 time: 2.17 +field ' n' [explicit solve]: current solution: 9.729206e+01, current residual:5.877431e+01 +wall time: 0.000299931s + +time increment:2180 time: 2.18 +field ' n' [explicit solve]: current solution: 9.729733e+01, current residual:5.877734e+01 +wall time: 0.000407219s + +time increment:2190 time: 2.19 +field ' n' [explicit solve]: current solution: 9.730261e+01, current residual:5.878039e+01 +wall time: 0.000347137s + +time increment:2200 time: 2.2 +field ' n' [explicit solve]: current solution: 9.730791e+01, current residual:5.878344e+01 +wall time: 0.000294924s + +time increment:2210 time: 2.21 +field ' n' [explicit solve]: current solution: 9.731323e+01, current residual:5.878651e+01 +wall time: 0.000347853s + +time increment:2220 time: 2.22 +field ' n' [explicit solve]: current solution: 9.731856e+01, current residual:5.878958e+01 +wall time: 0.000384808s + +time increment:2230 time: 2.23 +field ' n' [explicit solve]: current solution: 9.732391e+01, current residual:5.879267e+01 +wall time: 0.000316143s + +time increment:2240 time: 2.24 +field ' n' [explicit solve]: current solution: 9.732927e+01, current residual:5.879576e+01 +wall time: 0.000365973s + +time increment:2250 time: 2.25 +field ' n' [explicit solve]: current solution: 9.733464e+01, current residual:5.879886e+01 +wall time: 0.000371933s + +time increment:2260 time: 2.26 +field ' n' [explicit solve]: current solution: 9.734002e+01, current residual:5.880197e+01 +wall time: 0.000343084s + +time increment:2270 time: 2.27 +field ' n' [explicit solve]: current solution: 9.734542e+01, current residual:5.880509e+01 +wall time: 0.000303984s + +time increment:2280 time: 2.28 +field ' n' [explicit solve]: current solution: 9.735082e+01, current residual:5.880822e+01 +wall time: 0.00031209s + +time increment:2290 time: 2.29 +field ' n' [explicit solve]: current solution: 9.735624e+01, current residual:5.881135e+01 +wall time: 0.000293016s + +time increment:2300 time: 2.3 +field ' n' [explicit solve]: current solution: 9.736166e+01, current residual:5.881449e+01 +wall time: 0.000333071s + +time increment:2310 time: 2.31 +field ' n' [explicit solve]: current solution: 9.736710e+01, current residual:5.881764e+01 +wall time: 0.000300169s + +time increment:2320 time: 2.32 +field ' n' [explicit solve]: current solution: 9.737254e+01, current residual:5.882079e+01 +wall time: 0.000353098s + +time increment:2330 time: 2.33 +field ' n' [explicit solve]: current solution: 9.737799e+01, current residual:5.882395e+01 +wall time: 0.000340939s + +time increment:2340 time: 2.34 +field ' n' [explicit solve]: current solution: 9.738345e+01, current residual:5.882711e+01 +wall time: 0.000375986s + +time increment:2350 time: 2.35 +field ' n' [explicit solve]: current solution: 9.738891e+01, current residual:5.883028e+01 +wall time: 0.000321865s + +time increment:2360 time: 2.36 +field ' n' [explicit solve]: current solution: 9.739437e+01, current residual:5.883345e+01 +wall time: 0.000334978s + +time increment:2370 time: 2.37 +field ' n' [explicit solve]: current solution: 9.739985e+01, current residual:5.883662e+01 +wall time: 0.000299931s + +time increment:2380 time: 2.38 +field ' n' [explicit solve]: current solution: 9.740532e+01, current residual:5.883980e+01 +wall time: 0.000360966s + +time increment:2390 time: 2.39 +field ' n' [explicit solve]: current solution: 9.741080e+01, current residual:5.884298e+01 +wall time: 0.000408173s + +time increment:2400 time: 2.4 +field ' n' [explicit solve]: current solution: 9.741628e+01, current residual:5.884617e+01 +wall time: 0.000370026s + +time increment:2410 time: 2.41 +field ' n' [explicit solve]: current solution: 9.742177e+01, current residual:5.884935e+01 +wall time: 0.000322104s + +time increment:2420 time: 2.42 +field ' n' [explicit solve]: current solution: 9.742725e+01, current residual:5.885254e+01 +wall time: 0.000343084s + +time increment:2430 time: 2.43 +field ' n' [explicit solve]: current solution: 9.743274e+01, current residual:5.885573e+01 +wall time: 0.000360966s + +time increment:2440 time: 2.44 +field ' n' [explicit solve]: current solution: 9.743823e+01, current residual:5.885892e+01 +wall time: 0.000298977s + +time increment:2450 time: 2.45 +field ' n' [explicit solve]: current solution: 9.744372e+01, current residual:5.886211e+01 +wall time: 0.000334978s + +time increment:2460 time: 2.46 +field ' n' [explicit solve]: current solution: 9.744920e+01, current residual:5.886530e+01 +wall time: 0.000313044s + +time increment:2470 time: 2.47 +field ' n' [explicit solve]: current solution: 9.745469e+01, current residual:5.886849e+01 +wall time: 0.000366926s + +time increment:2480 time: 2.48 +field ' n' [explicit solve]: current solution: 9.746017e+01, current residual:5.887168e+01 +wall time: 0.000336885s + +time increment:2490 time: 2.49 +field ' n' [explicit solve]: current solution: 9.746565e+01, current residual:5.887487e+01 +wall time: 0.000322104s + +time increment:2500 time: 2.5 +field ' n' [explicit solve]: current solution: 9.747113e+01, current residual:5.887806e+01 +wall time: 0.000313997s + +time increment:2510 time: 2.51 +field ' n' [explicit solve]: current solution: 9.747661e+01, current residual:5.888125e+01 +wall time: 0.000307083s + +time increment:2520 time: 2.52 +field ' n' [explicit solve]: current solution: 9.748208e+01, current residual:5.888444e+01 +wall time: 0.000340939s + +time increment:2530 time: 2.53 +field ' n' [explicit solve]: current solution: 9.748755e+01, current residual:5.888762e+01 +wall time: 0.000303984s + +time increment:2540 time: 2.54 +field ' n' [explicit solve]: current solution: 9.749301e+01, current residual:5.889080e+01 +wall time: 0.00040102s + +time increment:2550 time: 2.55 +field ' n' [explicit solve]: current solution: 9.749847e+01, current residual:5.889398e+01 +wall time: 0.000303984s + +time increment:2560 time: 2.56 +field ' n' [explicit solve]: current solution: 9.750392e+01, current residual:5.889716e+01 +wall time: 0.00039196s + +time increment:2570 time: 2.57 +field ' n' [explicit solve]: current solution: 9.750937e+01, current residual:5.890033e+01 +wall time: 0.000298023s + +time increment:2580 time: 2.58 +field ' n' [explicit solve]: current solution: 9.751481e+01, current residual:5.890350e+01 +wall time: 0.000288963s + +time increment:2590 time: 2.59 +field ' n' [explicit solve]: current solution: 9.752025e+01, current residual:5.890667e+01 +wall time: 0.000319958s + +time increment:2600 time: 2.6 +field ' n' [explicit solve]: current solution: 9.752567e+01, current residual:5.890983e+01 +wall time: 0.000344992s + +time increment:2610 time: 2.61 +field ' n' [explicit solve]: current solution: 9.753109e+01, current residual:5.891299e+01 +wall time: 0.000371933s + +time increment:2620 time: 2.62 +field ' n' [explicit solve]: current solution: 9.753650e+01, current residual:5.891615e+01 +wall time: 0.000349998s + +time increment:2630 time: 2.63 +field ' n' [explicit solve]: current solution: 9.754190e+01, current residual:5.891930e+01 +wall time: 0.000319004s + +time increment:2640 time: 2.64 +field ' n' [explicit solve]: current solution: 9.754730e+01, current residual:5.892244e+01 +wall time: 0.000289917s + +time increment:2650 time: 2.65 +field ' n' [explicit solve]: current solution: 9.755268e+01, current residual:5.892558e+01 +wall time: 0.000329018s + +time increment:2660 time: 2.66 +field ' n' [explicit solve]: current solution: 9.755806e+01, current residual:5.892872e+01 +wall time: 0.000364065s + +time increment:2670 time: 2.67 +field ' n' [explicit solve]: current solution: 9.756342e+01, current residual:5.893185e+01 +wall time: 0.000344038s + +time increment:2680 time: 2.68 +field ' n' [explicit solve]: current solution: 9.756878e+01, current residual:5.893497e+01 +wall time: 0.000365019s + +time increment:2690 time: 2.69 +field ' n' [explicit solve]: current solution: 9.757412e+01, current residual:5.893809e+01 +wall time: 0.000334024s + +time increment:2700 time: 2.7 +field ' n' [explicit solve]: current solution: 9.757946e+01, current residual:5.894120e+01 +wall time: 0.000320911s + +time increment:2710 time: 2.71 +field ' n' [explicit solve]: current solution: 9.758478e+01, current residual:5.894431e+01 +wall time: 0.000312805s + +time increment:2720 time: 2.72 +field ' n' [explicit solve]: current solution: 9.759009e+01, current residual:5.894741e+01 +wall time: 0.000331163s + +time increment:2730 time: 2.73 +field ' n' [explicit solve]: current solution: 9.759539e+01, current residual:5.895050e+01 +wall time: 0.000352144s + +time increment:2740 time: 2.74 +field ' n' [explicit solve]: current solution: 9.760068e+01, current residual:5.895359e+01 +wall time: 0.000345945s + +time increment:2750 time: 2.75 +field ' n' [explicit solve]: current solution: 9.760596e+01, current residual:5.895667e+01 +wall time: 0.000325203s + +time increment:2760 time: 2.76 +field ' n' [explicit solve]: current solution: 9.761122e+01, current residual:5.895974e+01 +wall time: 0.000378132s + +time increment:2770 time: 2.77 +field ' n' [explicit solve]: current solution: 9.761648e+01, current residual:5.896281e+01 +wall time: 0.000346899s + +time increment:2780 time: 2.78 +field ' n' [explicit solve]: current solution: 9.762172e+01, current residual:5.896587e+01 +wall time: 0.000390053s + +time increment:2790 time: 2.79 +field ' n' [explicit solve]: current solution: 9.762694e+01, current residual:5.896892e+01 +wall time: 0.000374079s + +time increment:2800 time: 2.8 +field ' n' [explicit solve]: current solution: 9.763216e+01, current residual:5.897196e+01 +wall time: 0.000322819s + +time increment:2810 time: 2.81 +field ' n' [explicit solve]: current solution: 9.763736e+01, current residual:5.897500e+01 +wall time: 0.000349998s + +time increment:2820 time: 2.82 +field ' n' [explicit solve]: current solution: 9.764254e+01, current residual:5.897802e+01 +wall time: 0.000394106s + +time increment:2830 time: 2.83 +field ' n' [explicit solve]: current solution: 9.764772e+01, current residual:5.898104e+01 +wall time: 0.000352144s + +time increment:2840 time: 2.84 +field ' n' [explicit solve]: current solution: 9.765288e+01, current residual:5.898405e+01 +wall time: 0.000503063s + +time increment:2850 time: 2.85 +field ' n' [explicit solve]: current solution: 9.765802e+01, current residual:5.898706e+01 +wall time: 0.000406981s + +time increment:2860 time: 2.86 +field ' n' [explicit solve]: current solution: 9.766316e+01, current residual:5.899005e+01 +wall time: 0.000387907s + +time increment:2870 time: 2.87 +field ' n' [explicit solve]: current solution: 9.766827e+01, current residual:5.899304e+01 +wall time: 0.00039506s + +time increment:2880 time: 2.88 +field ' n' [explicit solve]: current solution: 9.767338e+01, current residual:5.899602e+01 +wall time: 0.000429869s + +time increment:2890 time: 2.89 +field ' n' [explicit solve]: current solution: 9.767847e+01, current residual:5.899899e+01 +wall time: 0.000399828s + +time increment:2900 time: 2.9 +field ' n' [explicit solve]: current solution: 9.768354e+01, current residual:5.900195e+01 +wall time: 0.000402927s + +time increment:2910 time: 2.91 +field ' n' [explicit solve]: current solution: 9.768860e+01, current residual:5.900491e+01 +wall time: 0.000340939s + +time increment:2920 time: 2.92 +field ' n' [explicit solve]: current solution: 9.769365e+01, current residual:5.900785e+01 +wall time: 0.000398159s + +time increment:2930 time: 2.93 +field ' n' [explicit solve]: current solution: 9.769868e+01, current residual:5.901079e+01 +wall time: 0.000340939s + +time increment:2940 time: 2.94 +field ' n' [explicit solve]: current solution: 9.770370e+01, current residual:5.901372e+01 +wall time: 0.000386s + +time increment:2950 time: 2.95 +field ' n' [explicit solve]: current solution: 9.770870e+01, current residual:5.901664e+01 +wall time: 0.000352859s + +time increment:2960 time: 2.96 +field ' n' [explicit solve]: current solution: 9.771369e+01, current residual:5.901955e+01 +wall time: 0.000354052s + +time increment:2970 time: 2.97 +field ' n' [explicit solve]: current solution: 9.771866e+01, current residual:5.902245e+01 +wall time: 0.000351191s + +time increment:2980 time: 2.98 +field ' n' [explicit solve]: current solution: 9.772361e+01, current residual:5.902534e+01 +wall time: 0.000400066s + +time increment:2990 time: 2.99 +field ' n' [explicit solve]: current solution: 9.772856e+01, current residual:5.902822e+01 +wall time: 0.000340223s + +time increment:3000 time: 3 +field ' n' [explicit solve]: current solution: 9.773348e+01, current residual:5.903110e+01 +wall time: 0.000400782s + +time increment:3010 time: 3.01 +field ' n' [explicit solve]: current solution: 9.773840e+01, current residual:5.903397e+01 +wall time: 0.000405073s + +time increment:3020 time: 3.02 +field ' n' [explicit solve]: current solution: 9.774329e+01, current residual:5.903682e+01 +wall time: 0.000338078s + +time increment:3030 time: 3.03 +field ' n' [explicit solve]: current solution: 9.774818e+01, current residual:5.903967e+01 +wall time: 0.000374079s + +time increment:3040 time: 3.04 +field ' n' [explicit solve]: current solution: 9.775304e+01, current residual:5.904251e+01 +wall time: 0.000369072s + +time increment:3050 time: 3.05 +field ' n' [explicit solve]: current solution: 9.775789e+01, current residual:5.904534e+01 +wall time: 0.000353098s + +time increment:3060 time: 3.06 +field ' n' [explicit solve]: current solution: 9.776273e+01, current residual:5.904816e+01 +wall time: 0.00028801s + +time increment:3070 time: 3.07 +field ' n' [explicit solve]: current solution: 9.776755e+01, current residual:5.905098e+01 +wall time: 0.000398874s + +time increment:3080 time: 3.08 +field ' n' [explicit solve]: current solution: 9.777236e+01, current residual:5.905378e+01 +wall time: 0.000360966s + +time increment:3090 time: 3.09 +field ' n' [explicit solve]: current solution: 9.777715e+01, current residual:5.905657e+01 +wall time: 0.000339031s + +time increment:3100 time: 3.1 +field ' n' [explicit solve]: current solution: 9.778193e+01, current residual:5.905936e+01 +wall time: 0.000399828s + +time increment:3110 time: 3.11 +field ' n' [explicit solve]: current solution: 9.778669e+01, current residual:5.906214e+01 +wall time: 0.000344992s + +time increment:3120 time: 3.12 +field ' n' [explicit solve]: current solution: 9.779144e+01, current residual:5.906491e+01 +wall time: 0.000345945s + +time increment:3130 time: 3.13 +field ' n' [explicit solve]: current solution: 9.779617e+01, current residual:5.906767e+01 +wall time: 0.000381947s + +time increment:3140 time: 3.14 +field ' n' [explicit solve]: current solution: 9.780089e+01, current residual:5.907042e+01 +wall time: 0.000362873s + +time increment:3150 time: 3.15 +field ' n' [explicit solve]: current solution: 9.780560e+01, current residual:5.907316e+01 +wall time: 0.000348091s + +time increment:3160 time: 3.16 +field ' n' [explicit solve]: current solution: 9.781029e+01, current residual:5.907589e+01 +wall time: 0.000375986s + +time increment:3170 time: 3.17 +field ' n' [explicit solve]: current solution: 9.781496e+01, current residual:5.907862e+01 +wall time: 0.000391006s + +time increment:3180 time: 3.18 +field ' n' [explicit solve]: current solution: 9.781962e+01, current residual:5.908133e+01 +wall time: 0.000351191s + +time increment:3190 time: 3.19 +field ' n' [explicit solve]: current solution: 9.782427e+01, current residual:5.908404e+01 +wall time: 0.000335217s + +time increment:3200 time: 3.2 +field ' n' [explicit solve]: current solution: 9.782890e+01, current residual:5.908674e+01 +wall time: 0.000348091s + +time increment:3210 time: 3.21 +field ' n' [explicit solve]: current solution: 9.783351e+01, current residual:5.908943e+01 +wall time: 0.000359058s + +time increment:3220 time: 3.22 +field ' n' [explicit solve]: current solution: 9.783812e+01, current residual:5.909211e+01 +wall time: 0.000342846s + +time increment:3230 time: 3.23 +field ' n' [explicit solve]: current solution: 9.784271e+01, current residual:5.909479e+01 +wall time: 0.000361919s + +time increment:3240 time: 3.24 +field ' n' [explicit solve]: current solution: 9.784728e+01, current residual:5.909745e+01 +wall time: 0.000346899s + +time increment:3250 time: 3.25 +field ' n' [explicit solve]: current solution: 9.785184e+01, current residual:5.910011e+01 +wall time: 0.000365973s + +time increment:3260 time: 3.26 +field ' n' [explicit solve]: current solution: 9.785639e+01, current residual:5.910276e+01 +wall time: 0.000344038s + +time increment:3270 time: 3.27 +field ' n' [explicit solve]: current solution: 9.786092e+01, current residual:5.910540e+01 +wall time: 0.000335217s + +time increment:3280 time: 3.28 +field ' n' [explicit solve]: current solution: 9.786544e+01, current residual:5.910803e+01 +wall time: 0.000339031s + +time increment:3290 time: 3.29 +field ' n' [explicit solve]: current solution: 9.786995e+01, current residual:5.911066e+01 +wall time: 0.000357151s + +time increment:3300 time: 3.3 +field ' n' [explicit solve]: current solution: 9.787444e+01, current residual:5.911327e+01 +wall time: 0.000355959s + +time increment:3310 time: 3.31 +field ' n' [explicit solve]: current solution: 9.787892e+01, current residual:5.911588e+01 +wall time: 0.000360966s + +time increment:3320 time: 3.32 +field ' n' [explicit solve]: current solution: 9.788339e+01, current residual:5.911848e+01 +wall time: 0.000363827s + +time increment:3330 time: 3.33 +field ' n' [explicit solve]: current solution: 9.788784e+01, current residual:5.912108e+01 +wall time: 0.000337839s + +time increment:3340 time: 3.34 +field ' n' [explicit solve]: current solution: 9.789228e+01, current residual:5.912366e+01 +wall time: 0.000352144s + +time increment:3350 time: 3.35 +field ' n' [explicit solve]: current solution: 9.789671e+01, current residual:5.912624e+01 +wall time: 0.000371933s + +time increment:3360 time: 3.36 +field ' n' [explicit solve]: current solution: 9.790113e+01, current residual:5.912881e+01 +wall time: 0.000400066s + +time increment:3370 time: 3.37 +field ' n' [explicit solve]: current solution: 9.790553e+01, current residual:5.913137e+01 +wall time: 0.000370979s + +time increment:3380 time: 3.38 +field ' n' [explicit solve]: current solution: 9.790992e+01, current residual:5.913392e+01 +wall time: 0.000355005s + +time increment:3390 time: 3.39 +field ' n' [explicit solve]: current solution: 9.791430e+01, current residual:5.913647e+01 +wall time: 0.000340939s + +time increment:3400 time: 3.4 +field ' n' [explicit solve]: current solution: 9.791866e+01, current residual:5.913901e+01 +wall time: 0.000355005s + +time increment:3410 time: 3.41 +field ' n' [explicit solve]: current solution: 9.792301e+01, current residual:5.914154e+01 +wall time: 0.000336885s + +time increment:3420 time: 3.42 +field ' n' [explicit solve]: current solution: 9.792735e+01, current residual:5.914407e+01 +wall time: 0.000424147s + +time increment:3430 time: 3.43 +field ' n' [explicit solve]: current solution: 9.793168e+01, current residual:5.914659e+01 +wall time: 0.000372171s + +time increment:3440 time: 3.44 +field ' n' [explicit solve]: current solution: 9.793600e+01, current residual:5.914910e+01 +wall time: 0.000436068s + +time increment:3450 time: 3.45 +field ' n' [explicit solve]: current solution: 9.794031e+01, current residual:5.915160e+01 +wall time: 0.000355005s + +time increment:3460 time: 3.46 +field ' n' [explicit solve]: current solution: 9.794460e+01, current residual:5.915410e+01 +wall time: 0.000377893s + +time increment:3470 time: 3.47 +field ' n' [explicit solve]: current solution: 9.794888e+01, current residual:5.915659e+01 +wall time: 0.000333071s + +time increment:3480 time: 3.48 +field ' n' [explicit solve]: current solution: 9.795315e+01, current residual:5.915907e+01 +wall time: 0.000358105s + +time increment:3490 time: 3.49 +field ' n' [explicit solve]: current solution: 9.795741e+01, current residual:5.916155e+01 +wall time: 0.000422001s + +time increment:3500 time: 3.5 +field ' n' [explicit solve]: current solution: 9.796166e+01, current residual:5.916402e+01 +wall time: 0.000306129s + +time increment:3510 time: 3.51 +field ' n' [explicit solve]: current solution: 9.796590e+01, current residual:5.916648e+01 +wall time: 0.000332832s + +time increment:3520 time: 3.52 +field ' n' [explicit solve]: current solution: 9.797013e+01, current residual:5.916894e+01 +wall time: 0.000377893s + +time increment:3530 time: 3.53 +field ' n' [explicit solve]: current solution: 9.797434e+01, current residual:5.917139e+01 +wall time: 0.000360966s + +time increment:3540 time: 3.54 +field ' n' [explicit solve]: current solution: 9.797855e+01, current residual:5.917383e+01 +wall time: 0.000334978s + +time increment:3550 time: 3.55 +field ' n' [explicit solve]: current solution: 9.798274e+01, current residual:5.917627e+01 +wall time: 0.000332117s + +time increment:3560 time: 3.56 +field ' n' [explicit solve]: current solution: 9.798693e+01, current residual:5.917870e+01 +wall time: 0.000298023s + +time increment:3570 time: 3.57 +field ' n' [explicit solve]: current solution: 9.799110e+01, current residual:5.918113e+01 +wall time: 0.000280857s + +time increment:3580 time: 3.58 +field ' n' [explicit solve]: current solution: 9.799527e+01, current residual:5.918355e+01 +wall time: 0.000300169s + +time increment:3590 time: 3.59 +field ' n' [explicit solve]: current solution: 9.799942e+01, current residual:5.918596e+01 +wall time: 0.000310898s + +time increment:3600 time: 3.6 +field ' n' [explicit solve]: current solution: 9.800357e+01, current residual:5.918837e+01 +wall time: 0.000337124s + +time increment:3610 time: 3.61 +field ' n' [explicit solve]: current solution: 9.800770e+01, current residual:5.919077e+01 +wall time: 0.000324965s + +time increment:3620 time: 3.62 +field ' n' [explicit solve]: current solution: 9.801183e+01, current residual:5.919316e+01 +wall time: 0.000422001s + +time increment:3630 time: 3.63 +field ' n' [explicit solve]: current solution: 9.801594e+01, current residual:5.919555e+01 +wall time: 0.000354052s + +time increment:3640 time: 3.64 +field ' n' [explicit solve]: current solution: 9.802005e+01, current residual:5.919794e+01 +wall time: 0.000304937s + +time increment:3650 time: 3.65 +field ' n' [explicit solve]: current solution: 9.802414e+01, current residual:5.920031e+01 +wall time: 0.00029397s + +time increment:3660 time: 3.66 +field ' n' [explicit solve]: current solution: 9.802823e+01, current residual:5.920269e+01 +wall time: 0.000370979s + +time increment:3670 time: 3.67 +field ' n' [explicit solve]: current solution: 9.803231e+01, current residual:5.920505e+01 +wall time: 0.000374079s + +time increment:3680 time: 3.68 +field ' n' [explicit solve]: current solution: 9.803638e+01, current residual:5.920742e+01 +wall time: 0.000339985s + +time increment:3690 time: 3.69 +field ' n' [explicit solve]: current solution: 9.804044e+01, current residual:5.920977e+01 +wall time: 0.000308037s + +time increment:3700 time: 3.7 +field ' n' [explicit solve]: current solution: 9.804449e+01, current residual:5.921212e+01 +wall time: 0.000341177s + +time increment:3710 time: 3.71 +field ' n' [explicit solve]: current solution: 9.804854e+01, current residual:5.921447e+01 +wall time: 0.000428915s + +time increment:3720 time: 3.72 +field ' n' [explicit solve]: current solution: 9.805257e+01, current residual:5.921681e+01 +wall time: 0.000426054s + +time increment:3730 time: 3.73 +field ' n' [explicit solve]: current solution: 9.805660e+01, current residual:5.921915e+01 +wall time: 0.000338078s + +time increment:3740 time: 3.74 +field ' n' [explicit solve]: current solution: 9.806062e+01, current residual:5.922148e+01 +wall time: 0.000337124s + +time increment:3750 time: 3.75 +field ' n' [explicit solve]: current solution: 9.806463e+01, current residual:5.922381e+01 +wall time: 0.000355959s + +time increment:3760 time: 3.76 +field ' n' [explicit solve]: current solution: 9.806864e+01, current residual:5.922613e+01 +wall time: 0.000298023s + +time increment:3770 time: 3.77 +field ' n' [explicit solve]: current solution: 9.807263e+01, current residual:5.922844e+01 +wall time: 0.000293016s + +time increment:3780 time: 3.78 +field ' n' [explicit solve]: current solution: 9.807662e+01, current residual:5.923076e+01 +wall time: 0.000305176s + +time increment:3790 time: 3.79 +field ' n' [explicit solve]: current solution: 9.808060e+01, current residual:5.923306e+01 +wall time: 0.000334978s + +time increment:3800 time: 3.8 +field ' n' [explicit solve]: current solution: 9.808457e+01, current residual:5.923537e+01 +wall time: 0.000302076s + +time increment:3810 time: 3.81 +field ' n' [explicit solve]: current solution: 9.808854e+01, current residual:5.923767e+01 +wall time: 0.000355959s + +time increment:3820 time: 3.82 +field ' n' [explicit solve]: current solution: 9.809249e+01, current residual:5.923996e+01 +wall time: 0.000347853s + +time increment:3830 time: 3.83 +field ' n' [explicit solve]: current solution: 9.809644e+01, current residual:5.924225e+01 +wall time: 0.000281096s + +time increment:3840 time: 3.84 +field ' n' [explicit solve]: current solution: 9.810039e+01, current residual:5.924453e+01 +wall time: 0.000364065s + +time increment:3850 time: 3.85 +field ' n' [explicit solve]: current solution: 9.810432e+01, current residual:5.924682e+01 +wall time: 0.000339985s + +time increment:3860 time: 3.86 +field ' n' [explicit solve]: current solution: 9.810825e+01, current residual:5.924909e+01 +wall time: 0.000356913s + +time increment:3870 time: 3.87 +field ' n' [explicit solve]: current solution: 9.811218e+01, current residual:5.925137e+01 +wall time: 0.000290155s + +time increment:3880 time: 3.88 +field ' n' [explicit solve]: current solution: 9.811609e+01, current residual:5.925364e+01 +wall time: 0.000334024s + +time increment:3890 time: 3.89 +field ' n' [explicit solve]: current solution: 9.812000e+01, current residual:5.925590e+01 +wall time: 0.000314951s + +time increment:3900 time: 3.9 +field ' n' [explicit solve]: current solution: 9.812391e+01, current residual:5.925816e+01 +wall time: 0.000315905s + +time increment:3910 time: 3.91 +field ' n' [explicit solve]: current solution: 9.812781e+01, current residual:5.926042e+01 +wall time: 0.00028491s + +time increment:3920 time: 3.92 +field ' n' [explicit solve]: current solution: 9.813170e+01, current residual:5.926267e+01 +wall time: 0.00030303s + +time increment:3930 time: 3.93 +field ' n' [explicit solve]: current solution: 9.813558e+01, current residual:5.926492e+01 +wall time: 0.000349998s + +time increment:3940 time: 3.94 +field ' n' [explicit solve]: current solution: 9.813946e+01, current residual:5.926717e+01 +wall time: 0.00029707s + +time increment:3950 time: 3.95 +field ' n' [explicit solve]: current solution: 9.814333e+01, current residual:5.926941e+01 +wall time: 0.000317097s + +time increment:3960 time: 3.96 +field ' n' [explicit solve]: current solution: 9.814720e+01, current residual:5.927165e+01 +wall time: 0.00030899s + +time increment:3970 time: 3.97 +field ' n' [explicit solve]: current solution: 9.815106e+01, current residual:5.927388e+01 +wall time: 0.000363827s + +time increment:3980 time: 3.98 +field ' n' [explicit solve]: current solution: 9.815492e+01, current residual:5.927612e+01 +wall time: 0.000315189s + +time increment:3990 time: 3.99 +field ' n' [explicit solve]: current solution: 9.815877e+01, current residual:5.927834e+01 +wall time: 0.000344038s + +time increment:4000 time: 4 +field ' n' [explicit solve]: current solution: 9.816262e+01, current residual:5.928057e+01 +wall time: 0.000312805s +fields: 1 +fields: 1 +fields: 1 +fields: 1 +Output written to:solution-004000.pvtu + +Energy: 230.703 +Energy Components: 105.732 124.97 0 + +time increment:4010 time: 4.01 +field ' n' [explicit solve]: current solution: 9.816646e+01, current residual:5.928279e+01 +wall time: 0.00039506s + +time increment:4020 time: 4.02 +field ' n' [explicit solve]: current solution: 9.817029e+01, current residual:5.928501e+01 +wall time: 0.000326157s + +time increment:4030 time: 4.03 +field ' n' [explicit solve]: current solution: 9.817412e+01, current residual:5.928723e+01 +wall time: 0.000355005s + +time increment:4040 time: 4.04 +field ' n' [explicit solve]: current solution: 9.817794e+01, current residual:5.928944e+01 +wall time: 0.000406027s + +time increment:4050 time: 4.05 +field ' n' [explicit solve]: current solution: 9.818176e+01, current residual:5.929165e+01 +wall time: 0.000344992s + +time increment:4060 time: 4.06 +field ' n' [explicit solve]: current solution: 9.818558e+01, current residual:5.929386e+01 +wall time: 0.000356913s + +time increment:4070 time: 4.07 +field ' n' [explicit solve]: current solution: 9.818939e+01, current residual:5.929606e+01 +wall time: 0.000415087s + +time increment:4080 time: 4.08 +field ' n' [explicit solve]: current solution: 9.819320e+01, current residual:5.929826e+01 +wall time: 0.000342846s + +time increment:4090 time: 4.09 +field ' n' [explicit solve]: current solution: 9.819700e+01, current residual:5.930046e+01 +wall time: 0.00037694s + +time increment:4100 time: 4.1 +field ' n' [explicit solve]: current solution: 9.820079e+01, current residual:5.930265e+01 +wall time: 0.000387907s + +time increment:4110 time: 4.11 +field ' n' [explicit solve]: current solution: 9.820459e+01, current residual:5.930485e+01 +wall time: 0.000375986s + +time increment:4120 time: 4.12 +field ' n' [explicit solve]: current solution: 9.820837e+01, current residual:5.930704e+01 +wall time: 0.000342846s + +time increment:4130 time: 4.13 +field ' n' [explicit solve]: current solution: 9.821216e+01, current residual:5.930922e+01 +wall time: 0.000329018s + +time increment:4140 time: 4.14 +field ' n' [explicit solve]: current solution: 9.821594e+01, current residual:5.931141e+01 +wall time: 0.0003829s + +time increment:4150 time: 4.15 +field ' n' [explicit solve]: current solution: 9.821971e+01, current residual:5.931359e+01 +wall time: 0.000316858s + +time increment:4160 time: 4.16 +field ' n' [explicit solve]: current solution: 9.822349e+01, current residual:5.931577e+01 +wall time: 0.000357151s + +time increment:4170 time: 4.17 +field ' n' [explicit solve]: current solution: 9.822726e+01, current residual:5.931795e+01 +wall time: 0.000361919s + +time increment:4180 time: 4.18 +field ' n' [explicit solve]: current solution: 9.823102e+01, current residual:5.932012e+01 +wall time: 0.000300884s + +time increment:4190 time: 4.19 +field ' n' [explicit solve]: current solution: 9.823478e+01, current residual:5.932230e+01 +wall time: 0.000362158s + +time increment:4200 time: 4.2 +field ' n' [explicit solve]: current solution: 9.823854e+01, current residual:5.932447e+01 +wall time: 0.00041604s + +time increment:4210 time: 4.21 +field ' n' [explicit solve]: current solution: 9.824229e+01, current residual:5.932664e+01 +wall time: 0.000324965s + +time increment:4220 time: 4.22 +field ' n' [explicit solve]: current solution: 9.824604e+01, current residual:5.932880e+01 +wall time: 0.000357866s + +time increment:4230 time: 4.23 +field ' n' [explicit solve]: current solution: 9.824979e+01, current residual:5.933097e+01 +wall time: 0.000320911s + +time increment:4240 time: 4.24 +field ' n' [explicit solve]: current solution: 9.825353e+01, current residual:5.933313e+01 +wall time: 0.000365019s + +time increment:4250 time: 4.25 +field ' n' [explicit solve]: current solution: 9.825727e+01, current residual:5.933529e+01 +wall time: 0.000389099s + +time increment:4260 time: 4.26 +field ' n' [explicit solve]: current solution: 9.826101e+01, current residual:5.933745e+01 +wall time: 0.000349045s + +time increment:4270 time: 4.27 +field ' n' [explicit solve]: current solution: 9.826475e+01, current residual:5.933960e+01 +wall time: 0.00040102s + +time increment:4280 time: 4.28 +field ' n' [explicit solve]: current solution: 9.826848e+01, current residual:5.934176e+01 +wall time: 0.000349045s + +time increment:4290 time: 4.29 +field ' n' [explicit solve]: current solution: 9.827221e+01, current residual:5.934391e+01 +wall time: 0.000379086s + +time increment:4300 time: 4.3 +field ' n' [explicit solve]: current solution: 9.827593e+01, current residual:5.934606e+01 +wall time: 0.000405073s + +time increment:4310 time: 4.31 +field ' n' [explicit solve]: current solution: 9.827966e+01, current residual:5.934821e+01 +wall time: 0.000344992s + +time increment:4320 time: 4.32 +field ' n' [explicit solve]: current solution: 9.828338e+01, current residual:5.935036e+01 +wall time: 0.000335932s + +time increment:4330 time: 4.33 +field ' n' [explicit solve]: current solution: 9.828710e+01, current residual:5.935250e+01 +wall time: 0.000411987s + +time increment:4340 time: 4.34 +field ' n' [explicit solve]: current solution: 9.829081e+01, current residual:5.935465e+01 +wall time: 0.000411987s + +time increment:4350 time: 4.35 +field ' n' [explicit solve]: current solution: 9.829453e+01, current residual:5.935679e+01 +wall time: 0.000344038s + +time increment:4360 time: 4.36 +field ' n' [explicit solve]: current solution: 9.829824e+01, current residual:5.935893e+01 +wall time: 0.000396013s + +time increment:4370 time: 4.37 +field ' n' [explicit solve]: current solution: 9.830195e+01, current residual:5.936107e+01 +wall time: 0.000374794s + +time increment:4380 time: 4.38 +field ' n' [explicit solve]: current solution: 9.830565e+01, current residual:5.936321e+01 +wall time: 0.000433922s + +time increment:4390 time: 4.39 +field ' n' [explicit solve]: current solution: 9.830936e+01, current residual:5.936535e+01 +wall time: 0.000319004s + +time increment:4400 time: 4.4 +field ' n' [explicit solve]: current solution: 9.831306e+01, current residual:5.936748e+01 +wall time: 0.000336885s + +time increment:4410 time: 4.41 +field ' n' [explicit solve]: current solution: 9.831676e+01, current residual:5.936961e+01 +wall time: 0.000363111s + +time increment:4420 time: 4.42 +field ' n' [explicit solve]: current solution: 9.832046e+01, current residual:5.937175e+01 +wall time: 0.000398874s + +time increment:4430 time: 4.43 +field ' n' [explicit solve]: current solution: 9.832416e+01, current residual:5.937388e+01 +wall time: 0.000357151s + +time increment:4440 time: 4.44 +field ' n' [explicit solve]: current solution: 9.832786e+01, current residual:5.937601e+01 +wall time: 0.000335932s + +time increment:4450 time: 4.45 +field ' n' [explicit solve]: current solution: 9.833155e+01, current residual:5.937814e+01 +wall time: 0.000344038s + +time increment:4460 time: 4.46 +field ' n' [explicit solve]: current solution: 9.833524e+01, current residual:5.938027e+01 +wall time: 0.000365019s + +time increment:4470 time: 4.47 +field ' n' [explicit solve]: current solution: 9.833893e+01, current residual:5.938239e+01 +wall time: 0.000325918s + +time increment:4480 time: 4.48 +field ' n' [explicit solve]: current solution: 9.834262e+01, current residual:5.938452e+01 +wall time: 0.000323057s + +time increment:4490 time: 4.49 +field ' n' [explicit solve]: current solution: 9.834631e+01, current residual:5.938665e+01 +wall time: 0.000339031s + +time increment:4500 time: 4.5 +field ' n' [explicit solve]: current solution: 9.835000e+01, current residual:5.938877e+01 +wall time: 0.000353098s + +time increment:4510 time: 4.51 +field ' n' [explicit solve]: current solution: 9.835368e+01, current residual:5.939089e+01 +wall time: 0.000305891s + +time increment:4520 time: 4.52 +field ' n' [explicit solve]: current solution: 9.835737e+01, current residual:5.939302e+01 +wall time: 0.000326872s + +time increment:4530 time: 4.53 +field ' n' [explicit solve]: current solution: 9.836105e+01, current residual:5.939514e+01 +wall time: 0.000329018s + +time increment:4540 time: 4.54 +field ' n' [explicit solve]: current solution: 9.836473e+01, current residual:5.939726e+01 +wall time: 0.000388861s + +time increment:4550 time: 4.55 +field ' n' [explicit solve]: current solution: 9.836842e+01, current residual:5.939938e+01 +wall time: 0.000339985s + +time increment:4560 time: 4.56 +field ' n' [explicit solve]: current solution: 9.837210e+01, current residual:5.940150e+01 +wall time: 0.000361919s + +time increment:4570 time: 4.57 +field ' n' [explicit solve]: current solution: 9.837578e+01, current residual:5.940362e+01 +wall time: 0.00030899s + +time increment:4580 time: 4.58 +field ' n' [explicit solve]: current solution: 9.837946e+01, current residual:5.940573e+01 +wall time: 0.000295877s + +time increment:4590 time: 4.59 +field ' n' [explicit solve]: current solution: 9.838313e+01, current residual:5.940785e+01 +wall time: 0.000335932s + +time increment:4600 time: 4.6 +field ' n' [explicit solve]: current solution: 9.838681e+01, current residual:5.940997e+01 +wall time: 0.00039196s + +time increment:4610 time: 4.61 +field ' n' [explicit solve]: current solution: 9.839049e+01, current residual:5.941209e+01 +wall time: 0.00029707s + +time increment:4620 time: 4.62 +field ' n' [explicit solve]: current solution: 9.839417e+01, current residual:5.941420e+01 +wall time: 0.000355005s + +time increment:4630 time: 4.63 +field ' n' [explicit solve]: current solution: 9.839784e+01, current residual:5.941632e+01 +wall time: 0.000295162s + +time increment:4640 time: 4.64 +field ' n' [explicit solve]: current solution: 9.840152e+01, current residual:5.941843e+01 +wall time: 0.000369072s + +time increment:4650 time: 4.65 +field ' n' [explicit solve]: current solution: 9.840519e+01, current residual:5.942055e+01 +wall time: 0.00030899s + +time increment:4660 time: 4.66 +field ' n' [explicit solve]: current solution: 9.840887e+01, current residual:5.942266e+01 +wall time: 0.000300884s + +time increment:4670 time: 4.67 +field ' n' [explicit solve]: current solution: 9.841254e+01, current residual:5.942477e+01 +wall time: 0.000371933s + +time increment:4680 time: 4.68 +field ' n' [explicit solve]: current solution: 9.841622e+01, current residual:5.942689e+01 +wall time: 0.000320196s + +time increment:4690 time: 4.69 +field ' n' [explicit solve]: current solution: 9.841989e+01, current residual:5.942900e+01 +wall time: 0.000356913s + +time increment:4700 time: 4.7 +field ' n' [explicit solve]: current solution: 9.842357e+01, current residual:5.943112e+01 +wall time: 0.000342131s + +time increment:4710 time: 4.71 +field ' n' [explicit solve]: current solution: 9.842724e+01, current residual:5.943323e+01 +wall time: 0.000343084s + +time increment:4720 time: 4.72 +field ' n' [explicit solve]: current solution: 9.843092e+01, current residual:5.943534e+01 +wall time: 0.000304937s + +time increment:4730 time: 4.73 +field ' n' [explicit solve]: current solution: 9.843459e+01, current residual:5.943745e+01 +wall time: 0.000290155s + +time increment:4740 time: 4.74 +field ' n' [explicit solve]: current solution: 9.843827e+01, current residual:5.943957e+01 +wall time: 0.000307083s + +time increment:4750 time: 4.75 +field ' n' [explicit solve]: current solution: 9.844194e+01, current residual:5.944168e+01 +wall time: 0.000383139s + +time increment:4760 time: 4.76 +field ' n' [explicit solve]: current solution: 9.844562e+01, current residual:5.944379e+01 +wall time: 0.000351906s + +time increment:4770 time: 4.77 +field ' n' [explicit solve]: current solution: 9.844930e+01, current residual:5.944590e+01 +wall time: 0.0003829s + +time increment:4780 time: 4.78 +field ' n' [explicit solve]: current solution: 9.845297e+01, current residual:5.944802e+01 +wall time: 0.00033617s + +time increment:4790 time: 4.79 +field ' n' [explicit solve]: current solution: 9.845665e+01, current residual:5.945013e+01 +wall time: 0.000386953s + +time increment:4800 time: 4.8 +field ' n' [explicit solve]: current solution: 9.846033e+01, current residual:5.945224e+01 +wall time: 0.000312805s + +time increment:4810 time: 4.81 +field ' n' [explicit solve]: current solution: 9.846401e+01, current residual:5.945436e+01 +wall time: 0.000296831s + +time increment:4820 time: 4.82 +field ' n' [explicit solve]: current solution: 9.846769e+01, current residual:5.945647e+01 +wall time: 0.000381947s + +time increment:4830 time: 4.83 +field ' n' [explicit solve]: current solution: 9.847137e+01, current residual:5.945858e+01 +wall time: 0.000304937s + +time increment:4840 time: 4.84 +field ' n' [explicit solve]: current solution: 9.847505e+01, current residual:5.946070e+01 +wall time: 0.00032711s + +time increment:4850 time: 4.85 +field ' n' [explicit solve]: current solution: 9.847873e+01, current residual:5.946281e+01 +wall time: 0.000373125s + +time increment:4860 time: 4.86 +field ' n' [explicit solve]: current solution: 9.848241e+01, current residual:5.946492e+01 +wall time: 0.000317097s + +time increment:4870 time: 4.87 +field ' n' [explicit solve]: current solution: 9.848610e+01, current residual:5.946704e+01 +wall time: 0.000311852s + +time increment:4880 time: 4.88 +field ' n' [explicit solve]: current solution: 9.848978e+01, current residual:5.946915e+01 +wall time: 0.000302076s + +time increment:4890 time: 4.89 +field ' n' [explicit solve]: current solution: 9.849347e+01, current residual:5.947127e+01 +wall time: 0.000341177s + +time increment:4900 time: 4.9 +field ' n' [explicit solve]: current solution: 9.849715e+01, current residual:5.947338e+01 +wall time: 0.000338078s + +time increment:4910 time: 4.91 +field ' n' [explicit solve]: current solution: 9.850084e+01, current residual:5.947550e+01 +wall time: 0.000334024s + +time increment:4920 time: 4.92 +field ' n' [explicit solve]: current solution: 9.850453e+01, current residual:5.947762e+01 +wall time: 0.000350952s + +time increment:4930 time: 4.93 +field ' n' [explicit solve]: current solution: 9.850822e+01, current residual:5.947973e+01 +wall time: 0.00035882s + +time increment:4940 time: 4.94 +field ' n' [explicit solve]: current solution: 9.851191e+01, current residual:5.948185e+01 +wall time: 0.000358105s + +time increment:4950 time: 4.95 +field ' n' [explicit solve]: current solution: 9.851561e+01, current residual:5.948397e+01 +wall time: 0.000315905s + +time increment:4960 time: 4.96 +field ' n' [explicit solve]: current solution: 9.851930e+01, current residual:5.948609e+01 +wall time: 0.00029707s + +time increment:4970 time: 4.97 +field ' n' [explicit solve]: current solution: 9.852300e+01, current residual:5.948821e+01 +wall time: 0.000341892s + +time increment:4980 time: 4.98 +field ' n' [explicit solve]: current solution: 9.852670e+01, current residual:5.949033e+01 +wall time: 0.00029707s + +time increment:4990 time: 4.99 +field ' n' [explicit solve]: current solution: 9.853040e+01, current residual:5.949245e+01 +wall time: 0.00033021s + +time increment:5000 time: 5 +field ' n' [explicit solve]: current solution: 9.853410e+01, current residual:5.949457e+01 +wall time: 0.000327826s + +time increment:5010 time: 5.01 +field ' n' [explicit solve]: current solution: 9.853780e+01, current residual:5.949669e+01 +wall time: 0.00039506s + +time increment:5020 time: 5.02 +field ' n' [explicit solve]: current solution: 9.854151e+01, current residual:5.949882e+01 +wall time: 0.000338078s + +time increment:5030 time: 5.03 +field ' n' [explicit solve]: current solution: 9.854521e+01, current residual:5.950094e+01 +wall time: 0.000365019s + +time increment:5040 time: 5.04 +field ' n' [explicit solve]: current solution: 9.854892e+01, current residual:5.950306e+01 +wall time: 0.000369072s + +time increment:5050 time: 5.05 +field ' n' [explicit solve]: current solution: 9.855263e+01, current residual:5.950519e+01 +wall time: 0.000350952s + +time increment:5060 time: 5.06 +field ' n' [explicit solve]: current solution: 9.855635e+01, current residual:5.950732e+01 +wall time: 0.000323057s + +time increment:5070 time: 5.07 +field ' n' [explicit solve]: current solution: 9.856006e+01, current residual:5.950944e+01 +wall time: 0.000324011s + +time increment:5080 time: 5.08 +field ' n' [explicit solve]: current solution: 9.856378e+01, current residual:5.951157e+01 +wall time: 0.00031209s + +time increment:5090 time: 5.09 +field ' n' [explicit solve]: current solution: 9.856750e+01, current residual:5.951370e+01 +wall time: 0.000381947s + +time increment:5100 time: 5.1 +field ' n' [explicit solve]: current solution: 9.857122e+01, current residual:5.951583e+01 +wall time: 0.000344992s + +time increment:5110 time: 5.11 +field ' n' [explicit solve]: current solution: 9.857494e+01, current residual:5.951796e+01 +wall time: 0.000349045s + +time increment:5120 time: 5.12 +field ' n' [explicit solve]: current solution: 9.857867e+01, current residual:5.952010e+01 +wall time: 0.000365019s + +time increment:5130 time: 5.13 +field ' n' [explicit solve]: current solution: 9.858240e+01, current residual:5.952223e+01 +wall time: 0.000301838s + +time increment:5140 time: 5.14 +field ' n' [explicit solve]: current solution: 9.858613e+01, current residual:5.952436e+01 +wall time: 0.000291109s + +time increment:5150 time: 5.15 +field ' n' [explicit solve]: current solution: 9.858986e+01, current residual:5.952650e+01 +wall time: 0.000357151s + +time increment:5160 time: 5.16 +field ' n' [explicit solve]: current solution: 9.859360e+01, current residual:5.952864e+01 +wall time: 0.000335932s + +time increment:5170 time: 5.17 +field ' n' [explicit solve]: current solution: 9.859734e+01, current residual:5.953078e+01 +wall time: 0.000340939s + +time increment:5180 time: 5.18 +field ' n' [explicit solve]: current solution: 9.860108e+01, current residual:5.953292e+01 +wall time: 0.000340939s + +time increment:5190 time: 5.19 +field ' n' [explicit solve]: current solution: 9.860482e+01, current residual:5.953506e+01 +wall time: 0.000306845s + +time increment:5200 time: 5.2 +field ' n' [explicit solve]: current solution: 9.860857e+01, current residual:5.953720e+01 +wall time: 0.000321865s + +time increment:5210 time: 5.21 +field ' n' [explicit solve]: current solution: 9.861232e+01, current residual:5.953934e+01 +wall time: 0.000291824s + +time increment:5220 time: 5.22 +field ' n' [explicit solve]: current solution: 9.861607e+01, current residual:5.954149e+01 +wall time: 0.000349045s + +time increment:5230 time: 5.23 +field ' n' [explicit solve]: current solution: 9.861983e+01, current residual:5.954363e+01 +wall time: 0.000355959s + +time increment:5240 time: 5.24 +field ' n' [explicit solve]: current solution: 9.862358e+01, current residual:5.954578e+01 +wall time: 0.00029397s + +time increment:5250 time: 5.25 +field ' n' [explicit solve]: current solution: 9.862735e+01, current residual:5.954793e+01 +wall time: 0.000333071s + +time increment:5260 time: 5.26 +field ' n' [explicit solve]: current solution: 9.863111e+01, current residual:5.955008e+01 +wall time: 0.000288963s + +time increment:5270 time: 5.27 +field ' n' [explicit solve]: current solution: 9.863488e+01, current residual:5.955223e+01 +wall time: 0.000375032s + +time increment:5280 time: 5.28 +field ' n' [explicit solve]: current solution: 9.863865e+01, current residual:5.955438e+01 +wall time: 0.000329018s + +time increment:5290 time: 5.29 +field ' n' [explicit solve]: current solution: 9.864242e+01, current residual:5.955654e+01 +wall time: 0.000340939s + +time increment:5300 time: 5.3 +field ' n' [explicit solve]: current solution: 9.864620e+01, current residual:5.955869e+01 +wall time: 0.000311852s + +time increment:5310 time: 5.31 +field ' n' [explicit solve]: current solution: 9.864998e+01, current residual:5.956085e+01 +wall time: 0.00036788s + +time increment:5320 time: 5.32 +field ' n' [explicit solve]: current solution: 9.865376e+01, current residual:5.956301e+01 +wall time: 0.000307083s + +time increment:5330 time: 5.33 +field ' n' [explicit solve]: current solution: 9.865755e+01, current residual:5.956517e+01 +wall time: 0.000334024s + +time increment:5340 time: 5.34 +field ' n' [explicit solve]: current solution: 9.866134e+01, current residual:5.956733e+01 +wall time: 0.000342846s + +time increment:5350 time: 5.35 +field ' n' [explicit solve]: current solution: 9.866513e+01, current residual:5.956950e+01 +wall time: 0.000309944s + +time increment:5360 time: 5.36 +field ' n' [explicit solve]: current solution: 9.866893e+01, current residual:5.957166e+01 +wall time: 0.000320911s + +time increment:5370 time: 5.37 +field ' n' [explicit solve]: current solution: 9.867273e+01, current residual:5.957383e+01 +wall time: 0.000378132s + +time increment:5380 time: 5.38 +field ' n' [explicit solve]: current solution: 9.867653e+01, current residual:5.957600e+01 +wall time: 0.000338078s + +time increment:5390 time: 5.39 +field ' n' [explicit solve]: current solution: 9.868034e+01, current residual:5.957817e+01 +wall time: 0.000298977s + +time increment:5400 time: 5.4 +field ' n' [explicit solve]: current solution: 9.868415e+01, current residual:5.958034e+01 +wall time: 0.000308037s + +time increment:5410 time: 5.41 +field ' n' [explicit solve]: current solution: 9.868797e+01, current residual:5.958252e+01 +wall time: 0.000294924s + +time increment:5420 time: 5.42 +field ' n' [explicit solve]: current solution: 9.869178e+01, current residual:5.958469e+01 +wall time: 0.00029397s + +time increment:5430 time: 5.43 +field ' n' [explicit solve]: current solution: 9.869561e+01, current residual:5.958687e+01 +wall time: 0.000313044s + +time increment:5440 time: 5.44 +field ' n' [explicit solve]: current solution: 9.869943e+01, current residual:5.958905e+01 +wall time: 0.000375986s + +time increment:5450 time: 5.45 +field ' n' [explicit solve]: current solution: 9.870326e+01, current residual:5.959123e+01 +wall time: 0.00031209s + +time increment:5460 time: 5.46 +field ' n' [explicit solve]: current solution: 9.870710e+01, current residual:5.959341e+01 +wall time: 0.000334978s + +time increment:5470 time: 5.47 +field ' n' [explicit solve]: current solution: 9.871093e+01, current residual:5.959560e+01 +wall time: 0.00030303s + +time increment:5480 time: 5.48 +field ' n' [explicit solve]: current solution: 9.871478e+01, current residual:5.959779e+01 +wall time: 0.000365973s + +time increment:5490 time: 5.49 +field ' n' [explicit solve]: current solution: 9.871862e+01, current residual:5.959997e+01 +wall time: 0.00031209s + +time increment:5500 time: 5.5 +field ' n' [explicit solve]: current solution: 9.872247e+01, current residual:5.960217e+01 +wall time: 0.000383854s + +time increment:5510 time: 5.51 +field ' n' [explicit solve]: current solution: 9.872633e+01, current residual:5.960436e+01 +wall time: 0.000320196s + +time increment:5520 time: 5.52 +field ' n' [explicit solve]: current solution: 9.873019e+01, current residual:5.960655e+01 +wall time: 0.000341177s + +time increment:5530 time: 5.53 +field ' n' [explicit solve]: current solution: 9.873405e+01, current residual:5.960875e+01 +wall time: 0.000316858s + +time increment:5540 time: 5.54 +field ' n' [explicit solve]: current solution: 9.873791e+01, current residual:5.961095e+01 +wall time: 0.000344992s + +time increment:5550 time: 5.55 +field ' n' [explicit solve]: current solution: 9.874178e+01, current residual:5.961315e+01 +wall time: 0.00039196s + +time increment:5560 time: 5.56 +field ' n' [explicit solve]: current solution: 9.874566e+01, current residual:5.961535e+01 +wall time: 0.00031209s + +time increment:5570 time: 5.57 +field ' n' [explicit solve]: current solution: 9.874954e+01, current residual:5.961756e+01 +wall time: 0.000335932s + +time increment:5580 time: 5.58 +field ' n' [explicit solve]: current solution: 9.875342e+01, current residual:5.961977e+01 +wall time: 0.000324011s + +time increment:5590 time: 5.59 +field ' n' [explicit solve]: current solution: 9.875731e+01, current residual:5.962197e+01 +wall time: 0.000319958s + +time increment:5600 time: 5.6 +field ' n' [explicit solve]: current solution: 9.876120e+01, current residual:5.962419e+01 +wall time: 0.000298023s + +time increment:5610 time: 5.61 +field ' n' [explicit solve]: current solution: 9.876510e+01, current residual:5.962640e+01 +wall time: 0.000293016s + +time increment:5620 time: 5.62 +field ' n' [explicit solve]: current solution: 9.876900e+01, current residual:5.962861e+01 +wall time: 0.000307083s + +time increment:5630 time: 5.63 +field ' n' [explicit solve]: current solution: 9.877291e+01, current residual:5.963083e+01 +wall time: 0.000340939s + +time increment:5640 time: 5.64 +field ' n' [explicit solve]: current solution: 9.877682e+01, current residual:5.963305e+01 +wall time: 0.000365973s + +time increment:5650 time: 5.65 +field ' n' [explicit solve]: current solution: 9.878073e+01, current residual:5.963527e+01 +wall time: 0.000329018s + +time increment:5660 time: 5.66 +field ' n' [explicit solve]: current solution: 9.878465e+01, current residual:5.963750e+01 +wall time: 0.000323057s + +time increment:5670 time: 5.67 +field ' n' [explicit solve]: current solution: 9.878857e+01, current residual:5.963972e+01 +wall time: 0.0003829s + +time increment:5680 time: 5.68 +field ' n' [explicit solve]: current solution: 9.879250e+01, current residual:5.964195e+01 +wall time: 0.00032115s + +time increment:5690 time: 5.69 +field ' n' [explicit solve]: current solution: 9.879644e+01, current residual:5.964418e+01 +wall time: 0.000355005s + +time increment:5700 time: 5.7 +field ' n' [explicit solve]: current solution: 9.880037e+01, current residual:5.964642e+01 +wall time: 0.000346899s + +time increment:5710 time: 5.71 +field ' n' [explicit solve]: current solution: 9.880432e+01, current residual:5.964865e+01 +wall time: 0.000374794s + +time increment:5720 time: 5.72 +field ' n' [explicit solve]: current solution: 9.880826e+01, current residual:5.965089e+01 +wall time: 0.000358105s + +time increment:5730 time: 5.73 +field ' n' [explicit solve]: current solution: 9.881221e+01, current residual:5.965313e+01 +wall time: 0.0003829s + +time increment:5740 time: 5.74 +field ' n' [explicit solve]: current solution: 9.881617e+01, current residual:5.965537e+01 +wall time: 0.000371933s + +time increment:5750 time: 5.75 +field ' n' [explicit solve]: current solution: 9.882013e+01, current residual:5.965761e+01 +wall time: 0.000400066s + +time increment:5760 time: 5.76 +field ' n' [explicit solve]: current solution: 9.882410e+01, current residual:5.965986e+01 +wall time: 0.000378847s + +time increment:5770 time: 5.77 +field ' n' [explicit solve]: current solution: 9.882807e+01, current residual:5.966211e+01 +wall time: 0.000334024s + +time increment:5780 time: 5.78 +field ' n' [explicit solve]: current solution: 9.883204e+01, current residual:5.966436e+01 +wall time: 0.000363827s + +time increment:5790 time: 5.79 +field ' n' [explicit solve]: current solution: 9.883602e+01, current residual:5.966661e+01 +wall time: 0.000422955s + +time increment:5800 time: 5.8 +field ' n' [explicit solve]: current solution: 9.884000e+01, current residual:5.966886e+01 +wall time: 0.000392914s + +time increment:5810 time: 5.81 +field ' n' [explicit solve]: current solution: 9.884399e+01, current residual:5.967112e+01 +wall time: 0.000391006s + +time increment:5820 time: 5.82 +field ' n' [explicit solve]: current solution: 9.884799e+01, current residual:5.967338e+01 +wall time: 0.000334024s + +time increment:5830 time: 5.83 +field ' n' [explicit solve]: current solution: 9.885198e+01, current residual:5.967564e+01 +wall time: 0.000387907s + +time increment:5840 time: 5.84 +field ' n' [explicit solve]: current solution: 9.885599e+01, current residual:5.967790e+01 +wall time: 0.000322819s + +time increment:5850 time: 5.85 +field ' n' [explicit solve]: current solution: 9.885999e+01, current residual:5.968017e+01 +wall time: 0.000349045s + +time increment:5860 time: 5.86 +field ' n' [explicit solve]: current solution: 9.886401e+01, current residual:5.968244e+01 +wall time: 0.000368834s + +time increment:5870 time: 5.87 +field ' n' [explicit solve]: current solution: 9.886802e+01, current residual:5.968471e+01 +wall time: 0.0003438s + +time increment:5880 time: 5.88 +field ' n' [explicit solve]: current solution: 9.887204e+01, current residual:5.968698e+01 +wall time: 0.000380993s + +time increment:5890 time: 5.89 +field ' n' [explicit solve]: current solution: 9.887607e+01, current residual:5.968925e+01 +wall time: 0.000348091s + +time increment:5900 time: 5.9 +field ' n' [explicit solve]: current solution: 9.888010e+01, current residual:5.969153e+01 +wall time: 0.000346899s + +time increment:5910 time: 5.91 +field ' n' [explicit solve]: current solution: 9.888413e+01, current residual:5.969381e+01 +wall time: 0.000345945s + +time increment:5920 time: 5.92 +field ' n' [explicit solve]: current solution: 9.888817e+01, current residual:5.969609e+01 +wall time: 0.000307083s + +time increment:5930 time: 5.93 +field ' n' [explicit solve]: current solution: 9.889222e+01, current residual:5.969837e+01 +wall time: 0.000405073s + +time increment:5940 time: 5.94 +field ' n' [explicit solve]: current solution: 9.889627e+01, current residual:5.970065e+01 +wall time: 0.000468969s + +time increment:5950 time: 5.95 +field ' n' [explicit solve]: current solution: 9.890032e+01, current residual:5.970294e+01 +wall time: 0.000397921s + +time increment:5960 time: 5.96 +field ' n' [explicit solve]: current solution: 9.890438e+01, current residual:5.970523e+01 +wall time: 0.000319958s + +time increment:5970 time: 5.97 +field ' n' [explicit solve]: current solution: 9.890844e+01, current residual:5.970752e+01 +wall time: 0.000346184s + +time increment:5980 time: 5.98 +field ' n' [explicit solve]: current solution: 9.891250e+01, current residual:5.970981e+01 +wall time: 0.000370026s + +time increment:5990 time: 5.99 +field ' n' [explicit solve]: current solution: 9.891657e+01, current residual:5.971210e+01 +wall time: 0.000379086s + +time increment:6000 time: 6 +field ' n' [explicit solve]: current solution: 9.892065e+01, current residual:5.971440e+01 +wall time: 0.000355005s +fields: 1 +fields: 1 +fields: 1 +fields: 1 +Output written to:solution-006000.pvtu + +Energy: 219.201 +Energy Components: 98.903 120.298 0 + +time increment:6010 time: 6.01 +field ' n' [explicit solve]: current solution: 9.892472e+01, current residual:5.971670e+01 +wall time: 0.000339985s + +time increment:6020 time: 6.02 +field ' n' [explicit solve]: current solution: 9.892881e+01, current residual:5.971900e+01 +wall time: 0.000329971s + +time increment:6030 time: 6.03 +field ' n' [explicit solve]: current solution: 9.893289e+01, current residual:5.972130e+01 +wall time: 0.000342131s + +time increment:6040 time: 6.04 +field ' n' [explicit solve]: current solution: 9.893698e+01, current residual:5.972360e+01 +wall time: 0.00031805s + +time increment:6050 time: 6.05 +field ' n' [explicit solve]: current solution: 9.894108e+01, current residual:5.972591e+01 +wall time: 0.000344992s + +time increment:6060 time: 6.06 +field ' n' [explicit solve]: current solution: 9.894517e+01, current residual:5.972821e+01 +wall time: 0.000300884s + +time increment:6070 time: 6.07 +field ' n' [explicit solve]: current solution: 9.894927e+01, current residual:5.973052e+01 +wall time: 0.0003438s + +time increment:6080 time: 6.08 +field ' n' [explicit solve]: current solution: 9.895338e+01, current residual:5.973283e+01 +wall time: 0.00028801s + +time increment:6090 time: 6.09 +field ' n' [explicit solve]: current solution: 9.895749e+01, current residual:5.973514e+01 +wall time: 0.000319004s + +time increment:6100 time: 6.1 +field ' n' [explicit solve]: current solution: 9.896160e+01, current residual:5.973745e+01 +wall time: 0.000346184s + +time increment:6110 time: 6.11 +field ' n' [explicit solve]: current solution: 9.896571e+01, current residual:5.973977e+01 +wall time: 0.000363111s + +time increment:6120 time: 6.12 +field ' n' [explicit solve]: current solution: 9.896983e+01, current residual:5.974208e+01 +wall time: 0.000380039s + +time increment:6130 time: 6.13 +field ' n' [explicit solve]: current solution: 9.897395e+01, current residual:5.974440e+01 +wall time: 0.000299931s + +time increment:6140 time: 6.14 +field ' n' [explicit solve]: current solution: 9.897808e+01, current residual:5.974671e+01 +wall time: 0.00033617s + +time increment:6150 time: 6.15 +field ' n' [explicit solve]: current solution: 9.898220e+01, current residual:5.974903e+01 +wall time: 0.000354052s + +time increment:6160 time: 6.16 +field ' n' [explicit solve]: current solution: 9.898633e+01, current residual:5.975135e+01 +wall time: 0.000331879s + +time increment:6170 time: 6.17 +field ' n' [explicit solve]: current solution: 9.899047e+01, current residual:5.975367e+01 +wall time: 0.000369072s + +time increment:6180 time: 6.18 +field ' n' [explicit solve]: current solution: 9.899460e+01, current residual:5.975599e+01 +wall time: 0.000310898s + +time increment:6190 time: 6.19 +field ' n' [explicit solve]: current solution: 9.899874e+01, current residual:5.975832e+01 +wall time: 0.00030899s + +time increment:6200 time: 6.2 +field ' n' [explicit solve]: current solution: 9.900288e+01, current residual:5.976064e+01 +wall time: 0.000298023s + +time increment:6210 time: 6.21 +field ' n' [explicit solve]: current solution: 9.900702e+01, current residual:5.976296e+01 +wall time: 0.000356913s + +time increment:6220 time: 6.22 +field ' n' [explicit solve]: current solution: 9.901116e+01, current residual:5.976529e+01 +wall time: 0.000294924s + +time increment:6230 time: 6.23 +field ' n' [explicit solve]: current solution: 9.901531e+01, current residual:5.976761e+01 +wall time: 0.000289917s + +time increment:6240 time: 6.24 +field ' n' [explicit solve]: current solution: 9.901945e+01, current residual:5.976994e+01 +wall time: 0.000301123s + +time increment:6250 time: 6.25 +field ' n' [explicit solve]: current solution: 9.902360e+01, current residual:5.977226e+01 +wall time: 0.000329971s + +time increment:6260 time: 6.26 +field ' n' [explicit solve]: current solution: 9.902775e+01, current residual:5.977459e+01 +wall time: 0.000315905s + +time increment:6270 time: 6.27 +field ' n' [explicit solve]: current solution: 9.903190e+01, current residual:5.977691e+01 +wall time: 0.000302076s + +time increment:6280 time: 6.28 +field ' n' [explicit solve]: current solution: 9.903605e+01, current residual:5.977924e+01 +wall time: 0.00030899s + +time increment:6290 time: 6.29 +field ' n' [explicit solve]: current solution: 9.904020e+01, current residual:5.978157e+01 +wall time: 0.000406027s + +time increment:6300 time: 6.3 +field ' n' [explicit solve]: current solution: 9.904435e+01, current residual:5.978389e+01 +wall time: 0.000341177s + +time increment:6310 time: 6.31 +field ' n' [explicit solve]: current solution: 9.904851e+01, current residual:5.978622e+01 +wall time: 0.000341892s + +time increment:6320 time: 6.32 +field ' n' [explicit solve]: current solution: 9.905266e+01, current residual:5.978854e+01 +wall time: 0.000294924s + +time increment:6330 time: 6.33 +field ' n' [explicit solve]: current solution: 9.905681e+01, current residual:5.979087e+01 +wall time: 0.000329018s + +time increment:6340 time: 6.34 +field ' n' [explicit solve]: current solution: 9.906096e+01, current residual:5.979319e+01 +wall time: 0.000322104s + +time increment:6350 time: 6.35 +field ' n' [explicit solve]: current solution: 9.906511e+01, current residual:5.979552e+01 +wall time: 0.000290871s + +time increment:6360 time: 6.36 +field ' n' [explicit solve]: current solution: 9.906926e+01, current residual:5.979784e+01 +wall time: 0.000354052s + +time increment:6370 time: 6.37 +field ' n' [explicit solve]: current solution: 9.907341e+01, current residual:5.980016e+01 +wall time: 0.000344992s + +time increment:6380 time: 6.38 +field ' n' [explicit solve]: current solution: 9.907756e+01, current residual:5.980248e+01 +wall time: 0.000319958s + +time increment:6390 time: 6.39 +field ' n' [explicit solve]: current solution: 9.908171e+01, current residual:5.980480e+01 +wall time: 0.00029397s + +time increment:6400 time: 6.4 +field ' n' [explicit solve]: current solution: 9.908585e+01, current residual:5.980712e+01 +wall time: 0.000298023s + +time increment:6410 time: 6.41 +field ' n' [explicit solve]: current solution: 9.908999e+01, current residual:5.980944e+01 +wall time: 0.000341177s + +time increment:6420 time: 6.42 +field ' n' [explicit solve]: current solution: 9.909413e+01, current residual:5.981175e+01 +wall time: 0.00040102s + +time increment:6430 time: 6.43 +field ' n' [explicit solve]: current solution: 9.909827e+01, current residual:5.981406e+01 +wall time: 0.000290155s + +time increment:6440 time: 6.44 +field ' n' [explicit solve]: current solution: 9.910241e+01, current residual:5.981638e+01 +wall time: 0.000422001s + +time increment:6450 time: 6.45 +field ' n' [explicit solve]: current solution: 9.910654e+01, current residual:5.981869e+01 +wall time: 0.00037694s + +time increment:6460 time: 6.46 +field ' n' [explicit solve]: current solution: 9.911067e+01, current residual:5.982099e+01 +wall time: 0.000312805s + +time increment:6470 time: 6.47 +field ' n' [explicit solve]: current solution: 9.911479e+01, current residual:5.982330e+01 +wall time: 0.000334024s + +time increment:6480 time: 6.48 +field ' n' [explicit solve]: current solution: 9.911891e+01, current residual:5.982560e+01 +wall time: 0.000294209s + +time increment:6490 time: 6.49 +field ' n' [explicit solve]: current solution: 9.912303e+01, current residual:5.982790e+01 +wall time: 0.000336885s + +time increment:6500 time: 6.5 +field ' n' [explicit solve]: current solution: 9.912714e+01, current residual:5.983020e+01 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current solution: 9.915577e+01, current residual:5.984620e+01 +wall time: 0.000322819s + +time increment:6580 time: 6.58 +field ' n' [explicit solve]: current solution: 9.915984e+01, current residual:5.984847e+01 +wall time: 0.000349998s + +time increment:6590 time: 6.59 +field ' n' [explicit solve]: current solution: 9.916390e+01, current residual:5.985074e+01 +wall time: 0.000359058s + +time increment:6600 time: 6.6 +field ' n' [explicit solve]: current solution: 9.916795e+01, current residual:5.985300e+01 +wall time: 0.000372887s + +time increment:6610 time: 6.61 +field ' n' [explicit solve]: current solution: 9.917199e+01, current residual:5.985526e+01 +wall time: 0.000396967s + +time increment:6620 time: 6.62 +field ' n' [explicit solve]: current solution: 9.917602e+01, current residual:5.985751e+01 +wall time: 0.000367165s + +time increment:6630 time: 6.63 +field ' n' [explicit solve]: current solution: 9.918005e+01, current residual:5.985976e+01 +wall time: 0.000344038s + +time increment:6640 time: 6.64 +field ' n' [explicit solve]: current solution: 9.918407e+01, current residual:5.986200e+01 +wall time: 0.000335932s + +time increment:6650 time: 6.65 +field ' n' [explicit solve]: current solution: 9.918808e+01, current residual:5.986424e+01 +wall time: 0.000392914s + +time increment:6660 time: 6.66 +field ' n' [explicit solve]: current solution: 9.919207e+01, current residual:5.986648e+01 +wall time: 0.000396013s + +time increment:6670 time: 6.67 +field ' n' [explicit solve]: current solution: 9.919606e+01, current residual:5.986871e+01 +wall time: 0.000342846s + +time increment:6680 time: 6.68 +field ' n' [explicit solve]: current solution: 9.920004e+01, current residual:5.987093e+01 +wall time: 0.000377893s + +time increment:6690 time: 6.69 +field ' n' [explicit solve]: current solution: 9.920401e+01, current residual:5.987315e+01 +wall time: 0.000367165s + +time increment:6700 time: 6.7 +field ' n' [explicit solve]: current solution: 9.920797e+01, current residual:5.987536e+01 +wall time: 0.000370026s + +time increment:6710 time: 6.71 +field ' n' [explicit solve]: current solution: 9.921192e+01, current residual:5.987757e+01 +wall time: 0.000334024s + +time increment:6720 time: 6.72 +field ' n' [explicit solve]: current solution: 9.921585e+01, current residual:5.987977e+01 +wall time: 0.000396013s + +time increment:6730 time: 6.73 +field ' n' [explicit solve]: current solution: 9.921978e+01, current residual:5.988196e+01 +wall time: 0.000352144s + +time increment:6740 time: 6.74 +field ' n' [explicit solve]: current solution: 9.922369e+01, current residual:5.988415e+01 +wall time: 0.000341892s + +time increment:6750 time: 6.75 +field ' n' [explicit solve]: current solution: 9.922759e+01, current residual:5.988633e+01 +wall time: 0.000374079s + +time increment:6760 time: 6.76 +field ' n' [explicit solve]: current solution: 9.923148e+01, current residual:5.988850e+01 +wall time: 0.000355005s + +time increment:6770 time: 6.77 +field ' n' [explicit solve]: current solution: 9.923535e+01, current residual:5.989067e+01 +wall time: 0.000324965s + +time increment:6780 time: 6.78 +field ' n' [explicit solve]: current solution: 9.923922e+01, current residual:5.989283e+01 +wall time: 0.000344038s + +time increment:6790 time: 6.79 +field ' n' [explicit solve]: current solution: 9.924307e+01, current residual:5.989499e+01 +wall time: 0.000344992s + +time increment:6800 time: 6.8 +field ' n' [explicit solve]: current solution: 9.924690e+01, current residual:5.989713e+01 +wall time: 0.00037384s + +time increment:6810 time: 6.81 +field ' n' [explicit solve]: current solution: 9.925072e+01, current residual:5.989927e+01 +wall time: 0.000361919s + +time increment:6820 time: 6.82 +field ' n' [explicit solve]: current solution: 9.925453e+01, current residual:5.990140e+01 +wall time: 0.000365019s + +time increment:6830 time: 6.83 +field ' n' [explicit solve]: current solution: 9.925833e+01, current residual:5.990353e+01 +wall time: 0.00033617s + +time increment:6840 time: 6.84 +field ' n' [explicit solve]: current solution: 9.926211e+01, current residual:5.990565e+01 +wall time: 0.000386s + +time increment:6850 time: 6.85 +field ' n' [explicit solve]: current solution: 9.926587e+01, current residual:5.990776e+01 +wall time: 0.000370979s + +time increment:6860 time: 6.86 +field ' n' [explicit solve]: current solution: 9.926963e+01, current residual:5.990986e+01 +wall time: 0.000334024s + +time increment:6870 time: 6.87 +field ' n' [explicit solve]: current solution: 9.927336e+01, current residual:5.991195e+01 +wall time: 0.000355005s + +time increment:6880 time: 6.88 +field ' n' [explicit solve]: current solution: 9.927708e+01, current residual:5.991404e+01 +wall time: 0.000355005s + +time increment:6890 time: 6.89 +field ' n' [explicit solve]: current solution: 9.928079e+01, current residual:5.991611e+01 +wall time: 0.000355959s + +time increment:6900 time: 6.9 +field ' n' [explicit solve]: current solution: 9.928448e+01, current residual:5.991818e+01 +wall time: 0.000344038s + +time increment:6910 time: 6.91 +field ' n' [explicit solve]: current solution: 9.928815e+01, current residual:5.992024e+01 +wall time: 0.000340939s + +time increment:6920 time: 6.92 +field ' n' [explicit solve]: current solution: 9.929181e+01, current residual:5.992230e+01 +wall time: 0.000444889s + +time increment:6930 time: 6.93 +field ' n' [explicit solve]: current solution: 9.929545e+01, current residual:5.992434e+01 +wall time: 0.000348091s + +time increment:6940 time: 6.94 +field ' n' [explicit solve]: current solution: 9.929907e+01, current residual:5.992638e+01 +wall time: 0.000334978s + +time increment:6950 time: 6.95 +field ' n' [explicit solve]: current solution: 9.930268e+01, current residual:5.992840e+01 +wall time: 0.000340939s + +time increment:6960 time: 6.96 +field ' n' [explicit solve]: current solution: 9.930628e+01, current residual:5.993042e+01 +wall time: 0.000350952s + +time increment:6970 time: 6.97 +field ' n' [explicit solve]: current solution: 9.930985e+01, current residual:5.993243e+01 +wall time: 0.000366926s + +time increment:6980 time: 6.98 +field ' n' [explicit solve]: current solution: 9.931341e+01, current residual:5.993443e+01 +wall time: 0.000380993s + +time increment:6990 time: 6.99 +field ' n' [explicit solve]: current solution: 9.931695e+01, current residual:5.993642e+01 +wall time: 0.000344992s + +time increment:7000 time: 7 +field ' n' [explicit solve]: current solution: 9.932047e+01, current residual:5.993840e+01 +wall time: 0.000355005s + +time increment:7010 time: 7.01 +field ' n' [explicit solve]: current solution: 9.932398e+01, current residual:5.994038e+01 +wall time: 0.000370026s + +time increment:7020 time: 7.02 +field ' n' [explicit solve]: current solution: 9.932747e+01, current residual:5.994234e+01 +wall time: 0.000360012s + +time increment:7030 time: 7.03 +field ' n' [explicit solve]: current solution: 9.933094e+01, current residual:5.994430e+01 +wall time: 0.000384808s + +time increment:7040 time: 7.04 +field ' n' [explicit solve]: current solution: 9.933440e+01, current residual:5.994624e+01 +wall time: 0.000339985s + +time increment:7050 time: 7.05 +field ' n' [explicit solve]: current solution: 9.933784e+01, current residual:5.994818e+01 +wall time: 0.000344038s + +time increment:7060 time: 7.06 +field ' n' [explicit solve]: current solution: 9.934125e+01, current residual:5.995011e+01 +wall time: 0.000349998s + +time increment:7070 time: 7.07 +field ' n' [explicit solve]: current solution: 9.934466e+01, current residual:5.995202e+01 +wall time: 0.000362158s + +time increment:7080 time: 7.08 +field ' n' [explicit solve]: current solution: 9.934804e+01, current residual:5.995393e+01 +wall time: 0.000335932s + +time increment:7090 time: 7.09 +field ' n' [explicit solve]: current solution: 9.935141e+01, current residual:5.995583e+01 +wall time: 0.000338078s + +time increment:7100 time: 7.1 +field ' n' [explicit solve]: current solution: 9.935476e+01, current residual:5.995772e+01 +wall time: 0.000347853s + +time increment:7110 time: 7.11 +field ' n' [explicit solve]: current solution: 9.935809e+01, current residual:5.995960e+01 +wall time: 0.000400066s + +time increment:7120 time: 7.12 +field ' n' [explicit solve]: current solution: 9.936140e+01, current residual:5.996147e+01 +wall time: 0.000372887s + +time increment:7130 time: 7.13 +field ' n' [explicit solve]: current solution: 9.936469e+01, current residual:5.996334e+01 +wall time: 0.000311852s + +time increment:7140 time: 7.14 +field ' n' [explicit solve]: current solution: 9.936797e+01, current residual:5.996519e+01 +wall time: 0.000368118s + +time increment:7150 time: 7.15 +field ' n' [explicit solve]: current solution: 9.937123e+01, current residual:5.996703e+01 +wall time: 0.000360966s + +time increment:7160 time: 7.16 +field ' n' [explicit solve]: current solution: 9.937447e+01, current residual:5.996887e+01 +wall time: 0.000374079s + +time increment:7170 time: 7.17 +field ' n' [explicit solve]: current solution: 9.937769e+01, current residual:5.997069e+01 +wall time: 0.000407934s + +time increment:7180 time: 7.18 +field ' n' [explicit solve]: current solution: 9.938090e+01, current residual:5.997251e+01 +wall time: 0.0003438s + +time increment:7190 time: 7.19 +field ' n' [explicit solve]: current solution: 9.938409e+01, current residual:5.997431e+01 +wall time: 0.000377893s + +time increment:7200 time: 7.2 +field ' n' [explicit solve]: current solution: 9.938726e+01, current residual:5.997611e+01 +wall time: 0.000343084s + +time increment:7210 time: 7.21 +field ' n' [explicit solve]: current solution: 9.939041e+01, current residual:5.997790e+01 +wall time: 0.000353098s + +time increment:7220 time: 7.22 +field ' n' [explicit solve]: current solution: 9.939354e+01, current residual:5.997968e+01 +wall time: 0.000355959s + +time increment:7230 time: 7.23 +field ' n' [explicit solve]: current solution: 9.939666e+01, current residual:5.998144e+01 +wall time: 0.00033021s + +time increment:7240 time: 7.24 +field ' n' [explicit solve]: current solution: 9.939976e+01, current residual:5.998320e+01 +wall time: 0.000360966s + +time increment:7250 time: 7.25 +field ' n' [explicit solve]: current solution: 9.940284e+01, current residual:5.998495e+01 +wall time: 0.000382185s + +time increment:7260 time: 7.26 +field ' n' [explicit solve]: current solution: 9.940590e+01, current residual:5.998670e+01 +wall time: 0.000298977s + +time increment:7270 time: 7.27 +field ' n' [explicit solve]: current solution: 9.940895e+01, current residual:5.998843e+01 +wall time: 0.000306129s + +time increment:7280 time: 7.28 +field ' n' [explicit solve]: current solution: 9.941198e+01, current residual:5.999015e+01 +wall time: 0.000334978s + +time increment:7290 time: 7.29 +field ' n' [explicit solve]: current solution: 9.941499e+01, current residual:5.999186e+01 +wall time: 0.000348091s + +time increment:7300 time: 7.3 +field ' n' [explicit solve]: current solution: 9.941799e+01, current residual:5.999357e+01 +wall time: 0.000331163s + +time increment:7310 time: 7.31 +field ' n' [explicit solve]: current solution: 9.942096e+01, current residual:5.999527e+01 +wall time: 0.000319958s + +time increment:7320 time: 7.32 +field ' n' [explicit solve]: current solution: 9.942392e+01, current residual:5.999695e+01 +wall time: 0.000421047s + +time increment:7330 time: 7.33 +field ' n' [explicit solve]: current solution: 9.942687e+01, current residual:5.999863e+01 +wall time: 0.00037694s + +time increment:7340 time: 7.34 +field ' n' [explicit solve]: current solution: 9.942980e+01, current residual:6.000030e+01 +wall time: 0.000352859s + +time increment:7350 time: 7.35 +field ' n' [explicit solve]: current solution: 9.943271e+01, current residual:6.000196e+01 +wall time: 0.000348091s + +time increment:7360 time: 7.36 +field ' n' [explicit solve]: current solution: 9.943560e+01, current residual:6.000361e+01 +wall time: 0.000313044s + +time increment:7370 time: 7.37 +field ' n' [explicit solve]: current solution: 9.943848e+01, current residual:6.000526e+01 +wall time: 0.000359058s + +time increment:7380 time: 7.38 +field ' n' [explicit solve]: current solution: 9.944134e+01, current residual:6.000689e+01 +wall time: 0.000383139s + +time increment:7390 time: 7.39 +field ' n' [explicit solve]: current solution: 9.944418e+01, current residual:6.000852e+01 +wall time: 0.000308037s + +time increment:7400 time: 7.4 +field ' n' [explicit solve]: current solution: 9.944701e+01, current residual:6.001014e+01 +wall time: 0.000360966s + +time increment:7410 time: 7.41 +field ' n' [explicit solve]: current solution: 9.944982e+01, current residual:6.001175e+01 +wall time: 0.000311136s + +time increment:7420 time: 7.42 +field ' n' [explicit solve]: current solution: 9.945262e+01, current residual:6.001335e+01 +wall time: 0.000299931s + +time increment:7430 time: 7.43 +field ' n' [explicit solve]: current solution: 9.945540e+01, current residual:6.001494e+01 +wall time: 0.000334024s + +time increment:7440 time: 7.44 +field ' n' [explicit solve]: current solution: 9.945817e+01, current residual:6.001652e+01 +wall time: 0.000385046s + +time increment:7450 time: 7.45 +field ' n' [explicit solve]: current solution: 9.946092e+01, current residual:6.001810e+01 +wall time: 0.000359058s + +time increment:7460 time: 7.46 +field ' n' [explicit solve]: current solution: 9.946365e+01, current residual:6.001967e+01 +wall time: 0.000356913s + +time increment:7470 time: 7.47 +field ' n' [explicit solve]: current solution: 9.946637e+01, current residual:6.002123e+01 +wall time: 0.000366926s + +time increment:7480 time: 7.48 +field ' n' [explicit solve]: current solution: 9.946908e+01, current residual:6.002278e+01 +wall time: 0.000374079s + +time increment:7490 time: 7.49 +field ' n' [explicit solve]: current solution: 9.947177e+01, current residual:6.002433e+01 +wall time: 0.000342846s + +time increment:7500 time: 7.5 +field ' n' [explicit solve]: current solution: 9.947444e+01, current residual:6.002586e+01 +wall time: 0.000348806s + +time increment:7510 time: 7.51 +field ' n' [explicit solve]: current solution: 9.947710e+01, current residual:6.002739e+01 +wall time: 0.000302076s + +time increment:7520 time: 7.52 +field ' n' [explicit solve]: current solution: 9.947975e+01, current residual:6.002891e+01 +wall time: 0.000295162s + +time increment:7530 time: 7.53 +field ' n' [explicit solve]: current solution: 9.948238e+01, current residual:6.003043e+01 +wall time: 0.000380993s + +time increment:7540 time: 7.54 +field ' n' [explicit solve]: current solution: 9.948499e+01, current residual:6.003193e+01 +wall time: 0.000287056s + +time increment:7550 time: 7.55 +field ' n' [explicit solve]: current solution: 9.948760e+01, current residual:6.003343e+01 +wall time: 0.000377893s + +time increment:7560 time: 7.56 +field ' n' [explicit solve]: current solution: 9.949018e+01, current residual:6.003492e+01 +wall time: 0.00031209s + +time increment:7570 time: 7.57 +field ' n' [explicit solve]: current solution: 9.949276e+01, current residual:6.003641e+01 +wall time: 0.000362873s + +time increment:7580 time: 7.58 +field ' n' [explicit solve]: current solution: 9.949532e+01, current residual:6.003788e+01 +wall time: 0.000319958s + +time increment:7590 time: 7.59 +field ' n' [explicit solve]: current solution: 9.949787e+01, current residual:6.003935e+01 +wall time: 0.000292063s + +time increment:7600 time: 7.6 +field ' n' [explicit solve]: current solution: 9.950040e+01, current residual:6.004082e+01 +wall time: 0.000340939s + +time increment:7610 time: 7.61 +field ' n' [explicit solve]: current solution: 9.950292e+01, current residual:6.004227e+01 +wall time: 0.00030899s + +time increment:7620 time: 7.62 +field ' n' [explicit solve]: current solution: 9.950543e+01, current residual:6.004372e+01 +wall time: 0.000325918s + +time increment:7630 time: 7.63 +field ' n' [explicit solve]: current solution: 9.950792e+01, current residual:6.004516e+01 +wall time: 0.000360012s + +time increment:7640 time: 7.64 +field ' n' [explicit solve]: current solution: 9.951040e+01, current residual:6.004660e+01 +wall time: 0.000375032s + +time increment:7650 time: 7.65 +field ' n' [explicit solve]: current solution: 9.951287e+01, current residual:6.004802e+01 +wall time: 0.000305891s + +time increment:7660 time: 7.66 +field ' n' [explicit solve]: current solution: 9.951533e+01, current residual:6.004944e+01 +wall time: 0.000328064s + +time increment:7670 time: 7.67 +field ' n' [explicit solve]: current solution: 9.951777e+01, current residual:6.005086e+01 +wall time: 0.00031805s + +time increment:7680 time: 7.68 +field ' n' [explicit solve]: current solution: 9.952020e+01, current residual:6.005227e+01 +wall time: 0.000298023s + +time increment:7690 time: 7.69 +field ' n' [explicit solve]: current solution: 9.952262e+01, current residual:6.005367e+01 +wall time: 0.000334978s + +time increment:7700 time: 7.7 +field ' n' [explicit solve]: current solution: 9.952503e+01, current residual:6.005506e+01 +wall time: 0.000322819s + +time increment:7710 time: 7.71 +field ' n' [explicit solve]: current solution: 9.952743e+01, current residual:6.005645e+01 +wall time: 0.000396013s + +time increment:7720 time: 7.72 +field ' n' [explicit solve]: current solution: 9.952981e+01, current residual:6.005784e+01 +wall time: 0.000340939s + +time increment:7730 time: 7.73 +field ' n' [explicit solve]: current solution: 9.953218e+01, current residual:6.005921e+01 +wall time: 0.000372171s + +time increment:7740 time: 7.74 +field ' n' [explicit solve]: current solution: 9.953454e+01, current residual:6.006058e+01 +wall time: 0.00035882s + +time increment:7750 time: 7.75 +field ' n' [explicit solve]: current solution: 9.953689e+01, current residual:6.006195e+01 +wall time: 0.000339031s + +time increment:7760 time: 7.76 +field ' n' [explicit solve]: current solution: 9.953923e+01, current residual:6.006330e+01 +wall time: 0.000316143s + +time increment:7770 time: 7.77 +field ' n' [explicit solve]: current solution: 9.954155e+01, current residual:6.006466e+01 +wall time: 0.000302076s + +time increment:7780 time: 7.78 +field ' n' [explicit solve]: current solution: 9.954387e+01, current residual:6.006600e+01 +wall time: 0.000360012s + +time increment:7790 time: 7.79 +field ' n' [explicit solve]: current solution: 9.954617e+01, current residual:6.006734e+01 +wall time: 0.000337124s + +time increment:7800 time: 7.8 +field ' n' [explicit solve]: current solution: 9.954847e+01, current residual:6.006868e+01 +wall time: 0.000346899s + +time increment:7810 time: 7.81 +field ' n' [explicit solve]: current solution: 9.955075e+01, current residual:6.007001e+01 +wall time: 0.000357151s + +time increment:7820 time: 7.82 +field ' n' [explicit solve]: current solution: 9.955302e+01, current residual:6.007133e+01 +wall time: 0.00030303s + +time increment:7830 time: 7.83 +field ' n' [explicit solve]: current solution: 9.955529e+01, current residual:6.007265e+01 +wall time: 0.000349998s + +time increment:7840 time: 7.84 +field ' n' [explicit solve]: current solution: 9.955754e+01, current residual:6.007396e+01 +wall time: 0.000308037s + +time increment:7850 time: 7.85 +field ' n' [explicit solve]: current solution: 9.955978e+01, current residual:6.007527e+01 +wall time: 0.000291109s + +time increment:7860 time: 7.86 +field ' n' [explicit solve]: current solution: 9.956201e+01, current residual:6.007657e+01 +wall time: 0.000331879s + +time increment:7870 time: 7.87 +field ' n' [explicit solve]: current solution: 9.956423e+01, current residual:6.007787e+01 +wall time: 0.00032115s + +time increment:7880 time: 7.88 +field ' n' [explicit solve]: current solution: 9.956645e+01, current residual:6.007916e+01 +wall time: 0.000308037s + +time increment:7890 time: 7.89 +field ' n' [explicit solve]: current solution: 9.956865e+01, current residual:6.008044e+01 +wall time: 0.00030303s + +time increment:7900 time: 7.9 +field ' n' [explicit solve]: current solution: 9.957084e+01, current residual:6.008173e+01 +wall time: 0.000304937s + +time increment:7910 time: 7.91 +field ' n' [explicit solve]: current solution: 9.957302e+01, current residual:6.008300e+01 +wall time: 0.000303984s + +time increment:7920 time: 7.92 +field ' n' [explicit solve]: current solution: 9.957520e+01, current residual:6.008427e+01 +wall time: 0.000316143s + +time increment:7930 time: 7.93 +field ' n' [explicit solve]: current solution: 9.957736e+01, current residual:6.008554e+01 +wall time: 0.000356913s + +time increment:7940 time: 7.94 +field ' n' [explicit solve]: current solution: 9.957952e+01, current residual:6.008680e+01 +wall time: 0.000404119s + +time increment:7950 time: 7.95 +field ' n' [explicit solve]: current solution: 9.958167e+01, current residual:6.008806e+01 +wall time: 0.000359058s + +time increment:7960 time: 7.96 +field ' n' [explicit solve]: current solution: 9.958380e+01, current residual:6.008931e+01 +wall time: 0.000370026s + +time increment:7970 time: 7.97 +field ' n' [explicit solve]: current solution: 9.958593e+01, current residual:6.009055e+01 +wall time: 0.000342131s + +time increment:7980 time: 7.98 +field ' n' [explicit solve]: current solution: 9.958805e+01, current residual:6.009180e+01 +wall time: 0.000407934s + +time increment:7990 time: 7.99 +field ' n' [explicit solve]: current solution: 9.959016e+01, current residual:6.009303e+01 +wall time: 0.000286102s + +time increment:8000 time: 8 +field ' n' [explicit solve]: current solution: 9.959227e+01, current residual:6.009427e+01 +wall time: 0.00036788s +fields: 1 +fields: 1 +fields: 1 +fields: 1 +Output written to:solution-008000.pvtu + +Energy: 209.139 +Energy Components: 92.264 116.875 0 + +time increment:8010 time: 8.01 +field ' n' [explicit solve]: current solution: 9.959436e+01, current residual:6.009550e+01 +wall time: 0.000338078s + +time increment:8020 time: 8.02 +field ' n' [explicit solve]: current solution: 9.959645e+01, current residual:6.009672e+01 +wall time: 0.000375986s + +time increment:8030 time: 8.03 +field ' n' [explicit solve]: current solution: 9.959853e+01, current residual:6.009794e+01 +wall time: 0.000355005s + +time increment:8040 time: 8.04 +field ' n' [explicit solve]: current solution: 9.960060e+01, current residual:6.009915e+01 +wall time: 0.000343084s + +time increment:8050 time: 8.05 +field ' n' [explicit solve]: current solution: 9.960266e+01, current residual:6.010036e+01 +wall time: 0.000334024s + +time increment:8060 time: 8.06 +field ' n' [explicit solve]: current solution: 9.960471e+01, current residual:6.010157e+01 +wall time: 0.000411034s + +time increment:8070 time: 8.07 +field ' n' [explicit solve]: current solution: 9.960676e+01, current residual:6.010277e+01 +wall time: 0.000355959s + +time increment:8080 time: 8.08 +field ' n' [explicit solve]: current solution: 9.960880e+01, current residual:6.010397e+01 +wall time: 0.000360012s + +time increment:8090 time: 8.09 +field ' n' [explicit solve]: current solution: 9.961083e+01, current residual:6.010517e+01 +wall time: 0.000435829s + +time increment:8100 time: 8.1 +field ' n' [explicit solve]: current solution: 9.961285e+01, current residual:6.010636e+01 +wall time: 0.000351906s + +time increment:8110 time: 8.11 +field ' n' [explicit solve]: current solution: 9.961487e+01, current residual:6.010754e+01 +wall time: 0.000311136s + +time increment:8120 time: 8.12 +field ' n' [explicit solve]: current solution: 9.961688e+01, current residual:6.010872e+01 +wall time: 0.000377178s + +time increment:8130 time: 8.13 +field ' n' [explicit solve]: current solution: 9.961888e+01, current residual:6.010990e+01 +wall time: 0.000374079s + +time increment:8140 time: 8.14 +field ' n' [explicit solve]: current solution: 9.962087e+01, current residual:6.011108e+01 +wall time: 0.000400066s + +time increment:8150 time: 8.15 +field ' n' [explicit solve]: current solution: 9.962286e+01, current residual:6.011225e+01 +wall time: 0.000333071s + +time increment:8160 time: 8.16 +field ' n' [explicit solve]: current solution: 9.962484e+01, current residual:6.011341e+01 +wall time: 0.000311852s + +time increment:8170 time: 8.17 +field ' n' [explicit solve]: current solution: 9.962681e+01, current residual:6.011457e+01 +wall time: 0.000404119s + +time increment:8180 time: 8.18 +field ' n' [explicit solve]: current solution: 9.962878e+01, current residual:6.011573e+01 +wall time: 0.000386s + +time increment:8190 time: 8.19 +field ' n' [explicit solve]: current solution: 9.963074e+01, current residual:6.011689e+01 +wall time: 0.000355959s + +time increment:8200 time: 8.2 +field ' n' [explicit solve]: current solution: 9.963269e+01, current residual:6.011804e+01 +wall time: 0.000327826s + +time increment:8210 time: 8.21 +field ' n' [explicit solve]: current solution: 9.963464e+01, current residual:6.011919e+01 +wall time: 0.000334024s + +time increment:8220 time: 8.22 +field ' n' [explicit solve]: current solution: 9.963657e+01, current residual:6.012033e+01 +wall time: 0.000361919s + +time increment:8230 time: 8.23 +field ' n' [explicit solve]: current solution: 9.963851e+01, current residual:6.012147e+01 +wall time: 0.000442982s + +time increment:8240 time: 8.24 +field ' n' [explicit solve]: current solution: 9.964043e+01, current residual:6.012261e+01 +wall time: 0.000334024s + +time increment:8250 time: 8.25 +field ' n' [explicit solve]: current solution: 9.964235e+01, current residual:6.012374e+01 +wall time: 0.000331879s + +time increment:8260 time: 8.26 +field ' n' [explicit solve]: current solution: 9.964427e+01, current residual:6.012487e+01 +wall time: 0.000400066s + +time increment:8270 time: 8.27 +field ' n' [explicit solve]: current solution: 9.964617e+01, current residual:6.012600e+01 +wall time: 0.000354052s + +time increment:8280 time: 8.28 +field ' n' [explicit solve]: current solution: 9.964807e+01, current residual:6.012712e+01 +wall time: 0.000354052s + +time increment:8290 time: 8.29 +field ' n' [explicit solve]: current solution: 9.964997e+01, current residual:6.012824e+01 +wall time: 0.000339985s + +time increment:8300 time: 8.3 +field ' n' [explicit solve]: current solution: 9.965186e+01, current residual:6.012936e+01 +wall time: 0.000380993s + +time increment:8310 time: 8.31 +field ' n' [explicit solve]: current solution: 9.965374e+01, current residual:6.013047e+01 +wall time: 0.000356913s + +time increment:8320 time: 8.32 +field ' n' [explicit solve]: current solution: 9.965562e+01, current residual:6.013158e+01 +wall time: 0.000332117s + +time increment:8330 time: 8.33 +field ' n' [explicit solve]: current solution: 9.965749e+01, current residual:6.013269e+01 +wall time: 0.000379086s + +time increment:8340 time: 8.34 +field ' n' [explicit solve]: current solution: 9.965935e+01, current residual:6.013379e+01 +wall time: 0.000347853s + +time increment:8350 time: 8.35 +field ' n' [explicit solve]: current solution: 9.966121e+01, current residual:6.013490e+01 +wall time: 0.000348091s + +time increment:8360 time: 8.36 +field ' n' [explicit solve]: current solution: 9.966306e+01, current residual:6.013599e+01 +wall time: 0.000377178s + +time increment:8370 time: 8.37 +field ' n' [explicit solve]: current solution: 9.966491e+01, current residual:6.013709e+01 +wall time: 0.000385046s + +time increment:8380 time: 8.38 +field ' n' [explicit solve]: current solution: 9.966675e+01, current residual:6.013818e+01 +wall time: 0.000344038s + +time increment:8390 time: 8.39 +field ' n' [explicit solve]: current solution: 9.966859e+01, current residual:6.013927e+01 +wall time: 0.000386s + +time increment:8400 time: 8.4 +field ' n' [explicit solve]: current solution: 9.967042e+01, current residual:6.014035e+01 +wall time: 0.000356913s + +time increment:8410 time: 8.41 +field ' n' [explicit solve]: current solution: 9.967225e+01, current residual:6.014144e+01 +wall time: 0.000371933s + +time increment:8420 time: 8.42 +field ' n' [explicit solve]: current solution: 9.967407e+01, current residual:6.014251e+01 +wall time: 0.000360966s + +time increment:8430 time: 8.43 +field ' n' [explicit solve]: current solution: 9.967588e+01, current residual:6.014359e+01 +wall time: 0.000339031s + +time increment:8440 time: 8.44 +field ' n' [explicit solve]: current solution: 9.967769e+01, current residual:6.014467e+01 +wall time: 0.000329971s + +time increment:8450 time: 8.45 +field ' n' [explicit solve]: current solution: 9.967950e+01, current residual:6.014574e+01 +wall time: 0.000344038s + +time increment:8460 time: 8.46 +field ' n' [explicit solve]: current solution: 9.968130e+01, current residual:6.014680e+01 +wall time: 0.000368834s + +time increment:8470 time: 8.47 +field ' n' [explicit solve]: current solution: 9.968309e+01, current residual:6.014787e+01 +wall time: 0.000324965s + +time increment:8480 time: 8.48 +field ' n' [explicit solve]: current solution: 9.968488e+01, current residual:6.014893e+01 +wall time: 0.000375032s + +time increment:8490 time: 8.49 +field ' n' [explicit solve]: current solution: 9.968667e+01, current residual:6.014999e+01 +wall time: 0.000389099s + +time increment:8500 time: 8.5 +field ' n' [explicit solve]: current solution: 9.968845e+01, current residual:6.015105e+01 +wall time: 0.000350952s + +time increment:8510 time: 8.51 +field ' n' [explicit solve]: current solution: 9.969022e+01, current residual:6.015210e+01 +wall time: 0.000360012s + +time increment:8520 time: 8.52 +field ' n' [explicit solve]: current solution: 9.969199e+01, current residual:6.015315e+01 +wall time: 0.000323057s + +time increment:8530 time: 8.53 +field ' n' [explicit solve]: current solution: 9.969375e+01, current residual:6.015420e+01 +wall time: 0.000355005s + +time increment:8540 time: 8.54 +field ' n' [explicit solve]: current solution: 9.969551e+01, current residual:6.015525e+01 +wall time: 0.000334978s + +time increment:8550 time: 8.55 +field ' n' [explicit solve]: current solution: 9.969727e+01, current residual:6.015629e+01 +wall time: 0.000347853s + +time increment:8560 time: 8.56 +field ' n' [explicit solve]: current solution: 9.969902e+01, current residual:6.015733e+01 +wall time: 0.000336885s + +time increment:8570 time: 8.57 +field ' n' [explicit solve]: current solution: 9.970077e+01, current residual:6.015837e+01 +wall time: 0.000301123s + +time increment:8580 time: 8.58 +field ' n' [explicit solve]: current solution: 9.970251e+01, current residual:6.015941e+01 +wall time: 0.000311136s + +time increment:8590 time: 8.59 +field ' n' [explicit solve]: current solution: 9.970424e+01, current residual:6.016044e+01 +wall time: 0.000298023s + +time increment:8600 time: 8.6 +field ' n' [explicit solve]: current solution: 9.970598e+01, current residual:6.016147e+01 +wall time: 0.00031209s + +time increment:8610 time: 8.61 +field ' n' [explicit solve]: current solution: 9.970770e+01, current residual:6.016250e+01 +wall time: 0.000352144s + +time increment:8620 time: 8.62 +field ' n' [explicit solve]: current solution: 9.970943e+01, current residual:6.016353e+01 +wall time: 0.000381947s + +time increment:8630 time: 8.63 +field ' n' [explicit solve]: current solution: 9.971114e+01, current residual:6.016455e+01 +wall time: 0.000324965s + +time increment:8640 time: 8.64 +field ' n' [explicit solve]: current solution: 9.971286e+01, current residual:6.016557e+01 +wall time: 0.000294924s + +time increment:8650 time: 8.65 +field ' n' [explicit solve]: current solution: 9.971457e+01, current residual:6.016659e+01 +wall time: 0.000328064s + +time increment:8660 time: 8.66 +field ' n' [explicit solve]: current solution: 9.971627e+01, current residual:6.016760e+01 +wall time: 0.000328064s + +time increment:8670 time: 8.67 +field ' n' [explicit solve]: current solution: 9.971798e+01, current residual:6.016862e+01 +wall time: 0.000380039s + +time increment:8680 time: 8.68 +field ' n' [explicit solve]: current solution: 9.971967e+01, current residual:6.016963e+01 +wall time: 0.000301123s + +time increment:8690 time: 8.69 +field ' n' [explicit solve]: current solution: 9.972136e+01, current residual:6.017064e+01 +wall time: 0.000325918s + +time increment:8700 time: 8.7 +field ' n' [explicit solve]: current solution: 9.972305e+01, current residual:6.017164e+01 +wall time: 0.00033021s + +time increment:8710 time: 8.71 +field ' n' [explicit solve]: current solution: 9.972474e+01, current residual:6.017265e+01 +wall time: 0.000374079s + +time increment:8720 time: 8.72 +field ' n' [explicit solve]: current solution: 9.972642e+01, current residual:6.017365e+01 +wall time: 0.000308037s + +time increment:8730 time: 8.73 +field ' n' [explicit solve]: current solution: 9.972809e+01, current residual:6.017465e+01 +wall time: 0.000329971s + +time increment:8740 time: 8.74 +field ' n' [explicit solve]: current solution: 9.972977e+01, current residual:6.017565e+01 +wall time: 0.000298023s + +time increment:8750 time: 8.75 +field ' n' [explicit solve]: current solution: 9.973143e+01, current residual:6.017664e+01 +wall time: 0.000296116s + +time increment:8760 time: 8.76 +field ' n' [explicit solve]: current solution: 9.973310e+01, current residual:6.017764e+01 +wall time: 0.000310898s + +time increment:8770 time: 8.77 +field ' n' [explicit solve]: current solution: 9.973476e+01, current residual:6.017863e+01 +wall time: 0.000349045s + +time increment:8780 time: 8.78 +field ' n' [explicit solve]: current solution: 9.973642e+01, current residual:6.017962e+01 +wall time: 0.000377178s + +time increment:8790 time: 8.79 +field ' n' [explicit solve]: current solution: 9.973807e+01, current residual:6.018060e+01 +wall time: 0.000296831s + +time increment:8800 time: 8.8 +field ' n' [explicit solve]: current solution: 9.973972e+01, current residual:6.018159e+01 +wall time: 0.0003438s + +time increment:8810 time: 8.81 +field ' n' [explicit solve]: current solution: 9.974136e+01, current residual:6.018257e+01 +wall time: 0.000369072s + +time increment:8820 time: 8.82 +field ' n' [explicit solve]: current solution: 9.974300e+01, current residual:6.018355e+01 +wall time: 0.000307083s + +time increment:8830 time: 8.83 +field ' n' [explicit solve]: current solution: 9.974464e+01, current residual:6.018453e+01 +wall time: 0.000333071s + +time increment:8840 time: 8.84 +field ' n' [explicit solve]: current solution: 9.974627e+01, current residual:6.018550e+01 +wall time: 0.000346184s + +time increment:8850 time: 8.85 +field ' n' [explicit solve]: current solution: 9.974790e+01, current residual:6.018648e+01 +wall time: 0.000277996s + +time increment:8860 time: 8.86 +field ' n' [explicit solve]: current solution: 9.974953e+01, current residual:6.018745e+01 +wall time: 0.000348806s + +time increment:8870 time: 8.87 +field ' n' [explicit solve]: current solution: 9.975115e+01, current residual:6.018842e+01 +wall time: 0.00030303s + +time increment:8880 time: 8.88 +field ' n' [explicit solve]: current solution: 9.975277e+01, current residual:6.018938e+01 +wall time: 0.000324965s + +time increment:8890 time: 8.89 +field ' n' [explicit solve]: current solution: 9.975438e+01, current residual:6.019035e+01 +wall time: 0.000349998s + +time increment:8900 time: 8.9 +field ' n' [explicit solve]: current solution: 9.975599e+01, current residual:6.019131e+01 +wall time: 0.000386s + +time increment:8910 time: 8.91 +field ' n' [explicit solve]: current solution: 9.975760e+01, current residual:6.019227e+01 +wall time: 0.000380993s + +time increment:8920 time: 8.92 +field ' n' [explicit solve]: current solution: 9.975920e+01, current residual:6.019323e+01 +wall time: 0.000389099s + +time increment:8930 time: 8.93 +field ' n' [explicit solve]: current solution: 9.976080e+01, current residual:6.019419e+01 +wall time: 0.000316858s + +time increment:8940 time: 8.94 +field ' n' [explicit solve]: current solution: 9.976240e+01, current residual:6.019515e+01 +wall time: 0.000360966s + +time increment:8950 time: 8.95 +field ' n' [explicit solve]: current solution: 9.976399e+01, current residual:6.019610e+01 +wall time: 0.000344038s + +time increment:8960 time: 8.96 +field ' n' [explicit solve]: current solution: 9.976558e+01, current residual:6.019705e+01 +wall time: 0.000337839s + +time increment:8970 time: 8.97 +field ' n' [explicit solve]: current solution: 9.976717e+01, current residual:6.019800e+01 +wall time: 0.000325918s + +time increment:8980 time: 8.98 +field ' n' [explicit solve]: current solution: 9.976875e+01, current residual:6.019895e+01 +wall time: 0.000303984s + +time increment:8990 time: 8.99 +field ' n' [explicit solve]: current solution: 9.977033e+01, current residual:6.019989e+01 +wall time: 0.000356913s + +time increment:9000 time: 9 +field ' n' [explicit solve]: current solution: 9.977191e+01, current residual:6.020084e+01 +wall time: 0.000300884s + +time increment:9010 time: 9.01 +field ' n' [explicit solve]: current solution: 9.977348e+01, current residual:6.020178e+01 +wall time: 0.000355005s + +time increment:9020 time: 9.02 +field ' n' [explicit solve]: current solution: 9.977505e+01, current residual:6.020272e+01 +wall time: 0.000303984s + +time increment:9030 time: 9.03 +field ' n' [explicit solve]: current solution: 9.977662e+01, current residual:6.020366e+01 +wall time: 0.000359058s + +time increment:9040 time: 9.04 +field ' n' [explicit solve]: current solution: 9.977818e+01, current residual:6.020459e+01 +wall time: 0.000375032s + +time increment:9050 time: 9.05 +field ' n' [explicit solve]: current solution: 9.977974e+01, current residual:6.020553e+01 +wall time: 0.000319958s + +time increment:9060 time: 9.06 +field ' n' [explicit solve]: current solution: 9.978130e+01, current residual:6.020646e+01 +wall time: 0.000339031s + +time increment:9070 time: 9.07 +field ' n' [explicit solve]: current solution: 9.978285e+01, current residual:6.020739e+01 +wall time: 0.000349998s + +time increment:9080 time: 9.08 +field ' n' [explicit solve]: current solution: 9.978440e+01, current residual:6.020832e+01 +wall time: 0.000363111s + +time increment:9090 time: 9.09 +field ' n' [explicit solve]: current solution: 9.978594e+01, current residual:6.020924e+01 +wall time: 0.000311852s + +time increment:9100 time: 9.1 +field ' n' [explicit solve]: current solution: 9.978749e+01, current residual:6.021017e+01 +wall time: 0.000374079s + +time increment:9110 time: 9.11 +field ' n' [explicit solve]: current solution: 9.978903e+01, current residual:6.021109e+01 +wall time: 0.000370026s + +time increment:9120 time: 9.12 +field ' n' [explicit solve]: current solution: 9.979056e+01, current residual:6.021201e+01 +wall time: 0.000300884s + +time increment:9130 time: 9.13 +field ' n' [explicit solve]: current solution: 9.979210e+01, current residual:6.021293e+01 +wall time: 0.000300169s + +time increment:9140 time: 9.14 +field ' n' [explicit solve]: current solution: 9.979363e+01, current residual:6.021385e+01 +wall time: 0.000313997s + +time increment:9150 time: 9.15 +field ' n' [explicit solve]: current solution: 9.979515e+01, current residual:6.021476e+01 +wall time: 0.000353098s + +time increment:9160 time: 9.16 +field ' n' [explicit solve]: current solution: 9.979668e+01, current residual:6.021568e+01 +wall time: 0.000390053s + +time increment:9170 time: 9.17 +field ' n' [explicit solve]: current solution: 9.979820e+01, current residual:6.021659e+01 +wall time: 0.000334024s + +time increment:9180 time: 9.18 +field ' n' [explicit solve]: current solution: 9.979972e+01, current residual:6.021750e+01 +wall time: 0.000295162s + +time increment:9190 time: 9.19 +field ' n' [explicit solve]: current solution: 9.980123e+01, current residual:6.021841e+01 +wall time: 0.000294924s + +time increment:9200 time: 9.2 +field ' n' [explicit solve]: current solution: 9.980275e+01, current residual:6.021932e+01 +wall time: 0.000304222s + +time increment:9210 time: 9.21 +field ' n' [explicit solve]: current solution: 9.980425e+01, current residual:6.022022e+01 +wall time: 0.000334024s + +time increment:9220 time: 9.22 +field ' n' [explicit solve]: current solution: 9.980576e+01, current residual:6.022113e+01 +wall time: 0.000396967s + +time increment:9230 time: 9.23 +field ' n' [explicit solve]: current solution: 9.980726e+01, current residual:6.022203e+01 +wall time: 0.000355959s + +time increment:9240 time: 9.24 +field ' n' [explicit solve]: current solution: 9.980876e+01, current residual:6.022293e+01 +wall time: 0.000299931s + +time increment:9250 time: 9.25 +field ' n' [explicit solve]: current solution: 9.981026e+01, current residual:6.022383e+01 +wall time: 0.000314951s + +time increment:9260 time: 9.26 +field ' n' [explicit solve]: current solution: 9.981175e+01, current residual:6.022472e+01 +wall time: 0.000332832s + +time increment:9270 time: 9.27 +field ' n' [explicit solve]: current solution: 9.981324e+01, current residual:6.022562e+01 +wall time: 0.000314951s + +time increment:9280 time: 9.28 +field ' n' [explicit solve]: current solution: 9.981473e+01, current residual:6.022651e+01 +wall time: 0.000370026s + +time increment:9290 time: 9.29 +field ' n' [explicit solve]: current solution: 9.981622e+01, current residual:6.022740e+01 +wall time: 0.00030899s + +time increment:9300 time: 9.3 +field ' n' [explicit solve]: current solution: 9.981770e+01, current residual:6.022829e+01 +wall time: 0.000294924s + +time increment:9310 time: 9.31 +field ' n' [explicit solve]: current solution: 9.981918e+01, current residual:6.022918e+01 +wall time: 0.000393867s + +time increment:9320 time: 9.32 +field ' n' [explicit solve]: current solution: 9.982066e+01, current residual:6.023007e+01 +wall time: 0.000363111s + +time increment:9330 time: 9.33 +field ' n' [explicit solve]: current solution: 9.982213e+01, current residual:6.023095e+01 +wall time: 0.000366926s + +time increment:9340 time: 9.34 +field ' n' [explicit solve]: current solution: 9.982360e+01, current residual:6.023184e+01 +wall time: 0.000323772s + +time increment:9350 time: 9.35 +field ' n' [explicit solve]: current solution: 9.982507e+01, current residual:6.023272e+01 +wall time: 0.000337124s + +time increment:9360 time: 9.36 +field ' n' [explicit solve]: current solution: 9.982653e+01, current residual:6.023360e+01 +wall time: 0.000349045s + +time increment:9370 time: 9.37 +field ' n' [explicit solve]: current solution: 9.982799e+01, current residual:6.023448e+01 +wall time: 0.000387907s + +time increment:9380 time: 9.38 +field ' n' [explicit solve]: current solution: 9.982945e+01, current residual:6.023535e+01 +wall time: 0.000361919s + +time increment:9390 time: 9.39 +field ' n' [explicit solve]: current solution: 9.983091e+01, current residual:6.023623e+01 +wall time: 0.00037694s + +time increment:9400 time: 9.4 +field ' n' [explicit solve]: current solution: 9.983236e+01, current residual:6.023710e+01 +wall time: 0.000345945s + +time increment:9410 time: 9.41 +field ' n' [explicit solve]: current solution: 9.983381e+01, current residual:6.023797e+01 +wall time: 0.000371933s + +time increment:9420 time: 9.42 +field ' n' [explicit solve]: current solution: 9.983526e+01, current residual:6.023885e+01 +wall time: 0.000355959s + +time increment:9430 time: 9.43 +field ' n' [explicit solve]: current solution: 9.983671e+01, current residual:6.023971e+01 +wall time: 0.000364065s + +time increment:9440 time: 9.44 +field ' n' [explicit solve]: current solution: 9.983815e+01, current residual:6.024058e+01 +wall time: 0.000334978s + +time increment:9450 time: 9.45 +field ' n' [explicit solve]: current solution: 9.983959e+01, current residual:6.024145e+01 +wall time: 0.000329018s + +time increment:9460 time: 9.46 +field ' n' [explicit solve]: current solution: 9.984103e+01, current residual:6.024231e+01 +wall time: 0.000348091s + +time increment:9470 time: 9.47 +field ' n' [explicit solve]: current solution: 9.984246e+01, current residual:6.024317e+01 +wall time: 0.000380039s + +time increment:9480 time: 9.48 +field ' n' [explicit solve]: current solution: 9.984389e+01, current residual:6.024403e+01 +wall time: 0.000386s + +time increment:9490 time: 9.49 +field ' n' [explicit solve]: current solution: 9.984532e+01, current residual:6.024489e+01 +wall time: 0.00039506s + +time increment:9500 time: 9.5 +field ' n' [explicit solve]: current solution: 9.984675e+01, current residual:6.024575e+01 +wall time: 0.000360012s + +time increment:9510 time: 9.51 +field ' n' [explicit solve]: current solution: 9.984817e+01, current residual:6.024661e+01 +wall time: 0.000324011s + +time increment:9520 time: 9.52 +field ' n' [explicit solve]: current solution: 9.984959e+01, current residual:6.024746e+01 +wall time: 0.000334024s + +time increment:9530 time: 9.53 +field ' n' [explicit solve]: current solution: 9.985101e+01, current residual:6.024832e+01 +wall time: 0.000320196s + +time increment:9540 time: 9.54 +field ' n' [explicit solve]: current solution: 9.985243e+01, current residual:6.024917e+01 +wall time: 0.000355005s + +time increment:9550 time: 9.55 +field ' n' [explicit solve]: current solution: 9.985384e+01, current residual:6.025002e+01 +wall time: 0.000344992s + +time increment:9560 time: 9.56 +field ' n' [explicit solve]: current solution: 9.985525e+01, current residual:6.025087e+01 +wall time: 0.000448942s + +time increment:9570 time: 9.57 +field ' n' [explicit solve]: current solution: 9.985666e+01, current residual:6.025171e+01 +wall time: 0.000396013s + +time increment:9580 time: 9.58 +field ' n' [explicit solve]: current solution: 9.985806e+01, current residual:6.025256e+01 +wall time: 0.000339985s + +time increment:9590 time: 9.59 +field ' n' [explicit solve]: current solution: 9.985947e+01, current residual:6.025340e+01 +wall time: 0.000335932s + +time increment:9600 time: 9.6 +field ' n' [explicit solve]: current solution: 9.986087e+01, current residual:6.025424e+01 +wall time: 0.000355005s + +time increment:9610 time: 9.61 +field ' n' [explicit solve]: current solution: 9.986226e+01, current residual:6.025509e+01 +wall time: 0.000352859s + +time increment:9620 time: 9.62 +field ' n' [explicit solve]: current solution: 9.986366e+01, current residual:6.025593e+01 +wall time: 0.000344992s + +time increment:9630 time: 9.63 +field ' n' [explicit solve]: current solution: 9.986505e+01, current residual:6.025676e+01 +wall time: 0.000341892s + +time increment:9640 time: 9.64 +field ' n' [explicit solve]: current solution: 9.986644e+01, current residual:6.025760e+01 +wall time: 0.000396013s + +time increment:9650 time: 9.65 +field ' n' [explicit solve]: current solution: 9.986783e+01, current residual:6.025843e+01 +wall time: 0.00031805s + +time increment:9660 time: 9.66 +field ' n' [explicit solve]: current solution: 9.986921e+01, current residual:6.025927e+01 +wall time: 0.000353098s + +time increment:9670 time: 9.67 +field ' n' [explicit solve]: current solution: 9.987059e+01, current residual:6.026010e+01 +wall time: 0.000356913s + +time increment:9680 time: 9.68 +field ' n' [explicit solve]: current solution: 9.987197e+01, current residual:6.026093e+01 +wall time: 0.000388145s + +time increment:9690 time: 9.69 +field ' n' [explicit solve]: current solution: 9.987335e+01, current residual:6.026176e+01 +wall time: 0.000347853s + +time increment:9700 time: 9.7 +field ' n' [explicit solve]: current solution: 9.987473e+01, current residual:6.026259e+01 +wall time: 0.000354052s + +time increment:9710 time: 9.71 +field ' n' [explicit solve]: current solution: 9.987610e+01, current residual:6.026341e+01 +wall time: 0.000340939s + +time increment:9720 time: 9.72 +field ' n' [explicit solve]: current solution: 9.987747e+01, current residual:6.026424e+01 +wall time: 0.000373125s + +time increment:9730 time: 9.73 +field ' n' [explicit solve]: current solution: 9.987883e+01, current residual:6.026506e+01 +wall time: 0.000365019s + +time increment:9740 time: 9.74 +field ' n' [explicit solve]: current solution: 9.988020e+01, current residual:6.026588e+01 +wall time: 0.000427008s + +time increment:9750 time: 9.75 +field ' n' [explicit solve]: current solution: 9.988156e+01, current residual:6.026670e+01 +wall time: 0.000365973s + +time increment:9760 time: 9.76 +field ' n' [explicit solve]: current solution: 9.988292e+01, current residual:6.026752e+01 +wall time: 0.000330925s + +time increment:9770 time: 9.77 +field ' n' [explicit solve]: current solution: 9.988428e+01, current residual:6.026834e+01 +wall time: 0.000396013s + +time increment:9780 time: 9.78 +field ' n' [explicit solve]: current solution: 9.988563e+01, current residual:6.026916e+01 +wall time: 0.000365019s + +time increment:9790 time: 9.79 +field ' n' [explicit solve]: current solution: 9.988698e+01, current residual:6.026997e+01 +wall time: 0.000370026s + +time increment:9800 time: 9.8 +field ' n' [explicit solve]: current solution: 9.988833e+01, current residual:6.027078e+01 +wall time: 0.000342846s + +time increment:9810 time: 9.81 +field ' n' [explicit solve]: current solution: 9.988968e+01, current residual:6.027159e+01 +wall time: 0.0003438s + +time increment:9820 time: 9.82 +field ' n' [explicit solve]: current solution: 9.989103e+01, current residual:6.027240e+01 +wall time: 0.000338078s + +time increment:9830 time: 9.83 +field ' n' [explicit solve]: current solution: 9.989237e+01, current residual:6.027321e+01 +wall time: 0.000350952s + +time increment:9840 time: 9.84 +field ' n' [explicit solve]: current solution: 9.989371e+01, current residual:6.027402e+01 +wall time: 0.000370026s + +time increment:9850 time: 9.85 +field ' n' [explicit solve]: current solution: 9.989505e+01, current residual:6.027483e+01 +wall time: 0.000372887s + +time increment:9860 time: 9.86 +field ' n' [explicit solve]: current solution: 9.989638e+01, current residual:6.027563e+01 +wall time: 0.000386953s + +time increment:9870 time: 9.87 +field ' n' [explicit solve]: current solution: 9.989771e+01, current residual:6.027643e+01 +wall time: 0.000388861s + +time increment:9880 time: 9.88 +field ' n' [explicit solve]: current solution: 9.989904e+01, current residual:6.027724e+01 +wall time: 0.000349045s + +time increment:9890 time: 9.89 +field ' n' [explicit solve]: current solution: 9.990037e+01, current residual:6.027804e+01 +wall time: 0.000372887s + +time increment:9900 time: 9.9 +field ' n' [explicit solve]: current solution: 9.990170e+01, current residual:6.027884e+01 +wall time: 0.000372171s + +time increment:9910 time: 9.91 +field ' n' [explicit solve]: current solution: 9.990302e+01, current residual:6.027963e+01 +wall time: 0.000386s + +time increment:9920 time: 9.92 +field ' n' [explicit solve]: current solution: 9.990434e+01, current residual:6.028043e+01 +wall time: 0.000319004s + +time increment:9930 time: 9.93 +field ' n' [explicit solve]: current solution: 9.990566e+01, current residual:6.028122e+01 +wall time: 0.000381947s + +time increment:9940 time: 9.94 +field ' n' [explicit solve]: current solution: 9.990698e+01, current residual:6.028202e+01 +wall time: 0.000348806s + +time increment:9950 time: 9.95 +field ' n' [explicit solve]: current solution: 9.990829e+01, current residual:6.028281e+01 +wall time: 0.000311852s + +time increment:9960 time: 9.96 +field ' n' [explicit solve]: current solution: 9.990960e+01, current residual:6.028360e+01 +wall time: 0.000338078s + +time increment:9970 time: 9.97 +field ' n' [explicit solve]: current solution: 9.991091e+01, current residual:6.028439e+01 +wall time: 0.0003829s + +time increment:9980 time: 9.98 +field ' n' [explicit solve]: current solution: 9.991222e+01, current residual:6.028518e+01 +wall time: 0.000319004s + +time increment:9990 time: 9.99 +field ' n' [explicit solve]: current solution: 9.991352e+01, current residual:6.028596e+01 +wall time: 0.000303984s + +time increment:10000 time: 10 +field ' n' [explicit solve]: current solution: 9.991482e+01, current residual:6.028675e+01 +wall time: 0.000360012s +fields: 1 +fields: 1 +fields: 1 +fields: 1 +Output written to:solution-010000.pvtu + +Energy: 203.464 +Energy Components: 88.6234 114.841 0 + +time increment:10010 time: 10.01 +field ' n' [explicit solve]: current solution: 9.991612e+01, current residual:6.028753e+01 +wall time: 0.000380993s + +time increment:10020 time: 10.02 +field ' n' [explicit solve]: current solution: 9.991742e+01, current residual:6.028832e+01 +wall time: 0.000366211s + +time increment:10030 time: 10.03 +field ' n' [explicit solve]: current solution: 9.991872e+01, current residual:6.028910e+01 +wall time: 0.000391006s + +time increment:10040 time: 10.04 +field ' n' [explicit solve]: current solution: 9.992001e+01, current residual:6.028988e+01 +wall time: 0.000291824s + +time increment:10050 time: 10.05 +field ' n' [explicit solve]: current solution: 9.992130e+01, current residual:6.029065e+01 +wall time: 0.000373125s + +time increment:10060 time: 10.06 +field ' n' [explicit solve]: current solution: 9.992259e+01, current residual:6.029143e+01 +wall time: 0.000329018s + +time increment:10070 time: 10.07 +field ' n' [explicit solve]: current solution: 9.992387e+01, current residual:6.029221e+01 +wall time: 0.00032115s + +time increment:10080 time: 10.08 +field ' n' [explicit solve]: current solution: 9.992516e+01, current residual:6.029298e+01 +wall time: 0.000365019s + +time increment:10090 time: 10.09 +field ' n' [explicit solve]: current solution: 9.992644e+01, current residual:6.029375e+01 +wall time: 0.000298023s + +time increment:10100 time: 10.1 +field ' n' [explicit solve]: current solution: 9.992772e+01, current residual:6.029453e+01 +wall time: 0.000364065s + +time increment:10110 time: 10.11 +field ' n' [explicit solve]: current solution: 9.992900e+01, current residual:6.029530e+01 +wall time: 0.000338793s + +time increment:10120 time: 10.12 +field ' n' [explicit solve]: current solution: 9.993027e+01, current residual:6.029607e+01 +wall time: 0.000306129s + +time increment:10130 time: 10.13 +field ' n' [explicit solve]: current solution: 9.993154e+01, current residual:6.029683e+01 +wall time: 0.000340939s + +time increment:10140 time: 10.14 +field ' n' [explicit solve]: current solution: 9.993281e+01, current residual:6.029760e+01 +wall time: 0.000339985s + +time increment:10150 time: 10.15 +field ' n' [explicit solve]: current solution: 9.993408e+01, current residual:6.029836e+01 +wall time: 0.000319958s + +time increment:10160 time: 10.16 +field ' n' [explicit solve]: current solution: 9.993535e+01, current residual:6.029913e+01 +wall time: 0.000360012s + +time increment:10170 time: 10.17 +field ' n' [explicit solve]: current solution: 9.993661e+01, current residual:6.029989e+01 +wall time: 0.000336885s + +time increment:10180 time: 10.18 +field ' n' [explicit solve]: current solution: 9.993787e+01, current residual:6.030065e+01 +wall time: 0.000351191s + +time increment:10190 time: 10.19 +field ' n' [explicit solve]: current solution: 9.993913e+01, current residual:6.030141e+01 +wall time: 0.000311136s + +time increment:10200 time: 10.2 +field ' n' [explicit solve]: current solution: 9.994039e+01, current residual:6.030217e+01 +wall time: 0.000338078s + +time increment:10210 time: 10.21 +field ' n' [explicit solve]: current solution: 9.994164e+01, current residual:6.030293e+01 +wall time: 0.000309944s + +time increment:10220 time: 10.22 +field ' n' [explicit solve]: current solution: 9.994289e+01, current residual:6.030368e+01 +wall time: 0.00030899s + +time increment:10230 time: 10.23 +field ' n' [explicit solve]: current solution: 9.994414e+01, current residual:6.030444e+01 +wall time: 0.000406981s + +time increment:10240 time: 10.24 +field ' n' [explicit solve]: current solution: 9.994539e+01, current residual:6.030519e+01 +wall time: 0.000378847s + +time increment:10250 time: 10.25 +field ' n' [explicit solve]: current solution: 9.994664e+01, current residual:6.030594e+01 +wall time: 0.000308037s + +time increment:10260 time: 10.26 +field ' n' [explicit solve]: current solution: 9.994788e+01, current residual:6.030669e+01 +wall time: 0.00033021s + +time increment:10270 time: 10.27 +field ' n' [explicit solve]: current solution: 9.994912e+01, current residual:6.030744e+01 +wall time: 0.000339031s + +time increment:10280 time: 10.28 +field ' n' [explicit solve]: current solution: 9.995036e+01, current residual:6.030819e+01 +wall time: 0.000342846s + +time increment:10290 time: 10.29 +field ' n' [explicit solve]: current solution: 9.995160e+01, current residual:6.030894e+01 +wall time: 0.000338078s + +time increment:10300 time: 10.3 +field ' n' [explicit solve]: current solution: 9.995284e+01, current residual:6.030968e+01 +wall time: 0.000365019s + +time increment:10310 time: 10.31 +field ' n' [explicit solve]: current solution: 9.995407e+01, current residual:6.031043e+01 +wall time: 0.000363111s + +time increment:10320 time: 10.32 +field ' n' [explicit solve]: current solution: 9.995530e+01, current residual:6.031117e+01 +wall time: 0.000353098s + +time increment:10330 time: 10.33 +field ' n' [explicit solve]: current solution: 9.995653e+01, current residual:6.031191e+01 +wall time: 0.000337124s + +time increment:10340 time: 10.34 +field ' n' [explicit solve]: current solution: 9.995775e+01, current residual:6.031265e+01 +wall time: 0.000334978s + +time increment:10350 time: 10.35 +field ' n' [explicit solve]: current solution: 9.995898e+01, current residual:6.031339e+01 +wall time: 0.000366926s + +time increment:10360 time: 10.36 +field ' n' [explicit solve]: current solution: 9.996020e+01, current residual:6.031413e+01 +wall time: 0.000323057s + +time increment:10370 time: 10.37 +field ' n' [explicit solve]: current solution: 9.996142e+01, current residual:6.031486e+01 +wall time: 0.000369072s + +time increment:10380 time: 10.38 +field ' n' [explicit solve]: current solution: 9.996264e+01, current residual:6.031560e+01 +wall time: 0.000396967s + +time increment:10390 time: 10.39 +field ' n' [explicit solve]: current solution: 9.996385e+01, current residual:6.031633e+01 +wall time: 0.000344992s + +time increment:10400 time: 10.4 +field ' n' [explicit solve]: current solution: 9.996507e+01, current residual:6.031706e+01 +wall time: 0.000355005s + +time increment:10410 time: 10.41 +field ' n' [explicit solve]: current solution: 9.996628e+01, current residual:6.031780e+01 +wall time: 0.000355959s + +time increment:10420 time: 10.42 +field ' n' [explicit solve]: current solution: 9.996749e+01, current residual:6.031853e+01 +wall time: 0.000340939s + +time increment:10430 time: 10.43 +field ' n' [explicit solve]: current solution: 9.996870e+01, current residual:6.031926e+01 +wall time: 0.00036788s + +time increment:10440 time: 10.44 +field ' n' [explicit solve]: current solution: 9.996990e+01, current residual:6.031998e+01 +wall time: 0.000349045s + +time increment:10450 time: 10.45 +field ' n' [explicit solve]: current solution: 9.997110e+01, current residual:6.032071e+01 +wall time: 0.000344992s + +time increment:10460 time: 10.46 +field ' n' [explicit solve]: current solution: 9.997231e+01, current residual:6.032143e+01 +wall time: 0.000319958s + +time increment:10470 time: 10.47 +field ' n' [explicit solve]: current solution: 9.997350e+01, current residual:6.032216e+01 +wall time: 0.000353098s + +time increment:10480 time: 10.48 +field ' n' [explicit solve]: current solution: 9.997470e+01, current residual:6.032288e+01 +wall time: 0.000362873s + +time increment:10490 time: 10.49 +field ' n' [explicit solve]: current solution: 9.997590e+01, current residual:6.032360e+01 +wall time: 0.00036788s + +time increment:10500 time: 10.5 +field ' n' [explicit solve]: current solution: 9.997709e+01, current residual:6.032432e+01 +wall time: 0.0003438s + +time increment:10510 time: 10.51 +field ' n' [explicit solve]: current solution: 9.997828e+01, current residual:6.032504e+01 +wall time: 0.000401974s + +time increment:10520 time: 10.52 +field ' n' [explicit solve]: current solution: 9.997947e+01, current residual:6.032576e+01 +wall time: 0.000406027s + +time increment:10530 time: 10.53 +field ' n' [explicit solve]: current solution: 9.998065e+01, current residual:6.032648e+01 +wall time: 0.000366926s + +time increment:10540 time: 10.54 +field ' n' [explicit solve]: current solution: 9.998184e+01, current residual:6.032719e+01 +wall time: 0.000339031s + +time increment:10550 time: 10.55 +field ' n' [explicit solve]: current solution: 9.998302e+01, current residual:6.032791e+01 +wall time: 0.000384092s + +time increment:10560 time: 10.56 +field ' n' [explicit solve]: current solution: 9.998420e+01, current residual:6.032862e+01 +wall time: 0.000363111s + +time increment:10570 time: 10.57 +field ' n' [explicit solve]: current solution: 9.998538e+01, current residual:6.032933e+01 +wall time: 0.000342846s + +time increment:10580 time: 10.58 +field ' n' [explicit solve]: current solution: 9.998656e+01, current residual:6.033004e+01 +wall time: 0.000401974s + +time increment:10590 time: 10.59 +field ' n' [explicit solve]: current solution: 9.998773e+01, current residual:6.033075e+01 +wall time: 0.000405788s + +time increment:10600 time: 10.6 +field ' n' [explicit solve]: current solution: 9.998890e+01, current residual:6.033146e+01 +wall time: 0.000333786s + +time increment:10610 time: 10.61 +field ' n' [explicit solve]: current solution: 9.999007e+01, current residual:6.033216e+01 +wall time: 0.00041604s + +time increment:10620 time: 10.62 +field ' n' [explicit solve]: current solution: 9.999124e+01, current residual:6.033287e+01 +wall time: 0.000352859s + +time increment:10630 time: 10.63 +field ' n' [explicit solve]: current solution: 9.999241e+01, current residual:6.033357e+01 +wall time: 0.000372887s + +time increment:10640 time: 10.64 +field ' n' [explicit solve]: current solution: 9.999357e+01, current residual:6.033428e+01 +wall time: 0.000386s + +time increment:10650 time: 10.65 +field ' n' [explicit solve]: current solution: 9.999473e+01, current residual:6.033498e+01 +wall time: 0.000291824s + +time increment:10660 time: 10.66 +field ' n' [explicit solve]: current solution: 9.999589e+01, current residual:6.033568e+01 +wall time: 0.000344038s + +time increment:10670 time: 10.67 +field ' n' [explicit solve]: current solution: 9.999705e+01, current residual:6.033638e+01 +wall time: 0.000333786s + +time increment:10680 time: 10.68 +field ' n' [explicit solve]: current solution: 9.999821e+01, current residual:6.033708e+01 +wall time: 0.000355959s + +time increment:10690 time: 10.69 +field ' n' [explicit solve]: current solution: 9.999936e+01, current residual:6.033777e+01 +wall time: 0.000365973s + +time increment:10700 time: 10.7 +field ' n' [explicit solve]: current solution: 1.000005e+02, current residual:6.033847e+01 +wall time: 0.000361919s + +time increment:10710 time: 10.71 +field ' n' [explicit solve]: current solution: 1.000017e+02, current residual:6.033917e+01 +wall time: 0.000357151s + +time increment:10720 time: 10.72 +field ' n' [explicit solve]: current solution: 1.000028e+02, current residual:6.033986e+01 +wall time: 0.000405073s + +time increment:10730 time: 10.73 +field ' n' [explicit solve]: current solution: 1.000040e+02, current residual:6.034055e+01 +wall time: 0.000354052s + +time increment:10740 time: 10.74 +field ' n' [explicit solve]: current solution: 1.000051e+02, current residual:6.034124e+01 +wall time: 0.000344038s + +time increment:10750 time: 10.75 +field ' n' [explicit solve]: current solution: 1.000062e+02, current residual:6.034193e+01 +wall time: 0.000371933s + +time increment:10760 time: 10.76 +field ' n' [explicit solve]: current solution: 1.000074e+02, current residual:6.034262e+01 +wall time: 0.000404835s + +time increment:10770 time: 10.77 +field ' n' [explicit solve]: current solution: 1.000085e+02, current residual:6.034331e+01 +wall time: 0.00040102s + +time increment:10780 time: 10.78 +field ' n' [explicit solve]: current solution: 1.000097e+02, current residual:6.034400e+01 +wall time: 0.000365973s + +time increment:10790 time: 10.79 +field ' n' [explicit solve]: current solution: 1.000108e+02, current residual:6.034468e+01 +wall time: 0.000362158s + +time increment:10800 time: 10.8 +field ' n' [explicit solve]: current solution: 1.000119e+02, current residual:6.034537e+01 +wall time: 0.000403881s + +time increment:10810 time: 10.81 +field ' n' [explicit solve]: current solution: 1.000131e+02, current residual:6.034605e+01 +wall time: 0.000422001s + +time increment:10820 time: 10.82 +field ' n' [explicit solve]: current solution: 1.000142e+02, current residual:6.034673e+01 +wall time: 0.000391006s + +time increment:10830 time: 10.83 +field ' n' [explicit solve]: current solution: 1.000153e+02, current residual:6.034741e+01 +wall time: 0.000372171s + +time increment:10840 time: 10.84 +field ' n' [explicit solve]: current solution: 1.000164e+02, current residual:6.034809e+01 +wall time: 0.000334978s + +time increment:10850 time: 10.85 +field ' n' [explicit solve]: current solution: 1.000176e+02, current residual:6.034877e+01 +wall time: 0.000350237s + +time increment:10860 time: 10.86 +field ' n' [explicit solve]: current solution: 1.000187e+02, current residual:6.034945e+01 +wall time: 0.000344992s + +time increment:10870 time: 10.87 +field ' n' [explicit solve]: current solution: 1.000198e+02, current residual:6.035012e+01 +wall time: 0.000337839s + +time increment:10880 time: 10.88 +field ' n' [explicit solve]: current solution: 1.000209e+02, current residual:6.035080e+01 +wall time: 0.000405073s + +time increment:10890 time: 10.89 +field ' n' [explicit solve]: current solution: 1.000220e+02, current residual:6.035147e+01 +wall time: 0.000387907s + +time increment:10900 time: 10.9 +field ' n' [explicit solve]: current solution: 1.000232e+02, current residual:6.035215e+01 +wall time: 0.000394106s + +time increment:10910 time: 10.91 +field ' n' [explicit solve]: current solution: 1.000243e+02, current residual:6.035282e+01 +wall time: 0.000380039s + +time increment:10920 time: 10.92 +field ' n' [explicit solve]: current solution: 1.000254e+02, current residual:6.035349e+01 +wall time: 0.000364065s + +time increment:10930 time: 10.93 +field ' n' [explicit solve]: current solution: 1.000265e+02, current residual:6.035416e+01 +wall time: 0.000325918s + +time increment:10940 time: 10.94 +field ' n' [explicit solve]: current solution: 1.000276e+02, current residual:6.035482e+01 +wall time: 0.000385046s + +time increment:10950 time: 10.95 +field ' n' [explicit solve]: current solution: 1.000287e+02, current residual:6.035549e+01 +wall time: 0.000339985s + +time increment:10960 time: 10.96 +field ' n' [explicit solve]: current solution: 1.000298e+02, current residual:6.035616e+01 +wall time: 0.00029397s + +time increment:10970 time: 10.97 +field ' n' [explicit solve]: current solution: 1.000309e+02, current residual:6.035682e+01 +wall time: 0.000305891s + +time increment:10980 time: 10.98 +field ' n' [explicit solve]: current solution: 1.000320e+02, current residual:6.035749e+01 +wall time: 0.000359058s + +time increment:10990 time: 10.99 +field ' n' [explicit solve]: current solution: 1.000331e+02, current residual:6.035815e+01 +wall time: 0.00035882s + +time increment:11000 time: 11 +field ' n' [explicit solve]: current solution: 1.000342e+02, current residual:6.035881e+01 +wall time: 0.00031209s + +time increment:11010 time: 11.01 +field ' n' [explicit solve]: current solution: 1.000353e+02, current residual:6.035947e+01 +wall time: 0.000303984s + +time increment:11020 time: 11.02 +field ' n' [explicit solve]: current solution: 1.000364e+02, current residual:6.036013e+01 +wall time: 0.000387192s + +time increment:11030 time: 11.03 +field ' n' [explicit solve]: current solution: 1.000375e+02, current residual:6.036079e+01 +wall time: 0.000307083s + +time increment:11040 time: 11.04 +field ' n' [explicit solve]: current solution: 1.000385e+02, current residual:6.036145e+01 +wall time: 0.00030899s + +time increment:11050 time: 11.05 +field ' n' [explicit solve]: current solution: 1.000396e+02, current residual:6.036210e+01 +wall time: 0.000346899s + +time increment:11060 time: 11.06 +field ' n' [explicit solve]: current solution: 1.000407e+02, current residual:6.036276e+01 +wall time: 0.000388861s + +time increment:11070 time: 11.07 +field ' n' [explicit solve]: current solution: 1.000418e+02, current residual:6.036341e+01 +wall time: 0.00030303s + +time increment:11080 time: 11.08 +field ' n' [explicit solve]: current solution: 1.000429e+02, current residual:6.036406e+01 +wall time: 0.000343084s + +time increment:11090 time: 11.09 +field ' n' [explicit solve]: current solution: 1.000440e+02, current residual:6.036471e+01 +wall time: 0.000411987s + +time increment:11100 time: 11.1 +field ' n' [explicit solve]: current solution: 1.000450e+02, current residual:6.036537e+01 +wall time: 0.000396967s + +time increment:11110 time: 11.11 +field ' n' [explicit solve]: current solution: 1.000461e+02, current residual:6.036601e+01 +wall time: 0.000315905s + +time increment:11120 time: 11.12 +field ' n' [explicit solve]: current solution: 1.000472e+02, current residual:6.036666e+01 +wall time: 0.000355959s + +time increment:11130 time: 11.13 +field ' n' [explicit solve]: current solution: 1.000483e+02, current residual:6.036731e+01 +wall time: 0.000386953s + +time increment:11140 time: 11.14 +field ' n' [explicit solve]: current solution: 1.000493e+02, current residual:6.036796e+01 +wall time: 0.000346899s + +time increment:11150 time: 11.15 +field ' n' [explicit solve]: current solution: 1.000504e+02, current residual:6.036860e+01 +wall time: 0.000339031s + +time increment:11160 time: 11.16 +field ' n' [explicit solve]: current solution: 1.000515e+02, current residual:6.036925e+01 +wall time: 0.000394821s + +time increment:11170 time: 11.17 +field ' n' [explicit solve]: current solution: 1.000525e+02, current residual:6.036989e+01 +wall time: 0.000345945s + +time increment:11180 time: 11.18 +field ' n' [explicit solve]: current solution: 1.000536e+02, current residual:6.037053e+01 +wall time: 0.000365019s + +time increment:11190 time: 11.19 +field ' n' [explicit solve]: current solution: 1.000546e+02, current residual:6.037117e+01 +wall time: 0.000323057s + +time increment:11200 time: 11.2 +field ' n' [explicit solve]: current solution: 1.000557e+02, current residual:6.037181e+01 +wall time: 0.000360966s + +time increment:11210 time: 11.21 +field ' n' [explicit solve]: current solution: 1.000568e+02, current residual:6.037245e+01 +wall time: 0.00029707s + +time increment:11220 time: 11.22 +field ' n' [explicit solve]: current solution: 1.000578e+02, current residual:6.037309e+01 +wall time: 0.000298023s + +time increment:11230 time: 11.23 +field ' n' [explicit solve]: current solution: 1.000589e+02, current residual:6.037372e+01 +wall time: 0.000337839s + +time increment:11240 time: 11.24 +field ' n' [explicit solve]: current solution: 1.000599e+02, current residual:6.037436e+01 +wall time: 0.000334978s + +time increment:11250 time: 11.25 +field ' n' [explicit solve]: current solution: 1.000610e+02, current residual:6.037499e+01 +wall time: 0.000398874s + +time increment:11260 time: 11.26 +field ' n' [explicit solve]: current solution: 1.000620e+02, current residual:6.037563e+01 +wall time: 0.00030303s + +time increment:11270 time: 11.27 +field ' n' [explicit solve]: current solution: 1.000631e+02, current residual:6.037626e+01 +wall time: 0.000375986s + +time increment:11280 time: 11.28 +field ' n' [explicit solve]: current solution: 1.000641e+02, current residual:6.037689e+01 +wall time: 0.000381947s + +time increment:11290 time: 11.29 +field ' n' [explicit solve]: current solution: 1.000652e+02, current residual:6.037752e+01 +wall time: 0.000302076s + +time increment:11300 time: 11.3 +field ' n' [explicit solve]: current solution: 1.000662e+02, current residual:6.037815e+01 +wall time: 0.000340939s + +time increment:11310 time: 11.31 +field ' n' [explicit solve]: current solution: 1.000672e+02, current residual:6.037878e+01 +wall time: 0.000365973s + +time increment:11320 time: 11.32 +field ' n' [explicit solve]: current solution: 1.000683e+02, current residual:6.037941e+01 +wall time: 0.000317812s + +time increment:11330 time: 11.33 +field ' n' [explicit solve]: current solution: 1.000693e+02, current residual:6.038003e+01 +wall time: 0.000313044s + +time increment:11340 time: 11.34 +field ' n' [explicit solve]: current solution: 1.000703e+02, current residual:6.038066e+01 +wall time: 0.000340939s + +time increment:11350 time: 11.35 +field ' n' [explicit solve]: current solution: 1.000714e+02, current residual:6.038128e+01 +wall time: 0.000307798s + +time increment:11360 time: 11.36 +field ' n' [explicit solve]: current solution: 1.000724e+02, current residual:6.038191e+01 +wall time: 0.000355005s + +time increment:11370 time: 11.37 +field ' n' [explicit solve]: current solution: 1.000734e+02, current residual:6.038253e+01 +wall time: 0.000331879s + +time increment:11380 time: 11.38 +field ' n' [explicit solve]: current solution: 1.000745e+02, current residual:6.038315e+01 +wall time: 0.000328064s + +time increment:11390 time: 11.39 +field ' n' [explicit solve]: current solution: 1.000755e+02, current residual:6.038377e+01 +wall time: 0.000366926s + +time increment:11400 time: 11.4 +field ' n' [explicit solve]: current solution: 1.000765e+02, current residual:6.038439e+01 +wall time: 0.000375032s + +time increment:11410 time: 11.41 +field ' n' [explicit solve]: current solution: 1.000775e+02, current residual:6.038501e+01 +wall time: 0.00040102s + +time increment:11420 time: 11.42 +field ' n' [explicit solve]: current solution: 1.000786e+02, current residual:6.038562e+01 +wall time: 0.000309944s + +time increment:11430 time: 11.43 +field ' n' [explicit solve]: current solution: 1.000796e+02, current residual:6.038624e+01 +wall time: 0.000345945s + +time increment:11440 time: 11.44 +field ' n' [explicit solve]: current solution: 1.000806e+02, current residual:6.038686e+01 +wall time: 0.000369787s + +time increment:11450 time: 11.45 +field ' n' [explicit solve]: current solution: 1.000816e+02, current residual:6.038747e+01 +wall time: 0.000346899s + +time increment:11460 time: 11.46 +field ' n' [explicit solve]: current solution: 1.000826e+02, current residual:6.038808e+01 +wall time: 0.000296831s + +time increment:11470 time: 11.47 +field ' n' [explicit solve]: current solution: 1.000836e+02, current residual:6.038869e+01 +wall time: 0.000371933s + +time increment:11480 time: 11.48 +field ' n' [explicit solve]: current solution: 1.000847e+02, current residual:6.038931e+01 +wall time: 0.000365019s + +time increment:11490 time: 11.49 +field ' n' [explicit solve]: current solution: 1.000857e+02, current residual:6.038992e+01 +wall time: 0.000340939s + +time increment:11500 time: 11.5 +field ' n' [explicit solve]: current solution: 1.000867e+02, current residual:6.039053e+01 +wall time: 0.000339985s + +time increment:11510 time: 11.51 +field ' n' [explicit solve]: current solution: 1.000877e+02, current residual:6.039113e+01 +wall time: 0.000370026s + +time increment:11520 time: 11.52 +field ' n' [explicit solve]: current solution: 1.000887e+02, current residual:6.039174e+01 +wall time: 0.000355959s + +time increment:11530 time: 11.53 +field ' n' [explicit solve]: current solution: 1.000897e+02, current residual:6.039235e+01 +wall time: 0.000409842s + +time increment:11540 time: 11.54 +field ' n' [explicit solve]: current solution: 1.000907e+02, current residual:6.039295e+01 +wall time: 0.000342846s + +time increment:11550 time: 11.55 +field ' n' [explicit solve]: current solution: 1.000917e+02, current residual:6.039356e+01 +wall time: 0.000373125s + +time increment:11560 time: 11.56 +field ' n' [explicit solve]: current solution: 1.000927e+02, current residual:6.039416e+01 +wall time: 0.000347853s + +time increment:11570 time: 11.57 +field ' n' [explicit solve]: current solution: 1.000937e+02, current residual:6.039476e+01 +wall time: 0.000325918s + +time increment:11580 time: 11.58 +field ' n' [explicit solve]: current solution: 1.000947e+02, current residual:6.039536e+01 +wall time: 0.000354052s + +time increment:11590 time: 11.59 +field ' n' [explicit solve]: current solution: 1.000957e+02, current residual:6.039596e+01 +wall time: 0.000355005s + +time increment:11600 time: 11.6 +field ' n' [explicit solve]: current solution: 1.000967e+02, current residual:6.039656e+01 +wall time: 0.000370026s + +time increment:11610 time: 11.61 +field ' n' [explicit solve]: current solution: 1.000977e+02, current residual:6.039716e+01 +wall time: 0.000370026s + +time increment:11620 time: 11.62 +field ' n' [explicit solve]: current solution: 1.000986e+02, current residual:6.039776e+01 +wall time: 0.000294924s + +time increment:11630 time: 11.63 +field ' n' [explicit solve]: current solution: 1.000996e+02, current residual:6.039836e+01 +wall time: 0.000348806s + +time increment:11640 time: 11.64 +field ' n' [explicit solve]: current solution: 1.001006e+02, current residual:6.039895e+01 +wall time: 0.000349045s + +time increment:11650 time: 11.65 +field ' n' [explicit solve]: current solution: 1.001016e+02, current residual:6.039955e+01 +wall time: 0.000339031s + +time increment:11660 time: 11.66 +field ' n' [explicit solve]: current solution: 1.001026e+02, current residual:6.040014e+01 +wall time: 0.000329971s + +time increment:11670 time: 11.67 +field ' n' [explicit solve]: current solution: 1.001036e+02, current residual:6.040073e+01 +wall time: 0.000371933s + +time increment:11680 time: 11.68 +field ' n' [explicit solve]: current solution: 1.001045e+02, current residual:6.040133e+01 +wall time: 0.000349998s + +time increment:11690 time: 11.69 +field ' n' [explicit solve]: current solution: 1.001055e+02, current residual:6.040192e+01 +wall time: 0.000347137s + +time increment:11700 time: 11.7 +field ' n' [explicit solve]: current solution: 1.001065e+02, current residual:6.040251e+01 +wall time: 0.000351906s + +time increment:11710 time: 11.71 +field ' n' [explicit solve]: current solution: 1.001075e+02, current residual:6.040310e+01 +wall time: 0.000380039s + +time increment:11720 time: 11.72 +field ' n' [explicit solve]: current solution: 1.001084e+02, current residual:6.040368e+01 +wall time: 0.000349998s + +time increment:11730 time: 11.73 +field ' n' [explicit solve]: current solution: 1.001094e+02, current residual:6.040427e+01 +wall time: 0.0003829s + +time increment:11740 time: 11.74 +field ' n' [explicit solve]: current solution: 1.001104e+02, current residual:6.040486e+01 +wall time: 0.000361204s + +time increment:11750 time: 11.75 +field ' n' [explicit solve]: current solution: 1.001114e+02, current residual:6.040544e+01 +wall time: 0.000404835s + +time increment:11760 time: 11.76 +field ' n' [explicit solve]: current solution: 1.001123e+02, current residual:6.040603e+01 +wall time: 0.000344992s + +time increment:11770 time: 11.77 +field ' n' [explicit solve]: current solution: 1.001133e+02, current residual:6.040661e+01 +wall time: 0.000340939s + +time increment:11780 time: 11.78 +field ' n' [explicit solve]: current solution: 1.001143e+02, current residual:6.040719e+01 +wall time: 0.000377893s + +time increment:11790 time: 11.79 +field ' n' [explicit solve]: current solution: 1.001152e+02, current residual:6.040778e+01 +wall time: 0.000488997s + +time increment:11800 time: 11.8 +field ' n' [explicit solve]: current solution: 1.001162e+02, current residual:6.040836e+01 +wall time: 0.000398874s + +time increment:11810 time: 11.81 +field ' n' [explicit solve]: current solution: 1.001171e+02, current residual:6.040894e+01 +wall time: 0.000345945s + +time increment:11820 time: 11.82 +field ' n' [explicit solve]: current solution: 1.001181e+02, current residual:6.040952e+01 +wall time: 0.000338078s + +time increment:11830 time: 11.83 +field ' n' [explicit solve]: current solution: 1.001191e+02, current residual:6.041009e+01 +wall time: 0.000354052s + +time increment:11840 time: 11.84 +field ' n' [explicit solve]: current solution: 1.001200e+02, current residual:6.041067e+01 +wall time: 0.00041008s + +time increment:11850 time: 11.85 +field ' n' [explicit solve]: current solution: 1.001210e+02, current residual:6.041125e+01 +wall time: 0.000329018s + +time increment:11860 time: 11.86 +field ' n' [explicit solve]: current solution: 1.001219e+02, current residual:6.041182e+01 +wall time: 0.000387192s + +time increment:11870 time: 11.87 +field ' n' [explicit solve]: current solution: 1.001229e+02, current residual:6.041240e+01 +wall time: 0.000366926s + +time increment:11880 time: 11.88 +field ' n' [explicit solve]: current solution: 1.001238e+02, current residual:6.041297e+01 +wall time: 0.000351906s + +time increment:11890 time: 11.89 +field ' n' [explicit solve]: current solution: 1.001248e+02, current residual:6.041354e+01 +wall time: 0.000355959s + +time increment:11900 time: 11.9 +field ' n' [explicit solve]: current solution: 1.001257e+02, current residual:6.041412e+01 +wall time: 0.000375986s + +time increment:11910 time: 11.91 +field ' n' [explicit solve]: current solution: 1.001267e+02, current residual:6.041469e+01 +wall time: 0.000340939s + +time increment:11920 time: 11.92 +field ' n' [explicit solve]: current solution: 1.001276e+02, current residual:6.041526e+01 +wall time: 0.000344992s + +time increment:11930 time: 11.93 +field ' n' [explicit solve]: current solution: 1.001285e+02, current residual:6.041583e+01 +wall time: 0.000375032s + +time increment:11940 time: 11.94 +field ' n' [explicit solve]: current solution: 1.001295e+02, current residual:6.041640e+01 +wall time: 0.000364065s + +time increment:11950 time: 11.95 +field ' n' [explicit solve]: current solution: 1.001304e+02, current residual:6.041696e+01 +wall time: 0.000347137s + +time increment:11960 time: 11.96 +field ' n' [explicit solve]: current solution: 1.001314e+02, current residual:6.041753e+01 +wall time: 0.000357151s + +time increment:11970 time: 11.97 +field ' n' [explicit solve]: current solution: 1.001323e+02, current residual:6.041810e+01 +wall time: 0.000344992s + +time increment:11980 time: 11.98 +field ' n' [explicit solve]: current solution: 1.001332e+02, current residual:6.041866e+01 +wall time: 0.000349998s + +time increment:11990 time: 11.99 +field ' n' [explicit solve]: current solution: 1.001342e+02, current residual:6.041923e+01 +wall time: 0.000333071s + +time increment:12000 time: 12 +field ' n' [explicit solve]: current solution: 1.001351e+02, current residual:6.041979e+01 +wall time: 0.000337839s +fields: 1 +fields: 1 +fields: 1 +fields: 1 +Output written to:solution-012000.pvtu + +Energy: 198.924 +Energy Components: 85.2998 113.624 0 + +time increment:12010 time: 12.01 +field ' n' [explicit solve]: current solution: 1.001360e+02, current residual:6.042035e+01 +wall time: 0.00031805s + +time increment:12020 time: 12.02 +field ' n' [explicit solve]: current solution: 1.001370e+02, current residual:6.042091e+01 +wall time: 0.000301123s + +time increment:12030 time: 12.03 +field ' n' [explicit solve]: current solution: 1.001379e+02, current residual:6.042147e+01 +wall time: 0.000396013s + +time increment:12040 time: 12.04 +field ' n' [explicit solve]: current solution: 1.001388e+02, current residual:6.042203e+01 +wall time: 0.000406981s + +time increment:12050 time: 12.05 +field ' n' [explicit solve]: current solution: 1.001397e+02, current residual:6.042259e+01 +wall time: 0.000328064s + +time increment:12060 time: 12.06 +field ' n' [explicit solve]: current solution: 1.001407e+02, current residual:6.042315e+01 +wall time: 0.000304937s + +time increment:12070 time: 12.07 +field ' n' [explicit solve]: current solution: 1.001416e+02, current residual:6.042371e+01 +wall time: 0.00033617s + +time increment:12080 time: 12.08 +field ' n' [explicit solve]: current solution: 1.001425e+02, current residual:6.042427e+01 +wall time: 0.000320911s + +time increment:12090 time: 12.09 +field ' n' [explicit solve]: current solution: 1.001434e+02, current residual:6.042482e+01 +wall time: 0.000355005s + +time increment:12100 time: 12.1 +field ' n' [explicit solve]: current solution: 1.001443e+02, current residual:6.042538e+01 +wall time: 0.000290871s + +time increment:12110 time: 12.11 +field ' n' [explicit solve]: current solution: 1.001453e+02, current residual:6.042593e+01 +wall time: 0.000378132s + +time increment:12120 time: 12.12 +field ' n' [explicit solve]: current solution: 1.001462e+02, current residual:6.042648e+01 +wall time: 0.000377178s + +time increment:12130 time: 12.13 +field ' n' [explicit solve]: current solution: 1.001471e+02, current residual:6.042704e+01 +wall time: 0.000313997s + +time increment:12140 time: 12.14 +field ' n' [explicit solve]: current solution: 1.001480e+02, current residual:6.042759e+01 +wall time: 0.000351906s + +time increment:12150 time: 12.15 +field ' n' [explicit solve]: current solution: 1.001489e+02, current residual:6.042814e+01 +wall time: 0.000335932s + +time increment:12160 time: 12.16 +field ' n' [explicit solve]: current solution: 1.001498e+02, current residual:6.042869e+01 +wall time: 0.000342846s + +time increment:12170 time: 12.17 +field ' n' [explicit solve]: current solution: 1.001507e+02, current residual:6.042924e+01 +wall time: 0.000432968s + +time increment:12180 time: 12.18 +field ' n' [explicit solve]: current solution: 1.001517e+02, current residual:6.042979e+01 +wall time: 0.000344038s + +time increment:12190 time: 12.19 +field ' n' [explicit solve]: current solution: 1.001526e+02, current residual:6.043034e+01 +wall time: 0.000388861s + +time increment:12200 time: 12.2 +field ' n' [explicit solve]: current solution: 1.001535e+02, current residual:6.043088e+01 +wall time: 0.000324965s + +time increment:12210 time: 12.21 +field ' n' [explicit solve]: current solution: 1.001544e+02, current residual:6.043143e+01 +wall time: 0.000349045s + +time increment:12220 time: 12.22 +field ' n' [explicit solve]: current solution: 1.001553e+02, current residual:6.043197e+01 +wall time: 0.000379801s + +time increment:12230 time: 12.23 +field ' n' [explicit solve]: current solution: 1.001562e+02, current residual:6.043252e+01 +wall time: 0.000340223s + +time increment:12240 time: 12.24 +field ' n' [explicit solve]: current solution: 1.001571e+02, current residual:6.043306e+01 +wall time: 0.000349045s + +time increment:12250 time: 12.25 +field ' n' [explicit solve]: current solution: 1.001580e+02, current residual:6.043361e+01 +wall time: 0.000329971s + +time increment:12260 time: 12.26 +field ' n' [explicit solve]: current solution: 1.001589e+02, current residual:6.043415e+01 +wall time: 0.000337124s + +time increment:12270 time: 12.27 +field ' n' [explicit solve]: current solution: 1.001598e+02, current residual:6.043469e+01 +wall time: 0.000355959s + +time increment:12280 time: 12.28 +field ' n' [explicit solve]: current solution: 1.001607e+02, current residual:6.043523e+01 +wall time: 0.000355959s + +time increment:12290 time: 12.29 +field ' n' [explicit solve]: current solution: 1.001616e+02, current residual:6.043577e+01 +wall time: 0.000355005s + +time increment:12300 time: 12.3 +field ' n' [explicit solve]: current solution: 1.001624e+02, current residual:6.043631e+01 +wall time: 0.000350952s + +time increment:12310 time: 12.31 +field ' n' [explicit solve]: current solution: 1.001633e+02, current residual:6.043685e+01 +wall time: 0.000351191s + +time increment:12320 time: 12.32 +field ' n' [explicit solve]: current solution: 1.001642e+02, current residual:6.043738e+01 +wall time: 0.000414848s + +time increment:12330 time: 12.33 +field ' n' [explicit solve]: current solution: 1.001651e+02, current residual:6.043792e+01 +wall time: 0.000377893s + +time increment:12340 time: 12.34 +field ' n' [explicit solve]: current solution: 1.001660e+02, current residual:6.043846e+01 +wall time: 0.000360966s + +time increment:12350 time: 12.35 +field ' n' [explicit solve]: current solution: 1.001669e+02, current residual:6.043899e+01 +wall time: 0.000407934s + +time increment:12360 time: 12.36 +field ' n' [explicit solve]: current solution: 1.001678e+02, current residual:6.043953e+01 +wall time: 0.000415087s + +time increment:12370 time: 12.37 +field ' n' [explicit solve]: current solution: 1.001687e+02, current residual:6.044006e+01 +wall time: 0.00041008s + +time increment:12380 time: 12.38 +field ' n' [explicit solve]: current solution: 1.001695e+02, current residual:6.044059e+01 +wall time: 0.000339031s + +time increment:12390 time: 12.39 +field ' n' [explicit solve]: current solution: 1.001704e+02, current residual:6.044113e+01 +wall time: 0.000352144s + +time increment:12400 time: 12.4 +field ' n' [explicit solve]: current solution: 1.001713e+02, current residual:6.044166e+01 +wall time: 0.000384092s + +time increment:12410 time: 12.41 +field ' n' [explicit solve]: current solution: 1.001722e+02, current residual:6.044219e+01 +wall time: 0.000344038s + +time increment:12420 time: 12.42 +field ' n' [explicit solve]: current solution: 1.001731e+02, current residual:6.044272e+01 +wall time: 0.000334978s + +time increment:12430 time: 12.43 +field ' n' [explicit solve]: current solution: 1.001739e+02, current residual:6.044325e+01 +wall time: 0.000334024s + +time increment:12440 time: 12.44 +field ' n' [explicit solve]: current solution: 1.001748e+02, current residual:6.044378e+01 +wall time: 0.000389814s + +time increment:12450 time: 12.45 +field ' n' [explicit solve]: current solution: 1.001757e+02, current residual:6.044431e+01 +wall time: 0.000373125s + +time increment:12460 time: 12.46 +field ' n' [explicit solve]: current solution: 1.001766e+02, current residual:6.044483e+01 +wall time: 0.000324965s + +time increment:12470 time: 12.47 +field ' n' [explicit solve]: current solution: 1.001774e+02, current residual:6.044536e+01 +wall time: 0.000359058s + +time increment:12480 time: 12.48 +field ' n' [explicit solve]: current solution: 1.001783e+02, current residual:6.044588e+01 +wall time: 0.000348091s + +time increment:12490 time: 12.49 +field ' n' [explicit solve]: current solution: 1.001792e+02, current residual:6.044641e+01 +wall time: 0.000334024s + +time increment:12500 time: 12.5 +field ' n' [explicit solve]: current solution: 1.001800e+02, current residual:6.044693e+01 +wall time: 0.000380039s + +time increment:12510 time: 12.51 +field ' n' [explicit solve]: current solution: 1.001809e+02, current residual:6.044746e+01 +wall time: 0.000345945s + +time increment:12520 time: 12.52 +field ' n' [explicit solve]: current solution: 1.001818e+02, current residual:6.044798e+01 +wall time: 0.000340939s + +time increment:12530 time: 12.53 +field ' n' [explicit solve]: current solution: 1.001826e+02, current residual:6.044850e+01 +wall time: 0.000349998s + +time increment:12540 time: 12.54 +field ' n' [explicit solve]: current solution: 1.001835e+02, current residual:6.044902e+01 +wall time: 0.000384092s + +time increment:12550 time: 12.55 +field ' n' [explicit solve]: current solution: 1.001844e+02, current residual:6.044954e+01 +wall time: 0.00039196s + +time increment:12560 time: 12.56 +field ' n' [explicit solve]: current solution: 1.001852e+02, current residual:6.045006e+01 +wall time: 0.000406981s + +time increment:12570 time: 12.57 +field ' n' [explicit solve]: current solution: 1.001861e+02, current residual:6.045058e+01 +wall time: 0.000340939s + +time increment:12580 time: 12.58 +field ' n' [explicit solve]: current solution: 1.001869e+02, current residual:6.045110e+01 +wall time: 0.000400066s + +time increment:12590 time: 12.59 +field ' n' [explicit solve]: current solution: 1.001878e+02, current residual:6.045162e+01 +wall time: 0.000374079s + +time increment:12600 time: 12.6 +field ' n' [explicit solve]: current solution: 1.001887e+02, current residual:6.045214e+01 +wall time: 0.000344038s + +time increment:12610 time: 12.61 +field ' n' [explicit solve]: current solution: 1.001895e+02, current residual:6.045265e+01 +wall time: 0.000344992s + +time increment:12620 time: 12.62 +field ' n' [explicit solve]: current solution: 1.001904e+02, current residual:6.045317e+01 +wall time: 0.000330925s + +time increment:12630 time: 12.63 +field ' n' [explicit solve]: current solution: 1.001912e+02, current residual:6.045368e+01 +wall time: 0.000351906s + +time increment:12640 time: 12.64 +field ' n' [explicit solve]: current solution: 1.001921e+02, current residual:6.045420e+01 +wall time: 0.000404835s + +time increment:12650 time: 12.65 +field ' n' [explicit solve]: current solution: 1.001929e+02, current residual:6.045471e+01 +wall time: 0.000360012s + +time increment:12660 time: 12.66 +field ' n' [explicit solve]: current solution: 1.001938e+02, current residual:6.045523e+01 +wall time: 0.000322819s + +time increment:12670 time: 12.67 +field ' n' [explicit solve]: current solution: 1.001946e+02, current residual:6.045574e+01 +wall time: 0.000354052s + +time increment:12680 time: 12.68 +field ' n' [explicit solve]: current solution: 1.001955e+02, current residual:6.045625e+01 +wall time: 0.000341892s + +time increment:12690 time: 12.69 +field ' n' [explicit solve]: current solution: 1.001963e+02, current residual:6.045676e+01 +wall time: 0.000355959s + +time increment:12700 time: 12.7 +field ' n' [explicit solve]: current solution: 1.001972e+02, current residual:6.045727e+01 +wall time: 0.000355005s + +time increment:12710 time: 12.71 +field ' n' [explicit solve]: current solution: 1.001980e+02, current residual:6.045778e+01 +wall time: 0.000352144s + +time increment:12720 time: 12.72 +field ' n' [explicit solve]: current solution: 1.001988e+02, current residual:6.045829e+01 +wall time: 0.000314951s + +time increment:12730 time: 12.73 +field ' n' [explicit solve]: current solution: 1.001997e+02, current residual:6.045880e+01 +wall time: 0.000416994s + +time increment:12740 time: 12.74 +field ' n' [explicit solve]: current solution: 1.002005e+02, current residual:6.045931e+01 +wall time: 0.00039196s + +time increment:12750 time: 12.75 +field ' n' [explicit solve]: current solution: 1.002014e+02, current residual:6.045981e+01 +wall time: 0.000368118s + +time increment:12760 time: 12.76 +field ' n' [explicit solve]: current solution: 1.002022e+02, current residual:6.046032e+01 +wall time: 0.000384092s + +time increment:12770 time: 12.77 +field ' n' [explicit solve]: current solution: 1.002030e+02, current residual:6.046083e+01 +wall time: 0.000331879s + +time increment:12780 time: 12.78 +field ' n' [explicit solve]: current solution: 1.002039e+02, current residual:6.046133e+01 +wall time: 0.000390053s + +time increment:12790 time: 12.79 +field ' n' [explicit solve]: current solution: 1.002047e+02, current residual:6.046184e+01 +wall time: 0.000362158s + +time increment:12800 time: 12.8 +field ' n' [explicit solve]: current solution: 1.002056e+02, current residual:6.046234e+01 +wall time: 0.000379086s + +time increment:12810 time: 12.81 +field ' n' [explicit solve]: current solution: 1.002064e+02, current residual:6.046284e+01 +wall time: 0.000389099s + +time increment:12820 time: 12.82 +field ' n' [explicit solve]: current solution: 1.002072e+02, current residual:6.046334e+01 +wall time: 0.000375032s + +time increment:12830 time: 12.83 +field ' n' [explicit solve]: current solution: 1.002080e+02, current residual:6.046385e+01 +wall time: 0.000359774s + +time increment:12840 time: 12.84 +field ' n' [explicit solve]: current solution: 1.002089e+02, current residual:6.046435e+01 +wall time: 0.000365019s + +time increment:12850 time: 12.85 +field ' n' [explicit solve]: current solution: 1.002097e+02, current residual:6.046485e+01 +wall time: 0.000360966s + +time increment:12860 time: 12.86 +field ' n' [explicit solve]: current solution: 1.002105e+02, current residual:6.046535e+01 +wall time: 0.000348091s + +time increment:12870 time: 12.87 +field ' n' [explicit solve]: current solution: 1.002114e+02, current residual:6.046585e+01 +wall time: 0.000381947s + +time increment:12880 time: 12.88 +field ' n' [explicit solve]: current solution: 1.002122e+02, current residual:6.046635e+01 +wall time: 0.000370979s + +time increment:12890 time: 12.89 +field ' n' [explicit solve]: current solution: 1.002130e+02, current residual:6.046684e+01 +wall time: 0.000394106s + +time increment:12900 time: 12.9 +field ' n' [explicit solve]: current solution: 1.002138e+02, current residual:6.046734e+01 +wall time: 0.000347137s + +time increment:12910 time: 12.91 +field ' n' [explicit solve]: current solution: 1.002147e+02, current residual:6.046784e+01 +wall time: 0.000344038s + +time increment:12920 time: 12.92 +field ' n' [explicit solve]: current solution: 1.002155e+02, current residual:6.046834e+01 +wall time: 0.000353098s + +time increment:12930 time: 12.93 +field ' n' [explicit solve]: current solution: 1.002163e+02, current residual:6.046883e+01 +wall time: 0.000336885s + +time increment:12940 time: 12.94 +field ' n' [explicit solve]: current solution: 1.002171e+02, current residual:6.046933e+01 +wall time: 0.000340939s + +time increment:12950 time: 12.95 +field ' n' [explicit solve]: current solution: 1.002179e+02, current residual:6.046982e+01 +wall time: 0.000405073s + +time increment:12960 time: 12.96 +field ' n' [explicit solve]: current solution: 1.002188e+02, current residual:6.047031e+01 +wall time: 0.000365973s + +time increment:12970 time: 12.97 +field ' n' [explicit solve]: current solution: 1.002196e+02, current residual:6.047081e+01 +wall time: 0.000349045s + +time increment:12980 time: 12.98 +field ' n' [explicit solve]: current solution: 1.002204e+02, current residual:6.047130e+01 +wall time: 0.000329018s + +time increment:12990 time: 12.99 +field ' n' [explicit solve]: current solution: 1.002212e+02, current residual:6.047179e+01 +wall time: 0.000360012s + +time increment:13000 time: 13 +field ' n' [explicit solve]: current solution: 1.002220e+02, current residual:6.047228e+01 +wall time: 0.000380993s + +time increment:13010 time: 13.01 +field ' n' [explicit solve]: current solution: 1.002228e+02, current residual:6.047277e+01 +wall time: 0.000373125s + +time increment:13020 time: 13.02 +field ' n' [explicit solve]: current solution: 1.002237e+02, current residual:6.047326e+01 +wall time: 0.000319958s + +time increment:13030 time: 13.03 +field ' n' [explicit solve]: current solution: 1.002245e+02, current residual:6.047375e+01 +wall time: 0.000337124s + +time increment:13040 time: 13.04 +field ' n' [explicit solve]: current solution: 1.002253e+02, current residual:6.047424e+01 +wall time: 0.000317812s + +time increment:13050 time: 13.05 +field ' n' [explicit solve]: current solution: 1.002261e+02, current residual:6.047473e+01 +wall time: 0.000386s + +time increment:13060 time: 13.06 +field ' n' [explicit solve]: current solution: 1.002269e+02, current residual:6.047522e+01 +wall time: 0.000356913s + +time increment:13070 time: 13.07 +field ' n' [explicit solve]: current solution: 1.002277e+02, current residual:6.047571e+01 +wall time: 0.000392914s + +time increment:13080 time: 13.08 +field ' n' [explicit solve]: current solution: 1.002285e+02, current residual:6.047619e+01 +wall time: 0.000327826s + +time increment:13090 time: 13.09 +field ' n' [explicit solve]: current solution: 1.002293e+02, current residual:6.047668e+01 +wall time: 0.000383139s + +time increment:13100 time: 13.1 +field ' n' [explicit solve]: current solution: 1.002301e+02, current residual:6.047716e+01 +wall time: 0.000364065s + +time increment:13110 time: 13.11 +field ' n' [explicit solve]: current solution: 1.002309e+02, current residual:6.047765e+01 +wall time: 0.00039506s + +time increment:13120 time: 13.12 +field ' n' [explicit solve]: current solution: 1.002317e+02, current residual:6.047813e+01 +wall time: 0.000365019s + +time increment:13130 time: 13.13 +field ' n' [explicit solve]: current solution: 1.002325e+02, current residual:6.047862e+01 +wall time: 0.000355005s + +time increment:13140 time: 13.14 +field ' n' [explicit solve]: current solution: 1.002333e+02, current residual:6.047910e+01 +wall time: 0.000361919s + +time increment:13150 time: 13.15 +field ' n' [explicit solve]: current solution: 1.002341e+02, current residual:6.047958e+01 +wall time: 0.000341892s + +time increment:13160 time: 13.16 +field ' n' [explicit solve]: current solution: 1.002349e+02, current residual:6.048007e+01 +wall time: 0.000351906s + +time increment:13170 time: 13.17 +field ' n' [explicit solve]: current solution: 1.002357e+02, current residual:6.048055e+01 +wall time: 0.000373125s + +time increment:13180 time: 13.18 +field ' n' [explicit solve]: current solution: 1.002365e+02, current residual:6.048103e+01 +wall time: 0.000328064s + +time increment:13190 time: 13.19 +field ' n' [explicit solve]: current solution: 1.002373e+02, current residual:6.048151e+01 +wall time: 0.000344038s + +time increment:13200 time: 13.2 +field ' n' [explicit solve]: current solution: 1.002381e+02, current residual:6.048199e+01 +wall time: 0.000355005s + +time increment:13210 time: 13.21 +field ' n' [explicit solve]: current solution: 1.002389e+02, current residual:6.048247e+01 +wall time: 0.000414133s + +time increment:13220 time: 13.22 +field ' n' [explicit solve]: current solution: 1.002397e+02, current residual:6.048295e+01 +wall time: 0.00035882s + +time increment:13230 time: 13.23 +field ' n' [explicit solve]: current solution: 1.002405e+02, current residual:6.048342e+01 +wall time: 0.000354052s + +time increment:13240 time: 13.24 +field ' n' [explicit solve]: current solution: 1.002413e+02, current residual:6.048390e+01 +wall time: 0.000379086s + +time increment:13250 time: 13.25 +field ' n' [explicit solve]: current solution: 1.002421e+02, current residual:6.048438e+01 +wall time: 0.000311852s + +time increment:13260 time: 13.26 +field ' n' [explicit solve]: current solution: 1.002429e+02, current residual:6.048486e+01 +wall time: 0.000337839s + +time increment:13270 time: 13.27 +field ' n' [explicit solve]: current solution: 1.002436e+02, current residual:6.048533e+01 +wall time: 0.00040102s + +time increment:13280 time: 13.28 +field ' n' [explicit solve]: current solution: 1.002444e+02, current residual:6.048581e+01 +wall time: 0.000358105s + +time increment:13290 time: 13.29 +field ' n' [explicit solve]: current solution: 1.002452e+02, current residual:6.048628e+01 +wall time: 0.000405073s + +time increment:13300 time: 13.3 +field ' n' [explicit solve]: current solution: 1.002460e+02, current residual:6.048676e+01 +wall time: 0.000370026s + +time increment:13310 time: 13.31 +field ' n' [explicit solve]: current solution: 1.002468e+02, current residual:6.048723e+01 +wall time: 0.000340939s + +time increment:13320 time: 13.32 +field ' n' [explicit solve]: current solution: 1.002476e+02, current residual:6.048770e+01 +wall time: 0.000378132s + +time increment:13330 time: 13.33 +field ' n' [explicit solve]: current solution: 1.002484e+02, current residual:6.048818e+01 +wall time: 0.000338078s + +time increment:13340 time: 13.34 +field ' n' [explicit solve]: current solution: 1.002491e+02, current residual:6.048865e+01 +wall time: 0.000375986s + +time increment:13350 time: 13.35 +field ' n' [explicit solve]: current solution: 1.002499e+02, current residual:6.048912e+01 +wall time: 0.000365019s + +time increment:13360 time: 13.36 +field ' n' [explicit solve]: current solution: 1.002507e+02, current residual:6.048959e+01 +wall time: 0.000332832s + +time increment:13370 time: 13.37 +field ' n' [explicit solve]: current solution: 1.002515e+02, current residual:6.049006e+01 +wall time: 0.00039196s + +time increment:13380 time: 13.38 +field ' n' [explicit solve]: current solution: 1.002523e+02, current residual:6.049053e+01 +wall time: 0.000335932s + +time increment:13390 time: 13.39 +field ' n' [explicit solve]: current solution: 1.002530e+02, current residual:6.049100e+01 +wall time: 0.00037694s + +time increment:13400 time: 13.4 +field ' n' [explicit solve]: current solution: 1.002538e+02, current residual:6.049147e+01 +wall time: 0.000348091s + +time increment:13410 time: 13.41 +field ' n' [explicit solve]: current solution: 1.002546e+02, current residual:6.049194e+01 +wall time: 0.000343084s + +time increment:13420 time: 13.42 +field ' n' [explicit solve]: current solution: 1.002554e+02, current residual:6.049241e+01 +wall time: 0.000305891s + +time increment:13430 time: 13.43 +field ' n' [explicit solve]: current solution: 1.002562e+02, current residual:6.049288e+01 +wall time: 0.000387907s + +time increment:13440 time: 13.44 +field ' n' [explicit solve]: current solution: 1.002569e+02, current residual:6.049334e+01 +wall time: 0.000302076s + +time increment:13450 time: 13.45 +field ' n' [explicit solve]: current solution: 1.002577e+02, current residual:6.049381e+01 +wall time: 0.000290155s + +time increment:13460 time: 13.46 +field ' n' [explicit solve]: current solution: 1.002585e+02, current residual:6.049428e+01 +wall time: 0.000286102s + +time increment:13470 time: 13.47 +field ' n' [explicit solve]: current solution: 1.002592e+02, current residual:6.049474e+01 +wall time: 0.000323057s + +time increment:13480 time: 13.48 +field ' n' [explicit solve]: current solution: 1.002600e+02, current residual:6.049521e+01 +wall time: 0.000333071s + +time increment:13490 time: 13.49 +field ' n' [explicit solve]: current solution: 1.002608e+02, current residual:6.049567e+01 +wall time: 0.000342131s + +time increment:13500 time: 13.5 +field ' n' [explicit solve]: current solution: 1.002616e+02, current residual:6.049614e+01 +wall time: 0.00030303s + +time increment:13510 time: 13.51 +field ' n' [explicit solve]: current solution: 1.002623e+02, current residual:6.049660e+01 +wall time: 0.000340939s + +time increment:13520 time: 13.52 +field ' n' [explicit solve]: current solution: 1.002631e+02, current residual:6.049706e+01 +wall time: 0.00032711s + +time increment:13530 time: 13.53 +field ' n' [explicit solve]: current solution: 1.002639e+02, current residual:6.049753e+01 +wall time: 0.000289917s + +time increment:13540 time: 13.54 +field ' n' [explicit solve]: current solution: 1.002646e+02, current residual:6.049799e+01 +wall time: 0.000303984s + +time increment:13550 time: 13.55 +field ' n' [explicit solve]: current solution: 1.002654e+02, current residual:6.049845e+01 +wall time: 0.000332832s + +time increment:13560 time: 13.56 +field ' n' [explicit solve]: current solution: 1.002662e+02, current residual:6.049891e+01 +wall time: 0.000348091s + +time increment:13570 time: 13.57 +field ' n' [explicit solve]: current solution: 1.002669e+02, current residual:6.049937e+01 +wall time: 0.000383854s + +time increment:13580 time: 13.58 +field ' n' [explicit solve]: current solution: 1.002677e+02, current residual:6.049983e+01 +wall time: 0.000334024s + +time increment:13590 time: 13.59 +field ' n' [explicit solve]: current solution: 1.002684e+02, current residual:6.050029e+01 +wall time: 0.000313044s + +time increment:13600 time: 13.6 +field ' n' [explicit solve]: current solution: 1.002692e+02, current residual:6.050075e+01 +wall time: 0.000308037s + +time increment:13610 time: 13.61 +field ' n' [explicit solve]: current solution: 1.002700e+02, current residual:6.050121e+01 +wall time: 0.000420094s + +time increment:13620 time: 13.62 +field ' n' [explicit solve]: current solution: 1.002707e+02, current residual:6.050167e+01 +wall time: 0.000282049s + +time increment:13630 time: 13.63 +field ' n' [explicit solve]: current solution: 1.002715e+02, current residual:6.050213e+01 +wall time: 0.000355005s + +time increment:13640 time: 13.64 +field ' n' [explicit solve]: current solution: 1.002722e+02, current residual:6.050258e+01 +wall time: 0.000299215s + +time increment:13650 time: 13.65 +field ' n' [explicit solve]: current solution: 1.002730e+02, current residual:6.050304e+01 +wall time: 0.000331163s + +time increment:13660 time: 13.66 +field ' n' [explicit solve]: current solution: 1.002738e+02, current residual:6.050350e+01 +wall time: 0.000313044s + +time increment:13670 time: 13.67 +field ' n' [explicit solve]: current solution: 1.002745e+02, current residual:6.050395e+01 +wall time: 0.00031209s + +time increment:13680 time: 13.68 +field ' n' [explicit solve]: current solution: 1.002753e+02, current residual:6.050441e+01 +wall time: 0.000370026s + +time increment:13690 time: 13.69 +field ' n' [explicit solve]: current solution: 1.002760e+02, current residual:6.050486e+01 +wall time: 0.000294924s + +time increment:13700 time: 13.7 +field ' n' [explicit solve]: current solution: 1.002768e+02, current residual:6.050532e+01 +wall time: 0.000295877s + +time increment:13710 time: 13.71 +field ' n' [explicit solve]: current solution: 1.002775e+02, current residual:6.050577e+01 +wall time: 0.000390053s + +time increment:13720 time: 13.72 +field ' n' [explicit solve]: current solution: 1.002783e+02, current residual:6.050623e+01 +wall time: 0.000326872s + +time increment:13730 time: 13.73 +field ' n' [explicit solve]: current solution: 1.002790e+02, current residual:6.050668e+01 +wall time: 0.000365973s + +time increment:13740 time: 13.74 +field ' n' [explicit solve]: current solution: 1.002798e+02, current residual:6.050713e+01 +wall time: 0.00029397s + +time increment:13750 time: 13.75 +field ' n' [explicit solve]: current solution: 1.002805e+02, current residual:6.050758e+01 +wall time: 0.000326157s + +time increment:13760 time: 13.76 +field ' n' [explicit solve]: current solution: 1.002813e+02, current residual:6.050804e+01 +wall time: 0.000336885s + +time increment:13770 time: 13.77 +field ' n' [explicit solve]: current solution: 1.002820e+02, current residual:6.050849e+01 +wall time: 0.000295162s + +time increment:13780 time: 13.78 +field ' n' [explicit solve]: current solution: 1.002828e+02, current residual:6.050894e+01 +wall time: 0.000355005s + +time increment:13790 time: 13.79 +field ' n' [explicit solve]: current solution: 1.002835e+02, current residual:6.050939e+01 +wall time: 0.000303984s + +time increment:13800 time: 13.8 +field ' n' [explicit solve]: current solution: 1.002843e+02, current residual:6.050984e+01 +wall time: 0.000352859s + +time increment:13810 time: 13.81 +field ' n' [explicit solve]: current solution: 1.002850e+02, current residual:6.051029e+01 +wall time: 0.00033617s + +time increment:13820 time: 13.82 +field ' n' [explicit solve]: current solution: 1.002858e+02, current residual:6.051074e+01 +wall time: 0.000344992s + +time increment:13830 time: 13.83 +field ' n' [explicit solve]: current solution: 1.002865e+02, current residual:6.051119e+01 +wall time: 0.000326157s + +time increment:13840 time: 13.84 +field ' n' [explicit solve]: current solution: 1.002873e+02, current residual:6.051164e+01 +wall time: 0.000351906s + +time increment:13850 time: 13.85 +field ' n' [explicit solve]: current solution: 1.002880e+02, current residual:6.051208e+01 +wall time: 0.000317097s + +time increment:13860 time: 13.86 +field ' n' [explicit solve]: current solution: 1.002887e+02, current residual:6.051253e+01 +wall time: 0.000338078s + +time increment:13870 time: 13.87 +field ' n' [explicit solve]: current solution: 1.002895e+02, current residual:6.051298e+01 +wall time: 0.00032711s + +time increment:13880 time: 13.88 +field ' n' [explicit solve]: current solution: 1.002902e+02, current residual:6.051343e+01 +wall time: 0.000319004s + +time increment:13890 time: 13.89 +field ' n' [explicit solve]: current solution: 1.002910e+02, current residual:6.051387e+01 +wall time: 0.000381947s + +time increment:13900 time: 13.9 +field ' n' [explicit solve]: current solution: 1.002917e+02, current residual:6.051432e+01 +wall time: 0.000332117s + +time increment:13910 time: 13.91 +field ' n' [explicit solve]: current solution: 1.002924e+02, current residual:6.051476e+01 +wall time: 0.00032115s + +time increment:13920 time: 13.92 +field ' n' [explicit solve]: current solution: 1.002932e+02, current residual:6.051521e+01 +wall time: 0.000355959s + +time increment:13930 time: 13.93 +field ' n' [explicit solve]: current solution: 1.002939e+02, current residual:6.051565e+01 +wall time: 0.000353098s + +time increment:13940 time: 13.94 +field ' n' [explicit solve]: current solution: 1.002947e+02, current residual:6.051610e+01 +wall time: 0.000339031s + +time increment:13950 time: 13.95 +field ' n' [explicit solve]: current solution: 1.002954e+02, current residual:6.051654e+01 +wall time: 0.000329971s + +time increment:13960 time: 13.96 +field ' n' [explicit solve]: current solution: 1.002961e+02, current residual:6.051698e+01 +wall time: 0.000303984s + +time increment:13970 time: 13.97 +field ' n' [explicit solve]: current solution: 1.002969e+02, current residual:6.051743e+01 +wall time: 0.000336885s + +time increment:13980 time: 13.98 +field ' n' [explicit solve]: current solution: 1.002976e+02, current residual:6.051787e+01 +wall time: 0.000344038s + +time increment:13990 time: 13.99 +field ' n' [explicit solve]: current solution: 1.002983e+02, current residual:6.051831e+01 +wall time: 0.000294924s + +time increment:14000 time: 14 +field ' n' [explicit solve]: current solution: 1.002991e+02, current residual:6.051875e+01 +wall time: 0.000323057s +fields: 1 +fields: 1 +fields: 1 +fields: 1 +Output written to:solution-014000.pvtu + +Energy: 194.748 +Energy Components: 82.2327 112.516 0 + +time increment:14010 time: 14.01 +field ' n' [explicit solve]: current solution: 1.002998e+02, current residual:6.051919e+01 +wall time: 0.000316858s + +time increment:14020 time: 14.02 +field ' n' [explicit solve]: current solution: 1.003005e+02, current residual:6.051964e+01 +wall time: 0.000378132s + +time increment:14030 time: 14.03 +field ' n' [explicit solve]: current solution: 1.003013e+02, current residual:6.052008e+01 +wall time: 0.000350952s + +time increment:14040 time: 14.04 +field ' n' [explicit solve]: current solution: 1.003020e+02, current residual:6.052052e+01 +wall time: 0.000362873s + +time increment:14050 time: 14.05 +field ' n' [explicit solve]: current solution: 1.003027e+02, current residual:6.052096e+01 +wall time: 0.000325918s + +time increment:14060 time: 14.06 +field ' n' [explicit solve]: current solution: 1.003034e+02, current residual:6.052140e+01 +wall time: 0.000366926s + +time increment:14070 time: 14.07 +field ' n' [explicit solve]: current solution: 1.003042e+02, current residual:6.052183e+01 +wall time: 0.000473022s + +time increment:14080 time: 14.08 +field ' n' [explicit solve]: current solution: 1.003049e+02, current residual:6.052227e+01 +wall time: 0.000364065s + +time increment:14090 time: 14.09 +field ' n' [explicit solve]: current solution: 1.003056e+02, current residual:6.052271e+01 +wall time: 0.000311136s + +time increment:14100 time: 14.1 +field ' n' [explicit solve]: current solution: 1.003064e+02, current residual:6.052315e+01 +wall time: 0.000408173s + +time increment:14110 time: 14.11 +field ' n' [explicit solve]: current solution: 1.003071e+02, current residual:6.052359e+01 +wall time: 0.000396013s + +time increment:14120 time: 14.12 +field ' n' [explicit solve]: current solution: 1.003078e+02, current residual:6.052402e+01 +wall time: 0.000375986s + +time increment:14130 time: 14.13 +field ' n' [explicit solve]: current solution: 1.003085e+02, current residual:6.052446e+01 +wall time: 0.000360012s + +time increment:14140 time: 14.14 +field ' n' [explicit solve]: current solution: 1.003092e+02, current residual:6.052490e+01 +wall time: 0.000364065s + +time increment:14150 time: 14.15 +field ' n' [explicit solve]: current solution: 1.003100e+02, current residual:6.052533e+01 +wall time: 0.000385046s + +time increment:14160 time: 14.16 +field ' n' [explicit solve]: current solution: 1.003107e+02, current residual:6.052577e+01 +wall time: 0.000348091s + +time increment:14170 time: 14.17 +field ' n' [explicit solve]: current solution: 1.003114e+02, current residual:6.052620e+01 +wall time: 0.000392199s + +time increment:14180 time: 14.18 +field ' n' [explicit solve]: current solution: 1.003121e+02, current residual:6.052664e+01 +wall time: 0.00030303s + +time increment:14190 time: 14.19 +field ' n' [explicit solve]: current solution: 1.003129e+02, current residual:6.052707e+01 +wall time: 0.000328779s + +time increment:14200 time: 14.2 +field ' n' [explicit solve]: current solution: 1.003136e+02, current residual:6.052751e+01 +wall time: 0.000353098s + +time increment:14210 time: 14.21 +field ' n' [explicit solve]: current solution: 1.003143e+02, current residual:6.052794e+01 +wall time: 0.000373125s + +time increment:14220 time: 14.22 +field ' n' [explicit solve]: current solution: 1.003150e+02, current residual:6.052837e+01 +wall time: 0.000414848s + +time increment:14230 time: 14.23 +field ' n' [explicit solve]: current solution: 1.003157e+02, current residual:6.052881e+01 +wall time: 0.000407934s + +time increment:14240 time: 14.24 +field ' n' [explicit solve]: current solution: 1.003165e+02, current residual:6.052924e+01 +wall time: 0.000365019s + +time increment:14250 time: 14.25 +field ' n' [explicit solve]: current solution: 1.003172e+02, current residual:6.052967e+01 +wall time: 0.000354052s + +time increment:14260 time: 14.26 +field ' n' [explicit solve]: current solution: 1.003179e+02, current residual:6.053010e+01 +wall time: 0.000349998s + +time increment:14270 time: 14.27 +field ' n' [explicit solve]: current solution: 1.003186e+02, current residual:6.053053e+01 +wall time: 0.000378847s + +time increment:14280 time: 14.28 +field ' n' [explicit solve]: current solution: 1.003193e+02, current residual:6.053096e+01 +wall time: 0.000356913s + +time increment:14290 time: 14.29 +field ' n' [explicit solve]: current solution: 1.003200e+02, current residual:6.053140e+01 +wall time: 0.000396967s + +time increment:14300 time: 14.3 +field ' n' [explicit solve]: current solution: 1.003207e+02, current residual:6.053183e+01 +wall time: 0.000363827s + +time increment:14310 time: 14.31 +field ' n' [explicit solve]: current solution: 1.003215e+02, current residual:6.053226e+01 +wall time: 0.000340223s + +time increment:14320 time: 14.32 +field ' n' [explicit solve]: current solution: 1.003222e+02, current residual:6.053269e+01 +wall time: 0.00037694s + +time increment:14330 time: 14.33 +field ' n' [explicit solve]: current solution: 1.003229e+02, current residual:6.053311e+01 +wall time: 0.00077796s + +time increment:14340 time: 14.34 +field ' n' [explicit solve]: current solution: 1.003236e+02, current residual:6.053354e+01 +wall time: 0.000351906s + +time increment:14350 time: 14.35 +field ' n' [explicit solve]: current solution: 1.003243e+02, current residual:6.053397e+01 +wall time: 0.000370979s + +time increment:14360 time: 14.36 +field ' n' [explicit solve]: current solution: 1.003250e+02, current residual:6.053440e+01 +wall time: 0.000340939s + +time increment:14370 time: 14.37 +field ' n' [explicit solve]: current solution: 1.003257e+02, current residual:6.053483e+01 +wall time: 0.000393867s + +time increment:14380 time: 14.38 +field ' n' [explicit solve]: current solution: 1.003264e+02, current residual:6.053526e+01 +wall time: 0.000386953s + +time increment:14390 time: 14.39 +field ' n' [explicit solve]: current solution: 1.003271e+02, current residual:6.053568e+01 +wall time: 0.000401974s + +time increment:14400 time: 14.4 +field ' n' [explicit solve]: current solution: 1.003279e+02, current residual:6.053611e+01 +wall time: 0.000362158s + +time increment:14410 time: 14.41 +field ' n' [explicit solve]: current solution: 1.003286e+02, current residual:6.053654e+01 +wall time: 0.000401974s + +time increment:14420 time: 14.42 +field ' n' [explicit solve]: current solution: 1.003293e+02, current residual:6.053696e+01 +wall time: 0.000342846s + +time increment:14430 time: 14.43 +field ' n' [explicit solve]: current solution: 1.003300e+02, current residual:6.053739e+01 +wall time: 0.000377893s + +time increment:14440 time: 14.44 +field ' n' [explicit solve]: current solution: 1.003307e+02, current residual:6.053781e+01 +wall time: 0.000408888s + +time increment:14450 time: 14.45 +field ' n' [explicit solve]: current solution: 1.003314e+02, current residual:6.053824e+01 +wall time: 0.000363111s + +time increment:14460 time: 14.46 +field ' n' [explicit solve]: current solution: 1.003321e+02, current residual:6.053866e+01 +wall time: 0.000388861s + +time increment:14470 time: 14.47 +field ' n' [explicit solve]: current solution: 1.003328e+02, current residual:6.053909e+01 +wall time: 0.00037694s + +time increment:14480 time: 14.48 +field ' n' [explicit solve]: current solution: 1.003335e+02, current residual:6.053951e+01 +wall time: 0.000305891s + +time increment:14490 time: 14.49 +field ' n' [explicit solve]: current solution: 1.003342e+02, current residual:6.053994e+01 +wall time: 0.000342131s + +time increment:14500 time: 14.5 +field ' n' [explicit solve]: current solution: 1.003349e+02, current residual:6.054036e+01 +wall time: 0.000339985s + +time increment:14510 time: 14.51 +field ' n' [explicit solve]: current solution: 1.003356e+02, current residual:6.054078e+01 +wall time: 0.000402927s + +time increment:14520 time: 14.52 +field ' n' [explicit solve]: current solution: 1.003363e+02, current residual:6.054121e+01 +wall time: 0.000319004s + +time increment:14530 time: 14.53 +field ' n' [explicit solve]: current solution: 1.003370e+02, current residual:6.054163e+01 +wall time: 0.000402927s + +time increment:14540 time: 14.54 +field ' n' [explicit solve]: current solution: 1.003377e+02, current residual:6.054205e+01 +wall time: 0.000329971s + +time increment:14550 time: 14.55 +field ' n' [explicit solve]: current solution: 1.003384e+02, current residual:6.054247e+01 +wall time: 0.000364065s + +time increment:14560 time: 14.56 +field ' n' [explicit solve]: current solution: 1.003391e+02, current residual:6.054289e+01 +wall time: 0.000330925s + +time increment:14570 time: 14.57 +field ' n' [explicit solve]: current solution: 1.003398e+02, current residual:6.054332e+01 +wall time: 0.000324011s + +time increment:14580 time: 14.58 +field ' n' [explicit solve]: current solution: 1.003405e+02, current residual:6.054374e+01 +wall time: 0.000354052s + +time increment:14590 time: 14.59 +field ' n' [explicit solve]: current solution: 1.003412e+02, current residual:6.054416e+01 +wall time: 0.000325918s + +time increment:14600 time: 14.6 +field ' n' [explicit solve]: current solution: 1.003419e+02, current residual:6.054458e+01 +wall time: 0.000325918s + +time increment:14610 time: 14.61 +field ' n' [explicit solve]: current solution: 1.003426e+02, current residual:6.054500e+01 +wall time: 0.000350952s + +time increment:14620 time: 14.62 +field ' n' [explicit solve]: current solution: 1.003433e+02, current residual:6.054542e+01 +wall time: 0.000319004s + +time increment:14630 time: 14.63 +field ' n' [explicit solve]: current solution: 1.003440e+02, current residual:6.054584e+01 +wall time: 0.000355005s + +time increment:14640 time: 14.64 +field ' n' [explicit solve]: current solution: 1.003447e+02, current residual:6.054626e+01 +wall time: 0.000307083s + +time increment:14650 time: 14.65 +field ' n' [explicit solve]: current solution: 1.003454e+02, current residual:6.054667e+01 +wall time: 0.000345945s + +time increment:14660 time: 14.66 +field ' n' [explicit solve]: current solution: 1.003461e+02, current residual:6.054709e+01 +wall time: 0.000346899s + +time increment:14670 time: 14.67 +field ' n' [explicit solve]: current solution: 1.003468e+02, current residual:6.054751e+01 +wall time: 0.000324011s + +time increment:14680 time: 14.68 +field ' n' [explicit solve]: current solution: 1.003475e+02, current residual:6.054793e+01 +wall time: 0.00029397s + +time increment:14690 time: 14.69 +field ' n' [explicit solve]: current solution: 1.003482e+02, current residual:6.054835e+01 +wall time: 0.000311136s + +time increment:14700 time: 14.7 +field ' n' [explicit solve]: current solution: 1.003489e+02, current residual:6.054876e+01 +wall time: 0.000357866s + +time increment:14710 time: 14.71 +field ' n' [explicit solve]: current solution: 1.003496e+02, current residual:6.054918e+01 +wall time: 0.00027895s + +time increment:14720 time: 14.72 +field ' n' [explicit solve]: current solution: 1.003502e+02, current residual:6.054960e+01 +wall time: 0.000308037s + +time increment:14730 time: 14.73 +field ' n' [explicit solve]: current solution: 1.003509e+02, current residual:6.055001e+01 +wall time: 0.000372887s + +time increment:14740 time: 14.74 +field ' n' [explicit solve]: current solution: 1.003516e+02, current residual:6.055043e+01 +wall time: 0.000344038s + +time increment:14750 time: 14.75 +field ' n' [explicit solve]: current solution: 1.003523e+02, current residual:6.055084e+01 +wall time: 0.000325918s + +time increment:14760 time: 14.76 +field ' n' [explicit solve]: current solution: 1.003530e+02, current residual:6.055126e+01 +wall time: 0.000281811s + +time increment:14770 time: 14.77 +field ' n' [explicit solve]: current solution: 1.003537e+02, current residual:6.055168e+01 +wall time: 0.000316858s + +time increment:14780 time: 14.78 +field ' n' [explicit solve]: current solution: 1.003544e+02, current residual:6.055209e+01 +wall time: 0.000308037s + +time increment:14790 time: 14.79 +field ' n' [explicit solve]: current solution: 1.003551e+02, current residual:6.055250e+01 +wall time: 0.000298977s + +time increment:14800 time: 14.8 +field ' n' [explicit solve]: current solution: 1.003558e+02, current residual:6.055292e+01 +wall time: 0.000371933s + +time increment:14810 time: 14.81 +field ' n' [explicit solve]: current solution: 1.003564e+02, current residual:6.055333e+01 +wall time: 0.000298023s + +time increment:14820 time: 14.82 +field ' n' [explicit solve]: current solution: 1.003571e+02, current residual:6.055375e+01 +wall time: 0.000308037s + +time increment:14830 time: 14.83 +field ' n' [explicit solve]: current solution: 1.003578e+02, current residual:6.055416e+01 +wall time: 0.000294924s + +time increment:14840 time: 14.84 +field ' n' [explicit solve]: current solution: 1.003585e+02, current residual:6.055457e+01 +wall time: 0.000388861s + +time increment:14850 time: 14.85 +field ' n' [explicit solve]: current solution: 1.003592e+02, current residual:6.055499e+01 +wall time: 0.000339985s + +time increment:14860 time: 14.86 +field ' n' [explicit solve]: current solution: 1.003599e+02, current residual:6.055540e+01 +wall time: 0.000295877s + +time increment:14870 time: 14.87 +field ' n' [explicit solve]: current solution: 1.003606e+02, current residual:6.055581e+01 +wall time: 0.000360012s + +time increment:14880 time: 14.88 +field ' n' [explicit solve]: current solution: 1.003612e+02, current residual:6.055622e+01 +wall time: 0.000392914s + +time increment:14890 time: 14.89 +field ' n' [explicit solve]: current solution: 1.003619e+02, current residual:6.055663e+01 +wall time: 0.000355005s + +time increment:14900 time: 14.9 +field ' n' [explicit solve]: current solution: 1.003626e+02, current residual:6.055705e+01 +wall time: 0.000349998s + +time increment:14910 time: 14.91 +field ' n' [explicit solve]: current solution: 1.003633e+02, current residual:6.055746e+01 +wall time: 0.000296116s + +time increment:14920 time: 14.92 +field ' n' [explicit solve]: current solution: 1.003640e+02, current residual:6.055787e+01 +wall time: 0.000352144s + +time increment:14930 time: 14.93 +field ' n' [explicit solve]: current solution: 1.003647e+02, current residual:6.055828e+01 +wall time: 0.000332117s + +time increment:14940 time: 14.94 +field ' n' [explicit solve]: current solution: 1.003653e+02, current residual:6.055869e+01 +wall time: 0.000362158s + +time increment:14950 time: 14.95 +field ' n' [explicit solve]: current solution: 1.003660e+02, current residual:6.055910e+01 +wall time: 0.000327826s + +time increment:14960 time: 14.96 +field ' n' [explicit solve]: current solution: 1.003667e+02, current residual:6.055951e+01 +wall time: 0.000289917s + +time increment:14970 time: 14.97 +field ' n' [explicit solve]: current solution: 1.003674e+02, current residual:6.055992e+01 +wall time: 0.000304937s + +time increment:14980 time: 14.98 +field ' n' [explicit solve]: current solution: 1.003681e+02, current residual:6.056033e+01 +wall time: 0.000360966s + +time increment:14990 time: 14.99 +field ' n' [explicit solve]: current solution: 1.003687e+02, current residual:6.056074e+01 +wall time: 0.000360966s + +time increment:15000 time: 15 +field ' n' [explicit solve]: current solution: 1.003694e+02, current residual:6.056115e+01 +wall time: 0.000370979s + +time increment:15010 time: 15.01 +field ' n' [explicit solve]: current solution: 1.003701e+02, current residual:6.056155e+01 +wall time: 0.000334978s + +time increment:15020 time: 15.02 +field ' n' [explicit solve]: current solution: 1.003708e+02, current residual:6.056196e+01 +wall time: 0.000358105s + +time increment:15030 time: 15.03 +field ' n' [explicit solve]: current solution: 1.003715e+02, current residual:6.056237e+01 +wall time: 0.000344038s + +time increment:15040 time: 15.04 +field ' n' [explicit solve]: current solution: 1.003721e+02, current residual:6.056278e+01 +wall time: 0.00029707s + +time increment:15050 time: 15.05 +field ' n' [explicit solve]: current solution: 1.003728e+02, current residual:6.056319e+01 +wall time: 0.000314951s + +time increment:15060 time: 15.06 +field ' n' [explicit solve]: current solution: 1.003735e+02, current residual:6.056359e+01 +wall time: 0.000316858s + +time increment:15070 time: 15.07 +field ' n' [explicit solve]: current solution: 1.003742e+02, current residual:6.056400e+01 +wall time: 0.000343084s + +time increment:15080 time: 15.08 +field ' n' [explicit solve]: current solution: 1.003748e+02, current residual:6.056441e+01 +wall time: 0.000301838s + +time increment:15090 time: 15.09 +field ' n' [explicit solve]: current solution: 1.003755e+02, current residual:6.056481e+01 +wall time: 0.000396013s + +time increment:15100 time: 15.1 +field ' n' [explicit solve]: current solution: 1.003762e+02, current residual:6.056522e+01 +wall time: 0.000298977s + +time increment:15110 time: 15.11 +field ' n' [explicit solve]: current solution: 1.003769e+02, current residual:6.056562e+01 +wall time: 0.000374079s + +time increment:15120 time: 15.12 +field ' n' [explicit solve]: current solution: 1.003775e+02, current residual:6.056603e+01 +wall time: 0.000310898s + +time increment:15130 time: 15.13 +field ' n' [explicit solve]: current solution: 1.003782e+02, current residual:6.056644e+01 +wall time: 0.000351191s + +time increment:15140 time: 15.14 +field ' n' [explicit solve]: current solution: 1.003789e+02, current residual:6.056684e+01 +wall time: 0.000370979s + +time increment:15150 time: 15.15 +field ' n' [explicit solve]: current solution: 1.003795e+02, current residual:6.056725e+01 +wall time: 0.000374079s + +time increment:15160 time: 15.16 +field ' n' [explicit solve]: current solution: 1.003802e+02, current residual:6.056765e+01 +wall time: 0.000305891s + +time increment:15170 time: 15.17 +field ' n' [explicit solve]: current solution: 1.003809e+02, current residual:6.056805e+01 +wall time: 0.000301123s + +time increment:15180 time: 15.18 +field ' n' [explicit solve]: current solution: 1.003816e+02, current residual:6.056846e+01 +wall time: 0.000296831s + +time increment:15190 time: 15.19 +field ' n' [explicit solve]: current solution: 1.003822e+02, current residual:6.056886e+01 +wall time: 0.000321865s + +time increment:15200 time: 15.2 +field ' n' [explicit solve]: current solution: 1.003829e+02, current residual:6.056927e+01 +wall time: 0.000364065s + +time increment:15210 time: 15.21 +field ' n' [explicit solve]: current solution: 1.003836e+02, current residual:6.056967e+01 +wall time: 0.000336885s + +time increment:15220 time: 15.22 +field ' n' [explicit solve]: current solution: 1.003842e+02, current residual:6.057007e+01 +wall time: 0.000346184s + +time increment:15230 time: 15.23 +field ' n' [explicit solve]: current solution: 1.003849e+02, current residual:6.057048e+01 +wall time: 0.000290871s + +time increment:15240 time: 15.24 +field ' n' [explicit solve]: current solution: 1.003856e+02, current residual:6.057088e+01 +wall time: 0.000391006s + +time increment:15250 time: 15.25 +field ' n' [explicit solve]: current solution: 1.003863e+02, current residual:6.057128e+01 +wall time: 0.000319958s + +time increment:15260 time: 15.26 +field ' n' [explicit solve]: current solution: 1.003869e+02, current residual:6.057168e+01 +wall time: 0.000299931s + +time increment:15270 time: 15.27 +field ' n' [explicit solve]: current solution: 1.003876e+02, current residual:6.057209e+01 +wall time: 0.00029707s + +time increment:15280 time: 15.28 +field ' n' [explicit solve]: current solution: 1.003883e+02, current residual:6.057249e+01 +wall time: 0.00030303s + +time increment:15290 time: 15.29 +field ' n' [explicit solve]: current solution: 1.003889e+02, current residual:6.057289e+01 +wall time: 0.00028491s + +time increment:15300 time: 15.3 +field ' n' [explicit solve]: current solution: 1.003896e+02, current residual:6.057329e+01 +wall time: 0.000322104s + +time increment:15310 time: 15.31 +field ' n' [explicit solve]: current solution: 1.003903e+02, current residual:6.057369e+01 +wall time: 0.000343084s + +time increment:15320 time: 15.32 +field ' n' [explicit solve]: current solution: 1.003909e+02, current residual:6.057409e+01 +wall time: 0.000363111s + +time increment:15330 time: 15.33 +field ' n' [explicit solve]: current solution: 1.003916e+02, current residual:6.057449e+01 +wall time: 0.000340939s + +time increment:15340 time: 15.34 +field ' n' [explicit solve]: current solution: 1.003922e+02, current residual:6.057489e+01 +wall time: 0.00037694s + +time increment:15350 time: 15.35 +field ' n' [explicit solve]: current solution: 1.003929e+02, current residual:6.057529e+01 +wall time: 0.000344992s + +time increment:15360 time: 15.36 +field ' n' [explicit solve]: current solution: 1.003936e+02, current residual:6.057569e+01 +wall time: 0.000352859s + +time increment:15370 time: 15.37 +field ' n' [explicit solve]: current solution: 1.003942e+02, current residual:6.057609e+01 +wall time: 0.000334978s + +time increment:15380 time: 15.38 +field ' n' [explicit solve]: current solution: 1.003949e+02, current residual:6.057649e+01 +wall time: 0.00030303s + +time increment:15390 time: 15.39 +field ' n' [explicit solve]: current solution: 1.003956e+02, current residual:6.057689e+01 +wall time: 0.00032711s + +time increment:15400 time: 15.4 +field ' n' [explicit solve]: current solution: 1.003962e+02, current residual:6.057729e+01 +wall time: 0.00029707s + +time increment:15410 time: 15.41 +field ' n' [explicit solve]: current solution: 1.003969e+02, current residual:6.057769e+01 +wall time: 0.000305176s + +time increment:15420 time: 15.42 +field ' n' [explicit solve]: current solution: 1.003976e+02, current residual:6.057809e+01 +wall time: 0.000366926s + +time increment:15430 time: 15.43 +field ' n' [explicit solve]: current solution: 1.003982e+02, current residual:6.057849e+01 +wall time: 0.000322819s + +time increment:15440 time: 15.44 +field ' n' [explicit solve]: current solution: 1.003989e+02, current residual:6.057888e+01 +wall time: 0.000358105s + +time increment:15450 time: 15.45 +field ' n' [explicit solve]: current solution: 1.003995e+02, current residual:6.057928e+01 +wall time: 0.000289202s + +time increment:15460 time: 15.46 +field ' n' [explicit solve]: current solution: 1.004002e+02, current residual:6.057968e+01 +wall time: 0.000344992s + +time increment:15470 time: 15.47 +field ' n' [explicit solve]: current solution: 1.004009e+02, current residual:6.058008e+01 +wall time: 0.000345945s + +time increment:15480 time: 15.48 +field ' n' [explicit solve]: current solution: 1.004015e+02, current residual:6.058047e+01 +wall time: 0.000307083s + +time increment:15490 time: 15.49 +field ' n' [explicit solve]: current solution: 1.004022e+02, current residual:6.058087e+01 +wall time: 0.000361919s + +time increment:15500 time: 15.5 +field ' n' [explicit solve]: current solution: 1.004028e+02, current residual:6.058127e+01 +wall time: 0.000347853s + +time increment:15510 time: 15.51 +field ' n' [explicit solve]: current solution: 1.004035e+02, current residual:6.058167e+01 +wall time: 0.000303984s + +time increment:15520 time: 15.52 +field ' n' [explicit solve]: current solution: 1.004042e+02, current residual:6.058206e+01 +wall time: 0.000332117s + +time increment:15530 time: 15.53 +field ' n' [explicit solve]: current solution: 1.004048e+02, current residual:6.058246e+01 +wall time: 0.000305891s + +time increment:15540 time: 15.54 +field ' n' [explicit solve]: current solution: 1.004055e+02, current residual:6.058285e+01 +wall time: 0.000375032s + +time increment:15550 time: 15.55 +field ' n' [explicit solve]: current solution: 1.004061e+02, current residual:6.058325e+01 +wall time: 0.000387192s + +time increment:15560 time: 15.56 +field ' n' [explicit solve]: current solution: 1.004068e+02, current residual:6.058365e+01 +wall time: 0.000288963s + +time increment:15570 time: 15.57 +field ' n' [explicit solve]: current solution: 1.004074e+02, current residual:6.058404e+01 +wall time: 0.000350952s + +time increment:15580 time: 15.58 +field ' n' [explicit solve]: current solution: 1.004081e+02, current residual:6.058444e+01 +wall time: 0.00032711s + +time increment:15590 time: 15.59 +field ' n' [explicit solve]: current solution: 1.004088e+02, current residual:6.058483e+01 +wall time: 0.000298023s + +time increment:15600 time: 15.6 +field ' n' [explicit solve]: current solution: 1.004094e+02, current residual:6.058523e+01 +wall time: 0.000313997s + +time increment:15610 time: 15.61 +field ' n' [explicit solve]: current solution: 1.004101e+02, current residual:6.058562e+01 +wall time: 0.000365019s + +time increment:15620 time: 15.62 +field ' n' [explicit solve]: current solution: 1.004107e+02, current residual:6.058601e+01 +wall time: 0.000336885s + +time increment:15630 time: 15.63 +field ' n' [explicit solve]: current solution: 1.004114e+02, current residual:6.058641e+01 +wall time: 0.000335932s + +time increment:15640 time: 15.64 +field ' n' [explicit solve]: current solution: 1.004120e+02, current residual:6.058680e+01 +wall time: 0.000334024s + +time increment:15650 time: 15.65 +field ' n' [explicit solve]: current solution: 1.004127e+02, current residual:6.058720e+01 +wall time: 0.000317097s + +time increment:15660 time: 15.66 +field ' n' [explicit solve]: current solution: 1.004133e+02, current residual:6.058759e+01 +wall time: 0.000333071s + +time increment:15670 time: 15.67 +field ' n' [explicit solve]: current solution: 1.004140e+02, current residual:6.058798e+01 +wall time: 0.000309944s + +time increment:15680 time: 15.68 +field ' n' [explicit solve]: current solution: 1.004147e+02, current residual:6.058838e+01 +wall time: 0.00029707s + +time increment:15690 time: 15.69 +field ' n' [explicit solve]: current solution: 1.004153e+02, current residual:6.058877e+01 +wall time: 0.000293016s + +time increment:15700 time: 15.7 +field ' n' [explicit solve]: current solution: 1.004160e+02, current residual:6.058916e+01 +wall time: 0.000341892s + +time increment:15710 time: 15.71 +field ' n' [explicit solve]: current solution: 1.004166e+02, current residual:6.058956e+01 +wall time: 0.000354052s + +time increment:15720 time: 15.72 +field ' n' [explicit solve]: current solution: 1.004173e+02, current residual:6.058995e+01 +wall time: 0.00032711s + +time increment:15730 time: 15.73 +field ' n' [explicit solve]: current solution: 1.004179e+02, current residual:6.059034e+01 +wall time: 0.000307083s + +time increment:15740 time: 15.74 +field ' n' [explicit solve]: current solution: 1.004186e+02, current residual:6.059073e+01 +wall time: 0.000309944s + +time increment:15750 time: 15.75 +field ' n' [explicit solve]: current solution: 1.004192e+02, current residual:6.059112e+01 +wall time: 0.000324965s + +time increment:15760 time: 15.76 +field ' n' [explicit solve]: current solution: 1.004199e+02, current residual:6.059152e+01 +wall time: 0.000308037s + +time increment:15770 time: 15.77 +field ' n' [explicit solve]: current solution: 1.004205e+02, current residual:6.059191e+01 +wall time: 0.000334024s + +time increment:15780 time: 15.78 +field ' n' [explicit solve]: current solution: 1.004212e+02, current residual:6.059230e+01 +wall time: 0.000329971s + +time increment:15790 time: 15.79 +field ' n' [explicit solve]: current solution: 1.004218e+02, current residual:6.059269e+01 +wall time: 0.000358105s + +time increment:15800 time: 15.8 +field ' n' [explicit solve]: current solution: 1.004225e+02, current residual:6.059308e+01 +wall time: 0.000298023s + +time increment:15810 time: 15.81 +field ' n' [explicit solve]: current solution: 1.004231e+02, current residual:6.059347e+01 +wall time: 0.000299215s + +time increment:15820 time: 15.82 +field ' n' [explicit solve]: current solution: 1.004238e+02, current residual:6.059386e+01 +wall time: 0.000389099s + +time increment:15830 time: 15.83 +field ' n' [explicit solve]: current solution: 1.004244e+02, current residual:6.059425e+01 +wall time: 0.00030303s + +time increment:15840 time: 15.84 +field ' n' [explicit solve]: current solution: 1.004251e+02, current residual:6.059464e+01 +wall time: 0.000370026s + +time increment:15850 time: 15.85 +field ' n' [explicit solve]: current solution: 1.004257e+02, current residual:6.059503e+01 +wall time: 0.000349998s + +time increment:15860 time: 15.86 +field ' n' [explicit solve]: current solution: 1.004264e+02, current residual:6.059542e+01 +wall time: 0.000330925s + +time increment:15870 time: 15.87 +field ' n' [explicit solve]: current solution: 1.004270e+02, current residual:6.059581e+01 +wall time: 0.000386s + +time increment:15880 time: 15.88 +field ' n' [explicit solve]: current solution: 1.004277e+02, current residual:6.059620e+01 +wall time: 0.000348806s + +time increment:15890 time: 15.89 +field ' n' [explicit solve]: current solution: 1.004283e+02, current residual:6.059659e+01 +wall time: 0.000355959s + +time increment:15900 time: 15.9 +field ' n' [explicit solve]: current solution: 1.004290e+02, current residual:6.059698e+01 +wall time: 0.000330925s + +time increment:15910 time: 15.91 +field ' n' [explicit solve]: current solution: 1.004296e+02, current residual:6.059737e+01 +wall time: 0.000315189s + +time increment:15920 time: 15.92 +field ' n' [explicit solve]: current solution: 1.004302e+02, current residual:6.059776e+01 +wall time: 0.000373125s + +time increment:15930 time: 15.93 +field ' n' [explicit solve]: current solution: 1.004309e+02, current residual:6.059815e+01 +wall time: 0.000346899s + +time increment:15940 time: 15.94 +field ' n' [explicit solve]: current solution: 1.004315e+02, current residual:6.059854e+01 +wall time: 0.000363111s + +time increment:15950 time: 15.95 +field ' n' [explicit solve]: current solution: 1.004322e+02, current residual:6.059892e+01 +wall time: 0.000333786s + +time increment:15960 time: 15.96 +field ' n' [explicit solve]: current solution: 1.004328e+02, current residual:6.059931e+01 +wall time: 0.000345945s + +time increment:15970 time: 15.97 +field ' n' [explicit solve]: current solution: 1.004335e+02, current residual:6.059970e+01 +wall time: 0.00029707s + +time increment:15980 time: 15.98 +field ' n' [explicit solve]: current solution: 1.004341e+02, current residual:6.060009e+01 +wall time: 0.000283957s + +time increment:15990 time: 15.99 +field ' n' [explicit solve]: current solution: 1.004348e+02, current residual:6.060048e+01 +wall time: 0.000345945s + +time increment:16000 time: 16 +field ' n' [explicit solve]: current solution: 1.004354e+02, current residual:6.060086e+01 +wall time: 0.000313997s +fields: 1 +fields: 1 +fields: 1 +fields: 1 +Output written to:solution-016000.pvtu + +Energy: 190.606 +Energy Components: 79.5496 111.056 0 + +time increment:16010 time: 16.01 +field ' n' [explicit solve]: current solution: 1.004360e+02, current residual:6.060125e+01 +wall time: 0.000374079s + +time increment:16020 time: 16.02 +field ' n' [explicit solve]: current solution: 1.004367e+02, current residual:6.060164e+01 +wall time: 0.000344992s + +time increment:16030 time: 16.03 +field ' n' [explicit solve]: current solution: 1.004373e+02, current residual:6.060202e+01 +wall time: 0.000347137s + +time increment:16040 time: 16.04 +field ' n' [explicit solve]: current solution: 1.004380e+02, current residual:6.060241e+01 +wall time: 0.000335932s + +time increment:16050 time: 16.05 +field ' n' [explicit solve]: current solution: 1.004386e+02, current residual:6.060280e+01 +wall time: 0.000329971s + +time increment:16060 time: 16.06 +field ' n' [explicit solve]: current solution: 1.004393e+02, current residual:6.060318e+01 +wall time: 0.00033617s + +time increment:16070 time: 16.07 +field ' n' [explicit solve]: current solution: 1.004399e+02, current residual:6.060357e+01 +wall time: 0.000326872s + +time increment:16080 time: 16.08 +field ' n' [explicit solve]: current solution: 1.004405e+02, current residual:6.060396e+01 +wall time: 0.000353098s + +time increment:16090 time: 16.09 +field ' n' [explicit solve]: current solution: 1.004412e+02, current residual:6.060434e+01 +wall time: 0.000361919s + +time increment:16100 time: 16.1 +field ' n' [explicit solve]: current solution: 1.004418e+02, current residual:6.060473e+01 +wall time: 0.000341177s + +time increment:16110 time: 16.11 +field ' n' [explicit solve]: current solution: 1.004425e+02, current residual:6.060511e+01 +wall time: 0.000326157s + +time increment:16120 time: 16.12 +field ' n' [explicit solve]: current solution: 1.004431e+02, current residual:6.060550e+01 +wall time: 0.000359058s + +time increment:16130 time: 16.13 +field ' n' [explicit solve]: current solution: 1.004438e+02, current residual:6.060588e+01 +wall time: 0.000348091s + +time increment:16140 time: 16.14 +field ' n' [explicit solve]: current solution: 1.004444e+02, current residual:6.060627e+01 +wall time: 0.000385046s + +time increment:16150 time: 16.15 +field ' n' [explicit solve]: current solution: 1.004450e+02, current residual:6.060666e+01 +wall time: 0.000406981s + +time increment:16160 time: 16.16 +field ' n' [explicit solve]: current solution: 1.004457e+02, current residual:6.060704e+01 +wall time: 0.000371933s + +time increment:16170 time: 16.17 +field ' n' [explicit solve]: current solution: 1.004463e+02, current residual:6.060742e+01 +wall time: 0.000406027s + +time increment:16180 time: 16.18 +field ' n' [explicit solve]: current solution: 1.004470e+02, current residual:6.060781e+01 +wall time: 0.000396967s + +time increment:16190 time: 16.19 +field ' n' [explicit solve]: current solution: 1.004476e+02, current residual:6.060819e+01 +wall time: 0.000334024s + +time increment:16200 time: 16.2 +field ' n' [explicit solve]: current solution: 1.004482e+02, current residual:6.060858e+01 +wall time: 0.000308037s + +time increment:16210 time: 16.21 +field ' n' [explicit solve]: current solution: 1.004489e+02, current residual:6.060896e+01 +wall time: 0.000364065s + +time increment:16220 time: 16.22 +field ' n' [explicit solve]: current solution: 1.004495e+02, current residual:6.060935e+01 +wall time: 0.000345945s + +time increment:16230 time: 16.23 +field ' n' [explicit solve]: current solution: 1.004501e+02, current residual:6.060973e+01 +wall time: 0.000367165s + +time increment:16240 time: 16.24 +field ' n' [explicit solve]: current solution: 1.004508e+02, current residual:6.061011e+01 +wall time: 0.000349045s + +time increment:16250 time: 16.25 +field ' n' [explicit solve]: current solution: 1.004514e+02, current residual:6.061050e+01 +wall time: 0.000393867s + +time increment:16260 time: 16.26 +field ' n' [explicit solve]: current solution: 1.004521e+02, current residual:6.061088e+01 +wall time: 0.000346899s + +time increment:16270 time: 16.27 +field ' n' [explicit solve]: current solution: 1.004527e+02, current residual:6.061126e+01 +wall time: 0.000317097s + +time increment:16280 time: 16.28 +field ' n' [explicit solve]: current solution: 1.004533e+02, current residual:6.061165e+01 +wall time: 0.000360966s + +time increment:16290 time: 16.29 +field ' n' [explicit solve]: current solution: 1.004540e+02, current residual:6.061203e+01 +wall time: 0.000363111s + +time increment:16300 time: 16.3 +field ' n' [explicit solve]: current solution: 1.004546e+02, current residual:6.061241e+01 +wall time: 0.000310898s + +time increment:16310 time: 16.31 +field ' n' [explicit solve]: current solution: 1.004552e+02, current residual:6.061280e+01 +wall time: 0.000290155s + +time increment:16320 time: 16.32 +field ' n' [explicit solve]: current solution: 1.004559e+02, current residual:6.061318e+01 +wall time: 0.000333071s + +time increment:16330 time: 16.33 +field ' n' [explicit solve]: current solution: 1.004565e+02, current residual:6.061356e+01 +wall time: 0.000303984s + +time increment:16340 time: 16.34 +field ' n' [explicit solve]: current solution: 1.004572e+02, current residual:6.061394e+01 +wall time: 0.000344038s + +time increment:16350 time: 16.35 +field ' n' [explicit solve]: current solution: 1.004578e+02, current residual:6.061432e+01 +wall time: 0.000350952s + +time increment:16360 time: 16.36 +field ' n' [explicit solve]: current solution: 1.004584e+02, current residual:6.061471e+01 +wall time: 0.000343084s + +time increment:16370 time: 16.37 +field ' n' [explicit solve]: current solution: 1.004591e+02, current residual:6.061509e+01 +wall time: 0.000355005s + +time increment:16380 time: 16.38 +field ' n' [explicit solve]: current solution: 1.004597e+02, current residual:6.061547e+01 +wall time: 0.000365019s + +time increment:16390 time: 16.39 +field ' n' [explicit solve]: current solution: 1.004603e+02, current residual:6.061585e+01 +wall time: 0.00033617s + +time increment:16400 time: 16.4 +field ' n' [explicit solve]: current solution: 1.004610e+02, current residual:6.061623e+01 +wall time: 0.000347137s + +time increment:16410 time: 16.41 +field ' n' [explicit solve]: current solution: 1.004616e+02, current residual:6.061661e+01 +wall time: 0.000334024s + +time increment:16420 time: 16.42 +field ' n' [explicit solve]: current solution: 1.004622e+02, current residual:6.061700e+01 +wall time: 0.00036788s + +time increment:16430 time: 16.43 +field ' n' [explicit solve]: current solution: 1.004629e+02, current residual:6.061738e+01 +wall time: 0.000346899s + +time increment:16440 time: 16.44 +field ' n' [explicit solve]: current solution: 1.004635e+02, current residual:6.061776e+01 +wall time: 0.000322819s + +time increment:16450 time: 16.45 +field ' n' [explicit solve]: current solution: 1.004641e+02, current residual:6.061814e+01 +wall time: 0.000352144s + +time increment:16460 time: 16.46 +field ' n' [explicit solve]: current solution: 1.004648e+02, current residual:6.061852e+01 +wall time: 0.000365973s + +time increment:16470 time: 16.47 +field ' n' [explicit solve]: current solution: 1.004654e+02, current residual:6.061890e+01 +wall time: 0.000370026s + +time increment:16480 time: 16.48 +field ' n' [explicit solve]: current solution: 1.004660e+02, current residual:6.061928e+01 +wall time: 0.00030899s + +time increment:16490 time: 16.49 +field ' n' [explicit solve]: current solution: 1.004667e+02, current residual:6.061966e+01 +wall time: 0.000308037s + +time increment:16500 time: 16.5 +field ' n' [explicit solve]: current solution: 1.004673e+02, current residual:6.062004e+01 +wall time: 0.000313044s + +time increment:16510 time: 16.51 +field ' n' [explicit solve]: current solution: 1.004679e+02, current residual:6.062042e+01 +wall time: 0.000362158s + +time increment:16520 time: 16.52 +field ' n' [explicit solve]: current solution: 1.004686e+02, current residual:6.062080e+01 +wall time: 0.000378847s + +time increment:16530 time: 16.53 +field ' n' [explicit solve]: current solution: 1.004692e+02, current residual:6.062118e+01 +wall time: 0.000298977s + +time increment:16540 time: 16.54 +field ' n' [explicit solve]: current solution: 1.004698e+02, current residual:6.062156e+01 +wall time: 0.00029397s + +time increment:16550 time: 16.55 +field ' n' [explicit solve]: current solution: 1.004704e+02, current residual:6.062194e+01 +wall time: 0.000309944s + +time increment:16560 time: 16.56 +field ' n' [explicit solve]: current solution: 1.004711e+02, current residual:6.062232e+01 +wall time: 0.000305891s + +time increment:16570 time: 16.57 +field ' n' [explicit solve]: current solution: 1.004717e+02, current residual:6.062270e+01 +wall time: 0.000298023s + +time increment:16580 time: 16.58 +field ' n' [explicit solve]: current solution: 1.004723e+02, current residual:6.062308e+01 +wall time: 0.000342131s + +time increment:16590 time: 16.59 +field ' n' [explicit solve]: current solution: 1.004730e+02, current residual:6.062346e+01 +wall time: 0.000298977s + +time increment:16600 time: 16.6 +field ' n' [explicit solve]: current solution: 1.004736e+02, current residual:6.062383e+01 +wall time: 0.000367165s + +time increment:16610 time: 16.61 +field ' n' [explicit solve]: current solution: 1.004742e+02, current residual:6.062421e+01 +wall time: 0.000398874s + +time increment:16620 time: 16.62 +field ' n' [explicit solve]: current solution: 1.004749e+02, current residual:6.062459e+01 +wall time: 0.000496864s + +time increment:16630 time: 16.63 +field ' n' [explicit solve]: current solution: 1.004755e+02, current residual:6.062497e+01 +wall time: 0.000519991s + +time increment:16640 time: 16.64 +field ' n' [explicit solve]: current solution: 1.004761e+02, current residual:6.062535e+01 +wall time: 0.000465155s + +time increment:16650 time: 16.65 +field ' n' [explicit solve]: current solution: 1.004767e+02, current residual:6.062573e+01 +wall time: 0.000361919s + +time increment:16660 time: 16.66 +field ' n' [explicit solve]: current solution: 1.004774e+02, current residual:6.062611e+01 +wall time: 0.000303984s + +time increment:16670 time: 16.67 +field ' n' [explicit solve]: current solution: 1.004780e+02, current residual:6.062648e+01 +wall time: 0.000349998s + +time increment:16680 time: 16.68 +field ' n' [explicit solve]: current solution: 1.004786e+02, current residual:6.062686e+01 +wall time: 0.000324011s + +time increment:16690 time: 16.69 +field ' n' [explicit solve]: current solution: 1.004793e+02, current residual:6.062724e+01 +wall time: 0.000317097s + +time increment:16700 time: 16.7 +field ' n' [explicit solve]: current solution: 1.004799e+02, current residual:6.062762e+01 +wall time: 0.000313044s + +time increment:16710 time: 16.71 +field ' n' [explicit solve]: current solution: 1.004805e+02, current residual:6.062799e+01 +wall time: 0.000319958s + +time increment:16720 time: 16.72 +field ' n' [explicit solve]: current solution: 1.004811e+02, current residual:6.062837e+01 +wall time: 0.000360966s + +time increment:16730 time: 16.73 +field ' n' [explicit solve]: current solution: 1.004818e+02, current residual:6.062875e+01 +wall time: 0.000314951s + +time increment:16740 time: 16.74 +field ' n' [explicit solve]: current solution: 1.004824e+02, current residual:6.062913e+01 +wall time: 0.000382185s + +time increment:16750 time: 16.75 +field ' n' [explicit solve]: current solution: 1.004830e+02, current residual:6.062950e+01 +wall time: 0.000299931s + +time increment:16760 time: 16.76 +field ' n' [explicit solve]: current solution: 1.004837e+02, current residual:6.062988e+01 +wall time: 0.000308037s + +time increment:16770 time: 16.77 +field ' n' [explicit solve]: current solution: 1.004843e+02, current residual:6.063026e+01 +wall time: 0.000306129s + +time increment:16780 time: 16.78 +field ' n' [explicit solve]: current solution: 1.004849e+02, current residual:6.063063e+01 +wall time: 0.00028801s + +time increment:16790 time: 16.79 +field ' n' [explicit solve]: current solution: 1.004855e+02, current residual:6.063101e+01 +wall time: 0.000303984s + +time increment:16800 time: 16.8 +field ' n' [explicit solve]: current solution: 1.004862e+02, current residual:6.063139e+01 +wall time: 0.000344038s + +time increment:16810 time: 16.81 +field ' n' [explicit solve]: current solution: 1.004868e+02, current residual:6.063176e+01 +wall time: 0.000351906s + +time increment:16820 time: 16.82 +field ' n' [explicit solve]: current solution: 1.004874e+02, current residual:6.063214e+01 +wall time: 0.000306845s + +time increment:16830 time: 16.83 +field ' n' [explicit solve]: current solution: 1.004880e+02, current residual:6.063252e+01 +wall time: 0.000298977s + +time increment:16840 time: 16.84 +field ' n' [explicit solve]: current solution: 1.004887e+02, current residual:6.063289e+01 +wall time: 0.000314951s + +time increment:16850 time: 16.85 +field ' n' [explicit solve]: current solution: 1.004893e+02, current residual:6.063327e+01 +wall time: 0.000300884s + +time increment:16860 time: 16.86 +field ' n' [explicit solve]: current solution: 1.004899e+02, current residual:6.063364e+01 +wall time: 0.000370979s + +time increment:16870 time: 16.87 +field ' n' [explicit solve]: current solution: 1.004905e+02, current residual:6.063402e+01 +wall time: 0.000368834s + +time increment:16880 time: 16.88 +field ' n' [explicit solve]: current solution: 1.004912e+02, current residual:6.063440e+01 +wall time: 0.000294209s + +time increment:16890 time: 16.89 +field ' n' [explicit solve]: current solution: 1.004918e+02, current residual:6.063477e+01 +wall time: 0.000328064s + +time increment:16900 time: 16.9 +field ' n' [explicit solve]: current solution: 1.004924e+02, current residual:6.063515e+01 +wall time: 0.000316858s + +time increment:16910 time: 16.91 +field ' n' [explicit solve]: current solution: 1.004930e+02, current residual:6.063552e+01 +wall time: 0.000354052s + +time increment:16920 time: 16.92 +field ' n' [explicit solve]: current solution: 1.004937e+02, current residual:6.063590e+01 +wall time: 0.000365019s + +time increment:16930 time: 16.93 +field ' n' [explicit solve]: current solution: 1.004943e+02, current residual:6.063627e+01 +wall time: 0.000338078s + +time increment:16940 time: 16.94 +field ' n' [explicit solve]: current solution: 1.004949e+02, current residual:6.063665e+01 +wall time: 0.000339985s + +time increment:16950 time: 16.95 +field ' n' [explicit solve]: current solution: 1.004955e+02, current residual:6.063702e+01 +wall time: 0.00032115s + +time increment:16960 time: 16.96 +field ' n' [explicit solve]: current solution: 1.004962e+02, current residual:6.063740e+01 +wall time: 0.000334978s + +time increment:16970 time: 16.97 +field ' n' [explicit solve]: current solution: 1.004968e+02, current residual:6.063777e+01 +wall time: 0.000384092s + +time increment:16980 time: 16.98 +field ' n' [explicit solve]: current solution: 1.004974e+02, current residual:6.063815e+01 +wall time: 0.000309944s + +time increment:16990 time: 16.99 +field ' n' [explicit solve]: current solution: 1.004980e+02, current residual:6.063852e+01 +wall time: 0.000360966s + +time increment:17000 time: 17 +field ' n' [explicit solve]: current solution: 1.004987e+02, current residual:6.063890e+01 +wall time: 0.000329018s + +time increment:17010 time: 17.01 +field ' n' [explicit solve]: current solution: 1.004993e+02, current residual:6.063927e+01 +wall time: 0.00031209s + +time increment:17020 time: 17.02 +field ' n' [explicit solve]: current solution: 1.004999e+02, current residual:6.063965e+01 +wall time: 0.000329018s + +time increment:17030 time: 17.03 +field ' n' [explicit solve]: current solution: 1.005005e+02, current residual:6.064002e+01 +wall time: 0.000360966s + +time increment:17040 time: 17.04 +field ' n' [explicit solve]: current solution: 1.005011e+02, current residual:6.064039e+01 +wall time: 0.000309944s + +time increment:17050 time: 17.05 +field ' n' [explicit solve]: current solution: 1.005018e+02, current residual:6.064077e+01 +wall time: 0.000317812s + +time increment:17060 time: 17.06 +field ' n' [explicit solve]: current solution: 1.005024e+02, current residual:6.064114e+01 +wall time: 0.000301838s + +time increment:17070 time: 17.07 +field ' n' [explicit solve]: current solution: 1.005030e+02, current residual:6.064152e+01 +wall time: 0.000361919s + +time increment:17080 time: 17.08 +field ' n' [explicit solve]: current solution: 1.005036e+02, current residual:6.064189e+01 +wall time: 0.000302076s + +time increment:17090 time: 17.09 +field ' n' [explicit solve]: current solution: 1.005043e+02, current residual:6.064226e+01 +wall time: 0.00030303s + +time increment:17100 time: 17.1 +field ' n' [explicit solve]: current solution: 1.005049e+02, current residual:6.064264e+01 +wall time: 0.00031805s + +time increment:17110 time: 17.11 +field ' n' [explicit solve]: current solution: 1.005055e+02, current residual:6.064301e+01 +wall time: 0.000334978s + +time increment:17120 time: 17.12 +field ' n' [explicit solve]: current solution: 1.005061e+02, current residual:6.064338e+01 +wall time: 0.000375032s + +time increment:17130 time: 17.13 +field ' n' [explicit solve]: current solution: 1.005067e+02, current residual:6.064376e+01 +wall time: 0.000305891s + +time increment:17140 time: 17.14 +field ' n' [explicit solve]: current solution: 1.005074e+02, current residual:6.064413e+01 +wall time: 0.000447989s + +time increment:17150 time: 17.15 +field ' n' [explicit solve]: current solution: 1.005080e+02, current residual:6.064450e+01 +wall time: 0.000380993s + +time increment:17160 time: 17.16 +field ' n' [explicit solve]: current solution: 1.005086e+02, current residual:6.064488e+01 +wall time: 0.000348091s + +time increment:17170 time: 17.17 +field ' n' [explicit solve]: current solution: 1.005092e+02, current residual:6.064525e+01 +wall time: 0.000320196s + +time increment:17180 time: 17.18 +field ' n' [explicit solve]: current solution: 1.005098e+02, current residual:6.064562e+01 +wall time: 0.000365973s + +time increment:17190 time: 17.19 +field ' n' [explicit solve]: current solution: 1.005105e+02, current residual:6.064600e+01 +wall time: 0.000342131s + +time increment:17200 time: 17.2 +field ' n' [explicit solve]: current solution: 1.005111e+02, current residual:6.064637e+01 +wall time: 0.00035882s + +time increment:17210 time: 17.21 +field ' n' [explicit solve]: current solution: 1.005117e+02, current residual:6.064674e+01 +wall time: 0.000290871s + +time increment:17220 time: 17.22 +field ' n' [explicit solve]: current solution: 1.005123e+02, current residual:6.064711e+01 +wall time: 0.000337124s + +time increment:17230 time: 17.23 +field ' n' [explicit solve]: current solution: 1.005129e+02, current residual:6.064749e+01 +wall time: 0.000355959s + +time increment:17240 time: 17.24 +field ' n' [explicit solve]: current solution: 1.005136e+02, current residual:6.064786e+01 +wall time: 0.000452042s + +time increment:17250 time: 17.25 +field ' n' [explicit solve]: current solution: 1.005142e+02, current residual:6.064823e+01 +wall time: 0.000316143s + +time increment:17260 time: 17.26 +field ' n' [explicit solve]: current solution: 1.005148e+02, current residual:6.064860e+01 +wall time: 0.000351906s + +time increment:17270 time: 17.27 +field ' n' [explicit solve]: current solution: 1.005154e+02, current residual:6.064898e+01 +wall time: 0.000426054s + +time increment:17280 time: 17.28 +field ' n' [explicit solve]: current solution: 1.005160e+02, current residual:6.064935e+01 +wall time: 0.000346184s + +time increment:17290 time: 17.29 +field ' n' [explicit solve]: current solution: 1.005167e+02, current residual:6.064972e+01 +wall time: 0.000325918s + +time increment:17300 time: 17.3 +field ' n' [explicit solve]: current solution: 1.005173e+02, current residual:6.065009e+01 +wall time: 0.000363111s + +time increment:17310 time: 17.31 +field ' n' [explicit solve]: current solution: 1.005179e+02, current residual:6.065046e+01 +wall time: 0.000329018s + +time increment:17320 time: 17.32 +field ' n' [explicit solve]: current solution: 1.005185e+02, current residual:6.065084e+01 +wall time: 0.000422001s + +time increment:17330 time: 17.33 +field ' n' [explicit solve]: current solution: 1.005191e+02, current residual:6.065121e+01 +wall time: 0.000355005s + +time increment:17340 time: 17.34 +field ' n' [explicit solve]: current solution: 1.005198e+02, current residual:6.065158e+01 +wall time: 0.000364065s + +time increment:17350 time: 17.35 +field ' n' [explicit solve]: current solution: 1.005204e+02, current residual:6.065195e+01 +wall time: 0.000402927s + +time increment:17360 time: 17.36 +field ' n' [explicit solve]: current solution: 1.005210e+02, current residual:6.065232e+01 +wall time: 0.000371933s + +time increment:17370 time: 17.37 +field ' n' [explicit solve]: current solution: 1.005216e+02, current residual:6.065269e+01 +wall time: 0.000406981s + +time increment:17380 time: 17.38 +field ' n' [explicit solve]: current solution: 1.005222e+02, current residual:6.065307e+01 +wall time: 0.000412941s + +time increment:17390 time: 17.39 +field ' n' [explicit solve]: current solution: 1.005228e+02, current residual:6.065344e+01 +wall time: 0.000387907s + +time increment:17400 time: 17.4 +field ' n' [explicit solve]: current solution: 1.005235e+02, current residual:6.065381e+01 +wall time: 0.000371933s + +time increment:17410 time: 17.41 +field ' n' [explicit solve]: current solution: 1.005241e+02, current residual:6.065418e+01 +wall time: 0.000337839s + +time increment:17420 time: 17.42 +field ' n' [explicit solve]: current solution: 1.005247e+02, current residual:6.065455e+01 +wall time: 0.000380993s + +time increment:17430 time: 17.43 +field ' n' [explicit solve]: current solution: 1.005253e+02, current residual:6.065492e+01 +wall time: 0.000397921s + +time increment:17440 time: 17.44 +field ' n' [explicit solve]: current solution: 1.005259e+02, current residual:6.065529e+01 +wall time: 0.000340939s + +time increment:17450 time: 17.45 +field ' n' [explicit solve]: current solution: 1.005266e+02, current residual:6.065566e+01 +wall time: 0.000359058s + +time increment:17460 time: 17.46 +field ' n' [explicit solve]: current solution: 1.005272e+02, current residual:6.065603e+01 +wall time: 0.000356913s + +time increment:17470 time: 17.47 +field ' n' [explicit solve]: current solution: 1.005278e+02, current residual:6.065640e+01 +wall time: 0.000365973s + +time increment:17480 time: 17.48 +field ' n' [explicit solve]: current solution: 1.005284e+02, current residual:6.065677e+01 +wall time: 0.000390053s + +time increment:17490 time: 17.49 +field ' n' [explicit solve]: current solution: 1.005290e+02, current residual:6.065715e+01 +wall time: 0.0004251s + +time increment:17500 time: 17.5 +field ' n' [explicit solve]: current solution: 1.005296e+02, current residual:6.065752e+01 +wall time: 0.00039506s + +time increment:17510 time: 17.51 +field ' n' [explicit solve]: current solution: 1.005303e+02, current residual:6.065789e+01 +wall time: 0.000388145s + +time increment:17520 time: 17.52 +field ' n' [explicit solve]: current solution: 1.005309e+02, current residual:6.065826e+01 +wall time: 0.00039506s + +time increment:17530 time: 17.53 +field ' n' [explicit solve]: current solution: 1.005315e+02, current residual:6.065863e+01 +wall time: 0.000349998s + +time increment:17540 time: 17.54 +field ' n' [explicit solve]: current solution: 1.005321e+02, current residual:6.065900e+01 +wall time: 0.000344992s + +time increment:17550 time: 17.55 +field ' n' [explicit solve]: current solution: 1.005327e+02, current residual:6.065937e+01 +wall time: 0.000417948s + +time increment:17560 time: 17.56 +field ' n' [explicit solve]: current solution: 1.005333e+02, current residual:6.065974e+01 +wall time: 0.000340939s + +time increment:17570 time: 17.57 +field ' n' [explicit solve]: current solution: 1.005340e+02, current residual:6.066011e+01 +wall time: 0.000347853s + +time increment:17580 time: 17.58 +field ' n' [explicit solve]: current solution: 1.005346e+02, current residual:6.066048e+01 +wall time: 0.000345945s + +time increment:17590 time: 17.59 +field ' n' [explicit solve]: current solution: 1.005352e+02, current residual:6.066085e+01 +wall time: 0.000378847s + +time increment:17600 time: 17.6 +field ' n' [explicit solve]: current solution: 1.005358e+02, current residual:6.066122e+01 +wall time: 0.000349045s + +time increment:17610 time: 17.61 +field ' n' [explicit solve]: current solution: 1.005364e+02, current residual:6.066159e+01 +wall time: 0.000385046s + +time increment:17620 time: 17.62 +field ' n' [explicit solve]: current solution: 1.005370e+02, current residual:6.066196e+01 +wall time: 0.000372887s + +time increment:17630 time: 17.63 +field ' n' [explicit solve]: current solution: 1.005376e+02, current residual:6.066233e+01 +wall time: 0.000390053s + +time increment:17640 time: 17.64 +field ' n' [explicit solve]: current solution: 1.005383e+02, current residual:6.066270e+01 +wall time: 0.000404119s + +time increment:17650 time: 17.65 +field ' n' [explicit solve]: current solution: 1.005389e+02, current residual:6.066307e+01 +wall time: 0.000374079s + +time increment:17660 time: 17.66 +field ' n' [explicit solve]: current solution: 1.005395e+02, current residual:6.066344e+01 +wall time: 0.000349998s + +time increment:17670 time: 17.67 +field ' n' [explicit solve]: current solution: 1.005401e+02, current residual:6.066381e+01 +wall time: 0.000360966s + +time increment:17680 time: 17.68 +field ' n' [explicit solve]: current solution: 1.005407e+02, current residual:6.066417e+01 +wall time: 0.000389099s + +time increment:17690 time: 17.69 +field ' n' [explicit solve]: current solution: 1.005413e+02, current residual:6.066454e+01 +wall time: 0.000375032s + +time increment:17700 time: 17.7 +field ' n' [explicit solve]: current solution: 1.005420e+02, current residual:6.066491e+01 +wall time: 0.000347853s + +time increment:17710 time: 17.71 +field ' n' [explicit solve]: current solution: 1.005426e+02, current residual:6.066528e+01 +wall time: 0.000349045s + +time increment:17720 time: 17.72 +field ' n' [explicit solve]: current solution: 1.005432e+02, current residual:6.066565e+01 +wall time: 0.000356913s + +time increment:17730 time: 17.73 +field ' n' [explicit solve]: current solution: 1.005438e+02, current residual:6.066602e+01 +wall time: 0.000384808s + +time increment:17740 time: 17.74 +field ' n' [explicit solve]: current solution: 1.005444e+02, current residual:6.066639e+01 +wall time: 0.000370979s + +time increment:17750 time: 17.75 +field ' n' [explicit solve]: current solution: 1.005450e+02, current residual:6.066676e+01 +wall time: 0.000336885s + +time increment:17760 time: 17.76 +field ' n' [explicit solve]: current solution: 1.005456e+02, current residual:6.066713e+01 +wall time: 0.00035882s + +time increment:17770 time: 17.77 +field ' n' [explicit solve]: current solution: 1.005463e+02, current residual:6.066750e+01 +wall time: 0.000309944s + +time increment:17780 time: 17.78 +field ' n' [explicit solve]: current solution: 1.005469e+02, current residual:6.066787e+01 +wall time: 0.000430107s + +time increment:17790 time: 17.79 +field ' n' [explicit solve]: current solution: 1.005475e+02, current residual:6.066823e+01 +wall time: 0.000334024s + +time increment:17800 time: 17.8 +field ' n' [explicit solve]: current solution: 1.005481e+02, current residual:6.066860e+01 +wall time: 0.000364065s + +time increment:17810 time: 17.81 +field ' n' [explicit solve]: current solution: 1.005487e+02, current residual:6.066897e+01 +wall time: 0.000386953s + +time increment:17820 time: 17.82 +field ' n' [explicit solve]: current solution: 1.005493e+02, current residual:6.066934e+01 +wall time: 0.000356913s + +time increment:17830 time: 17.83 +field ' n' [explicit solve]: current solution: 1.005499e+02, current residual:6.066971e+01 +wall time: 0.000328064s + +time increment:17840 time: 17.84 +field ' n' [explicit solve]: current solution: 1.005505e+02, current residual:6.067008e+01 +wall time: 0.000375032s + +time increment:17850 time: 17.85 +field ' n' [explicit solve]: current solution: 1.005512e+02, current residual:6.067045e+01 +wall time: 0.000339985s + +time increment:17860 time: 17.86 +field ' n' [explicit solve]: current solution: 1.005518e+02, current residual:6.067082e+01 +wall time: 0.000308037s + +time increment:17870 time: 17.87 +field ' n' [explicit solve]: current solution: 1.005524e+02, current residual:6.067118e+01 +wall time: 0.000330925s + +time increment:17880 time: 17.88 +field ' n' [explicit solve]: current solution: 1.005530e+02, current residual:6.067155e+01 +wall time: 0.000344992s + +time increment:17890 time: 17.89 +field ' n' [explicit solve]: current solution: 1.005536e+02, current residual:6.067192e+01 +wall time: 0.000324965s + +time increment:17900 time: 17.9 +field ' n' [explicit solve]: current solution: 1.005542e+02, current residual:6.067229e+01 +wall time: 0.000406981s + +time increment:17910 time: 17.91 +field ' n' [explicit solve]: current solution: 1.005548e+02, current residual:6.067266e+01 +wall time: 0.000331879s + +time increment:17920 time: 17.92 +field ' n' [explicit solve]: current solution: 1.005555e+02, current residual:6.067303e+01 +wall time: 0.000359058s + +time increment:17930 time: 17.93 +field ' n' [explicit solve]: current solution: 1.005561e+02, current residual:6.067339e+01 +wall time: 0.000366926s + +time increment:17940 time: 17.94 +field ' n' [explicit solve]: current solution: 1.005567e+02, current residual:6.067376e+01 +wall time: 0.000331163s + +time increment:17950 time: 17.95 +field ' n' [explicit solve]: current solution: 1.005573e+02, current residual:6.067413e+01 +wall time: 0.000365973s + +time increment:17960 time: 17.96 +field ' n' [explicit solve]: current solution: 1.005579e+02, current residual:6.067450e+01 +wall time: 0.00030303s + +time increment:17970 time: 17.97 +field ' n' [explicit solve]: current solution: 1.005585e+02, current residual:6.067487e+01 +wall time: 0.000314951s + +time increment:17980 time: 17.98 +field ' n' [explicit solve]: current solution: 1.005591e+02, current residual:6.067523e+01 +wall time: 0.000405073s + +time increment:17990 time: 17.99 +field ' n' [explicit solve]: current solution: 1.005597e+02, current residual:6.067560e+01 +wall time: 0.000384092s + +time increment:18000 time: 18 +field ' n' [explicit solve]: current solution: 1.005604e+02, current residual:6.067597e+01 +wall time: 0.000347137s +fields: 1 +fields: 1 +fields: 1 +fields: 1 +Output written to:solution-018000.pvtu + +Energy: 186.417 +Energy Components: 77.0957 109.321 0 + +time increment:18010 time: 18.01 +field ' n' [explicit solve]: current solution: 1.005610e+02, current residual:6.067634e+01 +wall time: 0.000366926s + +time increment:18020 time: 18.02 +field ' n' [explicit solve]: current solution: 1.005616e+02, current residual:6.067670e+01 +wall time: 0.000378132s + +time increment:18030 time: 18.03 +field ' n' [explicit solve]: current solution: 1.005622e+02, current residual:6.067707e+01 +wall time: 0.000363111s + +time increment:18040 time: 18.04 +field ' n' [explicit solve]: current solution: 1.005628e+02, current residual:6.067744e+01 +wall time: 0.000389814s + +time increment:18050 time: 18.05 +field ' n' [explicit solve]: current solution: 1.005634e+02, current residual:6.067781e+01 +wall time: 0.000339985s + +time increment:18060 time: 18.06 +field ' n' [explicit solve]: current solution: 1.005640e+02, current residual:6.067818e+01 +wall time: 0.000294924s + +time increment:18070 time: 18.07 +field ' n' [explicit solve]: current solution: 1.005646e+02, current residual:6.067854e+01 +wall time: 0.000406027s + +time increment:18080 time: 18.08 +field ' n' [explicit solve]: current solution: 1.005653e+02, current residual:6.067891e+01 +wall time: 0.000392914s + +time increment:18090 time: 18.09 +field ' n' [explicit solve]: current solution: 1.005659e+02, current residual:6.067928e+01 +wall time: 0.000303984s + +time increment:18100 time: 18.1 +field ' n' [explicit solve]: current solution: 1.005665e+02, current residual:6.067965e+01 +wall time: 0.00029707s + +time increment:18110 time: 18.11 +field ' n' [explicit solve]: current solution: 1.005671e+02, current residual:6.068001e+01 +wall time: 0.000358105s + +time increment:18120 time: 18.12 +field ' n' [explicit solve]: current solution: 1.005677e+02, current residual:6.068038e+01 +wall time: 0.000319004s + +time increment:18130 time: 18.13 +field ' n' [explicit solve]: current solution: 1.005683e+02, current residual:6.068075e+01 +wall time: 0.000289917s + +time increment:18140 time: 18.14 +field ' n' [explicit solve]: current solution: 1.005689e+02, current residual:6.068112e+01 +wall time: 0.000319958s + +time increment:18150 time: 18.15 +field ' n' [explicit solve]: current solution: 1.005695e+02, current residual:6.068148e+01 +wall time: 0.000343084s + +time increment:18160 time: 18.16 +field ' n' [explicit solve]: current solution: 1.005702e+02, current residual:6.068185e+01 +wall time: 0.000322104s + +time increment:18170 time: 18.17 +field ' n' [explicit solve]: current solution: 1.005708e+02, current residual:6.068222e+01 +wall time: 0.000289202s + +time increment:18180 time: 18.18 +field ' n' [explicit solve]: current solution: 1.005714e+02, current residual:6.068258e+01 +wall time: 0.000334024s + +time increment:18190 time: 18.19 +field ' n' [explicit solve]: current solution: 1.005720e+02, current residual:6.068295e+01 +wall time: 0.00031805s + +time increment:18200 time: 18.2 +field ' n' [explicit solve]: current solution: 1.005726e+02, current residual:6.068332e+01 +wall time: 0.000345945s + +time increment:18210 time: 18.21 +field ' n' [explicit solve]: current solution: 1.005732e+02, current residual:6.068369e+01 +wall time: 0.000405073s + +time increment:18220 time: 18.22 +field ' n' [explicit solve]: current solution: 1.005738e+02, current residual:6.068405e+01 +wall time: 0.00031805s + +time increment:18230 time: 18.23 +field ' n' [explicit solve]: current solution: 1.005744e+02, current residual:6.068442e+01 +wall time: 0.000347853s + +time increment:18240 time: 18.24 +field ' n' [explicit solve]: current solution: 1.005750e+02, current residual:6.068479e+01 +wall time: 0.00030899s + +time increment:18250 time: 18.25 +field ' n' [explicit solve]: current solution: 1.005757e+02, current residual:6.068515e+01 +wall time: 0.000330925s + +time increment:18260 time: 18.26 +field ' n' [explicit solve]: current solution: 1.005763e+02, current residual:6.068552e+01 +wall time: 0.000344992s + +time increment:18270 time: 18.27 +field ' n' [explicit solve]: current solution: 1.005769e+02, current residual:6.068589e+01 +wall time: 0.000316858s + +time increment:18280 time: 18.28 +field ' n' [explicit solve]: current solution: 1.005775e+02, current residual:6.068626e+01 +wall time: 0.000334978s + +time increment:18290 time: 18.29 +field ' n' [explicit solve]: current solution: 1.005781e+02, current residual:6.068662e+01 +wall time: 0.00030303s + +time increment:18300 time: 18.3 +field ' n' [explicit solve]: current solution: 1.005787e+02, current residual:6.068699e+01 +wall time: 0.000307083s + +time increment:18310 time: 18.31 +field ' n' [explicit solve]: current solution: 1.005793e+02, current residual:6.068736e+01 +wall time: 0.000323057s + +time increment:18320 time: 18.32 +field ' n' [explicit solve]: current solution: 1.005799e+02, current residual:6.068772e+01 +wall time: 0.000351906s + +time increment:18330 time: 18.33 +field ' n' [explicit solve]: current solution: 1.005805e+02, current residual:6.068809e+01 +wall time: 0.000351906s + +time increment:18340 time: 18.34 +field ' n' [explicit solve]: current solution: 1.005812e+02, current residual:6.068846e+01 +wall time: 0.000334978s + +time increment:18350 time: 18.35 +field ' n' [explicit solve]: current solution: 1.005818e+02, current residual:6.068882e+01 +wall time: 0.00036788s + +time increment:18360 time: 18.36 +field ' n' [explicit solve]: current solution: 1.005824e+02, current residual:6.068919e+01 +wall time: 0.000357866s + +time increment:18370 time: 18.37 +field ' n' [explicit solve]: current solution: 1.005830e+02, current residual:6.068956e+01 +wall time: 0.000338078s + +time increment:18380 time: 18.38 +field ' n' [explicit solve]: current solution: 1.005836e+02, current residual:6.068992e+01 +wall time: 0.000352859s + +time increment:18390 time: 18.39 +field ' n' [explicit solve]: current solution: 1.005842e+02, current residual:6.069029e+01 +wall time: 0.00033617s + +time increment:18400 time: 18.4 +field ' n' [explicit solve]: current solution: 1.005848e+02, current residual:6.069066e+01 +wall time: 0.000323057s + +time increment:18410 time: 18.41 +field ' n' [explicit solve]: current solution: 1.005854e+02, current residual:6.069102e+01 +wall time: 0.00037384s + +time increment:18420 time: 18.42 +field ' n' [explicit solve]: current solution: 1.005860e+02, current residual:6.069139e+01 +wall time: 0.000344992s + +time increment:18430 time: 18.43 +field ' n' [explicit solve]: current solution: 1.005867e+02, current residual:6.069176e+01 +wall time: 0.000358105s + +time increment:18440 time: 18.44 +field ' n' [explicit solve]: current solution: 1.005873e+02, current residual:6.069212e+01 +wall time: 0.000349045s + +time increment:18450 time: 18.45 +field ' n' [explicit solve]: current solution: 1.005879e+02, current residual:6.069249e+01 +wall time: 0.000399113s + +time increment:18460 time: 18.46 +field ' n' [explicit solve]: current solution: 1.005885e+02, current residual:6.069286e+01 +wall time: 0.000388145s + +time increment:18470 time: 18.47 +field ' n' [explicit solve]: current solution: 1.005891e+02, current residual:6.069322e+01 +wall time: 0.000366926s + +time increment:18480 time: 18.48 +field ' n' [explicit solve]: current solution: 1.005897e+02, current residual:6.069359e+01 +wall time: 0.000369072s + +time increment:18490 time: 18.49 +field ' n' [explicit solve]: current solution: 1.005903e+02, current residual:6.069396e+01 +wall time: 0.000349045s + +time increment:18500 time: 18.5 +field ' n' [explicit solve]: current solution: 1.005909e+02, current residual:6.069432e+01 +wall time: 0.000381947s + +time increment:18510 time: 18.51 +field ' n' [explicit solve]: current solution: 1.005915e+02, current residual:6.069469e+01 +wall time: 0.000330925s + +time increment:18520 time: 18.52 +field ' n' [explicit solve]: current solution: 1.005921e+02, current residual:6.069506e+01 +wall time: 0.000344992s + +time increment:18530 time: 18.53 +field ' n' [explicit solve]: current solution: 1.005928e+02, current residual:6.069542e+01 +wall time: 0.000351906s + +time increment:18540 time: 18.54 +field ' n' [explicit solve]: current solution: 1.005934e+02, current residual:6.069579e+01 +wall time: 0.000334024s + +time increment:18550 time: 18.55 +field ' n' [explicit solve]: current solution: 1.005940e+02, current residual:6.069615e+01 +wall time: 0.000355005s + +time increment:18560 time: 18.56 +field ' n' [explicit solve]: current solution: 1.005946e+02, current residual:6.069652e+01 +wall time: 0.000339031s + +time increment:18570 time: 18.57 +field ' n' [explicit solve]: current solution: 1.005952e+02, current residual:6.069689e+01 +wall time: 0.000349045s + +time increment:18580 time: 18.58 +field ' n' [explicit solve]: current solution: 1.005958e+02, current residual:6.069725e+01 +wall time: 0.000370979s + +time increment:18590 time: 18.59 +field ' n' [explicit solve]: current solution: 1.005964e+02, current residual:6.069762e+01 +wall time: 0.000345945s + +time increment:18600 time: 18.6 +field ' n' [explicit solve]: current solution: 1.005970e+02, current residual:6.069799e+01 +wall time: 0.000385046s + +time increment:18610 time: 18.61 +field ' n' [explicit solve]: current solution: 1.005976e+02, current residual:6.069835e+01 +wall time: 0.000343084s + +time increment:18620 time: 18.62 +field ' n' [explicit solve]: current solution: 1.005983e+02, current residual:6.069872e+01 +wall time: 0.000343084s + +time increment:18630 time: 18.63 +field ' n' [explicit solve]: current solution: 1.005989e+02, current residual:6.069908e+01 +wall time: 0.000359058s + +time increment:18640 time: 18.64 +field ' n' [explicit solve]: current solution: 1.005995e+02, current residual:6.069945e+01 +wall time: 0.000386953s + +time increment:18650 time: 18.65 +field ' n' [explicit solve]: current solution: 1.006001e+02, current residual:6.069982e+01 +wall time: 0.000353098s + +time increment:18660 time: 18.66 +field ' n' [explicit solve]: current solution: 1.006007e+02, current residual:6.070018e+01 +wall time: 0.000411987s + +time increment:18670 time: 18.67 +field ' n' [explicit solve]: current solution: 1.006013e+02, current residual:6.070055e+01 +wall time: 0.000411987s + +time increment:18680 time: 18.68 +field ' n' [explicit solve]: current solution: 1.006019e+02, current residual:6.070092e+01 +wall time: 0.000381947s + +time increment:18690 time: 18.69 +field ' n' [explicit solve]: current solution: 1.006025e+02, current residual:6.070128e+01 +wall time: 0.000440121s + +time increment:18700 time: 18.7 +field ' n' [explicit solve]: current solution: 1.006031e+02, current residual:6.070165e+01 +wall time: 0.000356197s + +time increment:18710 time: 18.71 +field ' n' [explicit solve]: current solution: 1.006037e+02, current residual:6.070201e+01 +wall time: 0.000371933s + +time increment:18720 time: 18.72 +field ' n' [explicit solve]: current solution: 1.006044e+02, current residual:6.070238e+01 +wall time: 0.000408888s + +time increment:18730 time: 18.73 +field ' n' [explicit solve]: current solution: 1.006050e+02, current residual:6.070275e+01 +wall time: 0.000354052s + +time increment:18740 time: 18.74 +field ' n' [explicit solve]: current solution: 1.006056e+02, current residual:6.070311e+01 +wall time: 0.000386s + +time increment:18750 time: 18.75 +field ' n' [explicit solve]: current solution: 1.006062e+02, current residual:6.070348e+01 +wall time: 0.000374079s + +time increment:18760 time: 18.76 +field ' n' [explicit solve]: current solution: 1.006068e+02, current residual:6.070385e+01 +wall time: 0.000345945s + +time increment:18770 time: 18.77 +field ' n' [explicit solve]: current solution: 1.006074e+02, current residual:6.070421e+01 +wall time: 0.000381947s + +time increment:18780 time: 18.78 +field ' n' [explicit solve]: current solution: 1.006080e+02, current residual:6.070458e+01 +wall time: 0.0003829s + +time increment:18790 time: 18.79 +field ' n' [explicit solve]: current solution: 1.006086e+02, current residual:6.070494e+01 +wall time: 0.000396013s + +time increment:18800 time: 18.8 +field ' n' [explicit solve]: current solution: 1.006092e+02, current residual:6.070531e+01 +wall time: 0.000437975s + +time increment:18810 time: 18.81 +field ' n' [explicit solve]: current solution: 1.006098e+02, current residual:6.070568e+01 +wall time: 0.000349045s + +time increment:18820 time: 18.82 +field ' n' [explicit solve]: current solution: 1.006105e+02, current residual:6.070604e+01 +wall time: 0.000386953s + +time increment:18830 time: 18.83 +field ' n' [explicit solve]: current solution: 1.006111e+02, current residual:6.070641e+01 +wall time: 0.000483036s + +time increment:18840 time: 18.84 +field ' n' [explicit solve]: current solution: 1.006117e+02, current residual:6.070678e+01 +wall time: 0.000380039s + +time increment:18850 time: 18.85 +field ' n' [explicit solve]: current solution: 1.006123e+02, current residual:6.070714e+01 +wall time: 0.000348091s + +time increment:18860 time: 18.86 +field ' n' [explicit solve]: current solution: 1.006129e+02, current residual:6.070751e+01 +wall time: 0.000342131s + +time increment:18870 time: 18.87 +field ' n' [explicit solve]: current solution: 1.006135e+02, current residual:6.070787e+01 +wall time: 0.000424862s + +time increment:18880 time: 18.88 +field ' n' [explicit solve]: current solution: 1.006141e+02, current residual:6.070824e+01 +wall time: 0.000366211s + +time increment:18890 time: 18.89 +field ' n' [explicit solve]: current solution: 1.006147e+02, current residual:6.070861e+01 +wall time: 0.000371218s + +time increment:18900 time: 18.9 +field ' n' [explicit solve]: current solution: 1.006153e+02, current residual:6.070897e+01 +wall time: 0.000602961s + +time increment:18910 time: 18.91 +field ' n' [explicit solve]: current solution: 1.006159e+02, current residual:6.070934e+01 +wall time: 0.000390053s + +time increment:18920 time: 18.92 +field ' n' [explicit solve]: current solution: 1.006166e+02, current residual:6.070970e+01 +wall time: 0.000396013s + +time increment:18930 time: 18.93 +field ' n' [explicit solve]: current solution: 1.006172e+02, current residual:6.071007e+01 +wall time: 0.000403166s + +time increment:18940 time: 18.94 +field ' n' [explicit solve]: current solution: 1.006178e+02, current residual:6.071044e+01 +wall time: 0.000422001s + +time increment:18950 time: 18.95 +field ' n' [explicit solve]: current solution: 1.006184e+02, current residual:6.071080e+01 +wall time: 0.000390053s + +time increment:18960 time: 18.96 +field ' n' [explicit solve]: current solution: 1.006190e+02, current residual:6.071117e+01 +wall time: 0.000344992s + +time increment:18970 time: 18.97 +field ' n' [explicit solve]: current solution: 1.006196e+02, current residual:6.071154e+01 +wall time: 0.000396967s + +time increment:18980 time: 18.98 +field ' n' [explicit solve]: current solution: 1.006202e+02, current residual:6.071190e+01 +wall time: 0.000408888s + +time increment:18990 time: 18.99 +field ' n' [explicit solve]: current solution: 1.006208e+02, current residual:6.071227e+01 +wall time: 0.0004251s + +time increment:19000 time: 19 +field ' n' [explicit solve]: current solution: 1.006214e+02, current residual:6.071263e+01 +wall time: 0.000478029s + +time increment:19010 time: 19.01 +field ' n' [explicit solve]: current solution: 1.006221e+02, current residual:6.071300e+01 +wall time: 0.000342131s + +time increment:19020 time: 19.02 +field ' n' [explicit solve]: current solution: 1.006227e+02, current residual:6.071337e+01 +wall time: 0.000389099s + +time increment:19030 time: 19.03 +field ' n' [explicit solve]: current solution: 1.006233e+02, current residual:6.071373e+01 +wall time: 0.000422001s + +time increment:19040 time: 19.04 +field ' n' [explicit solve]: current solution: 1.006239e+02, current residual:6.071410e+01 +wall time: 0.000375032s + +time increment:19050 time: 19.05 +field ' n' [explicit solve]: current solution: 1.006245e+02, current residual:6.071447e+01 +wall time: 0.00040102s + +time increment:19060 time: 19.06 +field ' n' [explicit solve]: current solution: 1.006251e+02, current residual:6.071483e+01 +wall time: 0.000339031s + +time increment:19070 time: 19.07 +field ' n' [explicit solve]: current solution: 1.006257e+02, current residual:6.071520e+01 +wall time: 0.00037694s + +time increment:19080 time: 19.08 +field ' n' [explicit solve]: current solution: 1.006263e+02, current residual:6.071556e+01 +wall time: 0.000391006s + +time increment:19090 time: 19.09 +field ' n' [explicit solve]: current solution: 1.006269e+02, current residual:6.071593e+01 +wall time: 0.000363827s + +time increment:19100 time: 19.1 +field ' n' [explicit solve]: current solution: 1.006275e+02, current residual:6.071630e+01 +wall time: 0.000358105s + +time increment:19110 time: 19.11 +field ' n' [explicit solve]: current solution: 1.006282e+02, current residual:6.071666e+01 +wall time: 0.000335932s + +time increment:19120 time: 19.12 +field ' n' [explicit solve]: current solution: 1.006288e+02, current residual:6.071703e+01 +wall time: 0.00037384s + +time increment:19130 time: 19.13 +field ' n' [explicit solve]: current solution: 1.006294e+02, current residual:6.071740e+01 +wall time: 0.000398874s + +time increment:19140 time: 19.14 +field ' n' [explicit solve]: current solution: 1.006300e+02, current residual:6.071776e+01 +wall time: 0.000381947s + +time increment:19150 time: 19.15 +field ' n' [explicit solve]: current solution: 1.006306e+02, current residual:6.071813e+01 +wall time: 0.000341892s + +time increment:19160 time: 19.16 +field ' n' [explicit solve]: current solution: 1.006312e+02, current residual:6.071850e+01 +wall time: 0.000400782s + +time increment:19170 time: 19.17 +field ' n' [explicit solve]: current solution: 1.006318e+02, current residual:6.071886e+01 +wall time: 0.000355005s + +time increment:19180 time: 19.18 +field ' n' [explicit solve]: current solution: 1.006324e+02, current residual:6.071923e+01 +wall time: 0.000355959s + +time increment:19190 time: 19.19 +field ' n' [explicit solve]: current solution: 1.006330e+02, current residual:6.071959e+01 +wall time: 0.000348806s + +time increment:19200 time: 19.2 +field ' n' [explicit solve]: current solution: 1.006337e+02, current residual:6.071996e+01 +wall time: 0.000406981s + +time increment:19210 time: 19.21 +field ' n' [explicit solve]: current solution: 1.006343e+02, current residual:6.072033e+01 +wall time: 0.000436783s + +time increment:19220 time: 19.22 +field ' n' [explicit solve]: current solution: 1.006349e+02, current residual:6.072069e+01 +wall time: 0.000400066s + +time increment:19230 time: 19.23 +field ' n' [explicit solve]: current solution: 1.006355e+02, current residual:6.072106e+01 +wall time: 0.000367165s + +time increment:19240 time: 19.24 +field ' n' [explicit solve]: current solution: 1.006361e+02, current residual:6.072143e+01 +wall time: 0.00041008s + +time increment:19250 time: 19.25 +field ' n' [explicit solve]: current solution: 1.006367e+02, current residual:6.072179e+01 +wall time: 0.000380039s + +time increment:19260 time: 19.26 +field ' n' [explicit solve]: current solution: 1.006373e+02, current residual:6.072216e+01 +wall time: 0.000375986s + +time increment:19270 time: 19.27 +field ' n' [explicit solve]: current solution: 1.006379e+02, current residual:6.072253e+01 +wall time: 0.000401974s + +time increment:19280 time: 19.28 +field ' n' [explicit solve]: current solution: 1.006385e+02, current residual:6.072289e+01 +wall time: 0.000427008s + +time increment:19290 time: 19.29 +field ' n' [explicit solve]: current solution: 1.006392e+02, current residual:6.072326e+01 +wall time: 0.000409842s + +time increment:19300 time: 19.3 +field ' n' [explicit solve]: current solution: 1.006398e+02, current residual:6.072363e+01 +wall time: 0.000344038s + +time increment:19310 time: 19.31 +field ' n' [explicit solve]: current solution: 1.006404e+02, current residual:6.072399e+01 +wall time: 0.000360966s + +time increment:19320 time: 19.32 +field ' n' [explicit solve]: current solution: 1.006410e+02, current residual:6.072436e+01 +wall time: 0.000360012s + +time increment:19330 time: 19.33 +field ' n' [explicit solve]: current solution: 1.006416e+02, current residual:6.072473e+01 +wall time: 0.000355005s + +time increment:19340 time: 19.34 +field ' n' [explicit solve]: current solution: 1.006422e+02, current residual:6.072509e+01 +wall time: 0.000386953s + +time increment:19350 time: 19.35 +field ' n' [explicit solve]: current solution: 1.006428e+02, current residual:6.072546e+01 +wall time: 0.000337124s + +time increment:19360 time: 19.36 +field ' n' [explicit solve]: current solution: 1.006434e+02, current residual:6.072583e+01 +wall time: 0.000345945s + +time increment:19370 time: 19.37 +field ' n' [explicit solve]: current solution: 1.006440e+02, current residual:6.072619e+01 +wall time: 0.000338078s + +time increment:19380 time: 19.38 +field ' n' [explicit solve]: current solution: 1.006447e+02, current residual:6.072656e+01 +wall time: 0.000378847s + +time increment:19390 time: 19.39 +field ' n' [explicit solve]: current solution: 1.006453e+02, current residual:6.072693e+01 +wall time: 0.0003829s + +time increment:19400 time: 19.4 +field ' n' [explicit solve]: current solution: 1.006459e+02, current residual:6.072729e+01 +wall time: 0.000429869s + +time increment:19410 time: 19.41 +field ' n' [explicit solve]: current solution: 1.006465e+02, current residual:6.072766e+01 +wall time: 0.000394106s + +time increment:19420 time: 19.42 +field ' n' [explicit solve]: current solution: 1.006471e+02, current residual:6.072803e+01 +wall time: 0.000345945s + +time increment:19430 time: 19.43 +field ' n' [explicit solve]: current solution: 1.006477e+02, current residual:6.072839e+01 +wall time: 0.000396967s + +time increment:19440 time: 19.44 +field ' n' [explicit solve]: current solution: 1.006483e+02, current residual:6.072876e+01 +wall time: 0.000349998s + +time 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time: 19.58 +field ' n' [explicit solve]: current solution: 1.006569e+02, current residual:6.073390e+01 +wall time: 0.000404119s + +time increment:19590 time: 19.59 +field ' n' [explicit solve]: current solution: 1.006575e+02, current residual:6.073427e+01 +wall time: 0.000377893s + +time increment:19600 time: 19.6 +field ' n' [explicit solve]: current solution: 1.006581e+02, current residual:6.073464e+01 +wall time: 0.000339985s + +time increment:19610 time: 19.61 +field ' n' [explicit solve]: current solution: 1.006587e+02, current residual:6.073500e+01 +wall time: 0.00030899s + +time increment:19620 time: 19.62 +field ' n' [explicit solve]: current solution: 1.006594e+02, current residual:6.073537e+01 +wall time: 0.000325918s + +time increment:19630 time: 19.63 +field ' n' [explicit solve]: current solution: 1.006600e+02, current residual:6.073574e+01 +wall time: 0.000334978s + +time increment:19640 time: 19.64 +field ' n' [explicit solve]: current solution: 1.006606e+02, current 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residual:6.074089e+01 +wall time: 0.000333071s + +time increment:19780 time: 19.78 +field ' n' [explicit solve]: current solution: 1.006692e+02, current residual:6.074126e+01 +wall time: 0.000316858s + +time increment:19790 time: 19.79 +field ' n' [explicit solve]: current solution: 1.006698e+02, current residual:6.074162e+01 +wall time: 0.000339031s + +time increment:19800 time: 19.8 +field ' n' [explicit solve]: current solution: 1.006704e+02, current residual:6.074199e+01 +wall time: 0.00031209s + +time increment:19810 time: 19.81 +field ' n' [explicit solve]: current solution: 1.006710e+02, current residual:6.074236e+01 +wall time: 0.000396013s + +time increment:19820 time: 19.82 +field ' n' [explicit solve]: current solution: 1.006716e+02, current residual:6.074273e+01 +wall time: 0.000381231s + +time increment:19830 time: 19.83 +field ' n' [explicit solve]: current solution: 1.006722e+02, current residual:6.074310e+01 +wall time: 0.000355005s + +time increment:19840 time: 19.84 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residual:6.074568e+01 +wall time: 0.000347137s + +time increment:19910 time: 19.91 +field ' n' [explicit solve]: current solution: 1.006772e+02, current residual:6.074605e+01 +wall time: 0.000316143s + +time increment:19920 time: 19.92 +field ' n' [explicit solve]: current solution: 1.006778e+02, current residual:6.074641e+01 +wall time: 0.000353098s + +time increment:19930 time: 19.93 +field ' n' [explicit solve]: current solution: 1.006784e+02, current residual:6.074678e+01 +wall time: 0.000342131s + +time increment:19940 time: 19.94 +field ' n' [explicit solve]: current solution: 1.006790e+02, current residual:6.074715e+01 +wall time: 0.00030303s + +time increment:19950 time: 19.95 +field ' n' [explicit solve]: current solution: 1.006796e+02, current residual:6.074752e+01 +wall time: 0.000349045s + +time increment:19960 time: 19.96 +field ' n' [explicit solve]: current solution: 1.006802e+02, current residual:6.074789e+01 +wall time: 0.00034523s + +time increment:19970 time: 19.97 +field ' n' [explicit solve]: current solution: 1.006809e+02, current residual:6.074826e+01 +wall time: 0.000378132s + +time increment:19980 time: 19.98 +field ' n' [explicit solve]: current solution: 1.006815e+02, current residual:6.074863e+01 +wall time: 0.000313044s + +time increment:19990 time: 19.99 +field ' n' [explicit solve]: current solution: 1.006821e+02, current residual:6.074900e+01 +wall time: 0.000313044s + +time increment:20000 time: 20 +field ' n' [explicit solve]: current solution: 1.006827e+02, current residual:6.074937e+01 +wall time: 0.000344038s +fields: 1 +fields: 1 +fields: 1 +fields: 1 +Output written to:solution-020000.pvtu + +Energy: 182.087 +Energy Components: 74.7191 107.367 0 + + ++---------------------------------------------+------------+------------+ +| Total wallclock time elapsed since start | 9.33s | | +| | | | +| Section | no. calls | wall time | % of total | ++---------------------------------+-----------+------------+------------+ +| matrixFreePDE: AMR | 20000 | 0.0714s | 0.77% | +| matrixFreePDE: computeEnergy | 11 | 0.734s | 7.9% | +| matrixFreePDE: computeRHS | 20000 | 4.27s | 46% | +| matrixFreePDE: initialization | 1 | 0.0422s | 0.45% | +| matrixFreePDE: output | 11 | 1.24s | 13% | +| matrixFreePDE: solve | 1 | 9.29s | 1e+02% | +| matrixFreePDE: solveIncrements | 20000 | 6.5s | 70% | ++---------------------------------+-----------+------------+------------+ + diff --git a/applications/allenCahn/parameters.h b/applications/allenCahn/parameters.h index d74bc762b..5a90427b6 100644 --- a/applications/allenCahn/parameters.h +++ b/applications/allenCahn/parameters.h @@ -56,6 +56,9 @@ // outputCondition is "LIST" #define outputList {} +// Status is printed to the screen every skipPrintSteps +#define skipPrintSteps 10 + // ================================================================================= // Set the flag determining if the total free energy is calculated for each output // ================================================================================= diff --git a/applications/allenCahn_pfield/parameters.h b/applications/allenCahn_pfield/parameters.h index e7b037c3f..2d6b2f342 100644 --- a/applications/allenCahn_pfield/parameters.h +++ b/applications/allenCahn_pfield/parameters.h @@ -60,6 +60,9 @@ // outputCondition is "LIST" #define outputList {} +// Status is printed to the screen every skipPrintSteps +#define skipPrintSteps 10 + // ================================================================================= // Set the flag determining if the total free energy is calculated for each output // ================================================================================= diff --git a/applications/cahnHilliard/freeEnergy.txt b/applications/cahnHilliard/freeEnergy.txt deleted file mode 100644 index 50887771f..000000000 --- a/applications/cahnHilliard/freeEnergy.txt +++ /dev/null @@ -1,7 +0,0 @@ -1035 -264.2545604 -246.1340928 -236.4666817 -230.7025334 -225.3561466 -219.2010184 diff --git a/applications/cahnHilliard/output.txt b/applications/cahnHilliard/output.txt new file mode 100644 index 000000000..499546bd9 --- /dev/null +++ b/applications/cahnHilliard/output.txt @@ -0,0 +1,50171 @@ +rm: *vtu: No such file or directory +-- Autopilot invoked +### +# +# Project main set up with deal.II-8.4.1 found at +# /Applications/deal.II.app/Contents/Resources +# +# CMAKE_BUILD_TYPE: Debug +# +# You can now run +# $ make - to compile and link the program +# $ make run - to (compile, link and) run the program +# +# $ make sign - to sign the executable with the supplied OSX developer key +# +# $ make debug - to switch the build type to 'Debug' +# $ make release - to switch the build type to 'Release' +# +# $ make edit_cache - to change (cached) configuration variables +# and rerun the configure and generate phases of CMake +# +# $ make strip_comments - to strip the source files in this +# directory off the documentation comments +# $ make clean - to remove the generated executable as well as +# all intermediate compilation files +# $ make runclean - to remove all output generated by the program +# $ make distclean - to clean the directory from _all_ generated +# files (includes clean, runclean and the removal +# of the generated build system) +# $ make info - to view this message again +# +# Have a nice day! +# +### +-- Configuring done +-- Generating done +-- Build files have been written to: /Users/stephendewitt/Documents/workspace/PRISMS_workspace/phaseField_dev/applications/cahnHilliard +Scanning dependencies of target release +[100%] Switch CMAKE_BUILD_TYPE to Release +-- Autopilot invoked +-- Run $ make info to print a detailed help message +-- Configuring done +-- Generating done +-- Build files have been written to: /Users/stephendewitt/Documents/workspace/PRISMS_workspace/phaseField_dev/applications/cahnHilliard +Scanning dependencies of target main +[ 50%] Building CXX object CMakeFiles/main.dir/main.cc.o +[100%] Linking CXX executable main +[100%] Built target main +Built target release +creating problem mesh... +periodic facepairs: 0 +problem dimensions: 100x100x100 +number of elements: 16384 + +initializing matrix free object +initializing finite element space P^1 for PARABOLIC:SCALAR field 'c' +field ' c' DOF : 16641 (Constraint DOF : 0) +initializing finite element space P^1 for PARABOLIC:SCALAR field 'mu' +field 'mu' DOF : 16641 (Constraint DOF : 0) +total DOF : 33282 +initializing parallel::distributed residual and solution vectors +computed mass matrix (using FE space for field: 0) + +solving... + +fields: 2 +fields: 2 +fields: 2 +fields: 2 +Output written to:solution-000000.pvtu + +Energy: 1035 +Energy Components: 0 1035 0 + +Time stepping parameters: timeStep: 0.001 timeFinal: 100 timeIncrements: 100000 + +time increment:10 time: 0.01 +field ' c' [explicit solve]: current solution: 1.011724e+02, current residual:6.122213e+01 +field 'mu' [explicit solve]: current solution: 1.168229e+02, current residual:7.011531e+01 +wall time: 0.000633955s + +time increment:20 time: 0.02 +field ' c' [explicit solve]: current solution: 1.009440e+02, current residual:6.107982e+01 +field 'mu' [explicit solve]: current solution: 8.348559e+01, current residual:5.003455e+01 +wall time: 0.000656843s + +time increment:30 time: 0.03 +field ' c' [explicit solve]: current solution: 1.008016e+02, current residual:6.099146e+01 +field 'mu' [explicit solve]: current solution: 6.987276e+01, current residual:4.186412e+01 +wall time: 0.000668049s + +time increment:40 time: 0.04 +field ' c' [explicit solve]: current solution: 1.006946e+02, current residual:6.092515e+01 +field 'mu' [explicit solve]: current solution: 6.184963e+01, current residual:3.705559e+01 +wall time: 0.000629902s + +time increment:50 time: 0.05 +field ' c' [explicit solve]: current solution: 1.006075e+02, current residual:6.087120e+01 +field 'mu' [explicit solve]: current solution: 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residual:2.656789e+01 +wall time: 0.000642061s + +time increment:100 time: 0.1 +field ' c' [explicit solve]: current solution: 1.003102e+02, current residual:6.068682e+01 +field 'mu' [explicit solve]: current solution: 4.248168e+01, current residual:2.545918e+01 +wall time: 0.000573874s + +time increment:110 time: 0.11 +field ' c' [explicit solve]: current solution: 1.002657e+02, current residual:6.065921e+01 +field 'mu' [explicit solve]: current solution: 4.087260e+01, current residual:2.449676e+01 +wall time: 0.000617981s + +time increment:120 time: 0.12 +field ' c' [explicit solve]: current solution: 1.002242e+02, current residual:6.063348e+01 +field 'mu' [explicit solve]: current solution: 3.945656e+01, current residual:2.364990e+01 +wall time: 0.00058198s + +time increment:130 time: 0.13 +field ' c' [explicit solve]: current solution: 1.001853e+02, current residual:6.060936e+01 +field 'mu' [explicit solve]: current solution: 3.819637e+01, current residual:2.289631e+01 +wall time: 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increment:180 time: 0.18 +field ' c' [explicit solve]: current solution: 1.000200e+02, current residual:6.050671e+01 +field 'mu' [explicit solve]: current solution: 3.345616e+01, current residual:2.006197e+01 +wall time: 0.000618935s + +time increment:190 time: 0.19 +field ' c' [explicit solve]: current solution: 9.999133e+01, current residual:6.048891e+01 +field 'mu' [explicit solve]: current solution: 3.272364e+01, current residual:1.962397e+01 +wall time: 0.000570059s + +time increment:200 time: 0.2 +field ' c' [explicit solve]: current solution: 9.996383e+01, current residual:6.047182e+01 +field 'mu' [explicit solve]: current solution: 3.204222e+01, current residual:1.921651e+01 +wall time: 0.000672102s + +time increment:210 time: 0.21 +field ' c' [explicit solve]: current solution: 9.993738e+01, current residual:6.045539e+01 +field 'mu' [explicit solve]: current solution: 3.140600e+01, current residual:1.883608e+01 +wall time: 0.000658035s + +time increment:220 time: 0.22 +field ' c' [explicit solve]: current solution: 9.991189e+01, current residual:6.043956e+01 +field 'mu' [explicit solve]: current solution: 3.081002e+01, current residual:1.847968e+01 +wall time: 0.000577927s + +time increment:230 time: 0.23 +field ' c' [explicit solve]: current solution: 9.988730e+01, current residual:6.042428e+01 +field 'mu' [explicit solve]: current solution: 3.025005e+01, current residual:1.814481e+01 +wall time: 0.000643015s + +time increment:240 time: 0.24 +field ' c' [explicit solve]: current solution: 9.986353e+01, current residual:6.040950e+01 +field 'mu' [explicit solve]: current solution: 2.972248e+01, current residual:1.782930e+01 +wall time: 0.000568867s + +time increment:250 time: 0.25 +field ' c' [explicit solve]: current solution: 9.984052e+01, current residual:6.039520e+01 +field 'mu' [explicit solve]: current solution: 2.922421e+01, current residual:1.753130e+01 +wall time: 0.000586033s + +time increment:260 time: 0.26 +field ' c' [explicit solve]: current 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[explicit solve]: current solution: 1.620848e+01, current residual:9.726368e+00 +wall time: 0.000706911s + +time increment:1010 time: 1.01 +field ' c' [explicit solve]: current solution: 9.892878e+01, current residual:5.982744e+01 +field 'mu' [explicit solve]: current solution: 1.613874e+01, current residual:9.684387e+00 +wall time: 0.000762939s + +time increment:1020 time: 1.02 +field ' c' [explicit solve]: current solution: 9.892150e+01, current residual:5.982289e+01 +field 'mu' [explicit solve]: current solution: 1.606997e+01, current residual:9.642991e+00 +wall time: 0.00067687s + +time increment:1030 time: 1.03 +field ' c' [explicit solve]: current solution: 9.891427e+01, current residual:5.981838e+01 +field 'mu' [explicit solve]: current solution: 1.600216e+01, current residual:9.602168e+00 +wall time: 0.000734091s + +time increment:1040 time: 1.04 +field ' c' [explicit solve]: current solution: 9.890711e+01, current residual:5.981391e+01 +field 'mu' [explicit solve]: current 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9.810664e+01, current residual:5.931127e+01 +field 'mu' [explicit solve]: current solution: 1.038794e+01, current residual:6.218400e+00 +wall time: 0.000751019s + +time increment:2860 time: 2.86 +field ' c' [explicit solve]: current solution: 9.810370e+01, current residual:5.930941e+01 +field 'mu' [explicit solve]: current solution: 1.037322e+01, current residual:6.209506e+00 +wall time: 0.000699043s + +time increment:2870 time: 2.87 +field ' c' [explicit solve]: current solution: 9.810077e+01, current residual:5.930756e+01 +field 'mu' [explicit solve]: current solution: 1.035857e+01, current residual:6.200658e+00 +wall time: 0.000667095s + +time increment:2880 time: 2.88 +field ' c' [explicit solve]: current solution: 9.809784e+01, current residual:5.930571e+01 +field 'mu' [explicit solve]: current solution: 1.034399e+01, current residual:6.191857e+00 +wall time: 0.000694036s + +time increment:2890 time: 2.89 +field ' c' [explicit solve]: current solution: 9.809493e+01, current 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current solution: 1.021619e+01, current residual:6.114661e+00 +wall time: 0.000674963s + +time increment:2980 time: 2.98 +field ' c' [explicit solve]: current solution: 9.806910e+01, current residual:5.928756e+01 +field 'mu' [explicit solve]: current solution: 1.020234e+01, current residual:6.106301e+00 +wall time: 0.000648975s + +time increment:2990 time: 2.99 +field ' c' [explicit solve]: current solution: 9.806628e+01, current residual:5.928577e+01 +field 'mu' [explicit solve]: current solution: 1.018857e+01, current residual:6.097982e+00 +wall time: 0.000653982s + +time increment:3000 time: 3 +field ' c' [explicit solve]: current solution: 9.806346e+01, current residual:5.928399e+01 +field 'mu' [explicit solve]: current solution: 1.017487e+01, current residual:6.089705e+00 +wall time: 0.000700951s + +time increment:3010 time: 3.01 +field ' c' [explicit solve]: current solution: 9.806065e+01, current residual:5.928222e+01 +field 'mu' [explicit solve]: current solution: 1.016123e+01, 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3.14 +field ' c' [explicit solve]: current solution: 9.802490e+01, current residual:5.925962e+01 +field 'mu' [explicit solve]: current solution: 9.989931e+00, current residual:5.977994e+00 +wall time: 0.000634909s + +time increment:3150 time: 3.15 +field ' c' [explicit solve]: current solution: 9.802221e+01, current residual:5.925792e+01 +field 'mu' [explicit solve]: current solution: 9.977192e+00, current residual:5.970299e+00 +wall time: 0.000585079s + +time increment:3160 time: 3.16 +field ' c' [explicit solve]: current solution: 9.801952e+01, current residual:5.925622e+01 +field 'mu' [explicit solve]: current solution: 9.964513e+00, current residual:5.962640e+00 +wall time: 0.000680923s + +time increment:3170 time: 3.17 +field ' c' [explicit solve]: current solution: 9.801684e+01, current residual:5.925452e+01 +field 'mu' [explicit solve]: current solution: 9.951894e+00, current residual:5.955018e+00 +wall time: 0.000564098s + +time increment:3180 time: 3.18 +field ' c' [explicit 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9.800357e+01, current residual:5.924613e+01 +field 'mu' [explicit solve]: current solution: 9.889671e+00, current residual:5.917432e+00 +wall time: 0.00058198s + +time increment:3230 time: 3.23 +field ' c' [explicit solve]: current solution: 9.800094e+01, current residual:5.924446e+01 +field 'mu' [explicit solve]: current solution: 9.877399e+00, current residual:5.910018e+00 +wall time: 0.000607967s + +time increment:3240 time: 3.24 +field ' c' [explicit solve]: current solution: 9.799831e+01, current residual:5.924280e+01 +field 'mu' [explicit solve]: current solution: 9.865182e+00, current residual:5.902639e+00 +wall time: 0.000570059s + +time increment:3250 time: 3.25 +field ' c' [explicit solve]: current solution: 9.799569e+01, current residual:5.924115e+01 +field 'mu' [explicit solve]: current solution: 9.853021e+00, current residual:5.895293e+00 +wall time: 0.000567913s + +time increment:3260 time: 3.26 +field ' c' [explicit solve]: current solution: 9.799308e+01, current 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'mu' [explicit solve]: current solution: 9.793034e+00, current residual:5.859058e+00 +wall time: 0.000689983s + +time increment:3310 time: 3.31 +field ' c' [explicit solve]: current solution: 9.798014e+01, current residual:5.923131e+01 +field 'mu' [explicit solve]: current solution: 9.781197e+00, current residual:5.851908e+00 +wall time: 0.00069499s + +time increment:3320 time: 3.32 +field ' c' [explicit solve]: current solution: 9.797758e+01, current residual:5.922968e+01 +field 'mu' [explicit solve]: current solution: 9.769413e+00, current residual:5.844790e+00 +wall time: 0.000667095s + +time increment:3330 time: 3.33 +field ' c' [explicit solve]: current solution: 9.797502e+01, current residual:5.922806e+01 +field 'mu' [explicit solve]: current solution: 9.757681e+00, current residual:5.837703e+00 +wall time: 0.000666857s + +time increment:3340 time: 3.34 +field ' c' [explicit solve]: current solution: 9.797246e+01, current residual:5.922645e+01 +field 'mu' [explicit solve]: current solution: 9.746001e+00, current residual:5.830648e+00 +wall time: 0.000702143s + +time increment:3350 time: 3.35 +field ' c' [explicit solve]: current solution: 9.796992e+01, current residual:5.922484e+01 +field 'mu' [explicit solve]: current solution: 9.734372e+00, current residual:5.823624e+00 +wall time: 0.000784874s + +time increment:3360 time: 3.36 +field ' c' [explicit solve]: current solution: 9.796738e+01, current residual:5.922323e+01 +field 'mu' [explicit solve]: current solution: 9.722794e+00, current residual:5.816630e+00 +wall time: 0.000738144s + +time increment:3370 time: 3.37 +field ' c' [explicit solve]: current solution: 9.796485e+01, current residual:5.922163e+01 +field 'mu' [explicit solve]: current solution: 9.711266e+00, current residual:5.809667e+00 +wall time: 0.000710964s + +time increment:3380 time: 3.38 +field ' c' [explicit solve]: current solution: 9.796232e+01, current residual:5.922003e+01 +field 'mu' [explicit solve]: current solution: 9.699789e+00, current residual:5.802735e+00 +wall time: 0.000683069s + +time increment:3390 time: 3.39 +field ' c' [explicit solve]: current solution: 9.795981e+01, current residual:5.921844e+01 +field 'mu' [explicit solve]: current solution: 9.688361e+00, current residual:5.795832e+00 +wall time: 0.000622988s + +time increment:3400 time: 3.4 +field ' c' [explicit solve]: current solution: 9.795730e+01, current residual:5.921685e+01 +field 'mu' [explicit solve]: current solution: 9.676983e+00, current residual:5.788959e+00 +wall time: 0.000682831s + +time increment:3410 time: 3.41 +field ' c' [explicit solve]: current solution: 9.795479e+01, current residual:5.921526e+01 +field 'mu' [explicit solve]: current solution: 9.665653e+00, current residual:5.782116e+00 +wall time: 0.000721931s + +time increment:3420 time: 3.42 +field ' c' [explicit solve]: current solution: 9.795229e+01, current residual:5.921368e+01 +field 'mu' [explicit solve]: current solution: 9.654372e+00, current residual:5.775302e+00 +wall time: 0.00065279s + +time increment:3430 time: 3.43 +field ' c' [explicit solve]: current solution: 9.794980e+01, current residual:5.921211e+01 +field 'mu' [explicit solve]: current solution: 9.643140e+00, current residual:5.768518e+00 +wall time: 0.000780106s + +time increment:3440 time: 3.44 +field ' c' [explicit solve]: current solution: 9.794732e+01, current residual:5.921053e+01 +field 'mu' [explicit solve]: current solution: 9.631955e+00, current residual:5.761762e+00 +wall time: 0.00067091s + +time increment:3450 time: 3.45 +field ' c' [explicit solve]: current solution: 9.794484e+01, current residual:5.920897e+01 +field 'mu' [explicit solve]: current solution: 9.620817e+00, current residual:5.755035e+00 +wall time: 0.000854015s + +time increment:3460 time: 3.46 +field ' c' [explicit solve]: current solution: 9.794237e+01, current residual:5.920740e+01 +field 'mu' [explicit solve]: current solution: 9.609727e+00, current residual:5.748336e+00 +wall time: 0.000714064s + +time increment:3470 time: 3.47 +field ' c' [explicit solve]: current solution: 9.793991e+01, current residual:5.920584e+01 +field 'mu' [explicit solve]: current solution: 9.598683e+00, current residual:5.741666e+00 +wall time: 0.000595093s + +time increment:3480 time: 3.48 +field ' c' [explicit solve]: current solution: 9.793745e+01, current residual:5.920429e+01 +field 'mu' [explicit solve]: current solution: 9.587685e+00, current residual:5.735024e+00 +wall time: 0.000636816s + +time increment:3490 time: 3.49 +field ' c' [explicit solve]: current solution: 9.793500e+01, current residual:5.920273e+01 +field 'mu' [explicit solve]: current solution: 9.576734e+00, current residual:5.728409e+00 +wall time: 0.000734091s + +time increment:3500 time: 3.5 +field ' c' [explicit solve]: current solution: 9.793256e+01, current residual:5.920119e+01 +field 'mu' [explicit solve]: current solution: 9.565828e+00, current residual:5.721823e+00 +wall time: 0.000638962s + +time increment:3510 time: 3.51 +field ' c' [explicit solve]: current solution: 9.793012e+01, current residual:5.919964e+01 +field 'mu' [explicit solve]: current solution: 9.554968e+00, current residual:5.715263e+00 +wall time: 0.000647068s + +time increment:3520 time: 3.52 +field ' c' [explicit solve]: current solution: 9.792769e+01, current residual:5.919810e+01 +field 'mu' [explicit solve]: current solution: 9.544152e+00, current residual:5.708731e+00 +wall time: 0.000663996s + +time increment:3530 time: 3.53 +field ' c' [explicit solve]: current solution: 9.792526e+01, current residual:5.919657e+01 +field 'mu' [explicit solve]: current solution: 9.533381e+00, current residual:5.702226e+00 +wall time: 0.000653028s + +time increment:3540 time: 3.54 +field ' c' [explicit solve]: current solution: 9.792284e+01, current residual:5.919504e+01 +field 'mu' [explicit solve]: current solution: 9.522655e+00, current residual:5.695747e+00 +wall time: 0.000666142s + +time increment:3550 time: 3.55 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current solution: 9.791084e+01, current residual:5.918743e+01 +field 'mu' [explicit solve]: current solution: 9.469674e+00, current residual:5.663751e+00 +wall time: 0.000658989s + +time increment:3600 time: 3.6 +field ' c' [explicit solve]: current solution: 9.790846e+01, current residual:5.918593e+01 +field 'mu' [explicit solve]: current solution: 9.459206e+00, current residual:5.657429e+00 +wall time: 0.000628948s + +time increment:3610 time: 3.61 +field ' c' [explicit solve]: current solution: 9.790608e+01, current residual:5.918442e+01 +field 'mu' [explicit solve]: current solution: 9.448780e+00, current residual:5.651133e+00 +wall time: 0.000639915s + +time increment:3620 time: 3.62 +field ' c' [explicit solve]: current solution: 9.790372e+01, current residual:5.918292e+01 +field 'mu' [explicit solve]: current solution: 9.438396e+00, current residual:5.644862e+00 +wall time: 0.000677109s + +time increment:3630 time: 3.63 +field ' c' [explicit solve]: current solution: 9.790135e+01, current residual:5.918142e+01 +field 'mu' [explicit solve]: current solution: 9.428053e+00, current residual:5.638616e+00 +wall time: 0.00067687s + +time increment:3640 time: 3.64 +field ' c' [explicit solve]: current solution: 9.789899e+01, current residual:5.917993e+01 +field 'mu' [explicit solve]: current solution: 9.417752e+00, current residual:5.632395e+00 +wall time: 0.000740051s + +time increment:3650 time: 3.65 +field ' c' [explicit solve]: current solution: 9.789664e+01, current residual:5.917844e+01 +field 'mu' [explicit solve]: current solution: 9.407491e+00, current residual:5.626199e+00 +wall time: 0.000725985s + +time increment:3660 time: 3.66 +field ' c' [explicit solve]: current solution: 9.789430e+01, current residual:5.917696e+01 +field 'mu' [explicit solve]: current solution: 9.397271e+00, current residual:5.620027e+00 +wall time: 0.000561953s + +time increment:3670 time: 3.67 +field ' c' [explicit solve]: current solution: 9.789196e+01, current residual:5.917547e+01 +field 'mu' [explicit solve]: current solution: 9.387091e+00, current residual:5.613880e+00 +wall time: 0.000558138s + +time increment:3680 time: 3.68 +field ' c' [explicit solve]: current solution: 9.788963e+01, current residual:5.917399e+01 +field 'mu' [explicit solve]: current solution: 9.376951e+00, current residual:5.607757e+00 +wall time: 0.000674009s + +time increment:3690 time: 3.69 +field ' c' [explicit solve]: current solution: 9.788730e+01, current residual:5.917252e+01 +field 'mu' [explicit solve]: current solution: 9.366850e+00, current residual:5.601658e+00 +wall time: 0.000576973s + +time increment:3700 time: 3.7 +field ' c' [explicit solve]: current solution: 9.788498e+01, current residual:5.917105e+01 +field 'mu' [explicit solve]: current solution: 9.356789e+00, current residual:5.595582e+00 +wall time: 0.000653028s + +time increment:3710 time: 3.71 +field ' c' [explicit solve]: current solution: 9.788266e+01, current residual:5.916958e+01 +field 'mu' [explicit solve]: current solution: 9.346767e+00, current residual:5.589531e+00 +wall time: 0.000720024s + +time increment:3720 time: 3.72 +field ' c' [explicit solve]: current solution: 9.788035e+01, current residual:5.916812e+01 +field 'mu' [explicit solve]: current solution: 9.336784e+00, current residual:5.583503e+00 +wall time: 0.00067687s + +time increment:3730 time: 3.73 +field ' c' [explicit solve]: current solution: 9.787805e+01, current residual:5.916666e+01 +field 'mu' [explicit solve]: current solution: 9.326840e+00, current residual:5.577498e+00 +wall time: 0.000656843s + +time increment:3740 time: 3.74 +field ' c' [explicit solve]: current solution: 9.787575e+01, current residual:5.916520e+01 +field 'mu' [explicit solve]: current solution: 9.316933e+00, current residual:5.571517e+00 +wall time: 0.000751019s + +time increment:3750 time: 3.75 +field ' c' [explicit solve]: current solution: 9.787346e+01, current residual:5.916375e+01 +field 'mu' [explicit solve]: current solution: 9.307065e+00, current residual:5.565558e+00 +wall time: 0.000620127s + +time increment:3760 time: 3.76 +field ' c' [explicit solve]: current solution: 9.787117e+01, current residual:5.916230e+01 +field 'mu' [explicit solve]: current solution: 9.297234e+00, current residual:5.559623e+00 +wall time: 0.000593901s + +time increment:3770 time: 3.77 +field ' c' [explicit solve]: current solution: 9.786889e+01, current residual:5.916085e+01 +field 'mu' [explicit solve]: current solution: 9.287441e+00, current residual:5.553710e+00 +wall time: 0.000626087s + +time increment:3780 time: 3.78 +field ' c' [explicit solve]: current solution: 9.786661e+01, current residual:5.915941e+01 +field 'mu' [explicit solve]: current solution: 9.277685e+00, current residual:5.547820e+00 +wall time: 0.000566006s + +time increment:3790 time: 3.79 +field ' c' [explicit solve]: current solution: 9.786434e+01, current residual:5.915797e+01 +field 'mu' [explicit solve]: current solution: 9.267966e+00, current residual:5.541952e+00 +wall time: 0.000623941s + +time increment:3800 time: 3.8 +field ' c' [explicit solve]: current solution: 9.786208e+01, current residual:5.915654e+01 +field 'mu' [explicit solve]: current solution: 9.258284e+00, current residual:5.536106e+00 +wall time: 0.000659943s + +time increment:3810 time: 3.81 +field ' c' [explicit solve]: current solution: 9.785982e+01, current residual:5.915510e+01 +field 'mu' [explicit solve]: current solution: 9.248638e+00, current residual:5.530283e+00 +wall time: 0.00058198s + +time increment:3820 time: 3.82 +field ' c' [explicit solve]: current solution: 9.785756e+01, current residual:5.915368e+01 +field 'mu' [explicit solve]: current solution: 9.239028e+00, current residual:5.524481e+00 +wall time: 0.000570059s + +time increment:3830 time: 3.83 +field ' c' [explicit solve]: current solution: 9.785532e+01, current residual:5.915225e+01 +field 'mu' [explicit solve]: current solution: 9.229454e+00, current residual:5.518701e+00 +wall time: 0.000633001s + +time increment:3840 time: 3.84 +field ' c' [explicit solve]: current solution: 9.785307e+01, current residual:5.915083e+01 +field 'mu' [explicit solve]: current solution: 9.219916e+00, current residual:5.512943e+00 +wall time: 0.000565052s + +time increment:3850 time: 3.85 +field ' c' [explicit solve]: current solution: 9.785084e+01, current residual:5.914941e+01 +field 'mu' [explicit solve]: current solution: 9.210413e+00, current residual:5.507206e+00 +wall time: 0.000579834s + +time increment:3860 time: 3.86 +field ' c' [explicit solve]: current solution: 9.784860e+01, current residual:5.914800e+01 +field 'mu' [explicit solve]: current solution: 9.200945e+00, current residual:5.501491e+00 +wall time: 0.000613213s + +time increment:3870 time: 3.87 +field ' c' [explicit solve]: current solution: 9.784638e+01, current residual:5.914658e+01 +field 'mu' [explicit solve]: current solution: 9.191513e+00, current residual:5.495797e+00 +wall time: 0.000575066s + +time increment:3880 time: 3.88 +field ' c' [explicit solve]: current solution: 9.784416e+01, current residual:5.914518e+01 +field 'mu' [explicit solve]: current solution: 9.182115e+00, current residual:5.490124e+00 +wall time: 0.00062108s + +time increment:3890 time: 3.89 +field ' c' [explicit solve]: current solution: 9.784194e+01, current residual:5.914377e+01 +field 'mu' [explicit solve]: current solution: 9.172752e+00, current residual:5.484471e+00 +wall time: 0.000565052s + +time increment:3900 time: 3.9 +field ' c' [explicit solve]: current solution: 9.783973e+01, current residual:5.914237e+01 +field 'mu' [explicit solve]: current solution: 9.163423e+00, current residual:5.478840e+00 +wall time: 0.000985861s + +time increment:3910 time: 3.91 +field ' c' [explicit solve]: current solution: 9.783753e+01, current residual:5.914097e+01 +field 'mu' [explicit solve]: current solution: 9.154127e+00, current residual:5.473229e+00 +wall time: 0.000689983s + +time increment:3920 time: 3.92 +field ' c' [explicit solve]: current solution: 9.783533e+01, current residual:5.913958e+01 +field 'mu' [explicit solve]: current solution: 9.144866e+00, current residual:5.467639e+00 +wall time: 0.000673056s + +time increment:3930 time: 3.93 +field ' c' [explicit solve]: current solution: 9.783313e+01, current residual:5.913818e+01 +field 'mu' [explicit solve]: current solution: 9.135638e+00, current residual:5.462069e+00 +wall time: 0.000710964s + +time increment:3940 time: 3.94 +field ' c' [explicit solve]: current solution: 9.783094e+01, current residual:5.913680e+01 +field 'mu' [explicit solve]: current solution: 9.126444e+00, current residual:5.456520e+00 +wall time: 0.000705004s + +time increment:3950 time: 3.95 +field ' c' [explicit solve]: current solution: 9.782876e+01, current residual:5.913541e+01 +field 'mu' [explicit solve]: current solution: 9.117283e+00, current residual:5.450990e+00 +wall time: 0.000722885s + +time increment:3960 time: 3.96 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current solution: 9.781792e+01, current residual:5.912853e+01 +field 'mu' [explicit solve]: current solution: 9.071966e+00, current residual:5.423640e+00 +wall time: 0.000579119s + +time increment:4010 time: 4.01 +field ' c' [explicit solve]: current solution: 9.781576e+01, current residual:5.912717e+01 +field 'mu' [explicit solve]: current solution: 9.062999e+00, current residual:5.418228e+00 +wall time: 0.000594854s + +time increment:4020 time: 4.02 +field ' c' [explicit solve]: current solution: 9.781361e+01, current residual:5.912581e+01 +field 'mu' [explicit solve]: current solution: 9.054063e+00, current residual:5.412836e+00 +wall time: 0.000626802s + +time increment:4030 time: 4.03 +field ' c' [explicit solve]: current solution: 9.781147e+01, current residual:5.912445e+01 +field 'mu' [explicit solve]: current solution: 9.045159e+00, current residual:5.407463e+00 +wall time: 0.00059104s + +time increment:4040 time: 4.04 +field ' c' [explicit solve]: current solution: 9.780933e+01, current residual:5.912309e+01 +field 'mu' [explicit solve]: current solution: 9.036287e+00, current residual:5.402108e+00 +wall time: 0.000669956s + +time increment:4050 time: 4.05 +field ' c' [explicit solve]: current solution: 9.780720e+01, current residual:5.912174e+01 +field 'mu' [explicit solve]: current solution: 9.027445e+00, current residual:5.396773e+00 +wall time: 0.000638008s + +time increment:4060 time: 4.06 +field ' c' [explicit solve]: current solution: 9.780507e+01, current residual:5.912039e+01 +field 'mu' [explicit solve]: current solution: 9.018634e+00, current residual:5.391456e+00 +wall time: 0.000688076s + +time increment:4070 time: 4.07 +field ' c' [explicit solve]: current solution: 9.780295e+01, current residual:5.911904e+01 +field 'mu' [explicit solve]: current solution: 9.009854e+00, current residual:5.386158e+00 +wall time: 0.000668049s + +time increment:4080 time: 4.08 +field ' c' [explicit solve]: current solution: 9.780083e+01, current residual:5.911769e+01 +field 'mu' [explicit solve]: current solution: 9.001104e+00, current residual:5.380878e+00 +wall time: 0.000796795s + +time increment:4090 time: 4.09 +field ' c' [explicit solve]: current solution: 9.779872e+01, current residual:5.911635e+01 +field 'mu' [explicit solve]: current solution: 8.992384e+00, current residual:5.375617e+00 +wall time: 0.000649929s + +time increment:4100 time: 4.1 +field ' c' [explicit solve]: current solution: 9.779661e+01, current residual:5.911501e+01 +field 'mu' [explicit solve]: current solution: 8.983695e+00, current residual:5.370374e+00 +wall time: 0.000778913s + +time increment:4110 time: 4.11 +field ' c' [explicit solve]: current solution: 9.779450e+01, current residual:5.911368e+01 +field 'mu' [explicit solve]: current solution: 8.975035e+00, current residual:5.365149e+00 +wall time: 0.000566006s + +time increment:4120 time: 4.12 +field ' c' [explicit solve]: current solution: 9.779240e+01, current residual:5.911235e+01 +field 'mu' [explicit solve]: current solution: 8.966405e+00, current residual:5.359942e+00 +wall time: 0.000678062s + +time increment:4130 time: 4.13 +field ' c' [explicit solve]: current solution: 9.779031e+01, current residual:5.911102e+01 +field 'mu' [explicit solve]: current solution: 8.957805e+00, current residual:5.354753e+00 +wall time: 0.00064683s + +time increment:4140 time: 4.14 +field ' c' [explicit solve]: current solution: 9.778822e+01, current residual:5.910969e+01 +field 'mu' [explicit solve]: current solution: 8.949234e+00, current residual:5.349582e+00 +wall time: 0.00060606s + +time increment:4150 time: 4.15 +field ' c' [explicit solve]: current solution: 9.778613e+01, current residual:5.910837e+01 +field 'mu' [explicit solve]: current solution: 8.940692e+00, current residual:5.344429e+00 +wall time: 0.000706911s + +time increment:4160 time: 4.16 +field ' c' [explicit solve]: current solution: 9.778405e+01, current residual:5.910705e+01 +field 'mu' [explicit solve]: current solution: 8.932178e+00, current residual:5.339293e+00 +wall time: 0.000614882s + +time increment:4170 time: 4.17 +field ' c' [explicit solve]: current solution: 9.778198e+01, current residual:5.910573e+01 +field 'mu' [explicit solve]: current solution: 8.923694e+00, current residual:5.334174e+00 +wall time: 0.000705004s + +time increment:4180 time: 4.18 +field ' c' [explicit solve]: current solution: 9.777991e+01, current residual:5.910442e+01 +field 'mu' [explicit solve]: current solution: 8.915238e+00, current residual:5.329073e+00 +wall time: 0.000700951s + +time increment:4190 time: 4.19 +field ' c' [explicit solve]: current solution: 9.777784e+01, current residual:5.910311e+01 +field 'mu' [explicit solve]: current solution: 8.906811e+00, current residual:5.323990e+00 +wall time: 0.000693798s + +time increment:4200 time: 4.2 +field ' c' [explicit solve]: current solution: 9.777578e+01, current residual:5.910180e+01 +field 'mu' [explicit solve]: current solution: 8.898412e+00, current residual:5.318923e+00 +wall time: 0.000725985s + +time increment:4210 time: 4.21 +field ' c' [explicit solve]: current solution: 9.777372e+01, current residual:5.910049e+01 +field 'mu' [explicit solve]: current solution: 8.890041e+00, current residual:5.313874e+00 +wall time: 0.000667095s + +time increment:4220 time: 4.22 +field ' c' [explicit solve]: current solution: 9.777167e+01, current residual:5.909919e+01 +field 'mu' [explicit solve]: current solution: 8.881698e+00, current residual:5.308841e+00 +wall time: 0.000657082s + +time increment:4230 time: 4.23 +field ' c' [explicit solve]: current solution: 9.776962e+01, current residual:5.909789e+01 +field 'mu' [explicit solve]: current solution: 8.873382e+00, current residual:5.303825e+00 +wall time: 0.000661135s + +time increment:4240 time: 4.24 +field ' c' [explicit solve]: current solution: 9.776758e+01, current residual:5.909659e+01 +field 'mu' [explicit solve]: current solution: 8.865094e+00, current residual:5.298826e+00 +wall time: 0.000643969s + +time increment:4250 time: 4.25 +field ' c' [explicit solve]: current solution: 9.776554e+01, current residual:5.909530e+01 +field 'mu' [explicit solve]: current solution: 8.856834e+00, current residual:5.293844e+00 +wall time: 0.000664949s + +time increment:4260 time: 4.26 +field ' c' [explicit solve]: current solution: 9.776351e+01, current residual:5.909401e+01 +field 'mu' [explicit solve]: current solution: 8.848600e+00, current residual:5.288878e+00 +wall time: 0.000761032s + +time increment:4270 time: 4.27 +field ' c' [explicit solve]: current solution: 9.776148e+01, current residual:5.909272e+01 +field 'mu' [explicit solve]: current solution: 8.840394e+00, current residual:5.283929e+00 +wall time: 0.000656843s + +time increment:4280 time: 4.28 +field ' c' [explicit solve]: current solution: 9.775945e+01, current residual:5.909143e+01 +field 'mu' [explicit solve]: current solution: 8.832215e+00, current residual:5.278996e+00 +wall time: 0.000642061s + +time increment:4290 time: 4.29 +field ' c' [explicit solve]: current solution: 9.775743e+01, current residual:5.909015e+01 +field 'mu' [explicit solve]: current solution: 8.824062e+00, current residual:5.274079e+00 +wall time: 0.000636101s + +time increment:4300 time: 4.3 +field ' c' [explicit solve]: current solution: 9.775542e+01, current residual:5.908887e+01 +field 'mu' [explicit solve]: current solution: 8.815936e+00, current residual:5.269179e+00 +wall time: 0.000731945s + +time increment:4310 time: 4.31 +field ' c' [explicit solve]: current solution: 9.775340e+01, current residual:5.908759e+01 +field 'mu' [explicit solve]: current solution: 8.807836e+00, current residual:5.264294e+00 +wall time: 0.000648022s + +time increment:4320 time: 4.32 +field ' c' [explicit solve]: current solution: 9.775140e+01, current residual:5.908632e+01 +field 'mu' [explicit solve]: current solution: 8.799763e+00, current residual:5.259426e+00 +wall time: 0.000637054s + +time increment:4330 time: 4.33 +field ' c' [explicit solve]: current solution: 9.774939e+01, current residual:5.908505e+01 +field 'mu' [explicit solve]: current solution: 8.791716e+00, current residual:5.254573e+00 +wall time: 0.000718117s + +time increment:4340 time: 4.34 +field ' c' [explicit solve]: current solution: 9.774739e+01, current residual:5.908378e+01 +field 'mu' [explicit solve]: current solution: 8.783694e+00, current residual:5.249737e+00 +wall time: 0.000745058s + +time increment:4350 time: 4.35 +field ' c' [explicit solve]: current solution: 9.774540e+01, current residual:5.908251e+01 +field 'mu' [explicit solve]: current solution: 8.775699e+00, current residual:5.244915e+00 +wall time: 0.000663042s + +time increment:4360 time: 4.36 +field ' c' [explicit solve]: current solution: 9.774341e+01, current residual:5.908125e+01 +field 'mu' [explicit solve]: current solution: 8.767729e+00, current residual:5.240110e+00 +wall time: 0.000697136s + +time increment:4370 time: 4.37 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current solution: 9.773353e+01, current residual:5.907497e+01 +field 'mu' [explicit solve]: current solution: 8.728259e+00, current residual:5.216313e+00 +wall time: 0.00071311s + +time increment:4420 time: 4.42 +field ' c' [explicit solve]: current solution: 9.773156e+01, current residual:5.907372e+01 +field 'mu' [explicit solve]: current solution: 8.720440e+00, current residual:5.211599e+00 +wall time: 0.000696898s + +time increment:4430 time: 4.43 +field ' c' [explicit solve]: current solution: 9.772960e+01, current residual:5.907248e+01 +field 'mu' [explicit solve]: current solution: 8.712645e+00, current residual:5.206901e+00 +wall time: 0.000771999s + +time increment:4440 time: 4.44 +field ' c' [explicit solve]: current solution: 9.772765e+01, current residual:5.907123e+01 +field 'mu' [explicit solve]: current solution: 8.704875e+00, current residual:5.202217e+00 +wall time: 0.000748873s + +time increment:4450 time: 4.45 +field ' c' [explicit solve]: current solution: 9.772569e+01, current residual:5.906999e+01 +field 'mu' [explicit solve]: current solution: 8.697130e+00, current residual:5.197548e+00 +wall time: 0.000691891s + +time increment:4460 time: 4.46 +field ' c' [explicit solve]: current solution: 9.772375e+01, current residual:5.906876e+01 +field 'mu' [explicit solve]: current solution: 8.689408e+00, current residual:5.192893e+00 +wall time: 0.000628948s + +time increment:4470 time: 4.47 +field ' c' [explicit solve]: current solution: 9.772180e+01, current residual:5.906752e+01 +field 'mu' [explicit solve]: current solution: 8.681711e+00, current residual:5.188254e+00 +wall time: 0.000659943s + +time increment:4480 time: 4.48 +field ' c' [explicit solve]: current solution: 9.771986e+01, current residual:5.906629e+01 +field 'mu' [explicit solve]: current solution: 8.674037e+00, current residual:5.183629e+00 +wall time: 0.000622034s + +time increment:4490 time: 4.49 +field ' c' [explicit solve]: current solution: 9.771793e+01, current residual:5.906506e+01 +field 'mu' [explicit solve]: current solution: 8.666388e+00, current residual:5.179018e+00 +wall time: 0.000749826s + +time increment:4500 time: 4.5 +field ' c' [explicit solve]: current solution: 9.771600e+01, current residual:5.906384e+01 +field 'mu' [explicit solve]: current solution: 8.658762e+00, current residual:5.174422e+00 +wall time: 0.00071311s + +time increment:4510 time: 4.51 +field ' c' [explicit solve]: current solution: 9.771407e+01, current residual:5.906261e+01 +field 'mu' [explicit solve]: current solution: 8.651159e+00, current residual:5.169840e+00 +wall time: 0.00069499s + +time increment:4520 time: 4.52 +field ' c' [explicit solve]: current solution: 9.771215e+01, current residual:5.906139e+01 +field 'mu' [explicit solve]: current solution: 8.643580e+00, current residual:5.165272e+00 +wall time: 0.000693083s + +time increment:4530 time: 4.53 +field ' c' [explicit solve]: current solution: 9.771023e+01, current residual:5.906017e+01 +field 'mu' [explicit solve]: current solution: 8.636024e+00, current residual:5.160719e+00 +wall time: 0.000559092s + +time increment:4540 time: 4.54 +field ' c' [explicit solve]: current solution: 9.770831e+01, current residual:5.905895e+01 +field 'mu' [explicit solve]: current solution: 8.628492e+00, current residual:5.156179e+00 +wall time: 0.000627995s + +time increment:4550 time: 4.55 +field ' c' [explicit solve]: current solution: 9.770640e+01, current residual:5.905774e+01 +field 'mu' [explicit solve]: current solution: 8.620982e+00, current residual:5.151654e+00 +wall time: 0.000627041s + +time increment:4560 time: 4.56 +field ' c' [explicit solve]: current solution: 9.770450e+01, current residual:5.905653e+01 +field 'mu' [explicit solve]: current solution: 8.613495e+00, current residual:5.147143e+00 +wall time: 0.000631809s + +time increment:4570 time: 4.57 +field ' c' [explicit solve]: current solution: 9.770259e+01, current residual:5.905532e+01 +field 'mu' [explicit solve]: current solution: 8.606032e+00, current residual:5.142645e+00 +wall time: 0.000733852s + +time increment:4580 time: 4.58 +field ' c' [explicit solve]: current solution: 9.770070e+01, current residual:5.905411e+01 +field 'mu' [explicit solve]: current solution: 8.598590e+00, current residual:5.138161e+00 +wall time: 0.000571966s + +time increment:4590 time: 4.59 +field ' c' [explicit solve]: current solution: 9.769880e+01, current residual:5.905291e+01 +field 'mu' [explicit solve]: current solution: 8.591171e+00, current residual:5.133691e+00 +wall time: 0.000604868s + +time increment:4600 time: 4.6 +field ' c' [explicit solve]: current solution: 9.769691e+01, current residual:5.905171e+01 +field 'mu' [explicit solve]: current solution: 8.583775e+00, current residual:5.129235e+00 +wall time: 0.000736952s + +time increment:4610 time: 4.61 +field ' c' [explicit solve]: current solution: 9.769502e+01, current residual:5.905051e+01 +field 'mu' [explicit solve]: current solution: 8.576401e+00, current residual:5.124792e+00 +wall time: 0.000566006s + +time increment:4620 time: 4.62 +field ' c' [explicit solve]: current solution: 9.769314e+01, current residual:5.904931e+01 +field 'mu' [explicit solve]: current solution: 8.569049e+00, current residual:5.120363e+00 +wall time: 0.00056386s + +time increment:4630 time: 4.63 +field ' c' [explicit solve]: current solution: 9.769126e+01, current residual:5.904812e+01 +field 'mu' [explicit solve]: current solution: 8.561719e+00, current residual:5.115947e+00 +wall time: 0.000683069s + +time increment:4640 time: 4.64 +field ' c' [explicit solve]: current solution: 9.768938e+01, current residual:5.904692e+01 +field 'mu' [explicit solve]: current solution: 8.554411e+00, current residual:5.111544e+00 +wall time: 0.000594139s + +time increment:4650 time: 4.65 +field ' c' [explicit solve]: current solution: 9.768751e+01, current residual:5.904573e+01 +field 'mu' [explicit solve]: current solution: 8.547125e+00, current residual:5.107155e+00 +wall time: 0.000560045s + +time increment:4660 time: 4.66 +field ' c' [explicit solve]: current solution: 9.768565e+01, current residual:5.904455e+01 +field 'mu' [explicit solve]: current solution: 8.539860e+00, current residual:5.102779e+00 +wall time: 0.000583887s + +time increment:4670 time: 4.67 +field ' c' [explicit solve]: current solution: 9.768378e+01, current residual:5.904336e+01 +field 'mu' [explicit solve]: current solution: 8.532617e+00, current residual:5.098416e+00 +wall time: 0.000602961s + +time increment:4680 time: 4.68 +field ' c' [explicit solve]: current solution: 9.768192e+01, current residual:5.904218e+01 +field 'mu' [explicit solve]: current solution: 8.525396e+00, current residual:5.094066e+00 +wall time: 0.000636101s + +time increment:4690 time: 4.69 +field ' c' [explicit solve]: current solution: 9.768007e+01, current residual:5.904100e+01 +field 'mu' [explicit solve]: current solution: 8.518195e+00, current residual:5.089729e+00 +wall time: 0.000643969s + +time increment:4700 time: 4.7 +field ' c' [explicit solve]: current solution: 9.767821e+01, current residual:5.903982e+01 +field 'mu' [explicit solve]: current solution: 8.511016e+00, current residual:5.085405e+00 +wall time: 0.000607967s + +time increment:4710 time: 4.71 +field ' c' [explicit solve]: current solution: 9.767636e+01, current residual:5.903865e+01 +field 'mu' [explicit solve]: current solution: 8.503859e+00, current residual:5.081094e+00 +wall time: 0.000569105s + +time increment:4720 time: 4.72 +field ' c' [explicit solve]: current solution: 9.767452e+01, current residual:5.903748e+01 +field 'mu' [explicit solve]: current solution: 8.496722e+00, current residual:5.076796e+00 +wall time: 0.000652075s + +time increment:4730 time: 4.73 +field ' c' [explicit solve]: current solution: 9.767268e+01, current residual:5.903630e+01 +field 'mu' [explicit solve]: current solution: 8.489606e+00, current residual:5.072510e+00 +wall time: 0.000628948s + +time increment:4740 time: 4.74 +field ' c' [explicit solve]: current solution: 9.767084e+01, current residual:5.903514e+01 +field 'mu' [explicit solve]: current solution: 8.482511e+00, current residual:5.068238e+00 +wall time: 0.000571012s + +time increment:4750 time: 4.75 +field ' c' [explicit solve]: current solution: 9.766901e+01, current residual:5.903397e+01 +field 'mu' [explicit solve]: current solution: 8.475436e+00, current residual:5.063977e+00 +wall time: 0.000649929s + +time increment:4760 time: 4.76 +field ' c' [explicit solve]: current solution: 9.766718e+01, current residual:5.903281e+01 +field 'mu' [explicit solve]: current solution: 8.468382e+00, current residual:5.059730e+00 +wall time: 0.000631094s + +time increment:4770 time: 4.77 +field ' c' [explicit solve]: current solution: 9.766535e+01, current residual:5.903165e+01 +field 'mu' [explicit solve]: current solution: 8.461349e+00, current residual:5.055494e+00 +wall time: 0.000568867s + +time increment:4780 time: 4.78 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current solution: 9.765627e+01, current residual:5.902587e+01 +field 'mu' [explicit solve]: current solution: 8.426484e+00, current residual:5.034503e+00 +wall time: 0.000563145s + +time increment:4830 time: 4.83 +field ' c' [explicit solve]: current solution: 9.765447e+01, current residual:5.902473e+01 +field 'mu' [explicit solve]: current solution: 8.419571e+00, current residual:5.030341e+00 +wall time: 0.000567913s + +time increment:4840 time: 4.84 +field ' c' [explicit solve]: current solution: 9.765267e+01, current residual:5.902358e+01 +field 'mu' [explicit solve]: current solution: 8.412678e+00, current residual:5.026191e+00 +wall time: 0.000584126s + +time increment:4850 time: 4.85 +field ' c' [explicit solve]: current solution: 9.765087e+01, current residual:5.902244e+01 +field 'mu' [explicit solve]: current solution: 8.405805e+00, current residual:5.022054e+00 +wall time: 0.000746965s + +time increment:4860 time: 4.86 +field ' c' [explicit solve]: current solution: 9.764908e+01, current residual:5.902130e+01 +field 'mu' [explicit solve]: current solution: 8.398951e+00, current residual:5.017928e+00 +wall time: 0.000649929s + +time increment:4870 time: 4.87 +field ' c' [explicit solve]: current solution: 9.764729e+01, current residual:5.902016e+01 +field 'mu' [explicit solve]: current solution: 8.392116e+00, current residual:5.013814e+00 +wall time: 0.000632048s + +time increment:4880 time: 4.88 +field ' c' [explicit solve]: current solution: 9.764550e+01, current residual:5.901902e+01 +field 'mu' [explicit solve]: current solution: 8.385301e+00, current residual:5.009712e+00 +wall time: 0.000692844s + +time increment:4890 time: 4.89 +field ' c' [explicit solve]: current solution: 9.764372e+01, current residual:5.901789e+01 +field 'mu' [explicit solve]: current solution: 8.378505e+00, current residual:5.005621e+00 +wall time: 0.000693083s + +time increment:4900 time: 4.9 +field ' c' [explicit solve]: current solution: 9.764194e+01, current residual:5.901676e+01 +field 'mu' [explicit solve]: current solution: 8.371728e+00, current residual:5.001543e+00 +wall time: 0.000694036s + +time increment:4910 time: 4.91 +field ' c' [explicit solve]: current solution: 9.764016e+01, current residual:5.901563e+01 +field 'mu' [explicit solve]: current solution: 8.364970e+00, current residual:4.997475e+00 +wall time: 0.000674963s + +time increment:4920 time: 4.92 +field ' c' [explicit solve]: current solution: 9.763839e+01, current residual:5.901450e+01 +field 'mu' [explicit solve]: current solution: 8.358232e+00, current residual:4.993420e+00 +wall time: 0.000653028s + +time increment:4930 time: 4.93 +field ' c' [explicit solve]: current solution: 9.763662e+01, current residual:5.901337e+01 +field 'mu' [explicit solve]: current solution: 8.351512e+00, current residual:4.989376e+00 +wall time: 0.000673056s + +time increment:4940 time: 4.94 +field ' c' [explicit solve]: current solution: 9.763485e+01, current residual:5.901225e+01 +field 'mu' [explicit solve]: current solution: 8.344811e+00, current residual:4.985343e+00 +wall time: 0.000920057s + +time increment:4950 time: 4.95 +field ' c' [explicit solve]: current solution: 9.763309e+01, current residual:5.901113e+01 +field 'mu' [explicit solve]: current solution: 8.338128e+00, current residual:4.981322e+00 +wall time: 0.000682116s + +time increment:4960 time: 4.96 +field ' c' [explicit solve]: current solution: 9.763133e+01, current residual:5.901001e+01 +field 'mu' [explicit solve]: current solution: 8.331464e+00, current residual:4.977312e+00 +wall time: 0.000722885s + +time increment:4970 time: 4.97 +field ' c' [explicit solve]: current solution: 9.762958e+01, current residual:5.900889e+01 +field 'mu' [explicit solve]: current solution: 8.324819e+00, current residual:4.973314e+00 +wall time: 0.000664949s + +time increment:4980 time: 4.98 +field ' c' [explicit solve]: current solution: 9.762782e+01, current residual:5.900778e+01 +field 'mu' [explicit solve]: current solution: 8.318192e+00, current residual:4.969326e+00 +wall time: 0.000638962s + +time increment:4990 time: 4.99 +field ' c' [explicit solve]: current solution: 9.762608e+01, current residual:5.900667e+01 +field 'mu' [explicit solve]: current solution: 8.311583e+00, current residual:4.965350e+00 +wall time: 0.000720978s + +time increment:5000 time: 5 +field ' c' [explicit solve]: current solution: 9.762433e+01, current residual:5.900556e+01 +field 'mu' [explicit solve]: current solution: 8.304993e+00, current residual:4.961385e+00 +wall time: 0.000669003s + +time increment:5010 time: 5.01 +field ' c' [explicit solve]: current solution: 9.762259e+01, current residual:5.900445e+01 +field 'mu' [explicit solve]: current solution: 8.298421e+00, current residual:4.957431e+00 +wall time: 0.00067091s + +time increment:5020 time: 5.02 +field ' c' [explicit solve]: current solution: 9.762085e+01, current residual:5.900334e+01 +field 'mu' [explicit solve]: current solution: 8.291867e+00, current residual:4.953488e+00 +wall time: 0.000701904s + +time increment:5030 time: 5.03 +field ' c' [explicit solve]: current solution: 9.761911e+01, current residual:5.900224e+01 +field 'mu' [explicit solve]: current solution: 8.285330e+00, current residual:4.949556e+00 +wall time: 0.000660896s + +time increment:5040 time: 5.04 +field ' c' [explicit solve]: current solution: 9.761738e+01, current residual:5.900114e+01 +field 'mu' [explicit solve]: current solution: 8.278812e+00, current residual:4.945635e+00 +wall time: 0.000702858s + +time increment:5050 time: 5.05 +field ' c' [explicit solve]: current solution: 9.761565e+01, current residual:5.900004e+01 +field 'mu' [explicit solve]: current solution: 8.272312e+00, current residual:4.941725e+00 +wall time: 0.00063014s + +time increment:5060 time: 5.06 +field ' c' [explicit solve]: current solution: 9.761393e+01, current residual:5.899894e+01 +field 'mu' [explicit solve]: current solution: 8.265829e+00, current residual:4.937826e+00 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5.15 +field ' c' [explicit solve]: current solution: 9.759855e+01, current residual:5.898915e+01 +field 'mu' [explicit solve]: current solution: 8.208267e+00, current residual:4.903209e+00 +wall time: 0.000690222s + +time increment:5160 time: 5.16 +field ' c' [explicit solve]: current solution: 9.759685e+01, current residual:5.898807e+01 +field 'mu' [explicit solve]: current solution: 8.201957e+00, current residual:4.899415e+00 +wall time: 0.000674963s + +time increment:5170 time: 5.17 +field ' c' [explicit solve]: current solution: 9.759516e+01, current residual:5.898700e+01 +field 'mu' [explicit solve]: current solution: 8.195664e+00, current residual:4.895632e+00 +wall time: 0.00077486s + +time increment:5180 time: 5.18 +field ' c' [explicit solve]: current solution: 9.759348e+01, current residual:5.898592e+01 +field 'mu' [explicit solve]: current solution: 8.189388e+00, current residual:4.891858e+00 +wall time: 0.000777006s + +time increment:5190 time: 5.19 +field ' c' [explicit solve]: current solution: 9.759179e+01, current residual:5.898485e+01 +field 'mu' [explicit solve]: current solution: 8.183128e+00, current residual:4.888095e+00 +wall time: 0.00072813s + +time increment:5200 time: 5.2 +field ' c' [explicit solve]: current solution: 9.759011e+01, current residual:5.898378e+01 +field 'mu' [explicit solve]: current solution: 8.176884e+00, current residual:4.884342e+00 +wall time: 0.000729084s + +time increment:5210 time: 5.21 +field ' c' [explicit solve]: current solution: 9.758844e+01, current residual:5.898272e+01 +field 'mu' [explicit solve]: current solution: 8.170658e+00, current residual:4.880599e+00 +wall time: 0.00072217s + +time increment:5220 time: 5.22 +field ' c' [explicit solve]: current solution: 9.758676e+01, current residual:5.898165e+01 +field 'mu' [explicit solve]: current solution: 8.164447e+00, current residual:4.876866e+00 +wall time: 0.000616074s + +time increment:5230 time: 5.23 +field ' c' [explicit solve]: current solution: 9.758509e+01, current residual:5.898059e+01 +field 'mu' [explicit solve]: current solution: 8.158254e+00, current residual:4.873143e+00 +wall time: 0.000584126s + +time increment:5240 time: 5.24 +field ' c' [explicit solve]: current solution: 9.758343e+01, current residual:5.897953e+01 +field 'mu' [explicit solve]: current solution: 8.152076e+00, current residual:4.869429e+00 +wall time: 0.000574827s + +time increment:5250 time: 5.25 +field ' c' [explicit solve]: current solution: 9.758176e+01, current residual:5.897847e+01 +field 'mu' [explicit solve]: current solution: 8.145915e+00, current residual:4.865726e+00 +wall time: 0.000644922s + +time increment:5260 time: 5.26 +field ' c' [explicit solve]: current solution: 9.758010e+01, current residual:5.897741e+01 +field 'mu' [explicit solve]: current solution: 8.139769e+00, current residual:4.862033e+00 +wall time: 0.000564098s + +time increment:5270 time: 5.27 +field ' c' [explicit solve]: current solution: 9.757844e+01, current residual:5.897635e+01 +field 'mu' [explicit solve]: current solution: 8.133640e+00, current residual:4.858349e+00 +wall time: 0.000571966s + +time increment:5280 time: 5.28 +field ' c' [explicit solve]: current solution: 9.757679e+01, current residual:5.897530e+01 +field 'mu' [explicit solve]: current solution: 8.127527e+00, current residual:4.854676e+00 +wall time: 0.000573158s + +time increment:5290 time: 5.29 +field ' c' [explicit solve]: current solution: 9.757513e+01, current residual:5.897425e+01 +field 'mu' [explicit solve]: current solution: 8.121430e+00, current residual:4.851012e+00 +wall time: 0.000688076s + +time increment:5300 time: 5.3 +field ' c' [explicit solve]: current solution: 9.757348e+01, current residual:5.897320e+01 +field 'mu' [explicit solve]: current solution: 8.115349e+00, current residual:4.847357e+00 +wall time: 0.000563145s + +time increment:5310 time: 5.31 +field ' c' [explicit solve]: current solution: 9.757184e+01, current residual:5.897215e+01 +field 'mu' [explicit solve]: current solution: 8.109283e+00, current residual:4.843713e+00 +wall time: 0.000616074s + +time increment:5320 time: 5.32 +field ' c' [explicit solve]: current solution: 9.757019e+01, current residual:5.897110e+01 +field 'mu' [explicit solve]: current solution: 8.103233e+00, current residual:4.840078e+00 +wall time: 0.000584841s + +time increment:5330 time: 5.33 +field ' c' [explicit solve]: current solution: 9.756855e+01, current residual:5.897006e+01 +field 'mu' [explicit solve]: current solution: 8.097199e+00, current residual:4.836452e+00 +wall time: 0.000669003s + +time increment:5340 time: 5.34 +field ' c' [explicit solve]: current solution: 9.756692e+01, current residual:5.896902e+01 +field 'mu' [explicit solve]: current solution: 8.091181e+00, current residual:4.832836e+00 +wall time: 0.000676155s + +time increment:5350 time: 5.35 +field ' c' [explicit solve]: current solution: 9.756528e+01, current residual:5.896797e+01 +field 'mu' [explicit solve]: current solution: 8.085178e+00, current residual:4.829230e+00 +wall time: 0.000586033s + +time increment:5360 time: 5.36 +field ' c' [explicit solve]: current solution: 9.756365e+01, current residual:5.896694e+01 +field 'mu' [explicit solve]: current solution: 8.079190e+00, current residual:4.825633e+00 +wall time: 0.000560045s + +time increment:5370 time: 5.37 +field ' c' [explicit solve]: current solution: 9.756202e+01, current residual:5.896590e+01 +field 'mu' [explicit solve]: current solution: 8.073218e+00, current residual:4.822045e+00 +wall time: 0.000553131s + +time increment:5380 time: 5.38 +field ' c' [explicit solve]: current solution: 9.756040e+01, current residual:5.896486e+01 +field 'mu' [explicit solve]: current solution: 8.067261e+00, current residual:4.818467e+00 +wall time: 0.000767946s + +time increment:5390 time: 5.39 +field ' c' [explicit solve]: current solution: 9.755878e+01, current residual:5.896383e+01 +field 'mu' [explicit solve]: current solution: 8.061320e+00, current residual:4.814898e+00 +wall time: 0.000558138s + +time increment:5400 time: 5.4 +field ' c' [explicit solve]: current solution: 9.755716e+01, current residual:5.896280e+01 +field 'mu' [explicit solve]: current solution: 8.055393e+00, current residual:4.811338e+00 +wall time: 0.000557899s + +time increment:5410 time: 5.41 +field ' c' [explicit solve]: current solution: 9.755554e+01, current residual:5.896177e+01 +field 'mu' [explicit solve]: current solution: 8.049482e+00, current residual:4.807788e+00 +wall time: 0.000574112s + +time increment:5420 time: 5.42 +field ' c' [explicit solve]: current solution: 9.755393e+01, current residual:5.896074e+01 +field 'mu' [explicit solve]: current solution: 8.043586e+00, current residual:4.804246e+00 +wall time: 0.000576973s + +time increment:5430 time: 5.43 +field ' c' [explicit solve]: current solution: 9.755232e+01, current residual:5.895972e+01 +field 'mu' [explicit solve]: current solution: 8.037705e+00, current residual:4.800714e+00 +wall time: 0.000854969s + +time increment:5440 time: 5.44 +field ' c' [explicit solve]: current solution: 9.755071e+01, current residual:5.895869e+01 +field 'mu' [explicit solve]: current solution: 8.031838e+00, current residual:4.797191e+00 +wall time: 0.000694036s + +time increment:5450 time: 5.45 +field ' c' [explicit solve]: current solution: 9.754910e+01, current residual:5.895767e+01 +field 'mu' [explicit solve]: current solution: 8.025987e+00, current residual:4.793677e+00 +wall time: 0.000683069s + +time increment:5460 time: 5.46 +field ' c' [explicit solve]: current solution: 9.754750e+01, current residual:5.895665e+01 +field 'mu' [explicit solve]: current solution: 8.020150e+00, current residual:4.790173e+00 +wall time: 0.000664949s + +time increment:5470 time: 5.47 +field ' c' [explicit solve]: current solution: 9.754590e+01, current residual:5.895563e+01 +field 'mu' [explicit solve]: current solution: 8.014329e+00, current residual:4.786677e+00 +wall time: 0.000635862s + +time increment:5480 time: 5.48 +field ' c' [explicit solve]: current solution: 9.754431e+01, current residual:5.895462e+01 +field 'mu' [explicit solve]: current solution: 8.008521e+00, current residual:4.783190e+00 +wall time: 0.000756979s + +time increment:5490 time: 5.49 +field ' c' [explicit solve]: current solution: 9.754271e+01, current residual:5.895360e+01 +field 'mu' [explicit solve]: current solution: 8.002729e+00, current residual:4.779712e+00 +wall time: 0.000602007s + +time increment:5500 time: 5.5 +field ' c' [explicit solve]: current solution: 9.754112e+01, current residual:5.895259e+01 +field 'mu' [explicit solve]: current solution: 7.996951e+00, current residual:4.776243e+00 +wall time: 0.000565052s + +time increment:5510 time: 5.51 +field ' c' [explicit solve]: current solution: 9.753953e+01, current residual:5.895158e+01 +field 'mu' [explicit solve]: current solution: 7.991187e+00, current residual:4.772783e+00 +wall time: 0.000627995s + +time increment:5520 time: 5.52 +field ' c' [explicit solve]: current solution: 9.753795e+01, current residual:5.895057e+01 +field 'mu' [explicit solve]: current solution: 7.985438e+00, current residual:4.769331e+00 +wall time: 0.000558138s + +time increment:5530 time: 5.53 +field ' c' [explicit solve]: current solution: 9.753637e+01, current residual:5.894956e+01 +field 'mu' [explicit solve]: current solution: 7.979704e+00, current residual:4.765889e+00 +wall time: 0.000659943s + +time increment:5540 time: 5.54 +field ' c' [explicit solve]: current solution: 9.753479e+01, current residual:5.894855e+01 +field 'mu' [explicit solve]: current solution: 7.973983e+00, current residual:4.762455e+00 +wall time: 0.000566006s + +time increment:5550 time: 5.55 +field ' c' [explicit solve]: current solution: 9.753321e+01, current residual:5.894755e+01 +field 'mu' [explicit solve]: current solution: 7.968277e+00, current residual:4.759030e+00 +wall time: 0.000567913s + +time increment:5560 time: 5.56 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current solution: 9.752537e+01, current residual:5.894255e+01 +field 'mu' [explicit solve]: current solution: 7.939959e+00, current residual:4.742034e+00 +wall time: 0.000652075s + +time increment:5610 time: 5.61 +field ' c' [explicit solve]: current solution: 9.752381e+01, current residual:5.894156e+01 +field 'mu' [explicit solve]: current solution: 7.934337e+00, current residual:4.738660e+00 +wall time: 0.000684023s + +time increment:5620 time: 5.62 +field ' c' [explicit solve]: current solution: 9.752225e+01, current residual:5.894057e+01 +field 'mu' [explicit solve]: current solution: 7.928729e+00, current residual:4.735295e+00 +wall time: 0.000658035s + +time increment:5630 time: 5.63 +field ' c' [explicit solve]: current solution: 9.752070e+01, current residual:5.893958e+01 +field 'mu' [explicit solve]: current solution: 7.923135e+00, current residual:4.731938e+00 +wall time: 0.00066781s + +time increment:5640 time: 5.64 +field ' c' [explicit solve]: current solution: 9.751915e+01, current residual:5.893859e+01 +field 'mu' [explicit solve]: current solution: 7.917555e+00, current residual:4.728590e+00 +wall time: 0.000649929s + +time increment:5650 time: 5.65 +field ' c' [explicit solve]: current solution: 9.751760e+01, current residual:5.893760e+01 +field 'mu' [explicit solve]: current solution: 7.911988e+00, current residual:4.725250e+00 +wall time: 0.000855207s + +time increment:5660 time: 5.66 +field ' c' [explicit solve]: current solution: 9.751605e+01, current residual:5.893662e+01 +field 'mu' [explicit solve]: current solution: 7.906435e+00, current residual:4.721918e+00 +wall time: 0.000672102s + +time increment:5670 time: 5.67 +field ' c' [explicit solve]: current solution: 9.751451e+01, current residual:5.893563e+01 +field 'mu' [explicit solve]: current solution: 7.900896e+00, current residual:4.718595e+00 +wall time: 0.00067091s + +time increment:5680 time: 5.68 +field ' c' [explicit solve]: current solution: 9.751297e+01, current residual:5.893465e+01 +field 'mu' [explicit solve]: current solution: 7.895370e+00, current residual:4.715280e+00 +wall time: 0.000684977s + +time increment:5690 time: 5.69 +field ' c' [explicit solve]: current solution: 9.751143e+01, current residual:5.893367e+01 +field 'mu' [explicit solve]: current solution: 7.889857e+00, current residual:4.711973e+00 +wall time: 0.000637054s + +time increment:5700 time: 5.7 +field ' c' [explicit solve]: current solution: 9.750989e+01, current residual:5.893269e+01 +field 'mu' [explicit solve]: current solution: 7.884358e+00, current residual:4.708674e+00 +wall time: 0.000654936s + +time increment:5710 time: 5.71 +field ' c' [explicit solve]: current solution: 9.750836e+01, current residual:5.893171e+01 +field 'mu' [explicit solve]: current solution: 7.878873e+00, current residual:4.705384e+00 +wall time: 0.000633001s + +time increment:5720 time: 5.72 +field ' c' [explicit solve]: current solution: 9.750683e+01, current residual:5.893074e+01 +field 'mu' [explicit solve]: current solution: 7.873400e+00, current residual:4.702102e+00 +wall time: 0.000692129s + +time increment:5730 time: 5.73 +field ' c' [explicit solve]: current solution: 9.750530e+01, current residual:5.892977e+01 +field 'mu' [explicit solve]: current solution: 7.867941e+00, current residual:4.698827e+00 +wall time: 0.000672102s + +time increment:5740 time: 5.74 +field ' c' [explicit solve]: current solution: 9.750378e+01, current residual:5.892879e+01 +field 'mu' [explicit solve]: current solution: 7.862495e+00, current residual:4.695561e+00 +wall time: 0.000684023s + +time increment:5750 time: 5.75 +field ' c' [explicit solve]: current solution: 9.750226e+01, current residual:5.892782e+01 +field 'mu' [explicit solve]: current solution: 7.857063e+00, current residual:4.692303e+00 +wall time: 0.000669956s + +time increment:5760 time: 5.76 +field ' c' [explicit solve]: current solution: 9.750074e+01, current residual:5.892686e+01 +field 'mu' [explicit solve]: current solution: 7.851643e+00, current residual:4.689053e+00 +wall time: 0.000712872s + +time increment:5770 time: 5.77 +field ' c' [explicit solve]: current solution: 9.749922e+01, current residual:5.892589e+01 +field 'mu' [explicit solve]: current solution: 7.846236e+00, current residual:4.685811e+00 +wall time: 0.000672102s + +time increment:5780 time: 5.78 +field ' c' [explicit solve]: current solution: 9.749771e+01, current residual:5.892492e+01 +field 'mu' [explicit solve]: current solution: 7.840843e+00, current residual:4.682577e+00 +wall time: 0.000694036s + +time increment:5790 time: 5.79 +field ' c' [explicit solve]: current solution: 9.749619e+01, current residual:5.892396e+01 +field 'mu' [explicit solve]: current solution: 7.835462e+00, current residual:4.679350e+00 +wall time: 0.000674963s + +time increment:5800 time: 5.8 +field ' c' [explicit solve]: current solution: 9.749468e+01, current residual:5.892300e+01 +field 'mu' [explicit solve]: current solution: 7.830095e+00, current residual:4.676132e+00 +wall time: 0.000627041s + +time increment:5810 time: 5.81 +field ' c' [explicit solve]: current solution: 9.749318e+01, current residual:5.892204e+01 +field 'mu' [explicit solve]: current solution: 7.824740e+00, current residual:4.672922e+00 +wall time: 0.000669956s + +time increment:5820 time: 5.82 +field ' c' [explicit solve]: current solution: 9.749167e+01, current residual:5.892108e+01 +field 'mu' [explicit solve]: current solution: 7.819398e+00, current residual:4.669719e+00 +wall time: 0.000637054s + +time increment:5830 time: 5.83 +field ' c' [explicit solve]: current solution: 9.749017e+01, current residual:5.892012e+01 +field 'mu' [explicit solve]: current solution: 7.814069e+00, current residual:4.666524e+00 +wall time: 0.000751972s + +time increment:5840 time: 5.84 +field ' c' [explicit solve]: current solution: 9.748867e+01, current residual:5.891917e+01 +field 'mu' [explicit solve]: current solution: 7.808752e+00, current residual:4.663337e+00 +wall time: 0.000681162s + +time increment:5850 time: 5.85 +field ' c' [explicit solve]: current solution: 9.748718e+01, current residual:5.891821e+01 +field 'mu' [explicit solve]: current solution: 7.803448e+00, current residual:4.660157e+00 +wall time: 0.000690937s + +time increment:5860 time: 5.86 +field ' c' [explicit solve]: current solution: 9.748568e+01, current residual:5.891726e+01 +field 'mu' [explicit solve]: current solution: 7.798157e+00, current residual:4.656986e+00 +wall time: 0.000651121s + +time increment:5870 time: 5.87 +field ' c' [explicit solve]: current solution: 9.748419e+01, current residual:5.891631e+01 +field 'mu' [explicit solve]: current solution: 7.792878e+00, current residual:4.653822e+00 +wall time: 0.000680923s + +time increment:5880 time: 5.88 +field ' c' [explicit solve]: current solution: 9.748270e+01, current residual:5.891536e+01 +field 'mu' [explicit solve]: current solution: 7.787612e+00, current residual:4.650665e+00 +wall time: 0.000677824s + +time increment:5890 time: 5.89 +field ' c' [explicit solve]: current solution: 9.748122e+01, current residual:5.891441e+01 +field 'mu' [explicit solve]: current solution: 7.782358e+00, current residual:4.647517e+00 +wall time: 0.000684023s + +time increment:5900 time: 5.9 +field ' c' [explicit solve]: current solution: 9.747973e+01, current residual:5.891347e+01 +field 'mu' [explicit solve]: current solution: 7.777117e+00, current residual:4.644375e+00 +wall time: 0.000699997s + +time increment:5910 time: 5.91 +field ' c' [explicit solve]: current solution: 9.747825e+01, current residual:5.891252e+01 +field 'mu' [explicit solve]: current solution: 7.771888e+00, current residual:4.641242e+00 +wall time: 0.000636101s + +time increment:5920 time: 5.92 +field ' c' [explicit solve]: current solution: 9.747677e+01, current residual:5.891158e+01 +field 'mu' [explicit solve]: current solution: 7.766671e+00, current residual:4.638116e+00 +wall time: 0.000638962s + +time increment:5930 time: 5.93 +field ' c' [explicit solve]: current solution: 9.747529e+01, current residual:5.891064e+01 +field 'mu' [explicit solve]: current solution: 7.761467e+00, current residual:4.634997e+00 +wall time: 0.000649929s + +time increment:5940 time: 5.94 +field ' c' [explicit solve]: current solution: 9.747382e+01, current residual:5.890970e+01 +field 'mu' [explicit solve]: current solution: 7.756275e+00, current residual:4.631886e+00 +wall time: 0.000576019s + +time increment:5950 time: 5.95 +field ' c' [explicit solve]: current solution: 9.747235e+01, current residual:5.890876e+01 +field 'mu' [explicit solve]: current solution: 7.751095e+00, current residual:4.628783e+00 +wall time: 0.000600815s + +time increment:5960 time: 5.96 +field ' c' [explicit solve]: current solution: 9.747088e+01, current residual:5.890782e+01 +field 'mu' [explicit solve]: current solution: 7.745927e+00, current residual:4.625686e+00 +wall time: 0.000702858s + +time increment:5970 time: 5.97 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current solution: 9.746357e+01, current residual:5.890316e+01 +field 'mu' [explicit solve]: current solution: 7.720269e+00, current residual:4.610316e+00 +wall time: 0.00062418s + +time increment:6020 time: 6.02 +field ' c' [explicit solve]: current solution: 9.746212e+01, current residual:5.890223e+01 +field 'mu' [explicit solve]: current solution: 7.715173e+00, current residual:4.607263e+00 +wall time: 0.000642061s + +time increment:6030 time: 6.03 +field ' c' [explicit solve]: current solution: 9.746067e+01, current residual:5.890131e+01 +field 'mu' [explicit solve]: current solution: 7.710089e+00, current residual:4.604218e+00 +wall time: 0.000736952s + +time increment:6040 time: 6.04 +field ' c' [explicit solve]: current solution: 9.745922e+01, current residual:5.890038e+01 +field 'mu' [explicit solve]: current solution: 7.705017e+00, current residual:4.601181e+00 +wall time: 0.000571966s + +time increment:6050 time: 6.05 +field ' c' [explicit solve]: current solution: 9.745777e+01, current residual:5.889946e+01 +field 'mu' [explicit solve]: current solution: 7.699957e+00, current residual:4.598150e+00 +wall time: 0.000566006s + +time increment:6060 time: 6.06 +field ' c' [explicit solve]: current solution: 9.745632e+01, current residual:5.889854e+01 +field 'mu' [explicit solve]: current solution: 7.694908e+00, current residual:4.595127e+00 +wall time: 0.000563145s + +time increment:6070 time: 6.07 +field ' c' [explicit solve]: current solution: 9.745488e+01, current residual:5.889762e+01 +field 'mu' [explicit solve]: current solution: 7.689871e+00, current residual:4.592110e+00 +wall time: 0.000642776s + +time increment:6080 time: 6.08 +field ' c' [explicit solve]: current solution: 9.745344e+01, current residual:5.889670e+01 +field 'mu' [explicit solve]: current solution: 7.684846e+00, current residual:4.589101e+00 +wall time: 0.000564098s + +time increment:6090 time: 6.09 +field ' c' [explicit solve]: current solution: 9.745200e+01, current residual:5.889578e+01 +field 'mu' [explicit solve]: current solution: 7.679832e+00, current residual:4.586099e+00 +wall time: 0.000646114s + +time increment:6100 time: 6.1 +field ' c' [explicit solve]: current solution: 9.745057e+01, current residual:5.889487e+01 +field 'mu' [explicit solve]: current solution: 7.674829e+00, current residual:4.583104e+00 +wall time: 0.000604153s + +time increment:6110 time: 6.11 +field ' c' [explicit solve]: current solution: 9.744914e+01, current residual:5.889396e+01 +field 'mu' [explicit solve]: current solution: 7.669839e+00, current residual:4.580116e+00 +wall time: 0.000571012s + +time increment:6120 time: 6.12 +field ' c' [explicit solve]: current solution: 9.744771e+01, current residual:5.889304e+01 +field 'mu' [explicit solve]: current solution: 7.664859e+00, current residual:4.577135e+00 +wall time: 0.000570059s + +time increment:6130 time: 6.13 +field ' c' [explicit solve]: current solution: 9.744628e+01, current residual:5.889213e+01 +field 'mu' [explicit solve]: current solution: 7.659891e+00, current residual:4.574161e+00 +wall time: 0.000616074s + +time increment:6140 time: 6.14 +field ' c' [explicit solve]: current solution: 9.744485e+01, current residual:5.889122e+01 +field 'mu' [explicit solve]: current solution: 7.654935e+00, current residual:4.571194e+00 +wall time: 0.00056982s + +time increment:6150 time: 6.15 +field ' c' [explicit solve]: current solution: 9.744343e+01, current residual:5.889031e+01 +field 'mu' [explicit solve]: current solution: 7.649990e+00, current residual:4.568233e+00 +wall time: 0.000694036s + +time increment:6160 time: 6.16 +field ' c' [explicit solve]: current solution: 9.744201e+01, current residual:5.888941e+01 +field 'mu' [explicit solve]: current solution: 7.645056e+00, current residual:4.565280e+00 +wall time: 0.000602961s + +time increment:6170 time: 6.17 +field ' c' [explicit solve]: current solution: 9.744059e+01, current residual:5.888850e+01 +field 'mu' [explicit solve]: current solution: 7.640133e+00, current residual:4.562334e+00 +wall time: 0.00070405s + +time increment:6180 time: 6.18 +field ' c' [explicit solve]: current solution: 9.743917e+01, current residual:5.888760e+01 +field 'mu' [explicit solve]: current solution: 7.635222e+00, current residual:4.559394e+00 +wall time: 0.000633001s + +time increment:6190 time: 6.19 +field ' c' [explicit solve]: current solution: 9.743776e+01, current residual:5.888670e+01 +field 'mu' [explicit solve]: current solution: 7.630322e+00, current residual:4.556461e+00 +wall time: 0.000576973s + +time increment:6200 time: 6.2 +field ' c' [explicit solve]: current solution: 9.743634e+01, current residual:5.888580e+01 +field 'mu' [explicit solve]: current solution: 7.625433e+00, current residual:4.553536e+00 +wall time: 0.000627041s + +time increment:6210 time: 6.21 +field ' c' [explicit solve]: current solution: 9.743493e+01, current residual:5.888490e+01 +field 'mu' [explicit solve]: current solution: 7.620555e+00, current residual:4.550616e+00 +wall time: 0.000571012s + +time increment:6220 time: 6.22 +field ' c' [explicit solve]: current solution: 9.743353e+01, current residual:5.888400e+01 +field 'mu' [explicit solve]: current solution: 7.615688e+00, current residual:4.547704e+00 +wall time: 0.000594854s + +time increment:6230 time: 6.23 +field ' c' [explicit solve]: current solution: 9.743212e+01, current residual:5.888310e+01 +field 'mu' [explicit solve]: current solution: 7.610832e+00, current residual:4.544798e+00 +wall time: 0.000669003s + +time increment:6240 time: 6.24 +field ' c' [explicit solve]: current solution: 9.743072e+01, current residual:5.888221e+01 +field 'mu' [explicit solve]: current solution: 7.605987e+00, current residual:4.541900e+00 +wall time: 0.000623941s + +time increment:6250 time: 6.25 +field ' c' [explicit solve]: current solution: 9.742932e+01, current residual:5.888131e+01 +field 'mu' [explicit solve]: current solution: 7.601153e+00, current residual:4.539007e+00 +wall time: 0.000658035s + +time increment:6260 time: 6.26 +field ' c' [explicit solve]: current solution: 9.742792e+01, current residual:5.888042e+01 +field 'mu' [explicit solve]: current solution: 7.596330e+00, current residual:4.536122e+00 +wall time: 0.000576973s + +time increment:6270 time: 6.27 +field ' c' [explicit solve]: current solution: 9.742652e+01, current residual:5.887953e+01 +field 'mu' [explicit solve]: current solution: 7.591518e+00, current residual:4.533243e+00 +wall time: 0.000585079s + +time increment:6280 time: 6.28 +field ' c' [explicit solve]: current solution: 9.742513e+01, current residual:5.887864e+01 +field 'mu' [explicit solve]: current solution: 7.586717e+00, current residual:4.530371e+00 +wall time: 0.000657797s + +time increment:6290 time: 6.29 +field ' c' [explicit solve]: current solution: 9.742374e+01, current residual:5.887775e+01 +field 'mu' [explicit solve]: current solution: 7.581926e+00, current residual:4.527505e+00 +wall time: 0.000647068s + +time increment:6300 time: 6.3 +field ' c' [explicit solve]: current solution: 9.742235e+01, current residual:5.887687e+01 +field 'mu' [explicit solve]: current solution: 7.577147e+00, current residual:4.524646e+00 +wall time: 0.000624895s + +time increment:6310 time: 6.31 +field ' c' [explicit solve]: current solution: 9.742096e+01, current residual:5.887598e+01 +field 'mu' [explicit solve]: current solution: 7.572378e+00, current residual:4.521793e+00 +wall time: 0.000582933s + +time increment:6320 time: 6.32 +field ' c' [explicit solve]: current solution: 9.741958e+01, current residual:5.887510e+01 +field 'mu' [explicit solve]: current solution: 7.567619e+00, current residual:4.518947e+00 +wall time: 0.000677109s + +time increment:6330 time: 6.33 +field ' c' [explicit solve]: current solution: 9.741819e+01, current residual:5.887422e+01 +field 'mu' [explicit solve]: current solution: 7.562872e+00, current residual:4.516108e+00 +wall time: 0.000627041s + +time increment:6340 time: 6.34 +field ' c' [explicit solve]: current solution: 9.741681e+01, current residual:5.887333e+01 +field 'mu' [explicit solve]: current solution: 7.558135e+00, current residual:4.513275e+00 +wall time: 0.000646114s + +time increment:6350 time: 6.35 +field ' c' [explicit solve]: current solution: 9.741543e+01, current residual:5.887245e+01 +field 'mu' [explicit solve]: current solution: 7.553408e+00, current residual:4.510449e+00 +wall time: 0.000656128s + +time increment:6360 time: 6.36 +field ' c' [explicit solve]: current solution: 9.741406e+01, current residual:5.887158e+01 +field 'mu' [explicit solve]: current solution: 7.548693e+00, current residual:4.507628e+00 +wall time: 0.000617981s + +time increment:6370 time: 6.37 +field ' c' [explicit solve]: current solution: 9.741268e+01, current residual:5.887070e+01 +field 'mu' [explicit solve]: current solution: 7.543987e+00, current residual:4.504815e+00 +wall time: 0.00070715s + +time increment:6380 time: 6.38 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current solution: 9.740585e+01, current residual:5.886634e+01 +field 'mu' [explicit solve]: current solution: 7.520617e+00, current residual:4.490842e+00 +wall time: 0.000632048s + +time increment:6430 time: 6.43 +field ' c' [explicit solve]: current solution: 9.740448e+01, current residual:5.886547e+01 +field 'mu' [explicit solve]: current solution: 7.515974e+00, current residual:4.488067e+00 +wall time: 0.000784874s + +time increment:6440 time: 6.44 +field ' c' [explicit solve]: current solution: 9.740313e+01, current residual:5.886460e+01 +field 'mu' [explicit solve]: current solution: 7.511341e+00, current residual:4.485297e+00 +wall time: 0.000689983s + +time increment:6450 time: 6.45 +field ' c' [explicit solve]: current solution: 9.740177e+01, current residual:5.886373e+01 +field 'mu' [explicit solve]: current solution: 7.506719e+00, current residual:4.482534e+00 +wall time: 0.000716925s + +time increment:6460 time: 6.46 +field ' c' [explicit solve]: current solution: 9.740041e+01, current residual:5.886287e+01 +field 'mu' [explicit solve]: current solution: 7.502107e+00, current residual:4.479778e+00 +wall time: 0.000723124s + +time increment:6470 time: 6.47 +field ' c' [explicit solve]: current solution: 9.739906e+01, current residual:5.886201e+01 +field 'mu' [explicit solve]: current solution: 7.497505e+00, current residual:4.477027e+00 +wall time: 0.000637054s + +time increment:6480 time: 6.48 +field ' c' [explicit solve]: current solution: 9.739771e+01, current residual:5.886114e+01 +field 'mu' [explicit solve]: current solution: 7.492913e+00, current residual:4.474283e+00 +wall time: 0.000665903s + +time increment:6490 time: 6.49 +field ' c' [explicit solve]: current solution: 9.739636e+01, current residual:5.886028e+01 +field 'mu' [explicit solve]: current solution: 7.488331e+00, current residual:4.471545e+00 +wall time: 0.00067091s + +time increment:6500 time: 6.5 +field ' c' [explicit solve]: current solution: 9.739502e+01, current residual:5.885942e+01 +field 'mu' [explicit solve]: current solution: 7.483759e+00, current residual:4.468813e+00 +wall time: 0.000679016s + +time increment:6510 time: 6.51 +field ' c' [explicit solve]: current solution: 9.739367e+01, current residual:5.885857e+01 +field 'mu' [explicit solve]: current solution: 7.479198e+00, current residual:4.466087e+00 +wall time: 0.000710011s + +time increment:6520 time: 6.52 +field ' c' [explicit solve]: current solution: 9.739233e+01, current residual:5.885771e+01 +field 'mu' [explicit solve]: current solution: 7.474646e+00, current residual:4.463367e+00 +wall time: 0.000699043s + +time increment:6530 time: 6.53 +field ' c' [explicit solve]: current solution: 9.739099e+01, current residual:5.885685e+01 +field 'mu' [explicit solve]: current solution: 7.470104e+00, current residual:4.460653e+00 +wall time: 0.000707865s + +time increment:6540 time: 6.54 +field ' c' [explicit solve]: current solution: 9.738965e+01, current residual:5.885600e+01 +field 'mu' [explicit solve]: current solution: 7.465572e+00, current residual:4.457946e+00 +wall time: 0.000659943s + +time increment:6550 time: 6.55 +field ' c' [explicit solve]: current solution: 9.738832e+01, current residual:5.885515e+01 +field 'mu' [explicit solve]: current solution: 7.461051e+00, current residual:4.455244e+00 +wall time: 0.000671148s + +time increment:6560 time: 6.56 +field ' c' [explicit solve]: current solution: 9.738698e+01, current residual:5.885430e+01 +field 'mu' [explicit solve]: current solution: 7.456539e+00, current residual:4.452549e+00 +wall time: 0.000663042s + +time increment:6570 time: 6.57 +field ' c' [explicit solve]: current solution: 9.738565e+01, current residual:5.885345e+01 +field 'mu' [explicit solve]: current solution: 7.452037e+00, current residual:4.449859e+00 +wall time: 0.000652075s + +time increment:6580 time: 6.58 +field ' c' [explicit solve]: current solution: 9.738432e+01, current residual:5.885260e+01 +field 'mu' [explicit solve]: current solution: 7.447544e+00, current residual:4.447176e+00 +wall time: 0.000699997s + +time increment:6590 time: 6.59 +field ' c' [explicit solve]: current solution: 9.738299e+01, current residual:5.885175e+01 +field 'mu' [explicit solve]: current solution: 7.443062e+00, current residual:4.444498e+00 +wall time: 0.000674009s + +time increment:6600 time: 6.6 +field ' c' [explicit solve]: current solution: 9.738167e+01, current residual:5.885090e+01 +field 'mu' [explicit solve]: current solution: 7.438589e+00, current residual:4.441827e+00 +wall time: 0.000651121s + +time increment:6610 time: 6.61 +field ' c' [explicit solve]: current solution: 9.738034e+01, current residual:5.885006e+01 +field 'mu' [explicit solve]: current solution: 7.434126e+00, current residual:4.439161e+00 +wall time: 0.000681877s + +time increment:6620 time: 6.62 +field ' c' [explicit solve]: current solution: 9.737902e+01, current residual:5.884922e+01 +field 'mu' [explicit solve]: current solution: 7.429673e+00, current residual:4.436501e+00 +wall time: 0.000691175s + +time increment:6630 time: 6.63 +field ' c' [explicit solve]: current solution: 9.737770e+01, current residual:5.884837e+01 +field 'mu' [explicit solve]: current solution: 7.425229e+00, current residual:4.433848e+00 +wall time: 0.00065589s + +time increment:6640 time: 6.64 +field ' c' [explicit solve]: current solution: 9.737639e+01, current residual:5.884753e+01 +field 'mu' [explicit solve]: current solution: 7.420795e+00, current residual:4.431200e+00 +wall time: 0.000627041s + +time increment:6650 time: 6.65 +field ' c' [explicit solve]: current solution: 9.737507e+01, current residual:5.884669e+01 +field 'mu' [explicit solve]: current solution: 7.416371e+00, current residual:4.428558e+00 +wall time: 0.000652075s + +time increment:6660 time: 6.66 +field ' c' [explicit solve]: current solution: 9.737376e+01, current residual:5.884585e+01 +field 'mu' [explicit solve]: current solution: 7.411956e+00, current residual:4.425921e+00 +wall time: 0.000683069s + +time increment:6670 time: 6.67 +field ' c' [explicit solve]: current solution: 9.737245e+01, current residual:5.884502e+01 +field 'mu' [explicit solve]: current solution: 7.407551e+00, current residual:4.423291e+00 +wall time: 0.000755072s + +time increment:6680 time: 6.68 +field ' c' [explicit solve]: current solution: 9.737114e+01, current residual:5.884418e+01 +field 'mu' [explicit solve]: current solution: 7.403155e+00, current residual:4.420667e+00 +wall time: 0.000782013s + +time increment:6690 time: 6.69 +field ' c' [explicit solve]: current solution: 9.736983e+01, current residual:5.884335e+01 +field 'mu' [explicit solve]: current solution: 7.398769e+00, current residual:4.418048e+00 +wall time: 0.000649929s + +time increment:6700 time: 6.7 +field ' c' [explicit solve]: current solution: 9.736852e+01, current residual:5.884251e+01 +field 'mu' [explicit solve]: current solution: 7.394392e+00, current residual:4.415435e+00 +wall time: 0.000746012s + +time increment:6710 time: 6.71 +field ' c' [explicit solve]: current solution: 9.736722e+01, current residual:5.884168e+01 +field 'mu' [explicit solve]: current solution: 7.390025e+00, current residual:4.412828e+00 +wall time: 0.00068593s + +time increment:6720 time: 6.72 +field ' c' [explicit solve]: current solution: 9.736592e+01, current residual:5.884085e+01 +field 'mu' [explicit solve]: current solution: 7.385667e+00, current residual:4.410226e+00 +wall time: 0.000693083s + +time increment:6730 time: 6.73 +field ' c' [explicit solve]: current solution: 9.736462e+01, current residual:5.884002e+01 +field 'mu' [explicit solve]: current solution: 7.381318e+00, current residual:4.407630e+00 +wall time: 0.00062108s + +time increment:6740 time: 6.74 +field ' c' [explicit solve]: current solution: 9.736332e+01, current residual:5.883919e+01 +field 'mu' [explicit solve]: current solution: 7.376979e+00, current residual:4.405040e+00 +wall time: 0.000660896s + +time increment:6750 time: 6.75 +field ' c' [explicit solve]: current solution: 9.736203e+01, current residual:5.883836e+01 +field 'mu' [explicit solve]: current solution: 7.372649e+00, current residual:4.402456e+00 +wall time: 0.00067997s + +time increment:6760 time: 6.76 +field ' c' [explicit solve]: current solution: 9.736073e+01, current residual:5.883754e+01 +field 'mu' [explicit solve]: current solution: 7.368328e+00, current residual:4.399877e+00 +wall time: 0.00065589s + +time increment:6770 time: 6.77 +field ' c' [explicit solve]: current solution: 9.735944e+01, current residual:5.883671e+01 +field 'mu' [explicit solve]: current solution: 7.364017e+00, current residual:4.397304e+00 +wall time: 0.000626802s + +time increment:6780 time: 6.78 +field ' c' [explicit solve]: current solution: 9.735815e+01, current residual:5.883589e+01 +field 'mu' [explicit solve]: current solution: 7.359715e+00, current residual:4.394737e+00 +wall time: 0.000573158s + +time increment:6790 time: 6.79 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current solution: 9.735173e+01, current residual:5.883179e+01 +field 'mu' [explicit solve]: current solution: 7.338342e+00, current residual:4.381984e+00 +wall time: 0.000567913s + +time increment:6840 time: 6.84 +field ' c' [explicit solve]: current solution: 9.735046e+01, current residual:5.883098e+01 +field 'mu' [explicit solve]: current solution: 7.334094e+00, current residual:4.379450e+00 +wall time: 0.000669956s + +time increment:6850 time: 6.85 +field ' c' [explicit solve]: current solution: 9.734918e+01, current residual:5.883016e+01 +field 'mu' [explicit solve]: current solution: 7.329856e+00, current residual:4.376922e+00 +wall time: 0.000554085s + +time increment:6860 time: 6.86 +field ' c' [explicit solve]: current solution: 9.734791e+01, current residual:5.882935e+01 +field 'mu' [explicit solve]: current solution: 7.325627e+00, current residual:4.374399e+00 +wall time: 0.000567913s + +time increment:6870 time: 6.87 +field ' c' [explicit solve]: current solution: 9.734663e+01, current residual:5.882853e+01 +field 'mu' [explicit solve]: current solution: 7.321406e+00, current residual:4.371881e+00 +wall time: 0.000576973s + +time increment:6880 time: 6.88 +field ' c' [explicit solve]: current solution: 9.734536e+01, current residual:5.882772e+01 +field 'mu' [explicit solve]: current solution: 7.317195e+00, current residual:4.369369e+00 +wall time: 0.000828981s + +time increment:6890 time: 6.89 +field ' c' [explicit solve]: current solution: 9.734410e+01, current residual:5.882691e+01 +field 'mu' [explicit solve]: current solution: 7.312992e+00, current residual:4.366863e+00 +wall time: 0.000663996s + +time increment:6900 time: 6.9 +field ' c' [explicit solve]: current solution: 9.734283e+01, current residual:5.882610e+01 +field 'mu' [explicit solve]: current solution: 7.308799e+00, current residual:4.364362e+00 +wall time: 0.000658035s + +time increment:6910 time: 6.91 +field ' c' [explicit solve]: current solution: 9.734156e+01, current residual:5.882530e+01 +field 'mu' [explicit solve]: current solution: 7.304614e+00, current residual:4.361866e+00 +wall time: 0.000599146s + +time increment:6920 time: 6.92 +field ' c' [explicit solve]: current solution: 9.734030e+01, current residual:5.882449e+01 +field 'mu' [explicit solve]: current solution: 7.300438e+00, current residual:4.359376e+00 +wall time: 0.000739813s + +time increment:6930 time: 6.93 +field ' c' [explicit solve]: current solution: 9.733904e+01, current residual:5.882368e+01 +field 'mu' [explicit solve]: current solution: 7.296272e+00, current residual:4.356891e+00 +wall time: 0.000684023s + +time increment:6940 time: 6.94 +field ' c' [explicit solve]: current solution: 9.733778e+01, current residual:5.882288e+01 +field 'mu' [explicit solve]: current solution: 7.292113e+00, current residual:4.354412e+00 +wall time: 0.000677109s + +time increment:6950 time: 6.95 +field ' c' [explicit solve]: current solution: 9.733653e+01, current residual:5.882208e+01 +field 'mu' [explicit solve]: current solution: 7.287964e+00, current residual:4.351937e+00 +wall time: 0.000626802s + +time increment:6960 time: 6.96 +field ' c' [explicit solve]: current solution: 9.733527e+01, current residual:5.882128e+01 +field 'mu' [explicit solve]: current solution: 7.283823e+00, current residual:4.349469e+00 +wall time: 0.000745058s + +time increment:6970 time: 6.97 +field ' c' [explicit solve]: current solution: 9.733402e+01, current residual:5.882048e+01 +field 'mu' [explicit solve]: current solution: 7.279691e+00, current residual:4.347005e+00 +wall time: 0.000648975s + +time increment:6980 time: 6.98 +field ' c' [explicit solve]: current solution: 9.733276e+01, current residual:5.881968e+01 +field 'mu' [explicit solve]: current solution: 7.275568e+00, current residual:4.344547e+00 +wall time: 0.000625134s + +time increment:6990 time: 6.99 +field ' c' [explicit solve]: current solution: 9.733152e+01, current residual:5.881888e+01 +field 'mu' [explicit solve]: current solution: 7.271454e+00, current residual:4.342094e+00 +wall time: 0.00065589s + +time increment:7000 time: 7 +field ' c' [explicit solve]: current solution: 9.733027e+01, current residual:5.881808e+01 +field 'mu' [explicit solve]: current solution: 7.267348e+00, current residual:4.339646e+00 +wall time: 0.000734806s + +time increment:7010 time: 7.01 +field ' c' [explicit solve]: current solution: 9.732902e+01, current residual:5.881728e+01 +field 'mu' [explicit solve]: current solution: 7.263250e+00, current residual:4.337204e+00 +wall time: 0.000731945s + +time increment:7020 time: 7.02 +field ' c' [explicit solve]: current solution: 9.732778e+01, current residual:5.881649e+01 +field 'mu' [explicit solve]: current solution: 7.259162e+00, current residual:4.334767e+00 +wall time: 0.000651836s + +time increment:7030 time: 7.03 +field ' c' [explicit solve]: current solution: 9.732653e+01, current residual:5.881570e+01 +field 'mu' [explicit solve]: current solution: 7.255082e+00, 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7.16 +field ' c' [explicit solve]: current solution: 9.731055e+01, current residual:5.880548e+01 +field 'mu' [explicit solve]: current solution: 7.202809e+00, current residual:4.301190e+00 +wall time: 0.000684023s + +time increment:7170 time: 7.17 +field ' c' [explicit solve]: current solution: 9.730933e+01, current residual:5.880470e+01 +field 'mu' [explicit solve]: current solution: 7.198847e+00, current residual:4.298830e+00 +wall time: 0.000678062s + +time increment:7180 time: 7.18 +field ' c' [explicit solve]: current solution: 9.730812e+01, current residual:5.880393e+01 +field 'mu' [explicit solve]: current solution: 7.194892e+00, current residual:4.296475e+00 +wall time: 0.000721931s + +time increment:7190 time: 7.19 +field ' c' [explicit solve]: current solution: 9.730690e+01, current residual:5.880315e+01 +field 'mu' [explicit solve]: current solution: 7.190946e+00, current residual:4.294125e+00 +wall time: 0.000759125s + +time increment:7200 time: 7.2 +field ' c' [explicit solve]: current solution: 9.730569e+01, current residual:5.880238e+01 +field 'mu' [explicit solve]: current solution: 7.187008e+00, current residual:4.291779e+00 +wall time: 0.000680923s + +time increment:7210 time: 7.21 +field ' c' [explicit solve]: current solution: 9.730448e+01, current residual:5.880161e+01 +field 'mu' [explicit solve]: current solution: 7.183078e+00, current residual:4.289439e+00 +wall time: 0.000699997s + +time increment:7220 time: 7.22 +field ' c' [explicit solve]: current solution: 9.730327e+01, current residual:5.880083e+01 +field 'mu' [explicit solve]: current solution: 7.179156e+00, current residual:4.287104e+00 +wall time: 0.000652075s + +time increment:7230 time: 7.23 +field ' c' [explicit solve]: current solution: 9.730206e+01, current residual:5.880006e+01 +field 'mu' [explicit solve]: current solution: 7.175243e+00, current residual:4.284774e+00 +wall time: 0.000654936s + +time increment:7240 time: 7.24 +field ' c' [explicit solve]: current solution: 9.730086e+01, current residual:5.879929e+01 +field 'mu' [explicit solve]: current solution: 7.171337e+00, current residual:4.282448e+00 +wall time: 0.000702143s + +time increment:7250 time: 7.25 +field ' c' [explicit solve]: current solution: 9.729966e+01, current residual:5.879852e+01 +field 'mu' [explicit solve]: current solution: 7.167440e+00, current residual:4.280128e+00 +wall time: 0.000709057s + +time increment:7260 time: 7.26 +field ' c' [explicit solve]: current solution: 9.729845e+01, current residual:5.879776e+01 +field 'mu' [explicit solve]: current solution: 7.163550e+00, current residual:4.277812e+00 +wall time: 0.000653028s + +time increment:7270 time: 7.27 +field ' c' [explicit solve]: current solution: 9.729726e+01, current residual:5.879699e+01 +field 'mu' [explicit solve]: current solution: 7.159669e+00, current residual:4.275502e+00 +wall time: 0.000680923s + +time increment:7280 time: 7.28 +field ' c' [explicit solve]: current solution: 9.729606e+01, current residual:5.879622e+01 +field 'mu' [explicit solve]: current solution: 7.155795e+00, current residual:4.273196e+00 +wall time: 0.000689983s + +time increment:7290 time: 7.29 +field ' c' [explicit solve]: current solution: 9.729486e+01, current residual:5.879546e+01 +field 'mu' [explicit solve]: current solution: 7.151930e+00, current residual:4.270895e+00 +wall time: 0.000669003s + +time increment:7300 time: 7.3 +field ' c' [explicit solve]: current solution: 9.729367e+01, current residual:5.879469e+01 +field 'mu' [explicit solve]: current solution: 7.148072e+00, current residual:4.268599e+00 +wall time: 0.000706196s + +time increment:7310 time: 7.31 +field ' c' [explicit solve]: current solution: 9.729247e+01, current residual:5.879393e+01 +field 'mu' [explicit solve]: current solution: 7.144222e+00, current residual:4.266307e+00 +wall time: 0.000706911s + +time increment:7320 time: 7.32 +field ' c' [explicit solve]: current solution: 9.729128e+01, current residual:5.879317e+01 +field 'mu' [explicit solve]: current solution: 7.140381e+00, current residual:4.264021e+00 +wall time: 0.000629187s + +time increment:7330 time: 7.33 +field ' c' [explicit solve]: current solution: 9.729009e+01, current residual:5.879241e+01 +field 'mu' [explicit solve]: current solution: 7.136547e+00, current residual:4.261739e+00 +wall time: 0.000645876s + +time increment:7340 time: 7.34 +field ' c' [explicit solve]: current solution: 9.728890e+01, current residual:5.879165e+01 +field 'mu' [explicit solve]: current solution: 7.132721e+00, current residual:4.259462e+00 +wall time: 0.000684023s + +time increment:7350 time: 7.35 +field ' c' [explicit solve]: current solution: 9.728772e+01, current residual:5.879089e+01 +field 'mu' [explicit solve]: current solution: 7.128903e+00, current residual:4.257190e+00 +wall time: 0.000571966s + +time increment:7360 time: 7.36 +field ' c' [explicit solve]: current solution: 9.728653e+01, current residual:5.879014e+01 +field 'mu' [explicit solve]: current solution: 7.125092e+00, current residual:4.254923e+00 +wall time: 0.000565052s + +time increment:7370 time: 7.37 +field ' c' [explicit solve]: current solution: 9.728535e+01, current residual:5.878938e+01 +field 'mu' [explicit solve]: current solution: 7.121290e+00, current residual:4.252660e+00 +wall time: 0.000559807s + +time increment:7380 time: 7.38 +field ' c' [explicit solve]: current solution: 9.728417e+01, current residual:5.878863e+01 +field 'mu' [explicit solve]: current solution: 7.117495e+00, current residual:4.250403e+00 +wall time: 0.000640869s + +time increment:7390 time: 7.39 +field ' c' [explicit solve]: current solution: 9.728299e+01, current residual:5.878787e+01 +field 'mu' [explicit solve]: current solution: 7.113708e+00, current residual:4.248149e+00 +wall time: 0.000695944s + +time increment:7400 time: 7.4 +field ' c' [explicit solve]: current solution: 9.728181e+01, current residual:5.878712e+01 +field 'mu' [explicit solve]: current solution: 7.109929e+00, current residual:4.245901e+00 +wall time: 0.000553131s + +time increment:7410 time: 7.41 +field ' c' [explicit solve]: current solution: 9.728064e+01, current residual:5.878637e+01 +field 'mu' [explicit solve]: current solution: 7.106157e+00, current residual:4.243657e+00 +wall time: 0.000635862s + +time increment:7420 time: 7.42 +field ' c' [explicit solve]: current solution: 9.727946e+01, current residual:5.878562e+01 +field 'mu' [explicit solve]: current solution: 7.102393e+00, current residual:4.241418e+00 +wall time: 0.000560045s + +time increment:7430 time: 7.43 +field ' c' [explicit solve]: current solution: 9.727829e+01, current residual:5.878487e+01 +field 'mu' [explicit solve]: current solution: 7.098637e+00, current residual:4.239184e+00 +wall time: 0.000558853s + +time increment:7440 time: 7.44 +field ' c' [explicit solve]: current solution: 9.727712e+01, current residual:5.878412e+01 +field 'mu' [explicit solve]: current solution: 7.094889e+00, current residual:4.236954e+00 +wall time: 0.00068593s + +time increment:7450 time: 7.45 +field ' c' [explicit solve]: current solution: 9.727595e+01, current residual:5.878337e+01 +field 'mu' [explicit solve]: current solution: 7.091148e+00, current residual:4.234729e+00 +wall time: 0.00069499s + +time increment:7460 time: 7.46 +field ' c' [explicit solve]: current solution: 9.727478e+01, current residual:5.878262e+01 +field 'mu' [explicit solve]: current solution: 7.087415e+00, current residual:4.232509e+00 +wall time: 0.000585794s + +time increment:7470 time: 7.47 +field ' c' [explicit solve]: current solution: 9.727361e+01, current residual:5.878188e+01 +field 'mu' [explicit solve]: current solution: 7.083689e+00, current residual:4.230293e+00 +wall time: 0.000561953s + +time increment:7480 time: 7.48 +field ' c' [explicit solve]: current solution: 9.727245e+01, current residual:5.878113e+01 +field 'mu' [explicit solve]: current solution: 7.079971e+00, current residual:4.228082e+00 +wall time: 0.000599861s + +time increment:7490 time: 7.49 +field ' c' [explicit solve]: current solution: 9.727129e+01, current residual:5.878039e+01 +field 'mu' [explicit solve]: current solution: 7.076260e+00, current residual:4.225876e+00 +wall time: 0.000566006s + +time increment:7500 time: 7.5 +field ' c' [explicit solve]: current solution: 9.727013e+01, current residual:5.877965e+01 +field 'mu' [explicit solve]: current solution: 7.072557e+00, current residual:4.223674e+00 +wall time: 0.000602007s + +time increment:7510 time: 7.51 +field ' c' [explicit solve]: current solution: 9.726897e+01, current residual:5.877891e+01 +field 'mu' [explicit solve]: current solution: 7.068862e+00, current residual:4.221476e+00 +wall time: 0.000663042s + +time increment:7520 time: 7.52 +field ' c' [explicit solve]: current solution: 9.726781e+01, current residual:5.877817e+01 +field 'mu' [explicit solve]: current solution: 7.065174e+00, current residual:4.219284e+00 +wall time: 0.000691175s + +time increment:7530 time: 7.53 +field ' c' [explicit solve]: current solution: 9.726665e+01, current residual:5.877743e+01 +field 'mu' [explicit solve]: current solution: 7.061493e+00, current residual:4.217095e+00 +wall time: 0.000644207s + +time increment:7540 time: 7.54 +field ' c' [explicit solve]: current solution: 9.726550e+01, current residual:5.877669e+01 +field 'mu' [explicit solve]: current solution: 7.057820e+00, current residual:4.214912e+00 +wall time: 0.000566006s + +time increment:7550 time: 7.55 +field ' c' [explicit solve]: current solution: 9.726434e+01, current residual:5.877595e+01 +field 'mu' [explicit solve]: current solution: 7.054154e+00, current residual:4.212733e+00 +wall time: 0.000617027s + +time increment:7560 time: 7.56 +field ' c' [explicit solve]: current solution: 9.726319e+01, current residual:5.877522e+01 +field 'mu' [explicit solve]: current solution: 7.050496e+00, current residual:4.210558e+00 +wall time: 0.000610113s + +time increment:7570 time: 7.57 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current solution: 9.725746e+01, current residual:5.877155e+01 +field 'mu' [explicit solve]: current solution: 7.032316e+00, current residual:4.199752e+00 +wall time: 0.000555038s + +time increment:7620 time: 7.62 +field ' c' [explicit solve]: current solution: 9.725632e+01, current residual:5.877082e+01 +field 'mu' [explicit solve]: current solution: 7.028702e+00, current residual:4.197605e+00 +wall time: 0.000766993s + +time increment:7630 time: 7.63 +field ' c' [explicit solve]: current solution: 9.725518e+01, current residual:5.877009e+01 +field 'mu' [explicit solve]: current solution: 7.025095e+00, current residual:4.195461e+00 +wall time: 0.000622988s + +time increment:7640 time: 7.64 +field ' c' [explicit solve]: current solution: 9.725404e+01, current residual:5.876936e+01 +field 'mu' [explicit solve]: current solution: 7.021495e+00, current residual:4.193322e+00 +wall time: 0.000568151s + +time increment:7650 time: 7.65 +field ' c' [explicit solve]: current solution: 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residual:5.876574e+01 +field 'mu' [explicit solve]: current solution: 7.003606e+00, current residual:4.182694e+00 +wall time: 0.000571966s + +time increment:7700 time: 7.7 +field ' c' [explicit solve]: current solution: 9.724724e+01, current residual:5.876502e+01 +field 'mu' [explicit solve]: current solution: 7.000049e+00, current residual:4.180581e+00 +wall time: 0.000648022s + +time increment:7710 time: 7.71 +field ' c' [explicit solve]: current solution: 9.724611e+01, current residual:5.876429e+01 +field 'mu' [explicit solve]: current solution: 6.996500e+00, current residual:4.178473e+00 +wall time: 0.00067091s + +time increment:7720 time: 7.72 +field ' c' [explicit solve]: current solution: 9.724498e+01, current residual:5.876357e+01 +field 'mu' [explicit solve]: current solution: 6.992958e+00, current residual:4.176369e+00 +wall time: 0.000651121s + +time increment:7730 time: 7.73 +field ' c' [explicit solve]: current solution: 9.724386e+01, current residual:5.876285e+01 +field 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6.961397e+00, current residual:4.157627e+00 +wall time: 0.000571012s + +time increment:7820 time: 7.82 +field ' c' [explicit solve]: current solution: 9.723380e+01, current residual:5.875643e+01 +field 'mu' [explicit solve]: current solution: 6.957925e+00, current residual:4.155566e+00 +wall time: 0.000568151s + +time increment:7830 time: 7.83 +field ' c' [explicit solve]: current solution: 9.723269e+01, current residual:5.875572e+01 +field 'mu' [explicit solve]: current solution: 6.954460e+00, current residual:4.153509e+00 +wall time: 0.000608921s + +time increment:7840 time: 7.84 +field ' c' [explicit solve]: current solution: 9.723158e+01, current residual:5.875501e+01 +field 'mu' [explicit solve]: current solution: 6.951002e+00, current residual:4.151456e+00 +wall time: 0.000662088s + +time increment:7850 time: 7.85 +field ' c' [explicit solve]: current solution: 9.723048e+01, current residual:5.875430e+01 +field 'mu' [explicit solve]: current solution: 6.947551e+00, current residual:4.149408e+00 +wall time: 0.000686884s + +time increment:7860 time: 7.86 +field ' c' [explicit solve]: current solution: 9.722937e+01, current residual:5.875359e+01 +field 'mu' [explicit solve]: current solution: 6.944106e+00, current residual:4.147363e+00 +wall time: 0.000561953s + +time increment:7870 time: 7.87 +field ' c' [explicit solve]: current solution: 9.722827e+01, current residual:5.875289e+01 +field 'mu' [explicit solve]: current solution: 6.940669e+00, current residual:4.145323e+00 +wall time: 0.000559092s + +time increment:7880 time: 7.88 +field ' c' [explicit solve]: current solution: 9.722717e+01, current residual:5.875218e+01 +field 'mu' [explicit solve]: current solution: 6.937238e+00, current residual:4.143287e+00 +wall time: 0.000594139s + +time increment:7890 time: 7.89 +field ' c' [explicit solve]: current solution: 9.722607e+01, current residual:5.875148e+01 +field 'mu' [explicit solve]: current solution: 6.933815e+00, current residual:4.141255e+00 +wall time: 0.000593901s + +time increment:7900 time: 7.9 +field ' c' [explicit solve]: current solution: 9.722497e+01, current residual:5.875077e+01 +field 'mu' [explicit solve]: current solution: 6.930398e+00, current residual:4.139227e+00 +wall time: 0.000660181s + +time increment:7910 time: 7.91 +field ' c' [explicit solve]: current solution: 9.722387e+01, current residual:5.875007e+01 +field 'mu' [explicit solve]: current solution: 6.926987e+00, current residual:4.137204e+00 +wall time: 0.000705957s + +time increment:7920 time: 7.92 +field ' c' [explicit solve]: current solution: 9.722277e+01, current residual:5.874937e+01 +field 'mu' [explicit solve]: current solution: 6.923584e+00, current residual:4.135184e+00 +wall time: 0.000614882s + +time increment:7930 time: 7.93 +field ' c' [explicit solve]: current solution: 9.722168e+01, current residual:5.874867e+01 +field 'mu' [explicit solve]: current solution: 6.920187e+00, current residual:4.133169e+00 +wall time: 0.000601053s + +time increment:7940 time: 7.94 +field ' c' [explicit solve]: current solution: 9.722058e+01, current residual:5.874797e+01 +field 'mu' [explicit solve]: current solution: 6.916798e+00, current residual:4.131158e+00 +wall time: 0.000567913s + +time increment:7950 time: 7.95 +field ' c' [explicit solve]: current solution: 9.721949e+01, current residual:5.874727e+01 +field 'mu' [explicit solve]: current solution: 6.913414e+00, current residual:4.129150e+00 +wall time: 0.000693083s + +time increment:7960 time: 7.96 +field ' c' [explicit solve]: current solution: 9.721840e+01, current residual:5.874658e+01 +field 'mu' [explicit solve]: current solution: 6.910038e+00, current residual:4.127147e+00 +wall time: 0.000595808s + +time increment:7970 time: 7.97 +field ' c' [explicit solve]: current solution: 9.721731e+01, current residual:5.874588e+01 +field 'mu' [explicit solve]: current solution: 6.906668e+00, current residual:4.125148e+00 +wall time: 0.000612974s + +time increment:7980 time: 7.98 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current solution: 9.721189e+01, current residual:5.874241e+01 +field 'mu' [explicit solve]: current solution: 6.889919e+00, current residual:4.115214e+00 +wall time: 0.000623941s + +time increment:8030 time: 8.03 +field ' c' [explicit solve]: current solution: 9.721081e+01, current residual:5.874172e+01 +field 'mu' [explicit solve]: current solution: 6.886589e+00, current residual:4.113239e+00 +wall time: 0.000643969s + +time increment:8040 time: 8.04 +field ' c' [explicit solve]: current solution: 9.720973e+01, current residual:5.874103e+01 +field 'mu' [explicit solve]: current solution: 6.883266e+00, current residual:4.111268e+00 +wall time: 0.000653028s + +time increment:8050 time: 8.05 +field ' c' [explicit solve]: current solution: 9.720865e+01, current residual:5.874034e+01 +field 'mu' [explicit solve]: current solution: 6.879949e+00, current residual:4.109301e+00 +wall time: 0.000632048s + +time increment:8060 time: 8.06 +field ' c' [explicit solve]: current solution: 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residual:5.873691e+01 +field 'mu' [explicit solve]: current solution: 6.863463e+00, current residual:4.099527e+00 +wall time: 0.000657797s + +time increment:8110 time: 8.11 +field ' c' [explicit solve]: current solution: 9.720222e+01, current residual:5.873623e+01 +field 'mu' [explicit solve]: current solution: 6.860185e+00, current residual:4.097584e+00 +wall time: 0.000763893s + +time increment:8120 time: 8.12 +field ' c' [explicit solve]: current solution: 9.720115e+01, current residual:5.873554e+01 +field 'mu' [explicit solve]: current solution: 6.856914e+00, current residual:4.095644e+00 +wall time: 0.00068903s + +time increment:8130 time: 8.13 +field ' c' [explicit solve]: current solution: 9.720009e+01, current residual:5.873486e+01 +field 'mu' [explicit solve]: current solution: 6.853649e+00, current residual:4.093709e+00 +wall time: 0.000646114s + +time increment:8140 time: 8.14 +field ' c' [explicit solve]: current solution: 9.719902e+01, current residual:5.873418e+01 +field 'mu' [explicit solve]: current solution: 6.850391e+00, current residual:4.091778e+00 +wall time: 0.000678062s + +time increment:8150 time: 8.15 +field ' c' [explicit solve]: current solution: 9.719796e+01, current residual:5.873350e+01 +field 'mu' [explicit solve]: current solution: 6.847139e+00, current residual:4.089850e+00 +wall time: 0.000654936s + +time increment:8160 time: 8.16 +field ' c' [explicit solve]: current solution: 9.719690e+01, current residual:5.873282e+01 +field 'mu' [explicit solve]: current solution: 6.843893e+00, current residual:4.087927e+00 +wall time: 0.00069499s + +time increment:8170 time: 8.17 +field ' c' [explicit solve]: current solution: 9.719584e+01, current residual:5.873214e+01 +field 'mu' [explicit solve]: current solution: 6.840654e+00, current residual:4.086007e+00 +wall time: 0.000688076s + +time increment:8180 time: 8.18 +field ' c' [explicit solve]: current solution: 9.719478e+01, current residual:5.873147e+01 +field 'mu' [explicit solve]: current solution: 6.837421e+00, current residual:4.084091e+00 +wall time: 0.000809908s + +time increment:8190 time: 8.19 +field ' c' [explicit solve]: current solution: 9.719372e+01, current residual:5.873079e+01 +field 'mu' [explicit solve]: current solution: 6.834194e+00, current residual:4.082179e+00 +wall time: 0.000723839s + +time increment:8200 time: 8.2 +field ' c' [explicit solve]: current solution: 9.719266e+01, current residual:5.873011e+01 +field 'mu' [explicit solve]: current solution: 6.830974e+00, current residual:4.080271e+00 +wall time: 0.0008111s + +time increment:8210 time: 8.21 +field ' c' [explicit solve]: current solution: 9.719161e+01, current residual:5.872944e+01 +field 'mu' [explicit solve]: current solution: 6.827760e+00, current residual:4.078367e+00 +wall time: 0.000677109s + +time increment:8220 time: 8.22 +field ' c' [explicit solve]: current solution: 9.719056e+01, current residual:5.872876e+01 +field 'mu' [explicit solve]: current solution: 6.824553e+00, 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residual:4.068903e+00 +wall time: 0.000648975s + +time increment:8270 time: 8.27 +field ' c' [explicit solve]: current solution: 9.718531e+01, current residual:5.872541e+01 +field 'mu' [explicit solve]: current solution: 6.808609e+00, current residual:4.067022e+00 +wall time: 0.000644922s + +time increment:8280 time: 8.28 +field ' c' [explicit solve]: current solution: 9.718426e+01, current residual:5.872474e+01 +field 'mu' [explicit solve]: current solution: 6.805439e+00, current residual:4.065144e+00 +wall time: 0.000675917s + +time increment:8290 time: 8.29 +field ' c' [explicit solve]: current solution: 9.718322e+01, current residual:5.872407e+01 +field 'mu' [explicit solve]: current solution: 6.802275e+00, current residual:4.063270e+00 +wall time: 0.000675917s + +time increment:8300 time: 8.3 +field ' c' [explicit solve]: current solution: 9.718218e+01, current residual:5.872340e+01 +field 'mu' [explicit solve]: current solution: 6.799117e+00, current residual:4.061400e+00 +wall time: 0.00067997s + +time increment:8310 time: 8.31 +field ' c' [explicit solve]: current solution: 9.718114e+01, current residual:5.872274e+01 +field 'mu' [explicit solve]: current solution: 6.795966e+00, current residual:4.059534e+00 +wall time: 0.000674963s + +time increment:8320 time: 8.32 +field ' c' [explicit solve]: current solution: 9.718010e+01, current residual:5.872207e+01 +field 'mu' [explicit solve]: current solution: 6.792821e+00, current residual:4.057671e+00 +wall time: 0.000700951s + +time increment:8330 time: 8.33 +field ' c' [explicit solve]: current solution: 9.717906e+01, current residual:5.872141e+01 +field 'mu' [explicit solve]: current solution: 6.789682e+00, current residual:4.055812e+00 +wall time: 0.000608921s + +time increment:8340 time: 8.34 +field ' c' [explicit solve]: current solution: 9.717802e+01, current residual:5.872075e+01 +field 'mu' [explicit solve]: current solution: 6.786549e+00, current residual:4.053957e+00 +wall time: 0.000659943s + +time increment:8350 time: 8.35 +field ' c' [explicit solve]: current solution: 9.717699e+01, current residual:5.872008e+01 +field 'mu' [explicit solve]: current solution: 6.783422e+00, current residual:4.052106e+00 +wall time: 0.000700951s + +time increment:8360 time: 8.36 +field ' c' [explicit solve]: current solution: 9.717595e+01, current residual:5.871942e+01 +field 'mu' [explicit solve]: current solution: 6.780301e+00, current residual:4.050258e+00 +wall time: 0.000679016s + +time increment:8370 time: 8.37 +field ' c' [explicit solve]: current solution: 9.717492e+01, current residual:5.871876e+01 +field 'mu' [explicit solve]: current solution: 6.777186e+00, current residual:4.048414e+00 +wall time: 0.000699043s + +time increment:8380 time: 8.38 +field ' c' [explicit solve]: current solution: 9.717389e+01, current residual:5.871810e+01 +field 'mu' [explicit solve]: current solution: 6.774077e+00, current residual:4.046573e+00 +wall time: 0.000671864s + +time increment:8390 time: 8.39 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current solution: 9.716875e+01, current residual:5.871481e+01 +field 'mu' [explicit solve]: current solution: 6.758625e+00, current residual:4.037427e+00 +wall time: 0.000763893s + +time increment:8440 time: 8.44 +field ' c' [explicit solve]: current solution: 9.716773e+01, current residual:5.871416e+01 +field 'mu' [explicit solve]: current solution: 6.755552e+00, current residual:4.035608e+00 +wall time: 0.000708103s + +time increment:8450 time: 8.45 +field ' c' [explicit solve]: current solution: 9.716670e+01, current residual:5.871350e+01 +field 'mu' [explicit solve]: current solution: 6.752486e+00, current residual:4.033794e+00 +wall time: 0.00069809s + +time increment:8460 time: 8.46 +field ' c' [explicit solve]: current solution: 9.716568e+01, current residual:5.871285e+01 +field 'mu' [explicit solve]: current solution: 6.749425e+00, current residual:4.031983e+00 +wall time: 0.00066781s + +time increment:8470 time: 8.47 +field ' c' [explicit solve]: current solution: 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current solution: 6.710176e+00, current residual:4.008764e+00 +wall time: 0.000560999s + +time increment:8600 time: 8.6 +field ' c' [explicit solve]: current solution: 9.715152e+01, current residual:5.870379e+01 +field 'mu' [explicit solve]: current solution: 6.707197e+00, current residual:4.007002e+00 +wall time: 0.000571012s + +time increment:8610 time: 8.61 +field ' c' [explicit solve]: current solution: 9.715052e+01, current residual:5.870315e+01 +field 'mu' [explicit solve]: current solution: 6.704225e+00, current residual:4.005245e+00 +wall time: 0.000581026s + +time increment:8620 time: 8.62 +field ' c' [explicit solve]: current solution: 9.714952e+01, current residual:5.870251e+01 +field 'mu' [explicit solve]: current solution: 6.701258e+00, current residual:4.003490e+00 +wall time: 0.000643015s + +time increment:8630 time: 8.63 +field ' c' [explicit solve]: current solution: 9.714852e+01, current residual:5.870187e+01 +field 'mu' [explicit solve]: current solution: 6.698297e+00, current residual:4.001740e+00 +wall time: 0.000648975s + +time increment:8640 time: 8.64 +field ' c' [explicit solve]: current solution: 9.714752e+01, current residual:5.870123e+01 +field 'mu' [explicit solve]: current solution: 6.695341e+00, current residual:3.999992e+00 +wall time: 0.000683069s + +time increment:8650 time: 8.65 +field ' c' [explicit solve]: current solution: 9.714653e+01, current residual:5.870059e+01 +field 'mu' [explicit solve]: current solution: 6.692392e+00, current residual:3.998248e+00 +wall time: 0.000652075s + +time increment:8660 time: 8.66 +field ' c' [explicit solve]: current solution: 9.714553e+01, current residual:5.869995e+01 +field 'mu' [explicit solve]: current solution: 6.689448e+00, current residual:3.996508e+00 +wall time: 0.000690937s + +time increment:8670 time: 8.67 +field ' c' [explicit solve]: current solution: 9.714454e+01, current residual:5.869932e+01 +field 'mu' [explicit solve]: current solution: 6.686510e+00, current residual:3.994771e+00 +wall time: 0.000564814s + +time increment:8680 time: 8.68 +field ' c' [explicit solve]: current solution: 9.714354e+01, current residual:5.869868e+01 +field 'mu' [explicit solve]: current solution: 6.683577e+00, current residual:3.993038e+00 +wall time: 0.00067091s + +time increment:8690 time: 8.69 +field ' c' [explicit solve]: current solution: 9.714255e+01, current residual:5.869805e+01 +field 'mu' [explicit solve]: current solution: 6.680650e+00, current residual:3.991307e+00 +wall time: 0.000566959s + +time increment:8700 time: 8.7 +field ' c' [explicit solve]: current solution: 9.714156e+01, current residual:5.869741e+01 +field 'mu' [explicit solve]: current solution: 6.677729e+00, current residual:3.989581e+00 +wall time: 0.000691175s + +time increment:8710 time: 8.71 +field ' c' [explicit solve]: current solution: 9.714057e+01, current residual:5.869678e+01 +field 'mu' [explicit solve]: current solution: 6.674813e+00, current residual:3.987858e+00 +wall time: 0.00070405s + +time increment:8720 time: 8.72 +field ' c' [explicit solve]: current solution: 9.713959e+01, current residual:5.869615e+01 +field 'mu' [explicit solve]: current solution: 6.671903e+00, current residual:3.986138e+00 +wall time: 0.000653028s + +time increment:8730 time: 8.73 +field ' c' [explicit solve]: current solution: 9.713860e+01, current residual:5.869552e+01 +field 'mu' [explicit solve]: current solution: 6.668999e+00, current residual:3.984421e+00 +wall time: 0.000643015s + +time increment:8740 time: 8.74 +field ' c' [explicit solve]: current solution: 9.713761e+01, current residual:5.869489e+01 +field 'mu' [explicit solve]: current solution: 6.666100e+00, current residual:3.982708e+00 +wall time: 0.000673056s + +time increment:8750 time: 8.75 +field ' c' [explicit solve]: current solution: 9.713663e+01, current residual:5.869426e+01 +field 'mu' [explicit solve]: current solution: 6.663207e+00, current residual:3.980999e+00 +wall time: 0.000763178s + +time 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current solution: 9.712783e+01, current residual:5.868862e+01 +field 'mu' [explicit solve]: current solution: 6.637419e+00, current residual:3.965764e+00 +wall time: 0.000567198s + +time increment:8850 time: 8.85 +field ' c' [explicit solve]: current solution: 9.712686e+01, current residual:5.868800e+01 +field 'mu' [explicit solve]: current solution: 6.634581e+00, current residual:3.964088e+00 +wall time: 0.00056386s + +time increment:8860 time: 8.86 +field ' c' [explicit solve]: current solution: 9.712588e+01, current residual:5.868738e+01 +field 'mu' [explicit solve]: current solution: 6.631749e+00, current residual:3.962415e+00 +wall time: 0.000648975s + +time increment:8870 time: 8.87 +field ' c' [explicit solve]: current solution: 9.712492e+01, current residual:5.868676e+01 +field 'mu' [explicit solve]: current solution: 6.628922e+00, current residual:3.960746e+00 +wall time: 0.000747204s + +time increment:8880 time: 8.88 +field ' c' [explicit solve]: current solution: 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'mu' [explicit solve]: current solution: 6.603723e+00, current residual:3.945868e+00 +wall time: 0.000624895s + +time increment:8970 time: 8.97 +field ' c' [explicit solve]: current solution: 9.711529e+01, current residual:5.868059e+01 +field 'mu' [explicit solve]: current solution: 6.600950e+00, current residual:3.944231e+00 +wall time: 0.000622034s + +time increment:8980 time: 8.98 +field ' c' [explicit solve]: current solution: 9.711433e+01, current residual:5.867998e+01 +field 'mu' [explicit solve]: current solution: 6.598182e+00, current residual:3.942597e+00 +wall time: 0.000608921s + +time increment:8990 time: 8.99 +field ' c' [explicit solve]: current solution: 9.711337e+01, current residual:5.867937e+01 +field 'mu' [explicit solve]: current solution: 6.595420e+00, current residual:3.940967e+00 +wall time: 0.000622988s + +time increment:9000 time: 9 +field ' c' [explicit solve]: current solution: 9.711242e+01, current residual:5.867876e+01 +field 'mu' [explicit solve]: current solution: 6.592663e+00, current residual:3.939340e+00 +wall time: 0.000637054s + +time increment:9010 time: 9.01 +field ' c' [explicit solve]: current solution: 9.711147e+01, current residual:5.867815e+01 +field 'mu' [explicit solve]: current solution: 6.589911e+00, current residual:3.937716e+00 +wall time: 0.000725985s + +time increment:9020 time: 9.02 +field ' c' [explicit solve]: current solution: 9.711052e+01, current residual:5.867754e+01 +field 'mu' [explicit solve]: current solution: 6.587164e+00, current residual:3.936095e+00 +wall time: 0.000565052s + +time increment:9030 time: 9.03 +field ' c' [explicit solve]: current solution: 9.710957e+01, current residual:5.867693e+01 +field 'mu' [explicit solve]: current solution: 6.584423e+00, current residual:3.934477e+00 +wall time: 0.00057292s + +time increment:9040 time: 9.04 +field ' c' [explicit solve]: current solution: 9.710862e+01, current residual:5.867632e+01 +field 'mu' [explicit solve]: current solution: 6.581687e+00, 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9.17 +field ' c' [explicit solve]: current solution: 9.709638e+01, current residual:5.866849e+01 +field 'mu' [explicit solve]: current solution: 6.546592e+00, current residual:3.912162e+00 +wall time: 0.000694036s + +time increment:9180 time: 9.18 +field ' c' [explicit solve]: current solution: 9.709545e+01, current residual:5.866789e+01 +field 'mu' [explicit solve]: current solution: 6.543929e+00, current residual:3.910592e+00 +wall time: 0.000718117s + +time increment:9190 time: 9.19 +field ' c' [explicit solve]: current solution: 9.709451e+01, current residual:5.866729e+01 +field 'mu' [explicit solve]: current solution: 6.541270e+00, current residual:3.909024e+00 +wall time: 0.000650883s + +time increment:9200 time: 9.2 +field ' c' [explicit solve]: current solution: 9.709358e+01, current residual:5.866670e+01 +field 'mu' [explicit solve]: current solution: 6.538617e+00, current residual:3.907460e+00 +wall time: 0.000653028s + +time increment:9210 time: 9.21 +field ' c' [explicit solve]: current solution: 9.709265e+01, current residual:5.866610e+01 +field 'mu' [explicit solve]: current solution: 6.535969e+00, current residual:3.905899e+00 +wall time: 0.000677109s + +time increment:9220 time: 9.22 +field ' c' [explicit solve]: current solution: 9.709172e+01, current residual:5.866551e+01 +field 'mu' [explicit solve]: current solution: 6.533326e+00, current residual:3.904340e+00 +wall time: 0.000669003s + +time increment:9230 time: 9.23 +field ' c' [explicit solve]: current solution: 9.709080e+01, current residual:5.866491e+01 +field 'mu' [explicit solve]: current solution: 6.530687e+00, current residual:3.902785e+00 +wall time: 0.000720024s + +time increment:9240 time: 9.24 +field ' c' [explicit solve]: current solution: 9.708987e+01, current residual:5.866432e+01 +field 'mu' [explicit solve]: current solution: 6.528054e+00, current residual:3.901233e+00 +wall time: 0.000658035s + +time increment:9250 time: 9.25 +field ' c' [explicit solve]: current solution: 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residual:5.866136e+01 +field 'mu' [explicit solve]: current solution: 6.514964e+00, current residual:3.893518e+00 +wall time: 0.000679016s + +time increment:9300 time: 9.3 +field ' c' [explicit solve]: current solution: 9.708433e+01, current residual:5.866077e+01 +field 'mu' [explicit solve]: current solution: 6.512361e+00, current residual:3.891984e+00 +wall time: 0.00069499s + +time increment:9310 time: 9.31 +field ' c' [explicit solve]: current solution: 9.708342e+01, current residual:5.866019e+01 +field 'mu' [explicit solve]: current solution: 6.509763e+00, current residual:3.890453e+00 +wall time: 0.000632048s + +time increment:9320 time: 9.32 +field ' c' [explicit solve]: current solution: 9.708250e+01, current residual:5.865960e+01 +field 'mu' [explicit solve]: current solution: 6.507170e+00, current residual:3.888925e+00 +wall time: 0.000656843s + +time increment:9330 time: 9.33 +field ' c' [explicit solve]: current solution: 9.708158e+01, current residual:5.865901e+01 +field 'mu' [explicit solve]: current solution: 6.504581e+00, current residual:3.887400e+00 +wall time: 0.000726938s + +time increment:9340 time: 9.34 +field ' c' [explicit solve]: current solution: 9.708067e+01, current residual:5.865843e+01 +field 'mu' [explicit solve]: current solution: 6.501998e+00, current residual:3.885878e+00 +wall time: 0.000633001s + +time increment:9350 time: 9.35 +field ' c' [explicit solve]: current solution: 9.707975e+01, current residual:5.865784e+01 +field 'mu' [explicit solve]: current solution: 6.499420e+00, current residual:3.884359e+00 +wall time: 0.000665903s + +time increment:9360 time: 9.36 +field ' c' [explicit solve]: current solution: 9.707884e+01, current residual:5.865726e+01 +field 'mu' [explicit solve]: current solution: 6.496846e+00, current residual:3.882843e+00 +wall time: 0.000723124s + +time increment:9370 time: 9.37 +field ' c' [explicit solve]: current solution: 9.707793e+01, current residual:5.865667e+01 +field 'mu' [explicit solve]: current solution: 6.494278e+00, current residual:3.881330e+00 +wall time: 0.000659943s + +time increment:9380 time: 9.38 +field ' c' [explicit solve]: current solution: 9.707701e+01, current residual:5.865609e+01 +field 'mu' [explicit solve]: current solution: 6.491714e+00, current residual:3.879820e+00 +wall time: 0.000769138s + +time increment:9390 time: 9.39 +field ' c' [explicit solve]: current solution: 9.707610e+01, current residual:5.865550e+01 +field 'mu' [explicit solve]: current solution: 6.489156e+00, current residual:3.878313e+00 +wall time: 0.000669003s + +time increment:9400 time: 9.4 +field ' c' [explicit solve]: current solution: 9.707520e+01, current residual:5.865492e+01 +field 'mu' [explicit solve]: current solution: 6.486602e+00, current residual:3.876808e+00 +wall time: 0.000765085s + +time increment:9410 time: 9.41 +field ' c' [explicit solve]: current solution: 9.707429e+01, current residual:5.865434e+01 +field 'mu' [explicit solve]: current solution: 6.484053e+00, current residual:3.875307e+00 +wall time: 0.000858068s + +time increment:9420 time: 9.42 +field ' c' [explicit solve]: current solution: 9.707338e+01, current residual:5.865376e+01 +field 'mu' [explicit solve]: current solution: 6.481509e+00, current residual:3.873809e+00 +wall time: 0.000653982s + +time increment:9430 time: 9.43 +field ' c' [explicit solve]: current solution: 9.707248e+01, current residual:5.865318e+01 +field 'mu' [explicit solve]: current solution: 6.478970e+00, current residual:3.872313e+00 +wall time: 0.000653028s + +time increment:9440 time: 9.44 +field ' c' [explicit solve]: current solution: 9.707157e+01, current residual:5.865260e+01 +field 'mu' [explicit solve]: current solution: 6.476435e+00, current residual:3.870821e+00 +wall time: 0.00069809s + +time increment:9450 time: 9.45 +field ' c' [explicit solve]: current solution: 9.707067e+01, current residual:5.865202e+01 +field 'mu' [explicit solve]: current solution: 6.473906e+00, current residual:3.869331e+00 +wall time: 0.000683069s + +time increment:9460 time: 9.46 +field ' c' [explicit solve]: current solution: 9.706977e+01, current residual:5.865144e+01 +field 'mu' [explicit solve]: current solution: 6.471381e+00, current residual:3.867845e+00 +wall time: 0.000684023s + +time increment:9470 time: 9.47 +field ' c' [explicit solve]: current solution: 9.706886e+01, current residual:5.865087e+01 +field 'mu' [explicit solve]: current solution: 6.468861e+00, current residual:3.866361e+00 +wall time: 0.000640869s + +time increment:9480 time: 9.48 +field ' c' [explicit solve]: current solution: 9.706796e+01, current residual:5.865029e+01 +field 'mu' [explicit solve]: current solution: 6.466346e+00, current residual:3.864880e+00 +wall time: 0.000580788s + +time increment:9490 time: 9.49 +field ' c' [explicit solve]: current solution: 9.706706e+01, current residual:5.864972e+01 +field 'mu' [explicit solve]: current solution: 6.463835e+00, current residual:3.863402e+00 +wall time: 0.000619888s + +time increment:9500 time: 9.5 +field ' c' [explicit solve]: current solution: 9.706617e+01, current residual:5.864914e+01 +field 'mu' [explicit solve]: current solution: 6.461330e+00, current residual:3.861927e+00 +wall time: 0.000611782s + +time increment:9510 time: 9.51 +field ' c' [explicit solve]: current solution: 9.706527e+01, current residual:5.864857e+01 +field 'mu' [explicit solve]: current solution: 6.458829e+00, current residual:3.860455e+00 +wall time: 0.000684023s + +time increment:9520 time: 9.52 +field ' c' [explicit solve]: current solution: 9.706437e+01, current residual:5.864799e+01 +field 'mu' [explicit solve]: current solution: 6.456333e+00, current residual:3.858986e+00 +wall time: 0.000574112s + +time increment:9530 time: 9.53 +field ' c' [explicit solve]: current solution: 9.706348e+01, current residual:5.864742e+01 +field 'mu' [explicit solve]: current solution: 6.453842e+00, current residual:3.857519e+00 +wall time: 0.000573158s + +time increment:9540 time: 9.54 +field ' c' [explicit solve]: current solution: 9.706258e+01, current residual:5.864685e+01 +field 'mu' [explicit solve]: current solution: 6.451355e+00, current residual:3.856056e+00 +wall time: 0.000593901s + +time increment:9550 time: 9.55 +field ' c' [explicit solve]: current solution: 9.706169e+01, current residual:5.864627e+01 +field 'mu' [explicit solve]: current solution: 6.448873e+00, current residual:3.854595e+00 +wall time: 0.000617981s + +time increment:9560 time: 9.56 +field ' c' [explicit solve]: current solution: 9.706080e+01, current residual:5.864570e+01 +field 'mu' [explicit solve]: current solution: 6.446396e+00, current residual:3.853137e+00 +wall time: 0.000706911s + +time increment:9570 time: 9.57 +field ' c' [explicit solve]: current solution: 9.705991e+01, current residual:5.864513e+01 +field 'mu' [explicit solve]: current solution: 6.443924e+00, current residual:3.851682e+00 +wall time: 0.000658989s + +time increment:9580 time: 9.58 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current solution: 9.705547e+01, current residual:5.864229e+01 +field 'mu' [explicit solve]: current solution: 6.431632e+00, current residual:3.844450e+00 +wall time: 0.000575066s + +time increment:9630 time: 9.63 +field ' c' [explicit solve]: current solution: 9.705459e+01, current residual:5.864172e+01 +field 'mu' [explicit solve]: current solution: 6.429188e+00, current residual:3.843012e+00 +wall time: 0.000576973s + +time increment:9640 time: 9.64 +field ' c' [explicit solve]: current solution: 9.705370e+01, current residual:5.864116e+01 +field 'mu' [explicit solve]: current solution: 6.426749e+00, current residual:3.841577e+00 +wall time: 0.000733137s + +time increment:9650 time: 9.65 +field ' c' [explicit solve]: current solution: 9.705282e+01, current residual:5.864059e+01 +field 'mu' [explicit solve]: current solution: 6.424314e+00, current residual:3.840144e+00 +wall time: 0.000658035s + +time increment:9660 time: 9.66 +field ' c' [explicit solve]: current solution: 9.705194e+01, current residual:5.864003e+01 +field 'mu' [explicit solve]: current solution: 6.421883e+00, current residual:3.838715e+00 +wall time: 0.000720024s + +time increment:9670 time: 9.67 +field ' c' [explicit solve]: current solution: 9.705106e+01, current residual:5.863946e+01 +field 'mu' [explicit solve]: current solution: 6.419457e+00, current residual:3.837288e+00 +wall time: 0.000710011s + +time increment:9680 time: 9.68 +field ' c' [explicit solve]: current solution: 9.705018e+01, current residual:5.863890e+01 +field 'mu' [explicit solve]: current solution: 6.417036e+00, current residual:3.835864e+00 +wall time: 0.000643969s + +time increment:9690 time: 9.69 +field ' c' [explicit solve]: current solution: 9.704930e+01, current residual:5.863834e+01 +field 'mu' [explicit solve]: current solution: 6.414620e+00, current residual:3.834442e+00 +wall time: 0.000763893s + +time increment:9700 time: 9.7 +field ' c' [explicit solve]: current solution: 9.704843e+01, current residual:5.863778e+01 +field 'mu' [explicit solve]: current solution: 6.412208e+00, current residual:3.833024e+00 +wall time: 0.000723839s + +time increment:9710 time: 9.71 +field ' c' [explicit solve]: current solution: 9.704755e+01, current residual:5.863722e+01 +field 'mu' [explicit solve]: current solution: 6.409801e+00, current residual:3.831608e+00 +wall time: 0.000720024s + +time increment:9720 time: 9.72 +field ' c' [explicit solve]: current solution: 9.704667e+01, current residual:5.863665e+01 +field 'mu' [explicit solve]: current solution: 6.407398e+00, current residual:3.830195e+00 +wall time: 0.000677109s + +time increment:9730 time: 9.73 +field ' c' [explicit solve]: current solution: 9.704580e+01, current residual:5.863609e+01 +field 'mu' [explicit solve]: current solution: 6.405000e+00, current residual:3.828785e+00 +wall time: 0.000705004s + +time increment:9740 time: 9.74 +field ' c' [explicit solve]: current solution: 9.704493e+01, current residual:5.863554e+01 +field 'mu' [explicit solve]: current solution: 6.402606e+00, current residual:3.827378e+00 +wall time: 0.000650883s + +time increment:9750 time: 9.75 +field ' c' [explicit solve]: current solution: 9.704405e+01, current residual:5.863498e+01 +field 'mu' [explicit solve]: current solution: 6.400217e+00, current residual:3.825973e+00 +wall time: 0.000745058s + +time increment:9760 time: 9.76 +field ' c' [explicit solve]: current solution: 9.704318e+01, current residual:5.863442e+01 +field 'mu' [explicit solve]: current solution: 6.397832e+00, current residual:3.824571e+00 +wall time: 0.000567913s + +time increment:9770 time: 9.77 +field ' c' [explicit solve]: current solution: 9.704231e+01, current residual:5.863386e+01 +field 'mu' [explicit solve]: current solution: 6.395452e+00, current residual:3.823172e+00 +wall time: 0.000555992s + +time increment:9780 time: 9.78 +field ' c' [explicit solve]: current solution: 9.704144e+01, current residual:5.863331e+01 +field 'mu' [explicit solve]: current solution: 6.393077e+00, current residual:3.821775e+00 +wall time: 0.000672102s + +time increment:9790 time: 9.79 +field ' c' [explicit solve]: current solution: 9.704058e+01, current residual:5.863275e+01 +field 'mu' [explicit solve]: current solution: 6.390706e+00, current residual:3.820381e+00 +wall time: 0.000567913s + +time increment:9800 time: 9.8 +field ' c' [explicit solve]: current solution: 9.703971e+01, current residual:5.863219e+01 +field 'mu' [explicit solve]: current solution: 6.388339e+00, current residual:3.818990e+00 +wall time: 0.000719786s + +time increment:9810 time: 9.81 +field ' c' [explicit solve]: current solution: 9.703884e+01, current residual:5.863164e+01 +field 'mu' [explicit solve]: current solution: 6.385977e+00, current residual:3.817602e+00 +wall time: 0.000715971s + +time increment:9820 time: 9.82 +field ' c' [explicit solve]: current solution: 9.703798e+01, current residual:5.863108e+01 +field 'mu' [explicit solve]: current solution: 6.383620e+00, current residual:3.816216e+00 +wall time: 0.000587225s + +time increment:9830 time: 9.83 +field ' c' [explicit solve]: current solution: 9.703711e+01, current residual:5.863053e+01 +field 'mu' [explicit solve]: current solution: 6.381267e+00, current residual:3.814833e+00 +wall time: 0.000638008s + +time increment:9840 time: 9.84 +field ' c' [explicit solve]: current solution: 9.703625e+01, current residual:5.862998e+01 +field 'mu' [explicit solve]: current solution: 6.378918e+00, current residual:3.813453e+00 +wall time: 0.000656843s + +time increment:9850 time: 9.85 +field ' c' [explicit solve]: current solution: 9.703539e+01, current residual:5.862943e+01 +field 'mu' [explicit solve]: current solution: 6.376574e+00, current residual:3.812076e+00 +wall time: 0.00056982s + +time increment:9860 time: 9.86 +field ' c' [explicit solve]: current solution: 9.703453e+01, current residual:5.862887e+01 +field 'mu' [explicit solve]: current solution: 6.374235e+00, current residual:3.810701e+00 +wall time: 0.000664949s + +time increment:9870 time: 9.87 +field ' c' [explicit solve]: current solution: 9.703367e+01, current residual:5.862832e+01 +field 'mu' [explicit solve]: current solution: 6.371899e+00, current residual:3.809328e+00 +wall time: 0.000758886s + +time increment:9880 time: 9.88 +field ' c' [explicit solve]: current solution: 9.703281e+01, current residual:5.862777e+01 +field 'mu' [explicit solve]: current solution: 6.369569e+00, current residual:3.807959e+00 +wall time: 0.000643015s + +time increment:9890 time: 9.89 +field ' c' [explicit solve]: current solution: 9.703195e+01, current residual:5.862722e+01 +field 'mu' [explicit solve]: current solution: 6.367242e+00, current residual:3.806592e+00 +wall time: 0.000774145s + +time increment:9900 time: 9.9 +field ' c' [explicit solve]: current solution: 9.703109e+01, current residual:5.862667e+01 +field 'mu' [explicit solve]: current solution: 6.364920e+00, current residual:3.805228e+00 +wall time: 0.000742912s + +time increment:9910 time: 9.91 +field ' c' [explicit solve]: current solution: 9.703024e+01, current residual:5.862613e+01 +field 'mu' [explicit solve]: current solution: 6.362603e+00, current residual:3.803866e+00 +wall time: 0.000634909s + +time increment:9920 time: 9.92 +field ' c' [explicit solve]: current solution: 9.702938e+01, current residual:5.862558e+01 +field 'mu' [explicit solve]: current solution: 6.360290e+00, current residual:3.802507e+00 +wall time: 0.000603914s + +time increment:9930 time: 9.93 +field ' c' [explicit solve]: current solution: 9.702853e+01, current residual:5.862503e+01 +field 'mu' [explicit solve]: current solution: 6.357981e+00, current residual:3.801151e+00 +wall time: 0.000631809s + +time increment:9940 time: 9.94 +field ' c' [explicit solve]: current solution: 9.702768e+01, current residual:5.862448e+01 +field 'mu' [explicit solve]: current solution: 6.355677e+00, current residual:3.799798e+00 +wall time: 0.000560999s + +time increment:9950 time: 9.95 +field ' c' [explicit solve]: current solution: 9.702682e+01, current residual:5.862394e+01 +field 'mu' [explicit solve]: current solution: 6.353377e+00, current residual:3.798447e+00 +wall time: 0.000591993s + +time increment:9960 time: 9.96 +field ' c' [explicit solve]: current solution: 9.702597e+01, current residual:5.862339e+01 +field 'mu' [explicit solve]: current solution: 6.351081e+00, current residual:3.797098e+00 +wall time: 0.000726938s + +time increment:9970 time: 9.97 +field ' c' [explicit solve]: current solution: 9.702512e+01, current residual:5.862285e+01 +field 'mu' [explicit solve]: current solution: 6.348790e+00, current residual:3.795753e+00 +wall time: 0.000679016s + +time increment:9980 time: 9.98 +field ' c' [explicit solve]: current solution: 9.702427e+01, current residual:5.862231e+01 +field 'mu' [explicit solve]: current solution: 6.346503e+00, current residual:3.794410e+00 +wall time: 0.000641108s + +time increment:9990 time: 9.99 +field ' c' [explicit solve]: current solution: 9.702343e+01, current residual:5.862176e+01 +field 'mu' [explicit solve]: current solution: 6.344220e+00, current residual:3.793069e+00 +wall time: 0.000629902s + +time increment:10000 time: 10 +field ' c' [explicit solve]: current solution: 9.702258e+01, current residual:5.862122e+01 +field 'mu' [explicit solve]: current solution: 6.341942e+00, current residual:3.791731e+00 +wall time: 0.00059104s +fields: 2 +fields: 2 +fields: 2 +fields: 2 +Output written to:solution-010000.pvtu + +Energy: 261.089 +Energy Components: 115.445 145.644 0 + +time increment:10010 time: 10.01 +field ' c' [explicit solve]: current solution: 9.702173e+01, current residual:5.862068e+01 +field 'mu' [explicit solve]: current solution: 6.339668e+00, current residual:3.790396e+00 +wall time: 0.000656128s + +time increment:10020 time: 10.02 +field ' c' [explicit solve]: current solution: 9.702089e+01, current residual:5.862014e+01 +field 'mu' [explicit solve]: current solution: 6.337399e+00, current residual:3.789063e+00 +wall time: 0.000640154s + +time increment:10030 time: 10.03 +field ' c' [explicit solve]: current solution: 9.702004e+01, current residual:5.861959e+01 +field 'mu' [explicit solve]: current solution: 6.335133e+00, current residual:3.787733e+00 +wall time: 0.00067997s + +time increment:10040 time: 10.04 +field ' c' [explicit solve]: current solution: 9.701920e+01, current residual:5.861905e+01 +field 'mu' [explicit solve]: current solution: 6.332872e+00, current residual:3.786406e+00 +wall time: 0.000723124s + +time increment:10050 time: 10.05 +field ' c' [explicit solve]: current solution: 9.701836e+01, current residual:5.861851e+01 +field 'mu' [explicit solve]: current solution: 6.330616e+00, current residual:3.785081e+00 +wall time: 0.000810862s + +time increment:10060 time: 10.06 +field ' c' [explicit solve]: current solution: 9.701752e+01, current residual:5.861798e+01 +field 'mu' [explicit solve]: current solution: 6.328363e+00, current residual:3.783759e+00 +wall time: 0.000703096s + +time increment:10070 time: 10.07 +field ' c' [explicit solve]: current solution: 9.701668e+01, current residual:5.861744e+01 +field 'mu' [explicit solve]: current solution: 6.326115e+00, current residual:3.782439e+00 +wall time: 0.000745058s + +time increment:10080 time: 10.08 +field ' c' [explicit solve]: current solution: 9.701584e+01, current residual:5.861690e+01 +field 'mu' [explicit solve]: current solution: 6.323871e+00, current residual:3.781122e+00 +wall time: 0.000669956s + +time increment:10090 time: 10.09 +field ' c' [explicit solve]: current solution: 9.701500e+01, current residual:5.861636e+01 +field 'mu' [explicit solve]: current solution: 6.321632e+00, current residual:3.779807e+00 +wall time: 0.000643969s + +time increment:10100 time: 10.1 +field ' c' [explicit solve]: current solution: 9.701416e+01, current residual:5.861582e+01 +field 'mu' [explicit solve]: current solution: 6.319396e+00, current residual:3.778495e+00 +wall time: 0.000654936s + +time increment:10110 time: 10.11 +field ' c' [explicit solve]: current solution: 9.701332e+01, current residual:5.861529e+01 +field 'mu' [explicit solve]: current solution: 6.317165e+00, current residual:3.777186e+00 +wall time: 0.000576973s + +time increment:10120 time: 10.12 +field ' c' [explicit solve]: current solution: 9.701249e+01, current residual:5.861475e+01 +field 'mu' [explicit solve]: current solution: 6.314938e+00, current residual:3.775879e+00 +wall time: 0.000642776s + +time increment:10130 time: 10.13 +field ' c' [explicit solve]: current solution: 9.701165e+01, current residual:5.861422e+01 +field 'mu' [explicit solve]: current solution: 6.312715e+00, current residual:3.774574e+00 +wall time: 0.000561953s + +time increment:10140 time: 10.14 +field ' c' [explicit solve]: current solution: 9.701082e+01, current residual:5.861368e+01 +field 'mu' [explicit solve]: current solution: 6.310497e+00, current residual:3.773272e+00 +wall time: 0.000555992s + +time increment:10150 time: 10.15 +field ' c' [explicit solve]: current solution: 9.700999e+01, current residual:5.861315e+01 +field 'mu' [explicit solve]: current solution: 6.308283e+00, current residual:3.771973e+00 +wall time: 0.000596046s + +time increment:10160 time: 10.16 +field ' c' [explicit solve]: current solution: 9.700915e+01, current residual:5.861262e+01 +field 'mu' [explicit solve]: current solution: 6.306073e+00, current residual:3.770676e+00 +wall time: 0.000615835s + +time increment:10170 time: 10.17 +field ' c' [explicit solve]: current solution: 9.700832e+01, current residual:5.861209e+01 +field 'mu' [explicit solve]: current solution: 6.303867e+00, current residual:3.769382e+00 +wall time: 0.000573874s + +time increment:10180 time: 10.18 +field ' c' [explicit solve]: current solution: 9.700749e+01, current residual:5.861155e+01 +field 'mu' [explicit solve]: current solution: 6.301665e+00, current residual:3.768090e+00 +wall time: 0.000575066s + +time increment:10190 time: 10.19 +field ' c' [explicit solve]: current solution: 9.700666e+01, current residual:5.861102e+01 +field 'mu' [explicit solve]: current solution: 6.299468e+00, current residual:3.766801e+00 +wall time: 0.000598907s + +time increment:10200 time: 10.2 +field ' c' [explicit solve]: current solution: 9.700584e+01, current residual:5.861049e+01 +field 'mu' [explicit solve]: current solution: 6.297274e+00, current residual:3.765514e+00 +wall time: 0.000576019s + +time increment:10210 time: 10.21 +field ' c' [explicit solve]: current solution: 9.700501e+01, current residual:5.860996e+01 +field 'mu' [explicit solve]: current solution: 6.295085e+00, current residual:3.764230e+00 +wall time: 0.000569105s + +time increment:10220 time: 10.22 +field ' c' [explicit solve]: current solution: 9.700418e+01, current residual:5.860943e+01 +field 'mu' [explicit solve]: current solution: 6.292900e+00, current residual:3.762948e+00 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0.000571012s + +time increment:10270 time: 10.27 +field ' c' [explicit solve]: current solution: 9.700007e+01, current residual:5.860679e+01 +field 'mu' [explicit solve]: current solution: 6.282037e+00, current residual:3.756577e+00 +wall time: 0.0005548s + +time increment:10280 time: 10.28 +field ' c' [explicit solve]: current solution: 9.699925e+01, current residual:5.860627e+01 +field 'mu' [explicit solve]: current solution: 6.279877e+00, current residual:3.755310e+00 +wall time: 0.000570059s + +time increment:10290 time: 10.29 +field ' c' [explicit solve]: current solution: 9.699843e+01, current residual:5.860574e+01 +field 'mu' [explicit solve]: current solution: 6.277720e+00, current residual:3.754046e+00 +wall time: 0.000641108s + +time increment:10300 time: 10.3 +field ' c' [explicit solve]: current solution: 9.699761e+01, current residual:5.860522e+01 +field 'mu' [explicit solve]: current solution: 6.275568e+00, current residual:3.752784e+00 +wall time: 0.000567913s + +time increment:10310 time: 10.31 +field ' c' [explicit solve]: current solution: 9.699679e+01, current residual:5.860469e+01 +field 'mu' [explicit solve]: current solution: 6.273420e+00, current residual:3.751525e+00 +wall time: 0.000571012s + +time increment:10320 time: 10.32 +field ' c' [explicit solve]: current solution: 9.699597e+01, current residual:5.860417e+01 +field 'mu' [explicit solve]: current solution: 6.271276e+00, current residual:3.750268e+00 +wall time: 0.000566006s + +time increment:10330 time: 10.33 +field ' c' [explicit solve]: current solution: 9.699516e+01, current residual:5.860365e+01 +field 'mu' [explicit solve]: current solution: 6.269136e+00, current residual:3.749013e+00 +wall time: 0.00070715s + +time increment:10340 time: 10.34 +field ' c' [explicit solve]: current solution: 9.699434e+01, current residual:5.860313e+01 +field 'mu' [explicit solve]: current solution: 6.267001e+00, current residual:3.747761e+00 +wall time: 0.000614882s + +time increment:10350 time: 10.35 +field ' c' [explicit solve]: current solution: 9.699353e+01, current residual:5.860260e+01 +field 'mu' [explicit solve]: current solution: 6.264869e+00, current residual:3.746511e+00 +wall time: 0.000565052s + +time increment:10360 time: 10.36 +field ' c' [explicit solve]: current solution: 9.699271e+01, current residual:5.860208e+01 +field 'mu' [explicit solve]: current solution: 6.262741e+00, current residual:3.745264e+00 +wall time: 0.000710011s + +time increment:10370 time: 10.37 +field ' c' [explicit solve]: current solution: 9.699190e+01, current residual:5.860156e+01 +field 'mu' [explicit solve]: current solution: 6.260617e+00, current residual:3.744019e+00 +wall time: 0.000686169s + +time increment:10380 time: 10.38 +field ' c' [explicit solve]: current solution: 9.699109e+01, current residual:5.860104e+01 +field 'mu' [explicit solve]: current solution: 6.258498e+00, current residual:3.742777e+00 +wall time: 0.000675917s + +time increment:10390 time: 10.39 +field ' c' [explicit solve]: current solution: 9.699028e+01, current residual:5.860052e+01 +field 'mu' [explicit solve]: current solution: 6.256382e+00, current residual:3.741537e+00 +wall time: 0.00059104s + +time increment:10400 time: 10.4 +field ' c' [explicit solve]: current solution: 9.698947e+01, current residual:5.860000e+01 +field 'mu' [explicit solve]: current solution: 6.254270e+00, current residual:3.740300e+00 +wall time: 0.000577927s + +time increment:10410 time: 10.41 +field ' c' [explicit solve]: current solution: 9.698866e+01, current residual:5.859949e+01 +field 'mu' [explicit solve]: current solution: 6.252163e+00, current residual:3.739064e+00 +wall time: 0.000562906s + +time increment:10420 time: 10.42 +field ' c' [explicit solve]: current solution: 9.698785e+01, current residual:5.859897e+01 +field 'mu' [explicit solve]: current solution: 6.250059e+00, current residual:3.737832e+00 +wall time: 0.000622988s + +time increment:10430 time: 10.43 +field ' c' [explicit solve]: current solution: 9.698705e+01, current residual:5.859845e+01 +field 'mu' [explicit solve]: current solution: 6.247959e+00, current residual:3.736601e+00 +wall time: 0.000566959s + +time increment:10440 time: 10.44 +field ' c' [explicit solve]: current solution: 9.698624e+01, current residual:5.859793e+01 +field 'mu' [explicit solve]: current solution: 6.245864e+00, current residual:3.735373e+00 +wall time: 0.000604868s + +time increment:10450 time: 10.45 +field ' c' [explicit solve]: current solution: 9.698544e+01, current residual:5.859742e+01 +field 'mu' [explicit solve]: current solution: 6.243772e+00, current residual:3.734148e+00 +wall time: 0.000582933s + +time increment:10460 time: 10.46 +field ' c' [explicit solve]: current solution: 9.698463e+01, current residual:5.859690e+01 +field 'mu' [explicit solve]: current solution: 6.241684e+00, current residual:3.732925e+00 +wall time: 0.000617981s + +time increment:10470 time: 10.47 +field ' c' [explicit solve]: current solution: 9.698383e+01, current residual:5.859639e+01 +field 'mu' [explicit solve]: current solution: 6.239600e+00, current residual:3.731704e+00 +wall time: 0.000678062s + +time increment:10480 time: 10.48 +field ' c' [explicit solve]: current solution: 9.698303e+01, current residual:5.859587e+01 +field 'mu' [explicit solve]: current solution: 6.237521e+00, current residual:3.730485e+00 +wall time: 0.000663996s + +time increment:10490 time: 10.49 +field ' c' [explicit solve]: current solution: 9.698222e+01, current residual:5.859536e+01 +field 'mu' [explicit solve]: current solution: 6.235445e+00, current residual:3.729269e+00 +wall time: 0.000573874s + +time increment:10500 time: 10.5 +field ' c' [explicit solve]: current solution: 9.698142e+01, current residual:5.859485e+01 +field 'mu' [explicit solve]: current solution: 6.233373e+00, current residual:3.728056e+00 +wall time: 0.000588894s + +time increment:10510 time: 10.51 +field ' c' [explicit solve]: current solution: 9.698062e+01, current residual:5.859433e+01 +field 'mu' [explicit solve]: current solution: 6.231305e+00, current residual:3.726844e+00 +wall time: 0.000654221s + +time increment:10520 time: 10.52 +field ' c' [explicit solve]: current solution: 9.697982e+01, current residual:5.859382e+01 +field 'mu' [explicit solve]: current solution: 6.229241e+00, current residual:3.725635e+00 +wall time: 0.000572205s + +time increment:10530 time: 10.53 +field ' c' [explicit solve]: current solution: 9.697903e+01, current residual:5.859331e+01 +field 'mu' [explicit solve]: current solution: 6.227180e+00, current residual:3.724429e+00 +wall time: 0.000710964s + +time increment:10540 time: 10.54 +field ' c' [explicit solve]: current solution: 9.697823e+01, current residual:5.859280e+01 +field 'mu' [explicit solve]: current solution: 6.225124e+00, current residual:3.723225e+00 +wall time: 0.000569105s + +time increment:10550 time: 10.55 +field ' c' [explicit solve]: current solution: 9.697743e+01, current residual:5.859229e+01 +field 'mu' [explicit solve]: current solution: 6.223072e+00, current residual:3.722023e+00 +wall time: 0.000568867s + +time increment:10560 time: 10.56 +field ' c' [explicit solve]: current solution: 9.697664e+01, current residual:5.859178e+01 +field 'mu' [explicit solve]: current solution: 6.221023e+00, current residual:3.720823e+00 +wall time: 0.00058198s + +time increment:10570 time: 10.57 +field ' c' [explicit solve]: current solution: 9.697584e+01, current residual:5.859127e+01 +field 'mu' [explicit solve]: current solution: 6.218979e+00, current residual:3.719626e+00 +wall time: 0.000631094s + +time increment:10580 time: 10.58 +field ' c' [explicit solve]: current solution: 9.697505e+01, current residual:5.859076e+01 +field 'mu' [explicit solve]: current solution: 6.216938e+00, current residual:3.718431e+00 +wall time: 0.000653982s + +time increment:10590 time: 10.59 +field ' c' [explicit solve]: current solution: 9.697426e+01, current residual:5.859025e+01 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solve]: current solution: 6.206792e+00, current residual:3.712491e+00 +wall time: 0.000653028s + +time increment:10640 time: 10.64 +field ' c' [explicit solve]: current solution: 9.697031e+01, current residual:5.858772e+01 +field 'mu' [explicit solve]: current solution: 6.204774e+00, current residual:3.711310e+00 +wall time: 0.000631094s + +time increment:10650 time: 10.65 +field ' c' [explicit solve]: current solution: 9.696952e+01, current residual:5.858722e+01 +field 'mu' [explicit solve]: current solution: 6.202761e+00, current residual:3.710132e+00 +wall time: 0.000586987s + +time increment:10660 time: 10.66 +field ' c' [explicit solve]: current solution: 9.696873e+01, current residual:5.858671e+01 +field 'mu' [explicit solve]: current solution: 6.200751e+00, current residual:3.708955e+00 +wall time: 0.000689983s + +time increment:10670 time: 10.67 +field ' c' [explicit solve]: current solution: 9.696795e+01, current residual:5.858621e+01 +field 'mu' [explicit solve]: current solution: 6.198744e+00, current residual:3.707781e+00 +wall time: 0.000653028s + +time increment:10680 time: 10.68 +field ' c' [explicit solve]: current solution: 9.696716e+01, current residual:5.858571e+01 +field 'mu' [explicit solve]: current solution: 6.196742e+00, current residual:3.706609e+00 +wall time: 0.00066185s + +time increment:10690 time: 10.69 +field ' c' [explicit solve]: current solution: 9.696638e+01, current residual:5.858520e+01 +field 'mu' [explicit solve]: current solution: 6.194744e+00, current residual:3.705440e+00 +wall time: 0.000748158s + +time increment:10700 time: 10.7 +field ' c' [explicit solve]: current solution: 9.696560e+01, current residual:5.858470e+01 +field 'mu' [explicit solve]: current solution: 6.192749e+00, current residual:3.704272e+00 +wall time: 0.000781059s + +time increment:10710 time: 10.71 +field ' c' [explicit solve]: current solution: 9.696481e+01, current residual:5.858420e+01 +field 'mu' [explicit solve]: current solution: 6.190758e+00, current residual:3.703107e+00 +wall time: 0.000804901s + +time increment:10720 time: 10.72 +field ' c' [explicit solve]: current solution: 9.696403e+01, current residual:5.858370e+01 +field 'mu' [explicit solve]: current solution: 6.188771e+00, current residual:3.701945e+00 +wall time: 0.000731945s + +time increment:10730 time: 10.73 +field ' c' [explicit solve]: current solution: 9.696325e+01, current residual:5.858320e+01 +field 'mu' [explicit solve]: current solution: 6.186787e+00, current residual:3.700784e+00 +wall time: 0.000690222s + +time increment:10740 time: 10.74 +field ' c' [explicit solve]: current solution: 9.696247e+01, current residual:5.858270e+01 +field 'mu' [explicit solve]: current solution: 6.184807e+00, current residual:3.699626e+00 +wall time: 0.000673056s + +time increment:10750 time: 10.75 +field ' c' [explicit solve]: current solution: 9.696169e+01, current residual:5.858220e+01 +field 'mu' [explicit solve]: current solution: 6.182832e+00, 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residual:3.693869e+00 +wall time: 0.000854015s + +time increment:10800 time: 10.8 +field ' c' [explicit solve]: current solution: 9.695781e+01, current residual:5.857971e+01 +field 'mu' [explicit solve]: current solution: 6.173008e+00, current residual:3.692724e+00 +wall time: 0.000658989s + +time increment:10810 time: 10.81 +field ' c' [explicit solve]: current solution: 9.695704e+01, current residual:5.857922e+01 +field 'mu' [explicit solve]: current solution: 6.171054e+00, current residual:3.691581e+00 +wall time: 0.000744104s + +time increment:10820 time: 10.82 +field ' c' [explicit solve]: current solution: 9.695626e+01, current residual:5.857872e+01 +field 'mu' [explicit solve]: current solution: 6.169105e+00, current residual:3.690441e+00 +wall time: 0.000712872s + +time increment:10830 time: 10.83 +field ' c' [explicit solve]: current solution: 9.695549e+01, current residual:5.857823e+01 +field 'mu' [explicit solve]: current solution: 6.167158e+00, current residual:3.689303e+00 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0.00065589s + +time increment:10880 time: 10.88 +field ' c' [explicit solve]: current solution: 9.695164e+01, current residual:5.857576e+01 +field 'mu' [explicit solve]: current solution: 6.157483e+00, current residual:3.683646e+00 +wall time: 0.000622034s + +time increment:10890 time: 10.89 +field ' c' [explicit solve]: current solution: 9.695088e+01, current residual:5.857527e+01 +field 'mu' [explicit solve]: current solution: 6.155559e+00, current residual:3.682521e+00 +wall time: 0.000640154s + +time increment:10900 time: 10.9 +field ' c' [explicit solve]: current solution: 9.695011e+01, current residual:5.857478e+01 +field 'mu' [explicit solve]: current solution: 6.153639e+00, current residual:3.681398e+00 +wall time: 0.000732899s + +time increment:10910 time: 10.91 +field ' c' [explicit solve]: current solution: 9.694935e+01, current residual:5.857429e+01 +field 'mu' [explicit solve]: current solution: 6.151722e+00, current residual:3.680278e+00 +wall time: 0.00067997s + +time increment:10920 time: 10.92 +field ' c' [explicit solve]: current solution: 9.694858e+01, current residual:5.857380e+01 +field 'mu' [explicit solve]: current solution: 6.149809e+00, current residual:3.679160e+00 +wall time: 0.000691891s + +time increment:10930 time: 10.93 +field ' c' [explicit solve]: current solution: 9.694782e+01, current residual:5.857331e+01 +field 'mu' [explicit solve]: current solution: 6.147899e+00, current residual:3.678044e+00 +wall time: 0.000645876s + +time increment:10940 time: 10.94 +field ' c' [explicit solve]: current solution: 9.694706e+01, current residual:5.857282e+01 +field 'mu' [explicit solve]: current solution: 6.145993e+00, current residual:3.676930e+00 +wall time: 0.000673056s + +time increment:10950 time: 10.95 +field ' c' [explicit solve]: current solution: 9.694629e+01, current residual:5.857233e+01 +field 'mu' [explicit solve]: current solution: 6.144091e+00, current residual:3.675818e+00 +wall time: 0.000674009s + +time increment:10960 time: 10.96 +field ' c' [explicit solve]: current solution: 9.694553e+01, current residual:5.857184e+01 +field 'mu' [explicit solve]: current solution: 6.142192e+00, current residual:3.674708e+00 +wall time: 0.000678062s + +time increment:10970 time: 10.97 +field ' c' [explicit solve]: current solution: 9.694477e+01, current residual:5.857135e+01 +field 'mu' [explicit solve]: current solution: 6.140297e+00, current residual:3.673601e+00 +wall time: 0.000633955s + +time increment:10980 time: 10.98 +field ' c' [explicit solve]: current solution: 9.694401e+01, current residual:5.857087e+01 +field 'mu' [explicit solve]: current solution: 6.138406e+00, current residual:3.672496e+00 +wall time: 0.000691891s + +time increment:10990 time: 10.99 +field ' c' [explicit solve]: current solution: 9.694325e+01, current residual:5.857038e+01 +field 'mu' [explicit solve]: current solution: 6.136518e+00, current residual:3.671393e+00 +wall time: 0.000658035s + +time increment:11000 time: 11 +field ' c' [explicit solve]: current solution: 9.694250e+01, current residual:5.856990e+01 +field 'mu' [explicit solve]: current solution: 6.134634e+00, current residual:3.670292e+00 +wall time: 0.000649929s + +time increment:11010 time: 11.01 +field ' c' [explicit solve]: current solution: 9.694174e+01, current residual:5.856941e+01 +field 'mu' [explicit solve]: current solution: 6.132753e+00, current residual:3.669193e+00 +wall time: 0.000663996s + +time increment:11020 time: 11.02 +field ' c' [explicit solve]: current solution: 9.694098e+01, current residual:5.856893e+01 +field 'mu' [explicit solve]: current solution: 6.130876e+00, current residual:3.668096e+00 +wall time: 0.000813007s + +time increment:11030 time: 11.03 +field ' c' [explicit solve]: current solution: 9.694023e+01, current residual:5.856844e+01 +field 'mu' [explicit solve]: current solution: 6.129002e+00, current residual:3.667002e+00 +wall time: 0.000695944s + +time increment:11040 time: 11.04 +field ' c' [explicit solve]: current solution: 9.693947e+01, current residual:5.856796e+01 +field 'mu' [explicit solve]: current solution: 6.127132e+00, current residual:3.665910e+00 +wall time: 0.00067997s + +time increment:11050 time: 11.05 +field ' c' [explicit solve]: current solution: 9.693872e+01, current residual:5.856747e+01 +field 'mu' [explicit solve]: current solution: 6.125266e+00, current residual:3.664819e+00 +wall time: 0.000726938s + +time increment:11060 time: 11.06 +field ' c' [explicit solve]: current solution: 9.693797e+01, current residual:5.856699e+01 +field 'mu' [explicit solve]: current solution: 6.123403e+00, current residual:3.663731e+00 +wall time: 0.000695944s + +time increment:11070 time: 11.07 +field ' c' [explicit solve]: current solution: 9.693721e+01, current residual:5.856651e+01 +field 'mu' [explicit solve]: current solution: 6.121544e+00, current residual:3.662645e+00 +wall time: 0.00069499s + +time increment:11080 time: 11.08 +field ' c' [explicit solve]: current solution: 9.693646e+01, current residual:5.856603e+01 +field 'mu' [explicit solve]: current solution: 6.119688e+00, current residual:3.661561e+00 +wall time: 0.000631094s + +time increment:11090 time: 11.09 +field ' c' [explicit solve]: current solution: 9.693571e+01, current residual:5.856555e+01 +field 'mu' [explicit solve]: current solution: 6.117836e+00, current residual:3.660480e+00 +wall time: 0.000631094s + +time increment:11100 time: 11.1 +field ' c' [explicit solve]: current solution: 9.693496e+01, current residual:5.856507e+01 +field 'mu' [explicit solve]: current solution: 6.115987e+00, current residual:3.659400e+00 +wall time: 0.000687122s + +time increment:11110 time: 11.11 +field ' c' [explicit solve]: current solution: 9.693421e+01, current residual:5.856459e+01 +field 'mu' [explicit solve]: current solution: 6.114141e+00, current residual:3.658322e+00 +wall time: 0.000648975s + +time increment:11120 time: 11.12 +field ' c' [explicit solve]: current solution: 9.693346e+01, current residual:5.856411e+01 +field 'mu' [explicit solve]: current solution: 6.112300e+00, current residual:3.657247e+00 +wall time: 0.000669003s + +time increment:11130 time: 11.13 +field ' c' [explicit solve]: current solution: 9.693272e+01, current residual:5.856363e+01 +field 'mu' [explicit solve]: current solution: 6.110461e+00, current residual:3.656174e+00 +wall time: 0.000725985s + +time increment:11140 time: 11.14 +field ' c' [explicit solve]: current solution: 9.693197e+01, current residual:5.856315e+01 +field 'mu' [explicit solve]: current solution: 6.108627e+00, current residual:3.655102e+00 +wall time: 0.000648975s + +time increment:11150 time: 11.15 +field ' c' [explicit solve]: current solution: 9.693122e+01, current residual:5.856267e+01 +field 'mu' [explicit solve]: current solution: 6.106795e+00, current residual:3.654033e+00 +wall time: 0.000644922s + +time increment:11160 time: 11.16 +field ' c' [explicit solve]: current solution: 9.693048e+01, current residual:5.856219e+01 +field 'mu' [explicit solve]: current solution: 6.104968e+00, current residual:3.652966e+00 +wall time: 0.000677109s + +time increment:11170 time: 11.17 +field ' c' [explicit solve]: current solution: 9.692974e+01, current residual:5.856172e+01 +field 'mu' [explicit solve]: current solution: 6.103143e+00, current residual:3.651901e+00 +wall time: 0.000666857s + +time increment:11180 time: 11.18 +field ' c' [explicit solve]: current solution: 9.692899e+01, current residual:5.856124e+01 +field 'mu' [explicit solve]: current solution: 6.101323e+00, current residual:3.650838e+00 +wall time: 0.000683069s + +time increment:11190 time: 11.19 +field ' c' [explicit solve]: current solution: 9.692825e+01, current residual:5.856076e+01 +field 'mu' [explicit solve]: current solution: 6.099505e+00, current residual:3.649778e+00 +wall time: 0.000663996s + +time increment:11200 time: 11.2 +field ' c' [explicit solve]: current solution: 9.692751e+01, current residual:5.856029e+01 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[explicit solve]: current solution: 6.090470e+00, current residual:3.644505e+00 +wall time: 0.000683069s + +time increment:11250 time: 11.25 +field ' c' [explicit solve]: current solution: 9.692381e+01, current residual:5.855792e+01 +field 'mu' [explicit solve]: current solution: 6.088673e+00, current residual:3.643456e+00 +wall time: 0.000669003s + +time increment:11260 time: 11.26 +field ' c' [explicit solve]: current solution: 9.692307e+01, current residual:5.855744e+01 +field 'mu' [explicit solve]: current solution: 6.086880e+00, current residual:3.642410e+00 +wall time: 0.000614166s + +time increment:11270 time: 11.27 +field ' c' [explicit solve]: current solution: 9.692234e+01, current residual:5.855697e+01 +field 'mu' [explicit solve]: current solution: 6.085090e+00, current residual:3.641365e+00 +wall time: 0.000712872s + +time increment:11280 time: 11.28 +field ' c' [explicit solve]: current solution: 9.692160e+01, current residual:5.855650e+01 +field 'mu' [explicit solve]: current solution: 6.083303e+00, current residual:3.640323e+00 +wall time: 0.000721931s + +time increment:11290 time: 11.29 +field ' c' [explicit solve]: current solution: 9.692087e+01, current residual:5.855603e+01 +field 'mu' [explicit solve]: current solution: 6.081520e+00, current residual:3.639283e+00 +wall time: 0.000643015s + +time increment:11300 time: 11.3 +field ' c' [explicit solve]: current solution: 9.692013e+01, current residual:5.855556e+01 +field 'mu' [explicit solve]: current solution: 6.079741e+00, current residual:3.638245e+00 +wall time: 0.000636816s + +time increment:11310 time: 11.31 +field ' c' [explicit solve]: current solution: 9.691940e+01, current residual:5.855509e+01 +field 'mu' [explicit solve]: current solution: 6.077964e+00, current residual:3.637208e+00 +wall time: 0.000666857s + +time increment:11320 time: 11.32 +field ' c' [explicit solve]: current solution: 9.691867e+01, current residual:5.855462e+01 +field 'mu' [explicit solve]: current solution: 6.076191e+00, current residual:3.636174e+00 +wall time: 0.000670195s + +time increment:11330 time: 11.33 +field ' c' [explicit solve]: current solution: 9.691794e+01, current residual:5.855415e+01 +field 'mu' [explicit solve]: current solution: 6.074422e+00, current residual:3.635142e+00 +wall time: 0.000599861s + +time increment:11340 time: 11.34 +field ' c' [explicit solve]: current solution: 9.691721e+01, current residual:5.855368e+01 +field 'mu' [explicit solve]: current solution: 6.072656e+00, current residual:3.634112e+00 +wall time: 0.000579834s + +time increment:11350 time: 11.35 +field ' c' [explicit solve]: current solution: 9.691648e+01, current residual:5.855321e+01 +field 'mu' [explicit solve]: current solution: 6.070893e+00, current residual:3.633084e+00 +wall time: 0.000617027s + +time increment:11360 time: 11.36 +field ' c' [explicit solve]: current solution: 9.691575e+01, current residual:5.855275e+01 +field 'mu' [explicit solve]: current solution: 6.069133e+00, current residual:3.632058e+00 +wall time: 0.000648975s + +time increment:11370 time: 11.37 +field ' c' [explicit solve]: current solution: 9.691502e+01, current residual:5.855228e+01 +field 'mu' [explicit solve]: current solution: 6.067377e+00, current residual:3.631034e+00 +wall time: 0.000628948s + +time increment:11380 time: 11.38 +field ' c' [explicit solve]: current solution: 9.691429e+01, current residual:5.855181e+01 +field 'mu' [explicit solve]: current solution: 6.065624e+00, current residual:3.630011e+00 +wall time: 0.000552893s + +time increment:11390 time: 11.39 +field ' c' [explicit solve]: current solution: 9.691356e+01, current residual:5.855135e+01 +field 'mu' [explicit solve]: current solution: 6.063875e+00, current residual:3.628991e+00 +wall time: 0.000654936s + +time increment:11400 time: 11.4 +field ' c' [explicit solve]: current solution: 9.691284e+01, current residual:5.855088e+01 +field 'mu' [explicit solve]: current solution: 6.062129e+00, current residual:3.627973e+00 +wall time: 0.000555038s + +time increment:11410 time: 11.41 +field ' c' [explicit solve]: current solution: 9.691211e+01, current residual:5.855042e+01 +field 'mu' [explicit solve]: current solution: 6.060386e+00, current residual:3.626957e+00 +wall time: 0.00056386s + +time increment:11420 time: 11.42 +field ' c' [explicit solve]: current solution: 9.691139e+01, current residual:5.854995e+01 +field 'mu' [explicit solve]: current solution: 6.058647e+00, current residual:3.625943e+00 +wall time: 0.000653982s + +time increment:11430 time: 11.43 +field ' c' [explicit solve]: current solution: 9.691066e+01, current residual:5.854949e+01 +field 'mu' [explicit solve]: current solution: 6.056910e+00, current residual:3.624931e+00 +wall time: 0.000569105s + +time increment:11440 time: 11.44 +field ' c' [explicit solve]: current solution: 9.690994e+01, current residual:5.854902e+01 +field 'mu' [explicit solve]: current solution: 6.055178e+00, current residual:3.623921e+00 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0.000742912s + +time increment:11490 time: 11.49 +field ' c' [explicit solve]: current solution: 9.690634e+01, current residual:5.854671e+01 +field 'mu' [explicit solve]: current solution: 6.046563e+00, current residual:3.618899e+00 +wall time: 0.000599146s + +time increment:11500 time: 11.5 +field ' c' [explicit solve]: current solution: 9.690562e+01, current residual:5.854625e+01 +field 'mu' [explicit solve]: current solution: 6.044850e+00, current residual:3.617901e+00 +wall time: 0.000649929s + +time increment:11510 time: 11.51 +field ' c' [explicit solve]: current solution: 9.690490e+01, current residual:5.854579e+01 +field 'mu' [explicit solve]: current solution: 6.043140e+00, current residual:3.616905e+00 +wall time: 0.000711203s + +time increment:11520 time: 11.52 +field ' c' [explicit solve]: current solution: 9.690418e+01, current residual:5.854533e+01 +field 'mu' [explicit solve]: current solution: 6.041433e+00, current residual:3.615910e+00 +wall time: 0.000714064s + +time increment:11530 time: 11.53 +field ' c' [explicit solve]: current solution: 9.690347e+01, current residual:5.854487e+01 +field 'mu' [explicit solve]: current solution: 6.039730e+00, current residual:3.614918e+00 +wall time: 0.000681162s + +time increment:11540 time: 11.54 +field ' c' [explicit solve]: current solution: 9.690275e+01, current residual:5.854441e+01 +field 'mu' [explicit solve]: current solution: 6.038030e+00, current residual:3.613927e+00 +wall time: 0.000663996s + +time increment:11550 time: 11.55 +field ' c' [explicit solve]: current solution: 9.690204e+01, current residual:5.854396e+01 +field 'mu' [explicit solve]: current solution: 6.036333e+00, current residual:3.612939e+00 +wall time: 0.00067687s + +time increment:11560 time: 11.56 +field ' c' [explicit solve]: current solution: 9.690132e+01, current residual:5.854350e+01 +field 'mu' [explicit solve]: current solution: 6.034639e+00, current residual:3.611952e+00 +wall time: 0.000672102s + +time increment:11570 time: 11.57 +field ' c' [explicit solve]: current solution: 9.690061e+01, current residual:5.854304e+01 +field 'mu' [explicit solve]: current solution: 6.032949e+00, current residual:3.610967e+00 +wall time: 0.000823975s + +time increment:11580 time: 11.58 +field ' c' [explicit solve]: current solution: 9.689990e+01, current residual:5.854258e+01 +field 'mu' [explicit solve]: current solution: 6.031262e+00, current residual:3.609984e+00 +wall time: 0.000726938s + +time increment:11590 time: 11.59 +field ' c' [explicit solve]: current solution: 9.689918e+01, current residual:5.854213e+01 +field 'mu' [explicit solve]: current solution: 6.029578e+00, current residual:3.609003e+00 +wall time: 0.000670195s + +time increment:11600 time: 11.6 +field ' c' [explicit solve]: current solution: 9.689847e+01, current residual:5.854167e+01 +field 'mu' [explicit solve]: current solution: 6.027897e+00, current residual:3.608025e+00 +wall time: 0.00075388s + +time increment:11610 time: 11.61 +field ' c' [explicit solve]: current solution: 9.689776e+01, current residual:5.854122e+01 +field 'mu' [explicit solve]: current solution: 6.026220e+00, current residual:3.607047e+00 +wall time: 0.000653028s + +time increment:11620 time: 11.62 +field ' c' [explicit solve]: current solution: 9.689705e+01, current residual:5.854076e+01 +field 'mu' [explicit solve]: current solution: 6.024545e+00, current residual:3.606072e+00 +wall time: 0.000681877s + +time increment:11630 time: 11.63 +field ' c' [explicit solve]: current solution: 9.689634e+01, current residual:5.854031e+01 +field 'mu' [explicit solve]: current solution: 6.022874e+00, current residual:3.605099e+00 +wall time: 0.00079298s + +time increment:11640 time: 11.64 +field ' c' [explicit solve]: current solution: 9.689564e+01, current residual:5.853985e+01 +field 'mu' [explicit solve]: current solution: 6.021206e+00, current residual:3.604128e+00 +wall time: 0.000700951s + +time increment:11650 time: 11.65 +field ' c' [explicit solve]: current solution: 9.689493e+01, current residual:5.853940e+01 +field 'mu' [explicit solve]: current solution: 6.019541e+00, current residual:3.603158e+00 +wall time: 0.000690937s + +time increment:11660 time: 11.66 +field ' c' [explicit solve]: current solution: 9.689422e+01, current residual:5.853895e+01 +field 'mu' [explicit solve]: current solution: 6.017880e+00, current residual:3.602191e+00 +wall time: 0.000689983s + +time increment:11670 time: 11.67 +field ' c' [explicit solve]: current solution: 9.689352e+01, current residual:5.853849e+01 +field 'mu' [explicit solve]: current solution: 6.016221e+00, current residual:3.601225e+00 +wall time: 0.000669003s + +time increment:11680 time: 11.68 +field ' c' [explicit solve]: current solution: 9.689281e+01, current residual:5.853804e+01 +field 'mu' [explicit solve]: current solution: 6.014566e+00, current residual:3.600262e+00 +wall time: 0.00067091s + +time increment:11690 time: 11.69 +field ' c' [explicit solve]: current solution: 9.689211e+01, current residual:5.853759e+01 +field 'mu' [explicit solve]: current solution: 6.012914e+00, current residual:3.599300e+00 +wall time: 0.00070405s + +time increment:11700 time: 11.7 +field ' c' [explicit solve]: current solution: 9.689140e+01, current residual:5.853714e+01 +field 'mu' [explicit solve]: current solution: 6.011265e+00, current residual:3.598340e+00 +wall time: 0.000686884s + +time increment:11710 time: 11.71 +field ' c' [explicit solve]: current solution: 9.689070e+01, current residual:5.853669e+01 +field 'mu' [explicit solve]: current solution: 6.009620e+00, current residual:3.597382e+00 +wall time: 0.000754118s + +time increment:11720 time: 11.72 +field ' c' [explicit solve]: current solution: 9.689000e+01, current residual:5.853624e+01 +field 'mu' [explicit solve]: current solution: 6.007977e+00, current residual:3.596426e+00 +wall time: 0.000674963s + +time increment:11730 time: 11.73 +field ' c' [explicit solve]: current solution: 9.688930e+01, current residual:5.853579e+01 +field 'mu' [explicit solve]: current solution: 6.006337e+00, current residual:3.595471e+00 +wall time: 0.000663042s + +time increment:11740 time: 11.74 +field ' c' [explicit solve]: current solution: 9.688860e+01, current residual:5.853534e+01 +field 'mu' [explicit solve]: current solution: 6.004701e+00, current residual:3.594519e+00 +wall time: 0.000672817s + +time increment:11750 time: 11.75 +field ' c' [explicit solve]: current solution: 9.688790e+01, current residual:5.853489e+01 +field 'mu' [explicit solve]: current solution: 6.003068e+00, current residual:3.593568e+00 +wall time: 0.000668049s + +time increment:11760 time: 11.76 +field ' c' [explicit solve]: current solution: 9.688720e+01, current residual:5.853444e+01 +field 'mu' [explicit solve]: current solution: 6.001438e+00, current residual:3.592620e+00 +wall time: 0.000627995s + +time increment:11770 time: 11.77 +field ' c' [explicit solve]: current solution: 9.688650e+01, current residual:5.853399e+01 +field 'mu' [explicit solve]: current solution: 5.999811e+00, current residual:3.591673e+00 +wall time: 0.000696182s + +time increment:11780 time: 11.78 +field ' c' [explicit solve]: current solution: 9.688580e+01, current residual:5.853354e+01 +field 'mu' [explicit solve]: current solution: 5.998187e+00, current residual:3.590728e+00 +wall time: 0.000623941s + +time increment:11790 time: 11.79 +field ' c' [explicit solve]: current solution: 9.688510e+01, current residual:5.853310e+01 +field 'mu' [explicit solve]: current solution: 5.996566e+00, current residual:3.589785e+00 +wall time: 0.000642776s + +time increment:11800 time: 11.8 +field ' c' [explicit solve]: current solution: 9.688441e+01, current residual:5.853265e+01 +field 'mu' [explicit solve]: current solution: 5.994949e+00, current residual:3.588844e+00 +wall time: 0.000730991s + +time increment:11810 time: 11.81 +field ' c' [explicit solve]: current solution: 9.688371e+01, current residual:5.853220e+01 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solve]: current solution: 5.986907e+00, current residual:3.584165e+00 +wall time: 0.000669003s + +time increment:11860 time: 11.86 +field ' c' [explicit solve]: current solution: 9.688024e+01, current residual:5.852998e+01 +field 'mu' [explicit solve]: current solution: 5.985307e+00, current residual:3.583235e+00 +wall time: 0.000667095s + +time increment:11870 time: 11.87 +field ' c' [explicit solve]: current solution: 9.687955e+01, current residual:5.852954e+01 +field 'mu' [explicit solve]: current solution: 5.983711e+00, current residual:3.582307e+00 +wall time: 0.000571012s + +time increment:11880 time: 11.88 +field ' c' [explicit solve]: current solution: 9.687886e+01, current residual:5.852909e+01 +field 'mu' [explicit solve]: current solution: 5.982118e+00, current residual:3.581380e+00 +wall time: 0.000670195s + +time increment:11890 time: 11.89 +field ' c' [explicit solve]: current solution: 9.687817e+01, current residual:5.852865e+01 +field 'mu' [explicit solve]: current solution: 5.980528e+00, current residual:3.580455e+00 +wall time: 0.000652075s + +time increment:11900 time: 11.9 +field ' c' [explicit solve]: current solution: 9.687748e+01, current residual:5.852821e+01 +field 'mu' [explicit solve]: current solution: 5.978941e+00, current residual:3.579533e+00 +wall time: 0.000706911s + +time increment:11910 time: 11.91 +field ' c' [explicit solve]: current solution: 9.687680e+01, current residual:5.852777e+01 +field 'mu' [explicit solve]: current solution: 5.977357e+00, current residual:3.578612e+00 +wall time: 0.000560999s + +time increment:11920 time: 11.92 +field ' c' [explicit solve]: current solution: 9.687611e+01, current residual:5.852733e+01 +field 'mu' [explicit solve]: current solution: 5.975776e+00, current residual:3.577692e+00 +wall time: 0.000572205s + +time increment:11930 time: 11.93 +field ' c' [explicit solve]: current solution: 9.687542e+01, current residual:5.852689e+01 +field 'mu' [explicit solve]: current solution: 5.974198e+00, current residual:3.576775e+00 +wall time: 0.000696898s + +time increment:11940 time: 11.94 +field ' c' [explicit solve]: current solution: 9.687473e+01, current residual:5.852645e+01 +field 'mu' [explicit solve]: current solution: 5.972624e+00, current residual:3.575859e+00 +wall time: 0.000560999s + +time increment:11950 time: 11.95 +field ' c' [explicit solve]: current solution: 9.687405e+01, current residual:5.852601e+01 +field 'mu' [explicit solve]: current solution: 5.971052e+00, current residual:3.574945e+00 +wall time: 0.000566959s + +time increment:11960 time: 11.96 +field ' c' [explicit solve]: current solution: 9.687336e+01, current residual:5.852557e+01 +field 'mu' [explicit solve]: current solution: 5.969483e+00, current residual:3.574033e+00 +wall time: 0.000573158s + +time increment:11970 time: 11.97 +field ' c' [explicit solve]: current solution: 9.687268e+01, current residual:5.852513e+01 +field 'mu' [explicit solve]: current solution: 5.967917e+00, current residual:3.573123e+00 +wall time: 0.000577927s + +time increment:11980 time: 11.98 +field ' c' [explicit solve]: current solution: 9.687200e+01, current residual:5.852469e+01 +field 'mu' [explicit solve]: current solution: 5.966354e+00, current residual:3.572215e+00 +wall time: 0.000663996s + +time increment:11990 time: 11.99 +field ' c' [explicit solve]: current solution: 9.687131e+01, current residual:5.852425e+01 +field 'mu' [explicit solve]: current solution: 5.964795e+00, current residual:3.571308e+00 +wall time: 0.000613928s + +time increment:12000 time: 12 +field ' c' [explicit solve]: current solution: 9.687063e+01, current residual:5.852382e+01 +field 'mu' [explicit solve]: current solution: 5.963238e+00, current residual:3.570403e+00 +wall time: 0.000746965s + +time increment:12010 time: 12.01 +field ' c' [explicit solve]: current solution: 9.686995e+01, current residual:5.852338e+01 +field 'mu' [explicit solve]: current solution: 5.961684e+00, current residual:3.569500e+00 +wall time: 0.000623941s + +time increment:12020 time: 12.02 +field ' c' [explicit solve]: current solution: 9.686927e+01, current residual:5.852294e+01 +field 'mu' [explicit solve]: current solution: 5.960133e+00, current residual:3.568599e+00 +wall time: 0.000723839s + +time increment:12030 time: 12.03 +field ' c' [explicit solve]: current solution: 9.686859e+01, current residual:5.852251e+01 +field 'mu' [explicit solve]: current solution: 5.958585e+00, current residual:3.567699e+00 +wall time: 0.000568867s + +time increment:12040 time: 12.04 +field ' c' [explicit solve]: current solution: 9.686791e+01, current residual:5.852207e+01 +field 'mu' [explicit solve]: current solution: 5.957040e+00, current residual:3.566801e+00 +wall time: 0.000669003s + +time increment:12050 time: 12.05 +field ' c' [explicit solve]: current solution: 9.686723e+01, current residual:5.852163e+01 +field 'mu' [explicit solve]: current solution: 5.955498e+00, current residual:3.565905e+00 +wall time: 0.000566959s + +time increment:12060 time: 12.06 +field ' c' [explicit solve]: current solution: 9.686655e+01, current residual:5.852120e+01 +field 'mu' [explicit solve]: current solution: 5.953959e+00, current residual:3.565011e+00 +wall time: 0.000677824s + +time increment:12070 time: 12.07 +field ' c' [explicit solve]: current solution: 9.686588e+01, current residual:5.852077e+01 +field 'mu' [explicit solve]: current solution: 5.952423e+00, current residual:3.564119e+00 +wall time: 0.000658989s + +time increment:12080 time: 12.08 +field ' c' [explicit solve]: current solution: 9.686520e+01, current residual:5.852033e+01 +field 'mu' [explicit solve]: current solution: 5.950890e+00, current residual:3.563228e+00 +wall time: 0.000758886s + +time increment:12090 time: 12.09 +field ' c' [explicit solve]: current solution: 9.686452e+01, current residual:5.851990e+01 +field 'mu' [explicit solve]: current solution: 5.949360e+00, current residual:3.562339e+00 +wall time: 0.000684023s + +time increment:12100 time: 12.1 +field ' c' [explicit solve]: current solution: 9.686385e+01, current residual:5.851946e+01 +field 'mu' [explicit solve]: current solution: 5.947833e+00, current residual:3.561452e+00 +wall time: 0.000571966s + +time increment:12110 time: 12.11 +field ' c' [explicit solve]: current solution: 9.686317e+01, current residual:5.851903e+01 +field 'mu' [explicit solve]: current solution: 5.946308e+00, current residual:3.560566e+00 +wall time: 0.000679016s + +time increment:12120 time: 12.12 +field ' c' [explicit solve]: current solution: 9.686250e+01, current residual:5.851860e+01 +field 'mu' [explicit solve]: current solution: 5.944787e+00, current residual:3.559683e+00 +wall time: 0.000662088s + +time increment:12130 time: 12.13 +field ' c' [explicit solve]: current solution: 9.686183e+01, current residual:5.851817e+01 +field 'mu' [explicit solve]: current solution: 5.943269e+00, current residual:3.558801e+00 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0.000560999s + +time increment:12180 time: 12.18 +field ' c' [explicit solve]: current solution: 9.685847e+01, current residual:5.851602e+01 +field 'mu' [explicit solve]: current solution: 5.935720e+00, current residual:3.554417e+00 +wall time: 0.000653982s + +time increment:12190 time: 12.19 +field ' c' [explicit solve]: current solution: 9.685780e+01, current residual:5.851559e+01 +field 'mu' [explicit solve]: current solution: 5.934219e+00, current residual:3.553546e+00 +wall time: 0.000706911s + +time increment:12200 time: 12.2 +field ' c' [explicit solve]: current solution: 9.685713e+01, current residual:5.851516e+01 +field 'mu' [explicit solve]: current solution: 5.932721e+00, current residual:3.552676e+00 +wall time: 0.000576019s + +time increment:12210 time: 12.21 +field ' c' [explicit solve]: current solution: 9.685647e+01, current residual:5.851473e+01 +field 'mu' [explicit solve]: current solution: 5.931226e+00, current residual:3.551808e+00 +wall time: 0.000662088s + +time increment:12220 time: 12.22 +field ' c' [explicit solve]: current solution: 9.685580e+01, current residual:5.851430e+01 +field 'mu' [explicit solve]: current solution: 5.929733e+00, current residual:3.550941e+00 +wall time: 0.000660896s + +time increment:12230 time: 12.23 +field ' c' [explicit solve]: current solution: 9.685513e+01, current residual:5.851388e+01 +field 'mu' [explicit solve]: current solution: 5.928244e+00, current residual:3.550077e+00 +wall time: 0.000674009s + +time increment:12240 time: 12.24 +field ' c' [explicit solve]: current solution: 9.685447e+01, current residual:5.851345e+01 +field 'mu' [explicit solve]: current solution: 5.926757e+00, current residual:3.549214e+00 +wall time: 0.000650883s + +time increment:12250 time: 12.25 +field ' c' [explicit solve]: current solution: 9.685380e+01, current residual:5.851302e+01 +field 'mu' [explicit solve]: current solution: 5.925273e+00, current residual:3.548352e+00 +wall time: 0.000660181s + +time increment:12260 time: 12.26 +field ' c' [explicit solve]: current solution: 9.685314e+01, current residual:5.851260e+01 +field 'mu' [explicit solve]: current solution: 5.923792e+00, current residual:3.547493e+00 +wall time: 0.000777006s + +time increment:12270 time: 12.27 +field ' c' [explicit solve]: current solution: 9.685248e+01, current residual:5.851217e+01 +field 'mu' [explicit solve]: current solution: 5.922314e+00, current residual:3.546635e+00 +wall time: 0.000801802s + +time increment:12280 time: 12.28 +field ' c' [explicit solve]: current solution: 9.685181e+01, current residual:5.851174e+01 +field 'mu' [explicit solve]: current solution: 5.920839e+00, current residual:3.545779e+00 +wall time: 0.00067687s + +time increment:12290 time: 12.29 +field ' c' [explicit solve]: current solution: 9.685115e+01, current residual:5.851132e+01 +field 'mu' [explicit solve]: current solution: 5.919367e+00, current residual:3.544924e+00 +wall time: 0.00067997s + +time increment:12300 time: 12.3 +field ' c' [explicit solve]: current solution: 9.685049e+01, current residual:5.851090e+01 +field 'mu' [explicit solve]: current solution: 5.917897e+00, current residual:3.544071e+00 +wall time: 0.000825882s + +time increment:12310 time: 12.31 +field ' c' [explicit solve]: current solution: 9.684983e+01, current residual:5.851047e+01 +field 'mu' [explicit solve]: current solution: 5.916430e+00, current residual:3.543220e+00 +wall time: 0.000742912s + +time increment:12320 time: 12.32 +field ' c' [explicit solve]: current solution: 9.684917e+01, current residual:5.851005e+01 +field 'mu' [explicit solve]: current solution: 5.914966e+00, current residual:3.542371e+00 +wall time: 0.000646114s + +time increment:12330 time: 12.33 +field ' c' [explicit solve]: current solution: 9.684851e+01, current residual:5.850963e+01 +field 'mu' [explicit solve]: current solution: 5.913505e+00, current residual:3.541523e+00 +wall time: 0.00067687s + +time increment:12340 time: 12.34 +field ' c' [explicit solve]: current solution: 9.684785e+01, current residual:5.850920e+01 +field 'mu' [explicit solve]: current solution: 5.912047e+00, current residual:3.540677e+00 +wall time: 0.000728846s + +time increment:12350 time: 12.35 +field ' c' [explicit solve]: current solution: 9.684719e+01, current residual:5.850878e+01 +field 'mu' [explicit solve]: current solution: 5.910592e+00, current residual:3.539833e+00 +wall time: 0.000675917s + +time increment:12360 time: 12.36 +field ' c' [explicit solve]: current solution: 9.684653e+01, current residual:5.850836e+01 +field 'mu' [explicit solve]: current solution: 5.909139e+00, current residual:3.538990e+00 +wall time: 0.000696182s + +time increment:12370 time: 12.37 +field ' c' [explicit solve]: current solution: 9.684588e+01, current residual:5.850794e+01 +field 'mu' [explicit solve]: current solution: 5.907689e+00, current residual:3.538149e+00 +wall time: 0.000710964s + +time increment:12380 time: 12.38 +field ' c' [explicit solve]: current solution: 9.684522e+01, current residual:5.850752e+01 +field 'mu' [explicit solve]: current solution: 5.906242e+00, current residual:3.537310e+00 +wall time: 0.000674009s + +time increment:12390 time: 12.39 +field ' c' [explicit solve]: current solution: 9.684456e+01, current residual:5.850710e+01 +field 'mu' [explicit solve]: current solution: 5.904798e+00, current residual:3.536472e+00 +wall time: 0.000746965s + +time increment:12400 time: 12.4 +field ' c' [explicit solve]: current solution: 9.684391e+01, current residual:5.850668e+01 +field 'mu' [explicit solve]: current solution: 5.903356e+00, current residual:3.535636e+00 +wall time: 0.00062418s + +time increment:12410 time: 12.41 +field ' c' [explicit solve]: current solution: 9.684326e+01, current residual:5.850626e+01 +field 'mu' [explicit solve]: current solution: 5.901917e+00, current residual:3.534802e+00 +wall time: 0.000638008s + +time increment:12420 time: 12.42 +field ' c' [explicit solve]: current solution: 9.684260e+01, current residual:5.850584e+01 +field 'mu' [explicit solve]: current solution: 5.900482e+00, current residual:3.533969e+00 +wall time: 0.00072813s + +time increment:12430 time: 12.43 +field ' c' [explicit solve]: current solution: 9.684195e+01, current residual:5.850542e+01 +field 'mu' [explicit solve]: current solution: 5.899048e+00, current residual:3.533138e+00 +wall time: 0.000793934s + +time increment:12440 time: 12.44 +field ' c' [explicit solve]: current solution: 9.684130e+01, current residual:5.850500e+01 +field 'mu' [explicit solve]: current solution: 5.897618e+00, current residual:3.532308e+00 +wall time: 0.000656128s + +time increment:12450 time: 12.45 +field ' c' [explicit solve]: current solution: 9.684065e+01, current residual:5.850458e+01 +field 'mu' [explicit solve]: current solution: 5.896190e+00, current residual:3.531481e+00 +wall time: 0.000656128s + +time increment:12460 time: 12.46 +field ' c' [explicit solve]: current solution: 9.683999e+01, current residual:5.850416e+01 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solve]: current solution: 5.889093e+00, current residual:3.527366e+00 +wall time: 0.00067091s + +time increment:12510 time: 12.51 +field ' c' [explicit solve]: current solution: 9.683675e+01, current residual:5.850208e+01 +field 'mu' [explicit solve]: current solution: 5.887682e+00, current residual:3.526548e+00 +wall time: 0.000652075s + +time increment:12520 time: 12.52 +field ' c' [explicit solve]: current solution: 9.683610e+01, current residual:5.850167e+01 +field 'mu' [explicit solve]: current solution: 5.886273e+00, current residual:3.525732e+00 +wall time: 0.000740051s + +time increment:12530 time: 12.53 +field ' c' [explicit solve]: current solution: 9.683546e+01, current residual:5.850125e+01 +field 'mu' [explicit solve]: current solution: 5.884868e+00, current residual:3.524917e+00 +wall time: 0.000661135s + +time increment:12540 time: 12.54 +field ' c' [explicit solve]: current solution: 9.683481e+01, current residual:5.850084e+01 +field 'mu' [explicit solve]: current solution: 5.883465e+00, current residual:3.524104e+00 +wall time: 0.000708103s + +time increment:12550 time: 12.55 +field ' c' [explicit solve]: current solution: 9.683417e+01, current residual:5.850042e+01 +field 'mu' [explicit solve]: current solution: 5.882064e+00, current residual:3.523293e+00 +wall time: 0.000678062s + +time increment:12560 time: 12.56 +field ' c' [explicit solve]: current solution: 9.683352e+01, current residual:5.850001e+01 +field 'mu' [explicit solve]: current solution: 5.880667e+00, current residual:3.522483e+00 +wall time: 0.000663996s + +time increment:12570 time: 12.57 +field ' c' [explicit solve]: current solution: 9.683288e+01, current residual:5.849960e+01 +field 'mu' [explicit solve]: current solution: 5.879272e+00, current residual:3.521674e+00 +wall time: 0.000660181s + +time increment:12580 time: 12.58 +field ' c' [explicit solve]: current solution: 9.683223e+01, current residual:5.849919e+01 +field 'mu' [explicit solve]: current solution: 5.877879e+00, current residual:3.520868e+00 +wall time: 0.000628948s + +time increment:12590 time: 12.59 +field ' c' [explicit solve]: current solution: 9.683159e+01, current residual:5.849877e+01 +field 'mu' [explicit solve]: current solution: 5.876490e+00, current residual:3.520063e+00 +wall time: 0.00064683s + +time increment:12600 time: 12.6 +field ' c' [explicit solve]: current solution: 9.683095e+01, current residual:5.849836e+01 +field 'mu' [explicit solve]: current solution: 5.875103e+00, current residual:3.519259e+00 +wall time: 0.000567913s + +time increment:12610 time: 12.61 +field ' c' [explicit solve]: current solution: 9.683031e+01, current residual:5.849795e+01 +field 'mu' [explicit solve]: current solution: 5.873719e+00, current residual:3.518457e+00 +wall time: 0.000576019s + +time increment:12620 time: 12.62 +field ' c' [explicit solve]: current solution: 9.682967e+01, current residual:5.849754e+01 +field 'mu' [explicit solve]: current solution: 5.872337e+00, current residual:3.517657e+00 +wall time: 0.000702143s + +time increment:12630 time: 12.63 +field ' c' [explicit solve]: current solution: 9.682903e+01, current residual:5.849713e+01 +field 'mu' [explicit solve]: current solution: 5.870958e+00, current residual:3.516858e+00 +wall time: 0.000556946s + +time increment:12640 time: 12.64 +field ' c' [explicit solve]: current solution: 9.682839e+01, current residual:5.849672e+01 +field 'mu' [explicit solve]: current solution: 5.869582e+00, current residual:3.516061e+00 +wall time: 0.000571966s + +time increment:12650 time: 12.65 +field ' c' [explicit solve]: current solution: 9.682775e+01, current residual:5.849631e+01 +field 'mu' [explicit solve]: current solution: 5.868209e+00, current residual:3.515266e+00 +wall time: 0.000566959s + +time increment:12660 time: 12.66 +field ' c' [explicit solve]: current solution: 9.682711e+01, current residual:5.849590e+01 +field 'mu' [explicit solve]: current solution: 5.866838e+00, current 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12.83 +field ' c' [explicit solve]: current solution: 9.681636e+01, current residual:5.848900e+01 +field 'mu' [explicit solve]: current solution: 5.843935e+00, current residual:3.501215e+00 +wall time: 0.00062108s + +time increment:12840 time: 12.84 +field ' c' [explicit solve]: current solution: 9.681574e+01, current residual:5.848860e+01 +field 'mu' [explicit solve]: current solution: 5.842612e+00, current residual:3.500449e+00 +wall time: 0.000569105s + +time increment:12850 time: 12.85 +field ' c' [explicit solve]: current solution: 9.681511e+01, current residual:5.848820e+01 +field 'mu' [explicit solve]: current solution: 5.841290e+00, current residual:3.499684e+00 +wall time: 0.000649214s + +time increment:12860 time: 12.86 +field ' c' [explicit solve]: current solution: 9.681448e+01, current residual:5.848780e+01 +field 'mu' [explicit solve]: current solution: 5.839972e+00, current residual:3.498922e+00 +wall time: 0.000564098s + +time increment:12870 time: 12.87 +field ' c' [explicit solve]: current solution: 9.681386e+01, current residual:5.848740e+01 +field 'mu' [explicit solve]: current solution: 5.838656e+00, current residual:3.498160e+00 +wall time: 0.000661135s + +time increment:12880 time: 12.88 +field ' c' [explicit solve]: current solution: 9.681324e+01, current residual:5.848700e+01 +field 'mu' [explicit solve]: current solution: 5.837342e+00, current residual:3.497400e+00 +wall time: 0.000577927s + +time increment:12890 time: 12.89 +field ' c' [explicit solve]: current solution: 9.681261e+01, current residual:5.848660e+01 +field 'mu' [explicit solve]: current solution: 5.836031e+00, current residual:3.496642e+00 +wall time: 0.000566959s + +time increment:12900 time: 12.9 +field ' c' [explicit solve]: current solution: 9.681199e+01, current residual:5.848620e+01 +field 'mu' [explicit solve]: current solution: 5.834723e+00, current residual:3.495885e+00 +wall time: 0.000632048s + +time increment:12910 time: 12.91 +field ' c' [explicit solve]: current solution: 9.681137e+01, current residual:5.848580e+01 +field 'mu' [explicit solve]: current solution: 5.833417e+00, current residual:3.495130e+00 +wall time: 0.000691175s + +time increment:12920 time: 12.92 +field ' c' [explicit solve]: current solution: 9.681074e+01, current residual:5.848540e+01 +field 'mu' [explicit solve]: current solution: 5.832113e+00, current residual:3.494376e+00 +wall time: 0.000589132s + +time increment:12930 time: 12.93 +field ' c' [explicit solve]: current solution: 9.681012e+01, current residual:5.848500e+01 +field 'mu' [explicit solve]: current solution: 5.830812e+00, current residual:3.493624e+00 +wall time: 0.000622034s + +time increment:12940 time: 12.94 +field ' c' [explicit solve]: current solution: 9.680950e+01, current residual:5.848460e+01 +field 'mu' [explicit solve]: current solution: 5.829514e+00, current residual:3.492873e+00 +wall time: 0.00058794s + +time increment:12950 time: 12.95 +field ' c' [explicit solve]: current solution: 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[explicit solve]: current solution: 5.802825e+00, current residual:3.477451e+00 +wall time: 0.000566959s + +time increment:13160 time: 13.16 +field ' c' [explicit solve]: current solution: 9.679600e+01, current residual:5.847594e+01 +field 'mu' [explicit solve]: current solution: 5.801581e+00, current residual:3.476732e+00 +wall time: 0.000586987s + +time increment:13170 time: 13.17 +field ' c' [explicit solve]: current solution: 9.679540e+01, current residual:5.847555e+01 +field 'mu' [explicit solve]: current solution: 5.800340e+00, current residual:3.476016e+00 +wall time: 0.000777006s + +time increment:13180 time: 13.18 +field ' c' [explicit solve]: current solution: 9.679479e+01, current residual:5.847516e+01 +field 'mu' [explicit solve]: current solution: 5.799101e+00, current residual:3.475300e+00 +wall time: 0.000662088s + +time increment:13190 time: 13.19 +field ' c' [explicit solve]: current solution: 9.679419e+01, current residual:5.847477e+01 +field 'mu' [explicit solve]: 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residual:3.466129e+00 +wall time: 0.000663042s + +time increment:13320 time: 13.32 +field ' c' [explicit solve]: current solution: 9.678637e+01, current residual:5.846975e+01 +field 'mu' [explicit solve]: current solution: 5.782007e+00, current residual:3.465434e+00 +wall time: 0.000656843s + +time increment:13330 time: 13.33 +field ' c' [explicit solve]: current solution: 9.678577e+01, current residual:5.846937e+01 +field 'mu' [explicit solve]: current solution: 5.780804e+00, current residual:3.464740e+00 +wall time: 0.00065589s + +time increment:13340 time: 13.34 +field ' c' [explicit solve]: current solution: 9.678517e+01, current residual:5.846899e+01 +field 'mu' [explicit solve]: current solution: 5.779603e+00, current residual:3.464047e+00 +wall time: 0.000678062s + +time increment:13350 time: 13.35 +field ' c' [explicit solve]: current solution: 9.678458e+01, current residual:5.846861e+01 +field 'mu' [explicit solve]: current solution: 5.778405e+00, current residual:3.463356e+00 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0.000751972s + +time increment:13400 time: 13.4 +field ' c' [explicit solve]: current solution: 9.678161e+01, current residual:5.846670e+01 +field 'mu' [explicit solve]: current solution: 5.772448e+00, current residual:3.459920e+00 +wall time: 0.000666142s + +time increment:13410 time: 13.41 +field ' c' [explicit solve]: current solution: 9.678101e+01, current residual:5.846632e+01 +field 'mu' [explicit solve]: current solution: 5.771263e+00, current residual:3.459237e+00 +wall time: 0.000663996s + +time increment:13420 time: 13.42 +field ' c' [explicit solve]: current solution: 9.678042e+01, current residual:5.846594e+01 +field 'mu' [explicit solve]: current solution: 5.770081e+00, current residual:3.458555e+00 +wall time: 0.000644922s + +time increment:13430 time: 13.43 +field ' c' [explicit solve]: current solution: 9.677983e+01, current residual:5.846556e+01 +field 'mu' [explicit solve]: current solution: 5.768901e+00, current residual:3.457875e+00 +wall time: 0.000643969s + +time increment:13440 time: 13.44 +field ' c' [explicit solve]: current solution: 9.677924e+01, current residual:5.846518e+01 +field 'mu' [explicit solve]: current solution: 5.767724e+00, current residual:3.457196e+00 +wall time: 0.000748873s + +time increment:13450 time: 13.45 +field ' c' [explicit solve]: current solution: 9.677865e+01, current residual:5.846480e+01 +field 'mu' [explicit solve]: current solution: 5.766549e+00, current residual:3.456518e+00 +wall time: 0.000696182s + +time increment:13460 time: 13.46 +field ' c' [explicit solve]: current solution: 9.677806e+01, current residual:5.846442e+01 +field 'mu' [explicit solve]: current solution: 5.765376e+00, current residual:3.455842e+00 +wall time: 0.000645876s + +time increment:13470 time: 13.47 +field ' c' [explicit solve]: current solution: 9.677747e+01, current residual:5.846404e+01 +field 'mu' [explicit solve]: current solution: 5.764205e+00, current residual:3.455168e+00 +wall time: 0.000692844s + +time increment:13480 time: 13.48 +field ' c' [explicit solve]: current solution: 9.677688e+01, current residual:5.846367e+01 +field 'mu' [explicit solve]: current solution: 5.763037e+00, current residual:3.454494e+00 +wall time: 0.00068593s + +time increment:13490 time: 13.49 +field ' c' [explicit solve]: current solution: 9.677629e+01, current residual:5.846329e+01 +field 'mu' [explicit solve]: current solution: 5.761871e+00, current residual:3.453822e+00 +wall time: 0.000629187s + +time increment:13500 time: 13.5 +field ' c' [explicit solve]: current solution: 9.677570e+01, current residual:5.846291e+01 +field 'mu' [explicit solve]: current solution: 5.760707e+00, current residual:3.453151e+00 +wall time: 0.000893116s + +time increment:13510 time: 13.51 +field ' c' [explicit solve]: current solution: 9.677512e+01, current residual:5.846253e+01 +field 'mu' [explicit solve]: current solution: 5.759546e+00, current residual:3.452482e+00 +wall time: 0.000665903s + +time increment:13520 time: 13.52 +field ' 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current solution: 9.677219e+01, current residual:5.846066e+01 +field 'mu' [explicit solve]: current solution: 5.753773e+00, current residual:3.449156e+00 +wall time: 0.00068903s + +time increment:13570 time: 13.57 +field ' c' [explicit solve]: current solution: 9.677161e+01, current residual:5.846028e+01 +field 'mu' [explicit solve]: current solution: 5.752625e+00, current residual:3.448494e+00 +wall time: 0.000668049s + +time increment:13580 time: 13.58 +field ' c' [explicit solve]: current solution: 9.677102e+01, current residual:5.845991e+01 +field 'mu' [explicit solve]: current solution: 5.751479e+00, current residual:3.447834e+00 +wall time: 0.000739098s + +time increment:13590 time: 13.59 +field ' c' [explicit solve]: current solution: 9.677044e+01, current residual:5.845953e+01 +field 'mu' [explicit solve]: current solution: 5.750336e+00, current residual:3.447176e+00 +wall time: 0.000676155s + +time increment:13600 time: 13.6 +field ' c' [explicit solve]: current solution: 9.676986e+01, current residual:5.845916e+01 +field 'mu' [explicit solve]: current solution: 5.749195e+00, current residual:3.446519e+00 +wall time: 0.00074482s + +time increment:13610 time: 13.61 +field ' c' [explicit solve]: current solution: 9.676928e+01, current residual:5.845879e+01 +field 'mu' [explicit solve]: current solution: 5.748056e+00, current residual:3.445863e+00 +wall time: 0.000638962s + +time increment:13620 time: 13.62 +field ' c' [explicit solve]: current solution: 9.676870e+01, current residual:5.845841e+01 +field 'mu' [explicit solve]: current solution: 5.746920e+00, current residual:3.445208e+00 +wall time: 0.000643969s + +time increment:13630 time: 13.63 +field ' c' [explicit solve]: current solution: 9.676812e+01, current residual:5.845804e+01 +field 'mu' [explicit solve]: current solution: 5.745785e+00, current residual:3.444555e+00 +wall time: 0.000568151s + +time increment:13640 time: 13.64 +field ' c' [explicit solve]: current solution: 9.676754e+01, current 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solution: 5.731241e+00, current residual:3.436182e+00 +wall time: 0.000720978s + +time increment:13770 time: 13.77 +field ' c' [explicit solve]: current solution: 9.676005e+01, current residual:5.845286e+01 +field 'mu' [explicit solve]: current solution: 5.730138e+00, current residual:3.435547e+00 +wall time: 0.000663996s + +time increment:13780 time: 13.78 +field ' c' [explicit solve]: current solution: 9.675948e+01, current residual:5.845250e+01 +field 'mu' [explicit solve]: current solution: 5.729036e+00, current residual:3.434914e+00 +wall time: 0.000648975s + +time increment:13790 time: 13.79 +field ' c' [explicit solve]: current solution: 9.675891e+01, current residual:5.845213e+01 +field 'mu' [explicit solve]: current solution: 5.727937e+00, current residual:3.434281e+00 +wall time: 0.000657082s + +time increment:13800 time: 13.8 +field ' c' [explicit solve]: current solution: 9.675834e+01, current residual:5.845176e+01 +field 'mu' [explicit solve]: current solution: 5.726840e+00, current residual:3.433650e+00 +wall time: 0.000710011s + +time increment:13810 time: 13.81 +field ' c' [explicit solve]: current solution: 9.675777e+01, current residual:5.845140e+01 +field 'mu' [explicit solve]: current solution: 5.725746e+00, current residual:3.433020e+00 +wall time: 0.000636101s + +time increment:13820 time: 13.82 +field ' c' [explicit solve]: current solution: 9.675720e+01, current residual:5.845103e+01 +field 'mu' [explicit solve]: current solution: 5.724653e+00, current residual:3.432392e+00 +wall time: 0.000664949s + +time increment:13830 time: 13.83 +field ' c' [explicit solve]: current solution: 9.675663e+01, current residual:5.845067e+01 +field 'mu' [explicit solve]: current solution: 5.723563e+00, current residual:3.431765e+00 +wall time: 0.000597954s + +time increment:13840 time: 13.84 +field ' c' [explicit solve]: current solution: 9.675606e+01, current residual:5.845030e+01 +field 'mu' [explicit solve]: current solution: 5.722474e+00, 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residual:3.428648e+00 +wall time: 0.000648022s + +time increment:13890 time: 13.89 +field ' c' [explicit solve]: current solution: 9.675322e+01, current residual:5.844848e+01 +field 'mu' [explicit solve]: current solution: 5.717065e+00, current residual:3.428028e+00 +wall time: 0.000573874s + +time increment:13900 time: 13.9 +field ' c' [explicit solve]: current solution: 9.675266e+01, current residual:5.844812e+01 +field 'mu' [explicit solve]: current solution: 5.715990e+00, current residual:3.427410e+00 +wall time: 0.000684023s + +time increment:13910 time: 13.91 +field ' c' [explicit solve]: current solution: 9.675209e+01, current residual:5.844776e+01 +field 'mu' [explicit solve]: current solution: 5.714916e+00, current residual:3.426793e+00 +wall time: 0.000573158s + +time increment:13920 time: 13.92 +field ' c' [explicit solve]: current solution: 9.675153e+01, current residual:5.844739e+01 +field 'mu' [explicit solve]: current solution: 5.713845e+00, current residual:3.426177e+00 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increment:14010 time: 14.01 +field ' c' [explicit solve]: current solution: 9.674647e+01, current residual:5.844415e+01 +field 'mu' [explicit solve]: current solution: 5.704299e+00, current residual:3.420691e+00 +wall time: 0.000746965s + +time increment:14020 time: 14.02 +field ' c' [explicit solve]: current solution: 9.674591e+01, current residual:5.844379e+01 +field 'mu' [explicit solve]: current solution: 5.703249e+00, current residual:3.420088e+00 +wall time: 0.000610113s + +time increment:14030 time: 14.03 +field ' c' [explicit solve]: current solution: 9.674536e+01, current residual:5.844343e+01 +field 'mu' [explicit solve]: current solution: 5.702201e+00, current residual:3.419485e+00 +wall time: 0.000642061s + +time increment:14040 time: 14.04 +field ' c' [explicit solve]: current solution: 9.674480e+01, current residual:5.844307e+01 +field 'mu' [explicit solve]: current solution: 5.701155e+00, current residual:3.418885e+00 +wall time: 0.000561953s + +time increment:14050 time: 14.05 +field ' c' [explicit solve]: current solution: 9.674424e+01, current residual:5.844271e+01 +field 'mu' [explicit solve]: current solution: 5.700111e+00, current residual:3.418285e+00 +wall time: 0.000593901s + +time increment:14060 time: 14.06 +field ' c' [explicit solve]: current solution: 9.674368e+01, current residual:5.844236e+01 +field 'mu' [explicit solve]: current solution: 5.699069e+00, current residual:3.417686e+00 +wall time: 0.000567913s + +time increment:14070 time: 14.07 +field ' c' [explicit solve]: current solution: 9.674313e+01, current residual:5.844200e+01 +field 'mu' [explicit solve]: current solution: 5.698030e+00, current residual:3.417089e+00 +wall time: 0.0005548s + +time increment:14080 time: 14.08 +field ' c' [explicit solve]: current solution: 9.674257e+01, current residual:5.844164e+01 +field 'mu' [explicit solve]: current solution: 5.696992e+00, current residual:3.416493e+00 +wall time: 0.000718117s + +time increment:14090 time: 14.09 +field ' c' [explicit solve]: current solution: 9.674202e+01, current residual:5.844129e+01 +field 'mu' [explicit solve]: current solution: 5.695956e+00, current residual:3.415899e+00 +wall time: 0.000665188s + +time increment:14100 time: 14.1 +field ' c' [explicit solve]: current solution: 9.674146e+01, current residual:5.844093e+01 +field 'mu' [explicit solve]: current solution: 5.694923e+00, current residual:3.415305e+00 +wall time: 0.000766993s + +time increment:14110 time: 14.11 +field ' c' [explicit solve]: current solution: 9.674091e+01, current residual:5.844057e+01 +field 'mu' [explicit solve]: current solution: 5.693891e+00, current residual:3.414713e+00 +wall time: 0.000635862s + +time increment:14120 time: 14.12 +field ' c' [explicit solve]: current solution: 9.674035e+01, current residual:5.844022e+01 +field 'mu' [explicit solve]: current solution: 5.692862e+00, current residual:3.414122e+00 +wall time: 0.000727892s + +time increment:14130 time: 14.13 +field ' c' [explicit solve]: current solution: 9.673980e+01, current residual:5.843986e+01 +field 'mu' [explicit solve]: current solution: 5.691834e+00, current residual:3.413532e+00 +wall time: 0.00069809s + +time increment:14140 time: 14.14 +field ' c' [explicit solve]: current solution: 9.673925e+01, current residual:5.843951e+01 +field 'mu' [explicit solve]: current solution: 5.690809e+00, current residual:3.412943e+00 +wall time: 0.000664949s + +time increment:14150 time: 14.15 +field ' c' [explicit solve]: current solution: 9.673869e+01, current residual:5.843915e+01 +field 'mu' [explicit solve]: current solution: 5.689785e+00, current residual:3.412355e+00 +wall time: 0.000769138s + +time increment:14160 time: 14.16 +field ' c' [explicit solve]: current solution: 9.673814e+01, current residual:5.843880e+01 +field 'mu' [explicit solve]: current solution: 5.688764e+00, current residual:3.411769e+00 +wall time: 0.000674009s + +time increment:14170 time: 14.17 +field ' c' [explicit solve]: current solution: 9.673759e+01, current residual:5.843845e+01 +field 'mu' [explicit solve]: current solution: 5.687745e+00, current residual:3.411184e+00 +wall time: 0.000718117s + +time increment:14180 time: 14.18 +field ' c' [explicit solve]: current solution: 9.673704e+01, current residual:5.843809e+01 +field 'mu' [explicit solve]: current solution: 5.686727e+00, current residual:3.410600e+00 +wall time: 0.000726938s + +time increment:14190 time: 14.19 +field ' c' [explicit solve]: current solution: 9.673649e+01, current residual:5.843774e+01 +field 'mu' [explicit solve]: current solution: 5.685712e+00, current residual:3.410018e+00 +wall time: 0.000687122s + +time increment:14200 time: 14.2 +field ' c' [explicit solve]: current solution: 9.673594e+01, current residual:5.843739e+01 +field 'mu' [explicit solve]: current solution: 5.684699e+00, current residual:3.409436e+00 +wall time: 0.000669956s + +time increment:14210 time: 14.21 +field ' c' [explicit solve]: current solution: 9.673539e+01, current 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solve]: current solution: 5.675670e+00, current residual:3.404256e+00 +wall time: 0.000680923s + +time increment:14300 time: 14.3 +field ' c' [explicit solve]: current solution: 9.673047e+01, current residual:5.843388e+01 +field 'mu' [explicit solve]: current solution: 5.674677e+00, current residual:3.403687e+00 +wall time: 0.000677824s + +time increment:14310 time: 14.31 +field ' c' [explicit solve]: current solution: 9.672993e+01, current residual:5.843353e+01 +field 'mu' [explicit solve]: current solution: 5.673685e+00, current residual:3.403118e+00 +wall time: 0.000683784s + +time increment:14320 time: 14.32 +field ' c' [explicit solve]: current solution: 9.672939e+01, current residual:5.843318e+01 +field 'mu' [explicit solve]: current solution: 5.672696e+00, current residual:3.402551e+00 +wall time: 0.000674963s + +time increment:14330 time: 14.33 +field ' c' [explicit solve]: current solution: 9.672884e+01, current residual:5.843283e+01 +field 'mu' [explicit solve]: current solution: 5.671709e+00, current residual:3.401985e+00 +wall time: 0.000663996s + +time increment:14340 time: 14.34 +field ' c' [explicit solve]: current solution: 9.672830e+01, current residual:5.843248e+01 +field 'mu' [explicit solve]: current solution: 5.670723e+00, current residual:3.401420e+00 +wall time: 0.000648975s + +time increment:14350 time: 14.35 +field ' c' [explicit solve]: current solution: 9.672776e+01, current residual:5.843213e+01 +field 'mu' [explicit solve]: current solution: 5.669740e+00, current residual:3.400856e+00 +wall time: 0.000679016s + +time increment:14360 time: 14.36 +field ' c' [explicit solve]: current solution: 9.672722e+01, current residual:5.843178e+01 +field 'mu' [explicit solve]: current solution: 5.668758e+00, current residual:3.400293e+00 +wall time: 0.000637054s + +time increment:14370 time: 14.37 +field ' c' [explicit solve]: current solution: 9.672668e+01, current residual:5.843144e+01 +field 'mu' [explicit solve]: current solution: 5.667779e+00, current residual:3.399732e+00 +wall time: 0.000718832s + +time increment:14380 time: 14.38 +field ' c' [explicit solve]: current solution: 9.672614e+01, current residual:5.843109e+01 +field 'mu' [explicit solve]: current solution: 5.666801e+00, current residual:3.399171e+00 +wall time: 0.000695944s + +time increment:14390 time: 14.39 +field ' c' [explicit solve]: current solution: 9.672560e+01, current residual:5.843074e+01 +field 'mu' [explicit solve]: current solution: 5.665826e+00, current residual:3.398612e+00 +wall time: 0.000652075s + +time increment:14400 time: 14.4 +field ' c' [explicit solve]: current solution: 9.672506e+01, current residual:5.843040e+01 +field 'mu' [explicit solve]: current solution: 5.664852e+00, current residual:3.398054e+00 +wall time: 0.000665903s + +time increment:14410 time: 14.41 +field ' c' [explicit solve]: current solution: 9.672452e+01, current residual:5.843005e+01 +field 'mu' [explicit solve]: current solution: 5.663881e+00, current residual:3.397497e+00 +wall time: 0.000673056s + +time increment:14420 time: 14.42 +field ' c' [explicit solve]: current solution: 9.672398e+01, current residual:5.842971e+01 +field 'mu' [explicit solve]: current solution: 5.662911e+00, current residual:3.396942e+00 +wall time: 0.00068903s + +time increment:14430 time: 14.43 +field ' c' [explicit solve]: current solution: 9.672344e+01, current residual:5.842936e+01 +field 'mu' [explicit solve]: current solution: 5.661943e+00, current residual:3.396387e+00 +wall time: 0.000744104s + +time increment:14440 time: 14.44 +field ' c' [explicit solve]: current solution: 9.672290e+01, current residual:5.842902e+01 +field 'mu' [explicit solve]: current solution: 5.660977e+00, current residual:3.395834e+00 +wall time: 0.00065279s + +time increment:14450 time: 14.45 +field ' c' [explicit solve]: current solution: 9.672237e+01, current residual:5.842867e+01 +field 'mu' [explicit solve]: current solution: 5.660013e+00, current residual:3.395281e+00 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0.000684977s + +time increment:14500 time: 14.5 +field ' c' [explicit solve]: current solution: 9.671969e+01, current residual:5.842695e+01 +field 'mu' [explicit solve]: current solution: 5.655222e+00, current residual:3.392537e+00 +wall time: 0.000647068s + +time increment:14510 time: 14.51 +field ' c' [explicit solve]: current solution: 9.671916e+01, current residual:5.842661e+01 +field 'mu' [explicit solve]: current solution: 5.654270e+00, current residual:3.391991e+00 +wall time: 0.000571012s + +time increment:14520 time: 14.52 +field ' c' [explicit solve]: current solution: 9.671862e+01, current residual:5.842627e+01 +field 'mu' [explicit solve]: current solution: 5.653319e+00, current residual:3.391447e+00 +wall time: 0.000577927s + +time increment:14530 time: 14.53 +field ' c' [explicit solve]: current solution: 9.671809e+01, current residual:5.842592e+01 +field 'mu' [explicit solve]: current solution: 5.652371e+00, current residual:3.390904e+00 +wall time: 0.000580072s + +time increment:14540 time: 14.54 +field ' c' [explicit solve]: current solution: 9.671756e+01, current residual:5.842558e+01 +field 'mu' [explicit solve]: current solution: 5.651424e+00, current residual:3.390362e+00 +wall time: 0.000686169s + +time increment:14550 time: 14.55 +field ' c' [explicit solve]: current solution: 9.671703e+01, current residual:5.842524e+01 +field 'mu' [explicit solve]: current solution: 5.650479e+00, current residual:3.389821e+00 +wall time: 0.000554085s + +time increment:14560 time: 14.56 +field ' c' [explicit solve]: current solution: 9.671649e+01, current residual:5.842490e+01 +field 'mu' [explicit solve]: current solution: 5.649536e+00, current residual:3.389281e+00 +wall time: 0.000567913s + +time increment:14570 time: 14.57 +field ' c' [explicit solve]: current solution: 9.671596e+01, current residual:5.842456e+01 +field 'mu' [explicit solve]: current solution: 5.648595e+00, current residual:3.388742e+00 +wall time: 0.000580072s + +time increment:14580 time: 14.58 +field ' c' [explicit solve]: current solution: 9.671543e+01, current residual:5.842422e+01 +field 'mu' [explicit solve]: current solution: 5.647656e+00, current residual:3.388204e+00 +wall time: 0.000629902s + +time increment:14590 time: 14.59 +field ' c' [explicit solve]: current solution: 9.671490e+01, current residual:5.842388e+01 +field 'mu' [explicit solve]: current solution: 5.646719e+00, current residual:3.387668e+00 +wall time: 0.000564098s + +time increment:14600 time: 14.6 +field ' c' [explicit solve]: current solution: 9.671437e+01, current residual:5.842354e+01 +field 'mu' [explicit solve]: current solution: 5.645783e+00, current residual:3.387132e+00 +wall time: 0.000656128s + +time increment:14610 time: 14.61 +field ' c' [explicit solve]: current solution: 9.671384e+01, current residual:5.842320e+01 +field 'mu' [explicit solve]: current solution: 5.644850e+00, current residual:3.386598e+00 +wall time: 0.000562906s + +time increment:14620 time: 14.62 +field ' c' [explicit solve]: current solution: 9.671332e+01, current residual:5.842286e+01 +field 'mu' [explicit solve]: current solution: 5.643918e+00, current residual:3.386065e+00 +wall time: 0.000657082s + +time increment:14630 time: 14.63 +field ' c' [explicit solve]: current solution: 9.671279e+01, current residual:5.842252e+01 +field 'mu' [explicit solve]: current solution: 5.642988e+00, current residual:3.385533e+00 +wall time: 0.000617981s + +time increment:14640 time: 14.64 +field ' c' [explicit solve]: current solution: 9.671226e+01, current residual:5.842218e+01 +field 'mu' [explicit solve]: current solution: 5.642060e+00, current residual:3.385002e+00 +wall time: 0.000577927s + +time increment:14650 time: 14.65 +field ' c' [explicit solve]: current solution: 9.671173e+01, current residual:5.842184e+01 +field 'mu' [explicit solve]: current solution: 5.641134e+00, current residual:3.384472e+00 +wall time: 0.000635862s + +time increment:14660 time: 14.66 +field ' c' [explicit solve]: current solution: 9.671121e+01, current residual:5.842150e+01 +field 'mu' [explicit solve]: current solution: 5.640210e+00, current residual:3.383943e+00 +wall time: 0.000559092s + +time increment:14670 time: 14.67 +field ' c' [explicit solve]: current solution: 9.671068e+01, current residual:5.842117e+01 +field 'mu' [explicit solve]: current solution: 5.639288e+00, current residual:3.383415e+00 +wall time: 0.000558853s + +time increment:14680 time: 14.68 +field ' c' [explicit solve]: current solution: 9.671016e+01, current residual:5.842083e+01 +field 'mu' [explicit solve]: current solution: 5.638367e+00, current residual:3.382889e+00 +wall time: 0.00058794s + +time increment:14690 time: 14.69 +field ' c' [explicit solve]: current solution: 9.670963e+01, current residual:5.842049e+01 +field 'mu' [explicit solve]: current solution: 5.637449e+00, current residual:3.382363e+00 +wall time: 0.000584126s + +time increment:14700 time: 14.7 +field ' c' [explicit solve]: current solution: 9.670911e+01, current residual:5.842015e+01 +field 'mu' [explicit solve]: current solution: 5.636532e+00, current residual:3.381839e+00 +wall time: 0.000710011s + +time increment:14710 time: 14.71 +field ' c' [explicit solve]: current solution: 9.670858e+01, current residual:5.841982e+01 +field 'mu' [explicit solve]: current solution: 5.635617e+00, current residual:3.381316e+00 +wall time: 0.000715017s + +time increment:14720 time: 14.72 +field ' c' [explicit solve]: current solution: 9.670806e+01, current residual:5.841948e+01 +field 'mu' [explicit solve]: current solution: 5.634704e+00, current residual:3.380793e+00 +wall time: 0.000558138s + +time increment:14730 time: 14.73 +field ' c' [explicit solve]: current solution: 9.670754e+01, current residual:5.841915e+01 +field 'mu' [explicit solve]: current solution: 5.633792e+00, current residual:3.380272e+00 +wall time: 0.000715017s + +time increment:14740 time: 14.74 +field ' c' [explicit solve]: current solution: 9.670701e+01, current residual:5.841881e+01 +field 'mu' [explicit solve]: current solution: 5.632883e+00, current residual:3.379752e+00 +wall time: 0.000710011s + +time increment:14750 time: 14.75 +field ' c' [explicit solve]: current solution: 9.670649e+01, current residual:5.841848e+01 +field 'mu' [explicit solve]: current solution: 5.631975e+00, current residual:3.379233e+00 +wall time: 0.000559092s + +time increment:14760 time: 14.76 +field ' c' [explicit solve]: current solution: 9.670597e+01, current residual:5.841814e+01 +field 'mu' [explicit solve]: current solution: 5.631070e+00, current residual:3.378715e+00 +wall time: 0.00061202s + +time increment:14770 time: 14.77 +field ' c' [explicit solve]: current solution: 9.670545e+01, current residual:5.841781e+01 +field 'mu' [explicit solve]: current solution: 5.630166e+00, current residual:3.378198e+00 +wall time: 0.000671148s + +time increment:14780 time: 14.78 +field ' c' [explicit solve]: current solution: 9.670493e+01, current residual:5.841747e+01 +field 'mu' [explicit solve]: current solution: 5.629263e+00, current residual:3.377683e+00 +wall time: 0.000622988s + +time increment:14790 time: 14.79 +field ' c' [explicit solve]: current solution: 9.670441e+01, current residual:5.841714e+01 +field 'mu' [explicit solve]: current solution: 5.628363e+00, current residual:3.377168e+00 +wall time: 0.000636816s + +time increment:14800 time: 14.8 +field ' c' [explicit solve]: current solution: 9.670389e+01, current residual:5.841680e+01 +field 'mu' [explicit solve]: current solution: 5.627465e+00, current residual:3.376654e+00 +wall time: 0.000584841s + +time increment:14810 time: 14.81 +field ' c' [explicit solve]: current solution: 9.670337e+01, current residual:5.841647e+01 +field 'mu' [explicit solve]: current solution: 5.626568e+00, current residual:3.376142e+00 +wall time: 0.000798941s + +time increment:14820 time: 14.82 +field ' c' [explicit solve]: current solution: 9.670285e+01, current residual:5.841614e+01 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solve]: current solution: 5.622111e+00, current residual:3.373595e+00 +wall time: 0.000562906s + +time increment:14870 time: 14.87 +field ' c' [explicit solve]: current solution: 9.670026e+01, current residual:5.841448e+01 +field 'mu' [explicit solve]: current solution: 5.621225e+00, current residual:3.373089e+00 +wall time: 0.00059104s + +time increment:14880 time: 14.88 +field ' c' [explicit solve]: current solution: 9.669975e+01, current residual:5.841414e+01 +field 'mu' [explicit solve]: current solution: 5.620341e+00, current residual:3.372583e+00 +wall time: 0.000585079s + +time increment:14890 time: 14.89 +field ' c' [explicit solve]: current solution: 9.669923e+01, current residual:5.841381e+01 +field 'mu' [explicit solve]: current solution: 5.619459e+00, current residual:3.372079e+00 +wall time: 0.00056386s + +time increment:14900 time: 14.9 +field ' c' [explicit solve]: current solution: 9.669872e+01, current residual:5.841348e+01 +field 'mu' [explicit solve]: current solution: 5.618578e+00, current residual:3.371576e+00 +wall time: 0.000622988s + +time increment:14910 time: 14.91 +field ' c' [explicit solve]: current solution: 9.669820e+01, current residual:5.841315e+01 +field 'mu' [explicit solve]: current solution: 5.617699e+00, current residual:3.371074e+00 +wall time: 0.00062418s + +time increment:14920 time: 14.92 +field ' c' [explicit solve]: current solution: 9.669769e+01, current residual:5.841282e+01 +field 'mu' [explicit solve]: current solution: 5.616822e+00, current residual:3.370573e+00 +wall time: 0.000617981s + +time increment:14930 time: 14.93 +field ' c' [explicit solve]: current solution: 9.669717e+01, current residual:5.841249e+01 +field 'mu' [explicit solve]: current solution: 5.615947e+00, current residual:3.370074e+00 +wall time: 0.000622988s + +time increment:14940 time: 14.94 +field ' c' [explicit solve]: current solution: 9.669666e+01, current residual:5.841216e+01 +field 'mu' [explicit solve]: current solution: 5.615073e+00, current residual:3.369575e+00 +wall time: 0.000607014s + +time increment:14950 time: 14.95 +field ' c' [explicit solve]: current solution: 9.669615e+01, current residual:5.841183e+01 +field 'mu' [explicit solve]: current solution: 5.614202e+00, current residual:3.369077e+00 +wall time: 0.000697851s + +time increment:14960 time: 14.96 +field ' c' [explicit solve]: current solution: 9.669564e+01, current residual:5.841150e+01 +field 'mu' [explicit solve]: current solution: 5.613332e+00, current residual:3.368580e+00 +wall time: 0.000567913s + +time increment:14970 time: 14.97 +field ' c' [explicit solve]: current solution: 9.669512e+01, current residual:5.841118e+01 +field 'mu' [explicit solve]: current solution: 5.612463e+00, current residual:3.368084e+00 +wall time: 0.000560045s + +time increment:14980 time: 14.98 +field ' c' [explicit solve]: current solution: 9.669461e+01, current residual:5.841085e+01 +field 'mu' [explicit solve]: current solution: 5.611597e+00, current residual:3.367590e+00 +wall time: 0.00065589s + +time increment:14990 time: 14.99 +field ' c' [explicit solve]: current solution: 9.669410e+01, current residual:5.841052e+01 +field 'mu' [explicit solve]: current solution: 5.610732e+00, current residual:3.367096e+00 +wall time: 0.000676155s + +time increment:15000 time: 15 +field ' c' [explicit solve]: current solution: 9.669359e+01, current residual:5.841019e+01 +field 'mu' [explicit solve]: current solution: 5.609869e+00, current residual:3.366603e+00 +wall time: 0.000734091s + +time increment:15010 time: 15.01 +field ' c' [explicit solve]: current solution: 9.669308e+01, current residual:5.840986e+01 +field 'mu' [explicit solve]: current solution: 5.609008e+00, current residual:3.366112e+00 +wall time: 0.000566959s + +time increment:15020 time: 15.02 +field ' c' [explicit solve]: current solution: 9.669257e+01, current residual:5.840954e+01 +field 'mu' [explicit solve]: current solution: 5.608148e+00, current 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residual:3.363669e+00 +wall time: 0.000712872s + +time increment:15070 time: 15.07 +field ' c' [explicit solve]: current solution: 9.669003e+01, current residual:5.840791e+01 +field 'mu' [explicit solve]: current solution: 5.603877e+00, current residual:3.363184e+00 +wall time: 0.000731945s + +time increment:15080 time: 15.08 +field ' c' [explicit solve]: current solution: 9.668953e+01, current residual:5.840758e+01 +field 'mu' [explicit solve]: current solution: 5.603028e+00, current residual:3.362699e+00 +wall time: 0.000679016s + +time increment:15090 time: 15.09 +field ' c' [explicit solve]: current solution: 9.668902e+01, current residual:5.840726e+01 +field 'mu' [explicit solve]: current solution: 5.602180e+00, current residual:3.362216e+00 +wall time: 0.000751972s + +time increment:15100 time: 15.1 +field ' c' [explicit solve]: current solution: 9.668852e+01, current residual:5.840693e+01 +field 'mu' [explicit solve]: current solution: 5.601335e+00, current residual:3.361733e+00 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increment:15190 time: 15.19 +field ' c' [explicit solve]: current solution: 9.668399e+01, current residual:5.840402e+01 +field 'mu' [explicit solve]: current solution: 5.593800e+00, current residual:3.357437e+00 +wall time: 0.000686169s + +time increment:15200 time: 15.2 +field ' c' [explicit solve]: current solution: 9.668349e+01, current residual:5.840370e+01 +field 'mu' [explicit solve]: current solution: 5.592971e+00, current residual:3.356964e+00 +wall time: 0.000638962s + +time increment:15210 time: 15.21 +field ' c' [explicit solve]: current solution: 9.668299e+01, current residual:5.840338e+01 +field 'mu' [explicit solve]: current solution: 5.592144e+00, current residual:3.356493e+00 +wall time: 0.000649214s + +time increment:15220 time: 15.22 +field ' c' [explicit solve]: current solution: 9.668248e+01, current residual:5.840306e+01 +field 'mu' [explicit solve]: current solution: 5.591319e+00, current residual:3.356022e+00 +wall time: 0.000634909s + +time increment:15230 time: 15.23 +field ' c' [explicit solve]: current solution: 9.668199e+01, current residual:5.840274e+01 +field 'mu' [explicit solve]: current solution: 5.590495e+00, current residual:3.355553e+00 +wall time: 0.000654936s + +time increment:15240 time: 15.24 +field ' c' [explicit solve]: current solution: 9.668149e+01, current residual:5.840241e+01 +field 'mu' [explicit solve]: current solution: 5.589673e+00, current residual:3.355085e+00 +wall time: 0.000761986s + +time increment:15250 time: 15.25 +field ' c' [explicit solve]: current solution: 9.668099e+01, current residual:5.840209e+01 +field 'mu' [explicit solve]: current solution: 5.588853e+00, current residual:3.354617e+00 +wall time: 0.000725985s + +time increment:15260 time: 15.26 +field ' c' [explicit solve]: current solution: 9.668049e+01, current residual:5.840177e+01 +field 'mu' [explicit solve]: current solution: 5.588034e+00, current residual:3.354151e+00 +wall time: 0.000770092s + +time increment:15270 time: 15.27 +field ' c' [explicit solve]: current solution: 9.667999e+01, current residual:5.840145e+01 +field 'mu' [explicit solve]: current solution: 5.587217e+00, current residual:3.353685e+00 +wall time: 0.000684023s + +time increment:15280 time: 15.28 +field ' c' [explicit solve]: current solution: 9.667949e+01, current residual:5.840114e+01 +field 'mu' [explicit solve]: current solution: 5.586402e+00, current residual:3.353221e+00 +wall time: 0.000675917s + +time increment:15290 time: 15.29 +field ' c' [explicit solve]: current solution: 9.667900e+01, current residual:5.840082e+01 +field 'mu' [explicit solve]: current solution: 5.585588e+00, current residual:3.352757e+00 +wall time: 0.000735998s + +time increment:15300 time: 15.3 +field ' c' [explicit solve]: current solution: 9.667850e+01, current residual:5.840050e+01 +field 'mu' [explicit solve]: current solution: 5.584776e+00, current residual:3.352294e+00 +wall time: 0.000699997s + +time increment:15310 time: 15.31 +field ' c' [explicit solve]: current solution: 9.667800e+01, current residual:5.840018e+01 +field 'mu' [explicit solve]: current solution: 5.583966e+00, current residual:3.351833e+00 +wall time: 0.000675917s + +time increment:15320 time: 15.32 +field ' c' [explicit solve]: current solution: 9.667751e+01, current residual:5.839986e+01 +field 'mu' [explicit solve]: current solution: 5.583157e+00, current residual:3.351372e+00 +wall time: 0.000680923s + +time increment:15330 time: 15.33 +field ' c' [explicit solve]: current solution: 9.667701e+01, current residual:5.839954e+01 +field 'mu' [explicit solve]: current solution: 5.582350e+00, current residual:3.350912e+00 +wall time: 0.000710011s + +time increment:15340 time: 15.34 +field ' c' [explicit solve]: current solution: 9.667652e+01, current residual:5.839922e+01 +field 'mu' [explicit solve]: current solution: 5.581544e+00, current residual:3.350454e+00 +wall time: 0.000687122s + +time increment:15350 time: 15.35 +field ' c' [explicit solve]: current solution: 9.667603e+01, current residual:5.839891e+01 +field 'mu' [explicit solve]: current solution: 5.580741e+00, current residual:3.349996e+00 +wall time: 0.00071311s + +time increment:15360 time: 15.36 +field ' c' [explicit solve]: current solution: 9.667553e+01, current residual:5.839859e+01 +field 'mu' [explicit solve]: current solution: 5.579938e+00, current residual:3.349539e+00 +wall time: 0.00070715s + +time increment:15370 time: 15.37 +field ' c' [explicit solve]: current solution: 9.667504e+01, current residual:5.839827e+01 +field 'mu' [explicit solve]: current solution: 5.579138e+00, current residual:3.349083e+00 +wall time: 0.000685215s + +time increment:15380 time: 15.38 +field ' c' [explicit solve]: current solution: 9.667455e+01, current residual:5.839796e+01 +field 'mu' [explicit solve]: current solution: 5.578339e+00, current residual:3.348629e+00 +wall time: 0.00067687s + +time increment:15390 time: 15.39 +field ' c' [explicit solve]: current solution: 9.667405e+01, current residual:5.839764e+01 +field 'mu' [explicit solve]: current solution: 5.577542e+00, current residual:3.348175e+00 +wall time: 0.000686169s + +time increment:15400 time: 15.4 +field ' c' [explicit solve]: current solution: 9.667356e+01, current residual:5.839732e+01 +field 'mu' [explicit solve]: current solution: 5.576746e+00, current residual:3.347722e+00 +wall time: 0.000684023s + +time increment:15410 time: 15.41 +field ' c' [explicit solve]: current solution: 9.667307e+01, current residual:5.839701e+01 +field 'mu' [explicit solve]: current solution: 5.575952e+00, current residual:3.347270e+00 +wall time: 0.000684023s + +time increment:15420 time: 15.42 +field ' c' [explicit solve]: current solution: 9.667258e+01, current residual:5.839669e+01 +field 'mu' [explicit solve]: current solution: 5.575159e+00, current residual:3.346819e+00 +wall time: 0.000732899s + +time increment:15430 time: 15.43 +field ' c' [explicit solve]: current solution: 9.667209e+01, current residual:5.839638e+01 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solve]: current solution: 5.571221e+00, current residual:3.344578e+00 +wall time: 0.000571966s + +time increment:15480 time: 15.48 +field ' c' [explicit solve]: current solution: 9.666964e+01, current residual:5.839481e+01 +field 'mu' [explicit solve]: current solution: 5.570439e+00, current residual:3.344133e+00 +wall time: 0.000892878s + +time increment:15490 time: 15.49 +field ' c' [explicit solve]: current solution: 9.666915e+01, current residual:5.839449e+01 +field 'mu' [explicit solve]: current solution: 5.569657e+00, current residual:3.343688e+00 +wall time: 0.000633001s + +time increment:15500 time: 15.5 +field ' c' [explicit solve]: current solution: 9.666867e+01, current residual:5.839418e+01 +field 'mu' [explicit solve]: current solution: 5.568878e+00, current residual:3.343245e+00 +wall time: 0.000674009s + +time increment:15510 time: 15.51 +field ' c' [explicit solve]: current solution: 9.666818e+01, current residual:5.839387e+01 +field 'mu' [explicit solve]: current solution: 5.568100e+00, current residual:3.342802e+00 +wall time: 0.000705004s + +time increment:15520 time: 15.52 +field ' c' [explicit solve]: current solution: 9.666769e+01, current residual:5.839355e+01 +field 'mu' [explicit solve]: current solution: 5.567323e+00, current residual:3.342361e+00 +wall time: 0.000653028s + +time increment:15530 time: 15.53 +field ' c' [explicit solve]: current solution: 9.666721e+01, current residual:5.839324e+01 +field 'mu' [explicit solve]: current solution: 5.566548e+00, current residual:3.341920e+00 +wall time: 0.000665903s + +time increment:15540 time: 15.54 +field ' c' [explicit solve]: current solution: 9.666672e+01, current residual:5.839293e+01 +field 'mu' [explicit solve]: current solution: 5.565775e+00, current residual:3.341480e+00 +wall time: 0.000710011s + +time increment:15550 time: 15.55 +field ' c' [explicit solve]: current solution: 9.666624e+01, current residual:5.839262e+01 +field 'mu' [explicit solve]: current solution: 5.565003e+00, current residual:3.341041e+00 +wall time: 0.00067091s + +time increment:15560 time: 15.56 +field ' c' [explicit solve]: current solution: 9.666575e+01, current residual:5.839231e+01 +field 'mu' [explicit solve]: current solution: 5.564233e+00, current residual:3.340603e+00 +wall time: 0.000689983s + +time increment:15570 time: 15.57 +field ' c' [explicit solve]: current solution: 9.666527e+01, current residual:5.839199e+01 +field 'mu' [explicit solve]: current solution: 5.563465e+00, current residual:3.340167e+00 +wall time: 0.00070405s + +time increment:15580 time: 15.58 +field ' c' [explicit solve]: current solution: 9.666478e+01, current residual:5.839168e+01 +field 'mu' [explicit solve]: current solution: 5.562698e+00, current residual:3.339731e+00 +wall time: 0.000703812s + +time increment:15590 time: 15.59 +field ' c' [explicit solve]: current solution: 9.666430e+01, current residual:5.839137e+01 +field 'mu' [explicit solve]: current solution: 5.561932e+00, current residual:3.339295e+00 +wall time: 0.00065589s + +time increment:15600 time: 15.6 +field ' c' [explicit solve]: current solution: 9.666382e+01, current residual:5.839106e+01 +field 'mu' [explicit solve]: current solution: 5.561169e+00, current residual:3.338861e+00 +wall time: 0.000673056s + +time increment:15610 time: 15.61 +field ' c' [explicit solve]: current solution: 9.666333e+01, current residual:5.839075e+01 +field 'mu' [explicit solve]: current solution: 5.560406e+00, current residual:3.338428e+00 +wall time: 0.000686169s + +time increment:15620 time: 15.62 +field ' c' [explicit solve]: current solution: 9.666285e+01, current residual:5.839044e+01 +field 'mu' [explicit solve]: current solution: 5.559646e+00, current residual:3.337996e+00 +wall time: 0.000668049s + +time increment:15630 time: 15.63 +field ' c' [explicit solve]: current solution: 9.666237e+01, current residual:5.839013e+01 +field 'mu' [explicit solve]: current solution: 5.558886e+00, current residual:3.337564e+00 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0.000622988s + +time increment:15680 time: 15.68 +field ' c' [explicit solve]: current solution: 9.665997e+01, current residual:5.838859e+01 +field 'mu' [explicit solve]: current solution: 5.555114e+00, current residual:3.335421e+00 +wall time: 0.000683069s + +time increment:15690 time: 15.69 +field ' c' [explicit solve]: current solution: 9.665949e+01, current residual:5.838828e+01 +field 'mu' [explicit solve]: current solution: 5.554364e+00, current residual:3.334995e+00 +wall time: 0.000627041s + +time increment:15700 time: 15.7 +field ' c' [explicit solve]: current solution: 9.665901e+01, current residual:5.838797e+01 +field 'mu' [explicit solve]: current solution: 5.553615e+00, current residual:3.334570e+00 +wall time: 0.000652075s + +time increment:15710 time: 15.71 +field ' c' [explicit solve]: current solution: 9.665853e+01, current residual:5.838767e+01 +field 'mu' [explicit solve]: current solution: 5.552869e+00, current residual:3.334146e+00 +wall time: 0.000648975s + +time increment:15720 time: 15.72 +field ' c' [explicit solve]: current solution: 9.665805e+01, current residual:5.838736e+01 +field 'mu' [explicit solve]: current solution: 5.552123e+00, current residual:3.333722e+00 +wall time: 0.000668049s + +time increment:15730 time: 15.73 +field ' c' [explicit solve]: current solution: 9.665758e+01, current residual:5.838705e+01 +field 'mu' [explicit solve]: current solution: 5.551380e+00, current residual:3.333300e+00 +wall time: 0.000696182s + +time increment:15740 time: 15.74 +field ' c' [explicit solve]: current solution: 9.665710e+01, current residual:5.838675e+01 +field 'mu' [explicit solve]: current solution: 5.550637e+00, current residual:3.332879e+00 +wall time: 0.000763893s + +time increment:15750 time: 15.75 +field ' c' [explicit solve]: current solution: 9.665662e+01, current residual:5.838644e+01 +field 'mu' [explicit solve]: current solution: 5.549897e+00, current residual:3.332458e+00 +wall time: 0.000672102s + +time increment:15760 time: 15.76 +field ' c' [explicit solve]: current solution: 9.665615e+01, current residual:5.838613e+01 +field 'mu' [explicit solve]: current solution: 5.549157e+00, current residual:3.332038e+00 +wall time: 0.000716925s + +time increment:15770 time: 15.77 +field ' c' [explicit solve]: current solution: 9.665567e+01, current residual:5.838583e+01 +field 'mu' [explicit solve]: current solution: 5.548420e+00, current residual:3.331620e+00 +wall time: 0.000641823s + +time increment:15780 time: 15.78 +field ' c' [explicit solve]: current solution: 9.665519e+01, current residual:5.838552e+01 +field 'mu' [explicit solve]: current solution: 5.547683e+00, current residual:3.331202e+00 +wall time: 0.00066185s + +time increment:15790 time: 15.79 +field ' c' [explicit solve]: current solution: 9.665472e+01, current residual:5.838522e+01 +field 'mu' [explicit solve]: current solution: 5.546949e+00, current residual:3.330785e+00 +wall time: 0.000613928s + +time increment:15800 time: 15.8 +field ' c' [explicit solve]: current solution: 9.665425e+01, current residual:5.838491e+01 +field 'mu' [explicit solve]: current solution: 5.546216e+00, current residual:3.330368e+00 +wall time: 0.000682116s + +time increment:15810 time: 15.81 +field ' c' [explicit solve]: current solution: 9.665377e+01, current residual:5.838461e+01 +field 'mu' [explicit solve]: current solution: 5.545484e+00, current residual:3.329953e+00 +wall time: 0.000567198s + +time increment:15820 time: 15.82 +field ' c' [explicit solve]: current solution: 9.665330e+01, current residual:5.838430e+01 +field 'mu' [explicit solve]: current solution: 5.544754e+00, current residual:3.329539e+00 +wall time: 0.0006392s + +time increment:15830 time: 15.83 +field ' c' [explicit solve]: current solution: 9.665282e+01, current residual:5.838400e+01 +field 'mu' [explicit solve]: current solution: 5.544025e+00, current residual:3.329125e+00 +wall time: 0.000649929s + +time increment:15840 time: 15.84 +field ' c' [explicit solve]: current solution: 9.665235e+01, current residual:5.838370e+01 +field 'mu' [explicit solve]: current solution: 5.543298e+00, current residual:3.328713e+00 +wall time: 0.000685215s + +time increment:15850 time: 15.85 +field ' c' [explicit solve]: current solution: 9.665188e+01, current residual:5.838339e+01 +field 'mu' [explicit solve]: current solution: 5.542572e+00, current residual:3.328301e+00 +wall time: 0.000648975s + +time increment:15860 time: 15.86 +field ' c' [explicit solve]: current solution: 9.665141e+01, current residual:5.838309e+01 +field 'mu' [explicit solve]: current solution: 5.541848e+00, current residual:3.327890e+00 +wall time: 0.000680923s + +time increment:15870 time: 15.87 +field ' c' [explicit solve]: current solution: 9.665094e+01, current residual:5.838279e+01 +field 'mu' [explicit solve]: current solution: 5.541125e+00, current residual:3.327480e+00 +wall time: 0.000595093s + +time increment:15880 time: 15.88 +field ' c' [explicit solve]: current solution: 9.665047e+01, current residual:5.838248e+01 +field 'mu' [explicit solve]: current solution: 5.540404e+00, current residual:3.327071e+00 +wall time: 0.000572205s + +time increment:15890 time: 15.89 +field ' c' [explicit solve]: current solution: 9.664999e+01, current residual:5.838218e+01 +field 'mu' [explicit solve]: current solution: 5.539684e+00, current residual:3.326663e+00 +wall time: 0.000566006s + +time increment:15900 time: 15.9 +field ' c' [explicit solve]: current solution: 9.664952e+01, current residual:5.838188e+01 +field 'mu' [explicit solve]: current solution: 5.538966e+00, current residual:3.326256e+00 +wall time: 0.000699043s + +time increment:15910 time: 15.91 +field ' c' [explicit solve]: current solution: 9.664905e+01, current residual:5.838158e+01 +field 'mu' [explicit solve]: current solution: 5.538249e+00, current residual:3.325849e+00 +wall time: 0.0006001s + +time increment:15920 time: 15.92 +field ' c' [explicit solve]: current solution: 9.664859e+01, current residual:5.838128e+01 +field 'mu' [explicit solve]: current solution: 5.537534e+00, current residual:3.325444e+00 +wall time: 0.000621796s + +time increment:15930 time: 15.93 +field ' c' [explicit solve]: current solution: 9.664812e+01, current residual:5.838097e+01 +field 'mu' [explicit solve]: current solution: 5.536820e+00, current residual:3.325039e+00 +wall time: 0.000680923s + +time increment:15940 time: 15.94 +field ' c' [explicit solve]: current solution: 9.664765e+01, current residual:5.838067e+01 +field 'mu' [explicit solve]: current solution: 5.536108e+00, current residual:3.324635e+00 +wall time: 0.000674009s + +time increment:15950 time: 15.95 +field ' c' [explicit solve]: current solution: 9.664718e+01, current residual:5.838037e+01 +field 'mu' [explicit solve]: current solution: 5.535397e+00, current residual:3.324232e+00 +wall time: 0.000566959s + +time increment:15960 time: 15.96 +field ' c' [explicit solve]: current solution: 9.664671e+01, current 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[explicit solve]: current solution: 5.529064e+00, current residual:3.320643e+00 +wall time: 0.00075984s + +time increment:16050 time: 16.05 +field ' c' [explicit solve]: current solution: 9.664252e+01, current residual:5.837738e+01 +field 'mu' [explicit solve]: current solution: 5.528368e+00, current residual:3.320249e+00 +wall time: 0.000658989s + +time increment:16060 time: 16.06 +field ' c' [explicit solve]: current solution: 9.664206e+01, current residual:5.837708e+01 +field 'mu' [explicit solve]: current solution: 5.527673e+00, current residual:3.319855e+00 +wall time: 0.000714064s + +time increment:16070 time: 16.07 +field ' c' [explicit solve]: current solution: 9.664160e+01, current residual:5.837678e+01 +field 'mu' [explicit solve]: current solution: 5.526979e+00, current residual:3.319462e+00 +wall time: 0.000725031s + +time increment:16080 time: 16.08 +field ' c' [explicit solve]: current solution: 9.664113e+01, current residual:5.837649e+01 +field 'mu' [explicit solve]: 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5.523533e+00, current residual:3.317511e+00 +wall time: 0.000686884s + +time increment:16130 time: 16.13 +field ' c' [explicit solve]: current solution: 9.663883e+01, current residual:5.837500e+01 +field 'mu' [explicit solve]: current solution: 5.522848e+00, current residual:3.317123e+00 +wall time: 0.000687122s + +time increment:16140 time: 16.14 +field ' c' [explicit solve]: current solution: 9.663837e+01, current residual:5.837471e+01 +field 'mu' [explicit solve]: current solution: 5.522164e+00, current residual:3.316736e+00 +wall time: 0.000647783s + +time increment:16150 time: 16.15 +field ' c' [explicit solve]: current solution: 9.663791e+01, current residual:5.837441e+01 +field 'mu' [explicit solve]: current solution: 5.521482e+00, current residual:3.316350e+00 +wall time: 0.000646114s + +time increment:16160 time: 16.16 +field ' c' [explicit solve]: current solution: 9.663745e+01, current residual:5.837412e+01 +field 'mu' [explicit solve]: current solution: 5.520801e+00, 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residual:3.314432e+00 +wall time: 0.000694036s + +time increment:16210 time: 16.21 +field ' c' [explicit solve]: current solution: 9.663516e+01, current residual:5.837264e+01 +field 'mu' [explicit solve]: current solution: 5.517419e+00, current residual:3.314050e+00 +wall time: 0.000696182s + +time increment:16220 time: 16.22 +field ' c' [explicit solve]: current solution: 9.663470e+01, current residual:5.837235e+01 +field 'mu' [explicit solve]: current solution: 5.516746e+00, current residual:3.313670e+00 +wall time: 0.000716925s + +time increment:16230 time: 16.23 +field ' c' [explicit solve]: current solution: 9.663424e+01, current residual:5.837206e+01 +field 'mu' [explicit solve]: current solution: 5.516075e+00, current residual:3.313290e+00 +wall time: 0.000735044s + +time increment:16240 time: 16.24 +field ' c' [explicit solve]: current solution: 9.663379e+01, current residual:5.837176e+01 +field 'mu' [explicit solve]: current solution: 5.515406e+00, current residual:3.312912e+00 +wall time: 0.000746012s + +time increment:16250 time: 16.25 +field ' c' [explicit solve]: current solution: 9.663333e+01, current residual:5.837147e+01 +field 'mu' [explicit solve]: current solution: 5.514737e+00, current residual:3.312534e+00 +wall time: 0.000692844s + +time increment:16260 time: 16.26 +field ' c' [explicit solve]: current solution: 9.663287e+01, current residual:5.837118e+01 +field 'mu' [explicit solve]: current solution: 5.514071e+00, current residual:3.312157e+00 +wall time: 0.000644922s + +time increment:16270 time: 16.27 +field ' c' [explicit solve]: current solution: 9.663242e+01, current residual:5.837089e+01 +field 'mu' [explicit solve]: current solution: 5.513405e+00, current residual:3.311780e+00 +wall time: 0.00072217s + +time increment:16280 time: 16.28 +field ' c' [explicit solve]: current solution: 9.663197e+01, current residual:5.837059e+01 +field 'mu' [explicit solve]: current solution: 5.512741e+00, current residual:3.311405e+00 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0.000668049s + +time increment:16330 time: 16.33 +field ' c' [explicit solve]: current solution: 9.662970e+01, current residual:5.836914e+01 +field 'mu' [explicit solve]: current solution: 5.509441e+00, current residual:3.309539e+00 +wall time: 0.000723124s + +time increment:16340 time: 16.34 +field ' c' [explicit solve]: current solution: 9.662925e+01, current residual:5.836885e+01 +field 'mu' [explicit solve]: current solution: 5.508785e+00, current residual:3.309169e+00 +wall time: 0.000633001s + +time increment:16350 time: 16.35 +field ' c' [explicit solve]: current solution: 9.662879e+01, current residual:5.836856e+01 +field 'mu' [explicit solve]: current solution: 5.508131e+00, current residual:3.308799e+00 +wall time: 0.000662088s + +time increment:16360 time: 16.36 +field ' c' [explicit solve]: current solution: 9.662834e+01, current residual:5.836827e+01 +field 'mu' [explicit solve]: current solution: 5.507478e+00, current residual:3.308430e+00 +wall time: 0.000715971s + +time increment:16370 time: 16.37 +field ' c' [explicit solve]: current solution: 9.662789e+01, current residual:5.836798e+01 +field 'mu' [explicit solve]: current solution: 5.506826e+00, current residual:3.308061e+00 +wall time: 0.000662088s + +time increment:16380 time: 16.38 +field ' c' [explicit solve]: current solution: 9.662744e+01, current residual:5.836769e+01 +field 'mu' [explicit solve]: current solution: 5.506175e+00, current residual:3.307694e+00 +wall time: 0.000672817s + +time increment:16390 time: 16.39 +field ' c' [explicit solve]: current solution: 9.662699e+01, current residual:5.836740e+01 +field 'mu' [explicit solve]: current solution: 5.505526e+00, current residual:3.307327e+00 +wall time: 0.000663042s + +time increment:16400 time: 16.4 +field ' c' [explicit solve]: current solution: 9.662654e+01, current residual:5.836711e+01 +field 'mu' [explicit solve]: current solution: 5.504878e+00, current residual:3.306961e+00 +wall time: 0.000699043s + +time increment:16410 time: 16.41 +field ' c' [explicit solve]: current solution: 9.662609e+01, current residual:5.836682e+01 +field 'mu' [explicit solve]: current solution: 5.504232e+00, current residual:3.306596e+00 +wall time: 0.000560045s + +time increment:16420 time: 16.42 +field ' c' [explicit solve]: current solution: 9.662564e+01, current residual:5.836653e+01 +field 'mu' [explicit solve]: current solution: 5.503587e+00, current residual:3.306232e+00 +wall time: 0.000628948s + +time increment:16430 time: 16.43 +field ' c' [explicit solve]: current solution: 9.662520e+01, current residual:5.836624e+01 +field 'mu' [explicit solve]: current solution: 5.502943e+00, current residual:3.305868e+00 +wall time: 0.000626087s + +time increment:16440 time: 16.44 +field ' c' [explicit solve]: current solution: 9.662475e+01, current residual:5.836595e+01 +field 'mu' [explicit solve]: current solution: 5.502301e+00, current residual:3.305505e+00 +wall time: 0.00057888s + +time increment:16450 time: 16.45 +field ' c' [explicit solve]: current solution: 9.662430e+01, current residual:5.836567e+01 +field 'mu' [explicit solve]: current solution: 5.501660e+00, current residual:3.305143e+00 +wall time: 0.000678062s + +time increment:16460 time: 16.46 +field ' c' [explicit solve]: current solution: 9.662385e+01, current residual:5.836538e+01 +field 'mu' [explicit solve]: current solution: 5.501020e+00, current residual:3.304782e+00 +wall time: 0.0006001s + +time increment:16470 time: 16.47 +field ' c' [explicit solve]: current solution: 9.662341e+01, current residual:5.836509e+01 +field 'mu' [explicit solve]: current solution: 5.500381e+00, current residual:3.304422e+00 +wall time: 0.000854969s + +time increment:16480 time: 16.48 +field ' c' [explicit solve]: current solution: 9.662296e+01, current residual:5.836480e+01 +field 'mu' [explicit solve]: current solution: 5.499744e+00, current residual:3.304062e+00 +wall time: 0.000653028s + +time increment:16490 time: 16.49 +field ' c' [explicit solve]: current solution: 9.662251e+01, current residual:5.836452e+01 +field 'mu' [explicit solve]: current solution: 5.499108e+00, current residual:3.303703e+00 +wall time: 0.000675917s + +time increment:16500 time: 16.5 +field ' c' [explicit solve]: current solution: 9.662207e+01, current residual:5.836423e+01 +field 'mu' [explicit solve]: current solution: 5.498474e+00, current residual:3.303345e+00 +wall time: 0.000653028s + +time increment:16510 time: 16.51 +field ' c' [explicit solve]: current solution: 9.662162e+01, current residual:5.836395e+01 +field 'mu' [explicit solve]: current solution: 5.497841e+00, current residual:3.302987e+00 +wall time: 0.000654936s + +time increment:16520 time: 16.52 +field ' c' [explicit solve]: current solution: 9.662118e+01, current residual:5.836366e+01 +field 'mu' [explicit solve]: current solution: 5.497209e+00, current residual:3.302631e+00 +wall time: 0.000609159s + +time increment:16530 time: 16.53 +field ' c' [explicit solve]: current solution: 9.662073e+01, current residual:5.836337e+01 +field 'mu' [explicit solve]: current solution: 5.496578e+00, current residual:3.302275e+00 +wall time: 0.000658035s + +time increment:16540 time: 16.54 +field ' c' [explicit solve]: current solution: 9.662029e+01, current residual:5.836309e+01 +field 'mu' [explicit solve]: current solution: 5.495949e+00, current residual:3.301920e+00 +wall time: 0.000679016s + +time increment:16550 time: 16.55 +field ' c' [explicit solve]: current solution: 9.661984e+01, current residual:5.836280e+01 +field 'mu' [explicit solve]: current solution: 5.495321e+00, current residual:3.301566e+00 +wall time: 0.000565052s + +time increment:16560 time: 16.56 +field ' c' [explicit solve]: current solution: 9.661940e+01, current residual:5.836252e+01 +field 'mu' [explicit solve]: current solution: 5.494695e+00, current residual:3.301212e+00 +wall time: 0.000677109s + +time increment:16570 time: 16.57 +field ' c' [explicit solve]: current solution: 9.661896e+01, 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residual:5.836110e+01 +field 'mu' [explicit solve]: current solution: 5.491581e+00, current residual:3.299456e+00 +wall time: 0.000622988s + +time increment:16620 time: 16.62 +field ' c' [explicit solve]: current solution: 9.661675e+01, current residual:5.836081e+01 +field 'mu' [explicit solve]: current solution: 5.490962e+00, current residual:3.299107e+00 +wall time: 0.000657082s + +time increment:16630 time: 16.63 +field ' c' [explicit solve]: current solution: 9.661631e+01, current residual:5.836053e+01 +field 'mu' [explicit solve]: current solution: 5.490345e+00, current residual:3.298759e+00 +wall time: 0.000590086s + +time increment:16640 time: 16.64 +field ' c' [explicit solve]: current solution: 9.661587e+01, current residual:5.836025e+01 +field 'mu' [explicit solve]: current solution: 5.489728e+00, current residual:3.298411e+00 +wall time: 0.000671148s + +time increment:16650 time: 16.65 +field ' c' [explicit solve]: current solution: 9.661543e+01, current residual:5.835996e+01 +field 'mu' [explicit solve]: current solution: 5.489113e+00, current residual:3.298065e+00 +wall time: 0.000617981s + +time increment:16660 time: 16.66 +field ' c' [explicit solve]: current solution: 9.661499e+01, current residual:5.835968e+01 +field 'mu' [explicit solve]: current solution: 5.488500e+00, current residual:3.297719e+00 +wall time: 0.000586033s + +time increment:16670 time: 16.67 +field ' c' [explicit solve]: current solution: 9.661455e+01, current residual:5.835940e+01 +field 'mu' [explicit solve]: current solution: 5.487887e+00, current residual:3.297374e+00 +wall time: 0.000712872s + +time increment:16680 time: 16.68 +field ' c' [explicit solve]: current solution: 9.661411e+01, current residual:5.835912e+01 +field 'mu' [explicit solve]: current solution: 5.487276e+00, current residual:3.297029e+00 +wall time: 0.000593901s + +time increment:16690 time: 16.69 +field ' c' [explicit solve]: current solution: 9.661367e+01, current residual:5.835883e+01 +field 'mu' [explicit solve]: current solution: 5.486666e+00, current residual:3.296685e+00 +wall time: 0.000669003s + +time increment:16700 time: 16.7 +field ' c' [explicit solve]: current solution: 9.661324e+01, current residual:5.835855e+01 +field 'mu' [explicit solve]: current solution: 5.486058e+00, current residual:3.296342e+00 +wall time: 0.000563145s + +time increment:16710 time: 16.71 +field ' c' [explicit solve]: current solution: 9.661280e+01, current residual:5.835827e+01 +field 'mu' [explicit solve]: current solution: 5.485450e+00, current residual:3.296000e+00 +wall time: 0.000648975s + +time increment:16720 time: 16.72 +field ' c' [explicit solve]: current solution: 9.661236e+01, current residual:5.835799e+01 +field 'mu' [explicit solve]: current solution: 5.484844e+00, current residual:3.295659e+00 +wall time: 0.000571012s + +time increment:16730 time: 16.73 +field ' c' [explicit solve]: current solution: 9.661192e+01, current residual:5.835771e+01 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solve]: current solution: 5.481833e+00, current residual:3.293962e+00 +wall time: 0.000744104s + +time increment:16780 time: 16.78 +field ' c' [explicit solve]: current solution: 9.660975e+01, current residual:5.835631e+01 +field 'mu' [explicit solve]: current solution: 5.481234e+00, current residual:3.293625e+00 +wall time: 0.000641108s + +time increment:16790 time: 16.79 +field ' c' [explicit solve]: current solution: 9.660931e+01, current residual:5.835603e+01 +field 'mu' [explicit solve]: current solution: 5.480637e+00, current residual:3.293289e+00 +wall time: 0.000650167s + +time increment:16800 time: 16.8 +field ' c' [explicit solve]: current solution: 9.660888e+01, current residual:5.835575e+01 +field 'mu' [explicit solve]: current solution: 5.480041e+00, current residual:3.292953e+00 +wall time: 0.000566959s + +time increment:16810 time: 16.81 +field ' c' [explicit solve]: current solution: 9.660844e+01, current residual:5.835547e+01 +field 'mu' [explicit solve]: current solution: 5.479446e+00, current residual:3.292619e+00 +wall time: 0.000725985s + +time increment:16820 time: 16.82 +field ' c' [explicit solve]: current solution: 9.660801e+01, current residual:5.835519e+01 +field 'mu' [explicit solve]: current solution: 5.478852e+00, current residual:3.292284e+00 +wall time: 0.000611067s + +time increment:16830 time: 16.83 +field ' c' [explicit solve]: current solution: 9.660758e+01, current residual:5.835492e+01 +field 'mu' [explicit solve]: current solution: 5.478260e+00, current residual:3.291951e+00 +wall time: 0.00069809s + +time increment:16840 time: 16.84 +field ' c' [explicit solve]: current solution: 9.660715e+01, current residual:5.835464e+01 +field 'mu' [explicit solve]: current solution: 5.477669e+00, current residual:3.291618e+00 +wall time: 0.000588894s + +time increment:16850 time: 16.85 +field ' c' [explicit solve]: current solution: 9.660671e+01, current residual:5.835436e+01 +field 'mu' [explicit solve]: current solution: 5.477079e+00, current residual:3.291286e+00 +wall time: 0.000633001s + +time increment:16860 time: 16.86 +field ' c' [explicit solve]: current solution: 9.660628e+01, current residual:5.835408e+01 +field 'mu' [explicit solve]: current solution: 5.476490e+00, current residual:3.290955e+00 +wall time: 0.000659943s + +time increment:16870 time: 16.87 +field ' c' [explicit solve]: current solution: 9.660585e+01, current residual:5.835380e+01 +field 'mu' [explicit solve]: current solution: 5.475903e+00, current residual:3.290625e+00 +wall time: 0.000674009s + +time increment:16880 time: 16.88 +field ' c' [explicit solve]: current solution: 9.660542e+01, current residual:5.835353e+01 +field 'mu' [explicit solve]: current solution: 5.475317e+00, current residual:3.290295e+00 +wall time: 0.000575066s + +time increment:16890 time: 16.89 +field ' c' [explicit solve]: current solution: 9.660499e+01, current residual:5.835325e+01 +field 'mu' [explicit solve]: current solution: 5.474732e+00, 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residual:3.288656e+00 +wall time: 0.000581026s + +time increment:16940 time: 16.94 +field ' c' [explicit solve]: current solution: 9.660284e+01, current residual:5.835187e+01 +field 'mu' [explicit solve]: current solution: 5.471825e+00, current residual:3.288331e+00 +wall time: 0.000682116s + +time increment:16950 time: 16.95 +field ' c' [explicit solve]: current solution: 9.660241e+01, current residual:5.835159e+01 +field 'mu' [explicit solve]: current solution: 5.471248e+00, current residual:3.288006e+00 +wall time: 0.000720978s + +time increment:16960 time: 16.96 +field ' c' [explicit solve]: current solution: 9.660198e+01, current residual:5.835132e+01 +field 'mu' [explicit solve]: current solution: 5.470671e+00, current residual:3.287682e+00 +wall time: 0.00069499s + +time increment:16970 time: 16.97 +field ' c' [explicit solve]: current solution: 9.660156e+01, current residual:5.835104e+01 +field 'mu' [explicit solve]: current solution: 5.470096e+00, current residual:3.287358e+00 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0.000672817s + +time increment:17020 time: 17.02 +field ' c' [explicit solve]: current solution: 9.659942e+01, current residual:5.834967e+01 +field 'mu' [explicit solve]: current solution: 5.467237e+00, current residual:3.285752e+00 +wall time: 0.000702858s + +time increment:17030 time: 17.03 +field ' c' [explicit solve]: current solution: 9.659900e+01, current residual:5.834940e+01 +field 'mu' [explicit solve]: current solution: 5.466669e+00, current residual:3.285432e+00 +wall time: 0.000756025s + +time increment:17040 time: 17.04 +field ' c' [explicit solve]: current solution: 9.659857e+01, current residual:5.834913e+01 +field 'mu' [explicit solve]: current solution: 5.466102e+00, current residual:3.285114e+00 +wall time: 0.000642061s + +time increment:17050 time: 17.05 +field ' c' [explicit solve]: current solution: 9.659815e+01, current residual:5.834885e+01 +field 'mu' [explicit solve]: current solution: 5.465537e+00, current residual:3.284796e+00 +wall time: 0.000564098s + +time increment:17060 time: 17.06 +field ' c' [explicit solve]: current solution: 9.659772e+01, current residual:5.834858e+01 +field 'mu' [explicit solve]: current solution: 5.464972e+00, current residual:3.284479e+00 +wall time: 0.000614882s + +time increment:17070 time: 17.07 +field ' c' [explicit solve]: current solution: 9.659730e+01, current residual:5.834831e+01 +field 'mu' [explicit solve]: current solution: 5.464409e+00, current residual:3.284163e+00 +wall time: 0.00071907s + +time increment:17080 time: 17.08 +field ' c' [explicit solve]: current solution: 9.659688e+01, current residual:5.834803e+01 +field 'mu' [explicit solve]: current solution: 5.463847e+00, current residual:3.283847e+00 +wall time: 0.000685215s + +time increment:17090 time: 17.09 +field ' c' [explicit solve]: current solution: 9.659645e+01, current residual:5.834776e+01 +field 'mu' [explicit solve]: current solution: 5.463286e+00, current residual:3.283532e+00 +wall time: 0.000672102s + +time increment:17100 time: 17.1 +field ' c' [explicit solve]: current solution: 9.659603e+01, current residual:5.834749e+01 +field 'mu' [explicit solve]: current solution: 5.462726e+00, current residual:3.283217e+00 +wall time: 0.000692129s + +time increment:17110 time: 17.11 +field ' c' [explicit solve]: current solution: 9.659561e+01, current residual:5.834722e+01 +field 'mu' [explicit solve]: current solution: 5.462167e+00, current residual:3.282904e+00 +wall time: 0.000659943s + +time increment:17120 time: 17.12 +field ' c' [explicit solve]: current solution: 9.659519e+01, current residual:5.834695e+01 +field 'mu' [explicit solve]: current solution: 5.461610e+00, current residual:3.282591e+00 +wall time: 0.000684977s + +time increment:17130 time: 17.13 +field ' c' [explicit solve]: current solution: 9.659477e+01, current residual:5.834668e+01 +field 'mu' [explicit solve]: current solution: 5.461053e+00, current residual:3.282278e+00 +wall time: 0.000631094s + +time increment:17140 time: 17.14 +field ' c' [explicit solve]: current solution: 9.659434e+01, current residual:5.834641e+01 +field 'mu' [explicit solve]: current solution: 5.460498e+00, current residual:3.281967e+00 +wall time: 0.000679016s + +time increment:17150 time: 17.15 +field ' c' [explicit solve]: current solution: 9.659392e+01, current residual:5.834613e+01 +field 'mu' [explicit solve]: current solution: 5.459944e+00, current residual:3.281656e+00 +wall time: 0.000720024s + +time increment:17160 time: 17.16 +field ' c' [explicit solve]: current solution: 9.659350e+01, current residual:5.834586e+01 +field 'mu' [explicit solve]: current solution: 5.459391e+00, current residual:3.281345e+00 +wall time: 0.000671148s + +time increment:17170 time: 17.17 +field ' c' [explicit solve]: current solution: 9.659308e+01, current residual:5.834559e+01 +field 'mu' [explicit solve]: current solution: 5.458840e+00, current residual:3.281036e+00 +wall time: 0.00064683s + +time increment:17180 time: 17.18 +field ' c' [explicit solve]: current solution: 9.659266e+01, current residual:5.834532e+01 +field 'mu' [explicit solve]: current solution: 5.458289e+00, current residual:3.280727e+00 +wall time: 0.000652075s + +time increment:17190 time: 17.19 +field ' c' [explicit solve]: current solution: 9.659224e+01, current residual:5.834505e+01 +field 'mu' [explicit solve]: current solution: 5.457740e+00, current residual:3.280418e+00 +wall time: 0.000681877s + +time increment:17200 time: 17.2 +field ' c' [explicit solve]: current solution: 9.659182e+01, current residual:5.834478e+01 +field 'mu' [explicit solve]: current solution: 5.457192e+00, current residual:3.280111e+00 +wall time: 0.000663042s + +time increment:17210 time: 17.21 +field ' c' [explicit solve]: current solution: 9.659141e+01, current residual:5.834451e+01 +field 'mu' [explicit solve]: current solution: 5.456645e+00, current residual:3.279804e+00 +wall time: 0.000684023s + +time increment:17220 time: 17.22 +field ' c' [explicit solve]: current solution: 9.659099e+01, current residual:5.834425e+01 +field 'mu' [explicit solve]: current solution: 5.456099e+00, current residual:3.279498e+00 +wall time: 0.000651836s + +time increment:17230 time: 17.23 +field ' c' [explicit solve]: current solution: 9.659057e+01, current residual:5.834398e+01 +field 'mu' [explicit solve]: current solution: 5.455554e+00, current residual:3.279192e+00 +wall time: 0.000664949s + +time increment:17240 time: 17.24 +field ' c' [explicit solve]: current solution: 9.659015e+01, current residual:5.834371e+01 +field 'mu' [explicit solve]: current solution: 5.455010e+00, current residual:3.278887e+00 +wall time: 0.000703812s + +time increment:17250 time: 17.25 +field ' c' [explicit solve]: current solution: 9.658973e+01, current residual:5.834344e+01 +field 'mu' [explicit solve]: current solution: 5.454468e+00, current residual:3.278583e+00 +wall time: 0.00064683s + +time increment:17260 time: 17.26 +field ' c' [explicit solve]: current solution: 9.658932e+01, current residual:5.834317e+01 +field 'mu' [explicit solve]: current solution: 5.453926e+00, current residual:3.278279e+00 +wall time: 0.000686884s + +time increment:17270 time: 17.27 +field ' c' [explicit solve]: current solution: 9.658890e+01, current residual:5.834290e+01 +field 'mu' [explicit solve]: current solution: 5.453386e+00, current residual:3.277976e+00 +wall time: 0.000627041s + +time increment:17280 time: 17.28 +field ' c' [explicit solve]: current solution: 9.658848e+01, current residual:5.834264e+01 +field 'mu' [explicit solve]: current solution: 5.452847e+00, current residual:3.277674e+00 +wall time: 0.000701904s + +time increment:17290 time: 17.29 +field ' c' [explicit solve]: current solution: 9.658807e+01, current residual:5.834237e+01 +field 'mu' [explicit solve]: current solution: 5.452309e+00, current residual:3.277372e+00 +wall time: 0.000682116s + +time increment:17300 time: 17.3 +field ' c' [explicit solve]: current solution: 9.658765e+01, current residual:5.834210e+01 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solve]: current solution: 5.449636e+00, current residual:3.275874e+00 +wall time: 0.000658035s + +time increment:17350 time: 17.35 +field ' c' [explicit solve]: current solution: 9.658558e+01, current residual:5.834077e+01 +field 'mu' [explicit solve]: current solution: 5.449105e+00, current residual:3.275577e+00 +wall time: 0.00067687s + +time increment:17360 time: 17.36 +field ' c' [explicit solve]: current solution: 9.658517e+01, current residual:5.834050e+01 +field 'mu' [explicit solve]: current solution: 5.448575e+00, current residual:3.275279e+00 +wall time: 0.000638962s + +time increment:17370 time: 17.37 +field ' c' [explicit solve]: current solution: 9.658476e+01, current residual:5.834024e+01 +field 'mu' [explicit solve]: current solution: 5.448046e+00, current residual:3.274983e+00 +wall time: 0.000662804s + +time increment:17380 time: 17.38 +field ' c' [explicit solve]: current solution: 9.658434e+01, current residual:5.833997e+01 +field 'mu' [explicit solve]: current solution: 5.447518e+00, current residual:3.274687e+00 +wall time: 0.000771999s + +time increment:17390 time: 17.39 +field ' c' [explicit solve]: current solution: 9.658393e+01, current residual:5.833971e+01 +field 'mu' [explicit solve]: current solution: 5.446991e+00, current residual:3.274392e+00 +wall time: 0.000672102s + +time increment:17400 time: 17.4 +field ' c' [explicit solve]: current solution: 9.658352e+01, current residual:5.833944e+01 +field 'mu' [explicit solve]: current solution: 5.446465e+00, current residual:3.274098e+00 +wall time: 0.000670195s + +time increment:17410 time: 17.41 +field ' c' [explicit solve]: current solution: 9.658311e+01, current residual:5.833918e+01 +field 'mu' [explicit solve]: current solution: 5.445941e+00, current residual:3.273804e+00 +wall time: 0.000669003s + +time increment:17420 time: 17.42 +field ' c' [explicit solve]: current solution: 9.658270e+01, current residual:5.833891e+01 +field 'mu' [explicit solve]: current solution: 5.445417e+00, current residual:3.273511e+00 +wall time: 0.000770092s + +time increment:17430 time: 17.43 +field ' c' [explicit solve]: current solution: 9.658229e+01, current residual:5.833865e+01 +field 'mu' [explicit solve]: current solution: 5.444895e+00, current residual:3.273218e+00 +wall time: 0.000631094s + +time increment:17440 time: 17.44 +field ' c' [explicit solve]: current solution: 9.658187e+01, current residual:5.833838e+01 +field 'mu' [explicit solve]: current solution: 5.444374e+00, current residual:3.272927e+00 +wall time: 0.000658035s + +time increment:17450 time: 17.45 +field ' c' [explicit solve]: current solution: 9.658146e+01, current residual:5.833812e+01 +field 'mu' [explicit solve]: current solution: 5.443853e+00, current residual:3.272635e+00 +wall time: 0.000676155s + +time increment:17460 time: 17.46 +field ' c' [explicit solve]: current solution: 9.658105e+01, current residual:5.833786e+01 +field 'mu' [explicit solve]: current solution: 5.443334e+00, current residual:3.272345e+00 +wall time: 0.000566006s + +time increment:17470 time: 17.47 +field ' c' [explicit solve]: current solution: 9.658065e+01, current residual:5.833759e+01 +field 'mu' [explicit solve]: current solution: 5.442816e+00, current residual:3.272055e+00 +wall time: 0.000631809s + +time increment:17480 time: 17.48 +field ' c' [explicit solve]: current solution: 9.658024e+01, current residual:5.833733e+01 +field 'mu' [explicit solve]: current solution: 5.442299e+00, current residual:3.271765e+00 +wall time: 0.00057292s + +time increment:17490 time: 17.49 +field ' c' [explicit solve]: current solution: 9.657983e+01, current residual:5.833707e+01 +field 'mu' [explicit solve]: current solution: 5.441783e+00, current residual:3.271477e+00 +wall time: 0.000702858s + +time increment:17500 time: 17.5 +field ' c' [explicit solve]: current solution: 9.657942e+01, current residual:5.833681e+01 +field 'mu' [explicit solve]: current solution: 5.441269e+00, current residual:3.271189e+00 +wall time: 0.000586033s + +time increment:17510 time: 17.51 +field ' c' [explicit solve]: current solution: 9.657901e+01, current residual:5.833654e+01 +field 'mu' [explicit solve]: current solution: 5.440755e+00, current residual:3.270901e+00 +wall time: 0.000637054s + +time increment:17520 time: 17.52 +field ' c' [explicit solve]: current solution: 9.657860e+01, current residual:5.833628e+01 +field 'mu' [explicit solve]: current solution: 5.440242e+00, current residual:3.270615e+00 +wall time: 0.000638008s + +time increment:17530 time: 17.53 +field ' c' [explicit solve]: current solution: 9.657820e+01, current residual:5.833602e+01 +field 'mu' [explicit solve]: current solution: 5.439731e+00, current residual:3.270328e+00 +wall time: 0.000597954s + +time increment:17540 time: 17.54 +field ' c' [explicit solve]: current solution: 9.657779e+01, current residual:5.833576e+01 +field 'mu' [explicit solve]: current solution: 5.439220e+00, current residual:3.270043e+00 +wall time: 0.000576019s + +time increment:17550 time: 17.55 +field ' c' [explicit solve]: current solution: 9.657738e+01, current residual:5.833550e+01 +field 'mu' [explicit solve]: current solution: 5.438711e+00, current residual:3.269758e+00 +wall time: 0.000650167s + +time increment:17560 time: 17.56 +field ' c' [explicit solve]: current solution: 9.657698e+01, current residual:5.833523e+01 +field 'mu' [explicit solve]: current solution: 5.438203e+00, current residual:3.269474e+00 +wall time: 0.000693083s + +time increment:17570 time: 17.57 +field ' c' [explicit solve]: current solution: 9.657657e+01, current residual:5.833497e+01 +field 'mu' [explicit solve]: current solution: 5.437696e+00, current residual:3.269190e+00 +wall time: 0.000564098s + +time increment:17580 time: 17.58 +field ' c' [explicit solve]: current solution: 9.657617e+01, current residual:5.833471e+01 +field 'mu' [explicit solve]: current solution: 5.437189e+00, current residual:3.268907e+00 +wall time: 0.000633001s + +time increment:17590 time: 17.59 +field ' c' [explicit solve]: current solution: 9.657576e+01, current residual:5.833445e+01 +field 'mu' [explicit solve]: current solution: 5.436684e+00, current residual:3.268625e+00 +wall time: 0.000571012s + +time increment:17600 time: 17.6 +field ' c' [explicit solve]: current solution: 9.657536e+01, current residual:5.833419e+01 +field 'mu' [explicit solve]: current solution: 5.436180e+00, current residual:3.268343e+00 +wall time: 0.000678778s + +time increment:17610 time: 17.61 +field ' c' [explicit solve]: current solution: 9.657495e+01, current residual:5.833393e+01 +field 'mu' [explicit solve]: current solution: 5.435677e+00, current residual:3.268062e+00 +wall time: 0.000622034s + +time increment:17620 time: 17.62 +field ' c' [explicit solve]: current solution: 9.657455e+01, current residual:5.833367e+01 +field 'mu' [explicit solve]: current solution: 5.435175e+00, current residual:3.267781e+00 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0.000580072s + +time increment:17670 time: 17.67 +field ' c' [explicit solve]: current solution: 9.657253e+01, current residual:5.833237e+01 +field 'mu' [explicit solve]: current solution: 5.432681e+00, current residual:3.266387e+00 +wall time: 0.000634909s + +time increment:17680 time: 17.68 +field ' c' [explicit solve]: current solution: 9.657213e+01, current residual:5.833212e+01 +field 'mu' [explicit solve]: current solution: 5.432186e+00, current residual:3.266111e+00 +wall time: 0.000558853s + +time increment:17690 time: 17.69 +field ' c' [explicit solve]: current solution: 9.657173e+01, current residual:5.833186e+01 +field 'mu' [explicit solve]: current solution: 5.431691e+00, current residual:3.265834e+00 +wall time: 0.00056386s + +time increment:17700 time: 17.7 +field ' c' [explicit solve]: current solution: 9.657133e+01, current residual:5.833160e+01 +field 'mu' [explicit solve]: current solution: 5.431198e+00, current residual:3.265559e+00 +wall time: 0.000724077s + +time increment:17710 time: 17.71 +field ' c' [explicit solve]: current solution: 9.657093e+01, current residual:5.833134e+01 +field 'mu' [explicit solve]: current solution: 5.430705e+00, current residual:3.265284e+00 +wall time: 0.00060606s + +time increment:17720 time: 17.72 +field ' c' [explicit solve]: current solution: 9.657053e+01, current residual:5.833108e+01 +field 'mu' [explicit solve]: current solution: 5.430214e+00, current residual:3.265009e+00 +wall time: 0.000660181s + +time increment:17730 time: 17.73 +field ' c' [explicit solve]: current solution: 9.657013e+01, current residual:5.833083e+01 +field 'mu' [explicit solve]: current solution: 5.429723e+00, current residual:3.264735e+00 +wall time: 0.000553846s + +time increment:17740 time: 17.74 +field ' c' [explicit solve]: current solution: 9.656973e+01, current residual:5.833057e+01 +field 'mu' [explicit solve]: current solution: 5.429234e+00, current residual:3.264462e+00 +wall time: 0.000655174s + +time increment:17750 time: 17.75 +field ' c' [explicit solve]: current solution: 9.656933e+01, current residual:5.833031e+01 +field 'mu' [explicit solve]: current solution: 5.428745e+00, current residual:3.264189e+00 +wall time: 0.000684977s + +time increment:17760 time: 17.76 +field ' c' [explicit solve]: current solution: 9.656893e+01, current residual:5.833005e+01 +field 'mu' [explicit solve]: current solution: 5.428258e+00, current residual:3.263917e+00 +wall time: 0.000570059s + +time increment:17770 time: 17.77 +field ' c' [explicit solve]: current solution: 9.656853e+01, current residual:5.832980e+01 +field 'mu' [explicit solve]: current solution: 5.427772e+00, current residual:3.263646e+00 +wall time: 0.000760794s + +time increment:17780 time: 17.78 +field ' c' [explicit solve]: current solution: 9.656813e+01, current residual:5.832954e+01 +field 'mu' [explicit solve]: current solution: 5.427287e+00, current residual:3.263375e+00 +wall time: 0.000665188s + +time increment:17790 time: 17.79 +field ' c' [explicit solve]: current solution: 9.656773e+01, current residual:5.832929e+01 +field 'mu' [explicit solve]: current solution: 5.426802e+00, current residual:3.263105e+00 +wall time: 0.000630856s + +time increment:17800 time: 17.8 +field ' c' [explicit solve]: current solution: 9.656733e+01, current residual:5.832903e+01 +field 'mu' [explicit solve]: current solution: 5.426319e+00, current residual:3.262835e+00 +wall time: 0.000570059s + +time increment:17810 time: 17.81 +field ' c' [explicit solve]: current solution: 9.656694e+01, current residual:5.832877e+01 +field 'mu' [explicit solve]: current solution: 5.425837e+00, current residual:3.262566e+00 +wall time: 0.000576019s + +time increment:17820 time: 17.82 +field ' c' [explicit solve]: current solution: 9.656654e+01, current residual:5.832852e+01 +field 'mu' [explicit solve]: current solution: 5.425355e+00, current residual:3.262297e+00 +wall time: 0.000602961s + +time increment:17830 time: 17.83 +field ' c' [explicit solve]: current solution: 9.656614e+01, current residual:5.832826e+01 +field 'mu' [explicit solve]: current solution: 5.424875e+00, current residual:3.262029e+00 +wall time: 0.000686884s + +time increment:17840 time: 17.84 +field ' c' [explicit solve]: current solution: 9.656575e+01, current residual:5.832801e+01 +field 'mu' [explicit solve]: current solution: 5.424396e+00, current residual:3.261762e+00 +wall time: 0.000749826s + +time increment:17850 time: 17.85 +field ' c' [explicit solve]: current solution: 9.656535e+01, current residual:5.832775e+01 +field 'mu' [explicit solve]: current solution: 5.423918e+00, current residual:3.261495e+00 +wall time: 0.00067997s + +time increment:17860 time: 17.86 +field ' c' [explicit solve]: current solution: 9.656496e+01, current residual:5.832750e+01 +field 'mu' [explicit solve]: current solution: 5.423441e+00, current residual:3.261229e+00 +wall time: 0.00067091s + +time increment:17870 time: 17.87 +field ' c' [explicit solve]: current solution: 9.656456e+01, current residual:5.832724e+01 +field 'mu' [explicit solve]: current solution: 5.422964e+00, current residual:3.260964e+00 +wall time: 0.000694036s + +time increment:17880 time: 17.88 +field ' c' [explicit solve]: current solution: 9.656416e+01, current residual:5.832699e+01 +field 'mu' [explicit solve]: current solution: 5.422489e+00, current residual:3.260699e+00 +wall time: 0.00069809s + +time increment:17890 time: 17.89 +field ' c' [explicit solve]: current solution: 9.656377e+01, current residual:5.832674e+01 +field 'mu' [explicit solve]: current solution: 5.422015e+00, current residual:3.260434e+00 +wall time: 0.000674963s + +time increment:17900 time: 17.9 +field ' c' [explicit solve]: current solution: 9.656338e+01, current residual:5.832648e+01 +field 'mu' [explicit solve]: current solution: 5.421542e+00, current residual:3.260171e+00 +wall time: 0.000696898s + +time increment:17910 time: 17.91 +field ' c' [explicit solve]: current solution: 9.656298e+01, current residual:5.832623e+01 +field 'mu' [explicit solve]: current solution: 5.421070e+00, current residual:3.259907e+00 +wall time: 0.000700951s + +time increment:17920 time: 17.92 +field ' c' [explicit solve]: current solution: 9.656259e+01, current residual:5.832598e+01 +field 'mu' [explicit solve]: current solution: 5.420599e+00, current residual:3.259645e+00 +wall time: 0.00067687s + +time increment:17930 time: 17.93 +field ' c' [explicit solve]: current solution: 9.656219e+01, current residual:5.832572e+01 +field 'mu' [explicit solve]: current solution: 5.420128e+00, current residual:3.259383e+00 +wall time: 0.000633001s + +time increment:17940 time: 17.94 +field ' c' [explicit solve]: current solution: 9.656180e+01, current residual:5.832547e+01 +field 'mu' [explicit solve]: current solution: 5.419659e+00, current residual:3.259121e+00 +wall time: 0.000664949s + +time increment:17950 time: 17.95 +field ' c' [explicit solve]: current solution: 9.656141e+01, current residual:5.832522e+01 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[explicit solve]: current solution: 5.417328e+00, current residual:3.257822e+00 +wall time: 0.000642061s + +time increment:18000 time: 18 +field ' c' [explicit solve]: current solution: 9.655945e+01, current residual:5.832396e+01 +field 'mu' [explicit solve]: current solution: 5.416865e+00, current residual:3.257564e+00 +wall time: 0.00068903s + +time increment:18010 time: 18.01 +field ' c' [explicit solve]: current solution: 9.655906e+01, current residual:5.832370e+01 +field 'mu' [explicit solve]: current solution: 5.416402e+00, current residual:3.257307e+00 +wall time: 0.000666857s + +time increment:18020 time: 18.02 +field ' c' [explicit solve]: current solution: 9.655867e+01, current residual:5.832345e+01 +field 'mu' [explicit solve]: current solution: 5.415941e+00, current residual:3.257050e+00 +wall time: 0.000734806s + +time increment:18030 time: 18.03 +field ' c' [explicit solve]: current solution: 9.655828e+01, current residual:5.832320e+01 +field 'mu' [explicit solve]: current solution: 5.415480e+00, current residual:3.256794e+00 +wall time: 0.000634909s + +time increment:18040 time: 18.04 +field ' c' [explicit solve]: current solution: 9.655789e+01, current residual:5.832295e+01 +field 'mu' [explicit solve]: current solution: 5.415021e+00, current residual:3.256538e+00 +wall time: 0.000663042s + +time increment:18050 time: 18.05 +field ' c' [explicit solve]: current solution: 9.655750e+01, current residual:5.832270e+01 +field 'mu' [explicit solve]: current solution: 5.414563e+00, current residual:3.256283e+00 +wall time: 0.000657797s + +time increment:18060 time: 18.06 +field ' c' [explicit solve]: current solution: 9.655711e+01, current residual:5.832245e+01 +field 'mu' [explicit solve]: current solution: 5.414105e+00, current residual:3.256028e+00 +wall time: 0.000657082s + +time increment:18070 time: 18.07 +field ' c' [explicit solve]: current solution: 9.655672e+01, current residual:5.832220e+01 +field 'mu' [explicit solve]: current solution: 5.413649e+00, current residual:3.255774e+00 +wall time: 0.000667095s + +time increment:18080 time: 18.08 +field ' c' [explicit solve]: current solution: 9.655633e+01, current residual:5.832195e+01 +field 'mu' [explicit solve]: current solution: 5.413193e+00, current residual:3.255520e+00 +wall time: 0.00074482s + +time increment:18090 time: 18.09 +field ' c' [explicit solve]: current solution: 9.655595e+01, current residual:5.832170e+01 +field 'mu' [explicit solve]: current solution: 5.412738e+00, current residual:3.255267e+00 +wall time: 0.000679016s + +time increment:18100 time: 18.1 +field ' c' [explicit solve]: current solution: 9.655556e+01, current residual:5.832145e+01 +field 'mu' [explicit solve]: current solution: 5.412285e+00, current residual:3.255015e+00 +wall time: 0.000676155s + +time increment:18110 time: 18.11 +field ' c' [explicit solve]: current solution: 9.655517e+01, current residual:5.832120e+01 +field 'mu' [explicit solve]: current solution: 5.411832e+00, current residual:3.254763e+00 +wall time: 0.000696898s + +time increment:18120 time: 18.12 +field ' c' [explicit solve]: current solution: 9.655479e+01, current residual:5.832095e+01 +field 'mu' [explicit solve]: current solution: 5.411380e+00, current residual:3.254512e+00 +wall time: 0.000674963s + +time increment:18130 time: 18.13 +field ' c' [explicit solve]: current solution: 9.655440e+01, current residual:5.832070e+01 +field 'mu' [explicit solve]: current solution: 5.410929e+00, current residual:3.254261e+00 +wall time: 0.000653982s + +time increment:18140 time: 18.14 +field ' c' [explicit solve]: current solution: 9.655401e+01, current residual:5.832046e+01 +field 'mu' [explicit solve]: current solution: 5.410480e+00, current residual:3.254011e+00 +wall time: 0.000718117s + +time increment:18150 time: 18.15 +field ' c' [explicit solve]: current solution: 9.655363e+01, current residual:5.832021e+01 +field 'mu' [explicit solve]: current solution: 5.410031e+00, current residual:3.253761e+00 +wall time: 0.000642061s + +time increment:18160 time: 18.16 +field ' c' [explicit solve]: current solution: 9.655324e+01, current residual:5.831996e+01 +field 'mu' [explicit solve]: current solution: 5.409583e+00, current residual:3.253512e+00 +wall time: 0.00068903s + +time increment:18170 time: 18.17 +field ' c' [explicit solve]: current solution: 9.655286e+01, current residual:5.831971e+01 +field 'mu' [explicit solve]: current solution: 5.409136e+00, current residual:3.253263e+00 +wall time: 0.000648975s + +time increment:18180 time: 18.18 +field ' c' [explicit solve]: current solution: 9.655247e+01, current residual:5.831946e+01 +field 'mu' [explicit solve]: current solution: 5.408690e+00, current residual:3.253015e+00 +wall time: 0.000742912s + +time increment:18190 time: 18.19 +field ' c' [explicit solve]: current solution: 9.655209e+01, current residual:5.831922e+01 +field 'mu' [explicit solve]: current solution: 5.408245e+00, current residual:3.252768e+00 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0.000926018s + +time increment:18240 time: 18.24 +field ' c' [explicit solve]: current solution: 9.655017e+01, current residual:5.831798e+01 +field 'mu' [explicit solve]: current solution: 5.406033e+00, current residual:3.251539e+00 +wall time: 0.00079608s + +time increment:18250 time: 18.25 +field ' c' [explicit solve]: current solution: 9.654979e+01, current residual:5.831773e+01 +field 'mu' [explicit solve]: current solution: 5.405593e+00, current residual:3.251295e+00 +wall time: 0.000878096s + +time increment:18260 time: 18.26 +field ' c' [explicit solve]: current solution: 9.654940e+01, current residual:5.831749e+01 +field 'mu' [explicit solve]: current solution: 5.405155e+00, current residual:3.251051e+00 +wall time: 0.000808001s + +time increment:18270 time: 18.27 +field ' c' [explicit solve]: current solution: 9.654902e+01, current residual:5.831724e+01 +field 'mu' [explicit solve]: current solution: 5.404717e+00, current residual:3.250808e+00 +wall time: 0.000714064s + +time increment:18280 time: 18.28 +field ' c' [explicit solve]: current solution: 9.654864e+01, current residual:5.831700e+01 +field 'mu' [explicit solve]: current solution: 5.404280e+00, current residual:3.250565e+00 +wall time: 0.000705957s + +time increment:18290 time: 18.29 +field ' c' [explicit solve]: current solution: 9.654826e+01, current residual:5.831675e+01 +field 'mu' [explicit solve]: current solution: 5.403845e+00, current residual:3.250323e+00 +wall time: 0.000678062s + +time increment:18300 time: 18.3 +field ' c' [explicit solve]: current solution: 9.654788e+01, current residual:5.831651e+01 +field 'mu' [explicit solve]: current solution: 5.403410e+00, current residual:3.250082e+00 +wall time: 0.000777006s + +time increment:18310 time: 18.31 +field ' c' [explicit solve]: current solution: 9.654750e+01, current residual:5.831626e+01 +field 'mu' [explicit solve]: current solution: 5.402976e+00, current residual:3.249841e+00 +wall time: 0.00071907s + +time increment:18320 time: 18.32 +field ' c' [explicit solve]: current solution: 9.654712e+01, current residual:5.831602e+01 +field 'mu' [explicit solve]: current solution: 5.402543e+00, current residual:3.249600e+00 +wall time: 0.00067997s + +time increment:18330 time: 18.33 +field ' c' [explicit solve]: current solution: 9.654674e+01, current residual:5.831577e+01 +field 'mu' [explicit solve]: current solution: 5.402110e+00, current residual:3.249360e+00 +wall time: 0.000690937s + +time increment:18340 time: 18.34 +field ' c' [explicit solve]: current solution: 9.654636e+01, current residual:5.831553e+01 +field 'mu' [explicit solve]: current solution: 5.401679e+00, current residual:3.249121e+00 +wall time: 0.000724792s + +time increment:18350 time: 18.35 +field ' c' [explicit solve]: current solution: 9.654598e+01, current residual:5.831528e+01 +field 'mu' [explicit solve]: current solution: 5.401249e+00, current residual:3.248882e+00 +wall time: 0.00072813s + +time increment:18360 time: 18.36 +field ' c' [explicit solve]: current solution: 9.654560e+01, current residual:5.831504e+01 +field 'mu' [explicit solve]: current solution: 5.400819e+00, current residual:3.248644e+00 +wall time: 0.000699997s + +time increment:18370 time: 18.37 +field ' c' [explicit solve]: current solution: 9.654522e+01, current residual:5.831480e+01 +field 'mu' [explicit solve]: current solution: 5.400391e+00, current residual:3.248406e+00 +wall time: 0.000694036s + +time increment:18380 time: 18.38 +field ' c' [explicit solve]: current solution: 9.654484e+01, current residual:5.831455e+01 +field 'mu' [explicit solve]: current solution: 5.399963e+00, current residual:3.248168e+00 +wall time: 0.00067091s + +time increment:18390 time: 18.39 +field ' c' [explicit solve]: current solution: 9.654446e+01, current residual:5.831431e+01 +field 'mu' [explicit solve]: current solution: 5.399536e+00, current residual:3.247932e+00 +wall time: 0.000684977s + +time increment:18400 time: 18.4 +field ' c' [explicit solve]: current solution: 9.654408e+01, current residual:5.831407e+01 +field 'mu' [explicit solve]: current solution: 5.399111e+00, current residual:3.247695e+00 +wall time: 0.000726938s + +time increment:18410 time: 18.41 +field ' c' [explicit solve]: current solution: 9.654371e+01, current residual:5.831382e+01 +field 'mu' [explicit solve]: current solution: 5.398686e+00, current residual:3.247460e+00 +wall time: 0.000707865s + +time increment:18420 time: 18.42 +field ' c' [explicit solve]: current solution: 9.654333e+01, current residual:5.831358e+01 +field 'mu' [explicit solve]: current solution: 5.398262e+00, current residual:3.247224e+00 +wall time: 0.000660181s + +time increment:18430 time: 18.43 +field ' c' [explicit solve]: current solution: 9.654295e+01, current residual:5.831334e+01 +field 'mu' [explicit solve]: current solution: 5.397838e+00, current residual:3.246990e+00 +wall time: 0.00088191s + +time increment:18440 time: 18.44 +field ' c' [explicit solve]: current solution: 9.654258e+01, current residual:5.831309e+01 +field 'mu' [explicit solve]: current solution: 5.397416e+00, current residual:3.246756e+00 +wall time: 0.000671148s + +time increment:18450 time: 18.45 +field ' c' [explicit solve]: current solution: 9.654220e+01, current residual:5.831285e+01 +field 'mu' [explicit solve]: current solution: 5.396995e+00, current residual:3.246522e+00 +wall time: 0.000677109s + +time increment:18460 time: 18.46 +field ' c' [explicit solve]: current solution: 9.654183e+01, current residual:5.831261e+01 +field 'mu' [explicit solve]: current solution: 5.396574e+00, current residual:3.246289e+00 +wall time: 0.000669003s + +time increment:18470 time: 18.47 +field ' c' [explicit solve]: current solution: 9.654145e+01, current residual:5.831237e+01 +field 'mu' [explicit solve]: current solution: 5.396155e+00, current residual:3.246056e+00 +wall time: 0.000667095s + +time increment:18480 time: 18.48 +field ' c' [explicit solve]: current solution: 9.654107e+01, current residual:5.831213e+01 +field 'mu' [explicit solve]: current solution: 5.395736e+00, current residual:3.245824e+00 +wall time: 0.000730991s + +time increment:18490 time: 18.49 +field ' c' [explicit solve]: current solution: 9.654070e+01, current residual:5.831189e+01 +field 'mu' [explicit solve]: current solution: 5.395318e+00, current residual:3.245592e+00 +wall time: 0.000685215s + +time increment:18500 time: 18.5 +field ' c' [explicit solve]: current solution: 9.654033e+01, current residual:5.831164e+01 +field 'mu' [explicit solve]: current solution: 5.394901e+00, current residual:3.245361e+00 +wall time: 0.000684977s + +time increment:18510 time: 18.51 +field ' c' [explicit solve]: current solution: 9.653995e+01, current residual:5.831140e+01 +field 'mu' [explicit solve]: current solution: 5.394485e+00, current residual:3.245131e+00 +wall time: 0.000607967s + +time increment:18520 time: 18.52 +field ' c' [explicit solve]: current solution: 9.653958e+01, current residual:5.831116e+01 +field 'mu' [explicit solve]: current solution: 5.394070e+00, current residual:3.244901e+00 +wall time: 0.000569105s + +time increment:18530 time: 18.53 +field ' c' [explicit solve]: current solution: 9.653920e+01, current residual:5.831092e+01 +field 'mu' [explicit solve]: current solution: 5.393656e+00, current residual:3.244671e+00 +wall time: 0.000602007s + +time increment:18540 time: 18.54 +field ' c' [explicit solve]: current solution: 9.653883e+01, current residual:5.831068e+01 +field 'mu' [explicit solve]: current solution: 5.393242e+00, current residual:3.244442e+00 +wall time: 0.000559092s + +time increment:18550 time: 18.55 +field ' c' [explicit solve]: current solution: 9.653846e+01, current residual:5.831044e+01 +field 'mu' [explicit solve]: current solution: 5.392829e+00, current residual:3.244214e+00 +wall time: 0.000697136s + +time increment:18560 time: 18.56 +field ' c' [explicit solve]: current solution: 9.653808e+01, current residual:5.831020e+01 +field 'mu' [explicit solve]: current solution: 5.392418e+00, current residual:3.243985e+00 +wall time: 0.000627041s + +time increment:18570 time: 18.57 +field ' c' [explicit solve]: current solution: 9.653771e+01, current residual:5.830996e+01 +field 'mu' [explicit solve]: current solution: 5.392007e+00, current residual:3.243758e+00 +wall time: 0.000571966s + +time increment:18580 time: 18.58 +field ' c' [explicit solve]: current solution: 9.653734e+01, current residual:5.830972e+01 +field 'mu' [explicit solve]: current solution: 5.391597e+00, current residual:3.243531e+00 +wall time: 0.000563145s + +time increment:18590 time: 18.59 +field ' c' [explicit solve]: current solution: 9.653697e+01, current residual:5.830948e+01 +field 'mu' [explicit solve]: current solution: 5.391188e+00, current residual:3.243304e+00 +wall time: 0.000555992s + +time increment:18600 time: 18.6 +field ' c' [explicit solve]: current solution: 9.653660e+01, current residual:5.830924e+01 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[explicit solve]: current solution: 5.389155e+00, current residual:3.242179e+00 +wall time: 0.000644922s + +time increment:18650 time: 18.65 +field ' c' [explicit solve]: current solution: 9.653475e+01, current residual:5.830805e+01 +field 'mu' [explicit solve]: current solution: 5.388751e+00, current residual:3.241955e+00 +wall time: 0.000674009s + +time increment:18660 time: 18.66 +field ' c' [explicit solve]: current solution: 9.653438e+01, current residual:5.830781e+01 +field 'mu' [explicit solve]: current solution: 5.388348e+00, current residual:3.241732e+00 +wall time: 0.000761032s + +time increment:18670 time: 18.67 +field ' c' [explicit solve]: current solution: 9.653401e+01, current residual:5.830758e+01 +field 'mu' [explicit solve]: current solution: 5.387946e+00, current residual:3.241510e+00 +wall time: 0.000729799s + +time increment:18680 time: 18.68 +field ' c' [explicit solve]: current solution: 9.653364e+01, current residual:5.830734e+01 +field 'mu' [explicit solve]: current solution: 5.387544e+00, current residual:3.241288e+00 +wall time: 0.000711918s + +time increment:18690 time: 18.69 +field ' c' [explicit solve]: current solution: 9.653327e+01, current residual:5.830710e+01 +field 'mu' [explicit solve]: current solution: 5.387144e+00, current residual:3.241066e+00 +wall time: 0.000629902s + +time increment:18700 time: 18.7 +field ' c' [explicit solve]: current solution: 9.653290e+01, current residual:5.830686e+01 +field 'mu' [explicit solve]: current solution: 5.386744e+00, current residual:3.240845e+00 +wall time: 0.000662088s + +time increment:18710 time: 18.71 +field ' c' [explicit solve]: current solution: 9.653253e+01, current residual:5.830663e+01 +field 'mu' [explicit solve]: current solution: 5.386345e+00, current residual:3.240624e+00 +wall time: 0.00058198s + +time increment:18720 time: 18.72 +field ' c' [explicit solve]: current solution: 9.653217e+01, current residual:5.830639e+01 +field 'mu' [explicit solve]: current solution: 5.385947e+00, current residual:3.240404e+00 +wall time: 0.000684023s + +time increment:18730 time: 18.73 +field ' c' [explicit solve]: current solution: 9.653180e+01, current residual:5.830615e+01 +field 'mu' [explicit solve]: current solution: 5.385550e+00, current residual:3.240185e+00 +wall time: 0.000576019s + +time increment:18740 time: 18.74 +field ' c' [explicit solve]: current solution: 9.653143e+01, current residual:5.830592e+01 +field 'mu' [explicit solve]: current solution: 5.385153e+00, current residual:3.239965e+00 +wall time: 0.000603914s + +time increment:18750 time: 18.75 +field ' c' [explicit solve]: current solution: 9.653107e+01, current residual:5.830568e+01 +field 'mu' [explicit solve]: current solution: 5.384758e+00, current residual:3.239747e+00 +wall time: 0.000645161s + +time increment:18760 time: 18.76 +field ' c' [explicit solve]: current solution: 9.653070e+01, current residual:5.830545e+01 +field 'mu' [explicit solve]: current solution: 5.384363e+00, current residual:3.239529e+00 +wall time: 0.000571966s + +time increment:18770 time: 18.77 +field ' c' [explicit solve]: current solution: 9.653033e+01, current residual:5.830521e+01 +field 'mu' [explicit solve]: current solution: 5.383969e+00, current residual:3.239311e+00 +wall time: 0.000591993s + +time increment:18780 time: 18.78 +field ' c' [explicit solve]: current solution: 9.652997e+01, current residual:5.830498e+01 +field 'mu' [explicit solve]: current solution: 5.383576e+00, current residual:3.239094e+00 +wall time: 0.000605822s + +time increment:18790 time: 18.79 +field ' c' [explicit solve]: current solution: 9.652960e+01, current residual:5.830474e+01 +field 'mu' [explicit solve]: current solution: 5.383184e+00, current residual:3.238877e+00 +wall time: 0.000636101s + +time increment:18800 time: 18.8 +field ' c' [explicit solve]: current solution: 9.652924e+01, current residual:5.830450e+01 +field 'mu' [explicit solve]: current solution: 5.382792e+00, current residual:3.238661e+00 +wall time: 0.000639915s + +time increment:18810 time: 18.81 +field ' c' [explicit solve]: current solution: 9.652887e+01, current residual:5.830427e+01 +field 'mu' [explicit solve]: current solution: 5.382402e+00, current residual:3.238445e+00 +wall time: 0.000596046s + +time increment:18820 time: 18.82 +field ' c' [explicit solve]: current solution: 9.652851e+01, current residual:5.830404e+01 +field 'mu' [explicit solve]: current solution: 5.382012e+00, current residual:3.238229e+00 +wall time: 0.000643969s + +time increment:18830 time: 18.83 +field ' c' [explicit solve]: current solution: 9.652814e+01, current residual:5.830380e+01 +field 'mu' [explicit solve]: current solution: 5.381623e+00, current residual:3.238015e+00 +wall time: 0.00068903s + +time increment:18840 time: 18.84 +field ' c' [explicit solve]: current solution: 9.652778e+01, current residual:5.830357e+01 +field 'mu' [explicit solve]: current solution: 5.381235e+00, current residual:3.237800e+00 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0.000617027s + +time increment:18890 time: 18.89 +field ' c' [explicit solve]: current solution: 9.652597e+01, current residual:5.830240e+01 +field 'mu' [explicit solve]: current solution: 5.379306e+00, current residual:3.236735e+00 +wall time: 0.000575066s + +time increment:18900 time: 18.9 +field ' c' [explicit solve]: current solution: 9.652560e+01, current residual:5.830217e+01 +field 'mu' [explicit solve]: current solution: 5.378923e+00, current residual:3.236524e+00 +wall time: 0.000557899s + +time increment:18910 time: 18.91 +field ' c' [explicit solve]: current solution: 9.652524e+01, current residual:5.830193e+01 +field 'mu' [explicit solve]: current solution: 5.378540e+00, current residual:3.236313e+00 +wall time: 0.000643969s + +time increment:18920 time: 18.92 +field ' c' [explicit solve]: current solution: 9.652488e+01, current residual:5.830170e+01 +field 'mu' [explicit solve]: current solution: 5.378159e+00, current residual:3.236102e+00 +wall time: 0.000656843s + +time increment:18930 time: 18.93 +field ' c' [explicit solve]: current solution: 9.652452e+01, current residual:5.830147e+01 +field 'mu' [explicit solve]: current solution: 5.377778e+00, current residual:3.235892e+00 +wall time: 0.000682116s + +time increment:18940 time: 18.94 +field ' c' [explicit solve]: current solution: 9.652416e+01, current residual:5.830123e+01 +field 'mu' [explicit solve]: current solution: 5.377398e+00, current residual:3.235682e+00 +wall time: 0.000569105s + +time increment:18950 time: 18.95 +field ' c' [explicit solve]: current solution: 9.652380e+01, current residual:5.830100e+01 +field 'mu' [explicit solve]: current solution: 5.377018e+00, current residual:3.235473e+00 +wall time: 0.000636101s + +time increment:18960 time: 18.96 +field ' c' [explicit solve]: current solution: 9.652344e+01, current residual:5.830077e+01 +field 'mu' [explicit solve]: current solution: 5.376640e+00, current residual:3.235264e+00 +wall time: 0.000567913s + +time increment:18970 time: 18.97 +field ' c' [explicit solve]: current solution: 9.652308e+01, current residual:5.830054e+01 +field 'mu' [explicit solve]: current solution: 5.376262e+00, current residual:3.235056e+00 +wall time: 0.000646114s + +time increment:18980 time: 18.98 +field ' c' [explicit solve]: current solution: 9.652272e+01, current residual:5.830031e+01 +field 'mu' [explicit solve]: current solution: 5.375885e+00, current residual:3.234848e+00 +wall time: 0.000647068s + +time increment:18990 time: 18.99 +field ' c' [explicit solve]: current solution: 9.652236e+01, current residual:5.830008e+01 +field 'mu' [explicit solve]: current solution: 5.375509e+00, current residual:3.234641e+00 +wall time: 0.000561953s + +time increment:19000 time: 19 +field ' c' [explicit solve]: current solution: 9.652200e+01, current residual:5.829984e+01 +field 'mu' [explicit solve]: current solution: 5.375134e+00, current residual:3.234434e+00 +wall time: 0.000617027s + +time increment:19010 time: 19.01 +field ' c' [explicit solve]: current solution: 9.652164e+01, current residual:5.829961e+01 +field 'mu' [explicit solve]: current solution: 5.374759e+00, current residual:3.234227e+00 +wall time: 0.000643015s + +time increment:19020 time: 19.02 +field ' c' [explicit solve]: current solution: 9.652129e+01, current residual:5.829938e+01 +field 'mu' [explicit solve]: current solution: 5.374386e+00, current residual:3.234021e+00 +wall time: 0.00061202s + +time increment:19030 time: 19.03 +field ' c' [explicit solve]: current solution: 9.652093e+01, current residual:5.829915e+01 +field 'mu' [explicit solve]: current solution: 5.374013e+00, current residual:3.233815e+00 +wall time: 0.000652075s + +time increment:19040 time: 19.04 +field ' c' [explicit solve]: current solution: 9.652057e+01, current residual:5.829892e+01 +field 'mu' [explicit solve]: current solution: 5.373640e+00, current residual:3.233610e+00 +wall time: 0.000558853s + +time increment:19050 time: 19.05 +field ' c' [explicit solve]: current solution: 9.652021e+01, current residual:5.829869e+01 +field 'mu' [explicit solve]: current solution: 5.373269e+00, current residual:3.233406e+00 +wall time: 0.000654936s + +time increment:19060 time: 19.06 +field ' c' [explicit solve]: current solution: 9.651986e+01, current residual:5.829846e+01 +field 'mu' [explicit solve]: current solution: 5.372898e+00, current residual:3.233201e+00 +wall time: 0.000680923s + +time increment:19070 time: 19.07 +field ' c' [explicit solve]: current solution: 9.651950e+01, current residual:5.829823e+01 +field 'mu' [explicit solve]: current solution: 5.372529e+00, current residual:3.232998e+00 +wall time: 0.000583887s + +time increment:19080 time: 19.08 +field ' c' [explicit solve]: current solution: 9.651914e+01, current residual:5.829800e+01 +field 'mu' [explicit solve]: current solution: 5.372159e+00, current residual:3.232794e+00 +wall time: 0.000578165s + +time increment:19090 time: 19.09 +field ' c' [explicit solve]: current solution: 9.651879e+01, current residual:5.829777e+01 +field 'mu' [explicit solve]: current solution: 5.371791e+00, current residual:3.232591e+00 +wall time: 0.000709057s + +time increment:19100 time: 19.1 +field ' c' [explicit solve]: current solution: 9.651843e+01, current residual:5.829754e+01 +field 'mu' [explicit solve]: current solution: 5.371424e+00, current residual:3.232389e+00 +wall time: 0.000579834s + +time increment:19110 time: 19.11 +field ' c' [explicit solve]: current solution: 9.651808e+01, current residual:5.829732e+01 +field 'mu' [explicit solve]: current solution: 5.371057e+00, current residual:3.232187e+00 +wall time: 0.000652075s + +time increment:19120 time: 19.12 +field ' c' [explicit solve]: current solution: 9.651772e+01, current residual:5.829709e+01 +field 'mu' [explicit solve]: current solution: 5.370691e+00, current residual:3.231985e+00 +wall time: 0.000695944s + +time increment:19130 time: 19.13 +field ' c' [explicit solve]: current solution: 9.651737e+01, current residual:5.829686e+01 +field 'mu' [explicit solve]: current solution: 5.370326e+00, current residual:3.231784e+00 +wall time: 0.000620127s + +time increment:19140 time: 19.14 +field ' c' [explicit solve]: current solution: 9.651701e+01, current residual:5.829663e+01 +field 'mu' [explicit solve]: current solution: 5.369961e+00, current residual:3.231584e+00 +wall time: 0.000659943s + +time increment:19150 time: 19.15 +field ' c' [explicit solve]: current solution: 9.651666e+01, current residual:5.829640e+01 +field 'mu' [explicit solve]: current solution: 5.369597e+00, current residual:3.231383e+00 +wall time: 0.000555992s + +time increment:19160 time: 19.16 +field ' c' [explicit solve]: current solution: 9.651630e+01, current residual:5.829617e+01 +field 'mu' [explicit solve]: current solution: 5.369235e+00, current residual:3.231184e+00 +wall time: 0.000558138s + +time increment:19170 time: 19.17 +field ' c' [explicit solve]: current solution: 9.651595e+01, current residual:5.829595e+01 +field 'mu' [explicit solve]: current solution: 5.368872e+00, current residual:3.230984e+00 +wall time: 0.000627995s + +time increment:19180 time: 19.18 +field ' c' [explicit solve]: current solution: 9.651560e+01, current residual:5.829572e+01 +field 'mu' [explicit solve]: current solution: 5.368511e+00, current residual:3.230785e+00 +wall time: 0.000575066s + +time increment:19190 time: 19.19 +field ' c' [explicit solve]: current solution: 9.651524e+01, current residual:5.829549e+01 +field 'mu' [explicit solve]: current solution: 5.368150e+00, current residual:3.230587e+00 +wall time: 0.000686884s + +time increment:19200 time: 19.2 +field ' c' [explicit solve]: current solution: 9.651489e+01, current residual:5.829526e+01 +field 'mu' [explicit solve]: current solution: 5.367790e+00, current residual:3.230389e+00 +wall time: 0.000560999s + +time increment:19210 time: 19.21 +field ' c' [explicit solve]: current solution: 9.651454e+01, current residual:5.829504e+01 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[explicit solve]: current solution: 5.366002e+00, current residual:3.229405e+00 +wall time: 0.000667095s + +time increment:19260 time: 19.26 +field ' c' [explicit solve]: current solution: 9.651278e+01, current residual:5.829390e+01 +field 'mu' [explicit solve]: current solution: 5.365647e+00, current residual:3.229210e+00 +wall time: 0.000701904s + +time increment:19270 time: 19.27 +field ' c' [explicit solve]: current solution: 9.651243e+01, current residual:5.829368e+01 +field 'mu' [explicit solve]: current solution: 5.365292e+00, current residual:3.229015e+00 +wall time: 0.000563145s + +time increment:19280 time: 19.28 +field ' c' [explicit solve]: current solution: 9.651208e+01, current residual:5.829345e+01 +field 'mu' [explicit solve]: current solution: 5.364938e+00, current residual:3.228820e+00 +wall time: 0.000571966s + +time increment:19290 time: 19.29 +field ' c' [explicit solve]: current solution: 9.651173e+01, current residual:5.829323e+01 +field 'mu' [explicit solve]: current solution: 5.364585e+00, current residual:3.228626e+00 +wall time: 0.000650883s + +time increment:19300 time: 19.3 +field ' c' [explicit solve]: current solution: 9.651138e+01, current residual:5.829300e+01 +field 'mu' [explicit solve]: current solution: 5.364232e+00, current residual:3.228432e+00 +wall time: 0.000566006s + +time increment:19310 time: 19.31 +field ' c' [explicit solve]: current solution: 9.651103e+01, current residual:5.829278e+01 +field 'mu' [explicit solve]: current solution: 5.363881e+00, current residual:3.228239e+00 +wall time: 0.000606775s + +time increment:19320 time: 19.32 +field ' c' [explicit solve]: current solution: 9.651068e+01, current residual:5.829255e+01 +field 'mu' [explicit solve]: current solution: 5.363530e+00, current residual:3.228046e+00 +wall time: 0.000692129s + +time increment:19330 time: 19.33 +field ' c' [explicit solve]: current solution: 9.651033e+01, current residual:5.829233e+01 +field 'mu' [explicit solve]: current solution: 5.363179e+00, current residual:3.227853e+00 +wall time: 0.000577211s + +time increment:19340 time: 19.34 +field ' c' [explicit solve]: current solution: 9.650998e+01, current residual:5.829210e+01 +field 'mu' [explicit solve]: current solution: 5.362830e+00, current residual:3.227661e+00 +wall time: 0.000715017s + +time increment:19350 time: 19.35 +field ' c' [explicit solve]: current solution: 9.650963e+01, current residual:5.829188e+01 +field 'mu' [explicit solve]: current solution: 5.362481e+00, current residual:3.227470e+00 +wall time: 0.000709057s + +time increment:19360 time: 19.36 +field ' c' [explicit solve]: current solution: 9.650929e+01, current residual:5.829165e+01 +field 'mu' [explicit solve]: current solution: 5.362133e+00, current residual:3.227279e+00 +wall time: 0.00067091s + +time increment:19370 time: 19.37 +field ' c' [explicit solve]: current solution: 9.650894e+01, current residual:5.829143e+01 +field 'mu' [explicit solve]: current solution: 5.361786e+00, current residual:3.227088e+00 +wall time: 0.00068903s + +time increment:19380 time: 19.38 +field ' c' [explicit solve]: current solution: 9.650859e+01, current residual:5.829120e+01 +field 'mu' [explicit solve]: current solution: 5.361439e+00, current residual:3.226897e+00 +wall time: 0.000625134s + +time increment:19390 time: 19.39 +field ' c' [explicit solve]: current solution: 9.650824e+01, current residual:5.829098e+01 +field 'mu' [explicit solve]: current solution: 5.361093e+00, current residual:3.226708e+00 +wall time: 0.000652075s + +time increment:19400 time: 19.4 +field ' c' [explicit solve]: current solution: 9.650790e+01, current residual:5.829076e+01 +field 'mu' [explicit solve]: current solution: 5.360748e+00, current residual:3.226518e+00 +wall time: 0.000638962s + +time increment:19410 time: 19.41 +field ' c' [explicit solve]: current solution: 9.650755e+01, current residual:5.829053e+01 +field 'mu' [explicit solve]: current solution: 5.360403e+00, current residual:3.226329e+00 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0.000757933s + +time increment:19460 time: 19.46 +field ' c' [explicit solve]: current solution: 9.650582e+01, current residual:5.828942e+01 +field 'mu' [explicit solve]: current solution: 5.358692e+00, current residual:3.225389e+00 +wall time: 0.000739813s + +time increment:19470 time: 19.47 +field ' c' [explicit solve]: current solution: 9.650548e+01, current residual:5.828920e+01 +field 'mu' [explicit solve]: current solution: 5.358352e+00, current residual:3.225203e+00 +wall time: 0.000682831s + +time increment:19480 time: 19.48 +field ' c' [explicit solve]: current solution: 9.650513e+01, current residual:5.828898e+01 +field 'mu' [explicit solve]: current solution: 5.358012e+00, current residual:3.225017e+00 +wall time: 0.000663996s + +time increment:19490 time: 19.49 +field ' c' [explicit solve]: current solution: 9.650479e+01, current residual:5.828875e+01 +field 'mu' [explicit solve]: current solution: 5.357673e+00, current residual:3.224831e+00 +wall time: 0.000689983s + +time increment:19500 time: 19.5 +field ' c' [explicit solve]: current solution: 9.650444e+01, current residual:5.828853e+01 +field 'mu' [explicit solve]: current solution: 5.357335e+00, current residual:3.224645e+00 +wall time: 0.000658035s + +time increment:19510 time: 19.51 +field ' c' [explicit solve]: current solution: 9.650410e+01, current residual:5.828831e+01 +field 'mu' [explicit solve]: current solution: 5.356998e+00, current residual:3.224460e+00 +wall time: 0.000692129s + +time increment:19520 time: 19.52 +field ' c' [explicit solve]: current solution: 9.650376e+01, current residual:5.828809e+01 +field 'mu' [explicit solve]: current solution: 5.356661e+00, current residual:3.224276e+00 +wall time: 0.000638962s + +time increment:19530 time: 19.53 +field ' c' [explicit solve]: current solution: 9.650341e+01, current residual:5.828787e+01 +field 'mu' [explicit solve]: current solution: 5.356325e+00, current residual:3.224092e+00 +wall time: 0.00069499s + +time increment:19540 time: 19.54 +field ' c' [explicit solve]: current solution: 9.650307e+01, current residual:5.828765e+01 +field 'mu' [explicit solve]: current solution: 5.355990e+00, current residual:3.223908e+00 +wall time: 0.000672817s + +time increment:19550 time: 19.55 +field ' c' [explicit solve]: current solution: 9.650273e+01, current residual:5.828743e+01 +field 'mu' [explicit solve]: current solution: 5.355656e+00, current residual:3.223724e+00 +wall time: 0.000684023s + +time increment:19560 time: 19.56 +field ' c' [explicit solve]: current solution: 9.650239e+01, current residual:5.828721e+01 +field 'mu' [explicit solve]: current solution: 5.355322e+00, current residual:3.223541e+00 +wall time: 0.000673056s + +time increment:19570 time: 19.57 +field ' c' [explicit solve]: current solution: 9.650205e+01, current residual:5.828699e+01 +field 'mu' [explicit solve]: current solution: 5.354989e+00, current residual:3.223359e+00 +wall time: 0.000693798s + +time increment:19580 time: 19.58 +field ' c' [explicit solve]: current solution: 9.650170e+01, current residual:5.828677e+01 +field 'mu' [explicit solve]: current solution: 5.354656e+00, current residual:3.223176e+00 +wall time: 0.000675201s + +time increment:19590 time: 19.59 +field ' c' [explicit solve]: current solution: 9.650136e+01, current residual:5.828655e+01 +field 'mu' [explicit solve]: current solution: 5.354324e+00, current residual:3.222995e+00 +wall time: 0.000631094s + +time increment:19600 time: 19.6 +field ' c' [explicit solve]: current solution: 9.650102e+01, current residual:5.828633e+01 +field 'mu' [explicit solve]: current solution: 5.353993e+00, current residual:3.222813e+00 +wall time: 0.000648022s + +time increment:19610 time: 19.61 +field ' c' [explicit solve]: current solution: 9.650068e+01, current residual:5.828611e+01 +field 'mu' [explicit solve]: current solution: 5.353663e+00, current residual:3.222632e+00 +wall time: 0.000696898s + +time increment:19620 time: 19.62 +field ' c' [explicit solve]: current solution: 9.650034e+01, current residual:5.828589e+01 +field 'mu' [explicit solve]: current solution: 5.353333e+00, current residual:3.222452e+00 +wall time: 0.000727892s + +time increment:19630 time: 19.63 +field ' c' [explicit solve]: current solution: 9.650000e+01, current residual:5.828567e+01 +field 'mu' [explicit solve]: current solution: 5.353004e+00, current residual:3.222271e+00 +wall time: 0.000688076s + +time increment:19640 time: 19.64 +field ' c' [explicit solve]: current solution: 9.649966e+01, current residual:5.828545e+01 +field 'mu' [explicit solve]: current solution: 5.352676e+00, current residual:3.222092e+00 +wall time: 0.000677109s + +time increment:19650 time: 19.65 +field ' c' [explicit solve]: current solution: 9.649932e+01, current residual:5.828523e+01 +field 'mu' [explicit solve]: current solution: 5.352348e+00, current residual:3.221912e+00 +wall time: 0.000675917s + +time increment:19660 time: 19.66 +field ' c' [explicit solve]: current solution: 9.649898e+01, current residual:5.828501e+01 +field 'mu' [explicit solve]: current solution: 5.352021e+00, current residual:3.221733e+00 +wall time: 0.000703096s + +time increment:19670 time: 19.67 +field ' c' [explicit solve]: current solution: 9.649864e+01, current residual:5.828479e+01 +field 'mu' [explicit solve]: current solution: 5.351695e+00, current residual:3.221554e+00 +wall time: 0.000558138s + +time increment:19680 time: 19.68 +field ' c' [explicit solve]: current solution: 9.649830e+01, current residual:5.828457e+01 +field 'mu' [explicit solve]: current solution: 5.351369e+00, current residual:3.221376e+00 +wall time: 0.000560999s + +time increment:19690 time: 19.69 +field ' c' [explicit solve]: current solution: 9.649797e+01, current residual:5.828436e+01 +field 'mu' [explicit solve]: current solution: 5.351044e+00, current residual:3.221198e+00 +wall time: 0.000658989s + +time increment:19700 time: 19.7 +field ' c' [explicit solve]: current solution: 9.649763e+01, current residual:5.828414e+01 +field 'mu' [explicit solve]: current solution: 5.350720e+00, current residual:3.221021e+00 +wall time: 0.000689983s + +time increment:19710 time: 19.71 +field ' c' [explicit solve]: current solution: 9.649729e+01, current residual:5.828392e+01 +field 'mu' [explicit solve]: current solution: 5.350396e+00, current residual:3.220844e+00 +wall time: 0.000711918s + +time increment:19720 time: 19.72 +field ' c' [explicit solve]: current solution: 9.649695e+01, current residual:5.828370e+01 +field 'mu' [explicit solve]: current solution: 5.350073e+00, current residual:3.220667e+00 +wall time: 0.000715017s + +time increment:19730 time: 19.73 +field ' c' [explicit solve]: current solution: 9.649662e+01, current residual:5.828349e+01 +field 'mu' [explicit solve]: current solution: 5.349751e+00, current residual:3.220491e+00 +wall time: 0.000571966s + +time increment:19740 time: 19.74 +field ' c' [explicit solve]: current solution: 9.649628e+01, current residual:5.828327e+01 +field 'mu' [explicit solve]: current solution: 5.349429e+00, current residual:3.220315e+00 +wall time: 0.000567913s + +time increment:19750 time: 19.75 +field ' c' [explicit solve]: current solution: 9.649594e+01, current residual:5.828305e+01 +field 'mu' [explicit solve]: current solution: 5.349108e+00, current residual:3.220139e+00 +wall time: 0.000665188s + +time increment:19760 time: 19.76 +field ' c' [explicit solve]: current solution: 9.649561e+01, current residual:5.828284e+01 +field 'mu' [explicit solve]: current solution: 5.348788e+00, current residual:3.219964e+00 +wall time: 0.00056386s + +time increment:19770 time: 19.77 +field ' c' [explicit solve]: current solution: 9.649527e+01, current residual:5.828262e+01 +field 'mu' [explicit solve]: current solution: 5.348468e+00, current residual:3.219789e+00 +wall time: 0.000566959s + +time increment:19780 time: 19.78 +field ' c' [explicit solve]: current solution: 9.649493e+01, current residual:5.828240e+01 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solve]: current solution: 5.346880e+00, current residual:3.218921e+00 +wall time: 0.000597s + +time increment:19830 time: 19.83 +field ' c' [explicit solve]: current solution: 9.649326e+01, current residual:5.828132e+01 +field 'mu' [explicit solve]: current solution: 5.346564e+00, current residual:3.218748e+00 +wall time: 0.000662088s + +time increment:19840 time: 19.84 +field ' c' [explicit solve]: current solution: 9.649292e+01, current residual:5.828111e+01 +field 'mu' [explicit solve]: current solution: 5.346249e+00, current residual:3.218576e+00 +wall time: 0.00066185s + +time increment:19850 time: 19.85 +field ' c' [explicit solve]: current solution: 9.649259e+01, current residual:5.828089e+01 +field 'mu' [explicit solve]: current solution: 5.345935e+00, current residual:3.218404e+00 +wall time: 0.000602007s + +time increment:19860 time: 19.86 +field ' c' [explicit solve]: current solution: 9.649226e+01, current residual:5.828068e+01 +field 'mu' [explicit solve]: current solution: 5.345621e+00, current residual:3.218233e+00 +wall time: 0.000659943s + +time increment:19870 time: 19.87 +field ' c' [explicit solve]: current solution: 9.649192e+01, current residual:5.828046e+01 +field 'mu' [explicit solve]: current solution: 5.345308e+00, current residual:3.218062e+00 +wall time: 0.000617027s + +time increment:19880 time: 19.88 +field ' c' [explicit solve]: current solution: 9.649159e+01, current residual:5.828025e+01 +field 'mu' [explicit solve]: current solution: 5.344995e+00, current residual:3.217891e+00 +wall time: 0.000573158s + +time increment:19890 time: 19.89 +field ' c' [explicit solve]: current solution: 9.649126e+01, current residual:5.828003e+01 +field 'mu' [explicit solve]: current solution: 5.344683e+00, current residual:3.217721e+00 +wall time: 0.000570059s + +time increment:19900 time: 19.9 +field ' c' [explicit solve]: current solution: 9.649093e+01, current residual:5.827982e+01 +field 'mu' [explicit solve]: current solution: 5.344372e+00, current residual:3.217551e+00 +wall time: 0.000597s + +time increment:19910 time: 19.91 +field ' c' [explicit solve]: current solution: 9.649059e+01, current residual:5.827960e+01 +field 'mu' [explicit solve]: current solution: 5.344062e+00, current residual:3.217381e+00 +wall time: 0.000708103s + +time increment:19920 time: 19.92 +field ' c' [explicit solve]: current solution: 9.649026e+01, current residual:5.827939e+01 +field 'mu' [explicit solve]: current solution: 5.343752e+00, current residual:3.217212e+00 +wall time: 0.000780821s + +time increment:19930 time: 19.93 +field ' c' [explicit solve]: current solution: 9.648993e+01, current residual:5.827918e+01 +field 'mu' [explicit solve]: current solution: 5.343442e+00, current residual:3.217043e+00 +wall time: 0.000784874s + +time increment:19940 time: 19.94 +field ' c' [explicit solve]: current solution: 9.648960e+01, current residual:5.827896e+01 +field 'mu' [explicit solve]: current solution: 5.343134e+00, current residual:3.216874e+00 +wall time: 0.000741959s + +time increment:19950 time: 19.95 +field ' c' [explicit solve]: current solution: 9.648927e+01, current residual:5.827875e+01 +field 'mu' [explicit solve]: current solution: 5.342826e+00, current residual:3.216706e+00 +wall time: 0.000686884s + +time increment:19960 time: 19.96 +field ' c' [explicit solve]: current solution: 9.648894e+01, current residual:5.827854e+01 +field 'mu' [explicit solve]: current solution: 5.342518e+00, current residual:3.216539e+00 +wall time: 0.000627041s + +time increment:19970 time: 19.97 +field ' c' [explicit solve]: current solution: 9.648861e+01, current residual:5.827832e+01 +field 'mu' [explicit solve]: current solution: 5.342212e+00, current residual:3.216371e+00 +wall time: 0.000688076s + +time increment:19980 time: 19.98 +field ' c' [explicit solve]: current solution: 9.648828e+01, current residual:5.827811e+01 +field 'mu' [explicit solve]: current solution: 5.341906e+00, current residual:3.216204e+00 +wall time: 0.000648975s + +time increment:19990 time: 19.99 +field ' c' [explicit solve]: current solution: 9.648795e+01, current residual:5.827790e+01 +field 'mu' [explicit solve]: current solution: 5.341600e+00, current residual:3.216037e+00 +wall time: 0.000649929s + +time increment:20000 time: 20 +field ' c' [explicit solve]: current solution: 9.648762e+01, current residual:5.827768e+01 +field 'mu' [explicit solve]: current solution: 5.341295e+00, current residual:3.215871e+00 +wall time: 0.00067687s +fields: 2 +fields: 2 +fields: 2 +fields: 2 +Output written to:solution-020000.pvtu + +Energy: 252.839 +Energy Components: 118.096 134.743 0 + +time increment:20010 time: 20.01 +field ' c' [explicit solve]: current solution: 9.648729e+01, current residual:5.827747e+01 +field 'mu' [explicit solve]: current solution: 5.340991e+00, current residual:3.215705e+00 +wall time: 0.000687122s + +time increment:20020 time: 20.02 +field ' c' [explicit solve]: current solution: 9.648696e+01, current residual:5.827726e+01 +field 'mu' [explicit solve]: current solution: 5.340687e+00, current residual:3.215540e+00 +wall time: 0.000718117s + +time increment:20030 time: 20.03 +field ' c' [explicit solve]: current solution: 9.648663e+01, current residual:5.827705e+01 +field 'mu' [explicit solve]: current solution: 5.340384e+00, current residual:3.215374e+00 +wall time: 0.000701904s + +time increment:20040 time: 20.04 +field ' c' [explicit solve]: current solution: 9.648630e+01, current residual:5.827684e+01 +field 'mu' [explicit solve]: current solution: 5.340082e+00, current residual:3.215209e+00 +wall time: 0.000687838s + +time increment:20050 time: 20.05 +field ' c' [explicit solve]: current solution: 9.648597e+01, current residual:5.827662e+01 +field 'mu' [explicit solve]: current solution: 5.339780e+00, current residual:3.215045e+00 +wall time: 0.000627995s + +time increment:20060 time: 20.06 +field ' c' [explicit solve]: current solution: 9.648564e+01, current residual:5.827641e+01 +field 'mu' [explicit solve]: current solution: 5.339479e+00, current residual:3.214881e+00 +wall time: 0.000761032s + +time increment:20070 time: 20.07 +field ' c' [explicit solve]: current solution: 9.648532e+01, current residual:5.827620e+01 +field 'mu' [explicit solve]: current solution: 5.339179e+00, current residual:3.214717e+00 +wall time: 0.00067997s + +time increment:20080 time: 20.08 +field ' c' [explicit solve]: current solution: 9.648499e+01, current residual:5.827599e+01 +field 'mu' [explicit solve]: current solution: 5.338879e+00, current residual:3.214553e+00 +wall time: 0.000637054s + +time increment:20090 time: 20.09 +field ' c' [explicit solve]: current solution: 9.648466e+01, current residual:5.827578e+01 +field 'mu' [explicit solve]: current solution: 5.338579e+00, current residual:3.214390e+00 +wall time: 0.000633001s + +time increment:20100 time: 20.1 +field ' c' [explicit solve]: current solution: 9.648434e+01, current 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solution: 5.332433e+00, current residual:3.211045e+00 +wall time: 0.000629902s + +time increment:20310 time: 20.31 +field ' c' [explicit solve]: current solution: 9.647754e+01, current residual:5.827118e+01 +field 'mu' [explicit solve]: current solution: 5.332147e+00, current residual:3.210889e+00 +wall time: 0.000562906s + +time increment:20320 time: 20.32 +field ' c' [explicit solve]: current solution: 9.647721e+01, current residual:5.827098e+01 +field 'mu' [explicit solve]: current solution: 5.331861e+00, current residual:3.210734e+00 +wall time: 0.000565052s + +time increment:20330 time: 20.33 +field ' c' [explicit solve]: current solution: 9.647689e+01, current residual:5.827077e+01 +field 'mu' [explicit solve]: current solution: 5.331576e+00, current residual:3.210579e+00 +wall time: 0.000684023s + +time increment:20340 time: 20.34 +field ' c' [explicit solve]: current solution: 9.647657e+01, current residual:5.827056e+01 +field 'mu' [explicit solve]: current solution: 5.331292e+00, current residual:3.210424e+00 +wall time: 0.000618935s + +time increment:20350 time: 20.35 +field ' c' [explicit solve]: current solution: 9.647625e+01, current residual:5.827036e+01 +field 'mu' [explicit solve]: current solution: 5.331008e+00, current residual:3.210270e+00 +wall time: 0.000613928s + +time increment:20360 time: 20.36 +field ' c' [explicit solve]: current solution: 9.647593e+01, current residual:5.827015e+01 +field 'mu' [explicit solve]: current solution: 5.330725e+00, current residual:3.210116e+00 +wall time: 0.000647068s + +time increment:20370 time: 20.37 +field ' c' [explicit solve]: current solution: 9.647561e+01, current residual:5.826994e+01 +field 'mu' [explicit solve]: current solution: 5.330442e+00, current residual:3.209962e+00 +wall time: 0.000581026s + +time increment:20380 time: 20.38 +field ' c' [explicit solve]: current solution: 9.647530e+01, current residual:5.826974e+01 +field 'mu' [explicit solve]: current solution: 5.330160e+00, current residual:3.209809e+00 +wall time: 0.00060606s + +time increment:20390 time: 20.39 +field ' c' [explicit solve]: current solution: 9.647498e+01, current residual:5.826953e+01 +field 'mu' [explicit solve]: current solution: 5.329878e+00, current residual:3.209656e+00 +wall time: 0.000564814s + +time increment:20400 time: 20.4 +field ' c' [explicit solve]: current solution: 9.647466e+01, current residual:5.826933e+01 +field 'mu' [explicit solve]: current solution: 5.329597e+00, current residual:3.209503e+00 +wall time: 0.000574112s + +time increment:20410 time: 20.41 +field ' c' [explicit solve]: current solution: 9.647434e+01, current residual:5.826912e+01 +field 'mu' [explicit solve]: current solution: 5.329317e+00, current residual:3.209351e+00 +wall time: 0.000569105s + +time increment:20420 time: 20.42 +field ' c' [explicit solve]: current solution: 9.647402e+01, current residual:5.826892e+01 +field 'mu' [explicit solve]: current solution: 5.329037e+00, current residual:3.209199e+00 +wall time: 0.000638008s + +time increment:20430 time: 20.43 +field ' c' [explicit solve]: current solution: 9.647370e+01, current residual:5.826871e+01 +field 'mu' [explicit solve]: current solution: 5.328758e+00, current residual:3.209047e+00 +wall time: 0.000712156s + +time increment:20440 time: 20.44 +field ' c' [explicit solve]: current solution: 9.647339e+01, current residual:5.826851e+01 +field 'mu' [explicit solve]: current solution: 5.328479e+00, current residual:3.208896e+00 +wall time: 0.000673056s + +time increment:20450 time: 20.45 +field ' c' [explicit solve]: current solution: 9.647307e+01, current residual:5.826830e+01 +field 'mu' [explicit solve]: current solution: 5.328201e+00, current residual:3.208745e+00 +wall time: 0.000569105s + +time increment:20460 time: 20.46 +field ' c' [explicit solve]: current solution: 9.647275e+01, current residual:5.826810e+01 +field 'mu' [explicit solve]: current solution: 5.327923e+00, current residual:3.208594e+00 +wall time: 0.000616789s + +time increment:20470 time: 20.47 +field ' c' [explicit solve]: current solution: 9.647243e+01, current residual:5.826789e+01 +field 'mu' [explicit solve]: current solution: 5.327646e+00, current residual:3.208444e+00 +wall time: 0.000627995s + +time increment:20480 time: 20.48 +field ' c' [explicit solve]: current solution: 9.647212e+01, current residual:5.826769e+01 +field 'mu' [explicit solve]: current solution: 5.327370e+00, current residual:3.208294e+00 +wall time: 0.00067687s + +time increment:20490 time: 20.49 +field ' c' [explicit solve]: current solution: 9.647180e+01, current residual:5.826749e+01 +field 'mu' [explicit solve]: current solution: 5.327094e+00, current residual:3.208144e+00 +wall time: 0.00066185s + +time increment:20500 time: 20.5 +field ' c' [explicit solve]: current solution: 9.647149e+01, current residual:5.826728e+01 +field 'mu' [explicit solve]: current solution: 5.326818e+00, current residual:3.207995e+00 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increment:20590 time: 20.59 +field ' c' [explicit solve]: current solution: 9.646865e+01, current residual:5.826545e+01 +field 'mu' [explicit solve]: current solution: 5.324364e+00, current residual:3.206664e+00 +wall time: 0.000569105s + +time increment:20600 time: 20.6 +field ' c' [explicit solve]: current solution: 9.646834e+01, current residual:5.826525e+01 +field 'mu' [explicit solve]: current solution: 5.324094e+00, current residual:3.206518e+00 +wall time: 0.00056386s + +time increment:20610 time: 20.61 +field ' c' [explicit solve]: current solution: 9.646803e+01, current residual:5.826505e+01 +field 'mu' [explicit solve]: current solution: 5.323825e+00, current residual:3.206372e+00 +wall time: 0.000677109s + +time increment:20620 time: 20.62 +field ' c' [explicit solve]: current solution: 9.646772e+01, current residual:5.826485e+01 +field 'mu' [explicit solve]: current solution: 5.323556e+00, current residual:3.206226e+00 +wall time: 0.00059104s + +time increment:20630 time: 20.63 +field ' c' [explicit solve]: current solution: 9.646740e+01, current residual:5.826465e+01 +field 'mu' [explicit solve]: current solution: 5.323288e+00, current residual:3.206081e+00 +wall time: 0.000669956s + +time increment:20640 time: 20.64 +field ' c' [explicit solve]: current solution: 9.646709e+01, current residual:5.826445e+01 +field 'mu' [explicit solve]: current solution: 5.323020e+00, current residual:3.205935e+00 +wall time: 0.000633001s + +time increment:20650 time: 20.65 +field ' c' [explicit solve]: current solution: 9.646678e+01, current residual:5.826424e+01 +field 'mu' [explicit solve]: current solution: 5.322752e+00, current residual:3.205791e+00 +wall time: 0.000576019s + +time increment:20660 time: 20.66 +field ' c' [explicit solve]: current solution: 9.646647e+01, current residual:5.826404e+01 +field 'mu' [explicit solve]: current solution: 5.322486e+00, current residual:3.205646e+00 +wall time: 0.00059104s + +time increment:20670 time: 20.67 +field ' c' [explicit solve]: current solution: 9.646616e+01, current residual:5.826384e+01 +field 'mu' [explicit solve]: current solution: 5.322220e+00, current residual:3.205502e+00 +wall time: 0.000670195s + +time increment:20680 time: 20.68 +field ' c' [explicit solve]: current solution: 9.646584e+01, current residual:5.826364e+01 +field 'mu' [explicit solve]: current solution: 5.321954e+00, current residual:3.205358e+00 +wall time: 0.000629902s + +time increment:20690 time: 20.69 +field ' c' [explicit solve]: current solution: 9.646553e+01, current residual:5.826344e+01 +field 'mu' [explicit solve]: current solution: 5.321689e+00, current residual:3.205215e+00 +wall time: 0.000577211s + +time increment:20700 time: 20.7 +field ' c' [explicit solve]: current solution: 9.646522e+01, current residual:5.826324e+01 +field 'mu' [explicit solve]: current solution: 5.321424e+00, current residual:3.205071e+00 +wall time: 0.00057292s + +time increment:20710 time: 20.71 +field ' c' [explicit solve]: current solution: 9.646491e+01, current residual:5.826304e+01 +field 'mu' [explicit solve]: current solution: 5.321160e+00, current residual:3.204928e+00 +wall time: 0.0006001s + +time increment:20720 time: 20.72 +field ' c' [explicit solve]: current solution: 9.646460e+01, current residual:5.826284e+01 +field 'mu' [explicit solve]: current solution: 5.320896e+00, current residual:3.204786e+00 +wall time: 0.00071311s + +time increment:20730 time: 20.73 +field ' c' [explicit solve]: current solution: 9.646429e+01, current residual:5.826264e+01 +field 'mu' [explicit solve]: current solution: 5.320633e+00, current residual:3.204643e+00 +wall time: 0.000569105s + +time increment:20740 time: 20.74 +field ' c' [explicit solve]: current solution: 9.646398e+01, current residual:5.826244e+01 +field 'mu' [explicit solve]: current solution: 5.320371e+00, current residual:3.204501e+00 +wall time: 0.000612974s + +time increment:20750 time: 20.75 +field ' c' [explicit solve]: current solution: 9.646367e+01, current residual:5.826224e+01 +field 'mu' [explicit solve]: current solution: 5.320109e+00, current residual:3.204359e+00 +wall time: 0.000560999s + +time increment:20760 time: 20.76 +field ' c' [explicit solve]: current solution: 9.646336e+01, current residual:5.826204e+01 +field 'mu' [explicit solve]: current solution: 5.319847e+00, current residual:3.204218e+00 +wall time: 0.00067997s + +time increment:20770 time: 20.77 +field ' c' [explicit solve]: current solution: 9.646305e+01, current residual:5.826184e+01 +field 'mu' [explicit solve]: current solution: 5.319586e+00, current residual:3.204077e+00 +wall time: 0.000601053s + +time increment:20780 time: 20.78 +field ' c' [explicit solve]: current solution: 9.646275e+01, current residual:5.826164e+01 +field 'mu' [explicit solve]: current solution: 5.319326e+00, current residual:3.203936e+00 +wall time: 0.000558853s + +time increment:20790 time: 20.79 +field ' c' [explicit solve]: current solution: 9.646244e+01, current residual:5.826144e+01 +field 'mu' [explicit solve]: current solution: 5.319066e+00, current residual:3.203795e+00 +wall time: 0.000693798s + +time increment:20800 time: 20.8 +field ' c' [explicit solve]: current solution: 9.646213e+01, current residual:5.826124e+01 +field 'mu' [explicit solve]: current solution: 5.318807e+00, current residual:3.203655e+00 +wall time: 0.000678778s + +time increment:20810 time: 20.81 +field ' c' [explicit solve]: current solution: 9.646182e+01, current residual:5.826105e+01 +field 'mu' [explicit solve]: current solution: 5.318548e+00, current residual:3.203515e+00 +wall time: 0.000667095s + +time increment:20820 time: 20.82 +field ' c' [explicit solve]: current solution: 9.646151e+01, current residual:5.826085e+01 +field 'mu' [explicit solve]: current solution: 5.318289e+00, current residual:3.203375e+00 +wall time: 0.000652075s + +time increment:20830 time: 20.83 +field ' c' [explicit solve]: current solution: 9.646121e+01, current residual:5.826065e+01 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solve]: current solution: 5.317005e+00, current residual:3.202681e+00 +wall time: 0.000599861s + +time increment:20880 time: 20.88 +field ' c' [explicit solve]: current solution: 9.645967e+01, current residual:5.825966e+01 +field 'mu' [explicit solve]: current solution: 5.316750e+00, current residual:3.202543e+00 +wall time: 0.000570059s + +time increment:20890 time: 20.89 +field ' c' [explicit solve]: current solution: 9.645937e+01, current residual:5.825946e+01 +field 'mu' [explicit solve]: current solution: 5.316495e+00, current residual:3.202405e+00 +wall time: 0.000607967s + +time increment:20900 time: 20.9 +field ' c' [explicit solve]: current solution: 9.645906e+01, current residual:5.825926e+01 +field 'mu' [explicit solve]: current solution: 5.316240e+00, current residual:3.202268e+00 +wall time: 0.000597s + +time increment:20910 time: 20.91 +field ' c' [explicit solve]: current solution: 9.645876e+01, current residual:5.825907e+01 +field 'mu' [explicit solve]: current solution: 5.315986e+00, current residual:3.202131e+00 +wall time: 0.000577927s + +time increment:20920 time: 20.92 +field ' c' [explicit solve]: current solution: 9.645845e+01, current residual:5.825887e+01 +field 'mu' [explicit solve]: current solution: 5.315733e+00, current residual:3.201994e+00 +wall time: 0.000653028s + +time increment:20930 time: 20.93 +field ' c' [explicit solve]: current solution: 9.645815e+01, current residual:5.825867e+01 +field 'mu' [explicit solve]: current solution: 5.315480e+00, current residual:3.201857e+00 +wall time: 0.000575066s + +time increment:20940 time: 20.94 +field ' c' [explicit solve]: current solution: 9.645784e+01, current residual:5.825848e+01 +field 'mu' [explicit solve]: current solution: 5.315228e+00, current residual:3.201721e+00 +wall time: 0.000578165s + +time increment:20950 time: 20.95 +field ' c' [explicit solve]: current solution: 9.645754e+01, current residual:5.825828e+01 +field 'mu' [explicit solve]: current solution: 5.314976e+00, current residual:3.201585e+00 +wall time: 0.00069809s + +time increment:20960 time: 20.96 +field ' c' [explicit solve]: current solution: 9.645724e+01, current residual:5.825809e+01 +field 'mu' [explicit solve]: current solution: 5.314725e+00, current residual:3.201449e+00 +wall time: 0.000550032s + +time increment:20970 time: 20.97 +field ' c' [explicit solve]: current solution: 9.645693e+01, current residual:5.825789e+01 +field 'mu' [explicit solve]: current solution: 5.314474e+00, current residual:3.201314e+00 +wall time: 0.000686884s + +time increment:20980 time: 20.98 +field ' c' [explicit solve]: current solution: 9.645663e+01, current residual:5.825769e+01 +field 'mu' [explicit solve]: current solution: 5.314223e+00, current residual:3.201178e+00 +wall time: 0.000605822s + +time increment:20990 time: 20.99 +field ' c' [explicit solve]: current solution: 9.645633e+01, current residual:5.825750e+01 +field 'mu' [explicit solve]: current solution: 5.313973e+00, current residual:3.201044e+00 +wall time: 0.000662088s + +time increment:21000 time: 21 +field ' c' [explicit solve]: current solution: 9.645602e+01, current residual:5.825730e+01 +field 'mu' [explicit solve]: current solution: 5.313724e+00, current residual:3.200909e+00 +wall time: 0.00067687s + +time increment:21010 time: 21.01 +field ' c' [explicit solve]: current solution: 9.645572e+01, current residual:5.825711e+01 +field 'mu' [explicit solve]: current solution: 5.313475e+00, current residual:3.200775e+00 +wall time: 0.000729084s + +time increment:21020 time: 21.02 +field ' c' [explicit solve]: current solution: 9.645542e+01, current residual:5.825691e+01 +field 'mu' [explicit solve]: current solution: 5.313226e+00, current residual:3.200640e+00 +wall time: 0.000700951s + +time increment:21030 time: 21.03 +field ' c' [explicit solve]: current solution: 9.645512e+01, current residual:5.825672e+01 +field 'mu' [explicit solve]: current solution: 5.312979e+00, current residual:3.200507e+00 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0.000670195s + +time increment:21080 time: 21.08 +field ' c' [explicit solve]: current solution: 9.645361e+01, current residual:5.825574e+01 +field 'mu' [explicit solve]: current solution: 5.311746e+00, current residual:3.199842e+00 +wall time: 0.000653982s + +time increment:21090 time: 21.09 +field ' c' [explicit solve]: current solution: 9.645331e+01, current residual:5.825555e+01 +field 'mu' [explicit solve]: current solution: 5.311501e+00, current residual:3.199710e+00 +wall time: 0.000686884s + +time increment:21100 time: 21.1 +field ' c' [explicit solve]: current solution: 9.645301e+01, current residual:5.825536e+01 +field 'mu' [explicit solve]: current solution: 5.311256e+00, current residual:3.199578e+00 +wall time: 0.000710964s + +time increment:21110 time: 21.11 +field ' c' [explicit solve]: current solution: 9.645271e+01, current residual:5.825516e+01 +field 'mu' [explicit solve]: current solution: 5.311012e+00, current residual:3.199446e+00 +wall time: 0.000690222s + +time increment:21120 time: 21.12 +field ' c' [explicit solve]: current solution: 9.645241e+01, current residual:5.825497e+01 +field 'mu' [explicit solve]: current solution: 5.310768e+00, current residual:3.199315e+00 +wall time: 0.000640869s + +time increment:21130 time: 21.13 +field ' c' [explicit solve]: current solution: 9.645211e+01, current residual:5.825477e+01 +field 'mu' [explicit solve]: current solution: 5.310525e+00, current residual:3.199184e+00 +wall time: 0.000733137s + +time increment:21140 time: 21.14 +field ' c' [explicit solve]: current solution: 9.645181e+01, current residual:5.825458e+01 +field 'mu' [explicit solve]: current solution: 5.310282e+00, current residual:3.199053e+00 +wall time: 0.000673056s + +time increment:21150 time: 21.15 +field ' c' [explicit solve]: current solution: 9.645151e+01, current residual:5.825439e+01 +field 'mu' [explicit solve]: current solution: 5.310040e+00, current residual:3.198922e+00 +wall time: 0.000622034s + +time increment:21160 time: 21.16 +field ' c' [explicit solve]: current solution: 9.645121e+01, current residual:5.825420e+01 +field 'mu' [explicit solve]: current solution: 5.309798e+00, current residual:3.198792e+00 +wall time: 0.000673056s + +time increment:21170 time: 21.17 +field ' c' [explicit solve]: current solution: 9.645091e+01, current residual:5.825400e+01 +field 'mu' [explicit solve]: current solution: 5.309557e+00, current residual:3.198662e+00 +wall time: 0.000697851s + +time increment:21180 time: 21.18 +field ' c' [explicit solve]: current solution: 9.645061e+01, current residual:5.825381e+01 +field 'mu' [explicit solve]: current solution: 5.309316e+00, current residual:3.198532e+00 +wall time: 0.000646114s + +time increment:21190 time: 21.19 +field ' c' [explicit solve]: current solution: 9.645032e+01, current residual:5.825362e+01 +field 'mu' [explicit solve]: current solution: 5.309076e+00, current residual:3.198403e+00 +wall time: 0.000689983s + +time increment:21200 time: 21.2 +field ' c' [explicit solve]: current solution: 9.645002e+01, current residual:5.825342e+01 +field 'mu' [explicit solve]: current solution: 5.308836e+00, current residual:3.198273e+00 +wall time: 0.000666142s + +time increment:21210 time: 21.21 +field ' c' [explicit solve]: current solution: 9.644972e+01, current residual:5.825323e+01 +field 'mu' [explicit solve]: current solution: 5.308596e+00, current residual:3.198145e+00 +wall time: 0.000684023s + +time increment:21220 time: 21.22 +field ' c' [explicit solve]: current solution: 9.644942e+01, current residual:5.825304e+01 +field 'mu' [explicit solve]: current solution: 5.308357e+00, current residual:3.198016e+00 +wall time: 0.000671864s + +time increment:21230 time: 21.23 +field ' c' [explicit solve]: current solution: 9.644913e+01, current residual:5.825285e+01 +field 'mu' [explicit solve]: current solution: 5.308119e+00, current residual:3.197887e+00 +wall time: 0.000671148s + +time increment:21240 time: 21.24 +field ' c' [explicit solve]: current solution: 9.644883e+01, current residual:5.825266e+01 +field 'mu' [explicit solve]: current solution: 5.307881e+00, current residual:3.197759e+00 +wall time: 0.000659943s + +time increment:21250 time: 21.25 +field ' c' [explicit solve]: current solution: 9.644853e+01, current residual:5.825247e+01 +field 'mu' [explicit solve]: current solution: 5.307643e+00, current residual:3.197631e+00 +wall time: 0.000653028s + +time increment:21260 time: 21.26 +field ' c' [explicit solve]: current solution: 9.644824e+01, current residual:5.825227e+01 +field 'mu' [explicit solve]: current solution: 5.307406e+00, current residual:3.197504e+00 +wall time: 0.000674963s + +time increment:21270 time: 21.27 +field ' c' [explicit solve]: current solution: 9.644794e+01, current residual:5.825208e+01 +field 'mu' [explicit solve]: current solution: 5.307169e+00, current residual:3.197376e+00 +wall time: 0.000633001s + +time increment:21280 time: 21.28 +field ' c' [explicit solve]: current solution: 9.644764e+01, current residual:5.825189e+01 +field 'mu' [explicit solve]: current solution: 5.306933e+00, current residual:3.197249e+00 +wall time: 0.000700951s + +time increment:21290 time: 21.29 +field ' c' [explicit solve]: current solution: 9.644735e+01, current residual:5.825170e+01 +field 'mu' [explicit solve]: current solution: 5.306697e+00, current residual:3.197122e+00 +wall time: 0.00069499s + +time increment:21300 time: 21.3 +field ' c' [explicit solve]: current solution: 9.644705e+01, current residual:5.825151e+01 +field 'mu' [explicit solve]: current solution: 5.306461e+00, current residual:3.196995e+00 +wall time: 0.000651121s + +time increment:21310 time: 21.31 +field ' c' [explicit solve]: current solution: 9.644676e+01, current residual:5.825132e+01 +field 'mu' [explicit solve]: current solution: 5.306226e+00, current residual:3.196869e+00 +wall time: 0.000666142s + +time increment:21320 time: 21.32 +field ' c' [explicit solve]: current solution: 9.644646e+01, current residual:5.825113e+01 +field 'mu' [explicit solve]: current solution: 5.305992e+00, current residual:3.196743e+00 +wall time: 0.000748158s + +time increment:21330 time: 21.33 +field ' c' [explicit solve]: current solution: 9.644617e+01, current residual:5.825094e+01 +field 'mu' [explicit solve]: current solution: 5.305758e+00, current residual:3.196617e+00 +wall time: 0.000653982s + +time increment:21340 time: 21.34 +field ' c' [explicit solve]: current solution: 9.644587e+01, current residual:5.825075e+01 +field 'mu' [explicit solve]: current solution: 5.305524e+00, current residual:3.196491e+00 +wall time: 0.000631094s + +time increment:21350 time: 21.35 +field ' c' [explicit solve]: current solution: 9.644558e+01, current residual:5.825056e+01 +field 'mu' [explicit solve]: current solution: 5.305291e+00, current residual:3.196366e+00 +wall time: 0.000759125s + +time increment:21360 time: 21.36 +field ' c' [explicit solve]: current solution: 9.644529e+01, current residual:5.825037e+01 +field 'mu' [explicit solve]: current solution: 5.305058e+00, current residual:3.196241e+00 +wall time: 0.000708103s + +time increment:21370 time: 21.37 +field ' c' [explicit solve]: current solution: 9.644499e+01, current residual:5.825018e+01 +field 'mu' [explicit solve]: current solution: 5.304826e+00, current residual:3.196116e+00 +wall time: 0.000653028s + +time increment:21380 time: 21.38 +field ' c' [explicit solve]: current solution: 9.644470e+01, current residual:5.824999e+01 +field 'mu' [explicit solve]: current solution: 5.304594e+00, current residual:3.195991e+00 +wall time: 0.000693798s + +time increment:21390 time: 21.39 +field ' c' [explicit solve]: current solution: 9.644441e+01, current residual:5.824980e+01 +field 'mu' [explicit solve]: current solution: 5.304363e+00, current residual:3.195867e+00 +wall time: 0.000665903s + +time increment:21400 time: 21.4 +field ' c' [explicit solve]: current solution: 9.644411e+01, current residual:5.824961e+01 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solve]: current solution: 5.303213e+00, current residual:3.195249e+00 +wall time: 0.000573874s + +time increment:21450 time: 21.45 +field ' c' [explicit solve]: current solution: 9.644265e+01, current residual:5.824867e+01 +field 'mu' [explicit solve]: current solution: 5.302984e+00, current residual:3.195126e+00 +wall time: 0.000635147s + +time increment:21460 time: 21.46 +field ' c' [explicit solve]: current solution: 9.644236e+01, current residual:5.824848e+01 +field 'mu' [explicit solve]: current solution: 5.302755e+00, current residual:3.195003e+00 +wall time: 0.000580072s + +time increment:21470 time: 21.47 +field ' c' [explicit solve]: current solution: 9.644207e+01, current residual:5.824829e+01 +field 'mu' [explicit solve]: current solution: 5.302527e+00, current residual:3.194881e+00 +wall time: 0.000595808s + +time increment:21480 time: 21.48 +field ' c' [explicit solve]: current solution: 9.644178e+01, current residual:5.824810e+01 +field 'mu' [explicit solve]: current solution: 5.302300e+00, current residual:3.194758e+00 +wall time: 0.000607014s + +time increment:21490 time: 21.49 +field ' c' [explicit solve]: current solution: 9.644149e+01, current residual:5.824792e+01 +field 'mu' [explicit solve]: current solution: 5.302073e+00, current residual:3.194636e+00 +wall time: 0.000633955s + +time increment:21500 time: 21.5 +field ' c' [explicit solve]: current solution: 9.644120e+01, current residual:5.824773e+01 +field 'mu' [explicit solve]: current solution: 5.301846e+00, current residual:3.194515e+00 +wall time: 0.000566006s + +time increment:21510 time: 21.51 +field ' c' [explicit solve]: current solution: 9.644091e+01, current residual:5.824754e+01 +field 'mu' [explicit solve]: current solution: 5.301620e+00, current residual:3.194393e+00 +wall time: 0.000702143s + +time increment:21520 time: 21.52 +field ' c' [explicit solve]: current solution: 9.644062e+01, current residual:5.824735e+01 +field 'mu' [explicit solve]: current solution: 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residual:3.193311e+00 +wall time: 0.000568867s + +time increment:21610 time: 21.61 +field ' c' [explicit solve]: current solution: 9.643802e+01, current residual:5.824568e+01 +field 'mu' [explicit solve]: current solution: 5.299381e+00, current residual:3.193192e+00 +wall time: 0.000601053s + +time increment:21620 time: 21.62 +field ' c' [explicit solve]: current solution: 9.643773e+01, current residual:5.824549e+01 +field 'mu' [explicit solve]: current solution: 5.299159e+00, current residual:3.193073e+00 +wall time: 0.000733852s + +time increment:21630 time: 21.63 +field ' c' [explicit solve]: current solution: 9.643744e+01, current residual:5.824530e+01 +field 'mu' [explicit solve]: current solution: 5.298938e+00, current residual:3.192954e+00 +wall time: 0.000561953s + +time increment:21640 time: 21.64 +field ' c' [explicit solve]: current solution: 9.643716e+01, current residual:5.824512e+01 +field 'mu' [explicit solve]: current solution: 5.298717e+00, current residual:3.192836e+00 +wall time: 0.000617981s + +time increment:21650 time: 21.65 +field ' c' [explicit solve]: current solution: 9.643687e+01, current residual:5.824493e+01 +field 'mu' [explicit solve]: current solution: 5.298497e+00, current residual:3.192718e+00 +wall time: 0.000571012s + +time increment:21660 time: 21.66 +field ' c' [explicit solve]: current solution: 9.643658e+01, current residual:5.824475e+01 +field 'mu' [explicit solve]: current solution: 5.298277e+00, current residual:3.192600e+00 +wall time: 0.000585079s + +time increment:21670 time: 21.67 +field ' c' [explicit solve]: current solution: 9.643630e+01, current residual:5.824456e+01 +field 'mu' [explicit solve]: current solution: 5.298057e+00, current residual:3.192482e+00 +wall time: 0.000730991s + +time increment:21680 time: 21.68 +field ' c' [explicit solve]: current solution: 9.643601e+01, current residual:5.824438e+01 +field 'mu' [explicit solve]: current solution: 5.297838e+00, current residual:3.192364e+00 +wall time: 0.000556946s + +time increment:21690 time: 21.69 +field ' c' [explicit solve]: current solution: 9.643572e+01, current residual:5.824419e+01 +field 'mu' [explicit solve]: current solution: 5.297620e+00, current residual:3.192247e+00 +wall time: 0.000660896s + +time increment:21700 time: 21.7 +field ' c' [explicit solve]: current solution: 9.643544e+01, current residual:5.824401e+01 +field 'mu' [explicit solve]: current solution: 5.297401e+00, current residual:3.192130e+00 +wall time: 0.000638962s + +time increment:21710 time: 21.71 +field ' c' [explicit solve]: current solution: 9.643515e+01, current residual:5.824382e+01 +field 'mu' [explicit solve]: current solution: 5.297183e+00, current residual:3.192013e+00 +wall time: 0.000577927s + +time increment:21720 time: 21.72 +field ' c' [explicit solve]: current solution: 9.643487e+01, current residual:5.824364e+01 +field 'mu' [explicit solve]: current solution: 5.296966e+00, current residual:3.191897e+00 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0.000551939s + +time increment:21770 time: 21.77 +field ' c' [explicit solve]: current solution: 9.643345e+01, current residual:5.824272e+01 +field 'mu' [explicit solve]: current solution: 5.295884e+00, current residual:3.191317e+00 +wall time: 0.000654936s + +time increment:21780 time: 21.78 +field ' c' [explicit solve]: current solution: 9.643316e+01, current residual:5.824254e+01 +field 'mu' [explicit solve]: current solution: 5.295669e+00, current residual:3.191202e+00 +wall time: 0.000634909s + +time increment:21790 time: 21.79 +field ' c' [explicit solve]: current solution: 9.643288e+01, current residual:5.824236e+01 +field 'mu' [explicit solve]: current solution: 5.295454e+00, current residual:3.191087e+00 +wall time: 0.000617981s + +time increment:21800 time: 21.8 +field ' c' [explicit solve]: current solution: 9.643260e+01, current residual:5.824217e+01 +field 'mu' [explicit solve]: current solution: 5.295240e+00, current residual:3.190972e+00 +wall time: 0.000597954s + +time increment:21810 time: 21.81 +field ' c' [explicit solve]: current solution: 9.643231e+01, current residual:5.824199e+01 +field 'mu' [explicit solve]: current solution: 5.295026e+00, current residual:3.190858e+00 +wall time: 0.000910997s + +time increment:21820 time: 21.82 +field ' c' [explicit solve]: current solution: 9.643203e+01, current residual:5.824181e+01 +field 'mu' [explicit solve]: current solution: 5.294812e+00, current residual:3.190743e+00 +wall time: 0.000579119s + +time increment:21830 time: 21.83 +field ' c' [explicit solve]: current solution: 9.643175e+01, current residual:5.824162e+01 +field 'mu' [explicit solve]: current solution: 5.294599e+00, current residual:3.190629e+00 +wall time: 0.000566959s + +time increment:21840 time: 21.84 +field ' c' [explicit solve]: current solution: 9.643147e+01, current residual:5.824144e+01 +field 'mu' [explicit solve]: current solution: 5.294386e+00, current residual:3.190515e+00 +wall time: 0.000653028s + +time increment:21850 time: 21.85 +field ' c' [explicit solve]: current solution: 9.643118e+01, current residual:5.824126e+01 +field 'mu' [explicit solve]: current solution: 5.294173e+00, current residual:3.190402e+00 +wall time: 0.000560999s + +time increment:21860 time: 21.86 +field ' c' [explicit solve]: current solution: 9.643090e+01, current residual:5.824108e+01 +field 'mu' [explicit solve]: current solution: 5.293961e+00, current residual:3.190288e+00 +wall time: 0.000653982s + +time increment:21870 time: 21.87 +field ' c' [explicit solve]: current solution: 9.643062e+01, current residual:5.824090e+01 +field 'mu' [explicit solve]: current solution: 5.293750e+00, current residual:3.190175e+00 +wall time: 0.000627041s + +time increment:21880 time: 21.88 +field ' c' [explicit solve]: current solution: 9.643034e+01, current residual:5.824071e+01 +field 'mu' [explicit solve]: current solution: 5.293538e+00, current residual:3.190062e+00 +wall time: 0.00059104s + +time increment:21890 time: 21.89 +field ' c' [explicit solve]: current solution: 9.643006e+01, current residual:5.824053e+01 +field 'mu' [explicit solve]: current solution: 5.293328e+00, current residual:3.189949e+00 +wall time: 0.000622988s + +time increment:21900 time: 21.9 +field ' c' [explicit solve]: current solution: 9.642978e+01, current residual:5.824035e+01 +field 'mu' [explicit solve]: current solution: 5.293117e+00, current residual:3.189836e+00 +wall time: 0.000565767s + +time increment:21910 time: 21.91 +field ' c' [explicit solve]: current solution: 9.642950e+01, current residual:5.824017e+01 +field 'mu' [explicit solve]: current solution: 5.292907e+00, current residual:3.189724e+00 +wall time: 0.000589848s + +time increment:21920 time: 21.92 +field ' c' [explicit solve]: current solution: 9.642922e+01, current residual:5.823999e+01 +field 'mu' [explicit solve]: current solution: 5.292697e+00, current residual:3.189612e+00 +wall time: 0.000574112s + +time increment:21930 time: 21.93 +field ' c' [explicit solve]: current solution: 9.642894e+01, current residual:5.823981e+01 +field 'mu' [explicit solve]: current solution: 5.292488e+00, current residual:3.189500e+00 +wall time: 0.000641823s + +time increment:21940 time: 21.94 +field ' c' [explicit solve]: current solution: 9.642866e+01, current residual:5.823963e+01 +field 'mu' [explicit solve]: current solution: 5.292279e+00, current residual:3.189388e+00 +wall time: 0.000570059s + +time increment:21950 time: 21.95 +field ' c' [explicit solve]: current solution: 9.642838e+01, current residual:5.823945e+01 +field 'mu' [explicit solve]: current solution: 5.292070e+00, current residual:3.189276e+00 +wall time: 0.000657797s + +time increment:21960 time: 21.96 +field ' c' [explicit solve]: current solution: 9.642810e+01, current residual:5.823926e+01 +field 'mu' [explicit solve]: current solution: 5.291862e+00, current residual:3.189165e+00 +wall time: 0.000633955s + +time increment:21970 time: 21.97 +field ' c' [explicit solve]: current solution: 9.642782e+01, current residual:5.823908e+01 +field 'mu' [explicit solve]: current solution: 5.291654e+00, current residual:3.189054e+00 +wall time: 0.000594139s + +time increment:21980 time: 21.98 +field ' c' [explicit solve]: current solution: 9.642754e+01, current residual:5.823890e+01 +field 'mu' [explicit solve]: current solution: 5.291446e+00, current residual:3.188943e+00 +wall time: 0.000635862s + +time increment:21990 time: 21.99 +field ' c' [explicit solve]: current solution: 9.642726e+01, current residual:5.823872e+01 +field 'mu' [explicit solve]: current solution: 5.291239e+00, current residual:3.188832e+00 +wall time: 0.00065589s + +time increment:22000 time: 22 +field ' c' [explicit solve]: current solution: 9.642698e+01, current residual:5.823854e+01 +field 'mu' [explicit solve]: current solution: 5.291032e+00, current residual:3.188722e+00 +wall time: 0.00056386s + +time increment:22010 time: 22.01 +field ' c' [explicit solve]: current solution: 9.642670e+01, current 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[explicit solve]: current solution: 5.289188e+00, current residual:3.187736e+00 +wall time: 0.000672102s + +time increment:22100 time: 22.1 +field ' c' [explicit solve]: current solution: 9.642421e+01, current residual:5.823675e+01 +field 'mu' [explicit solve]: current solution: 5.288985e+00, current residual:3.187628e+00 +wall time: 0.00057292s + +time increment:22110 time: 22.11 +field ' c' [explicit solve]: current solution: 9.642394e+01, current residual:5.823657e+01 +field 'mu' [explicit solve]: current solution: 5.288782e+00, current residual:3.187519e+00 +wall time: 0.000613928s + +time increment:22120 time: 22.12 +field ' c' [explicit solve]: current solution: 9.642366e+01, current residual:5.823640e+01 +field 'mu' [explicit solve]: current solution: 5.288580e+00, current residual:3.187411e+00 +wall time: 0.000593901s + +time increment:22130 time: 22.13 +field ' c' [explicit solve]: current solution: 9.642338e+01, current residual:5.823622e+01 +field 'mu' [explicit solve]: current solution: 5.288377e+00, current residual:3.187303e+00 +wall time: 0.000571012s + +time increment:22140 time: 22.14 +field ' c' [explicit solve]: current solution: 9.642311e+01, current residual:5.823604e+01 +field 'mu' [explicit solve]: current solution: 5.288176e+00, current residual:3.187196e+00 +wall time: 0.000571966s + +time increment:22150 time: 22.15 +field ' c' [explicit solve]: current solution: 9.642283e+01, current residual:5.823586e+01 +field 'mu' [explicit solve]: current solution: 5.287974e+00, current residual:3.187088e+00 +wall time: 0.000643015s + +time increment:22160 time: 22.16 +field ' c' [explicit solve]: current solution: 9.642256e+01, current residual:5.823568e+01 +field 'mu' [explicit solve]: current solution: 5.287773e+00, current residual:3.186981e+00 +wall time: 0.000574827s + +time increment:22170 time: 22.17 +field ' c' [explicit solve]: current solution: 9.642228e+01, current residual:5.823551e+01 +field 'mu' [explicit solve]: current solution: 5.287573e+00, current residual:3.186874e+00 +wall time: 0.000563145s + +time increment:22180 time: 22.18 +field ' c' [explicit solve]: current solution: 9.642201e+01, current residual:5.823533e+01 +field 'mu' [explicit solve]: current solution: 5.287372e+00, current residual:3.186767e+00 +wall time: 0.00058794s + +time increment:22190 time: 22.19 +field ' c' [explicit solve]: current solution: 9.642174e+01, current residual:5.823515e+01 +field 'mu' [explicit solve]: current solution: 5.287173e+00, current residual:3.186660e+00 +wall time: 0.000558853s + +time increment:22200 time: 22.2 +field ' c' [explicit solve]: current solution: 9.642146e+01, current residual:5.823497e+01 +field 'mu' [explicit solve]: current solution: 5.286973e+00, current residual:3.186554e+00 +wall time: 0.00066185s + +time increment:22210 time: 22.21 +field ' c' [explicit solve]: current solution: 9.642119e+01, current residual:5.823480e+01 +field 'mu' [explicit solve]: current solution: 5.286774e+00, current residual:3.186447e+00 +wall time: 0.000669003s + +time increment:22220 time: 22.22 +field ' c' [explicit solve]: current solution: 9.642092e+01, current residual:5.823462e+01 +field 'mu' [explicit solve]: current solution: 5.286575e+00, current residual:3.186341e+00 +wall time: 0.000694036s + +time increment:22230 time: 22.23 +field ' c' [explicit solve]: current solution: 9.642064e+01, current residual:5.823444e+01 +field 'mu' [explicit solve]: current solution: 5.286376e+00, current residual:3.186235e+00 +wall time: 0.000653982s + +time increment:22240 time: 22.24 +field ' c' [explicit solve]: current solution: 9.642037e+01, current residual:5.823427e+01 +field 'mu' [explicit solve]: current solution: 5.286178e+00, current residual:3.186130e+00 +wall time: 0.000699997s + +time increment:22250 time: 22.25 +field ' c' [explicit solve]: current solution: 9.642010e+01, current residual:5.823409e+01 +field 'mu' [explicit solve]: current solution: 5.285980e+00, current residual:3.186024e+00 +wall time: 0.000631094s + +time increment:22260 time: 22.26 +field ' c' [explicit solve]: current solution: 9.641982e+01, current residual:5.823392e+01 +field 'mu' [explicit solve]: current solution: 5.285783e+00, current residual:3.185919e+00 +wall time: 0.000639915s + +time increment:22270 time: 22.27 +field ' c' [explicit solve]: current solution: 9.641955e+01, current residual:5.823374e+01 +field 'mu' [explicit solve]: current solution: 5.285585e+00, current residual:3.185814e+00 +wall time: 0.000649929s + +time increment:22280 time: 22.28 +field ' c' [explicit solve]: current solution: 9.641928e+01, current residual:5.823356e+01 +field 'mu' [explicit solve]: current solution: 5.285389e+00, current residual:3.185709e+00 +wall time: 0.000746965s + +time increment:22290 time: 22.29 +field ' c' [explicit solve]: current solution: 9.641901e+01, current residual:5.823339e+01 +field 'mu' [explicit solve]: current solution: 5.285192e+00, current residual:3.185604e+00 +wall time: 0.000633955s + +time increment:22300 time: 22.3 +field ' c' [explicit solve]: current solution: 9.641874e+01, current residual:5.823321e+01 +field 'mu' [explicit solve]: current solution: 5.284996e+00, current residual:3.185499e+00 +wall time: 0.000662088s + +time increment:22310 time: 22.31 +field ' c' [explicit solve]: current solution: 9.641847e+01, current residual:5.823304e+01 +field 'mu' [explicit solve]: current solution: 5.284800e+00, current residual:3.185395e+00 +wall time: 0.000653982s + +time increment:22320 time: 22.32 +field ' c' [explicit solve]: current solution: 9.641819e+01, current residual:5.823286e+01 +field 'mu' [explicit solve]: current solution: 5.284605e+00, current residual:3.185291e+00 +wall time: 0.000735044s + +time increment:22330 time: 22.33 +field ' c' [explicit solve]: current solution: 9.641792e+01, current residual:5.823269e+01 +field 'mu' [explicit solve]: current solution: 5.284409e+00, current residual:3.185187e+00 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0.000691891s + +time increment:22380 time: 22.38 +field ' c' [explicit solve]: current solution: 9.641657e+01, current residual:5.823181e+01 +field 'mu' [explicit solve]: current solution: 5.283439e+00, current residual:3.184669e+00 +wall time: 0.000626087s + +time increment:22390 time: 22.39 +field ' c' [explicit solve]: current solution: 9.641630e+01, current residual:5.823164e+01 +field 'mu' [explicit solve]: current solution: 5.283245e+00, current residual:3.184566e+00 +wall time: 0.000660896s + +time increment:22400 time: 22.4 +field ' c' [explicit solve]: current solution: 9.641603e+01, current residual:5.823146e+01 +field 'mu' [explicit solve]: current solution: 5.283053e+00, current residual:3.184464e+00 +wall time: 0.000674963s + +time increment:22410 time: 22.41 +field ' c' [explicit solve]: current solution: 9.641577e+01, current residual:5.823129e+01 +field 'mu' [explicit solve]: current solution: 5.282860e+00, current residual:3.184361e+00 +wall time: 0.000680923s + +time increment:22420 time: 22.42 +field ' c' [explicit solve]: current solution: 9.641550e+01, current residual:5.823112e+01 +field 'mu' [explicit solve]: current solution: 5.282668e+00, current residual:3.184259e+00 +wall time: 0.000664949s + +time increment:22430 time: 22.43 +field ' c' [explicit solve]: current solution: 9.641523e+01, current residual:5.823094e+01 +field 'mu' [explicit solve]: current solution: 5.282476e+00, current residual:3.184157e+00 +wall time: 0.000638962s + +time increment:22440 time: 22.44 +field ' c' [explicit solve]: current solution: 9.641496e+01, current residual:5.823077e+01 +field 'mu' [explicit solve]: current solution: 5.282285e+00, current residual:3.184055e+00 +wall time: 0.000657082s + +time increment:22450 time: 22.45 +field ' c' [explicit solve]: current solution: 9.641469e+01, current residual:5.823060e+01 +field 'mu' [explicit solve]: current solution: 5.282093e+00, current residual:3.183953e+00 +wall time: 0.000693083s + +time increment:22460 time: 22.46 +field ' c' [explicit solve]: current solution: 9.641442e+01, current residual:5.823042e+01 +field 'mu' [explicit solve]: current solution: 5.281903e+00, current residual:3.183851e+00 +wall time: 0.000644922s + +time increment:22470 time: 22.47 +field ' c' [explicit solve]: current solution: 9.641416e+01, current residual:5.823025e+01 +field 'mu' [explicit solve]: current solution: 5.281712e+00, current residual:3.183750e+00 +wall time: 0.000663996s + +time increment:22480 time: 22.48 +field ' c' [explicit solve]: current solution: 9.641389e+01, current residual:5.823008e+01 +field 'mu' [explicit solve]: current solution: 5.281522e+00, current residual:3.183648e+00 +wall time: 0.000660896s + +time increment:22490 time: 22.49 +field ' c' [explicit solve]: current solution: 9.641362e+01, current residual:5.822990e+01 +field 'mu' [explicit solve]: current solution: 5.281332e+00, current residual:3.183547e+00 +wall time: 0.000654936s + +time increment:22500 time: 22.5 +field ' c' [explicit solve]: current solution: 9.641335e+01, current residual:5.822973e+01 +field 'mu' [explicit solve]: current solution: 5.281142e+00, current residual:3.183446e+00 +wall time: 0.00067687s + +time increment:22510 time: 22.51 +field ' c' [explicit solve]: current solution: 9.641309e+01, current residual:5.822956e+01 +field 'mu' [explicit solve]: current solution: 5.280953e+00, current residual:3.183346e+00 +wall time: 0.000679016s + +time increment:22520 time: 22.52 +field ' c' [explicit solve]: current solution: 9.641282e+01, current residual:5.822939e+01 +field 'mu' [explicit solve]: current solution: 5.280764e+00, current residual:3.183245e+00 +wall time: 0.000937939s + +time increment:22530 time: 22.53 +field ' c' [explicit solve]: current solution: 9.641255e+01, current residual:5.822921e+01 +field 'mu' [explicit solve]: current solution: 5.280575e+00, current residual:3.183145e+00 +wall time: 0.000644922s + +time increment:22540 time: 22.54 +field ' c' [explicit solve]: current solution: 9.641229e+01, current residual:5.822904e+01 +field 'mu' [explicit solve]: current solution: 5.280387e+00, current residual:3.183044e+00 +wall time: 0.000589848s + +time increment:22550 time: 22.55 +field ' c' [explicit solve]: current solution: 9.641202e+01, current residual:5.822887e+01 +field 'mu' [explicit solve]: current solution: 5.280199e+00, current residual:3.182944e+00 +wall time: 0.000697136s + +time increment:22560 time: 22.56 +field ' c' [explicit solve]: current solution: 9.641176e+01, current residual:5.822870e+01 +field 'mu' [explicit solve]: current solution: 5.280011e+00, current residual:3.182844e+00 +wall time: 0.000619173s + +time increment:22570 time: 22.57 +field ' c' [explicit solve]: current solution: 9.641149e+01, current residual:5.822853e+01 +field 'mu' [explicit solve]: current solution: 5.279824e+00, current residual:3.182745e+00 +wall time: 0.000653028s + +time increment:22580 time: 22.58 +field ' c' [explicit solve]: current solution: 9.641123e+01, current residual:5.822835e+01 +field 'mu' [explicit solve]: current solution: 5.279637e+00, current residual:3.182645e+00 +wall time: 0.00069809s + +time increment:22590 time: 22.59 +field ' c' [explicit solve]: current solution: 9.641096e+01, current residual:5.822818e+01 +field 'mu' [explicit solve]: current solution: 5.279450e+00, current residual:3.182546e+00 +wall time: 0.000593901s + +time increment:22600 time: 22.6 +field ' c' [explicit solve]: current solution: 9.641070e+01, current residual:5.822801e+01 +field 'mu' [explicit solve]: current solution: 5.279264e+00, current residual:3.182447e+00 +wall time: 0.000705004s + +time increment:22610 time: 22.61 +field ' c' [explicit solve]: current solution: 9.641043e+01, current residual:5.822784e+01 +field 'mu' [explicit solve]: current solution: 5.279077e+00, current residual:3.182348e+00 +wall time: 0.000608921s + +time increment:22620 time: 22.62 +field ' c' [explicit solve]: current solution: 9.641017e+01, current residual:5.822767e+01 +field 'mu' [explicit solve]: current solution: 5.278892e+00, current residual:3.182249e+00 +wall time: 0.000562906s + +time increment:22630 time: 22.63 +field ' c' [explicit solve]: current solution: 9.640990e+01, current residual:5.822750e+01 +field 'mu' [explicit solve]: current solution: 5.278706e+00, current residual:3.182150e+00 +wall time: 0.0005548s + +time increment:22640 time: 22.64 +field ' c' [explicit solve]: current solution: 9.640964e+01, current residual:5.822733e+01 +field 'mu' [explicit solve]: current solution: 5.278521e+00, current residual:3.182052e+00 +wall time: 0.000643969s + +time increment:22650 time: 22.65 +field ' c' [explicit solve]: current solution: 9.640938e+01, current residual:5.822716e+01 +field 'mu' [explicit solve]: current solution: 5.278336e+00, current residual:3.181953e+00 +wall time: 0.000574112s + +time increment:22660 time: 22.66 +field ' c' [explicit solve]: current solution: 9.640911e+01, current residual:5.822699e+01 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solve]: current solution: 5.277416e+00, current residual:3.181464e+00 +wall time: 0.000559092s + +time increment:22710 time: 22.71 +field ' c' [explicit solve]: current solution: 9.640780e+01, current residual:5.822614e+01 +field 'mu' [explicit solve]: current solution: 5.277233e+00, current residual:3.181367e+00 +wall time: 0.00067091s + +time increment:22720 time: 22.72 +field ' c' [explicit solve]: current solution: 9.640754e+01, current residual:5.822597e+01 +field 'mu' [explicit solve]: current solution: 5.277050e+00, current residual:3.181269e+00 +wall time: 0.000566959s + +time increment:22730 time: 22.73 +field ' c' [explicit solve]: current solution: 9.640728e+01, current residual:5.822580e+01 +field 'mu' [explicit solve]: current solution: 5.276867e+00, current residual:3.181172e+00 +wall time: 0.000571012s + +time increment:22740 time: 22.74 +field ' c' [explicit solve]: current solution: 9.640701e+01, current residual:5.822563e+01 +field 'mu' [explicit solve]: current solution: 5.276685e+00, current residual:3.181076e+00 +wall time: 0.00056386s + +time increment:22750 time: 22.75 +field ' c' [explicit solve]: current solution: 9.640675e+01, current residual:5.822546e+01 +field 'mu' [explicit solve]: current solution: 5.276503e+00, current residual:3.180979e+00 +wall time: 0.000677109s + +time increment:22760 time: 22.76 +field ' c' [explicit solve]: current solution: 9.640649e+01, current residual:5.822529e+01 +field 'mu' [explicit solve]: current solution: 5.276321e+00, current residual:3.180882e+00 +wall time: 0.000574827s + +time increment:22770 time: 22.77 +field ' c' [explicit solve]: current solution: 9.640623e+01, current residual:5.822512e+01 +field 'mu' [explicit solve]: current solution: 5.276140e+00, current residual:3.180786e+00 +wall time: 0.000714064s + +time increment:22780 time: 22.78 +field ' c' [explicit solve]: current solution: 9.640597e+01, current residual:5.822495e+01 +field 'mu' [explicit solve]: current solution: 5.275959e+00, current residual:3.180690e+00 +wall time: 0.000571966s + +time increment:22790 time: 22.79 +field ' c' [explicit solve]: current solution: 9.640571e+01, current residual:5.822478e+01 +field 'mu' [explicit solve]: current solution: 5.275778e+00, current residual:3.180594e+00 +wall time: 0.00065589s + +time increment:22800 time: 22.8 +field ' c' [explicit solve]: current solution: 9.640545e+01, current residual:5.822462e+01 +field 'mu' [explicit solve]: current solution: 5.275598e+00, current residual:3.180498e+00 +wall time: 0.000568867s + +time increment:22810 time: 22.81 +field ' c' [explicit solve]: current solution: 9.640519e+01, current residual:5.822445e+01 +field 'mu' [explicit solve]: current solution: 5.275418e+00, current residual:3.180402e+00 +wall time: 0.000584841s + +time increment:22820 time: 22.82 +field ' c' [explicit solve]: current solution: 9.640493e+01, current residual:5.822428e+01 +field 'mu' [explicit solve]: current solution: 5.275238e+00, current residual:3.180307e+00 +wall time: 0.000679016s + +time increment:22830 time: 22.83 +field ' c' [explicit solve]: current solution: 9.640467e+01, current residual:5.822411e+01 +field 'mu' [explicit solve]: current solution: 5.275058e+00, current residual:3.180211e+00 +wall time: 0.000577927s + +time increment:22840 time: 22.84 +field ' c' [explicit solve]: current solution: 9.640441e+01, current residual:5.822394e+01 +field 'mu' [explicit solve]: current solution: 5.274879e+00, current residual:3.180116e+00 +wall time: 0.000564098s + +time increment:22850 time: 22.85 +field ' c' [explicit solve]: current solution: 9.640415e+01, current residual:5.822378e+01 +field 'mu' [explicit solve]: current solution: 5.274700e+00, current residual:3.180021e+00 +wall time: 0.000561953s + +time increment:22860 time: 22.86 +field ' c' [explicit solve]: current solution: 9.640389e+01, current residual:5.822361e+01 +field 'mu' [explicit solve]: current solution: 5.274521e+00, current residual:3.179926e+00 +wall time: 0.000686884s + +time increment:22870 time: 22.87 +field ' c' [explicit solve]: current solution: 9.640363e+01, current residual:5.822344e+01 +field 'mu' [explicit solve]: current solution: 5.274342e+00, current residual:3.179831e+00 +wall time: 0.000635862s + +time increment:22880 time: 22.88 +field ' c' [explicit solve]: current solution: 9.640337e+01, current residual:5.822327e+01 +field 'mu' [explicit solve]: current solution: 5.274164e+00, current residual:3.179737e+00 +wall time: 0.00065589s + +time increment:22890 time: 22.89 +field ' c' [explicit solve]: current solution: 9.640312e+01, current residual:5.822311e+01 +field 'mu' [explicit solve]: current solution: 5.273986e+00, current residual:3.179642e+00 +wall time: 0.000595808s + +time increment:22900 time: 22.9 +field ' c' [explicit solve]: current solution: 9.640286e+01, current residual:5.822294e+01 +field 'mu' [explicit solve]: current solution: 5.273809e+00, current residual:3.179548e+00 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0.000660896s + +time increment:22950 time: 22.95 +field ' c' [explicit solve]: current solution: 9.640157e+01, current residual:5.822211e+01 +field 'mu' [explicit solve]: current solution: 5.272925e+00, current residual:3.179079e+00 +wall time: 0.000658035s + +time increment:22960 time: 22.96 +field ' c' [explicit solve]: current solution: 9.640131e+01, current residual:5.822194e+01 +field 'mu' [explicit solve]: current solution: 5.272749e+00, current residual:3.178985e+00 +wall time: 0.000663996s + +time increment:22970 time: 22.97 +field ' c' [explicit solve]: current solution: 9.640106e+01, current residual:5.822177e+01 +field 'mu' [explicit solve]: current solution: 5.272573e+00, current residual:3.178892e+00 +wall time: 0.00062108s + +time increment:22980 time: 22.98 +field ' c' [explicit solve]: current solution: 9.640080e+01, current residual:5.822161e+01 +field 'mu' [explicit solve]: current solution: 5.272398e+00, current residual:3.178799e+00 +wall time: 0.000697851s + +time increment:22990 time: 22.99 +field ' c' [explicit solve]: current solution: 9.640054e+01, current residual:5.822144e+01 +field 'mu' [explicit solve]: current solution: 5.272223e+00, current residual:3.178706e+00 +wall time: 0.000695944s + +time increment:23000 time: 23 +field ' c' [explicit solve]: current solution: 9.640029e+01, current residual:5.822128e+01 +field 'mu' [explicit solve]: current solution: 5.272048e+00, current residual:3.178613e+00 +wall time: 0.000708103s + +time increment:23010 time: 23.01 +field ' c' [explicit solve]: current solution: 9.640003e+01, current residual:5.822111e+01 +field 'mu' [explicit solve]: current solution: 5.271873e+00, current residual:3.178521e+00 +wall time: 0.000730038s + +time increment:23020 time: 23.02 +field ' c' [explicit solve]: current solution: 9.639978e+01, current residual:5.822095e+01 +field 'mu' [explicit solve]: current solution: 5.271699e+00, current residual:3.178428e+00 +wall time: 0.000642061s + +time increment:23030 time: 23.03 +field ' c' [explicit solve]: current solution: 9.639952e+01, current residual:5.822078e+01 +field 'mu' [explicit solve]: current solution: 5.271525e+00, current residual:3.178336e+00 +wall time: 0.000648022s + +time increment:23040 time: 23.04 +field ' c' [explicit solve]: current solution: 9.639927e+01, current residual:5.822061e+01 +field 'mu' [explicit solve]: current solution: 5.271351e+00, current residual:3.178244e+00 +wall time: 0.000638962s + +time increment:23050 time: 23.05 +field ' c' [explicit solve]: current solution: 9.639901e+01, current residual:5.822045e+01 +field 'mu' [explicit solve]: current solution: 5.271178e+00, current residual:3.178152e+00 +wall time: 0.000749111s + +time increment:23060 time: 23.06 +field ' c' [explicit solve]: current solution: 9.639876e+01, current residual:5.822028e+01 +field 'mu' [explicit solve]: current solution: 5.271004e+00, current residual:3.178060e+00 +wall time: 0.000666857s + +time increment:23070 time: 23.07 +field ' c' [explicit solve]: current solution: 9.639850e+01, current residual:5.822012e+01 +field 'mu' [explicit solve]: current solution: 5.270831e+00, current residual:3.177968e+00 +wall time: 0.000564098s + +time increment:23080 time: 23.08 +field ' c' [explicit solve]: current solution: 9.639825e+01, current residual:5.821996e+01 +field 'mu' [explicit solve]: current solution: 5.270659e+00, current residual:3.177876e+00 +wall time: 0.000591993s + +time increment:23090 time: 23.09 +field ' c' [explicit solve]: current solution: 9.639799e+01, current residual:5.821979e+01 +field 'mu' [explicit solve]: current solution: 5.270486e+00, current residual:3.177785e+00 +wall time: 0.000557899s + +time increment:23100 time: 23.1 +field ' c' [explicit solve]: current solution: 9.639774e+01, current residual:5.821963e+01 +field 'mu' [explicit solve]: current solution: 5.270314e+00, current residual:3.177694e+00 +wall time: 0.000602961s + +time increment:23110 time: 23.11 +field ' c' [explicit solve]: current solution: 9.639749e+01, current residual:5.821946e+01 +field 'mu' [explicit solve]: current solution: 5.270142e+00, current residual:3.177602e+00 +wall time: 0.00057292s + +time increment:23120 time: 23.12 +field ' c' [explicit solve]: current solution: 9.639723e+01, current residual:5.821930e+01 +field 'mu' [explicit solve]: current solution: 5.269970e+00, current residual:3.177511e+00 +wall time: 0.000638008s + +time increment:23130 time: 23.13 +field ' c' [explicit solve]: current solution: 9.639698e+01, current residual:5.821913e+01 +field 'mu' [explicit solve]: current solution: 5.269799e+00, current residual:3.177420e+00 +wall time: 0.000578165s + +time increment:23140 time: 23.14 +field ' c' [explicit solve]: current solution: 9.639673e+01, current residual:5.821897e+01 +field 'mu' [explicit solve]: current solution: 5.269628e+00, current residual:3.177330e+00 +wall time: 0.000644922s + +time increment:23150 time: 23.15 +field ' c' [explicit solve]: current solution: 9.639647e+01, current residual:5.821881e+01 +field 'mu' [explicit solve]: current solution: 5.269457e+00, current residual:3.177239e+00 +wall time: 0.000623941s + +time increment:23160 time: 23.16 +field ' c' [explicit solve]: current solution: 9.639622e+01, current residual:5.821864e+01 +field 'mu' [explicit solve]: current solution: 5.269286e+00, current residual:3.177149e+00 +wall time: 0.000744104s + +time increment:23170 time: 23.17 +field ' c' [explicit solve]: current solution: 9.639597e+01, current residual:5.821848e+01 +field 'mu' [explicit solve]: current solution: 5.269116e+00, current residual:3.177058e+00 +wall time: 0.000659943s + +time increment:23180 time: 23.18 +field ' c' [explicit solve]: current solution: 9.639572e+01, current residual:5.821832e+01 +field 'mu' [explicit solve]: current solution: 5.268946e+00, current residual:3.176968e+00 +wall time: 0.000655174s + +time increment:23190 time: 23.19 +field ' c' [explicit solve]: current solution: 9.639547e+01, current residual:5.821815e+01 +field 'mu' [explicit solve]: current solution: 5.268776e+00, current residual:3.176878e+00 +wall time: 0.000646114s + +time increment:23200 time: 23.2 +field ' c' [explicit solve]: current solution: 9.639521e+01, current residual:5.821799e+01 +field 'mu' [explicit solve]: current solution: 5.268606e+00, current residual:3.176788e+00 +wall time: 0.000696898s + +time increment:23210 time: 23.21 +field ' c' [explicit solve]: current solution: 9.639496e+01, current residual:5.821783e+01 +field 'mu' [explicit solve]: current solution: 5.268437e+00, current residual:3.176698e+00 +wall time: 0.000646114s + +time increment:23220 time: 23.22 +field ' c' [explicit solve]: current solution: 9.639471e+01, current residual:5.821767e+01 +field 'mu' [explicit solve]: current solution: 5.268268e+00, current residual:3.176609e+00 +wall time: 0.000610113s + +time increment:23230 time: 23.23 +field ' c' [explicit solve]: current solution: 9.639446e+01, current residual:5.821750e+01 +field 'mu' [explicit solve]: current solution: 5.268099e+00, current residual:3.176519e+00 +wall time: 0.000648975s + +time increment:23240 time: 23.24 +field ' c' [explicit solve]: current solution: 9.639421e+01, current residual:5.821734e+01 +field 'mu' [explicit solve]: current solution: 5.267931e+00, current residual:3.176430e+00 +wall time: 0.000582218s + +time increment:23250 time: 23.25 +field ' c' [explicit solve]: current solution: 9.639396e+01, current residual:5.821718e+01 +field 'mu' [explicit solve]: current solution: 5.267762e+00, current residual:3.176341e+00 +wall time: 0.000576973s + +time increment:23260 time: 23.26 +field ' c' [explicit solve]: current solution: 9.639371e+01, current residual:5.821702e+01 +field 'mu' [explicit solve]: current solution: 5.267594e+00, current residual:3.176252e+00 +wall time: 0.000592947s + +time increment:23270 time: 23.27 +field ' c' [explicit solve]: current solution: 9.639346e+01, current residual:5.821685e+01 +field 'mu' [explicit solve]: current solution: 5.267426e+00, current residual:3.176163e+00 +wall time: 0.000567913s + +time increment:23280 time: 23.28 +field ' c' [explicit solve]: current solution: 9.639321e+01, current residual:5.821669e+01 +field 'mu' [explicit solve]: current solution: 5.267259e+00, current residual:3.176074e+00 +wall time: 0.000709057s + +time increment:23290 time: 23.29 +field ' c' [explicit solve]: current solution: 9.639296e+01, current residual:5.821653e+01 +field 'mu' [explicit solve]: current solution: 5.267091e+00, current residual:3.175985e+00 +wall time: 0.000690937s + +time increment:23300 time: 23.3 +field ' c' [explicit solve]: current solution: 9.639271e+01, current residual:5.821637e+01 +field 'mu' [explicit solve]: current solution: 5.266924e+00, current residual:3.175897e+00 +wall time: 0.000688076s + +time increment:23310 time: 23.31 +field ' c' [explicit solve]: current solution: 9.639246e+01, current residual:5.821621e+01 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[explicit solve]: current solution: 5.266093e+00, current residual:3.175456e+00 +wall time: 0.000636816s + +time increment:23360 time: 23.36 +field ' c' [explicit solve]: current solution: 9.639122e+01, current residual:5.821540e+01 +field 'mu' [explicit solve]: current solution: 5.265927e+00, current residual:3.175368e+00 +wall time: 0.000653982s + +time increment:23370 time: 23.37 +field ' c' [explicit solve]: current solution: 9.639097e+01, current residual:5.821524e+01 +field 'mu' [explicit solve]: current solution: 5.265762e+00, current residual:3.175281e+00 +wall time: 0.000727892s + +time increment:23380 time: 23.38 +field ' c' [explicit solve]: current solution: 9.639072e+01, current residual:5.821508e+01 +field 'mu' [explicit solve]: current solution: 5.265596e+00, current residual:3.175193e+00 +wall time: 0.000650883s + +time increment:23390 time: 23.39 +field ' c' [explicit solve]: current solution: 9.639047e+01, current residual:5.821492e+01 +field 'mu' [explicit solve]: current solution: 5.265431e+00, current residual:3.175106e+00 +wall time: 0.00069499s + +time increment:23400 time: 23.4 +field ' c' [explicit solve]: current solution: 9.639023e+01, current residual:5.821476e+01 +field 'mu' [explicit solve]: current solution: 5.265267e+00, current residual:3.175019e+00 +wall time: 0.000635862s + +time increment:23410 time: 23.41 +field ' c' [explicit solve]: current solution: 9.638998e+01, current residual:5.821460e+01 +field 'mu' [explicit solve]: current solution: 5.265102e+00, current residual:3.174932e+00 +wall time: 0.000755072s + +time increment:23420 time: 23.42 +field ' c' [explicit solve]: current solution: 9.638973e+01, current residual:5.821444e+01 +field 'mu' [explicit solve]: current solution: 5.264938e+00, current residual:3.174845e+00 +wall time: 0.000708103s + +time increment:23430 time: 23.43 +field ' c' [explicit solve]: current solution: 9.638948e+01, current residual:5.821428e+01 +field 'mu' [explicit solve]: current solution: 5.264774e+00, current residual:3.174758e+00 +wall time: 0.000648022s + +time increment:23440 time: 23.44 +field ' c' [explicit solve]: current solution: 9.638924e+01, current residual:5.821412e+01 +field 'mu' [explicit solve]: current solution: 5.264610e+00, current residual:3.174671e+00 +wall time: 0.00067997s + +time increment:23450 time: 23.45 +field ' c' [explicit solve]: current solution: 9.638899e+01, current residual:5.821396e+01 +field 'mu' [explicit solve]: current solution: 5.264447e+00, current residual:3.174585e+00 +wall time: 0.000638962s + +time increment:23460 time: 23.46 +field ' c' [explicit solve]: current solution: 9.638874e+01, current residual:5.821380e+01 +field 'mu' [explicit solve]: current solution: 5.264284e+00, current residual:3.174498e+00 +wall time: 0.000665903s + +time increment:23470 time: 23.47 +field ' c' [explicit solve]: current solution: 9.638850e+01, current residual:5.821364e+01 +field 'mu' [explicit solve]: current solution: 5.264121e+00, current residual:3.174412e+00 +wall time: 0.000658035s + +time increment:23480 time: 23.48 +field ' c' [explicit solve]: current solution: 9.638825e+01, current residual:5.821348e+01 +field 'mu' [explicit solve]: current solution: 5.263958e+00, current residual:3.174326e+00 +wall time: 0.000675917s + +time increment:23490 time: 23.49 +field ' c' [explicit solve]: current solution: 9.638801e+01, current residual:5.821332e+01 +field 'mu' [explicit solve]: current solution: 5.263795e+00, current residual:3.174240e+00 +wall time: 0.000653982s + +time increment:23500 time: 23.5 +field ' c' [explicit solve]: current solution: 9.638776e+01, current residual:5.821316e+01 +field 'mu' [explicit solve]: current solution: 5.263633e+00, current residual:3.174154e+00 +wall time: 0.000688076s + +time increment:23510 time: 23.51 +field ' c' [explicit solve]: current solution: 9.638752e+01, current residual:5.821300e+01 +field 'mu' [explicit solve]: current solution: 5.263471e+00, current residual:3.174068e+00 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0.000705004s + +time increment:23560 time: 23.56 +field ' c' [explicit solve]: current solution: 9.638629e+01, current residual:5.821221e+01 +field 'mu' [explicit solve]: current solution: 5.262664e+00, current residual:3.173641e+00 +wall time: 0.000661135s + +time increment:23570 time: 23.57 +field ' c' [explicit solve]: current solution: 9.638605e+01, current residual:5.821205e+01 +field 'mu' [explicit solve]: current solution: 5.262503e+00, current residual:3.173556e+00 +wall time: 0.000779152s + +time increment:23580 time: 23.58 +field ' c' [explicit solve]: current solution: 9.638581e+01, current residual:5.821190e+01 +field 'mu' [explicit solve]: current solution: 5.262343e+00, current residual:3.173471e+00 +wall time: 0.000682831s + +time increment:23590 time: 23.59 +field ' c' [explicit solve]: current solution: 9.638556e+01, current residual:5.821174e+01 +field 'mu' [explicit solve]: current solution: 5.262182e+00, current residual:3.173386e+00 +wall time: 0.000782013s + +time increment:23600 time: 23.6 +field ' c' [explicit solve]: current solution: 9.638532e+01, current residual:5.821158e+01 +field 'mu' [explicit solve]: current solution: 5.262022e+00, current residual:3.173301e+00 +wall time: 0.000656843s + +time increment:23610 time: 23.61 +field ' c' [explicit solve]: current solution: 9.638508e+01, current residual:5.821142e+01 +field 'mu' [explicit solve]: current solution: 5.261862e+00, current residual:3.173216e+00 +wall time: 0.000756979s + +time increment:23620 time: 23.62 +field ' c' [explicit solve]: current solution: 9.638483e+01, current residual:5.821127e+01 +field 'mu' [explicit solve]: current solution: 5.261703e+00, current residual:3.173132e+00 +wall time: 0.000756979s + +time increment:23630 time: 23.63 +field ' c' [explicit solve]: current solution: 9.638459e+01, current residual:5.821111e+01 +field 'mu' [explicit solve]: current solution: 5.261543e+00, current residual:3.173048e+00 +wall time: 0.00078702s + +time increment:23640 time: 23.64 +field ' c' [explicit solve]: current solution: 9.638435e+01, current residual:5.821095e+01 +field 'mu' [explicit solve]: current solution: 5.261384e+00, current residual:3.172963e+00 +wall time: 0.000685215s + +time increment:23650 time: 23.65 +field ' c' [explicit solve]: current solution: 9.638411e+01, current residual:5.821079e+01 +field 'mu' [explicit solve]: current solution: 5.261225e+00, current residual:3.172879e+00 +wall time: 0.000672102s + +time increment:23660 time: 23.66 +field ' c' [explicit solve]: current solution: 9.638386e+01, current residual:5.821064e+01 +field 'mu' [explicit solve]: current solution: 5.261066e+00, current residual:3.172795e+00 +wall time: 0.000668049s + +time increment:23670 time: 23.67 +field ' c' [explicit solve]: current solution: 9.638362e+01, current residual:5.821048e+01 +field 'mu' [explicit solve]: current solution: 5.260908e+00, current residual:3.172711e+00 +wall time: 0.000691891s + +time increment:23680 time: 23.68 +field ' c' [explicit solve]: current solution: 9.638338e+01, current residual:5.821032e+01 +field 'mu' [explicit solve]: current solution: 5.260749e+00, current residual:3.172628e+00 +wall time: 0.000697136s + +time increment:23690 time: 23.69 +field ' c' [explicit solve]: current solution: 9.638314e+01, current residual:5.821017e+01 +field 'mu' [explicit solve]: current solution: 5.260591e+00, current residual:3.172544e+00 +wall time: 0.000668049s + +time increment:23700 time: 23.7 +field ' c' [explicit solve]: current solution: 9.638290e+01, current residual:5.821001e+01 +field 'mu' [explicit solve]: current solution: 5.260433e+00, current residual:3.172460e+00 +wall time: 0.00067091s + +time increment:23710 time: 23.71 +field ' c' [explicit solve]: current solution: 9.638266e+01, current residual:5.820986e+01 +field 'mu' [explicit solve]: current solution: 5.260276e+00, current residual:3.172377e+00 +wall time: 0.000585079s + +time increment:23720 time: 23.72 +field ' c' [explicit solve]: current solution: 9.638242e+01, current residual:5.820970e+01 +field 'mu' [explicit solve]: current solution: 5.260118e+00, current residual:3.172294e+00 +wall time: 0.000710964s + +time increment:23730 time: 23.73 +field ' c' [explicit solve]: current solution: 9.638217e+01, current residual:5.820954e+01 +field 'mu' [explicit solve]: current solution: 5.259961e+00, current residual:3.172210e+00 +wall time: 0.000658035s + +time increment:23740 time: 23.74 +field ' c' [explicit solve]: current solution: 9.638193e+01, current residual:5.820939e+01 +field 'mu' [explicit solve]: current solution: 5.259804e+00, current residual:3.172127e+00 +wall time: 0.000568867s + +time increment:23750 time: 23.75 +field ' c' [explicit solve]: current solution: 9.638169e+01, current residual:5.820923e+01 +field 'mu' [explicit solve]: current solution: 5.259647e+00, current residual:3.172044e+00 +wall time: 0.000652075s + +time increment:23760 time: 23.76 +field ' c' [explicit solve]: current solution: 9.638145e+01, current residual:5.820908e+01 +field 'mu' [explicit solve]: current solution: 5.259491e+00, current residual:3.171961e+00 +wall time: 0.000607967s + +time increment:23770 time: 23.77 +field ' c' [explicit solve]: current solution: 9.638121e+01, current residual:5.820892e+01 +field 'mu' [explicit solve]: current solution: 5.259334e+00, current residual:3.171879e+00 +wall time: 0.000669956s + +time increment:23780 time: 23.78 +field ' c' [explicit solve]: current solution: 9.638097e+01, current residual:5.820877e+01 +field 'mu' [explicit solve]: current solution: 5.259178e+00, current residual:3.171796e+00 +wall time: 0.00064683s + +time increment:23790 time: 23.79 +field ' c' [explicit solve]: current solution: 9.638074e+01, current residual:5.820861e+01 +field 'mu' [explicit solve]: current solution: 5.259022e+00, current residual:3.171714e+00 +wall time: 0.000667095s + +time increment:23800 time: 23.8 +field ' c' [explicit solve]: current solution: 9.638050e+01, current residual:5.820845e+01 +field 'mu' [explicit solve]: current solution: 5.258866e+00, current residual:3.171631e+00 +wall time: 0.000694036s + +time increment:23810 time: 23.81 +field ' c' [explicit solve]: current solution: 9.638026e+01, current residual:5.820830e+01 +field 'mu' [explicit solve]: current solution: 5.258711e+00, current residual:3.171549e+00 +wall time: 0.000678062s + +time increment:23820 time: 23.82 +field ' c' [explicit solve]: current solution: 9.638002e+01, current residual:5.820815e+01 +field 'mu' [explicit solve]: current solution: 5.258555e+00, current residual:3.171467e+00 +wall time: 0.000644922s + +time increment:23830 time: 23.83 +field ' c' [explicit solve]: current solution: 9.637978e+01, current residual:5.820799e+01 +field 'mu' [explicit solve]: current solution: 5.258400e+00, current residual:3.171385e+00 +wall time: 0.00067091s + +time increment:23840 time: 23.84 +field ' c' [explicit solve]: current solution: 9.637954e+01, current residual:5.820784e+01 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[explicit solve]: current solution: 5.257627e+00, current residual:3.170976e+00 +wall time: 0.00065589s + +time increment:23890 time: 23.89 +field ' c' [explicit solve]: current solution: 9.637835e+01, current residual:5.820706e+01 +field 'mu' [explicit solve]: current solution: 5.257473e+00, current residual:3.170894e+00 +wall time: 0.000586033s + +time increment:23900 time: 23.9 +field ' c' [explicit solve]: current solution: 9.637811e+01, current residual:5.820691e+01 +field 'mu' [explicit solve]: current solution: 5.257320e+00, current residual:3.170813e+00 +wall time: 0.00070715s + +time increment:23910 time: 23.91 +field ' c' [explicit solve]: current solution: 9.637788e+01, current residual:5.820676e+01 +field 'mu' [explicit solve]: current solution: 5.257166e+00, current residual:3.170732e+00 +wall time: 0.000616074s + +time increment:23920 time: 23.92 +field ' c' [explicit solve]: current solution: 9.637764e+01, current residual:5.820660e+01 +field 'mu' [explicit solve]: current solution: 5.257013e+00, current residual:3.170651e+00 +wall time: 0.000577927s + +time increment:23930 time: 23.93 +field ' c' [explicit solve]: current solution: 9.637740e+01, current residual:5.820645e+01 +field 'mu' [explicit solve]: current solution: 5.256859e+00, current residual:3.170570e+00 +wall time: 0.000582933s + +time increment:23940 time: 23.94 +field ' c' [explicit solve]: current solution: 9.637717e+01, current residual:5.820630e+01 +field 'mu' [explicit solve]: current solution: 5.256707e+00, current residual:3.170489e+00 +wall time: 0.000573158s + +time increment:23950 time: 23.95 +field ' c' [explicit solve]: current solution: 9.637693e+01, current residual:5.820614e+01 +field 'mu' [explicit solve]: current solution: 5.256554e+00, current residual:3.170408e+00 +wall time: 0.000564098s + +time increment:23960 time: 23.96 +field ' c' [explicit solve]: current solution: 9.637670e+01, current residual:5.820599e+01 +field 'mu' [explicit solve]: current solution: 5.256401e+00, current residual:3.170327e+00 +wall time: 0.000562906s + +time increment:23970 time: 23.97 +field ' c' [explicit solve]: current solution: 9.637646e+01, current residual:5.820584e+01 +field 'mu' [explicit solve]: current solution: 5.256249e+00, current residual:3.170247e+00 +wall time: 0.000596046s + +time increment:23980 time: 23.98 +field ' c' [explicit solve]: current solution: 9.637622e+01, current residual:5.820569e+01 +field 'mu' [explicit solve]: current solution: 5.256097e+00, current residual:3.170166e+00 +wall time: 0.000557899s + +time increment:23990 time: 23.99 +field ' c' [explicit solve]: current solution: 9.637599e+01, current residual:5.820553e+01 +field 'mu' [explicit solve]: current solution: 5.255945e+00, current residual:3.170086e+00 +wall time: 0.000673056s + +time increment:24000 time: 24 +field ' c' [explicit solve]: current solution: 9.637575e+01, current residual:5.820538e+01 +field 'mu' [explicit solve]: current solution: 5.255793e+00, current residual:3.170006e+00 +wall time: 0.000561953s + +time increment:24010 time: 24.01 +field ' c' [explicit solve]: current solution: 9.637552e+01, current residual:5.820523e+01 +field 'mu' [explicit solve]: current solution: 5.255641e+00, current residual:3.169925e+00 +wall time: 0.000582933s + +time increment:24020 time: 24.02 +field ' c' [explicit solve]: current solution: 9.637528e+01, current residual:5.820508e+01 +field 'mu' [explicit solve]: current solution: 5.255490e+00, current residual:3.169845e+00 +wall time: 0.00056386s + +time increment:24030 time: 24.03 +field ' c' [explicit solve]: current solution: 9.637505e+01, current residual:5.820492e+01 +field 'mu' [explicit solve]: current solution: 5.255339e+00, current residual:3.169765e+00 +wall time: 0.000742912s + +time increment:24040 time: 24.04 +field ' c' [explicit solve]: current solution: 9.637481e+01, current residual:5.820477e+01 +field 'mu' [explicit solve]: current solution: 5.255188e+00, current residual:3.169686e+00 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0.000656843s + +time increment:24090 time: 24.09 +field ' c' [explicit solve]: current solution: 9.637365e+01, current residual:5.820401e+01 +field 'mu' [explicit solve]: current solution: 5.254435e+00, current residual:3.169288e+00 +wall time: 0.000637054s + +time increment:24100 time: 24.1 +field ' c' [explicit solve]: current solution: 9.637341e+01, current residual:5.820386e+01 +field 'mu' [explicit solve]: current solution: 5.254285e+00, current residual:3.169208e+00 +wall time: 0.000684977s + +time increment:24110 time: 24.11 +field ' c' [explicit solve]: current solution: 9.637318e+01, current residual:5.820371e+01 +field 'mu' [explicit solve]: current solution: 5.254136e+00, current residual:3.169129e+00 +wall time: 0.000674009s + +time increment:24120 time: 24.12 +field ' c' [explicit solve]: current solution: 9.637295e+01, current residual:5.820356e+01 +field 'mu' [explicit solve]: current solution: 5.253986e+00, current residual:3.169050e+00 +wall time: 0.000666857s + +time increment:24130 time: 24.13 +field ' c' [explicit solve]: current solution: 9.637271e+01, current residual:5.820341e+01 +field 'mu' [explicit solve]: current solution: 5.253837e+00, current residual:3.168971e+00 +wall time: 0.000627995s + +time increment:24140 time: 24.14 +field ' c' [explicit solve]: current solution: 9.637248e+01, current residual:5.820326e+01 +field 'mu' [explicit solve]: current solution: 5.253687e+00, current residual:3.168892e+00 +wall time: 0.000712156s + +time increment:24150 time: 24.15 +field ' c' [explicit solve]: current solution: 9.637225e+01, current residual:5.820311e+01 +field 'mu' [explicit solve]: current solution: 5.253538e+00, current residual:3.168814e+00 +wall time: 0.000632048s + +time increment:24160 time: 24.16 +field ' c' [explicit solve]: current solution: 9.637202e+01, current residual:5.820296e+01 +field 'mu' [explicit solve]: current solution: 5.253390e+00, current residual:3.168735e+00 +wall time: 0.000643015s + +time increment:24170 time: 24.17 +field ' c' [explicit solve]: current solution: 9.637179e+01, current residual:5.820281e+01 +field 'mu' [explicit solve]: current solution: 5.253241e+00, current residual:3.168656e+00 +wall time: 0.000693798s + +time increment:24180 time: 24.18 +field ' c' [explicit solve]: current solution: 9.637155e+01, current residual:5.820266e+01 +field 'mu' [explicit solve]: current solution: 5.253093e+00, current residual:3.168578e+00 +wall time: 0.000689983s + +time increment:24190 time: 24.19 +field ' c' [explicit solve]: current solution: 9.637132e+01, current residual:5.820251e+01 +field 'mu' [explicit solve]: current solution: 5.252944e+00, current residual:3.168499e+00 +wall time: 0.00066185s + +time increment:24200 time: 24.2 +field ' c' [explicit solve]: current solution: 9.637109e+01, current residual:5.820236e+01 +field 'mu' [explicit solve]: current solution: 5.252796e+00, current residual:3.168421e+00 +wall time: 0.000669003s + +time increment:24210 time: 24.21 +field ' c' [explicit solve]: current solution: 9.637086e+01, current residual:5.820221e+01 +field 'mu' [explicit solve]: current solution: 5.252648e+00, current residual:3.168343e+00 +wall time: 0.000652075s + +time increment:24220 time: 24.22 +field ' c' [explicit solve]: current solution: 9.637063e+01, current residual:5.820206e+01 +field 'mu' [explicit solve]: current solution: 5.252500e+00, current residual:3.168265e+00 +wall time: 0.00068903s + +time increment:24230 time: 24.23 +field ' c' [explicit solve]: current solution: 9.637040e+01, current residual:5.820191e+01 +field 'mu' [explicit solve]: current solution: 5.252353e+00, current residual:3.168187e+00 +wall time: 0.000662088s + +time increment:24240 time: 24.24 +field ' c' [explicit solve]: current solution: 9.637017e+01, current residual:5.820176e+01 +field 'mu' [explicit solve]: current solution: 5.252206e+00, current residual:3.168109e+00 +wall time: 0.000674963s + +time increment:24250 time: 24.25 +field ' c' [explicit solve]: current solution: 9.636994e+01, current residual:5.820161e+01 +field 'mu' [explicit solve]: current solution: 5.252058e+00, current residual:3.168031e+00 +wall time: 0.000622034s + +time increment:24260 time: 24.26 +field ' c' [explicit solve]: current solution: 9.636971e+01, current residual:5.820146e+01 +field 'mu' [explicit solve]: current solution: 5.251911e+00, current residual:3.167953e+00 +wall time: 0.000683069s + +time increment:24270 time: 24.27 +field ' c' [explicit solve]: current solution: 9.636948e+01, current residual:5.820131e+01 +field 'mu' [explicit solve]: current solution: 5.251764e+00, current residual:3.167875e+00 +wall time: 0.000690937s + +time increment:24280 time: 24.28 +field ' c' [explicit solve]: current solution: 9.636925e+01, current residual:5.820116e+01 +field 'mu' [explicit solve]: current solution: 5.251618e+00, current residual:3.167798e+00 +wall time: 0.000671148s + +time increment:24290 time: 24.29 +field ' c' [explicit solve]: current solution: 9.636902e+01, current residual:5.820101e+01 +field 'mu' [explicit solve]: current solution: 5.251471e+00, current residual:3.167720e+00 +wall time: 0.000633001s + +time increment:24300 time: 24.3 +field ' c' [explicit solve]: current solution: 9.636879e+01, current residual:5.820086e+01 +field 'mu' [explicit solve]: current solution: 5.251325e+00, current residual:3.167643e+00 +wall time: 0.000654936s + +time increment:24310 time: 24.31 +field ' c' [explicit solve]: current solution: 9.636856e+01, current residual:5.820071e+01 +field 'mu' [explicit solve]: current solution: 5.251179e+00, current residual:3.167566e+00 +wall time: 0.000626087s + +time increment:24320 time: 24.32 +field ' c' [explicit solve]: current solution: 9.636833e+01, current residual:5.820057e+01 +field 'mu' [explicit solve]: current solution: 5.251033e+00, current residual:3.167488e+00 +wall time: 0.000640869s + +time increment:24330 time: 24.33 +field ' c' [explicit solve]: current solution: 9.636810e+01, current 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solution: 5.249150e+00, current residual:3.166493e+00 +wall time: 0.00067997s + +time increment:24460 time: 24.46 +field ' c' [explicit solve]: current solution: 9.636515e+01, current residual:5.819850e+01 +field 'mu' [explicit solve]: current solution: 5.249006e+00, current residual:3.166417e+00 +wall time: 0.000680923s + +time increment:24470 time: 24.47 +field ' c' [explicit solve]: current solution: 9.636492e+01, current residual:5.819835e+01 +field 'mu' [explicit solve]: current solution: 5.248862e+00, current residual:3.166341e+00 +wall time: 0.000710964s + +time increment:24480 time: 24.48 +field ' c' [explicit solve]: current solution: 9.636469e+01, current residual:5.819821e+01 +field 'mu' [explicit solve]: current solution: 5.248719e+00, current residual:3.166265e+00 +wall time: 0.00067091s + +time increment:24490 time: 24.49 +field ' c' [explicit solve]: current solution: 9.636447e+01, current residual:5.819806e+01 +field 'mu' [explicit solve]: current solution: 5.248576e+00, current residual:3.166189e+00 +wall time: 0.000628948s + +time increment:24500 time: 24.5 +field ' c' [explicit solve]: current solution: 9.636424e+01, current residual:5.819791e+01 +field 'mu' [explicit solve]: current solution: 5.248433e+00, current residual:3.166114e+00 +wall time: 0.000677824s + +time increment:24510 time: 24.51 +field ' c' [explicit solve]: current solution: 9.636402e+01, current residual:5.819777e+01 +field 'mu' [explicit solve]: current solution: 5.248290e+00, current residual:3.166038e+00 +wall time: 0.000641108s + +time increment:24520 time: 24.52 +field ' c' [explicit solve]: current solution: 9.636379e+01, current residual:5.819762e+01 +field 'mu' [explicit solve]: current solution: 5.248147e+00, current residual:3.165962e+00 +wall time: 0.000694036s + +time increment:24530 time: 24.53 +field ' c' [explicit solve]: current solution: 9.636357e+01, current residual:5.819747e+01 +field 'mu' [explicit solve]: current solution: 5.248005e+00, current residual:3.165887e+00 +wall time: 0.000699043s + +time increment:24540 time: 24.54 +field ' c' [explicit solve]: current solution: 9.636334e+01, current residual:5.819733e+01 +field 'mu' [explicit solve]: current solution: 5.247862e+00, current residual:3.165812e+00 +wall time: 0.000636101s + +time increment:24550 time: 24.55 +field ' c' [explicit solve]: current solution: 9.636312e+01, current residual:5.819718e+01 +field 'mu' [explicit solve]: current solution: 5.247720e+00, current residual:3.165736e+00 +wall time: 0.000715017s + +time increment:24560 time: 24.56 +field ' c' [explicit solve]: current solution: 9.636289e+01, current residual:5.819704e+01 +field 'mu' [explicit solve]: current solution: 5.247578e+00, current residual:3.165661e+00 +wall time: 0.000667095s + +time increment:24570 time: 24.57 +field ' c' [explicit solve]: current solution: 9.636267e+01, current residual:5.819689e+01 +field 'mu' [explicit solve]: current solution: 5.247436e+00, current residual:3.165586e+00 +wall time: 0.00071907s + +time increment:24580 time: 24.58 +field ' c' [explicit solve]: current solution: 9.636245e+01, current residual:5.819675e+01 +field 'mu' [explicit solve]: current solution: 5.247294e+00, current residual:3.165511e+00 +wall time: 0.000694036s + +time increment:24590 time: 24.59 +field ' c' [explicit solve]: current solution: 9.636222e+01, current residual:5.819660e+01 +field 'mu' [explicit solve]: current solution: 5.247152e+00, current residual:3.165436e+00 +wall time: 0.000669003s + +time increment:24600 time: 24.6 +field ' c' [explicit solve]: current solution: 9.636200e+01, current residual:5.819646e+01 +field 'mu' [explicit solve]: current solution: 5.247011e+00, current residual:3.165361e+00 +wall time: 0.000659943s + +time increment:24610 time: 24.61 +field ' c' [explicit solve]: current solution: 9.636178e+01, current residual:5.819631e+01 +field 'mu' [explicit solve]: current solution: 5.246870e+00, current residual:3.165287e+00 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increment:24700 time: 24.7 +field ' c' [explicit solve]: current solution: 9.635977e+01, current residual:5.819501e+01 +field 'mu' [explicit solve]: current solution: 5.245604e+00, current residual:3.164617e+00 +wall time: 0.000625134s + +time increment:24710 time: 24.71 +field ' c' [explicit solve]: current solution: 9.635955e+01, current residual:5.819487e+01 +field 'mu' [explicit solve]: current solution: 5.245464e+00, current residual:3.164543e+00 +wall time: 0.000638962s + +time increment:24720 time: 24.72 +field ' c' [explicit solve]: current solution: 9.635933e+01, current residual:5.819472e+01 +field 'mu' [explicit solve]: current solution: 5.245324e+00, current residual:3.164469e+00 +wall time: 0.000634909s + +time increment:24730 time: 24.73 +field ' c' [explicit solve]: current solution: 9.635911e+01, current residual:5.819458e+01 +field 'mu' [explicit solve]: current solution: 5.245185e+00, current residual:3.164395e+00 +wall time: 0.000566006s + +time increment:24740 time: 24.74 +field ' c' [explicit solve]: current solution: 9.635889e+01, current residual:5.819444e+01 +field 'mu' [explicit solve]: current solution: 5.245045e+00, current residual:3.164321e+00 +wall time: 0.0007658s + +time increment:24750 time: 24.75 +field ' c' [explicit solve]: current solution: 9.635867e+01, current residual:5.819429e+01 +field 'mu' [explicit solve]: current solution: 5.244906e+00, current residual:3.164248e+00 +wall time: 0.000566006s + +time increment:24760 time: 24.76 +field ' c' [explicit solve]: current solution: 9.635845e+01, current residual:5.819415e+01 +field 'mu' [explicit solve]: current solution: 5.244767e+00, current residual:3.164174e+00 +wall time: 0.000597s + +time increment:24770 time: 24.77 +field ' c' [explicit solve]: current solution: 9.635823e+01, current residual:5.819401e+01 +field 'mu' [explicit solve]: current solution: 5.244628e+00, current residual:3.164101e+00 +wall time: 0.000660896s + +time increment:24780 time: 24.78 +field ' c' [explicit solve]: current solution: 9.635801e+01, current residual:5.819386e+01 +field 'mu' [explicit solve]: current solution: 5.244489e+00, current residual:3.164027e+00 +wall time: 0.000654936s + +time increment:24790 time: 24.79 +field ' c' [explicit solve]: current solution: 9.635779e+01, current residual:5.819372e+01 +field 'mu' [explicit solve]: current solution: 5.244350e+00, current residual:3.163954e+00 +wall time: 0.000581026s + +time increment:24800 time: 24.8 +field ' c' [explicit solve]: current solution: 9.635757e+01, current residual:5.819358e+01 +field 'mu' [explicit solve]: current solution: 5.244212e+00, current residual:3.163880e+00 +wall time: 0.000562906s + +time increment:24810 time: 24.81 +field ' c' [explicit solve]: current solution: 9.635735e+01, current residual:5.819344e+01 +field 'mu' [explicit solve]: current solution: 5.244073e+00, current residual:3.163807e+00 +wall time: 0.000629187s + +time increment:24820 time: 24.82 +field ' c' [explicit solve]: current solution: 9.635713e+01, current residual:5.819329e+01 +field 'mu' [explicit solve]: current solution: 5.243935e+00, current residual:3.163734e+00 +wall time: 0.000617981s + +time increment:24830 time: 24.83 +field ' c' [explicit solve]: current solution: 9.635691e+01, current residual:5.819315e+01 +field 'mu' [explicit solve]: current solution: 5.243797e+00, current residual:3.163661e+00 +wall time: 0.000613213s + +time increment:24840 time: 24.84 +field ' c' [explicit solve]: current solution: 9.635669e+01, current residual:5.819301e+01 +field 'mu' [explicit solve]: current solution: 5.243659e+00, current residual:3.163588e+00 +wall time: 0.000628948s + +time increment:24850 time: 24.85 +field ' c' [explicit solve]: current solution: 9.635647e+01, current residual:5.819287e+01 +field 'mu' [explicit solve]: current solution: 5.243521e+00, current residual:3.163515e+00 +wall time: 0.000668049s + +time increment:24860 time: 24.86 +field ' c' [explicit solve]: current solution: 9.635625e+01, current residual:5.819272e+01 +field 'mu' [explicit solve]: current solution: 5.243383e+00, current residual:3.163442e+00 +wall time: 0.000635147s + +time increment:24870 time: 24.87 +field ' c' [explicit solve]: current solution: 9.635604e+01, current residual:5.819258e+01 +field 'mu' [explicit solve]: current solution: 5.243246e+00, current residual:3.163369e+00 +wall time: 0.000594854s + +time increment:24880 time: 24.88 +field ' c' [explicit solve]: current solution: 9.635582e+01, current residual:5.819244e+01 +field 'mu' [explicit solve]: current solution: 5.243108e+00, current residual:3.163296e+00 +wall time: 0.000563145s + +time increment:24890 time: 24.89 +field ' c' [explicit solve]: current solution: 9.635560e+01, current residual:5.819230e+01 +field 'mu' [explicit solve]: current solution: 5.242971e+00, current residual:3.163223e+00 +wall time: 0.000644922s + +time increment:24900 time: 24.9 +field ' c' [explicit solve]: current solution: 9.635538e+01, current residual:5.819216e+01 +field 'mu' [explicit solve]: current solution: 5.242834e+00, current residual:3.163151e+00 +wall time: 0.000684977s + +time increment:24910 time: 24.91 +field ' c' [explicit solve]: current solution: 9.635516e+01, current residual:5.819202e+01 +field 'mu' [explicit solve]: current solution: 5.242697e+00, current residual:3.163078e+00 +wall time: 0.00056982s + +time increment:24920 time: 24.92 +field ' c' [explicit solve]: current solution: 9.635495e+01, current residual:5.819188e+01 +field 'mu' [explicit solve]: current solution: 5.242560e+00, current residual:3.163006e+00 +wall time: 0.000558138s + +time increment:24930 time: 24.93 +field ' c' [explicit solve]: current solution: 9.635473e+01, current residual:5.819173e+01 +field 'mu' [explicit solve]: current solution: 5.242423e+00, current residual:3.162933e+00 +wall time: 0.000708103s + +time increment:24940 time: 24.94 +field ' c' [explicit solve]: current solution: 9.635451e+01, current residual:5.819159e+01 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[explicit solve]: current solution: 5.241741e+00, current residual:3.162572e+00 +wall time: 0.000576019s + +time increment:24990 time: 24.99 +field ' c' [explicit solve]: current solution: 9.635343e+01, current residual:5.819089e+01 +field 'mu' [explicit solve]: current solution: 5.241605e+00, current residual:3.162500e+00 +wall time: 0.000563145s + +time increment:25000 time: 25 +field ' c' [explicit solve]: current solution: 9.635321e+01, current residual:5.819075e+01 +field 'mu' [explicit solve]: current solution: 5.241469e+00, current residual:3.162428e+00 +wall time: 0.000627995s + +time increment:25010 time: 25.01 +field ' c' [explicit solve]: current solution: 9.635300e+01, current residual:5.819061e+01 +field 'mu' [explicit solve]: current solution: 5.241333e+00, current residual:3.162356e+00 +wall time: 0.000560999s + +time increment:25020 time: 25.02 +field ' c' [explicit solve]: current solution: 9.635278e+01, current residual:5.819047e+01 +field 'mu' [explicit solve]: current solution: 5.241197e+00, current residual:3.162284e+00 +wall time: 0.000647068s + +time increment:25030 time: 25.03 +field ' c' [explicit solve]: current solution: 9.635256e+01, current residual:5.819033e+01 +field 'mu' [explicit solve]: current solution: 5.241062e+00, current residual:3.162212e+00 +wall time: 0.000772953s + +time increment:25040 time: 25.04 +field ' c' [explicit solve]: current solution: 9.635235e+01, current residual:5.819019e+01 +field 'mu' [explicit solve]: current solution: 5.240926e+00, current residual:3.162141e+00 +wall time: 0.000740051s + +time increment:25050 time: 25.05 +field ' c' [explicit solve]: current solution: 9.635213e+01, current residual:5.819005e+01 +field 'mu' [explicit solve]: current solution: 5.240791e+00, current residual:3.162069e+00 +wall time: 0.000622034s + +time increment:25060 time: 25.06 +field ' c' [explicit solve]: current solution: 9.635192e+01, current residual:5.818991e+01 +field 'mu' [explicit solve]: current solution: 5.240656e+00, current residual:3.161997e+00 +wall time: 0.000601053s + +time increment:25070 time: 25.07 +field ' c' [explicit solve]: current solution: 9.635170e+01, current residual:5.818977e+01 +field 'mu' [explicit solve]: current solution: 5.240521e+00, current residual:3.161926e+00 +wall time: 0.000609159s + +time increment:25080 time: 25.08 +field ' c' [explicit solve]: current solution: 9.635149e+01, current residual:5.818963e+01 +field 'mu' [explicit solve]: current solution: 5.240386e+00, current residual:3.161854e+00 +wall time: 0.000555038s + +time increment:25090 time: 25.09 +field ' c' [explicit solve]: current solution: 9.635128e+01, current residual:5.818949e+01 +field 'mu' [explicit solve]: current solution: 5.240251e+00, current residual:3.161783e+00 +wall time: 0.000564814s + +time increment:25100 time: 25.1 +field ' c' [explicit solve]: current solution: 9.635106e+01, current residual:5.818935e+01 +field 'mu' [explicit solve]: current solution: 5.240117e+00, current residual:3.161711e+00 +wall time: 0.000676155s + +time increment:25110 time: 25.11 +field ' c' [explicit solve]: current solution: 9.635085e+01, current residual:5.818921e+01 +field 'mu' [explicit solve]: current solution: 5.239982e+00, current residual:3.161640e+00 +wall time: 0.000643969s + +time increment:25120 time: 25.12 +field ' c' [explicit solve]: current solution: 9.635063e+01, current residual:5.818907e+01 +field 'mu' [explicit solve]: current solution: 5.239848e+00, current residual:3.161569e+00 +wall time: 0.000581026s + +time increment:25130 time: 25.13 +field ' c' [explicit solve]: current solution: 9.635042e+01, current residual:5.818893e+01 +field 'mu' [explicit solve]: current solution: 5.239714e+00, current residual:3.161498e+00 +wall time: 0.000641108s + +time increment:25140 time: 25.14 +field ' c' [explicit solve]: current solution: 9.635021e+01, current residual:5.818880e+01 +field 'mu' [explicit solve]: current solution: 5.239579e+00, current residual:3.161427e+00 +wall time: 0.000563145s + +time increment:25150 time: 25.15 +field ' c' [explicit solve]: current solution: 9.634999e+01, current residual:5.818866e+01 +field 'mu' [explicit solve]: current solution: 5.239445e+00, current residual:3.161356e+00 +wall time: 0.00063014s + +time increment:25160 time: 25.16 +field ' c' [explicit solve]: current solution: 9.634978e+01, current residual:5.818852e+01 +field 'mu' [explicit solve]: current solution: 5.239311e+00, current residual:3.161285e+00 +wall time: 0.000583172s + +time increment:25170 time: 25.17 +field ' c' [explicit solve]: current solution: 9.634957e+01, current residual:5.818838e+01 +field 'mu' [explicit solve]: current solution: 5.239178e+00, current residual:3.161214e+00 +wall time: 0.000653028s + +time increment:25180 time: 25.18 +field ' c' [explicit solve]: current solution: 9.634935e+01, current residual:5.818824e+01 +field 'mu' [explicit solve]: current solution: 5.239044e+00, current residual:3.161143e+00 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0.000568867s + +time increment:25230 time: 25.23 +field ' c' [explicit solve]: current solution: 9.634829e+01, current residual:5.818755e+01 +field 'mu' [explicit solve]: current solution: 5.238377e+00, current residual:3.160789e+00 +wall time: 0.000718117s + +time increment:25240 time: 25.24 +field ' c' [explicit solve]: current solution: 9.634808e+01, current residual:5.818741e+01 +field 'mu' [explicit solve]: current solution: 5.238244e+00, current residual:3.160719e+00 +wall time: 0.000583887s + +time increment:25250 time: 25.25 +field ' c' [explicit solve]: current solution: 9.634787e+01, current residual:5.818728e+01 +field 'mu' [explicit solve]: current solution: 5.238111e+00, current residual:3.160648e+00 +wall time: 0.000638962s + +time increment:25260 time: 25.26 +field ' c' [explicit solve]: current solution: 9.634766e+01, current residual:5.818714e+01 +field 'mu' [explicit solve]: current solution: 5.237979e+00, current residual:3.160578e+00 +wall time: 0.00061202s + +time increment:25270 time: 25.27 +field ' c' [explicit solve]: current solution: 9.634745e+01, current residual:5.818700e+01 +field 'mu' [explicit solve]: current solution: 5.237846e+00, current residual:3.160508e+00 +wall time: 0.00057888s + +time increment:25280 time: 25.28 +field ' c' [explicit solve]: current solution: 9.634724e+01, current residual:5.818687e+01 +field 'mu' [explicit solve]: current solution: 5.237714e+00, current residual:3.160437e+00 +wall time: 0.000645876s + +time increment:25290 time: 25.29 +field ' c' [explicit solve]: current solution: 9.634703e+01, current residual:5.818673e+01 +field 'mu' [explicit solve]: current solution: 5.237581e+00, current residual:3.160367e+00 +wall time: 0.000694036s + +time increment:25300 time: 25.3 +field ' c' [explicit solve]: current solution: 9.634681e+01, current residual:5.818659e+01 +field 'mu' [explicit solve]: current solution: 5.237449e+00, current residual:3.160297e+00 +wall time: 0.00067997s + +time increment:25310 time: 25.31 +field ' c' [explicit solve]: current solution: 9.634660e+01, current residual:5.818645e+01 +field 'mu' [explicit solve]: current solution: 5.237317e+00, current residual:3.160227e+00 +wall time: 0.000671864s + +time increment:25320 time: 25.32 +field ' c' [explicit solve]: current solution: 9.634639e+01, current residual:5.818632e+01 +field 'mu' [explicit solve]: current solution: 5.237185e+00, current residual:3.160157e+00 +wall time: 0.000673056s + +time increment:25330 time: 25.33 +field ' c' [explicit solve]: current solution: 9.634618e+01, current residual:5.818618e+01 +field 'mu' [explicit solve]: current solution: 5.237053e+00, current residual:3.160087e+00 +wall time: 0.000608206s + +time increment:25340 time: 25.34 +field ' c' [explicit solve]: current solution: 9.634597e+01, current residual:5.818605e+01 +field 'mu' [explicit solve]: current solution: 5.236921e+00, current residual:3.160017e+00 +wall time: 0.000630856s + +time increment:25350 time: 25.35 +field ' c' [explicit solve]: current solution: 9.634576e+01, current residual:5.818591e+01 +field 'mu' [explicit solve]: current solution: 5.236789e+00, current residual:3.159947e+00 +wall time: 0.000706911s + +time increment:25360 time: 25.36 +field ' c' [explicit solve]: current solution: 9.634556e+01, current residual:5.818577e+01 +field 'mu' [explicit solve]: current solution: 5.236657e+00, current residual:3.159877e+00 +wall time: 0.000658035s + +time increment:25370 time: 25.37 +field ' c' [explicit solve]: current solution: 9.634535e+01, current residual:5.818564e+01 +field 'mu' [explicit solve]: current solution: 5.236526e+00, current residual:3.159807e+00 +wall time: 0.000592947s + +time increment:25380 time: 25.38 +field ' c' [explicit solve]: current solution: 9.634514e+01, current residual:5.818550e+01 +field 'mu' [explicit solve]: current solution: 5.236395e+00, current residual:3.159738e+00 +wall time: 0.000622034s + +time increment:25390 time: 25.39 +field ' c' [explicit solve]: current solution: 9.634493e+01, current residual:5.818536e+01 +field 'mu' [explicit solve]: current solution: 5.236263e+00, current residual:3.159668e+00 +wall time: 0.000677109s + +time increment:25400 time: 25.4 +field ' c' [explicit solve]: current solution: 9.634472e+01, current residual:5.818523e+01 +field 'mu' [explicit solve]: current solution: 5.236132e+00, current residual:3.159598e+00 +wall time: 0.000648022s + +time increment:25410 time: 25.41 +field ' c' [explicit solve]: current solution: 9.634451e+01, current residual:5.818509e+01 +field 'mu' [explicit solve]: current solution: 5.236001e+00, current residual:3.159529e+00 +wall time: 0.00071907s + +time increment:25420 time: 25.42 +field ' c' [explicit solve]: current solution: 9.634430e+01, current residual:5.818496e+01 +field 'mu' [explicit solve]: current solution: 5.235870e+00, current residual:3.159459e+00 +wall time: 0.00058794s + +time increment:25430 time: 25.43 +field ' c' [explicit solve]: current solution: 9.634409e+01, current residual:5.818482e+01 +field 'mu' [explicit solve]: current solution: 5.235739e+00, current residual:3.159390e+00 +wall time: 0.000647068s + +time increment:25440 time: 25.44 +field ' c' [explicit solve]: current solution: 9.634389e+01, current residual:5.818469e+01 +field 'mu' [explicit solve]: current solution: 5.235608e+00, current residual:3.159320e+00 +wall time: 0.000693083s + +time increment:25450 time: 25.45 +field ' c' [explicit solve]: current solution: 9.634368e+01, current residual:5.818455e+01 +field 'mu' [explicit solve]: current solution: 5.235478e+00, current residual:3.159251e+00 +wall time: 0.000622988s + +time increment:25460 time: 25.46 +field ' c' [explicit solve]: current solution: 9.634347e+01, current residual:5.818442e+01 +field 'mu' [explicit solve]: current solution: 5.235347e+00, current residual:3.159182e+00 +wall time: 0.000689983s + +time increment:25470 time: 25.47 +field ' c' [explicit solve]: current solution: 9.634326e+01, 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residual:5.818374e+01 +field 'mu' [explicit solve]: current solution: 5.234696e+00, current residual:3.158836e+00 +wall time: 0.000678062s + +time increment:25520 time: 25.52 +field ' c' [explicit solve]: current solution: 9.634223e+01, current residual:5.818361e+01 +field 'mu' [explicit solve]: current solution: 5.234566e+00, current residual:3.158767e+00 +wall time: 0.000671864s + +time increment:25530 time: 25.53 +field ' c' [explicit solve]: current solution: 9.634202e+01, current residual:5.818347e+01 +field 'mu' [explicit solve]: current solution: 5.234436e+00, current residual:3.158698e+00 +wall time: 0.000746012s + +time increment:25540 time: 25.54 +field ' c' [explicit solve]: current solution: 9.634181e+01, current residual:5.818334e+01 +field 'mu' [explicit solve]: current solution: 5.234306e+00, current residual:3.158629e+00 +wall time: 0.000869989s + +time increment:25550 time: 25.55 +field ' c' [explicit solve]: current solution: 9.634161e+01, current residual:5.818321e+01 +field 'mu' [explicit solve]: current solution: 5.234177e+00, current residual:3.158560e+00 +wall time: 0.000655174s + +time increment:25560 time: 25.56 +field ' c' [explicit solve]: current solution: 9.634140e+01, current residual:5.818307e+01 +field 'mu' [explicit solve]: current solution: 5.234047e+00, current residual:3.158491e+00 +wall time: 0.000692844s + +time increment:25570 time: 25.57 +field ' c' [explicit solve]: current solution: 9.634120e+01, current residual:5.818294e+01 +field 'mu' [explicit solve]: current solution: 5.233918e+00, current residual:3.158422e+00 +wall time: 0.000665903s + +time increment:25580 time: 25.58 +field ' c' [explicit solve]: current solution: 9.634099e+01, current residual:5.818280e+01 +field 'mu' [explicit solve]: current solution: 5.233788e+00, current residual:3.158353e+00 +wall time: 0.000663996s + +time increment:25590 time: 25.59 +field ' c' [explicit solve]: current solution: 9.634079e+01, current residual:5.818267e+01 +field 'mu' [explicit solve]: current solution: 5.233659e+00, current residual:3.158285e+00 +wall time: 0.00070405s + +time increment:25600 time: 25.6 +field ' c' [explicit solve]: current solution: 9.634058e+01, current residual:5.818254e+01 +field 'mu' [explicit solve]: current solution: 5.233530e+00, current residual:3.158216e+00 +wall time: 0.000706196s + +time increment:25610 time: 25.61 +field ' c' [explicit solve]: current solution: 9.634037e+01, current residual:5.818240e+01 +field 'mu' [explicit solve]: current solution: 5.233401e+00, current residual:3.158148e+00 +wall time: 0.000658035s + +time increment:25620 time: 25.62 +field ' c' [explicit solve]: current solution: 9.634017e+01, current residual:5.818227e+01 +field 'mu' [explicit solve]: current solution: 5.233272e+00, current residual:3.158079e+00 +wall time: 0.000675917s + +time increment:25630 time: 25.63 +field ' c' [explicit solve]: current solution: 9.633997e+01, current residual:5.818214e+01 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solve]: current solution: 5.232628e+00, current residual:3.157737e+00 +wall time: 0.000670195s + +time increment:25680 time: 25.68 +field ' c' [explicit solve]: current solution: 9.633894e+01, current residual:5.818147e+01 +field 'mu' [explicit solve]: current solution: 5.232500e+00, current residual:3.157669e+00 +wall time: 0.00067997s + +time increment:25690 time: 25.69 +field ' c' [explicit solve]: current solution: 9.633874e+01, current residual:5.818134e+01 +field 'mu' [explicit solve]: current solution: 5.232372e+00, current residual:3.157600e+00 +wall time: 0.000674963s + +time increment:25700 time: 25.7 +field ' c' [explicit solve]: current solution: 9.633854e+01, current residual:5.818121e+01 +field 'mu' [explicit solve]: current solution: 5.232244e+00, current residual:3.157532e+00 +wall time: 0.000724077s + +time increment:25710 time: 25.71 +field ' c' [explicit solve]: current solution: 9.633833e+01, current residual:5.818108e+01 +field 'mu' [explicit solve]: current solution: 5.232115e+00, current residual:3.157464e+00 +wall time: 0.000702143s + +time increment:25720 time: 25.72 +field ' c' [explicit solve]: current solution: 9.633813e+01, current residual:5.818094e+01 +field 'mu' [explicit solve]: current solution: 5.231987e+00, current residual:3.157396e+00 +wall time: 0.000628948s + +time increment:25730 time: 25.73 +field ' c' [explicit solve]: current solution: 9.633793e+01, current residual:5.818081e+01 +field 'mu' [explicit solve]: current solution: 5.231860e+00, current residual:3.157328e+00 +wall time: 0.000671148s + +time increment:25740 time: 25.74 +field ' c' [explicit solve]: current solution: 9.633772e+01, current residual:5.818068e+01 +field 'mu' [explicit solve]: current solution: 5.231732e+00, current residual:3.157260e+00 +wall time: 0.000668049s + +time increment:25750 time: 25.75 +field ' c' [explicit solve]: current solution: 9.633752e+01, current residual:5.818055e+01 +field 'mu' [explicit solve]: current solution: 5.231604e+00, current residual:3.157192e+00 +wall time: 0.00069809s + +time increment:25760 time: 25.76 +field ' c' [explicit solve]: current solution: 9.633732e+01, current residual:5.818042e+01 +field 'mu' [explicit solve]: current solution: 5.231476e+00, current residual:3.157124e+00 +wall time: 0.000663996s + +time increment:25770 time: 25.77 +field ' c' [explicit solve]: current solution: 9.633712e+01, current residual:5.818028e+01 +field 'mu' [explicit solve]: current solution: 5.231349e+00, current residual:3.157056e+00 +wall time: 0.000649929s + +time increment:25780 time: 25.78 +field ' c' [explicit solve]: current solution: 9.633692e+01, current residual:5.818015e+01 +field 'mu' [explicit solve]: current solution: 5.231221e+00, current residual:3.156988e+00 +wall time: 0.000683069s + +time increment:25790 time: 25.79 +field ' c' [explicit solve]: current solution: 9.633671e+01, current residual:5.818002e+01 +field 'mu' [explicit solve]: current solution: 5.231094e+00, current residual:3.156920e+00 +wall time: 0.000671148s + +time increment:25800 time: 25.8 +field ' c' [explicit solve]: current solution: 9.633651e+01, current residual:5.817989e+01 +field 'mu' [explicit solve]: current solution: 5.230967e+00, current residual:3.156853e+00 +wall time: 0.000566006s + +time increment:25810 time: 25.81 +field ' c' [explicit solve]: current solution: 9.633631e+01, current residual:5.817976e+01 +field 'mu' [explicit solve]: current solution: 5.230839e+00, current residual:3.156785e+00 +wall time: 0.00067091s + +time increment:25820 time: 25.82 +field ' c' [explicit solve]: current solution: 9.633611e+01, current residual:5.817963e+01 +field 'mu' [explicit solve]: current solution: 5.230712e+00, current residual:3.156717e+00 +wall time: 0.000651121s + +time increment:25830 time: 25.83 +field ' c' [explicit solve]: current solution: 9.633591e+01, current residual:5.817950e+01 +field 'mu' [explicit solve]: current solution: 5.230585e+00, current residual:3.156650e+00 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0.000565052s + +time increment:25880 time: 25.88 +field ' c' [explicit solve]: current solution: 9.633490e+01, current residual:5.817884e+01 +field 'mu' [explicit solve]: current solution: 5.229951e+00, current residual:3.156312e+00 +wall time: 0.000715971s + +time increment:25890 time: 25.89 +field ' c' [explicit solve]: current solution: 9.633470e+01, current residual:5.817871e+01 +field 'mu' [explicit solve]: current solution: 5.229825e+00, current residual:3.156245e+00 +wall time: 0.000618935s + +time increment:25900 time: 25.9 +field ' c' [explicit solve]: current solution: 9.633450e+01, current residual:5.817858e+01 +field 'mu' [explicit solve]: current solution: 5.229698e+00, current residual:3.156177e+00 +wall time: 0.000664949s + +time increment:25910 time: 25.91 +field ' c' [explicit solve]: current solution: 9.633430e+01, current residual:5.817845e+01 +field 'mu' [explicit solve]: current solution: 5.229572e+00, current residual:3.156110e+00 +wall time: 0.000572205s + +time increment:25920 time: 25.92 +field ' c' [explicit solve]: current solution: 9.633410e+01, current residual:5.817832e+01 +field 'mu' [explicit solve]: current solution: 5.229446e+00, current residual:3.156043e+00 +wall time: 0.000668049s + +time increment:25930 time: 25.93 +field ' c' [explicit solve]: current solution: 9.633390e+01, current residual:5.817819e+01 +field 'mu' [explicit solve]: current solution: 5.229319e+00, current residual:3.155976e+00 +wall time: 0.000673056s + +time increment:25940 time: 25.94 +field ' c' [explicit solve]: current solution: 9.633370e+01, current residual:5.817806e+01 +field 'mu' [explicit solve]: current solution: 5.229193e+00, current residual:3.155908e+00 +wall time: 0.000566959s + +time increment:25950 time: 25.95 +field ' c' [explicit solve]: current solution: 9.633351e+01, current residual:5.817793e+01 +field 'mu' [explicit solve]: current solution: 5.229067e+00, current residual:3.155841e+00 +wall time: 0.000564098s + +time increment:25960 time: 25.96 +field ' c' [explicit solve]: current solution: 9.633331e+01, current residual:5.817780e+01 +field 'mu' [explicit solve]: current solution: 5.228941e+00, current residual:3.155774e+00 +wall time: 0.000609159s + +time increment:25970 time: 25.97 +field ' c' [explicit solve]: current solution: 9.633311e+01, current residual:5.817767e+01 +field 'mu' [explicit solve]: current solution: 5.228816e+00, current residual:3.155707e+00 +wall time: 0.000575066s + +time increment:25980 time: 25.98 +field ' c' [explicit solve]: current solution: 9.633291e+01, current residual:5.817754e+01 +field 'mu' [explicit solve]: current solution: 5.228690e+00, current residual:3.155640e+00 +wall time: 0.000589848s + +time increment:25990 time: 25.99 +field ' c' [explicit solve]: current solution: 9.633271e+01, current residual:5.817742e+01 +field 'mu' [explicit solve]: current solution: 5.228564e+00, current residual:3.155573e+00 +wall time: 0.000571012s + +time increment:26000 time: 26 +field ' c' [explicit solve]: current solution: 9.633251e+01, current residual:5.817729e+01 +field 'mu' [explicit solve]: current solution: 5.228438e+00, current residual:3.155506e+00 +wall time: 0.00058794s + +time increment:26010 time: 26.01 +field ' c' [explicit solve]: current solution: 9.633231e+01, current residual:5.817716e+01 +field 'mu' [explicit solve]: current solution: 5.228313e+00, current residual:3.155439e+00 +wall time: 0.000628948s + +time increment:26020 time: 26.02 +field ' c' [explicit solve]: current solution: 9.633212e+01, current residual:5.817703e+01 +field 'mu' [explicit solve]: current solution: 5.228187e+00, current residual:3.155372e+00 +wall time: 0.000663042s + +time increment:26030 time: 26.03 +field ' c' [explicit solve]: current solution: 9.633192e+01, current residual:5.817690e+01 +field 'mu' [explicit solve]: current solution: 5.228062e+00, current residual:3.155305e+00 +wall time: 0.000661135s + +time increment:26040 time: 26.04 +field ' c' [explicit solve]: current solution: 9.633172e+01, current residual:5.817677e+01 +field 'mu' [explicit solve]: current solution: 5.227937e+00, current residual:3.155238e+00 +wall time: 0.000692129s + +time increment:26050 time: 26.05 +field ' c' [explicit solve]: current solution: 9.633152e+01, current residual:5.817664e+01 +field 'mu' [explicit solve]: current solution: 5.227811e+00, current residual:3.155171e+00 +wall time: 0.000735044s + +time increment:26060 time: 26.06 +field ' c' [explicit solve]: current solution: 9.633133e+01, current residual:5.817651e+01 +field 'mu' [explicit solve]: current solution: 5.227686e+00, current residual:3.155105e+00 +wall time: 0.000640154s + +time increment:26070 time: 26.07 +field ' c' [explicit solve]: current solution: 9.633113e+01, current residual:5.817639e+01 +field 'mu' [explicit solve]: current solution: 5.227561e+00, current residual:3.155038e+00 +wall time: 0.000570059s + +time increment:26080 time: 26.08 +field ' c' [explicit solve]: current solution: 9.633093e+01, current residual:5.817626e+01 +field 'mu' [explicit solve]: current solution: 5.227436e+00, current residual:3.154971e+00 +wall time: 0.000652075s + +time increment:26090 time: 26.09 +field ' c' [explicit solve]: current solution: 9.633073e+01, current residual:5.817613e+01 +field 'mu' [explicit solve]: current solution: 5.227311e+00, current residual:3.154905e+00 +wall time: 0.000554085s + +time increment:26100 time: 26.1 +field ' c' [explicit solve]: current solution: 9.633054e+01, current residual:5.817600e+01 +field 'mu' [explicit solve]: current solution: 5.227186e+00, current residual:3.154838e+00 +wall time: 0.000560045s + +time increment:26110 time: 26.11 +field ' c' [explicit solve]: current solution: 9.633034e+01, current residual:5.817587e+01 +field 'mu' [explicit solve]: current solution: 5.227062e+00, current residual:3.154771e+00 +wall time: 0.000619888s + +time increment:26120 time: 26.12 +field ' c' [explicit solve]: current solution: 9.633015e+01, current residual:5.817574e+01 +field 'mu' [explicit solve]: current solution: 5.226937e+00, current residual:3.154705e+00 +wall time: 0.000677109s + +time increment:26130 time: 26.13 +field ' c' [explicit solve]: current solution: 9.632995e+01, current residual:5.817562e+01 +field 'mu' [explicit solve]: current solution: 5.226812e+00, current residual:3.154638e+00 +wall time: 0.000575066s + +time increment:26140 time: 26.14 +field ' c' [explicit solve]: current solution: 9.632975e+01, current residual:5.817549e+01 +field 'mu' [explicit solve]: current solution: 5.226688e+00, current residual:3.154572e+00 +wall time: 0.000581026s + +time increment:26150 time: 26.15 +field ' c' [explicit solve]: current solution: 9.632956e+01, current residual:5.817536e+01 +field 'mu' [explicit solve]: current solution: 5.226563e+00, current residual:3.154505e+00 +wall time: 0.000651121s + +time increment:26160 time: 26.16 +field ' c' [explicit solve]: current solution: 9.632936e+01, current residual:5.817523e+01 +field 'mu' [explicit solve]: current solution: 5.226439e+00, current residual:3.154439e+00 +wall time: 0.00057292s + +time increment:26170 time: 26.17 +field ' c' [explicit solve]: current solution: 9.632917e+01, current residual:5.817511e+01 +field 'mu' [explicit solve]: current solution: 5.226314e+00, current residual:3.154372e+00 +wall time: 0.000566959s + +time increment:26180 time: 26.18 +field ' c' [explicit solve]: current solution: 9.632897e+01, current residual:5.817498e+01 +field 'mu' [explicit solve]: current solution: 5.226190e+00, current residual:3.154306e+00 +wall time: 0.000618935s + +time increment:26190 time: 26.19 +field ' c' [explicit solve]: current solution: 9.632878e+01, current residual:5.817485e+01 +field 'mu' [explicit solve]: current solution: 5.226066e+00, current residual:3.154240e+00 +wall time: 0.000756025s + +time increment:26200 time: 26.2 +field ' c' [explicit solve]: current solution: 9.632858e+01, current residual:5.817473e+01 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[explicit solve]: current solution: 5.225446e+00, current residual:3.153909e+00 +wall time: 0.000563145s + +time increment:26250 time: 26.25 +field ' c' [explicit solve]: current solution: 9.632761e+01, current residual:5.817409e+01 +field 'mu' [explicit solve]: current solution: 5.225322e+00, current residual:3.153842e+00 +wall time: 0.000622988s + +time increment:26260 time: 26.26 +field ' c' [explicit solve]: current solution: 9.632741e+01, current residual:5.817397e+01 +field 'mu' [explicit solve]: current solution: 5.225199e+00, current residual:3.153776e+00 +wall time: 0.000619888s + +time increment:26270 time: 26.27 +field ' c' [explicit solve]: current solution: 9.632722e+01, current residual:5.817384e+01 +field 'mu' [explicit solve]: current solution: 5.225075e+00, current residual:3.153710e+00 +wall time: 0.000663996s + +time increment:26280 time: 26.28 +field ' c' [explicit solve]: current solution: 9.632703e+01, current residual:5.817371e+01 +field 'mu' [explicit solve]: current solution: 5.224951e+00, current residual:3.153644e+00 +wall time: 0.000622988s + +time increment:26290 time: 26.29 +field ' c' [explicit solve]: current solution: 9.632683e+01, current residual:5.817359e+01 +field 'mu' [explicit solve]: current solution: 5.224828e+00, current residual:3.153578e+00 +wall time: 0.000664949s + +time increment:26300 time: 26.3 +field ' c' [explicit solve]: current solution: 9.632664e+01, current residual:5.817346e+01 +field 'mu' [explicit solve]: current solution: 5.224704e+00, current residual:3.153512e+00 +wall time: 0.000561953s + +time increment:26310 time: 26.31 +field ' c' [explicit solve]: current solution: 9.632645e+01, current residual:5.817334e+01 +field 'mu' [explicit solve]: current solution: 5.224581e+00, current residual:3.153446e+00 +wall time: 0.000571966s + +time increment:26320 time: 26.32 +field ' c' [explicit solve]: current solution: 9.632625e+01, current residual:5.817321e+01 +field 'mu' [explicit solve]: current solution: 5.224458e+00, current residual:3.153380e+00 +wall time: 0.000560045s + +time increment:26330 time: 26.33 +field ' c' [explicit solve]: current solution: 9.632606e+01, current residual:5.817308e+01 +field 'mu' [explicit solve]: current solution: 5.224335e+00, current residual:3.153314e+00 +wall time: 0.000642061s + +time increment:26340 time: 26.34 +field ' c' [explicit solve]: current solution: 9.632587e+01, current residual:5.817296e+01 +field 'mu' [explicit solve]: current solution: 5.224211e+00, current residual:3.153248e+00 +wall time: 0.00056982s + +time increment:26350 time: 26.35 +field ' c' [explicit solve]: current solution: 9.632568e+01, current residual:5.817283e+01 +field 'mu' [explicit solve]: current solution: 5.224088e+00, current residual:3.153183e+00 +wall time: 0.00069499s + +time increment:26360 time: 26.36 +field ' c' [explicit solve]: current solution: 9.632548e+01, current residual:5.817271e+01 +field 'mu' [explicit solve]: current solution: 5.223965e+00, current residual:3.153117e+00 +wall time: 0.000647068s + +time increment:26370 time: 26.37 +field ' c' [explicit solve]: current solution: 9.632529e+01, current residual:5.817258e+01 +field 'mu' [explicit solve]: current solution: 5.223842e+00, current residual:3.153051e+00 +wall time: 0.000562906s + +time increment:26380 time: 26.38 +field ' c' [explicit solve]: current solution: 9.632510e+01, current residual:5.817246e+01 +field 'mu' [explicit solve]: current solution: 5.223719e+00, current residual:3.152985e+00 +wall time: 0.000558138s + +time increment:26390 time: 26.39 +field ' c' [explicit solve]: current solution: 9.632491e+01, current residual:5.817233e+01 +field 'mu' [explicit solve]: current solution: 5.223596e+00, current residual:3.152919e+00 +wall time: 0.000628948s + +time increment:26400 time: 26.4 +field ' c' [explicit solve]: current solution: 9.632472e+01, current residual:5.817221e+01 +field 'mu' [explicit solve]: current solution: 5.223474e+00, current residual:3.152854e+00 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increment:26490 time: 26.49 +field ' c' [explicit solve]: current solution: 9.632300e+01, current residual:5.817109e+01 +field 'mu' [explicit solve]: current solution: 5.222371e+00, current residual:3.152263e+00 +wall time: 0.000658035s + +time increment:26500 time: 26.5 +field ' c' [explicit solve]: current solution: 9.632281e+01, current residual:5.817097e+01 +field 'mu' [explicit solve]: current solution: 5.222249e+00, current residual:3.152198e+00 +wall time: 0.000562906s + +time increment:26510 time: 26.51 +field ' c' [explicit solve]: current solution: 9.632262e+01, current residual:5.817084e+01 +field 'mu' [explicit solve]: current solution: 5.222127e+00, current residual:3.152133e+00 +wall time: 0.000613928s + +time increment:26520 time: 26.52 +field ' c' [explicit solve]: current solution: 9.632243e+01, current residual:5.817072e+01 +field 'mu' [explicit solve]: current solution: 5.222005e+00, current residual:3.152067e+00 +wall time: 0.000610828s + +time increment:26530 time: 26.53 +field ' c' [explicit solve]: current solution: 9.632224e+01, current residual:5.817059e+01 +field 'mu' [explicit solve]: current solution: 5.221883e+00, current residual:3.152002e+00 +wall time: 0.000641108s + +time increment:26540 time: 26.54 +field ' c' [explicit solve]: current solution: 9.632205e+01, current residual:5.817047e+01 +field 'mu' [explicit solve]: current solution: 5.221761e+00, current residual:3.151936e+00 +wall time: 0.000627995s + +time increment:26550 time: 26.55 +field ' c' [explicit solve]: current solution: 9.632186e+01, current residual:5.817035e+01 +field 'mu' [explicit solve]: current solution: 5.221639e+00, current residual:3.151871e+00 +wall time: 0.000715017s + +time increment:26560 time: 26.56 +field ' c' [explicit solve]: current solution: 9.632167e+01, current residual:5.817022e+01 +field 'mu' [explicit solve]: current solution: 5.221517e+00, current residual:3.151806e+00 +wall time: 0.000730991s + +time increment:26570 time: 26.57 +field ' c' [explicit solve]: current solution: 9.632148e+01, current residual:5.817010e+01 +field 'mu' [explicit solve]: current solution: 5.221396e+00, current residual:3.151740e+00 +wall time: 0.000650167s + +time increment:26580 time: 26.58 +field ' c' [explicit solve]: current solution: 9.632130e+01, current residual:5.816998e+01 +field 'mu' [explicit solve]: current solution: 5.221274e+00, current residual:3.151675e+00 +wall time: 0.000570059s + +time increment:26590 time: 26.59 +field ' c' [explicit solve]: current solution: 9.632111e+01, current residual:5.816986e+01 +field 'mu' [explicit solve]: current solution: 5.221152e+00, current residual:3.151610e+00 +wall time: 0.000658989s + +time increment:26600 time: 26.6 +field ' c' [explicit solve]: current solution: 9.632092e+01, current residual:5.816973e+01 +field 'mu' [explicit solve]: current solution: 5.221031e+00, current residual:3.151545e+00 +wall time: 0.000569105s + +time increment:26610 time: 26.61 +field ' c' [explicit solve]: current solution: 9.632073e+01, current residual:5.816961e+01 +field 'mu' [explicit solve]: current solution: 5.220909e+00, current residual:3.151480e+00 +wall time: 0.000688076s + +time increment:26620 time: 26.62 +field ' c' [explicit solve]: current solution: 9.632054e+01, current residual:5.816949e+01 +field 'mu' [explicit solve]: current solution: 5.220788e+00, current residual:3.151414e+00 +wall time: 0.000663996s + +time increment:26630 time: 26.63 +field ' c' [explicit solve]: current solution: 9.632036e+01, current residual:5.816936e+01 +field 'mu' [explicit solve]: current solution: 5.220666e+00, current residual:3.151349e+00 +wall time: 0.000588894s + +time increment:26640 time: 26.64 +field ' c' [explicit solve]: current solution: 9.632017e+01, current residual:5.816924e+01 +field 'mu' [explicit solve]: current solution: 5.220545e+00, current residual:3.151284e+00 +wall time: 0.000654936s + +time increment:26650 time: 26.65 +field ' c' [explicit solve]: current solution: 9.631998e+01, current residual:5.816912e+01 +field 'mu' [explicit solve]: current solution: 5.220424e+00, current residual:3.151219e+00 +wall time: 0.000622988s + +time increment:26660 time: 26.66 +field ' c' [explicit solve]: current solution: 9.631979e+01, current residual:5.816900e+01 +field 'mu' [explicit solve]: current solution: 5.220302e+00, current residual:3.151154e+00 +wall time: 0.00067687s + +time increment:26670 time: 26.67 +field ' c' [explicit solve]: current solution: 9.631961e+01, current residual:5.816888e+01 +field 'mu' [explicit solve]: current solution: 5.220181e+00, current residual:3.151089e+00 +wall time: 0.000749111s + +time increment:26680 time: 26.68 +field ' c' [explicit solve]: current solution: 9.631942e+01, current residual:5.816875e+01 +field 'mu' [explicit solve]: current solution: 5.220060e+00, current residual:3.151024e+00 +wall time: 0.000599146s + +time increment:26690 time: 26.69 +field ' c' [explicit solve]: current solution: 9.631923e+01, current residual:5.816863e+01 +field 'mu' [explicit solve]: current solution: 5.219939e+00, current residual:3.150959e+00 +wall time: 0.000644922s + +time increment:26700 time: 26.7 +field ' c' [explicit solve]: current solution: 9.631904e+01, current residual:5.816851e+01 +field 'mu' [explicit solve]: current solution: 5.219818e+00, current residual:3.150894e+00 +wall time: 0.000644922s + +time increment:26710 time: 26.71 +field ' c' [explicit solve]: current solution: 9.631886e+01, current residual:5.816839e+01 +field 'mu' [explicit solve]: current solution: 5.219697e+00, current residual:3.150829e+00 +wall time: 0.00063014s + +time increment:26720 time: 26.72 +field ' c' [explicit solve]: current solution: 9.631867e+01, current residual:5.816827e+01 +field 'mu' [explicit solve]: current solution: 5.219576e+00, current residual:3.150764e+00 +wall time: 0.000688791s + +time increment:26730 time: 26.73 +field ' c' [explicit solve]: current solution: 9.631849e+01, current residual:5.816815e+01 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solve]: current solution: 5.218972e+00, current residual:3.150439e+00 +wall time: 0.000658989s + +time increment:26780 time: 26.78 +field ' c' [explicit solve]: current solution: 9.631756e+01, current residual:5.816754e+01 +field 'mu' [explicit solve]: current solution: 5.218851e+00, current residual:3.150374e+00 +wall time: 0.000664949s + +time increment:26790 time: 26.79 +field ' c' [explicit solve]: current solution: 9.631737e+01, current residual:5.816742e+01 +field 'mu' [explicit solve]: current solution: 5.218731e+00, current residual:3.150310e+00 +wall time: 0.0006392s + +time increment:26800 time: 26.8 +field ' c' [explicit solve]: current solution: 9.631719e+01, current residual:5.816730e+01 +field 'mu' [explicit solve]: current solution: 5.218610e+00, current residual:3.150245e+00 +wall time: 0.000689983s + +time increment:26810 time: 26.81 +field ' c' [explicit solve]: current solution: 9.631700e+01, current residual:5.816718e+01 +field 'mu' [explicit solve]: current solution: 5.218490e+00, current residual:3.150180e+00 +wall time: 0.000761032s + +time increment:26820 time: 26.82 +field ' c' [explicit solve]: current solution: 9.631682e+01, current residual:5.816706e+01 +field 'mu' [explicit solve]: current solution: 5.218369e+00, current residual:3.150115e+00 +wall time: 0.000735998s + +time increment:26830 time: 26.83 +field ' c' [explicit solve]: current solution: 9.631663e+01, current residual:5.816694e+01 +field 'mu' [explicit solve]: current solution: 5.218249e+00, current residual:3.150051e+00 +wall time: 0.000697851s + +time increment:26840 time: 26.84 +field ' c' [explicit solve]: current solution: 9.631645e+01, current residual:5.816682e+01 +field 'mu' [explicit solve]: current solution: 5.218128e+00, current residual:3.149986e+00 +wall time: 0.000672102s + +time increment:26850 time: 26.85 +field ' c' [explicit solve]: current solution: 9.631626e+01, current residual:5.816670e+01 +field 'mu' [explicit solve]: current solution: 5.218008e+00, current residual:3.149921e+00 +wall time: 0.000693083s + +time increment:26860 time: 26.86 +field ' c' [explicit solve]: current solution: 9.631608e+01, current residual:5.816658e+01 +field 'mu' [explicit solve]: current solution: 5.217888e+00, current residual:3.149856e+00 +wall time: 0.000788927s + +time increment:26870 time: 26.87 +field ' c' [explicit solve]: current solution: 9.631589e+01, current residual:5.816646e+01 +field 'mu' [explicit solve]: current solution: 5.217768e+00, current residual:3.149792e+00 +wall time: 0.000646114s + +time increment:26880 time: 26.88 +field ' c' [explicit solve]: current solution: 9.631571e+01, current residual:5.816634e+01 +field 'mu' [explicit solve]: current solution: 5.217647e+00, current residual:3.149727e+00 +wall time: 0.000718117s + +time increment:26890 time: 26.89 +field ' c' [explicit solve]: current solution: 9.631553e+01, current residual:5.816622e+01 +field 'mu' [explicit solve]: current solution: 5.217527e+00, current residual:3.149662e+00 +wall time: 0.00056386s + +time increment:26900 time: 26.9 +field ' c' [explicit solve]: current solution: 9.631534e+01, current residual:5.816610e+01 +field 'mu' [explicit solve]: current solution: 5.217407e+00, current residual:3.149598e+00 +wall time: 0.000689983s + +time increment:26910 time: 26.91 +field ' c' [explicit solve]: current solution: 9.631516e+01, current residual:5.816598e+01 +field 'mu' [explicit solve]: current solution: 5.217287e+00, current residual:3.149533e+00 +wall time: 0.00067997s + +time increment:26920 time: 26.92 +field ' c' [explicit solve]: current solution: 9.631498e+01, current residual:5.816586e+01 +field 'mu' [explicit solve]: current solution: 5.217167e+00, current residual:3.149469e+00 +wall time: 0.000607014s + +time increment:26930 time: 26.93 +field ' c' [explicit solve]: current solution: 9.631479e+01, current residual:5.816574e+01 +field 'mu' [explicit solve]: current solution: 5.217047e+00, current residual:3.149404e+00 +wall time: 0.000658989s + +time increment:26940 time: 26.94 +field ' c' [explicit solve]: current solution: 9.631461e+01, current residual:5.816562e+01 +field 'mu' [explicit solve]: current solution: 5.216927e+00, current residual:3.149340e+00 +wall time: 0.000694036s + +time increment:26950 time: 26.95 +field ' c' [explicit solve]: current solution: 9.631443e+01, current residual:5.816550e+01 +field 'mu' [explicit solve]: current solution: 5.216808e+00, current residual:3.149275e+00 +wall time: 0.000640869s + +time increment:26960 time: 26.96 +field ' c' [explicit solve]: current solution: 9.631425e+01, current residual:5.816538e+01 +field 'mu' [explicit solve]: current solution: 5.216688e+00, current residual:3.149210e+00 +wall time: 0.000658035s + +time increment:26970 time: 26.97 +field ' c' [explicit solve]: current solution: 9.631406e+01, current residual:5.816526e+01 +field 'mu' [explicit solve]: current solution: 5.216568e+00, current residual:3.149146e+00 +wall time: 0.000665188s + +time increment:26980 time: 26.98 +field ' c' [explicit solve]: current solution: 9.631388e+01, current residual:5.816514e+01 +field 'mu' [explicit solve]: current solution: 5.216448e+00, current residual:3.149081e+00 +wall time: 0.000628948s + +time increment:26990 time: 26.99 +field ' c' [explicit solve]: current solution: 9.631370e+01, current residual:5.816502e+01 +field 'mu' [explicit solve]: current solution: 5.216329e+00, current residual:3.149017e+00 +wall time: 0.000642061s + +time increment:27000 time: 27 +field ' c' [explicit solve]: current solution: 9.631352e+01, current residual:5.816490e+01 +field 'mu' [explicit solve]: current solution: 5.216209e+00, current residual:3.148953e+00 +wall time: 0.000718117s + +time increment:27010 time: 27.01 +field ' c' [explicit solve]: current solution: 9.631334e+01, current residual:5.816479e+01 +field 'mu' [explicit solve]: current solution: 5.216089e+00, current residual:3.148888e+00 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0.000565052s + +time increment:27060 time: 27.06 +field ' c' [explicit solve]: current solution: 9.631243e+01, current residual:5.816419e+01 +field 'mu' [explicit solve]: current solution: 5.215492e+00, current residual:3.148566e+00 +wall time: 0.000691175s + +time increment:27070 time: 27.07 +field ' c' [explicit solve]: current solution: 9.631225e+01, current residual:5.816408e+01 +field 'mu' [explicit solve]: current solution: 5.215373e+00, current residual:3.148502e+00 +wall time: 0.000658035s + +time increment:27080 time: 27.08 +field ' c' [explicit solve]: current solution: 9.631207e+01, current residual:5.816396e+01 +field 'mu' [explicit solve]: current solution: 5.215253e+00, current residual:3.148437e+00 +wall time: 0.000597954s + +time increment:27090 time: 27.09 +field ' c' [explicit solve]: current solution: 9.631189e+01, current residual:5.816384e+01 +field 'mu' [explicit solve]: current solution: 5.215134e+00, current residual:3.148373e+00 +wall time: 0.000571012s + +time increment:27100 time: 27.1 +field ' c' [explicit solve]: current solution: 9.631171e+01, current residual:5.816372e+01 +field 'mu' [explicit solve]: current solution: 5.215015e+00, current residual:3.148309e+00 +wall time: 0.00056386s + +time increment:27110 time: 27.11 +field ' c' [explicit solve]: current solution: 9.631153e+01, current residual:5.816360e+01 +field 'mu' [explicit solve]: current solution: 5.214896e+00, current residual:3.148244e+00 +wall time: 0.000557899s + +time increment:27120 time: 27.12 +field ' c' [explicit solve]: current solution: 9.631135e+01, current residual:5.816349e+01 +field 'mu' [explicit solve]: current solution: 5.214777e+00, current residual:3.148180e+00 +wall time: 0.000649929s + +time increment:27130 time: 27.13 +field ' c' [explicit solve]: current solution: 9.631117e+01, current residual:5.816337e+01 +field 'mu' [explicit solve]: current solution: 5.214658e+00, current residual:3.148116e+00 +wall time: 0.000564098s + +time increment:27140 time: 27.14 +field ' c' [explicit solve]: current solution: 9.631099e+01, current residual:5.816325e+01 +field 'mu' [explicit solve]: current solution: 5.214539e+00, current residual:3.148052e+00 +wall time: 0.000669003s + +time increment:27150 time: 27.15 +field ' c' [explicit solve]: current solution: 9.631081e+01, current residual:5.816313e+01 +field 'mu' [explicit solve]: current solution: 5.214420e+00, current residual:3.147987e+00 +wall time: 0.00058198s + +time increment:27160 time: 27.16 +field ' c' [explicit solve]: current solution: 9.631063e+01, current residual:5.816302e+01 +field 'mu' [explicit solve]: current solution: 5.214301e+00, current residual:3.147923e+00 +wall time: 0.000657082s + +time increment:27170 time: 27.17 +field ' c' [explicit solve]: current solution: 9.631045e+01, current residual:5.816290e+01 +field 'mu' [explicit solve]: current solution: 5.214182e+00, current residual:3.147859e+00 +wall time: 0.000571966s + +time increment:27180 time: 27.18 +field ' c' [explicit solve]: current solution: 9.631027e+01, current residual:5.816278e+01 +field 'mu' [explicit solve]: current solution: 5.214063e+00, current residual:3.147795e+00 +wall time: 0.000568867s + +time increment:27190 time: 27.19 +field ' c' [explicit solve]: current solution: 9.631009e+01, current residual:5.816267e+01 +field 'mu' [explicit solve]: current solution: 5.213944e+00, current residual:3.147730e+00 +wall time: 0.000582933s + +time increment:27200 time: 27.2 +field ' c' [explicit solve]: current solution: 9.630991e+01, current residual:5.816255e+01 +field 'mu' [explicit solve]: current solution: 5.213825e+00, current residual:3.147666e+00 +wall time: 0.000566006s + +time increment:27210 time: 27.21 +field ' c' [explicit solve]: current solution: 9.630973e+01, current residual:5.816243e+01 +field 'mu' [explicit solve]: current solution: 5.213706e+00, current residual:3.147602e+00 +wall time: 0.000709057s + +time increment:27220 time: 27.22 +field ' c' [explicit solve]: current solution: 9.630955e+01, current residual:5.816232e+01 +field 'mu' [explicit solve]: current solution: 5.213587e+00, current residual:3.147538e+00 +wall time: 0.000625849s + +time increment:27230 time: 27.23 +field ' c' [explicit solve]: current solution: 9.630938e+01, current residual:5.816220e+01 +field 'mu' [explicit solve]: current solution: 5.213469e+00, current residual:3.147474e+00 +wall time: 0.000570059s + +time increment:27240 time: 27.24 +field ' c' [explicit solve]: current solution: 9.630920e+01, current residual:5.816208e+01 +field 'mu' [explicit solve]: current solution: 5.213350e+00, current residual:3.147410e+00 +wall time: 0.000577927s + +time increment:27250 time: 27.25 +field ' c' [explicit solve]: current solution: 9.630902e+01, current residual:5.816197e+01 +field 'mu' [explicit solve]: current solution: 5.213231e+00, current residual:3.147345e+00 +wall time: 0.000720978s + +time increment:27260 time: 27.26 +field ' c' [explicit solve]: current solution: 9.630884e+01, current residual:5.816185e+01 +field 'mu' [explicit solve]: current solution: 5.213113e+00, current residual:3.147281e+00 +wall time: 0.000608921s + +time increment:27270 time: 27.27 +field ' c' [explicit solve]: current solution: 9.630866e+01, current residual:5.816174e+01 +field 'mu' [explicit solve]: current solution: 5.212994e+00, current residual:3.147217e+00 +wall time: 0.000597s + +time increment:27280 time: 27.28 +field ' c' [explicit solve]: current solution: 9.630849e+01, current residual:5.816162e+01 +field 'mu' [explicit solve]: current solution: 5.212876e+00, current residual:3.147153e+00 +wall time: 0.000573874s + +time increment:27290 time: 27.29 +field ' c' [explicit solve]: current solution: 9.630831e+01, current residual:5.816150e+01 +field 'mu' [explicit solve]: current solution: 5.212757e+00, current residual:3.147089e+00 +wall time: 0.00075078s + +time increment:27300 time: 27.3 +field ' c' [explicit solve]: current solution: 9.630813e+01, current residual:5.816139e+01 +field 'mu' [explicit solve]: current solution: 5.212639e+00, current residual:3.147025e+00 +wall time: 0.000584126s + +time increment:27310 time: 27.31 +field ' c' [explicit solve]: current solution: 9.630796e+01, current residual:5.816127e+01 +field 'mu' [explicit solve]: current solution: 5.212520e+00, current residual:3.146961e+00 +wall time: 0.000653028s + +time increment:27320 time: 27.32 +field ' c' [explicit solve]: current solution: 9.630778e+01, current residual:5.816116e+01 +field 'mu' [explicit solve]: current solution: 5.212402e+00, current residual:3.146897e+00 +wall time: 0.000597s + +time increment:27330 time: 27.33 +field ' c' [explicit solve]: current solution: 9.630760e+01, current residual:5.816104e+01 +field 'mu' [explicit solve]: current solution: 5.212283e+00, current residual:3.146833e+00 +wall time: 0.000571012s + +time increment:27340 time: 27.34 +field ' c' [explicit solve]: current solution: 9.630743e+01, current residual:5.816093e+01 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[explicit solve]: current solution: 5.211692e+00, current residual:3.146513e+00 +wall time: 0.000675917s + +time increment:27390 time: 27.39 +field ' c' [explicit solve]: current solution: 9.630654e+01, current residual:5.816035e+01 +field 'mu' [explicit solve]: current solution: 5.211574e+00, current residual:3.146449e+00 +wall time: 0.000634909s + +time increment:27400 time: 27.4 +field ' c' [explicit solve]: current solution: 9.630637e+01, current residual:5.816024e+01 +field 'mu' [explicit solve]: current solution: 5.211456e+00, current residual:3.146385e+00 +wall time: 0.000668049s + +time increment:27410 time: 27.41 +field ' c' [explicit solve]: current solution: 9.630619e+01, current residual:5.816012e+01 +field 'mu' [explicit solve]: current solution: 5.211337e+00, current residual:3.146321e+00 +wall time: 0.000585079s + +time increment:27420 time: 27.42 +field ' c' [explicit solve]: current solution: 9.630602e+01, current residual:5.816001e+01 +field 'mu' [explicit solve]: current solution: 5.211219e+00, current residual:3.146257e+00 +wall time: 0.000611782s + +time increment:27430 time: 27.43 +field ' c' [explicit solve]: current solution: 9.630584e+01, current residual:5.815989e+01 +field 'mu' [explicit solve]: current solution: 5.211101e+00, current residual:3.146193e+00 +wall time: 0.000664949s + +time increment:27440 time: 27.44 +field ' c' [explicit solve]: current solution: 9.630567e+01, current residual:5.815978e+01 +field 'mu' [explicit solve]: current solution: 5.210983e+00, current residual:3.146129e+00 +wall time: 0.000740051s + +time increment:27450 time: 27.45 +field ' c' [explicit solve]: current solution: 9.630549e+01, current residual:5.815966e+01 +field 'mu' [explicit solve]: current solution: 5.210865e+00, current residual:3.146065e+00 +wall time: 0.000626087s + +time increment:27460 time: 27.46 +field ' c' [explicit solve]: current solution: 9.630532e+01, current residual:5.815955e+01 +field 'mu' [explicit solve]: current solution: 5.210747e+00, current residual:3.146001e+00 +wall time: 0.000612974s + +time increment:27470 time: 27.47 +field ' c' [explicit solve]: current solution: 9.630514e+01, current residual:5.815943e+01 +field 'mu' [explicit solve]: current solution: 5.210629e+00, current residual:3.145937e+00 +wall time: 0.000573874s + +time increment:27480 time: 27.48 +field ' c' [explicit solve]: current solution: 9.630497e+01, current residual:5.815932e+01 +field 'mu' [explicit solve]: current solution: 5.210511e+00, current residual:3.145873e+00 +wall time: 0.000590086s + +time increment:27490 time: 27.49 +field ' c' [explicit solve]: current solution: 9.630479e+01, current residual:5.815921e+01 +field 'mu' [explicit solve]: current solution: 5.210394e+00, current residual:3.145809e+00 +wall time: 0.000674009s + +time increment:27500 time: 27.5 +field ' c' [explicit solve]: current solution: 9.630462e+01, current residual:5.815909e+01 +field 'mu' [explicit solve]: current solution: 5.210276e+00, current residual:3.145745e+00 +wall time: 0.000688076s + +time increment:27510 time: 27.51 +field ' c' [explicit solve]: current solution: 9.630445e+01, current residual:5.815898e+01 +field 'mu' [explicit solve]: current solution: 5.210158e+00, current residual:3.145681e+00 +wall time: 0.000673056s + +time increment:27520 time: 27.52 +field ' c' [explicit solve]: current solution: 9.630427e+01, current residual:5.815887e+01 +field 'mu' [explicit solve]: current solution: 5.210040e+00, current residual:3.145618e+00 +wall time: 0.000569105s + +time increment:27530 time: 27.53 +field ' c' [explicit solve]: current solution: 9.630410e+01, current residual:5.815875e+01 +field 'mu' [explicit solve]: current solution: 5.209922e+00, current residual:3.145554e+00 +wall time: 0.000577211s + +time increment:27540 time: 27.54 +field ' c' [explicit solve]: current solution: 9.630392e+01, current residual:5.815864e+01 +field 'mu' [explicit solve]: current solution: 5.209805e+00, current residual:3.145490e+00 +wall time: 0.000616074s + +time increment:27550 time: 27.55 +field ' c' [explicit solve]: current solution: 9.630375e+01, current residual:5.815853e+01 +field 'mu' [explicit solve]: current solution: 5.209687e+00, current residual:3.145426e+00 +wall time: 0.000662088s + +time increment:27560 time: 27.56 +field ' c' [explicit solve]: current solution: 9.630358e+01, current residual:5.815841e+01 +field 'mu' [explicit solve]: current solution: 5.209569e+00, current residual:3.145362e+00 +wall time: 0.000633955s + +time increment:27570 time: 27.57 +field ' c' [explicit solve]: current solution: 9.630340e+01, current residual:5.815830e+01 +field 'mu' [explicit solve]: current solution: 5.209451e+00, current residual:3.145298e+00 +wall time: 0.000571012s + +time increment:27580 time: 27.58 +field ' c' [explicit solve]: current solution: 9.630323e+01, current residual:5.815819e+01 +field 'mu' [explicit solve]: current solution: 5.209334e+00, current residual:3.145234e+00 +wall time: 0.000586033s + +time increment:27590 time: 27.59 +field ' c' [explicit solve]: current solution: 9.630306e+01, current residual:5.815807e+01 +field 'mu' [explicit solve]: current solution: 5.209216e+00, current residual:3.145171e+00 +wall time: 0.00069809s + +time increment:27600 time: 27.6 +field ' c' [explicit solve]: current solution: 9.630289e+01, current residual:5.815796e+01 +field 'mu' [explicit solve]: current solution: 5.209099e+00, current residual:3.145107e+00 +wall time: 0.000664949s + +time increment:27610 time: 27.61 +field ' c' [explicit solve]: current solution: 9.630271e+01, current residual:5.815785e+01 +field 'mu' [explicit solve]: current solution: 5.208981e+00, current residual:3.145043e+00 +wall time: 0.000648975s + +time increment:27620 time: 27.62 +field ' c' [explicit solve]: current solution: 9.630254e+01, current residual:5.815773e+01 +field 'mu' [explicit solve]: current solution: 5.208864e+00, current residual:3.144979e+00 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0.000633001s + +time increment:27670 time: 27.67 +field ' c' [explicit solve]: current solution: 9.630168e+01, current residual:5.815717e+01 +field 'mu' [explicit solve]: current solution: 5.208276e+00, current residual:3.144660e+00 +wall time: 0.000700951s + +time increment:27680 time: 27.68 +field ' c' [explicit solve]: current solution: 9.630151e+01, current residual:5.815706e+01 +field 'mu' [explicit solve]: current solution: 5.208159e+00, current residual:3.144596e+00 +wall time: 0.000559092s + +time increment:27690 time: 27.69 +field ' c' [explicit solve]: current solution: 9.630134e+01, current residual:5.815695e+01 +field 'mu' [explicit solve]: current solution: 5.208042e+00, current residual:3.144532e+00 +wall time: 0.00072217s + +time increment:27700 time: 27.7 +field ' c' [explicit solve]: current solution: 9.630117e+01, current residual:5.815684e+01 +field 'mu' [explicit solve]: current solution: 5.207924e+00, current residual:3.144469e+00 +wall time: 0.000659943s + +time increment:27710 time: 27.71 +field ' c' [explicit solve]: current solution: 9.630100e+01, current residual:5.815672e+01 +field 'mu' [explicit solve]: current solution: 5.207807e+00, current residual:3.144405e+00 +wall time: 0.000664949s + +time increment:27720 time: 27.72 +field ' c' [explicit solve]: current solution: 9.630083e+01, current residual:5.815661e+01 +field 'mu' [explicit solve]: current solution: 5.207690e+00, current residual:3.144341e+00 +wall time: 0.000710011s + +time increment:27730 time: 27.73 +field ' c' [explicit solve]: current solution: 9.630066e+01, current residual:5.815650e+01 +field 'mu' [explicit solve]: current solution: 5.207572e+00, current residual:3.144277e+00 +wall time: 0.000588894s + +time increment:27740 time: 27.74 +field ' c' [explicit solve]: current solution: 9.630048e+01, current residual:5.815639e+01 +field 'mu' [explicit solve]: current solution: 5.207455e+00, current residual:3.144214e+00 +wall time: 0.000584126s + +time increment:27750 time: 27.75 +field ' c' [explicit solve]: current solution: 9.630031e+01, current residual:5.815628e+01 +field 'mu' [explicit solve]: current solution: 5.207338e+00, current residual:3.144150e+00 +wall time: 0.000555038s + +time increment:27760 time: 27.76 +field ' c' [explicit solve]: current solution: 9.630014e+01, current residual:5.815617e+01 +field 'mu' [explicit solve]: current solution: 5.207221e+00, current residual:3.144086e+00 +wall time: 0.000636101s + +time increment:27770 time: 27.77 +field ' c' [explicit solve]: current solution: 9.629997e+01, current residual:5.815606e+01 +field 'mu' [explicit solve]: current solution: 5.207104e+00, current residual:3.144022e+00 +wall time: 0.000647068s + +time increment:27780 time: 27.78 +field ' c' [explicit solve]: current solution: 9.629980e+01, current residual:5.815594e+01 +field 'mu' [explicit solve]: current solution: 5.206986e+00, current residual:3.143958e+00 +wall time: 0.000691891s + +time increment:27790 time: 27.79 +field ' c' [explicit solve]: current solution: 9.629963e+01, current residual:5.815583e+01 +field 'mu' [explicit solve]: current solution: 5.206869e+00, current residual:3.143895e+00 +wall time: 0.000741005s + +time increment:27800 time: 27.8 +field ' c' [explicit solve]: current solution: 9.629946e+01, current residual:5.815572e+01 +field 'mu' [explicit solve]: current solution: 5.206752e+00, current residual:3.143831e+00 +wall time: 0.000699043s + +time increment:27810 time: 27.81 +field ' c' [explicit solve]: current solution: 9.629930e+01, current residual:5.815561e+01 +field 'mu' [explicit solve]: current solution: 5.206635e+00, current residual:3.143767e+00 +wall time: 0.000708103s + +time increment:27820 time: 27.82 +field ' c' [explicit solve]: current solution: 9.629913e+01, current residual:5.815550e+01 +field 'mu' [explicit solve]: current solution: 5.206518e+00, current residual:3.143703e+00 +wall time: 0.000716925s + +time increment:27830 time: 27.83 +field ' c' [explicit solve]: current solution: 9.629896e+01, current residual:5.815539e+01 +field 'mu' [explicit solve]: current solution: 5.206401e+00, current residual:3.143640e+00 +wall time: 0.00067687s + +time increment:27840 time: 27.84 +field ' c' [explicit solve]: current solution: 9.629879e+01, current residual:5.815528e+01 +field 'mu' [explicit solve]: current solution: 5.206284e+00, current residual:3.143576e+00 +wall time: 0.000641108s + +time increment:27850 time: 27.85 +field ' c' [explicit solve]: current solution: 9.629862e+01, current residual:5.815517e+01 +field 'mu' [explicit solve]: current solution: 5.206167e+00, current residual:3.143512e+00 +wall time: 0.000683069s + +time increment:27860 time: 27.86 +field ' c' [explicit solve]: current solution: 9.629845e+01, current residual:5.815506e+01 +field 'mu' [explicit solve]: current solution: 5.206050e+00, current residual:3.143449e+00 +wall time: 0.000712156s + +time increment:27870 time: 27.87 +field ' c' [explicit solve]: current solution: 9.629828e+01, current residual:5.815495e+01 +field 'mu' [explicit solve]: current solution: 5.205933e+00, current residual:3.143385e+00 +wall time: 0.000696898s + +time increment:27880 time: 27.88 +field ' c' [explicit solve]: current solution: 9.629811e+01, current residual:5.815484e+01 +field 'mu' [explicit solve]: current solution: 5.205816e+00, current residual:3.143321e+00 +wall time: 0.000696898s + +time increment:27890 time: 27.89 +field ' c' [explicit solve]: current solution: 9.629794e+01, current residual:5.815473e+01 +field 'mu' [explicit solve]: current solution: 5.205699e+00, current residual:3.143257e+00 +wall time: 0.000683069s + +time increment:27900 time: 27.9 +field ' c' [explicit solve]: current solution: 9.629778e+01, current residual:5.815462e+01 +field 'mu' [explicit solve]: current solution: 5.205582e+00, current residual:3.143194e+00 +wall time: 0.000677109s + +time increment:27910 time: 27.91 +field ' c' [explicit solve]: current solution: 9.629761e+01, current residual:5.815451e+01 +field 'mu' [explicit solve]: current solution: 5.205465e+00, current residual:3.143130e+00 +wall time: 0.000645876s + +time increment:27920 time: 27.92 +field ' c' [explicit solve]: current solution: 9.629744e+01, current residual:5.815440e+01 +field 'mu' [explicit solve]: current solution: 5.205348e+00, current residual:3.143066e+00 +wall time: 0.000720024s + +time increment:27930 time: 27.93 +field ' c' [explicit solve]: current solution: 9.629727e+01, current residual:5.815429e+01 +field 'mu' [explicit solve]: current solution: 5.205232e+00, current residual:3.143002e+00 +wall time: 0.000609159s + +time increment:27940 time: 27.94 +field ' c' [explicit solve]: current solution: 9.629710e+01, current residual:5.815418e+01 +field 'mu' [explicit solve]: current solution: 5.205115e+00, current residual:3.142939e+00 +wall time: 0.000667095s + +time increment:27950 time: 27.95 +field ' c' [explicit solve]: current solution: 9.629694e+01, current residual:5.815407e+01 +field 'mu' [explicit solve]: current solution: 5.204998e+00, current residual:3.142875e+00 +wall time: 0.000627995s + +time increment:27960 time: 27.96 +field ' c' [explicit solve]: current solution: 9.629677e+01, current residual:5.815396e+01 +field 'mu' [explicit solve]: current solution: 5.204881e+00, current residual:3.142811e+00 +wall time: 0.000673056s + +time increment:27970 time: 27.97 +field ' c' [explicit solve]: current solution: 9.629660e+01, current residual:5.815385e+01 +field 'mu' [explicit solve]: current solution: 5.204764e+00, current residual:3.142748e+00 +wall time: 0.00075388s + +time increment:27980 time: 27.98 +field ' c' [explicit solve]: current solution: 9.629644e+01, current residual:5.815374e+01 +field 'mu' [explicit solve]: current solution: 5.204648e+00, current residual:3.142684e+00 +wall time: 0.00068593s + +time increment:27990 time: 27.99 +field ' c' [explicit solve]: current solution: 9.629627e+01, current residual:5.815363e+01 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solve]: current solution: 5.204064e+00, current residual:3.142365e+00 +wall time: 0.000654936s + +time increment:28040 time: 28.04 +field ' c' [explicit solve]: current solution: 9.629544e+01, current residual:5.815309e+01 +field 'mu' [explicit solve]: current solution: 5.203947e+00, current residual:3.142302e+00 +wall time: 0.000688076s + +time increment:28050 time: 28.05 +field ' c' [explicit solve]: current solution: 9.629527e+01, current residual:5.815298e+01 +field 'mu' [explicit solve]: current solution: 5.203831e+00, current residual:3.142238e+00 +wall time: 0.000723124s + +time increment:28060 time: 28.06 +field ' c' [explicit solve]: current solution: 9.629510e+01, current residual:5.815287e+01 +field 'mu' [explicit solve]: current solution: 5.203714e+00, current residual:3.142174e+00 +wall time: 0.000689983s + +time increment:28070 time: 28.07 +field ' c' [explicit solve]: current solution: 9.629494e+01, current residual:5.815276e+01 +field 'mu' [explicit solve]: current solution: 5.203598e+00, current residual:3.142110e+00 +wall time: 0.00062108s + +time increment:28080 time: 28.08 +field ' c' [explicit solve]: current solution: 9.629477e+01, current residual:5.815265e+01 +field 'mu' [explicit solve]: current solution: 5.203481e+00, current residual:3.142047e+00 +wall time: 0.000676155s + +time increment:28090 time: 28.09 +field ' c' [explicit solve]: current solution: 9.629461e+01, current residual:5.815254e+01 +field 'mu' [explicit solve]: current solution: 5.203364e+00, current residual:3.141983e+00 +wall time: 0.000649929s + +time increment:28100 time: 28.1 +field ' c' [explicit solve]: current solution: 9.629444e+01, current residual:5.815244e+01 +field 'mu' [explicit solve]: current solution: 5.203248e+00, current residual:3.141919e+00 +wall time: 0.000639915s + +time increment:28110 time: 28.11 +field ' c' [explicit solve]: current solution: 9.629428e+01, current residual:5.815233e+01 +field 'mu' [explicit solve]: current solution: 5.203131e+00, current residual:3.141856e+00 +wall time: 0.000638962s + +time increment:28120 time: 28.12 +field ' c' [explicit solve]: current solution: 9.629411e+01, current residual:5.815222e+01 +field 'mu' [explicit solve]: current solution: 5.203015e+00, current residual:3.141792e+00 +wall time: 0.000728846s + +time increment:28130 time: 28.13 +field ' c' [explicit solve]: current solution: 9.629395e+01, current residual:5.815211e+01 +field 'mu' [explicit solve]: current solution: 5.202898e+00, current residual:3.141728e+00 +wall time: 0.000617027s + +time increment:28140 time: 28.14 +field ' c' [explicit solve]: current solution: 9.629378e+01, current residual:5.815200e+01 +field 'mu' [explicit solve]: current solution: 5.202782e+00, current residual:3.141665e+00 +wall time: 0.00061202s + +time increment:28150 time: 28.15 +field ' c' [explicit solve]: current solution: 9.629362e+01, current residual:5.815190e+01 +field 'mu' [explicit solve]: current solution: 5.202665e+00, current residual:3.141601e+00 +wall time: 0.000613928s + +time increment:28160 time: 28.16 +field ' c' [explicit solve]: current solution: 9.629345e+01, current residual:5.815179e+01 +field 'mu' [explicit solve]: current solution: 5.202549e+00, current residual:3.141537e+00 +wall time: 0.000604868s + +time increment:28170 time: 28.17 +field ' c' [explicit solve]: current solution: 9.629329e+01, current residual:5.815168e+01 +field 'mu' [explicit solve]: current solution: 5.202432e+00, current residual:3.141473e+00 +wall time: 0.000567913s + +time increment:28180 time: 28.18 +field ' c' [explicit solve]: current solution: 9.629313e+01, current residual:5.815157e+01 +field 'mu' [explicit solve]: current solution: 5.202316e+00, current residual:3.141410e+00 +wall time: 0.000641823s + +time increment:28190 time: 28.19 +field ' c' [explicit solve]: current solution: 9.629296e+01, current residual:5.815147e+01 +field 'mu' [explicit solve]: current solution: 5.202199e+00, current residual:3.141346e+00 +wall time: 0.000604153s + +time increment:28200 time: 28.2 +field ' c' [explicit solve]: current solution: 9.629280e+01, current residual:5.815136e+01 +field 'mu' [explicit solve]: current solution: 5.202083e+00, current residual:3.141282e+00 +wall time: 0.000658989s + +time increment:28210 time: 28.21 +field ' c' [explicit solve]: current solution: 9.629263e+01, current residual:5.815125e+01 +field 'mu' [explicit solve]: current solution: 5.201967e+00, current residual:3.141218e+00 +wall time: 0.000600815s + +time increment:28220 time: 28.22 +field ' c' [explicit solve]: current solution: 9.629247e+01, current residual:5.815115e+01 +field 'mu' [explicit solve]: current solution: 5.201850e+00, current residual:3.141155e+00 +wall time: 0.000567913s + +time increment:28230 time: 28.23 +field ' c' [explicit solve]: current solution: 9.629231e+01, current residual:5.815104e+01 +field 'mu' [explicit solve]: current solution: 5.201734e+00, current residual:3.141091e+00 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0.000728846s + +time increment:28280 time: 28.28 +field ' c' [explicit solve]: current solution: 9.629149e+01, current residual:5.815050e+01 +field 'mu' [explicit solve]: current solution: 5.201152e+00, current residual:3.140772e+00 +wall time: 0.000744104s + +time increment:28290 time: 28.29 +field ' c' [explicit solve]: current solution: 9.629133e+01, current residual:5.815040e+01 +field 'mu' [explicit solve]: current solution: 5.201036e+00, current residual:3.140709e+00 +wall time: 0.00067091s + +time increment:28300 time: 28.3 +field ' c' [explicit solve]: current solution: 9.629117e+01, current residual:5.815029e+01 +field 'mu' [explicit solve]: current solution: 5.200919e+00, current residual:3.140645e+00 +wall time: 0.000668049s + +time increment:28310 time: 28.31 +field ' c' [explicit solve]: current solution: 9.629101e+01, current residual:5.815019e+01 +field 'mu' [explicit solve]: current solution: 5.200803e+00, current residual:3.140581e+00 +wall time: 0.000651121s + +time increment:28320 time: 28.32 +field ' c' [explicit solve]: current solution: 9.629084e+01, current residual:5.815008e+01 +field 'mu' [explicit solve]: current solution: 5.200687e+00, current residual:3.140517e+00 +wall time: 0.000739098s + +time increment:28330 time: 28.33 +field ' c' [explicit solve]: current solution: 9.629068e+01, current residual:5.814997e+01 +field 'mu' [explicit solve]: current solution: 5.200570e+00, current residual:3.140454e+00 +wall time: 0.000621796s + +time increment:28340 time: 28.34 +field ' c' [explicit solve]: current solution: 9.629052e+01, current residual:5.814987e+01 +field 'mu' [explicit solve]: current solution: 5.200454e+00, current residual:3.140390e+00 +wall time: 0.000700951s + +time increment:28350 time: 28.35 +field ' c' [explicit solve]: current solution: 9.629036e+01, current residual:5.814976e+01 +field 'mu' [explicit solve]: current solution: 5.200338e+00, current residual:3.140326e+00 +wall time: 0.000585079s + +time increment:28360 time: 28.36 +field ' c' [explicit solve]: current solution: 9.629020e+01, current residual:5.814966e+01 +field 'mu' [explicit solve]: current solution: 5.200222e+00, current residual:3.140262e+00 +wall time: 0.000699997s + +time increment:28370 time: 28.37 +field ' c' [explicit solve]: current solution: 9.629004e+01, current residual:5.814955e+01 +field 'mu' [explicit solve]: current solution: 5.200105e+00, current residual:3.140199e+00 +wall time: 0.000571012s + +time increment:28380 time: 28.38 +field ' c' [explicit solve]: current solution: 9.628987e+01, current residual:5.814944e+01 +field 'mu' [explicit solve]: current solution: 5.199989e+00, current residual:3.140135e+00 +wall time: 0.000691891s + +time increment:28390 time: 28.39 +field ' c' [explicit solve]: current solution: 9.628971e+01, current residual:5.814934e+01 +field 'mu' [explicit solve]: current solution: 5.199873e+00, current residual:3.140071e+00 +wall time: 0.00069809s + +time increment:28400 time: 28.4 +field ' c' [explicit solve]: current solution: 9.628955e+01, current residual:5.814923e+01 +field 'mu' [explicit solve]: current solution: 5.199757e+00, current residual:3.140007e+00 +wall time: 0.000570059s + +time increment:28410 time: 28.41 +field ' c' [explicit solve]: current solution: 9.628939e+01, current residual:5.814913e+01 +field 'mu' [explicit solve]: current solution: 5.199641e+00, current residual:3.139944e+00 +wall time: 0.000874043s + +time increment:28420 time: 28.42 +field ' c' [explicit solve]: current solution: 9.628923e+01, current residual:5.814902e+01 +field 'mu' [explicit solve]: current solution: 5.199524e+00, current residual:3.139880e+00 +wall time: 0.000617027s + +time increment:28430 time: 28.43 +field ' c' [explicit solve]: current solution: 9.628907e+01, current residual:5.814892e+01 +field 'mu' [explicit solve]: current solution: 5.199408e+00, current residual:3.139816e+00 +wall time: 0.000650883s + +time increment:28440 time: 28.44 +field ' c' [explicit solve]: current solution: 9.628891e+01, current residual:5.814881e+01 +field 'mu' [explicit solve]: current solution: 5.199292e+00, current residual:3.139752e+00 +wall time: 0.000638008s + +time increment:28450 time: 28.45 +field ' c' [explicit solve]: current solution: 9.628875e+01, current residual:5.814871e+01 +field 'mu' [explicit solve]: current solution: 5.199176e+00, current residual:3.139688e+00 +wall time: 0.000659943s + +time increment:28460 time: 28.46 +field ' c' [explicit solve]: current solution: 9.628859e+01, current residual:5.814860e+01 +field 'mu' [explicit solve]: current solution: 5.199060e+00, current residual:3.139625e+00 +wall time: 0.000644922s + +time increment:28470 time: 28.47 +field ' c' [explicit solve]: current solution: 9.628843e+01, current residual:5.814850e+01 +field 'mu' [explicit solve]: current solution: 5.198944e+00, current residual:3.139561e+00 +wall time: 0.000594854s + +time increment:28480 time: 28.48 +field ' c' [explicit solve]: current solution: 9.628827e+01, current residual:5.814839e+01 +field 'mu' [explicit solve]: current solution: 5.198828e+00, current residual:3.139497e+00 +wall time: 0.000580072s + +time increment:28490 time: 28.49 +field ' c' [explicit solve]: current solution: 9.628811e+01, current residual:5.814829e+01 +field 'mu' [explicit solve]: current solution: 5.198711e+00, current residual:3.139433e+00 +wall time: 0.000568867s + +time increment:28500 time: 28.5 +field ' c' [explicit solve]: current solution: 9.628795e+01, current residual:5.814818e+01 +field 'mu' [explicit solve]: current solution: 5.198595e+00, current residual:3.139369e+00 +wall time: 0.00067091s + +time increment:28510 time: 28.51 +field ' c' [explicit solve]: current solution: 9.628779e+01, current residual:5.814808e+01 +field 'mu' [explicit solve]: current solution: 5.198479e+00, current residual:3.139306e+00 +wall time: 0.000898838s + +time increment:28520 time: 28.52 +field ' c' [explicit solve]: current solution: 9.628763e+01, current residual:5.814797e+01 +field 'mu' [explicit solve]: current solution: 5.198363e+00, current residual:3.139242e+00 +wall time: 0.000669956s + +time increment:28530 time: 28.53 +field ' c' [explicit solve]: current solution: 9.628747e+01, current residual:5.814787e+01 +field 'mu' [explicit solve]: current solution: 5.198247e+00, current residual:3.139178e+00 +wall time: 0.000684977s + +time increment:28540 time: 28.54 +field ' c' [explicit solve]: current solution: 9.628731e+01, current residual:5.814777e+01 +field 'mu' [explicit solve]: current solution: 5.198131e+00, current residual:3.139114e+00 +wall time: 0.000609159s + +time increment:28550 time: 28.55 +field ' c' [explicit solve]: current solution: 9.628715e+01, current residual:5.814766e+01 +field 'mu' [explicit solve]: current solution: 5.198015e+00, current residual:3.139050e+00 +wall time: 0.000647068s + +time increment:28560 time: 28.56 +field ' c' [explicit solve]: current solution: 9.628700e+01, current 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[explicit solve]: current solution: 5.196970e+00, current residual:3.138476e+00 +wall time: 0.00066781s + +time increment:28650 time: 28.65 +field ' c' [explicit solve]: current solution: 9.628557e+01, current residual:5.814663e+01 +field 'mu' [explicit solve]: current solution: 5.196854e+00, current residual:3.138412e+00 +wall time: 0.000653028s + +time increment:28660 time: 28.66 +field ' c' [explicit solve]: current solution: 9.628542e+01, current residual:5.814652e+01 +field 'mu' [explicit solve]: current solution: 5.196738e+00, current residual:3.138348e+00 +wall time: 0.000668049s + +time increment:28670 time: 28.67 +field ' c' [explicit solve]: current solution: 9.628526e+01, current residual:5.814642e+01 +field 'mu' [explicit solve]: current solution: 5.196622e+00, current residual:3.138284e+00 +wall time: 0.000665903s + +time increment:28680 time: 28.68 +field ' c' [explicit solve]: current solution: 9.628510e+01, current residual:5.814632e+01 +field 'mu' [explicit solve]: current solution: 5.196506e+00, current residual:3.138220e+00 +wall time: 0.000659943s + +time increment:28690 time: 28.69 +field ' c' [explicit solve]: current solution: 9.628495e+01, current residual:5.814621e+01 +field 'mu' [explicit solve]: current solution: 5.196390e+00, current residual:3.138156e+00 +wall time: 0.000664949s + +time increment:28700 time: 28.7 +field ' c' [explicit solve]: current solution: 9.628479e+01, current residual:5.814611e+01 +field 'mu' [explicit solve]: current solution: 5.196274e+00, current residual:3.138092e+00 +wall time: 0.000722885s + +time increment:28710 time: 28.71 +field ' c' [explicit solve]: current solution: 9.628463e+01, current residual:5.814601e+01 +field 'mu' [explicit solve]: current solution: 5.196158e+00, current residual:3.138028e+00 +wall time: 0.000703812s + +time increment:28720 time: 28.72 +field ' c' [explicit solve]: current solution: 9.628448e+01, current residual:5.814591e+01 +field 'mu' [explicit solve]: current solution: 5.196042e+00, current residual:3.137964e+00 +wall time: 0.000677109s + +time increment:28730 time: 28.73 +field ' c' [explicit solve]: current solution: 9.628432e+01, current residual:5.814580e+01 +field 'mu' [explicit solve]: current solution: 5.195926e+00, current residual:3.137900e+00 +wall time: 0.000656128s + +time increment:28740 time: 28.74 +field ' c' [explicit solve]: current solution: 9.628416e+01, current residual:5.814570e+01 +field 'mu' [explicit solve]: current solution: 5.195810e+00, current residual:3.137836e+00 +wall time: 0.000667095s + +time increment:28750 time: 28.75 +field ' c' [explicit solve]: current solution: 9.628401e+01, current residual:5.814560e+01 +field 'mu' [explicit solve]: current solution: 5.195694e+00, current residual:3.137772e+00 +wall time: 0.000692844s + +time increment:28760 time: 28.76 +field ' c' [explicit solve]: current solution: 9.628385e+01, current residual:5.814550e+01 +field 'mu' [explicit solve]: current solution: 5.195578e+00, current residual:3.137708e+00 +wall time: 0.000668049s + +time increment:28770 time: 28.77 +field ' c' [explicit solve]: current solution: 9.628370e+01, current residual:5.814539e+01 +field 'mu' [explicit solve]: current solution: 5.195462e+00, current residual:3.137644e+00 +wall time: 0.000669956s + +time increment:28780 time: 28.78 +field ' c' [explicit solve]: current solution: 9.628354e+01, current residual:5.814529e+01 +field 'mu' [explicit solve]: current solution: 5.195346e+00, current residual:3.137580e+00 +wall time: 0.000720978s + +time increment:28790 time: 28.79 +field ' c' [explicit solve]: current solution: 9.628338e+01, current residual:5.814519e+01 +field 'mu' [explicit solve]: current solution: 5.195230e+00, current residual:3.137516e+00 +wall time: 0.000678062s + +time increment:28800 time: 28.8 +field ' c' [explicit solve]: current solution: 9.628323e+01, current residual:5.814509e+01 +field 'mu' [explicit solve]: current solution: 5.195115e+00, current residual:3.137453e+00 +wall time: 0.000691891s + +time increment:28810 time: 28.81 +field ' c' [explicit solve]: current solution: 9.628307e+01, current residual:5.814499e+01 +field 'mu' [explicit solve]: current solution: 5.194999e+00, current residual:3.137389e+00 +wall time: 0.000676155s + +time increment:28820 time: 28.82 +field ' c' [explicit solve]: current solution: 9.628292e+01, current residual:5.814488e+01 +field 'mu' [explicit solve]: current solution: 5.194883e+00, current residual:3.137325e+00 +wall time: 0.000638008s + +time increment:28830 time: 28.83 +field ' c' [explicit solve]: current solution: 9.628276e+01, current residual:5.814478e+01 +field 'mu' [explicit solve]: current solution: 5.194767e+00, current residual:3.137260e+00 +wall time: 0.000702143s + +time increment:28840 time: 28.84 +field ' c' [explicit solve]: current solution: 9.628261e+01, current residual:5.814468e+01 +field 'mu' [explicit solve]: current solution: 5.194651e+00, current residual:3.137196e+00 +wall time: 0.000644922s + +time increment:28850 time: 28.85 +field ' c' [explicit solve]: current solution: 9.628245e+01, current residual:5.814458e+01 +field 'mu' [explicit solve]: current solution: 5.194535e+00, current residual:3.137132e+00 +wall time: 0.000640154s + +time increment:28860 time: 28.86 +field ' c' [explicit solve]: current solution: 9.628230e+01, current residual:5.814448e+01 +field 'mu' [explicit solve]: current solution: 5.194419e+00, current residual:3.137068e+00 +wall time: 0.000658035s + +time increment:28870 time: 28.87 +field ' c' [explicit solve]: current solution: 9.628215e+01, current residual:5.814438e+01 +field 'mu' [explicit solve]: current solution: 5.194303e+00, current residual:3.137004e+00 +wall time: 0.000697136s + +time increment:28880 time: 28.88 +field ' c' [explicit solve]: current solution: 9.628199e+01, current residual:5.814428e+01 +field 'mu' [explicit solve]: current solution: 5.194187e+00, current residual:3.136940e+00 +wall time: 0.000671864s + +time increment:28890 time: 28.89 +field ' c' [explicit solve]: current solution: 9.628184e+01, current residual:5.814417e+01 +field 'mu' [explicit solve]: current solution: 5.194071e+00, current residual:3.136876e+00 +wall time: 0.000659227s + +time increment:28900 time: 28.9 +field ' c' [explicit solve]: current solution: 9.628168e+01, current residual:5.814407e+01 +field 'mu' [explicit solve]: current solution: 5.193955e+00, current residual:3.136812e+00 +wall time: 0.000647068s + +time increment:28910 time: 28.91 +field ' c' [explicit solve]: current solution: 9.628153e+01, current residual:5.814397e+01 +field 'mu' [explicit solve]: current solution: 5.193839e+00, current residual:3.136748e+00 +wall time: 0.000735044s + +time increment:28920 time: 28.92 +field ' c' [explicit solve]: current solution: 9.628138e+01, current residual:5.814387e+01 +field 'mu' [explicit solve]: current solution: 5.193723e+00, current residual:3.136684e+00 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0.000623941s + +time increment:28970 time: 28.97 +field ' c' [explicit solve]: current solution: 9.628061e+01, current residual:5.814337e+01 +field 'mu' [explicit solve]: current solution: 5.193144e+00, current residual:3.136364e+00 +wall time: 0.000669003s + +time increment:28980 time: 28.98 +field ' c' [explicit solve]: current solution: 9.628046e+01, current residual:5.814327e+01 +field 'mu' [explicit solve]: current solution: 5.193028e+00, current residual:3.136299e+00 +wall time: 0.000669956s + +time increment:28990 time: 28.99 +field ' c' [explicit solve]: current solution: 9.628030e+01, current residual:5.814317e+01 +field 'mu' [explicit solve]: current solution: 5.192912e+00, current residual:3.136235e+00 +wall time: 0.000708818s + +time increment:29000 time: 29 +field ' c' [explicit solve]: current solution: 9.628015e+01, current residual:5.814307e+01 +field 'mu' [explicit solve]: current solution: 5.192796e+00, current residual:3.136171e+00 +wall time: 0.000697136s + +time increment:29010 time: 29.01 +field ' c' [explicit solve]: current solution: 9.628000e+01, current residual:5.814297e+01 +field 'mu' [explicit solve]: current solution: 5.192680e+00, current residual:3.136107e+00 +wall time: 0.000651836s + +time increment:29020 time: 29.02 +field ' c' [explicit solve]: current solution: 9.627985e+01, current residual:5.814287e+01 +field 'mu' [explicit solve]: current solution: 5.192564e+00, current residual:3.136043e+00 +wall time: 0.000703096s + +time increment:29030 time: 29.03 +field ' c' [explicit solve]: current solution: 9.627969e+01, current residual:5.814277e+01 +field 'mu' [explicit solve]: current solution: 5.192448e+00, current residual:3.135979e+00 +wall time: 0.000695944s + +time increment:29040 time: 29.04 +field ' c' [explicit solve]: current solution: 9.627954e+01, current residual:5.814267e+01 +field 'mu' [explicit solve]: current solution: 5.192332e+00, current residual:3.135915e+00 +wall time: 0.000738144s + +time increment:29050 time: 29.05 +field ' c' [explicit solve]: current solution: 9.627939e+01, current residual:5.814257e+01 +field 'mu' [explicit solve]: current solution: 5.192216e+00, current residual:3.135850e+00 +wall time: 0.000674963s + +time increment:29060 time: 29.06 +field ' c' [explicit solve]: current solution: 9.627924e+01, current residual:5.814247e+01 +field 'mu' [explicit solve]: current solution: 5.192100e+00, current residual:3.135786e+00 +wall time: 0.00069499s + +time increment:29070 time: 29.07 +field ' c' [explicit solve]: current solution: 9.627909e+01, current residual:5.814237e+01 +field 'mu' [explicit solve]: current solution: 5.191984e+00, current residual:3.135722e+00 +wall time: 0.000683069s + +time increment:29080 time: 29.08 +field ' c' [explicit solve]: current solution: 9.627893e+01, current residual:5.814227e+01 +field 'mu' [explicit solve]: current solution: 5.191868e+00, current residual:3.135658e+00 +wall time: 0.000674963s + +time increment:29090 time: 29.09 +field ' c' [explicit solve]: current solution: 9.627878e+01, current residual:5.814217e+01 +field 'mu' [explicit solve]: current solution: 5.191752e+00, current residual:3.135593e+00 +wall time: 0.000737906s + +time increment:29100 time: 29.1 +field ' c' [explicit solve]: current solution: 9.627863e+01, current residual:5.814207e+01 +field 'mu' [explicit solve]: current solution: 5.191636e+00, current residual:3.135529e+00 +wall time: 0.000701904s + +time increment:29110 time: 29.11 +field ' c' [explicit solve]: current solution: 9.627848e+01, current residual:5.814197e+01 +field 'mu' [explicit solve]: current solution: 5.191520e+00, current residual:3.135465e+00 +wall time: 0.000658989s + +time increment:29120 time: 29.12 +field ' c' [explicit solve]: current solution: 9.627833e+01, current residual:5.814187e+01 +field 'mu' [explicit solve]: current solution: 5.191405e+00, current residual:3.135401e+00 +wall time: 0.000619888s + +time increment:29130 time: 29.13 +field ' c' [explicit solve]: current solution: 9.627818e+01, current residual:5.814177e+01 +field 'mu' [explicit solve]: current solution: 5.191289e+00, current residual:3.135336e+00 +wall time: 0.000590086s + +time increment:29140 time: 29.14 +field ' c' [explicit solve]: current solution: 9.627803e+01, current residual:5.814167e+01 +field 'mu' [explicit solve]: current solution: 5.191173e+00, current residual:3.135272e+00 +wall time: 0.000681877s + +time increment:29150 time: 29.15 +field ' c' [explicit solve]: current solution: 9.627788e+01, current residual:5.814158e+01 +field 'mu' [explicit solve]: current solution: 5.191057e+00, current residual:3.135208e+00 +wall time: 0.000720978s + +time increment:29160 time: 29.16 +field ' c' [explicit solve]: current solution: 9.627773e+01, current residual:5.814148e+01 +field 'mu' [explicit solve]: current solution: 5.190941e+00, current residual:3.135144e+00 +wall time: 0.000659943s + +time increment:29170 time: 29.17 +field ' c' [explicit solve]: current solution: 9.627758e+01, current residual:5.814138e+01 +field 'mu' [explicit solve]: current solution: 5.190825e+00, current residual:3.135079e+00 +wall time: 0.000694036s + +time increment:29180 time: 29.18 +field ' c' [explicit solve]: current solution: 9.627743e+01, current residual:5.814128e+01 +field 'mu' [explicit solve]: current solution: 5.190709e+00, current residual:3.135015e+00 +wall time: 0.00067687s + +time increment:29190 time: 29.19 +field ' c' [explicit solve]: current solution: 9.627728e+01, current residual:5.814118e+01 +field 'mu' [explicit solve]: current solution: 5.190593e+00, current residual:3.134951e+00 +wall time: 0.000705004s + +time increment:29200 time: 29.2 +field ' c' [explicit solve]: current solution: 9.627713e+01, current residual:5.814108e+01 +field 'mu' [explicit solve]: current solution: 5.190477e+00, current residual:3.134886e+00 +wall time: 0.000564814s + +time increment:29210 time: 29.21 +field ' c' [explicit solve]: current solution: 9.627698e+01, current residual:5.814098e+01 +field 'mu' [explicit solve]: current solution: 5.190361e+00, current residual:3.134822e+00 +wall time: 0.000660896s + +time increment:29220 time: 29.22 +field ' c' [explicit solve]: current solution: 9.627683e+01, current residual:5.814089e+01 +field 'mu' [explicit solve]: current solution: 5.190245e+00, current residual:3.134758e+00 +wall time: 0.000659943s + +time increment:29230 time: 29.23 +field ' c' [explicit solve]: current solution: 9.627668e+01, current residual:5.814079e+01 +field 'mu' [explicit solve]: current solution: 5.190129e+00, current residual:3.134693e+00 +wall time: 0.000686884s + +time increment:29240 time: 29.24 +field ' c' [explicit solve]: current solution: 9.627653e+01, current residual:5.814069e+01 +field 'mu' [explicit solve]: current solution: 5.190013e+00, current residual:3.134629e+00 +wall time: 0.000616074s + +time increment:29250 time: 29.25 +field ' c' [explicit solve]: current solution: 9.627638e+01, current residual:5.814059e+01 +field 'mu' [explicit solve]: current solution: 5.189897e+00, current residual:3.134565e+00 +wall time: 0.000566006s + +time increment:29260 time: 29.26 +field ' c' [explicit solve]: current solution: 9.627623e+01, current residual:5.814049e+01 +field 'mu' [explicit solve]: current solution: 5.189781e+00, current residual:3.134500e+00 +wall time: 0.000559092s + +time increment:29270 time: 29.27 +field ' c' [explicit solve]: current solution: 9.627608e+01, current residual:5.814039e+01 +field 'mu' [explicit solve]: current solution: 5.189665e+00, current residual:3.134436e+00 +wall time: 0.000677109s + +time increment:29280 time: 29.28 +field ' c' [explicit solve]: current solution: 9.627593e+01, current residual:5.814030e+01 +field 'mu' [explicit solve]: current solution: 5.189549e+00, current residual:3.134371e+00 +wall time: 0.000576019s + +time increment:29290 time: 29.29 +field ' c' [explicit solve]: current solution: 9.627578e+01, current residual:5.814020e+01 +field 'mu' [explicit solve]: current solution: 5.189433e+00, current residual:3.134307e+00 +wall time: 0.000632048s + +time increment:29300 time: 29.3 +field ' c' [explicit solve]: current solution: 9.627563e+01, current residual:5.814010e+01 +field 'mu' [explicit solve]: current solution: 5.189317e+00, current residual:3.134243e+00 +wall time: 0.00067091s + +time increment:29310 time: 29.31 +field ' c' [explicit solve]: current solution: 9.627549e+01, current residual:5.814000e+01 +field 'mu' [explicit solve]: current solution: 5.189201e+00, current residual:3.134178e+00 +wall time: 0.000597s + +time increment:29320 time: 29.32 +field ' c' [explicit solve]: current solution: 9.627534e+01, current residual:5.813991e+01 +field 'mu' [explicit solve]: current solution: 5.189085e+00, current residual:3.134114e+00 +wall time: 0.000609875s + +time increment:29330 time: 29.33 +field ' c' [explicit solve]: current solution: 9.627519e+01, current residual:5.813981e+01 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[explicit solve]: current solution: 5.188505e+00, current residual:3.133791e+00 +wall time: 0.000568867s + +time increment:29380 time: 29.38 +field ' c' [explicit solve]: current solution: 9.627445e+01, current residual:5.813932e+01 +field 'mu' [explicit solve]: current solution: 5.188389e+00, current residual:3.133727e+00 +wall time: 0.000562191s + +time increment:29390 time: 29.39 +field ' c' [explicit solve]: current solution: 9.627430e+01, current residual:5.813923e+01 +field 'mu' [explicit solve]: current solution: 5.188273e+00, current residual:3.133662e+00 +wall time: 0.00067091s + +time increment:29400 time: 29.4 +field ' c' [explicit solve]: current solution: 9.627416e+01, current residual:5.813913e+01 +field 'mu' [explicit solve]: current solution: 5.188157e+00, current residual:3.133598e+00 +wall time: 0.000561953s + +time increment:29410 time: 29.41 +field ' c' [explicit solve]: current solution: 9.627401e+01, current residual:5.813903e+01 +field 'mu' [explicit solve]: current solution: 5.188041e+00, current residual:3.133533e+00 +wall time: 0.000625134s + +time increment:29420 time: 29.42 +field ' c' [explicit solve]: current solution: 9.627386e+01, current residual:5.813894e+01 +field 'mu' [explicit solve]: current solution: 5.187925e+00, current residual:3.133469e+00 +wall time: 0.000650883s + +time increment:29430 time: 29.43 +field ' c' [explicit solve]: current solution: 9.627372e+01, current residual:5.813884e+01 +field 'mu' [explicit solve]: current solution: 5.187809e+00, current residual:3.133404e+00 +wall time: 0.000612974s + +time increment:29440 time: 29.44 +field ' c' [explicit solve]: current solution: 9.627357e+01, current residual:5.813874e+01 +field 'mu' [explicit solve]: current solution: 5.187693e+00, current residual:3.133339e+00 +wall time: 0.000571966s + +time increment:29450 time: 29.45 +field ' c' [explicit solve]: current solution: 9.627342e+01, current residual:5.813865e+01 +field 'mu' [explicit solve]: current solution: 5.187577e+00, current residual:3.133275e+00 +wall time: 0.000616074s + +time increment:29460 time: 29.46 +field ' c' [explicit solve]: current solution: 9.627328e+01, current residual:5.813855e+01 +field 'mu' [explicit solve]: current solution: 5.187461e+00, current residual:3.133210e+00 +wall time: 0.000656128s + +time increment:29470 time: 29.47 +field ' c' [explicit solve]: current solution: 9.627313e+01, current residual:5.813845e+01 +field 'mu' [explicit solve]: current solution: 5.187345e+00, current residual:3.133146e+00 +wall time: 0.000663996s + +time increment:29480 time: 29.48 +field ' c' [explicit solve]: current solution: 9.627298e+01, current residual:5.813836e+01 +field 'mu' [explicit solve]: current solution: 5.187229e+00, current residual:3.133081e+00 +wall time: 0.000608921s + +time increment:29490 time: 29.49 +field ' c' [explicit solve]: current solution: 9.627284e+01, current residual:5.813826e+01 +field 'mu' [explicit solve]: current solution: 5.187113e+00, current residual:3.133016e+00 +wall time: 0.000633955s + +time increment:29500 time: 29.5 +field ' c' [explicit solve]: current solution: 9.627269e+01, current residual:5.813817e+01 +field 'mu' [explicit solve]: current solution: 5.186997e+00, current residual:3.132952e+00 +wall time: 0.000674963s + +time increment:29510 time: 29.51 +field ' c' [explicit solve]: current solution: 9.627254e+01, current residual:5.813807e+01 +field 'mu' [explicit solve]: current solution: 5.186881e+00, current residual:3.132887e+00 +wall time: 0.000665903s + +time increment:29520 time: 29.52 +field ' c' [explicit solve]: current solution: 9.627240e+01, current residual:5.813797e+01 +field 'mu' [explicit solve]: current solution: 5.186765e+00, current residual:3.132822e+00 +wall time: 0.00057292s + +time increment:29530 time: 29.53 +field ' c' [explicit solve]: current solution: 9.627225e+01, current residual:5.813788e+01 +field 'mu' [explicit solve]: current solution: 5.186649e+00, current residual:3.132758e+00 +wall time: 0.000653982s + +time increment:29540 time: 29.54 +field ' c' [explicit solve]: current solution: 9.627211e+01, current residual:5.813778e+01 +field 'mu' [explicit solve]: current solution: 5.186533e+00, current residual:3.132693e+00 +wall time: 0.000804901s + +time increment:29550 time: 29.55 +field ' c' [explicit solve]: current solution: 9.627196e+01, current residual:5.813769e+01 +field 'mu' [explicit solve]: current solution: 5.186417e+00, current residual:3.132628e+00 +wall time: 0.000560045s + +time increment:29560 time: 29.56 +field ' c' [explicit solve]: current solution: 9.627182e+01, current residual:5.813759e+01 +field 'mu' [explicit solve]: current solution: 5.186301e+00, current residual:3.132564e+00 +wall time: 0.00058198s + +time increment:29570 time: 29.57 +field ' c' [explicit solve]: current solution: 9.627167e+01, current residual:5.813750e+01 +field 'mu' [explicit solve]: current solution: 5.186184e+00, current residual:3.132499e+00 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0.000566006s + +time increment:29620 time: 29.62 +field ' c' [explicit solve]: current solution: 9.627095e+01, current residual:5.813702e+01 +field 'mu' [explicit solve]: current solution: 5.185604e+00, current residual:3.132175e+00 +wall time: 0.000654936s + +time increment:29630 time: 29.63 +field ' c' [explicit solve]: current solution: 9.627081e+01, current residual:5.813693e+01 +field 'mu' [explicit solve]: current solution: 5.185488e+00, current residual:3.132110e+00 +wall time: 0.000653028s + +time increment:29640 time: 29.64 +field ' c' [explicit solve]: current solution: 9.627066e+01, current residual:5.813683e+01 +field 'mu' [explicit solve]: current solution: 5.185372e+00, current residual:3.132045e+00 +wall time: 0.000563145s + +time increment:29650 time: 29.65 +field ' c' [explicit solve]: current solution: 9.627052e+01, current residual:5.813674e+01 +field 'mu' [explicit solve]: current solution: 5.185255e+00, current residual:3.131981e+00 +wall time: 0.000574827s + +time increment:29660 time: 29.66 +field ' c' [explicit solve]: current solution: 9.627037e+01, current residual:5.813664e+01 +field 'mu' [explicit solve]: current solution: 5.185139e+00, current residual:3.131916e+00 +wall time: 0.000566959s + +time increment:29670 time: 29.67 +field ' c' [explicit solve]: current solution: 9.627023e+01, current residual:5.813655e+01 +field 'mu' [explicit solve]: current solution: 5.185023e+00, current residual:3.131851e+00 +wall time: 0.000698805s + +time increment:29680 time: 29.68 +field ' c' [explicit solve]: current solution: 9.627009e+01, current residual:5.813645e+01 +field 'mu' [explicit solve]: current solution: 5.184907e+00, current residual:3.131786e+00 +wall time: 0.000570059s + +time increment:29690 time: 29.69 +field ' c' [explicit solve]: current solution: 9.626994e+01, current residual:5.813636e+01 +field 'mu' [explicit solve]: current solution: 5.184791e+00, current residual:3.131721e+00 +wall time: 0.000622988s + +time increment:29700 time: 29.7 +field ' c' [explicit solve]: current solution: 9.626980e+01, current residual:5.813626e+01 +field 'mu' [explicit solve]: current solution: 5.184675e+00, current residual:3.131656e+00 +wall time: 0.000633955s + +time increment:29710 time: 29.71 +field ' c' [explicit solve]: current solution: 9.626966e+01, current residual:5.813617e+01 +field 'mu' [explicit solve]: current solution: 5.184558e+00, current residual:3.131591e+00 +wall time: 0.00067687s + +time increment:29720 time: 29.72 +field ' c' [explicit solve]: current solution: 9.626951e+01, current residual:5.813608e+01 +field 'mu' [explicit solve]: current solution: 5.184442e+00, current residual:3.131527e+00 +wall time: 0.000602961s + +time increment:29730 time: 29.73 +field ' c' [explicit solve]: current solution: 9.626937e+01, current residual:5.813598e+01 +field 'mu' [explicit solve]: current solution: 5.184326e+00, current residual:3.131462e+00 +wall time: 0.000562191s + +time increment:29740 time: 29.74 +field ' c' [explicit solve]: current solution: 9.626923e+01, current residual:5.813589e+01 +field 'mu' [explicit solve]: current solution: 5.184210e+00, current residual:3.131397e+00 +wall time: 0.00059104s + +time increment:29750 time: 29.75 +field ' c' [explicit solve]: current solution: 9.626908e+01, current residual:5.813579e+01 +field 'mu' [explicit solve]: current solution: 5.184094e+00, current residual:3.131332e+00 +wall time: 0.000626802s + +time increment:29760 time: 29.76 +field ' c' [explicit solve]: current solution: 9.626894e+01, current residual:5.813570e+01 +field 'mu' [explicit solve]: current solution: 5.183977e+00, current residual:3.131267e+00 +wall time: 0.000571966s + +time increment:29770 time: 29.77 +field ' c' [explicit solve]: current solution: 9.626880e+01, current residual:5.813561e+01 +field 'mu' [explicit solve]: current solution: 5.183861e+00, current residual:3.131202e+00 +wall time: 0.000669003s + +time increment:29780 time: 29.78 +field ' c' [explicit solve]: current solution: 9.626866e+01, current residual:5.813551e+01 +field 'mu' [explicit solve]: current solution: 5.183745e+00, current residual:3.131137e+00 +wall time: 0.000745058s + +time increment:29790 time: 29.79 +field ' c' [explicit solve]: current solution: 9.626851e+01, current residual:5.813542e+01 +field 'mu' [explicit solve]: current solution: 5.183629e+00, current residual:3.131072e+00 +wall time: 0.000687122s + +time increment:29800 time: 29.8 +field ' c' [explicit solve]: current solution: 9.626837e+01, current residual:5.813533e+01 +field 'mu' [explicit solve]: current solution: 5.183512e+00, current residual:3.131007e+00 +wall time: 0.000761986s + +time increment:29810 time: 29.81 +field ' c' [explicit solve]: current solution: 9.626823e+01, current residual:5.813523e+01 +field 'mu' [explicit solve]: current solution: 5.183396e+00, current residual:3.130942e+00 +wall time: 0.000775099s + +time increment:29820 time: 29.82 +field ' c' [explicit solve]: current solution: 9.626809e+01, current residual:5.813514e+01 +field 'mu' [explicit solve]: current solution: 5.183280e+00, current residual:3.130877e+00 +wall time: 0.000777006s + +time increment:29830 time: 29.83 +field ' c' [explicit solve]: current solution: 9.626795e+01, current residual:5.813505e+01 +field 'mu' [explicit solve]: current solution: 5.183163e+00, current residual:3.130812e+00 +wall time: 0.000671864s + +time increment:29840 time: 29.84 +field ' c' [explicit solve]: current solution: 9.626781e+01, current residual:5.813495e+01 +field 'mu' [explicit solve]: current solution: 5.183047e+00, current residual:3.130747e+00 +wall time: 0.000645161s + +time increment:29850 time: 29.85 +field ' c' [explicit solve]: current solution: 9.626766e+01, current residual:5.813486e+01 +field 'mu' [explicit solve]: current solution: 5.182931e+00, current residual:3.130682e+00 +wall time: 0.000693798s + +time increment:29860 time: 29.86 +field ' c' [explicit solve]: current solution: 9.626752e+01, current residual:5.813477e+01 +field 'mu' [explicit solve]: current solution: 5.182815e+00, current residual:3.130616e+00 +wall time: 0.000833035s + +time increment:29870 time: 29.87 +field ' c' [explicit solve]: current solution: 9.626738e+01, current residual:5.813467e+01 +field 'mu' [explicit solve]: current solution: 5.182698e+00, current residual:3.130551e+00 +wall time: 0.000666142s + +time increment:29880 time: 29.88 +field ' c' [explicit solve]: current solution: 9.626724e+01, current residual:5.813458e+01 +field 'mu' [explicit solve]: current solution: 5.182582e+00, current residual:3.130486e+00 +wall time: 0.000631094s + +time increment:29890 time: 29.89 +field ' c' [explicit solve]: current solution: 9.626710e+01, current residual:5.813449e+01 +field 'mu' [explicit solve]: current solution: 5.182466e+00, current residual:3.130421e+00 +wall time: 0.000686169s + +time increment:29900 time: 29.9 +field ' c' [explicit solve]: current solution: 9.626696e+01, current residual:5.813439e+01 +field 'mu' [explicit solve]: current solution: 5.182349e+00, current residual:3.130356e+00 +wall time: 0.000743151s + +time increment:29910 time: 29.91 +field ' c' [explicit solve]: current solution: 9.626682e+01, current residual:5.813430e+01 +field 'mu' [explicit solve]: current solution: 5.182233e+00, current residual:3.130291e+00 +wall time: 0.000716925s + +time increment:29920 time: 29.92 +field ' c' [explicit solve]: current solution: 9.626668e+01, current residual:5.813421e+01 +field 'mu' [explicit solve]: current solution: 5.182117e+00, current residual:3.130226e+00 +wall time: 0.000706911s + +time increment:29930 time: 29.93 +field ' c' [explicit solve]: current solution: 9.626654e+01, current residual:5.813412e+01 +field 'mu' [explicit solve]: current solution: 5.182000e+00, current residual:3.130160e+00 +wall time: 0.000684023s + +time increment:29940 time: 29.94 +field ' c' [explicit solve]: current solution: 9.626640e+01, current residual:5.813402e+01 +field 'mu' [explicit solve]: current solution: 5.181884e+00, current residual:3.130095e+00 +wall time: 0.00066781s + +time increment:29950 time: 29.95 +field ' c' [explicit solve]: current solution: 9.626626e+01, current residual:5.813393e+01 +field 'mu' [explicit solve]: current solution: 5.181767e+00, current residual:3.130030e+00 +wall time: 0.000631809s + +time increment:29960 time: 29.96 +field ' c' [explicit solve]: current solution: 9.626612e+01, current residual:5.813384e+01 +field 'mu' [explicit solve]: current solution: 5.181651e+00, current residual:3.129965e+00 +wall time: 0.000664949s + +time increment:29970 time: 29.97 +field ' c' [explicit solve]: current solution: 9.626598e+01, current residual:5.813375e+01 +field 'mu' [explicit solve]: current solution: 5.181535e+00, current residual:3.129900e+00 +wall time: 0.000722885s + +time increment:29980 time: 29.98 +field ' c' [explicit solve]: current solution: 9.626584e+01, current residual:5.813366e+01 +field 'mu' [explicit solve]: current solution: 5.181418e+00, current residual:3.129834e+00 +wall time: 0.00077486s + +time increment:29990 time: 29.99 +field ' c' [explicit solve]: current solution: 9.626570e+01, current residual:5.813356e+01 +field 'mu' [explicit solve]: current solution: 5.181302e+00, current residual:3.129769e+00 +wall time: 0.000656128s + +time increment:30000 time: 30 +field ' c' [explicit solve]: current solution: 9.626556e+01, current residual:5.813347e+01 +field 'mu' [explicit solve]: current solution: 5.181185e+00, current residual:3.129704e+00 +wall time: 0.000658989s +fields: 2 +fields: 2 +fields: 2 +fields: 2 +Output written to:solution-030000.pvtu + +Energy: 247.958 +Energy Components: 117.751 130.207 0 + +time increment:30010 time: 30.01 +field ' c' [explicit solve]: current solution: 9.626542e+01, current residual:5.813338e+01 +field 'mu' [explicit solve]: current solution: 5.181069e+00, current residual:3.129639e+00 +wall time: 0.000569105s + +time increment:30020 time: 30.02 +field ' c' [explicit solve]: current solution: 9.626528e+01, current residual:5.813329e+01 +field 'mu' [explicit solve]: current solution: 5.180952e+00, current residual:3.129573e+00 +wall time: 0.000674009s + +time increment:30030 time: 30.03 +field ' c' [explicit solve]: current solution: 9.626514e+01, current residual:5.813320e+01 +field 'mu' [explicit solve]: current solution: 5.180836e+00, current residual:3.129508e+00 +wall time: 0.000756025s + +time increment:30040 time: 30.04 +field ' c' [explicit solve]: current solution: 9.626500e+01, current residual:5.813311e+01 +field 'mu' [explicit solve]: current solution: 5.180720e+00, current residual:3.129443e+00 +wall time: 0.000754118s + +time increment:30050 time: 30.05 +field ' c' [explicit solve]: current solution: 9.626486e+01, current residual:5.813301e+01 +field 'mu' [explicit solve]: current solution: 5.180603e+00, current residual:3.129377e+00 +wall time: 0.000688076s + +time increment:30060 time: 30.06 +field ' c' [explicit solve]: current solution: 9.626472e+01, current residual:5.813292e+01 +field 'mu' [explicit solve]: current solution: 5.180487e+00, current residual:3.129312e+00 +wall time: 0.000675917s + +time increment:30070 time: 30.07 +field ' c' [explicit solve]: current solution: 9.626458e+01, current residual:5.813283e+01 +field 'mu' [explicit solve]: current solution: 5.180370e+00, current residual:3.129247e+00 +wall time: 0.000571012s + +time increment:30080 time: 30.08 +field ' c' [explicit solve]: current solution: 9.626445e+01, current residual:5.813274e+01 +field 'mu' [explicit solve]: current solution: 5.180254e+00, current residual:3.129181e+00 +wall time: 0.000607014s + +time increment:30090 time: 30.09 +field ' c' [explicit solve]: current solution: 9.626431e+01, current residual:5.813265e+01 +field 'mu' [explicit solve]: current solution: 5.180137e+00, current residual:3.129116e+00 +wall time: 0.000573158s + +time increment:30100 time: 30.1 +field ' c' [explicit solve]: current solution: 9.626417e+01, current residual:5.813256e+01 +field 'mu' [explicit solve]: current solution: 5.180021e+00, current residual:3.129050e+00 +wall time: 0.000694036s + +time increment:30110 time: 30.11 +field ' c' [explicit solve]: current solution: 9.626403e+01, current residual:5.813247e+01 +field 'mu' [explicit solve]: current solution: 5.179904e+00, current residual:3.128985e+00 +wall time: 0.000584841s + +time increment:30120 time: 30.12 +field ' c' [explicit solve]: current solution: 9.626389e+01, current residual:5.813238e+01 +field 'mu' [explicit solve]: current solution: 5.179787e+00, current residual:3.128920e+00 +wall time: 0.000602007s + +time increment:30130 time: 30.13 +field ' c' [explicit solve]: current solution: 9.626375e+01, current residual:5.813228e+01 +field 'mu' [explicit solve]: current solution: 5.179671e+00, current residual:3.128854e+00 +wall time: 0.000641108s + +time increment:30140 time: 30.14 +field ' c' [explicit solve]: current solution: 9.626362e+01, current residual:5.813219e+01 +field 'mu' [explicit solve]: current solution: 5.179554e+00, current residual:3.128789e+00 +wall time: 0.00062108s + +time increment:30150 time: 30.15 +field ' c' [explicit solve]: current solution: 9.626348e+01, current residual:5.813210e+01 +field 'mu' [explicit solve]: current solution: 5.179438e+00, current residual:3.128723e+00 +wall time: 0.000597954s + +time increment:30160 time: 30.16 +field ' c' [explicit solve]: current solution: 9.626334e+01, current residual:5.813201e+01 +field 'mu' [explicit solve]: current solution: 5.179321e+00, current residual:3.128658e+00 +wall time: 0.000610828s + +time increment:30170 time: 30.17 +field ' c' [explicit solve]: current solution: 9.626320e+01, current residual:5.813192e+01 +field 'mu' [explicit solve]: current solution: 5.179205e+00, current residual:3.128592e+00 +wall time: 0.000797987s + +time increment:30180 time: 30.18 +field ' c' [explicit solve]: current solution: 9.626307e+01, current residual:5.813183e+01 +field 'mu' [explicit solve]: current solution: 5.179088e+00, current residual:3.128527e+00 +wall time: 0.000653028s + +time increment:30190 time: 30.19 +field ' c' [explicit solve]: current solution: 9.626293e+01, current residual:5.813174e+01 +field 'mu' [explicit solve]: current solution: 5.178971e+00, current residual:3.128461e+00 +wall time: 0.000656843s + +time increment:30200 time: 30.2 +field ' c' [explicit solve]: current solution: 9.626279e+01, current residual:5.813165e+01 +field 'mu' [explicit solve]: current solution: 5.178855e+00, current residual:3.128396e+00 +wall time: 0.000760078s + +time increment:30210 time: 30.21 +field ' c' [explicit solve]: current solution: 9.626266e+01, current residual:5.813156e+01 +field 'mu' [explicit solve]: current solution: 5.178738e+00, current residual:3.128330e+00 +wall time: 0.000662088s + +time increment:30220 time: 30.22 +field ' c' [explicit solve]: current solution: 9.626252e+01, current residual:5.813147e+01 +field 'mu' [explicit solve]: current solution: 5.178621e+00, current residual:3.128264e+00 +wall time: 0.000711918s + +time increment:30230 time: 30.23 +field ' c' [explicit solve]: current solution: 9.626238e+01, current residual:5.813138e+01 +field 'mu' [explicit solve]: current solution: 5.178505e+00, current residual:3.128199e+00 +wall time: 0.000691891s + +time increment:30240 time: 30.24 +field ' c' [explicit solve]: current solution: 9.626225e+01, current residual:5.813129e+01 +field 'mu' [explicit solve]: current solution: 5.178388e+00, current residual:3.128133e+00 +wall time: 0.000663042s + +time increment:30250 time: 30.25 +field ' c' [explicit solve]: current solution: 9.626211e+01, current residual:5.813120e+01 +field 'mu' [explicit solve]: current solution: 5.178271e+00, current residual:3.128068e+00 +wall time: 0.00059104s + +time increment:30260 time: 30.26 +field ' c' [explicit solve]: current solution: 9.626197e+01, current residual:5.813111e+01 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solve]: current solution: 5.177688e+00, current residual:3.127739e+00 +wall time: 0.000561953s + +time increment:30310 time: 30.31 +field ' c' [explicit solve]: current solution: 9.626129e+01, current residual:5.813066e+01 +field 'mu' [explicit solve]: current solution: 5.177571e+00, current residual:3.127674e+00 +wall time: 0.000637054s + +time increment:30320 time: 30.32 +field ' c' [explicit solve]: current solution: 9.626116e+01, current residual:5.813057e+01 +field 'mu' [explicit solve]: current solution: 5.177454e+00, current residual:3.127608e+00 +wall time: 0.000558853s + +time increment:30330 time: 30.33 +field ' c' [explicit solve]: current solution: 9.626102e+01, current residual:5.813048e+01 +field 'mu' [explicit solve]: current solution: 5.177338e+00, current residual:3.127542e+00 +wall time: 0.000589848s + +time increment:30340 time: 30.34 +field ' c' [explicit solve]: current solution: 9.626089e+01, current residual:5.813039e+01 +field 'mu' [explicit solve]: current solution: 5.177221e+00, current residual:3.127476e+00 +wall time: 0.000627041s + +time increment:30350 time: 30.35 +field ' c' [explicit solve]: current solution: 9.626075e+01, current residual:5.813030e+01 +field 'mu' [explicit solve]: current solution: 5.177104e+00, current residual:3.127411e+00 +wall time: 0.000586033s + +time increment:30360 time: 30.36 +field ' c' [explicit solve]: current solution: 9.626062e+01, current residual:5.813022e+01 +field 'mu' [explicit solve]: current solution: 5.176987e+00, current residual:3.127345e+00 +wall time: 0.000651121s + +time increment:30370 time: 30.37 +field ' c' [explicit solve]: current solution: 9.626048e+01, current residual:5.813013e+01 +field 'mu' [explicit solve]: current solution: 5.176870e+00, current residual:3.127279e+00 +wall time: 0.000690937s + +time increment:30380 time: 30.38 +field ' c' [explicit solve]: current solution: 9.626035e+01, current residual:5.813004e+01 +field 'mu' [explicit solve]: current solution: 5.176754e+00, current residual:3.127213e+00 +wall time: 0.000710011s + +time increment:30390 time: 30.39 +field ' c' [explicit solve]: current solution: 9.626021e+01, current residual:5.812995e+01 +field 'mu' [explicit solve]: current solution: 5.176637e+00, current residual:3.127147e+00 +wall time: 0.000741005s + +time increment:30400 time: 30.4 +field ' c' [explicit solve]: current solution: 9.626008e+01, current residual:5.812986e+01 +field 'mu' [explicit solve]: current solution: 5.176520e+00, current residual:3.127082e+00 +wall time: 0.000691891s + +time increment:30410 time: 30.41 +field ' c' [explicit solve]: current solution: 9.625994e+01, current residual:5.812977e+01 +field 'mu' [explicit solve]: current solution: 5.176403e+00, current residual:3.127016e+00 +wall time: 0.000669003s + +time increment:30420 time: 30.42 +field ' c' [explicit solve]: current solution: 9.625981e+01, current residual:5.812968e+01 +field 'mu' [explicit solve]: current solution: 5.176286e+00, current residual:3.126950e+00 +wall time: 0.000781059s + +time increment:30430 time: 30.43 +field ' c' [explicit solve]: current solution: 9.625968e+01, current residual:5.812959e+01 +field 'mu' [explicit solve]: current solution: 5.176169e+00, current residual:3.126884e+00 +wall time: 0.000695944s + +time increment:30440 time: 30.44 +field ' c' [explicit solve]: current solution: 9.625954e+01, current residual:5.812951e+01 +field 'mu' [explicit solve]: current solution: 5.176052e+00, current residual:3.126818e+00 +wall time: 0.000807047s + +time increment:30450 time: 30.45 +field ' c' [explicit solve]: current solution: 9.625941e+01, current residual:5.812942e+01 +field 'mu' [explicit solve]: current solution: 5.175935e+00, current residual:3.126752e+00 +wall time: 0.000718832s + +time increment:30460 time: 30.46 +field ' c' [explicit solve]: current solution: 9.625927e+01, current residual:5.812933e+01 +field 'mu' [explicit solve]: current solution: 5.175819e+00, current residual:3.126686e+00 +wall time: 0.000702858s + +time increment:30470 time: 30.47 +field ' c' [explicit solve]: current solution: 9.625914e+01, current residual:5.812924e+01 +field 'mu' [explicit solve]: current solution: 5.175702e+00, current residual:3.126620e+00 +wall time: 0.000706911s + +time increment:30480 time: 30.48 +field ' c' [explicit solve]: current solution: 9.625901e+01, current residual:5.812915e+01 +field 'mu' [explicit solve]: current solution: 5.175585e+00, current residual:3.126554e+00 +wall time: 0.000749826s + +time increment:30490 time: 30.49 +field ' c' [explicit solve]: current solution: 9.625887e+01, current residual:5.812906e+01 +field 'mu' [explicit solve]: current solution: 5.175468e+00, current residual:3.126488e+00 +wall time: 0.000726938s + +time increment:30500 time: 30.5 +field ' c' [explicit solve]: current solution: 9.625874e+01, current residual:5.812898e+01 +field 'mu' [explicit solve]: current solution: 5.175351e+00, current residual:3.126422e+00 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0.000791073s + +time increment:30550 time: 30.55 +field ' c' [explicit solve]: current solution: 9.625807e+01, current residual:5.812854e+01 +field 'mu' [explicit solve]: current solution: 5.174766e+00, current residual:3.126092e+00 +wall time: 0.000652075s + +time increment:30560 time: 30.56 +field ' c' [explicit solve]: current solution: 9.625794e+01, current residual:5.812845e+01 +field 'mu' [explicit solve]: current solution: 5.174649e+00, current residual:3.126026e+00 +wall time: 0.00072217s + +time increment:30570 time: 30.57 +field ' c' [explicit solve]: current solution: 9.625781e+01, current residual:5.812836e+01 +field 'mu' [explicit solve]: current solution: 5.174532e+00, current residual:3.125960e+00 +wall time: 0.000715017s + +time increment:30580 time: 30.58 +field ' c' [explicit solve]: current solution: 9.625768e+01, current residual:5.812828e+01 +field 'mu' [explicit solve]: current solution: 5.174415e+00, current residual:3.125894e+00 +wall time: 0.000566006s + +time increment:30590 time: 30.59 +field ' c' [explicit solve]: current solution: 9.625754e+01, current residual:5.812819e+01 +field 'mu' [explicit solve]: current solution: 5.174298e+00, current residual:3.125828e+00 +wall time: 0.00071907s + +time increment:30600 time: 30.6 +field ' c' [explicit solve]: current solution: 9.625741e+01, current residual:5.812810e+01 +field 'mu' [explicit solve]: current solution: 5.174181e+00, current residual:3.125762e+00 +wall time: 0.00071907s + +time increment:30610 time: 30.61 +field ' c' [explicit solve]: current solution: 9.625728e+01, current residual:5.812801e+01 +field 'mu' [explicit solve]: current solution: 5.174063e+00, current residual:3.125696e+00 +wall time: 0.000788927s + +time increment:30620 time: 30.62 +field ' c' [explicit solve]: current solution: 9.625715e+01, current residual:5.812793e+01 +field 'mu' [explicit solve]: current solution: 5.173946e+00, current residual:3.125630e+00 +wall time: 0.000718117s + +time increment:30630 time: 30.63 +field ' c' [explicit solve]: current solution: 9.625702e+01, current residual:5.812784e+01 +field 'mu' [explicit solve]: current solution: 5.173829e+00, current residual:3.125563e+00 +wall time: 0.00070405s + +time increment:30640 time: 30.64 +field ' c' [explicit solve]: current solution: 9.625688e+01, current residual:5.812775e+01 +field 'mu' [explicit solve]: current solution: 5.173712e+00, current residual:3.125497e+00 +wall time: 0.000743866s + +time increment:30650 time: 30.65 +field ' c' [explicit solve]: current solution: 9.625675e+01, current residual:5.812767e+01 +field 'mu' [explicit solve]: current solution: 5.173595e+00, current residual:3.125431e+00 +wall time: 0.00065589s + +time increment:30660 time: 30.66 +field ' c' [explicit solve]: current solution: 9.625662e+01, current residual:5.812758e+01 +field 'mu' [explicit solve]: current solution: 5.173478e+00, current residual:3.125365e+00 +wall time: 0.00065589s + +time increment:30670 time: 30.67 +field ' c' [explicit solve]: current solution: 9.625649e+01, current residual:5.812749e+01 +field 'mu' [explicit solve]: current solution: 5.173361e+00, current residual:3.125299e+00 +wall time: 0.000695944s + +time increment:30680 time: 30.68 +field ' c' [explicit solve]: current solution: 9.625636e+01, current residual:5.812741e+01 +field 'mu' [explicit solve]: current solution: 5.173243e+00, current residual:3.125232e+00 +wall time: 0.000647068s + +time increment:30690 time: 30.69 +field ' c' [explicit solve]: current solution: 9.625623e+01, current residual:5.812732e+01 +field 'mu' [explicit solve]: current solution: 5.173126e+00, current residual:3.125166e+00 +wall time: 0.000649929s + +time increment:30700 time: 30.7 +field ' c' [explicit solve]: current solution: 9.625610e+01, current residual:5.812723e+01 +field 'mu' [explicit solve]: current solution: 5.173009e+00, current residual:3.125100e+00 +wall time: 0.000701904s + +time increment:30710 time: 30.71 +field ' c' [explicit solve]: current solution: 9.625597e+01, current residual:5.812715e+01 +field 'mu' [explicit solve]: current solution: 5.172892e+00, current residual:3.125034e+00 +wall time: 0.000654936s + +time increment:30720 time: 30.72 +field ' c' [explicit solve]: current solution: 9.625584e+01, current residual:5.812706e+01 +field 'mu' [explicit solve]: current solution: 5.172775e+00, current residual:3.124967e+00 +wall time: 0.00067997s + +time increment:30730 time: 30.73 +field ' c' [explicit solve]: current solution: 9.625571e+01, current residual:5.812697e+01 +field 'mu' [explicit solve]: current solution: 5.172657e+00, current residual:3.124901e+00 +wall time: 0.000609875s + +time increment:30740 time: 30.74 +field ' c' [explicit solve]: current solution: 9.625557e+01, current residual:5.812689e+01 +field 'mu' [explicit solve]: current solution: 5.172540e+00, current residual:3.124835e+00 +wall time: 0.000559092s + +time increment:30750 time: 30.75 +field ' c' [explicit solve]: current solution: 9.625544e+01, current residual:5.812680e+01 +field 'mu' [explicit solve]: current solution: 5.172423e+00, current residual:3.124768e+00 +wall time: 0.000558138s + +time increment:30760 time: 30.76 +field ' c' [explicit solve]: current solution: 9.625531e+01, current residual:5.812672e+01 +field 'mu' [explicit solve]: current solution: 5.172306e+00, current residual:3.124702e+00 +wall time: 0.00056982s + +time increment:30770 time: 30.77 +field ' c' [explicit solve]: current solution: 9.625518e+01, current residual:5.812663e+01 +field 'mu' [explicit solve]: current solution: 5.172188e+00, current residual:3.124636e+00 +wall time: 0.000657082s + +time increment:30780 time: 30.78 +field ' c' [explicit solve]: current solution: 9.625505e+01, current residual:5.812654e+01 +field 'mu' [explicit solve]: current solution: 5.172071e+00, current residual:3.124569e+00 +wall time: 0.000718117s + +time increment:30790 time: 30.79 +field ' c' [explicit solve]: current solution: 9.625492e+01, current residual:5.812646e+01 +field 'mu' [explicit solve]: current solution: 5.171954e+00, current residual:3.124503e+00 +wall time: 0.000571012s + +time increment:30800 time: 30.8 +field ' c' [explicit solve]: current solution: 9.625479e+01, current residual:5.812637e+01 +field 'mu' [explicit solve]: current solution: 5.171836e+00, current residual:3.124436e+00 +wall time: 0.000569105s + +time increment:30810 time: 30.81 +field ' c' [explicit solve]: current solution: 9.625466e+01, current residual:5.812629e+01 +field 'mu' [explicit solve]: current solution: 5.171719e+00, current residual:3.124370e+00 +wall time: 0.00066185s + +time increment:30820 time: 30.82 +field ' c' [explicit solve]: current solution: 9.625454e+01, current residual:5.812620e+01 +field 'mu' [explicit solve]: current solution: 5.171602e+00, current residual:3.124303e+00 +wall time: 0.000662804s + +time increment:30830 time: 30.83 +field ' c' [explicit solve]: current solution: 9.625441e+01, current residual:5.812612e+01 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solve]: current solution: 5.171015e+00, current residual:3.123971e+00 +wall time: 0.000586987s + +time increment:30880 time: 30.88 +field ' c' [explicit solve]: current solution: 9.625376e+01, current residual:5.812569e+01 +field 'mu' [explicit solve]: current solution: 5.170897e+00, current residual:3.123904e+00 +wall time: 0.000672102s + +time increment:30890 time: 30.89 +field ' c' [explicit solve]: current solution: 9.625363e+01, current residual:5.812560e+01 +field 'mu' [explicit solve]: current solution: 5.170780e+00, current residual:3.123838e+00 +wall time: 0.000696182s + +time increment:30900 time: 30.9 +field ' c' [explicit solve]: current solution: 9.625350e+01, current residual:5.812552e+01 +field 'mu' [explicit solve]: current solution: 5.170662e+00, current residual:3.123771e+00 +wall time: 0.000672102s + +time increment:30910 time: 30.91 +field ' c' [explicit solve]: current solution: 9.625337e+01, current residual:5.812543e+01 +field 'mu' [explicit solve]: current solution: 5.170545e+00, current residual:3.123705e+00 +wall time: 0.00067997s + +time increment:30920 time: 30.92 +field ' c' [explicit solve]: current solution: 9.625325e+01, current residual:5.812535e+01 +field 'mu' [explicit solve]: current solution: 5.170427e+00, current residual:3.123638e+00 +wall time: 0.000716925s + +time increment:30930 time: 30.93 +field ' c' [explicit solve]: current solution: 9.625312e+01, current residual:5.812526e+01 +field 'mu' [explicit solve]: current solution: 5.170310e+00, current residual:3.123571e+00 +wall time: 0.000711203s + +time increment:30940 time: 30.94 +field ' c' [explicit solve]: current solution: 9.625299e+01, current residual:5.812518e+01 +field 'mu' [explicit solve]: current solution: 5.170192e+00, current residual:3.123505e+00 +wall time: 0.000584841s + +time increment:30950 time: 30.95 +field ' c' [explicit solve]: current solution: 9.625286e+01, current residual:5.812510e+01 +field 'mu' [explicit solve]: current solution: 5.170075e+00, current residual:3.123438e+00 +wall time: 0.000710011s + +time increment:30960 time: 30.96 +field ' c' [explicit solve]: current solution: 9.625273e+01, current residual:5.812501e+01 +field 'mu' [explicit solve]: current solution: 5.169957e+00, current residual:3.123372e+00 +wall time: 0.000681877s + +time increment:30970 time: 30.97 +field ' c' [explicit solve]: current solution: 9.625261e+01, current residual:5.812493e+01 +field 'mu' [explicit solve]: current solution: 5.169840e+00, current residual:3.123305e+00 +wall time: 0.000607967s + +time increment:30980 time: 30.98 +field ' c' [explicit solve]: current solution: 9.625248e+01, current residual:5.812484e+01 +field 'mu' [explicit solve]: current solution: 5.169722e+00, current residual:3.123238e+00 +wall time: 0.000683069s + +time increment:30990 time: 30.99 +field ' c' [explicit solve]: current solution: 9.625235e+01, current residual:5.812476e+01 +field 'mu' [explicit solve]: current solution: 5.169605e+00, current residual:3.123171e+00 +wall time: 0.000604868s + +time increment:31000 time: 31 +field ' c' [explicit solve]: current solution: 9.625222e+01, current residual:5.812467e+01 +field 'mu' [explicit solve]: current solution: 5.169487e+00, current residual:3.123105e+00 +wall time: 0.000557899s + +time increment:31010 time: 31.01 +field ' c' [explicit solve]: current solution: 9.625210e+01, current residual:5.812459e+01 +field 'mu' [explicit solve]: current solution: 5.169369e+00, current residual:3.123038e+00 +wall time: 0.000672102s + +time increment:31020 time: 31.02 +field ' c' [explicit solve]: current solution: 9.625197e+01, current residual:5.812451e+01 +field 'mu' [explicit solve]: current solution: 5.169252e+00, current residual:3.122971e+00 +wall time: 0.000672102s + +time increment:31030 time: 31.03 +field ' c' [explicit solve]: current solution: 9.625184e+01, current residual:5.812442e+01 +field 'mu' [explicit solve]: current solution: 5.169134e+00, current residual:3.122905e+00 +wall time: 0.000564098s + +time increment:31040 time: 31.04 +field ' c' [explicit solve]: current solution: 9.625172e+01, current residual:5.812434e+01 +field 'mu' [explicit solve]: current solution: 5.169016e+00, current residual:3.122838e+00 +wall time: 0.000622988s + +time increment:31050 time: 31.05 +field ' c' [explicit solve]: current solution: 9.625159e+01, current residual:5.812425e+01 +field 'mu' [explicit solve]: current solution: 5.168899e+00, current residual:3.122771e+00 +wall time: 0.00057888s + +time increment:31060 time: 31.06 +field ' c' [explicit solve]: current solution: 9.625146e+01, current residual:5.812417e+01 +field 'mu' [explicit solve]: current solution: 5.168781e+00, current residual:3.122704e+00 +wall time: 0.000668049s + +time increment:31070 time: 31.07 +field ' c' [explicit solve]: current solution: 9.625134e+01, current residual:5.812409e+01 +field 'mu' [explicit solve]: current solution: 5.168663e+00, current residual:3.122637e+00 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0.000849009s + +time increment:31120 time: 31.12 +field ' c' [explicit solve]: current solution: 9.625070e+01, current residual:5.812367e+01 +field 'mu' [explicit solve]: current solution: 5.168075e+00, current residual:3.122303e+00 +wall time: 0.000577927s + +time increment:31130 time: 31.13 +field ' c' [explicit solve]: current solution: 9.625058e+01, current residual:5.812359e+01 +field 'mu' [explicit solve]: current solution: 5.167957e+00, current residual:3.122236e+00 +wall time: 0.000566959s + +time increment:31140 time: 31.14 +field ' c' [explicit solve]: current solution: 9.625045e+01, current residual:5.812350e+01 +field 'mu' [explicit solve]: current solution: 5.167839e+00, current residual:3.122169e+00 +wall time: 0.000684023s + +time increment:31150 time: 31.15 +field ' c' [explicit solve]: current solution: 9.625033e+01, current residual:5.812342e+01 +field 'mu' [explicit solve]: current solution: 5.167721e+00, current residual:3.122102e+00 +wall time: 0.000589132s + +time increment:31160 time: 31.16 +field ' c' [explicit solve]: current solution: 9.625020e+01, current residual:5.812334e+01 +field 'mu' [explicit solve]: current solution: 5.167604e+00, current residual:3.122035e+00 +wall time: 0.000564098s + +time increment:31170 time: 31.17 +field ' c' [explicit solve]: current solution: 9.625008e+01, current residual:5.812325e+01 +field 'mu' [explicit solve]: current solution: 5.167486e+00, current residual:3.121968e+00 +wall time: 0.000566959s + +time increment:31180 time: 31.18 +field ' c' [explicit solve]: current solution: 9.624995e+01, current residual:5.812317e+01 +field 'mu' [explicit solve]: current solution: 5.167368e+00, current residual:3.121901e+00 +wall time: 0.000628948s + +time increment:31190 time: 31.19 +field ' c' [explicit solve]: current solution: 9.624983e+01, current residual:5.812309e+01 +field 'mu' [explicit solve]: current solution: 5.167250e+00, current residual:3.121834e+00 +wall time: 0.000653982s + +time increment:31200 time: 31.2 +field ' c' [explicit solve]: current solution: 9.624970e+01, current residual:5.812300e+01 +field 'mu' [explicit solve]: current solution: 5.167132e+00, current residual:3.121767e+00 +wall time: 0.000586033s + +time increment:31210 time: 31.21 +field ' c' [explicit solve]: current solution: 9.624958e+01, current residual:5.812292e+01 +field 'mu' [explicit solve]: current solution: 5.167014e+00, current residual:3.121700e+00 +wall time: 0.000663042s + +time increment:31220 time: 31.22 +field ' c' [explicit solve]: current solution: 9.624945e+01, current residual:5.812284e+01 +field 'mu' [explicit solve]: current solution: 5.166897e+00, current residual:3.121633e+00 +wall time: 0.000618935s + +time increment:31230 time: 31.23 +field ' c' [explicit solve]: current solution: 9.624933e+01, current residual:5.812276e+01 +field 'mu' [explicit solve]: current solution: 5.166779e+00, current residual:3.121566e+00 +wall time: 0.00059104s + +time increment:31240 time: 31.24 +field ' c' [explicit solve]: current solution: 9.624920e+01, current residual:5.812267e+01 +field 'mu' [explicit solve]: current solution: 5.166661e+00, current residual:3.121499e+00 +wall time: 0.000622988s + +time increment:31250 time: 31.25 +field ' c' [explicit solve]: current solution: 9.624908e+01, current residual:5.812259e+01 +field 'mu' [explicit solve]: current solution: 5.166543e+00, current residual:3.121432e+00 +wall time: 0.000741959s + +time increment:31260 time: 31.26 +field ' c' [explicit solve]: current solution: 9.624895e+01, current residual:5.812251e+01 +field 'mu' [explicit solve]: current solution: 5.166425e+00, current residual:3.121365e+00 +wall time: 0.000638962s + +time increment:31270 time: 31.27 +field ' c' [explicit solve]: current solution: 9.624883e+01, current residual:5.812243e+01 +field 'mu' [explicit solve]: current solution: 5.166307e+00, current residual:3.121298e+00 +wall time: 0.000617981s + +time increment:31280 time: 31.28 +field ' c' [explicit solve]: current solution: 9.624870e+01, current residual:5.812234e+01 +field 'mu' [explicit solve]: current solution: 5.166189e+00, current residual:3.121230e+00 +wall time: 0.000684023s + +time increment:31290 time: 31.29 +field ' c' [explicit solve]: current solution: 9.624858e+01, current residual:5.812226e+01 +field 'mu' [explicit solve]: current solution: 5.166071e+00, current residual:3.121163e+00 +wall time: 0.000595093s + +time increment:31300 time: 31.3 +field ' c' [explicit solve]: current solution: 9.624846e+01, current residual:5.812218e+01 +field 'mu' [explicit solve]: current solution: 5.165953e+00, current residual:3.121096e+00 +wall time: 0.000626087s + +time increment:31310 time: 31.31 +field ' c' [explicit solve]: current solution: 9.624833e+01, current residual:5.812210e+01 +field 'mu' [explicit solve]: current solution: 5.165835e+00, current residual:3.121029e+00 +wall time: 0.000687838s + +time increment:31320 time: 31.32 +field ' c' [explicit solve]: current solution: 9.624821e+01, current residual:5.812202e+01 +field 'mu' [explicit solve]: current solution: 5.165717e+00, current residual:3.120962e+00 +wall time: 0.000571012s + +time increment:31330 time: 31.33 +field ' c' [explicit solve]: current solution: 9.624808e+01, current residual:5.812193e+01 +field 'mu' [explicit solve]: current solution: 5.165599e+00, current residual:3.120895e+00 +wall time: 0.000653028s + +time increment:31340 time: 31.34 +field ' c' [explicit solve]: current solution: 9.624796e+01, current residual:5.812185e+01 +field 'mu' [explicit solve]: current solution: 5.165481e+00, current residual:3.120827e+00 +wall time: 0.000661135s + +time increment:31350 time: 31.35 +field ' c' [explicit solve]: current solution: 9.624784e+01, current residual:5.812177e+01 +field 'mu' [explicit solve]: current solution: 5.165363e+00, current residual:3.120760e+00 +wall time: 0.00061202s + +time increment:31360 time: 31.36 +field ' c' [explicit solve]: current solution: 9.624771e+01, current residual:5.812169e+01 +field 'mu' [explicit solve]: current solution: 5.165245e+00, current residual:3.120693e+00 +wall time: 0.000658035s + +time increment:31370 time: 31.37 +field ' c' [explicit solve]: current solution: 9.624759e+01, current residual:5.812161e+01 +field 'mu' [explicit solve]: current solution: 5.165127e+00, current residual:3.120626e+00 +wall time: 0.000569105s + +time increment:31380 time: 31.38 +field ' c' [explicit solve]: current solution: 9.624747e+01, current residual:5.812153e+01 +field 'mu' [explicit solve]: current solution: 5.165009e+00, current residual:3.120558e+00 +wall time: 0.00068903s + +time increment:31390 time: 31.39 +field ' c' [explicit solve]: current solution: 9.624735e+01, current residual:5.812145e+01 +field 'mu' [explicit solve]: current solution: 5.164890e+00, current residual:3.120491e+00 +wall time: 0.000564098s + +time increment:31400 time: 31.4 +field ' c' [explicit solve]: current solution: 9.624722e+01, current residual:5.812136e+01 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[explicit solve]: current solution: 5.164300e+00, current residual:3.120154e+00 +wall time: 0.00072217s + +time increment:31450 time: 31.45 +field ' c' [explicit solve]: current solution: 9.624661e+01, current residual:5.812096e+01 +field 'mu' [explicit solve]: current solution: 5.164181e+00, current residual:3.120087e+00 +wall time: 0.00075388s + +time increment:31460 time: 31.46 +field ' c' [explicit solve]: current solution: 9.624649e+01, current residual:5.812088e+01 +field 'mu' [explicit solve]: current solution: 5.164063e+00, current residual:3.120019e+00 +wall time: 0.000674009s + +time increment:31470 time: 31.47 +field ' c' [explicit solve]: current solution: 9.624637e+01, current residual:5.812080e+01 +field 'mu' [explicit solve]: current solution: 5.163945e+00, current residual:3.119952e+00 +wall time: 0.000683069s + +time increment:31480 time: 31.48 +field ' c' [explicit solve]: current solution: 9.624624e+01, current residual:5.812072e+01 +field 'mu' [explicit solve]: current solution: 5.163827e+00, current residual:3.119885e+00 +wall time: 0.000629902s + +time increment:31490 time: 31.49 +field ' c' [explicit solve]: current solution: 9.624612e+01, current residual:5.812064e+01 +field 'mu' [explicit solve]: current solution: 5.163709e+00, current residual:3.119817e+00 +wall time: 0.00067687s + +time increment:31500 time: 31.5 +field ' c' [explicit solve]: current solution: 9.624600e+01, current residual:5.812055e+01 +field 'mu' [explicit solve]: current solution: 5.163590e+00, current residual:3.119750e+00 +wall time: 0.000635862s + +time increment:31510 time: 31.51 +field ' c' [explicit solve]: current solution: 9.624588e+01, current residual:5.812047e+01 +field 'mu' [explicit solve]: current solution: 5.163472e+00, current residual:3.119682e+00 +wall time: 0.000664949s + +time increment:31520 time: 31.52 +field ' c' [explicit solve]: current solution: 9.624576e+01, current residual:5.812039e+01 +field 'mu' [explicit solve]: current solution: 5.163354e+00, current residual:3.119615e+00 +wall time: 0.000690937s + +time increment:31530 time: 31.53 +field ' c' [explicit solve]: current solution: 9.624564e+01, current residual:5.812031e+01 +field 'mu' [explicit solve]: current solution: 5.163235e+00, current residual:3.119547e+00 +wall time: 0.00066185s + +time increment:31540 time: 31.54 +field ' c' [explicit solve]: current solution: 9.624552e+01, current residual:5.812023e+01 +field 'mu' [explicit solve]: current solution: 5.163117e+00, current residual:3.119480e+00 +wall time: 0.00088501s + +time increment:31550 time: 31.55 +field ' c' [explicit solve]: current solution: 9.624539e+01, current residual:5.812015e+01 +field 'mu' [explicit solve]: current solution: 5.162999e+00, current residual:3.119412e+00 +wall time: 0.000689983s + +time increment:31560 time: 31.56 +field ' c' [explicit solve]: current solution: 9.624527e+01, current residual:5.812007e+01 +field 'mu' [explicit solve]: current solution: 5.162880e+00, current residual:3.119344e+00 +wall time: 0.000679016s + +time increment:31570 time: 31.57 +field ' c' [explicit solve]: current solution: 9.624515e+01, current residual:5.811999e+01 +field 'mu' [explicit solve]: current solution: 5.162762e+00, current residual:3.119277e+00 +wall time: 0.000683069s + +time increment:31580 time: 31.58 +field ' c' [explicit solve]: current solution: 9.624503e+01, current residual:5.811991e+01 +field 'mu' [explicit solve]: current solution: 5.162644e+00, current residual:3.119209e+00 +wall time: 0.000632048s + +time increment:31590 time: 31.59 +field ' c' [explicit solve]: current solution: 9.624491e+01, current residual:5.811983e+01 +field 'mu' [explicit solve]: current solution: 5.162525e+00, current residual:3.119142e+00 +wall time: 0.000662088s + +time increment:31600 time: 31.6 +field ' c' [explicit solve]: current solution: 9.624479e+01, current residual:5.811975e+01 +field 'mu' [explicit solve]: current solution: 5.162407e+00, current residual:3.119074e+00 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0.000692844s + +time increment:31650 time: 31.65 +field ' c' [explicit solve]: current solution: 9.624419e+01, current residual:5.811935e+01 +field 'mu' [explicit solve]: current solution: 5.161815e+00, current residual:3.118736e+00 +wall time: 0.000763893s + +time increment:31660 time: 31.66 +field ' c' [explicit solve]: current solution: 9.624407e+01, current residual:5.811927e+01 +field 'mu' [explicit solve]: current solution: 5.161696e+00, current residual:3.118668e+00 +wall time: 0.000666857s + +time increment:31670 time: 31.67 +field ' c' [explicit solve]: current solution: 9.624395e+01, current residual:5.811920e+01 +field 'mu' [explicit solve]: current solution: 5.161578e+00, current residual:3.118600e+00 +wall time: 0.00066185s + +time increment:31680 time: 31.68 +field ' c' [explicit solve]: current solution: 9.624383e+01, current residual:5.811912e+01 +field 'mu' [explicit solve]: current solution: 5.161459e+00, current residual:3.118533e+00 +wall time: 0.000588179s + +time increment:31690 time: 31.69 +field ' c' [explicit solve]: current solution: 9.624371e+01, current residual:5.811904e+01 +field 'mu' [explicit solve]: current solution: 5.161341e+00, current residual:3.118465e+00 +wall time: 0.000689983s + +time increment:31700 time: 31.7 +field ' c' [explicit solve]: current solution: 9.624359e+01, current residual:5.811896e+01 +field 'mu' [explicit solve]: current solution: 5.161222e+00, current residual:3.118397e+00 +wall time: 0.00069499s + +time increment:31710 time: 31.71 +field ' c' [explicit solve]: current solution: 9.624347e+01, current residual:5.811888e+01 +field 'mu' [explicit solve]: current solution: 5.161104e+00, current residual:3.118329e+00 +wall time: 0.000649929s + +time increment:31720 time: 31.72 +field ' c' [explicit solve]: current solution: 9.624335e+01, current residual:5.811880e+01 +field 'mu' [explicit solve]: current solution: 5.160985e+00, current residual:3.118262e+00 +wall time: 0.000693083s + +time increment:31730 time: 31.73 +field ' c' [explicit solve]: current solution: 9.624323e+01, current residual:5.811872e+01 +field 'mu' [explicit solve]: current solution: 5.160867e+00, current residual:3.118194e+00 +wall time: 0.000682116s + +time increment:31740 time: 31.74 +field ' c' [explicit solve]: current solution: 9.624311e+01, current residual:5.811864e+01 +field 'mu' [explicit solve]: current solution: 5.160748e+00, current residual:3.118126e+00 +wall time: 0.000684977s + +time increment:31750 time: 31.75 +field ' c' [explicit solve]: current solution: 9.624300e+01, current residual:5.811856e+01 +field 'mu' [explicit solve]: current solution: 5.160629e+00, current residual:3.118058e+00 +wall time: 0.000632048s + +time increment:31760 time: 31.76 +field ' c' [explicit solve]: current solution: 9.624288e+01, current residual:5.811848e+01 +field 'mu' [explicit solve]: current solution: 5.160511e+00, current residual:3.117990e+00 +wall time: 0.000774145s + +time increment:31770 time: 31.77 +field ' c' [explicit solve]: current solution: 9.624276e+01, current residual:5.811841e+01 +field 'mu' [explicit solve]: current solution: 5.160392e+00, current residual:3.117922e+00 +wall time: 0.000576973s + +time increment:31780 time: 31.78 +field ' c' [explicit solve]: current solution: 9.624264e+01, current residual:5.811833e+01 +field 'mu' [explicit solve]: current solution: 5.160273e+00, current residual:3.117854e+00 +wall time: 0.00060606s + +time increment:31790 time: 31.79 +field ' c' [explicit solve]: current solution: 9.624252e+01, current residual:5.811825e+01 +field 'mu' [explicit solve]: current solution: 5.160155e+00, current residual:3.117787e+00 +wall time: 0.000643015s + +time increment:31800 time: 31.8 +field ' c' [explicit solve]: current solution: 9.624240e+01, current residual:5.811817e+01 +field 'mu' [explicit solve]: current solution: 5.160036e+00, current residual:3.117719e+00 +wall time: 0.000715017s + +time increment:31810 time: 31.81 +field ' c' [explicit solve]: current solution: 9.624228e+01, current residual:5.811809e+01 +field 'mu' [explicit solve]: current solution: 5.159917e+00, current residual:3.117651e+00 +wall time: 0.000683069s + +time increment:31820 time: 31.82 +field ' c' [explicit solve]: current solution: 9.624217e+01, current residual:5.811801e+01 +field 'mu' [explicit solve]: current solution: 5.159799e+00, current residual:3.117583e+00 +wall time: 0.000560999s + +time increment:31830 time: 31.83 +field ' c' [explicit solve]: current solution: 9.624205e+01, current residual:5.811793e+01 +field 'mu' [explicit solve]: current solution: 5.159680e+00, current residual:3.117515e+00 +wall time: 0.000595808s + +time increment:31840 time: 31.84 +field ' c' [explicit solve]: current solution: 9.624193e+01, current residual:5.811786e+01 +field 'mu' [explicit solve]: current solution: 5.159561e+00, current residual:3.117447e+00 +wall time: 0.000555038s + +time increment:31850 time: 31.85 +field ' c' [explicit solve]: current solution: 9.624181e+01, current residual:5.811778e+01 +field 'mu' [explicit solve]: current solution: 5.159443e+00, current residual:3.117379e+00 +wall time: 0.000647068s + +time increment:31860 time: 31.86 +field ' c' [explicit solve]: current solution: 9.624170e+01, current residual:5.811770e+01 +field 'mu' [explicit solve]: current solution: 5.159324e+00, current residual:3.117311e+00 +wall time: 0.000680923s + +time increment:31870 time: 31.87 +field ' c' [explicit solve]: current solution: 9.624158e+01, current residual:5.811762e+01 +field 'mu' [explicit solve]: current solution: 5.159205e+00, current residual:3.117243e+00 +wall time: 0.000560999s + +time increment:31880 time: 31.88 +field ' c' [explicit solve]: current solution: 9.624146e+01, current residual:5.811754e+01 +field 'mu' [explicit solve]: current solution: 5.159086e+00, current residual:3.117175e+00 +wall time: 0.0006001s + +time increment:31890 time: 31.89 +field ' c' [explicit solve]: current solution: 9.624134e+01, current residual:5.811747e+01 +field 'mu' [explicit solve]: current solution: 5.158968e+00, current residual:3.117107e+00 +wall time: 0.000634909s + +time increment:31900 time: 31.9 +field ' c' [explicit solve]: current solution: 9.624123e+01, current residual:5.811739e+01 +field 'mu' [explicit solve]: current solution: 5.158849e+00, current residual:3.117039e+00 +wall time: 0.000663996s + +time increment:31910 time: 31.91 +field ' c' [explicit solve]: current solution: 9.624111e+01, current residual:5.811731e+01 +field 'mu' [explicit solve]: current solution: 5.158730e+00, current residual:3.116971e+00 +wall time: 0.000622034s + +time increment:31920 time: 31.92 +field ' c' [explicit solve]: current solution: 9.624099e+01, current residual:5.811723e+01 +field 'mu' [explicit solve]: current solution: 5.158611e+00, current residual:3.116903e+00 +wall time: 0.00067091s + +time increment:31930 time: 31.93 +field ' c' [explicit solve]: current solution: 9.624087e+01, current residual:5.811716e+01 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solve]: current solution: 5.158017e+00, current residual:3.116562e+00 +wall time: 0.000602961s + +time increment:31980 time: 31.98 +field ' c' [explicit solve]: current solution: 9.624029e+01, current residual:5.811677e+01 +field 'mu' [explicit solve]: current solution: 5.157898e+00, current residual:3.116494e+00 +wall time: 0.000672102s + +time increment:31990 time: 31.99 +field ' c' [explicit solve]: current solution: 9.624018e+01, current residual:5.811669e+01 +field 'mu' [explicit solve]: current solution: 5.157779e+00, current residual:3.116426e+00 +wall time: 0.000724077s + +time increment:32000 time: 32 +field ' c' [explicit solve]: current solution: 9.624006e+01, current residual:5.811661e+01 +field 'mu' [explicit solve]: current solution: 5.157660e+00, current residual:3.116357e+00 +wall time: 0.000668049s + +time increment:32010 time: 32.01 +field ' c' [explicit solve]: current solution: 9.623994e+01, current residual:5.811654e+01 +field 'mu' [explicit solve]: current solution: 5.157541e+00, current residual:3.116289e+00 +wall time: 0.000577211s + +time increment:32020 time: 32.02 +field ' c' [explicit solve]: current solution: 9.623983e+01, current residual:5.811646e+01 +field 'mu' [explicit solve]: current solution: 5.157422e+00, current residual:3.116221e+00 +wall time: 0.000622034s + +time increment:32030 time: 32.03 +field ' c' [explicit solve]: current solution: 9.623971e+01, current residual:5.811638e+01 +field 'mu' [explicit solve]: current solution: 5.157303e+00, current residual:3.116153e+00 +wall time: 0.000620127s + +time increment:32040 time: 32.04 +field ' c' [explicit solve]: current solution: 9.623960e+01, current residual:5.811631e+01 +field 'mu' [explicit solve]: current solution: 5.157184e+00, current residual:3.116084e+00 +wall time: 0.000663042s + +time increment:32050 time: 32.05 +field ' c' [explicit solve]: current solution: 9.623948e+01, current residual:5.811623e+01 +field 'mu' [explicit solve]: current solution: 5.157065e+00, current residual:3.116016e+00 +wall time: 0.000564098s + +time increment:32060 time: 32.06 +field ' c' [explicit solve]: current solution: 9.623937e+01, current residual:5.811615e+01 +field 'mu' [explicit solve]: current solution: 5.156946e+00, current residual:3.115948e+00 +wall time: 0.000695944s + +time increment:32070 time: 32.07 +field ' c' [explicit solve]: current solution: 9.623925e+01, current residual:5.811608e+01 +field 'mu' [explicit solve]: current solution: 5.156827e+00, current residual:3.115880e+00 +wall time: 0.000592947s + +time increment:32080 time: 32.08 +field ' c' [explicit solve]: current solution: 9.623913e+01, current residual:5.811600e+01 +field 'mu' [explicit solve]: current solution: 5.156708e+00, current residual:3.115811e+00 +wall time: 0.000607014s + +time increment:32090 time: 32.09 +field ' c' [explicit solve]: current solution: 9.623902e+01, current residual:5.811592e+01 +field 'mu' [explicit solve]: current solution: 5.156589e+00, current residual:3.115743e+00 +wall time: 0.000608921s + +time increment:32100 time: 32.1 +field ' c' [explicit solve]: current solution: 9.623890e+01, current residual:5.811585e+01 +field 'mu' [explicit solve]: current solution: 5.156470e+00, current residual:3.115675e+00 +wall time: 0.000575066s + +time increment:32110 time: 32.11 +field ' c' [explicit solve]: current solution: 9.623879e+01, current residual:5.811577e+01 +field 'mu' [explicit solve]: current solution: 5.156351e+00, current residual:3.115606e+00 +wall time: 0.000591993s + +time increment:32120 time: 32.12 +field ' c' [explicit solve]: current solution: 9.623867e+01, current residual:5.811569e+01 +field 'mu' [explicit solve]: current solution: 5.156232e+00, current residual:3.115538e+00 +wall time: 0.000706911s + +time increment:32130 time: 32.13 +field ' c' [explicit solve]: current solution: 9.623856e+01, current residual:5.811562e+01 +field 'mu' [explicit solve]: current solution: 5.156113e+00, current residual:3.115469e+00 +wall time: 0.000878096s + +time increment:32140 time: 32.14 +field ' c' [explicit solve]: current solution: 9.623845e+01, current residual:5.811554e+01 +field 'mu' [explicit solve]: current solution: 5.155994e+00, current residual:3.115401e+00 +wall time: 0.000673056s + +time increment:32150 time: 32.15 +field ' c' [explicit solve]: current solution: 9.623833e+01, current residual:5.811547e+01 +field 'mu' [explicit solve]: current solution: 5.155875e+00, current residual:3.115333e+00 +wall time: 0.00058794s + +time increment:32160 time: 32.16 +field ' c' [explicit solve]: current solution: 9.623822e+01, current residual:5.811539e+01 +field 'mu' [explicit solve]: current solution: 5.155755e+00, current residual:3.115264e+00 +wall time: 0.000588894s + +time increment:32170 time: 32.17 +field ' c' [explicit solve]: current solution: 9.623810e+01, current residual:5.811531e+01 +field 'mu' [explicit solve]: current solution: 5.155636e+00, current residual:3.115196e+00 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0.000728846s + +time increment:32220 time: 32.22 +field ' c' [explicit solve]: current solution: 9.623753e+01, current residual:5.811493e+01 +field 'mu' [explicit solve]: current solution: 5.155040e+00, current residual:3.114853e+00 +wall time: 0.00068903s + +time increment:32230 time: 32.23 +field ' c' [explicit solve]: current solution: 9.623742e+01, current residual:5.811486e+01 +field 'mu' [explicit solve]: current solution: 5.154921e+00, current residual:3.114785e+00 +wall time: 0.000663996s + +time increment:32240 time: 32.24 +field ' c' [explicit solve]: current solution: 9.623730e+01, current residual:5.811478e+01 +field 'mu' [explicit solve]: current solution: 5.154802e+00, current residual:3.114716e+00 +wall time: 0.000633001s + +time increment:32250 time: 32.25 +field ' c' [explicit solve]: current solution: 9.623719e+01, current residual:5.811471e+01 +field 'mu' [explicit solve]: current solution: 5.154683e+00, current residual:3.114648e+00 +wall time: 0.000696898s + +time increment:32260 time: 32.26 +field ' c' [explicit solve]: current solution: 9.623708e+01, current residual:5.811463e+01 +field 'mu' [explicit solve]: current solution: 5.154563e+00, current residual:3.114579e+00 +wall time: 0.000672817s + +time increment:32270 time: 32.27 +field ' c' [explicit solve]: current solution: 9.623696e+01, current residual:5.811456e+01 +field 'mu' [explicit solve]: current solution: 5.154444e+00, current residual:3.114511e+00 +wall time: 0.000663996s + +time increment:32280 time: 32.28 +field ' c' [explicit solve]: current solution: 9.623685e+01, current residual:5.811448e+01 +field 'mu' [explicit solve]: current solution: 5.154325e+00, current residual:3.114442e+00 +wall time: 0.000735998s + +time increment:32290 time: 32.29 +field ' c' [explicit solve]: current solution: 9.623674e+01, current residual:5.811441e+01 +field 'mu' [explicit solve]: current solution: 5.154206e+00, current residual:3.114373e+00 +wall time: 0.000689983s + +time increment:32300 time: 32.3 +field ' c' [explicit solve]: current solution: 9.623663e+01, current residual:5.811433e+01 +field 'mu' [explicit solve]: current solution: 5.154086e+00, current residual:3.114305e+00 +wall time: 0.000630856s + +time increment:32310 time: 32.31 +field ' c' [explicit solve]: current solution: 9.623651e+01, current residual:5.811426e+01 +field 'mu' [explicit solve]: current solution: 5.153967e+00, current residual:3.114236e+00 +wall time: 0.000730038s + +time increment:32320 time: 32.32 +field ' c' [explicit solve]: current solution: 9.623640e+01, current residual:5.811418e+01 +field 'mu' [explicit solve]: current solution: 5.153848e+00, current residual:3.114168e+00 +wall time: 0.000631809s + +time increment:32330 time: 32.33 +field ' c' [explicit solve]: current solution: 9.623629e+01, current residual:5.811411e+01 +field 'mu' [explicit solve]: current solution: 5.153728e+00, current residual:3.114099e+00 +wall time: 0.000673056s + +time increment:32340 time: 32.34 +field ' c' [explicit solve]: current solution: 9.623618e+01, current residual:5.811403e+01 +field 'mu' [explicit solve]: current solution: 5.153609e+00, current residual:3.114030e+00 +wall time: 0.000683069s + +time increment:32350 time: 32.35 +field ' c' [explicit solve]: current solution: 9.623606e+01, current residual:5.811396e+01 +field 'mu' [explicit solve]: current solution: 5.153490e+00, current residual:3.113962e+00 +wall time: 0.000638008s + +time increment:32360 time: 32.36 +field ' c' [explicit solve]: current solution: 9.623595e+01, current residual:5.811388e+01 +field 'mu' [explicit solve]: current solution: 5.153370e+00, current residual:3.113893e+00 +wall time: 0.000660896s + +time increment:32370 time: 32.37 +field ' c' [explicit solve]: current solution: 9.623584e+01, current residual:5.811381e+01 +field 'mu' [explicit solve]: current solution: 5.153251e+00, current residual:3.113824e+00 +wall time: 0.000682116s + +time increment:32380 time: 32.38 +field ' c' [explicit solve]: current solution: 9.623573e+01, current residual:5.811373e+01 +field 'mu' [explicit solve]: current solution: 5.153131e+00, current residual:3.113755e+00 +wall time: 0.00068593s + +time increment:32390 time: 32.39 +field ' c' [explicit solve]: current solution: 9.623561e+01, current residual:5.811366e+01 +field 'mu' [explicit solve]: current solution: 5.153012e+00, current residual:3.113687e+00 +wall time: 0.000782013s + +time increment:32400 time: 32.4 +field ' c' [explicit solve]: current solution: 9.623550e+01, current residual:5.811359e+01 +field 'mu' [explicit solve]: current solution: 5.152892e+00, current residual:3.113618e+00 +wall time: 0.000717163s + +time increment:32410 time: 32.41 +field ' c' [explicit solve]: current solution: 9.623539e+01, current residual:5.811351e+01 +field 'mu' [explicit solve]: current solution: 5.152773e+00, current residual:3.113549e+00 +wall time: 0.00069499s + +time increment:32420 time: 32.42 +field ' c' [explicit solve]: current solution: 9.623528e+01, current residual:5.811344e+01 +field 'mu' [explicit solve]: current solution: 5.152654e+00, current residual:3.113480e+00 +wall time: 0.000665903s + +time increment:32430 time: 32.43 +field ' c' [explicit solve]: current solution: 9.623517e+01, current residual:5.811336e+01 +field 'mu' [explicit solve]: current solution: 5.152534e+00, current residual:3.113412e+00 +wall time: 0.000801802s + +time increment:32440 time: 32.44 +field ' c' [explicit solve]: current solution: 9.623506e+01, current residual:5.811329e+01 +field 'mu' [explicit solve]: current solution: 5.152415e+00, current residual:3.113343e+00 +wall time: 0.000783205s + +time increment:32450 time: 32.45 +field ' c' [explicit solve]: current solution: 9.623494e+01, current residual:5.811321e+01 +field 'mu' [explicit solve]: current solution: 5.152295e+00, current residual:3.113274e+00 +wall time: 0.000769138s + +time increment:32460 time: 32.46 +field ' c' [explicit solve]: current solution: 9.623483e+01, current residual:5.811314e+01 +field 'mu' [explicit solve]: current solution: 5.152176e+00, current residual:3.113205e+00 +wall time: 0.000773907s + +time increment:32470 time: 32.47 +field ' c' [explicit solve]: current solution: 9.623472e+01, current residual:5.811307e+01 +field 'mu' [explicit solve]: current solution: 5.152056e+00, current residual:3.113136e+00 +wall time: 0.000672102s + +time increment:32480 time: 32.48 +field ' c' [explicit solve]: current solution: 9.623461e+01, current residual:5.811299e+01 +field 'mu' [explicit solve]: current solution: 5.151937e+00, current residual:3.113067e+00 +wall time: 0.000657082s + +time increment:32490 time: 32.49 +field ' c' [explicit solve]: current solution: 9.623450e+01, current residual:5.811292e+01 +field 'mu' [explicit solve]: current solution: 5.151817e+00, current residual:3.112999e+00 +wall time: 0.000725031s + +time increment:32500 time: 32.5 +field ' c' [explicit solve]: current solution: 9.623439e+01, current residual:5.811284e+01 +field 'mu' [explicit solve]: current solution: 5.151697e+00, current residual:3.112930e+00 +wall time: 0.000622034s + +time increment:32510 time: 32.51 +field ' c' [explicit solve]: current solution: 9.623428e+01, current residual:5.811277e+01 +field 'mu' [explicit solve]: current solution: 5.151578e+00, current residual:3.112861e+00 +wall time: 0.000656843s + +time increment:32520 time: 32.52 +field ' c' [explicit solve]: current solution: 9.623417e+01, current residual:5.811270e+01 +field 'mu' [explicit solve]: current solution: 5.151458e+00, current residual:3.112792e+00 +wall time: 0.000688791s + +time increment:32530 time: 32.53 +field ' c' [explicit solve]: current solution: 9.623406e+01, current residual:5.811262e+01 +field 'mu' [explicit solve]: current solution: 5.151339e+00, current residual:3.112723e+00 +wall time: 0.000666142s + +time increment:32540 time: 32.54 +field ' c' [explicit solve]: current solution: 9.623395e+01, current residual:5.811255e+01 +field 'mu' [explicit solve]: current solution: 5.151219e+00, current residual:3.112654e+00 +wall time: 0.00066781s + +time increment:32550 time: 32.55 +field ' c' [explicit solve]: current solution: 9.623384e+01, current residual:5.811248e+01 +field 'mu' [explicit solve]: current solution: 5.151100e+00, current residual:3.112585e+00 +wall time: 0.000602007s + +time increment:32560 time: 32.56 +field ' c' [explicit solve]: current solution: 9.623373e+01, current residual:5.811240e+01 +field 'mu' [explicit solve]: current solution: 5.150980e+00, current residual:3.112516e+00 +wall time: 0.000571966s + +time increment:32570 time: 32.57 +field ' c' [explicit solve]: current solution: 9.623362e+01, current residual:5.811233e+01 +field 'mu' [explicit solve]: current solution: 5.150860e+00, current residual:3.112447e+00 +wall time: 0.000580072s + +time increment:32580 time: 32.58 +field ' c' [explicit solve]: current solution: 9.623351e+01, current residual:5.811226e+01 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solve]: current solution: 5.150262e+00, current residual:3.112102e+00 +wall time: 0.000707865s + +time increment:32630 time: 32.63 +field ' c' [explicit solve]: current solution: 9.623296e+01, current residual:5.811189e+01 +field 'mu' [explicit solve]: current solution: 5.150142e+00, current residual:3.112033e+00 +wall time: 0.00067997s + +time increment:32640 time: 32.64 +field ' c' [explicit solve]: current solution: 9.623285e+01, current residual:5.811182e+01 +field 'mu' [explicit solve]: current solution: 5.150023e+00, current residual:3.111964e+00 +wall time: 0.000674009s + +time increment:32650 time: 32.65 +field ' c' [explicit solve]: current solution: 9.623274e+01, current residual:5.811175e+01 +field 'mu' [explicit solve]: current solution: 5.149903e+00, current residual:3.111895e+00 +wall time: 0.000653982s + +time increment:32660 time: 32.66 +field ' c' [explicit solve]: current solution: 9.623263e+01, current residual:5.811167e+01 +field 'mu' [explicit solve]: current solution: 5.149783e+00, current residual:3.111826e+00 +wall time: 0.000708818s + +time increment:32670 time: 32.67 +field ' c' [explicit solve]: current solution: 9.623252e+01, current residual:5.811160e+01 +field 'mu' [explicit solve]: current solution: 5.149663e+00, current residual:3.111757e+00 +wall time: 0.000751019s + +time increment:32680 time: 32.68 +field ' c' [explicit solve]: current solution: 9.623241e+01, current residual:5.811153e+01 +field 'mu' [explicit solve]: current solution: 5.149544e+00, current residual:3.111687e+00 +wall time: 0.000647068s + +time increment:32690 time: 32.69 +field ' c' [explicit solve]: current solution: 9.623230e+01, current residual:5.811146e+01 +field 'mu' [explicit solve]: current solution: 5.149424e+00, current residual:3.111618e+00 +wall time: 0.000682116s + +time increment:32700 time: 32.7 +field ' c' [explicit solve]: current solution: 9.623220e+01, current residual:5.811138e+01 +field 'mu' [explicit solve]: current solution: 5.149304e+00, current residual:3.111549e+00 +wall time: 0.000653028s + +time increment:32710 time: 32.71 +field ' c' [explicit solve]: current solution: 9.623209e+01, current residual:5.811131e+01 +field 'mu' [explicit solve]: current solution: 5.149184e+00, current residual:3.111480e+00 +wall time: 0.000715017s + +time increment:32720 time: 32.72 +field ' c' [explicit solve]: current solution: 9.623198e+01, current residual:5.811124e+01 +field 'mu' [explicit solve]: current solution: 5.149064e+00, current residual:3.111411e+00 +wall time: 0.000647068s + +time increment:32730 time: 32.73 +field ' c' [explicit solve]: current solution: 9.623187e+01, current residual:5.811117e+01 +field 'mu' [explicit solve]: current solution: 5.148945e+00, current residual:3.111342e+00 +wall time: 0.000675917s + +time increment:32740 time: 32.74 +field ' c' [explicit solve]: current solution: 9.623176e+01, current residual:5.811109e+01 +field 'mu' [explicit solve]: current solution: 5.148825e+00, current residual:3.111273e+00 +wall time: 0.000658035s + +time increment:32750 time: 32.75 +field ' c' [explicit solve]: current solution: 9.623165e+01, current residual:5.811102e+01 +field 'mu' [explicit solve]: current solution: 5.148705e+00, current residual:3.111203e+00 +wall time: 0.000701189s + +time increment:32760 time: 32.76 +field ' c' [explicit solve]: current solution: 9.623155e+01, current residual:5.811095e+01 +field 'mu' [explicit solve]: current solution: 5.148585e+00, current residual:3.111134e+00 +wall time: 0.000674963s + +time increment:32770 time: 32.77 +field ' c' [explicit solve]: current solution: 9.623144e+01, current residual:5.811088e+01 +field 'mu' [explicit solve]: current solution: 5.148465e+00, current residual:3.111065e+00 +wall time: 0.000656843s + +time increment:32780 time: 32.78 +field ' c' [explicit solve]: current solution: 9.623133e+01, current residual:5.811081e+01 +field 'mu' [explicit solve]: current solution: 5.148345e+00, current residual:3.110996e+00 +wall time: 0.000667095s + +time increment:32790 time: 32.79 +field ' c' [explicit solve]: current solution: 9.623122e+01, current residual:5.811073e+01 +field 'mu' [explicit solve]: current solution: 5.148225e+00, current residual:3.110926e+00 +wall time: 0.000702858s + +time increment:32800 time: 32.8 +field ' c' [explicit solve]: current solution: 9.623111e+01, current residual:5.811066e+01 +field 'mu' [explicit solve]: current solution: 5.148106e+00, current residual:3.110857e+00 +wall time: 0.000633955s + +time increment:32810 time: 32.81 +field ' c' [explicit solve]: current solution: 9.623101e+01, current residual:5.811059e+01 +field 'mu' [explicit solve]: current solution: 5.147986e+00, current residual:3.110788e+00 +wall time: 0.000720024s + +time increment:32820 time: 32.82 +field ' c' [explicit solve]: current solution: 9.623090e+01, current residual:5.811052e+01 +field 'mu' [explicit solve]: current solution: 5.147866e+00, current residual:3.110719e+00 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0.000662088s + +time increment:32870 time: 32.87 +field ' c' [explicit solve]: current solution: 9.623036e+01, current residual:5.811016e+01 +field 'mu' [explicit solve]: current solution: 5.147266e+00, current residual:3.110372e+00 +wall time: 0.000724077s + +time increment:32880 time: 32.88 +field ' c' [explicit solve]: current solution: 9.623026e+01, current residual:5.811009e+01 +field 'mu' [explicit solve]: current solution: 5.147146e+00, current residual:3.110303e+00 +wall time: 0.000639915s + +time increment:32890 time: 32.89 +field ' c' [explicit solve]: current solution: 9.623015e+01, current residual:5.811002e+01 +field 'mu' [explicit solve]: current solution: 5.147026e+00, current residual:3.110233e+00 +wall time: 0.000658989s + +time increment:32900 time: 32.9 +field ' c' [explicit solve]: current solution: 9.623004e+01, current residual:5.810995e+01 +field 'mu' [explicit solve]: current solution: 5.146906e+00, current residual:3.110164e+00 +wall time: 0.000699043s + +time increment:32910 time: 32.91 +field ' c' [explicit solve]: current solution: 9.622994e+01, current residual:5.810988e+01 +field 'mu' [explicit solve]: current solution: 5.146786e+00, current residual:3.110094e+00 +wall time: 0.000684023s + +time increment:32920 time: 32.92 +field ' c' [explicit solve]: current solution: 9.622983e+01, current residual:5.810981e+01 +field 'mu' [explicit solve]: current solution: 5.146666e+00, current residual:3.110025e+00 +wall time: 0.000602007s + +time increment:32930 time: 32.93 +field ' c' [explicit solve]: current solution: 9.622972e+01, current residual:5.810974e+01 +field 'mu' [explicit solve]: current solution: 5.146546e+00, current residual:3.109956e+00 +wall time: 0.000567913s + +time increment:32940 time: 32.94 +field ' c' [explicit solve]: current solution: 9.622962e+01, current residual:5.810967e+01 +field 'mu' [explicit solve]: current solution: 5.146426e+00, current residual:3.109886e+00 +wall time: 0.000580072s + +time increment:32950 time: 32.95 +field ' c' [explicit solve]: current solution: 9.622951e+01, current residual:5.810959e+01 +field 'mu' [explicit solve]: current solution: 5.146306e+00, current residual:3.109817e+00 +wall time: 0.000711918s + +time increment:32960 time: 32.96 +field ' c' [explicit solve]: current solution: 9.622940e+01, current residual:5.810952e+01 +field 'mu' [explicit solve]: current solution: 5.146186e+00, current residual:3.109747e+00 +wall time: 0.000579834s + +time increment:32970 time: 32.97 +field ' c' [explicit solve]: current solution: 9.622930e+01, current residual:5.810945e+01 +field 'mu' [explicit solve]: current solution: 5.146066e+00, current residual:3.109678e+00 +wall time: 0.000740051s + +time increment:32980 time: 32.98 +field ' c' [explicit solve]: current solution: 9.622919e+01, current residual:5.810938e+01 +field 'mu' [explicit solve]: current solution: 5.145946e+00, current residual:3.109608e+00 +wall time: 0.000608921s + +time increment:32990 time: 32.99 +field ' c' [explicit solve]: current solution: 9.622909e+01, current residual:5.810931e+01 +field 'mu' [explicit solve]: current solution: 5.145826e+00, current residual:3.109539e+00 +wall time: 0.000552893s + +time increment:33000 time: 33 +field ' c' [explicit solve]: current solution: 9.622898e+01, current residual:5.810924e+01 +field 'mu' [explicit solve]: current solution: 5.145706e+00, current residual:3.109469e+00 +wall time: 0.000665903s + +time increment:33010 time: 33.01 +field ' c' [explicit solve]: current solution: 9.622888e+01, current residual:5.810917e+01 +field 'mu' [explicit solve]: current solution: 5.145585e+00, current residual:3.109400e+00 +wall time: 0.00063014s + +time increment:33020 time: 33.02 +field ' c' [explicit solve]: current solution: 9.622877e+01, current residual:5.810910e+01 +field 'mu' [explicit solve]: current solution: 5.145465e+00, current residual:3.109330e+00 +wall time: 0.000693083s + +time increment:33030 time: 33.03 +field ' c' [explicit solve]: current solution: 9.622867e+01, current residual:5.810903e+01 +field 'mu' [explicit solve]: current solution: 5.145345e+00, current residual:3.109261e+00 +wall time: 0.000658989s + +time increment:33040 time: 33.04 +field ' c' [explicit solve]: current solution: 9.622856e+01, current residual:5.810896e+01 +field 'mu' [explicit solve]: current solution: 5.145225e+00, current residual:3.109191e+00 +wall time: 0.000654936s + +time increment:33050 time: 33.05 +field ' c' [explicit solve]: current solution: 9.622845e+01, current residual:5.810889e+01 +field 'mu' [explicit solve]: current solution: 5.145105e+00, current residual:3.109121e+00 +wall time: 0.000586033s + +time increment:33060 time: 33.06 +field ' c' [explicit solve]: current solution: 9.622835e+01, current residual:5.810882e+01 +field 'mu' [explicit solve]: current solution: 5.144985e+00, current residual:3.109052e+00 +wall time: 0.000577927s + +time increment:33070 time: 33.07 +field ' c' [explicit solve]: current solution: 9.622824e+01, current residual:5.810875e+01 +field 'mu' [explicit solve]: current solution: 5.144865e+00, current residual:3.108982e+00 +wall time: 0.000591993s + +time increment:33080 time: 33.08 +field ' c' [explicit solve]: current solution: 9.622814e+01, current residual:5.810868e+01 +field 'mu' [explicit solve]: current solution: 5.144744e+00, current residual:3.108913e+00 +wall time: 0.000677824s + +time increment:33090 time: 33.09 +field ' c' [explicit solve]: current solution: 9.622803e+01, current residual:5.810861e+01 +field 'mu' [explicit solve]: current solution: 5.144624e+00, current residual:3.108843e+00 +wall time: 0.00070405s + +time increment:33100 time: 33.1 +field ' c' [explicit solve]: current solution: 9.622793e+01, current residual:5.810854e+01 +field 'mu' [explicit solve]: current solution: 5.144504e+00, current residual:3.108773e+00 +wall time: 0.000651836s + +time increment:33110 time: 33.11 +field ' c' [explicit solve]: current solution: 9.622783e+01, current residual:5.810847e+01 +field 'mu' [explicit solve]: current solution: 5.144384e+00, current residual:3.108704e+00 +wall time: 0.000755072s + +time increment:33120 time: 33.12 +field ' c' [explicit solve]: current solution: 9.622772e+01, current residual:5.810840e+01 +field 'mu' [explicit solve]: current solution: 5.144264e+00, current residual:3.108634e+00 +wall time: 0.00070715s + +time increment:33130 time: 33.13 +field ' c' [explicit solve]: current solution: 9.622762e+01, current residual:5.810833e+01 +field 'mu' [explicit solve]: current solution: 5.144143e+00, current residual:3.108565e+00 +wall time: 0.000658989s + +time increment:33140 time: 33.14 +field ' c' [explicit solve]: current solution: 9.622751e+01, current residual:5.810826e+01 +field 'mu' [explicit solve]: current solution: 5.144023e+00, current residual:3.108495e+00 +wall time: 0.000674963s + +time increment:33150 time: 33.15 +field ' c' [explicit solve]: current solution: 9.622741e+01, current residual:5.810819e+01 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[explicit solve]: current solution: 5.143422e+00, current residual:3.108146e+00 +wall time: 0.000709057s + +time increment:33200 time: 33.2 +field ' c' [explicit solve]: current solution: 9.622689e+01, current residual:5.810784e+01 +field 'mu' [explicit solve]: current solution: 5.143302e+00, current residual:3.108077e+00 +wall time: 0.000688791s + +time increment:33210 time: 33.21 +field ' c' [explicit solve]: current solution: 9.622679e+01, current residual:5.810778e+01 +field 'mu' [explicit solve]: current solution: 5.143181e+00, current residual:3.108007e+00 +wall time: 0.000715017s + +time increment:33220 time: 33.22 +field ' c' [explicit solve]: current solution: 9.622668e+01, current residual:5.810771e+01 +field 'mu' [explicit solve]: current solution: 5.143061e+00, current residual:3.107937e+00 +wall time: 0.000731945s + +time increment:33230 time: 33.23 +field ' c' [explicit solve]: current solution: 9.622658e+01, current residual:5.810764e+01 +field 'mu' [explicit solve]: current solution: 5.142941e+00, current residual:3.107867e+00 +wall time: 0.000699043s + +time increment:33240 time: 33.24 +field ' c' [explicit solve]: current solution: 9.622648e+01, current residual:5.810757e+01 +field 'mu' [explicit solve]: current solution: 5.142820e+00, current residual:3.107798e+00 +wall time: 0.000739098s + +time increment:33250 time: 33.25 +field ' c' [explicit solve]: current solution: 9.622637e+01, current residual:5.810750e+01 +field 'mu' [explicit solve]: current solution: 5.142700e+00, current residual:3.107728e+00 +wall time: 0.000638962s + +time increment:33260 time: 33.26 +field ' c' [explicit solve]: current solution: 9.622627e+01, current residual:5.810743e+01 +field 'mu' [explicit solve]: current solution: 5.142580e+00, current residual:3.107658e+00 +wall time: 0.000638962s + +time increment:33270 time: 33.27 +field ' c' [explicit solve]: current solution: 9.622617e+01, current residual:5.810736e+01 +field 'mu' [explicit solve]: current solution: 5.142459e+00, current residual:3.107588e+00 +wall time: 0.000637054s + +time increment:33280 time: 33.28 +field ' c' [explicit solve]: current solution: 9.622606e+01, current residual:5.810729e+01 +field 'mu' [explicit solve]: current solution: 5.142339e+00, current residual:3.107518e+00 +wall time: 0.00067091s + +time increment:33290 time: 33.29 +field ' c' [explicit solve]: current solution: 9.622596e+01, current residual:5.810722e+01 +field 'mu' [explicit solve]: current solution: 5.142218e+00, current residual:3.107448e+00 +wall time: 0.000726938s + +time increment:33300 time: 33.3 +field ' c' [explicit solve]: current solution: 9.622586e+01, current residual:5.810716e+01 +field 'mu' [explicit solve]: current solution: 5.142098e+00, current residual:3.107379e+00 +wall time: 0.000708103s + +time increment:33310 time: 33.31 +field ' c' [explicit solve]: current solution: 9.622576e+01, current residual:5.810709e+01 +field 'mu' [explicit solve]: current solution: 5.141978e+00, current residual:3.107309e+00 +wall time: 0.000675201s + +time increment:33320 time: 33.32 +field ' c' [explicit solve]: current solution: 9.622565e+01, current residual:5.810702e+01 +field 'mu' [explicit solve]: current solution: 5.141857e+00, current residual:3.107239e+00 +wall time: 0.000683069s + +time increment:33330 time: 33.33 +field ' c' [explicit solve]: current solution: 9.622555e+01, current residual:5.810695e+01 +field 'mu' [explicit solve]: current solution: 5.141737e+00, current residual:3.107169e+00 +wall time: 0.000644207s + +time increment:33340 time: 33.34 +field ' c' [explicit solve]: current solution: 9.622545e+01, current residual:5.810688e+01 +field 'mu' [explicit solve]: current solution: 5.141616e+00, current residual:3.107099e+00 +wall time: 0.000720024s + +time increment:33350 time: 33.35 +field ' c' [explicit solve]: current solution: 9.622535e+01, current residual:5.810681e+01 +field 'mu' [explicit solve]: current solution: 5.141496e+00, current residual:3.107029e+00 +wall time: 0.000701189s + +time increment:33360 time: 33.36 +field ' c' [explicit solve]: current solution: 9.622524e+01, current residual:5.810675e+01 +field 'mu' [explicit solve]: current solution: 5.141376e+00, current residual:3.106959e+00 +wall time: 0.000684977s + +time increment:33370 time: 33.37 +field ' c' [explicit solve]: current solution: 9.622514e+01, current residual:5.810668e+01 +field 'mu' [explicit solve]: current solution: 5.141255e+00, current residual:3.106889e+00 +wall time: 0.000648975s + +time increment:33380 time: 33.38 +field ' c' [explicit solve]: current solution: 9.622504e+01, current residual:5.810661e+01 +field 'mu' [explicit solve]: current solution: 5.141135e+00, current residual:3.106819e+00 +wall time: 0.000674963s + +time increment:33390 time: 33.39 +field ' c' [explicit solve]: current solution: 9.622494e+01, current residual:5.810654e+01 +field 'mu' [explicit solve]: current solution: 5.141014e+00, current residual:3.106750e+00 +wall time: 0.000701904s + +time increment:33400 time: 33.4 +field ' c' [explicit solve]: current solution: 9.622484e+01, current residual:5.810647e+01 +field 'mu' [explicit solve]: current solution: 5.140894e+00, current residual:3.106680e+00 +wall time: 0.000689983s + +time increment:33410 time: 33.41 +field ' c' [explicit solve]: current solution: 9.622474e+01, current residual:5.810641e+01 +field 'mu' [explicit solve]: current solution: 5.140773e+00, current residual:3.106610e+00 +wall time: 0.000732899s + +time increment:33420 time: 33.42 +field ' c' [explicit solve]: current solution: 9.622463e+01, current residual:5.810634e+01 +field 'mu' [explicit solve]: current solution: 5.140653e+00, current residual:3.106540e+00 +wall time: 0.000707865s + +time increment:33430 time: 33.43 +field ' c' [explicit solve]: current solution: 9.622453e+01, current residual:5.810627e+01 +field 'mu' [explicit solve]: current solution: 5.140532e+00, current residual:3.106470e+00 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0.000575066s + +time increment:33480 time: 33.48 +field ' c' [explicit solve]: current solution: 9.622403e+01, current residual:5.810593e+01 +field 'mu' [explicit solve]: current solution: 5.139930e+00, current residual:3.106120e+00 +wall time: 0.000597954s + +time increment:33490 time: 33.49 +field ' c' [explicit solve]: current solution: 9.622393e+01, current residual:5.810586e+01 +field 'mu' [explicit solve]: current solution: 5.139809e+00, current residual:3.106050e+00 +wall time: 0.000727892s + +time increment:33500 time: 33.5 +field ' c' [explicit solve]: current solution: 9.622383e+01, current residual:5.810580e+01 +field 'mu' [explicit solve]: current solution: 5.139689e+00, current residual:3.105979e+00 +wall time: 0.000643015s + +time increment:33510 time: 33.51 +field ' c' [explicit solve]: current solution: 9.622372e+01, current residual:5.810573e+01 +field 'mu' [explicit solve]: current solution: 5.139568e+00, current residual:3.105909e+00 +wall time: 0.000557184s + +time increment:33520 time: 33.52 +field ' c' [explicit solve]: current solution: 9.622362e+01, current residual:5.810566e+01 +field 'mu' [explicit solve]: current solution: 5.139448e+00, current residual:3.105839e+00 +wall time: 0.000555038s + +time increment:33530 time: 33.53 +field ' c' [explicit solve]: current solution: 9.622352e+01, current residual:5.810559e+01 +field 'mu' [explicit solve]: current solution: 5.139327e+00, current residual:3.105769e+00 +wall time: 0.000652075s + +time increment:33540 time: 33.54 +field ' c' [explicit solve]: current solution: 9.622342e+01, current residual:5.810553e+01 +field 'mu' [explicit solve]: current solution: 5.139206e+00, current residual:3.105699e+00 +wall time: 0.000576973s + +time increment:33550 time: 33.55 +field ' c' [explicit solve]: current solution: 9.622332e+01, current residual:5.810546e+01 +field 'mu' [explicit solve]: current solution: 5.139086e+00, current residual:3.105629e+00 +wall time: 0.000551939s + +time increment:33560 time: 33.56 +field ' c' [explicit solve]: current solution: 9.622322e+01, current residual:5.810539e+01 +field 'mu' [explicit solve]: current solution: 5.138965e+00, current residual:3.105559e+00 +wall time: 0.000643969s + +time increment:33570 time: 33.57 +field ' c' [explicit solve]: current solution: 9.622312e+01, current residual:5.810533e+01 +field 'mu' [explicit solve]: current solution: 5.138845e+00, current residual:3.105489e+00 +wall time: 0.000569105s + +time increment:33580 time: 33.58 +field ' c' [explicit solve]: current solution: 9.622302e+01, current residual:5.810526e+01 +field 'mu' [explicit solve]: current solution: 5.138724e+00, current residual:3.105419e+00 +wall time: 0.000681877s + +time increment:33590 time: 33.59 +field ' c' [explicit solve]: current solution: 9.622292e+01, current residual:5.810519e+01 +field 'mu' [explicit solve]: current solution: 5.138603e+00, current residual:3.105349e+00 +wall time: 0.000607014s + +time increment:33600 time: 33.6 +field ' c' [explicit solve]: current solution: 9.622282e+01, current residual:5.810513e+01 +field 'mu' [explicit solve]: current solution: 5.138483e+00, current residual:3.105278e+00 +wall time: 0.00069499s + +time increment:33610 time: 33.61 +field ' c' [explicit solve]: current solution: 9.622272e+01, current residual:5.810506e+01 +field 'mu' [explicit solve]: current solution: 5.138362e+00, current residual:3.105208e+00 +wall time: 0.000725031s + +time increment:33620 time: 33.62 +field ' c' [explicit solve]: current solution: 9.622262e+01, current residual:5.810499e+01 +field 'mu' [explicit solve]: current solution: 5.138242e+00, current residual:3.105138e+00 +wall time: 0.000617981s + +time increment:33630 time: 33.63 +field ' c' [explicit solve]: current solution: 9.622252e+01, current residual:5.810493e+01 +field 'mu' [explicit solve]: current solution: 5.138121e+00, current residual:3.105068e+00 +wall time: 0.000575066s + +time increment:33640 time: 33.64 +field ' c' [explicit solve]: current solution: 9.622243e+01, current residual:5.810486e+01 +field 'mu' [explicit solve]: current solution: 5.138000e+00, current residual:3.104998e+00 +wall time: 0.000627995s + +time increment:33650 time: 33.65 +field ' c' [explicit solve]: current solution: 9.622233e+01, current residual:5.810479e+01 +field 'mu' [explicit solve]: current solution: 5.137880e+00, current residual:3.104927e+00 +wall time: 0.000669003s + +time increment:33660 time: 33.66 +field ' c' [explicit solve]: current solution: 9.622223e+01, current residual:5.810473e+01 +field 'mu' [explicit solve]: current solution: 5.137759e+00, current residual:3.104857e+00 +wall time: 0.000603914s + +time increment:33670 time: 33.67 +field ' c' [explicit solve]: current solution: 9.622213e+01, current residual:5.810466e+01 +field 'mu' [explicit solve]: current solution: 5.137638e+00, current residual:3.104787e+00 +wall time: 0.000622988s + +time increment:33680 time: 33.68 +field ' c' [explicit solve]: current solution: 9.622203e+01, current residual:5.810459e+01 +field 'mu' [explicit solve]: current solution: 5.137518e+00, current residual:3.104717e+00 +wall time: 0.000601053s + +time increment:33690 time: 33.69 +field ' c' [explicit solve]: current solution: 9.622193e+01, current residual:5.810453e+01 +field 'mu' [explicit solve]: current solution: 5.137397e+00, current residual:3.104647e+00 +wall time: 0.000673056s + +time increment:33700 time: 33.7 +field ' c' [explicit solve]: current solution: 9.622183e+01, current residual:5.810446e+01 +field 'mu' [explicit solve]: current solution: 5.137276e+00, current residual:3.104576e+00 +wall time: 0.000597s + +time increment:33710 time: 33.71 +field ' c' [explicit solve]: current solution: 9.622173e+01, current residual:5.810440e+01 +field 'mu' [explicit solve]: current solution: 5.137155e+00, current residual:3.104506e+00 +wall time: 0.00057292s + +time increment:33720 time: 33.72 +field ' c' [explicit solve]: current solution: 9.622163e+01, current residual:5.810433e+01 +field 'mu' [explicit solve]: current solution: 5.137035e+00, current residual:3.104436e+00 +wall time: 0.000690937s + +time increment:33730 time: 33.73 +field ' c' [explicit solve]: current solution: 9.622154e+01, current residual:5.810426e+01 +field 'mu' [explicit solve]: current solution: 5.136914e+00, current residual:3.104365e+00 +wall time: 0.000675917s + +time increment:33740 time: 33.74 +field ' c' [explicit solve]: current solution: 9.622144e+01, current residual:5.810420e+01 +field 'mu' [explicit solve]: current solution: 5.136793e+00, current residual:3.104295e+00 +wall time: 0.000690222s + +time increment:33750 time: 33.75 +field ' c' [explicit solve]: current solution: 9.622134e+01, current residual:5.810413e+01 +field 'mu' [explicit solve]: current solution: 5.136673e+00, current residual:3.104225e+00 +wall time: 0.000605106s + +time increment:33760 time: 33.76 +field ' c' [explicit solve]: current solution: 9.622124e+01, current residual:5.810407e+01 +field 'mu' [explicit solve]: current solution: 5.136552e+00, current residual:3.104155e+00 +wall time: 0.000575066s + +time increment:33770 time: 33.77 +field ' c' [explicit solve]: current solution: 9.622114e+01, current residual:5.810400e+01 +field 'mu' [explicit solve]: current solution: 5.136431e+00, current residual:3.104084e+00 +wall time: 0.000593185s + +time increment:33780 time: 33.78 +field ' c' [explicit solve]: current solution: 9.622104e+01, current residual:5.810394e+01 +field 'mu' [explicit solve]: current solution: 5.136310e+00, current residual:3.104014e+00 +wall time: 0.000703096s + +time increment:33790 time: 33.79 +field ' c' [explicit solve]: current solution: 9.622095e+01, current residual:5.810387e+01 +field 'mu' [explicit solve]: current solution: 5.136190e+00, current residual:3.103944e+00 +wall time: 0.000613928s + +time increment:33800 time: 33.8 +field ' c' [explicit solve]: current solution: 9.622085e+01, current residual:5.810380e+01 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solve]: current solution: 5.135586e+00, current residual:3.103592e+00 +wall time: 0.000686884s + +time increment:33850 time: 33.85 +field ' c' [explicit solve]: current solution: 9.622036e+01, current residual:5.810348e+01 +field 'mu' [explicit solve]: current solution: 5.135465e+00, current residual:3.103521e+00 +wall time: 0.000658989s + +time increment:33860 time: 33.86 +field ' c' [explicit solve]: current solution: 9.622026e+01, current residual:5.810341e+01 +field 'mu' [explicit solve]: current solution: 5.135344e+00, current residual:3.103451e+00 +wall time: 0.000687122s + +time increment:33870 time: 33.87 +field ' c' [explicit solve]: current solution: 9.622017e+01, current residual:5.810335e+01 +field 'mu' [explicit solve]: current solution: 5.135223e+00, current residual:3.103381e+00 +wall time: 0.000697851s + +time increment:33880 time: 33.88 +field ' c' [explicit solve]: current solution: 9.622007e+01, current residual:5.810328e+01 +field 'mu' [explicit solve]: current solution: 5.135103e+00, current residual:3.103310e+00 +wall time: 0.000677109s + +time increment:33890 time: 33.89 +field ' c' [explicit solve]: current solution: 9.621997e+01, current residual:5.810322e+01 +field 'mu' [explicit solve]: current solution: 5.134982e+00, current residual:3.103240e+00 +wall time: 0.000674009s + +time increment:33900 time: 33.9 +field ' c' [explicit solve]: current solution: 9.621988e+01, current residual:5.810315e+01 +field 'mu' [explicit solve]: current solution: 5.134861e+00, current residual:3.103169e+00 +wall time: 0.000607967s + +time increment:33910 time: 33.91 +field ' c' [explicit solve]: current solution: 9.621978e+01, current residual:5.810309e+01 +field 'mu' [explicit solve]: current solution: 5.134740e+00, current residual:3.103099e+00 +wall time: 0.000595093s + +time increment:33920 time: 33.92 +field ' c' [explicit solve]: current solution: 9.621968e+01, current residual:5.810302e+01 +field 'mu' [explicit solve]: current solution: 5.134619e+00, current residual:3.103028e+00 +wall time: 0.000736952s + +time increment:33930 time: 33.93 +field ' c' [explicit solve]: current solution: 9.621959e+01, current residual:5.810296e+01 +field 'mu' [explicit solve]: current solution: 5.134498e+00, current residual:3.102958e+00 +wall time: 0.000681162s + +time increment:33940 time: 33.94 +field ' c' [explicit solve]: current solution: 9.621949e+01, current residual:5.810289e+01 +field 'mu' [explicit solve]: current solution: 5.134378e+00, current residual:3.102888e+00 +wall time: 0.000705004s + +time increment:33950 time: 33.95 +field ' c' [explicit solve]: current solution: 9.621939e+01, current residual:5.810283e+01 +field 'mu' [explicit solve]: current solution: 5.134257e+00, current residual:3.102817e+00 +wall time: 0.000648022s + +time increment:33960 time: 33.96 +field ' c' [explicit solve]: current solution: 9.621930e+01, current residual:5.810276e+01 +field 'mu' [explicit solve]: current solution: 5.134136e+00, current residual:3.102747e+00 +wall time: 0.000794888s + +time increment:33970 time: 33.97 +field ' c' [explicit solve]: current solution: 9.621920e+01, current residual:5.810270e+01 +field 'mu' [explicit solve]: current solution: 5.134015e+00, current residual:3.102676e+00 +wall time: 0.000674009s + +time increment:33980 time: 33.98 +field ' c' [explicit solve]: current solution: 9.621910e+01, current residual:5.810263e+01 +field 'mu' [explicit solve]: current solution: 5.133894e+00, current residual:3.102606e+00 +wall time: 0.000620127s + +time increment:33990 time: 33.99 +field ' c' [explicit solve]: current solution: 9.621901e+01, current residual:5.810257e+01 +field 'mu' [explicit solve]: current solution: 5.133773e+00, current residual:3.102535e+00 +wall time: 0.00068593s + +time increment:34000 time: 34 +field ' c' [explicit solve]: current solution: 9.621891e+01, current residual:5.810251e+01 +field 'mu' [explicit solve]: current solution: 5.133652e+00, current residual:3.102465e+00 +wall time: 0.00072813s + +time increment:34010 time: 34.01 +field ' c' [explicit solve]: current solution: 9.621882e+01, current residual:5.810244e+01 +field 'mu' [explicit solve]: current solution: 5.133532e+00, current residual:3.102394e+00 +wall time: 0.00069499s + +time increment:34020 time: 34.02 +field ' c' [explicit solve]: current solution: 9.621872e+01, current residual:5.810238e+01 +field 'mu' [explicit solve]: current solution: 5.133411e+00, current residual:3.102323e+00 +wall time: 0.000688076s + +time increment:34030 time: 34.03 +field ' c' [explicit solve]: current solution: 9.621863e+01, current residual:5.810231e+01 +field 'mu' [explicit solve]: current solution: 5.133290e+00, current residual:3.102253e+00 +wall time: 0.000703096s + +time increment:34040 time: 34.04 +field ' c' [explicit solve]: current solution: 9.621853e+01, current residual:5.810225e+01 +field 'mu' [explicit solve]: current solution: 5.133169e+00, current residual:3.102182e+00 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0.000683069s + +time increment:34090 time: 34.09 +field ' c' [explicit solve]: current solution: 9.621805e+01, current residual:5.810193e+01 +field 'mu' [explicit solve]: current solution: 5.132564e+00, current residual:3.101830e+00 +wall time: 0.000790834s + +time increment:34100 time: 34.1 +field ' c' [explicit solve]: current solution: 9.621796e+01, current residual:5.810187e+01 +field 'mu' [explicit solve]: current solution: 5.132443e+00, current residual:3.101759e+00 +wall time: 0.000721931s + +time increment:34110 time: 34.11 +field ' c' [explicit solve]: current solution: 9.621786e+01, current residual:5.810180e+01 +field 'mu' [explicit solve]: current solution: 5.132323e+00, current residual:3.101688e+00 +wall time: 0.000755072s + +time increment:34120 time: 34.12 +field ' c' [explicit solve]: current solution: 9.621777e+01, current residual:5.810174e+01 +field 'mu' [explicit solve]: current solution: 5.132202e+00, current residual:3.101618e+00 +wall time: 0.000726938s + +time increment:34130 time: 34.13 +field ' c' [explicit solve]: current solution: 9.621767e+01, current residual:5.810167e+01 +field 'mu' [explicit solve]: current solution: 5.132081e+00, current residual:3.101547e+00 +wall time: 0.000654936s + +time increment:34140 time: 34.14 +field ' c' [explicit solve]: current solution: 9.621758e+01, current residual:5.810161e+01 +field 'mu' [explicit solve]: current solution: 5.131960e+00, current residual:3.101476e+00 +wall time: 0.000788927s + +time increment:34150 time: 34.15 +field ' c' [explicit solve]: current solution: 9.621748e+01, current residual:5.810155e+01 +field 'mu' [explicit solve]: current solution: 5.131839e+00, current residual:3.101406e+00 +wall time: 0.00077796s + +time increment:34160 time: 34.16 +field ' c' [explicit solve]: current solution: 9.621739e+01, current residual:5.810148e+01 +field 'mu' [explicit solve]: current solution: 5.131718e+00, current residual:3.101335e+00 +wall time: 0.000727892s + +time increment:34170 time: 34.17 +field ' c' [explicit solve]: current solution: 9.621730e+01, current residual:5.810142e+01 +field 'mu' [explicit solve]: current solution: 5.131597e+00, current residual:3.101265e+00 +wall time: 0.000785828s + +time increment:34180 time: 34.18 +field ' c' [explicit solve]: current solution: 9.621720e+01, current residual:5.810136e+01 +field 'mu' [explicit solve]: current solution: 5.131476e+00, current residual:3.101194e+00 +wall time: 0.000625134s + +time increment:34190 time: 34.19 +field ' c' [explicit solve]: current solution: 9.621711e+01, current residual:5.810129e+01 +field 'mu' [explicit solve]: current solution: 5.131355e+00, current residual:3.101123e+00 +wall time: 0.000662088s + +time increment:34200 time: 34.2 +field ' c' [explicit solve]: current solution: 9.621701e+01, current residual:5.810123e+01 +field 'mu' [explicit solve]: current solution: 5.131234e+00, current residual:3.101053e+00 +wall time: 0.000711918s + +time increment:34210 time: 34.21 +field ' c' [explicit solve]: current solution: 9.621692e+01, current residual:5.810117e+01 +field 'mu' [explicit solve]: current solution: 5.131113e+00, current residual:3.100982e+00 +wall time: 0.000694036s + +time increment:34220 time: 34.22 +field ' c' [explicit solve]: current solution: 9.621683e+01, current residual:5.810110e+01 +field 'mu' [explicit solve]: current solution: 5.130992e+00, current residual:3.100911e+00 +wall time: 0.000673056s + +time increment:34230 time: 34.23 +field ' c' [explicit solve]: current solution: 9.621673e+01, current residual:5.810104e+01 +field 'mu' [explicit solve]: current solution: 5.130871e+00, current residual:3.100840e+00 +wall time: 0.000696898s + +time increment:34240 time: 34.24 +field ' c' [explicit solve]: current solution: 9.621664e+01, current residual:5.810098e+01 +field 'mu' [explicit solve]: current solution: 5.130750e+00, current residual:3.100770e+00 +wall time: 0.000714064s + +time increment:34250 time: 34.25 +field ' c' [explicit solve]: current solution: 9.621654e+01, current residual:5.810092e+01 +field 'mu' [explicit solve]: current solution: 5.130629e+00, current residual:3.100699e+00 +wall time: 0.000634909s + +time increment:34260 time: 34.26 +field ' c' [explicit solve]: current solution: 9.621645e+01, current residual:5.810085e+01 +field 'mu' [explicit solve]: current solution: 5.130508e+00, current residual:3.100628e+00 +wall time: 0.000757217s + +time increment:34270 time: 34.27 +field ' c' [explicit solve]: current solution: 9.621636e+01, current residual:5.810079e+01 +field 'mu' [explicit solve]: current solution: 5.130387e+00, current residual:3.100558e+00 +wall time: 0.000735044s + +time increment:34280 time: 34.28 +field ' c' [explicit solve]: current solution: 9.621626e+01, current residual:5.810073e+01 +field 'mu' [explicit solve]: current solution: 5.130266e+00, current residual:3.100487e+00 +wall time: 0.000672102s + +time increment:34290 time: 34.29 +field ' c' [explicit solve]: current solution: 9.621617e+01, current residual:5.810067e+01 +field 'mu' [explicit solve]: current solution: 5.130145e+00, current residual:3.100416e+00 +wall time: 0.000751972s + +time increment:34300 time: 34.3 +field ' c' [explicit solve]: current solution: 9.621608e+01, current residual:5.810060e+01 +field 'mu' [explicit solve]: current solution: 5.130024e+00, current residual:3.100345e+00 +wall time: 0.000718832s + +time increment:34310 time: 34.31 +field ' c' [explicit solve]: current solution: 9.621598e+01, current residual:5.810054e+01 +field 'mu' [explicit solve]: current solution: 5.129903e+00, current residual:3.100275e+00 +wall time: 0.00061512s + +time increment:34320 time: 34.32 +field ' c' [explicit solve]: current solution: 9.621589e+01, current residual:5.810048e+01 +field 'mu' [explicit solve]: current solution: 5.129782e+00, current residual:3.100204e+00 +wall time: 0.000569105s + +time increment:34330 time: 34.33 +field ' c' [explicit solve]: current solution: 9.621580e+01, current residual:5.810042e+01 +field 'mu' [explicit solve]: current solution: 5.129661e+00, current residual:3.100133e+00 +wall time: 0.000592232s + +time increment:34340 time: 34.34 +field ' c' [explicit solve]: current solution: 9.621571e+01, current residual:5.810035e+01 +field 'mu' [explicit solve]: current solution: 5.129540e+00, current residual:3.100062e+00 +wall time: 0.000566959s + +time increment:34350 time: 34.35 +field ' c' [explicit solve]: current solution: 9.621561e+01, current residual:5.810029e+01 +field 'mu' [explicit solve]: current solution: 5.129419e+00, current residual:3.099992e+00 +wall time: 0.000609159s + +time increment:34360 time: 34.36 +field ' c' [explicit solve]: current solution: 9.621552e+01, current residual:5.810023e+01 +field 'mu' [explicit solve]: current solution: 5.129298e+00, current residual:3.099921e+00 +wall time: 0.000558138s + +time increment:34370 time: 34.37 +field ' c' [explicit solve]: current solution: 9.621543e+01, current residual:5.810017e+01 +field 'mu' [explicit solve]: current solution: 5.129177e+00, current residual:3.099850e+00 +wall time: 0.000609875s + +time increment:34380 time: 34.38 +field ' c' [explicit solve]: current solution: 9.621534e+01, current residual:5.810010e+01 +field 'mu' [explicit solve]: current solution: 5.129056e+00, current residual:3.099779e+00 +wall time: 0.000591993s + +time increment:34390 time: 34.39 +field ' c' [explicit solve]: current solution: 9.621524e+01, current residual:5.810004e+01 +field 'mu' [explicit solve]: current solution: 5.128935e+00, current residual:3.099708e+00 +wall time: 0.000690937s + +time increment:34400 time: 34.4 +field ' c' [explicit solve]: current solution: 9.621515e+01, current residual:5.809998e+01 +field 'mu' [explicit solve]: current solution: 5.128814e+00, current residual:3.099638e+00 +wall time: 0.000663042s + +time increment:34410 time: 34.41 +field ' c' [explicit solve]: current solution: 9.621506e+01, current residual:5.809992e+01 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[explicit solve]: current solution: 5.128209e+00, current residual:3.099283e+00 +wall time: 0.000640154s + +time increment:34460 time: 34.46 +field ' c' [explicit solve]: current solution: 9.621460e+01, current residual:5.809961e+01 +field 'mu' [explicit solve]: current solution: 5.128087e+00, current residual:3.099213e+00 +wall time: 0.000602007s + +time increment:34470 time: 34.47 +field ' c' [explicit solve]: current solution: 9.621451e+01, current residual:5.809955e+01 +field 'mu' [explicit solve]: current solution: 5.127966e+00, current residual:3.099142e+00 +wall time: 0.000596046s + +time increment:34480 time: 34.48 +field ' c' [explicit solve]: current solution: 9.621442e+01, current residual:5.809949e+01 +field 'mu' [explicit solve]: current solution: 5.127845e+00, current residual:3.099071e+00 +wall time: 0.000571966s + +time increment:34490 time: 34.49 +field ' c' [explicit solve]: current solution: 9.621433e+01, current residual:5.809942e+01 +field 'mu' [explicit solve]: current solution: 5.127724e+00, current residual:3.099000e+00 +wall time: 0.000666142s + +time increment:34500 time: 34.5 +field ' c' [explicit solve]: current solution: 9.621423e+01, current residual:5.809936e+01 +field 'mu' [explicit solve]: current solution: 5.127603e+00, current residual:3.098929e+00 +wall time: 0.00071311s + +time increment:34510 time: 34.51 +field ' c' [explicit solve]: current solution: 9.621414e+01, current residual:5.809930e+01 +field 'mu' [explicit solve]: current solution: 5.127482e+00, current residual:3.098858e+00 +wall time: 0.000583172s + +time increment:34520 time: 34.52 +field ' c' [explicit solve]: current solution: 9.621405e+01, current residual:5.809924e+01 +field 'mu' [explicit solve]: current solution: 5.127361e+00, current residual:3.098787e+00 +wall time: 0.000673056s + +time increment:34530 time: 34.53 +field ' c' [explicit solve]: current solution: 9.621396e+01, current residual:5.809918e+01 +field 'mu' [explicit solve]: current solution: 5.127240e+00, current residual:3.098716e+00 +wall time: 0.000568867s + +time increment:34540 time: 34.54 +field ' c' [explicit solve]: current solution: 9.621387e+01, current residual:5.809912e+01 +field 'mu' [explicit solve]: current solution: 5.127119e+00, current residual:3.098645e+00 +wall time: 0.000674009s + +time increment:34550 time: 34.55 +field ' c' [explicit solve]: current solution: 9.621378e+01, current residual:5.809906e+01 +field 'mu' [explicit solve]: current solution: 5.126998e+00, current residual:3.098575e+00 +wall time: 0.000597s + +time increment:34560 time: 34.56 +field ' c' [explicit solve]: current solution: 9.621369e+01, current residual:5.809900e+01 +field 'mu' [explicit solve]: current solution: 5.126877e+00, current residual:3.098504e+00 +wall time: 0.000568151s + +time increment:34570 time: 34.57 +field ' c' [explicit solve]: current solution: 9.621360e+01, current residual:5.809893e+01 +field 'mu' [explicit solve]: current solution: 5.126756e+00, current residual:3.098433e+00 +wall time: 0.00067091s + +time increment:34580 time: 34.58 +field ' c' [explicit solve]: current solution: 9.621351e+01, current residual:5.809887e+01 +field 'mu' [explicit solve]: current solution: 5.126635e+00, current residual:3.098362e+00 +wall time: 0.000674963s + +time increment:34590 time: 34.59 +field ' c' [explicit solve]: current solution: 9.621342e+01, current residual:5.809881e+01 +field 'mu' [explicit solve]: current solution: 5.126513e+00, current residual:3.098291e+00 +wall time: 0.000680923s + +time increment:34600 time: 34.6 +field ' c' [explicit solve]: current solution: 9.621333e+01, current residual:5.809875e+01 +field 'mu' [explicit solve]: current solution: 5.126392e+00, current residual:3.098220e+00 +wall time: 0.00057888s + +time increment:34610 time: 34.61 +field ' c' [explicit solve]: current solution: 9.621324e+01, current residual:5.809869e+01 +field 'mu' [explicit solve]: current solution: 5.126271e+00, current residual:3.098149e+00 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0.000677109s + +time increment:34660 time: 34.66 +field ' c' [explicit solve]: current solution: 9.621278e+01, current residual:5.809839e+01 +field 'mu' [explicit solve]: current solution: 5.125666e+00, current residual:3.097794e+00 +wall time: 0.000631094s + +time increment:34670 time: 34.67 +field ' c' [explicit solve]: current solution: 9.621270e+01, current residual:5.809833e+01 +field 'mu' [explicit solve]: current solution: 5.125545e+00, current residual:3.097723e+00 +wall time: 0.000700951s + +time increment:34680 time: 34.68 +field ' c' [explicit solve]: current solution: 9.621261e+01, current residual:5.809827e+01 +field 'mu' [explicit solve]: current solution: 5.125424e+00, current residual:3.097652e+00 +wall time: 0.000669956s + +time increment:34690 time: 34.69 +field ' c' [explicit solve]: current solution: 9.621252e+01, current residual:5.809821e+01 +field 'mu' [explicit solve]: current solution: 5.125302e+00, current residual:3.097581e+00 +wall time: 0.000562906s + +time increment:34700 time: 34.7 +field ' c' [explicit solve]: current solution: 9.621243e+01, current residual:5.809815e+01 +field 'mu' [explicit solve]: current solution: 5.125181e+00, current residual:3.097510e+00 +wall time: 0.000571966s + +time increment:34710 time: 34.71 +field ' c' [explicit solve]: current solution: 9.621234e+01, current residual:5.809809e+01 +field 'mu' [explicit solve]: current solution: 5.125060e+00, current residual:3.097439e+00 +wall time: 0.000549078s + +time increment:34720 time: 34.72 +field ' c' [explicit solve]: current solution: 9.621225e+01, current residual:5.809803e+01 +field 'mu' [explicit solve]: current solution: 5.124939e+00, current residual:3.097368e+00 +wall time: 0.000631094s + +time increment:34730 time: 34.73 +field ' c' [explicit solve]: current solution: 9.621216e+01, current residual:5.809797e+01 +field 'mu' [explicit solve]: current solution: 5.124818e+00, current residual:3.097297e+00 +wall time: 0.000710011s + +time increment:34740 time: 34.74 +field ' c' [explicit solve]: current solution: 9.621207e+01, current residual:5.809791e+01 +field 'mu' [explicit solve]: current solution: 5.124697e+00, current residual:3.097226e+00 +wall time: 0.000639915s + +time increment:34750 time: 34.75 +field ' c' [explicit solve]: current solution: 9.621198e+01, current residual:5.809785e+01 +field 'mu' [explicit solve]: current solution: 5.124576e+00, current residual:3.097155e+00 +wall time: 0.000722885s + +time increment:34760 time: 34.76 +field ' c' [explicit solve]: current solution: 9.621189e+01, current residual:5.809779e+01 +field 'mu' [explicit solve]: current solution: 5.124455e+00, current residual:3.097084e+00 +wall time: 0.000665903s + +time increment:34770 time: 34.77 +field ' c' [explicit solve]: current solution: 9.621180e+01, current residual:5.809773e+01 +field 'mu' [explicit solve]: current solution: 5.124334e+00, current residual:3.097013e+00 +wall time: 0.000814199s + +time increment:34780 time: 34.78 +field ' c' [explicit solve]: current solution: 9.621171e+01, current residual:5.809767e+01 +field 'mu' [explicit solve]: current solution: 5.124212e+00, current residual:3.096942e+00 +wall time: 0.000653982s + +time increment:34790 time: 34.79 +field ' c' [explicit solve]: current solution: 9.621162e+01, current residual:5.809761e+01 +field 'mu' [explicit solve]: current solution: 5.124091e+00, current residual:3.096871e+00 +wall time: 0.000637054s + +time increment:34800 time: 34.8 +field ' c' [explicit solve]: current solution: 9.621154e+01, current residual:5.809755e+01 +field 'mu' [explicit solve]: current solution: 5.123970e+00, current residual:3.096800e+00 +wall time: 0.000632048s + +time increment:34810 time: 34.81 +field ' c' [explicit solve]: current solution: 9.621145e+01, current residual:5.809749e+01 +field 'mu' [explicit solve]: current solution: 5.123849e+00, current residual:3.096729e+00 +wall time: 0.000715971s + +time increment:34820 time: 34.82 +field ' c' [explicit solve]: current solution: 9.621136e+01, current residual:5.809743e+01 +field 'mu' [explicit solve]: current solution: 5.123728e+00, current residual:3.096658e+00 +wall time: 0.00078392s + +time increment:34830 time: 34.83 +field ' c' [explicit solve]: current solution: 9.621127e+01, current residual:5.809737e+01 +field 'mu' [explicit solve]: current solution: 5.123607e+00, current residual:3.096587e+00 +wall time: 0.000583887s + +time increment:34840 time: 34.84 +field ' c' [explicit solve]: current solution: 9.621118e+01, current residual:5.809731e+01 +field 'mu' [explicit solve]: current solution: 5.123486e+00, current residual:3.096516e+00 +wall time: 0.000720978s + +time increment:34850 time: 34.85 +field ' c' [explicit solve]: current solution: 9.621109e+01, current residual:5.809725e+01 +field 'mu' [explicit solve]: current solution: 5.123365e+00, current residual:3.096445e+00 +wall time: 0.000604868s + +time increment:34860 time: 34.86 +field ' c' [explicit solve]: current solution: 9.621101e+01, current residual:5.809719e+01 +field 'mu' [explicit solve]: current solution: 5.123244e+00, current residual:3.096373e+00 +wall time: 0.000697136s + +time increment:34870 time: 34.87 +field ' c' [explicit solve]: current solution: 9.621092e+01, current residual:5.809713e+01 +field 'mu' [explicit solve]: current solution: 5.123122e+00, current residual:3.096302e+00 +wall time: 0.000595093s + +time increment:34880 time: 34.88 +field ' c' [explicit solve]: current solution: 9.621083e+01, current residual:5.809707e+01 +field 'mu' [explicit solve]: current solution: 5.123001e+00, current residual:3.096231e+00 +wall time: 0.000591993s + +time increment:34890 time: 34.89 +field ' c' [explicit solve]: current solution: 9.621074e+01, current residual:5.809701e+01 +field 'mu' [explicit solve]: current solution: 5.122880e+00, current residual:3.096160e+00 +wall time: 0.000653028s + +time increment:34900 time: 34.9 +field ' c' [explicit solve]: current solution: 9.621065e+01, current residual:5.809695e+01 +field 'mu' [explicit solve]: current solution: 5.122759e+00, current residual:3.096089e+00 +wall time: 0.000695944s + +time increment:34910 time: 34.91 +field ' c' [explicit solve]: current solution: 9.621057e+01, current residual:5.809689e+01 +field 'mu' [explicit solve]: current solution: 5.122638e+00, current residual:3.096018e+00 +wall time: 0.000576019s + +time increment:34920 time: 34.92 +field ' c' [explicit solve]: current solution: 9.621048e+01, current residual:5.809683e+01 +field 'mu' [explicit solve]: current solution: 5.122517e+00, current residual:3.095947e+00 +wall time: 0.000743866s + +time increment:34930 time: 34.93 +field ' c' [explicit solve]: current solution: 9.621039e+01, current residual:5.809677e+01 +field 'mu' [explicit solve]: current solution: 5.122396e+00, current residual:3.095876e+00 +wall time: 0.000576019s + +time increment:34940 time: 34.94 +field ' c' [explicit solve]: current solution: 9.621030e+01, current residual:5.809672e+01 +field 'mu' [explicit solve]: current solution: 5.122275e+00, current residual:3.095805e+00 +wall time: 0.000656128s + +time increment:34950 time: 34.95 +field ' c' [explicit solve]: current solution: 9.621022e+01, current residual:5.809666e+01 +field 'mu' [explicit solve]: current solution: 5.122153e+00, current residual:3.095734e+00 +wall time: 0.000782967s + +time increment:34960 time: 34.96 +field ' c' [explicit solve]: current solution: 9.621013e+01, current residual:5.809660e+01 +field 'mu' [explicit solve]: current solution: 5.122032e+00, current residual:3.095662e+00 +wall time: 0.000710011s + +time increment:34970 time: 34.97 +field ' c' [explicit solve]: current solution: 9.621004e+01, current residual:5.809654e+01 +field 'mu' [explicit solve]: current solution: 5.121911e+00, current residual:3.095591e+00 +wall time: 0.000597s + +time increment:34980 time: 34.98 +field ' c' [explicit solve]: current solution: 9.620996e+01, current residual:5.809648e+01 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solve]: current solution: 5.121306e+00, current residual:3.095236e+00 +wall time: 0.000703096s + +time increment:35030 time: 35.03 +field ' c' [explicit solve]: current solution: 9.620952e+01, current residual:5.809619e+01 +field 'mu' [explicit solve]: current solution: 5.121184e+00, current residual:3.095164e+00 +wall time: 0.000694036s + +time increment:35040 time: 35.04 +field ' c' [explicit solve]: current solution: 9.620943e+01, current residual:5.809613e+01 +field 'mu' [explicit solve]: current solution: 5.121063e+00, current residual:3.095093e+00 +wall time: 0.000798941s + +time increment:35050 time: 35.05 +field ' c' [explicit solve]: current solution: 9.620935e+01, current residual:5.809607e+01 +field 'mu' [explicit solve]: current solution: 5.120942e+00, current residual:3.095022e+00 +wall time: 0.000725031s + +time increment:35060 time: 35.06 +field ' c' [explicit solve]: current solution: 9.620926e+01, current residual:5.809601e+01 +field 'mu' [explicit solve]: current solution: 5.120821e+00, current residual:3.094951e+00 +wall time: 0.000632048s + +time increment:35070 time: 35.07 +field ' c' [explicit solve]: current solution: 9.620918e+01, current residual:5.809595e+01 +field 'mu' [explicit solve]: current solution: 5.120700e+00, current residual:3.094880e+00 +wall time: 0.000715017s + +time increment:35080 time: 35.08 +field ' c' [explicit solve]: current solution: 9.620909e+01, current residual:5.809590e+01 +field 'mu' [explicit solve]: current solution: 5.120579e+00, current residual:3.094809e+00 +wall time: 0.000640154s + +time increment:35090 time: 35.09 +field ' c' [explicit solve]: current solution: 9.620900e+01, current residual:5.809584e+01 +field 'mu' [explicit solve]: current solution: 5.120458e+00, current residual:3.094737e+00 +wall time: 0.000640154s + +time increment:35100 time: 35.1 +field ' c' [explicit solve]: current solution: 9.620892e+01, current residual:5.809578e+01 +field 'mu' [explicit solve]: current solution: 5.120337e+00, current residual:3.094666e+00 +wall time: 0.000691891s + +time increment:35110 time: 35.11 +field ' c' [explicit solve]: current solution: 9.620883e+01, current residual:5.809572e+01 +field 'mu' [explicit solve]: current solution: 5.120216e+00, current residual:3.094595e+00 +wall time: 0.000825167s + +time increment:35120 time: 35.12 +field ' c' [explicit solve]: current solution: 9.620875e+01, current residual:5.809566e+01 +field 'mu' [explicit solve]: current solution: 5.120094e+00, current residual:3.094524e+00 +wall time: 0.0007689s + +time increment:35130 time: 35.13 +field ' c' [explicit solve]: current solution: 9.620866e+01, current residual:5.809561e+01 +field 'mu' [explicit solve]: current solution: 5.119973e+00, current residual:3.094453e+00 +wall time: 0.000741005s + +time increment:35140 time: 35.14 +field ' c' [explicit solve]: current solution: 9.620857e+01, current residual:5.809555e+01 +field 'mu' [explicit solve]: current solution: 5.119852e+00, current residual:3.094381e+00 +wall time: 0.00075984s + +time increment:35150 time: 35.15 +field ' c' [explicit solve]: current solution: 9.620849e+01, current residual:5.809549e+01 +field 'mu' [explicit solve]: current solution: 5.119731e+00, current residual:3.094310e+00 +wall time: 0.00067091s + +time increment:35160 time: 35.16 +field ' c' [explicit solve]: current solution: 9.620840e+01, current residual:5.809543e+01 +field 'mu' [explicit solve]: current solution: 5.119610e+00, current residual:3.094239e+00 +wall time: 0.00063014s + +time increment:35170 time: 35.17 +field ' c' [explicit solve]: current solution: 9.620832e+01, current residual:5.809537e+01 +field 'mu' [explicit solve]: current solution: 5.119489e+00, current residual:3.094168e+00 +wall time: 0.000669956s + +time increment:35180 time: 35.18 +field ' c' [explicit solve]: current solution: 9.620823e+01, current residual:5.809532e+01 +field 'mu' [explicit solve]: current solution: 5.119368e+00, current residual:3.094097e+00 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0.000650167s + +time increment:35230 time: 35.23 +field ' c' [explicit solve]: current solution: 9.620781e+01, current residual:5.809503e+01 +field 'mu' [explicit solve]: current solution: 5.118762e+00, current residual:3.093740e+00 +wall time: 0.000642061s + +time increment:35240 time: 35.24 +field ' c' [explicit solve]: current solution: 9.620772e+01, current residual:5.809497e+01 +field 'mu' [explicit solve]: current solution: 5.118641e+00, current residual:3.093669e+00 +wall time: 0.000733137s + +time increment:35250 time: 35.25 +field ' c' [explicit solve]: current solution: 9.620764e+01, current residual:5.809491e+01 +field 'mu' [explicit solve]: current solution: 5.118520e+00, current residual:3.093598e+00 +wall time: 0.000680923s + +time increment:35260 time: 35.26 +field ' c' [explicit solve]: current solution: 9.620755e+01, current residual:5.809486e+01 +field 'mu' [explicit solve]: current solution: 5.118399e+00, current residual:3.093527e+00 +wall time: 0.000828028s + +time increment:35270 time: 35.27 +field ' c' [explicit solve]: current solution: 9.620747e+01, current residual:5.809480e+01 +field 'mu' [explicit solve]: current solution: 5.118278e+00, current residual:3.093455e+00 +wall time: 0.000617027s + +time increment:35280 time: 35.28 +field ' c' [explicit solve]: current solution: 9.620738e+01, current residual:5.809474e+01 +field 'mu' [explicit solve]: current solution: 5.118157e+00, current residual:3.093384e+00 +wall time: 0.000786066s + +time increment:35290 time: 35.29 +field ' c' [explicit solve]: current solution: 9.620730e+01, current residual:5.809469e+01 +field 'mu' [explicit solve]: current solution: 5.118036e+00, current residual:3.093313e+00 +wall time: 0.000706911s + +time increment:35300 time: 35.3 +field ' c' [explicit solve]: current solution: 9.620722e+01, current residual:5.809463e+01 +field 'mu' [explicit solve]: current solution: 5.117915e+00, current residual:3.093242e+00 +wall time: 0.000664949s + +time increment:35310 time: 35.31 +field ' c' [explicit solve]: current solution: 9.620713e+01, current residual:5.809457e+01 +field 'mu' [explicit solve]: current solution: 5.117794e+00, current residual:3.093170e+00 +wall time: 0.000653982s + +time increment:35320 time: 35.32 +field ' c' [explicit solve]: current solution: 9.620705e+01, current residual:5.809452e+01 +field 'mu' [explicit solve]: current solution: 5.117673e+00, current residual:3.093099e+00 +wall time: 0.000633001s + +time increment:35330 time: 35.33 +field ' c' [explicit solve]: current solution: 9.620696e+01, current residual:5.809446e+01 +field 'mu' [explicit solve]: current solution: 5.117551e+00, current residual:3.093028e+00 +wall time: 0.000730991s + +time increment:35340 time: 35.34 +field ' c' [explicit solve]: current solution: 9.620688e+01, current residual:5.809440e+01 +field 'mu' [explicit solve]: current solution: 5.117430e+00, current residual:3.092957e+00 +wall time: 0.000691175s + +time increment:35350 time: 35.35 +field ' c' [explicit solve]: current solution: 9.620680e+01, current residual:5.809435e+01 +field 'mu' [explicit solve]: current solution: 5.117309e+00, current residual:3.092885e+00 +wall time: 0.000693083s + +time increment:35360 time: 35.36 +field ' c' [explicit solve]: current solution: 9.620671e+01, current residual:5.809429e+01 +field 'mu' [explicit solve]: current solution: 5.117188e+00, current residual:3.092814e+00 +wall time: 0.000701904s + +time increment:35370 time: 35.37 +field ' c' [explicit solve]: current solution: 9.620663e+01, current residual:5.809423e+01 +field 'mu' [explicit solve]: current solution: 5.117067e+00, current residual:3.092743e+00 +wall time: 0.000729084s + +time increment:35380 time: 35.38 +field ' c' [explicit solve]: current solution: 9.620654e+01, current residual:5.809418e+01 +field 'mu' [explicit solve]: current solution: 5.116946e+00, current residual:3.092672e+00 +wall time: 0.000694036s + +time increment:35390 time: 35.39 +field ' c' [explicit solve]: current solution: 9.620646e+01, current residual:5.809412e+01 +field 'mu' [explicit solve]: current solution: 5.116825e+00, current residual:3.092600e+00 +wall time: 0.000720024s + +time increment:35400 time: 35.4 +field ' c' [explicit solve]: current solution: 9.620638e+01, current residual:5.809406e+01 +field 'mu' [explicit solve]: current solution: 5.116704e+00, current residual:3.092529e+00 +wall time: 0.000678062s + +time increment:35410 time: 35.41 +field ' c' [explicit solve]: current solution: 9.620629e+01, current residual:5.809401e+01 +field 'mu' [explicit solve]: current solution: 5.116583e+00, current residual:3.092458e+00 +wall time: 0.00059104s + +time increment:35420 time: 35.42 +field ' c' [explicit solve]: current solution: 9.620621e+01, current residual:5.809395e+01 +field 'mu' [explicit solve]: current solution: 5.116462e+00, current residual:3.092386e+00 +wall time: 0.000579119s + +time increment:35430 time: 35.43 +field ' c' [explicit solve]: current solution: 9.620613e+01, current residual:5.809389e+01 +field 'mu' [explicit solve]: current solution: 5.116341e+00, current residual:3.092315e+00 +wall time: 0.000681162s + +time increment:35440 time: 35.44 +field ' c' [explicit solve]: current solution: 9.620604e+01, current residual:5.809384e+01 +field 'mu' [explicit solve]: current solution: 5.116220e+00, current residual:3.092244e+00 +wall time: 0.000712872s + +time increment:35450 time: 35.45 +field ' c' [explicit solve]: current solution: 9.620596e+01, current residual:5.809378e+01 +field 'mu' [explicit solve]: current solution: 5.116099e+00, current residual:3.092173e+00 +wall time: 0.000622988s + +time increment:35460 time: 35.46 +field ' c' [explicit solve]: current solution: 9.620588e+01, current residual:5.809372e+01 +field 'mu' [explicit solve]: current solution: 5.115978e+00, current residual:3.092101e+00 +wall time: 0.000569105s + +time increment:35470 time: 35.47 +field ' c' [explicit solve]: current solution: 9.620580e+01, current residual:5.809367e+01 +field 'mu' [explicit solve]: current solution: 5.115857e+00, current residual:3.092030e+00 +wall time: 0.000563145s + +time increment:35480 time: 35.48 +field ' c' [explicit solve]: current solution: 9.620571e+01, current residual:5.809361e+01 +field 'mu' [explicit solve]: current solution: 5.115736e+00, current residual:3.091959e+00 +wall time: 0.000627995s + +time increment:35490 time: 35.49 +field ' c' [explicit solve]: current solution: 9.620563e+01, current residual:5.809356e+01 +field 'mu' [explicit solve]: current solution: 5.115615e+00, current residual:3.091887e+00 +wall time: 0.000622034s + +time increment:35500 time: 35.5 +field ' c' [explicit solve]: current solution: 9.620555e+01, current residual:5.809350e+01 +field 'mu' [explicit solve]: current solution: 5.115494e+00, current residual:3.091816e+00 +wall time: 0.000567198s + +time increment:35510 time: 35.51 +field ' c' [explicit solve]: current solution: 9.620546e+01, current residual:5.809344e+01 +field 'mu' [explicit solve]: current solution: 5.115373e+00, current residual:3.091745e+00 +wall time: 0.000584841s + +time increment:35520 time: 35.52 +field ' c' [explicit solve]: current solution: 9.620538e+01, current residual:5.809339e+01 +field 'mu' [explicit solve]: current solution: 5.115252e+00, current residual:3.091673e+00 +wall time: 0.000601053s + +time increment:35530 time: 35.53 +field ' c' [explicit solve]: current solution: 9.620530e+01, current residual:5.809333e+01 +field 'mu' [explicit solve]: current solution: 5.115131e+00, current residual:3.091602e+00 +wall time: 0.000579119s + +time increment:35540 time: 35.54 +field ' c' [explicit solve]: current solution: 9.620522e+01, current residual:5.809328e+01 +field 'mu' [explicit solve]: current solution: 5.115010e+00, current residual:3.091531e+00 +wall time: 0.00067091s + +time increment:35550 time: 35.55 +field ' c' [explicit solve]: current solution: 9.620514e+01, current residual:5.809322e+01 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[explicit solve]: current solution: 5.114405e+00, current residual:3.091174e+00 +wall time: 0.000599861s + +time increment:35600 time: 35.6 +field ' c' [explicit solve]: current solution: 9.620473e+01, current residual:5.809294e+01 +field 'mu' [explicit solve]: current solution: 5.114284e+00, current residual:3.091103e+00 +wall time: 0.000582933s + +time increment:35610 time: 35.61 +field ' c' [explicit solve]: current solution: 9.620464e+01, current residual:5.809289e+01 +field 'mu' [explicit solve]: current solution: 5.114163e+00, current residual:3.091031e+00 +wall time: 0.000558853s + +time increment:35620 time: 35.62 +field ' c' [explicit solve]: current solution: 9.620456e+01, current residual:5.809283e+01 +field 'mu' [explicit solve]: current solution: 5.114042e+00, current residual:3.090960e+00 +wall time: 0.000641823s + +time increment:35630 time: 35.63 +field ' c' [explicit solve]: current solution: 9.620448e+01, current residual:5.809278e+01 +field 'mu' [explicit solve]: current solution: 5.113921e+00, current residual:3.090889e+00 +wall time: 0.000707865s + +time increment:35640 time: 35.64 +field ' c' [explicit solve]: current solution: 9.620440e+01, current residual:5.809272e+01 +field 'mu' [explicit solve]: current solution: 5.113800e+00, current residual:3.090817e+00 +wall time: 0.000571966s + +time increment:35650 time: 35.65 +field ' c' [explicit solve]: current solution: 9.620432e+01, current residual:5.809267e+01 +field 'mu' [explicit solve]: current solution: 5.113679e+00, current residual:3.090746e+00 +wall time: 0.000564814s + +time increment:35660 time: 35.66 +field ' c' [explicit solve]: current solution: 9.620424e+01, current residual:5.809261e+01 +field 'mu' [explicit solve]: current solution: 5.113558e+00, current residual:3.090675e+00 +wall time: 0.00062108s + +time increment:35670 time: 35.67 +field ' c' [explicit solve]: current solution: 9.620416e+01, current residual:5.809256e+01 +field 'mu' [explicit solve]: current solution: 5.113437e+00, current residual:3.090603e+00 +wall time: 0.000555038s + +time increment:35680 time: 35.68 +field ' c' [explicit solve]: current solution: 9.620408e+01, current residual:5.809250e+01 +field 'mu' [explicit solve]: current solution: 5.113316e+00, current residual:3.090532e+00 +wall time: 0.000647068s + +time increment:35690 time: 35.69 +field ' c' [explicit solve]: current solution: 9.620399e+01, current residual:5.809245e+01 +field 'mu' [explicit solve]: current solution: 5.113195e+00, current residual:3.090461e+00 +wall time: 0.00071311s + +time increment:35700 time: 35.7 +field ' c' [explicit solve]: current solution: 9.620391e+01, current residual:5.809239e+01 +field 'mu' [explicit solve]: current solution: 5.113074e+00, current residual:3.090389e+00 +wall time: 0.000571012s + +time increment:35710 time: 35.71 +field ' c' [explicit solve]: current solution: 9.620383e+01, current residual:5.809234e+01 +field 'mu' [explicit solve]: current solution: 5.112953e+00, current residual:3.090318e+00 +wall time: 0.000702858s + +time increment:35720 time: 35.72 +field ' c' [explicit solve]: current solution: 9.620375e+01, current residual:5.809228e+01 +field 'mu' [explicit solve]: current solution: 5.112832e+00, current residual:3.090247e+00 +wall time: 0.000689983s + +time increment:35730 time: 35.73 +field ' c' [explicit solve]: current solution: 9.620367e+01, current residual:5.809223e+01 +field 'mu' [explicit solve]: current solution: 5.112711e+00, current residual:3.090175e+00 +wall time: 0.00065589s + +time increment:35740 time: 35.74 +field ' c' [explicit solve]: current solution: 9.620359e+01, current residual:5.809217e+01 +field 'mu' [explicit solve]: current solution: 5.112590e+00, current residual:3.090104e+00 +wall time: 0.000709057s + +time increment:35750 time: 35.75 +field ' c' [explicit solve]: current solution: 9.620351e+01, current residual:5.809212e+01 +field 'mu' [explicit solve]: current solution: 5.112469e+00, current residual:3.090033e+00 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0.000664949s + +time increment:35800 time: 35.8 +field ' c' [explicit solve]: current solution: 9.620311e+01, current residual:5.809185e+01 +field 'mu' [explicit solve]: current solution: 5.111865e+00, current residual:3.089676e+00 +wall time: 0.000588179s + +time increment:35810 time: 35.81 +field ' c' [explicit solve]: current solution: 9.620303e+01, current residual:5.809179e+01 +field 'mu' [explicit solve]: current solution: 5.111744e+00, current residual:3.089605e+00 +wall time: 0.000656128s + +time increment:35820 time: 35.82 +field ' c' [explicit solve]: current solution: 9.620295e+01, current residual:5.809174e+01 +field 'mu' [explicit solve]: current solution: 5.111623e+00, current residual:3.089533e+00 +wall time: 0.000554085s + +time increment:35830 time: 35.83 +field ' c' [explicit solve]: current solution: 9.620287e+01, current residual:5.809168e+01 +field 'mu' [explicit solve]: current solution: 5.111502e+00, current residual:3.089462e+00 +wall time: 0.000596046s + +time increment:35840 time: 35.84 +field ' c' [explicit solve]: current solution: 9.620279e+01, current residual:5.809163e+01 +field 'mu' [explicit solve]: current solution: 5.111381e+00, current residual:3.089391e+00 +wall time: 0.000726938s + +time increment:35850 time: 35.85 +field ' c' [explicit solve]: current solution: 9.620271e+01, current residual:5.809158e+01 +field 'mu' [explicit solve]: current solution: 5.111260e+00, current residual:3.089319e+00 +wall time: 0.000684977s + +time increment:35860 time: 35.86 +field ' c' [explicit solve]: current solution: 9.620263e+01, current residual:5.809152e+01 +field 'mu' [explicit solve]: current solution: 5.111140e+00, current residual:3.089248e+00 +wall time: 0.000705004s + +time increment:35870 time: 35.87 +field ' c' [explicit solve]: current solution: 9.620255e+01, current residual:5.809147e+01 +field 'mu' [explicit solve]: current solution: 5.111019e+00, current residual:3.089176e+00 +wall time: 0.000574112s + +time increment:35880 time: 35.88 +field ' c' [explicit solve]: current solution: 9.620247e+01, current residual:5.809141e+01 +field 'mu' [explicit solve]: current solution: 5.110898e+00, current residual:3.089105e+00 +wall time: 0.000591993s + +time increment:35890 time: 35.89 +field ' c' [explicit solve]: current solution: 9.620239e+01, current residual:5.809136e+01 +field 'mu' [explicit solve]: current solution: 5.110777e+00, current residual:3.089034e+00 +wall time: 0.000652075s + +time increment:35900 time: 35.9 +field ' c' [explicit solve]: current solution: 9.620231e+01, current residual:5.809131e+01 +field 'mu' [explicit solve]: current solution: 5.110656e+00, current residual:3.088962e+00 +wall time: 0.000710011s + +time increment:35910 time: 35.91 +field ' c' [explicit solve]: current solution: 9.620223e+01, current residual:5.809125e+01 +field 'mu' [explicit solve]: current solution: 5.110535e+00, current residual:3.088891e+00 +wall time: 0.000564098s + +time increment:35920 time: 35.92 +field ' c' [explicit solve]: current solution: 9.620215e+01, current residual:5.809120e+01 +field 'mu' [explicit solve]: current solution: 5.110415e+00, current residual:3.088820e+00 +wall time: 0.000565052s + +time increment:35930 time: 35.93 +field ' c' [explicit solve]: current solution: 9.620207e+01, current residual:5.809115e+01 +field 'mu' [explicit solve]: current solution: 5.110294e+00, current residual:3.088748e+00 +wall time: 0.000571012s + +time increment:35940 time: 35.94 +field ' c' [explicit solve]: current solution: 9.620200e+01, current residual:5.809109e+01 +field 'mu' [explicit solve]: current solution: 5.110173e+00, current residual:3.088677e+00 +wall time: 0.000561953s + +time increment:35950 time: 35.95 +field ' c' [explicit solve]: current solution: 9.620192e+01, current residual:5.809104e+01 +field 'mu' [explicit solve]: current solution: 5.110052e+00, current residual:3.088606e+00 +wall time: 0.000601053s + +time increment:35960 time: 35.96 +field ' c' [explicit solve]: current solution: 9.620184e+01, current residual:5.809098e+01 +field 'mu' [explicit solve]: current solution: 5.109931e+00, current residual:3.088534e+00 +wall time: 0.000638962s + +time increment:35970 time: 35.97 +field ' c' [explicit solve]: current solution: 9.620176e+01, current residual:5.809093e+01 +field 'mu' [explicit solve]: current solution: 5.109810e+00, current residual:3.088463e+00 +wall time: 0.000619173s + +time increment:35980 time: 35.98 +field ' c' [explicit solve]: current solution: 9.620168e+01, current residual:5.809088e+01 +field 'mu' [explicit solve]: current solution: 5.109690e+00, current residual:3.088391e+00 +wall time: 0.000593185s + +time increment:35990 time: 35.99 +field ' c' [explicit solve]: current solution: 9.620160e+01, current residual:5.809082e+01 +field 'mu' [explicit solve]: current solution: 5.109569e+00, current residual:3.088320e+00 +wall time: 0.000639915s + +time increment:36000 time: 36 +field ' c' [explicit solve]: current solution: 9.620152e+01, current residual:5.809077e+01 +field 'mu' [explicit solve]: current solution: 5.109448e+00, current residual:3.088249e+00 +wall time: 0.00061512s + +time increment:36010 time: 36.01 +field ' c' [explicit solve]: current solution: 9.620145e+01, current residual:5.809072e+01 +field 'mu' [explicit solve]: current solution: 5.109327e+00, current residual:3.088177e+00 +wall time: 0.000627995s + +time increment:36020 time: 36.02 +field ' c' [explicit solve]: current solution: 9.620137e+01, current residual:5.809066e+01 +field 'mu' [explicit solve]: current solution: 5.109207e+00, current residual:3.088106e+00 +wall time: 0.000597s + +time increment:36030 time: 36.03 +field ' c' [explicit solve]: current solution: 9.620129e+01, current residual:5.809061e+01 +field 'mu' [explicit solve]: current solution: 5.109086e+00, current residual:3.088035e+00 +wall time: 0.000647068s + +time increment:36040 time: 36.04 +field ' c' [explicit solve]: current solution: 9.620121e+01, current residual:5.809056e+01 +field 'mu' [explicit solve]: current solution: 5.108965e+00, current residual:3.087963e+00 +wall time: 0.000615835s + +time increment:36050 time: 36.05 +field ' c' [explicit solve]: current solution: 9.620113e+01, current residual:5.809051e+01 +field 'mu' [explicit solve]: current solution: 5.108844e+00, current residual:3.087892e+00 +wall time: 0.000620842s + +time increment:36060 time: 36.06 +field ' c' [explicit solve]: current solution: 9.620106e+01, current residual:5.809045e+01 +field 'mu' [explicit solve]: current solution: 5.108724e+00, current residual:3.087820e+00 +wall time: 0.000637054s + +time increment:36070 time: 36.07 +field ' c' [explicit solve]: current solution: 9.620098e+01, current residual:5.809040e+01 +field 'mu' [explicit solve]: current solution: 5.108603e+00, current residual:3.087749e+00 +wall time: 0.000563145s + +time increment:36080 time: 36.08 +field ' c' [explicit solve]: current solution: 9.620090e+01, current residual:5.809035e+01 +field 'mu' [explicit solve]: current solution: 5.108482e+00, current residual:3.087678e+00 +wall time: 0.000719786s + +time increment:36090 time: 36.09 +field ' c' [explicit solve]: current solution: 9.620082e+01, current residual:5.809029e+01 +field 'mu' [explicit solve]: current solution: 5.108361e+00, current residual:3.087606e+00 +wall time: 0.000636101s + +time increment:36100 time: 36.1 +field ' c' [explicit solve]: current solution: 9.620074e+01, current residual:5.809024e+01 +field 'mu' [explicit solve]: current solution: 5.108241e+00, current residual:3.087535e+00 +wall time: 0.000579119s + +time increment:36110 time: 36.11 +field ' c' [explicit solve]: current solution: 9.620067e+01, current residual:5.809019e+01 +field 'mu' [explicit solve]: current solution: 5.108120e+00, current residual:3.087464e+00 +wall time: 0.000567913s + +time increment:36120 time: 36.12 +field ' c' [explicit solve]: current solution: 9.620059e+01, current residual:5.809014e+01 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[explicit solve]: current solution: 5.107517e+00, current residual:3.087107e+00 +wall time: 0.000568151s + +time increment:36170 time: 36.17 +field ' c' [explicit solve]: current solution: 9.620020e+01, current residual:5.808987e+01 +field 'mu' [explicit solve]: current solution: 5.107396e+00, current residual:3.087035e+00 +wall time: 0.000729084s + +time increment:36180 time: 36.18 +field ' c' [explicit solve]: current solution: 9.620013e+01, current residual:5.808982e+01 +field 'mu' [explicit solve]: current solution: 5.107275e+00, current residual:3.086964e+00 +wall time: 0.000669956s + +time increment:36190 time: 36.19 +field ' c' [explicit solve]: current solution: 9.620005e+01, current residual:5.808977e+01 +field 'mu' [explicit solve]: current solution: 5.107155e+00, current residual:3.086893e+00 +wall time: 0.000631094s + +time increment:36200 time: 36.2 +field ' c' [explicit solve]: current solution: 9.619997e+01, current residual:5.808972e+01 +field 'mu' [explicit solve]: current solution: 5.107034e+00, current residual:3.086821e+00 +wall time: 0.000701904s + +time increment:36210 time: 36.21 +field ' c' [explicit solve]: current solution: 9.619990e+01, current residual:5.808966e+01 +field 'mu' [explicit solve]: current solution: 5.106913e+00, current residual:3.086750e+00 +wall time: 0.000624895s + +time increment:36220 time: 36.22 +field ' c' [explicit solve]: current solution: 9.619982e+01, current residual:5.808961e+01 +field 'mu' [explicit solve]: current solution: 5.106793e+00, current residual:3.086679e+00 +wall time: 0.00070405s + +time increment:36230 time: 36.23 +field ' c' [explicit solve]: current solution: 9.619974e+01, current residual:5.808956e+01 +field 'mu' [explicit solve]: current solution: 5.106672e+00, current residual:3.086607e+00 +wall time: 0.000723124s + +time increment:36240 time: 36.24 +field ' c' [explicit solve]: current solution: 9.619967e+01, current residual:5.808951e+01 +field 'mu' [explicit solve]: current solution: 5.106552e+00, current residual:3.086536e+00 +wall time: 0.00069809s + +time increment:36250 time: 36.25 +field ' c' [explicit solve]: current solution: 9.619959e+01, current residual:5.808945e+01 +field 'mu' [explicit solve]: current solution: 5.106431e+00, current residual:3.086465e+00 +wall time: 0.000686169s + +time increment:36260 time: 36.26 +field ' c' [explicit solve]: current solution: 9.619951e+01, current residual:5.808940e+01 +field 'mu' [explicit solve]: current solution: 5.106310e+00, current residual:3.086393e+00 +wall time: 0.000715971s + +time increment:36270 time: 36.27 +field ' c' [explicit solve]: current solution: 9.619944e+01, current residual:5.808935e+01 +field 'mu' [explicit solve]: current solution: 5.106190e+00, current residual:3.086322e+00 +wall time: 0.000694036s + +time increment:36280 time: 36.28 +field ' c' [explicit solve]: current solution: 9.619936e+01, current residual:5.808930e+01 +field 'mu' [explicit solve]: current solution: 5.106069e+00, current residual:3.086251e+00 +wall time: 0.000702143s + +time increment:36290 time: 36.29 +field ' c' [explicit solve]: current solution: 9.619928e+01, current residual:5.808925e+01 +field 'mu' [explicit solve]: current solution: 5.105949e+00, current residual:3.086179e+00 +wall time: 0.000684023s + +time increment:36300 time: 36.3 +field ' c' [explicit solve]: current solution: 9.619921e+01, current residual:5.808920e+01 +field 'mu' [explicit solve]: current solution: 5.105828e+00, current residual:3.086108e+00 +wall time: 0.000660896s + +time increment:36310 time: 36.31 +field ' c' [explicit solve]: current solution: 9.619913e+01, current residual:5.808914e+01 +field 'mu' [explicit solve]: current solution: 5.105708e+00, current residual:3.086036e+00 +wall time: 0.000675917s + +time increment:36320 time: 36.32 +field ' c' [explicit solve]: current solution: 9.619906e+01, current residual:5.808909e+01 +field 'mu' [explicit solve]: current solution: 5.105587e+00, current residual:3.085965e+00 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0.000674009s + +time increment:36370 time: 36.37 +field ' c' [explicit solve]: current solution: 9.619868e+01, current residual:5.808883e+01 +field 'mu' [explicit solve]: current solution: 5.104984e+00, current residual:3.085608e+00 +wall time: 0.000673056s + +time increment:36380 time: 36.38 +field ' c' [explicit solve]: current solution: 9.619860e+01, current residual:5.808878e+01 +field 'mu' [explicit solve]: current solution: 5.104864e+00, current residual:3.085537e+00 +wall time: 0.000696182s + +time increment:36390 time: 36.39 +field ' c' [explicit solve]: current solution: 9.619853e+01, current residual:5.808873e+01 +field 'mu' [explicit solve]: current solution: 5.104743e+00, current residual:3.085466e+00 +wall time: 0.000666857s + +time increment:36400 time: 36.4 +field ' c' [explicit solve]: current solution: 9.619845e+01, current residual:5.808868e+01 +field 'mu' [explicit solve]: current solution: 5.104623e+00, current residual:3.085394e+00 +wall time: 0.000735998s + +time increment:36410 time: 36.41 +field ' c' [explicit solve]: current solution: 9.619838e+01, current residual:5.808863e+01 +field 'mu' [explicit solve]: current solution: 5.104503e+00, current residual:3.085323e+00 +wall time: 0.00069499s + +time increment:36420 time: 36.42 +field ' c' [explicit solve]: current solution: 9.619830e+01, current residual:5.808858e+01 +field 'mu' [explicit solve]: current solution: 5.104382e+00, current residual:3.085252e+00 +wall time: 0.00067997s + +time increment:36430 time: 36.43 +field ' c' [explicit solve]: current solution: 9.619823e+01, current residual:5.808853e+01 +field 'mu' [explicit solve]: current solution: 5.104262e+00, current residual:3.085180e+00 +wall time: 0.000669956s + +time increment:36440 time: 36.44 +field ' c' [explicit solve]: current solution: 9.619815e+01, current residual:5.808848e+01 +field 'mu' [explicit solve]: current solution: 5.104141e+00, current residual:3.085109e+00 +wall time: 0.000687838s + +time increment:36450 time: 36.45 +field ' c' [explicit solve]: current solution: 9.619808e+01, current residual:5.808842e+01 +field 'mu' [explicit solve]: current solution: 5.104021e+00, current residual:3.085038e+00 +wall time: 0.000592947s + +time increment:36460 time: 36.46 +field ' c' [explicit solve]: current solution: 9.619800e+01, current residual:5.808837e+01 +field 'mu' [explicit solve]: current solution: 5.103900e+00, current residual:3.084966e+00 +wall time: 0.00065589s + +time increment:36470 time: 36.47 +field ' c' [explicit solve]: current solution: 9.619793e+01, current residual:5.808832e+01 +field 'mu' [explicit solve]: current solution: 5.103780e+00, current residual:3.084895e+00 +wall time: 0.000684977s + +time increment:36480 time: 36.48 +field ' c' [explicit solve]: current solution: 9.619785e+01, current residual:5.808827e+01 +field 'mu' [explicit solve]: current solution: 5.103660e+00, current residual:3.084824e+00 +wall time: 0.00067997s + +time increment:36490 time: 36.49 +field ' c' [explicit solve]: current solution: 9.619778e+01, current residual:5.808822e+01 +field 'mu' [explicit solve]: current solution: 5.103539e+00, current residual:3.084752e+00 +wall time: 0.000660896s + +time increment:36500 time: 36.5 +field ' c' [explicit solve]: current solution: 9.619770e+01, current residual:5.808817e+01 +field 'mu' [explicit solve]: current solution: 5.103419e+00, current residual:3.084681e+00 +wall time: 0.000688076s + +time increment:36510 time: 36.51 +field ' c' [explicit solve]: current solution: 9.619763e+01, current residual:5.808812e+01 +field 'mu' [explicit solve]: current solution: 5.103298e+00, current residual:3.084610e+00 +wall time: 0.000669003s + +time increment:36520 time: 36.52 +field ' c' [explicit solve]: current solution: 9.619756e+01, current residual:5.808807e+01 +field 'mu' [explicit solve]: current solution: 5.103178e+00, current residual:3.084538e+00 +wall time: 0.000689983s + +time increment:36530 time: 36.53 +field ' c' [explicit solve]: current solution: 9.619748e+01, current residual:5.808802e+01 +field 'mu' [explicit solve]: current solution: 5.103058e+00, current residual:3.084467e+00 +wall time: 0.000586033s + +time increment:36540 time: 36.54 +field ' c' [explicit solve]: current solution: 9.619741e+01, current residual:5.808797e+01 +field 'mu' [explicit solve]: current solution: 5.102937e+00, current residual:3.084396e+00 +wall time: 0.000715971s + +time increment:36550 time: 36.55 +field ' c' [explicit solve]: current solution: 9.619733e+01, current residual:5.808792e+01 +field 'mu' [explicit solve]: current solution: 5.102817e+00, current residual:3.084324e+00 +wall time: 0.000577211s + +time increment:36560 time: 36.56 +field ' c' [explicit solve]: current solution: 9.619726e+01, current residual:5.808787e+01 +field 'mu' [explicit solve]: current solution: 5.102697e+00, current residual:3.084253e+00 +wall time: 0.000674009s + +time increment:36570 time: 36.57 +field ' c' [explicit solve]: current solution: 9.619718e+01, current residual:5.808782e+01 +field 'mu' [explicit solve]: current solution: 5.102576e+00, current residual:3.084182e+00 +wall time: 0.000636101s + +time increment:36580 time: 36.58 +field ' c' [explicit solve]: current solution: 9.619711e+01, current residual:5.808777e+01 +field 'mu' [explicit solve]: current solution: 5.102456e+00, current residual:3.084111e+00 +wall time: 0.000780106s + +time increment:36590 time: 36.59 +field ' c' [explicit solve]: current solution: 9.619704e+01, current residual:5.808772e+01 +field 'mu' [explicit solve]: current solution: 5.102336e+00, current residual:3.084039e+00 +wall time: 0.000692844s + +time increment:36600 time: 36.6 +field ' c' [explicit solve]: current solution: 9.619696e+01, current residual:5.808766e+01 +field 'mu' [explicit solve]: current solution: 5.102215e+00, current residual:3.083968e+00 +wall time: 0.000560045s + +time increment:36610 time: 36.61 +field ' c' [explicit solve]: current solution: 9.619689e+01, current residual:5.808761e+01 +field 'mu' [explicit solve]: current solution: 5.102095e+00, current residual:3.083897e+00 +wall time: 0.000624895s + +time increment:36620 time: 36.62 +field ' c' [explicit solve]: current solution: 9.619682e+01, current residual:5.808756e+01 +field 'mu' [explicit solve]: current solution: 5.101975e+00, current residual:3.083825e+00 +wall time: 0.000656128s + +time increment:36630 time: 36.63 +field ' c' [explicit solve]: current solution: 9.619674e+01, current residual:5.808751e+01 +field 'mu' [explicit solve]: current solution: 5.101855e+00, current residual:3.083754e+00 +wall time: 0.000562906s + +time increment:36640 time: 36.64 +field ' c' [explicit solve]: current solution: 9.619667e+01, current residual:5.808746e+01 +field 'mu' [explicit solve]: current solution: 5.101734e+00, current residual:3.083683e+00 +wall time: 0.000564814s + +time increment:36650 time: 36.65 +field ' c' [explicit solve]: current solution: 9.619660e+01, current residual:5.808741e+01 +field 'mu' [explicit solve]: current solution: 5.101614e+00, current residual:3.083611e+00 +wall time: 0.000576019s + +time increment:36660 time: 36.66 +field ' c' [explicit solve]: current solution: 9.619652e+01, current residual:5.808736e+01 +field 'mu' [explicit solve]: current solution: 5.101494e+00, current residual:3.083540e+00 +wall time: 0.000562906s + +time increment:36670 time: 36.67 +field ' c' [explicit solve]: current solution: 9.619645e+01, current residual:5.808731e+01 +field 'mu' [explicit solve]: current solution: 5.101374e+00, current residual:3.083469e+00 +wall time: 0.000566959s + +time increment:36680 time: 36.68 +field ' c' [explicit solve]: current solution: 9.619638e+01, current residual:5.808726e+01 +field 'mu' [explicit solve]: current solution: 5.101254e+00, current residual:3.083398e+00 +wall time: 0.000597s + +time increment:36690 time: 36.69 +field ' c' [explicit solve]: current solution: 9.619630e+01, current residual:5.808721e+01 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solve]: current solution: 5.100653e+00, current residual:3.083041e+00 +wall time: 0.000667095s + +time increment:36740 time: 36.74 +field ' c' [explicit solve]: current solution: 9.619594e+01, current residual:5.808697e+01 +field 'mu' [explicit solve]: current solution: 5.100533e+00, current residual:3.082970e+00 +wall time: 0.000607967s + +time increment:36750 time: 36.75 +field ' c' [explicit solve]: current solution: 9.619587e+01, current residual:5.808692e+01 +field 'mu' [explicit solve]: current solution: 5.100412e+00, current residual:3.082899e+00 +wall time: 0.000566006s + +time increment:36760 time: 36.76 +field ' c' [explicit solve]: current solution: 9.619580e+01, current residual:5.808687e+01 +field 'mu' [explicit solve]: current solution: 5.100292e+00, current residual:3.082827e+00 +wall time: 0.000597s + +time increment:36770 time: 36.77 +field ' c' [explicit solve]: current solution: 9.619572e+01, current residual:5.808682e+01 +field 'mu' [explicit solve]: current solution: 5.100172e+00, current residual:3.082756e+00 +wall time: 0.000584126s + +time increment:36780 time: 36.78 +field ' c' [explicit solve]: current solution: 9.619565e+01, current residual:5.808677e+01 +field 'mu' [explicit solve]: current solution: 5.100052e+00, current residual:3.082685e+00 +wall time: 0.000596046s + +time increment:36790 time: 36.79 +field ' c' [explicit solve]: current solution: 9.619558e+01, current residual:5.808672e+01 +field 'mu' [explicit solve]: current solution: 5.099932e+00, current residual:3.082614e+00 +wall time: 0.0006001s + +time increment:36800 time: 36.8 +field ' c' [explicit solve]: current solution: 9.619551e+01, current residual:5.808667e+01 +field 'mu' [explicit solve]: current solution: 5.099812e+00, current residual:3.082542e+00 +wall time: 0.000607967s + +time increment:36810 time: 36.81 +field ' c' [explicit solve]: current solution: 9.619543e+01, current residual:5.808662e+01 +field 'mu' [explicit solve]: current solution: 5.099692e+00, current residual:3.082471e+00 +wall time: 0.000591993s + +time increment:36820 time: 36.82 +field ' c' [explicit solve]: current solution: 9.619536e+01, current residual:5.808657e+01 +field 'mu' [explicit solve]: current solution: 5.099572e+00, current residual:3.082400e+00 +wall time: 0.000643015s + +time increment:36830 time: 36.83 +field ' c' [explicit solve]: current solution: 9.619529e+01, current residual:5.808652e+01 +field 'mu' [explicit solve]: current solution: 5.099452e+00, current residual:3.082329e+00 +wall time: 0.000569105s + +time increment:36840 time: 36.84 +field ' c' [explicit solve]: current solution: 9.619522e+01, current residual:5.808647e+01 +field 'mu' [explicit solve]: current solution: 5.099332e+00, current residual:3.082257e+00 +wall time: 0.000678778s + +time increment:36850 time: 36.85 +field ' c' [explicit solve]: current solution: 9.619515e+01, current residual:5.808642e+01 +field 'mu' [explicit solve]: current solution: 5.099212e+00, current residual:3.082186e+00 +wall time: 0.00069499s + +time increment:36860 time: 36.86 +field ' c' [explicit solve]: current solution: 9.619508e+01, current residual:5.808637e+01 +field 'mu' [explicit solve]: current solution: 5.099092e+00, current residual:3.082115e+00 +wall time: 0.000658989s + +time increment:36870 time: 36.87 +field ' c' [explicit solve]: current solution: 9.619500e+01, current residual:5.808633e+01 +field 'mu' [explicit solve]: current solution: 5.098972e+00, current residual:3.082044e+00 +wall time: 0.000733852s + +time increment:36880 time: 36.88 +field ' c' [explicit solve]: current solution: 9.619493e+01, current residual:5.808628e+01 +field 'mu' [explicit solve]: current solution: 5.098852e+00, current residual:3.081972e+00 +wall time: 0.000722885s + +time increment:36890 time: 36.89 +field ' c' [explicit solve]: current solution: 9.619486e+01, current residual:5.808623e+01 +field 'mu' [explicit solve]: current solution: 5.098732e+00, current residual:3.081901e+00 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0.000573874s + +time increment:36940 time: 36.94 +field ' c' [explicit solve]: current solution: 9.619450e+01, current residual:5.808598e+01 +field 'mu' [explicit solve]: current solution: 5.098132e+00, current residual:3.081545e+00 +wall time: 0.000625134s + +time increment:36950 time: 36.95 +field ' c' [explicit solve]: current solution: 9.619443e+01, current residual:5.808593e+01 +field 'mu' [explicit solve]: current solution: 5.098012e+00, current residual:3.081474e+00 +wall time: 0.000570059s + +time increment:36960 time: 36.96 +field ' c' [explicit solve]: current solution: 9.619436e+01, current residual:5.808589e+01 +field 'mu' [explicit solve]: current solution: 5.097892e+00, current residual:3.081403e+00 +wall time: 0.000562906s + +time increment:36970 time: 36.97 +field ' c' [explicit solve]: current solution: 9.619429e+01, current residual:5.808584e+01 +field 'mu' [explicit solve]: current solution: 5.097773e+00, current residual:3.081332e+00 +wall time: 0.000608921s + +time increment:36980 time: 36.98 +field ' c' [explicit solve]: current solution: 9.619422e+01, current residual:5.808579e+01 +field 'mu' [explicit solve]: current solution: 5.097653e+00, current residual:3.081260e+00 +wall time: 0.000741005s + +time increment:36990 time: 36.99 +field ' c' [explicit solve]: current solution: 9.619415e+01, current residual:5.808574e+01 +field 'mu' [explicit solve]: current solution: 5.097533e+00, current residual:3.081189e+00 +wall time: 0.000571966s + +time increment:37000 time: 37 +field ' c' [explicit solve]: current solution: 9.619408e+01, current residual:5.808569e+01 +field 'mu' [explicit solve]: current solution: 5.097413e+00, current residual:3.081118e+00 +wall time: 0.000681162s + +time increment:37010 time: 37.01 +field ' c' [explicit solve]: current solution: 9.619401e+01, current residual:5.808564e+01 +field 'mu' [explicit solve]: current solution: 5.097293e+00, current residual:3.081047e+00 +wall time: 0.00056386s + +time increment:37020 time: 37.02 +field ' c' [explicit solve]: current solution: 9.619394e+01, current residual:5.808560e+01 +field 'mu' [explicit solve]: current solution: 5.097173e+00, current residual:3.080976e+00 +wall time: 0.000581026s + +time increment:37030 time: 37.03 +field ' c' [explicit solve]: current solution: 9.619387e+01, current residual:5.808555e+01 +field 'mu' [explicit solve]: current solution: 5.097054e+00, current residual:3.080905e+00 +wall time: 0.000602007s + +time increment:37040 time: 37.04 +field ' c' [explicit solve]: current solution: 9.619380e+01, current residual:5.808550e+01 +field 'mu' [explicit solve]: current solution: 5.096934e+00, current residual:3.080833e+00 +wall time: 0.000749111s + +time increment:37050 time: 37.05 +field ' c' [explicit solve]: current solution: 9.619373e+01, current residual:5.808545e+01 +field 'mu' [explicit solve]: current solution: 5.096814e+00, current residual:3.080762e+00 +wall time: 0.000663042s + +time increment:37060 time: 37.06 +field ' c' [explicit solve]: current solution: 9.619366e+01, current residual:5.808540e+01 +field 'mu' [explicit solve]: current solution: 5.096694e+00, current residual:3.080691e+00 +wall time: 0.000630856s + +time increment:37070 time: 37.07 +field ' c' [explicit solve]: current solution: 9.619359e+01, current residual:5.808536e+01 +field 'mu' [explicit solve]: current solution: 5.096574e+00, current residual:3.080620e+00 +wall time: 0.000597954s + +time increment:37080 time: 37.08 +field ' c' [explicit solve]: current solution: 9.619352e+01, current residual:5.808531e+01 +field 'mu' [explicit solve]: current solution: 5.096455e+00, current residual:3.080549e+00 +wall time: 0.00067997s + +time increment:37090 time: 37.09 +field ' c' [explicit solve]: current solution: 9.619345e+01, current residual:5.808526e+01 +field 'mu' [explicit solve]: current solution: 5.096335e+00, current residual:3.080478e+00 +wall time: 0.000591993s + +time increment:37100 time: 37.1 +field ' c' [explicit solve]: current solution: 9.619338e+01, current residual:5.808521e+01 +field 'mu' [explicit solve]: current solution: 5.096215e+00, current residual:3.080406e+00 +wall time: 0.000647068s + +time increment:37110 time: 37.11 +field ' c' [explicit solve]: current solution: 9.619331e+01, current residual:5.808516e+01 +field 'mu' [explicit solve]: current solution: 5.096096e+00, current residual:3.080335e+00 +wall time: 0.000695944s + +time increment:37120 time: 37.12 +field ' c' [explicit solve]: current solution: 9.619324e+01, current residual:5.808512e+01 +field 'mu' [explicit solve]: current solution: 5.095976e+00, current residual:3.080264e+00 +wall time: 0.000635147s + +time increment:37130 time: 37.13 +field ' c' [explicit solve]: current solution: 9.619317e+01, current residual:5.808507e+01 +field 'mu' [explicit solve]: current solution: 5.095856e+00, current residual:3.080193e+00 +wall time: 0.00068903s + +time increment:37140 time: 37.14 +field ' c' [explicit solve]: current solution: 9.619310e+01, current residual:5.808502e+01 +field 'mu' [explicit solve]: current solution: 5.095737e+00, current residual:3.080122e+00 +wall time: 0.000677109s + +time increment:37150 time: 37.15 +field ' c' [explicit solve]: current solution: 9.619303e+01, current residual:5.808497e+01 +field 'mu' [explicit solve]: current solution: 5.095617e+00, current residual:3.080051e+00 +wall time: 0.000734091s + +time increment:37160 time: 37.16 +field ' c' [explicit solve]: current solution: 9.619296e+01, current residual:5.808493e+01 +field 'mu' [explicit solve]: current solution: 5.095497e+00, current residual:3.079980e+00 +wall time: 0.000746965s + +time increment:37170 time: 37.17 +field ' c' [explicit solve]: current solution: 9.619289e+01, current residual:5.808488e+01 +field 'mu' [explicit solve]: current solution: 5.095378e+00, current residual:3.079909e+00 +wall time: 0.000701904s + +time increment:37180 time: 37.18 +field ' c' [explicit solve]: current solution: 9.619282e+01, 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residual:5.808464e+01 +field 'mu' [explicit solve]: current solution: 5.094780e+00, current residual:3.079553e+00 +wall time: 0.00069809s + +time increment:37230 time: 37.23 +field ' c' [explicit solve]: current solution: 9.619248e+01, current residual:5.808459e+01 +field 'mu' [explicit solve]: current solution: 5.094660e+00, current residual:3.079482e+00 +wall time: 0.000741005s + +time increment:37240 time: 37.24 +field ' c' [explicit solve]: current solution: 9.619241e+01, current residual:5.808455e+01 +field 'mu' [explicit solve]: current solution: 5.094541e+00, current residual:3.079411e+00 +wall time: 0.000692844s + +time increment:37250 time: 37.25 +field ' c' [explicit solve]: current solution: 9.619234e+01, current residual:5.808450e+01 +field 'mu' [explicit solve]: current solution: 5.094421e+00, current residual:3.079340e+00 +wall time: 0.000717878s + +time increment:37260 time: 37.26 +field ' c' [explicit solve]: current solution: 9.619227e+01, current residual:5.808445e+01 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solve]: current solution: 5.093824e+00, current residual:3.078985e+00 +wall time: 0.000686884s + +time increment:37310 time: 37.31 +field ' c' [explicit solve]: current solution: 9.619193e+01, current residual:5.808422e+01 +field 'mu' [explicit solve]: current solution: 5.093704e+00, current residual:3.078913e+00 +wall time: 0.000744104s + +time increment:37320 time: 37.32 +field ' c' [explicit solve]: current solution: 9.619186e+01, current residual:5.808417e+01 +field 'mu' [explicit solve]: current solution: 5.093585e+00, current residual:3.078842e+00 +wall time: 0.000687838s + +time increment:37330 time: 37.33 +field ' c' [explicit solve]: current solution: 9.619179e+01, current residual:5.808412e+01 +field 'mu' [explicit solve]: current solution: 5.093466e+00, current residual:3.078771e+00 +wall time: 0.000622988s + +time increment:37340 time: 37.34 +field ' c' [explicit solve]: current solution: 9.619172e+01, current residual:5.808408e+01 +field 'mu' [explicit solve]: current solution: 5.093346e+00, current residual:3.078700e+00 +wall time: 0.000675917s + +time increment:37350 time: 37.35 +field ' c' [explicit solve]: current solution: 9.619166e+01, current residual:5.808403e+01 +field 'mu' [explicit solve]: current solution: 5.093227e+00, current residual:3.078629e+00 +wall time: 0.00071311s + +time increment:37360 time: 37.36 +field ' c' [explicit solve]: current solution: 9.619159e+01, current residual:5.808398e+01 +field 'mu' [explicit solve]: current solution: 5.093107e+00, current residual:3.078558e+00 +wall time: 0.00069499s + +time increment:37370 time: 37.37 +field ' c' [explicit solve]: current solution: 9.619152e+01, current residual:5.808394e+01 +field 'mu' [explicit solve]: current solution: 5.092988e+00, current residual:3.078487e+00 +wall time: 0.000653982s + +time increment:37380 time: 37.38 +field ' c' [explicit solve]: current solution: 9.619145e+01, current residual:5.808389e+01 +field 'mu' [explicit solve]: current solution: 5.092869e+00, current residual:3.078416e+00 +wall time: 0.000696898s + +time increment:37390 time: 37.39 +field ' c' [explicit solve]: current solution: 9.619139e+01, current residual:5.808384e+01 +field 'mu' [explicit solve]: current solution: 5.092749e+00, current residual:3.078345e+00 +wall time: 0.000656843s + +time increment:37400 time: 37.4 +field ' c' [explicit solve]: current solution: 9.619132e+01, current residual:5.808380e+01 +field 'mu' [explicit solve]: current solution: 5.092630e+00, current residual:3.078274e+00 +wall time: 0.000702143s + +time increment:37410 time: 37.41 +field ' c' [explicit solve]: current solution: 9.619125e+01, current residual:5.808375e+01 +field 'mu' [explicit solve]: current solution: 5.092511e+00, current residual:3.078203e+00 +wall time: 0.000667095s + +time increment:37420 time: 37.42 +field ' c' [explicit solve]: current solution: 9.619118e+01, current residual:5.808370e+01 +field 'mu' [explicit solve]: current solution: 5.092392e+00, current residual:3.078132e+00 +wall time: 0.000722885s + +time increment:37430 time: 37.43 +field ' c' [explicit solve]: current solution: 9.619112e+01, current residual:5.808366e+01 +field 'mu' [explicit solve]: current solution: 5.092272e+00, current residual:3.078061e+00 +wall time: 0.000677109s + +time increment:37440 time: 37.44 +field ' c' [explicit solve]: current solution: 9.619105e+01, current residual:5.808361e+01 +field 'mu' [explicit solve]: current solution: 5.092153e+00, current residual:3.077990e+00 +wall time: 0.000703096s + +time increment:37450 time: 37.45 +field ' c' [explicit solve]: current solution: 9.619098e+01, current residual:5.808357e+01 +field 'mu' [explicit solve]: current solution: 5.092034e+00, current residual:3.077919e+00 +wall time: 0.000648022s + +time increment:37460 time: 37.46 +field ' c' [explicit solve]: current solution: 9.619091e+01, current residual:5.808352e+01 +field 'mu' [explicit solve]: current solution: 5.091915e+00, current residual:3.077848e+00 +wall time: 0.000661135s + +time increment:37470 time: 37.47 +field ' c' [explicit solve]: current solution: 9.619085e+01, current residual:5.808347e+01 +field 'mu' [explicit solve]: current solution: 5.091795e+00, current residual:3.077777e+00 +wall time: 0.000664949s + +time increment:37480 time: 37.48 +field ' c' [explicit solve]: current solution: 9.619078e+01, current residual:5.808343e+01 +field 'mu' [explicit solve]: current solution: 5.091676e+00, current residual:3.077707e+00 +wall time: 0.000725031s + +time increment:37490 time: 37.49 +field ' c' [explicit solve]: current solution: 9.619071e+01, current residual:5.808338e+01 +field 'mu' [explicit solve]: current solution: 5.091557e+00, current residual:3.077636e+00 +wall time: 0.00078702s + +time increment:37500 time: 37.5 +field ' c' [explicit solve]: current solution: 9.619065e+01, current residual:5.808333e+01 +field 'mu' [explicit solve]: current solution: 5.091438e+00, current residual:3.077565e+00 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0.000638962s + +time increment:37550 time: 37.55 +field ' c' [explicit solve]: current solution: 9.619031e+01, current residual:5.808311e+01 +field 'mu' [explicit solve]: current solution: 5.090842e+00, current residual:3.077210e+00 +wall time: 0.000577927s + +time increment:37560 time: 37.56 +field ' c' [explicit solve]: current solution: 9.619025e+01, current residual:5.808306e+01 +field 'mu' [explicit solve]: current solution: 5.090723e+00, current residual:3.077139e+00 +wall time: 0.000664949s + +time increment:37570 time: 37.57 +field ' c' [explicit solve]: current solution: 9.619018e+01, current residual:5.808301e+01 +field 'mu' [explicit solve]: current solution: 5.090604e+00, current residual:3.077068e+00 +wall time: 0.000625134s + +time increment:37580 time: 37.58 +field ' c' [explicit solve]: current solution: 9.619011e+01, current residual:5.808297e+01 +field 'mu' [explicit solve]: current solution: 5.090485e+00, current residual:3.076997e+00 +wall time: 0.000586033s + +time increment:37590 time: 37.59 +field ' c' [explicit solve]: current solution: 9.619005e+01, current residual:5.808292e+01 +field 'mu' [explicit solve]: current solution: 5.090366e+00, current residual:3.076926e+00 +wall time: 0.000706911s + +time increment:37600 time: 37.6 +field ' c' [explicit solve]: current solution: 9.618998e+01, current residual:5.808288e+01 +field 'mu' [explicit solve]: current solution: 5.090247e+00, current residual:3.076855e+00 +wall time: 0.000696182s + +time increment:37610 time: 37.61 +field ' c' [explicit solve]: current solution: 9.618991e+01, current residual:5.808283e+01 +field 'mu' [explicit solve]: current solution: 5.090128e+00, current residual:3.076785e+00 +wall time: 0.000715971s + +time increment:37620 time: 37.62 +field ' c' [explicit solve]: current solution: 9.618985e+01, current residual:5.808279e+01 +field 'mu' [explicit solve]: current solution: 5.090009e+00, current residual:3.076714e+00 +wall time: 0.000575066s + +time increment:37630 time: 37.63 +field ' c' [explicit solve]: current solution: 9.618978e+01, current residual:5.808274e+01 +field 'mu' [explicit solve]: current solution: 5.089890e+00, current residual:3.076643e+00 +wall time: 0.000679016s + +time increment:37640 time: 37.64 +field ' c' [explicit solve]: current solution: 9.618972e+01, current residual:5.808270e+01 +field 'mu' [explicit solve]: current solution: 5.089771e+00, current residual:3.076572e+00 +wall time: 0.000622988s + +time increment:37650 time: 37.65 +field ' c' [explicit solve]: current solution: 9.618965e+01, current residual:5.808265e+01 +field 'mu' [explicit solve]: current solution: 5.089652e+00, current residual:3.076501e+00 +wall time: 0.000566959s + +time increment:37660 time: 37.66 +field ' c' [explicit solve]: current solution: 9.618959e+01, current residual:5.808260e+01 +field 'mu' [explicit solve]: current solution: 5.089533e+00, current residual:3.076430e+00 +wall time: 0.000732899s + +time increment:37670 time: 37.67 +field ' c' [explicit solve]: current solution: 9.618952e+01, current residual:5.808256e+01 +field 'mu' [explicit solve]: current solution: 5.089415e+00, current residual:3.076359e+00 +wall time: 0.000633001s + +time increment:37680 time: 37.68 +field ' c' [explicit solve]: current solution: 9.618945e+01, current residual:5.808251e+01 +field 'mu' [explicit solve]: current solution: 5.089296e+00, current residual:3.076289e+00 +wall time: 0.000633001s + +time increment:37690 time: 37.69 +field ' c' [explicit solve]: current solution: 9.618939e+01, current residual:5.808247e+01 +field 'mu' [explicit solve]: current solution: 5.089177e+00, current residual:3.076218e+00 +wall time: 0.000596046s + +time increment:37700 time: 37.7 +field ' c' [explicit solve]: current solution: 9.618932e+01, current residual:5.808242e+01 +field 'mu' [explicit solve]: current solution: 5.089058e+00, current residual:3.076147e+00 +wall time: 0.000608921s + +time increment:37710 time: 37.71 +field ' c' [explicit solve]: current solution: 9.618926e+01, current residual:5.808238e+01 +field 'mu' [explicit solve]: current solution: 5.088939e+00, current residual:3.076076e+00 +wall time: 0.000689983s + +time increment:37720 time: 37.72 +field ' c' [explicit solve]: current solution: 9.618919e+01, current residual:5.808233e+01 +field 'mu' [explicit solve]: current solution: 5.088820e+00, current residual:3.076005e+00 +wall time: 0.000706911s + +time increment:37730 time: 37.73 +field ' c' [explicit solve]: current solution: 9.618913e+01, current residual:5.808229e+01 +field 'mu' [explicit solve]: current solution: 5.088702e+00, current residual:3.075935e+00 +wall time: 0.000567913s + +time increment:37740 time: 37.74 +field ' c' [explicit solve]: current solution: 9.618906e+01, current residual:5.808224e+01 +field 'mu' [explicit solve]: current solution: 5.088583e+00, current residual:3.075864e+00 +wall time: 0.000560999s + +time increment:37750 time: 37.75 +field ' c' [explicit solve]: current solution: 9.618900e+01, current residual:5.808220e+01 +field 'mu' [explicit solve]: current solution: 5.088464e+00, current residual:3.075793e+00 +wall time: 0.000586033s + +time increment:37760 time: 37.76 +field ' c' [explicit solve]: current solution: 9.618893e+01, current residual:5.808215e+01 +field 'mu' [explicit solve]: current solution: 5.088345e+00, current residual:3.075722e+00 +wall time: 0.000628948s + +time increment:37770 time: 37.77 +field ' c' [explicit solve]: current solution: 9.618887e+01, current residual:5.808211e+01 +field 'mu' [explicit solve]: current solution: 5.088227e+00, current residual:3.075651e+00 +wall time: 0.000619888s + +time increment:37780 time: 37.78 +field ' c' [explicit solve]: current solution: 9.618880e+01, current residual:5.808207e+01 +field 'mu' [explicit solve]: current solution: 5.088108e+00, current residual:3.075581e+00 +wall time: 0.000622988s + +time increment:37790 time: 37.79 +field ' c' [explicit solve]: current solution: 9.618874e+01, current residual:5.808202e+01 +field 'mu' [explicit solve]: current solution: 5.087989e+00, current residual:3.075510e+00 +wall time: 0.000577927s + +time increment:37800 time: 37.8 +field ' c' [explicit solve]: current solution: 9.618867e+01, current residual:5.808198e+01 +field 'mu' [explicit solve]: current solution: 5.087870e+00, current residual:3.075439e+00 +wall time: 0.000571966s + +time increment:37810 time: 37.81 +field ' c' [explicit solve]: current solution: 9.618861e+01, current residual:5.808193e+01 +field 'mu' [explicit solve]: current solution: 5.087752e+00, current residual:3.075368e+00 +wall time: 0.000682831s + +time increment:37820 time: 37.82 +field ' c' [explicit solve]: current solution: 9.618854e+01, current residual:5.808189e+01 +field 'mu' [explicit solve]: current solution: 5.087633e+00, current residual:3.075298e+00 +wall time: 0.000616074s + +time increment:37830 time: 37.83 +field ' c' [explicit solve]: current solution: 9.618848e+01, current residual:5.808184e+01 +field 'mu' [explicit solve]: current solution: 5.087514e+00, current residual:3.075227e+00 +wall time: 0.000677109s + +time increment:37840 time: 37.84 +field ' c' [explicit solve]: current solution: 9.618842e+01, current residual:5.808180e+01 +field 'mu' [explicit solve]: current solution: 5.087396e+00, current residual:3.075156e+00 +wall time: 0.00069499s + +time increment:37850 time: 37.85 +field ' c' [explicit solve]: current solution: 9.618835e+01, current residual:5.808175e+01 +field 'mu' [explicit solve]: current solution: 5.087277e+00, current residual:3.075085e+00 +wall time: 0.000644922s + +time increment:37860 time: 37.86 +field ' c' [explicit solve]: current solution: 9.618829e+01, current residual:5.808171e+01 +field 'mu' [explicit solve]: current solution: 5.087159e+00, current residual:3.075015e+00 +wall time: 0.000576973s + +time increment:37870 time: 37.87 +field ' c' [explicit solve]: current solution: 9.618822e+01, current residual:5.808166e+01 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solve]: current solution: 5.086566e+00, current residual:3.074661e+00 +wall time: 0.000671148s + +time increment:37920 time: 37.92 +field ' c' [explicit solve]: current solution: 9.618790e+01, current residual:5.808144e+01 +field 'mu' [explicit solve]: current solution: 5.086448e+00, current residual:3.074591e+00 +wall time: 0.000634909s + +time increment:37930 time: 37.93 +field ' c' [explicit solve]: current solution: 9.618784e+01, current residual:5.808140e+01 +field 'mu' [explicit solve]: current solution: 5.086329e+00, current residual:3.074520e+00 +wall time: 0.000684977s + +time increment:37940 time: 37.94 +field ' c' [explicit solve]: current solution: 9.618778e+01, current residual:5.808136e+01 +field 'mu' [explicit solve]: current solution: 5.086211e+00, current residual:3.074449e+00 +wall time: 0.000671864s + +time increment:37950 time: 37.95 +field ' c' [explicit solve]: current solution: 9.618771e+01, current residual:5.808131e+01 +field 'mu' [explicit solve]: current solution: 5.086092e+00, current residual:3.074379e+00 +wall time: 0.000640869s + +time increment:37960 time: 37.96 +field ' c' [explicit solve]: current solution: 9.618765e+01, current residual:5.808127e+01 +field 'mu' [explicit solve]: current solution: 5.085974e+00, current residual:3.074308e+00 +wall time: 0.000632048s + +time increment:37970 time: 37.97 +field ' c' [explicit solve]: current solution: 9.618758e+01, current residual:5.808122e+01 +field 'mu' [explicit solve]: current solution: 5.085855e+00, current residual:3.074237e+00 +wall time: 0.000658035s + +time increment:37980 time: 37.98 +field ' c' [explicit solve]: current solution: 9.618752e+01, current residual:5.808118e+01 +field 'mu' [explicit solve]: current solution: 5.085737e+00, current residual:3.074167e+00 +wall time: 0.000692129s + +time increment:37990 time: 37.99 +field ' c' [explicit solve]: current solution: 9.618746e+01, current residual:5.808114e+01 +field 'mu' [explicit solve]: current solution: 5.085619e+00, current residual:3.074096e+00 +wall time: 0.000674009s + +time increment:38000 time: 38 +field ' c' [explicit solve]: current solution: 9.618739e+01, current residual:5.808109e+01 +field 'mu' [explicit solve]: current solution: 5.085500e+00, current residual:3.074025e+00 +wall time: 0.000661135s + +time increment:38010 time: 38.01 +field ' c' [explicit solve]: current solution: 9.618733e+01, current residual:5.808105e+01 +field 'mu' [explicit solve]: current solution: 5.085382e+00, current residual:3.073955e+00 +wall time: 0.000737906s + +time increment:38020 time: 38.02 +field ' c' [explicit solve]: current solution: 9.618727e+01, current residual:5.808101e+01 +field 'mu' [explicit solve]: current solution: 5.085264e+00, current residual:3.073884e+00 +wall time: 0.000716925s + +time increment:38030 time: 38.03 +field ' c' [explicit solve]: current solution: 9.618721e+01, current residual:5.808096e+01 +field 'mu' [explicit solve]: current solution: 5.085145e+00, current residual:3.073814e+00 +wall time: 0.000616789s + +time increment:38040 time: 38.04 +field ' c' [explicit solve]: current solution: 9.618714e+01, current residual:5.808092e+01 +field 'mu' [explicit solve]: current solution: 5.085027e+00, current residual:3.073743e+00 +wall time: 0.00062418s + +time increment:38050 time: 38.05 +field ' c' [explicit solve]: current solution: 9.618708e+01, current residual:5.808087e+01 +field 'mu' [explicit solve]: current solution: 5.084909e+00, current residual:3.073672e+00 +wall time: 0.000789881s + +time increment:38060 time: 38.06 +field ' c' [explicit solve]: current solution: 9.618702e+01, current residual:5.808083e+01 +field 'mu' [explicit solve]: current solution: 5.084791e+00, current residual:3.073602e+00 +wall time: 0.000674963s + +time increment:38070 time: 38.07 +field ' c' [explicit solve]: current solution: 9.618695e+01, current residual:5.808079e+01 +field 'mu' [explicit solve]: current solution: 5.084672e+00, current residual:3.073531e+00 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0.000645161s + +time increment:38120 time: 38.12 +field ' c' [explicit solve]: current solution: 9.618664e+01, current residual:5.808057e+01 +field 'mu' [explicit solve]: current solution: 5.084082e+00, current residual:3.073179e+00 +wall time: 0.000627041s + +time increment:38130 time: 38.13 +field ' c' [explicit solve]: current solution: 9.618658e+01, current residual:5.808053e+01 +field 'mu' [explicit solve]: current solution: 5.083964e+00, current residual:3.073108e+00 +wall time: 0.000739098s + +time increment:38140 time: 38.14 +field ' c' [explicit solve]: current solution: 9.618652e+01, current residual:5.808048e+01 +field 'mu' [explicit solve]: current solution: 5.083845e+00, current residual:3.073038e+00 +wall time: 0.000677109s + +time increment:38150 time: 38.15 +field ' c' [explicit solve]: current solution: 9.618645e+01, current residual:5.808044e+01 +field 'mu' [explicit solve]: current solution: 5.083727e+00, current residual:3.072967e+00 +wall time: 0.000646114s + +time increment:38160 time: 38.16 +field ' c' [explicit solve]: current solution: 9.618639e+01, current residual:5.808040e+01 +field 'mu' [explicit solve]: current solution: 5.083609e+00, current residual:3.072897e+00 +wall time: 0.000644922s + +time increment:38170 time: 38.17 +field ' c' [explicit solve]: current solution: 9.618633e+01, current residual:5.808036e+01 +field 'mu' [explicit solve]: current solution: 5.083491e+00, current residual:3.072826e+00 +wall time: 0.000746965s + +time increment:38180 time: 38.18 +field ' c' [explicit solve]: current solution: 9.618627e+01, current residual:5.808031e+01 +field 'mu' [explicit solve]: current solution: 5.083373e+00, current residual:3.072756e+00 +wall time: 0.000756025s + +time increment:38190 time: 38.19 +field ' c' [explicit solve]: current solution: 9.618621e+01, current residual:5.808027e+01 +field 'mu' [explicit solve]: current solution: 5.083255e+00, current residual:3.072685e+00 +wall time: 0.000666142s + +time increment:38200 time: 38.2 +field ' c' [explicit solve]: current solution: 9.618614e+01, current residual:5.808023e+01 +field 'mu' [explicit solve]: current solution: 5.083137e+00, current residual:3.072615e+00 +wall time: 0.000701904s + +time increment:38210 time: 38.21 +field ' c' [explicit solve]: current solution: 9.618608e+01, current residual:5.808018e+01 +field 'mu' [explicit solve]: current solution: 5.083019e+00, current residual:3.072544e+00 +wall time: 0.000710011s + +time increment:38220 time: 38.22 +field ' c' [explicit solve]: current solution: 9.618602e+01, current residual:5.808014e+01 +field 'mu' [explicit solve]: current solution: 5.082901e+00, current residual:3.072474e+00 +wall time: 0.000692129s + +time increment:38230 time: 38.23 +field ' c' [explicit solve]: current solution: 9.618596e+01, current residual:5.808010e+01 +field 'mu' [explicit solve]: current solution: 5.082784e+00, current residual:3.072403e+00 +wall time: 0.000692844s + +time increment:38240 time: 38.24 +field ' c' [explicit solve]: current solution: 9.618590e+01, current residual:5.808006e+01 +field 'mu' [explicit solve]: current solution: 5.082666e+00, current residual:3.072333e+00 +wall time: 0.000757217s + +time increment:38250 time: 38.25 +field ' c' [explicit solve]: current solution: 9.618584e+01, current residual:5.808001e+01 +field 'mu' [explicit solve]: current solution: 5.082548e+00, current residual:3.072262e+00 +wall time: 0.000705957s + +time increment:38260 time: 38.26 +field ' c' [explicit solve]: current solution: 9.618577e+01, current residual:5.807997e+01 +field 'mu' [explicit solve]: current solution: 5.082430e+00, current residual:3.072192e+00 +wall time: 0.000669956s + +time increment:38270 time: 38.27 +field ' c' [explicit solve]: current solution: 9.618571e+01, current residual:5.807993e+01 +field 'mu' [explicit solve]: current solution: 5.082312e+00, current residual:3.072122e+00 +wall time: 0.00058794s + +time increment:38280 time: 38.28 +field ' c' [explicit solve]: current solution: 9.618565e+01, current residual:5.807989e+01 +field 'mu' [explicit solve]: current solution: 5.082194e+00, current residual:3.072051e+00 +wall time: 0.000602007s + +time increment:38290 time: 38.29 +field ' c' [explicit solve]: current solution: 9.618559e+01, current residual:5.807984e+01 +field 'mu' [explicit solve]: current solution: 5.082076e+00, current residual:3.071981e+00 +wall time: 0.0006001s + +time increment:38300 time: 38.3 +field ' c' [explicit solve]: current solution: 9.618553e+01, current residual:5.807980e+01 +field 'mu' [explicit solve]: current solution: 5.081959e+00, current residual:3.071911e+00 +wall time: 0.000657082s + +time increment:38310 time: 38.31 +field ' c' [explicit solve]: current solution: 9.618547e+01, current residual:5.807976e+01 +field 'mu' [explicit solve]: current solution: 5.081841e+00, current residual:3.071840e+00 +wall time: 0.000688076s + +time increment:38320 time: 38.32 +field ' c' [explicit solve]: current solution: 9.618541e+01, current residual:5.807972e+01 +field 'mu' [explicit solve]: current solution: 5.081723e+00, current residual:3.071770e+00 +wall time: 0.000744104s + +time increment:38330 time: 38.33 +field ' c' [explicit solve]: current solution: 9.618535e+01, current residual:5.807967e+01 +field 'mu' [explicit solve]: current solution: 5.081605e+00, current residual:3.071699e+00 +wall time: 0.000674963s + +time increment:38340 time: 38.34 +field ' c' [explicit solve]: current solution: 9.618529e+01, current residual:5.807963e+01 +field 'mu' [explicit solve]: current solution: 5.081488e+00, current residual:3.071629e+00 +wall time: 0.000710964s + +time increment:38350 time: 38.35 +field ' c' [explicit solve]: current solution: 9.618522e+01, current residual:5.807959e+01 +field 'mu' [explicit solve]: current solution: 5.081370e+00, current residual:3.071559e+00 +wall time: 0.000746012s + +time increment:38360 time: 38.36 +field ' c' [explicit solve]: current solution: 9.618516e+01, current residual:5.807955e+01 +field 'mu' [explicit solve]: current solution: 5.081252e+00, current residual:3.071488e+00 +wall time: 0.000801802s + +time increment:38370 time: 38.37 +field ' c' [explicit solve]: current solution: 9.618510e+01, current residual:5.807951e+01 +field 'mu' [explicit solve]: current solution: 5.081135e+00, current residual:3.071418e+00 +wall time: 0.000652075s + +time increment:38380 time: 38.38 +field ' c' [explicit solve]: current solution: 9.618504e+01, current residual:5.807946e+01 +field 'mu' [explicit solve]: current solution: 5.081017e+00, current residual:3.071348e+00 +wall time: 0.000573874s + +time increment:38390 time: 38.39 +field ' c' [explicit solve]: current solution: 9.618498e+01, current residual:5.807942e+01 +field 'mu' [explicit solve]: current solution: 5.080899e+00, current residual:3.071277e+00 +wall time: 0.000586033s + +time increment:38400 time: 38.4 +field ' c' [explicit solve]: current solution: 9.618492e+01, current residual:5.807938e+01 +field 'mu' [explicit solve]: current solution: 5.080782e+00, current residual:3.071207e+00 +wall time: 0.000586987s + +time increment:38410 time: 38.41 +field ' c' [explicit solve]: current solution: 9.618486e+01, current residual:5.807934e+01 +field 'mu' [explicit solve]: current solution: 5.080664e+00, current residual:3.071137e+00 +wall time: 0.00056386s + +time increment:38420 time: 38.42 +field ' c' [explicit solve]: current solution: 9.618480e+01, current residual:5.807930e+01 +field 'mu' [explicit solve]: current solution: 5.080547e+00, current residual:3.071067e+00 +wall time: 0.000573158s + +time increment:38430 time: 38.43 +field ' c' [explicit solve]: current solution: 9.618474e+01, current residual:5.807925e+01 +field 'mu' [explicit solve]: current solution: 5.080429e+00, current residual:3.070996e+00 +wall time: 0.000553131s + +time increment:38440 time: 38.44 +field ' c' [explicit solve]: current solution: 9.618468e+01, current residual:5.807921e+01 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solve]: current solution: 5.079842e+00, current residual:3.070645e+00 +wall time: 0.000617027s + +time increment:38490 time: 38.49 +field ' c' [explicit solve]: current solution: 9.618438e+01, current residual:5.807900e+01 +field 'mu' [explicit solve]: current solution: 5.079724e+00, current residual:3.070575e+00 +wall time: 0.000632048s + +time increment:38500 time: 38.5 +field ' c' [explicit solve]: current solution: 9.618432e+01, current residual:5.807896e+01 +field 'mu' [explicit solve]: current solution: 5.079607e+00, current residual:3.070505e+00 +wall time: 0.000582933s + +time increment:38510 time: 38.51 +field ' c' [explicit solve]: current solution: 9.618426e+01, current residual:5.807892e+01 +field 'mu' [explicit solve]: current solution: 5.079490e+00, current residual:3.070435e+00 +wall time: 0.00057292s + +time increment:38520 time: 38.52 +field ' c' [explicit solve]: current solution: 9.618420e+01, current residual:5.807888e+01 +field 'mu' [explicit solve]: current solution: 5.079372e+00, current residual:3.070364e+00 +wall time: 0.000571012s + +time increment:38530 time: 38.53 +field ' c' [explicit solve]: current solution: 9.618414e+01, current residual:5.807884e+01 +field 'mu' [explicit solve]: current solution: 5.079255e+00, current residual:3.070294e+00 +wall time: 0.000695944s + +time increment:38540 time: 38.54 +field ' c' [explicit solve]: current solution: 9.618408e+01, current residual:5.807880e+01 +field 'mu' [explicit solve]: current solution: 5.079137e+00, current residual:3.070224e+00 +wall time: 0.000599146s + +time increment:38550 time: 38.55 +field ' c' [explicit solve]: current solution: 9.618402e+01, current residual:5.807875e+01 +field 'mu' [explicit solve]: current solution: 5.079020e+00, current residual:3.070154e+00 +wall time: 0.00071907s + +time increment:38560 time: 38.56 +field ' c' [explicit solve]: current solution: 9.618396e+01, current residual:5.807871e+01 +field 'mu' [explicit solve]: current solution: 5.078903e+00, current residual:3.070084e+00 +wall time: 0.000652075s + +time increment:38570 time: 38.57 +field ' c' [explicit solve]: current solution: 9.618390e+01, current residual:5.807867e+01 +field 'mu' [explicit solve]: current solution: 5.078786e+00, current residual:3.070014e+00 +wall time: 0.000653982s + +time increment:38580 time: 38.58 +field ' c' [explicit solve]: current solution: 9.618384e+01, current residual:5.807863e+01 +field 'mu' [explicit solve]: current solution: 5.078668e+00, current residual:3.069943e+00 +wall time: 0.000650883s + +time increment:38590 time: 38.59 +field ' c' [explicit solve]: current solution: 9.618379e+01, current residual:5.807859e+01 +field 'mu' [explicit solve]: current solution: 5.078551e+00, current residual:3.069873e+00 +wall time: 0.000746965s + +time increment:38600 time: 38.6 +field ' c' [explicit solve]: current solution: 9.618373e+01, current residual:5.807855e+01 +field 'mu' [explicit solve]: current solution: 5.078434e+00, current residual:3.069803e+00 +wall time: 0.000656128s + +time increment:38610 time: 38.61 +field ' c' [explicit solve]: current solution: 9.618367e+01, current residual:5.807851e+01 +field 'mu' [explicit solve]: current solution: 5.078317e+00, current residual:3.069733e+00 +wall time: 0.000634909s + +time increment:38620 time: 38.62 +field ' c' [explicit solve]: current solution: 9.618361e+01, current residual:5.807847e+01 +field 'mu' [explicit solve]: current solution: 5.078200e+00, current residual:3.069663e+00 +wall time: 0.000730038s + +time increment:38630 time: 38.63 +field ' c' [explicit solve]: current solution: 9.618355e+01, current residual:5.807843e+01 +field 'mu' [explicit solve]: current solution: 5.078083e+00, current residual:3.069593e+00 +wall time: 0.000633955s + +time increment:38640 time: 38.64 +field ' c' [explicit solve]: current solution: 9.618349e+01, current residual:5.807838e+01 +field 'mu' [explicit solve]: current solution: 5.077965e+00, current residual:3.069523e+00 +wall time: 0.000568151s + +time increment:38650 time: 38.65 +field ' c' [explicit solve]: current solution: 9.618343e+01, current residual:5.807834e+01 +field 'mu' [explicit solve]: current solution: 5.077848e+00, current residual:3.069453e+00 +wall time: 0.000692844s + +time increment:38660 time: 38.66 +field ' c' [explicit solve]: current solution: 9.618337e+01, current residual:5.807830e+01 +field 'mu' [explicit solve]: current solution: 5.077731e+00, current residual:3.069383e+00 +wall time: 0.000634909s + +time increment:38670 time: 38.67 +field ' c' [explicit solve]: current solution: 9.618331e+01, current residual:5.807826e+01 +field 'mu' [explicit solve]: current solution: 5.077614e+00, current residual:3.069313e+00 +wall time: 0.000710011s + +time increment:38680 time: 38.68 +field ' c' [explicit solve]: current solution: 9.618326e+01, current residual:5.807822e+01 +field 'mu' [explicit solve]: current solution: 5.077497e+00, current residual:3.069243e+00 +wall time: 0.000695944s + +time increment:38690 time: 38.69 +field ' c' [explicit solve]: current solution: 9.618320e+01, current residual:5.807818e+01 +field 'mu' [explicit solve]: current solution: 5.077380e+00, current residual:3.069173e+00 +wall time: 0.000740051s + +time increment:38700 time: 38.7 +field ' c' [explicit solve]: current solution: 9.618314e+01, current residual:5.807814e+01 +field 'mu' [explicit solve]: current solution: 5.077263e+00, current residual:3.069103e+00 +wall time: 0.000596046s + +time increment:38710 time: 38.71 +field ' c' [explicit solve]: current solution: 9.618308e+01, current residual:5.807810e+01 +field 'mu' [explicit solve]: current solution: 5.077146e+00, current residual:3.069033e+00 +wall time: 0.000612974s + +time increment:38720 time: 38.72 +field ' c' [explicit solve]: current solution: 9.618302e+01, current residual:5.807806e+01 +field 'mu' [explicit solve]: current solution: 5.077029e+00, current residual:3.068963e+00 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0.000586987s + +time increment:38770 time: 38.77 +field ' c' [explicit solve]: current solution: 9.618273e+01, current residual:5.807786e+01 +field 'mu' [explicit solve]: current solution: 5.076445e+00, current residual:3.068613e+00 +wall time: 0.000564098s + +time increment:38780 time: 38.78 +field ' c' [explicit solve]: current solution: 9.618267e+01, current residual:5.807782e+01 +field 'mu' [explicit solve]: current solution: 5.076328e+00, current residual:3.068543e+00 +wall time: 0.000579834s + +time increment:38790 time: 38.79 +field ' c' [explicit solve]: current solution: 9.618262e+01, current residual:5.807778e+01 +field 'mu' [explicit solve]: current solution: 5.076211e+00, current residual:3.068473e+00 +wall time: 0.000552177s + +time increment:38800 time: 38.8 +field ' c' [explicit solve]: current solution: 9.618256e+01, current residual:5.807774e+01 +field 'mu' [explicit solve]: current solution: 5.076094e+00, current residual:3.068403e+00 +wall time: 0.000598907s + +time increment:38810 time: 38.81 +field ' c' [explicit solve]: current solution: 9.618250e+01, current residual:5.807769e+01 +field 'mu' [explicit solve]: current solution: 5.075978e+00, current residual:3.068333e+00 +wall time: 0.000648975s + +time increment:38820 time: 38.82 +field ' c' [explicit solve]: current solution: 9.618244e+01, current residual:5.807765e+01 +field 'mu' [explicit solve]: current solution: 5.075861e+00, current residual:3.068263e+00 +wall time: 0.000610113s + +time increment:38830 time: 38.83 +field ' c' [explicit solve]: current solution: 9.618239e+01, current residual:5.807761e+01 +field 'mu' [explicit solve]: current solution: 5.075744e+00, current residual:3.068193e+00 +wall time: 0.000656128s + +time increment:38840 time: 38.84 +field ' c' [explicit solve]: current solution: 9.618233e+01, current residual:5.807757e+01 +field 'mu' [explicit solve]: current solution: 5.075628e+00, current residual:3.068123e+00 +wall time: 0.000688076s + +time increment:38850 time: 38.85 +field ' c' [explicit solve]: current solution: 9.618227e+01, current residual:5.807753e+01 +field 'mu' [explicit solve]: current solution: 5.075511e+00, current residual:3.068054e+00 +wall time: 0.00069499s + +time increment:38860 time: 38.86 +field ' c' [explicit solve]: current solution: 9.618221e+01, current residual:5.807749e+01 +field 'mu' [explicit solve]: current solution: 5.075394e+00, current residual:3.067984e+00 +wall time: 0.000586987s + +time increment:38870 time: 38.87 +field ' c' [explicit solve]: current solution: 9.618216e+01, current residual:5.807745e+01 +field 'mu' [explicit solve]: current solution: 5.075278e+00, current residual:3.067914e+00 +wall time: 0.000692129s + +time increment:38880 time: 38.88 +field ' c' [explicit solve]: current solution: 9.618210e+01, current residual:5.807741e+01 +field 'mu' [explicit solve]: current solution: 5.075161e+00, current residual:3.067844e+00 +wall time: 0.000705004s + +time increment:38890 time: 38.89 +field ' c' [explicit solve]: current solution: 9.618204e+01, current residual:5.807737e+01 +field 'mu' [explicit solve]: current solution: 5.075044e+00, current residual:3.067774e+00 +wall time: 0.00064683s + +time increment:38900 time: 38.9 +field ' c' [explicit solve]: current solution: 9.618198e+01, current residual:5.807733e+01 +field 'mu' [explicit solve]: current solution: 5.074928e+00, current residual:3.067704e+00 +wall time: 0.000660896s + +time increment:38910 time: 38.91 +field ' c' [explicit solve]: current solution: 9.618193e+01, current residual:5.807730e+01 +field 'mu' [explicit solve]: current solution: 5.074811e+00, current residual:3.067635e+00 +wall time: 0.000742912s + +time increment:38920 time: 38.92 +field ' c' [explicit solve]: current solution: 9.618187e+01, current residual:5.807726e+01 +field 'mu' [explicit solve]: current solution: 5.074695e+00, current residual:3.067565e+00 +wall time: 0.00067687s + +time increment:38930 time: 38.93 +field ' c' [explicit solve]: current solution: 9.618181e+01, current residual:5.807722e+01 +field 'mu' [explicit solve]: current solution: 5.074578e+00, current residual:3.067495e+00 +wall time: 0.000776052s + +time increment:38940 time: 38.94 +field ' c' [explicit solve]: current solution: 9.618176e+01, current residual:5.807718e+01 +field 'mu' [explicit solve]: current solution: 5.074462e+00, current residual:3.067425e+00 +wall time: 0.000679016s + +time increment:38950 time: 38.95 +field ' c' [explicit solve]: current solution: 9.618170e+01, current residual:5.807714e+01 +field 'mu' [explicit solve]: current solution: 5.074345e+00, current residual:3.067355e+00 +wall time: 0.000735044s + +time increment:38960 time: 38.96 +field ' c' [explicit solve]: current solution: 9.618164e+01, current residual:5.807710e+01 +field 'mu' [explicit solve]: current solution: 5.074229e+00, current residual:3.067286e+00 +wall time: 0.000638008s + +time increment:38970 time: 38.97 +field ' c' [explicit solve]: current solution: 9.618159e+01, current residual:5.807706e+01 +field 'mu' [explicit solve]: current solution: 5.074113e+00, current residual:3.067216e+00 +wall time: 0.000763893s + +time increment:38980 time: 38.98 +field ' c' [explicit solve]: current solution: 9.618153e+01, current residual:5.807702e+01 +field 'mu' [explicit solve]: current solution: 5.073996e+00, current residual:3.067146e+00 +wall time: 0.000689983s + +time increment:38990 time: 38.99 +field ' c' [explicit solve]: current solution: 9.618147e+01, current residual:5.807698e+01 +field 'mu' [explicit solve]: current solution: 5.073880e+00, current residual:3.067077e+00 +wall time: 0.000654936s + +time increment:39000 time: 39 +field ' c' [explicit solve]: current solution: 9.618142e+01, current residual:5.807694e+01 +field 'mu' [explicit solve]: current solution: 5.073763e+00, current residual:3.067007e+00 +wall time: 0.000663996s + +time increment:39010 time: 39.01 +field ' c' [explicit solve]: current solution: 9.618136e+01, current residual:5.807690e+01 +field 'mu' [explicit solve]: current solution: 5.073647e+00, current residual:3.066937e+00 +wall time: 0.000706911s + +time increment:39020 time: 39.02 +field ' c' [explicit solve]: current solution: 9.618130e+01, current residual:5.807686e+01 +field 'mu' [explicit solve]: current solution: 5.073531e+00, current residual:3.066867e+00 +wall time: 0.000648975s + +time increment:39030 time: 39.03 +field ' c' [explicit solve]: current solution: 9.618125e+01, current residual:5.807682e+01 +field 'mu' [explicit solve]: current solution: 5.073415e+00, current residual:3.066798e+00 +wall time: 0.000634909s + +time increment:39040 time: 39.04 +field ' c' [explicit solve]: current solution: 9.618119e+01, current residual:5.807678e+01 +field 'mu' [explicit solve]: current solution: 5.073298e+00, current residual:3.066728e+00 +wall time: 0.000663042s + +time increment:39050 time: 39.05 +field ' c' [explicit solve]: current solution: 9.618114e+01, current residual:5.807674e+01 +field 'mu' [explicit solve]: current solution: 5.073182e+00, current residual:3.066658e+00 +wall time: 0.000643969s + +time increment:39060 time: 39.06 +field ' c' [explicit solve]: current solution: 9.618108e+01, current residual:5.807670e+01 +field 'mu' [explicit solve]: current solution: 5.073066e+00, current residual:3.066589e+00 +wall time: 0.000702858s + +time increment:39070 time: 39.07 +field ' c' [explicit solve]: current solution: 9.618102e+01, current residual:5.807666e+01 +field 'mu' [explicit solve]: current solution: 5.072950e+00, current residual:3.066519e+00 +wall time: 0.00065589s + +time increment:39080 time: 39.08 +field ' c' [explicit solve]: current solution: 9.618097e+01, current residual:5.807662e+01 +field 'mu' [explicit solve]: current solution: 5.072834e+00, current residual:3.066450e+00 +wall time: 0.000732183s + +time increment:39090 time: 39.09 +field ' c' [explicit solve]: current solution: 9.618091e+01, current residual:5.807659e+01 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solve]: current solution: 5.072253e+00, current residual:3.066102e+00 +wall time: 0.000684023s + +time increment:39140 time: 39.14 +field ' c' [explicit solve]: current solution: 9.618063e+01, current residual:5.807639e+01 +field 'mu' [explicit solve]: current solution: 5.072137e+00, current residual:3.066032e+00 +wall time: 0.000684977s + +time increment:39150 time: 39.15 +field ' c' [explicit solve]: current solution: 9.618058e+01, current residual:5.807635e+01 +field 'mu' [explicit solve]: current solution: 5.072021e+00, current residual:3.065962e+00 +wall time: 0.000795841s + +time increment:39160 time: 39.16 +field ' c' [explicit solve]: current solution: 9.618052e+01, current residual:5.807631e+01 +field 'mu' [explicit solve]: current solution: 5.071905e+00, current residual:3.065893e+00 +wall time: 0.000674963s + +time increment:39170 time: 39.17 +field ' c' [explicit solve]: current solution: 9.618047e+01, current residual:5.807627e+01 +field 'mu' [explicit solve]: current solution: 5.071789e+00, current residual:3.065823e+00 +wall time: 0.000730038s + +time increment:39180 time: 39.18 +field ' c' [explicit solve]: current solution: 9.618041e+01, current residual:5.807624e+01 +field 'mu' [explicit solve]: current solution: 5.071673e+00, current residual:3.065754e+00 +wall time: 0.000711918s + +time increment:39190 time: 39.19 +field ' c' [explicit solve]: current solution: 9.618036e+01, current residual:5.807620e+01 +field 'mu' [explicit solve]: current solution: 5.071557e+00, current residual:3.065684e+00 +wall time: 0.000652075s + +time increment:39200 time: 39.2 +field ' c' [explicit solve]: current solution: 9.618030e+01, current residual:5.807616e+01 +field 'mu' [explicit solve]: current solution: 5.071441e+00, current residual:3.065615e+00 +wall time: 0.00071907s + +time increment:39210 time: 39.21 +field ' c' [explicit solve]: current solution: 9.618025e+01, current residual:5.807612e+01 +field 'mu' [explicit solve]: current solution: 5.071326e+00, current residual:3.065545e+00 +wall time: 0.000612974s + +time increment:39220 time: 39.22 +field ' c' [explicit solve]: current solution: 9.618019e+01, current residual:5.807608e+01 +field 'mu' [explicit solve]: current solution: 5.071210e+00, current residual:3.065476e+00 +wall time: 0.000644207s + +time increment:39230 time: 39.23 +field ' c' [explicit solve]: current solution: 9.618014e+01, current residual:5.807604e+01 +field 'mu' [explicit solve]: current solution: 5.071094e+00, current residual:3.065406e+00 +wall time: 0.000678062s + +time increment:39240 time: 39.24 +field ' c' [explicit solve]: current solution: 9.618008e+01, current residual:5.807600e+01 +field 'mu' [explicit solve]: current solution: 5.070978e+00, current residual:3.065337e+00 +wall time: 0.00071311s + +time increment:39250 time: 39.25 +field ' c' [explicit solve]: current solution: 9.618003e+01, current residual:5.807597e+01 +field 'mu' [explicit solve]: current solution: 5.070862e+00, current residual:3.065268e+00 +wall time: 0.000659943s + +time increment:39260 time: 39.26 +field ' c' [explicit solve]: current solution: 9.617997e+01, current residual:5.807593e+01 +field 'mu' [explicit solve]: current solution: 5.070747e+00, current residual:3.065198e+00 +wall time: 0.000699043s + +time increment:39270 time: 39.27 +field ' c' [explicit solve]: current solution: 9.617992e+01, current residual:5.807589e+01 +field 'mu' [explicit solve]: current solution: 5.070631e+00, current residual:3.065129e+00 +wall time: 0.000731945s + +time increment:39280 time: 39.28 +field ' c' [explicit solve]: current solution: 9.617986e+01, current residual:5.807585e+01 +field 'mu' [explicit solve]: current solution: 5.070515e+00, current residual:3.065059e+00 +wall time: 0.00057292s + +time increment:39290 time: 39.29 +field ' c' [explicit solve]: current solution: 9.617981e+01, current residual:5.807581e+01 +field 'mu' [explicit solve]: current solution: 5.070399e+00, current residual:3.064990e+00 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0.000567913s + +time increment:39340 time: 39.34 +field ' c' [explicit solve]: current solution: 9.617954e+01, current residual:5.807562e+01 +field 'mu' [explicit solve]: current solution: 5.069821e+00, current residual:3.064643e+00 +wall time: 0.000620127s + +time increment:39350 time: 39.35 +field ' c' [explicit solve]: current solution: 9.617948e+01, current residual:5.807558e+01 +field 'mu' [explicit solve]: current solution: 5.069706e+00, current residual:3.064574e+00 +wall time: 0.000631094s + +time increment:39360 time: 39.36 +field ' c' [explicit solve]: current solution: 9.617943e+01, current residual:5.807555e+01 +field 'mu' [explicit solve]: current solution: 5.069590e+00, current residual:3.064505e+00 +wall time: 0.000627995s + +time increment:39370 time: 39.37 +field ' c' [explicit solve]: current solution: 9.617938e+01, current residual:5.807551e+01 +field 'mu' [explicit solve]: current solution: 5.069475e+00, current residual:3.064435e+00 +wall time: 0.000574827s + +time increment:39380 time: 39.38 +field ' c' [explicit solve]: current solution: 9.617932e+01, current residual:5.807547e+01 +field 'mu' [explicit solve]: current solution: 5.069359e+00, current residual:3.064366e+00 +wall time: 0.000555038s + +time increment:39390 time: 39.39 +field ' c' [explicit solve]: current solution: 9.617927e+01, current residual:5.807543e+01 +field 'mu' [explicit solve]: current solution: 5.069244e+00, current residual:3.064297e+00 +wall time: 0.000674009s + +time increment:39400 time: 39.4 +field ' c' [explicit solve]: current solution: 9.617921e+01, current residual:5.807540e+01 +field 'mu' [explicit solve]: current solution: 5.069129e+00, current residual:3.064227e+00 +wall time: 0.000566959s + +time increment:39410 time: 39.41 +field ' c' [explicit solve]: current solution: 9.617916e+01, current residual:5.807536e+01 +field 'mu' [explicit solve]: current solution: 5.069013e+00, current residual:3.064158e+00 +wall time: 0.000624895s + +time increment:39420 time: 39.42 +field ' c' [explicit solve]: current solution: 9.617911e+01, current residual:5.807532e+01 +field 'mu' [explicit solve]: current solution: 5.068898e+00, current residual:3.064089e+00 +wall time: 0.000598907s + +time increment:39430 time: 39.43 +field ' c' [explicit solve]: current solution: 9.617905e+01, current residual:5.807528e+01 +field 'mu' [explicit solve]: current solution: 5.068782e+00, current residual:3.064020e+00 +wall time: 0.000602961s + +time increment:39440 time: 39.44 +field ' c' [explicit solve]: current solution: 9.617900e+01, current residual:5.807524e+01 +field 'mu' [explicit solve]: current solution: 5.068667e+00, current residual:3.063950e+00 +wall time: 0.000648975s + +time increment:39450 time: 39.45 +field ' c' [explicit solve]: current solution: 9.617895e+01, current residual:5.807521e+01 +field 'mu' [explicit solve]: current solution: 5.068552e+00, current residual:3.063881e+00 +wall time: 0.000593901s + +time increment:39460 time: 39.46 +field ' c' [explicit solve]: current solution: 9.617889e+01, current residual:5.807517e+01 +field 'mu' [explicit solve]: current solution: 5.068437e+00, current residual:3.063812e+00 +wall time: 0.000710964s + +time increment:39470 time: 39.47 +field ' c' [explicit solve]: current solution: 9.617884e+01, current residual:5.807513e+01 +field 'mu' [explicit solve]: current solution: 5.068321e+00, current residual:3.063743e+00 +wall time: 0.000619888s + +time increment:39480 time: 39.48 +field ' c' [explicit solve]: current solution: 9.617879e+01, current residual:5.807509e+01 +field 'mu' [explicit solve]: current solution: 5.068206e+00, current residual:3.063674e+00 +wall time: 0.000571012s + +time increment:39490 time: 39.49 +field ' c' [explicit solve]: current solution: 9.617873e+01, current residual:5.807506e+01 +field 'mu' [explicit solve]: current solution: 5.068091e+00, current residual:3.063605e+00 +wall time: 0.000660896s + +time increment:39500 time: 39.5 +field ' c' [explicit solve]: current solution: 9.617868e+01, current residual:5.807502e+01 +field 'mu' [explicit solve]: current solution: 5.067976e+00, current residual:3.063535e+00 +wall time: 0.000609875s + +time increment:39510 time: 39.51 +field ' c' [explicit solve]: current solution: 9.617863e+01, current residual:5.807498e+01 +field 'mu' [explicit solve]: current solution: 5.067861e+00, current residual:3.063466e+00 +wall time: 0.000645161s + +time increment:39520 time: 39.52 +field ' c' [explicit solve]: current solution: 9.617857e+01, current residual:5.807495e+01 +field 'mu' [explicit solve]: current solution: 5.067745e+00, current residual:3.063397e+00 +wall time: 0.000638008s + +time increment:39530 time: 39.53 +field ' c' [explicit solve]: current solution: 9.617852e+01, current residual:5.807491e+01 +field 'mu' [explicit solve]: current solution: 5.067630e+00, current residual:3.063328e+00 +wall time: 0.00068593s + +time increment:39540 time: 39.54 +field ' c' [explicit solve]: current solution: 9.617847e+01, current residual:5.807487e+01 +field 'mu' [explicit solve]: current solution: 5.067515e+00, current residual:3.063259e+00 +wall time: 0.00057292s + +time increment:39550 time: 39.55 +field ' c' [explicit solve]: current solution: 9.617841e+01, current residual:5.807483e+01 +field 'mu' [explicit solve]: current solution: 5.067400e+00, current residual:3.063190e+00 +wall time: 0.000714064s + +time increment:39560 time: 39.56 +field ' c' [explicit solve]: current solution: 9.617836e+01, current residual:5.807480e+01 +field 'mu' [explicit solve]: current solution: 5.067285e+00, current residual:3.063121e+00 +wall time: 0.000628948s + +time increment:39570 time: 39.57 +field ' c' [explicit solve]: current solution: 9.617831e+01, current residual:5.807476e+01 +field 'mu' [explicit solve]: current solution: 5.067170e+00, current residual:3.063052e+00 +wall time: 0.000735998s + +time increment:39580 time: 39.58 +field ' c' [explicit solve]: current solution: 9.617826e+01, current residual:5.807472e+01 +field 'mu' [explicit solve]: current solution: 5.067055e+00, current residual:3.062983e+00 +wall time: 0.000575066s + +time increment:39590 time: 39.59 +field ' c' [explicit solve]: current solution: 9.617820e+01, current residual:5.807469e+01 +field 'mu' [explicit solve]: current solution: 5.066940e+00, current residual:3.062914e+00 +wall time: 0.000579119s + +time increment:39600 time: 39.6 +field ' c' [explicit solve]: current solution: 9.617815e+01, current residual:5.807465e+01 +field 'mu' [explicit solve]: current solution: 5.066825e+00, current residual:3.062845e+00 +wall time: 0.000669956s + +time increment:39610 time: 39.61 +field ' c' [explicit solve]: current solution: 9.617810e+01, current residual:5.807461e+01 +field 'mu' [explicit solve]: current solution: 5.066710e+00, current residual:3.062776e+00 +wall time: 0.00066185s + +time increment:39620 time: 39.62 +field ' c' [explicit solve]: current solution: 9.617805e+01, current residual:5.807457e+01 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[explicit solve]: current solution: 5.066136e+00, current residual:3.062431e+00 +wall time: 0.000673056s + +time increment:39670 time: 39.67 +field ' c' [explicit solve]: current solution: 9.617778e+01, current residual:5.807439e+01 +field 'mu' [explicit solve]: current solution: 5.066022e+00, current residual:3.062362e+00 +wall time: 0.000710011s + +time increment:39680 time: 39.68 +field ' c' [explicit solve]: current solution: 9.617773e+01, current residual:5.807435e+01 +field 'mu' [explicit solve]: current solution: 5.065907e+00, current residual:3.062293e+00 +wall time: 0.000560045s + +time increment:39690 time: 39.69 +field ' c' [explicit solve]: current solution: 9.617768e+01, current residual:5.807432e+01 +field 'mu' [explicit solve]: current solution: 5.065792e+00, current residual:3.062224e+00 +wall time: 0.000565052s + +time increment:39700 time: 39.7 +field ' c' [explicit solve]: current solution: 9.617763e+01, current residual:5.807428e+01 +field 'mu' [explicit solve]: current solution: 5.065677e+00, current residual:3.062155e+00 +wall time: 0.000567913s + +time increment:39710 time: 39.71 +field ' c' [explicit solve]: current solution: 9.617758e+01, current residual:5.807424e+01 +field 'mu' [explicit solve]: current solution: 5.065563e+00, current residual:3.062086e+00 +wall time: 0.000720978s + +time increment:39720 time: 39.72 +field ' c' [explicit solve]: current solution: 9.617753e+01, current residual:5.807421e+01 +field 'mu' [explicit solve]: current solution: 5.065448e+00, current residual:3.062017e+00 +wall time: 0.000695944s + +time increment:39730 time: 39.73 +field ' c' [explicit solve]: current solution: 9.617747e+01, current residual:5.807417e+01 +field 'mu' [explicit solve]: current solution: 5.065333e+00, current residual:3.061948e+00 +wall time: 0.00060606s + +time increment:39740 time: 39.74 +field ' c' [explicit solve]: current solution: 9.617742e+01, current residual:5.807413e+01 +field 'mu' [explicit solve]: current solution: 5.065219e+00, current residual:3.061880e+00 +wall time: 0.000703096s + +time increment:39750 time: 39.75 +field ' c' [explicit solve]: current solution: 9.617737e+01, current residual:5.807410e+01 +field 'mu' [explicit solve]: current solution: 5.065104e+00, current residual:3.061811e+00 +wall time: 0.000662088s + +time increment:39760 time: 39.76 +field ' c' [explicit solve]: current solution: 9.617732e+01, current residual:5.807406e+01 +field 'mu' [explicit solve]: current solution: 5.064990e+00, current residual:3.061742e+00 +wall time: 0.000667095s + +time increment:39770 time: 39.77 +field ' c' [explicit solve]: current solution: 9.617727e+01, current residual:5.807403e+01 +field 'mu' [explicit solve]: current solution: 5.064875e+00, current residual:3.061673e+00 +wall time: 0.000602961s + +time increment:39780 time: 39.78 +field ' c' [explicit solve]: current solution: 9.617722e+01, current residual:5.807399e+01 +field 'mu' [explicit solve]: current solution: 5.064761e+00, current residual:3.061604e+00 +wall time: 0.000627995s + +time increment:39790 time: 39.79 +field ' c' [explicit solve]: current solution: 9.617716e+01, current residual:5.807395e+01 +field 'mu' [explicit solve]: current solution: 5.064646e+00, current residual:3.061535e+00 +wall time: 0.000576973s + +time increment:39800 time: 39.8 +field ' c' [explicit solve]: current solution: 9.617711e+01, current residual:5.807392e+01 +field 'mu' [explicit solve]: current solution: 5.064532e+00, current residual:3.061467e+00 +wall time: 0.000644922s + +time increment:39810 time: 39.81 +field ' c' [explicit solve]: current solution: 9.617706e+01, current residual:5.807388e+01 +field 'mu' [explicit solve]: current solution: 5.064418e+00, current residual:3.061398e+00 +wall time: 0.000663996s + +time increment:39820 time: 39.82 +field ' c' [explicit solve]: current solution: 9.617701e+01, current residual:5.807384e+01 +field 'mu' [explicit solve]: current solution: 5.064303e+00, current residual:3.061329e+00 +wall time: 0.000614882s + +time increment:39830 time: 39.83 +field ' c' [explicit solve]: current solution: 9.617696e+01, current residual:5.807381e+01 +field 'mu' [explicit solve]: current solution: 5.064189e+00, current residual:3.061260e+00 +wall time: 0.000800848s + +time increment:39840 time: 39.84 +field ' c' [explicit solve]: current solution: 9.617691e+01, current residual:5.807377e+01 +field 'mu' [explicit solve]: current solution: 5.064074e+00, current residual:3.061192e+00 +wall time: 0.000800133s + +time increment:39850 time: 39.85 +field ' c' [explicit solve]: current solution: 9.617686e+01, current residual:5.807374e+01 +field 'mu' [explicit solve]: current solution: 5.063960e+00, current residual:3.061123e+00 +wall time: 0.000738859s + +time increment:39860 time: 39.86 +field ' c' [explicit solve]: current solution: 9.617681e+01, current residual:5.807370e+01 +field 'mu' [explicit solve]: current solution: 5.063846e+00, current residual:3.061054e+00 +wall time: 0.000756025s + +time increment:39870 time: 39.87 +field ' c' [explicit solve]: current solution: 9.617676e+01, current residual:5.807366e+01 +field 'mu' [explicit solve]: current solution: 5.063732e+00, current residual:3.060986e+00 +wall time: 0.000755072s + +time increment:39880 time: 39.88 +field ' c' [explicit solve]: current solution: 9.617671e+01, current residual:5.807363e+01 +field 'mu' [explicit solve]: current solution: 5.063617e+00, current residual:3.060917e+00 +wall time: 0.000680923s + +time increment:39890 time: 39.89 +field ' c' [explicit solve]: current solution: 9.617665e+01, current residual:5.807359e+01 +field 'mu' [explicit solve]: current solution: 5.063503e+00, current residual:3.060848e+00 +wall time: 0.00070405s + +time increment:39900 time: 39.9 +field ' c' [explicit solve]: current solution: 9.617660e+01, current residual:5.807356e+01 +field 'mu' [explicit solve]: current solution: 5.063389e+00, current residual:3.060780e+00 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0.000706911s + +time increment:39950 time: 39.95 +field ' c' [explicit solve]: current solution: 9.617635e+01, current residual:5.807338e+01 +field 'mu' [explicit solve]: current solution: 5.062819e+00, current residual:3.060437e+00 +wall time: 0.000690937s + +time increment:39960 time: 39.96 +field ' c' [explicit solve]: current solution: 9.617630e+01, current residual:5.807334e+01 +field 'mu' [explicit solve]: current solution: 5.062705e+00, current residual:3.060368e+00 +wall time: 0.000672102s + +time increment:39970 time: 39.97 +field ' c' [explicit solve]: current solution: 9.617625e+01, current residual:5.807331e+01 +field 'mu' [explicit solve]: current solution: 5.062591e+00, current residual:3.060299e+00 +wall time: 0.000720024s + +time increment:39980 time: 39.98 +field ' c' [explicit solve]: current solution: 9.617620e+01, current residual:5.807327e+01 +field 'mu' [explicit solve]: current solution: 5.062477e+00, current residual:3.060231e+00 +wall time: 0.000672102s + +time increment:39990 time: 39.99 +field ' c' [explicit solve]: current solution: 9.617615e+01, current residual:5.807324e+01 +field 'mu' [explicit solve]: current solution: 5.062363e+00, current residual:3.060162e+00 +wall time: 0.000730038s + +time increment:40000 time: 40 +field ' c' [explicit solve]: current solution: 9.617610e+01, current residual:5.807320e+01 +field 'mu' [explicit solve]: current solution: 5.062249e+00, current residual:3.060094e+00 +wall time: 0.000717878s +fields: 2 +fields: 2 +fields: 2 +fields: 2 +Output written to:solution-040000.pvtu + +Energy: 244.052 +Energy Components: 116.492 127.56 0 + +time increment:40010 time: 40.01 +field ' c' [explicit solve]: current solution: 9.617605e+01, current residual:5.807317e+01 +field 'mu' [explicit solve]: current solution: 5.062135e+00, current residual:3.060025e+00 +wall time: 0.00070715s + +time increment:40020 time: 40.02 +field ' c' [explicit solve]: current solution: 9.617600e+01, current residual:5.807313e+01 +field 'mu' [explicit solve]: current solution: 5.062021e+00, current residual:3.059957e+00 +wall time: 0.000566959s + +time increment:40030 time: 40.03 +field ' c' [explicit solve]: current solution: 9.617595e+01, current residual:5.807310e+01 +field 'mu' [explicit solve]: current solution: 5.061907e+00, current residual:3.059888e+00 +wall time: 0.000576973s + +time increment:40040 time: 40.04 +field ' c' [explicit solve]: current solution: 9.617590e+01, current residual:5.807306e+01 +field 'mu' [explicit solve]: current solution: 5.061793e+00, current residual:3.059820e+00 +wall time: 0.000597s + +time increment:40050 time: 40.05 +field ' c' [explicit solve]: current solution: 9.617585e+01, current residual:5.807303e+01 +field 'mu' [explicit solve]: current solution: 5.061680e+00, current residual:3.059751e+00 +wall time: 0.000583172s + +time increment:40060 time: 40.06 +field ' c' [explicit solve]: current solution: 9.617580e+01, current residual:5.807299e+01 +field 'mu' [explicit solve]: current solution: 5.061566e+00, current residual:3.059683e+00 +wall time: 0.000575066s + +time increment:40070 time: 40.07 +field ' c' [explicit solve]: current solution: 9.617575e+01, current residual:5.807296e+01 +field 'mu' [explicit solve]: current solution: 5.061452e+00, current residual:3.059615e+00 +wall time: 0.000665903s + +time increment:40080 time: 40.08 +field ' c' [explicit solve]: current solution: 9.617570e+01, current residual:5.807292e+01 +field 'mu' [explicit solve]: current solution: 5.061338e+00, current residual:3.059546e+00 +wall time: 0.000592947s + +time increment:40090 time: 40.09 +field ' c' [explicit solve]: current solution: 9.617565e+01, current residual:5.807288e+01 +field 'mu' [explicit solve]: current solution: 5.061225e+00, current residual:3.059478e+00 +wall time: 0.000566006s + +time increment:40100 time: 40.1 +field ' c' [explicit solve]: current solution: 9.617560e+01, current residual:5.807285e+01 +field 'mu' [explicit solve]: current solution: 5.061111e+00, current residual:3.059409e+00 +wall time: 0.000580072s + +time increment:40110 time: 40.11 +field ' c' [explicit solve]: current solution: 9.617555e+01, current residual:5.807281e+01 +field 'mu' [explicit solve]: current solution: 5.060997e+00, current residual:3.059341e+00 +wall time: 0.000572205s + +time increment:40120 time: 40.12 +field ' c' [explicit solve]: current solution: 9.617551e+01, current residual:5.807278e+01 +field 'mu' [explicit solve]: current solution: 5.060884e+00, current residual:3.059273e+00 +wall time: 0.000608206s + +time increment:40130 time: 40.13 +field ' c' [explicit solve]: current solution: 9.617546e+01, current residual:5.807275e+01 +field 'mu' [explicit solve]: current solution: 5.060770e+00, current residual:3.059204e+00 +wall time: 0.000682116s + +time increment:40140 time: 40.14 +field ' c' [explicit solve]: current solution: 9.617541e+01, current residual:5.807271e+01 +field 'mu' [explicit solve]: current solution: 5.060657e+00, current residual:3.059136e+00 +wall time: 0.00057292s + +time increment:40150 time: 40.15 +field ' c' [explicit solve]: current solution: 9.617536e+01, current residual:5.807268e+01 +field 'mu' [explicit solve]: current solution: 5.060543e+00, current residual:3.059068e+00 +wall time: 0.000571966s + +time increment:40160 time: 40.16 +field ' c' [explicit solve]: current solution: 9.617531e+01, current residual:5.807264e+01 +field 'mu' [explicit solve]: current solution: 5.060430e+00, current residual:3.058999e+00 +wall time: 0.000652075s + +time increment:40170 time: 40.17 +field ' c' [explicit solve]: current solution: 9.617526e+01, current residual:5.807261e+01 +field 'mu' [explicit solve]: current solution: 5.060316e+00, current residual:3.058931e+00 +wall time: 0.000566006s + +time increment:40180 time: 40.18 +field ' c' [explicit solve]: current solution: 9.617521e+01, current residual:5.807257e+01 +field 'mu' [explicit solve]: current solution: 5.060203e+00, current residual:3.058863e+00 +wall time: 0.000591993s + +time increment:40190 time: 40.19 +field ' c' [explicit solve]: current solution: 9.617516e+01, current residual:5.807254e+01 +field 'mu' [explicit solve]: current solution: 5.060089e+00, current residual:3.058795e+00 +wall time: 0.00067091s + +time increment:40200 time: 40.2 +field ' c' [explicit solve]: current solution: 9.617511e+01, current residual:5.807250e+01 +field 'mu' [explicit solve]: current solution: 5.059976e+00, current residual:3.058726e+00 +wall time: 0.000574112s + +time increment:40210 time: 40.21 +field ' c' [explicit solve]: current solution: 9.617506e+01, current residual:5.807247e+01 +field 'mu' [explicit solve]: current solution: 5.059863e+00, current residual:3.058658e+00 +wall time: 0.000568867s + +time increment:40220 time: 40.22 +field ' c' [explicit solve]: current solution: 9.617502e+01, current residual:5.807243e+01 +field 'mu' [explicit solve]: current solution: 5.059749e+00, current residual:3.058590e+00 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0.000627995s + +time increment:40270 time: 40.27 +field ' c' [explicit solve]: current solution: 9.617477e+01, current residual:5.807226e+01 +field 'mu' [explicit solve]: current solution: 5.059183e+00, current residual:3.058249e+00 +wall time: 0.00057292s + +time increment:40280 time: 40.28 +field ' c' [explicit solve]: current solution: 9.617473e+01, current residual:5.807223e+01 +field 'mu' [explicit solve]: current solution: 5.059070e+00, current residual:3.058181e+00 +wall time: 0.000560999s + +time increment:40290 time: 40.29 +field ' c' [explicit solve]: current solution: 9.617468e+01, current residual:5.807219e+01 +field 'mu' [explicit solve]: current solution: 5.058957e+00, current residual:3.058113e+00 +wall time: 0.000569105s + +time increment:40300 time: 40.3 +field ' c' [explicit solve]: current solution: 9.617463e+01, current residual:5.807216e+01 +field 'mu' [explicit solve]: current solution: 5.058844e+00, current residual:3.058045e+00 +wall time: 0.000620127s + +time increment:40310 time: 40.31 +field ' c' [explicit solve]: current solution: 9.617458e+01, current residual:5.807212e+01 +field 'mu' [explicit solve]: current solution: 5.058730e+00, current residual:3.057977e+00 +wall time: 0.000581026s + +time increment:40320 time: 40.32 +field ' c' [explicit solve]: current solution: 9.617453e+01, current residual:5.807209e+01 +field 'mu' [explicit solve]: current solution: 5.058617e+00, current residual:3.057908e+00 +wall time: 0.00056386s + +time increment:40330 time: 40.33 +field ' c' [explicit solve]: current solution: 9.617448e+01, current residual:5.807206e+01 +field 'mu' [explicit solve]: current solution: 5.058504e+00, current residual:3.057840e+00 +wall time: 0.000679016s + +time increment:40340 time: 40.34 +field ' c' [explicit solve]: current solution: 9.617444e+01, current residual:5.807202e+01 +field 'mu' [explicit solve]: current solution: 5.058391e+00, current residual:3.057772e+00 +wall time: 0.00071907s + +time increment:40350 time: 40.35 +field ' c' [explicit solve]: current solution: 9.617439e+01, current residual:5.807199e+01 +field 'mu' [explicit solve]: current solution: 5.058278e+00, current residual:3.057704e+00 +wall time: 0.000640869s + +time increment:40360 time: 40.36 +field ' c' [explicit solve]: current solution: 9.617434e+01, current residual:5.807195e+01 +field 'mu' [explicit solve]: current solution: 5.058165e+00, current residual:3.057636e+00 +wall time: 0.000571012s + +time increment:40370 time: 40.37 +field ' c' [explicit solve]: current solution: 9.617429e+01, current residual:5.807192e+01 +field 'mu' [explicit solve]: current solution: 5.058053e+00, current residual:3.057568e+00 +wall time: 0.000602007s + +time increment:40380 time: 40.38 +field ' c' [explicit solve]: current solution: 9.617425e+01, current residual:5.807189e+01 +field 'mu' [explicit solve]: current solution: 5.057940e+00, current residual:3.057500e+00 +wall time: 0.000639915s + +time increment:40390 time: 40.39 +field ' c' [explicit solve]: current solution: 9.617420e+01, current residual:5.807185e+01 +field 'mu' [explicit solve]: current solution: 5.057827e+00, current residual:3.057432e+00 +wall time: 0.000723839s + +time increment:40400 time: 40.4 +field ' c' [explicit solve]: current solution: 9.617415e+01, current residual:5.807182e+01 +field 'mu' [explicit solve]: current solution: 5.057714e+00, current residual:3.057364e+00 +wall time: 0.00067997s + +time increment:40410 time: 40.41 +field ' c' [explicit solve]: current solution: 9.617410e+01, current residual:5.807178e+01 +field 'mu' [explicit solve]: current solution: 5.057601e+00, current residual:3.057296e+00 +wall time: 0.000569105s + +time increment:40420 time: 40.42 +field ' c' [explicit solve]: current solution: 9.617406e+01, current residual:5.807175e+01 +field 'mu' [explicit solve]: current solution: 5.057488e+00, current residual:3.057228e+00 +wall time: 0.000588894s + +time increment:40430 time: 40.43 +field ' c' [explicit solve]: current solution: 9.617401e+01, current residual:5.807172e+01 +field 'mu' [explicit solve]: current solution: 5.057376e+00, current residual:3.057161e+00 +wall time: 0.000766993s + +time increment:40440 time: 40.44 +field ' c' [explicit solve]: current solution: 9.617396e+01, current residual:5.807168e+01 +field 'mu' [explicit solve]: current solution: 5.057263e+00, current residual:3.057093e+00 +wall time: 0.00059104s + +time increment:40450 time: 40.45 +field ' c' [explicit solve]: current solution: 9.617391e+01, current residual:5.807165e+01 +field 'mu' [explicit solve]: current solution: 5.057150e+00, current residual:3.057025e+00 +wall time: 0.000628948s + +time increment:40460 time: 40.46 +field ' c' [explicit solve]: current solution: 9.617387e+01, current residual:5.807162e+01 +field 'mu' [explicit solve]: current solution: 5.057037e+00, current residual:3.056957e+00 +wall time: 0.000576019s + +time increment:40470 time: 40.47 +field ' c' [explicit solve]: current solution: 9.617382e+01, current residual:5.807158e+01 +field 'mu' [explicit solve]: current solution: 5.056925e+00, current residual:3.056889e+00 +wall time: 0.000590086s + +time increment:40480 time: 40.48 +field ' c' [explicit solve]: current solution: 9.617377e+01, current residual:5.807155e+01 +field 'mu' [explicit solve]: current solution: 5.056812e+00, current residual:3.056821e+00 +wall time: 0.000649929s + +time increment:40490 time: 40.49 +field ' c' [explicit solve]: current solution: 9.617372e+01, current residual:5.807151e+01 +field 'mu' [explicit solve]: current solution: 5.056700e+00, current residual:3.056753e+00 +wall time: 0.000763893s + +time increment:40500 time: 40.5 +field ' c' [explicit solve]: current solution: 9.617368e+01, current residual:5.807148e+01 +field 'mu' [explicit solve]: current solution: 5.056587e+00, current residual:3.056685e+00 +wall time: 0.000753164s + +time increment:40510 time: 40.51 +field ' c' [explicit solve]: current solution: 9.617363e+01, current residual:5.807145e+01 +field 'mu' [explicit solve]: current solution: 5.056474e+00, current residual:3.056618e+00 +wall time: 0.000682116s + +time increment:40520 time: 40.52 +field ' c' [explicit solve]: current solution: 9.617358e+01, current residual:5.807141e+01 +field 'mu' [explicit solve]: current solution: 5.056362e+00, current residual:3.056550e+00 +wall time: 0.000663996s + +time increment:40530 time: 40.53 +field ' c' [explicit solve]: current solution: 9.617354e+01, current residual:5.807138e+01 +field 'mu' [explicit solve]: current solution: 5.056249e+00, current residual:3.056482e+00 +wall time: 0.000683069s + +time increment:40540 time: 40.54 +field ' c' [explicit solve]: current solution: 9.617349e+01, current residual:5.807135e+01 +field 'mu' [explicit solve]: current solution: 5.056137e+00, current residual:3.056414e+00 +wall time: 0.00068593s + +time increment:40550 time: 40.55 +field ' c' [explicit solve]: current solution: 9.617344e+01, current residual:5.807131e+01 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[explicit solve]: current solution: 5.055575e+00, current residual:3.056076e+00 +wall time: 0.000701904s + +time increment:40600 time: 40.6 +field ' c' [explicit solve]: current solution: 9.617321e+01, current residual:5.807115e+01 +field 'mu' [explicit solve]: current solution: 5.055463e+00, current residual:3.056008e+00 +wall time: 0.00075388s + +time increment:40610 time: 40.61 +field ' c' [explicit solve]: current solution: 9.617317e+01, current residual:5.807112e+01 +field 'mu' [explicit solve]: current solution: 5.055351e+00, current residual:3.055940e+00 +wall time: 0.000671864s + +time increment:40620 time: 40.62 +field ' c' [explicit solve]: current solution: 9.617312e+01, current residual:5.807108e+01 +field 'mu' [explicit solve]: current solution: 5.055238e+00, current residual:3.055873e+00 +wall time: 0.000645876s + +time increment:40630 time: 40.63 +field ' c' [explicit solve]: current solution: 9.617307e+01, current residual:5.807105e+01 +field 'mu' [explicit solve]: current solution: 5.055126e+00, current residual:3.055805e+00 +wall time: 0.000752926s + +time increment:40640 time: 40.64 +field ' c' [explicit solve]: current solution: 9.617303e+01, current residual:5.807102e+01 +field 'mu' [explicit solve]: current solution: 5.055014e+00, current residual:3.055737e+00 +wall time: 0.000694036s + +time increment:40650 time: 40.65 +field ' c' [explicit solve]: current solution: 9.617298e+01, current residual:5.807098e+01 +field 'mu' [explicit solve]: current solution: 5.054902e+00, current residual:3.055670e+00 +wall time: 0.000641108s + +time increment:40660 time: 40.66 +field ' c' [explicit solve]: current solution: 9.617293e+01, current residual:5.807095e+01 +field 'mu' [explicit solve]: current solution: 5.054790e+00, current residual:3.055602e+00 +wall time: 0.000711918s + +time increment:40670 time: 40.67 +field ' c' [explicit solve]: current solution: 9.617289e+01, current residual:5.807092e+01 +field 'mu' [explicit solve]: current solution: 5.054678e+00, current residual:3.055535e+00 +wall time: 0.000568867s + +time increment:40680 time: 40.68 +field ' c' [explicit solve]: current solution: 9.617284e+01, current residual:5.807088e+01 +field 'mu' [explicit solve]: current solution: 5.054566e+00, current residual:3.055467e+00 +wall time: 0.000656843s + +time increment:40690 time: 40.69 +field ' c' [explicit solve]: current solution: 9.617280e+01, current residual:5.807085e+01 +field 'mu' [explicit solve]: current solution: 5.054453e+00, current residual:3.055400e+00 +wall time: 0.000895023s + +time increment:40700 time: 40.7 +field ' c' [explicit solve]: current solution: 9.617275e+01, current residual:5.807082e+01 +field 'mu' [explicit solve]: current solution: 5.054341e+00, current residual:3.055332e+00 +wall time: 0.000705957s + +time increment:40710 time: 40.71 +field ' c' [explicit solve]: current solution: 9.617271e+01, current residual:5.807079e+01 +field 'mu' [explicit solve]: current solution: 5.054229e+00, current residual:3.055264e+00 +wall time: 0.000724077s + +time increment:40720 time: 40.72 +field ' c' [explicit solve]: current solution: 9.617266e+01, current residual:5.807075e+01 +field 'mu' [explicit solve]: current solution: 5.054118e+00, current residual:3.055197e+00 +wall time: 0.000687122s + +time increment:40730 time: 40.73 +field ' c' [explicit solve]: current solution: 9.617261e+01, current residual:5.807072e+01 +field 'mu' [explicit solve]: current solution: 5.054006e+00, current residual:3.055129e+00 +wall time: 0.000682831s + +time increment:40740 time: 40.74 +field ' c' [explicit solve]: current solution: 9.617257e+01, current residual:5.807069e+01 +field 'mu' [explicit solve]: current solution: 5.053894e+00, current residual:3.055062e+00 +wall time: 0.000695944s + +time increment:40750 time: 40.75 +field ' c' [explicit solve]: current solution: 9.617252e+01, current residual:5.807066e+01 +field 'mu' [explicit solve]: current solution: 5.053782e+00, current residual:3.054995e+00 +wall time: 0.000670195s + +time increment:40760 time: 40.76 +field ' c' [explicit solve]: current solution: 9.617248e+01, current residual:5.807062e+01 +field 'mu' [explicit solve]: current solution: 5.053670e+00, current residual:3.054927e+00 +wall time: 0.000645161s + +time increment:40770 time: 40.77 +field ' c' [explicit solve]: current solution: 9.617243e+01, current residual:5.807059e+01 +field 'mu' [explicit solve]: current solution: 5.053558e+00, current residual:3.054860e+00 +wall time: 0.000659943s + +time increment:40780 time: 40.78 +field ' c' [explicit solve]: current solution: 9.617239e+01, current residual:5.807056e+01 +field 'mu' [explicit solve]: current solution: 5.053446e+00, current residual:3.054792e+00 +wall time: 0.000672102s + +time increment:40790 time: 40.79 +field ' c' [explicit solve]: current solution: 9.617234e+01, current residual:5.807053e+01 +field 'mu' [explicit solve]: current solution: 5.053335e+00, current residual:3.054725e+00 +wall time: 0.000632048s + +time increment:40800 time: 40.8 +field ' c' [explicit solve]: current solution: 9.617230e+01, current residual:5.807049e+01 +field 'mu' [explicit solve]: current solution: 5.053223e+00, current residual:3.054657e+00 +wall time: 0.00067997s + +time increment:40810 time: 40.81 +field ' c' [explicit solve]: current solution: 9.617225e+01, current residual:5.807046e+01 +field 'mu' [explicit solve]: current solution: 5.053111e+00, current residual:3.054590e+00 +wall time: 0.000660181s + +time increment:40820 time: 40.82 +field ' c' [explicit solve]: current solution: 9.617221e+01, current residual:5.807043e+01 +field 'mu' [explicit solve]: current solution: 5.053000e+00, current residual:3.054523e+00 +wall time: 0.000793934s + +time increment:40830 time: 40.83 +field ' c' [explicit solve]: current solution: 9.617216e+01, current residual:5.807040e+01 +field 'mu' [explicit solve]: current solution: 5.052888e+00, current residual:3.054455e+00 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0.000645161s + +time increment:40880 time: 40.88 +field ' c' [explicit solve]: current solution: 9.617194e+01, current residual:5.807024e+01 +field 'mu' [explicit solve]: current solution: 5.052330e+00, current residual:3.054119e+00 +wall time: 0.000742912s + +time increment:40890 time: 40.89 +field ' c' [explicit solve]: current solution: 9.617189e+01, current residual:5.807020e+01 +field 'mu' [explicit solve]: current solution: 5.052219e+00, current residual:3.054052e+00 +wall time: 0.000702143s + +time increment:40900 time: 40.9 +field ' c' [explicit solve]: current solution: 9.617185e+01, current residual:5.807017e+01 +field 'mu' [explicit solve]: current solution: 5.052107e+00, current residual:3.053984e+00 +wall time: 0.000682116s + +time increment:40910 time: 40.91 +field ' c' [explicit solve]: current solution: 9.617180e+01, current residual:5.807014e+01 +field 'mu' [explicit solve]: current solution: 5.051996e+00, current residual:3.053917e+00 +wall time: 0.000642061s + +time increment:40920 time: 40.92 +field ' c' [explicit solve]: current solution: 9.617176e+01, current residual:5.807011e+01 +field 'mu' [explicit solve]: current solution: 5.051885e+00, current residual:3.053850e+00 +wall time: 0.000550032s + +time increment:40930 time: 40.93 +field ' c' [explicit solve]: current solution: 9.617171e+01, current residual:5.807008e+01 +field 'mu' [explicit solve]: current solution: 5.051773e+00, current residual:3.053783e+00 +wall time: 0.000583172s + +time increment:40940 time: 40.94 +field ' c' [explicit solve]: current solution: 9.617167e+01, current residual:5.807004e+01 +field 'mu' [explicit solve]: current solution: 5.051662e+00, current residual:3.053716e+00 +wall time: 0.000636101s + +time increment:40950 time: 40.95 +field ' c' [explicit solve]: current solution: 9.617163e+01, current residual:5.807001e+01 +field 'mu' [explicit solve]: current solution: 5.051551e+00, current residual:3.053648e+00 +wall time: 0.00066781s + +time increment:40960 time: 40.96 +field ' c' [explicit solve]: current solution: 9.617158e+01, current residual:5.806998e+01 +field 'mu' [explicit solve]: current solution: 5.051439e+00, current residual:3.053581e+00 +wall time: 0.000761032s + +time increment:40970 time: 40.97 +field ' c' [explicit solve]: current solution: 9.617154e+01, current residual:5.806995e+01 +field 'mu' [explicit solve]: current solution: 5.051328e+00, current residual:3.053514e+00 +wall time: 0.00078702s + +time increment:40980 time: 40.98 +field ' c' [explicit solve]: current solution: 9.617149e+01, current residual:5.806992e+01 +field 'mu' [explicit solve]: current solution: 5.051217e+00, current residual:3.053447e+00 +wall time: 0.000729084s + +time increment:40990 time: 40.99 +field ' c' [explicit solve]: current solution: 9.617145e+01, current residual:5.806989e+01 +field 'mu' [explicit solve]: current solution: 5.051106e+00, current residual:3.053380e+00 +wall time: 0.000725031s + +time increment:41000 time: 41 +field ' c' [explicit solve]: current solution: 9.617140e+01, current residual:5.806985e+01 +field 'mu' [explicit solve]: current solution: 5.050995e+00, current residual:3.053313e+00 +wall time: 0.000573874s + +time increment:41010 time: 41.01 +field ' c' [explicit solve]: current solution: 9.617136e+01, current residual:5.806982e+01 +field 'mu' [explicit solve]: current solution: 5.050883e+00, current residual:3.053246e+00 +wall time: 0.000576019s + +time increment:41020 time: 41.02 +field ' c' [explicit solve]: current solution: 9.617132e+01, current residual:5.806979e+01 +field 'mu' [explicit solve]: current solution: 5.050772e+00, current residual:3.053179e+00 +wall time: 0.00060606s + +time increment:41030 time: 41.03 +field ' c' [explicit solve]: current solution: 9.617127e+01, current residual:5.806976e+01 +field 'mu' [explicit solve]: current solution: 5.050661e+00, current residual:3.053112e+00 +wall time: 0.000650883s + +time increment:41040 time: 41.04 +field ' c' [explicit solve]: current solution: 9.617123e+01, current residual:5.806973e+01 +field 'mu' [explicit solve]: current solution: 5.050550e+00, current residual:3.053045e+00 +wall time: 0.000660896s + +time increment:41050 time: 41.05 +field ' c' [explicit solve]: current solution: 9.617119e+01, current residual:5.806970e+01 +field 'mu' [explicit solve]: current solution: 5.050439e+00, current residual:3.052978e+00 +wall time: 0.000603914s + +time increment:41060 time: 41.06 +field ' c' [explicit solve]: current solution: 9.617114e+01, current residual:5.806967e+01 +field 'mu' [explicit solve]: current solution: 5.050328e+00, current residual:3.052911e+00 +wall time: 0.000615835s + +time increment:41070 time: 41.07 +field ' c' [explicit solve]: current solution: 9.617110e+01, current residual:5.806963e+01 +field 'mu' [explicit solve]: current solution: 5.050217e+00, current residual:3.052844e+00 +wall time: 0.000669956s + +time increment:41080 time: 41.08 +field ' c' [explicit solve]: current solution: 9.617105e+01, current residual:5.806960e+01 +field 'mu' [explicit solve]: current solution: 5.050106e+00, current residual:3.052777e+00 +wall time: 0.000622034s + +time increment:41090 time: 41.09 +field ' c' [explicit solve]: current solution: 9.617101e+01, current residual:5.806957e+01 +field 'mu' [explicit solve]: current solution: 5.049996e+00, current residual:3.052710e+00 +wall time: 0.000573158s + +time increment:41100 time: 41.1 +field ' c' [explicit solve]: current solution: 9.617097e+01, current residual:5.806954e+01 +field 'mu' [explicit solve]: current solution: 5.049885e+00, current residual:3.052643e+00 +wall time: 0.00056386s + +time increment:41110 time: 41.11 +field ' c' [explicit solve]: current solution: 9.617092e+01, current residual:5.806951e+01 +field 'mu' [explicit solve]: current solution: 5.049774e+00, current residual:3.052576e+00 +wall time: 0.000561953s + +time increment:41120 time: 41.12 +field ' c' [explicit solve]: current solution: 9.617088e+01, current residual:5.806948e+01 +field 'mu' [explicit solve]: current solution: 5.049663e+00, current residual:3.052509e+00 +wall time: 0.000568867s + +time increment:41130 time: 41.13 +field ' c' [explicit solve]: current solution: 9.617084e+01, current residual:5.806945e+01 +field 'mu' [explicit solve]: current solution: 5.049552e+00, current residual:3.052442e+00 +wall time: 0.000605822s + +time increment:41140 time: 41.14 +field ' c' [explicit solve]: current solution: 9.617079e+01, current residual:5.806942e+01 +field 'mu' [explicit solve]: current solution: 5.049442e+00, current residual:3.052375e+00 +wall time: 0.000622034s + +time increment:41150 time: 41.15 +field ' c' [explicit solve]: current solution: 9.617075e+01, current residual:5.806938e+01 +field 'mu' [explicit solve]: current solution: 5.049331e+00, current residual:3.052308e+00 +wall time: 0.000675917s + +time increment:41160 time: 41.16 +field ' c' [explicit solve]: current solution: 9.617071e+01, current residual:5.806935e+01 +field 'mu' [explicit solve]: current solution: 5.049220e+00, current residual:3.052242e+00 +wall time: 0.000577927s + +time increment:41170 time: 41.17 +field ' c' [explicit solve]: current solution: 9.617066e+01, current residual:5.806932e+01 +field 'mu' [explicit solve]: current solution: 5.049110e+00, current residual:3.052175e+00 +wall time: 0.000762939s + +time increment:41180 time: 41.18 +field ' c' [explicit solve]: current solution: 9.617062e+01, current residual:5.806929e+01 +field 'mu' [explicit solve]: current solution: 5.048999e+00, current residual:3.052108e+00 +wall time: 0.000699997s + +time increment:41190 time: 41.19 +field ' c' [explicit solve]: current solution: 9.617058e+01, current residual:5.806926e+01 +field 'mu' [explicit solve]: current solution: 5.048888e+00, current residual:3.052041e+00 +wall time: 0.000772953s + +time increment:41200 time: 41.2 +field ' c' [explicit solve]: current solution: 9.617054e+01, current residual:5.806923e+01 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[explicit solve]: current solution: 5.048336e+00, current residual:3.051708e+00 +wall time: 0.000693083s + +time increment:41250 time: 41.25 +field ' c' [explicit solve]: current solution: 9.617032e+01, current residual:5.806908e+01 +field 'mu' [explicit solve]: current solution: 5.048225e+00, current residual:3.051641e+00 +wall time: 0.000672102s + +time increment:41260 time: 41.26 +field ' c' [explicit solve]: current solution: 9.617028e+01, current residual:5.806905e+01 +field 'mu' [explicit solve]: current solution: 5.048115e+00, current residual:3.051574e+00 +wall time: 0.000757933s + +time increment:41270 time: 41.27 +field ' c' [explicit solve]: current solution: 9.617024e+01, current residual:5.806901e+01 +field 'mu' [explicit solve]: current solution: 5.048005e+00, current residual:3.051507e+00 +wall time: 0.000791073s + +time increment:41280 time: 41.28 +field ' c' [explicit solve]: current solution: 9.617020e+01, current residual:5.806898e+01 +field 'mu' [explicit solve]: current solution: 5.047894e+00, current residual:3.051441e+00 +wall time: 0.000717878s + +time increment:41290 time: 41.29 +field ' c' [explicit solve]: current solution: 9.617015e+01, current residual:5.806895e+01 +field 'mu' [explicit solve]: current solution: 5.047784e+00, current residual:3.051374e+00 +wall time: 0.000789165s + +time increment:41300 time: 41.3 +field ' c' [explicit solve]: current solution: 9.617011e+01, current residual:5.806892e+01 +field 'mu' [explicit solve]: current solution: 5.047674e+00, current residual:3.051308e+00 +wall time: 0.000767946s + +time increment:41310 time: 41.31 +field ' c' [explicit solve]: current solution: 9.617007e+01, current residual:5.806889e+01 +field 'mu' [explicit solve]: current solution: 5.047564e+00, current residual:3.051241e+00 +wall time: 0.000813007s + +time increment:41320 time: 41.32 +field ' c' [explicit solve]: current solution: 9.617003e+01, current residual:5.806886e+01 +field 'mu' [explicit solve]: current solution: 5.047453e+00, current residual:3.051174e+00 +wall time: 0.000645876s + +time increment:41330 time: 41.33 +field ' c' [explicit solve]: current solution: 9.616998e+01, current residual:5.806883e+01 +field 'mu' [explicit solve]: current solution: 5.047343e+00, current residual:3.051108e+00 +wall time: 0.000674963s + +time increment:41340 time: 41.34 +field ' c' [explicit solve]: current solution: 9.616994e+01, current residual:5.806880e+01 +field 'mu' [explicit solve]: current solution: 5.047233e+00, current residual:3.051041e+00 +wall time: 0.000695944s + +time increment:41350 time: 41.35 +field ' c' [explicit solve]: current solution: 9.616990e+01, current residual:5.806877e+01 +field 'mu' [explicit solve]: current solution: 5.047123e+00, current residual:3.050975e+00 +wall time: 0.000724077s + +time increment:41360 time: 41.36 +field ' c' [explicit solve]: current solution: 9.616986e+01, current residual:5.806874e+01 +field 'mu' [explicit solve]: current solution: 5.047013e+00, current residual:3.050908e+00 +wall time: 0.000799894s + +time increment:41370 time: 41.37 +field ' c' [explicit solve]: current solution: 9.616982e+01, current residual:5.806871e+01 +field 'mu' [explicit solve]: current solution: 5.046903e+00, current residual:3.050842e+00 +wall time: 0.00075078s + +time increment:41380 time: 41.38 +field ' c' [explicit solve]: current solution: 9.616977e+01, current residual:5.806868e+01 +field 'mu' [explicit solve]: current solution: 5.046793e+00, current residual:3.050775e+00 +wall time: 0.000687838s + +time increment:41390 time: 41.39 +field ' c' [explicit solve]: current solution: 9.616973e+01, current residual:5.806865e+01 +field 'mu' [explicit solve]: current solution: 5.046683e+00, current residual:3.050709e+00 +wall time: 0.000664949s + +time increment:41400 time: 41.4 +field ' c' [explicit solve]: current solution: 9.616969e+01, current residual:5.806862e+01 +field 'mu' [explicit solve]: current solution: 5.046573e+00, current residual:3.050642e+00 +wall time: 0.000795126s + +time increment:41410 time: 41.41 +field ' c' [explicit solve]: current solution: 9.616965e+01, current residual:5.806859e+01 +field 'mu' [explicit solve]: current solution: 5.046463e+00, current residual:3.050576e+00 +wall time: 0.000679016s + +time increment:41420 time: 41.42 +field ' c' [explicit solve]: current solution: 9.616961e+01, current residual:5.806856e+01 +field 'mu' [explicit solve]: current solution: 5.046353e+00, current residual:3.050509e+00 +wall time: 0.000755072s + +time increment:41430 time: 41.43 +field ' c' [explicit solve]: current solution: 9.616957e+01, current residual:5.806853e+01 +field 'mu' [explicit solve]: current solution: 5.046243e+00, current residual:3.050443e+00 +wall time: 0.000633955s + +time increment:41440 time: 41.44 +field ' c' [explicit solve]: current solution: 9.616952e+01, current residual:5.806850e+01 +field 'mu' [explicit solve]: current solution: 5.046133e+00, current residual:3.050377e+00 +wall time: 0.000731945s + +time increment:41450 time: 41.45 +field ' c' [explicit solve]: current solution: 9.616948e+01, current residual:5.806847e+01 +field 'mu' [explicit solve]: current solution: 5.046023e+00, current residual:3.050310e+00 +wall time: 0.000813007s + +time increment:41460 time: 41.46 +field ' c' [explicit solve]: current solution: 9.616944e+01, current residual:5.806844e+01 +field 'mu' [explicit solve]: current solution: 5.045914e+00, current residual:3.050244e+00 +wall time: 0.000702143s + +time increment:41470 time: 41.47 +field ' c' [explicit solve]: current solution: 9.616940e+01, current residual:5.806841e+01 +field 'mu' [explicit solve]: current solution: 5.045804e+00, current residual:3.050178e+00 +wall time: 0.000680208s + +time increment:41480 time: 41.48 +field ' c' [explicit solve]: current solution: 9.616936e+01, current residual:5.806838e+01 +field 'mu' [explicit solve]: current solution: 5.045694e+00, current residual:3.050111e+00 +wall time: 0.000687122s + +time increment:41490 time: 41.49 +field ' c' [explicit solve]: current solution: 9.616932e+01, current residual:5.806835e+01 +field 'mu' [explicit solve]: current solution: 5.045585e+00, current residual:3.050045e+00 +wall time: 0.000763893s + +time increment:41500 time: 41.5 +field ' c' [explicit solve]: current solution: 9.616928e+01, current residual:5.806832e+01 +field 'mu' [explicit solve]: current solution: 5.045475e+00, current residual:3.049979e+00 +wall time: 0.000697136s + +time increment:41510 time: 41.51 +field ' c' [explicit solve]: current solution: 9.616924e+01, current residual:5.806829e+01 +field 'mu' [explicit solve]: current solution: 5.045365e+00, current residual:3.049912e+00 +wall time: 0.000744104s + +time increment:41520 time: 41.52 +field ' c' [explicit solve]: current solution: 9.616919e+01, current residual:5.806826e+01 +field 'mu' [explicit solve]: current solution: 5.045256e+00, current residual:3.049846e+00 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0.000634909s + +time increment:41570 time: 41.57 +field ' c' [explicit solve]: current solution: 9.616899e+01, current residual:5.806811e+01 +field 'mu' [explicit solve]: current solution: 5.044708e+00, current residual:3.049515e+00 +wall time: 0.000620127s + +time increment:41580 time: 41.58 +field ' c' [explicit solve]: current solution: 9.616895e+01, current residual:5.806808e+01 +field 'mu' [explicit solve]: current solution: 5.044599e+00, current residual:3.049449e+00 +wall time: 0.000693083s + +time increment:41590 time: 41.59 +field ' c' [explicit solve]: current solution: 9.616891e+01, current residual:5.806805e+01 +field 'mu' [explicit solve]: current solution: 5.044489e+00, current residual:3.049383e+00 +wall time: 0.000564098s + +time increment:41600 time: 41.6 +field ' c' [explicit solve]: current solution: 9.616887e+01, current residual:5.806802e+01 +field 'mu' [explicit solve]: current solution: 5.044380e+00, current residual:3.049317e+00 +wall time: 0.000645161s + +time increment:41610 time: 41.61 +field ' c' [explicit solve]: current solution: 9.616883e+01, current residual:5.806799e+01 +field 'mu' [explicit solve]: current solution: 5.044271e+00, current residual:3.049250e+00 +wall time: 0.000564098s + +time increment:41620 time: 41.62 +field ' c' [explicit solve]: current solution: 9.616879e+01, current residual:5.806796e+01 +field 'mu' [explicit solve]: current solution: 5.044161e+00, current residual:3.049184e+00 +wall time: 0.000641823s + +time increment:41630 time: 41.63 +field ' c' [explicit solve]: current solution: 9.616875e+01, current residual:5.806793e+01 +field 'mu' [explicit solve]: current solution: 5.044052e+00, current residual:3.049118e+00 +wall time: 0.000658035s + +time increment:41640 time: 41.64 +field ' c' [explicit solve]: current solution: 9.616871e+01, current residual:5.806791e+01 +field 'mu' [explicit solve]: current solution: 5.043943e+00, current residual:3.049052e+00 +wall time: 0.000589848s + +time increment:41650 time: 41.65 +field ' c' [explicit solve]: current solution: 9.616867e+01, current residual:5.806788e+01 +field 'mu' [explicit solve]: current solution: 5.043834e+00, current residual:3.048986e+00 +wall time: 0.000694036s + +time increment:41660 time: 41.66 +field ' c' [explicit solve]: current solution: 9.616863e+01, current residual:5.806785e+01 +field 'mu' [explicit solve]: current solution: 5.043724e+00, current residual:3.048920e+00 +wall time: 0.000629187s + +time increment:41670 time: 41.67 +field ' c' [explicit solve]: current solution: 9.616858e+01, current residual:5.806782e+01 +field 'mu' [explicit solve]: current solution: 5.043615e+00, current residual:3.048854e+00 +wall time: 0.000589132s + +time increment:41680 time: 41.68 +field ' c' [explicit solve]: current solution: 9.616854e+01, current residual:5.806779e+01 +field 'mu' [explicit solve]: current solution: 5.043506e+00, current residual:3.048788e+00 +wall time: 0.000616074s + +time increment:41690 time: 41.69 +field ' c' [explicit solve]: current solution: 9.616850e+01, current residual:5.806776e+01 +field 'mu' [explicit solve]: current solution: 5.043397e+00, current residual:3.048722e+00 +wall time: 0.000567913s + +time increment:41700 time: 41.7 +field ' c' [explicit solve]: current solution: 9.616846e+01, current residual:5.806773e+01 +field 'mu' [explicit solve]: current solution: 5.043288e+00, current residual:3.048656e+00 +wall time: 0.000607967s + +time increment:41710 time: 41.71 +field ' c' [explicit solve]: current solution: 9.616842e+01, current residual:5.806770e+01 +field 'mu' [explicit solve]: current solution: 5.043179e+00, current residual:3.048590e+00 +wall time: 0.000561953s + +time increment:41720 time: 41.72 +field ' c' [explicit solve]: current solution: 9.616838e+01, current residual:5.806767e+01 +field 'mu' [explicit solve]: current solution: 5.043070e+00, current residual:3.048524e+00 +wall time: 0.000553131s + +time increment:41730 time: 41.73 +field ' c' [explicit solve]: current solution: 9.616834e+01, current residual:5.806764e+01 +field 'mu' [explicit solve]: current solution: 5.042961e+00, current residual:3.048458e+00 +wall time: 0.000717878s + +time increment:41740 time: 41.74 +field ' c' [explicit solve]: current solution: 9.616830e+01, current residual:5.806761e+01 +field 'mu' [explicit solve]: current solution: 5.042852e+00, current residual:3.048392e+00 +wall time: 0.000594854s + +time increment:41750 time: 41.75 +field ' c' [explicit solve]: current solution: 9.616827e+01, current residual:5.806759e+01 +field 'mu' [explicit solve]: current solution: 5.042743e+00, current residual:3.048327e+00 +wall time: 0.000567913s + +time increment:41760 time: 41.76 +field ' c' [explicit solve]: current solution: 9.616823e+01, current residual:5.806756e+01 +field 'mu' [explicit solve]: current solution: 5.042634e+00, current residual:3.048261e+00 +wall time: 0.000575066s + +time increment:41770 time: 41.77 +field ' c' [explicit solve]: current solution: 9.616819e+01, current residual:5.806753e+01 +field 'mu' [explicit solve]: current solution: 5.042526e+00, current residual:3.048195e+00 +wall time: 0.000624895s + +time increment:41780 time: 41.78 +field ' c' [explicit solve]: current solution: 9.616815e+01, current residual:5.806750e+01 +field 'mu' [explicit solve]: current solution: 5.042417e+00, current residual:3.048129e+00 +wall time: 0.000666142s + +time increment:41790 time: 41.79 +field ' c' [explicit solve]: current solution: 9.616811e+01, current residual:5.806747e+01 +field 'mu' [explicit solve]: current solution: 5.042308e+00, current residual:3.048063e+00 +wall time: 0.000638962s + +time increment:41800 time: 41.8 +field ' c' [explicit solve]: current solution: 9.616807e+01, current residual:5.806744e+01 +field 'mu' [explicit solve]: current solution: 5.042199e+00, current residual:3.047997e+00 +wall time: 0.000562906s + +time increment:41810 time: 41.81 +field ' c' [explicit solve]: current solution: 9.616803e+01, current residual:5.806741e+01 +field 'mu' [explicit solve]: current solution: 5.042091e+00, current residual:3.047932e+00 +wall time: 0.000591993s + +time increment:41820 time: 41.82 +field ' c' [explicit solve]: current solution: 9.616799e+01, current residual:5.806738e+01 +field 'mu' [explicit solve]: current solution: 5.041982e+00, current residual:3.047866e+00 +wall time: 0.000773907s + +time increment:41830 time: 41.83 +field ' c' [explicit solve]: current solution: 9.616795e+01, current residual:5.806735e+01 +field 'mu' [explicit solve]: current solution: 5.041873e+00, current residual:3.047800e+00 +wall time: 0.00069809s + +time increment:41840 time: 41.84 +field ' c' [explicit solve]: current solution: 9.616791e+01, current residual:5.806733e+01 +field 'mu' [explicit solve]: current solution: 5.041765e+00, current residual:3.047734e+00 +wall time: 0.000721931s + +time increment:41850 time: 41.85 +field ' c' [explicit solve]: current solution: 9.616787e+01, current residual:5.806730e+01 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[explicit solve]: current solution: 5.041222e+00, current residual:3.047406e+00 +wall time: 0.000648975s + +time increment:41900 time: 41.9 +field ' c' [explicit solve]: current solution: 9.616767e+01, current residual:5.806716e+01 +field 'mu' [explicit solve]: current solution: 5.041114e+00, current residual:3.047340e+00 +wall time: 0.000571012s + +time increment:41910 time: 41.91 +field ' c' [explicit solve]: current solution: 9.616764e+01, current residual:5.806713e+01 +field 'mu' [explicit solve]: current solution: 5.041005e+00, current residual:3.047275e+00 +wall time: 0.000701189s + +time increment:41920 time: 41.92 +field ' c' [explicit solve]: current solution: 9.616760e+01, current residual:5.806710e+01 +field 'mu' [explicit solve]: current solution: 5.040897e+00, current residual:3.047209e+00 +wall time: 0.000634909s + +time increment:41930 time: 41.93 +field ' c' [explicit solve]: current solution: 9.616756e+01, current residual:5.806707e+01 +field 'mu' [explicit solve]: current solution: 5.040789e+00, current residual:3.047143e+00 +wall time: 0.000662088s + +time increment:41940 time: 41.94 +field ' c' [explicit solve]: current solution: 9.616752e+01, current residual:5.806704e+01 +field 'mu' [explicit solve]: current solution: 5.040680e+00, current residual:3.047078e+00 +wall time: 0.000658035s + +time increment:41950 time: 41.95 +field ' c' [explicit solve]: current solution: 9.616748e+01, current residual:5.806701e+01 +field 'mu' [explicit solve]: current solution: 5.040572e+00, current residual:3.047012e+00 +wall time: 0.000557899s + +time increment:41960 time: 41.96 +field ' c' [explicit solve]: current solution: 9.616744e+01, current residual:5.806699e+01 +field 'mu' [explicit solve]: current solution: 5.040464e+00, current residual:3.046947e+00 +wall time: 0.000715017s + +time increment:41970 time: 41.97 +field ' c' [explicit solve]: current solution: 9.616740e+01, current residual:5.806696e+01 +field 'mu' [explicit solve]: current solution: 5.040355e+00, current residual:3.046881e+00 +wall time: 0.000574112s + +time increment:41980 time: 41.98 +field ' c' [explicit solve]: current solution: 9.616737e+01, current residual:5.806693e+01 +field 'mu' [explicit solve]: current solution: 5.040247e+00, current residual:3.046816e+00 +wall time: 0.000599861s + +time increment:41990 time: 41.99 +field ' c' [explicit solve]: current solution: 9.616733e+01, current residual:5.806690e+01 +field 'mu' [explicit solve]: current solution: 5.040139e+00, current residual:3.046750e+00 +wall time: 0.000560045s + +time increment:42000 time: 42 +field ' c' [explicit solve]: current solution: 9.616729e+01, current residual:5.806687e+01 +field 'mu' [explicit solve]: current solution: 5.040031e+00, current residual:3.046685e+00 +wall time: 0.000731945s + +time increment:42010 time: 42.01 +field ' c' [explicit solve]: current solution: 9.616725e+01, current residual:5.806684e+01 +field 'mu' [explicit solve]: current solution: 5.039923e+00, current 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0.000605106s + +time increment:42100 time: 42.1 +field ' c' [explicit solve]: current solution: 9.616691e+01, current residual:5.806659e+01 +field 'mu' [explicit solve]: current solution: 5.038952e+00, current residual:3.046031e+00 +wall time: 0.000561953s + +time increment:42110 time: 42.11 +field ' c' [explicit solve]: current solution: 9.616687e+01, current residual:5.806657e+01 +field 'mu' [explicit solve]: current solution: 5.038844e+00, current residual:3.045966e+00 +wall time: 0.000669003s + +time increment:42120 time: 42.12 +field ' c' [explicit solve]: current solution: 9.616683e+01, current residual:5.806654e+01 +field 'mu' [explicit solve]: current solution: 5.038736e+00, current residual:3.045901e+00 +wall time: 0.000725985s + +time increment:42130 time: 42.13 +field ' c' [explicit solve]: current solution: 9.616679e+01, current residual:5.806651e+01 +field 'mu' [explicit solve]: current solution: 5.038628e+00, current residual:3.045835e+00 +wall time: 0.000562906s + +time 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time: 42.18 +field ' c' [explicit solve]: current solution: 9.616661e+01, current residual:5.806637e+01 +field 'mu' [explicit solve]: current solution: 5.038090e+00, current residual:3.045509e+00 +wall time: 0.000746965s + +time increment:42190 time: 42.19 +field ' c' [explicit solve]: current solution: 9.616657e+01, current residual:5.806635e+01 +field 'mu' [explicit solve]: current solution: 5.037983e+00, current residual:3.045444e+00 +wall time: 0.000688791s + +time increment:42200 time: 42.2 +field ' c' [explicit solve]: current solution: 9.616653e+01, current residual:5.806632e+01 +field 'mu' [explicit solve]: current solution: 5.037875e+00, current residual:3.045379e+00 +wall time: 0.000688076s + +time increment:42210 time: 42.21 +field ' c' [explicit solve]: current solution: 9.616649e+01, current residual:5.806629e+01 +field 'mu' [explicit solve]: current solution: 5.037768e+00, current residual:3.045314e+00 +wall time: 0.000571012s + +time increment:42220 time: 42.22 +field ' c' [explicit solve]: current solution: 9.616646e+01, current residual:5.806626e+01 +field 'mu' [explicit solve]: current solution: 5.037660e+00, current residual:3.045249e+00 +wall time: 0.000700951s + +time increment:42230 time: 42.23 +field ' c' [explicit solve]: current solution: 9.616642e+01, current residual:5.806624e+01 +field 'mu' [explicit solve]: current solution: 5.037553e+00, current residual:3.045184e+00 +wall time: 0.000682831s + +time increment:42240 time: 42.24 +field ' c' [explicit solve]: current solution: 9.616638e+01, current residual:5.806621e+01 +field 'mu' [explicit solve]: current solution: 5.037446e+00, current residual:3.045119e+00 +wall time: 0.000684023s + +time increment:42250 time: 42.25 +field ' c' [explicit solve]: current solution: 9.616635e+01, current residual:5.806618e+01 +field 'mu' [explicit solve]: current solution: 5.037338e+00, current residual:3.045054e+00 +wall time: 0.000783205s + +time increment:42260 time: 42.26 +field ' c' [explicit solve]: current solution: 9.616631e+01, current residual:5.806615e+01 +field 'mu' [explicit solve]: current solution: 5.037231e+00, current residual:3.044989e+00 +wall time: 0.000701904s + +time increment:42270 time: 42.27 +field ' c' [explicit solve]: current solution: 9.616627e+01, current residual:5.806613e+01 +field 'mu' [explicit solve]: current solution: 5.037124e+00, current residual:3.044924e+00 +wall time: 0.000706911s + +time increment:42280 time: 42.28 +field ' c' [explicit solve]: current solution: 9.616623e+01, current residual:5.806610e+01 +field 'mu' [explicit solve]: current solution: 5.037017e+00, current residual:3.044859e+00 +wall time: 0.000667095s + +time increment:42290 time: 42.29 +field ' c' [explicit solve]: current solution: 9.616620e+01, current residual:5.806607e+01 +field 'mu' [explicit solve]: current solution: 5.036909e+00, current residual:3.044794e+00 +wall time: 0.000706196s + +time increment:42300 time: 42.3 +field ' c' [explicit solve]: current solution: 9.616616e+01, current residual:5.806605e+01 +field 'mu' [explicit solve]: current solution: 5.036802e+00, current residual:3.044729e+00 +wall time: 0.000755072s + +time increment:42310 time: 42.31 +field ' c' [explicit solve]: current solution: 9.616612e+01, current residual:5.806602e+01 +field 'mu' [explicit solve]: current solution: 5.036695e+00, current residual:3.044664e+00 +wall time: 0.00070405s + +time increment:42320 time: 42.32 +field ' c' [explicit solve]: current solution: 9.616609e+01, current residual:5.806599e+01 +field 'mu' [explicit solve]: current solution: 5.036588e+00, current residual:3.044599e+00 +wall time: 0.00071907s + +time increment:42330 time: 42.33 +field ' c' [explicit solve]: current solution: 9.616605e+01, current residual:5.806596e+01 +field 'mu' [explicit solve]: current solution: 5.036481e+00, current residual:3.044534e+00 +wall time: 0.000746012s + +time increment:42340 time: 42.34 +field ' c' [explicit solve]: current solution: 9.616601e+01, current residual:5.806594e+01 +field 'mu' [explicit solve]: current solution: 5.036374e+00, current residual:3.044469e+00 +wall time: 0.000696182s + +time increment:42350 time: 42.35 +field ' c' [explicit solve]: current solution: 9.616598e+01, current residual:5.806591e+01 +field 'mu' [explicit solve]: current solution: 5.036267e+00, current residual:3.044404e+00 +wall time: 0.000694036s + +time increment:42360 time: 42.36 +field ' c' [explicit solve]: current solution: 9.616594e+01, current residual:5.806588e+01 +field 'mu' [explicit solve]: current solution: 5.036160e+00, current residual:3.044339e+00 +wall time: 0.000715017s + +time increment:42370 time: 42.37 +field ' c' [explicit solve]: current solution: 9.616591e+01, current residual:5.806586e+01 +field 'mu' [explicit solve]: current solution: 5.036053e+00, current residual:3.044275e+00 +wall time: 0.00067997s + +time increment:42380 time: 42.38 +field ' c' [explicit solve]: current solution: 9.616587e+01, current residual:5.806583e+01 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solution: 5.035092e+00, current residual:3.043692e+00 +wall time: 0.000668049s + +time increment:42470 time: 42.47 +field ' c' [explicit solve]: current solution: 9.616554e+01, current residual:5.806559e+01 +field 'mu' [explicit solve]: current solution: 5.034985e+00, current residual:3.043627e+00 +wall time: 0.000715017s + +time increment:42480 time: 42.48 +field ' c' [explicit solve]: current solution: 9.616551e+01, current residual:5.806556e+01 +field 'mu' [explicit solve]: current solution: 5.034879e+00, current residual:3.043562e+00 +wall time: 0.000677824s + +time increment:42490 time: 42.49 +field ' c' [explicit solve]: current solution: 9.616547e+01, current residual:5.806554e+01 +field 'mu' [explicit solve]: current solution: 5.034772e+00, current residual:3.043498e+00 +wall time: 0.000664949s + +time increment:42500 time: 42.5 +field ' c' [explicit solve]: current solution: 9.616544e+01, current residual:5.806551e+01 +field 'mu' [explicit solve]: current solution: 5.034665e+00, current residual:3.043433e+00 +wall time: 0.000712156s + +time increment:42510 time: 42.51 +field ' c' [explicit solve]: current solution: 9.616540e+01, current residual:5.806548e+01 +field 'mu' [explicit solve]: current solution: 5.034559e+00, current residual:3.043369e+00 +wall time: 0.000760078s + +time increment:42520 time: 42.52 +field ' c' [explicit solve]: current solution: 9.616537e+01, current residual:5.806546e+01 +field 'mu' [explicit solve]: current solution: 5.034453e+00, current residual:3.043304e+00 +wall time: 0.00067997s + +time increment:42530 time: 42.53 +field ' c' [explicit solve]: current solution: 9.616533e+01, current residual:5.806543e+01 +field 'mu' [explicit solve]: current solution: 5.034346e+00, current residual:3.043240e+00 +wall time: 0.000686169s + +time increment:42540 time: 42.54 +field ' c' [explicit solve]: current solution: 9.616529e+01, current residual:5.806541e+01 +field 'mu' [explicit solve]: current solution: 5.034240e+00, current residual:3.043175e+00 +wall time: 0.000748873s + +time increment:42550 time: 42.55 +field ' c' [explicit solve]: current solution: 9.616526e+01, current residual:5.806538e+01 +field 'mu' [explicit solve]: current solution: 5.034133e+00, current residual:3.043110e+00 +wall time: 0.000651121s + +time increment:42560 time: 42.56 +field ' c' [explicit solve]: current solution: 9.616522e+01, current residual:5.806535e+01 +field 'mu' [explicit solve]: current solution: 5.034027e+00, current residual:3.043046e+00 +wall time: 0.000645876s + +time increment:42570 time: 42.57 +field ' c' [explicit solve]: current solution: 9.616519e+01, current residual:5.806533e+01 +field 'mu' [explicit solve]: current solution: 5.033921e+00, current residual:3.042982e+00 +wall time: 0.000638008s + +time increment:42580 time: 42.58 +field ' c' [explicit solve]: current solution: 9.616515e+01, current residual:5.806530e+01 +field 'mu' [explicit solve]: current solution: 5.033814e+00, current residual:3.042917e+00 +wall time: 0.000669003s + +time increment:42590 time: 42.59 +field ' c' [explicit solve]: current solution: 9.616512e+01, current residual:5.806528e+01 +field 'mu' [explicit solve]: current solution: 5.033708e+00, current residual:3.042853e+00 +wall time: 0.000673056s + +time increment:42600 time: 42.6 +field ' c' [explicit solve]: current solution: 9.616508e+01, current residual:5.806525e+01 +field 'mu' [explicit solve]: current solution: 5.033602e+00, current residual:3.042788e+00 +wall time: 0.00068593s + +time increment:42610 time: 42.61 +field ' c' [explicit solve]: current solution: 9.616505e+01, current residual:5.806522e+01 +field 'mu' [explicit solve]: current solution: 5.033496e+00, current residual:3.042724e+00 +wall time: 0.000761986s + +time increment:42620 time: 42.62 +field ' c' [explicit solve]: current solution: 9.616501e+01, current residual:5.806520e+01 +field 'mu' [explicit solve]: current solution: 5.033390e+00, current residual:3.042659e+00 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0.000675917s + +time increment:42670 time: 42.67 +field ' c' [explicit solve]: current solution: 9.616484e+01, current residual:5.806507e+01 +field 'mu' [explicit solve]: current solution: 5.032859e+00, current residual:3.042338e+00 +wall time: 0.000660896s + +time increment:42680 time: 42.68 +field ' c' [explicit solve]: current solution: 9.616480e+01, current residual:5.806504e+01 +field 'mu' [explicit solve]: current solution: 5.032753e+00, current residual:3.042273e+00 +wall time: 0.000677109s + +time increment:42690 time: 42.69 +field ' c' [explicit solve]: current solution: 9.616477e+01, current residual:5.806502e+01 +field 'mu' [explicit solve]: current solution: 5.032648e+00, current residual:3.042209e+00 +wall time: 0.000643969s + +time increment:42700 time: 42.7 +field ' c' [explicit solve]: current solution: 9.616473e+01, current residual:5.806499e+01 +field 'mu' [explicit solve]: current solution: 5.032542e+00, current residual:3.042145e+00 +wall time: 0.000808954s + +time 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time: 42.75 +field ' c' [explicit solve]: current solution: 9.616456e+01, current residual:5.806486e+01 +field 'mu' [explicit solve]: current solution: 5.032013e+00, current residual:3.041824e+00 +wall time: 0.00060606s + +time increment:42760 time: 42.76 +field ' c' [explicit solve]: current solution: 9.616453e+01, current residual:5.806484e+01 +field 'mu' [explicit solve]: current solution: 5.031907e+00, current residual:3.041760e+00 +wall time: 0.000609875s + +time increment:42770 time: 42.77 +field ' c' [explicit solve]: current solution: 9.616449e+01, current residual:5.806481e+01 +field 'mu' [explicit solve]: current solution: 5.031801e+00, current residual:3.041696e+00 +wall time: 0.000567913s + +time increment:42780 time: 42.78 +field ' c' [explicit solve]: current solution: 9.616446e+01, current residual:5.806479e+01 +field 'mu' [explicit solve]: current solution: 5.031696e+00, current residual:3.041631e+00 +wall time: 0.000668049s + +time increment:42790 time: 42.79 +field ' 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current solution: 9.616429e+01, current residual:5.806466e+01 +field 'mu' [explicit solve]: current solution: 5.031168e+00, current residual:3.041311e+00 +wall time: 0.000673056s + +time increment:42840 time: 42.84 +field ' c' [explicit solve]: current solution: 9.616425e+01, current residual:5.806463e+01 +field 'mu' [explicit solve]: current solution: 5.031063e+00, current residual:3.041247e+00 +wall time: 0.000662804s + +time increment:42850 time: 42.85 +field ' c' [explicit solve]: current solution: 9.616422e+01, current residual:5.806461e+01 +field 'mu' [explicit solve]: current solution: 5.030957e+00, current residual:3.041183e+00 +wall time: 0.000678062s + +time increment:42860 time: 42.86 +field ' c' [explicit solve]: current solution: 9.616418e+01, current residual:5.806458e+01 +field 'mu' [explicit solve]: current solution: 5.030852e+00, current residual:3.041119e+00 +wall time: 0.000581026s + +time increment:42870 time: 42.87 +field ' c' [explicit solve]: current solution: 9.616415e+01, current residual:5.806456e+01 +field 'mu' [explicit solve]: current solution: 5.030747e+00, current residual:3.041055e+00 +wall time: 0.000560045s + +time increment:42880 time: 42.88 +field ' c' [explicit solve]: current solution: 9.616412e+01, current residual:5.806453e+01 +field 'mu' [explicit solve]: current solution: 5.030641e+00, current residual:3.040991e+00 +wall time: 0.00072217s + +time increment:42890 time: 42.89 +field ' c' [explicit solve]: current solution: 9.616408e+01, current residual:5.806451e+01 +field 'mu' [explicit solve]: current solution: 5.030536e+00, current residual:3.040927e+00 +wall time: 0.00058198s + +time increment:42900 time: 42.9 +field ' c' [explicit solve]: current solution: 9.616405e+01, current residual:5.806448e+01 +field 'mu' [explicit solve]: current solution: 5.030431e+00, current residual:3.040863e+00 +wall time: 0.000626087s + +time increment:42910 time: 42.91 +field ' c' [explicit solve]: current solution: 9.616402e+01, current residual:5.806446e+01 +field 'mu' [explicit solve]: current solution: 5.030326e+00, current residual:3.040800e+00 +wall time: 0.000579119s + +time increment:42920 time: 42.92 +field ' c' [explicit solve]: current solution: 9.616398e+01, current residual:5.806443e+01 +field 'mu' [explicit solve]: current solution: 5.030220e+00, current residual:3.040736e+00 +wall time: 0.000680208s + +time increment:42930 time: 42.93 +field ' c' [explicit solve]: current solution: 9.616395e+01, current residual:5.806441e+01 +field 'mu' [explicit solve]: current solution: 5.030115e+00, current residual:3.040672e+00 +wall time: 0.000596046s + +time increment:42940 time: 42.94 +field ' c' [explicit solve]: current solution: 9.616392e+01, current residual:5.806438e+01 +field 'mu' [explicit solve]: current solution: 5.030010e+00, current residual:3.040608e+00 +wall time: 0.000633001s + +time increment:42950 time: 42.95 +field ' c' [explicit solve]: current solution: 9.616388e+01, current 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solve]: current solution: 5.029066e+00, current residual:3.040034e+00 +wall time: 0.000554085s + +time increment:43040 time: 43.04 +field ' c' [explicit solve]: current solution: 9.616358e+01, current residual:5.806413e+01 +field 'mu' [explicit solve]: current solution: 5.028961e+00, current residual:3.039971e+00 +wall time: 0.000744104s + +time increment:43050 time: 43.05 +field ' c' [explicit solve]: current solution: 9.616355e+01, current residual:5.806411e+01 +field 'mu' [explicit solve]: current solution: 5.028856e+00, current residual:3.039907e+00 +wall time: 0.000626087s + +time increment:43060 time: 43.06 +field ' c' [explicit solve]: current solution: 9.616352e+01, current residual:5.806408e+01 +field 'mu' [explicit solve]: current solution: 5.028751e+00, current residual:3.039843e+00 +wall time: 0.000602007s + +time increment:43070 time: 43.07 +field ' c' [explicit solve]: current solution: 9.616349e+01, current residual:5.806406e+01 +field 'mu' [explicit solve]: current solution: 5.028647e+00, current residual:3.039780e+00 +wall time: 0.000638008s + +time increment:43080 time: 43.08 +field ' c' [explicit solve]: current solution: 9.616345e+01, current residual:5.806404e+01 +field 'mu' [explicit solve]: current solution: 5.028542e+00, current residual:3.039716e+00 +wall time: 0.000709057s + +time increment:43090 time: 43.09 +field ' c' [explicit solve]: current solution: 9.616342e+01, current residual:5.806401e+01 +field 'mu' [explicit solve]: current solution: 5.028438e+00, current residual:3.039653e+00 +wall time: 0.000705004s + +time increment:43100 time: 43.1 +field ' c' [explicit solve]: current solution: 9.616339e+01, current residual:5.806399e+01 +field 'mu' [explicit solve]: current solution: 5.028333e+00, current residual:3.039589e+00 +wall time: 0.000589132s + +time increment:43110 time: 43.11 +field ' c' [explicit solve]: current solution: 9.616336e+01, current residual:5.806396e+01 +field 'mu' [explicit solve]: current solution: 5.028228e+00, current residual:3.039526e+00 +wall time: 0.000673056s + +time increment:43120 time: 43.12 +field ' c' [explicit solve]: current solution: 9.616332e+01, current residual:5.806394e+01 +field 'mu' [explicit solve]: current solution: 5.028124e+00, current residual:3.039462e+00 +wall time: 0.000549793s + +time increment:43130 time: 43.13 +field ' c' [explicit solve]: current solution: 9.616329e+01, current residual:5.806391e+01 +field 'mu' [explicit solve]: current solution: 5.028019e+00, current residual:3.039399e+00 +wall time: 0.000673056s + +time increment:43140 time: 43.14 +field ' c' [explicit solve]: current solution: 9.616326e+01, current residual:5.806389e+01 +field 'mu' [explicit solve]: current solution: 5.027915e+00, current residual:3.039335e+00 +wall time: 0.000663996s + +time increment:43150 time: 43.15 +field ' c' [explicit solve]: current solution: 9.616323e+01, current residual:5.806387e+01 +field 'mu' [explicit solve]: current solution: 5.027811e+00, 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residual:3.039018e+00 +wall time: 0.000639915s + +time increment:43200 time: 43.2 +field ' c' [explicit solve]: current solution: 9.616306e+01, current residual:5.806374e+01 +field 'mu' [explicit solve]: current solution: 5.027289e+00, current residual:3.038955e+00 +wall time: 0.000644922s + +time increment:43210 time: 43.21 +field ' c' [explicit solve]: current solution: 9.616303e+01, current residual:5.806372e+01 +field 'mu' [explicit solve]: current solution: 5.027185e+00, current residual:3.038891e+00 +wall time: 0.000743866s + +time increment:43220 time: 43.22 +field ' c' [explicit solve]: current solution: 9.616300e+01, current residual:5.806370e+01 +field 'mu' [explicit solve]: current solution: 5.027081e+00, current residual:3.038828e+00 +wall time: 0.000635147s + +time increment:43230 time: 43.23 +field ' c' [explicit solve]: current solution: 9.616297e+01, current residual:5.806367e+01 +field 'mu' [explicit solve]: current solution: 5.026976e+00, current residual:3.038765e+00 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0.000710964s + +time increment:43280 time: 43.28 +field ' c' [explicit solve]: current solution: 9.616281e+01, current residual:5.806355e+01 +field 'mu' [explicit solve]: current solution: 5.026456e+00, current residual:3.038449e+00 +wall time: 0.00062418s + +time increment:43290 time: 43.29 +field ' c' [explicit solve]: current solution: 9.616278e+01, current residual:5.806353e+01 +field 'mu' [explicit solve]: current solution: 5.026352e+00, current residual:3.038385e+00 +wall time: 0.000692129s + +time increment:43300 time: 43.3 +field ' c' [explicit solve]: current solution: 9.616275e+01, current residual:5.806350e+01 +field 'mu' [explicit solve]: current solution: 5.026248e+00, current residual:3.038322e+00 +wall time: 0.000690937s + +time increment:43310 time: 43.31 +field ' c' [explicit solve]: current solution: 9.616271e+01, current residual:5.806348e+01 +field 'mu' [explicit solve]: current solution: 5.026144e+00, current residual:3.038259e+00 +wall time: 0.000712156s + +time increment:43320 time: 43.32 +field ' c' [explicit solve]: current solution: 9.616268e+01, current residual:5.806346e+01 +field 'mu' [explicit solve]: current solution: 5.026041e+00, current residual:3.038196e+00 +wall time: 0.00071907s + +time increment:43330 time: 43.33 +field ' c' [explicit solve]: current solution: 9.616265e+01, current residual:5.806343e+01 +field 'mu' [explicit solve]: current solution: 5.025937e+00, current residual:3.038133e+00 +wall time: 0.000665903s + +time increment:43340 time: 43.34 +field ' c' [explicit solve]: current solution: 9.616262e+01, current residual:5.806341e+01 +field 'mu' [explicit solve]: current solution: 5.025833e+00, current residual:3.038070e+00 +wall time: 0.000632048s + +time increment:43350 time: 43.35 +field ' c' [explicit solve]: current solution: 9.616259e+01, current residual:5.806339e+01 +field 'mu' [explicit solve]: current solution: 5.025729e+00, current residual:3.038007e+00 +wall time: 0.000691891s + +time increment:43360 time: 43.36 +field ' c' [explicit solve]: current solution: 9.616256e+01, current residual:5.806336e+01 +field 'mu' [explicit solve]: current solution: 5.025625e+00, current residual:3.037944e+00 +wall time: 0.00069499s + +time increment:43370 time: 43.37 +field ' c' [explicit solve]: current solution: 9.616253e+01, current residual:5.806334e+01 +field 'mu' [explicit solve]: current solution: 5.025522e+00, current residual:3.037881e+00 +wall time: 0.00069809s + +time increment:43380 time: 43.38 +field ' c' [explicit solve]: current solution: 9.616249e+01, current residual:5.806331e+01 +field 'mu' [explicit solve]: current solution: 5.025418e+00, current residual:3.037817e+00 +wall time: 0.00070405s + +time increment:43390 time: 43.39 +field ' c' [explicit solve]: current solution: 9.616246e+01, current residual:5.806329e+01 +field 'mu' [explicit solve]: current solution: 5.025315e+00, current residual:3.037754e+00 +wall time: 0.000675917s + +time increment:43400 time: 43.4 +field ' c' [explicit solve]: current solution: 9.616243e+01, current residual:5.806327e+01 +field 'mu' [explicit solve]: current solution: 5.025211e+00, current residual:3.037691e+00 +wall time: 0.000658989s + +time increment:43410 time: 43.41 +field ' c' [explicit solve]: current solution: 9.616240e+01, current residual:5.806324e+01 +field 'mu' [explicit solve]: current solution: 5.025107e+00, current residual:3.037628e+00 +wall time: 0.000684977s + +time increment:43420 time: 43.42 +field ' c' [explicit solve]: current solution: 9.616237e+01, current residual:5.806322e+01 +field 'mu' [explicit solve]: current solution: 5.025004e+00, current residual:3.037566e+00 +wall time: 0.000660896s + +time increment:43430 time: 43.43 +field ' c' [explicit solve]: current solution: 9.616234e+01, current residual:5.806320e+01 +field 'mu' [explicit solve]: current solution: 5.024900e+00, current residual:3.037503e+00 +wall time: 0.000691891s + +time increment:43440 time: 43.44 +field ' c' [explicit solve]: current solution: 9.616231e+01, current residual:5.806317e+01 +field 'mu' [explicit solve]: current solution: 5.024797e+00, current residual:3.037440e+00 +wall time: 0.000690937s + +time increment:43450 time: 43.45 +field ' c' [explicit solve]: current solution: 9.616228e+01, current residual:5.806315e+01 +field 'mu' [explicit solve]: current solution: 5.024693e+00, current residual:3.037377e+00 +wall time: 0.000659943s + +time increment:43460 time: 43.46 +field ' c' [explicit solve]: current solution: 9.616225e+01, current residual:5.806313e+01 +field 'mu' [explicit solve]: current solution: 5.024590e+00, current residual:3.037314e+00 +wall time: 0.000693083s + +time increment:43470 time: 43.47 +field ' c' [explicit solve]: current solution: 9.616222e+01, current residual:5.806310e+01 +field 'mu' [explicit solve]: current solution: 5.024487e+00, current residual:3.037251e+00 +wall time: 0.000602007s + +time increment:43480 time: 43.48 +field ' c' [explicit solve]: current solution: 9.616219e+01, current residual:5.806308e+01 +field 'mu' [explicit solve]: current solution: 5.024383e+00, current residual:3.037188e+00 +wall time: 0.00068903s + +time increment:43490 time: 43.49 +field ' c' [explicit solve]: current solution: 9.616215e+01, current residual:5.806306e+01 +field 'mu' [explicit solve]: current solution: 5.024280e+00, current residual:3.037125e+00 +wall time: 0.000693798s + +time increment:43500 time: 43.5 +field ' c' [explicit solve]: current solution: 9.616212e+01, current residual:5.806303e+01 +field 'mu' [explicit solve]: current solution: 5.024177e+00, current residual:3.037063e+00 +wall time: 0.000705957s + +time increment:43510 time: 43.51 +field ' c' [explicit solve]: current solution: 9.616209e+01, current residual:5.806301e+01 +field 'mu' [explicit solve]: current solution: 5.024074e+00, current residual:3.037000e+00 +wall time: 0.000634909s + +time increment:43520 time: 43.52 +field ' c' [explicit solve]: current solution: 9.616206e+01, current residual:5.806299e+01 +field 'mu' [explicit solve]: current solution: 5.023970e+00, current residual:3.036937e+00 +wall time: 0.000669003s + +time increment:43530 time: 43.53 +field ' c' [explicit solve]: current solution: 9.616203e+01, current residual:5.806297e+01 +field 'mu' [explicit solve]: current solution: 5.023867e+00, current residual:3.036874e+00 +wall time: 0.000682116s + +time increment:43540 time: 43.54 +field ' c' [explicit solve]: current solution: 9.616200e+01, current residual:5.806294e+01 +field 'mu' [explicit solve]: current solution: 5.023764e+00, current residual:3.036811e+00 +wall time: 0.000656843s + +time increment:43550 time: 43.55 +field ' c' [explicit solve]: current solution: 9.616197e+01, current residual:5.806292e+01 +field 'mu' [explicit solve]: current solution: 5.023661e+00, current residual:3.036749e+00 +wall time: 0.000710964s + +time increment:43560 time: 43.56 +field ' c' [explicit solve]: current solution: 9.616194e+01, current 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time: 43.97 +field ' c' [explicit solve]: current solution: 9.616074e+01, current residual:5.806198e+01 +field 'mu' [explicit solve]: current solution: 5.019361e+00, current residual:3.034131e+00 +wall time: 0.000590086s + +time increment:43980 time: 43.98 +field ' c' [explicit solve]: current solution: 9.616071e+01, current residual:5.806196e+01 +field 'mu' [explicit solve]: current solution: 5.019260e+00, current residual:3.034069e+00 +wall time: 0.000713825s + +time increment:43990 time: 43.99 +field ' c' [explicit solve]: current solution: 9.616068e+01, current residual:5.806194e+01 +field 'mu' [explicit solve]: current solution: 5.019158e+00, current residual:3.034007e+00 +wall time: 0.000644922s + +time increment:44000 time: 44 +field ' c' [explicit solve]: current solution: 9.616066e+01, current residual:5.806191e+01 +field 'mu' [explicit solve]: current solution: 5.019056e+00, current residual:3.033945e+00 +wall time: 0.000566959s + +time increment:44010 time: 44.01 +field ' c' 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solution: 5.016528e+00, current residual:3.032404e+00 +wall time: 0.000711918s + +time increment:44260 time: 44.26 +field ' c' [explicit solve]: current solution: 9.615994e+01, current residual:5.806136e+01 +field 'mu' [explicit solve]: current solution: 5.016427e+00, current residual:3.032343e+00 +wall time: 0.000782013s + +time increment:44270 time: 44.27 +field ' c' [explicit solve]: current solution: 9.615991e+01, current residual:5.806134e+01 +field 'mu' [explicit solve]: current solution: 5.016326e+00, current residual:3.032282e+00 +wall time: 0.000761032s + +time increment:44280 time: 44.28 +field ' c' [explicit solve]: current solution: 9.615989e+01, current residual:5.806132e+01 +field 'mu' [explicit solve]: current solution: 5.016226e+00, current residual:3.032220e+00 +wall time: 0.000674009s + +time increment:44290 time: 44.29 +field ' c' [explicit solve]: current solution: 9.615986e+01, current residual:5.806130e+01 +field 'mu' [explicit solve]: current solution: 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residual:3.031914e+00 +wall time: 0.000689983s + +time increment:44340 time: 44.34 +field ' c' [explicit solve]: current solution: 9.615973e+01, current residual:5.806120e+01 +field 'mu' [explicit solve]: current solution: 5.015622e+00, current residual:3.031852e+00 +wall time: 0.000767946s + +time increment:44350 time: 44.35 +field ' c' [explicit solve]: current solution: 9.615970e+01, current residual:5.806117e+01 +field 'mu' [explicit solve]: current solution: 5.015522e+00, current residual:3.031791e+00 +wall time: 0.000623941s + +time increment:44360 time: 44.36 +field ' c' [explicit solve]: current solution: 9.615967e+01, current residual:5.806115e+01 +field 'mu' [explicit solve]: current solution: 5.015422e+00, current residual:3.031730e+00 +wall time: 0.000633001s + +time increment:44370 time: 44.37 +field ' c' [explicit solve]: current solution: 9.615965e+01, current residual:5.806113e+01 +field 'mu' [explicit solve]: current solution: 5.015321e+00, current residual:3.031669e+00 +wall time: 0.000657797s + +time increment:44380 time: 44.38 +field ' c' [explicit solve]: current solution: 9.615962e+01, current residual:5.806111e+01 +field 'mu' [explicit solve]: current solution: 5.015221e+00, current residual:3.031608e+00 +wall time: 0.000748158s + +time increment:44390 time: 44.39 +field ' c' [explicit solve]: current solution: 9.615959e+01, current residual:5.806109e+01 +field 'mu' [explicit solve]: current solution: 5.015121e+00, current residual:3.031546e+00 +wall time: 0.000699043s + +time increment:44400 time: 44.4 +field ' c' [explicit solve]: current solution: 9.615957e+01, current residual:5.806107e+01 +field 'mu' [explicit solve]: current solution: 5.015020e+00, current residual:3.031485e+00 +wall time: 0.000665903s + +time increment:44410 time: 44.41 +field ' c' [explicit solve]: current solution: 9.615954e+01, current residual:5.806105e+01 +field 'mu' [explicit solve]: current solution: 5.014920e+00, current residual:3.031424e+00 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0.000677109s + +time increment:44460 time: 44.46 +field ' c' [explicit solve]: current solution: 9.615941e+01, current residual:5.806095e+01 +field 'mu' [explicit solve]: current solution: 5.014420e+00, current residual:3.031119e+00 +wall time: 0.000661135s + +time increment:44470 time: 44.47 +field ' c' [explicit solve]: current solution: 9.615938e+01, current residual:5.806093e+01 +field 'mu' [explicit solve]: current solution: 5.014320e+00, current residual:3.031058e+00 +wall time: 0.000689983s + +time increment:44480 time: 44.48 +field ' c' [explicit solve]: current solution: 9.615936e+01, current residual:5.806091e+01 +field 'mu' [explicit solve]: current solution: 5.014220e+00, current residual:3.030997e+00 +wall time: 0.00062418s + +time increment:44490 time: 44.49 +field ' c' [explicit solve]: current solution: 9.615933e+01, current residual:5.806089e+01 +field 'mu' [explicit solve]: current solution: 5.014120e+00, current residual:3.030936e+00 +wall time: 0.000656128s + +time increment:44500 time: 44.5 +field ' c' [explicit solve]: current solution: 9.615931e+01, current residual:5.806087e+01 +field 'mu' [explicit solve]: current solution: 5.014020e+00, current residual:3.030875e+00 +wall time: 0.000570059s + +time increment:44510 time: 44.51 +field ' c' [explicit solve]: current solution: 9.615928e+01, current residual:5.806085e+01 +field 'mu' [explicit solve]: current solution: 5.013920e+00, current residual:3.030814e+00 +wall time: 0.000570059s + +time increment:44520 time: 44.52 +field ' c' [explicit solve]: current solution: 9.615926e+01, current residual:5.806083e+01 +field 'mu' [explicit solve]: current solution: 5.013820e+00, current residual:3.030753e+00 +wall time: 0.000675917s + +time increment:44530 time: 44.53 +field ' c' [explicit solve]: current solution: 9.615923e+01, current residual:5.806081e+01 +field 'mu' [explicit solve]: current solution: 5.013720e+00, current residual:3.030692e+00 +wall time: 0.000682116s + +time increment:44540 time: 44.54 +field ' c' [explicit solve]: current solution: 9.615920e+01, current residual:5.806079e+01 +field 'mu' [explicit solve]: current solution: 5.013620e+00, current residual:3.030631e+00 +wall time: 0.00057888s + +time increment:44550 time: 44.55 +field ' c' [explicit solve]: current solution: 9.615918e+01, current residual:5.806077e+01 +field 'mu' [explicit solve]: current solution: 5.013521e+00, current residual:3.030570e+00 +wall time: 0.000603914s + +time increment:44560 time: 44.56 +field ' c' [explicit solve]: current solution: 9.615915e+01, current residual:5.806075e+01 +field 'mu' [explicit solve]: current solution: 5.013421e+00, current residual:3.030510e+00 +wall time: 0.000591993s + +time increment:44570 time: 44.57 +field ' c' [explicit solve]: current solution: 9.615913e+01, current residual:5.806073e+01 +field 'mu' [explicit solve]: current solution: 5.013321e+00, current residual:3.030449e+00 +wall time: 0.000576019s + +time increment:44580 time: 44.58 +field ' c' [explicit solve]: current solution: 9.615910e+01, current residual:5.806071e+01 +field 'mu' [explicit solve]: current solution: 5.013222e+00, current residual:3.030388e+00 +wall time: 0.000623941s + +time increment:44590 time: 44.59 +field ' c' [explicit solve]: current solution: 9.615908e+01, current residual:5.806069e+01 +field 'mu' [explicit solve]: current solution: 5.013122e+00, current residual:3.030327e+00 +wall time: 0.000593901s + +time increment:44600 time: 44.6 +field ' c' [explicit solve]: current solution: 9.615905e+01, current residual:5.806067e+01 +field 'mu' [explicit solve]: current solution: 5.013022e+00, current residual:3.030266e+00 +wall time: 0.000576973s + +time increment:44610 time: 44.61 +field ' c' [explicit solve]: current solution: 9.615903e+01, current residual:5.806065e+01 +field 'mu' [explicit solve]: current solution: 5.012923e+00, current residual:3.030206e+00 +wall time: 0.000610828s + +time increment:44620 time: 44.62 +field ' c' [explicit solve]: current solution: 9.615900e+01, current residual:5.806063e+01 +field 'mu' [explicit solve]: current solution: 5.012823e+00, current residual:3.030145e+00 +wall time: 0.000609875s + +time increment:44630 time: 44.63 +field ' c' [explicit solve]: current solution: 9.615898e+01, current residual:5.806061e+01 +field 'mu' [explicit solve]: current solution: 5.012724e+00, current residual:3.030084e+00 +wall time: 0.00067687s + +time increment:44640 time: 44.64 +field ' c' [explicit solve]: current solution: 9.615895e+01, current residual:5.806059e+01 +field 'mu' [explicit solve]: current solution: 5.012624e+00, current residual:3.030024e+00 +wall time: 0.000664234s + +time increment:44650 time: 44.65 +field ' c' [explicit solve]: current solution: 9.615893e+01, current residual:5.806057e+01 +field 'mu' [explicit solve]: current solution: 5.012525e+00, current residual:3.029963e+00 +wall time: 0.000777006s + +time increment:44660 time: 44.66 +field ' c' [explicit solve]: current solution: 9.615890e+01, current residual:5.806055e+01 +field 'mu' [explicit solve]: current solution: 5.012426e+00, current residual:3.029902e+00 +wall time: 0.00072813s + +time increment:44670 time: 44.67 +field ' c' [explicit solve]: current solution: 9.615887e+01, current residual:5.806053e+01 +field 'mu' [explicit solve]: current solution: 5.012326e+00, current residual:3.029842e+00 +wall time: 0.000643015s + +time increment:44680 time: 44.68 +field ' c' [explicit solve]: current solution: 9.615885e+01, current residual:5.806051e+01 +field 'mu' [explicit solve]: current solution: 5.012227e+00, current residual:3.029781e+00 +wall time: 0.000673056s + +time increment:44690 time: 44.69 +field ' c' [explicit solve]: current solution: 9.615882e+01, current residual:5.806049e+01 +field 'mu' [explicit solve]: current solution: 5.012128e+00, current residual:3.029720e+00 +wall time: 0.000658035s + +time increment:44700 time: 44.7 +field ' c' [explicit solve]: current solution: 9.615880e+01, current residual:5.806047e+01 +field 'mu' [explicit solve]: current solution: 5.012029e+00, current residual:3.029660e+00 +wall time: 0.000715971s + +time increment:44710 time: 44.71 +field ' c' [explicit solve]: current solution: 9.615878e+01, current residual:5.806045e+01 +field 'mu' [explicit solve]: current solution: 5.011929e+00, current residual:3.029599e+00 +wall time: 0.000583887s + +time increment:44720 time: 44.72 +field ' c' [explicit solve]: current solution: 9.615875e+01, current residual:5.806043e+01 +field 'mu' [explicit solve]: current solution: 5.011830e+00, current residual:3.029539e+00 +wall time: 0.000617981s + +time increment:44730 time: 44.73 +field ' c' [explicit solve]: current solution: 9.615873e+01, current residual:5.806041e+01 +field 'mu' [explicit solve]: current solution: 5.011731e+00, current residual:3.029478e+00 +wall time: 0.000603914s + +time increment:44740 time: 44.74 +field ' c' [explicit solve]: current solution: 9.615870e+01, current residual:5.806039e+01 +field 'mu' [explicit solve]: current solution: 5.011632e+00, current residual:3.029418e+00 +wall time: 0.000711918s + +time increment:44750 time: 44.75 +field ' c' [explicit solve]: current solution: 9.615868e+01, current residual:5.806037e+01 +field 'mu' [explicit solve]: current solution: 5.011533e+00, current residual:3.029357e+00 +wall time: 0.000721931s + +time increment:44760 time: 44.76 +field ' c' [explicit solve]: current solution: 9.615865e+01, current residual:5.806035e+01 +field 'mu' [explicit solve]: current solution: 5.011434e+00, current residual:3.029297e+00 +wall time: 0.000640154s + +time increment:44770 time: 44.77 +field ' c' [explicit solve]: current solution: 9.615863e+01, current residual:5.806034e+01 +field 'mu' [explicit solve]: current solution: 5.011335e+00, current residual:3.029236e+00 +wall time: 0.00056982s + +time increment:44780 time: 44.78 +field ' c' [explicit solve]: current solution: 9.615860e+01, current residual:5.806032e+01 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solve]: current solution: 5.010840e+00, current residual:3.028934e+00 +wall time: 0.000628948s + +time increment:44830 time: 44.83 +field ' c' [explicit solve]: current solution: 9.615848e+01, current residual:5.806022e+01 +field 'mu' [explicit solve]: current solution: 5.010741e+00, current residual:3.028874e+00 +wall time: 0.000638962s + +time increment:44840 time: 44.84 +field ' c' [explicit solve]: current solution: 9.615846e+01, current residual:5.806020e+01 +field 'mu' [explicit solve]: current solution: 5.010643e+00, current residual:3.028814e+00 +wall time: 0.000665903s + +time increment:44850 time: 44.85 +field ' c' [explicit solve]: current solution: 9.615843e+01, current residual:5.806018e+01 +field 'mu' [explicit solve]: current solution: 5.010544e+00, current residual:3.028753e+00 +wall time: 0.00068593s + +time increment:44860 time: 44.86 +field ' c' [explicit solve]: current solution: 9.615841e+01, current residual:5.806016e+01 +field 'mu' [explicit solve]: current solution: 5.010445e+00, current residual:3.028693e+00 +wall time: 0.000714064s + +time increment:44870 time: 44.87 +field ' c' [explicit solve]: current solution: 9.615838e+01, current residual:5.806014e+01 +field 'mu' [explicit solve]: current solution: 5.010347e+00, current residual:3.028633e+00 +wall time: 0.000603199s + +time increment:44880 time: 44.88 +field ' c' [explicit solve]: current solution: 9.615836e+01, current residual:5.806012e+01 +field 'mu' [explicit solve]: current solution: 5.010248e+00, current residual:3.028573e+00 +wall time: 0.000559807s + +time increment:44890 time: 44.89 +field ' c' [explicit solve]: current solution: 9.615834e+01, current residual:5.806010e+01 +field 'mu' [explicit solve]: current solution: 5.010149e+00, current residual:3.028512e+00 +wall time: 0.000579834s + +time increment:44900 time: 44.9 +field ' c' [explicit solve]: current solution: 9.615831e+01, current residual:5.806009e+01 +field 'mu' [explicit solve]: current solution: 5.010051e+00, current residual:3.028452e+00 +wall time: 0.000644207s + +time increment:44910 time: 44.91 +field ' c' [explicit solve]: current solution: 9.615829e+01, current residual:5.806007e+01 +field 'mu' [explicit solve]: current solution: 5.009952e+00, current residual:3.028392e+00 +wall time: 0.000623941s + +time increment:44920 time: 44.92 +field ' c' [explicit solve]: current solution: 9.615826e+01, current residual:5.806005e+01 +field 'mu' [explicit solve]: current solution: 5.009854e+00, current residual:3.028332e+00 +wall time: 0.000566959s + +time increment:44930 time: 44.93 +field ' c' [explicit solve]: current solution: 9.615824e+01, current residual:5.806003e+01 +field 'mu' [explicit solve]: current solution: 5.009755e+00, current residual:3.028272e+00 +wall time: 0.000644207s + +time increment:44940 time: 44.94 +field ' c' [explicit solve]: current solution: 9.615822e+01, current residual:5.806001e+01 +field 'mu' [explicit solve]: current solution: 5.009657e+00, current residual:3.028212e+00 +wall time: 0.000571966s + +time increment:44950 time: 44.95 +field ' c' [explicit solve]: current solution: 9.615819e+01, current residual:5.805999e+01 +field 'mu' [explicit solve]: current solution: 5.009558e+00, current residual:3.028152e+00 +wall time: 0.000648975s + +time increment:44960 time: 44.96 +field ' c' [explicit solve]: current solution: 9.615817e+01, current residual:5.805997e+01 +field 'mu' [explicit solve]: current solution: 5.009460e+00, current residual:3.028091e+00 +wall time: 0.000649214s + +time increment:44970 time: 44.97 +field ' c' [explicit solve]: current solution: 9.615815e+01, current residual:5.805995e+01 +field 'mu' [explicit solve]: current solution: 5.009362e+00, current residual:3.028031e+00 +wall time: 0.000622988s + +time increment:44980 time: 44.98 +field ' c' [explicit solve]: current solution: 9.615812e+01, current residual:5.805993e+01 +field 'mu' [explicit solve]: current solution: 5.009263e+00, current residual:3.027971e+00 +wall time: 0.000642061s + +time increment:44990 time: 44.99 +field ' c' [explicit solve]: current solution: 9.615810e+01, current residual:5.805992e+01 +field 'mu' [explicit solve]: current solution: 5.009165e+00, current residual:3.027911e+00 +wall time: 0.000646114s + +time increment:45000 time: 45 +field ' c' [explicit solve]: current solution: 9.615807e+01, current residual:5.805990e+01 +field 'mu' [explicit solve]: current solution: 5.009067e+00, current residual:3.027851e+00 +wall time: 0.000583887s + +time increment:45010 time: 45.01 +field ' c' [explicit solve]: current solution: 9.615805e+01, current residual:5.805988e+01 +field 'mu' [explicit solve]: current solution: 5.008969e+00, current residual:3.027791e+00 +wall time: 0.000597s + +time increment:45020 time: 45.02 +field ' c' [explicit solve]: current solution: 9.615803e+01, current residual:5.805986e+01 +field 'mu' [explicit solve]: current solution: 5.008871e+00, current residual:3.027731e+00 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0.000617981s + +time increment:45070 time: 45.07 +field ' c' [explicit solve]: current solution: 9.615791e+01, current residual:5.805977e+01 +field 'mu' [explicit solve]: current solution: 5.008380e+00, current residual:3.027432e+00 +wall time: 0.000595093s + +time increment:45080 time: 45.08 +field ' c' [explicit solve]: current solution: 9.615789e+01, current residual:5.805975e+01 +field 'mu' [explicit solve]: current solution: 5.008282e+00, current residual:3.027372e+00 +wall time: 0.000599861s + +time increment:45090 time: 45.09 +field ' c' [explicit solve]: current solution: 9.615786e+01, current residual:5.805973e+01 +field 'mu' [explicit solve]: current solution: 5.008184e+00, current residual:3.027312e+00 +wall time: 0.000648022s + +time increment:45100 time: 45.1 +field ' c' [explicit solve]: current solution: 9.615784e+01, current residual:5.805971e+01 +field 'mu' [explicit solve]: current solution: 5.008087e+00, current residual:3.027252e+00 +wall time: 0.000648022s + +time increment:45110 time: 45.11 +field ' c' [explicit solve]: current solution: 9.615782e+01, current residual:5.805969e+01 +field 'mu' [explicit solve]: current solution: 5.007989e+00, current residual:3.027192e+00 +wall time: 0.000560999s + +time increment:45120 time: 45.12 +field ' c' [explicit solve]: current solution: 9.615780e+01, current residual:5.805967e+01 +field 'mu' [explicit solve]: current solution: 5.007891e+00, current residual:3.027133e+00 +wall time: 0.000611067s + +time increment:45130 time: 45.13 +field ' c' [explicit solve]: current solution: 9.615777e+01, current residual:5.805966e+01 +field 'mu' [explicit solve]: current solution: 5.007793e+00, current residual:3.027073e+00 +wall time: 0.000599146s + +time increment:45140 time: 45.14 +field ' c' [explicit solve]: current solution: 9.615775e+01, current residual:5.805964e+01 +field 'mu' [explicit solve]: current solution: 5.007695e+00, current residual:3.027013e+00 +wall time: 0.000796795s + +time increment:45150 time: 45.15 +field ' c' [explicit solve]: current solution: 9.615773e+01, current residual:5.805962e+01 +field 'mu' [explicit solve]: current solution: 5.007598e+00, current residual:3.026953e+00 +wall time: 0.000722885s + +time increment:45160 time: 45.16 +field ' c' [explicit solve]: current solution: 9.615770e+01, current residual:5.805960e+01 +field 'mu' [explicit solve]: current solution: 5.007500e+00, current residual:3.026894e+00 +wall time: 0.000625134s + +time increment:45170 time: 45.17 +field ' c' [explicit solve]: current solution: 9.615768e+01, current residual:5.805958e+01 +field 'mu' [explicit solve]: current solution: 5.007402e+00, current residual:3.026834e+00 +wall time: 0.000594854s + +time increment:45180 time: 45.18 +field ' c' [explicit solve]: current solution: 9.615766e+01, current residual:5.805956e+01 +field 'mu' [explicit solve]: current solution: 5.007305e+00, current residual:3.026774e+00 +wall time: 0.000573874s + +time increment:45190 time: 45.19 +field ' c' [explicit solve]: current solution: 9.615764e+01, current residual:5.805955e+01 +field 'mu' [explicit solve]: current solution: 5.007207e+00, current residual:3.026715e+00 +wall time: 0.000566006s + +time increment:45200 time: 45.2 +field ' c' [explicit solve]: current solution: 9.615761e+01, current residual:5.805953e+01 +field 'mu' [explicit solve]: current solution: 5.007109e+00, current residual:3.026655e+00 +wall time: 0.000636101s + +time increment:45210 time: 45.21 +field ' c' [explicit solve]: current solution: 9.615759e+01, current residual:5.805951e+01 +field 'mu' [explicit solve]: current solution: 5.007012e+00, current residual:3.026595e+00 +wall time: 0.000636101s + +time increment:45220 time: 45.22 +field ' c' [explicit solve]: current solution: 9.615757e+01, current residual:5.805949e+01 +field 'mu' [explicit solve]: current solution: 5.006914e+00, current residual:3.026536e+00 +wall time: 0.000561953s + +time increment:45230 time: 45.23 +field ' c' [explicit solve]: current solution: 9.615755e+01, current residual:5.805947e+01 +field 'mu' [explicit solve]: current solution: 5.006817e+00, current residual:3.026476e+00 +wall time: 0.000595093s + +time increment:45240 time: 45.24 +field ' c' [explicit solve]: current solution: 9.615752e+01, current residual:5.805946e+01 +field 'mu' [explicit solve]: current solution: 5.006719e+00, current residual:3.026417e+00 +wall time: 0.000602007s + +time increment:45250 time: 45.25 +field ' c' [explicit solve]: current solution: 9.615750e+01, current residual:5.805944e+01 +field 'mu' [explicit solve]: current solution: 5.006622e+00, current residual:3.026357e+00 +wall time: 0.000716925s + +time increment:45260 time: 45.26 +field ' c' [explicit solve]: current solution: 9.615748e+01, current residual:5.805942e+01 +field 'mu' [explicit solve]: current solution: 5.006525e+00, current residual:3.026298e+00 +wall time: 0.000609159s + +time increment:45270 time: 45.27 +field ' c' [explicit solve]: current solution: 9.615746e+01, current residual:5.805940e+01 +field 'mu' [explicit solve]: current solution: 5.006427e+00, current residual:3.026238e+00 +wall time: 0.000671148s + +time increment:45280 time: 45.28 +field ' c' [explicit solve]: current solution: 9.615743e+01, current residual:5.805938e+01 +field 'mu' [explicit solve]: current solution: 5.006330e+00, current residual:3.026179e+00 +wall time: 0.000763178s + +time increment:45290 time: 45.29 +field ' c' [explicit solve]: current solution: 9.615741e+01, current residual:5.805937e+01 +field 'mu' [explicit solve]: current solution: 5.006233e+00, current residual:3.026119e+00 +wall time: 0.000663042s + +time increment:45300 time: 45.3 +field ' c' [explicit solve]: current solution: 9.615739e+01, current residual:5.805935e+01 +field 'mu' [explicit solve]: current solution: 5.006136e+00, current residual:3.026060e+00 +wall time: 0.000743866s + +time increment:45310 time: 45.31 +field ' c' [explicit solve]: current solution: 9.615737e+01, current residual:5.805933e+01 +field 'mu' [explicit solve]: current solution: 5.006038e+00, current residual:3.026000e+00 +wall time: 0.000765085s + +time increment:45320 time: 45.32 +field ' c' [explicit solve]: current solution: 9.615735e+01, current residual:5.805931e+01 +field 'mu' [explicit solve]: current solution: 5.005941e+00, current residual:3.025941e+00 +wall time: 0.000664949s + +time increment:45330 time: 45.33 +field ' c' [explicit solve]: current solution: 9.615732e+01, current residual:5.805929e+01 +field 'mu' [explicit solve]: current solution: 5.005844e+00, current residual:3.025881e+00 +wall time: 0.000876904s + +time increment:45340 time: 45.34 +field ' c' [explicit solve]: current solution: 9.615730e+01, current residual:5.805928e+01 +field 'mu' [explicit solve]: current solution: 5.005747e+00, current residual:3.025822e+00 +wall time: 0.000702143s + +time increment:45350 time: 45.35 +field ' c' [explicit solve]: current solution: 9.615728e+01, current 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solution: 5.004488e+00, current residual:3.025052e+00 +wall time: 0.000645161s + +time increment:45480 time: 45.48 +field ' c' [explicit solve]: current solution: 9.615700e+01, current residual:5.805903e+01 +field 'mu' [explicit solve]: current solution: 5.004391e+00, current residual:3.024993e+00 +wall time: 0.000722885s + +time increment:45490 time: 45.49 +field ' c' [explicit solve]: current solution: 9.615698e+01, current residual:5.805901e+01 +field 'mu' [explicit solve]: current solution: 5.004295e+00, current residual:3.024934e+00 +wall time: 0.000706911s + +time increment:45500 time: 45.5 +field ' c' [explicit solve]: current solution: 9.615696e+01, current residual:5.805900e+01 +field 'mu' [explicit solve]: current solution: 5.004198e+00, current residual:3.024875e+00 +wall time: 0.000653028s + +time increment:45510 time: 45.51 +field ' c' [explicit solve]: current solution: 9.615693e+01, current residual:5.805898e+01 +field 'mu' [explicit solve]: current solution: 5.004102e+00, current residual:3.024816e+00 +wall time: 0.000649929s + +time increment:45520 time: 45.52 +field ' c' [explicit solve]: current solution: 9.615691e+01, current residual:5.805896e+01 +field 'mu' [explicit solve]: current solution: 5.004005e+00, current residual:3.024757e+00 +wall time: 0.00070405s + +time increment:45530 time: 45.53 +field ' c' [explicit solve]: current solution: 9.615689e+01, current residual:5.805894e+01 +field 'mu' [explicit solve]: current solution: 5.003909e+00, current residual:3.024698e+00 +wall time: 0.00067997s + +time increment:45540 time: 45.54 +field ' c' [explicit solve]: current solution: 9.615687e+01, current residual:5.805893e+01 +field 'mu' [explicit solve]: current solution: 5.003812e+00, current residual:3.024639e+00 +wall time: 0.000694036s + +time increment:45550 time: 45.55 +field ' c' [explicit solve]: current solution: 9.615685e+01, current residual:5.805891e+01 +field 'mu' [explicit solve]: current solution: 5.003716e+00, current residual:3.024580e+00 +wall time: 0.000709057s + +time increment:45560 time: 45.56 +field ' c' [explicit solve]: current solution: 9.615683e+01, current residual:5.805889e+01 +field 'mu' [explicit solve]: current solution: 5.003620e+00, current residual:3.024521e+00 +wall time: 0.000674963s + +time increment:45570 time: 45.57 +field ' c' [explicit solve]: current solution: 9.615681e+01, current residual:5.805888e+01 +field 'mu' [explicit solve]: current solution: 5.003523e+00, current residual:3.024462e+00 +wall time: 0.000669956s + +time increment:45580 time: 45.58 +field ' c' [explicit solve]: current solution: 9.615679e+01, current residual:5.805886e+01 +field 'mu' [explicit solve]: current solution: 5.003427e+00, current residual:3.024403e+00 +wall time: 0.000741005s + +time increment:45590 time: 45.59 +field ' c' [explicit solve]: current solution: 9.615677e+01, current residual:5.805884e+01 +field 'mu' [explicit solve]: current solution: 5.003331e+00, current residual:3.024344e+00 +wall time: 0.000648975s + +time increment:45600 time: 45.6 +field ' c' [explicit solve]: current solution: 9.615675e+01, current residual:5.805882e+01 +field 'mu' [explicit solve]: current solution: 5.003234e+00, current residual:3.024285e+00 +wall time: 0.000633955s + +time increment:45610 time: 45.61 +field ' c' [explicit solve]: current solution: 9.615672e+01, current residual:5.805881e+01 +field 'mu' [explicit solve]: current solution: 5.003138e+00, current residual:3.024226e+00 +wall time: 0.000666857s + +time increment:45620 time: 45.62 +field ' c' [explicit solve]: current solution: 9.615670e+01, current residual:5.805879e+01 +field 'mu' [explicit solve]: current solution: 5.003042e+00, current residual:3.024167e+00 +wall time: 0.000832796s + +time increment:45630 time: 45.63 +field ' c' [explicit solve]: current solution: 9.615668e+01, current residual:5.805877e+01 +field 'mu' [explicit solve]: current solution: 5.002946e+00, current residual:3.024109e+00 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0.00071907s + +time increment:45680 time: 45.68 +field ' c' [explicit solve]: current solution: 9.615658e+01, current residual:5.805869e+01 +field 'mu' [explicit solve]: current solution: 5.002466e+00, current residual:3.023815e+00 +wall time: 0.000670195s + +time increment:45690 time: 45.69 +field ' c' [explicit solve]: current solution: 9.615656e+01, current residual:5.805867e+01 +field 'mu' [explicit solve]: current solution: 5.002370e+00, current residual:3.023756e+00 +wall time: 0.000675201s + +time increment:45700 time: 45.7 +field ' c' [explicit solve]: current solution: 9.615654e+01, current residual:5.805866e+01 +field 'mu' [explicit solve]: current solution: 5.002274e+00, current residual:3.023697e+00 +wall time: 0.000636101s + +time increment:45710 time: 45.71 +field ' c' [explicit solve]: current solution: 9.615652e+01, current residual:5.805864e+01 +field 'mu' [explicit solve]: current solution: 5.002178e+00, current residual:3.023639e+00 +wall time: 0.00070715s + +time increment:45720 time: 45.72 +field ' c' [explicit solve]: current solution: 9.615650e+01, current residual:5.805862e+01 +field 'mu' [explicit solve]: current solution: 5.002082e+00, current residual:3.023580e+00 +wall time: 0.000613928s + +time increment:45730 time: 45.73 +field ' c' [explicit solve]: current solution: 9.615648e+01, current residual:5.805861e+01 +field 'mu' [explicit solve]: current solution: 5.001987e+00, current residual:3.023521e+00 +wall time: 0.000696898s + +time increment:45740 time: 45.74 +field ' c' [explicit solve]: current solution: 9.615646e+01, current residual:5.805859e+01 +field 'mu' [explicit solve]: current solution: 5.001891e+00, current residual:3.023463e+00 +wall time: 0.000675917s + +time increment:45750 time: 45.75 +field ' c' [explicit solve]: current solution: 9.615644e+01, current residual:5.805857e+01 +field 'mu' [explicit solve]: current solution: 5.001795e+00, current residual:3.023404e+00 +wall time: 0.000697851s + +time increment:45760 time: 45.76 +field ' c' [explicit solve]: current solution: 9.615642e+01, current residual:5.805856e+01 +field 'mu' [explicit solve]: current solution: 5.001700e+00, current residual:3.023345e+00 +wall time: 0.000765085s + +time increment:45770 time: 45.77 +field ' c' [explicit solve]: current solution: 9.615640e+01, current residual:5.805854e+01 +field 'mu' [explicit solve]: current solution: 5.001604e+00, current residual:3.023287e+00 +wall time: 0.000757933s + +time increment:45780 time: 45.78 +field ' c' [explicit solve]: current solution: 9.615638e+01, current residual:5.805852e+01 +field 'mu' [explicit solve]: current solution: 5.001508e+00, current residual:3.023228e+00 +wall time: 0.00069499s + +time increment:45790 time: 45.79 +field ' c' [explicit solve]: current solution: 9.615636e+01, current residual:5.805851e+01 +field 'mu' [explicit solve]: current solution: 5.001413e+00, current residual:3.023170e+00 +wall time: 0.000725031s + +time increment:45800 time: 45.8 +field ' c' [explicit solve]: current solution: 9.615634e+01, current residual:5.805849e+01 +field 'mu' [explicit solve]: current solution: 5.001317e+00, current residual:3.023111e+00 +wall time: 0.00069809s + +time increment:45810 time: 45.81 +field ' c' [explicit solve]: current solution: 9.615632e+01, current residual:5.805847e+01 +field 'mu' [explicit solve]: current solution: 5.001222e+00, current residual:3.023053e+00 +wall time: 0.000691891s + +time increment:45820 time: 45.82 +field ' c' [explicit solve]: current solution: 9.615630e+01, current residual:5.805846e+01 +field 'mu' [explicit solve]: current solution: 5.001126e+00, current residual:3.022994e+00 +wall time: 0.000667095s + +time increment:45830 time: 45.83 +field ' c' [explicit solve]: current solution: 9.615628e+01, current residual:5.805844e+01 +field 'mu' [explicit solve]: current solution: 5.001031e+00, current residual:3.022936e+00 +wall time: 0.000636101s + +time increment:45840 time: 45.84 +field ' c' [explicit solve]: current solution: 9.615626e+01, current residual:5.805842e+01 +field 'mu' [explicit solve]: current solution: 5.000935e+00, current residual:3.022878e+00 +wall time: 0.000695944s + +time increment:45850 time: 45.85 +field ' c' [explicit solve]: current solution: 9.615624e+01, current residual:5.805841e+01 +field 'mu' [explicit solve]: current solution: 5.000840e+00, current residual:3.022819e+00 +wall time: 0.000623941s + +time increment:45860 time: 45.86 +field ' c' [explicit solve]: current solution: 9.615622e+01, current residual:5.805839e+01 +field 'mu' [explicit solve]: current solution: 5.000744e+00, current residual:3.022761e+00 +wall time: 0.000627995s + +time increment:45870 time: 45.87 +field ' c' [explicit solve]: current solution: 9.615620e+01, current residual:5.805837e+01 +field 'mu' [explicit solve]: current solution: 5.000649e+00, current residual:3.022702e+00 +wall time: 0.000714064s + +time increment:45880 time: 45.88 +field ' c' [explicit solve]: current solution: 9.615618e+01, current residual:5.805836e+01 +field 'mu' [explicit solve]: current solution: 5.000554e+00, current residual:3.022644e+00 +wall time: 0.00070405s + +time increment:45890 time: 45.89 +field ' c' [explicit solve]: current solution: 9.615616e+01, current residual:5.805834e+01 +field 'mu' [explicit solve]: current solution: 5.000459e+00, current residual:3.022586e+00 +wall time: 0.000666142s + +time increment:45900 time: 45.9 +field ' c' [explicit solve]: current solution: 9.615614e+01, current residual:5.805833e+01 +field 'mu' [explicit solve]: current solution: 5.000363e+00, current residual:3.022527e+00 +wall time: 0.000635147s + +time increment:45910 time: 45.91 +field ' c' [explicit solve]: current solution: 9.615612e+01, current residual:5.805831e+01 +field 'mu' [explicit solve]: current solution: 5.000268e+00, current residual:3.022469e+00 +wall time: 0.000661135s + +time increment:45920 time: 45.92 +field ' c' [explicit solve]: current solution: 9.615610e+01, current residual:5.805829e+01 +field 'mu' [explicit solve]: current solution: 5.000173e+00, current residual:3.022411e+00 +wall time: 0.000728846s + +time increment:45930 time: 45.93 +field ' c' [explicit solve]: current solution: 9.615608e+01, current residual:5.805828e+01 +field 'mu' [explicit solve]: current solution: 5.000078e+00, current residual:3.022352e+00 +wall time: 0.000653028s + +time increment:45940 time: 45.94 +field ' c' [explicit solve]: current solution: 9.615606e+01, current residual:5.805826e+01 +field 'mu' [explicit solve]: current solution: 4.999983e+00, current residual:3.022294e+00 +wall time: 0.00056386s + +time increment:45950 time: 45.95 +field ' c' [explicit solve]: current solution: 9.615604e+01, current residual:5.805824e+01 +field 'mu' [explicit solve]: current solution: 4.999888e+00, current residual:3.022236e+00 +wall time: 0.000605106s + +time increment:45960 time: 45.96 +field ' c' [explicit solve]: current solution: 9.615602e+01, current residual:5.805823e+01 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solve]: current solution: 4.999413e+00, current residual:3.021945e+00 +wall time: 0.000558138s + +time increment:46010 time: 46.01 +field ' c' [explicit solve]: current solution: 9.615593e+01, current residual:5.805815e+01 +field 'mu' [explicit solve]: current solution: 4.999318e+00, current residual:3.021887e+00 +wall time: 0.000635147s + +time increment:46020 time: 46.02 +field ' c' [explicit solve]: current solution: 9.615591e+01, current residual:5.805813e+01 +field 'mu' [explicit solve]: current solution: 4.999223e+00, current residual:3.021829e+00 +wall time: 0.000560045s + +time increment:46030 time: 46.03 +field ' c' [explicit solve]: current solution: 9.615589e+01, current residual:5.805812e+01 +field 'mu' [explicit solve]: current solution: 4.999129e+00, current residual:3.021771e+00 +wall time: 0.000663996s + +time increment:46040 time: 46.04 +field ' c' [explicit solve]: current solution: 9.615587e+01, current residual:5.805810e+01 +field 'mu' [explicit solve]: current solution: 4.999034e+00, current residual:3.021713e+00 +wall time: 0.000661135s + +time increment:46050 time: 46.05 +field ' c' [explicit solve]: current solution: 9.615585e+01, current residual:5.805808e+01 +field 'mu' [explicit solve]: current solution: 4.998939e+00, current residual:3.021655e+00 +wall time: 0.000570059s + +time increment:46060 time: 46.06 +field ' c' [explicit solve]: current solution: 9.615583e+01, current residual:5.805807e+01 +field 'mu' [explicit solve]: current solution: 4.998844e+00, current residual:3.021597e+00 +wall time: 0.00059104s + +time increment:46070 time: 46.07 +field ' c' [explicit solve]: current solution: 9.615581e+01, current residual:5.805805e+01 +field 'mu' [explicit solve]: current solution: 4.998750e+00, current residual:3.021539e+00 +wall time: 0.000644922s + +time increment:46080 time: 46.08 +field ' c' [explicit solve]: current solution: 9.615579e+01, current residual:5.805804e+01 +field 'mu' [explicit solve]: current solution: 4.998655e+00, current residual:3.021481e+00 +wall time: 0.000620842s + +time increment:46090 time: 46.09 +field ' c' [explicit solve]: current solution: 9.615578e+01, current residual:5.805802e+01 +field 'mu' [explicit solve]: current solution: 4.998561e+00, current residual:3.021423e+00 +wall time: 0.000687122s + +time increment:46100 time: 46.1 +field ' c' [explicit solve]: current solution: 9.615576e+01, current residual:5.805801e+01 +field 'mu' [explicit solve]: current solution: 4.998466e+00, current residual:3.021365e+00 +wall time: 0.000602961s + +time increment:46110 time: 46.11 +field ' c' [explicit solve]: current solution: 9.615574e+01, current residual:5.805799e+01 +field 'mu' [explicit solve]: current solution: 4.998371e+00, current residual:3.021307e+00 +wall time: 0.000674009s + +time increment:46120 time: 46.12 +field ' c' [explicit solve]: current solution: 9.615572e+01, current residual:5.805797e+01 +field 'mu' [explicit solve]: current solution: 4.998277e+00, current residual:3.021249e+00 +wall time: 0.000648975s + +time increment:46130 time: 46.13 +field ' c' [explicit solve]: current solution: 9.615570e+01, current residual:5.805796e+01 +field 'mu' [explicit solve]: current solution: 4.998183e+00, current residual:3.021191e+00 +wall time: 0.000667095s + +time increment:46140 time: 46.14 +field ' c' [explicit solve]: current solution: 9.615568e+01, current residual:5.805794e+01 +field 'mu' [explicit solve]: current solution: 4.998088e+00, current residual:3.021133e+00 +wall time: 0.000632048s + +time increment:46150 time: 46.15 +field ' c' [explicit solve]: current solution: 9.615566e+01, current residual:5.805793e+01 +field 'mu' [explicit solve]: current solution: 4.997994e+00, current residual:3.021075e+00 +wall time: 0.000658035s + +time increment:46160 time: 46.16 +field ' c' [explicit solve]: current solution: 9.615565e+01, current residual:5.805791e+01 +field 'mu' [explicit solve]: current solution: 4.997899e+00, current residual:3.021018e+00 +wall time: 0.000736952s + +time increment:46170 time: 46.17 +field ' c' [explicit solve]: current solution: 9.615563e+01, current residual:5.805790e+01 +field 'mu' [explicit solve]: current solution: 4.997805e+00, current residual:3.020960e+00 +wall time: 0.000717878s + +time increment:46180 time: 46.18 +field ' c' [explicit solve]: current solution: 9.615561e+01, current residual:5.805788e+01 +field 'mu' [explicit solve]: current solution: 4.997711e+00, current residual:3.020902e+00 +wall time: 0.000649929s + +time increment:46190 time: 46.19 +field ' c' [explicit solve]: current solution: 9.615559e+01, current residual:5.805787e+01 +field 'mu' [explicit solve]: current solution: 4.997616e+00, current residual:3.020844e+00 +wall time: 0.000774145s + +time increment:46200 time: 46.2 +field ' c' [explicit solve]: current solution: 9.615557e+01, current residual:5.805785e+01 +field 'mu' [explicit solve]: current solution: 4.997522e+00, current residual:3.020787e+00 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0.000679016s + +time increment:46250 time: 46.25 +field ' c' [explicit solve]: current solution: 9.615548e+01, current residual:5.805777e+01 +field 'mu' [explicit solve]: current solution: 4.997052e+00, current residual:3.020498e+00 +wall time: 0.000716925s + +time increment:46260 time: 46.26 +field ' c' [explicit solve]: current solution: 9.615546e+01, current residual:5.805776e+01 +field 'mu' [explicit solve]: current solution: 4.996958e+00, current residual:3.020440e+00 +wall time: 0.000707865s + +time increment:46270 time: 46.27 +field ' c' [explicit solve]: current solution: 9.615545e+01, current residual:5.805774e+01 +field 'mu' [explicit solve]: current solution: 4.996864e+00, current residual:3.020383e+00 +wall time: 0.00067997s + +time increment:46280 time: 46.28 +field ' c' [explicit solve]: current solution: 9.615543e+01, current residual:5.805773e+01 +field 'mu' [explicit solve]: current solution: 4.996770e+00, current residual:3.020325e+00 +wall time: 0.000720024s + +time increment:46290 time: 46.29 +field ' c' [explicit solve]: current solution: 9.615541e+01, current residual:5.805771e+01 +field 'mu' [explicit solve]: current solution: 4.996676e+00, current residual:3.020268e+00 +wall time: 0.000604868s + +time increment:46300 time: 46.3 +field ' c' [explicit solve]: current solution: 9.615539e+01, current residual:5.805770e+01 +field 'mu' [explicit solve]: current solution: 4.996582e+00, current residual:3.020210e+00 +wall time: 0.000756025s + +time increment:46310 time: 46.31 +field ' c' [explicit solve]: current solution: 9.615537e+01, current residual:5.805768e+01 +field 'mu' [explicit solve]: current solution: 4.996488e+00, current residual:3.020152e+00 +wall time: 0.000731945s + +time increment:46320 time: 46.32 +field ' c' [explicit solve]: current solution: 9.615536e+01, current residual:5.805767e+01 +field 'mu' [explicit solve]: current solution: 4.996394e+00, current residual:3.020095e+00 +wall time: 0.000634909s + +time increment:46330 time: 46.33 +field ' c' [explicit solve]: current solution: 9.615534e+01, current residual:5.805765e+01 +field 'mu' [explicit solve]: current solution: 4.996300e+00, current residual:3.020037e+00 +wall time: 0.000686169s + +time increment:46340 time: 46.34 +field ' c' [explicit solve]: current solution: 9.615532e+01, current residual:5.805764e+01 +field 'mu' [explicit solve]: current solution: 4.996207e+00, current residual:3.019980e+00 +wall time: 0.000674009s + +time increment:46350 time: 46.35 +field ' c' [explicit solve]: current solution: 9.615530e+01, current residual:5.805762e+01 +field 'mu' [explicit solve]: current solution: 4.996113e+00, current residual:3.019922e+00 +wall time: 0.000663042s + +time increment:46360 time: 46.36 +field ' c' [explicit solve]: current solution: 9.615529e+01, current residual:5.805761e+01 +field 'mu' [explicit solve]: current solution: 4.996019e+00, current residual:3.019865e+00 +wall time: 0.000705957s + +time increment:46370 time: 46.37 +field ' c' [explicit solve]: current solution: 9.615527e+01, current residual:5.805759e+01 +field 'mu' [explicit solve]: current solution: 4.995925e+00, current residual:3.019808e+00 +wall time: 0.000720024s + +time increment:46380 time: 46.38 +field ' c' [explicit solve]: current solution: 9.615525e+01, current residual:5.805758e+01 +field 'mu' [explicit solve]: current solution: 4.995832e+00, current residual:3.019750e+00 +wall time: 0.000707865s + +time increment:46390 time: 46.39 +field ' c' [explicit solve]: current solution: 9.615523e+01, current residual:5.805756e+01 +field 'mu' [explicit solve]: current solution: 4.995738e+00, current residual:3.019693e+00 +wall time: 0.000657082s + +time increment:46400 time: 46.4 +field ' c' [explicit solve]: current solution: 9.615522e+01, current residual:5.805755e+01 +field 'mu' [explicit solve]: current solution: 4.995645e+00, current residual:3.019635e+00 +wall time: 0.000735998s + +time increment:46410 time: 46.41 +field ' c' [explicit solve]: current solution: 9.615520e+01, current residual:5.805753e+01 +field 'mu' [explicit solve]: current solution: 4.995551e+00, current residual:3.019578e+00 +wall time: 0.000603914s + +time increment:46420 time: 46.42 +field ' c' [explicit solve]: current solution: 9.615518e+01, current residual:5.805752e+01 +field 'mu' [explicit solve]: current solution: 4.995458e+00, current residual:3.019521e+00 +wall time: 0.000705957s + +time increment:46430 time: 46.43 +field ' c' [explicit solve]: current solution: 9.615516e+01, current residual:5.805750e+01 +field 'mu' [explicit solve]: current solution: 4.995364e+00, current residual:3.019463e+00 +wall time: 0.000669956s + +time increment:46440 time: 46.44 +field ' c' [explicit solve]: current solution: 9.615515e+01, current residual:5.805749e+01 +field 'mu' [explicit solve]: current solution: 4.995271e+00, current residual:3.019406e+00 +wall time: 0.00071907s + +time increment:46450 time: 46.45 +field ' c' [explicit solve]: current solution: 9.615513e+01, current residual:5.805747e+01 +field 'mu' [explicit solve]: current solution: 4.995177e+00, current residual:3.019349e+00 +wall time: 0.000687838s + +time increment:46460 time: 46.46 +field ' c' [explicit solve]: current solution: 9.615511e+01, current residual:5.805746e+01 +field 'mu' [explicit solve]: current solution: 4.995084e+00, current residual:3.019291e+00 +wall time: 0.00068903s + +time increment:46470 time: 46.47 +field ' c' [explicit solve]: current solution: 9.615509e+01, current residual:5.805744e+01 +field 'mu' [explicit solve]: current solution: 4.994991e+00, current residual:3.019234e+00 +wall time: 0.000683784s + +time increment:46480 time: 46.48 +field ' c' [explicit solve]: current solution: 9.615508e+01, current residual:5.805743e+01 +field 'mu' [explicit solve]: current solution: 4.994897e+00, current residual:3.019177e+00 +wall time: 0.000644922s + +time increment:46490 time: 46.49 +field ' c' [explicit solve]: current solution: 9.615506e+01, current residual:5.805741e+01 +field 'mu' [explicit solve]: current solution: 4.994804e+00, current residual:3.019120e+00 +wall time: 0.000733852s + +time increment:46500 time: 46.5 +field ' c' [explicit solve]: current solution: 9.615504e+01, current residual:5.805740e+01 +field 'mu' [explicit solve]: current solution: 4.994711e+00, current residual:3.019063e+00 +wall time: 0.000724077s + +time increment:46510 time: 46.51 +field ' c' [explicit solve]: current solution: 9.615503e+01, current residual:5.805738e+01 +field 'mu' [explicit solve]: current solution: 4.994618e+00, current residual:3.019005e+00 +wall time: 0.000626087s + +time increment:46520 time: 46.52 +field ' c' [explicit solve]: current solution: 9.615501e+01, current residual:5.805737e+01 +field 'mu' [explicit solve]: current solution: 4.994524e+00, current residual:3.018948e+00 +wall time: 0.000566006s + +time increment:46530 time: 46.53 +field ' c' [explicit solve]: current solution: 9.615499e+01, current residual:5.805735e+01 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solve]: current solution: 4.994059e+00, current residual:3.018663e+00 +wall time: 0.000566006s + +time increment:46580 time: 46.58 +field ' c' [explicit solve]: current solution: 9.615491e+01, current residual:5.805728e+01 +field 'mu' [explicit solve]: current solution: 4.993966e+00, current residual:3.018606e+00 +wall time: 0.000596046s + +time increment:46590 time: 46.59 +field ' c' [explicit solve]: current solution: 9.615489e+01, current residual:5.805727e+01 +field 'mu' [explicit solve]: current solution: 4.993873e+00, current residual:3.018549e+00 +wall time: 0.000643969s + +time increment:46600 time: 46.6 +field ' c' [explicit solve]: current solution: 9.615487e+01, current residual:5.805725e+01 +field 'mu' [explicit solve]: current solution: 4.993780e+00, current residual:3.018492e+00 +wall time: 0.000699043s + +time increment:46610 time: 46.61 +field ' c' [explicit solve]: current solution: 9.615486e+01, current residual:5.805724e+01 +field 'mu' [explicit solve]: current solution: 4.993687e+00, current residual:3.018435e+00 +wall time: 0.000760078s + +time increment:46620 time: 46.62 +field ' c' [explicit solve]: current solution: 9.615484e+01, current residual:5.805722e+01 +field 'mu' [explicit solve]: current solution: 4.993595e+00, current residual:3.018378e+00 +wall time: 0.000599861s + +time increment:46630 time: 46.63 +field ' c' [explicit solve]: current solution: 9.615483e+01, current residual:5.805721e+01 +field 'mu' [explicit solve]: current solution: 4.993502e+00, current residual:3.018321e+00 +wall time: 0.000640869s + +time increment:46640 time: 46.64 +field ' c' [explicit solve]: current solution: 9.615481e+01, current residual:5.805720e+01 +field 'mu' [explicit solve]: current solution: 4.993409e+00, current residual:3.018264e+00 +wall time: 0.000633001s + +time increment:46650 time: 46.65 +field ' c' [explicit solve]: current solution: 9.615479e+01, current residual:5.805718e+01 +field 'mu' [explicit solve]: current solution: 4.993316e+00, current residual:3.018207e+00 +wall time: 0.000679016s + +time increment:46660 time: 46.66 +field ' c' [explicit solve]: current solution: 9.615478e+01, current residual:5.805717e+01 +field 'mu' [explicit solve]: current solution: 4.993224e+00, current residual:3.018150e+00 +wall time: 0.000603914s + +time increment:46670 time: 46.67 +field ' c' [explicit solve]: current solution: 9.615476e+01, current residual:5.805715e+01 +field 'mu' [explicit solve]: current solution: 4.993131e+00, current residual:3.018093e+00 +wall time: 0.000644207s + +time increment:46680 time: 46.68 +field ' c' [explicit solve]: current solution: 9.615474e+01, current residual:5.805714e+01 +field 'mu' [explicit solve]: current solution: 4.993038e+00, current residual:3.018036e+00 +wall time: 0.000605822s + +time increment:46690 time: 46.69 +field ' c' [explicit solve]: current solution: 9.615473e+01, current residual:5.805712e+01 +field 'mu' [explicit solve]: current solution: 4.992946e+00, current residual:3.017979e+00 +wall time: 0.000724792s + +time increment:46700 time: 46.7 +field ' c' [explicit solve]: current solution: 9.615471e+01, current residual:5.805711e+01 +field 'mu' [explicit solve]: current solution: 4.992853e+00, current residual:3.017922e+00 +wall time: 0.000624895s + +time increment:46710 time: 46.71 +field ' c' [explicit solve]: current solution: 9.615469e+01, current residual:5.805710e+01 +field 'mu' [explicit solve]: current solution: 4.992760e+00, current residual:3.017866e+00 +wall time: 0.000684023s + +time increment:46720 time: 46.72 +field ' c' [explicit solve]: current solution: 9.615468e+01, current residual:5.805708e+01 +field 'mu' [explicit solve]: current solution: 4.992668e+00, current residual:3.017809e+00 +wall time: 0.000581026s + +time increment:46730 time: 46.73 +field ' c' [explicit solve]: current solution: 9.615466e+01, current residual:5.805707e+01 +field 'mu' [explicit solve]: current solution: 4.992575e+00, current residual:3.017752e+00 +wall time: 0.000625134s + +time increment:46740 time: 46.74 +field ' c' [explicit solve]: current solution: 9.615465e+01, current residual:5.805705e+01 +field 'mu' [explicit solve]: current solution: 4.992483e+00, current residual:3.017695e+00 +wall time: 0.000710964s + +time increment:46750 time: 46.75 +field ' c' [explicit solve]: current solution: 9.615463e+01, current residual:5.805704e+01 +field 'mu' [explicit solve]: current solution: 4.992391e+00, current residual:3.017639e+00 +wall time: 0.000659943s + +time increment:46760 time: 46.76 +field ' c' [explicit solve]: current solution: 9.615461e+01, current residual:5.805703e+01 +field 'mu' [explicit solve]: current solution: 4.992298e+00, current residual:3.017582e+00 +wall time: 0.000620842s + +time increment:46770 time: 46.77 +field ' c' [explicit solve]: current solution: 9.615460e+01, current residual:5.805701e+01 +field 'mu' [explicit solve]: current solution: 4.992206e+00, current residual:3.017525e+00 +wall time: 0.000688076s + +time increment:46780 time: 46.78 +field ' c' [explicit solve]: current solution: 9.615458e+01, current residual:5.805700e+01 +field 'mu' [explicit solve]: current solution: 4.992114e+00, current residual:3.017469e+00 +wall time: 0.000581026s + +time increment:46790 time: 46.79 +field ' c' [explicit solve]: current solution: 9.615457e+01, current residual:5.805698e+01 +field 'mu' [explicit solve]: current solution: 4.992021e+00, current residual:3.017412e+00 +wall time: 0.000615835s + +time increment:46800 time: 46.8 +field ' c' [explicit solve]: current solution: 9.615455e+01, current residual:5.805697e+01 +field 'mu' [explicit solve]: current solution: 4.991929e+00, current residual:3.017355e+00 +wall time: 0.000575066s + +time increment:46810 time: 46.81 +field ' c' [explicit solve]: current solution: 9.615454e+01, current residual:5.805696e+01 +field 'mu' [explicit solve]: current solution: 4.991837e+00, current residual:3.017299e+00 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0.000607967s + +time increment:46860 time: 46.86 +field ' c' [explicit solve]: current solution: 9.615446e+01, current residual:5.805689e+01 +field 'mu' [explicit solve]: current solution: 4.991376e+00, current residual:3.017016e+00 +wall time: 0.000692129s + +time increment:46870 time: 46.87 +field ' c' [explicit solve]: current solution: 9.615444e+01, current residual:5.805687e+01 +field 'mu' [explicit solve]: current solution: 4.991284e+00, current residual:3.016959e+00 +wall time: 0.000638962s + +time increment:46880 time: 46.88 +field ' c' [explicit solve]: current solution: 9.615443e+01, current residual:5.805686e+01 +field 'mu' [explicit solve]: current solution: 4.991192e+00, current residual:3.016903e+00 +wall time: 0.000608206s + +time increment:46890 time: 46.89 +field ' c' [explicit solve]: current solution: 9.615441e+01, current residual:5.805685e+01 +field 'mu' [explicit solve]: current solution: 4.991100e+00, current residual:3.016846e+00 +wall time: 0.000664949s + +time increment:46900 time: 46.9 +field ' c' [explicit solve]: current solution: 9.615440e+01, current residual:5.805683e+01 +field 'mu' [explicit solve]: current solution: 4.991008e+00, current residual:3.016790e+00 +wall time: 0.000745058s + +time increment:46910 time: 46.91 +field ' c' [explicit solve]: current solution: 9.615438e+01, current residual:5.805682e+01 +field 'mu' [explicit solve]: current solution: 4.990916e+00, current residual:3.016733e+00 +wall time: 0.000645876s + +time increment:46920 time: 46.92 +field ' c' [explicit solve]: current solution: 9.615436e+01, current residual:5.805681e+01 +field 'mu' [explicit solve]: current solution: 4.990824e+00, current residual:3.016677e+00 +wall time: 0.000666857s + +time increment:46930 time: 46.93 +field ' c' [explicit solve]: current solution: 9.615435e+01, current residual:5.805679e+01 +field 'mu' [explicit solve]: current solution: 4.990733e+00, current residual:3.016621e+00 +wall time: 0.000571966s + +time increment:46940 time: 46.94 +field ' c' [explicit solve]: current solution: 9.615433e+01, current residual:5.805678e+01 +field 'mu' [explicit solve]: current solution: 4.990641e+00, current residual:3.016564e+00 +wall time: 0.000576019s + +time increment:46950 time: 46.95 +field ' c' [explicit solve]: current solution: 9.615432e+01, current residual:5.805677e+01 +field 'mu' [explicit solve]: current solution: 4.990549e+00, current residual:3.016508e+00 +wall time: 0.000657082s + +time increment:46960 time: 46.96 +field ' c' [explicit solve]: current solution: 9.615430e+01, current residual:5.805675e+01 +field 'mu' [explicit solve]: current solution: 4.990457e+00, current residual:3.016452e+00 +wall time: 0.000588179s + +time increment:46970 time: 46.97 +field ' c' [explicit solve]: current solution: 9.615429e+01, current residual:5.805674e+01 +field 'mu' [explicit solve]: current solution: 4.990366e+00, current residual:3.016395e+00 +wall time: 0.000662088s + +time increment:46980 time: 46.98 +field ' c' [explicit solve]: current solution: 9.615427e+01, current residual:5.805672e+01 +field 'mu' [explicit solve]: current solution: 4.990274e+00, current residual:3.016339e+00 +wall time: 0.000665903s + +time increment:46990 time: 46.99 +field ' c' [explicit solve]: current solution: 9.615426e+01, current residual:5.805671e+01 +field 'mu' [explicit solve]: current solution: 4.990182e+00, current residual:3.016283e+00 +wall time: 0.000617981s + +time increment:47000 time: 47 +field ' c' [explicit solve]: current solution: 9.615424e+01, current residual:5.805670e+01 +field 'mu' [explicit solve]: current solution: 4.990091e+00, current residual:3.016226e+00 +wall time: 0.000729084s + +time increment:47010 time: 47.01 +field ' c' [explicit solve]: current solution: 9.615423e+01, current residual:5.805668e+01 +field 'mu' [explicit solve]: current solution: 4.989999e+00, current residual:3.016170e+00 +wall time: 0.000663996s + +time increment:47020 time: 47.02 +field ' c' [explicit solve]: current solution: 9.615421e+01, current residual:5.805667e+01 +field 'mu' [explicit solve]: current solution: 4.989908e+00, current residual:3.016114e+00 +wall time: 0.000725031s + +time increment:47030 time: 47.03 +field ' c' [explicit solve]: current solution: 9.615420e+01, current residual:5.805666e+01 +field 'mu' [explicit solve]: current solution: 4.989816e+00, current residual:3.016058e+00 +wall time: 0.000725985s + +time increment:47040 time: 47.04 +field ' c' [explicit solve]: current solution: 9.615418e+01, current residual:5.805664e+01 +field 'mu' [explicit solve]: current solution: 4.989725e+00, current residual:3.016001e+00 +wall time: 0.000634909s + +time increment:47050 time: 47.05 +field ' c' [explicit solve]: current solution: 9.615417e+01, current residual:5.805663e+01 +field 'mu' [explicit solve]: current solution: 4.989633e+00, current residual:3.015945e+00 +wall time: 0.000695944s + +time increment:47060 time: 47.06 +field ' c' [explicit solve]: current solution: 9.615415e+01, current residual:5.805662e+01 +field 'mu' [explicit solve]: current solution: 4.989542e+00, current residual:3.015889e+00 +wall time: 0.000643969s + +time increment:47070 time: 47.07 +field ' c' [explicit solve]: current solution: 9.615414e+01, current residual:5.805660e+01 +field 'mu' [explicit solve]: current solution: 4.989450e+00, current residual:3.015833e+00 +wall time: 0.000662088s + +time increment:47080 time: 47.08 +field ' c' [explicit solve]: current solution: 9.615412e+01, current residual:5.805659e+01 +field 'mu' [explicit solve]: current solution: 4.989359e+00, current residual:3.015777e+00 +wall time: 0.000657082s + +time increment:47090 time: 47.09 +field ' c' [explicit solve]: current solution: 9.615411e+01, current residual:5.805658e+01 +field 'mu' [explicit solve]: current solution: 4.989268e+00, current residual:3.015721e+00 +wall time: 0.000720978s + +time increment:47100 time: 47.1 +field ' c' [explicit solve]: current solution: 9.615410e+01, current residual:5.805657e+01 +field 'mu' [explicit solve]: current solution: 4.989176e+00, current residual:3.015665e+00 +wall time: 0.00195789s + +time increment:47110 time: 47.11 +field ' c' [explicit solve]: current solution: 9.615408e+01, current residual:5.805655e+01 +field 'mu' [explicit solve]: current solution: 4.989085e+00, current residual:3.015609e+00 +wall time: 0.000677824s + +time increment:47120 time: 47.12 +field ' c' [explicit solve]: current solution: 9.615407e+01, current residual:5.805654e+01 +field 'mu' [explicit solve]: current solution: 4.988994e+00, current residual:3.015552e+00 +wall time: 0.000661135s + +time increment:47130 time: 47.13 +field ' c' [explicit solve]: current solution: 9.615405e+01, current residual:5.805653e+01 +field 'mu' [explicit solve]: current solution: 4.988903e+00, current residual:3.015496e+00 +wall time: 0.000706911s + +time increment:47140 time: 47.14 +field ' c' [explicit solve]: current solution: 9.615404e+01, current residual:5.805651e+01 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[explicit solve]: current solution: 4.988447e+00, current residual:3.015217e+00 +wall time: 0.00068903s + +time increment:47190 time: 47.19 +field ' c' [explicit solve]: current solution: 9.615397e+01, current residual:5.805645e+01 +field 'mu' [explicit solve]: current solution: 4.988356e+00, current residual:3.015161e+00 +wall time: 0.000710011s + +time increment:47200 time: 47.2 +field ' c' [explicit solve]: current solution: 9.615395e+01, current residual:5.805644e+01 +field 'mu' [explicit solve]: current solution: 4.988265e+00, current residual:3.015105e+00 +wall time: 0.00069499s + +time increment:47210 time: 47.21 +field ' c' [explicit solve]: current solution: 9.615394e+01, current residual:5.805642e+01 +field 'mu' [explicit solve]: current solution: 4.988174e+00, current residual:3.015049e+00 +wall time: 0.000653028s + +time increment:47220 time: 47.22 +field ' c' [explicit solve]: current solution: 9.615392e+01, current residual:5.805641e+01 +field 'mu' [explicit solve]: current solution: 4.988083e+00, current residual:3.014993e+00 +wall time: 0.000756025s + +time increment:47230 time: 47.23 +field ' c' [explicit solve]: current solution: 9.615391e+01, current residual:5.805640e+01 +field 'mu' [explicit solve]: current solution: 4.987993e+00, current residual:3.014937e+00 +wall time: 0.000622034s + +time increment:47240 time: 47.24 +field ' c' [explicit solve]: current solution: 9.615390e+01, current residual:5.805638e+01 +field 'mu' [explicit solve]: current solution: 4.987902e+00, current residual:3.014881e+00 +wall time: 0.000677109s + +time increment:47250 time: 47.25 +field ' c' [explicit solve]: current solution: 9.615388e+01, current residual:5.805637e+01 +field 'mu' [explicit solve]: current solution: 4.987811e+00, current residual:3.014825e+00 +wall time: 0.000653982s + +time increment:47260 time: 47.26 +field ' c' [explicit solve]: current solution: 9.615387e+01, current residual:5.805636e+01 +field 'mu' [explicit solve]: current solution: 4.987720e+00, current residual:3.014770e+00 +wall time: 0.000650883s + +time increment:47270 time: 47.27 +field ' c' [explicit solve]: current solution: 9.615385e+01, current residual:5.805635e+01 +field 'mu' [explicit solve]: current solution: 4.987629e+00, current residual:3.014714e+00 +wall time: 0.000761032s + +time increment:47280 time: 47.28 +field ' c' [explicit solve]: current solution: 9.615384e+01, current residual:5.805633e+01 +field 'mu' [explicit solve]: current solution: 4.987539e+00, current residual:3.014658e+00 +wall time: 0.000726938s + +time increment:47290 time: 47.29 +field ' c' [explicit solve]: current solution: 9.615383e+01, current residual:5.805632e+01 +field 'mu' [explicit solve]: current solution: 4.987448e+00, current residual:3.014602e+00 +wall time: 0.000671864s + +time increment:47300 time: 47.3 +field ' c' [explicit solve]: current solution: 9.615381e+01, current residual:5.805631e+01 +field 'mu' [explicit solve]: current solution: 4.987357e+00, current residual:3.014547e+00 +wall time: 0.000663996s + +time increment:47310 time: 47.31 +field ' c' [explicit solve]: current solution: 9.615380e+01, current residual:5.805630e+01 +field 'mu' [explicit solve]: current solution: 4.987267e+00, current residual:3.014491e+00 +wall time: 0.000660181s + +time increment:47320 time: 47.32 +field ' c' [explicit solve]: current solution: 9.615378e+01, current residual:5.805628e+01 +field 'mu' [explicit solve]: current solution: 4.987176e+00, current residual:3.014435e+00 +wall time: 0.000658035s + +time increment:47330 time: 47.33 +field ' c' [explicit solve]: current solution: 9.615377e+01, current residual:5.805627e+01 +field 'mu' [explicit solve]: current solution: 4.987086e+00, current residual:3.014380e+00 +wall time: 0.00063014s + +time increment:47340 time: 47.34 +field ' c' [explicit solve]: current solution: 9.615376e+01, current residual:5.805626e+01 +field 'mu' [explicit solve]: current solution: 4.986995e+00, current residual:3.014324e+00 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0.000560045s + +time increment:47390 time: 47.39 +field ' c' [explicit solve]: current solution: 9.615369e+01, current residual:5.805620e+01 +field 'mu' [explicit solve]: current solution: 4.986543e+00, current residual:3.014046e+00 +wall time: 0.000574827s + +time increment:47400 time: 47.4 +field ' c' [explicit solve]: current solution: 9.615368e+01, current residual:5.805618e+01 +field 'mu' [explicit solve]: current solution: 4.986453e+00, current residual:3.013990e+00 +wall time: 0.000565052s + +time increment:47410 time: 47.41 +field ' c' [explicit solve]: current solution: 9.615366e+01, current residual:5.805617e+01 +field 'mu' [explicit solve]: current solution: 4.986363e+00, current residual:3.013935e+00 +wall time: 0.000684023s + +time increment:47420 time: 47.42 +field ' c' [explicit solve]: current solution: 9.615365e+01, current residual:5.805616e+01 +field 'mu' [explicit solve]: current solution: 4.986272e+00, current residual:3.013879e+00 +wall time: 0.000555992s + +time increment:47430 time: 47.43 +field ' c' [explicit solve]: current solution: 9.615364e+01, current residual:5.805615e+01 +field 'mu' [explicit solve]: current solution: 4.986182e+00, current residual:3.013824e+00 +wall time: 0.000565052s + +time increment:47440 time: 47.44 +field ' c' [explicit solve]: current solution: 9.615362e+01, current residual:5.805613e+01 +field 'mu' [explicit solve]: current solution: 4.986092e+00, current residual:3.013768e+00 +wall time: 0.000700951s + +time increment:47450 time: 47.45 +field ' c' [explicit solve]: current solution: 9.615361e+01, current residual:5.805612e+01 +field 'mu' [explicit solve]: current solution: 4.986002e+00, current residual:3.013713e+00 +wall time: 0.000645876s + +time increment:47460 time: 47.46 +field ' c' [explicit solve]: current solution: 9.615360e+01, current residual:5.805611e+01 +field 'mu' [explicit solve]: current solution: 4.985912e+00, current residual:3.013658e+00 +wall time: 0.000597s + +time increment:47470 time: 47.47 +field ' c' [explicit solve]: current solution: 9.615358e+01, current residual:5.805610e+01 +field 'mu' [explicit solve]: current solution: 4.985821e+00, current residual:3.013602e+00 +wall time: 0.000565052s + +time increment:47480 time: 47.48 +field ' c' [explicit solve]: current solution: 9.615357e+01, current residual:5.805608e+01 +field 'mu' [explicit solve]: current solution: 4.985731e+00, current residual:3.013547e+00 +wall time: 0.000566959s + +time increment:47490 time: 47.49 +field ' c' [explicit solve]: current solution: 9.615356e+01, current residual:5.805607e+01 +field 'mu' [explicit solve]: current solution: 4.985641e+00, current residual:3.013491e+00 +wall time: 0.000755072s + +time increment:47500 time: 47.5 +field ' c' [explicit solve]: current solution: 9.615354e+01, current residual:5.805606e+01 +field 'mu' [explicit solve]: current solution: 4.985551e+00, current residual:3.013436e+00 +wall time: 0.000560045s + +time increment:47510 time: 47.51 +field ' c' [explicit solve]: current solution: 9.615353e+01, current residual:5.805605e+01 +field 'mu' [explicit solve]: current solution: 4.985461e+00, current residual:3.013381e+00 +wall time: 0.000683069s + +time increment:47520 time: 47.52 +field ' c' [explicit solve]: current solution: 9.615352e+01, current residual:5.805604e+01 +field 'mu' [explicit solve]: current solution: 4.985371e+00, current residual:3.013325e+00 +wall time: 0.000671148s + +time increment:47530 time: 47.53 +field ' c' [explicit solve]: current solution: 9.615351e+01, current residual:5.805602e+01 +field 'mu' [explicit solve]: current solution: 4.985282e+00, current residual:3.013270e+00 +wall time: 0.000566006s + +time increment:47540 time: 47.54 +field ' c' [explicit solve]: current solution: 9.615349e+01, current residual:5.805601e+01 +field 'mu' [explicit solve]: current solution: 4.985192e+00, current residual:3.013215e+00 +wall time: 0.000781059s + +time increment:47550 time: 47.55 +field ' c' [explicit solve]: current solution: 9.615348e+01, current residual:5.805600e+01 +field 'mu' [explicit solve]: current solution: 4.985102e+00, current residual:3.013159e+00 +wall time: 0.000635862s + +time increment:47560 time: 47.56 +field ' c' [explicit solve]: current solution: 9.615347e+01, current residual:5.805599e+01 +field 'mu' [explicit solve]: current solution: 4.985012e+00, current residual:3.013104e+00 +wall time: 0.000710011s + +time increment:47570 time: 47.57 +field ' c' [explicit solve]: current solution: 9.615345e+01, current residual:5.805598e+01 +field 'mu' [explicit solve]: current solution: 4.984922e+00, current residual:3.013049e+00 +wall time: 0.000566006s + +time increment:47580 time: 47.58 +field ' c' [explicit solve]: current solution: 9.615344e+01, current residual:5.805596e+01 +field 'mu' [explicit solve]: current solution: 4.984832e+00, current residual:3.012994e+00 +wall time: 0.000591993s + +time increment:47590 time: 47.59 +field ' c' [explicit solve]: current solution: 9.615343e+01, current residual:5.805595e+01 +field 'mu' [explicit solve]: current solution: 4.984743e+00, current residual:3.012939e+00 +wall time: 0.000706911s + +time increment:47600 time: 47.6 +field ' c' [explicit solve]: current solution: 9.615342e+01, current residual:5.805594e+01 +field 'mu' [explicit solve]: current solution: 4.984653e+00, current residual:3.012883e+00 +wall time: 0.00056386s + +time increment:47610 time: 47.61 +field ' c' [explicit solve]: current solution: 9.615340e+01, current residual:5.805593e+01 +field 'mu' [explicit solve]: current solution: 4.984563e+00, current residual:3.012828e+00 +wall time: 0.000595093s + +time increment:47620 time: 47.62 +field ' c' [explicit solve]: current solution: 9.615339e+01, current residual:5.805592e+01 +field 'mu' [explicit solve]: current solution: 4.984474e+00, current residual:3.012773e+00 +wall time: 0.000668049s + +time increment:47630 time: 47.63 +field ' c' [explicit solve]: current solution: 9.615338e+01, current residual:5.805590e+01 +field 'mu' [explicit solve]: current solution: 4.984384e+00, current residual:3.012718e+00 +wall time: 0.0006001s + +time increment:47640 time: 47.64 +field ' c' [explicit solve]: current solution: 9.615337e+01, current residual:5.805589e+01 +field 'mu' [explicit solve]: current solution: 4.984295e+00, current residual:3.012663e+00 +wall time: 0.000617981s + +time increment:47650 time: 47.65 +field ' c' [explicit solve]: current solution: 9.615335e+01, current residual:5.805588e+01 +field 'mu' [explicit solve]: current solution: 4.984205e+00, current residual:3.012608e+00 +wall time: 0.000636101s + +time increment:47660 time: 47.66 +field ' c' [explicit solve]: current solution: 9.615334e+01, current residual:5.805587e+01 +field 'mu' [explicit solve]: current solution: 4.984116e+00, current residual:3.012553e+00 +wall time: 0.000715971s + +time increment:47670 time: 47.67 +field ' c' [explicit solve]: current solution: 9.615333e+01, current residual:5.805586e+01 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solve]: current solution: 4.983669e+00, current residual:3.012278e+00 +wall time: 0.000725031s + +time increment:47720 time: 47.72 +field ' c' [explicit solve]: current solution: 9.615327e+01, current residual:5.805580e+01 +field 'mu' [explicit solve]: current solution: 4.983579e+00, current residual:3.012223e+00 +wall time: 0.000636101s + +time increment:47730 time: 47.73 +field ' c' [explicit solve]: current solution: 9.615326e+01, current residual:5.805579e+01 +field 'mu' [explicit solve]: current solution: 4.983490e+00, current residual:3.012168e+00 +wall time: 0.000590086s + +time increment:47740 time: 47.74 +field ' c' [explicit solve]: current solution: 9.615324e+01, current residual:5.805578e+01 +field 'mu' [explicit solve]: current solution: 4.983401e+00, current residual:3.012113e+00 +wall time: 0.000570059s + +time increment:47750 time: 47.75 +field ' c' [explicit solve]: current solution: 9.615323e+01, current residual:5.805576e+01 +field 'mu' [explicit solve]: current solution: 4.983312e+00, current residual:3.012058e+00 +wall time: 0.000617981s + +time increment:47760 time: 47.76 +field ' c' [explicit solve]: current solution: 9.615322e+01, current residual:5.805575e+01 +field 'mu' [explicit solve]: current solution: 4.983222e+00, current residual:3.012003e+00 +wall time: 0.000630856s + +time increment:47770 time: 47.77 +field ' c' [explicit solve]: current solution: 9.615321e+01, current residual:5.805574e+01 +field 'mu' [explicit solve]: current solution: 4.983133e+00, current residual:3.011948e+00 +wall time: 0.000576973s + +time increment:47780 time: 47.78 +field ' c' [explicit solve]: current solution: 9.615319e+01, current residual:5.805573e+01 +field 'mu' [explicit solve]: current solution: 4.983044e+00, current residual:3.011893e+00 +wall time: 0.000697136s + +time increment:47790 time: 47.79 +field ' c' [explicit solve]: current solution: 9.615318e+01, current residual:5.805572e+01 +field 'mu' [explicit solve]: current solution: 4.982955e+00, current residual:3.011838e+00 +wall time: 0.000607014s + +time increment:47800 time: 47.8 +field ' c' [explicit solve]: current solution: 9.615317e+01, current residual:5.805571e+01 +field 'mu' [explicit solve]: current solution: 4.982866e+00, current residual:3.011784e+00 +wall time: 0.000604153s + +time increment:47810 time: 47.81 +field ' c' [explicit solve]: current solution: 9.615316e+01, current residual:5.805570e+01 +field 'mu' [explicit solve]: current solution: 4.982777e+00, current residual:3.011729e+00 +wall time: 0.000583887s + +time increment:47820 time: 47.82 +field ' c' [explicit solve]: current solution: 9.615315e+01, current residual:5.805568e+01 +field 'mu' [explicit solve]: current solution: 4.982688e+00, current residual:3.011674e+00 +wall time: 0.000566959s + +time increment:47830 time: 47.83 +field ' c' [explicit solve]: current solution: 9.615314e+01, current residual:5.805567e+01 +field 'mu' [explicit solve]: current solution: 4.982599e+00, current residual:3.011619e+00 +wall time: 0.000644922s + +time increment:47840 time: 47.84 +field ' c' [explicit solve]: current solution: 9.615312e+01, current residual:5.805566e+01 +field 'mu' [explicit solve]: current solution: 4.982510e+00, current residual:3.011565e+00 +wall time: 0.000576019s + +time increment:47850 time: 47.85 +field ' c' [explicit solve]: current solution: 9.615311e+01, current residual:5.805565e+01 +field 'mu' [explicit solve]: current solution: 4.982421e+00, current residual:3.011510e+00 +wall time: 0.000598907s + +time increment:47860 time: 47.86 +field ' c' [explicit solve]: current solution: 9.615310e+01, current residual:5.805564e+01 +field 'mu' [explicit solve]: current solution: 4.982332e+00, current residual:3.011455e+00 +wall time: 0.000592947s + +time increment:47870 time: 47.87 +field ' c' [explicit solve]: current solution: 9.615309e+01, current residual:5.805563e+01 +field 'mu' [explicit solve]: current solution: 4.982244e+00, current residual:3.011400e+00 +wall time: 0.000555992s + +time increment:47880 time: 47.88 +field ' c' [explicit solve]: current solution: 9.615308e+01, current residual:5.805562e+01 +field 'mu' [explicit solve]: current solution: 4.982155e+00, current residual:3.011346e+00 +wall time: 0.000649929s + +time increment:47890 time: 47.89 +field ' c' [explicit solve]: current solution: 9.615307e+01, current residual:5.805561e+01 +field 'mu' [explicit solve]: current solution: 4.982066e+00, current residual:3.011291e+00 +wall time: 0.000746012s + +time increment:47900 time: 47.9 +field ' c' [explicit solve]: current solution: 9.615305e+01, current residual:5.805559e+01 +field 'mu' [explicit solve]: current solution: 4.981977e+00, current residual:3.011236e+00 +wall time: 0.000566959s + +time increment:47910 time: 47.91 +field ' c' [explicit solve]: current solution: 9.615304e+01, current residual:5.805558e+01 +field 'mu' [explicit solve]: current solution: 4.981889e+00, current residual:3.011182e+00 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0.000594139s + +time increment:47960 time: 47.96 +field ' c' [explicit solve]: current solution: 9.615299e+01, current residual:5.805553e+01 +field 'mu' [explicit solve]: current solution: 4.981445e+00, current residual:3.010909e+00 +wall time: 0.000600815s + +time increment:47970 time: 47.97 +field ' c' [explicit solve]: current solution: 9.615298e+01, current residual:5.805552e+01 +field 'mu' [explicit solve]: current solution: 4.981357e+00, current residual:3.010854e+00 +wall time: 0.000634909s + +time increment:47980 time: 47.98 +field ' c' [explicit solve]: current solution: 9.615296e+01, current residual:5.805551e+01 +field 'mu' [explicit solve]: current solution: 4.981268e+00, current residual:3.010800e+00 +wall time: 0.000591993s + +time increment:47990 time: 47.99 +field ' c' [explicit solve]: current solution: 9.615295e+01, current residual:5.805549e+01 +field 'mu' [explicit solve]: current solution: 4.981180e+00, current residual:3.010745e+00 +wall time: 0.000574827s + +time increment:48000 time: 48 +field ' c' [explicit solve]: current solution: 9.615294e+01, current residual:5.805548e+01 +field 'mu' [explicit solve]: current solution: 4.981092e+00, current residual:3.010691e+00 +wall time: 0.000565052s + +time increment:48010 time: 48.01 +field ' c' [explicit solve]: current solution: 9.615293e+01, current residual:5.805547e+01 +field 'mu' [explicit solve]: current solution: 4.981003e+00, current residual:3.010637e+00 +wall time: 0.000589132s + +time increment:48020 time: 48.02 +field ' c' [explicit solve]: current solution: 9.615292e+01, current residual:5.805546e+01 +field 'mu' [explicit solve]: current solution: 4.980915e+00, current residual:3.010582e+00 +wall time: 0.000569105s + +time increment:48030 time: 48.03 +field ' c' [explicit solve]: current solution: 9.615291e+01, current residual:5.805545e+01 +field 'mu' [explicit solve]: current solution: 4.980826e+00, current residual:3.010528e+00 +wall time: 0.000584841s + +time increment:48040 time: 48.04 +field ' c' [explicit solve]: current solution: 9.615290e+01, current residual:5.805544e+01 +field 'mu' [explicit solve]: current solution: 4.980738e+00, current residual:3.010473e+00 +wall time: 0.000658989s + +time increment:48050 time: 48.05 +field ' c' [explicit solve]: current solution: 9.615289e+01, current residual:5.805543e+01 +field 'mu' [explicit solve]: current solution: 4.980650e+00, current residual:3.010419e+00 +wall time: 0.000620127s + +time increment:48060 time: 48.06 +field ' c' [explicit solve]: current solution: 9.615288e+01, current residual:5.805542e+01 +field 'mu' [explicit solve]: current solution: 4.980562e+00, current residual:3.010365e+00 +wall time: 0.000609875s + +time increment:48070 time: 48.07 +field ' c' [explicit solve]: current solution: 9.615287e+01, current residual:5.805541e+01 +field 'mu' [explicit solve]: current solution: 4.980473e+00, current residual:3.010310e+00 +wall time: 0.000766993s + +time increment:48080 time: 48.08 +field ' c' [explicit solve]: current solution: 9.615285e+01, current residual:5.805540e+01 +field 'mu' [explicit solve]: current solution: 4.980385e+00, current residual:3.010256e+00 +wall time: 0.000720978s + +time increment:48090 time: 48.09 +field ' c' [explicit solve]: current solution: 9.615284e+01, current residual:5.805539e+01 +field 'mu' [explicit solve]: current solution: 4.980297e+00, current residual:3.010202e+00 +wall time: 0.000715017s + +time increment:48100 time: 48.1 +field ' c' [explicit solve]: current solution: 9.615283e+01, current residual:5.805538e+01 +field 'mu' [explicit solve]: current solution: 4.980209e+00, current residual:3.010147e+00 +wall time: 0.000682831s + +time increment:48110 time: 48.11 +field ' c' [explicit solve]: current solution: 9.615282e+01, current residual:5.805536e+01 +field 'mu' [explicit solve]: current solution: 4.980121e+00, current residual:3.010093e+00 +wall time: 0.000841856s + +time increment:48120 time: 48.12 +field ' c' [explicit solve]: current solution: 9.615281e+01, current residual:5.805535e+01 +field 'mu' [explicit solve]: current solution: 4.980033e+00, current residual:3.010039e+00 +wall time: 0.000712156s + +time increment:48130 time: 48.13 +field ' c' [explicit solve]: current solution: 9.615280e+01, current residual:5.805534e+01 +field 'mu' [explicit solve]: current solution: 4.979945e+00, current residual:3.009985e+00 +wall time: 0.000663042s + +time increment:48140 time: 48.14 +field ' c' [explicit solve]: current solution: 9.615279e+01, current residual:5.805533e+01 +field 'mu' [explicit solve]: current solution: 4.979857e+00, current residual:3.009930e+00 +wall time: 0.00068903s + +time increment:48150 time: 48.15 +field ' c' [explicit solve]: current solution: 9.615278e+01, current residual:5.805532e+01 +field 'mu' [explicit solve]: current solution: 4.979769e+00, current residual:3.009876e+00 +wall time: 0.000696898s + +time increment:48160 time: 48.16 +field ' c' [explicit solve]: current solution: 9.615277e+01, current residual:5.805531e+01 +field 'mu' [explicit solve]: current solution: 4.979681e+00, current residual:3.009822e+00 +wall time: 0.000692129s + +time increment:48170 time: 48.17 +field ' c' [explicit solve]: current solution: 9.615276e+01, current residual:5.805530e+01 +field 'mu' [explicit solve]: current solution: 4.979593e+00, current residual:3.009768e+00 +wall time: 0.000647068s + +time increment:48180 time: 48.18 +field ' c' [explicit solve]: current solution: 9.615275e+01, current residual:5.805529e+01 +field 'mu' [explicit solve]: current solution: 4.979505e+00, current residual:3.009714e+00 +wall time: 0.000656128s + +time increment:48190 time: 48.19 +field ' c' [explicit solve]: current solution: 9.615274e+01, current residual:5.805528e+01 +field 'mu' [explicit solve]: current solution: 4.979417e+00, current residual:3.009660e+00 +wall time: 0.000635862s + +time increment:48200 time: 48.2 +field ' c' [explicit solve]: current solution: 9.615273e+01, current residual:5.805527e+01 +field 'mu' [explicit solve]: current solution: 4.979330e+00, current residual:3.009605e+00 +wall time: 0.00067997s + +time increment:48210 time: 48.21 +field ' c' [explicit solve]: current solution: 9.615272e+01, current residual:5.805526e+01 +field 'mu' [explicit solve]: current solution: 4.979242e+00, current residual:3.009551e+00 +wall time: 0.00071907s + +time increment:48220 time: 48.22 +field ' c' [explicit solve]: current solution: 9.615271e+01, current residual:5.805525e+01 +field 'mu' [explicit solve]: current solution: 4.979154e+00, current residual:3.009497e+00 +wall time: 0.000700951s + +time increment:48230 time: 48.23 +field ' c' [explicit solve]: current solution: 9.615270e+01, current residual:5.805524e+01 +field 'mu' [explicit solve]: current solution: 4.979066e+00, current residual:3.009443e+00 +wall time: 0.000653028s + +time increment:48240 time: 48.24 +field ' c' [explicit solve]: current solution: 9.615269e+01, current residual:5.805523e+01 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[explicit solve]: current solution: 4.978628e+00, current residual:3.009173e+00 +wall time: 0.000740051s + +time increment:48290 time: 48.29 +field ' c' [explicit solve]: current solution: 9.615264e+01, current residual:5.805518e+01 +field 'mu' [explicit solve]: current solution: 4.978541e+00, current residual:3.009119e+00 +wall time: 0.000726223s + +time increment:48300 time: 48.3 +field ' c' [explicit solve]: current solution: 9.615263e+01, current residual:5.805517e+01 +field 'mu' [explicit solve]: current solution: 4.978453e+00, current residual:3.009065e+00 +wall time: 0.000649929s + +time increment:48310 time: 48.31 +field ' c' [explicit solve]: current solution: 9.615262e+01, current residual:5.805516e+01 +field 'mu' [explicit solve]: current solution: 4.978366e+00, current residual:3.009011e+00 +wall time: 0.000651121s + +time increment:48320 time: 48.32 +field ' c' [explicit solve]: current solution: 9.615261e+01, current residual:5.805515e+01 +field 'mu' [explicit solve]: current solution: 4.978278e+00, current residual:3.008957e+00 +wall time: 0.000641108s + +time increment:48330 time: 48.33 +field ' c' [explicit solve]: current solution: 9.615260e+01, current residual:5.805514e+01 +field 'mu' [explicit solve]: current solution: 4.978191e+00, current residual:3.008904e+00 +wall time: 0.00070405s + +time increment:48340 time: 48.34 +field ' c' [explicit solve]: current solution: 9.615259e+01, current residual:5.805513e+01 +field 'mu' [explicit solve]: current solution: 4.978104e+00, current residual:3.008850e+00 +wall time: 0.000697136s + +time increment:48350 time: 48.35 +field ' c' [explicit solve]: current solution: 9.615258e+01, current residual:5.805511e+01 +field 'mu' [explicit solve]: current solution: 4.978016e+00, current residual:3.008796e+00 +wall time: 0.000714064s + +time increment:48360 time: 48.36 +field ' c' [explicit solve]: current solution: 9.615257e+01, current residual:5.805510e+01 +field 'mu' [explicit solve]: current solution: 4.977929e+00, current residual:3.008742e+00 +wall time: 0.000671864s + +time increment:48370 time: 48.37 +field ' c' [explicit solve]: current solution: 9.615256e+01, current residual:5.805509e+01 +field 'mu' [explicit solve]: current solution: 4.977842e+00, current residual:3.008688e+00 +wall time: 0.000697136s + +time increment:48380 time: 48.38 +field ' c' [explicit solve]: current solution: 9.615255e+01, current residual:5.805508e+01 +field 'mu' [explicit solve]: current solution: 4.977754e+00, current residual:3.008634e+00 +wall time: 0.000682831s + +time increment:48390 time: 48.39 +field ' c' [explicit solve]: current solution: 9.615254e+01, current residual:5.805507e+01 +field 'mu' [explicit solve]: current solution: 4.977667e+00, current residual:3.008581e+00 +wall time: 0.000651836s + +time increment:48400 time: 48.4 +field ' c' [explicit solve]: current solution: 9.615253e+01, current residual:5.805506e+01 +field 'mu' [explicit solve]: current solution: 4.977580e+00, current residual:3.008527e+00 +wall time: 0.000679016s + +time increment:48410 time: 48.41 +field ' c' [explicit solve]: current solution: 9.615252e+01, current residual:5.805505e+01 +field 'mu' [explicit solve]: current solution: 4.977493e+00, current residual:3.008473e+00 +wall time: 0.000669956s + +time increment:48420 time: 48.42 +field ' c' [explicit solve]: current solution: 9.615251e+01, current residual:5.805504e+01 +field 'mu' [explicit solve]: current solution: 4.977406e+00, current residual:3.008419e+00 +wall time: 0.00068903s + +time increment:48430 time: 48.43 +field ' c' [explicit solve]: current solution: 9.615250e+01, current residual:5.805503e+01 +field 'mu' [explicit solve]: current solution: 4.977319e+00, current residual:3.008366e+00 +wall time: 0.000573158s + +time increment:48440 time: 48.44 +field ' c' [explicit solve]: current solution: 9.615249e+01, current residual:5.805502e+01 +field 'mu' [explicit solve]: current solution: 4.977231e+00, current residual:3.008312e+00 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0.000648975s + +time increment:48490 time: 48.49 +field ' c' [explicit solve]: current solution: 9.615244e+01, current residual:5.805498e+01 +field 'mu' [explicit solve]: current solution: 4.976797e+00, current residual:3.008044e+00 +wall time: 0.000645876s + +time increment:48500 time: 48.5 +field ' c' [explicit solve]: current solution: 9.615243e+01, current residual:5.805497e+01 +field 'mu' [explicit solve]: current solution: 4.976710e+00, current residual:3.007990e+00 +wall time: 0.000661135s + +time increment:48510 time: 48.51 +field ' c' [explicit solve]: current solution: 9.615243e+01, current residual:5.805496e+01 +field 'mu' [explicit solve]: current solution: 4.976623e+00, current residual:3.007937e+00 +wall time: 0.000598907s + +time increment:48520 time: 48.52 +field ' c' [explicit solve]: current solution: 9.615242e+01, current residual:5.805495e+01 +field 'mu' [explicit solve]: current solution: 4.976536e+00, current residual:3.007883e+00 +wall time: 0.000618935s + +time increment:48530 time: 48.53 +field ' c' [explicit solve]: current solution: 9.615241e+01, current residual:5.805494e+01 +field 'mu' [explicit solve]: current solution: 4.976449e+00, current residual:3.007830e+00 +wall time: 0.000675917s + +time increment:48540 time: 48.54 +field ' c' [explicit solve]: current solution: 9.615240e+01, current residual:5.805493e+01 +field 'mu' [explicit solve]: current solution: 4.976362e+00, current residual:3.007776e+00 +wall time: 0.000638008s + +time increment:48550 time: 48.55 +field ' c' [explicit solve]: current solution: 9.615239e+01, current residual:5.805492e+01 +field 'mu' [explicit solve]: current solution: 4.976276e+00, current residual:3.007723e+00 +wall time: 0.000566006s + +time increment:48560 time: 48.56 +field ' c' [explicit solve]: current solution: 9.615238e+01, current residual:5.805491e+01 +field 'mu' [explicit solve]: current solution: 4.976189e+00, current residual:3.007669e+00 +wall time: 0.000619173s + +time increment:48570 time: 48.57 +field ' c' [explicit solve]: current solution: 9.615237e+01, current residual:5.805490e+01 +field 'mu' [explicit solve]: current solution: 4.976102e+00, current residual:3.007616e+00 +wall time: 0.000638008s + +time increment:48580 time: 48.58 +field ' c' [explicit solve]: current solution: 9.615236e+01, current residual:5.805489e+01 +field 'mu' [explicit solve]: current solution: 4.976016e+00, current residual:3.007562e+00 +wall time: 0.000602961s + +time increment:48590 time: 48.59 +field ' c' [explicit solve]: current solution: 9.615235e+01, current residual:5.805488e+01 +field 'mu' [explicit solve]: current solution: 4.975929e+00, current residual:3.007509e+00 +wall time: 0.000898123s + +time increment:48600 time: 48.6 +field ' c' [explicit solve]: current solution: 9.615234e+01, current residual:5.805487e+01 +field 'mu' [explicit solve]: current solution: 4.975843e+00, current residual:3.007455e+00 +wall time: 0.000674009s + +time increment:48610 time: 48.61 +field ' c' [explicit solve]: current solution: 9.615234e+01, current residual:5.805486e+01 +field 'mu' [explicit solve]: current solution: 4.975756e+00, current residual:3.007402e+00 +wall time: 0.000586033s + +time increment:48620 time: 48.62 +field ' c' [explicit solve]: current solution: 9.615233e+01, current residual:5.805485e+01 +field 'mu' [explicit solve]: current solution: 4.975669e+00, current residual:3.007349e+00 +wall time: 0.000663042s + +time increment:48630 time: 48.63 +field ' c' [explicit solve]: current solution: 9.615232e+01, current residual:5.805484e+01 +field 'mu' [explicit solve]: current solution: 4.975583e+00, current residual:3.007295e+00 +wall time: 0.000650883s + +time increment:48640 time: 48.64 +field ' c' [explicit solve]: current solution: 9.615231e+01, current residual:5.805483e+01 +field 'mu' [explicit solve]: current solution: 4.975497e+00, current residual:3.007242e+00 +wall time: 0.000710964s + +time increment:48650 time: 48.65 +field ' c' [explicit solve]: current solution: 9.615230e+01, current residual:5.805482e+01 +field 'mu' [explicit solve]: current solution: 4.975410e+00, current residual:3.007189e+00 +wall time: 0.000576019s + +time increment:48660 time: 48.66 +field ' c' [explicit solve]: current solution: 9.615229e+01, current residual:5.805481e+01 +field 'mu' [explicit solve]: current solution: 4.975324e+00, current residual:3.007135e+00 +wall time: 0.000717878s + +time increment:48670 time: 48.67 +field ' c' [explicit solve]: current solution: 9.615228e+01, current residual:5.805480e+01 +field 'mu' [explicit solve]: current solution: 4.975237e+00, current residual:3.007082e+00 +wall time: 0.000584126s + +time increment:48680 time: 48.68 +field ' c' [explicit solve]: current solution: 9.615227e+01, current residual:5.805479e+01 +field 'mu' [explicit solve]: current solution: 4.975151e+00, current residual:3.007029e+00 +wall time: 0.000674963s + +time increment:48690 time: 48.69 +field ' c' [explicit solve]: current solution: 9.615227e+01, current residual:5.805478e+01 +field 'mu' [explicit solve]: current solution: 4.975065e+00, current residual:3.006976e+00 +wall time: 0.000619888s + +time increment:48700 time: 48.7 +field ' c' [explicit solve]: current solution: 9.615226e+01, current residual:5.805478e+01 +field 'mu' [explicit solve]: current solution: 4.974978e+00, current residual:3.006922e+00 +wall time: 0.000733852s + +time increment:48710 time: 48.71 +field ' c' [explicit solve]: current solution: 9.615225e+01, current residual:5.805477e+01 +field 'mu' [explicit solve]: current solution: 4.974892e+00, current residual:3.006869e+00 +wall time: 0.000584126s + +time increment:48720 time: 48.72 +field ' c' [explicit solve]: current solution: 9.615224e+01, current residual:5.805476e+01 +field 'mu' [explicit solve]: current solution: 4.974806e+00, current residual:3.006816e+00 +wall time: 0.000701904s + +time increment:48730 time: 48.73 +field ' c' [explicit solve]: current solution: 9.615223e+01, current residual:5.805475e+01 +field 'mu' [explicit solve]: current solution: 4.974720e+00, current residual:3.006763e+00 +wall time: 0.000563145s + +time increment:48740 time: 48.74 +field ' c' [explicit solve]: current solution: 9.615222e+01, current residual:5.805474e+01 +field 'mu' [explicit solve]: current solution: 4.974634e+00, current residual:3.006710e+00 +wall time: 0.00057888s + +time increment:48750 time: 48.75 +field ' c' [explicit solve]: current solution: 9.615222e+01, current residual:5.805473e+01 +field 'mu' [explicit solve]: current solution: 4.974547e+00, current residual:3.006656e+00 +wall time: 0.000607967s + +time increment:48760 time: 48.76 +field ' c' [explicit solve]: current solution: 9.615221e+01, current residual:5.805472e+01 +field 'mu' [explicit solve]: current solution: 4.974461e+00, current residual:3.006603e+00 +wall time: 0.000686169s + +time increment:48770 time: 48.77 +field ' c' [explicit solve]: current solution: 9.615220e+01, current residual:5.805471e+01 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solve]: current solution: 4.974031e+00, current residual:3.006338e+00 +wall time: 0.000605106s + +time increment:48820 time: 48.82 +field ' c' [explicit solve]: current solution: 9.615216e+01, current residual:5.805466e+01 +field 'mu' [explicit solve]: current solution: 4.973945e+00, current residual:3.006285e+00 +wall time: 0.000647068s + +time increment:48830 time: 48.83 +field ' c' [explicit solve]: current solution: 9.615215e+01, current residual:5.805466e+01 +field 'mu' [explicit solve]: current solution: 4.973860e+00, current residual:3.006232e+00 +wall time: 0.00056386s + +time increment:48840 time: 48.84 +field ' c' [explicit solve]: current solution: 9.615214e+01, current residual:5.805465e+01 +field 'mu' [explicit solve]: current solution: 4.973774e+00, current residual:3.006179e+00 +wall time: 0.000625849s + +time increment:48850 time: 48.85 +field ' c' [explicit solve]: current solution: 9.615213e+01, current residual:5.805464e+01 +field 'mu' [explicit solve]: current solution: 4.973688e+00, current residual:3.006126e+00 +wall time: 0.000609875s + +time increment:48860 time: 48.86 +field ' c' [explicit solve]: current solution: 9.615213e+01, current residual:5.805463e+01 +field 'mu' [explicit solve]: current solution: 4.973602e+00, current residual:3.006073e+00 +wall time: 0.00057292s + +time increment:48870 time: 48.87 +field ' c' [explicit solve]: current solution: 9.615212e+01, current residual:5.805462e+01 +field 'mu' [explicit solve]: current solution: 4.973516e+00, current residual:3.006020e+00 +wall time: 0.000692129s + +time increment:48880 time: 48.88 +field ' c' [explicit solve]: current solution: 9.615211e+01, current residual:5.805461e+01 +field 'mu' [explicit solve]: current solution: 4.973431e+00, current residual:3.005967e+00 +wall time: 0.000565052s + +time increment:48890 time: 48.89 +field ' c' [explicit solve]: current solution: 9.615210e+01, current residual:5.805460e+01 +field 'mu' [explicit solve]: current solution: 4.973345e+00, current residual:3.005914e+00 +wall time: 0.000664949s + +time increment:48900 time: 48.9 +field ' c' [explicit solve]: current solution: 9.615209e+01, current residual:5.805459e+01 +field 'mu' [explicit solve]: current solution: 4.973259e+00, current residual:3.005861e+00 +wall time: 0.000664949s + +time increment:48910 time: 48.91 +field ' c' [explicit solve]: current solution: 9.615209e+01, current residual:5.805458e+01 +field 'mu' [explicit solve]: current solution: 4.973174e+00, current residual:3.005808e+00 +wall time: 0.00071311s + +time increment:48920 time: 48.92 +field ' c' [explicit solve]: current solution: 9.615208e+01, current residual:5.805458e+01 +field 'mu' [explicit solve]: current solution: 4.973088e+00, current residual:3.005755e+00 +wall time: 0.000730038s + +time increment:48930 time: 48.93 +field ' c' [explicit solve]: current solution: 9.615207e+01, current residual:5.805457e+01 +field 'mu' [explicit solve]: current solution: 4.973002e+00, current residual:3.005703e+00 +wall time: 0.000638962s + +time increment:48940 time: 48.94 +field ' c' [explicit solve]: current solution: 9.615206e+01, current residual:5.805456e+01 +field 'mu' [explicit solve]: current solution: 4.972917e+00, current residual:3.005650e+00 +wall time: 0.000775099s + +time increment:48950 time: 48.95 +field ' c' [explicit solve]: current solution: 9.615206e+01, current residual:5.805455e+01 +field 'mu' [explicit solve]: current solution: 4.972831e+00, current residual:3.005597e+00 +wall time: 0.000643969s + +time increment:48960 time: 48.96 +field ' c' [explicit solve]: current solution: 9.615205e+01, current residual:5.805454e+01 +field 'mu' [explicit solve]: current solution: 4.972746e+00, current residual:3.005544e+00 +wall time: 0.000746012s + +time increment:48970 time: 48.97 +field ' c' [explicit solve]: current solution: 9.615204e+01, current residual:5.805453e+01 +field 'mu' [explicit solve]: current solution: 4.972660e+00, current residual:3.005491e+00 +wall time: 0.000600815s + +time increment:48980 time: 48.98 +field ' c' [explicit solve]: current solution: 9.615203e+01, current residual:5.805452e+01 +field 'mu' [explicit solve]: current solution: 4.972575e+00, current residual:3.005439e+00 +wall time: 0.000612974s + +time increment:48990 time: 48.99 +field ' c' [explicit solve]: current solution: 9.615203e+01, current residual:5.805451e+01 +field 'mu' [explicit solve]: current solution: 4.972489e+00, current residual:3.005386e+00 +wall time: 0.000570059s + +time increment:49000 time: 49 +field ' c' [explicit solve]: current solution: 9.615202e+01, current residual:5.805451e+01 +field 'mu' [explicit solve]: current solution: 4.972404e+00, current residual:3.005333e+00 +wall time: 0.000597s + +time increment:49010 time: 49.01 +field ' c' [explicit solve]: current solution: 9.615201e+01, current residual:5.805450e+01 +field 'mu' [explicit solve]: current solution: 4.972319e+00, current residual:3.005280e+00 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0.000632048s + +time increment:49060 time: 49.06 +field ' c' [explicit solve]: current solution: 9.615197e+01, current residual:5.805445e+01 +field 'mu' [explicit solve]: current solution: 4.971892e+00, current residual:3.005017e+00 +wall time: 0.000560999s + +time increment:49070 time: 49.07 +field ' c' [explicit solve]: current solution: 9.615197e+01, current residual:5.805445e+01 +field 'mu' [explicit solve]: current solution: 4.971807e+00, current residual:3.004965e+00 +wall time: 0.000742912s + +time increment:49080 time: 49.08 +field ' c' [explicit solve]: current solution: 9.615196e+01, current residual:5.805444e+01 +field 'mu' [explicit solve]: current solution: 4.971722e+00, current residual:3.004912e+00 +wall time: 0.000720024s + +time increment:49090 time: 49.09 +field ' c' [explicit solve]: current solution: 9.615195e+01, current residual:5.805443e+01 +field 'mu' [explicit solve]: current solution: 4.971637e+00, current residual:3.004859e+00 +wall time: 0.000643969s + +time increment:49100 time: 49.1 +field ' c' [explicit solve]: current solution: 9.615195e+01, current residual:5.805442e+01 +field 'mu' [explicit solve]: current solution: 4.971552e+00, current residual:3.004807e+00 +wall time: 0.000707865s + +time increment:49110 time: 49.11 +field ' c' [explicit solve]: current solution: 9.615194e+01, current residual:5.805441e+01 +field 'mu' [explicit solve]: current solution: 4.971467e+00, current residual:3.004754e+00 +wall time: 0.000706911s + +time increment:49120 time: 49.12 +field ' c' [explicit solve]: current solution: 9.615193e+01, current residual:5.805440e+01 +field 'mu' [explicit solve]: current solution: 4.971382e+00, current residual:3.004702e+00 +wall time: 0.000628948s + +time increment:49130 time: 49.13 +field ' c' [explicit solve]: current solution: 9.615193e+01, current residual:5.805440e+01 +field 'mu' [explicit solve]: current solution: 4.971297e+00, current residual:3.004649e+00 +wall time: 0.000746012s + +time increment:49140 time: 49.14 +field ' c' [explicit solve]: current solution: 9.615192e+01, current residual:5.805439e+01 +field 'mu' [explicit solve]: current solution: 4.971212e+00, current residual:3.004597e+00 +wall time: 0.000712872s + +time increment:49150 time: 49.15 +field ' c' [explicit solve]: current solution: 9.615191e+01, current residual:5.805438e+01 +field 'mu' [explicit solve]: current solution: 4.971127e+00, current residual:3.004544e+00 +wall time: 0.000743866s + +time increment:49160 time: 49.16 +field ' c' [explicit solve]: current solution: 9.615190e+01, current residual:5.805437e+01 +field 'mu' [explicit solve]: current solution: 4.971042e+00, current residual:3.004492e+00 +wall time: 0.000700951s + +time increment:49170 time: 49.17 +field ' c' [explicit solve]: current solution: 9.615190e+01, current residual:5.805436e+01 +field 'mu' [explicit solve]: current solution: 4.970957e+00, current residual:3.004440e+00 +wall time: 0.000669003s + +time increment:49180 time: 49.18 +field ' c' [explicit solve]: current solution: 9.615189e+01, current residual:5.805435e+01 +field 'mu' [explicit solve]: current solution: 4.970872e+00, current residual:3.004387e+00 +wall time: 0.000693798s + +time increment:49190 time: 49.19 +field ' c' [explicit solve]: current solution: 9.615188e+01, current residual:5.805435e+01 +field 'mu' [explicit solve]: current solution: 4.970788e+00, current residual:3.004335e+00 +wall time: 0.000696898s + +time increment:49200 time: 49.2 +field ' c' [explicit solve]: current solution: 9.615188e+01, current residual:5.805434e+01 +field 'mu' [explicit solve]: current solution: 4.970703e+00, current residual:3.004282e+00 +wall time: 0.000701904s + +time increment:49210 time: 49.21 +field ' c' [explicit solve]: current solution: 9.615187e+01, current residual:5.805433e+01 +field 'mu' [explicit solve]: current solution: 4.970618e+00, current residual:3.004230e+00 +wall time: 0.000708103s + +time increment:49220 time: 49.22 +field ' c' [explicit solve]: current solution: 9.615186e+01, current residual:5.805432e+01 +field 'mu' [explicit solve]: current solution: 4.970533e+00, current residual:3.004178e+00 +wall time: 0.000667095s + +time increment:49230 time: 49.23 +field ' c' [explicit solve]: current solution: 9.615186e+01, current residual:5.805431e+01 +field 'mu' [explicit solve]: current solution: 4.970449e+00, current residual:3.004125e+00 +wall time: 0.000650883s + +time increment:49240 time: 49.24 +field ' c' [explicit solve]: current solution: 9.615185e+01, current residual:5.805431e+01 +field 'mu' [explicit solve]: current solution: 4.970364e+00, current residual:3.004073e+00 +wall time: 0.000649929s + +time increment:49250 time: 49.25 +field ' c' [explicit solve]: current solution: 9.615184e+01, current residual:5.805430e+01 +field 'mu' [explicit solve]: current solution: 4.970279e+00, current residual:3.004021e+00 +wall time: 0.000712156s + +time increment:49260 time: 49.26 +field ' c' [explicit solve]: current solution: 9.615184e+01, current residual:5.805429e+01 +field 'mu' [explicit solve]: current solution: 4.970195e+00, current residual:3.003968e+00 +wall time: 0.000683069s + +time increment:49270 time: 49.27 +field ' c' [explicit solve]: current solution: 9.615183e+01, current residual:5.805428e+01 +field 'mu' [explicit solve]: current solution: 4.970110e+00, current residual:3.003916e+00 +wall time: 0.000671864s + +time increment:49280 time: 49.28 +field ' c' [explicit solve]: current solution: 9.615183e+01, current residual:5.805427e+01 +field 'mu' [explicit solve]: current solution: 4.970026e+00, current residual:3.003864e+00 +wall time: 0.000705004s + +time increment:49290 time: 49.29 +field ' c' [explicit solve]: current solution: 9.615182e+01, current residual:5.805426e+01 +field 'mu' [explicit solve]: current solution: 4.969941e+00, current residual:3.003812e+00 +wall time: 0.000669003s + +time increment:49300 time: 49.3 +field ' c' [explicit solve]: current solution: 9.615181e+01, current residual:5.805426e+01 +field 'mu' [explicit solve]: current solution: 4.969857e+00, current residual:3.003759e+00 +wall time: 0.000709057s + +time increment:49310 time: 49.31 +field ' c' [explicit solve]: current solution: 9.615181e+01, current residual:5.805425e+01 +field 'mu' [explicit solve]: current solution: 4.969772e+00, current residual:3.003707e+00 +wall time: 0.000679016s + +time increment:49320 time: 49.32 +field ' c' [explicit solve]: current solution: 9.615180e+01, current residual:5.805424e+01 +field 'mu' [explicit solve]: current solution: 4.969688e+00, current residual:3.003655e+00 +wall time: 0.000702858s + +time increment:49330 time: 49.33 +field ' c' [explicit solve]: current solution: 9.615179e+01, current residual:5.805423e+01 +field 'mu' [explicit solve]: current solution: 4.969603e+00, current residual:3.003603e+00 +wall time: 0.000667095s + +time increment:49340 time: 49.34 +field ' c' [explicit solve]: current solution: 9.615179e+01, current residual:5.805423e+01 +field 'mu' [explicit solve]: current solution: 4.969519e+00, current residual:3.003551e+00 +wall time: 0.000730991s + +time increment:49350 time: 49.35 +field ' c' [explicit solve]: current solution: 9.615178e+01, current residual:5.805422e+01 +field 'mu' [explicit solve]: current solution: 4.969434e+00, current residual:3.003499e+00 +wall time: 0.000674009s + +time increment:49360 time: 49.36 +field ' c' [explicit solve]: current solution: 9.615177e+01, current residual:5.805421e+01 +field 'mu' [explicit solve]: current solution: 4.969350e+00, current residual:3.003446e+00 +wall time: 0.00069809s + +time increment:49370 time: 49.37 +field ' c' [explicit solve]: current solution: 9.615177e+01, current residual:5.805420e+01 +field 'mu' [explicit solve]: current solution: 4.969266e+00, current residual:3.003394e+00 +wall time: 0.000669003s + +time increment:49380 time: 49.38 +field ' c' [explicit solve]: current solution: 9.615176e+01, current residual:5.805419e+01 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solve]: current solution: 4.968845e+00, current residual:3.003134e+00 +wall time: 0.000771046s + +time increment:49430 time: 49.43 +field ' c' [explicit solve]: current solution: 9.615173e+01, current residual:5.805416e+01 +field 'mu' [explicit solve]: current solution: 4.968761e+00, current residual:3.003082e+00 +wall time: 0.000721931s + +time increment:49440 time: 49.44 +field ' c' [explicit solve]: current solution: 9.615173e+01, current residual:5.805415e+01 +field 'mu' [explicit solve]: current solution: 4.968677e+00, current residual:3.003030e+00 +wall time: 0.000689983s + +time increment:49450 time: 49.45 +field ' c' [explicit solve]: current solution: 9.615172e+01, current residual:5.805414e+01 +field 'mu' [explicit solve]: current solution: 4.968593e+00, current residual:3.002978e+00 +wall time: 0.000673056s + +time increment:49460 time: 49.46 +field ' c' [explicit solve]: current solution: 9.615172e+01, current residual:5.805413e+01 +field 'mu' [explicit solve]: current solution: 4.968509e+00, current residual:3.002926e+00 +wall time: 0.000675917s + +time increment:49470 time: 49.47 +field ' c' [explicit solve]: current solution: 9.615171e+01, current residual:5.805412e+01 +field 'mu' [explicit solve]: current solution: 4.968425e+00, current residual:3.002874e+00 +wall time: 0.000590801s + +time increment:49480 time: 49.48 +field ' c' [explicit solve]: current solution: 9.615170e+01, current residual:5.805412e+01 +field 'mu' [explicit solve]: current solution: 4.968341e+00, current residual:3.002822e+00 +wall time: 0.000609875s + +time increment:49490 time: 49.49 +field ' c' [explicit solve]: current solution: 9.615170e+01, current residual:5.805411e+01 +field 'mu' [explicit solve]: current solution: 4.968257e+00, current residual:3.002770e+00 +wall time: 0.00059104s + +time increment:49500 time: 49.5 +field ' c' [explicit solve]: current solution: 9.615169e+01, current residual:5.805410e+01 +field 'mu' [explicit solve]: current solution: 4.968173e+00, current residual:3.002719e+00 +wall time: 0.000562906s + +time increment:49510 time: 49.51 +field ' c' [explicit solve]: current solution: 9.615169e+01, current residual:5.805409e+01 +field 'mu' [explicit solve]: current solution: 4.968089e+00, current residual:3.002667e+00 +wall time: 0.000656843s + +time increment:49520 time: 49.52 +field ' c' [explicit solve]: current solution: 9.615168e+01, current residual:5.805409e+01 +field 'mu' [explicit solve]: current solution: 4.968005e+00, current residual:3.002615e+00 +wall time: 0.000594854s + +time increment:49530 time: 49.53 +field ' c' [explicit solve]: current solution: 9.615168e+01, current residual:5.805408e+01 +field 'mu' [explicit solve]: current solution: 4.967921e+00, current residual:3.002563e+00 +wall time: 0.000643015s + +time increment:49540 time: 49.54 +field ' c' [explicit solve]: current solution: 9.615167e+01, current residual:5.805407e+01 +field 'mu' [explicit solve]: current solution: 4.967837e+00, current residual:3.002511e+00 +wall time: 0.000622988s + +time increment:49550 time: 49.55 +field ' c' [explicit solve]: current solution: 9.615166e+01, current residual:5.805406e+01 +field 'mu' [explicit solve]: current solution: 4.967754e+00, current residual:3.002459e+00 +wall time: 0.000710011s + +time increment:49560 time: 49.56 +field ' c' [explicit solve]: current solution: 9.615166e+01, current residual:5.805406e+01 +field 'mu' [explicit solve]: current solution: 4.967670e+00, current residual:3.002408e+00 +wall time: 0.000614882s + +time increment:49570 time: 49.57 +field ' c' [explicit solve]: current solution: 9.615165e+01, current residual:5.805405e+01 +field 'mu' [explicit solve]: current solution: 4.967586e+00, current residual:3.002356e+00 +wall time: 0.000623941s + +time increment:49580 time: 49.58 +field ' c' [explicit solve]: current solution: 9.615165e+01, current residual:5.805404e+01 +field 'mu' [explicit solve]: current solution: 4.967503e+00, current 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time: 49.75 +field ' c' [explicit solve]: current solution: 9.615156e+01, current residual:5.805392e+01 +field 'mu' [explicit solve]: current solution: 4.966084e+00, current residual:3.001427e+00 +wall time: 0.00066185s + +time increment:49760 time: 49.76 +field ' c' [explicit solve]: current solution: 9.615156e+01, current residual:5.805391e+01 +field 'mu' [explicit solve]: current solution: 4.966001e+00, current residual:3.001375e+00 +wall time: 0.000663996s + +time increment:49770 time: 49.77 +field ' c' [explicit solve]: current solution: 9.615155e+01, current residual:5.805391e+01 +field 'mu' [explicit solve]: current solution: 4.965918e+00, current residual:3.001324e+00 +wall time: 0.000652075s + +time increment:49780 time: 49.78 +field ' c' [explicit solve]: current solution: 9.615155e+01, current residual:5.805390e+01 +field 'mu' [explicit solve]: current solution: 4.965835e+00, current residual:3.001273e+00 +wall time: 0.000756025s + +time increment:49790 time: 49.79 +field ' c' [explicit solve]: current solution: 9.615154e+01, current residual:5.805389e+01 +field 'mu' [explicit solve]: current solution: 4.965752e+00, current residual:3.001221e+00 +wall time: 0.000718117s + +time increment:49800 time: 49.8 +field ' c' [explicit solve]: current solution: 9.615154e+01, current residual:5.805389e+01 +field 'mu' [explicit solve]: current solution: 4.965669e+00, current residual:3.001170e+00 +wall time: 0.000716925s + +time increment:49810 time: 49.81 +field ' c' [explicit solve]: current solution: 9.615153e+01, current residual:5.805388e+01 +field 'mu' [explicit solve]: current solution: 4.965586e+00, current residual:3.001118e+00 +wall time: 0.000635147s + +time increment:49820 time: 49.82 +field ' c' [explicit solve]: current solution: 9.615153e+01, current residual:5.805387e+01 +field 'mu' [explicit solve]: current solution: 4.965503e+00, current residual:3.001067e+00 +wall time: 0.000626087s + +time increment:49830 time: 49.83 +field ' c' [explicit solve]: current solution: 9.615152e+01, current residual:5.805386e+01 +field 'mu' [explicit solve]: current solution: 4.965420e+00, current residual:3.001016e+00 +wall time: 0.000684977s + +time increment:49840 time: 49.84 +field ' c' [explicit solve]: current solution: 9.615152e+01, current residual:5.805386e+01 +field 'mu' [explicit solve]: current solution: 4.965337e+00, current residual:3.000964e+00 +wall time: 0.000674963s + +time increment:49850 time: 49.85 +field ' c' [explicit solve]: current solution: 9.615152e+01, current residual:5.805385e+01 +field 'mu' [explicit solve]: current solution: 4.965254e+00, current residual:3.000913e+00 +wall time: 0.000746965s + +time increment:49860 time: 49.86 +field ' c' [explicit solve]: current solution: 9.615151e+01, current residual:5.805384e+01 +field 'mu' [explicit solve]: current solution: 4.965171e+00, current residual:3.000862e+00 +wall time: 0.000718117s + +time increment:49870 time: 49.87 +field ' c' [explicit solve]: current solution: 9.615151e+01, current residual:5.805384e+01 +field 'mu' [explicit solve]: current solution: 4.965088e+00, current residual:3.000811e+00 +wall time: 0.000706196s + +time increment:49880 time: 49.88 +field ' c' [explicit solve]: current solution: 9.615150e+01, current residual:5.805383e+01 +field 'mu' [explicit solve]: current solution: 4.965006e+00, current residual:3.000759e+00 +wall time: 0.000716925s + +time increment:49890 time: 49.89 +field ' c' [explicit solve]: current solution: 9.615150e+01, current residual:5.805382e+01 +field 'mu' [explicit solve]: current solution: 4.964923e+00, current residual:3.000708e+00 +wall time: 0.000681877s + +time increment:49900 time: 49.9 +field ' c' [explicit solve]: current solution: 9.615149e+01, current residual:5.805382e+01 +field 'mu' [explicit solve]: current solution: 4.964840e+00, current residual:3.000657e+00 +wall time: 0.000700951s + +time increment:49910 time: 49.91 +field ' c' [explicit solve]: current solution: 9.615149e+01, current residual:5.805381e+01 +field 'mu' [explicit solve]: current solution: 4.964757e+00, current residual:3.000606e+00 +wall time: 0.000706911s + +time increment:49920 time: 49.92 +field ' c' [explicit solve]: current solution: 9.615149e+01, current residual:5.805380e+01 +field 'mu' [explicit solve]: current solution: 4.964675e+00, current residual:3.000554e+00 +wall time: 0.00067687s + +time increment:49930 time: 49.93 +field ' c' [explicit solve]: current solution: 9.615148e+01, current residual:5.805380e+01 +field 'mu' [explicit solve]: current solution: 4.964592e+00, current residual:3.000503e+00 +wall time: 0.000674009s + +time increment:49940 time: 49.94 +field ' c' [explicit solve]: current solution: 9.615148e+01, current residual:5.805379e+01 +field 'mu' [explicit solve]: current solution: 4.964509e+00, current residual:3.000452e+00 +wall time: 0.000692129s + +time increment:49950 time: 49.95 +field ' c' [explicit solve]: current solution: 9.615147e+01, current residual:5.805378e+01 +field 'mu' [explicit solve]: current solution: 4.964427e+00, current residual:3.000401e+00 +wall time: 0.000625134s + +time increment:49960 time: 49.96 +field ' c' [explicit solve]: current solution: 9.615147e+01, current residual:5.805378e+01 +field 'mu' [explicit solve]: current solution: 4.964344e+00, current residual:3.000350e+00 +wall time: 0.000673056s + +time increment:49970 time: 49.97 +field ' c' [explicit solve]: current solution: 9.615146e+01, current residual:5.805377e+01 +field 'mu' [explicit solve]: current solution: 4.964261e+00, current residual:3.000299e+00 +wall time: 0.000674963s + +time increment:49980 time: 49.98 +field ' c' [explicit solve]: current solution: 9.615146e+01, current residual:5.805376e+01 +field 'mu' [explicit solve]: current solution: 4.964179e+00, current residual:3.000248e+00 +wall time: 0.000695944s + +time increment:49990 time: 49.99 +field ' c' [explicit solve]: current solution: 9.615146e+01, current residual:5.805376e+01 +field 'mu' [explicit solve]: current solution: 4.964096e+00, current residual:3.000197e+00 +wall time: 0.000675917s + +time increment:50000 time: 50 +field ' c' [explicit solve]: current solution: 9.615145e+01, current residual:5.805375e+01 +field 'mu' [explicit solve]: current solution: 4.964014e+00, current residual:3.000145e+00 +wall time: 0.000756979s +fields: 2 +fields: 2 +fields: 2 +fields: 2 +Output written to:solution-050000.pvtu + +Energy: 240.65 +Energy Components: 115.045 125.605 0 + +time increment:50010 time: 50.01 +field ' c' [explicit solve]: current solution: 9.615145e+01, current residual:5.805375e+01 +field 'mu' [explicit solve]: current solution: 4.963931e+00, current residual:3.000094e+00 +wall time: 0.000681877s + +time increment:50020 time: 50.02 +field ' c' [explicit solve]: current solution: 9.615144e+01, current residual:5.805374e+01 +field 'mu' [explicit solve]: current solution: 4.963849e+00, current residual:3.000043e+00 +wall time: 0.00060606s + +time increment:50030 time: 50.03 +field ' c' [explicit solve]: current solution: 9.615144e+01, current residual:5.805373e+01 +field 'mu' [explicit solve]: current solution: 4.963767e+00, current residual:2.999992e+00 +wall time: 0.000750065s + +time increment:50040 time: 50.04 +field ' c' [explicit solve]: current solution: 9.615144e+01, current residual:5.805373e+01 +field 'mu' [explicit solve]: current solution: 4.963684e+00, current residual:2.999941e+00 +wall time: 0.000658989s + +time increment:50050 time: 50.05 +field ' c' [explicit solve]: current solution: 9.615143e+01, current residual:5.805372e+01 +field 'mu' [explicit solve]: current solution: 4.963602e+00, current residual:2.999890e+00 +wall time: 0.000631094s + +time increment:50060 time: 50.06 +field ' c' [explicit solve]: current solution: 9.615143e+01, current residual:5.805371e+01 +field 'mu' [explicit solve]: current solution: 4.963520e+00, current residual:2.999839e+00 +wall time: 0.000673056s + +time increment:50070 time: 50.07 +field ' c' [explicit solve]: current solution: 9.615143e+01, current residual:5.805371e+01 +field 'mu' [explicit solve]: current solution: 4.963437e+00, current residual:2.999788e+00 +wall time: 0.000597954s + +time increment:50080 time: 50.08 +field ' c' [explicit solve]: current solution: 9.615142e+01, current residual:5.805370e+01 +field 'mu' [explicit solve]: current solution: 4.963355e+00, current residual:2.999738e+00 +wall time: 0.00058198s + +time increment:50090 time: 50.09 +field ' c' [explicit solve]: current solution: 9.615142e+01, current residual:5.805369e+01 +field 'mu' [explicit solve]: current solution: 4.963273e+00, current residual:2.999687e+00 +wall time: 0.000643015s + +time increment:50100 time: 50.1 +field ' c' [explicit solve]: current solution: 9.615141e+01, current residual:5.805369e+01 +field 'mu' [explicit solve]: current solution: 4.963191e+00, current residual:2.999636e+00 +wall time: 0.000579119s + +time increment:50110 time: 50.11 +field ' c' [explicit solve]: current solution: 9.615141e+01, current residual:5.805368e+01 +field 'mu' [explicit solve]: current solution: 4.963108e+00, current residual:2.999585e+00 +wall time: 0.000653028s + +time increment:50120 time: 50.12 +field ' c' [explicit solve]: current solution: 9.615141e+01, current residual:5.805368e+01 +field 'mu' [explicit solve]: current solution: 4.963026e+00, current residual:2.999534e+00 +wall time: 0.000673056s + +time increment:50130 time: 50.13 +field ' c' [explicit solve]: current solution: 9.615140e+01, current residual:5.805367e+01 +field 'mu' [explicit solve]: current solution: 4.962944e+00, current residual:2.999483e+00 +wall time: 0.000598907s + +time increment:50140 time: 50.14 +field ' c' [explicit solve]: current solution: 9.615140e+01, current residual:5.805366e+01 +field 'mu' [explicit solve]: current solution: 4.962862e+00, current residual:2.999432e+00 +wall time: 0.000565052s + +time increment:50150 time: 50.15 +field ' c' [explicit solve]: current solution: 9.615140e+01, current residual:5.805366e+01 +field 'mu' [explicit solve]: current solution: 4.962780e+00, current residual:2.999381e+00 +wall time: 0.00066185s + +time increment:50160 time: 50.16 +field ' c' [explicit solve]: current solution: 9.615139e+01, current residual:5.805365e+01 +field 'mu' [explicit solve]: current solution: 4.962698e+00, current residual:2.999331e+00 +wall time: 0.000559092s + +time increment:50170 time: 50.17 +field ' c' [explicit solve]: current solution: 9.615139e+01, current residual:5.805365e+01 +field 'mu' [explicit solve]: current solution: 4.962616e+00, current residual:2.999280e+00 +wall time: 0.000636816s + +time increment:50180 time: 50.18 +field ' c' [explicit solve]: current solution: 9.615139e+01, current residual:5.805364e+01 +field 'mu' [explicit solve]: current solution: 4.962534e+00, current residual:2.999229e+00 +wall time: 0.00070715s + +time increment:50190 time: 50.19 +field ' c' [explicit solve]: current solution: 9.615138e+01, current residual:5.805363e+01 +field 'mu' [explicit solve]: current solution: 4.962452e+00, current residual:2.999178e+00 +wall time: 0.000730991s + +time increment:50200 time: 50.2 +field ' c' [explicit solve]: current solution: 9.615138e+01, current residual:5.805363e+01 +field 'mu' [explicit solve]: current solution: 4.962370e+00, current residual:2.999128e+00 +wall time: 0.000727892s + +time increment:50210 time: 50.21 +field ' c' [explicit solve]: current solution: 9.615138e+01, current residual:5.805362e+01 +field 'mu' [explicit solve]: current solution: 4.962288e+00, current residual:2.999077e+00 +wall time: 0.000749826s + +time increment:50220 time: 50.22 +field ' c' [explicit solve]: current solution: 9.615137e+01, current residual:5.805361e+01 +field 'mu' [explicit solve]: current solution: 4.962206e+00, current residual:2.999026e+00 +wall time: 0.000622988s + +time increment:50230 time: 50.23 +field ' c' [explicit solve]: current solution: 9.615137e+01, current residual:5.805361e+01 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solve]: current solution: 4.961798e+00, current residual:2.998773e+00 +wall time: 0.000682116s + +time increment:50280 time: 50.28 +field ' c' [explicit solve]: current solution: 9.615136e+01, current residual:5.805358e+01 +field 'mu' [explicit solve]: current solution: 4.961716e+00, current residual:2.998722e+00 +wall time: 0.000706911s + +time increment:50290 time: 50.29 +field ' c' [explicit solve]: current solution: 9.615135e+01, current residual:5.805357e+01 +field 'mu' [explicit solve]: current solution: 4.961634e+00, current residual:2.998672e+00 +wall time: 0.000659943s + +time increment:50300 time: 50.3 +field ' c' [explicit solve]: current solution: 9.615135e+01, current residual:5.805357e+01 +field 'mu' [explicit solve]: current solution: 4.961553e+00, current residual:2.998621e+00 +wall time: 0.000643969s + +time increment:50310 time: 50.31 +field ' c' [explicit solve]: current solution: 9.615135e+01, current residual:5.805356e+01 +field 'mu' [explicit solve]: current solution: 4.961471e+00, current residual:2.998570e+00 +wall time: 0.000653028s + +time increment:50320 time: 50.32 +field ' c' [explicit solve]: current solution: 9.615134e+01, current residual:5.805356e+01 +field 'mu' [explicit solve]: current solution: 4.961389e+00, current residual:2.998520e+00 +wall time: 0.000674963s + +time increment:50330 time: 50.33 +field ' c' [explicit solve]: current solution: 9.615134e+01, current residual:5.805355e+01 +field 'mu' [explicit solve]: current solution: 4.961308e+00, current residual:2.998469e+00 +wall time: 0.000693083s + +time increment:50340 time: 50.34 +field ' c' [explicit solve]: current solution: 9.615134e+01, current residual:5.805354e+01 +field 'mu' [explicit solve]: current solution: 4.961226e+00, current residual:2.998419e+00 +wall time: 0.000723124s + +time increment:50350 time: 50.35 +field ' c' [explicit solve]: current solution: 9.615134e+01, current residual:5.805354e+01 +field 'mu' [explicit solve]: current solution: 4.961145e+00, current residual:2.998368e+00 +wall time: 0.000699043s + +time increment:50360 time: 50.36 +field ' c' [explicit solve]: current solution: 9.615133e+01, current residual:5.805353e+01 +field 'mu' [explicit solve]: current solution: 4.961063e+00, current residual:2.998318e+00 +wall time: 0.000630856s + +time increment:50370 time: 50.37 +field ' c' [explicit solve]: current solution: 9.615133e+01, current residual:5.805353e+01 +field 'mu' [explicit solve]: current solution: 4.960982e+00, current residual:2.998267e+00 +wall time: 0.000630856s + +time increment:50380 time: 50.38 +field ' c' [explicit solve]: current solution: 9.615133e+01, current residual:5.805352e+01 +field 'mu' [explicit solve]: current solution: 4.960900e+00, current residual:2.998217e+00 +wall time: 0.000649929s + +time increment:50390 time: 50.39 +field ' c' [explicit solve]: current solution: 9.615132e+01, current residual:5.805352e+01 +field 'mu' [explicit solve]: current solution: 4.960819e+00, current residual:2.998167e+00 +wall time: 0.000626087s + +time increment:50400 time: 50.4 +field ' c' [explicit solve]: current solution: 9.615132e+01, current residual:5.805351e+01 +field 'mu' [explicit solve]: current solution: 4.960738e+00, current residual:2.998116e+00 +wall time: 0.000571012s + +time increment:50410 time: 50.41 +field ' c' [explicit solve]: current solution: 9.615132e+01, current residual:5.805350e+01 +field 'mu' [explicit solve]: current solution: 4.960656e+00, current residual:2.998066e+00 +wall time: 0.000766039s + +time increment:50420 time: 50.42 +field ' c' [explicit solve]: current solution: 9.615132e+01, current residual:5.805350e+01 +field 'mu' [explicit solve]: current solution: 4.960575e+00, current residual:2.998015e+00 +wall time: 0.000726938s + +time increment:50430 time: 50.43 +field ' c' [explicit solve]: current solution: 9.615131e+01, current residual:5.805349e+01 +field 'mu' [explicit solve]: current solution: 4.960494e+00, current residual:2.997965e+00 +wall time: 0.000654936s + +time increment:50440 time: 50.44 +field ' c' [explicit solve]: current solution: 9.615131e+01, current residual:5.805349e+01 +field 'mu' [explicit solve]: current solution: 4.960412e+00, current residual:2.997915e+00 +wall time: 0.000724077s + +time increment:50450 time: 50.45 +field ' c' [explicit solve]: current solution: 9.615131e+01, current residual:5.805348e+01 +field 'mu' [explicit solve]: current solution: 4.960331e+00, current residual:2.997864e+00 +wall time: 0.00069809s + +time increment:50460 time: 50.46 +field ' c' [explicit solve]: current solution: 9.615131e+01, current residual:5.805348e+01 +field 'mu' [explicit solve]: current solution: 4.960250e+00, current residual:2.997814e+00 +wall time: 0.000701904s + +time increment:50470 time: 50.47 +field ' c' [explicit solve]: current solution: 9.615130e+01, current residual:5.805347e+01 +field 'mu' [explicit solve]: current solution: 4.960169e+00, current residual:2.997764e+00 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0.000638008s + +time increment:50520 time: 50.52 +field ' c' [explicit solve]: current solution: 9.615129e+01, current residual:5.805344e+01 +field 'mu' [explicit solve]: current solution: 4.959763e+00, current residual:2.997512e+00 +wall time: 0.000682831s + +time increment:50530 time: 50.53 +field ' c' [explicit solve]: current solution: 9.615129e+01, current residual:5.805344e+01 +field 'mu' [explicit solve]: current solution: 4.959682e+00, current residual:2.997462e+00 +wall time: 0.000664949s + +time increment:50540 time: 50.54 +field ' c' [explicit solve]: current solution: 9.615129e+01, current residual:5.805343e+01 +field 'mu' [explicit solve]: current solution: 4.959601e+00, current residual:2.997412e+00 +wall time: 0.000601053s + +time increment:50550 time: 50.55 +field ' c' [explicit solve]: current solution: 9.615129e+01, current residual:5.805343e+01 +field 'mu' [explicit solve]: current solution: 4.959520e+00, current residual:2.997362e+00 +wall time: 0.000601053s + +time increment:50560 time: 50.56 +field ' c' [explicit solve]: current solution: 9.615128e+01, current residual:5.805342e+01 +field 'mu' [explicit solve]: current solution: 4.959439e+00, current residual:2.997311e+00 +wall time: 0.000656128s + +time increment:50570 time: 50.57 +field ' c' [explicit solve]: current solution: 9.615128e+01, current residual:5.805342e+01 +field 'mu' [explicit solve]: current solution: 4.959359e+00, current residual:2.997261e+00 +wall time: 0.000691891s + +time increment:50580 time: 50.58 +field ' c' [explicit solve]: current solution: 9.615128e+01, current residual:5.805341e+01 +field 'mu' [explicit solve]: current solution: 4.959278e+00, current residual:2.997211e+00 +wall time: 0.000702143s + +time increment:50590 time: 50.59 +field ' c' [explicit solve]: current solution: 9.615128e+01, current residual:5.805341e+01 +field 'mu' [explicit solve]: current solution: 4.959197e+00, current residual:2.997161e+00 +wall time: 0.000788927s + +time increment:50600 time: 50.6 +field ' c' [explicit solve]: current solution: 9.615128e+01, current residual:5.805340e+01 +field 'mu' [explicit solve]: current solution: 4.959116e+00, current residual:2.997111e+00 +wall time: 0.000599146s + +time increment:50610 time: 50.61 +field ' c' [explicit solve]: current solution: 9.615127e+01, current residual:5.805340e+01 +field 'mu' [explicit solve]: current solution: 4.959035e+00, current residual:2.997061e+00 +wall time: 0.00070405s + +time increment:50620 time: 50.62 +field ' c' [explicit solve]: current solution: 9.615127e+01, current residual:5.805339e+01 +field 'mu' [explicit solve]: current solution: 4.958954e+00, current residual:2.997011e+00 +wall time: 0.000668049s + +time increment:50630 time: 50.63 +field ' c' [explicit solve]: current solution: 9.615127e+01, current residual:5.805339e+01 +field 'mu' [explicit solve]: current solution: 4.958874e+00, current residual:2.996961e+00 +wall time: 0.000602961s + +time increment:50640 time: 50.64 +field ' c' [explicit solve]: current solution: 9.615127e+01, current residual:5.805338e+01 +field 'mu' [explicit solve]: current solution: 4.958793e+00, current residual:2.996911e+00 +wall time: 0.000619888s + +time increment:50650 time: 50.65 +field ' c' [explicit solve]: current solution: 9.615127e+01, current residual:5.805338e+01 +field 'mu' [explicit solve]: current solution: 4.958712e+00, current residual:2.996861e+00 +wall time: 0.000666857s + +time increment:50660 time: 50.66 +field ' c' [explicit solve]: current solution: 9.615126e+01, current residual:5.805337e+01 +field 'mu' [explicit solve]: current solution: 4.958632e+00, current residual:2.996811e+00 +wall time: 0.000560999s + +time increment:50670 time: 50.67 +field ' c' [explicit solve]: current solution: 9.615126e+01, current residual:5.805337e+01 +field 'mu' [explicit solve]: current solution: 4.958551e+00, current residual:2.996761e+00 +wall time: 0.000574827s + +time increment:50680 time: 50.68 +field ' c' [explicit solve]: current solution: 9.615126e+01, current residual:5.805336e+01 +field 'mu' [explicit solve]: current solution: 4.958470e+00, current residual:2.996711e+00 +wall time: 0.000625849s + +time increment:50690 time: 50.69 +field ' c' [explicit solve]: current solution: 9.615126e+01, current residual:5.805336e+01 +field 'mu' [explicit solve]: current solution: 4.958390e+00, current residual:2.996661e+00 +wall time: 0.000771046s + +time increment:50700 time: 50.7 +field ' c' [explicit solve]: current solution: 9.615126e+01, current residual:5.805335e+01 +field 'mu' [explicit solve]: current solution: 4.958309e+00, current residual:2.996611e+00 +wall time: 0.000656128s + +time increment:50710 time: 50.71 +field ' c' [explicit solve]: current solution: 9.615126e+01, current residual:5.805335e+01 +field 'mu' [explicit solve]: current solution: 4.958229e+00, current residual:2.996561e+00 +wall time: 0.000718117s + +time increment:50720 time: 50.72 +field ' c' [explicit solve]: current solution: 9.615125e+01, current residual:5.805334e+01 +field 'mu' [explicit solve]: current solution: 4.958148e+00, current residual:2.996511e+00 +wall time: 0.000586987s + +time increment:50730 time: 50.73 +field ' c' [explicit solve]: current solution: 9.615125e+01, current residual:5.805334e+01 +field 'mu' [explicit solve]: current solution: 4.958068e+00, current residual:2.996461e+00 +wall time: 0.00077796s + +time increment:50740 time: 50.74 +field ' c' [explicit solve]: current solution: 9.615125e+01, current residual:5.805333e+01 +field 'mu' [explicit solve]: current solution: 4.957987e+00, current residual:2.996411e+00 +wall time: 0.000646114s + +time increment:50750 time: 50.75 +field ' c' [explicit solve]: current solution: 9.615125e+01, current residual:5.805333e+01 +field 'mu' [explicit solve]: current solution: 4.957907e+00, current residual:2.996361e+00 +wall time: 0.000586033s + +time increment:50760 time: 50.76 +field ' c' [explicit solve]: current solution: 9.615125e+01, current residual:5.805332e+01 +field 'mu' [explicit solve]: current solution: 4.957826e+00, current residual:2.996311e+00 +wall time: 0.000626087s + +time increment:50770 time: 50.77 +field ' c' [explicit solve]: current solution: 9.615125e+01, current residual:5.805332e+01 +field 'mu' [explicit solve]: current solution: 4.957746e+00, current residual:2.996261e+00 +wall time: 0.000653028s + +time increment:50780 time: 50.78 +field ' c' [explicit solve]: current solution: 9.615125e+01, current residual:5.805331e+01 +field 'mu' [explicit solve]: current solution: 4.957666e+00, current residual:2.996212e+00 +wall time: 0.000574112s + +time increment:50790 time: 50.79 +field ' c' [explicit solve]: current solution: 9.615124e+01, current residual:5.805331e+01 +field 'mu' [explicit solve]: current solution: 4.957585e+00, current residual:2.996162e+00 +wall time: 0.000637054s + +time increment:50800 time: 50.8 +field ' c' [explicit solve]: current solution: 9.615124e+01, current residual:5.805330e+01 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[explicit solve]: current solution: 4.957184e+00, current residual:2.995913e+00 +wall time: 0.000583172s + +time increment:50850 time: 50.85 +field ' c' [explicit solve]: current solution: 9.615124e+01, current residual:5.805328e+01 +field 'mu' [explicit solve]: current solution: 4.957104e+00, current residual:2.995863e+00 +wall time: 0.000558138s + +time increment:50860 time: 50.86 +field ' c' [explicit solve]: current solution: 9.615124e+01, current residual:5.805327e+01 +field 'mu' [explicit solve]: current solution: 4.957024e+00, current residual:2.995813e+00 +wall time: 0.000680923s + +time increment:50870 time: 50.87 +field ' c' [explicit solve]: current solution: 9.615123e+01, current residual:5.805327e+01 +field 'mu' [explicit solve]: current solution: 4.956944e+00, current residual:2.995764e+00 +wall time: 0.000672102s + +time increment:50880 time: 50.88 +field ' c' [explicit solve]: current solution: 9.615123e+01, current residual:5.805326e+01 +field 'mu' [explicit solve]: current solution: 4.956864e+00, current residual:2.995714e+00 +wall time: 0.000564814s + +time increment:50890 time: 50.89 +field ' c' [explicit solve]: current solution: 9.615123e+01, current residual:5.805326e+01 +field 'mu' [explicit solve]: current solution: 4.956784e+00, current residual:2.995664e+00 +wall time: 0.000663042s + +time increment:50900 time: 50.9 +field ' c' [explicit solve]: current solution: 9.615123e+01, current residual:5.805325e+01 +field 'mu' [explicit solve]: current solution: 4.956704e+00, current residual:2.995615e+00 +wall time: 0.000580072s + +time increment:50910 time: 50.91 +field ' c' [explicit solve]: current solution: 9.615123e+01, current residual:5.805325e+01 +field 'mu' [explicit solve]: current solution: 4.956624e+00, current residual:2.995565e+00 +wall time: 0.000648975s + +time increment:50920 time: 50.92 +field ' c' [explicit solve]: current solution: 9.615123e+01, current residual:5.805324e+01 +field 'mu' [explicit solve]: current solution: 4.956544e+00, current residual:2.995515e+00 +wall time: 0.000612974s + +time increment:50930 time: 50.93 +field ' c' [explicit solve]: current solution: 9.615123e+01, current residual:5.805324e+01 +field 'mu' [explicit solve]: current solution: 4.956464e+00, current residual:2.995466e+00 +wall time: 0.00063014s + +time increment:50940 time: 50.94 +field ' c' [explicit solve]: current solution: 9.615123e+01, current residual:5.805324e+01 +field 'mu' [explicit solve]: current solution: 4.956384e+00, current residual:2.995416e+00 +wall time: 0.000669003s + +time increment:50950 time: 50.95 +field ' c' [explicit solve]: current solution: 9.615123e+01, current residual:5.805323e+01 +field 'mu' [explicit solve]: current solution: 4.956304e+00, current residual:2.995367e+00 +wall time: 0.000702143s + +time increment:50960 time: 50.96 +field ' c' [explicit solve]: current solution: 9.615123e+01, current residual:5.805323e+01 +field 'mu' [explicit solve]: current solution: 4.956224e+00, current residual:2.995317e+00 +wall time: 0.000748873s + +time increment:50970 time: 50.97 +field ' c' [explicit solve]: current solution: 9.615123e+01, current residual:5.805322e+01 +field 'mu' [explicit solve]: current solution: 4.956144e+00, current residual:2.995268e+00 +wall time: 0.000703096s + +time increment:50980 time: 50.98 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805322e+01 +field 'mu' [explicit solve]: current solution: 4.956065e+00, current residual:2.995218e+00 +wall time: 0.000743866s + +time increment:50990 time: 50.99 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805321e+01 +field 'mu' [explicit solve]: current solution: 4.955985e+00, current residual:2.995169e+00 +wall time: 0.000776052s + +time increment:51000 time: 51 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805321e+01 +field 'mu' [explicit solve]: current solution: 4.955905e+00, current residual:2.995119e+00 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0.000623941s + +time increment:51050 time: 51.05 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805319e+01 +field 'mu' [explicit solve]: current solution: 4.955507e+00, current residual:2.994872e+00 +wall time: 0.000689983s + +time increment:51060 time: 51.06 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805318e+01 +field 'mu' [explicit solve]: current solution: 4.955427e+00, current residual:2.994822e+00 +wall time: 0.000565052s + +time increment:51070 time: 51.07 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805318e+01 +field 'mu' [explicit solve]: current solution: 4.955348e+00, current residual:2.994773e+00 +wall time: 0.000571012s + +time increment:51080 time: 51.08 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805317e+01 +field 'mu' [explicit solve]: current solution: 4.955268e+00, current residual:2.994724e+00 +wall time: 0.000574112s + +time increment:51090 time: 51.09 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805317e+01 +field 'mu' [explicit solve]: current solution: 4.955189e+00, current residual:2.994674e+00 +wall time: 0.000659943s + +time increment:51100 time: 51.1 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805317e+01 +field 'mu' [explicit solve]: current solution: 4.955109e+00, current residual:2.994625e+00 +wall time: 0.000721931s + +time increment:51110 time: 51.11 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805316e+01 +field 'mu' [explicit solve]: current solution: 4.955030e+00, current residual:2.994576e+00 +wall time: 0.000630856s + +time increment:51120 time: 51.12 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805316e+01 +field 'mu' [explicit solve]: current solution: 4.954950e+00, current residual:2.994526e+00 +wall time: 0.000627041s + +time increment:51130 time: 51.13 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805315e+01 +field 'mu' [explicit solve]: current solution: 4.954871e+00, current residual:2.994477e+00 +wall time: 0.00074482s + +time increment:51140 time: 51.14 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805315e+01 +field 'mu' [explicit solve]: current solution: 4.954791e+00, current residual:2.994428e+00 +wall time: 0.000778913s + +time increment:51150 time: 51.15 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805314e+01 +field 'mu' [explicit solve]: current solution: 4.954712e+00, current residual:2.994378e+00 +wall time: 0.000695944s + +time increment:51160 time: 51.16 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805314e+01 +field 'mu' [explicit solve]: current solution: 4.954633e+00, current residual:2.994329e+00 +wall time: 0.000667095s + +time increment:51170 time: 51.17 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805314e+01 +field 'mu' [explicit solve]: current solution: 4.954553e+00, current residual:2.994280e+00 +wall time: 0.000771999s + +time increment:51180 time: 51.18 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805313e+01 +field 'mu' [explicit solve]: current solution: 4.954474e+00, current residual:2.994231e+00 +wall time: 0.00078702s + +time increment:51190 time: 51.19 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805313e+01 +field 'mu' [explicit solve]: current solution: 4.954395e+00, current residual:2.994181e+00 +wall time: 0.000663996s + +time increment:51200 time: 51.2 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805312e+01 +field 'mu' [explicit solve]: current solution: 4.954316e+00, current residual:2.994132e+00 +wall time: 0.000662804s + +time increment:51210 time: 51.21 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805312e+01 +field 'mu' [explicit solve]: current solution: 4.954236e+00, current residual:2.994083e+00 +wall time: 0.000735044s + +time increment:51220 time: 51.22 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805312e+01 +field 'mu' [explicit solve]: current solution: 4.954157e+00, current residual:2.994034e+00 +wall time: 0.000648975s + +time increment:51230 time: 51.23 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805311e+01 +field 'mu' [explicit solve]: current solution: 4.954078e+00, current residual:2.993985e+00 +wall time: 0.000731945s + +time increment:51240 time: 51.24 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805311e+01 +field 'mu' [explicit solve]: current solution: 4.953999e+00, current residual:2.993936e+00 +wall time: 0.000662088s + +time increment:51250 time: 51.25 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805310e+01 +field 'mu' [explicit solve]: current solution: 4.953920e+00, current residual:2.993887e+00 +wall time: 0.000594854s + +time increment:51260 time: 51.26 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805310e+01 +field 'mu' [explicit solve]: current solution: 4.953841e+00, current residual:2.993837e+00 +wall time: 0.000711918s + +time increment:51270 time: 51.27 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805310e+01 +field 'mu' [explicit solve]: current solution: 4.953762e+00, current residual:2.993788e+00 +wall time: 0.000607967s + +time increment:51280 time: 51.28 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805309e+01 +field 'mu' [explicit solve]: current solution: 4.953683e+00, current residual:2.993739e+00 +wall time: 0.000684977s + +time increment:51290 time: 51.29 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805309e+01 +field 'mu' [explicit solve]: current solution: 4.953604e+00, current residual:2.993690e+00 +wall time: 0.000627995s + +time increment:51300 time: 51.3 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805309e+01 +field 'mu' [explicit solve]: current solution: 4.953525e+00, current residual:2.993641e+00 +wall time: 0.000642061s + +time increment:51310 time: 51.31 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805308e+01 +field 'mu' [explicit solve]: current solution: 4.953446e+00, current residual:2.993592e+00 +wall time: 0.00067091s + +time increment:51320 time: 51.32 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805308e+01 +field 'mu' [explicit solve]: current solution: 4.953367e+00, current residual:2.993543e+00 +wall time: 0.000741005s + +time increment:51330 time: 51.33 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805307e+01 +field 'mu' [explicit solve]: current solution: 4.953288e+00, current residual:2.993494e+00 +wall time: 0.000643015s + +time increment:51340 time: 51.34 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805307e+01 +field 'mu' [explicit solve]: current solution: 4.953209e+00, current residual:2.993445e+00 +wall time: 0.000689983s + +time increment:51350 time: 51.35 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805307e+01 +field 'mu' [explicit solve]: current solution: 4.953130e+00, current residual:2.993396e+00 +wall time: 0.000684977s + +time increment:51360 time: 51.36 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805306e+01 +field 'mu' [explicit solve]: current solution: 4.953052e+00, current residual:2.993347e+00 +wall time: 0.000681877s + +time increment:51370 time: 51.37 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805306e+01 +field 'mu' [explicit solve]: current solution: 4.952973e+00, current residual:2.993298e+00 +wall time: 0.000803947s + +time increment:51380 time: 51.38 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805306e+01 +field 'mu' [explicit solve]: current solution: 4.952894e+00, current residual:2.993249e+00 +wall time: 0.000641823s + +time increment:51390 time: 51.39 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805305e+01 +field 'mu' [explicit solve]: current solution: 4.952815e+00, current residual:2.993200e+00 +wall time: 0.000730038s + +time increment:51400 time: 51.4 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805305e+01 +field 'mu' [explicit solve]: current solution: 4.952737e+00, current residual:2.993152e+00 +wall time: 0.000672817s + +time increment:51410 time: 51.41 +field ' c' [explicit solve]: current solution: 9.615122e+01, current residual:5.805304e+01 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solve]: current solution: 4.952343e+00, current residual:2.992907e+00 +wall time: 0.000756025s + +time increment:51460 time: 51.46 +field ' c' [explicit solve]: current solution: 9.615123e+01, current residual:5.805303e+01 +field 'mu' [explicit solve]: current solution: 4.952265e+00, current residual:2.992858e+00 +wall time: 0.000626087s + +time increment:51470 time: 51.47 +field ' c' [explicit solve]: current solution: 9.615123e+01, current residual:5.805302e+01 +field 'mu' [explicit solve]: current solution: 4.952186e+00, current residual:2.992810e+00 +wall time: 0.000665903s + +time increment:51480 time: 51.48 +field ' c' [explicit solve]: current solution: 9.615123e+01, current residual:5.805302e+01 +field 'mu' [explicit solve]: current solution: 4.952108e+00, current residual:2.992761e+00 +wall time: 0.000702858s + +time increment:51490 time: 51.49 +field ' c' [explicit solve]: current solution: 9.615123e+01, current residual:5.805302e+01 +field 'mu' [explicit solve]: current solution: 4.952029e+00, current residual:2.992712e+00 +wall time: 0.000746012s + +time increment:51500 time: 51.5 +field ' c' [explicit solve]: current solution: 9.615123e+01, current residual:5.805301e+01 +field 'mu' [explicit solve]: current solution: 4.951951e+00, current residual:2.992663e+00 +wall time: 0.00058794s + +time increment:51510 time: 51.51 +field ' c' [explicit solve]: current solution: 9.615123e+01, current residual:5.805301e+01 +field 'mu' [explicit solve]: current solution: 4.951872e+00, current residual:2.992615e+00 +wall time: 0.000633001s + +time increment:51520 time: 51.52 +field ' c' [explicit solve]: current solution: 9.615123e+01, current residual:5.805301e+01 +field 'mu' [explicit solve]: current solution: 4.951794e+00, current residual:2.992566e+00 +wall time: 0.00066781s + +time increment:51530 time: 51.53 +field ' c' [explicit solve]: current solution: 9.615123e+01, current residual:5.805300e+01 +field 'mu' [explicit solve]: current solution: 4.951716e+00, current residual:2.992517e+00 +wall time: 0.000762939s + +time increment:51540 time: 51.54 +field ' c' [explicit solve]: current solution: 9.615123e+01, current residual:5.805300e+01 +field 'mu' [explicit solve]: current solution: 4.951637e+00, current residual:2.992469e+00 +wall time: 0.000727892s + +time increment:51550 time: 51.55 +field ' c' [explicit solve]: current solution: 9.615123e+01, current residual:5.805300e+01 +field 'mu' [explicit solve]: current solution: 4.951559e+00, current residual:2.992420e+00 +wall time: 0.000708103s + +time increment:51560 time: 51.56 +field ' c' [explicit solve]: current solution: 9.615124e+01, current residual:5.805299e+01 +field 'mu' [explicit solve]: current solution: 4.951481e+00, current residual:2.992371e+00 +wall time: 0.000716925s + +time increment:51570 time: 51.57 +field ' c' [explicit solve]: current solution: 9.615124e+01, current residual:5.805299e+01 +field 'mu' [explicit solve]: current solution: 4.951402e+00, current residual:2.992323e+00 +wall time: 0.000653982s + +time increment:51580 time: 51.58 +field ' c' [explicit solve]: current solution: 9.615124e+01, current residual:5.805299e+01 +field 'mu' [explicit solve]: current solution: 4.951324e+00, current residual:2.992274e+00 +wall time: 0.000663996s + +time increment:51590 time: 51.59 +field ' c' [explicit solve]: current solution: 9.615124e+01, current residual:5.805298e+01 +field 'mu' [explicit solve]: current solution: 4.951246e+00, current residual:2.992225e+00 +wall time: 0.000628948s + +time increment:51600 time: 51.6 +field ' c' [explicit solve]: current solution: 9.615124e+01, current residual:5.805298e+01 +field 'mu' [explicit solve]: current solution: 4.951168e+00, current residual:2.992177e+00 +wall time: 0.000663996s + +time increment:51610 time: 51.61 +field ' c' [explicit solve]: current solution: 9.615124e+01, current residual:5.805298e+01 +field 'mu' [explicit solve]: current solution: 4.951090e+00, current residual:2.992128e+00 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0.00067997s + +time increment:51660 time: 51.66 +field ' c' [explicit solve]: current solution: 9.615125e+01, current residual:5.805296e+01 +field 'mu' [explicit solve]: current solution: 4.950699e+00, current residual:2.991885e+00 +wall time: 0.000656843s + +time increment:51670 time: 51.67 +field ' c' [explicit solve]: current solution: 9.615125e+01, current residual:5.805296e+01 +field 'mu' [explicit solve]: current solution: 4.950621e+00, current residual:2.991837e+00 +wall time: 0.000677109s + +time increment:51680 time: 51.68 +field ' c' [explicit solve]: current solution: 9.615125e+01, current residual:5.805295e+01 +field 'mu' [explicit solve]: current solution: 4.950543e+00, current residual:2.991788e+00 +wall time: 0.000722885s + +time increment:51690 time: 51.69 +field ' c' [explicit solve]: current solution: 9.615125e+01, current residual:5.805295e+01 +field 'mu' [explicit solve]: current solution: 4.950465e+00, current residual:2.991740e+00 +wall time: 0.000617027s + +time increment:51700 time: 51.7 +field ' c' [explicit solve]: current solution: 9.615125e+01, current residual:5.805295e+01 +field 'mu' [explicit solve]: current solution: 4.950387e+00, current residual:2.991691e+00 +wall time: 0.00057888s + +time increment:51710 time: 51.71 +field ' c' [explicit solve]: current solution: 9.615125e+01, current residual:5.805294e+01 +field 'mu' [explicit solve]: current solution: 4.950309e+00, current residual:2.991643e+00 +wall time: 0.000638008s + +time increment:51720 time: 51.72 +field ' c' [explicit solve]: current solution: 9.615126e+01, current residual:5.805294e+01 +field 'mu' [explicit solve]: current solution: 4.950231e+00, current residual:2.991595e+00 +wall time: 0.000592947s + +time increment:51730 time: 51.73 +field ' c' [explicit solve]: current solution: 9.615126e+01, current residual:5.805294e+01 +field 'mu' [explicit solve]: current solution: 4.950153e+00, current residual:2.991546e+00 +wall time: 0.000653028s + +time increment:51740 time: 51.74 +field ' c' [explicit solve]: current solution: 9.615126e+01, current residual:5.805294e+01 +field 'mu' [explicit solve]: current solution: 4.950076e+00, current residual:2.991498e+00 +wall time: 0.000616074s + +time increment:51750 time: 51.75 +field ' c' [explicit solve]: current solution: 9.615126e+01, current residual:5.805293e+01 +field 'mu' [explicit solve]: current solution: 4.949998e+00, current residual:2.991449e+00 +wall time: 0.000657797s + +time increment:51760 time: 51.76 +field ' c' [explicit solve]: current solution: 9.615126e+01, current residual:5.805293e+01 +field 'mu' [explicit solve]: current solution: 4.949920e+00, current residual:2.991401e+00 +wall time: 0.000567913s + +time increment:51770 time: 51.77 +field ' c' [explicit solve]: current solution: 9.615126e+01, current residual:5.805293e+01 +field 'mu' [explicit solve]: current solution: 4.949842e+00, current residual:2.991353e+00 +wall time: 0.000662804s + +time increment:51780 time: 51.78 +field ' c' [explicit solve]: current solution: 9.615127e+01, current residual:5.805292e+01 +field 'mu' [explicit solve]: current solution: 4.949764e+00, current residual:2.991304e+00 +wall time: 0.000683069s + +time increment:51790 time: 51.79 +field ' c' [explicit solve]: current solution: 9.615127e+01, current residual:5.805292e+01 +field 'mu' [explicit solve]: current solution: 4.949687e+00, current residual:2.991256e+00 +wall time: 0.000755072s + +time increment:51800 time: 51.8 +field ' c' [explicit solve]: current solution: 9.615127e+01, current residual:5.805292e+01 +field 'mu' [explicit solve]: current solution: 4.949609e+00, current residual:2.991208e+00 +wall time: 0.000561953s + +time increment:51810 time: 51.81 +field ' c' [explicit solve]: current solution: 9.615127e+01, current residual:5.805292e+01 +field 'mu' [explicit solve]: current solution: 4.949531e+00, current residual:2.991159e+00 +wall time: 0.000762939s + +time increment:51820 time: 51.82 +field ' c' [explicit solve]: current solution: 9.615127e+01, current residual:5.805291e+01 +field 'mu' [explicit solve]: current solution: 4.949454e+00, current residual:2.991111e+00 +wall time: 0.000583172s + +time increment:51830 time: 51.83 +field ' c' [explicit solve]: current solution: 9.615128e+01, current residual:5.805291e+01 +field 'mu' [explicit solve]: current solution: 4.949376e+00, current residual:2.991063e+00 +wall time: 0.00061202s + +time increment:51840 time: 51.84 +field ' c' [explicit solve]: current solution: 9.615128e+01, current residual:5.805291e+01 +field 'mu' [explicit solve]: current solution: 4.949299e+00, current residual:2.991015e+00 +wall time: 0.000571966s + +time increment:51850 time: 51.85 +field ' c' [explicit solve]: current solution: 9.615128e+01, current residual:5.805290e+01 +field 'mu' [explicit solve]: current solution: 4.949221e+00, current residual:2.990966e+00 +wall time: 0.000668049s + +time increment:51860 time: 51.86 +field ' c' [explicit solve]: current solution: 9.615128e+01, current residual:5.805290e+01 +field 'mu' [explicit solve]: current solution: 4.949143e+00, current residual:2.990918e+00 +wall time: 0.000756025s + +time increment:51870 time: 51.87 +field ' c' [explicit solve]: current solution: 9.615128e+01, current residual:5.805290e+01 +field 'mu' [explicit solve]: current solution: 4.949066e+00, current residual:2.990870e+00 +wall time: 0.000560999s + +time increment:51880 time: 51.88 +field ' c' [explicit solve]: current solution: 9.615129e+01, current residual:5.805290e+01 +field 'mu' [explicit solve]: current solution: 4.948988e+00, current residual:2.990822e+00 +wall time: 0.000574112s + +time increment:51890 time: 51.89 +field ' c' [explicit solve]: current solution: 9.615129e+01, current residual:5.805289e+01 +field 'mu' [explicit solve]: current solution: 4.948911e+00, current residual:2.990773e+00 +wall time: 0.000621796s + +time increment:51900 time: 51.9 +field ' c' [explicit solve]: current solution: 9.615129e+01, current residual:5.805289e+01 +field 'mu' [explicit solve]: current solution: 4.948833e+00, current residual:2.990725e+00 +wall time: 0.000696182s + +time increment:51910 time: 51.91 +field ' c' [explicit solve]: current solution: 9.615129e+01, current residual:5.805289e+01 +field 'mu' [explicit solve]: current solution: 4.948756e+00, current residual:2.990677e+00 +wall time: 0.000597954s + +time increment:51920 time: 51.92 +field ' c' [explicit solve]: current solution: 9.615129e+01, current residual:5.805289e+01 +field 'mu' [explicit solve]: current solution: 4.948679e+00, current residual:2.990629e+00 +wall time: 0.000673056s + +time increment:51930 time: 51.93 +field ' c' [explicit solve]: current solution: 9.615130e+01, current residual:5.805288e+01 +field 'mu' [explicit solve]: current solution: 4.948601e+00, current residual:2.990581e+00 +wall time: 0.000561953s + +time increment:51940 time: 51.94 +field ' c' [explicit solve]: current solution: 9.615130e+01, current residual:5.805288e+01 +field 'mu' [explicit solve]: current solution: 4.948524e+00, current residual:2.990533e+00 +wall time: 0.000652075s + +time increment:51950 time: 51.95 +field ' c' [explicit solve]: current solution: 9.615130e+01, current residual:5.805288e+01 +field 'mu' [explicit solve]: current solution: 4.948447e+00, current residual:2.990485e+00 +wall time: 0.000635862s + +time increment:51960 time: 51.96 +field ' c' [explicit solve]: current solution: 9.615130e+01, current residual:5.805287e+01 +field 'mu' [explicit solve]: current solution: 4.948369e+00, current residual:2.990437e+00 +wall time: 0.000648022s + +time increment:51970 time: 51.97 +field ' c' [explicit solve]: current solution: 9.615131e+01, current residual:5.805287e+01 +field 'mu' [explicit solve]: current solution: 4.948292e+00, current residual:2.990389e+00 +wall time: 0.000643015s + +time increment:51980 time: 51.98 +field ' c' [explicit solve]: current solution: 9.615131e+01, current residual:5.805287e+01 +field 'mu' [explicit solve]: current solution: 4.948215e+00, current residual:2.990340e+00 +wall time: 0.000673056s + +time increment:51990 time: 51.99 +field ' c' [explicit solve]: current solution: 9.615131e+01, current residual:5.805287e+01 +field 'mu' [explicit solve]: current solution: 4.948138e+00, current residual:2.990292e+00 +wall time: 0.000591993s + +time increment:52000 time: 52 +field ' c' [explicit solve]: current solution: 9.615131e+01, current residual:5.805286e+01 +field 'mu' [explicit solve]: current solution: 4.948060e+00, current residual:2.990244e+00 +wall time: 0.000561953s + +time increment:52010 time: 52.01 +field ' c' [explicit solve]: current solution: 9.615131e+01, current residual:5.805286e+01 +field 'mu' [explicit solve]: current solution: 4.947983e+00, current residual:2.990196e+00 +wall time: 0.000715017s + +time increment:52020 time: 52.02 +field ' c' [explicit solve]: current solution: 9.615132e+01, current residual:5.805286e+01 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solve]: current solution: 4.947598e+00, current residual:2.989957e+00 +wall time: 0.000617981s + +time increment:52070 time: 52.07 +field ' c' [explicit solve]: current solution: 9.615133e+01, current residual:5.805285e+01 +field 'mu' [explicit solve]: current solution: 4.947521e+00, current residual:2.989909e+00 +wall time: 0.000603199s + +time increment:52080 time: 52.08 +field ' c' [explicit solve]: current solution: 9.615133e+01, current residual:5.805285e+01 +field 'mu' [explicit solve]: current solution: 4.947444e+00, current residual:2.989861e+00 +wall time: 0.000560045s + +time increment:52090 time: 52.09 +field ' c' [explicit solve]: current solution: 9.615134e+01, current residual:5.805284e+01 +field 'mu' [explicit solve]: current solution: 4.947367e+00, current residual:2.989813e+00 +wall time: 0.0006392s + +time increment:52100 time: 52.1 +field ' c' [explicit solve]: current solution: 9.615134e+01, current residual:5.805284e+01 +field 'mu' [explicit solve]: current solution: 4.947290e+00, current residual:2.989765e+00 +wall time: 0.000657082s + +time increment:52110 time: 52.11 +field ' c' [explicit solve]: current solution: 9.615134e+01, current residual:5.805284e+01 +field 'mu' [explicit solve]: current solution: 4.947213e+00, current residual:2.989717e+00 +wall time: 0.000710011s + +time increment:52120 time: 52.12 +field ' c' [explicit solve]: current solution: 9.615134e+01, current residual:5.805284e+01 +field 'mu' [explicit solve]: current solution: 4.947136e+00, current residual:2.989669e+00 +wall time: 0.000635147s + +time increment:52130 time: 52.13 +field ' c' [explicit solve]: current solution: 9.615135e+01, current residual:5.805283e+01 +field 'mu' [explicit solve]: current solution: 4.947059e+00, current residual:2.989621e+00 +wall time: 0.000646114s + +time increment:52140 time: 52.14 +field ' c' [explicit solve]: current solution: 9.615135e+01, current residual:5.805283e+01 +field 'mu' [explicit solve]: current solution: 4.946982e+00, current residual:2.989573e+00 +wall time: 0.000663996s + +time increment:52150 time: 52.15 +field ' c' [explicit solve]: current solution: 9.615135e+01, current residual:5.805283e+01 +field 'mu' [explicit solve]: current solution: 4.946905e+00, current residual:2.989526e+00 +wall time: 0.000690937s + +time increment:52160 time: 52.16 +field ' c' [explicit solve]: current solution: 9.615135e+01, current residual:5.805283e+01 +field 'mu' [explicit solve]: current solution: 4.946829e+00, current residual:2.989478e+00 +wall time: 0.000669003s + +time increment:52170 time: 52.17 +field ' c' [explicit solve]: current solution: 9.615136e+01, current residual:5.805283e+01 +field 'mu' [explicit solve]: current solution: 4.946752e+00, current residual:2.989430e+00 +wall time: 0.000797987s + +time increment:52180 time: 52.18 +field ' c' [explicit solve]: current solution: 9.615136e+01, current residual:5.805282e+01 +field 'mu' [explicit solve]: current solution: 4.946675e+00, current residual:2.989382e+00 +wall time: 0.000645161s + +time increment:52190 time: 52.19 +field ' c' [explicit solve]: current solution: 9.615136e+01, current residual:5.805282e+01 +field 'mu' [explicit solve]: current solution: 4.946598e+00, current residual:2.989335e+00 +wall time: 0.000582933s + +time increment:52200 time: 52.2 +field ' c' [explicit solve]: current solution: 9.615137e+01, current residual:5.805282e+01 +field 'mu' [explicit solve]: current solution: 4.946522e+00, current residual:2.989287e+00 +wall time: 0.0006001s + +time increment:52210 time: 52.21 +field ' c' [explicit solve]: current solution: 9.615137e+01, current residual:5.805282e+01 +field 'mu' [explicit solve]: current solution: 4.946445e+00, current residual:2.989239e+00 +wall time: 0.000641108s + +time increment:52220 time: 52.22 +field ' c' [explicit solve]: current solution: 9.615137e+01, current residual:5.805281e+01 +field 'mu' [explicit solve]: current solution: 4.946368e+00, current residual:2.989191e+00 +wall time: 0.000582933s + +time increment:52230 time: 52.23 +field ' c' [explicit solve]: current solution: 9.615138e+01, current residual:5.805281e+01 +field 'mu' [explicit solve]: current solution: 4.946292e+00, current residual:2.989144e+00 +wall time: 0.000579834s + +time increment:52240 time: 52.24 +field ' c' [explicit solve]: current solution: 9.615138e+01, current residual:5.805281e+01 +field 'mu' [explicit solve]: current solution: 4.946215e+00, current residual:2.989096e+00 +wall time: 0.000643969s + +time increment:52250 time: 52.25 +field ' c' [explicit solve]: current solution: 9.615138e+01, current residual:5.805281e+01 +field 'mu' [explicit solve]: current solution: 4.946138e+00, current residual:2.989048e+00 +wall time: 0.000583887s + +time increment:52260 time: 52.26 +field ' c' [explicit solve]: current solution: 9.615139e+01, current residual:5.805281e+01 +field 'mu' [explicit solve]: current solution: 4.946062e+00, current residual:2.989001e+00 +wall time: 0.000716925s + +time increment:52270 time: 52.27 +field ' c' [explicit solve]: current solution: 9.615139e+01, current residual:5.805280e+01 +field 'mu' [explicit solve]: current solution: 4.945985e+00, current residual:2.988953e+00 +wall time: 0.000640154s + +time increment:52280 time: 52.28 +field ' c' [explicit solve]: current solution: 9.615139e+01, current residual:5.805280e+01 +field 'mu' [explicit solve]: current solution: 4.945909e+00, current residual:2.988905e+00 +wall time: 0.000699043s + +time increment:52290 time: 52.29 +field ' c' [explicit solve]: current solution: 9.615139e+01, current residual:5.805280e+01 +field 'mu' [explicit solve]: current solution: 4.945832e+00, current residual:2.988858e+00 +wall time: 0.000619888s + +time increment:52300 time: 52.3 +field ' c' [explicit solve]: current solution: 9.615140e+01, current residual:5.805280e+01 +field 'mu' [explicit solve]: current solution: 4.945756e+00, current residual:2.988810e+00 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0.000560045s + +time increment:52350 time: 52.35 +field ' c' [explicit solve]: current solution: 9.615142e+01, current residual:5.805279e+01 +field 'mu' [explicit solve]: current solution: 4.945374e+00, current residual:2.988572e+00 +wall time: 0.000751019s + +time increment:52360 time: 52.36 +field ' c' [explicit solve]: current solution: 9.615142e+01, current residual:5.805279e+01 +field 'mu' [explicit solve]: current solution: 4.945298e+00, current residual:2.988525e+00 +wall time: 0.000739098s + +time increment:52370 time: 52.37 +field ' c' [explicit solve]: current solution: 9.615142e+01, current residual:5.805279e+01 +field 'mu' [explicit solve]: current solution: 4.945221e+00, current residual:2.988477e+00 +wall time: 0.000663042s + +time increment:52380 time: 52.38 +field ' c' [explicit solve]: current solution: 9.615143e+01, current residual:5.805278e+01 +field 'mu' [explicit solve]: current solution: 4.945145e+00, current residual:2.988430e+00 +wall time: 0.000673056s + +time increment:52390 time: 52.39 +field ' c' [explicit solve]: current solution: 9.615143e+01, current residual:5.805278e+01 +field 'mu' [explicit solve]: current solution: 4.945069e+00, current residual:2.988382e+00 +wall time: 0.000716925s + +time increment:52400 time: 52.4 +field ' c' [explicit solve]: current solution: 9.615143e+01, current residual:5.805278e+01 +field 'mu' [explicit solve]: current solution: 4.944992e+00, current residual:2.988335e+00 +wall time: 0.000672102s + +time increment:52410 time: 52.41 +field ' c' [explicit solve]: current solution: 9.615144e+01, current residual:5.805278e+01 +field 'mu' [explicit solve]: current solution: 4.944916e+00, current residual:2.988287e+00 +wall time: 0.00070715s + +time increment:52420 time: 52.42 +field ' c' [explicit solve]: current solution: 9.615144e+01, current residual:5.805278e+01 +field 'mu' [explicit solve]: current solution: 4.944840e+00, current residual:2.988240e+00 +wall time: 0.000653028s + +time increment:52430 time: 52.43 +field ' c' [explicit solve]: current solution: 9.615144e+01, current residual:5.805278e+01 +field 'mu' [explicit solve]: current solution: 4.944764e+00, current residual:2.988192e+00 +wall time: 0.000716925s + +time increment:52440 time: 52.44 +field ' c' [explicit solve]: current solution: 9.615145e+01, current residual:5.805277e+01 +field 'mu' [explicit solve]: current solution: 4.944688e+00, current residual:2.988145e+00 +wall time: 0.000692844s + +time increment:52450 time: 52.45 +field ' c' [explicit solve]: current solution: 9.615145e+01, current residual:5.805277e+01 +field 'mu' [explicit solve]: current solution: 4.944612e+00, current residual:2.988098e+00 +wall time: 0.000770807s + +time increment:52460 time: 52.46 +field ' c' [explicit solve]: current solution: 9.615145e+01, current residual:5.805277e+01 +field 'mu' [explicit solve]: current solution: 4.944536e+00, current residual:2.988050e+00 +wall time: 0.000762224s + +time increment:52470 time: 52.47 +field ' c' [explicit solve]: current solution: 9.615146e+01, current residual:5.805277e+01 +field 'mu' [explicit solve]: current solution: 4.944460e+00, current residual:2.988003e+00 +wall time: 0.000662804s + +time increment:52480 time: 52.48 +field ' c' [explicit solve]: current solution: 9.615146e+01, current residual:5.805277e+01 +field 'mu' [explicit solve]: current solution: 4.944384e+00, current residual:2.987956e+00 +wall time: 0.000733852s + +time increment:52490 time: 52.49 +field ' c' [explicit solve]: current solution: 9.615147e+01, current residual:5.805277e+01 +field 'mu' [explicit solve]: current solution: 4.944308e+00, current residual:2.987908e+00 +wall time: 0.000696182s + +time increment:52500 time: 52.5 +field ' c' [explicit solve]: current solution: 9.615147e+01, current residual:5.805276e+01 +field 'mu' [explicit solve]: current solution: 4.944232e+00, current residual:2.987861e+00 +wall time: 0.000681877s + +time increment:52510 time: 52.51 +field ' c' [explicit solve]: current solution: 9.615147e+01, current residual:5.805276e+01 +field 'mu' [explicit solve]: current solution: 4.944156e+00, current residual:2.987814e+00 +wall time: 0.000752926s + +time increment:52520 time: 52.52 +field ' c' [explicit solve]: current solution: 9.615148e+01, current residual:5.805276e+01 +field 'mu' [explicit solve]: current solution: 4.944080e+00, current residual:2.987766e+00 +wall time: 0.000741005s + +time increment:52530 time: 52.53 +field ' c' [explicit solve]: current solution: 9.615148e+01, current residual:5.805276e+01 +field 'mu' [explicit solve]: current solution: 4.944004e+00, current residual:2.987719e+00 +wall time: 0.000746012s + +time increment:52540 time: 52.54 +field ' c' [explicit solve]: current solution: 9.615149e+01, current residual:5.805276e+01 +field 'mu' [explicit solve]: current solution: 4.943928e+00, current residual:2.987672e+00 +wall time: 0.000620127s + +time increment:52550 time: 52.55 +field ' c' [explicit solve]: current solution: 9.615149e+01, current residual:5.805276e+01 +field 'mu' [explicit solve]: current solution: 4.943852e+00, current residual:2.987624e+00 +wall time: 0.000669003s + +time increment:52560 time: 52.56 +field ' c' [explicit solve]: current solution: 9.615149e+01, current residual:5.805275e+01 +field 'mu' [explicit solve]: current solution: 4.943776e+00, current residual:2.987577e+00 +wall time: 0.00071907s + +time increment:52570 time: 52.57 +field ' c' [explicit solve]: current solution: 9.615150e+01, current residual:5.805275e+01 +field 'mu' [explicit solve]: current solution: 4.943700e+00, current residual:2.987530e+00 +wall time: 0.000658989s + +time increment:52580 time: 52.58 +field ' c' [explicit solve]: current solution: 9.615150e+01, current residual:5.805275e+01 +field 'mu' [explicit solve]: current solution: 4.943624e+00, current residual:2.987483e+00 +wall time: 0.000685215s + +time increment:52590 time: 52.59 +field ' c' [explicit solve]: current solution: 9.615151e+01, current residual:5.805275e+01 +field 'mu' [explicit solve]: current solution: 4.943549e+00, current residual:2.987435e+00 +wall time: 0.000793934s + +time increment:52600 time: 52.6 +field ' c' [explicit solve]: current solution: 9.615151e+01, current residual:5.805275e+01 +field 'mu' [explicit solve]: current solution: 4.943473e+00, current residual:2.987388e+00 +wall time: 0.00067997s + +time increment:52610 time: 52.61 +field ' c' [explicit solve]: current solution: 9.615151e+01, current residual:5.805275e+01 +field 'mu' [explicit solve]: current solution: 4.943397e+00, current residual:2.987341e+00 +wall time: 0.000680923s + +time increment:52620 time: 52.62 +field ' c' [explicit solve]: current solution: 9.615152e+01, current residual:5.805275e+01 +field 'mu' [explicit solve]: current solution: 4.943322e+00, current residual:2.987294e+00 +wall time: 0.000644922s + +time increment:52630 time: 52.63 +field ' c' [explicit solve]: current solution: 9.615152e+01, current residual:5.805275e+01 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[explicit solve]: current solution: 4.942943e+00, current residual:2.987058e+00 +wall time: 0.000680923s + +time increment:52680 time: 52.68 +field ' c' [explicit solve]: current solution: 9.615155e+01, current residual:5.805274e+01 +field 'mu' [explicit solve]: current solution: 4.942868e+00, current residual:2.987011e+00 +wall time: 0.000661135s + +time increment:52690 time: 52.69 +field ' c' [explicit solve]: current solution: 9.615155e+01, current residual:5.805274e+01 +field 'mu' [explicit solve]: current solution: 4.942792e+00, current residual:2.986964e+00 +wall time: 0.00065279s + +time increment:52700 time: 52.7 +field ' c' [explicit solve]: current solution: 9.615155e+01, current residual:5.805274e+01 +field 'mu' [explicit solve]: current solution: 4.942717e+00, current residual:2.986917e+00 +wall time: 0.000744104s + +time increment:52710 time: 52.71 +field ' c' [explicit solve]: current solution: 9.615156e+01, current residual:5.805274e+01 +field 'mu' [explicit solve]: current solution: 4.942641e+00, current residual:2.986870e+00 +wall time: 0.000743866s + +time increment:52720 time: 52.72 +field ' c' [explicit solve]: current solution: 9.615156e+01, current residual:5.805273e+01 +field 'mu' [explicit solve]: current solution: 4.942566e+00, current residual:2.986823e+00 +wall time: 0.000660896s + +time increment:52730 time: 52.73 +field ' c' [explicit solve]: current solution: 9.615157e+01, current residual:5.805273e+01 +field 'mu' [explicit solve]: current solution: 4.942490e+00, current residual:2.986776e+00 +wall time: 0.00068593s + +time increment:52740 time: 52.74 +field ' c' [explicit solve]: current solution: 9.615157e+01, current residual:5.805273e+01 +field 'mu' [explicit solve]: current solution: 4.942415e+00, current residual:2.986729e+00 +wall time: 0.000758886s + +time increment:52750 time: 52.75 +field ' c' [explicit solve]: current solution: 9.615158e+01, current residual:5.805273e+01 +field 'mu' [explicit solve]: current solution: 4.942339e+00, current residual:2.986682e+00 +wall time: 0.000649929s + +time increment:52760 time: 52.76 +field ' c' [explicit solve]: current solution: 9.615158e+01, current residual:5.805273e+01 +field 'mu' [explicit solve]: current solution: 4.942264e+00, current residual:2.986635e+00 +wall time: 0.000663042s + +time increment:52770 time: 52.77 +field ' c' [explicit solve]: current solution: 9.615159e+01, current residual:5.805273e+01 +field 'mu' [explicit solve]: current solution: 4.942189e+00, current residual:2.986588e+00 +wall time: 0.000573874s + +time increment:52780 time: 52.78 +field ' c' [explicit solve]: current solution: 9.615159e+01, current residual:5.805273e+01 +field 'mu' [explicit solve]: current solution: 4.942113e+00, current residual:2.986541e+00 +wall time: 0.000648975s + +time increment:52790 time: 52.79 +field ' c' [explicit solve]: current solution: 9.615160e+01, current residual:5.805273e+01 +field 'mu' [explicit solve]: current solution: 4.942038e+00, current residual:2.986494e+00 +wall time: 0.000566959s + +time increment:52800 time: 52.8 +field ' c' [explicit solve]: current solution: 9.615160e+01, current residual:5.805273e+01 +field 'mu' [explicit solve]: current solution: 4.941963e+00, current residual:2.986447e+00 +wall time: 0.000567913s + +time increment:52810 time: 52.81 +field ' c' [explicit solve]: current solution: 9.615160e+01, current residual:5.805272e+01 +field 'mu' [explicit solve]: current solution: 4.941888e+00, current residual:2.986400e+00 +wall time: 0.000589132s + +time increment:52820 time: 52.82 +field ' c' [explicit solve]: current solution: 9.615161e+01, current residual:5.805272e+01 +field 'mu' [explicit solve]: current solution: 4.941812e+00, current residual:2.986353e+00 +wall time: 0.00069499s + +time increment:52830 time: 52.83 +field ' c' [explicit solve]: current solution: 9.615161e+01, current residual:5.805272e+01 +field 'mu' [explicit solve]: current solution: 4.941737e+00, current residual:2.986307e+00 +wall time: 0.000684977s + +time increment:52840 time: 52.84 +field ' c' [explicit solve]: current solution: 9.615162e+01, current residual:5.805272e+01 +field 'mu' [explicit solve]: current solution: 4.941662e+00, current residual:2.986260e+00 +wall time: 0.000669003s + +time increment:52850 time: 52.85 +field ' c' [explicit solve]: current solution: 9.615162e+01, current residual:5.805272e+01 +field 'mu' [explicit solve]: current solution: 4.941587e+00, current residual:2.986213e+00 +wall time: 0.000581026s + +time increment:52860 time: 52.86 +field ' c' [explicit solve]: current solution: 9.615163e+01, current residual:5.805272e+01 +field 'mu' [explicit solve]: current solution: 4.941512e+00, current residual:2.986166e+00 +wall time: 0.000687122s + +time increment:52870 time: 52.87 +field ' c' [explicit solve]: current solution: 9.615163e+01, current residual:5.805272e+01 +field 'mu' [explicit solve]: current solution: 4.941436e+00, current residual:2.986119e+00 +wall time: 0.000595093s + +time increment:52880 time: 52.88 +field ' c' [explicit solve]: current solution: 9.615164e+01, current residual:5.805272e+01 +field 'mu' [explicit solve]: current solution: 4.941361e+00, current residual:2.986072e+00 +wall time: 0.000663996s + +time increment:52890 time: 52.89 +field ' c' [explicit solve]: current solution: 9.615164e+01, current residual:5.805272e+01 +field 'mu' [explicit solve]: current solution: 4.941286e+00, current residual:2.986026e+00 +wall time: 0.000571966s + +time increment:52900 time: 52.9 +field ' c' [explicit solve]: current solution: 9.615165e+01, current residual:5.805272e+01 +field 'mu' [explicit solve]: current solution: 4.941211e+00, current residual:2.985979e+00 +wall time: 0.000560999s + +time increment:52910 time: 52.91 +field ' c' [explicit solve]: current solution: 9.615165e+01, current residual:5.805272e+01 +field 'mu' [explicit solve]: current solution: 4.941136e+00, current residual:2.985932e+00 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0.000597954s + +time increment:52960 time: 52.96 +field ' c' [explicit solve]: current solution: 9.615168e+01, current residual:5.805271e+01 +field 'mu' [explicit solve]: current solution: 4.940761e+00, current residual:2.985698e+00 +wall time: 0.000612974s + +time increment:52970 time: 52.97 +field ' c' [explicit solve]: current solution: 9.615169e+01, current residual:5.805271e+01 +field 'mu' [explicit solve]: current solution: 4.940687e+00, current residual:2.985652e+00 +wall time: 0.000592947s + +time increment:52980 time: 52.98 +field ' c' [explicit solve]: current solution: 9.615169e+01, current residual:5.805271e+01 +field 'mu' [explicit solve]: current solution: 4.940612e+00, current residual:2.985605e+00 +wall time: 0.000652075s + +time increment:52990 time: 52.99 +field ' c' [explicit solve]: current solution: 9.615170e+01, current residual:5.805271e+01 +field 'mu' [explicit solve]: current solution: 4.940537e+00, current residual:2.985558e+00 +wall time: 0.000651121s + +time increment:53000 time: 53 +field ' c' [explicit solve]: current solution: 9.615170e+01, current residual:5.805271e+01 +field 'mu' [explicit solve]: current solution: 4.940462e+00, current residual:2.985512e+00 +wall time: 0.00065589s + +time increment:53010 time: 53.01 +field ' c' [explicit solve]: current solution: 9.615171e+01, current residual:5.805271e+01 +field 'mu' [explicit solve]: current solution: 4.940387e+00, current residual:2.985465e+00 +wall time: 0.000641108s + +time increment:53020 time: 53.02 +field ' c' [explicit solve]: current solution: 9.615171e+01, current residual:5.805271e+01 +field 'mu' [explicit solve]: current solution: 4.940312e+00, current residual:2.985418e+00 +wall time: 0.00069499s + +time increment:53030 time: 53.03 +field ' c' [explicit solve]: current solution: 9.615172e+01, current residual:5.805271e+01 +field 'mu' [explicit solve]: current solution: 4.940238e+00, current residual:2.985372e+00 +wall time: 0.00070405s + +time increment:53040 time: 53.04 +field ' c' [explicit solve]: current solution: 9.615172e+01, current residual:5.805271e+01 +field 'mu' [explicit solve]: current solution: 4.940163e+00, current residual:2.985325e+00 +wall time: 0.000655174s + +time increment:53050 time: 53.05 +field ' c' [explicit solve]: current solution: 9.615173e+01, current residual:5.805271e+01 +field 'mu' [explicit solve]: current solution: 4.940088e+00, current residual:2.985278e+00 +wall time: 0.000637054s + +time increment:53060 time: 53.06 +field ' c' [explicit solve]: current solution: 9.615173e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.940014e+00, current residual:2.985232e+00 +wall time: 0.000568867s + +time increment:53070 time: 53.07 +field ' c' [explicit solve]: current solution: 9.615174e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.939939e+00, current residual:2.985185e+00 +wall time: 0.000576019s + +time increment:53080 time: 53.08 +field ' c' [explicit solve]: current solution: 9.615174e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.939864e+00, current residual:2.985139e+00 +wall time: 0.000687122s + +time increment:53090 time: 53.09 +field ' c' [explicit solve]: current solution: 9.615175e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.939790e+00, current residual:2.985092e+00 +wall time: 0.000640154s + +time increment:53100 time: 53.1 +field ' c' [explicit solve]: current solution: 9.615176e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.939715e+00, current residual:2.985046e+00 +wall time: 0.000617027s + +time increment:53110 time: 53.11 +field ' c' [explicit solve]: current solution: 9.615176e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.939640e+00, current residual:2.984999e+00 +wall time: 0.000565052s + +time increment:53120 time: 53.12 +field ' c' [explicit solve]: current solution: 9.615177e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.939566e+00, current residual:2.984953e+00 +wall time: 0.000646114s + +time increment:53130 time: 53.13 +field ' c' [explicit solve]: current solution: 9.615177e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.939491e+00, current residual:2.984906e+00 +wall time: 0.000597s + +time increment:53140 time: 53.14 +field ' c' [explicit solve]: current solution: 9.615178e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.939417e+00, current residual:2.984860e+00 +wall time: 0.00065589s + +time increment:53150 time: 53.15 +field ' c' [explicit solve]: current solution: 9.615178e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.939342e+00, current residual:2.984813e+00 +wall time: 0.00062418s + +time increment:53160 time: 53.16 +field ' c' [explicit solve]: current solution: 9.615179e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.939268e+00, current residual:2.984767e+00 +wall time: 0.000563145s + +time increment:53170 time: 53.17 +field ' c' [explicit solve]: current solution: 9.615180e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.939193e+00, current residual:2.984720e+00 +wall time: 0.000619173s + +time increment:53180 time: 53.18 +field ' c' [explicit solve]: current solution: 9.615180e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.939119e+00, current residual:2.984674e+00 +wall time: 0.000559807s + +time increment:53190 time: 53.19 +field ' c' [explicit solve]: current solution: 9.615181e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.939045e+00, current residual:2.984627e+00 +wall time: 0.000571012s + +time increment:53200 time: 53.2 +field ' c' [explicit solve]: current solution: 9.615181e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.938970e+00, current residual:2.984581e+00 +wall time: 0.000691891s + +time increment:53210 time: 53.21 +field ' c' [explicit solve]: current solution: 9.615182e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.938896e+00, current residual:2.984535e+00 +wall time: 0.00065589s + +time increment:53220 time: 53.22 +field ' c' [explicit solve]: current solution: 9.615183e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.938822e+00, current residual:2.984488e+00 +wall time: 0.000637054s + +time increment:53230 time: 53.23 +field ' c' [explicit solve]: current solution: 9.615183e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.938747e+00, current residual:2.984442e+00 +wall time: 0.000579119s + +time increment:53240 time: 53.24 +field ' c' [explicit solve]: current solution: 9.615184e+01, current residual:5.805270e+01 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[explicit solve]: current solution: 4.938376e+00, current residual:2.984210e+00 +wall time: 0.000609875s + +time increment:53290 time: 53.29 +field ' c' [explicit solve]: current solution: 9.615187e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.938302e+00, current residual:2.984164e+00 +wall time: 0.000669003s + +time increment:53300 time: 53.3 +field ' c' [explicit solve]: current solution: 9.615187e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.938228e+00, current residual:2.984118e+00 +wall time: 0.000628948s + +time increment:53310 time: 53.31 +field ' c' [explicit solve]: current solution: 9.615188e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.938154e+00, current residual:2.984072e+00 +wall time: 0.000596046s + +time increment:53320 time: 53.32 +field ' c' [explicit solve]: current solution: 9.615189e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: 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4.937783e+00, current residual:2.983840e+00 +wall time: 0.000602961s + +time increment:53370 time: 53.37 +field ' c' [explicit solve]: current solution: 9.615192e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.937709e+00, current residual:2.983794e+00 +wall time: 0.00071907s + +time increment:53380 time: 53.38 +field ' c' [explicit solve]: current solution: 9.615192e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.937635e+00, current residual:2.983748e+00 +wall time: 0.000566959s + +time increment:53390 time: 53.39 +field ' c' [explicit solve]: current solution: 9.615193e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.937561e+00, current residual:2.983702e+00 +wall time: 0.000597954s + +time increment:53400 time: 53.4 +field ' c' [explicit solve]: current solution: 9.615194e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.937488e+00, current residual:2.983656e+00 +wall time: 0.000591993s + +time increment:53410 time: 53.41 +field ' c' [explicit solve]: current solution: 9.615194e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.937414e+00, current residual:2.983610e+00 +wall time: 0.000565052s + +time increment:53420 time: 53.42 +field ' c' [explicit solve]: current solution: 9.615195e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.937340e+00, current residual:2.983564e+00 +wall time: 0.000596046s + +time increment:53430 time: 53.43 +field ' c' [explicit solve]: current solution: 9.615196e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.937266e+00, current residual:2.983517e+00 +wall time: 0.000555992s + +time increment:53440 time: 53.44 +field ' c' [explicit solve]: current solution: 9.615196e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.937192e+00, current residual:2.983471e+00 +wall time: 0.000637054s + +time increment:53450 time: 53.45 +field ' c' [explicit solve]: current solution: 9.615197e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.937118e+00, current residual:2.983425e+00 +wall time: 0.000595093s + +time increment:53460 time: 53.46 +field ' c' [explicit solve]: current solution: 9.615198e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.937044e+00, current residual:2.983379e+00 +wall time: 0.00065589s + +time increment:53470 time: 53.47 +field ' c' [explicit solve]: current solution: 9.615198e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.936971e+00, current residual:2.983333e+00 +wall time: 0.000603914s + +time increment:53480 time: 53.48 +field ' c' [explicit solve]: current solution: 9.615199e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.936897e+00, current residual:2.983287e+00 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0.000573874s + +time increment:53530 time: 53.53 +field ' c' [explicit solve]: current solution: 9.615202e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.936529e+00, current residual:2.983057e+00 +wall time: 0.000683784s + +time increment:53540 time: 53.54 +field ' c' [explicit solve]: current solution: 9.615203e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.936455e+00, current residual:2.983011e+00 +wall time: 0.000567913s + +time increment:53550 time: 53.55 +field ' c' [explicit solve]: current solution: 9.615204e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.936381e+00, current residual:2.982965e+00 +wall time: 0.00065589s + +time increment:53560 time: 53.56 +field ' c' [explicit solve]: current solution: 9.615204e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.936308e+00, current residual:2.982919e+00 +wall time: 0.000652075s + +time increment:53570 time: 53.57 +field ' c' [explicit solve]: current solution: 9.615205e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.936234e+00, current residual:2.982873e+00 +wall time: 0.000735998s + +time increment:53580 time: 53.58 +field ' c' [explicit solve]: current solution: 9.615206e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.936161e+00, current residual:2.982827e+00 +wall time: 0.000638008s + +time increment:53590 time: 53.59 +field ' c' [explicit solve]: current solution: 9.615206e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.936087e+00, current residual:2.982782e+00 +wall time: 0.000742912s + +time increment:53600 time: 53.6 +field ' c' [explicit solve]: current solution: 9.615207e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.936014e+00, current residual:2.982736e+00 +wall time: 0.000805855s + +time increment:53610 time: 53.61 +field ' c' [explicit solve]: current solution: 9.615208e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.935940e+00, current residual:2.982690e+00 +wall time: 0.000680923s + +time increment:53620 time: 53.62 +field ' c' [explicit solve]: current solution: 9.615209e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.935867e+00, current residual:2.982644e+00 +wall time: 0.000780106s + +time increment:53630 time: 53.63 +field ' c' [explicit solve]: current solution: 9.615209e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.935793e+00, current residual:2.982598e+00 +wall time: 0.000631094s + +time increment:53640 time: 53.64 +field ' c' [explicit solve]: current solution: 9.615210e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.935720e+00, current residual:2.982552e+00 +wall time: 0.000648975s + +time increment:53650 time: 53.65 +field ' c' [explicit solve]: current solution: 9.615211e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.935647e+00, current residual:2.982506e+00 +wall time: 0.000660896s + +time increment:53660 time: 53.66 +field ' c' [explicit solve]: current solution: 9.615211e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.935573e+00, current residual:2.982461e+00 +wall time: 0.000643969s + +time increment:53670 time: 53.67 +field ' c' [explicit solve]: current solution: 9.615212e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.935500e+00, current residual:2.982415e+00 +wall time: 0.000735044s + +time increment:53680 time: 53.68 +field ' c' [explicit solve]: current solution: 9.615213e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.935427e+00, current residual:2.982369e+00 +wall time: 0.000654936s + +time increment:53690 time: 53.69 +field ' c' [explicit solve]: current solution: 9.615214e+01, current residual:5.805270e+01 +field 'mu' [explicit solve]: current solution: 4.935353e+00, current residual:2.982323e+00 +wall time: 0.000656843s + +time increment:53700 time: 53.7 +field ' c' [explicit solve]: current solution: 9.615214e+01, current residual:5.805271e+01 +field 'mu' [explicit solve]: current solution: 4.935280e+00, current residual:2.982277e+00 +wall time: 0.000669956s + +time increment:53710 time: 53.71 +field ' c' [explicit solve]: current solution: 9.615215e+01, current residual:5.805271e+01 +field 'mu' [explicit solve]: current solution: 4.935207e+00, current residual:2.982232e+00 +wall time: 0.000684977s + +time increment:53720 time: 53.72 +field ' c' [explicit solve]: current solution: 9.615216e+01, current residual:5.805271e+01 +field 'mu' [explicit solve]: current solution: 4.935134e+00, current residual:2.982186e+00 +wall time: 0.000731945s + +time increment:53730 time: 53.73 +field ' c' [explicit solve]: current solution: 9.615217e+01, current residual:5.805271e+01 +field 'mu' [explicit solve]: current solution: 4.935060e+00, current residual:2.982140e+00 +wall time: 0.000689983s + +time increment:53740 time: 53.74 +field ' c' [explicit solve]: current solution: 9.615217e+01, current residual:5.805271e+01 +field 'mu' [explicit solve]: current solution: 4.934987e+00, current residual:2.982095e+00 +wall time: 0.000725985s + +time increment:53750 time: 53.75 +field ' c' [explicit solve]: current solution: 9.615218e+01, current residual:5.805271e+01 +field 'mu' [explicit solve]: current solution: 4.934914e+00, current residual:2.982049e+00 +wall time: 0.000653028s + +time increment:53760 time: 53.76 +field ' c' [explicit solve]: current solution: 9.615219e+01, current residual:5.805271e+01 +field 'mu' [explicit solve]: current solution: 4.934841e+00, current residual:2.982003e+00 +wall time: 0.00067091s + +time increment:53770 time: 53.77 +field ' c' [explicit solve]: current solution: 9.615219e+01, current residual:5.805271e+01 +field 'mu' [explicit solve]: current solution: 4.934768e+00, current residual:2.981957e+00 +wall time: 0.000666142s + +time increment:53780 time: 53.78 +field ' c' [explicit solve]: current solution: 9.615220e+01, current residual:5.805271e+01 +field 'mu' [explicit solve]: current solution: 4.934695e+00, current residual:2.981912e+00 +wall time: 0.00074482s + +time increment:53790 time: 53.79 +field ' c' [explicit solve]: current solution: 9.615221e+01, current residual:5.805271e+01 +field 'mu' [explicit solve]: current solution: 4.934622e+00, current residual:2.981866e+00 +wall time: 0.000717878s + +time increment:53800 time: 53.8 +field ' c' [explicit solve]: current solution: 9.615222e+01, current residual:5.805271e+01 +field 'mu' [explicit solve]: current solution: 4.934549e+00, current residual:2.981821e+00 +wall time: 0.000693083s + +time increment:53810 time: 53.81 +field ' c' [explicit solve]: current solution: 9.615222e+01, current residual:5.805271e+01 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solve]: current solution: 4.934184e+00, current residual:2.981593e+00 +wall time: 0.000759125s + +time increment:53860 time: 53.86 +field ' c' [explicit solve]: current solution: 9.615226e+01, current residual:5.805272e+01 +field 'mu' [explicit solve]: current solution: 4.934111e+00, current residual:2.981547e+00 +wall time: 0.000730038s + +time increment:53870 time: 53.87 +field ' c' [explicit solve]: current solution: 9.615227e+01, current residual:5.805272e+01 +field 'mu' [explicit solve]: current solution: 4.934038e+00, current residual:2.981501e+00 +wall time: 0.000699997s + +time increment:53880 time: 53.88 +field ' c' [explicit solve]: current solution: 9.615228e+01, current residual:5.805272e+01 +field 'mu' [explicit solve]: current solution: 4.933965e+00, current residual:2.981456e+00 +wall time: 0.000663042s + +time increment:53890 time: 53.89 +field ' c' [explicit solve]: current solution: 9.615229e+01, current residual:5.805272e+01 +field 'mu' [explicit solve]: current solution: 4.933892e+00, current residual:2.981410e+00 +wall time: 0.000658989s + +time increment:53900 time: 53.9 +field ' c' [explicit solve]: current solution: 9.615229e+01, current residual:5.805272e+01 +field 'mu' [explicit solve]: current solution: 4.933819e+00, current residual:2.981365e+00 +wall time: 0.000684023s + +time increment:53910 time: 53.91 +field ' c' [explicit solve]: current solution: 9.615230e+01, current residual:5.805272e+01 +field 'mu' [explicit solve]: current solution: 4.933746e+00, current residual:2.981319e+00 +wall time: 0.000700951s + +time increment:53920 time: 53.92 +field ' c' [explicit solve]: current solution: 9.615231e+01, current residual:5.805272e+01 +field 'mu' [explicit solve]: current solution: 4.933674e+00, current residual:2.981274e+00 +wall time: 0.00067997s + +time increment:53930 time: 53.93 +field ' c' [explicit solve]: current solution: 9.615232e+01, current residual:5.805272e+01 +field 'mu' [explicit solve]: current solution: 4.933601e+00, current residual:2.981228e+00 +wall time: 0.000658035s + +time increment:53940 time: 53.94 +field ' c' [explicit solve]: current solution: 9.615233e+01, current residual:5.805272e+01 +field 'mu' [explicit solve]: current solution: 4.933528e+00, current residual:2.981183e+00 +wall time: 0.000674963s + +time increment:53950 time: 53.95 +field ' c' [explicit solve]: current solution: 9.615233e+01, current residual:5.805272e+01 +field 'mu' [explicit solve]: current solution: 4.933455e+00, current residual:2.981137e+00 +wall time: 0.000665903s + +time increment:53960 time: 53.96 +field ' c' [explicit solve]: current solution: 9.615234e+01, current residual:5.805273e+01 +field 'mu' [explicit solve]: current solution: 4.933383e+00, current residual:2.981092e+00 +wall time: 0.000740051s + +time increment:53970 time: 53.97 +field ' c' [explicit solve]: current solution: 9.615235e+01, current residual:5.805273e+01 +field 'mu' [explicit solve]: current solution: 4.933310e+00, current residual:2.981047e+00 +wall time: 0.000735998s + +time increment:53980 time: 53.98 +field ' c' [explicit solve]: current solution: 9.615236e+01, current residual:5.805273e+01 +field 'mu' [explicit solve]: current solution: 4.933237e+00, current residual:2.981001e+00 +wall time: 0.000636101s + +time increment:53990 time: 53.99 +field ' c' [explicit solve]: current solution: 9.615237e+01, current residual:5.805273e+01 +field 'mu' [explicit solve]: current solution: 4.933165e+00, current residual:2.980956e+00 +wall time: 0.000619888s + +time increment:54000 time: 54 +field ' c' [explicit solve]: current solution: 9.615237e+01, current residual:5.805273e+01 +field 'mu' [explicit solve]: current solution: 4.933092e+00, current residual:2.980910e+00 +wall time: 0.000715971s + +time increment:54010 time: 54.01 +field ' c' [explicit solve]: current solution: 9.615238e+01, current residual:5.805273e+01 +field 'mu' [explicit solve]: current solution: 4.933019e+00, current residual:2.980865e+00 +wall time: 0.00068593s + +time increment:54020 time: 54.02 +field ' c' [explicit solve]: current solution: 9.615239e+01, current residual:5.805273e+01 +field 'mu' [explicit solve]: current solution: 4.932947e+00, current residual:2.980820e+00 +wall time: 0.000680923s + +time increment:54030 time: 54.03 +field ' c' [explicit solve]: current solution: 9.615240e+01, current residual:5.805273e+01 +field 'mu' [explicit solve]: current solution: 4.932874e+00, current residual:2.980774e+00 +wall time: 0.000569105s + +time increment:54040 time: 54.04 +field ' c' [explicit solve]: current solution: 9.615241e+01, current residual:5.805273e+01 +field 'mu' [explicit solve]: current solution: 4.932802e+00, current residual:2.980729e+00 +wall time: 0.000715017s + +time increment:54050 time: 54.05 +field ' c' [explicit solve]: current solution: 9.615241e+01, current residual:5.805274e+01 +field 'mu' [explicit solve]: current solution: 4.932729e+00, current residual:2.980684e+00 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0.000586033s + +time increment:54100 time: 54.1 +field ' c' [explicit solve]: current solution: 9.615246e+01, current residual:5.805274e+01 +field 'mu' [explicit solve]: current solution: 4.932367e+00, current residual:2.980457e+00 +wall time: 0.000631809s + +time increment:54110 time: 54.11 +field ' c' [explicit solve]: current solution: 9.615246e+01, current residual:5.805274e+01 +field 'mu' [explicit solve]: current solution: 4.932294e+00, current residual:2.980412e+00 +wall time: 0.000708103s + +time increment:54120 time: 54.12 +field ' c' [explicit solve]: current solution: 9.615247e+01, current residual:5.805274e+01 +field 'mu' [explicit solve]: current solution: 4.932222e+00, current residual:2.980367e+00 +wall time: 0.000622034s + +time increment:54130 time: 54.13 +field ' c' [explicit solve]: current solution: 9.615248e+01, current residual:5.805274e+01 +field 'mu' [explicit solve]: current solution: 4.932150e+00, current residual:2.980321e+00 +wall time: 0.000715971s + +time increment:54140 time: 54.14 +field ' c' [explicit solve]: current solution: 9.615249e+01, current residual:5.805275e+01 +field 'mu' [explicit solve]: current solution: 4.932077e+00, current residual:2.980276e+00 +wall time: 0.000880003s + +time increment:54150 time: 54.15 +field ' c' [explicit solve]: current solution: 9.615250e+01, current residual:5.805275e+01 +field 'mu' [explicit solve]: current solution: 4.932005e+00, current residual:2.980231e+00 +wall time: 0.000725985s + +time increment:54160 time: 54.16 +field ' c' [explicit solve]: current solution: 9.615251e+01, current residual:5.805275e+01 +field 'mu' [explicit solve]: current solution: 4.931933e+00, current residual:2.980186e+00 +wall time: 0.000693083s + +time increment:54170 time: 54.17 +field ' c' [explicit solve]: current solution: 9.615252e+01, current residual:5.805275e+01 +field 'mu' [explicit solve]: current solution: 4.931860e+00, current residual:2.980141e+00 +wall time: 0.000637054s + +time increment:54180 time: 54.18 +field ' c' [explicit solve]: current solution: 9.615252e+01, current residual:5.805275e+01 +field 'mu' [explicit solve]: current solution: 4.931788e+00, current residual:2.980095e+00 +wall time: 0.000663996s + +time increment:54190 time: 54.19 +field ' c' [explicit solve]: current solution: 9.615253e+01, current residual:5.805275e+01 +field 'mu' [explicit solve]: current solution: 4.931716e+00, current residual:2.980050e+00 +wall time: 0.000663996s + +time increment:54200 time: 54.2 +field ' c' [explicit solve]: current solution: 9.615254e+01, current residual:5.805275e+01 +field 'mu' [explicit solve]: current solution: 4.931644e+00, current residual:2.980005e+00 +wall time: 0.000721931s + +time increment:54210 time: 54.21 +field ' c' [explicit solve]: current solution: 9.615255e+01, current residual:5.805276e+01 +field 'mu' [explicit solve]: current solution: 4.931572e+00, current residual:2.979960e+00 +wall time: 0.000728846s + +time increment:54220 time: 54.22 +field ' c' [explicit solve]: current solution: 9.615256e+01, current residual:5.805276e+01 +field 'mu' [explicit solve]: current solution: 4.931499e+00, current residual:2.979915e+00 +wall time: 0.00069809s + +time increment:54230 time: 54.23 +field ' c' [explicit solve]: current solution: 9.615257e+01, current residual:5.805276e+01 +field 'mu' [explicit solve]: current solution: 4.931427e+00, current residual:2.979870e+00 +wall time: 0.000720024s + +time increment:54240 time: 54.24 +field ' c' [explicit solve]: current solution: 9.615258e+01, current residual:5.805276e+01 +field 'mu' [explicit solve]: current solution: 4.931355e+00, current residual:2.979825e+00 +wall time: 0.000668049s + +time increment:54250 time: 54.25 +field ' c' [explicit solve]: current solution: 9.615258e+01, current residual:5.805276e+01 +field 'mu' [explicit solve]: current solution: 4.931283e+00, current residual:2.979779e+00 +wall time: 0.000668049s + +time increment:54260 time: 54.26 +field ' c' [explicit solve]: current solution: 9.615259e+01, current residual:5.805276e+01 +field 'mu' [explicit solve]: current solution: 4.931211e+00, current residual:2.979734e+00 +wall time: 0.000644922s + +time increment:54270 time: 54.27 +field ' c' [explicit solve]: current solution: 9.615260e+01, current residual:5.805276e+01 +field 'mu' [explicit solve]: current solution: 4.931139e+00, current residual:2.979689e+00 +wall time: 0.000720978s + +time increment:54280 time: 54.28 +field ' c' [explicit solve]: current solution: 9.615261e+01, current residual:5.805277e+01 +field 'mu' [explicit solve]: current solution: 4.931067e+00, current residual:2.979644e+00 +wall time: 0.000754833s + +time increment:54290 time: 54.29 +field ' c' [explicit solve]: current solution: 9.615262e+01, current residual:5.805277e+01 +field 'mu' [explicit solve]: current solution: 4.930995e+00, current residual:2.979599e+00 +wall time: 0.000673056s + +time increment:54300 time: 54.3 +field ' c' [explicit solve]: current solution: 9.615263e+01, current residual:5.805277e+01 +field 'mu' [explicit solve]: current solution: 4.930923e+00, current residual:2.979554e+00 +wall time: 0.000752926s + +time increment:54310 time: 54.31 +field ' c' [explicit solve]: current solution: 9.615264e+01, current residual:5.805277e+01 +field 'mu' [explicit solve]: current solution: 4.930851e+00, current residual:2.979509e+00 +wall time: 0.000710964s + +time increment:54320 time: 54.32 +field ' c' [explicit solve]: current solution: 9.615265e+01, current residual:5.805277e+01 +field 'mu' [explicit solve]: current solution: 4.930779e+00, current residual:2.979464e+00 +wall time: 0.000674009s + +time increment:54330 time: 54.33 +field ' c' [explicit solve]: current solution: 9.615266e+01, current residual:5.805277e+01 +field 'mu' [explicit solve]: current solution: 4.930707e+00, current residual:2.979419e+00 +wall time: 0.000720024s + +time increment:54340 time: 54.34 +field ' c' [explicit solve]: current solution: 9.615266e+01, 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residual:5.805278e+01 +field 'mu' [explicit solve]: current solution: 4.930347e+00, current residual:2.979194e+00 +wall time: 0.000705004s + +time increment:54390 time: 54.39 +field ' c' [explicit solve]: current solution: 9.615271e+01, current residual:5.805278e+01 +field 'mu' [explicit solve]: current solution: 4.930276e+00, current residual:2.979149e+00 +wall time: 0.000687122s + +time increment:54400 time: 54.4 +field ' c' [explicit solve]: current solution: 9.615272e+01, current residual:5.805279e+01 +field 'mu' [explicit solve]: current solution: 4.930204e+00, current residual:2.979105e+00 +wall time: 0.000709057s + +time increment:54410 time: 54.41 +field ' c' [explicit solve]: current solution: 9.615273e+01, current residual:5.805279e+01 +field 'mu' [explicit solve]: current solution: 4.930132e+00, current residual:2.979060e+00 +wall time: 0.000749826s + +time increment:54420 time: 54.42 +field ' c' [explicit solve]: current solution: 9.615274e+01, current residual:5.805279e+01 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solve]: current solution: 4.929773e+00, current residual:2.978835e+00 +wall time: 0.000710964s + +time increment:54470 time: 54.47 +field ' c' [explicit solve]: current solution: 9.615278e+01, current residual:5.805280e+01 +field 'mu' [explicit solve]: current solution: 4.929702e+00, current residual:2.978790e+00 +wall time: 0.000735998s + +time increment:54480 time: 54.48 +field ' c' [explicit solve]: current solution: 9.615279e+01, current residual:5.805280e+01 +field 'mu' [explicit solve]: current solution: 4.929630e+00, current residual:2.978746e+00 +wall time: 0.000625134s + +time increment:54490 time: 54.49 +field ' c' [explicit solve]: current solution: 9.615280e+01, current residual:5.805280e+01 +field 'mu' [explicit solve]: current solution: 4.929559e+00, current residual:2.978701e+00 +wall time: 0.000682831s + +time increment:54500 time: 54.5 +field ' c' [explicit solve]: current solution: 9.615281e+01, current residual:5.805280e+01 +field 'mu' [explicit solve]: current solution: 4.929487e+00, current residual:2.978656e+00 +wall time: 0.000636101s + +time increment:54510 time: 54.51 +field ' c' [explicit solve]: current solution: 9.615282e+01, current residual:5.805281e+01 +field 'mu' [explicit solve]: current solution: 4.929415e+00, current residual:2.978611e+00 +wall time: 0.000649214s + +time increment:54520 time: 54.52 +field ' c' [explicit solve]: current solution: 9.615283e+01, current residual:5.805281e+01 +field 'mu' [explicit solve]: current solution: 4.929344e+00, current residual:2.978566e+00 +wall time: 0.000579834s + +time increment:54530 time: 54.53 +field ' c' [explicit solve]: current solution: 9.615284e+01, current residual:5.805281e+01 +field 'mu' [explicit solve]: current solution: 4.929272e+00, current residual:2.978522e+00 +wall time: 0.000557899s + +time increment:54540 time: 54.54 +field ' c' [explicit solve]: current solution: 9.615285e+01, current residual:5.805281e+01 +field 'mu' [explicit solve]: current solution: 4.929201e+00, current residual:2.978477e+00 +wall time: 0.000658989s + +time increment:54550 time: 54.55 +field ' c' [explicit solve]: current solution: 9.615286e+01, current residual:5.805281e+01 +field 'mu' [explicit solve]: current solution: 4.929129e+00, current residual:2.978432e+00 +wall time: 0.000661135s + +time increment:54560 time: 54.56 +field ' c' [explicit solve]: current solution: 9.615287e+01, current residual:5.805282e+01 +field 'mu' [explicit solve]: current solution: 4.929058e+00, current residual:2.978388e+00 +wall time: 0.000682831s + +time increment:54570 time: 54.57 +field ' c' [explicit solve]: current solution: 9.615288e+01, current residual:5.805282e+01 +field 'mu' [explicit solve]: current solution: 4.928986e+00, current residual:2.978343e+00 +wall time: 0.000653982s + +time increment:54580 time: 54.58 +field ' c' [explicit solve]: current solution: 9.615289e+01, current residual:5.805282e+01 +field 'mu' [explicit solve]: current solution: 4.928915e+00, current residual:2.978298e+00 +wall time: 0.000669003s + +time increment:54590 time: 54.59 +field ' c' [explicit solve]: current solution: 9.615290e+01, current residual:5.805282e+01 +field 'mu' [explicit solve]: current solution: 4.928844e+00, current residual:2.978253e+00 +wall time: 0.000676155s + +time increment:54600 time: 54.6 +field ' c' [explicit solve]: current solution: 9.615291e+01, current residual:5.805282e+01 +field 'mu' [explicit solve]: current solution: 4.928772e+00, current residual:2.978209e+00 +wall time: 0.000744104s + +time increment:54610 time: 54.61 +field ' c' [explicit solve]: current solution: 9.615292e+01, current residual:5.805283e+01 +field 'mu' [explicit solve]: current solution: 4.928701e+00, current residual:2.978164e+00 +wall time: 0.000705957s + +time increment:54620 time: 54.62 +field ' c' [explicit solve]: current solution: 9.615293e+01, current residual:5.805283e+01 +field 'mu' [explicit solve]: current solution: 4.928629e+00, current residual:2.978119e+00 +wall time: 0.000834942s + +time increment:54630 time: 54.63 +field ' c' [explicit solve]: current solution: 9.615294e+01, current residual:5.805283e+01 +field 'mu' [explicit solve]: current solution: 4.928558e+00, current residual:2.978075e+00 +wall time: 0.000756025s + +time increment:54640 time: 54.64 +field ' c' [explicit solve]: current solution: 9.615295e+01, current residual:5.805283e+01 +field 'mu' [explicit solve]: current solution: 4.928487e+00, current residual:2.978030e+00 +wall time: 0.000747919s + +time increment:54650 time: 54.65 +field ' c' [explicit solve]: current solution: 9.615296e+01, current residual:5.805283e+01 +field 'mu' [explicit solve]: current solution: 4.928415e+00, current residual:2.977985e+00 +wall time: 0.00064683s + +time increment:54660 time: 54.66 +field ' c' [explicit solve]: current solution: 9.615297e+01, current residual:5.805284e+01 +field 'mu' [explicit solve]: current solution: 4.928344e+00, current residual:2.977941e+00 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54.79 +field ' c' [explicit solve]: current solution: 9.615309e+01, current residual:5.805287e+01 +field 'mu' [explicit solve]: current solution: 4.927419e+00, current residual:2.977362e+00 +wall time: 0.000687122s + +time increment:54800 time: 54.8 +field ' c' [explicit solve]: current solution: 9.615311e+01, current residual:5.805287e+01 +field 'mu' [explicit solve]: current solution: 4.927348e+00, current residual:2.977318e+00 +wall time: 0.000663996s + +time increment:54810 time: 54.81 +field ' c' [explicit solve]: current solution: 9.615312e+01, current residual:5.805287e+01 +field 'mu' [explicit solve]: current solution: 4.927277e+00, current residual:2.977273e+00 +wall time: 0.000625849s + +time increment:54820 time: 54.82 +field ' c' [explicit solve]: current solution: 9.615313e+01, current residual:5.805287e+01 +field 'mu' [explicit solve]: current solution: 4.927206e+00, current residual:2.977229e+00 +wall time: 0.000639915s + +time increment:54830 time: 54.83 +field ' c' 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9.615322e+01, current residual:5.805289e+01 +field 'mu' [explicit solve]: current solution: 4.926569e+00, current residual:2.976829e+00 +wall time: 0.000683784s + +time increment:54920 time: 54.92 +field ' c' [explicit solve]: current solution: 9.615323e+01, current residual:5.805290e+01 +field 'mu' [explicit solve]: current solution: 4.926498e+00, current residual:2.976785e+00 +wall time: 0.00070715s + +time increment:54930 time: 54.93 +field ' c' [explicit solve]: current solution: 9.615324e+01, current residual:5.805290e+01 +field 'mu' [explicit solve]: current solution: 4.926427e+00, current residual:2.976741e+00 +wall time: 0.000706911s + +time increment:54940 time: 54.94 +field ' c' [explicit solve]: current solution: 9.615325e+01, current residual:5.805290e+01 +field 'mu' [explicit solve]: current solution: 4.926356e+00, current residual:2.976696e+00 +wall time: 0.00069499s + +time increment:54950 time: 54.95 +field ' c' [explicit solve]: current solution: 9.615326e+01, current residual:5.805290e+01 +field 'mu' [explicit solve]: current solution: 4.926286e+00, current residual:2.976652e+00 +wall time: 0.000577927s + +time increment:54960 time: 54.96 +field ' c' [explicit solve]: current solution: 9.615327e+01, current residual:5.805291e+01 +field 'mu' [explicit solve]: current solution: 4.926215e+00, current residual:2.976608e+00 +wall time: 0.000711203s + +time increment:54970 time: 54.97 +field ' c' [explicit solve]: current solution: 9.615328e+01, current residual:5.805291e+01 +field 'mu' [explicit solve]: current solution: 4.926144e+00, current residual:2.976564e+00 +wall time: 0.000687122s + +time increment:54980 time: 54.98 +field ' c' [explicit solve]: current solution: 9.615329e+01, current residual:5.805291e+01 +field 'mu' [explicit solve]: current solution: 4.926074e+00, current residual:2.976519e+00 +wall time: 0.000573874s + +time increment:54990 time: 54.99 +field ' c' [explicit solve]: current solution: 9.615330e+01, current residual:5.805292e+01 +field 'mu' [explicit solve]: current solution: 4.926003e+00, current residual:2.976475e+00 +wall time: 0.000577927s + +time increment:55000 time: 55 +field ' c' [explicit solve]: current solution: 9.615331e+01, current residual:5.805292e+01 +field 'mu' [explicit solve]: current solution: 4.925933e+00, current residual:2.976431e+00 +wall time: 0.00065589s + +time increment:55010 time: 55.01 +field ' c' [explicit solve]: current solution: 9.615332e+01, current residual:5.805292e+01 +field 'mu' [explicit solve]: current solution: 4.925862e+00, current residual:2.976387e+00 +wall time: 0.000700951s + +time increment:55020 time: 55.02 +field ' c' [explicit solve]: current solution: 9.615334e+01, current residual:5.805292e+01 +field 'mu' [explicit solve]: current solution: 4.925791e+00, current residual:2.976343e+00 +wall time: 0.000619888s + +time increment:55030 time: 55.03 +field ' c' [explicit solve]: current solution: 9.615335e+01, current residual:5.805293e+01 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[explicit solve]: current solution: 4.925439e+00, current residual:2.976122e+00 +wall time: 0.000559807s + +time increment:55080 time: 55.08 +field ' c' [explicit solve]: current solution: 9.615340e+01, current residual:5.805294e+01 +field 'mu' [explicit solve]: current solution: 4.925368e+00, current residual:2.976078e+00 +wall time: 0.000597s + +time increment:55090 time: 55.09 +field ' c' [explicit solve]: current solution: 9.615341e+01, current residual:5.805294e+01 +field 'mu' [explicit solve]: current solution: 4.925298e+00, current residual:2.976033e+00 +wall time: 0.000735998s + +time increment:55100 time: 55.1 +field ' c' [explicit solve]: current solution: 9.615342e+01, current residual:5.805295e+01 +field 'mu' [explicit solve]: current solution: 4.925228e+00, current residual:2.975989e+00 +wall time: 0.000597954s + +time increment:55110 time: 55.11 +field ' c' [explicit solve]: current solution: 9.615343e+01, current residual:5.805295e+01 +field 'mu' [explicit solve]: current solution: 4.925157e+00, current residual:2.975945e+00 +wall time: 0.00057292s + +time increment:55120 time: 55.12 +field ' c' [explicit solve]: current solution: 9.615344e+01, current residual:5.805295e+01 +field 'mu' [explicit solve]: current solution: 4.925087e+00, current residual:2.975901e+00 +wall time: 0.000721931s + +time increment:55130 time: 55.13 +field ' c' [explicit solve]: current solution: 9.615345e+01, current residual:5.805295e+01 +field 'mu' [explicit solve]: current solution: 4.925016e+00, current residual:2.975857e+00 +wall time: 0.000986099s + +time increment:55140 time: 55.14 +field ' c' [explicit solve]: current solution: 9.615347e+01, current residual:5.805296e+01 +field 'mu' [explicit solve]: current solution: 4.924946e+00, current residual:2.975813e+00 +wall time: 0.000617027s + +time increment:55150 time: 55.15 +field ' c' [explicit solve]: current solution: 9.615348e+01, current residual:5.805296e+01 +field 'mu' [explicit solve]: current solution: 4.924876e+00, current residual:2.975769e+00 +wall time: 0.000589132s + +time increment:55160 time: 55.16 +field ' c' [explicit solve]: current solution: 9.615349e+01, current residual:5.805296e+01 +field 'mu' [explicit solve]: current solution: 4.924805e+00, current residual:2.975725e+00 +wall time: 0.000585079s + +time increment:55170 time: 55.17 +field ' c' [explicit solve]: current solution: 9.615350e+01, current residual:5.805297e+01 +field 'mu' [explicit solve]: current solution: 4.924735e+00, current residual:2.975681e+00 +wall time: 0.000675917s + +time increment:55180 time: 55.18 +field ' c' [explicit solve]: current solution: 9.615351e+01, current residual:5.805297e+01 +field 'mu' [explicit solve]: current solution: 4.924665e+00, current residual:2.975637e+00 +wall time: 0.000568867s + +time increment:55190 time: 55.19 +field ' c' [explicit solve]: current solution: 9.615352e+01, current residual:5.805297e+01 +field 'mu' [explicit solve]: current solution: 4.924595e+00, 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residual:2.975417e+00 +wall time: 0.000596046s + +time increment:55240 time: 55.24 +field ' c' [explicit solve]: current solution: 9.615358e+01, current residual:5.805299e+01 +field 'mu' [explicit solve]: current solution: 4.924244e+00, current residual:2.975373e+00 +wall time: 0.000664949s + +time increment:55250 time: 55.25 +field ' c' [explicit solve]: current solution: 9.615359e+01, current residual:5.805299e+01 +field 'mu' [explicit solve]: current solution: 4.924174e+00, current residual:2.975329e+00 +wall time: 0.000629902s + +time increment:55260 time: 55.26 +field ' c' [explicit solve]: current solution: 9.615360e+01, current residual:5.805299e+01 +field 'mu' [explicit solve]: current solution: 4.924103e+00, current residual:2.975285e+00 +wall time: 0.000708103s + +time increment:55270 time: 55.27 +field ' c' [explicit solve]: current solution: 9.615361e+01, current residual:5.805300e+01 +field 'mu' [explicit solve]: current solution: 4.924033e+00, current residual:2.975241e+00 +wall time: 0.000581026s + +time increment:55280 time: 55.28 +field ' c' [explicit solve]: current solution: 9.615362e+01, current residual:5.805300e+01 +field 'mu' [explicit solve]: current solution: 4.923963e+00, current residual:2.975197e+00 +wall time: 0.000697136s + +time increment:55290 time: 55.29 +field ' c' [explicit solve]: current solution: 9.615363e+01, current residual:5.805300e+01 +field 'mu' [explicit solve]: current solution: 4.923893e+00, current residual:2.975153e+00 +wall time: 0.000707865s + +time increment:55300 time: 55.3 +field ' c' [explicit solve]: current solution: 9.615365e+01, current residual:5.805301e+01 +field 'mu' [explicit solve]: current solution: 4.923823e+00, current residual:2.975109e+00 +wall time: 0.000566006s + +time increment:55310 time: 55.31 +field ' c' [explicit solve]: current solution: 9.615366e+01, current residual:5.805301e+01 +field 'mu' [explicit solve]: current solution: 4.923753e+00, current residual:2.975065e+00 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0.000566006s + +time increment:55360 time: 55.36 +field ' c' [explicit solve]: current solution: 9.615371e+01, current residual:5.805303e+01 +field 'mu' [explicit solve]: current solution: 4.923403e+00, current residual:2.974846e+00 +wall time: 0.000598192s + +time increment:55370 time: 55.37 +field ' c' [explicit solve]: current solution: 9.615373e+01, current residual:5.805303e+01 +field 'mu' [explicit solve]: current solution: 4.923333e+00, current residual:2.974802e+00 +wall time: 0.000623941s + +time increment:55380 time: 55.38 +field ' c' [explicit solve]: current solution: 9.615374e+01, current residual:5.805303e+01 +field 'mu' [explicit solve]: current solution: 4.923264e+00, current residual:2.974759e+00 +wall time: 0.000663996s + +time increment:55390 time: 55.39 +field ' c' [explicit solve]: current solution: 9.615375e+01, current residual:5.805303e+01 +field 'mu' [explicit solve]: current solution: 4.923194e+00, current residual:2.974715e+00 +wall time: 0.000691891s + +time increment:55400 time: 55.4 +field ' c' [explicit solve]: current solution: 9.615376e+01, current residual:5.805304e+01 +field 'mu' [explicit solve]: current solution: 4.923124e+00, current residual:2.974671e+00 +wall time: 0.000603914s + +time increment:55410 time: 55.41 +field ' c' [explicit solve]: current solution: 9.615377e+01, current residual:5.805304e+01 +field 'mu' [explicit solve]: current solution: 4.923054e+00, current residual:2.974627e+00 +wall time: 0.000674009s + +time increment:55420 time: 55.42 +field ' c' [explicit solve]: current solution: 9.615378e+01, current residual:5.805304e+01 +field 'mu' [explicit solve]: current solution: 4.922984e+00, current residual:2.974584e+00 +wall time: 0.000763178s + +time increment:55430 time: 55.43 +field ' c' [explicit solve]: current solution: 9.615380e+01, current residual:5.805305e+01 +field 'mu' [explicit solve]: current solution: 4.922914e+00, current residual:2.974540e+00 +wall time: 0.000720978s + +time increment:55440 time: 55.44 +field ' c' [explicit solve]: current solution: 9.615381e+01, current residual:5.805305e+01 +field 'mu' [explicit solve]: current solution: 4.922845e+00, current residual:2.974496e+00 +wall time: 0.000669003s + +time increment:55450 time: 55.45 +field ' c' [explicit solve]: current solution: 9.615382e+01, current residual:5.805306e+01 +field 'mu' [explicit solve]: current solution: 4.922775e+00, current residual:2.974452e+00 +wall time: 0.000773191s + +time increment:55460 time: 55.46 +field ' c' [explicit solve]: current solution: 9.615383e+01, current residual:5.805306e+01 +field 'mu' [explicit solve]: current solution: 4.922705e+00, current residual:2.974409e+00 +wall time: 0.00070405s + +time increment:55470 time: 55.47 +field ' c' [explicit solve]: current solution: 9.615384e+01, current residual:5.805306e+01 +field 'mu' [explicit solve]: current solution: 4.922635e+00, current residual:2.974365e+00 +wall time: 0.000679016s + +time increment:55480 time: 55.48 +field ' c' [explicit solve]: current solution: 9.615386e+01, current residual:5.805307e+01 +field 'mu' [explicit solve]: current solution: 4.922566e+00, current residual:2.974321e+00 +wall time: 0.000669003s + +time increment:55490 time: 55.49 +field ' c' [explicit solve]: current solution: 9.615387e+01, current residual:5.805307e+01 +field 'mu' [explicit solve]: current solution: 4.922496e+00, current residual:2.974277e+00 +wall time: 0.000690937s + +time increment:55500 time: 55.5 +field ' c' [explicit solve]: current solution: 9.615388e+01, current residual:5.805307e+01 +field 'mu' [explicit solve]: current solution: 4.922426e+00, current residual:2.974234e+00 +wall time: 0.000700235s + +time increment:55510 time: 55.51 +field ' c' [explicit solve]: current solution: 9.615389e+01, current residual:5.805308e+01 +field 'mu' [explicit solve]: current solution: 4.922357e+00, current residual:2.974190e+00 +wall time: 0.000687122s + +time increment:55520 time: 55.52 +field ' c' [explicit solve]: current solution: 9.615390e+01, current residual:5.805308e+01 +field 'mu' [explicit solve]: current solution: 4.922287e+00, current residual:2.974146e+00 +wall time: 0.000643015s + +time increment:55530 time: 55.53 +field ' c' [explicit solve]: current solution: 9.615392e+01, current residual:5.805308e+01 +field 'mu' [explicit solve]: current solution: 4.922218e+00, current residual:2.974103e+00 +wall time: 0.000697851s + +time increment:55540 time: 55.54 +field ' c' [explicit solve]: current solution: 9.615393e+01, current residual:5.805309e+01 +field 'mu' [explicit solve]: current solution: 4.922148e+00, current residual:2.974059e+00 +wall time: 0.00070715s + +time increment:55550 time: 55.55 +field ' c' [explicit solve]: current solution: 9.615394e+01, current residual:5.805309e+01 +field 'mu' [explicit solve]: current solution: 4.922078e+00, current residual:2.974016e+00 +wall time: 0.000683069s + +time increment:55560 time: 55.56 +field ' c' [explicit solve]: current solution: 9.615395e+01, current residual:5.805309e+01 +field 'mu' [explicit solve]: current solution: 4.922009e+00, current residual:2.973972e+00 +wall time: 0.000622988s + +time increment:55570 time: 55.57 +field ' c' [explicit solve]: current solution: 9.615396e+01, current residual:5.805310e+01 +field 'mu' [explicit solve]: current solution: 4.921939e+00, current residual:2.973928e+00 +wall time: 0.000651121s + +time increment:55580 time: 55.58 +field ' c' [explicit solve]: current solution: 9.615398e+01, current residual:5.805310e+01 +field 'mu' [explicit solve]: current solution: 4.921870e+00, current residual:2.973885e+00 +wall time: 0.000673056s + +time increment:55590 time: 55.59 +field ' c' [explicit solve]: current solution: 9.615399e+01, current residual:5.805310e+01 +field 'mu' [explicit solve]: current solution: 4.921800e+00, current residual:2.973841e+00 +wall time: 0.000741959s + +time increment:55600 time: 55.6 +field ' c' [explicit solve]: current solution: 9.615400e+01, current residual:5.805311e+01 +field 'mu' [explicit solve]: current solution: 4.921731e+00, current residual:2.973798e+00 +wall time: 0.000715971s + +time increment:55610 time: 55.61 +field ' c' [explicit solve]: current solution: 9.615401e+01, current residual:5.805311e+01 +field 'mu' [explicit solve]: current solution: 4.921661e+00, current residual:2.973754e+00 +wall time: 0.000699997s + +time increment:55620 time: 55.62 +field ' c' [explicit solve]: current solution: 9.615402e+01, current residual:5.805311e+01 +field 'mu' [explicit solve]: current solution: 4.921592e+00, current residual:2.973711e+00 +wall time: 0.00106907s + +time increment:55630 time: 55.63 +field ' c' [explicit solve]: current solution: 9.615404e+01, current residual:5.805312e+01 +field 'mu' [explicit solve]: current solution: 4.921522e+00, current residual:2.973667e+00 +wall time: 0.000746965s + +time increment:55640 time: 55.64 +field ' c' [explicit solve]: current solution: 9.615405e+01, current residual:5.805312e+01 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solve]: current solution: 4.921176e+00, current residual:2.973449e+00 +wall time: 0.00067997s + +time increment:55690 time: 55.69 +field ' c' [explicit solve]: current solution: 9.615411e+01, current residual:5.805314e+01 +field 'mu' [explicit solve]: current solution: 4.921106e+00, current residual:2.973406e+00 +wall time: 0.000710964s + +time increment:55700 time: 55.7 +field ' c' [explicit solve]: current solution: 9.615412e+01, current residual:5.805314e+01 +field 'mu' [explicit solve]: current solution: 4.921037e+00, current residual:2.973363e+00 +wall time: 0.000784159s + +time increment:55710 time: 55.71 +field ' c' [explicit solve]: current solution: 9.615414e+01, current residual:5.805315e+01 +field 'mu' [explicit solve]: current solution: 4.920968e+00, current residual:2.973319e+00 +wall time: 0.000813007s + +time increment:55720 time: 55.72 +field ' c' [explicit solve]: current solution: 9.615415e+01, current residual:5.805315e+01 +field 'mu' [explicit solve]: current solution: 4.920899e+00, current residual:2.973276e+00 +wall time: 0.000662088s + +time increment:55730 time: 55.73 +field ' c' [explicit solve]: current solution: 9.615416e+01, current residual:5.805316e+01 +field 'mu' [explicit solve]: current solution: 4.920829e+00, current residual:2.973232e+00 +wall time: 0.000716925s + +time increment:55740 time: 55.74 +field ' c' [explicit solve]: current solution: 9.615417e+01, current residual:5.805316e+01 +field 'mu' [explicit solve]: current solution: 4.920760e+00, current residual:2.973189e+00 +wall time: 0.000734091s + +time increment:55750 time: 55.75 +field ' c' [explicit solve]: current solution: 9.615419e+01, current residual:5.805316e+01 +field 'mu' [explicit solve]: current solution: 4.920691e+00, current residual:2.973145e+00 +wall time: 0.00065589s + +time increment:55760 time: 55.76 +field ' c' [explicit solve]: current solution: 9.615420e+01, current residual:5.805317e+01 +field 'mu' [explicit solve]: current solution: 4.920622e+00, current residual:2.973102e+00 +wall time: 0.00072217s + +time increment:55770 time: 55.77 +field ' c' [explicit solve]: current solution: 9.615421e+01, current residual:5.805317e+01 +field 'mu' [explicit solve]: current solution: 4.920552e+00, current residual:2.973059e+00 +wall time: 0.000646114s + +time increment:55780 time: 55.78 +field ' c' [explicit solve]: current solution: 9.615422e+01, current residual:5.805318e+01 +field 'mu' [explicit solve]: current solution: 4.920483e+00, current residual:2.973015e+00 +wall time: 0.00067997s + +time increment:55790 time: 55.79 +field ' c' [explicit solve]: current solution: 9.615424e+01, current residual:5.805318e+01 +field 'mu' [explicit solve]: current solution: 4.920414e+00, current residual:2.972972e+00 +wall time: 0.000684977s + +time increment:55800 time: 55.8 +field ' c' [explicit solve]: current solution: 9.615425e+01, current residual:5.805318e+01 +field 'mu' [explicit solve]: current solution: 4.920345e+00, current residual:2.972928e+00 +wall time: 0.000649929s + +time increment:55810 time: 55.81 +field ' c' [explicit solve]: current solution: 9.615426e+01, current residual:5.805319e+01 +field 'mu' [explicit solve]: current solution: 4.920276e+00, current residual:2.972885e+00 +wall time: 0.000576973s + +time increment:55820 time: 55.82 +field ' c' [explicit solve]: current solution: 9.615427e+01, current residual:5.805319e+01 +field 'mu' [explicit solve]: current solution: 4.920207e+00, current residual:2.972842e+00 +wall time: 0.000699997s + +time increment:55830 time: 55.83 +field ' c' [explicit solve]: current solution: 9.615429e+01, current residual:5.805319e+01 +field 'mu' [explicit solve]: current solution: 4.920138e+00, current residual:2.972798e+00 +wall time: 0.000732899s + +time increment:55840 time: 55.84 +field ' c' [explicit solve]: current solution: 9.615430e+01, current residual:5.805320e+01 +field 'mu' [explicit solve]: current solution: 4.920069e+00, current residual:2.972755e+00 +wall time: 0.000672102s + +time increment:55850 time: 55.85 +field ' c' [explicit solve]: current solution: 9.615431e+01, current residual:5.805320e+01 +field 'mu' [explicit solve]: current solution: 4.920000e+00, current residual:2.972712e+00 +wall time: 0.000682116s + +time increment:55860 time: 55.86 +field ' c' [explicit solve]: current solution: 9.615432e+01, current residual:5.805321e+01 +field 'mu' [explicit solve]: current solution: 4.919931e+00, current residual:2.972669e+00 +wall time: 0.000653982s + +time increment:55870 time: 55.87 +field ' c' [explicit solve]: current solution: 9.615434e+01, current residual:5.805321e+01 +field 'mu' [explicit solve]: current solution: 4.919862e+00, current residual:2.972625e+00 +wall time: 0.000710964s + +time increment:55880 time: 55.88 +field ' c' [explicit solve]: current solution: 9.615435e+01, current residual:5.805322e+01 +field 'mu' [explicit solve]: current solution: 4.919793e+00, current residual:2.972582e+00 +wall time: 0.000638008s + +time increment:55890 time: 55.89 +field ' c' [explicit solve]: current solution: 9.615436e+01, current residual:5.805322e+01 +field 'mu' [explicit solve]: current solution: 4.919724e+00, current residual:2.972539e+00 +wall time: 0.000634193s + +time increment:55900 time: 55.9 +field ' c' [explicit solve]: current solution: 9.615438e+01, current residual:5.805322e+01 +field 'mu' [explicit solve]: current solution: 4.919655e+00, current residual:2.972495e+00 +wall time: 0.000626087s + +time increment:55910 time: 55.91 +field ' c' [explicit solve]: current solution: 9.615439e+01, current residual:5.805323e+01 +field 'mu' [explicit solve]: current solution: 4.919586e+00, current residual:2.972452e+00 +wall time: 0.00078702s + +time increment:55920 time: 55.92 +field ' c' [explicit solve]: current solution: 9.615440e+01, current residual:5.805323e+01 +field 'mu' [explicit solve]: current solution: 4.919517e+00, current residual:2.972409e+00 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0.000671864s + +time increment:55970 time: 55.97 +field ' c' [explicit solve]: current solution: 9.615447e+01, current residual:5.805325e+01 +field 'mu' [explicit solve]: current solution: 4.919173e+00, current residual:2.972193e+00 +wall time: 0.000735044s + +time increment:55980 time: 55.98 +field ' c' [explicit solve]: current solution: 9.615448e+01, current residual:5.805326e+01 +field 'mu' [explicit solve]: current solution: 4.919104e+00, current residual:2.972150e+00 +wall time: 0.000633955s + +time increment:55990 time: 55.99 +field ' c' [explicit solve]: current solution: 9.615449e+01, current residual:5.805326e+01 +field 'mu' [explicit solve]: current solution: 4.919035e+00, current residual:2.972107e+00 +wall time: 0.000686169s + +time increment:56000 time: 56 +field ' c' [explicit solve]: current solution: 9.615451e+01, current residual:5.805326e+01 +field 'mu' [explicit solve]: current solution: 4.918967e+00, current residual:2.972063e+00 +wall time: 0.000883102s + +time increment:56010 time: 56.01 +field ' c' [explicit solve]: current solution: 9.615452e+01, current residual:5.805327e+01 +field 'mu' [explicit solve]: current solution: 4.918898e+00, current residual:2.972020e+00 +wall time: 0.000739098s + +time increment:56020 time: 56.02 +field ' c' [explicit solve]: current solution: 9.615453e+01, current residual:5.805327e+01 +field 'mu' [explicit solve]: current solution: 4.918829e+00, current residual:2.971977e+00 +wall time: 0.000748158s + +time increment:56030 time: 56.03 +field ' c' [explicit solve]: current solution: 9.615455e+01, current residual:5.805328e+01 +field 'mu' [explicit solve]: current solution: 4.918760e+00, current residual:2.971934e+00 +wall time: 0.000694036s + +time increment:56040 time: 56.04 +field ' c' [explicit solve]: current solution: 9.615456e+01, current residual:5.805328e+01 +field 'mu' [explicit solve]: current solution: 4.918692e+00, current residual:2.971891e+00 +wall time: 0.000784874s + +time increment:56050 time: 56.05 +field ' c' [explicit solve]: current solution: 9.615457e+01, current residual:5.805329e+01 +field 'mu' [explicit solve]: current solution: 4.918623e+00, current residual:2.971848e+00 +wall time: 0.000689983s + +time increment:56060 time: 56.06 +field ' c' [explicit solve]: current solution: 9.615459e+01, current residual:5.805329e+01 +field 'mu' [explicit solve]: current solution: 4.918554e+00, current residual:2.971805e+00 +wall time: 0.000712156s + +time increment:56070 time: 56.07 +field ' c' [explicit solve]: current solution: 9.615460e+01, current residual:5.805329e+01 +field 'mu' [explicit solve]: current solution: 4.918486e+00, current residual:2.971762e+00 +wall time: 0.000743151s + +time increment:56080 time: 56.08 +field ' c' [explicit solve]: current solution: 9.615461e+01, current residual:5.805330e+01 +field 'mu' [explicit solve]: current solution: 4.918417e+00, current residual:2.971719e+00 +wall time: 0.000705004s + +time increment:56090 time: 56.09 +field ' c' [explicit solve]: current solution: 9.615463e+01, current residual:5.805330e+01 +field 'mu' [explicit solve]: current solution: 4.918349e+00, current residual:2.971676e+00 +wall time: 0.000756025s + +time increment:56100 time: 56.1 +field ' c' [explicit solve]: current solution: 9.615464e+01, current residual:5.805331e+01 +field 'mu' [explicit solve]: current solution: 4.918280e+00, current residual:2.971633e+00 +wall time: 0.00067997s + +time increment:56110 time: 56.11 +field ' c' [explicit solve]: current solution: 9.615465e+01, current residual:5.805331e+01 +field 'mu' [explicit solve]: current solution: 4.918211e+00, current residual:2.971590e+00 +wall time: 0.000709057s + +time increment:56120 time: 56.12 +field ' c' [explicit solve]: current solution: 9.615467e+01, current residual:5.805332e+01 +field 'mu' [explicit solve]: current solution: 4.918143e+00, current residual:2.971546e+00 +wall time: 0.000717878s + +time increment:56130 time: 56.13 +field ' c' [explicit solve]: current solution: 9.615468e+01, current residual:5.805332e+01 +field 'mu' [explicit solve]: current solution: 4.918074e+00, current residual:2.971503e+00 +wall time: 0.000800133s + +time increment:56140 time: 56.14 +field ' c' [explicit solve]: current solution: 9.615469e+01, current residual:5.805333e+01 +field 'mu' [explicit solve]: current solution: 4.918006e+00, current residual:2.971460e+00 +wall time: 0.000674009s + +time increment:56150 time: 56.15 +field ' c' [explicit solve]: current solution: 9.615471e+01, current residual:5.805333e+01 +field 'mu' [explicit solve]: current solution: 4.917937e+00, current residual:2.971417e+00 +wall time: 0.000694036s + +time increment:56160 time: 56.16 +field ' c' [explicit solve]: current solution: 9.615472e+01, current residual:5.805333e+01 +field 'mu' [explicit solve]: current solution: 4.917869e+00, current residual:2.971374e+00 +wall time: 0.000689983s + +time increment:56170 time: 56.17 +field ' c' [explicit solve]: current solution: 9.615473e+01, current residual:5.805334e+01 +field 'mu' [explicit solve]: current solution: 4.917800e+00, current residual:2.971331e+00 +wall time: 0.00069809s + +time increment:56180 time: 56.18 +field ' c' [explicit solve]: current solution: 9.615475e+01, current residual:5.805334e+01 +field 'mu' [explicit solve]: current solution: 4.917732e+00, current residual:2.971289e+00 +wall time: 0.000690937s + +time increment:56190 time: 56.19 +field ' c' [explicit solve]: current solution: 9.615476e+01, current residual:5.805335e+01 +field 'mu' [explicit solve]: current solution: 4.917664e+00, current residual:2.971246e+00 +wall time: 0.000650883s + +time increment:56200 time: 56.2 +field ' c' [explicit solve]: current solution: 9.615477e+01, current residual:5.805335e+01 +field 'mu' [explicit solve]: current solution: 4.917595e+00, current residual:2.971203e+00 +wall time: 0.000684977s + +time increment:56210 time: 56.21 +field ' c' [explicit solve]: current solution: 9.615479e+01, current residual:5.805336e+01 +field 'mu' [explicit solve]: current solution: 4.917527e+00, current residual:2.971160e+00 +wall time: 0.000663042s + +time increment:56220 time: 56.22 +field ' c' [explicit solve]: current solution: 9.615480e+01, current residual:5.805336e+01 +field 'mu' [explicit solve]: current solution: 4.917458e+00, current residual:2.971117e+00 +wall time: 0.000648975s + +time increment:56230 time: 56.23 +field ' c' [explicit solve]: current solution: 9.615481e+01, current residual:5.805337e+01 +field 'mu' [explicit solve]: current solution: 4.917390e+00, current residual:2.971074e+00 +wall time: 0.000725031s + +time increment:56240 time: 56.24 +field ' c' [explicit solve]: current solution: 9.615483e+01, current residual:5.805337e+01 +field 'mu' [explicit solve]: current solution: 4.917322e+00, current residual:2.971031e+00 +wall time: 0.000864029s + +time increment:56250 time: 56.25 +field ' c' [explicit solve]: current solution: 9.615484e+01, current residual:5.805337e+01 +field 'mu' [explicit solve]: current solution: 4.917253e+00, current residual:2.970988e+00 +wall time: 0.000710964s + +time increment:56260 time: 56.26 +field ' c' [explicit solve]: current solution: 9.615486e+01, current residual:5.805338e+01 +field 'mu' [explicit solve]: current solution: 4.917185e+00, current residual:2.970945e+00 +wall time: 0.000687122s + +time increment:56270 time: 56.27 +field ' c' [explicit solve]: current solution: 9.615487e+01, current residual:5.805338e+01 +field 'mu' [explicit solve]: current solution: 4.917117e+00, current residual:2.970902e+00 +wall time: 0.00064683s + +time increment:56280 time: 56.28 +field ' c' [explicit solve]: current solution: 9.615488e+01, current residual:5.805339e+01 +field 'mu' [explicit solve]: current solution: 4.917048e+00, current residual:2.970859e+00 +wall time: 0.000688076s + +time increment:56290 time: 56.29 +field ' c' [explicit solve]: current solution: 9.615490e+01, current residual:5.805339e+01 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solve]: current solution: 4.916707e+00, current residual:2.970645e+00 +wall time: 0.00068593s + +time increment:56340 time: 56.34 +field ' c' [explicit solve]: current solution: 9.615497e+01, current residual:5.805342e+01 +field 'mu' [explicit solve]: current solution: 4.916639e+00, current residual:2.970602e+00 +wall time: 0.000678062s + +time increment:56350 time: 56.35 +field ' c' [explicit solve]: current solution: 9.615498e+01, current residual:5.805342e+01 +field 'mu' [explicit solve]: current solution: 4.916571e+00, current residual:2.970560e+00 +wall time: 0.000629187s + +time increment:56360 time: 56.36 +field ' c' [explicit solve]: current solution: 9.615499e+01, current residual:5.805343e+01 +field 'mu' [explicit solve]: current solution: 4.916503e+00, current residual:2.970517e+00 +wall time: 0.00071311s + +time increment:56370 time: 56.37 +field ' c' [explicit solve]: current solution: 9.615501e+01, current residual:5.805343e+01 +field 'mu' [explicit solve]: current solution: 4.916435e+00, current residual:2.970474e+00 +wall time: 0.000674009s + +time increment:56380 time: 56.38 +field ' c' [explicit solve]: current solution: 9.615502e+01, current residual:5.805344e+01 +field 'mu' [explicit solve]: current solution: 4.916367e+00, current residual:2.970431e+00 +wall time: 0.000668049s + +time increment:56390 time: 56.39 +field ' c' [explicit solve]: current solution: 9.615504e+01, current residual:5.805344e+01 +field 'mu' [explicit solve]: current solution: 4.916299e+00, current residual:2.970389e+00 +wall time: 0.000641108s + +time increment:56400 time: 56.4 +field ' c' [explicit solve]: current solution: 9.615505e+01, current residual:5.805345e+01 +field 'mu' [explicit solve]: current solution: 4.916231e+00, current residual:2.970346e+00 +wall time: 0.000607967s + +time increment:56410 time: 56.41 +field ' c' [explicit solve]: current solution: 9.615506e+01, current residual:5.805345e+01 +field 'mu' [explicit solve]: current solution: 4.916163e+00, current residual:2.970303e+00 +wall time: 0.000582933s + +time increment:56420 time: 56.42 +field ' c' [explicit solve]: current solution: 9.615508e+01, current residual:5.805346e+01 +field 'mu' [explicit solve]: current solution: 4.916095e+00, current residual:2.970260e+00 +wall time: 0.000633001s + +time increment:56430 time: 56.43 +field ' c' [explicit solve]: current solution: 9.615509e+01, current residual:5.805346e+01 +field 'mu' [explicit solve]: current solution: 4.916027e+00, current residual:2.970218e+00 +wall time: 0.000670195s + +time increment:56440 time: 56.44 +field ' c' [explicit solve]: current solution: 9.615511e+01, current residual:5.805346e+01 +field 'mu' [explicit solve]: current solution: 4.915959e+00, current residual:2.970175e+00 +wall time: 0.000732183s + +time increment:56450 time: 56.45 +field ' c' [explicit solve]: current solution: 9.615512e+01, current residual:5.805347e+01 +field 'mu' [explicit solve]: current solution: 4.915891e+00, current residual:2.970132e+00 +wall time: 0.000688076s + +time increment:56460 time: 56.46 +field ' c' [explicit solve]: current solution: 9.615514e+01, current residual:5.805347e+01 +field 'mu' [explicit solve]: current solution: 4.915823e+00, current residual:2.970090e+00 +wall time: 0.00064683s + +time increment:56470 time: 56.47 +field ' c' [explicit solve]: current solution: 9.615515e+01, current residual:5.805348e+01 +field 'mu' [explicit solve]: current solution: 4.915755e+00, current residual:2.970047e+00 +wall time: 0.000630856s + +time increment:56480 time: 56.48 +field ' c' [explicit solve]: current solution: 9.615516e+01, current residual:5.805348e+01 +field 'mu' [explicit solve]: current solution: 4.915687e+00, current residual:2.970004e+00 +wall time: 0.000684023s + +time increment:56490 time: 56.49 +field ' c' [explicit solve]: current solution: 9.615518e+01, current residual:5.805349e+01 +field 'mu' [explicit solve]: current solution: 4.915619e+00, current residual:2.969962e+00 +wall time: 0.00061512s + +time increment:56500 time: 56.5 +field ' c' [explicit solve]: current solution: 9.615519e+01, current residual:5.805349e+01 +field 'mu' [explicit solve]: current solution: 4.915551e+00, current residual:2.969919e+00 +wall time: 0.000564098s + +time increment:56510 time: 56.51 +field ' c' [explicit solve]: current solution: 9.615521e+01, current residual:5.805350e+01 +field 'mu' [explicit solve]: current solution: 4.915483e+00, current residual:2.969876e+00 +wall time: 0.000627041s + +time increment:56520 time: 56.52 +field ' c' [explicit solve]: current solution: 9.615522e+01, current residual:5.805350e+01 +field 'mu' [explicit solve]: current solution: 4.915415e+00, current residual:2.969834e+00 +wall time: 0.000554085s + +time increment:56530 time: 56.53 +field ' c' [explicit solve]: current solution: 9.615524e+01, current residual:5.805351e+01 +field 'mu' [explicit solve]: current solution: 4.915347e+00, current residual:2.969791e+00 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0.000658989s + +time increment:56580 time: 56.58 +field ' c' [explicit solve]: current solution: 9.615531e+01, current residual:5.805353e+01 +field 'mu' [explicit solve]: current solution: 4.915008e+00, current residual:2.969578e+00 +wall time: 0.000705957s + +time increment:56590 time: 56.59 +field ' c' [explicit solve]: current solution: 9.615532e+01, current residual:5.805354e+01 +field 'mu' [explicit solve]: current solution: 4.914941e+00, current residual:2.969535e+00 +wall time: 0.000700951s + +time increment:56600 time: 56.6 +field ' c' [explicit solve]: current solution: 9.615534e+01, current residual:5.805354e+01 +field 'mu' [explicit solve]: current solution: 4.914873e+00, current residual:2.969493e+00 +wall time: 0.000686884s + +time increment:56610 time: 56.61 +field ' c' [explicit solve]: current solution: 9.615535e+01, current residual:5.805355e+01 +field 'mu' [explicit solve]: current solution: 4.914805e+00, current residual:2.969450e+00 +wall time: 0.000611782s + +time increment:56620 time: 56.62 +field ' c' [explicit solve]: current solution: 9.615537e+01, current residual:5.805355e+01 +field 'mu' [explicit solve]: current solution: 4.914738e+00, current residual:2.969408e+00 +wall time: 0.000692129s + +time increment:56630 time: 56.63 +field ' c' [explicit solve]: current solution: 9.615538e+01, current residual:5.805356e+01 +field 'mu' [explicit solve]: current solution: 4.914670e+00, current residual:2.969365e+00 +wall time: 0.000568151s + +time increment:56640 time: 56.64 +field ' c' [explicit solve]: current solution: 9.615540e+01, current residual:5.805357e+01 +field 'mu' [explicit solve]: current solution: 4.914602e+00, current residual:2.969323e+00 +wall time: 0.00057292s + +time increment:56650 time: 56.65 +field ' c' [explicit solve]: current solution: 9.615541e+01, current residual:5.805357e+01 +field 'mu' [explicit solve]: current solution: 4.914535e+00, current residual:2.969280e+00 +wall time: 0.00062108s + +time increment:56660 time: 56.66 +field ' c' [explicit solve]: current solution: 9.615542e+01, current residual:5.805358e+01 +field 'mu' [explicit solve]: current solution: 4.914467e+00, current residual:2.969238e+00 +wall time: 0.000607014s + +time increment:56670 time: 56.67 +field ' c' [explicit solve]: current solution: 9.615544e+01, current residual:5.805358e+01 +field 'mu' [explicit solve]: current solution: 4.914399e+00, current residual:2.969195e+00 +wall time: 0.000576973s + +time increment:56680 time: 56.68 +field ' c' [explicit solve]: current solution: 9.615545e+01, current residual:5.805359e+01 +field 'mu' [explicit solve]: current solution: 4.914332e+00, current residual:2.969153e+00 +wall time: 0.000674963s + +time increment:56690 time: 56.69 +field ' c' [explicit solve]: current solution: 9.615547e+01, current residual:5.805359e+01 +field 'mu' [explicit solve]: current solution: 4.914264e+00, current residual:2.969110e+00 +wall time: 0.000694036s + +time increment:56700 time: 56.7 +field ' c' [explicit solve]: current solution: 9.615548e+01, current residual:5.805360e+01 +field 'mu' [explicit solve]: current solution: 4.914197e+00, current residual:2.969068e+00 +wall time: 0.000649929s + +time increment:56710 time: 56.71 +field ' c' [explicit solve]: current solution: 9.615550e+01, current residual:5.805360e+01 +field 'mu' [explicit solve]: current solution: 4.914129e+00, current residual:2.969025e+00 +wall time: 0.000658989s + +time increment:56720 time: 56.72 +field ' c' [explicit solve]: current solution: 9.615551e+01, current residual:5.805361e+01 +field 'mu' [explicit solve]: current solution: 4.914062e+00, current residual:2.968983e+00 +wall time: 0.000646114s + +time increment:56730 time: 56.73 +field ' c' [explicit solve]: current solution: 9.615553e+01, current residual:5.805361e+01 +field 'mu' [explicit solve]: current solution: 4.913994e+00, current residual:2.968941e+00 +wall time: 0.000634909s + +time increment:56740 time: 56.74 +field ' c' [explicit solve]: current solution: 9.615554e+01, current residual:5.805362e+01 +field 'mu' [explicit solve]: current solution: 4.913927e+00, current residual:2.968898e+00 +wall time: 0.000576973s + +time increment:56750 time: 56.75 +field ' c' [explicit solve]: current solution: 9.615556e+01, current residual:5.805362e+01 +field 'mu' [explicit solve]: current solution: 4.913859e+00, current residual:2.968856e+00 +wall time: 0.000666142s + +time increment:56760 time: 56.76 +field ' c' [explicit solve]: current solution: 9.615557e+01, current residual:5.805363e+01 +field 'mu' [explicit solve]: current solution: 4.913792e+00, current residual:2.968813e+00 +wall time: 0.000727892s + +time increment:56770 time: 56.77 +field ' c' [explicit solve]: current solution: 9.615559e+01, current residual:5.805363e+01 +field 'mu' [explicit solve]: current solution: 4.913724e+00, current residual:2.968771e+00 +wall time: 0.000694036s + +time increment:56780 time: 56.78 +field ' c' [explicit solve]: current solution: 9.615560e+01, current residual:5.805364e+01 +field 'mu' [explicit solve]: current solution: 4.913657e+00, current residual:2.968729e+00 +wall time: 0.000755072s + +time increment:56790 time: 56.79 +field ' c' [explicit solve]: current solution: 9.615562e+01, current residual:5.805364e+01 +field 'mu' [explicit solve]: current solution: 4.913589e+00, current residual:2.968686e+00 +wall time: 0.000686884s + +time increment:56800 time: 56.8 +field ' c' [explicit solve]: current solution: 9.615563e+01, current residual:5.805365e+01 +field 'mu' [explicit solve]: current solution: 4.913522e+00, current residual:2.968644e+00 +wall time: 0.000672102s + +time increment:56810 time: 56.81 +field ' c' [explicit solve]: current solution: 9.615565e+01, current residual:5.805366e+01 +field 'mu' [explicit solve]: current solution: 4.913455e+00, current residual:2.968602e+00 +wall time: 0.000707865s + +time increment:56820 time: 56.82 +field ' c' [explicit solve]: current solution: 9.615566e+01, current residual:5.805366e+01 +field 'mu' [explicit solve]: current solution: 4.913387e+00, current residual:2.968559e+00 +wall time: 0.00067997s + +time increment:56830 time: 56.83 +field ' c' [explicit solve]: current solution: 9.615568e+01, current residual:5.805367e+01 +field 'mu' [explicit solve]: current solution: 4.913320e+00, current residual:2.968517e+00 +wall time: 0.000763178s + +time increment:56840 time: 56.84 +field ' c' [explicit solve]: current solution: 9.615569e+01, current residual:5.805367e+01 +field 'mu' [explicit solve]: current solution: 4.913253e+00, current residual:2.968475e+00 +wall time: 0.000710011s + +time increment:56850 time: 56.85 +field ' c' [explicit solve]: current solution: 9.615571e+01, current residual:5.805368e+01 +field 'mu' [explicit solve]: current solution: 4.913185e+00, current residual:2.968432e+00 +wall time: 0.000723839s + +time increment:56860 time: 56.86 +field ' c' [explicit solve]: current solution: 9.615572e+01, current residual:5.805368e+01 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solve]: current solution: 4.912849e+00, current residual:2.968221e+00 +wall time: 0.000654936s + +time increment:56910 time: 56.91 +field ' c' [explicit solve]: current solution: 9.615580e+01, current residual:5.805371e+01 +field 'mu' [explicit solve]: current solution: 4.912782e+00, current residual:2.968179e+00 +wall time: 0.00071907s + +time increment:56920 time: 56.92 +field ' c' [explicit solve]: current solution: 9.615581e+01, current residual:5.805372e+01 +field 'mu' [explicit solve]: current solution: 4.912715e+00, current residual:2.968136e+00 +wall time: 0.000625134s + +time increment:56930 time: 56.93 +field ' c' [explicit solve]: current solution: 9.615583e+01, current residual:5.805372e+01 +field 'mu' [explicit solve]: current solution: 4.912647e+00, current residual:2.968094e+00 +wall time: 0.000629902s + +time increment:56940 time: 56.94 +field ' c' [explicit solve]: current solution: 9.615584e+01, current residual:5.805373e+01 +field 'mu' [explicit solve]: current solution: 4.912580e+00, current residual:2.968052e+00 +wall time: 0.000555992s + +time increment:56950 time: 56.95 +field ' c' [explicit solve]: current solution: 9.615586e+01, current residual:5.805373e+01 +field 'mu' [explicit solve]: current solution: 4.912513e+00, current residual:2.968010e+00 +wall time: 0.000598907s + +time increment:56960 time: 56.96 +field ' c' [explicit solve]: current solution: 9.615588e+01, current residual:5.805374e+01 +field 'mu' [explicit solve]: current solution: 4.912446e+00, current residual:2.967968e+00 +wall time: 0.000643015s + +time increment:56970 time: 56.97 +field ' c' [explicit solve]: current solution: 9.615589e+01, current residual:5.805374e+01 +field 'mu' [explicit solve]: current solution: 4.912379e+00, current residual:2.967925e+00 +wall time: 0.000761986s + +time increment:56980 time: 56.98 +field ' c' [explicit solve]: current solution: 9.615591e+01, current residual:5.805375e+01 +field 'mu' [explicit solve]: current solution: 4.912312e+00, current residual:2.967883e+00 +wall time: 0.00067091s + +time increment:56990 time: 56.99 +field ' c' [explicit solve]: current solution: 9.615592e+01, current residual:5.805375e+01 +field 'mu' [explicit solve]: current solution: 4.912245e+00, current residual:2.967841e+00 +wall time: 0.000643969s + +time increment:57000 time: 57 +field ' c' [explicit solve]: current solution: 9.615594e+01, current residual:5.805376e+01 +field 'mu' [explicit solve]: current solution: 4.912178e+00, current residual:2.967799e+00 +wall time: 0.000690937s + +time increment:57010 time: 57.01 +field ' c' [explicit solve]: current solution: 9.615595e+01, current residual:5.805377e+01 +field 'mu' [explicit solve]: current solution: 4.912111e+00, current residual:2.967757e+00 +wall time: 0.000673056s + +time increment:57020 time: 57.02 +field ' c' [explicit solve]: current solution: 9.615597e+01, current residual:5.805377e+01 +field 'mu' [explicit solve]: current solution: 4.912043e+00, current residual:2.967714e+00 +wall time: 0.000941992s + +time increment:57030 time: 57.03 +field ' c' [explicit solve]: current solution: 9.615598e+01, current residual:5.805378e+01 +field 'mu' [explicit solve]: current solution: 4.911976e+00, current residual:2.967672e+00 +wall time: 0.000711203s + +time increment:57040 time: 57.04 +field ' c' [explicit solve]: current solution: 9.615600e+01, current residual:5.805378e+01 +field 'mu' [explicit solve]: current solution: 4.911909e+00, current residual:2.967630e+00 +wall time: 0.00064683s + +time increment:57050 time: 57.05 +field ' c' [explicit solve]: current solution: 9.615601e+01, current residual:5.805379e+01 +field 'mu' [explicit solve]: current solution: 4.911842e+00, current residual:2.967588e+00 +wall time: 0.000646114s + +time increment:57060 time: 57.06 +field ' c' [explicit solve]: current solution: 9.615603e+01, current residual:5.805379e+01 +field 'mu' [explicit solve]: current solution: 4.911776e+00, current residual:2.967546e+00 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0.000640154s + +time increment:57110 time: 57.11 +field ' c' [explicit solve]: current solution: 9.615611e+01, current residual:5.805382e+01 +field 'mu' [explicit solve]: current solution: 4.911441e+00, current residual:2.967336e+00 +wall time: 0.000624895s + +time increment:57120 time: 57.12 +field ' c' [explicit solve]: current solution: 9.615612e+01, current residual:5.805383e+01 +field 'mu' [explicit solve]: current solution: 4.911374e+00, current residual:2.967293e+00 +wall time: 0.000638962s + +time increment:57130 time: 57.13 +field ' c' [explicit solve]: current solution: 9.615614e+01, current residual:5.805384e+01 +field 'mu' [explicit solve]: current solution: 4.911307e+00, current residual:2.967251e+00 +wall time: 0.000711918s + +time increment:57140 time: 57.14 +field ' c' [explicit solve]: current solution: 9.615616e+01, current residual:5.805384e+01 +field 'mu' [explicit solve]: current solution: 4.911240e+00, current residual:2.967209e+00 +wall time: 0.000746965s + +time increment:57150 time: 57.15 +field ' c' [explicit solve]: current solution: 9.615617e+01, current residual:5.805385e+01 +field 'mu' [explicit solve]: current solution: 4.911174e+00, current residual:2.967167e+00 +wall time: 0.00070405s + +time increment:57160 time: 57.16 +field ' c' [explicit solve]: current solution: 9.615619e+01, current residual:5.805385e+01 +field 'mu' [explicit solve]: current solution: 4.911107e+00, current residual:2.967125e+00 +wall time: 0.000683069s + +time increment:57170 time: 57.17 +field ' c' [explicit solve]: current solution: 9.615620e+01, current residual:5.805386e+01 +field 'mu' [explicit solve]: current solution: 4.911040e+00, current residual:2.967083e+00 +wall time: 0.000660896s + +time increment:57180 time: 57.18 +field ' c' [explicit solve]: current solution: 9.615622e+01, current residual:5.805386e+01 +field 'mu' [explicit solve]: current solution: 4.910973e+00, current residual:2.967041e+00 +wall time: 0.00067997s + +time increment:57190 time: 57.19 +field ' c' [explicit solve]: current solution: 9.615623e+01, current residual:5.805387e+01 +field 'mu' [explicit solve]: current solution: 4.910906e+00, current residual:2.966999e+00 +wall time: 0.000690937s + +time increment:57200 time: 57.2 +field ' c' [explicit solve]: current solution: 9.615625e+01, current residual:5.805388e+01 +field 'mu' [explicit solve]: current solution: 4.910840e+00, current residual:2.966957e+00 +wall time: 0.000669956s + +time increment:57210 time: 57.21 +field ' c' [explicit solve]: current solution: 9.615627e+01, current residual:5.805388e+01 +field 'mu' [explicit solve]: current solution: 4.910773e+00, current residual:2.966915e+00 +wall time: 0.000838995s + +time increment:57220 time: 57.22 +field ' c' [explicit solve]: current solution: 9.615628e+01, current residual:5.805389e+01 +field 'mu' [explicit solve]: current solution: 4.910706e+00, current residual:2.966873e+00 +wall time: 0.000560999s + +time increment:57230 time: 57.23 +field ' c' [explicit solve]: current solution: 9.615630e+01, current residual:5.805389e+01 +field 'mu' [explicit solve]: current solution: 4.910639e+00, current residual:2.966831e+00 +wall time: 0.000752926s + +time increment:57240 time: 57.24 +field ' c' [explicit solve]: current solution: 9.615631e+01, current residual:5.805390e+01 +field 'mu' [explicit solve]: current solution: 4.910573e+00, current residual:2.966790e+00 +wall time: 0.000761032s + +time increment:57250 time: 57.25 +field ' c' [explicit solve]: current solution: 9.615633e+01, current residual:5.805391e+01 +field 'mu' [explicit solve]: current solution: 4.910506e+00, current residual:2.966748e+00 +wall time: 0.000677109s + +time increment:57260 time: 57.26 +field ' c' [explicit solve]: current solution: 9.615635e+01, current residual:5.805391e+01 +field 'mu' [explicit solve]: current solution: 4.910439e+00, current residual:2.966706e+00 +wall time: 0.000741959s + +time increment:57270 time: 57.27 +field ' c' [explicit solve]: current solution: 9.615636e+01, current residual:5.805392e+01 +field 'mu' [explicit solve]: current solution: 4.910373e+00, current residual:2.966664e+00 +wall time: 0.000652075s + +time increment:57280 time: 57.28 +field ' c' [explicit solve]: current solution: 9.615638e+01, current residual:5.805392e+01 +field 'mu' [explicit solve]: current solution: 4.910306e+00, current residual:2.966622e+00 +wall time: 0.000673056s + +time increment:57290 time: 57.29 +field ' c' [explicit solve]: current solution: 9.615639e+01, current residual:5.805393e+01 +field 'mu' [explicit solve]: current solution: 4.910240e+00, current residual:2.966580e+00 +wall time: 0.000659227s + +time increment:57300 time: 57.3 +field ' c' [explicit solve]: current solution: 9.615641e+01, current residual:5.805394e+01 +field 'mu' [explicit solve]: current solution: 4.910173e+00, current residual:2.966538e+00 +wall time: 0.000649929s + +time increment:57310 time: 57.31 +field ' c' [explicit solve]: current solution: 9.615643e+01, current residual:5.805394e+01 +field 'mu' [explicit solve]: current solution: 4.910106e+00, current residual:2.966496e+00 +wall time: 0.000671864s + +time increment:57320 time: 57.32 +field ' c' [explicit solve]: current solution: 9.615644e+01, current residual:5.805395e+01 +field 'mu' [explicit solve]: current solution: 4.910040e+00, current residual:2.966454e+00 +wall time: 0.000735998s + +time increment:57330 time: 57.33 +field ' c' [explicit solve]: current solution: 9.615646e+01, current residual:5.805396e+01 +field 'mu' [explicit solve]: current solution: 4.909973e+00, current residual:2.966412e+00 +wall time: 0.000591993s + +time increment:57340 time: 57.34 +field ' c' [explicit solve]: current solution: 9.615648e+01, current residual:5.805396e+01 +field 'mu' [explicit solve]: current solution: 4.909907e+00, current residual:2.966371e+00 +wall time: 0.000709057s + +time increment:57350 time: 57.35 +field ' c' [explicit solve]: current solution: 9.615649e+01, 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residual:5.805399e+01 +field 'mu' [explicit solve]: current solution: 4.909574e+00, current residual:2.966161e+00 +wall time: 0.000622034s + +time increment:57400 time: 57.4 +field ' c' [explicit solve]: current solution: 9.615657e+01, current residual:5.805400e+01 +field 'mu' [explicit solve]: current solution: 4.909508e+00, current residual:2.966120e+00 +wall time: 0.000738144s + +time increment:57410 time: 57.41 +field ' c' [explicit solve]: current solution: 9.615659e+01, current residual:5.805400e+01 +field 'mu' [explicit solve]: current solution: 4.909442e+00, current residual:2.966078e+00 +wall time: 0.000697851s + +time increment:57420 time: 57.42 +field ' c' [explicit solve]: current solution: 9.615661e+01, current residual:5.805401e+01 +field 'mu' [explicit solve]: current solution: 4.909375e+00, current residual:2.966036e+00 +wall time: 0.00070405s + +time increment:57430 time: 57.43 +field ' c' [explicit solve]: current solution: 9.615662e+01, current residual:5.805402e+01 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solve]: current solution: 4.909043e+00, current residual:2.965827e+00 +wall time: 0.000594854s + +time increment:57480 time: 57.48 +field ' c' [explicit solve]: current solution: 9.615670e+01, current residual:5.805405e+01 +field 'mu' [explicit solve]: current solution: 4.908977e+00, current residual:2.965786e+00 +wall time: 0.000652075s + +time increment:57490 time: 57.49 +field ' c' [explicit solve]: current solution: 9.615672e+01, current residual:5.805406e+01 +field 'mu' [explicit solve]: current solution: 4.908911e+00, current residual:2.965744e+00 +wall time: 0.000593901s + +time increment:57500 time: 57.5 +field ' c' [explicit solve]: current solution: 9.615674e+01, current residual:5.805406e+01 +field 'mu' [explicit solve]: current solution: 4.908845e+00, current residual:2.965702e+00 +wall time: 0.000670195s + +time increment:57510 time: 57.51 +field ' c' [explicit solve]: current solution: 9.615675e+01, current residual:5.805407e+01 +field 'mu' [explicit solve]: current solution: 4.908778e+00, current residual:2.965660e+00 +wall time: 0.000588179s + +time increment:57520 time: 57.52 +field ' c' [explicit solve]: current solution: 9.615677e+01, current residual:5.805407e+01 +field 'mu' [explicit solve]: current solution: 4.908712e+00, current residual:2.965619e+00 +wall time: 0.000622988s + +time increment:57530 time: 57.53 +field ' c' [explicit solve]: current solution: 9.615679e+01, current residual:5.805408e+01 +field 'mu' [explicit solve]: current solution: 4.908646e+00, current residual:2.965577e+00 +wall time: 0.000562906s + +time increment:57540 time: 57.54 +field ' c' [explicit solve]: current solution: 9.615680e+01, current residual:5.805409e+01 +field 'mu' [explicit solve]: current solution: 4.908580e+00, current residual:2.965535e+00 +wall time: 0.00061512s + +time increment:57550 time: 57.55 +field ' c' [explicit solve]: current solution: 9.615682e+01, current residual:5.805409e+01 +field 'mu' [explicit solve]: current solution: 4.908513e+00, current residual:2.965494e+00 +wall time: 0.000681162s + +time increment:57560 time: 57.56 +field ' c' [explicit solve]: current solution: 9.615684e+01, current residual:5.805410e+01 +field 'mu' [explicit solve]: current solution: 4.908447e+00, current residual:2.965452e+00 +wall time: 0.000658989s + +time increment:57570 time: 57.57 +field ' c' [explicit solve]: current solution: 9.615685e+01, current residual:5.805411e+01 +field 'mu' [explicit solve]: current solution: 4.908381e+00, current residual:2.965410e+00 +wall time: 0.000575066s + +time increment:57580 time: 57.58 +field ' c' [explicit solve]: current solution: 9.615687e+01, current residual:5.805411e+01 +field 'mu' [explicit solve]: current solution: 4.908315e+00, current residual:2.965369e+00 +wall time: 0.000700951s + +time increment:57590 time: 57.59 +field ' c' [explicit solve]: current solution: 9.615689e+01, current residual:5.805412e+01 +field 'mu' [explicit solve]: current solution: 4.908249e+00, current residual:2.965327e+00 +wall time: 0.000597s + +time increment:57600 time: 57.6 +field ' c' [explicit solve]: current solution: 9.615690e+01, current residual:5.805413e+01 +field 'mu' [explicit solve]: current solution: 4.908183e+00, current residual:2.965286e+00 +wall time: 0.000789165s + +time increment:57610 time: 57.61 +field ' c' [explicit solve]: current solution: 9.615692e+01, current residual:5.805413e+01 +field 'mu' [explicit solve]: current solution: 4.908116e+00, current residual:2.965244e+00 +wall time: 0.000689983s + +time increment:57620 time: 57.62 +field ' c' [explicit solve]: current solution: 9.615694e+01, current residual:5.805414e+01 +field 'mu' [explicit solve]: current solution: 4.908050e+00, current residual:2.965202e+00 +wall time: 0.000614166s + +time increment:57630 time: 57.63 +field ' c' [explicit solve]: current solution: 9.615695e+01, current residual:5.805415e+01 +field 'mu' [explicit solve]: current solution: 4.907984e+00, current residual:2.965161e+00 +wall time: 0.000566959s + +time increment:57640 time: 57.64 +field ' c' [explicit solve]: current solution: 9.615697e+01, current residual:5.805415e+01 +field 'mu' [explicit solve]: current solution: 4.907918e+00, current residual:2.965119e+00 +wall time: 0.00056982s + +time increment:57650 time: 57.65 +field ' c' [explicit solve]: current solution: 9.615699e+01, current residual:5.805416e+01 +field 'mu' [explicit solve]: current solution: 4.907852e+00, current residual:2.965078e+00 +wall time: 0.000716925s + +time increment:57660 time: 57.66 +field ' c' [explicit solve]: current solution: 9.615701e+01, current residual:5.805417e+01 +field 'mu' [explicit solve]: current solution: 4.907786e+00, current residual:2.965036e+00 +wall time: 0.000633001s + +time increment:57670 time: 57.67 +field ' c' [explicit solve]: current solution: 9.615702e+01, current residual:5.805417e+01 +field 'mu' [explicit solve]: current solution: 4.907720e+00, current residual:2.964994e+00 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0.000761986s + +time increment:57720 time: 57.72 +field ' c' [explicit solve]: current solution: 9.615711e+01, current residual:5.805420e+01 +field 'mu' [explicit solve]: current solution: 4.907390e+00, current residual:2.964787e+00 +wall time: 0.000761986s + +time increment:57730 time: 57.73 +field ' c' [explicit solve]: current solution: 9.615712e+01, current residual:5.805421e+01 +field 'mu' [explicit solve]: current solution: 4.907324e+00, current residual:2.964745e+00 +wall time: 0.000560999s + +time increment:57740 time: 57.74 +field ' c' [explicit solve]: current solution: 9.615714e+01, current residual:5.805422e+01 +field 'mu' [explicit solve]: current solution: 4.907258e+00, current residual:2.964704e+00 +wall time: 0.000642061s + +time increment:57750 time: 57.75 +field ' c' [explicit solve]: current solution: 9.615716e+01, current residual:5.805423e+01 +field 'mu' [explicit solve]: current solution: 4.907193e+00, current residual:2.964662e+00 +wall time: 0.000725031s + +time increment:57760 time: 57.76 +field ' c' [explicit solve]: current solution: 9.615718e+01, current residual:5.805423e+01 +field 'mu' [explicit solve]: current solution: 4.907127e+00, current residual:2.964621e+00 +wall time: 0.000699997s + +time increment:57770 time: 57.77 +field ' c' [explicit solve]: current solution: 9.615719e+01, current residual:5.805424e+01 +field 'mu' [explicit solve]: current solution: 4.907061e+00, current residual:2.964579e+00 +wall time: 0.000692844s + +time increment:57780 time: 57.78 +field ' c' [explicit solve]: current solution: 9.615721e+01, current residual:5.805425e+01 +field 'mu' [explicit solve]: current solution: 4.906995e+00, current residual:2.964538e+00 +wall time: 0.000662088s + +time increment:57790 time: 57.79 +field ' c' [explicit solve]: current solution: 9.615723e+01, current residual:5.805425e+01 +field 'mu' [explicit solve]: current solution: 4.906929e+00, current residual:2.964496e+00 +wall time: 0.000586033s + +time increment:57800 time: 57.8 +field ' c' [explicit solve]: current solution: 9.615724e+01, current residual:5.805426e+01 +field 'mu' [explicit solve]: current solution: 4.906863e+00, current residual:2.964455e+00 +wall time: 0.000655174s + +time increment:57810 time: 57.81 +field ' c' [explicit solve]: current solution: 9.615726e+01, current residual:5.805427e+01 +field 'mu' [explicit solve]: current solution: 4.906797e+00, current residual:2.964414e+00 +wall time: 0.000645876s + +time increment:57820 time: 57.82 +field ' c' [explicit solve]: current solution: 9.615728e+01, current residual:5.805427e+01 +field 'mu' [explicit solve]: current solution: 4.906732e+00, current residual:2.964372e+00 +wall time: 0.000663996s + +time increment:57830 time: 57.83 +field ' c' [explicit solve]: current solution: 9.615730e+01, current residual:5.805428e+01 +field 'mu' [explicit solve]: current solution: 4.906666e+00, current residual:2.964331e+00 +wall time: 0.000581026s + +time increment:57840 time: 57.84 +field ' c' [explicit solve]: current solution: 9.615731e+01, current residual:5.805429e+01 +field 'mu' [explicit solve]: current solution: 4.906600e+00, current residual:2.964289e+00 +wall time: 0.000696182s + +time increment:57850 time: 57.85 +field ' c' [explicit solve]: current solution: 9.615733e+01, current residual:5.805429e+01 +field 'mu' [explicit solve]: current solution: 4.906534e+00, current residual:2.964248e+00 +wall time: 0.000673056s + +time increment:57860 time: 57.86 +field ' c' [explicit solve]: current solution: 9.615735e+01, current residual:5.805430e+01 +field 'mu' [explicit solve]: current solution: 4.906469e+00, current residual:2.964207e+00 +wall time: 0.000631094s + +time increment:57870 time: 57.87 +field ' c' [explicit solve]: current solution: 9.615737e+01, current residual:5.805431e+01 +field 'mu' [explicit solve]: current solution: 4.906403e+00, current residual:2.964165e+00 +wall time: 0.000687838s + +time increment:57880 time: 57.88 +field ' c' [explicit solve]: current solution: 9.615738e+01, current residual:5.805431e+01 +field 'mu' [explicit solve]: current solution: 4.906337e+00, current residual:2.964124e+00 +wall time: 0.000611067s + +time increment:57890 time: 57.89 +field ' c' [explicit solve]: current solution: 9.615740e+01, current residual:5.805432e+01 +field 'mu' [explicit solve]: current solution: 4.906271e+00, current residual:2.964082e+00 +wall time: 0.000582933s + +time increment:57900 time: 57.9 +field ' c' [explicit solve]: current solution: 9.615742e+01, current residual:5.805433e+01 +field 'mu' [explicit solve]: current solution: 4.906206e+00, current residual:2.964041e+00 +wall time: 0.000656128s + +time increment:57910 time: 57.91 +field ' c' [explicit solve]: current solution: 9.615743e+01, current residual:5.805433e+01 +field 'mu' [explicit solve]: current solution: 4.906140e+00, current residual:2.964000e+00 +wall time: 0.000658035s + +time increment:57920 time: 57.92 +field ' c' [explicit solve]: current solution: 9.615745e+01, current residual:5.805434e+01 +field 'mu' [explicit solve]: current solution: 4.906075e+00, current residual:2.963958e+00 +wall time: 0.000741005s + +time increment:57930 time: 57.93 +field ' c' [explicit solve]: current solution: 9.615747e+01, current residual:5.805435e+01 +field 'mu' [explicit solve]: current solution: 4.906009e+00, current residual:2.963917e+00 +wall time: 0.000638962s + +time increment:57940 time: 57.94 +field ' c' [explicit solve]: current solution: 9.615749e+01, current residual:5.805435e+01 +field 'mu' [explicit solve]: current solution: 4.905943e+00, current residual:2.963876e+00 +wall time: 0.000701904s + +time increment:57950 time: 57.95 +field ' c' [explicit solve]: current solution: 9.615750e+01, current residual:5.805436e+01 +field 'mu' [explicit solve]: current solution: 4.905878e+00, current residual:2.963834e+00 +wall time: 0.000678062s + +time increment:57960 time: 57.96 +field ' c' [explicit solve]: current solution: 9.615752e+01, current residual:5.805437e+01 +field 'mu' [explicit solve]: current solution: 4.905812e+00, current residual:2.963793e+00 +wall time: 0.00065589s + +time increment:57970 time: 57.97 +field ' c' [explicit solve]: current solution: 9.615754e+01, current residual:5.805438e+01 +field 'mu' [explicit solve]: current solution: 4.905747e+00, current residual:2.963752e+00 +wall time: 0.000643969s + +time increment:57980 time: 57.98 +field ' c' [explicit solve]: current solution: 9.615756e+01, current residual:5.805438e+01 +field 'mu' [explicit solve]: current solution: 4.905681e+00, current residual:2.963711e+00 +wall time: 0.000679016s + +time increment:57990 time: 57.99 +field ' c' [explicit solve]: current solution: 9.615758e+01, current residual:5.805439e+01 +field 'mu' [explicit solve]: current solution: 4.905615e+00, current residual:2.963669e+00 +wall time: 0.000644922s + +time increment:58000 time: 58 +field ' c' [explicit solve]: current solution: 9.615759e+01, current residual:5.805440e+01 +field 'mu' [explicit solve]: current solution: 4.905550e+00, current residual:2.963628e+00 +wall time: 0.00069499s + +time increment:58010 time: 58.01 +field ' c' [explicit solve]: current solution: 9.615761e+01, current residual:5.805440e+01 +field 'mu' [explicit solve]: current solution: 4.905484e+00, current residual:2.963587e+00 +wall time: 0.000643015s + +time increment:58020 time: 58.02 +field ' c' [explicit solve]: current solution: 9.615763e+01, current residual:5.805441e+01 +field 'mu' [explicit solve]: current solution: 4.905419e+00, current residual:2.963545e+00 +wall time: 0.000673056s + +time increment:58030 time: 58.03 +field ' c' [explicit solve]: current solution: 9.615765e+01, current residual:5.805442e+01 +field 'mu' [explicit solve]: current solution: 4.905353e+00, current residual:2.963504e+00 +wall time: 0.000729084s + +time increment:58040 time: 58.04 +field ' c' [explicit solve]: current solution: 9.615766e+01, current residual:5.805443e+01 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[explicit solve]: current solution: 4.905026e+00, current residual:2.963298e+00 +wall time: 0.000746012s + +time increment:58090 time: 58.09 +field ' c' [explicit solve]: current solution: 9.615775e+01, current residual:5.805446e+01 +field 'mu' [explicit solve]: current solution: 4.904961e+00, current residual:2.963257e+00 +wall time: 0.000614166s + +time increment:58100 time: 58.1 +field ' c' [explicit solve]: current solution: 9.615777e+01, current residual:5.805447e+01 +field 'mu' [explicit solve]: current solution: 4.904895e+00, current residual:2.963216e+00 +wall time: 0.000699997s + +time increment:58110 time: 58.11 +field ' c' [explicit solve]: current solution: 9.615779e+01, current residual:5.805448e+01 +field 'mu' [explicit solve]: current solution: 4.904830e+00, current residual:2.963175e+00 +wall time: 0.000723124s + +time increment:58120 time: 58.12 +field ' c' [explicit solve]: current solution: 9.615781e+01, current residual:5.805448e+01 +field 'mu' [explicit solve]: current solution: 4.904765e+00, current residual:2.963133e+00 +wall time: 0.000633001s + +time increment:58130 time: 58.13 +field ' c' [explicit solve]: current solution: 9.615782e+01, current residual:5.805449e+01 +field 'mu' [explicit solve]: current solution: 4.904699e+00, current residual:2.963092e+00 +wall time: 0.000764132s + +time increment:58140 time: 58.14 +field ' c' [explicit solve]: current solution: 9.615784e+01, current residual:5.805450e+01 +field 'mu' [explicit solve]: current solution: 4.904634e+00, current residual:2.963051e+00 +wall time: 0.000751019s + +time increment:58150 time: 58.15 +field ' c' [explicit solve]: current solution: 9.615786e+01, current residual:5.805450e+01 +field 'mu' [explicit solve]: current solution: 4.904569e+00, current residual:2.963010e+00 +wall time: 0.000663042s + +time increment:58160 time: 58.16 +field ' c' [explicit solve]: current solution: 9.615788e+01, current residual:5.805451e+01 +field 'mu' [explicit solve]: current solution: 4.904503e+00, current residual:2.962969e+00 +wall time: 0.000710011s + +time increment:58170 time: 58.17 +field ' c' [explicit solve]: current solution: 9.615790e+01, current residual:5.805452e+01 +field 'mu' [explicit solve]: current solution: 4.904438e+00, current residual:2.962928e+00 +wall time: 0.000728846s + +time increment:58180 time: 58.18 +field ' c' [explicit solve]: current solution: 9.615791e+01, current residual:5.805453e+01 +field 'mu' [explicit solve]: current solution: 4.904373e+00, current residual:2.962887e+00 +wall time: 0.000678062s + +time increment:58190 time: 58.19 +field ' c' [explicit solve]: current solution: 9.615793e+01, current residual:5.805453e+01 +field 'mu' [explicit solve]: current solution: 4.904308e+00, current residual:2.962845e+00 +wall time: 0.000691175s + +time increment:58200 time: 58.2 +field ' c' [explicit solve]: current solution: 9.615795e+01, current residual:5.805454e+01 +field 'mu' [explicit solve]: current solution: 4.904242e+00, current residual:2.962804e+00 +wall time: 0.000773907s + +time increment:58210 time: 58.21 +field ' c' [explicit solve]: current solution: 9.615797e+01, current residual:5.805455e+01 +field 'mu' [explicit solve]: current solution: 4.904177e+00, current residual:2.962763e+00 +wall time: 0.00068903s + +time increment:58220 time: 58.22 +field ' c' [explicit solve]: current solution: 9.615799e+01, current residual:5.805456e+01 +field 'mu' [explicit solve]: current solution: 4.904112e+00, current residual:2.962722e+00 +wall time: 0.000674009s + +time increment:58230 time: 58.23 +field ' c' [explicit solve]: current solution: 9.615800e+01, current residual:5.805456e+01 +field 'mu' [explicit solve]: current solution: 4.904047e+00, current residual:2.962681e+00 +wall time: 0.000655174s + +time increment:58240 time: 58.24 +field ' c' [explicit solve]: current solution: 9.615802e+01, current residual:5.805457e+01 +field 'mu' [explicit solve]: current solution: 4.903982e+00, current residual:2.962640e+00 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0.000632048s + +time increment:58290 time: 58.29 +field ' c' [explicit solve]: current solution: 9.615811e+01, current residual:5.805461e+01 +field 'mu' [explicit solve]: current solution: 4.903656e+00, current residual:2.962435e+00 +wall time: 0.000699043s + +time increment:58300 time: 58.3 +field ' c' [explicit solve]: current solution: 9.615813e+01, current residual:5.805461e+01 +field 'mu' [explicit solve]: current solution: 4.903591e+00, current residual:2.962394e+00 +wall time: 0.000697136s + +time increment:58310 time: 58.31 +field ' c' [explicit solve]: current solution: 9.615815e+01, current residual:5.805462e+01 +field 'mu' [explicit solve]: current solution: 4.903526e+00, current residual:2.962353e+00 +wall time: 0.000723124s + +time increment:58320 time: 58.32 +field ' c' [explicit solve]: current solution: 9.615817e+01, current residual:5.805463e+01 +field 'mu' [explicit solve]: current solution: 4.903461e+00, current residual:2.962312e+00 +wall time: 0.000595093s + +time increment:58330 time: 58.33 +field ' c' [explicit solve]: current solution: 9.615819e+01, current residual:5.805464e+01 +field 'mu' [explicit solve]: current solution: 4.903396e+00, current residual:2.962271e+00 +wall time: 0.000724077s + +time increment:58340 time: 58.34 +field ' c' [explicit solve]: current solution: 9.615820e+01, current residual:5.805464e+01 +field 'mu' [explicit solve]: current solution: 4.903331e+00, current residual:2.962230e+00 +wall time: 0.000721931s + +time increment:58350 time: 58.35 +field ' c' [explicit solve]: current solution: 9.615822e+01, current residual:5.805465e+01 +field 'mu' [explicit solve]: current solution: 4.903266e+00, current residual:2.962189e+00 +wall time: 0.000569105s + +time increment:58360 time: 58.36 +field ' c' [explicit solve]: current solution: 9.615824e+01, current residual:5.805466e+01 +field 'mu' [explicit solve]: current solution: 4.903201e+00, current residual:2.962148e+00 +wall time: 0.000614882s + +time increment:58370 time: 58.37 +field ' c' [explicit solve]: current solution: 9.615826e+01, current residual:5.805467e+01 +field 'mu' [explicit solve]: current solution: 4.903136e+00, current residual:2.962107e+00 +wall time: 0.00061202s + +time increment:58380 time: 58.38 +field ' c' [explicit solve]: current solution: 9.615828e+01, current residual:5.805467e+01 +field 'mu' [explicit solve]: current solution: 4.903071e+00, current residual:2.962066e+00 +wall time: 0.000635147s + +time increment:58390 time: 58.39 +field ' c' [explicit solve]: current solution: 9.615830e+01, current residual:5.805468e+01 +field 'mu' [explicit solve]: current solution: 4.903006e+00, current residual:2.962025e+00 +wall time: 0.000664949s + +time increment:58400 time: 58.4 +field ' c' [explicit solve]: current solution: 9.615832e+01, current residual:5.805469e+01 +field 'mu' [explicit solve]: current solution: 4.902941e+00, current residual:2.961984e+00 +wall time: 0.000684023s + +time increment:58410 time: 58.41 +field ' c' [explicit solve]: current solution: 9.615833e+01, current residual:5.805470e+01 +field 'mu' [explicit solve]: current solution: 4.902876e+00, current residual:2.961943e+00 +wall time: 0.00069809s + +time increment:58420 time: 58.42 +field ' c' [explicit solve]: current solution: 9.615835e+01, current residual:5.805470e+01 +field 'mu' [explicit solve]: current solution: 4.902811e+00, current residual:2.961902e+00 +wall time: 0.000638962s + +time increment:58430 time: 58.43 +field ' c' [explicit solve]: current solution: 9.615837e+01, current residual:5.805471e+01 +field 'mu' [explicit solve]: current solution: 4.902746e+00, current residual:2.961861e+00 +wall time: 0.000571012s + +time increment:58440 time: 58.44 +field ' c' [explicit solve]: current solution: 9.615839e+01, current residual:5.805472e+01 +field 'mu' [explicit solve]: current solution: 4.902681e+00, current residual:2.961820e+00 +wall time: 0.000566959s + +time increment:58450 time: 58.45 +field ' c' [explicit solve]: current solution: 9.615841e+01, current residual:5.805473e+01 +field 'mu' [explicit solve]: current solution: 4.902616e+00, current residual:2.961780e+00 +wall time: 0.000661135s + +time increment:58460 time: 58.46 +field ' c' [explicit solve]: current solution: 9.615843e+01, current residual:5.805473e+01 +field 'mu' [explicit solve]: current solution: 4.902551e+00, current residual:2.961739e+00 +wall time: 0.000594139s + +time increment:58470 time: 58.47 +field ' c' [explicit solve]: current solution: 9.615844e+01, current residual:5.805474e+01 +field 'mu' [explicit solve]: current solution: 4.902486e+00, current residual:2.961698e+00 +wall time: 0.000565052s + +time increment:58480 time: 58.48 +field ' c' [explicit solve]: current solution: 9.615846e+01, current residual:5.805475e+01 +field 'mu' [explicit solve]: current solution: 4.902422e+00, current residual:2.961657e+00 +wall time: 0.000701904s + +time increment:58490 time: 58.49 +field ' c' [explicit solve]: current solution: 9.615848e+01, current residual:5.805476e+01 +field 'mu' [explicit solve]: current solution: 4.902357e+00, current residual:2.961616e+00 +wall time: 0.000571012s + +time increment:58500 time: 58.5 +field ' c' [explicit solve]: current solution: 9.615850e+01, current residual:5.805477e+01 +field 'mu' [explicit solve]: current solution: 4.902292e+00, current residual:2.961575e+00 +wall time: 0.000993967s + +time increment:58510 time: 58.51 +field ' c' [explicit solve]: current solution: 9.615852e+01, current residual:5.805477e+01 +field 'mu' [explicit solve]: current solution: 4.902227e+00, current residual:2.961534e+00 +wall time: 0.000655174s + +time increment:58520 time: 58.52 +field ' c' [explicit solve]: current solution: 9.615854e+01, current residual:5.805478e+01 +field 'mu' [explicit solve]: current solution: 4.902162e+00, current residual:2.961493e+00 +wall time: 0.000706196s + +time increment:58530 time: 58.53 +field ' c' [explicit solve]: current solution: 9.615856e+01, current residual:5.805479e+01 +field 'mu' [explicit solve]: current solution: 4.902098e+00, current residual:2.961453e+00 +wall time: 0.000597s + +time increment:58540 time: 58.54 +field ' c' [explicit solve]: current solution: 9.615858e+01, current residual:5.805480e+01 +field 'mu' [explicit solve]: current solution: 4.902033e+00, current residual:2.961412e+00 +wall time: 0.000677824s + +time increment:58550 time: 58.55 +field ' c' [explicit solve]: current solution: 9.615859e+01, current residual:5.805480e+01 +field 'mu' [explicit solve]: current solution: 4.901968e+00, current residual:2.961371e+00 +wall time: 0.000771999s + +time increment:58560 time: 58.56 +field ' c' [explicit solve]: current solution: 9.615861e+01, current residual:5.805481e+01 +field 'mu' [explicit solve]: current solution: 4.901903e+00, current residual:2.961330e+00 +wall time: 0.000720978s + +time increment:58570 time: 58.57 +field ' c' [explicit solve]: current solution: 9.615863e+01, current residual:5.805482e+01 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solve]: current solution: 4.901580e+00, current residual:2.961126e+00 +wall time: 0.000765085s + +time increment:58620 time: 58.62 +field ' c' [explicit solve]: current solution: 9.615873e+01, current residual:5.805486e+01 +field 'mu' [explicit solve]: current solution: 4.901515e+00, current residual:2.961086e+00 +wall time: 0.00070405s + +time increment:58630 time: 58.63 +field ' c' [explicit solve]: current solution: 9.615875e+01, current residual:5.805487e+01 +field 'mu' [explicit solve]: current solution: 4.901451e+00, current residual:2.961045e+00 +wall time: 0.000717878s + +time increment:58640 time: 58.64 +field ' c' [explicit solve]: current solution: 9.615876e+01, current residual:5.805487e+01 +field 'mu' [explicit solve]: current solution: 4.901386e+00, current residual:2.961004e+00 +wall time: 0.000683069s + +time increment:58650 time: 58.65 +field ' c' [explicit solve]: current solution: 9.615878e+01, current residual:5.805488e+01 +field 'mu' [explicit solve]: current solution: 4.901321e+00, current residual:2.960963e+00 +wall time: 0.000740767s + +time increment:58660 time: 58.66 +field ' c' [explicit solve]: current solution: 9.615880e+01, current residual:5.805489e+01 +field 'mu' [explicit solve]: current solution: 4.901257e+00, current residual:2.960923e+00 +wall time: 0.00067997s + +time increment:58670 time: 58.67 +field ' c' [explicit solve]: current solution: 9.615882e+01, current residual:5.805490e+01 +field 'mu' [explicit solve]: current solution: 4.901192e+00, current residual:2.960882e+00 +wall time: 0.000684023s + +time increment:58680 time: 58.68 +field ' c' [explicit solve]: current solution: 9.615884e+01, current residual:5.805491e+01 +field 'mu' [explicit solve]: current solution: 4.901128e+00, current residual:2.960841e+00 +wall time: 0.000796795s + +time increment:58690 time: 58.69 +field ' c' [explicit solve]: current solution: 9.615886e+01, current residual:5.805491e+01 +field 'mu' [explicit solve]: current solution: 4.901063e+00, current residual:2.960801e+00 +wall time: 0.000763893s + +time increment:58700 time: 58.7 +field ' c' [explicit solve]: current solution: 9.615888e+01, current residual:5.805492e+01 +field 'mu' [explicit solve]: current solution: 4.900999e+00, current residual:2.960760e+00 +wall time: 0.000793934s + +time increment:58710 time: 58.71 +field ' c' [explicit solve]: current solution: 9.615890e+01, current residual:5.805493e+01 +field 'mu' [explicit solve]: current solution: 4.900934e+00, current residual:2.960719e+00 +wall time: 0.000669956s + +time increment:58720 time: 58.72 +field ' c' [explicit solve]: current solution: 9.615892e+01, current residual:5.805494e+01 +field 'mu' [explicit solve]: current solution: 4.900870e+00, current residual:2.960678e+00 +wall time: 0.000682831s + +time increment:58730 time: 58.73 +field ' c' [explicit solve]: current solution: 9.615894e+01, current residual:5.805495e+01 +field 'mu' [explicit solve]: current solution: 4.900805e+00, current residual:2.960638e+00 +wall time: 0.000692129s + +time increment:58740 time: 58.74 +field ' c' [explicit solve]: current solution: 9.615896e+01, current residual:5.805495e+01 +field 'mu' [explicit solve]: current solution: 4.900741e+00, current residual:2.960597e+00 +wall time: 0.000746965s + +time increment:58750 time: 58.75 +field ' c' [explicit solve]: current solution: 9.615897e+01, current residual:5.805496e+01 +field 'mu' [explicit solve]: current solution: 4.900676e+00, current residual:2.960556e+00 +wall time: 0.000674963s + +time increment:58760 time: 58.76 +field ' c' [explicit solve]: current solution: 9.615899e+01, current residual:5.805497e+01 +field 'mu' [explicit solve]: current solution: 4.900612e+00, current residual:2.960516e+00 +wall time: 0.000771046s + +time increment:58770 time: 58.77 +field ' c' [explicit solve]: current solution: 9.615901e+01, current residual:5.805498e+01 +field 'mu' [explicit solve]: current solution: 4.900547e+00, current residual:2.960475e+00 +wall time: 0.00068593s + +time increment:58780 time: 58.78 +field ' c' [explicit solve]: current solution: 9.615903e+01, current residual:5.805499e+01 +field 'mu' [explicit solve]: current solution: 4.900483e+00, current residual:2.960435e+00 +wall time: 0.00068903s + +time increment:58790 time: 58.79 +field ' c' [explicit solve]: current solution: 9.615905e+01, current residual:5.805499e+01 +field 'mu' [explicit solve]: current solution: 4.900418e+00, current residual:2.960394e+00 +wall time: 0.000657797s + +time increment:58800 time: 58.8 +field ' c' [explicit solve]: current solution: 9.615907e+01, current residual:5.805500e+01 +field 'mu' [explicit solve]: current solution: 4.900354e+00, current residual:2.960353e+00 +wall time: 0.000699043s + +time increment:58810 time: 58.81 +field ' c' [explicit solve]: current solution: 9.615909e+01, current residual:5.805501e+01 +field 'mu' [explicit solve]: current solution: 4.900290e+00, current residual:2.960313e+00 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0.000669956s + +time increment:58860 time: 58.86 +field ' c' [explicit solve]: current solution: 9.615919e+01, current residual:5.805505e+01 +field 'mu' [explicit solve]: current solution: 4.899968e+00, current residual:2.960110e+00 +wall time: 0.000741959s + +time increment:58870 time: 58.87 +field ' c' [explicit solve]: current solution: 9.615921e+01, current residual:5.805506e+01 +field 'mu' [explicit solve]: current solution: 4.899903e+00, current residual:2.960069e+00 +wall time: 0.000735044s + +time increment:58880 time: 58.88 +field ' c' [explicit solve]: current solution: 9.615923e+01, current residual:5.805507e+01 +field 'mu' [explicit solve]: current solution: 4.899839e+00, current residual:2.960029e+00 +wall time: 0.000684023s + +time increment:58890 time: 58.89 +field ' c' [explicit solve]: current solution: 9.615924e+01, current residual:5.805508e+01 +field 'mu' [explicit solve]: current solution: 4.899775e+00, current residual:2.959988e+00 +wall time: 0.000585079s + +time increment:58900 time: 58.9 +field ' c' [explicit solve]: current solution: 9.615926e+01, current residual:5.805508e+01 +field 'mu' [explicit solve]: current solution: 4.899711e+00, current residual:2.959948e+00 +wall time: 0.000652075s + +time increment:58910 time: 58.91 +field ' c' [explicit solve]: current solution: 9.615928e+01, current residual:5.805509e+01 +field 'mu' [explicit solve]: current solution: 4.899646e+00, current residual:2.959907e+00 +wall time: 0.000717163s + +time increment:58920 time: 58.92 +field ' c' [explicit solve]: current solution: 9.615930e+01, current residual:5.805510e+01 +field 'mu' [explicit solve]: current solution: 4.899582e+00, current residual:2.959867e+00 +wall time: 0.000718117s + +time increment:58930 time: 58.93 +field ' c' [explicit solve]: current solution: 9.615932e+01, current residual:5.805511e+01 +field 'mu' [explicit solve]: current solution: 4.899518e+00, current residual:2.959826e+00 +wall time: 0.00062108s + +time increment:58940 time: 58.94 +field ' c' [explicit solve]: current solution: 9.615934e+01, current residual:5.805512e+01 +field 'mu' [explicit solve]: current solution: 4.899454e+00, current residual:2.959786e+00 +wall time: 0.000567198s + +time increment:58950 time: 58.95 +field ' c' [explicit solve]: current solution: 9.615936e+01, current residual:5.805512e+01 +field 'mu' [explicit solve]: current solution: 4.899390e+00, current residual:2.959745e+00 +wall time: 0.00067997s + +time increment:58960 time: 58.96 +field ' c' [explicit solve]: current solution: 9.615938e+01, current residual:5.805513e+01 +field 'mu' [explicit solve]: current solution: 4.899325e+00, current residual:2.959705e+00 +wall time: 0.000664949s + +time increment:58970 time: 58.97 +field ' c' [explicit solve]: current solution: 9.615940e+01, current residual:5.805514e+01 +field 'mu' [explicit solve]: current solution: 4.899261e+00, current residual:2.959664e+00 +wall time: 0.000691891s + +time increment:58980 time: 58.98 +field ' c' [explicit solve]: current solution: 9.615942e+01, current residual:5.805515e+01 +field 'mu' [explicit solve]: current solution: 4.899197e+00, current residual:2.959624e+00 +wall time: 0.000694036s + +time increment:58990 time: 58.99 +field ' c' [explicit solve]: current solution: 9.615944e+01, current residual:5.805516e+01 +field 'mu' [explicit solve]: current solution: 4.899133e+00, current residual:2.959583e+00 +wall time: 0.000660896s + +time increment:59000 time: 59 +field ' c' [explicit solve]: current solution: 9.615946e+01, current residual:5.805517e+01 +field 'mu' [explicit solve]: current solution: 4.899069e+00, current residual:2.959543e+00 +wall time: 0.000766039s + +time increment:59010 time: 59.01 +field ' c' [explicit solve]: current solution: 9.615948e+01, current residual:5.805517e+01 +field 'mu' [explicit solve]: current solution: 4.899005e+00, current residual:2.959502e+00 +wall time: 0.000630856s + +time increment:59020 time: 59.02 +field ' c' [explicit solve]: current solution: 9.615950e+01, current residual:5.805518e+01 +field 'mu' [explicit solve]: current solution: 4.898941e+00, current residual:2.959462e+00 +wall time: 0.000607014s + +time increment:59030 time: 59.03 +field ' c' [explicit solve]: current solution: 9.615952e+01, current residual:5.805519e+01 +field 'mu' [explicit solve]: current solution: 4.898876e+00, current residual:2.959421e+00 +wall time: 0.000701904s + +time increment:59040 time: 59.04 +field ' c' [explicit solve]: current solution: 9.615954e+01, current residual:5.805520e+01 +field 'mu' [explicit solve]: current solution: 4.898812e+00, current residual:2.959381e+00 +wall time: 0.000571012s + +time increment:59050 time: 59.05 +field ' c' [explicit solve]: current solution: 9.615956e+01, current residual:5.805521e+01 +field 'mu' [explicit solve]: current solution: 4.898748e+00, current residual:2.959341e+00 +wall time: 0.000581026s + +time increment:59060 time: 59.06 +field ' c' [explicit solve]: current solution: 9.615958e+01, current residual:5.805522e+01 +field 'mu' [explicit solve]: current solution: 4.898684e+00, current residual:2.959300e+00 +wall time: 0.000643969s + +time increment:59070 time: 59.07 +field ' c' [explicit solve]: current solution: 9.615960e+01, current residual:5.805523e+01 +field 'mu' [explicit solve]: current solution: 4.898620e+00, current residual:2.959260e+00 +wall time: 0.000617981s + +time increment:59080 time: 59.08 +field ' c' [explicit solve]: current solution: 9.615962e+01, current residual:5.805523e+01 +field 'mu' [explicit solve]: current solution: 4.898556e+00, current residual:2.959219e+00 +wall time: 0.00058198s + +time increment:59090 time: 59.09 +field ' c' [explicit solve]: current solution: 9.615964e+01, current residual:5.805524e+01 +field 'mu' [explicit solve]: current solution: 4.898492e+00, current residual:2.959179e+00 +wall time: 0.000580072s + +time increment:59100 time: 59.1 +field ' c' [explicit solve]: current solution: 9.615966e+01, current residual:5.805525e+01 +field 'mu' [explicit solve]: current solution: 4.898428e+00, current residual:2.959139e+00 +wall time: 0.000611067s + +time increment:59110 time: 59.11 +field ' c' [explicit solve]: current solution: 9.615968e+01, current residual:5.805526e+01 +field 'mu' [explicit solve]: current solution: 4.898364e+00, current residual:2.959098e+00 +wall time: 0.000638008s + +time increment:59120 time: 59.12 +field ' c' [explicit solve]: current solution: 9.615970e+01, current residual:5.805527e+01 +field 'mu' [explicit solve]: current solution: 4.898300e+00, current residual:2.959058e+00 +wall time: 0.000622034s + +time increment:59130 time: 59.13 +field ' c' [explicit solve]: current solution: 9.615972e+01, current residual:5.805528e+01 +field 'mu' [explicit solve]: current solution: 4.898236e+00, current residual:2.959018e+00 +wall time: 0.000692844s + +time increment:59140 time: 59.14 +field ' c' [explicit solve]: current solution: 9.615974e+01, current residual:5.805528e+01 +field 'mu' [explicit solve]: current solution: 4.898172e+00, current residual:2.958977e+00 +wall time: 0.000633955s + +time increment:59150 time: 59.15 +field ' c' [explicit solve]: current solution: 9.615976e+01, current residual:5.805529e+01 +field 'mu' [explicit solve]: current solution: 4.898108e+00, current residual:2.958937e+00 +wall time: 0.000654936s + +time increment:59160 time: 59.16 +field ' c' [explicit solve]: current solution: 9.615978e+01, current residual:5.805530e+01 +field 'mu' [explicit solve]: current solution: 4.898044e+00, current residual:2.958897e+00 +wall time: 0.000649929s + +time increment:59170 time: 59.17 +field ' c' [explicit solve]: current solution: 9.615980e+01, current residual:5.805531e+01 +field 'mu' [explicit solve]: current solution: 4.897981e+00, current residual:2.958856e+00 +wall time: 0.000632048s + +time increment:59180 time: 59.18 +field ' c' [explicit solve]: current solution: 9.615982e+01, current residual:5.805532e+01 +field 'mu' [explicit solve]: current solution: 4.897917e+00, current residual:2.958816e+00 +wall time: 0.000725031s + +time increment:59190 time: 59.19 +field ' c' [explicit solve]: current solution: 9.615984e+01, current residual:5.805533e+01 +field 'mu' [explicit solve]: current solution: 4.897853e+00, current residual:2.958776e+00 +wall time: 0.000648022s + +time increment:59200 time: 59.2 +field ' c' [explicit solve]: current solution: 9.615986e+01, current residual:5.805534e+01 +field 'mu' [explicit solve]: current solution: 4.897789e+00, current residual:2.958735e+00 +wall time: 0.000566959s + +time increment:59210 time: 59.21 +field ' c' [explicit solve]: current solution: 9.615988e+01, current residual:5.805534e+01 +field 'mu' [explicit solve]: current solution: 4.897725e+00, current residual:2.958695e+00 +wall time: 0.000568867s + +time increment:59220 time: 59.22 +field ' c' [explicit solve]: current solution: 9.615990e+01, current residual:5.805535e+01 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[explicit solve]: current solution: 4.897406e+00, current residual:2.958494e+00 +wall time: 0.000568867s + +time increment:59270 time: 59.27 +field ' c' [explicit solve]: current solution: 9.616000e+01, current residual:5.805540e+01 +field 'mu' [explicit solve]: current solution: 4.897342e+00, current residual:2.958453e+00 +wall time: 0.000701904s + +time increment:59280 time: 59.28 +field ' c' [explicit solve]: current solution: 9.616002e+01, current residual:5.805540e+01 +field 'mu' [explicit solve]: current solution: 4.897278e+00, current residual:2.958413e+00 +wall time: 0.00057292s + +time increment:59290 time: 59.29 +field ' c' [explicit solve]: current solution: 9.616004e+01, current residual:5.805541e+01 +field 'mu' [explicit solve]: current solution: 4.897215e+00, current residual:2.958373e+00 +wall time: 0.000720978s + +time increment:59300 time: 59.3 +field ' c' [explicit solve]: current solution: 9.616006e+01, current residual:5.805542e+01 +field 'mu' [explicit solve]: current solution: 4.897151e+00, current residual:2.958333e+00 +wall time: 0.000568151s + +time increment:59310 time: 59.31 +field ' c' [explicit solve]: current solution: 9.616008e+01, current residual:5.805543e+01 +field 'mu' [explicit solve]: current solution: 4.897087e+00, current residual:2.958293e+00 +wall time: 0.000602007s + +time increment:59320 time: 59.32 +field ' c' [explicit solve]: current solution: 9.616010e+01, current residual:5.805544e+01 +field 'mu' [explicit solve]: current solution: 4.897023e+00, current residual:2.958252e+00 +wall time: 0.000597s + +time increment:59330 time: 59.33 +field ' c' [explicit solve]: current solution: 9.616012e+01, current residual:5.805545e+01 +field 'mu' [explicit solve]: current solution: 4.896960e+00, current residual:2.958212e+00 +wall time: 0.000694036s + +time increment:59340 time: 59.34 +field ' c' [explicit solve]: current solution: 9.616014e+01, current residual:5.805546e+01 +field 'mu' [explicit solve]: current solution: 4.896896e+00, current residual:2.958172e+00 +wall time: 0.000653028s + +time increment:59350 time: 59.35 +field ' c' [explicit solve]: current solution: 9.616016e+01, current residual:5.805547e+01 +field 'mu' [explicit solve]: current solution: 4.896832e+00, current residual:2.958132e+00 +wall time: 0.000633955s + +time increment:59360 time: 59.36 +field ' c' [explicit solve]: current solution: 9.616018e+01, current residual:5.805547e+01 +field 'mu' [explicit solve]: current solution: 4.896769e+00, current residual:2.958092e+00 +wall time: 0.00068903s + +time increment:59370 time: 59.37 +field ' c' [explicit solve]: current solution: 9.616020e+01, current residual:5.805548e+01 +field 'mu' [explicit solve]: current solution: 4.896705e+00, current residual:2.958051e+00 +wall time: 0.000646114s + +time increment:59380 time: 59.38 +field ' c' [explicit solve]: current solution: 9.616022e+01, current residual:5.805549e+01 +field 'mu' [explicit solve]: current solution: 4.896641e+00, current residual:2.958011e+00 +wall time: 0.000695944s + +time increment:59390 time: 59.39 +field ' c' [explicit solve]: current solution: 9.616024e+01, current residual:5.805550e+01 +field 'mu' [explicit solve]: current solution: 4.896578e+00, current residual:2.957971e+00 +wall time: 0.000571966s + +time increment:59400 time: 59.4 +field ' c' [explicit solve]: current solution: 9.616026e+01, current residual:5.805551e+01 +field 'mu' [explicit solve]: current solution: 4.896514e+00, current residual:2.957931e+00 +wall time: 0.000643969s + +time increment:59410 time: 59.41 +field ' c' [explicit solve]: current solution: 9.616028e+01, current residual:5.805552e+01 +field 'mu' [explicit solve]: current solution: 4.896451e+00, current residual:2.957891e+00 +wall time: 0.000658035s + +time increment:59420 time: 59.42 +field ' c' [explicit solve]: current solution: 9.616030e+01, current residual:5.805553e+01 +field 'mu' [explicit solve]: current solution: 4.896387e+00, current residual:2.957851e+00 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0.000699997s + +time increment:59470 time: 59.47 +field ' c' [explicit solve]: current solution: 9.616041e+01, current residual:5.805557e+01 +field 'mu' [explicit solve]: current solution: 4.896069e+00, current residual:2.957650e+00 +wall time: 0.000656843s + +time increment:59480 time: 59.48 +field ' c' [explicit solve]: current solution: 9.616043e+01, current residual:5.805558e+01 +field 'mu' [explicit solve]: current solution: 4.896006e+00, current residual:2.957610e+00 +wall time: 0.000566959s + +time increment:59490 time: 59.49 +field ' c' [explicit solve]: current solution: 9.616045e+01, current residual:5.805559e+01 +field 'mu' [explicit solve]: current solution: 4.895942e+00, current residual:2.957570e+00 +wall time: 0.000639915s + +time increment:59500 time: 59.5 +field ' c' [explicit solve]: current solution: 9.616047e+01, current residual:5.805560e+01 +field 'mu' [explicit solve]: current solution: 4.895879e+00, current residual:2.957530e+00 +wall time: 0.00082612s + +time increment:59510 time: 59.51 +field ' c' [explicit solve]: current solution: 9.616049e+01, current residual:5.805561e+01 +field 'mu' [explicit solve]: current solution: 4.895815e+00, current residual:2.957490e+00 +wall time: 0.000871897s + +time increment:59520 time: 59.52 +field ' c' [explicit solve]: current solution: 9.616051e+01, current residual:5.805562e+01 +field 'mu' [explicit solve]: current solution: 4.895752e+00, current residual:2.957450e+00 +wall time: 0.0006001s + +time increment:59530 time: 59.53 +field ' c' [explicit solve]: current solution: 9.616053e+01, current residual:5.805563e+01 +field 'mu' [explicit solve]: current solution: 4.895688e+00, current residual:2.957410e+00 +wall time: 0.000682831s + +time increment:59540 time: 59.54 +field ' c' [explicit solve]: current solution: 9.616055e+01, current residual:5.805563e+01 +field 'mu' [explicit solve]: current solution: 4.895625e+00, current residual:2.957370e+00 +wall time: 0.000669003s + +time increment:59550 time: 59.55 +field ' c' [explicit solve]: current solution: 9.616057e+01, current residual:5.805564e+01 +field 'mu' [explicit solve]: current solution: 4.895562e+00, current residual:2.957330e+00 +wall time: 0.000664949s + +time increment:59560 time: 59.56 +field ' c' [explicit solve]: current solution: 9.616059e+01, current residual:5.805565e+01 +field 'mu' [explicit solve]: current solution: 4.895498e+00, current residual:2.957290e+00 +wall time: 0.000684023s + +time increment:59570 time: 59.57 +field ' c' [explicit solve]: current solution: 9.616061e+01, current residual:5.805566e+01 +field 'mu' [explicit solve]: current solution: 4.895435e+00, current residual:2.957250e+00 +wall time: 0.000724077s + +time increment:59580 time: 59.58 +field ' c' [explicit solve]: current solution: 9.616064e+01, current residual:5.805567e+01 +field 'mu' [explicit solve]: current solution: 4.895371e+00, current residual:2.957210e+00 +wall time: 0.000638962s + +time increment:59590 time: 59.59 +field ' c' [explicit solve]: current solution: 9.616066e+01, current residual:5.805568e+01 +field 'mu' [explicit solve]: current solution: 4.895308e+00, current residual:2.957170e+00 +wall time: 0.000680923s + +time increment:59600 time: 59.6 +field ' c' [explicit solve]: current solution: 9.616068e+01, current residual:5.805569e+01 +field 'mu' [explicit solve]: current solution: 4.895245e+00, current residual:2.957130e+00 +wall time: 0.00071907s + +time increment:59610 time: 59.61 +field ' c' [explicit solve]: current solution: 9.616070e+01, current residual:5.805570e+01 +field 'mu' [explicit solve]: current solution: 4.895181e+00, current residual:2.957090e+00 +wall time: 0.000701904s + +time increment:59620 time: 59.62 +field ' c' [explicit solve]: current solution: 9.616072e+01, current residual:5.805571e+01 +field 'mu' [explicit solve]: current solution: 4.895118e+00, current residual:2.957050e+00 +wall time: 0.000643015s + +time increment:59630 time: 59.63 +field ' c' [explicit solve]: current solution: 9.616074e+01, current residual:5.805572e+01 +field 'mu' [explicit solve]: current solution: 4.895055e+00, current residual:2.957010e+00 +wall time: 0.00071311s + +time increment:59640 time: 59.64 +field ' c' [explicit solve]: current solution: 9.616076e+01, current residual:5.805573e+01 +field 'mu' [explicit solve]: current solution: 4.894991e+00, current residual:2.956970e+00 +wall time: 0.000642776s + +time increment:59650 time: 59.65 +field ' c' [explicit solve]: current solution: 9.616078e+01, current residual:5.805573e+01 +field 'mu' [explicit solve]: current solution: 4.894928e+00, current residual:2.956930e+00 +wall time: 0.00079298s + +time increment:59660 time: 59.66 +field ' c' [explicit solve]: current solution: 9.616080e+01, current residual:5.805574e+01 +field 'mu' [explicit solve]: current solution: 4.894865e+00, current residual:2.956890e+00 +wall time: 0.00067997s + +time increment:59670 time: 59.67 +field ' c' [explicit solve]: current solution: 9.616082e+01, current residual:5.805575e+01 +field 'mu' [explicit solve]: current solution: 4.894801e+00, current residual:2.956850e+00 +wall time: 0.000729799s + +time increment:59680 time: 59.68 +field ' c' [explicit solve]: current solution: 9.616085e+01, current residual:5.805576e+01 +field 'mu' [explicit solve]: current solution: 4.894738e+00, current residual:2.956810e+00 +wall time: 0.000743866s + +time increment:59690 time: 59.69 +field ' c' [explicit solve]: current solution: 9.616087e+01, current residual:5.805577e+01 +field 'mu' [explicit solve]: current solution: 4.894675e+00, current residual:2.956770e+00 +wall time: 0.000708103s + +time increment:59700 time: 59.7 +field ' c' [explicit solve]: current solution: 9.616089e+01, current residual:5.805578e+01 +field 'mu' [explicit solve]: current solution: 4.894612e+00, current residual:2.956730e+00 +wall time: 0.000662088s + +time increment:59710 time: 59.71 +field ' c' [explicit solve]: current solution: 9.616091e+01, current residual:5.805579e+01 +field 'mu' [explicit solve]: current solution: 4.894548e+00, current residual:2.956690e+00 +wall time: 0.000710011s + +time increment:59720 time: 59.72 +field ' c' [explicit solve]: current solution: 9.616093e+01, current residual:5.805580e+01 +field 'mu' [explicit solve]: current solution: 4.894485e+00, current residual:2.956650e+00 +wall time: 0.00067687s + +time increment:59730 time: 59.73 +field ' c' [explicit solve]: current solution: 9.616095e+01, current residual:5.805581e+01 +field 'mu' [explicit solve]: current solution: 4.894422e+00, current residual:2.956610e+00 +wall time: 0.00075388s + +time increment:59740 time: 59.74 +field ' c' [explicit solve]: current solution: 9.616097e+01, current residual:5.805582e+01 +field 'mu' [explicit solve]: current solution: 4.894359e+00, current residual:2.956570e+00 +wall time: 0.000711918s + +time increment:59750 time: 59.75 +field ' c' [explicit solve]: current solution: 9.616099e+01, current residual:5.805583e+01 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solve]: current solution: 4.894043e+00, current residual:2.956371e+00 +wall time: 0.000751972s + +time increment:59800 time: 59.8 +field ' c' [explicit solve]: current solution: 9.616110e+01, current residual:5.805587e+01 +field 'mu' [explicit solve]: current solution: 4.893980e+00, current residual:2.956331e+00 +wall time: 0.000674009s + +time increment:59810 time: 59.81 +field ' c' [explicit solve]: current solution: 9.616112e+01, current residual:5.805588e+01 +field 'mu' [explicit solve]: current solution: 4.893917e+00, current residual:2.956291e+00 +wall time: 0.000791073s + +time increment:59820 time: 59.82 +field ' c' [explicit solve]: current solution: 9.616114e+01, current residual:5.805589e+01 +field 'mu' [explicit solve]: current solution: 4.893854e+00, current residual:2.956251e+00 +wall time: 0.000639915s + +time increment:59830 time: 59.83 +field ' c' [explicit solve]: current solution: 9.616116e+01, current residual:5.805590e+01 +field 'mu' [explicit solve]: current solution: 4.893791e+00, current residual:2.956212e+00 +wall time: 0.000710964s + +time increment:59840 time: 59.84 +field ' c' [explicit solve]: current solution: 9.616118e+01, current residual:5.805591e+01 +field 'mu' [explicit solve]: current solution: 4.893728e+00, current residual:2.956172e+00 +wall time: 0.000689983s + +time increment:59850 time: 59.85 +field ' c' [explicit solve]: current solution: 9.616121e+01, current residual:5.805592e+01 +field 'mu' [explicit solve]: current solution: 4.893665e+00, current residual:2.956132e+00 +wall time: 0.000681877s + +time increment:59860 time: 59.86 +field ' c' [explicit solve]: current solution: 9.616123e+01, current residual:5.805593e+01 +field 'mu' [explicit solve]: current solution: 4.893602e+00, current residual:2.956092e+00 +wall time: 0.000709057s + +time increment:59870 time: 59.87 +field ' c' [explicit solve]: current solution: 9.616125e+01, current residual:5.805594e+01 +field 'mu' [explicit solve]: current solution: 4.893539e+00, current residual:2.956052e+00 +wall time: 0.000668049s + +time increment:59880 time: 59.88 +field ' c' [explicit solve]: current solution: 9.616127e+01, current residual:5.805595e+01 +field 'mu' [explicit solve]: current solution: 4.893476e+00, current residual:2.956012e+00 +wall time: 0.000738144s + +time increment:59890 time: 59.89 +field ' c' [explicit solve]: current solution: 9.616129e+01, current residual:5.805596e+01 +field 'mu' [explicit solve]: current solution: 4.893413e+00, current residual:2.955973e+00 +wall time: 0.000657082s + +time increment:59900 time: 59.9 +field ' c' [explicit solve]: current solution: 9.616131e+01, current residual:5.805597e+01 +field 'mu' [explicit solve]: current solution: 4.893350e+00, current residual:2.955933e+00 +wall time: 0.000653028s + +time increment:59910 time: 59.91 +field ' c' [explicit solve]: current solution: 9.616133e+01, current residual:5.805598e+01 +field 'mu' [explicit solve]: current solution: 4.893287e+00, current residual:2.955893e+00 +wall time: 0.000589132s + +time increment:59920 time: 59.92 +field ' c' [explicit solve]: current solution: 9.616136e+01, current residual:5.805599e+01 +field 'mu' [explicit solve]: current solution: 4.893224e+00, current residual:2.955853e+00 +wall time: 0.000589132s + +time increment:59930 time: 59.93 +field ' c' [explicit solve]: current solution: 9.616138e+01, current residual:5.805599e+01 +field 'mu' [explicit solve]: current solution: 4.893161e+00, current residual:2.955814e+00 +wall time: 0.000586987s + +time increment:59940 time: 59.94 +field ' c' [explicit solve]: current solution: 9.616140e+01, current residual:5.805600e+01 +field 'mu' [explicit solve]: current solution: 4.893098e+00, current residual:2.955774e+00 +wall time: 0.000709057s + +time increment:59950 time: 59.95 +field ' c' [explicit solve]: current solution: 9.616142e+01, current residual:5.805601e+01 +field 'mu' [explicit solve]: current solution: 4.893035e+00, current residual:2.955734e+00 +wall time: 0.000768185s + +time increment:59960 time: 59.96 +field ' c' [explicit solve]: current solution: 9.616144e+01, current residual:5.805602e+01 +field 'mu' [explicit solve]: current solution: 4.892972e+00, current residual:2.955694e+00 +wall time: 0.00068593s + +time increment:59970 time: 59.97 +field ' c' [explicit solve]: current solution: 9.616146e+01, current residual:5.805603e+01 +field 'mu' [explicit solve]: current solution: 4.892909e+00, current residual:2.955655e+00 +wall time: 0.00103116s + +time increment:59980 time: 59.98 +field ' c' [explicit solve]: current solution: 9.616148e+01, current residual:5.805604e+01 +field 'mu' [explicit solve]: current solution: 4.892846e+00, current residual:2.955615e+00 +wall time: 0.000715971s + +time increment:59990 time: 59.99 +field ' c' [explicit solve]: current solution: 9.616151e+01, current residual:5.805605e+01 +field 'mu' [explicit solve]: current solution: 4.892783e+00, current residual:2.955575e+00 +wall time: 0.000653028s + +time increment:60000 time: 60 +field ' c' [explicit solve]: current solution: 9.616153e+01, current residual:5.805606e+01 +field 'mu' [explicit solve]: current solution: 4.892720e+00, current residual:2.955535e+00 +wall time: 0.000650883s +fields: 2 +fields: 2 +fields: 2 +fields: 2 +Output written to:solution-060000.pvtu + +Energy: 237.566 +Energy Components: 113.563 124.002 0 + +time increment:60010 time: 60.01 +field ' c' [explicit solve]: current solution: 9.616155e+01, current residual:5.805607e+01 +field 'mu' [explicit solve]: current solution: 4.892657e+00, current residual:2.955496e+00 +wall time: 0.000638008s + +time increment:60020 time: 60.02 +field ' c' [explicit solve]: current solution: 9.616157e+01, current residual:5.805608e+01 +field 'mu' [explicit solve]: current solution: 4.892595e+00, current residual:2.955456e+00 +wall time: 0.000653028s + +time increment:60030 time: 60.03 +field ' c' [explicit solve]: current solution: 9.616159e+01, current residual:5.805609e+01 +field 'mu' [explicit solve]: current solution: 4.892532e+00, current residual:2.955416e+00 +wall time: 0.000668049s + +time increment:60040 time: 60.04 +field ' c' [explicit solve]: current solution: 9.616161e+01, current residual:5.805610e+01 +field 'mu' [explicit solve]: current solution: 4.892469e+00, current residual:2.955377e+00 +wall time: 0.000720024s + +time increment:60050 time: 60.05 +field ' c' [explicit solve]: current solution: 9.616164e+01, current residual:5.805611e+01 +field 'mu' [explicit solve]: current solution: 4.892406e+00, current residual:2.955337e+00 +wall time: 0.000653028s + +time increment:60060 time: 60.06 +field ' c' [explicit solve]: current solution: 9.616166e+01, current residual:5.805612e+01 +field 'mu' [explicit solve]: current solution: 4.892343e+00, current residual:2.955297e+00 +wall time: 0.000680923s + +time increment:60070 time: 60.07 +field ' c' [explicit solve]: current solution: 9.616168e+01, current residual:5.805613e+01 +field 'mu' [explicit solve]: current solution: 4.892281e+00, current residual:2.955258e+00 +wall time: 0.000669956s + +time increment:60080 time: 60.08 +field ' c' [explicit solve]: current solution: 9.616170e+01, current residual:5.805614e+01 +field 'mu' [explicit solve]: current solution: 4.892218e+00, current residual:2.955218e+00 +wall time: 0.000677824s + +time increment:60090 time: 60.09 +field ' c' [explicit solve]: current solution: 9.616172e+01, current residual:5.805615e+01 +field 'mu' [explicit solve]: current solution: 4.892155e+00, current residual:2.955178e+00 +wall time: 0.000696898s + +time increment:60100 time: 60.1 +field ' c' [explicit solve]: current solution: 9.616174e+01, current residual:5.805616e+01 +field 'mu' [explicit solve]: current solution: 4.892092e+00, current residual:2.955139e+00 +wall time: 0.000727177s + +time increment:60110 time: 60.11 +field ' c' [explicit solve]: current solution: 9.616177e+01, current residual:5.805617e+01 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solve]: current solution: 4.891779e+00, current residual:2.954941e+00 +wall time: 0.000708818s + +time increment:60160 time: 60.16 +field ' c' [explicit solve]: current solution: 9.616188e+01, current residual:5.805622e+01 +field 'mu' [explicit solve]: current solution: 4.891716e+00, current residual:2.954901e+00 +wall time: 0.000622034s + +time increment:60170 time: 60.17 +field ' c' [explicit solve]: current solution: 9.616190e+01, current residual:5.805623e+01 +field 'mu' [explicit solve]: current solution: 4.891653e+00, current residual:2.954861e+00 +wall time: 0.000694036s + +time increment:60180 time: 60.18 +field ' c' [explicit solve]: current solution: 9.616192e+01, current residual:5.805624e+01 +field 'mu' [explicit solve]: current solution: 4.891591e+00, current residual:2.954822e+00 +wall time: 0.000720024s + +time increment:60190 time: 60.19 +field ' c' [explicit solve]: current solution: 9.616194e+01, current residual:5.805624e+01 +field 'mu' [explicit solve]: current solution: 4.891528e+00, current residual:2.954782e+00 +wall time: 0.000724077s + +time increment:60200 time: 60.2 +field ' c' [explicit solve]: current solution: 9.616196e+01, current residual:5.805625e+01 +field 'mu' [explicit solve]: current solution: 4.891465e+00, current residual:2.954743e+00 +wall time: 0.000743866s + +time increment:60210 time: 60.21 +field ' c' [explicit solve]: current solution: 9.616199e+01, current residual:5.805626e+01 +field 'mu' [explicit solve]: current solution: 4.891403e+00, current residual:2.954703e+00 +wall time: 0.000699997s + +time increment:60220 time: 60.22 +field ' c' [explicit solve]: current solution: 9.616201e+01, current residual:5.805627e+01 +field 'mu' [explicit solve]: current solution: 4.891340e+00, current residual:2.954664e+00 +wall time: 0.000569105s + +time increment:60230 time: 60.23 +field ' c' [explicit solve]: current solution: 9.616203e+01, current residual:5.805628e+01 +field 'mu' [explicit solve]: current solution: 4.891278e+00, current residual:2.954624e+00 +wall time: 0.000669003s + +time increment:60240 time: 60.24 +field ' c' [explicit solve]: current solution: 9.616205e+01, current residual:5.805629e+01 +field 'mu' [explicit solve]: current solution: 4.891215e+00, current residual:2.954585e+00 +wall time: 0.000725985s + +time increment:60250 time: 60.25 +field ' c' [explicit solve]: current solution: 9.616207e+01, current residual:5.805630e+01 +field 'mu' [explicit solve]: current solution: 4.891152e+00, current residual:2.954545e+00 +wall time: 0.000724077s + +time increment:60260 time: 60.26 +field ' c' [explicit solve]: current solution: 9.616210e+01, current residual:5.805631e+01 +field 'mu' [explicit solve]: current solution: 4.891090e+00, current residual:2.954505e+00 +wall time: 0.000657797s + +time increment:60270 time: 60.27 +field ' c' [explicit solve]: current solution: 9.616212e+01, current residual:5.805632e+01 +field 'mu' [explicit solve]: current solution: 4.891027e+00, current residual:2.954466e+00 +wall time: 0.000675917s + +time increment:60280 time: 60.28 +field ' c' [explicit solve]: current solution: 9.616214e+01, current residual:5.805633e+01 +field 'mu' [explicit solve]: current solution: 4.890965e+00, current residual:2.954426e+00 +wall time: 0.000657082s + +time increment:60290 time: 60.29 +field ' c' [explicit solve]: current solution: 9.616216e+01, current residual:5.805634e+01 +field 'mu' [explicit solve]: current solution: 4.890902e+00, current residual:2.954387e+00 +wall time: 0.000642061s + +time increment:60300 time: 60.3 +field ' c' [explicit solve]: current solution: 9.616219e+01, current residual:5.805635e+01 +field 'mu' [explicit solve]: current solution: 4.890840e+00, current residual:2.954347e+00 +wall time: 0.000751019s + +time increment:60310 time: 60.31 +field ' c' [explicit solve]: current solution: 9.616221e+01, current residual:5.805636e+01 +field 'mu' [explicit solve]: current solution: 4.890777e+00, current residual:2.954308e+00 +wall time: 0.000673056s + +time increment:60320 time: 60.32 +field ' c' [explicit solve]: current solution: 9.616223e+01, current residual:5.805637e+01 +field 'mu' [explicit solve]: current solution: 4.890715e+00, current residual:2.954268e+00 +wall time: 0.000674009s + +time increment:60330 time: 60.33 +field ' c' [explicit solve]: current solution: 9.616225e+01, current residual:5.805638e+01 +field 'mu' [explicit solve]: current solution: 4.890652e+00, current residual:2.954229e+00 +wall time: 0.000692844s + +time increment:60340 time: 60.34 +field ' c' [explicit solve]: current solution: 9.616227e+01, current residual:5.805639e+01 +field 'mu' [explicit solve]: current solution: 4.890590e+00, current residual:2.954189e+00 +wall time: 0.00072813s + +time increment:60350 time: 60.35 +field ' c' [explicit solve]: current solution: 9.616230e+01, current residual:5.805640e+01 +field 'mu' [explicit solve]: current solution: 4.890527e+00, current residual:2.954150e+00 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0.000571966s + +time increment:60400 time: 60.4 +field ' c' [explicit solve]: current solution: 9.616241e+01, current residual:5.805645e+01 +field 'mu' [explicit solve]: current solution: 4.890215e+00, current residual:2.953953e+00 +wall time: 0.000641823s + +time increment:60410 time: 60.41 +field ' c' [explicit solve]: current solution: 9.616243e+01, current residual:5.805646e+01 +field 'mu' [explicit solve]: current solution: 4.890153e+00, current residual:2.953913e+00 +wall time: 0.000674009s + +time increment:60420 time: 60.42 +field ' c' [explicit solve]: current solution: 9.616245e+01, current residual:5.805647e+01 +field 'mu' [explicit solve]: current solution: 4.890090e+00, current residual:2.953874e+00 +wall time: 0.000637054s + +time increment:60430 time: 60.43 +field ' c' [explicit solve]: current solution: 9.616248e+01, current residual:5.805648e+01 +field 'mu' [explicit solve]: current solution: 4.890028e+00, current residual:2.953835e+00 +wall time: 0.000586033s + +time increment:60440 time: 60.44 +field ' c' [explicit solve]: current solution: 9.616250e+01, current residual:5.805649e+01 +field 'mu' [explicit solve]: current solution: 4.889966e+00, current residual:2.953795e+00 +wall time: 0.000655174s + +time increment:60450 time: 60.45 +field ' c' [explicit solve]: current solution: 9.616252e+01, current residual:5.805650e+01 +field 'mu' [explicit solve]: current solution: 4.889903e+00, current residual:2.953756e+00 +wall time: 0.000662088s + +time increment:60460 time: 60.46 +field ' c' [explicit solve]: current solution: 9.616254e+01, current residual:5.805651e+01 +field 'mu' [explicit solve]: current solution: 4.889841e+00, current residual:2.953716e+00 +wall time: 0.000610113s + +time increment:60470 time: 60.47 +field ' c' [explicit solve]: current solution: 9.616256e+01, current residual:5.805652e+01 +field 'mu' [explicit solve]: current solution: 4.889779e+00, current residual:2.953677e+00 +wall time: 0.000552893s + +time increment:60480 time: 60.48 +field ' c' [explicit solve]: current solution: 9.616259e+01, current residual:5.805653e+01 +field 'mu' [explicit solve]: current solution: 4.889716e+00, current residual:2.953638e+00 +wall time: 0.000610828s + +time increment:60490 time: 60.49 +field ' c' [explicit solve]: current solution: 9.616261e+01, current residual:5.805654e+01 +field 'mu' [explicit solve]: current solution: 4.889654e+00, current residual:2.953598e+00 +wall time: 0.000551939s + +time increment:60500 time: 60.5 +field ' c' [explicit solve]: current solution: 9.616263e+01, current residual:5.805655e+01 +field 'mu' [explicit solve]: current solution: 4.889592e+00, current residual:2.953559e+00 +wall time: 0.000729799s + +time increment:60510 time: 60.51 +field ' c' [explicit solve]: current solution: 9.616266e+01, current residual:5.805656e+01 +field 'mu' [explicit solve]: current solution: 4.889530e+00, current residual:2.953519e+00 +wall time: 0.000641108s + +time increment:60520 time: 60.52 +field ' c' [explicit solve]: current solution: 9.616268e+01, current residual:5.805657e+01 +field 'mu' [explicit solve]: current solution: 4.889467e+00, current residual:2.953480e+00 +wall time: 0.000662804s + +time increment:60530 time: 60.53 +field ' c' [explicit solve]: current solution: 9.616270e+01, current residual:5.805658e+01 +field 'mu' [explicit solve]: current solution: 4.889405e+00, current residual:2.953441e+00 +wall time: 0.000599146s + +time increment:60540 time: 60.54 +field ' c' [explicit solve]: current solution: 9.616272e+01, current residual:5.805659e+01 +field 'mu' [explicit solve]: current solution: 4.889343e+00, current residual:2.953401e+00 +wall time: 0.00059104s + +time increment:60550 time: 60.55 +field ' c' [explicit solve]: current solution: 9.616275e+01, current residual:5.805660e+01 +field 'mu' [explicit solve]: current solution: 4.889281e+00, current residual:2.953362e+00 +wall time: 0.000612974s + +time increment:60560 time: 60.56 +field ' c' [explicit solve]: current solution: 9.616277e+01, current residual:5.805661e+01 +field 'mu' [explicit solve]: current solution: 4.889218e+00, current residual:2.953323e+00 +wall time: 0.000705004s + +time increment:60570 time: 60.57 +field ' c' [explicit solve]: current solution: 9.616279e+01, current residual:5.805662e+01 +field 'mu' [explicit solve]: current solution: 4.889156e+00, current residual:2.953283e+00 +wall time: 0.000594139s + +time increment:60580 time: 60.58 +field ' c' [explicit solve]: current solution: 9.616281e+01, current residual:5.805664e+01 +field 'mu' [explicit solve]: current solution: 4.889094e+00, current residual:2.953244e+00 +wall time: 0.000616074s + +time increment:60590 time: 60.59 +field ' c' [explicit solve]: current solution: 9.616284e+01, current residual:5.805665e+01 +field 'mu' [explicit solve]: current solution: 4.889032e+00, current residual:2.953205e+00 +wall time: 0.000763893s + +time increment:60600 time: 60.6 +field ' c' [explicit solve]: current solution: 9.616286e+01, current residual:5.805666e+01 +field 'mu' [explicit solve]: current solution: 4.888970e+00, current residual:2.953165e+00 +wall time: 0.000677824s + +time increment:60610 time: 60.61 +field ' c' [explicit solve]: current solution: 9.616288e+01, current residual:5.805667e+01 +field 'mu' [explicit solve]: current solution: 4.888907e+00, current residual:2.953126e+00 +wall time: 0.000640154s + +time increment:60620 time: 60.62 +field ' c' [explicit solve]: current solution: 9.616290e+01, current residual:5.805668e+01 +field 'mu' [explicit solve]: current solution: 4.888845e+00, current residual:2.953087e+00 +wall time: 0.000683069s + +time increment:60630 time: 60.63 +field ' c' [explicit solve]: current solution: 9.616293e+01, current residual:5.805669e+01 +field 'mu' [explicit solve]: current solution: 4.888783e+00, current residual:2.953048e+00 +wall time: 0.00069809s + +time increment:60640 time: 60.64 +field ' c' [explicit solve]: current solution: 9.616295e+01, current 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solve]: current solution: 4.887976e+00, current residual:2.952538e+00 +wall time: 0.000617027s + +time increment:60770 time: 60.77 +field ' c' [explicit solve]: current solution: 9.616325e+01, current residual:5.805683e+01 +field 'mu' [explicit solve]: current solution: 4.887914e+00, current residual:2.952498e+00 +wall time: 0.000637054s + +time increment:60780 time: 60.78 +field ' c' [explicit solve]: current solution: 9.616327e+01, current residual:5.805684e+01 +field 'mu' [explicit solve]: current solution: 4.887852e+00, current residual:2.952459e+00 +wall time: 0.000627041s + +time increment:60790 time: 60.79 +field ' c' [explicit solve]: current solution: 9.616329e+01, current residual:5.805685e+01 +field 'mu' [explicit solve]: current solution: 4.887790e+00, current residual:2.952420e+00 +wall time: 0.000725031s + +time increment:60800 time: 60.8 +field ' c' [explicit solve]: current solution: 9.616332e+01, current residual:5.805686e+01 +field 'mu' [explicit solve]: current solution: 4.887728e+00, current residual:2.952381e+00 +wall time: 0.000756025s + +time increment:60810 time: 60.81 +field ' c' [explicit solve]: current solution: 9.616334e+01, current residual:5.805687e+01 +field 'mu' [explicit solve]: current solution: 4.887666e+00, current residual:2.952342e+00 +wall time: 0.000754118s + +time increment:60820 time: 60.82 +field ' c' [explicit solve]: current solution: 9.616336e+01, current residual:5.805688e+01 +field 'mu' [explicit solve]: current solution: 4.887605e+00, current residual:2.952303e+00 +wall time: 0.000702143s + +time increment:60830 time: 60.83 +field ' c' [explicit solve]: current solution: 9.616339e+01, current residual:5.805689e+01 +field 'mu' [explicit solve]: current solution: 4.887543e+00, current residual:2.952263e+00 +wall time: 0.000766039s + +time increment:60840 time: 60.84 +field ' c' [explicit solve]: current solution: 9.616341e+01, current residual:5.805690e+01 +field 'mu' [explicit solve]: current solution: 4.887481e+00, current residual:2.952224e+00 +wall time: 0.000781059s + +time increment:60850 time: 60.85 +field ' c' [explicit solve]: current solution: 9.616343e+01, current residual:5.805692e+01 +field 'mu' [explicit solve]: current solution: 4.887419e+00, current residual:2.952185e+00 +wall time: 0.000622988s + +time increment:60860 time: 60.86 +field ' c' [explicit solve]: current solution: 9.616346e+01, current residual:5.805693e+01 +field 'mu' [explicit solve]: current solution: 4.887357e+00, current residual:2.952146e+00 +wall time: 0.000711918s + +time increment:60870 time: 60.87 +field ' c' [explicit solve]: current solution: 9.616348e+01, current residual:5.805694e+01 +field 'mu' [explicit solve]: current solution: 4.887295e+00, current residual:2.952107e+00 +wall time: 0.000679016s + +time increment:60880 time: 60.88 +field ' c' [explicit solve]: current solution: 9.616350e+01, current residual:5.805695e+01 +field 'mu' [explicit solve]: current solution: 4.887233e+00, current residual:2.952068e+00 +wall time: 0.000825167s + +time increment:60890 time: 60.89 +field ' c' [explicit solve]: current solution: 9.616353e+01, current residual:5.805696e+01 +field 'mu' [explicit solve]: current solution: 4.887171e+00, current residual:2.952029e+00 +wall time: 0.000725985s + +time increment:60900 time: 60.9 +field ' c' [explicit solve]: current solution: 9.616355e+01, current residual:5.805697e+01 +field 'mu' [explicit solve]: current solution: 4.887110e+00, current residual:2.951990e+00 +wall time: 0.000751972s + +time increment:60910 time: 60.91 +field ' c' [explicit solve]: current solution: 9.616357e+01, current residual:5.805698e+01 +field 'mu' [explicit solve]: current solution: 4.887048e+00, current residual:2.951950e+00 +wall time: 0.000735044s + +time increment:60920 time: 60.92 +field ' c' [explicit solve]: current solution: 9.616360e+01, current residual:5.805699e+01 +field 'mu' [explicit solve]: current solution: 4.886986e+00, current residual:2.951911e+00 +wall time: 0.000637054s + +time increment:60930 time: 60.93 +field ' c' [explicit solve]: current solution: 9.616362e+01, current residual:5.805700e+01 +field 'mu' [explicit solve]: current solution: 4.886924e+00, current residual:2.951872e+00 +wall time: 0.000677109s + +time increment:60940 time: 60.94 +field ' c' [explicit solve]: current solution: 9.616364e+01, current residual:5.805701e+01 +field 'mu' [explicit solve]: current solution: 4.886862e+00, current residual:2.951833e+00 +wall time: 0.00069809s + +time increment:60950 time: 60.95 +field ' c' [explicit solve]: current solution: 9.616367e+01, current residual:5.805702e+01 +field 'mu' [explicit solve]: current solution: 4.886800e+00, current residual:2.951794e+00 +wall time: 0.000665903s + +time increment:60960 time: 60.96 +field ' c' [explicit solve]: current solution: 9.616369e+01, current residual:5.805703e+01 +field 'mu' [explicit solve]: current solution: 4.886739e+00, current residual:2.951755e+00 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increment:61050 time: 61.05 +field ' c' [explicit solve]: current solution: 9.616390e+01, current residual:5.805713e+01 +field 'mu' [explicit solve]: current solution: 4.886183e+00, current residual:2.951404e+00 +wall time: 0.000603914s + +time increment:61060 time: 61.06 +field ' c' [explicit solve]: current solution: 9.616392e+01, current residual:5.805714e+01 +field 'mu' [explicit solve]: current solution: 4.886122e+00, current residual:2.951365e+00 +wall time: 0.00146914s + +time increment:61070 time: 61.07 +field ' c' [explicit solve]: current solution: 9.616395e+01, current residual:5.805715e+01 +field 'mu' [explicit solve]: current solution: 4.886060e+00, current residual:2.951326e+00 +wall time: 0.000602961s + +time increment:61080 time: 61.08 +field ' c' [explicit solve]: current solution: 9.616397e+01, current residual:5.805716e+01 +field 'mu' [explicit solve]: current solution: 4.885998e+00, current residual:2.951287e+00 +wall time: 0.000647068s + +time increment:61090 time: 61.09 +field ' c' [explicit solve]: current solution: 9.616399e+01, current residual:5.805717e+01 +field 'mu' [explicit solve]: current solution: 4.885937e+00, current residual:2.951248e+00 +wall time: 0.000599146s + +time increment:61100 time: 61.1 +field ' c' [explicit solve]: current solution: 9.616402e+01, current residual:5.805718e+01 +field 'mu' [explicit solve]: current solution: 4.885875e+00, current residual:2.951209e+00 +wall time: 0.000709057s + +time increment:61110 time: 61.11 +field ' c' [explicit solve]: current solution: 9.616404e+01, current residual:5.805719e+01 +field 'mu' [explicit solve]: current solution: 4.885813e+00, current residual:2.951170e+00 +wall time: 0.00068593s + +time increment:61120 time: 61.12 +field ' c' [explicit solve]: current solution: 9.616407e+01, current residual:5.805720e+01 +field 'mu' [explicit solve]: current solution: 4.885752e+00, current residual:2.951131e+00 +wall time: 0.000571012s + +time increment:61130 time: 61.13 +field ' c' [explicit solve]: current solution: 9.616409e+01, current residual:5.805721e+01 +field 'mu' [explicit solve]: current solution: 4.885690e+00, current residual:2.951092e+00 +wall time: 0.000653982s + +time increment:61140 time: 61.14 +field ' c' [explicit solve]: current solution: 9.616411e+01, current residual:5.805723e+01 +field 'mu' [explicit solve]: current solution: 4.885629e+00, current residual:2.951053e+00 +wall time: 0.000715971s + +time increment:61150 time: 61.15 +field ' c' [explicit solve]: current solution: 9.616414e+01, current residual:5.805724e+01 +field 'mu' [explicit solve]: current solution: 4.885567e+00, current residual:2.951014e+00 +wall time: 0.000584841s + +time increment:61160 time: 61.16 +field ' c' [explicit solve]: current solution: 9.616416e+01, current residual:5.805725e+01 +field 'mu' [explicit solve]: current solution: 4.885505e+00, current residual:2.950975e+00 +wall time: 0.000666142s + +time increment:61170 time: 61.17 +field ' c' [explicit solve]: current solution: 9.616418e+01, current residual:5.805726e+01 +field 'mu' [explicit solve]: current solution: 4.885444e+00, current residual:2.950936e+00 +wall time: 0.000620127s + +time increment:61180 time: 61.18 +field ' c' [explicit solve]: current solution: 9.616421e+01, current residual:5.805727e+01 +field 'mu' [explicit solve]: current solution: 4.885382e+00, current residual:2.950897e+00 +wall time: 0.000648022s + +time increment:61190 time: 61.19 +field ' c' [explicit solve]: current solution: 9.616423e+01, current residual:5.805728e+01 +field 'mu' [explicit solve]: current solution: 4.885321e+00, current residual:2.950858e+00 +wall time: 0.000665903s + +time increment:61200 time: 61.2 +field ' c' [explicit solve]: current solution: 9.616426e+01, current residual:5.805729e+01 +field 'mu' [explicit solve]: current solution: 4.885259e+00, current residual:2.950819e+00 +wall time: 0.000604868s + +time increment:61210 time: 61.21 +field ' c' [explicit solve]: current solution: 9.616428e+01, current residual:5.805730e+01 +field 'mu' [explicit solve]: current solution: 4.885198e+00, current residual:2.950781e+00 +wall time: 0.000586987s + +time increment:61220 time: 61.22 +field ' c' [explicit solve]: current solution: 9.616430e+01, current residual:5.805731e+01 +field 'mu' [explicit solve]: current solution: 4.885136e+00, current residual:2.950742e+00 +wall time: 0.000663996s + +time increment:61230 time: 61.23 +field ' c' [explicit solve]: current solution: 9.616433e+01, current residual:5.805732e+01 +field 'mu' [explicit solve]: current solution: 4.885075e+00, current residual:2.950703e+00 +wall time: 0.00069809s + +time increment:61240 time: 61.24 +field ' c' [explicit solve]: current solution: 9.616435e+01, current residual:5.805733e+01 +field 'mu' [explicit solve]: current solution: 4.885013e+00, current residual:2.950664e+00 +wall time: 0.000638962s + +time increment:61250 time: 61.25 +field ' c' [explicit solve]: current solution: 9.616438e+01, current residual:5.805735e+01 +field 'mu' [explicit solve]: current solution: 4.884952e+00, current residual:2.950625e+00 +wall time: 0.000692844s + +time increment:61260 time: 61.26 +field ' c' [explicit solve]: current solution: 9.616440e+01, current residual:5.805736e+01 +field 'mu' [explicit solve]: current solution: 4.884890e+00, current residual:2.950586e+00 +wall time: 0.000631094s + +time increment:61270 time: 61.27 +field ' c' [explicit solve]: current solution: 9.616442e+01, current residual:5.805737e+01 +field 'mu' [explicit solve]: current solution: 4.884829e+00, current residual:2.950547e+00 +wall time: 0.000712156s + +time increment:61280 time: 61.28 +field ' c' [explicit solve]: current solution: 9.616445e+01, current residual:5.805738e+01 +field 'mu' [explicit solve]: current solution: 4.884767e+00, current residual:2.950508e+00 +wall time: 0.000726938s + +time increment:61290 time: 61.29 +field ' c' [explicit solve]: current solution: 9.616447e+01, current residual:5.805739e+01 +field 'mu' [explicit solve]: current solution: 4.884706e+00, current residual:2.950470e+00 +wall time: 0.000693798s + +time increment:61300 time: 61.3 +field ' c' [explicit solve]: current solution: 9.616449e+01, current residual:5.805740e+01 +field 'mu' [explicit solve]: current solution: 4.884645e+00, current residual:2.950431e+00 +wall time: 0.000659943s + +time increment:61310 time: 61.31 +field ' c' [explicit solve]: current solution: 9.616452e+01, current residual:5.805741e+01 +field 'mu' [explicit solve]: current solution: 4.884583e+00, current residual:2.950392e+00 +wall time: 0.000607967s + +time increment:61320 time: 61.32 +field ' c' [explicit solve]: current solution: 9.616454e+01, current residual:5.805742e+01 +field 'mu' [explicit solve]: current solution: 4.884522e+00, current residual:2.950353e+00 +wall time: 0.00060606s + +time increment:61330 time: 61.33 +field ' c' [explicit solve]: current solution: 9.616457e+01, current residual:5.805743e+01 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[explicit solve]: current solution: 4.884215e+00, current residual:2.950159e+00 +wall time: 0.000585079s + +time increment:61380 time: 61.38 +field ' c' [explicit solve]: current solution: 9.616469e+01, current residual:5.805749e+01 +field 'mu' [explicit solve]: current solution: 4.884154e+00, current residual:2.950120e+00 +wall time: 0.000622988s + +time increment:61390 time: 61.39 +field ' c' [explicit solve]: current solution: 9.616471e+01, current residual:5.805750e+01 +field 'mu' [explicit solve]: current solution: 4.884092e+00, current residual:2.950081e+00 +wall time: 0.00068593s + +time increment:61400 time: 61.4 +field ' c' [explicit solve]: current solution: 9.616474e+01, current residual:5.805751e+01 +field 'mu' [explicit solve]: current solution: 4.884031e+00, current residual:2.950043e+00 +wall time: 0.000585079s + +time increment:61410 time: 61.41 +field ' c' [explicit solve]: current solution: 9.616476e+01, current residual:5.805752e+01 +field 'mu' [explicit solve]: current solution: 4.883970e+00, current residual:2.950004e+00 +wall time: 0.000688791s + +time increment:61420 time: 61.42 +field ' c' [explicit solve]: current solution: 9.616478e+01, current residual:5.805753e+01 +field 'mu' [explicit solve]: current solution: 4.883908e+00, current residual:2.949965e+00 +wall time: 0.000562906s + +time increment:61430 time: 61.43 +field ' c' [explicit solve]: current solution: 9.616481e+01, current residual:5.805754e+01 +field 'mu' [explicit solve]: current solution: 4.883847e+00, current residual:2.949926e+00 +wall time: 0.000622988s + +time increment:61440 time: 61.44 +field ' c' [explicit solve]: current solution: 9.616483e+01, current residual:5.805756e+01 +field 'mu' [explicit solve]: current solution: 4.883786e+00, current residual:2.949887e+00 +wall time: 0.000592947s + +time increment:61450 time: 61.45 +field ' c' [explicit solve]: current solution: 9.616486e+01, current residual:5.805757e+01 +field 'mu' [explicit solve]: current solution: 4.883725e+00, current residual:2.949849e+00 +wall time: 0.000627995s + +time increment:61460 time: 61.46 +field ' c' [explicit solve]: current solution: 9.616488e+01, current residual:5.805758e+01 +field 'mu' [explicit solve]: current solution: 4.883663e+00, current residual:2.949810e+00 +wall time: 0.000578165s + +time increment:61470 time: 61.47 +field ' c' [explicit solve]: current solution: 9.616491e+01, current residual:5.805759e+01 +field 'mu' [explicit solve]: current solution: 4.883602e+00, current residual:2.949771e+00 +wall time: 0.000562906s + +time increment:61480 time: 61.48 +field ' c' [explicit solve]: current solution: 9.616493e+01, current residual:5.805760e+01 +field 'mu' [explicit solve]: current solution: 4.883541e+00, current residual:2.949733e+00 +wall time: 0.000616074s + +time increment:61490 time: 61.49 +field ' c' [explicit solve]: current solution: 9.616495e+01, current residual:5.805761e+01 +field 'mu' [explicit solve]: current solution: 4.883480e+00, current residual:2.949694e+00 +wall time: 0.000699997s + +time increment:61500 time: 61.5 +field ' c' [explicit solve]: current solution: 9.616498e+01, current residual:5.805762e+01 +field 'mu' [explicit solve]: current solution: 4.883418e+00, current residual:2.949655e+00 +wall time: 0.000619888s + +time increment:61510 time: 61.51 +field ' c' [explicit solve]: current solution: 9.616500e+01, current residual:5.805763e+01 +field 'mu' [explicit solve]: current solution: 4.883357e+00, current residual:2.949616e+00 +wall time: 0.000619888s + +time increment:61520 time: 61.52 +field ' c' [explicit solve]: current solution: 9.616503e+01, current residual:5.805765e+01 +field 'mu' [explicit solve]: current solution: 4.883296e+00, current residual:2.949578e+00 +wall time: 0.000628948s + +time increment:61530 time: 61.53 +field ' c' [explicit solve]: current solution: 9.616505e+01, current residual:5.805766e+01 +field 'mu' [explicit solve]: current solution: 4.883235e+00, current residual:2.949539e+00 +wall time: 0.000674963s + +time increment:61540 time: 61.54 +field ' c' [explicit solve]: current solution: 9.616508e+01, current residual:5.805767e+01 +field 'mu' [explicit solve]: current solution: 4.883174e+00, current residual:2.949500e+00 +wall time: 0.000611782s + +time increment:61550 time: 61.55 +field ' c' [explicit solve]: current solution: 9.616510e+01, current residual:5.805768e+01 +field 'mu' [explicit solve]: current solution: 4.883113e+00, current residual:2.949462e+00 +wall time: 0.000565052s + +time increment:61560 time: 61.56 +field ' c' [explicit solve]: current solution: 9.616512e+01, current residual:5.805769e+01 +field 'mu' [explicit solve]: current solution: 4.883051e+00, current residual:2.949423e+00 +wall time: 0.000712156s + +time increment:61570 time: 61.57 +field ' c' [explicit solve]: current solution: 9.616515e+01, current residual:5.805770e+01 +field 'mu' [explicit solve]: current solution: 4.882990e+00, current residual:2.949384e+00 +wall time: 0.000647068s + +time increment:61580 time: 61.58 +field ' c' [explicit solve]: current solution: 9.616517e+01, current residual:5.805771e+01 +field 'mu' [explicit solve]: current solution: 4.882929e+00, current residual:2.949345e+00 +wall time: 0.000647068s + +time increment:61590 time: 61.59 +field ' c' [explicit solve]: current solution: 9.616520e+01, current residual:5.805773e+01 +field 'mu' [explicit solve]: current solution: 4.882868e+00, current residual:2.949307e+00 +wall time: 0.000556946s + +time increment:61600 time: 61.6 +field ' c' [explicit solve]: current solution: 9.616522e+01, current residual:5.805774e+01 +field 'mu' [explicit solve]: current solution: 4.882807e+00, current residual:2.949268e+00 +wall time: 0.000628948s + +time increment:61610 time: 61.61 +field ' c' [explicit solve]: current solution: 9.616525e+01, current residual:5.805775e+01 +field 'mu' [explicit solve]: current solution: 4.882746e+00, current residual:2.949229e+00 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0.000688791s + +time increment:61660 time: 61.66 +field ' c' [explicit solve]: current solution: 9.616537e+01, current residual:5.805780e+01 +field 'mu' [explicit solve]: current solution: 4.882441e+00, current residual:2.949036e+00 +wall time: 0.00064683s + +time increment:61670 time: 61.67 +field ' c' [explicit solve]: current solution: 9.616539e+01, current residual:5.805782e+01 +field 'mu' [explicit solve]: current solution: 4.882379e+00, current residual:2.948998e+00 +wall time: 0.00067091s + +time increment:61680 time: 61.68 +field ' c' [explicit solve]: current solution: 9.616542e+01, current residual:5.805783e+01 +field 'mu' [explicit solve]: current solution: 4.882318e+00, current residual:2.948959e+00 +wall time: 0.000657082s + +time increment:61690 time: 61.69 +field ' c' [explicit solve]: current solution: 9.616544e+01, current residual:5.805784e+01 +field 'mu' [explicit solve]: current solution: 4.882257e+00, current residual:2.948920e+00 +wall time: 0.000676155s + +time increment:61700 time: 61.7 +field ' c' [explicit solve]: current solution: 9.616547e+01, current residual:5.805785e+01 +field 'mu' [explicit solve]: current solution: 4.882196e+00, current residual:2.948882e+00 +wall time: 0.000659943s + +time increment:61710 time: 61.71 +field ' c' [explicit solve]: current solution: 9.616549e+01, current residual:5.805786e+01 +field 'mu' [explicit solve]: current solution: 4.882135e+00, current residual:2.948843e+00 +wall time: 0.000643969s + +time increment:61720 time: 61.72 +field ' c' [explicit solve]: current solution: 9.616552e+01, current residual:5.805787e+01 +field 'mu' [explicit solve]: current solution: 4.882074e+00, current residual:2.948805e+00 +wall time: 0.000691891s + +time increment:61730 time: 61.73 +field ' c' [explicit solve]: current solution: 9.616554e+01, current residual:5.805789e+01 +field 'mu' [explicit solve]: current solution: 4.882013e+00, current residual:2.948766e+00 +wall time: 0.000705004s + +time increment:61740 time: 61.74 +field ' c' [explicit solve]: current solution: 9.616557e+01, current residual:5.805790e+01 +field 'mu' [explicit solve]: current solution: 4.881953e+00, current residual:2.948727e+00 +wall time: 0.000648022s + +time increment:61750 time: 61.75 +field ' c' [explicit solve]: current solution: 9.616559e+01, current residual:5.805791e+01 +field 'mu' [explicit solve]: current solution: 4.881892e+00, current residual:2.948689e+00 +wall time: 0.000705957s + +time increment:61760 time: 61.76 +field ' c' [explicit solve]: current solution: 9.616562e+01, current residual:5.805792e+01 +field 'mu' [explicit solve]: current solution: 4.881831e+00, current residual:2.948650e+00 +wall time: 0.000650167s + +time increment:61770 time: 61.77 +field ' c' [explicit solve]: current solution: 9.616564e+01, current residual:5.805793e+01 +field 'mu' [explicit solve]: current solution: 4.881770e+00, current residual:2.948612e+00 +wall time: 0.000694036s + +time increment:61780 time: 61.78 +field ' c' [explicit solve]: current solution: 9.616567e+01, current residual:5.805794e+01 +field 'mu' [explicit solve]: current solution: 4.881709e+00, current residual:2.948573e+00 +wall time: 0.000638962s + +time increment:61790 time: 61.79 +field ' c' [explicit solve]: current solution: 9.616569e+01, current residual:5.805795e+01 +field 'mu' [explicit solve]: current solution: 4.881648e+00, current residual:2.948535e+00 +wall time: 0.000750065s + +time increment:61800 time: 61.8 +field ' c' [explicit solve]: current solution: 9.616572e+01, current residual:5.805797e+01 +field 'mu' [explicit solve]: current solution: 4.881587e+00, current residual:2.948496e+00 +wall time: 0.000851154s + +time increment:61810 time: 61.81 +field ' c' [explicit solve]: current solution: 9.616574e+01, current residual:5.805798e+01 +field 'mu' [explicit solve]: current solution: 4.881526e+00, current residual:2.948458e+00 +wall time: 0.000740051s + +time increment:61820 time: 61.82 +field ' c' [explicit solve]: current solution: 9.616577e+01, current residual:5.805799e+01 +field 'mu' [explicit solve]: current solution: 4.881465e+00, current residual:2.948419e+00 +wall time: 0.000696898s + +time increment:61830 time: 61.83 +field ' c' [explicit solve]: current solution: 9.616579e+01, current residual:5.805800e+01 +field 'mu' [explicit solve]: current solution: 4.881404e+00, current residual:2.948380e+00 +wall time: 0.000663996s + +time increment:61840 time: 61.84 +field ' c' [explicit solve]: current solution: 9.616582e+01, current residual:5.805801e+01 +field 'mu' [explicit solve]: current solution: 4.881343e+00, current residual:2.948342e+00 +wall time: 0.000646114s + +time increment:61850 time: 61.85 +field ' c' [explicit solve]: current solution: 9.616584e+01, current residual:5.805802e+01 +field 'mu' [explicit solve]: current solution: 4.881283e+00, current residual:2.948303e+00 +wall time: 0.000741005s + +time increment:61860 time: 61.86 +field ' c' [explicit solve]: current solution: 9.616587e+01, current residual:5.805804e+01 +field 'mu' [explicit solve]: current solution: 4.881222e+00, current residual:2.948265e+00 +wall time: 0.00068903s + +time increment:61870 time: 61.87 +field ' c' [explicit solve]: current solution: 9.616589e+01, current residual:5.805805e+01 +field 'mu' [explicit solve]: current solution: 4.881161e+00, current residual:2.948226e+00 +wall time: 0.000666142s + +time increment:61880 time: 61.88 +field ' c' [explicit solve]: current solution: 9.616591e+01, current residual:5.805806e+01 +field 'mu' [explicit solve]: current solution: 4.881100e+00, current residual:2.948188e+00 +wall time: 0.000627041s + +time increment:61890 time: 61.89 +field ' c' [explicit solve]: current solution: 9.616594e+01, current residual:5.805807e+01 +field 'mu' [explicit solve]: current solution: 4.881039e+00, current residual:2.948149e+00 +wall time: 0.000650883s + +time increment:61900 time: 61.9 +field ' c' [explicit solve]: current solution: 9.616596e+01, current residual:5.805808e+01 +field 'mu' [explicit solve]: current solution: 4.880978e+00, current residual:2.948111e+00 +wall time: 0.000709772s + +time increment:61910 time: 61.91 +field ' c' [explicit solve]: current solution: 9.616599e+01, current residual:5.805809e+01 +field 'mu' [explicit solve]: current solution: 4.880918e+00, current residual:2.948072e+00 +wall time: 0.000725985s + +time increment:61920 time: 61.92 +field ' c' [explicit solve]: current solution: 9.616601e+01, current residual:5.805811e+01 +field 'mu' [explicit solve]: current solution: 4.880857e+00, current residual:2.948034e+00 +wall time: 0.000648975s + +time increment:61930 time: 61.93 +field ' c' [explicit solve]: current solution: 9.616604e+01, current residual:5.805812e+01 +field 'mu' [explicit solve]: current solution: 4.880796e+00, current residual:2.947996e+00 +wall time: 0.000696898s + +time increment:61940 time: 61.94 +field ' c' [explicit solve]: current solution: 9.616607e+01, current residual:5.805813e+01 +field 'mu' [explicit solve]: current solution: 4.880735e+00, current residual:2.947957e+00 +wall time: 0.000633001s + +time increment:61950 time: 61.95 +field ' c' [explicit solve]: current solution: 9.616609e+01, current residual:5.805814e+01 +field 'mu' [explicit solve]: current solution: 4.880674e+00, current residual:2.947919e+00 +wall time: 0.000646114s + +time increment:61960 time: 61.96 +field ' c' [explicit solve]: current solution: 9.616612e+01, current residual:5.805815e+01 +field 'mu' [explicit solve]: current solution: 4.880614e+00, current residual:2.947880e+00 +wall time: 0.000669003s + +time increment:61970 time: 61.97 +field ' c' [explicit solve]: current solution: 9.616614e+01, current residual:5.805816e+01 +field 'mu' [explicit solve]: current solution: 4.880553e+00, current residual:2.947842e+00 +wall time: 0.000715971s + +time increment:61980 time: 61.98 +field ' c' [explicit solve]: current solution: 9.616617e+01, current residual:5.805818e+01 +field 'mu' [explicit solve]: current solution: 4.880492e+00, current residual:2.947803e+00 +wall time: 0.000688076s + +time increment:61990 time: 61.99 +field ' c' [explicit solve]: current solution: 9.616619e+01, current residual:5.805819e+01 +field 'mu' [explicit solve]: current solution: 4.880432e+00, current residual:2.947765e+00 +wall time: 0.000658989s + +time increment:62000 time: 62 +field ' c' [explicit solve]: current solution: 9.616622e+01, current residual:5.805820e+01 +field 'mu' [explicit solve]: current solution: 4.880371e+00, current residual:2.947726e+00 +wall time: 0.000727177s + +time increment:62010 time: 62.01 +field ' c' [explicit solve]: current solution: 9.616624e+01, current residual:5.805821e+01 +field 'mu' [explicit solve]: current solution: 4.880310e+00, current residual:2.947688e+00 +wall time: 0.000622034s + +time increment:62020 time: 62.02 +field ' c' [explicit solve]: current solution: 9.616627e+01, current residual:5.805822e+01 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[explicit solve]: current solution: 4.880007e+00, current residual:2.947496e+00 +wall time: 0.000565052s + +time increment:62070 time: 62.07 +field ' c' [explicit solve]: current solution: 9.616639e+01, current residual:5.805828e+01 +field 'mu' [explicit solve]: current solution: 4.879946e+00, current residual:2.947458e+00 +wall time: 0.000767946s + +time increment:62080 time: 62.08 +field ' c' [explicit solve]: current solution: 9.616642e+01, current residual:5.805829e+01 +field 'mu' [explicit solve]: current solution: 4.879885e+00, current residual:2.947419e+00 +wall time: 0.00058198s + +time increment:62090 time: 62.09 +field ' c' [explicit solve]: current solution: 9.616644e+01, current residual:5.805831e+01 +field 'mu' [explicit solve]: current solution: 4.879825e+00, current residual:2.947381e+00 +wall time: 0.000643015s + +time increment:62100 time: 62.1 +field ' c' [explicit solve]: current solution: 9.616647e+01, current residual:5.805832e+01 +field 'mu' [explicit solve]: current solution: 4.879764e+00, current residual:2.947342e+00 +wall time: 0.000687122s + +time increment:62110 time: 62.11 +field ' c' [explicit solve]: current solution: 9.616649e+01, current residual:5.805833e+01 +field 'mu' [explicit solve]: current solution: 4.879704e+00, current residual:2.947304e+00 +wall time: 0.00067091s + +time increment:62120 time: 62.12 +field ' c' [explicit solve]: current solution: 9.616652e+01, current residual:5.805834e+01 +field 'mu' [explicit solve]: current solution: 4.879643e+00, current residual:2.947266e+00 +wall time: 0.000690937s + +time increment:62130 time: 62.13 +field ' c' [explicit solve]: current solution: 9.616654e+01, current residual:5.805835e+01 +field 'mu' [explicit solve]: current solution: 4.879582e+00, current residual:2.947227e+00 +wall time: 0.000693083s + +time increment:62140 time: 62.14 +field ' c' [explicit solve]: current solution: 9.616657e+01, current residual:5.805837e+01 +field 'mu' [explicit solve]: current solution: 4.879522e+00, current residual:2.947189e+00 +wall time: 0.000727892s + +time increment:62150 time: 62.15 +field ' c' [explicit solve]: current solution: 9.616660e+01, current residual:5.805838e+01 +field 'mu' [explicit solve]: current solution: 4.879461e+00, current residual:2.947151e+00 +wall time: 0.000636101s + +time increment:62160 time: 62.16 +field ' c' [explicit solve]: current solution: 9.616662e+01, current residual:5.805839e+01 +field 'mu' [explicit solve]: current solution: 4.879401e+00, current residual:2.947112e+00 +wall time: 0.000668049s + +time increment:62170 time: 62.17 +field ' c' [explicit solve]: current solution: 9.616665e+01, current residual:5.805840e+01 +field 'mu' [explicit solve]: current solution: 4.879340e+00, current residual:2.947074e+00 +wall time: 0.000581026s + +time increment:62180 time: 62.18 +field ' c' [explicit solve]: current solution: 9.616667e+01, current residual:5.805841e+01 +field 'mu' [explicit solve]: current solution: 4.879280e+00, 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62.43 +field ' c' [explicit solve]: current solution: 9.616731e+01, current residual:5.805872e+01 +field 'mu' [explicit solve]: current solution: 4.877769e+00, current residual:2.946079e+00 +wall time: 0.000701904s + +time increment:62440 time: 62.44 +field ' c' [explicit solve]: current solution: 9.616734e+01, current residual:5.805873e+01 +field 'mu' [explicit solve]: current solution: 4.877709e+00, current residual:2.946041e+00 +wall time: 0.000586987s + +time increment:62450 time: 62.45 +field ' c' [explicit solve]: current solution: 9.616737e+01, current residual:5.805874e+01 +field 'mu' [explicit solve]: current solution: 4.877649e+00, current residual:2.946003e+00 +wall time: 0.000597s + +time increment:62460 time: 62.46 +field ' c' [explicit solve]: current solution: 9.616739e+01, current residual:5.805875e+01 +field 'mu' [explicit solve]: current solution: 4.877588e+00, current residual:2.945965e+00 +wall time: 0.000571012s + +time increment:62470 time: 62.47 +field ' c' 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residual:2.944556e+00 +wall time: 0.000664949s + +time increment:62840 time: 62.84 +field ' c' [explicit solve]: current solution: 9.616839e+01, current residual:5.805923e+01 +field 'mu' [explicit solve]: current solution: 4.875304e+00, current residual:2.944518e+00 +wall time: 0.000664949s + +time increment:62850 time: 62.85 +field ' c' [explicit solve]: current solution: 9.616842e+01, current residual:5.805924e+01 +field 'mu' [explicit solve]: current solution: 4.875244e+00, current residual:2.944480e+00 +wall time: 0.000648022s + +time increment:62860 time: 62.86 +field ' c' [explicit solve]: current solution: 9.616844e+01, current residual:5.805925e+01 +field 'mu' [explicit solve]: current solution: 4.875184e+00, current residual:2.944442e+00 +wall time: 0.00069809s + +time increment:62870 time: 62.87 +field ' c' [explicit solve]: current solution: 9.616847e+01, current residual:5.805926e+01 +field 'mu' [explicit solve]: current solution: 4.875124e+00, current residual:2.944405e+00 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0.000699043s + +time increment:62920 time: 62.92 +field ' c' [explicit solve]: current solution: 9.616860e+01, current residual:5.805933e+01 +field 'mu' [explicit solve]: current solution: 4.874825e+00, current residual:2.944215e+00 +wall time: 0.00078392s + +time increment:62930 time: 62.93 +field ' c' [explicit solve]: current solution: 9.616863e+01, current residual:5.805934e+01 +field 'mu' [explicit solve]: current solution: 4.874765e+00, current residual:2.944177e+00 +wall time: 0.000706196s + +time increment:62940 time: 62.94 +field ' c' [explicit solve]: current solution: 9.616865e+01, current residual:5.805935e+01 +field 'mu' [explicit solve]: current solution: 4.874705e+00, current residual:2.944139e+00 +wall time: 0.000658989s + +time increment:62950 time: 62.95 +field ' c' [explicit solve]: current solution: 9.616868e+01, current residual:5.805937e+01 +field 'mu' [explicit solve]: current solution: 4.874645e+00, current residual:2.944101e+00 +wall time: 0.000635862s + +time increment:62960 time: 62.96 +field ' c' [explicit solve]: current solution: 9.616871e+01, current residual:5.805938e+01 +field 'mu' [explicit solve]: current solution: 4.874586e+00, current residual:2.944063e+00 +wall time: 0.000741959s + +time increment:62970 time: 62.97 +field ' c' [explicit solve]: current solution: 9.616874e+01, current residual:5.805939e+01 +field 'mu' [explicit solve]: current solution: 4.874526e+00, current residual:2.944025e+00 +wall time: 0.000660896s + +time increment:62980 time: 62.98 +field ' c' [explicit solve]: current solution: 9.616876e+01, current residual:5.805940e+01 +field 'mu' [explicit solve]: current solution: 4.874466e+00, current residual:2.943987e+00 +wall time: 0.000674963s + +time increment:62990 time: 62.99 +field ' c' [explicit solve]: current solution: 9.616879e+01, current residual:5.805942e+01 +field 'mu' [explicit solve]: current solution: 4.874406e+00, current residual:2.943950e+00 +wall time: 0.000586987s + +time increment:63000 time: 63 +field ' c' [explicit solve]: current solution: 9.616882e+01, current residual:5.805943e+01 +field 'mu' [explicit solve]: current solution: 4.874346e+00, current residual:2.943912e+00 +wall time: 0.000673056s + +time increment:63010 time: 63.01 +field ' c' [explicit solve]: current solution: 9.616884e+01, current residual:5.805944e+01 +field 'mu' [explicit solve]: current solution: 4.874287e+00, current residual:2.943874e+00 +wall time: 0.000604153s + +time increment:63020 time: 63.02 +field ' c' [explicit solve]: current solution: 9.616887e+01, current residual:5.805946e+01 +field 'mu' [explicit solve]: current solution: 4.874227e+00, current residual:2.943836e+00 +wall time: 0.000669003s + +time increment:63030 time: 63.03 +field ' c' [explicit solve]: current solution: 9.616890e+01, current residual:5.805947e+01 +field 'mu' [explicit solve]: current solution: 4.874167e+00, current residual:2.943798e+00 +wall time: 0.000632048s + +time increment:63040 time: 63.04 +field ' c' [explicit solve]: current solution: 9.616892e+01, current residual:5.805948e+01 +field 'mu' [explicit solve]: current solution: 4.874107e+00, current residual:2.943760e+00 +wall time: 0.000665903s + +time increment:63050 time: 63.05 +field ' c' [explicit solve]: current solution: 9.616895e+01, current residual:5.805949e+01 +field 'mu' [explicit solve]: current solution: 4.874047e+00, current residual:2.943722e+00 +wall time: 0.000683069s + +time increment:63060 time: 63.06 +field ' c' [explicit solve]: current solution: 9.616898e+01, current residual:5.805951e+01 +field 'mu' [explicit solve]: current solution: 4.873988e+00, current residual:2.943685e+00 +wall time: 0.00072813s + +time increment:63070 time: 63.07 +field ' c' [explicit solve]: current solution: 9.616900e+01, current residual:5.805952e+01 +field 'mu' [explicit solve]: current solution: 4.873928e+00, current residual:2.943647e+00 +wall time: 0.000562906s + +time increment:63080 time: 63.08 +field ' c' [explicit solve]: current solution: 9.616903e+01, current residual:5.805953e+01 +field 'mu' [explicit solve]: current solution: 4.873868e+00, current residual:2.943609e+00 +wall time: 0.000603199s + +time increment:63090 time: 63.09 +field ' c' [explicit solve]: current solution: 9.616906e+01, current residual:5.805955e+01 +field 'mu' [explicit solve]: current solution: 4.873809e+00, current residual:2.943571e+00 +wall time: 0.000607967s + +time increment:63100 time: 63.1 +field ' c' [explicit solve]: current solution: 9.616908e+01, current residual:5.805956e+01 +field 'mu' [explicit solve]: current solution: 4.873749e+00, current residual:2.943533e+00 +wall time: 0.000687838s + +time increment:63110 time: 63.11 +field ' c' [explicit solve]: current solution: 9.616911e+01, current residual:5.805957e+01 +field 'mu' [explicit solve]: current solution: 4.873689e+00, current residual:2.943495e+00 +wall time: 0.000699997s + +time increment:63120 time: 63.12 +field ' c' [explicit solve]: current solution: 9.616914e+01, current residual:5.805958e+01 +field 'mu' [explicit solve]: current solution: 4.873629e+00, current residual:2.943458e+00 +wall time: 0.000570059s + +time increment:63130 time: 63.13 +field ' c' [explicit solve]: current solution: 9.616917e+01, current residual:5.805960e+01 +field 'mu' [explicit solve]: current solution: 4.873570e+00, current residual:2.943420e+00 +wall time: 0.000640154s + +time increment:63140 time: 63.14 +field ' c' [explicit solve]: current solution: 9.616919e+01, current residual:5.805961e+01 +field 'mu' [explicit solve]: current solution: 4.873510e+00, current residual:2.943382e+00 +wall time: 0.000603199s + +time increment:63150 time: 63.15 +field ' c' [explicit solve]: current solution: 9.616922e+01, current residual:5.805962e+01 +field 'mu' [explicit solve]: current solution: 4.873450e+00, current residual:2.943344e+00 +wall time: 0.000670195s + +time increment:63160 time: 63.16 +field ' c' [explicit solve]: current solution: 9.616925e+01, current residual:5.805964e+01 +field 'mu' [explicit solve]: current solution: 4.873391e+00, current residual:2.943306e+00 +wall time: 0.000581026s + +time increment:63170 time: 63.17 +field ' c' [explicit solve]: current solution: 9.616927e+01, current residual:5.805965e+01 +field 'mu' [explicit solve]: current solution: 4.873331e+00, current residual:2.943269e+00 +wall time: 0.000605106s + +time increment:63180 time: 63.18 +field ' c' [explicit solve]: current solution: 9.616930e+01, current residual:5.805966e+01 +field 'mu' [explicit solve]: current solution: 4.873271e+00, current residual:2.943231e+00 +wall time: 0.000656128s + +time increment:63190 time: 63.19 +field ' c' [explicit solve]: current solution: 9.616933e+01, current residual:5.805967e+01 +field 'mu' [explicit solve]: current solution: 4.873212e+00, current residual:2.943193e+00 +wall time: 0.000689983s + +time increment:63200 time: 63.2 +field ' c' [explicit solve]: current solution: 9.616935e+01, current residual:5.805969e+01 +field 'mu' [explicit solve]: current solution: 4.873152e+00, current residual:2.943155e+00 +wall time: 0.000596046s + +time increment:63210 time: 63.21 +field ' c' [explicit solve]: current solution: 9.616938e+01, current residual:5.805970e+01 +field 'mu' [explicit solve]: current solution: 4.873092e+00, current residual:2.943117e+00 +wall time: 0.000577927s + +time increment:63220 time: 63.22 +field ' c' [explicit solve]: current solution: 9.616941e+01, current residual:5.805971e+01 +field 'mu' [explicit solve]: current solution: 4.873033e+00, current residual:2.943080e+00 +wall time: 0.000596046s + +time increment:63230 time: 63.23 +field ' c' [explicit solve]: current solution: 9.616944e+01, current residual:5.805973e+01 +field 'mu' [explicit solve]: current solution: 4.872973e+00, current residual:2.943042e+00 +wall time: 0.00069809s + +time increment:63240 time: 63.24 +field ' c' [explicit solve]: current solution: 9.616946e+01, current residual:5.805974e+01 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solve]: current solution: 4.872675e+00, current residual:2.942853e+00 +wall time: 0.000692844s + +time increment:63290 time: 63.29 +field ' c' [explicit solve]: current solution: 9.616960e+01, current residual:5.805981e+01 +field 'mu' [explicit solve]: current solution: 4.872616e+00, current residual:2.942815e+00 +wall time: 0.000644922s + +time increment:63300 time: 63.3 +field ' c' [explicit solve]: current solution: 9.616963e+01, current residual:5.805982e+01 +field 'mu' [explicit solve]: current solution: 4.872556e+00, current residual:2.942778e+00 +wall time: 0.000836849s + +time increment:63310 time: 63.31 +field ' c' [explicit solve]: current solution: 9.616965e+01, current residual:5.805983e+01 +field 'mu' [explicit solve]: current solution: 4.872497e+00, current residual:2.942740e+00 +wall time: 0.000684977s + +time increment:63320 time: 63.32 +field ' c' [explicit solve]: current solution: 9.616968e+01, current residual:5.805984e+01 +field 'mu' [explicit solve]: current solution: 4.872437e+00, current residual:2.942702e+00 +wall time: 0.000621796s + +time increment:63330 time: 63.33 +field ' c' [explicit solve]: current solution: 9.616971e+01, current residual:5.805986e+01 +field 'mu' [explicit solve]: current solution: 4.872378e+00, current residual:2.942665e+00 +wall time: 0.000568867s + +time increment:63340 time: 63.34 +field ' c' [explicit solve]: current solution: 9.616974e+01, current residual:5.805987e+01 +field 'mu' [explicit solve]: current solution: 4.872318e+00, current residual:2.942627e+00 +wall time: 0.000628948s + +time increment:63350 time: 63.35 +field ' c' [explicit solve]: current solution: 9.616976e+01, current residual:5.805988e+01 +field 'mu' [explicit solve]: current solution: 4.872259e+00, current residual:2.942589e+00 +wall time: 0.000689983s + +time increment:63360 time: 63.36 +field ' c' [explicit solve]: current solution: 9.616979e+01, current residual:5.805990e+01 +field 'mu' [explicit solve]: current solution: 4.872199e+00, current residual:2.942551e+00 +wall time: 0.000562191s + +time increment:63370 time: 63.37 +field ' c' [explicit solve]: current solution: 9.616982e+01, current residual:5.805991e+01 +field 'mu' [explicit solve]: current solution: 4.872140e+00, current residual:2.942514e+00 +wall time: 0.000605822s + +time increment:63380 time: 63.38 +field ' c' [explicit solve]: current solution: 9.616984e+01, current residual:5.805992e+01 +field 'mu' [explicit solve]: current solution: 4.872080e+00, current residual:2.942476e+00 +wall time: 0.000638962s + +time increment:63390 time: 63.39 +field ' c' [explicit solve]: current solution: 9.616987e+01, current residual:5.805994e+01 +field 'mu' [explicit solve]: current solution: 4.872021e+00, current residual:2.942438e+00 +wall time: 0.000631094s + +time increment:63400 time: 63.4 +field ' c' [explicit solve]: current solution: 9.616990e+01, current residual:5.805995e+01 +field 'mu' [explicit solve]: current solution: 4.871961e+00, current residual:2.942401e+00 +wall time: 0.000629902s + +time increment:63410 time: 63.41 +field ' c' [explicit solve]: current solution: 9.616993e+01, current residual:5.805996e+01 +field 'mu' [explicit solve]: current solution: 4.871902e+00, current residual:2.942363e+00 +wall time: 0.000622034s + +time increment:63420 time: 63.42 +field ' c' [explicit solve]: current solution: 9.616995e+01, current residual:5.805998e+01 +field 'mu' [explicit solve]: current solution: 4.871842e+00, current residual:2.942325e+00 +wall time: 0.000674009s + +time increment:63430 time: 63.43 +field ' c' [explicit solve]: current solution: 9.616998e+01, current residual:5.805999e+01 +field 'mu' [explicit solve]: current solution: 4.871783e+00, current residual:2.942288e+00 +wall time: 0.000559092s + +time increment:63440 time: 63.44 +field ' c' [explicit solve]: current solution: 9.617001e+01, current residual:5.806000e+01 +field 'mu' [explicit solve]: current solution: 4.871723e+00, current residual:2.942250e+00 +wall time: 0.000640869s + +time increment:63450 time: 63.45 +field ' c' [explicit solve]: current solution: 9.617004e+01, current residual:5.806002e+01 +field 'mu' [explicit solve]: current solution: 4.871664e+00, current residual:2.942212e+00 +wall time: 0.000665903s + +time increment:63460 time: 63.46 +field ' c' [explicit solve]: current solution: 9.617006e+01, current residual:5.806003e+01 +field 'mu' [explicit solve]: current solution: 4.871605e+00, current residual:2.942175e+00 +wall time: 0.000643969s + +time increment:63470 time: 63.47 +field ' c' [explicit solve]: current solution: 9.617009e+01, current residual:5.806004e+01 +field 'mu' [explicit solve]: current solution: 4.871545e+00, current residual:2.942137e+00 +wall time: 0.000622988s + +time increment:63480 time: 63.48 +field ' c' [explicit solve]: current solution: 9.617012e+01, current residual:5.806006e+01 +field 'mu' [explicit solve]: current solution: 4.871486e+00, current residual:2.942099e+00 +wall time: 0.000714064s + +time increment:63490 time: 63.49 +field ' c' [explicit solve]: current solution: 9.617015e+01, current residual:5.806007e+01 +field 'mu' [explicit solve]: current solution: 4.871426e+00, current residual:2.942062e+00 +wall time: 0.000611067s + +time increment:63500 time: 63.5 +field ' c' [explicit solve]: current solution: 9.617017e+01, current residual:5.806008e+01 +field 'mu' [explicit solve]: current solution: 4.871367e+00, current residual:2.942024e+00 +wall time: 0.000690937s + +time increment:63510 time: 63.51 +field ' c' [explicit solve]: current solution: 9.617020e+01, current residual:5.806010e+01 +field 'mu' [explicit solve]: current solution: 4.871308e+00, current residual:2.941987e+00 +wall time: 0.000662804s + +time increment:63520 time: 63.52 +field ' c' [explicit solve]: current solution: 9.617023e+01, current residual:5.806011e+01 +field 'mu' [explicit solve]: current solution: 4.871248e+00, current residual:2.941949e+00 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0.00077796s + +time increment:63570 time: 63.57 +field ' c' [explicit solve]: current solution: 9.617037e+01, current residual:5.806018e+01 +field 'mu' [explicit solve]: current solution: 4.870952e+00, current residual:2.941761e+00 +wall time: 0.000720978s + +time increment:63580 time: 63.58 +field ' c' [explicit solve]: current solution: 9.617040e+01, current residual:5.806019e+01 +field 'mu' [explicit solve]: current solution: 4.870892e+00, current residual:2.941723e+00 +wall time: 0.000653982s + +time increment:63590 time: 63.59 +field ' c' [explicit solve]: current solution: 9.617042e+01, current residual:5.806020e+01 +field 'mu' [explicit solve]: current solution: 4.870833e+00, current residual:2.941686e+00 +wall time: 0.000714064s + +time increment:63600 time: 63.6 +field ' c' [explicit solve]: current solution: 9.617045e+01, current residual:5.806022e+01 +field 'mu' [explicit solve]: current solution: 4.870774e+00, current residual:2.941648e+00 +wall time: 0.000732899s + +time increment:63610 time: 63.61 +field ' c' [explicit solve]: current solution: 9.617048e+01, current residual:5.806023e+01 +field 'mu' [explicit solve]: current solution: 4.870714e+00, current residual:2.941611e+00 +wall time: 0.000620842s + +time increment:63620 time: 63.62 +field ' c' [explicit solve]: current solution: 9.617051e+01, current residual:5.806024e+01 +field 'mu' [explicit solve]: current solution: 4.870655e+00, current residual:2.941573e+00 +wall time: 0.00066185s + +time increment:63630 time: 63.63 +field ' c' [explicit solve]: current solution: 9.617053e+01, current residual:5.806026e+01 +field 'mu' [explicit solve]: current solution: 4.870596e+00, current residual:2.941536e+00 +wall time: 0.000695944s + +time increment:63640 time: 63.64 +field ' c' [explicit solve]: current solution: 9.617056e+01, current residual:5.806027e+01 +field 'mu' [explicit solve]: current solution: 4.870536e+00, current residual:2.941498e+00 +wall time: 0.000757933s + +time increment:63650 time: 63.65 +field ' c' [explicit solve]: current solution: 9.617059e+01, current residual:5.806028e+01 +field 'mu' [explicit solve]: current solution: 4.870477e+00, current residual:2.941460e+00 +wall time: 0.000724077s + +time increment:63660 time: 63.66 +field ' c' [explicit solve]: current solution: 9.617062e+01, current residual:5.806030e+01 +field 'mu' [explicit solve]: current solution: 4.870418e+00, current residual:2.941423e+00 +wall time: 0.00072813s + +time increment:63670 time: 63.67 +field ' c' [explicit solve]: current solution: 9.617065e+01, current residual:5.806031e+01 +field 'mu' [explicit solve]: current solution: 4.870359e+00, current residual:2.941385e+00 +wall time: 0.000642061s + +time increment:63680 time: 63.68 +field ' c' [explicit solve]: current solution: 9.617067e+01, current residual:5.806032e+01 +field 'mu' [explicit solve]: current solution: 4.870299e+00, current residual:2.941348e+00 +wall time: 0.000745058s + +time increment:63690 time: 63.69 +field ' c' [explicit solve]: current solution: 9.617070e+01, current residual:5.806034e+01 +field 'mu' [explicit solve]: current solution: 4.870240e+00, current residual:2.941310e+00 +wall time: 0.000645876s + +time increment:63700 time: 63.7 +field ' c' [explicit solve]: current solution: 9.617073e+01, current residual:5.806035e+01 +field 'mu' [explicit solve]: current solution: 4.870181e+00, current residual:2.941273e+00 +wall time: 0.000700951s + +time increment:63710 time: 63.71 +field ' c' [explicit solve]: current solution: 9.617076e+01, current residual:5.806036e+01 +field 'mu' [explicit solve]: current solution: 4.870122e+00, current residual:2.941235e+00 +wall time: 0.000732899s + +time increment:63720 time: 63.72 +field ' c' [explicit solve]: current solution: 9.617078e+01, current residual:5.806038e+01 +field 'mu' [explicit solve]: current solution: 4.870063e+00, current residual:2.941198e+00 +wall time: 0.000664949s + +time increment:63730 time: 63.73 +field ' c' [explicit solve]: current solution: 9.617081e+01, current residual:5.806039e+01 +field 'mu' [explicit solve]: current solution: 4.870003e+00, current residual:2.941160e+00 +wall time: 0.000643015s + +time increment:63740 time: 63.74 +field ' c' [explicit solve]: current solution: 9.617084e+01, current residual:5.806040e+01 +field 'mu' [explicit solve]: current solution: 4.869944e+00, current residual:2.941123e+00 +wall time: 0.000771046s + +time increment:63750 time: 63.75 +field ' c' [explicit solve]: current solution: 9.617087e+01, current residual:5.806042e+01 +field 'mu' [explicit solve]: current solution: 4.869885e+00, current residual:2.941085e+00 +wall time: 0.000670195s + +time increment:63760 time: 63.76 +field ' c' [explicit solve]: current solution: 9.617090e+01, current residual:5.806043e+01 +field 'mu' [explicit solve]: current solution: 4.869826e+00, current residual:2.941048e+00 +wall time: 0.000715971s + +time increment:63770 time: 63.77 +field ' c' [explicit solve]: current solution: 9.617092e+01, current residual:5.806044e+01 +field 'mu' [explicit solve]: current solution: 4.869767e+00, current residual:2.941010e+00 +wall time: 0.000724077s + +time increment:63780 time: 63.78 +field ' c' [explicit solve]: current solution: 9.617095e+01, current residual:5.806046e+01 +field 'mu' [explicit solve]: current solution: 4.869707e+00, current residual:2.940973e+00 +wall time: 0.000640869s + +time increment:63790 time: 63.79 +field ' c' [explicit solve]: current solution: 9.617098e+01, current residual:5.806047e+01 +field 'mu' [explicit solve]: current solution: 4.869648e+00, current residual:2.940935e+00 +wall time: 0.000772953s + +time increment:63800 time: 63.8 +field ' c' [explicit solve]: current solution: 9.617101e+01, current residual:5.806049e+01 +field 'mu' [explicit solve]: current solution: 4.869589e+00, current residual:2.940898e+00 +wall time: 0.00065589s + +time increment:63810 time: 63.81 +field ' c' [explicit solve]: current solution: 9.617104e+01, current residual:5.806050e+01 +field 'mu' [explicit solve]: current solution: 4.869530e+00, current residual:2.940860e+00 +wall time: 0.000708103s + +time increment:63820 time: 63.82 +field ' c' [explicit solve]: current solution: 9.617106e+01, current residual:5.806051e+01 +field 'mu' [explicit solve]: current solution: 4.869471e+00, current residual:2.940823e+00 +wall time: 0.000705004s + +time increment:63830 time: 63.83 +field ' c' [explicit solve]: current solution: 9.617109e+01, current residual:5.806053e+01 +field 'mu' [explicit solve]: current solution: 4.869412e+00, current residual:2.940785e+00 +wall time: 0.000781059s + +time increment:63840 time: 63.84 +field ' c' [explicit solve]: current solution: 9.617112e+01, current residual:5.806054e+01 +field 'mu' [explicit solve]: current solution: 4.869353e+00, current residual:2.940748e+00 +wall time: 0.000730038s + +time increment:63850 time: 63.85 +field ' c' [explicit solve]: current solution: 9.617115e+01, current 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[explicit solve]: current solution: 4.868585e+00, current residual:2.940261e+00 +wall time: 0.000601053s + +time increment:63980 time: 63.98 +field ' c' [explicit solve]: current solution: 9.617152e+01, current residual:5.806073e+01 +field 'mu' [explicit solve]: current solution: 4.868526e+00, current residual:2.940224e+00 +wall time: 0.000629902s + +time increment:63990 time: 63.99 +field ' c' [explicit solve]: current solution: 9.617154e+01, current residual:5.806075e+01 +field 'mu' [explicit solve]: current solution: 4.868467e+00, current residual:2.940186e+00 +wall time: 0.000570059s + +time increment:64000 time: 64 +field ' c' [explicit solve]: current solution: 9.617157e+01, current residual:5.806076e+01 +field 'mu' [explicit solve]: current solution: 4.868408e+00, current residual:2.940149e+00 +wall time: 0.000582933s + +time increment:64010 time: 64.01 +field ' c' [explicit solve]: current solution: 9.617160e+01, current residual:5.806077e+01 +field 'mu' [explicit solve]: current solution: 4.868349e+00, current residual:2.940112e+00 +wall time: 0.000614166s + +time increment:64020 time: 64.02 +field ' c' [explicit solve]: current solution: 9.617163e+01, current residual:5.806079e+01 +field 'mu' [explicit solve]: current solution: 4.868290e+00, current residual:2.940074e+00 +wall time: 0.000721931s + +time increment:64030 time: 64.03 +field ' c' [explicit solve]: current solution: 9.617166e+01, current residual:5.806080e+01 +field 'mu' [explicit solve]: current solution: 4.868231e+00, current residual:2.940037e+00 +wall time: 0.000905991s + +time increment:64040 time: 64.04 +field ' c' [explicit solve]: current solution: 9.617169e+01, current residual:5.806081e+01 +field 'mu' [explicit solve]: current solution: 4.868172e+00, current residual:2.940000e+00 +wall time: 0.000663996s + +time increment:64050 time: 64.05 +field ' c' [explicit solve]: current solution: 9.617171e+01, current residual:5.806083e+01 +field 'mu' [explicit solve]: current solution: 4.868113e+00, current residual:2.939962e+00 +wall time: 0.00069499s + +time increment:64060 time: 64.06 +field ' c' [explicit solve]: current solution: 9.617174e+01, current residual:5.806084e+01 +field 'mu' [explicit solve]: current solution: 4.868054e+00, current residual:2.939925e+00 +wall time: 0.000629902s + +time increment:64070 time: 64.07 +field ' c' [explicit solve]: current solution: 9.617177e+01, current residual:5.806086e+01 +field 'mu' [explicit solve]: current solution: 4.867995e+00, current residual:2.939888e+00 +wall time: 0.000787973s + +time increment:64080 time: 64.08 +field ' c' [explicit solve]: current solution: 9.617180e+01, current residual:5.806087e+01 +field 'mu' [explicit solve]: current solution: 4.867936e+00, current residual:2.939850e+00 +wall time: 0.00068593s + +time increment:64090 time: 64.09 +field ' c' [explicit solve]: current solution: 9.617183e+01, current residual:5.806088e+01 +field 'mu' [explicit solve]: current solution: 4.867877e+00, current residual:2.939813e+00 +wall time: 0.000755072s + +time increment:64100 time: 64.1 +field ' c' [explicit solve]: current solution: 9.617186e+01, current residual:5.806090e+01 +field 'mu' [explicit solve]: current solution: 4.867818e+00, current residual:2.939776e+00 +wall time: 0.000668049s + +time increment:64110 time: 64.11 +field ' c' [explicit solve]: current solution: 9.617188e+01, current residual:5.806091e+01 +field 'mu' [explicit solve]: current solution: 4.867759e+00, current residual:2.939738e+00 +wall time: 0.000571966s + +time increment:64120 time: 64.12 +field ' c' [explicit solve]: current solution: 9.617191e+01, current residual:5.806093e+01 +field 'mu' [explicit solve]: current solution: 4.867701e+00, current residual:2.939701e+00 +wall time: 0.000648022s + +time increment:64130 time: 64.13 +field ' c' [explicit solve]: current solution: 9.617194e+01, current residual:5.806094e+01 +field 'mu' [explicit solve]: current solution: 4.867642e+00, current residual:2.939664e+00 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0.000680923s + +time increment:64180 time: 64.18 +field ' c' [explicit solve]: current solution: 9.617208e+01, current residual:5.806101e+01 +field 'mu' [explicit solve]: current solution: 4.867347e+00, current residual:2.939477e+00 +wall time: 0.00065589s + +time increment:64190 time: 64.19 +field ' c' [explicit solve]: current solution: 9.617211e+01, current residual:5.806102e+01 +field 'mu' [explicit solve]: current solution: 4.867288e+00, current residual:2.939440e+00 +wall time: 0.000709057s + +time increment:64200 time: 64.2 +field ' c' [explicit solve]: current solution: 9.617214e+01, current residual:5.806104e+01 +field 'mu' [explicit solve]: current solution: 4.867230e+00, current residual:2.939402e+00 +wall time: 0.000630856s + +time increment:64210 time: 64.21 +field ' c' [explicit solve]: current solution: 9.617217e+01, current residual:5.806105e+01 +field 'mu' [explicit solve]: current solution: 4.867171e+00, current residual:2.939365e+00 +wall time: 0.000648022s + +time increment:64220 time: 64.22 +field ' c' [explicit solve]: current solution: 9.617220e+01, current residual:5.806106e+01 +field 'mu' [explicit solve]: current solution: 4.867112e+00, current residual:2.939328e+00 +wall time: 0.000699997s + +time increment:64230 time: 64.23 +field ' c' [explicit solve]: current solution: 9.617223e+01, current residual:5.806108e+01 +field 'mu' [explicit solve]: current solution: 4.867053e+00, current residual:2.939291e+00 +wall time: 0.000636101s + +time increment:64240 time: 64.24 +field ' c' [explicit solve]: current solution: 9.617226e+01, current residual:5.806109e+01 +field 'mu' [explicit solve]: current solution: 4.866994e+00, current residual:2.939253e+00 +wall time: 0.000673056s + +time increment:64250 time: 64.25 +field ' c' [explicit solve]: current solution: 9.617228e+01, current residual:5.806111e+01 +field 'mu' [explicit solve]: current solution: 4.866936e+00, current residual:2.939216e+00 +wall time: 0.000658989s + +time increment:64260 time: 64.26 +field ' c' [explicit solve]: current solution: 9.617231e+01, current residual:5.806112e+01 +field 'mu' [explicit solve]: current solution: 4.866877e+00, current residual:2.939179e+00 +wall time: 0.000645876s + +time increment:64270 time: 64.27 +field ' c' [explicit solve]: current solution: 9.617234e+01, current residual:5.806113e+01 +field 'mu' [explicit solve]: current solution: 4.866818e+00, current residual:2.939141e+00 +wall time: 0.000696898s + +time increment:64280 time: 64.28 +field ' c' [explicit solve]: current solution: 9.617237e+01, current residual:5.806115e+01 +field 'mu' [explicit solve]: current solution: 4.866759e+00, current residual:2.939104e+00 +wall time: 0.000653028s + +time increment:64290 time: 64.29 +field ' c' [explicit solve]: current solution: 9.617240e+01, current residual:5.806116e+01 +field 'mu' [explicit solve]: current solution: 4.866700e+00, current residual:2.939067e+00 +wall time: 0.000628233s + +time increment:64300 time: 64.3 +field ' c' [explicit solve]: current solution: 9.617243e+01, current residual:5.806118e+01 +field 'mu' [explicit solve]: current solution: 4.866642e+00, current residual:2.939030e+00 +wall time: 0.000723839s + +time increment:64310 time: 64.31 +field ' c' [explicit solve]: current solution: 9.617246e+01, current residual:5.806119e+01 +field 'mu' [explicit solve]: current solution: 4.866583e+00, current residual:2.938992e+00 +wall time: 0.000638008s + +time increment:64320 time: 64.32 +field ' c' [explicit solve]: current solution: 9.617249e+01, current residual:5.806120e+01 +field 'mu' [explicit solve]: current solution: 4.866524e+00, current residual:2.938955e+00 +wall time: 0.000692129s + +time increment:64330 time: 64.33 +field ' c' [explicit solve]: current solution: 9.617251e+01, current residual:5.806122e+01 +field 'mu' [explicit solve]: current solution: 4.866465e+00, current residual:2.938918e+00 +wall time: 0.000643969s + +time increment:64340 time: 64.34 +field ' c' [explicit solve]: current solution: 9.617254e+01, current residual:5.806123e+01 +field 'mu' [explicit solve]: current solution: 4.866407e+00, current residual:2.938881e+00 +wall time: 0.000658035s + +time increment:64350 time: 64.35 +field ' c' [explicit solve]: current solution: 9.617257e+01, current residual:5.806125e+01 +field 'mu' [explicit solve]: current solution: 4.866348e+00, current residual:2.938844e+00 +wall time: 0.000627041s + +time increment:64360 time: 64.36 +field ' c' [explicit solve]: current solution: 9.617260e+01, current residual:5.806126e+01 +field 'mu' [explicit solve]: current solution: 4.866289e+00, current residual:2.938806e+00 +wall time: 0.000738144s + +time increment:64370 time: 64.37 +field ' c' [explicit solve]: current solution: 9.617263e+01, current residual:5.806128e+01 +field 'mu' [explicit solve]: current solution: 4.866230e+00, current residual:2.938769e+00 +wall time: 0.000700951s + +time increment:64380 time: 64.38 +field ' c' [explicit solve]: current solution: 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residual:5.806135e+01 +field 'mu' [explicit solve]: current solution: 4.865937e+00, current residual:2.938583e+00 +wall time: 0.000725985s + +time increment:64430 time: 64.43 +field ' c' [explicit solve]: current solution: 9.617280e+01, current residual:5.806136e+01 +field 'mu' [explicit solve]: current solution: 4.865878e+00, current residual:2.938546e+00 +wall time: 0.000722885s + +time increment:64440 time: 64.44 +field ' c' [explicit solve]: current solution: 9.617283e+01, current residual:5.806137e+01 +field 'mu' [explicit solve]: current solution: 4.865820e+00, current residual:2.938509e+00 +wall time: 0.000701189s + +time increment:64450 time: 64.45 +field ' c' [explicit solve]: current solution: 9.617286e+01, current residual:5.806139e+01 +field 'mu' [explicit solve]: current solution: 4.865761e+00, current residual:2.938471e+00 +wall time: 0.000627995s + +time increment:64460 time: 64.46 +field ' c' [explicit solve]: current solution: 9.617289e+01, current residual:5.806140e+01 +field 'mu' [explicit solve]: current solution: 4.865702e+00, current residual:2.938434e+00 +wall time: 0.000689983s + +time increment:64470 time: 64.47 +field ' c' [explicit solve]: current solution: 9.617292e+01, current residual:5.806142e+01 +field 'mu' [explicit solve]: current solution: 4.865644e+00, current residual:2.938397e+00 +wall time: 0.000684977s + +time increment:64480 time: 64.48 +field ' c' [explicit solve]: current solution: 9.617295e+01, current residual:5.806143e+01 +field 'mu' [explicit solve]: current solution: 4.865585e+00, current residual:2.938360e+00 +wall time: 0.000700951s + +time increment:64490 time: 64.49 +field ' c' [explicit solve]: current solution: 9.617298e+01, current residual:5.806145e+01 +field 'mu' [explicit solve]: current solution: 4.865526e+00, current residual:2.938323e+00 +wall time: 0.000716925s + +time increment:64500 time: 64.5 +field ' c' [explicit solve]: current solution: 9.617301e+01, current residual:5.806146e+01 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solve]: current solution: 4.865233e+00, current residual:2.938137e+00 +wall time: 0.000639915s + +time increment:64550 time: 64.55 +field ' c' [explicit solve]: current solution: 9.617315e+01, current residual:5.806153e+01 +field 'mu' [explicit solve]: current solution: 4.865175e+00, current residual:2.938100e+00 +wall time: 0.000657082s + +time increment:64560 time: 64.56 +field ' c' [explicit solve]: current solution: 9.617318e+01, current residual:5.806154e+01 +field 'mu' [explicit solve]: current solution: 4.865116e+00, current residual:2.938063e+00 +wall time: 0.000602007s + +time increment:64570 time: 64.57 +field ' c' [explicit solve]: current solution: 9.617321e+01, current residual:5.806156e+01 +field 'mu' [explicit solve]: current solution: 4.865058e+00, current residual:2.938026e+00 +wall time: 0.000599861s + +time increment:64580 time: 64.58 +field ' c' [explicit solve]: current solution: 9.617324e+01, current residual:5.806157e+01 +field 'mu' [explicit solve]: current solution: 4.864999e+00, current residual:2.937989e+00 +wall time: 0.000579834s + +time increment:64590 time: 64.59 +field ' c' [explicit solve]: current solution: 9.617327e+01, current residual:5.806159e+01 +field 'mu' [explicit solve]: current solution: 4.864940e+00, current residual:2.937951e+00 +wall time: 0.000574112s + +time increment:64600 time: 64.6 +field ' c' [explicit solve]: current solution: 9.617330e+01, current residual:5.806160e+01 +field 'mu' [explicit solve]: current solution: 4.864882e+00, current residual:2.937914e+00 +wall time: 0.000563145s + +time increment:64610 time: 64.61 +field ' c' [explicit solve]: current solution: 9.617333e+01, current residual:5.806162e+01 +field 'mu' [explicit solve]: current solution: 4.864823e+00, current residual:2.937877e+00 +wall time: 0.000595808s + +time increment:64620 time: 64.62 +field ' c' [explicit solve]: current solution: 9.617336e+01, current residual:5.806163e+01 +field 'mu' [explicit solve]: current solution: 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increment:64910 time: 64.91 +field ' c' [explicit solve]: current solution: 9.617421e+01, current residual:5.806205e+01 +field 'mu' [explicit solve]: current solution: 4.863070e+00, current residual:2.936766e+00 +wall time: 0.000586033s + +time increment:64920 time: 64.92 +field ' c' [explicit solve]: current solution: 9.617424e+01, current residual:5.806207e+01 +field 'mu' [explicit solve]: current solution: 4.863012e+00, current residual:2.936729e+00 +wall time: 0.000688791s + +time increment:64930 time: 64.93 +field ' c' [explicit solve]: current solution: 9.617427e+01, current residual:5.806208e+01 +field 'mu' [explicit solve]: current solution: 4.862954e+00, current residual:2.936692e+00 +wall time: 0.000560999s + +time increment:64940 time: 64.94 +field ' c' [explicit solve]: current solution: 9.617430e+01, current residual:5.806209e+01 +field 'mu' [explicit solve]: current solution: 4.862895e+00, current residual:2.936655e+00 +wall time: 0.000561953s + +time increment:64950 time: 64.95 +field ' c' [explicit solve]: current solution: 9.617433e+01, current residual:5.806211e+01 +field 'mu' [explicit solve]: current solution: 4.862837e+00, current residual:2.936618e+00 +wall time: 0.000564098s + +time increment:64960 time: 64.96 +field ' c' [explicit solve]: current solution: 9.617436e+01, current residual:5.806212e+01 +field 'mu' [explicit solve]: current solution: 4.862779e+00, current residual:2.936581e+00 +wall time: 0.000571012s + +time increment:64970 time: 64.97 +field ' c' [explicit solve]: current solution: 9.617439e+01, current residual:5.806214e+01 +field 'mu' [explicit solve]: current solution: 4.862720e+00, current residual:2.936544e+00 +wall time: 0.000677824s + +time increment:64980 time: 64.98 +field ' c' [explicit solve]: current solution: 9.617442e+01, current residual:5.806215e+01 +field 'mu' [explicit solve]: current solution: 4.862662e+00, current residual:2.936507e+00 +wall time: 0.00058198s + +time increment:64990 time: 64.99 +field ' c' [explicit solve]: current solution: 9.617445e+01, current residual:5.806217e+01 +field 'mu' [explicit solve]: current solution: 4.862604e+00, current residual:2.936470e+00 +wall time: 0.000671148s + +time increment:65000 time: 65 +field ' c' [explicit solve]: current solution: 9.617448e+01, current residual:5.806218e+01 +field 'mu' [explicit solve]: current solution: 4.862545e+00, current residual:2.936433e+00 +wall time: 0.000663042s + +time increment:65010 time: 65.01 +field ' c' [explicit solve]: current solution: 9.617451e+01, current residual:5.806220e+01 +field 'mu' [explicit solve]: current solution: 4.862487e+00, current residual:2.936396e+00 +wall time: 0.000654936s + +time increment:65020 time: 65.02 +field ' c' [explicit solve]: current solution: 9.617454e+01, current residual:5.806221e+01 +field 'mu' [explicit solve]: current solution: 4.862429e+00, current residual:2.936359e+00 +wall time: 0.000597s + +time increment:65030 time: 65.03 +field ' c' [explicit solve]: 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residual:5.806234e+01 +field 'mu' [explicit solve]: current solution: 4.861905e+00, current residual:2.936027e+00 +wall time: 0.000726938s + +time increment:65120 time: 65.12 +field ' c' [explicit solve]: current solution: 9.617483e+01, current residual:5.806236e+01 +field 'mu' [explicit solve]: current solution: 4.861847e+00, current residual:2.935990e+00 +wall time: 0.000801086s + +time increment:65130 time: 65.13 +field ' c' [explicit solve]: current solution: 9.617486e+01, current residual:5.806237e+01 +field 'mu' [explicit solve]: current solution: 4.861788e+00, current residual:2.935953e+00 +wall time: 0.000706196s + +time increment:65140 time: 65.14 +field ' c' [explicit solve]: current solution: 9.617489e+01, current residual:5.806239e+01 +field 'mu' [explicit solve]: current solution: 4.861730e+00, current residual:2.935916e+00 +wall time: 0.000712872s + +time increment:65150 time: 65.15 +field ' c' [explicit solve]: current solution: 9.617492e+01, current 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solve]: current solution: 4.861207e+00, current residual:2.935585e+00 +wall time: 0.000684023s + +time increment:65240 time: 65.24 +field ' c' [explicit solve]: current solution: 9.617519e+01, current residual:5.806254e+01 +field 'mu' [explicit solve]: current solution: 4.861149e+00, current residual:2.935548e+00 +wall time: 0.000681877s + +time increment:65250 time: 65.25 +field ' c' [explicit solve]: current solution: 9.617522e+01, current residual:5.806255e+01 +field 'mu' [explicit solve]: current solution: 4.861091e+00, current residual:2.935511e+00 +wall time: 0.000684023s + +time increment:65260 time: 65.26 +field ' c' [explicit solve]: current solution: 9.617525e+01, current residual:5.806257e+01 +field 'mu' [explicit solve]: current solution: 4.861033e+00, current residual:2.935474e+00 +wall time: 0.000715017s + +time increment:65270 time: 65.27 +field ' c' [explicit solve]: current solution: 9.617528e+01, current residual:5.806258e+01 +field 'mu' [explicit solve]: current solution: 4.860974e+00, current residual:2.935437e+00 +wall time: 0.000730038s + +time increment:65280 time: 65.28 +field ' c' [explicit solve]: current solution: 9.617531e+01, current residual:5.806260e+01 +field 'mu' [explicit solve]: current solution: 4.860916e+00, current residual:2.935400e+00 +wall time: 0.000693083s + +time increment:65290 time: 65.29 +field ' c' [explicit solve]: current solution: 9.617534e+01, current residual:5.806261e+01 +field 'mu' [explicit solve]: current solution: 4.860858e+00, current residual:2.935364e+00 +wall time: 0.000694036s + +time increment:65300 time: 65.3 +field ' c' [explicit solve]: current solution: 9.617537e+01, current residual:5.806263e+01 +field 'mu' [explicit solve]: current solution: 4.860800e+00, current residual:2.935327e+00 +wall time: 0.000617981s + +time increment:65310 time: 65.31 +field ' c' [explicit solve]: current solution: 9.617540e+01, current residual:5.806264e+01 +field 'mu' [explicit solve]: current solution: 4.860742e+00, current residual:2.935290e+00 +wall time: 0.000660896s + +time increment:65320 time: 65.32 +field ' c' [explicit solve]: current solution: 9.617543e+01, current residual:5.806266e+01 +field 'mu' [explicit solve]: current solution: 4.860684e+00, current residual:2.935253e+00 +wall time: 0.000820875s + +time increment:65330 time: 65.33 +field ' c' [explicit solve]: current solution: 9.617546e+01, current residual:5.806267e+01 +field 'mu' [explicit solve]: current solution: 4.860626e+00, current residual:2.935216e+00 +wall time: 0.000715017s + +time increment:65340 time: 65.34 +field ' c' [explicit solve]: current solution: 9.617550e+01, current residual:5.806269e+01 +field 'mu' [explicit solve]: current solution: 4.860568e+00, current residual:2.935180e+00 +wall time: 0.000716925s + +time increment:65350 time: 65.35 +field ' c' [explicit solve]: current solution: 9.617553e+01, current residual:5.806270e+01 +field 'mu' [explicit solve]: current solution: 4.860510e+00, 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residual:2.934996e+00 +wall time: 0.000653982s + +time increment:65400 time: 65.4 +field ' c' [explicit solve]: current solution: 9.617568e+01, current residual:5.806278e+01 +field 'mu' [explicit solve]: current solution: 4.860220e+00, current residual:2.934959e+00 +wall time: 0.000641108s + +time increment:65410 time: 65.41 +field ' c' [explicit solve]: current solution: 9.617571e+01, current residual:5.806279e+01 +field 'mu' [explicit solve]: current solution: 4.860162e+00, current residual:2.934922e+00 +wall time: 0.000622034s + +time increment:65420 time: 65.42 +field ' c' [explicit solve]: current solution: 9.617574e+01, current residual:5.806281e+01 +field 'mu' [explicit solve]: current solution: 4.860104e+00, current residual:2.934885e+00 +wall time: 0.000727177s + +time increment:65430 time: 65.43 +field ' c' [explicit solve]: current solution: 9.617577e+01, current residual:5.806282e+01 +field 'mu' [explicit solve]: current solution: 4.860046e+00, current residual:2.934849e+00 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0.000692129s + +time increment:65480 time: 65.48 +field ' c' [explicit solve]: current solution: 9.617592e+01, current residual:5.806290e+01 +field 'mu' [explicit solve]: current solution: 4.859756e+00, current residual:2.934665e+00 +wall time: 0.000586033s + +time increment:65490 time: 65.49 +field ' c' [explicit solve]: current solution: 9.617595e+01, current residual:5.806291e+01 +field 'mu' [explicit solve]: current solution: 4.859698e+00, current residual:2.934628e+00 +wall time: 0.000640869s + +time increment:65500 time: 65.5 +field ' c' [explicit solve]: current solution: 9.617598e+01, current residual:5.806293e+01 +field 'mu' [explicit solve]: current solution: 4.859640e+00, current residual:2.934591e+00 +wall time: 0.000765085s + +time increment:65510 time: 65.51 +field ' c' [explicit solve]: current solution: 9.617601e+01, current residual:5.806294e+01 +field 'mu' [explicit solve]: current solution: 4.859582e+00, current residual:2.934555e+00 +wall time: 0.00061202s + +time increment:65520 time: 65.52 +field ' c' [explicit solve]: current solution: 9.617604e+01, current residual:5.806296e+01 +field 'mu' [explicit solve]: current solution: 4.859524e+00, current residual:2.934518e+00 +wall time: 0.00069809s + +time increment:65530 time: 65.53 +field ' c' [explicit solve]: current solution: 9.617607e+01, current residual:5.806297e+01 +field 'mu' [explicit solve]: current solution: 4.859466e+00, current residual:2.934481e+00 +wall time: 0.000607967s + +time increment:65540 time: 65.54 +field ' c' [explicit solve]: current solution: 9.617610e+01, current residual:5.806299e+01 +field 'mu' [explicit solve]: current solution: 4.859408e+00, current residual:2.934444e+00 +wall time: 0.00068593s + +time increment:65550 time: 65.55 +field ' c' [explicit solve]: current solution: 9.617613e+01, current residual:5.806300e+01 +field 'mu' [explicit solve]: current solution: 4.859350e+00, current residual:2.934408e+00 +wall time: 0.000623941s + +time increment:65560 time: 65.56 +field ' c' [explicit solve]: current solution: 9.617616e+01, current residual:5.806302e+01 +field 'mu' [explicit solve]: current solution: 4.859292e+00, current residual:2.934371e+00 +wall time: 0.000560045s + +time increment:65570 time: 65.57 +field ' c' [explicit solve]: current solution: 9.617619e+01, current residual:5.806303e+01 +field 'mu' [explicit solve]: current solution: 4.859234e+00, current residual:2.934334e+00 +wall time: 0.000598907s + +time increment:65580 time: 65.58 +field ' c' [explicit solve]: current solution: 9.617622e+01, current residual:5.806305e+01 +field 'mu' [explicit solve]: current solution: 4.859177e+00, current residual:2.934297e+00 +wall time: 0.000669956s + +time increment:65590 time: 65.59 +field ' c' [explicit solve]: current solution: 9.617625e+01, current residual:5.806306e+01 +field 'mu' [explicit solve]: current solution: 4.859119e+00, current residual:2.934261e+00 +wall time: 0.000672817s + +time increment:65600 time: 65.6 +field ' c' [explicit solve]: current solution: 9.617629e+01, current residual:5.806308e+01 +field 'mu' [explicit solve]: current solution: 4.859061e+00, current residual:2.934224e+00 +wall time: 0.000603914s + +time increment:65610 time: 65.61 +field ' c' [explicit solve]: current solution: 9.617632e+01, current residual:5.806309e+01 +field 'mu' [explicit solve]: current solution: 4.859003e+00, current residual:2.934187e+00 +wall time: 0.00056386s + +time increment:65620 time: 65.62 +field ' c' [explicit solve]: current solution: 9.617635e+01, current residual:5.806311e+01 +field 'mu' [explicit solve]: current solution: 4.858945e+00, current residual:2.934151e+00 +wall time: 0.000626087s + +time increment:65630 time: 65.63 +field ' c' [explicit solve]: current solution: 9.617638e+01, current residual:5.806312e+01 +field 'mu' [explicit solve]: current solution: 4.858887e+00, current residual:2.934114e+00 +wall time: 0.000673056s + +time increment:65640 time: 65.64 +field ' c' [explicit solve]: current solution: 9.617641e+01, current residual:5.806314e+01 +field 'mu' [explicit solve]: current solution: 4.858829e+00, current residual:2.934077e+00 +wall time: 0.000567198s + +time increment:65650 time: 65.65 +field ' c' [explicit solve]: current solution: 9.617644e+01, current residual:5.806315e+01 +field 'mu' [explicit solve]: current solution: 4.858771e+00, current residual:2.934041e+00 +wall time: 0.000597954s + +time increment:65660 time: 65.66 +field ' c' [explicit solve]: current solution: 9.617647e+01, current residual:5.806317e+01 +field 'mu' [explicit solve]: current solution: 4.858713e+00, current residual:2.934004e+00 +wall time: 0.000625134s + +time increment:65670 time: 65.67 +field ' c' [explicit solve]: current solution: 9.617650e+01, current residual:5.806319e+01 +field 'mu' [explicit solve]: current solution: 4.858656e+00, current residual:2.933967e+00 +wall time: 0.000649929s + +time increment:65680 time: 65.68 +field ' c' [explicit solve]: current solution: 9.617653e+01, current residual:5.806320e+01 +field 'mu' [explicit solve]: current solution: 4.858598e+00, current residual:2.933931e+00 +wall time: 0.00067091s + +time increment:65690 time: 65.69 +field ' c' [explicit solve]: current solution: 9.617656e+01, current residual:5.806322e+01 +field 'mu' [explicit solve]: current solution: 4.858540e+00, current residual:2.933894e+00 +wall time: 0.000571966s + +time increment:65700 time: 65.7 +field ' c' [explicit solve]: current solution: 9.617659e+01, current residual:5.806323e+01 +field 'mu' [explicit solve]: current solution: 4.858482e+00, current residual:2.933857e+00 +wall time: 0.000697136s + +time increment:65710 time: 65.71 +field ' c' [explicit solve]: current solution: 9.617662e+01, current residual:5.806325e+01 +field 'mu' [explicit solve]: current solution: 4.858424e+00, current residual:2.933821e+00 +wall time: 0.000678062s + +time increment:65720 time: 65.72 +field ' c' [explicit solve]: current solution: 9.617665e+01, current residual:5.806326e+01 +field 'mu' [explicit solve]: current solution: 4.858366e+00, current residual:2.933784e+00 +wall time: 0.000648975s + +time increment:65730 time: 65.73 +field ' c' [explicit solve]: current solution: 9.617668e+01, current residual:5.806328e+01 +field 'mu' [explicit solve]: current solution: 4.858309e+00, current residual:2.933747e+00 +wall time: 0.000674009s + +time increment:65740 time: 65.74 +field ' c' [explicit solve]: current solution: 9.617671e+01, current residual:5.806329e+01 +field 'mu' [explicit solve]: current solution: 4.858251e+00, current residual:2.933711e+00 +wall time: 0.000576973s + +time increment:65750 time: 65.75 +field ' c' [explicit solve]: current solution: 9.617675e+01, current residual:5.806331e+01 +field 'mu' [explicit solve]: current solution: 4.858193e+00, current residual:2.933674e+00 +wall time: 0.000708103s + +time increment:65760 time: 65.76 +field ' c' [explicit solve]: current solution: 9.617678e+01, current residual:5.806332e+01 +field 'mu' [explicit solve]: current solution: 4.858135e+00, current residual:2.933637e+00 +wall time: 0.000571966s + +time increment:65770 time: 65.77 +field ' c' [explicit solve]: current solution: 9.617681e+01, current residual:5.806334e+01 +field 'mu' [explicit solve]: current solution: 4.858077e+00, current residual:2.933601e+00 +wall time: 0.000581026s + +time increment:65780 time: 65.78 +field ' c' [explicit solve]: current solution: 9.617684e+01, current residual:5.806335e+01 +field 'mu' [explicit solve]: current solution: 4.858020e+00, current residual:2.933564e+00 +wall time: 0.000594854s + +time increment:65790 time: 65.79 +field ' c' [explicit solve]: current solution: 9.617687e+01, current residual:5.806337e+01 +field 'mu' [explicit solve]: current solution: 4.857962e+00, current residual:2.933527e+00 +wall time: 0.000597s + +time increment:65800 time: 65.8 +field ' c' [explicit solve]: current solution: 9.617690e+01, current residual:5.806338e+01 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[explicit solve]: current solution: 4.857673e+00, current residual:2.933344e+00 +wall time: 0.00065589s + +time increment:65850 time: 65.85 +field ' c' [explicit solve]: current solution: 9.617705e+01, current residual:5.806346e+01 +field 'mu' [explicit solve]: current solution: 4.857615e+00, current residual:2.933308e+00 +wall time: 0.000634193s + +time increment:65860 time: 65.86 +field ' c' [explicit solve]: current solution: 9.617708e+01, current residual:5.806348e+01 +field 'mu' [explicit solve]: current solution: 4.857558e+00, current residual:2.933271e+00 +wall time: 0.00059104s + +time increment:65870 time: 65.87 +field ' c' [explicit solve]: current solution: 9.617712e+01, current residual:5.806349e+01 +field 'mu' [explicit solve]: current solution: 4.857500e+00, current residual:2.933235e+00 +wall time: 0.000576019s + +time increment:65880 time: 65.88 +field ' c' [explicit solve]: current solution: 9.617715e+01, current residual:5.806351e+01 +field 'mu' [explicit solve]: current solution: 4.857442e+00, current residual:2.933198e+00 +wall time: 0.000674009s + +time increment:65890 time: 65.89 +field ' c' [explicit solve]: current solution: 9.617718e+01, current residual:5.806352e+01 +field 'mu' [explicit solve]: current solution: 4.857384e+00, current residual:2.933161e+00 +wall time: 0.000586987s + +time increment:65900 time: 65.9 +field ' c' [explicit solve]: current solution: 9.617721e+01, current residual:5.806354e+01 +field 'mu' [explicit solve]: current solution: 4.857327e+00, current residual:2.933125e+00 +wall time: 0.000730991s + +time increment:65910 time: 65.91 +field ' c' [explicit solve]: current solution: 9.617724e+01, current residual:5.806355e+01 +field 'mu' [explicit solve]: current solution: 4.857269e+00, current residual:2.933088e+00 +wall time: 0.000696182s + +time increment:65920 time: 65.92 +field ' c' [explicit solve]: current solution: 9.617727e+01, current residual:5.806357e+01 +field 'mu' [explicit solve]: current solution: 4.857211e+00, current residual:2.933052e+00 +wall time: 0.000680923s + +time increment:65930 time: 65.93 +field ' c' [explicit solve]: current solution: 9.617730e+01, current residual:5.806359e+01 +field 'mu' [explicit solve]: current solution: 4.857154e+00, current residual:2.933015e+00 +wall time: 0.000559807s + +time increment:65940 time: 65.94 +field ' c' [explicit solve]: current solution: 9.617733e+01, current residual:5.806360e+01 +field 'mu' [explicit solve]: current solution: 4.857096e+00, current residual:2.932978e+00 +wall time: 0.00068903s + +time increment:65950 time: 65.95 +field ' c' [explicit solve]: current solution: 9.617736e+01, current residual:5.806362e+01 +field 'mu' [explicit solve]: current solution: 4.857038e+00, current residual:2.932942e+00 +wall time: 0.000635862s + +time increment:65960 time: 65.96 +field ' c' [explicit solve]: current solution: 9.617739e+01, current residual:5.806363e+01 +field 'mu' [explicit solve]: current solution: 4.856981e+00, current residual:2.932905e+00 +wall time: 0.000560045s + +time increment:65970 time: 65.97 +field ' c' [explicit solve]: current solution: 9.617743e+01, current residual:5.806365e+01 +field 'mu' [explicit solve]: current solution: 4.856923e+00, current residual:2.932869e+00 +wall time: 0.000687838s + +time increment:65980 time: 65.98 +field ' c' [explicit solve]: current solution: 9.617746e+01, current residual:5.806366e+01 +field 'mu' [explicit solve]: current solution: 4.856865e+00, current residual:2.932832e+00 +wall time: 0.000619888s + +time increment:65990 time: 65.99 +field ' c' [explicit solve]: current solution: 9.617749e+01, current residual:5.806368e+01 +field 'mu' [explicit solve]: current solution: 4.856808e+00, current residual:2.932796e+00 +wall time: 0.00072217s + +time increment:66000 time: 66 +field ' c' [explicit solve]: current solution: 9.617752e+01, current residual:5.806369e+01 +field 'mu' [explicit solve]: current solution: 4.856750e+00, current residual:2.932759e+00 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0.000741959s + +time increment:66050 time: 66.05 +field ' c' [explicit solve]: current solution: 9.617767e+01, current residual:5.806377e+01 +field 'mu' [explicit solve]: current solution: 4.856462e+00, current residual:2.932576e+00 +wall time: 0.000727892s + +time increment:66060 time: 66.06 +field ' c' [explicit solve]: current solution: 9.617771e+01, current residual:5.806379e+01 +field 'mu' [explicit solve]: current solution: 4.856404e+00, current residual:2.932540e+00 +wall time: 0.000626087s + +time increment:66070 time: 66.07 +field ' c' [explicit solve]: current solution: 9.617774e+01, current residual:5.806380e+01 +field 'mu' [explicit solve]: current solution: 4.856347e+00, current residual:2.932503e+00 +wall time: 0.000674009s + +time increment:66080 time: 66.08 +field ' c' [explicit solve]: current solution: 9.617777e+01, current residual:5.806382e+01 +field 'mu' [explicit solve]: current solution: 4.856289e+00, current residual:2.932467e+00 +wall time: 0.000627995s + +time increment:66090 time: 66.09 +field ' c' [explicit solve]: current solution: 9.617780e+01, current residual:5.806383e+01 +field 'mu' [explicit solve]: current solution: 4.856231e+00, current residual:2.932430e+00 +wall time: 0.000555992s + +time increment:66100 time: 66.1 +field ' c' [explicit solve]: current solution: 9.617783e+01, current residual:5.806385e+01 +field 'mu' [explicit solve]: current solution: 4.856174e+00, current residual:2.932394e+00 +wall time: 0.000699997s + +time increment:66110 time: 66.11 +field ' c' [explicit solve]: current solution: 9.617786e+01, current residual:5.806387e+01 +field 'mu' [explicit solve]: current solution: 4.856116e+00, current residual:2.932357e+00 +wall time: 0.000599146s + +time increment:66120 time: 66.12 +field ' c' [explicit solve]: current solution: 9.617789e+01, current residual:5.806388e+01 +field 'mu' [explicit solve]: current solution: 4.856059e+00, current residual:2.932321e+00 +wall time: 0.000653028s + +time increment:66130 time: 66.13 +field ' c' [explicit solve]: current solution: 9.617792e+01, current residual:5.806390e+01 +field 'mu' [explicit solve]: current solution: 4.856001e+00, current residual:2.932284e+00 +wall time: 0.000625849s + +time increment:66140 time: 66.14 +field ' c' [explicit solve]: current solution: 9.617795e+01, current residual:5.806391e+01 +field 'mu' [explicit solve]: current solution: 4.855943e+00, current residual:2.932248e+00 +wall time: 0.000618935s + +time increment:66150 time: 66.15 +field ' c' [explicit solve]: current solution: 9.617799e+01, current residual:5.806393e+01 +field 'mu' [explicit solve]: current solution: 4.855886e+00, current residual:2.932211e+00 +wall time: 0.000574112s + +time increment:66160 time: 66.16 +field ' c' [explicit solve]: current solution: 9.617802e+01, current residual:5.806394e+01 +field 'mu' [explicit solve]: current solution: 4.855828e+00, current residual:2.932175e+00 +wall time: 0.000598907s + +time increment:66170 time: 66.17 +field ' c' [explicit solve]: current solution: 9.617805e+01, current residual:5.806396e+01 +field 'mu' [explicit solve]: current solution: 4.855771e+00, current residual:2.932138e+00 +wall time: 0.000566006s + +time increment:66180 time: 66.18 +field ' c' [explicit solve]: current solution: 9.617808e+01, current residual:5.806397e+01 +field 'mu' [explicit solve]: current solution: 4.855713e+00, current residual:2.932102e+00 +wall time: 0.000584126s + +time increment:66190 time: 66.19 +field ' c' [explicit solve]: current solution: 9.617811e+01, current residual:5.806399e+01 +field 'mu' [explicit solve]: current solution: 4.855656e+00, current residual:2.932065e+00 +wall time: 0.000663996s + +time increment:66200 time: 66.2 +field ' c' [explicit solve]: current solution: 9.617814e+01, current residual:5.806401e+01 +field 'mu' [explicit solve]: current solution: 4.855598e+00, current residual:2.932029e+00 +wall time: 0.000570059s + +time increment:66210 time: 66.21 +field ' c' [explicit solve]: current solution: 9.617817e+01, current residual:5.806402e+01 +field 'mu' [explicit solve]: current solution: 4.855541e+00, current residual:2.931992e+00 +wall time: 0.00059104s + +time increment:66220 time: 66.22 +field ' c' [explicit solve]: current solution: 9.617821e+01, current residual:5.806404e+01 +field 'mu' [explicit solve]: current solution: 4.855483e+00, current residual:2.931956e+00 +wall time: 0.00075388s + +time increment:66230 time: 66.23 +field ' c' [explicit solve]: current solution: 9.617824e+01, current residual:5.806405e+01 +field 'mu' [explicit solve]: current solution: 4.855426e+00, current residual:2.931919e+00 +wall time: 0.00061512s + +time increment:66240 time: 66.24 +field ' c' [explicit solve]: current solution: 9.617827e+01, current residual:5.806407e+01 +field 'mu' [explicit solve]: current solution: 4.855368e+00, current residual:2.931883e+00 +wall time: 0.000668049s + +time increment:66250 time: 66.25 +field ' c' [explicit solve]: current solution: 9.617830e+01, current residual:5.806408e+01 +field 'mu' [explicit solve]: current solution: 4.855310e+00, current residual:2.931847e+00 +wall time: 0.000686169s + +time increment:66260 time: 66.26 +field ' c' [explicit solve]: current solution: 9.617833e+01, current residual:5.806410e+01 +field 'mu' [explicit solve]: current solution: 4.855253e+00, current residual:2.931810e+00 +wall time: 0.000684977s + +time increment:66270 time: 66.27 +field ' c' [explicit solve]: current solution: 9.617836e+01, current residual:5.806412e+01 +field 'mu' [explicit solve]: current solution: 4.855195e+00, current residual:2.931774e+00 +wall time: 0.000718832s + +time increment:66280 time: 66.28 +field ' c' [explicit solve]: current solution: 9.617839e+01, current residual:5.806413e+01 +field 'mu' [explicit solve]: current solution: 4.855138e+00, current residual:2.931737e+00 +wall time: 0.000689983s + +time increment:66290 time: 66.29 +field ' c' [explicit solve]: current solution: 9.617843e+01, current residual:5.806415e+01 +field 'mu' [explicit solve]: current solution: 4.855081e+00, current residual:2.931701e+00 +wall time: 0.00070715s + +time increment:66300 time: 66.3 +field ' c' [explicit solve]: current solution: 9.617846e+01, current residual:5.806416e+01 +field 'mu' [explicit solve]: current solution: 4.855023e+00, current residual:2.931664e+00 +wall time: 0.000709057s + +time increment:66310 time: 66.31 +field ' c' [explicit solve]: current solution: 9.617849e+01, current residual:5.806418e+01 +field 'mu' [explicit solve]: current solution: 4.854966e+00, current residual:2.931628e+00 +wall time: 0.00066185s + +time increment:66320 time: 66.32 +field ' c' [explicit solve]: current solution: 9.617852e+01, current residual:5.806420e+01 +field 'mu' [explicit solve]: current solution: 4.854908e+00, current residual:2.931591e+00 +wall time: 0.000688076s + +time increment:66330 time: 66.33 +field ' c' [explicit solve]: current solution: 9.617855e+01, current residual:5.806421e+01 +field 'mu' [explicit solve]: current solution: 4.854851e+00, current residual:2.931555e+00 +wall time: 0.00069809s + +time increment:66340 time: 66.34 +field ' c' [explicit solve]: current solution: 9.617858e+01, current residual:5.806423e+01 +field 'mu' [explicit solve]: current solution: 4.854793e+00, current residual:2.931519e+00 +wall time: 0.000693083s + +time increment:66350 time: 66.35 +field ' c' [explicit solve]: current solution: 9.617861e+01, current residual:5.806424e+01 +field 'mu' [explicit solve]: current solution: 4.854736e+00, current residual:2.931482e+00 +wall time: 0.000722885s + +time increment:66360 time: 66.36 +field ' c' [explicit solve]: current solution: 9.617865e+01, current residual:5.806426e+01 +field 'mu' [explicit solve]: current solution: 4.854678e+00, current residual:2.931446e+00 +wall time: 0.000715971s + +time increment:66370 time: 66.37 +field ' c' [explicit solve]: current solution: 9.617868e+01, current residual:5.806427e+01 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solve]: current solution: 4.854391e+00, current residual:2.931264e+00 +wall time: 0.000642061s + +time increment:66420 time: 66.42 +field ' c' [explicit solve]: current solution: 9.617884e+01, current residual:5.806435e+01 +field 'mu' [explicit solve]: current solution: 4.854334e+00, current residual:2.931227e+00 +wall time: 0.000653028s + +time increment:66430 time: 66.43 +field ' c' [explicit solve]: current solution: 9.617887e+01, current residual:5.806437e+01 +field 'mu' [explicit solve]: current solution: 4.854276e+00, current residual:2.931191e+00 +wall time: 0.000638962s + +time increment:66440 time: 66.44 +field ' c' [explicit solve]: current solution: 9.617890e+01, current residual:5.806439e+01 +field 'mu' [explicit solve]: current solution: 4.854219e+00, current residual:2.931154e+00 +wall time: 0.000733137s + +time increment:66450 time: 66.45 +field ' c' [explicit solve]: current solution: 9.617893e+01, current residual:5.806440e+01 +field 'mu' [explicit solve]: current solution: 4.854161e+00, current residual:2.931118e+00 +wall time: 0.000655174s + +time increment:66460 time: 66.46 +field ' c' [explicit solve]: current solution: 9.617896e+01, current residual:5.806442e+01 +field 'mu' [explicit solve]: current solution: 4.854104e+00, current residual:2.931082e+00 +wall time: 0.000699043s + +time increment:66470 time: 66.47 +field ' c' [explicit solve]: current solution: 9.617899e+01, current residual:5.806443e+01 +field 'mu' [explicit solve]: current solution: 4.854047e+00, current residual:2.931045e+00 +wall time: 0.000653982s + +time increment:66480 time: 66.48 +field ' c' [explicit solve]: current solution: 9.617903e+01, current residual:5.806445e+01 +field 'mu' [explicit solve]: current solution: 4.853989e+00, current residual:2.931009e+00 +wall time: 0.000560999s + +time increment:66490 time: 66.49 +field ' c' [explicit solve]: current solution: 9.617906e+01, current residual:5.806446e+01 +field 'mu' [explicit solve]: current solution: 4.853932e+00, current residual:2.930973e+00 +wall time: 0.000682831s + +time increment:66500 time: 66.5 +field ' c' [explicit solve]: current solution: 9.617909e+01, current residual:5.806448e+01 +field 'mu' [explicit solve]: current solution: 4.853875e+00, current residual:2.930936e+00 +wall time: 0.000646114s + +time increment:66510 time: 66.51 +field ' c' [explicit solve]: current solution: 9.617912e+01, current residual:5.806450e+01 +field 'mu' [explicit solve]: current solution: 4.853817e+00, current residual:2.930900e+00 +wall time: 0.000627995s + +time increment:66520 time: 66.52 +field ' c' [explicit solve]: current solution: 9.617915e+01, current residual:5.806451e+01 +field 'mu' [explicit solve]: current solution: 4.853760e+00, current residual:2.930863e+00 +wall time: 0.000802994s + +time increment:66530 time: 66.53 +field ' c' [explicit solve]: current solution: 9.617918e+01, current residual:5.806453e+01 +field 'mu' [explicit solve]: current solution: 4.853702e+00, current residual:2.930827e+00 +wall time: 0.000757933s + +time increment:66540 time: 66.54 +field ' c' [explicit solve]: current solution: 9.617922e+01, current residual:5.806454e+01 +field 'mu' [explicit solve]: current solution: 4.853645e+00, current residual:2.930791e+00 +wall time: 0.000674963s + +time increment:66550 time: 66.55 +field ' c' [explicit solve]: current solution: 9.617925e+01, current residual:5.806456e+01 +field 'mu' [explicit solve]: current solution: 4.853588e+00, current residual:2.930754e+00 +wall time: 0.000799894s + +time increment:66560 time: 66.56 +field ' c' [explicit solve]: current solution: 9.617928e+01, current residual:5.806458e+01 +field 'mu' [explicit solve]: current solution: 4.853530e+00, current residual:2.930718e+00 +wall time: 0.00079608s + +time increment:66570 time: 66.57 +field ' c' [explicit solve]: current solution: 9.617931e+01, current residual:5.806459e+01 +field 'mu' [explicit solve]: current solution: 4.853473e+00, current residual:2.930682e+00 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0.00068903s + +time increment:66620 time: 66.62 +field ' c' [explicit solve]: current solution: 9.617947e+01, current residual:5.806467e+01 +field 'mu' [explicit solve]: current solution: 4.853186e+00, current residual:2.930500e+00 +wall time: 0.000697136s + +time increment:66630 time: 66.63 +field ' c' [explicit solve]: current solution: 9.617950e+01, current residual:5.806469e+01 +field 'mu' [explicit solve]: current solution: 4.853129e+00, current residual:2.930464e+00 +wall time: 0.000712156s + +time increment:66640 time: 66.64 +field ' c' [explicit solve]: current solution: 9.617953e+01, current residual:5.806470e+01 +field 'mu' [explicit solve]: current solution: 4.853072e+00, current residual:2.930427e+00 +wall time: 0.000669956s + +time increment:66650 time: 66.65 +field ' c' [explicit solve]: current solution: 9.617957e+01, current residual:5.806472e+01 +field 'mu' [explicit solve]: current solution: 4.853015e+00, current residual:2.930391e+00 +wall time: 0.000718832s + +time increment:66660 time: 66.66 +field ' c' [explicit solve]: current solution: 9.617960e+01, current residual:5.806474e+01 +field 'mu' [explicit solve]: current solution: 4.852957e+00, current residual:2.930355e+00 +wall time: 0.000701904s + +time increment:66670 time: 66.67 +field ' c' [explicit solve]: current solution: 9.617963e+01, current residual:5.806475e+01 +field 'mu' [explicit solve]: current solution: 4.852900e+00, current residual:2.930318e+00 +wall time: 0.000668049s + +time increment:66680 time: 66.68 +field ' c' [explicit solve]: current solution: 9.617966e+01, current residual:5.806477e+01 +field 'mu' [explicit solve]: current solution: 4.852843e+00, current residual:2.930282e+00 +wall time: 0.000635862s + +time increment:66690 time: 66.69 +field ' c' [explicit solve]: current solution: 9.617969e+01, current residual:5.806479e+01 +field 'mu' [explicit solve]: current solution: 4.852785e+00, current residual:2.930246e+00 +wall time: 0.000654936s + +time increment:66700 time: 66.7 +field ' c' [explicit solve]: current solution: 9.617973e+01, current residual:5.806480e+01 +field 'mu' [explicit solve]: current solution: 4.852728e+00, current residual:2.930209e+00 +wall time: 0.000658035s + +time increment:66710 time: 66.71 +field ' c' [explicit solve]: current solution: 9.617976e+01, current residual:5.806482e+01 +field 'mu' [explicit solve]: current solution: 4.852671e+00, current residual:2.930173e+00 +wall time: 0.00070405s + +time increment:66720 time: 66.72 +field ' c' [explicit solve]: current solution: 9.617979e+01, current residual:5.806483e+01 +field 'mu' [explicit solve]: current solution: 4.852614e+00, current residual:2.930137e+00 +wall time: 0.000669956s + +time increment:66730 time: 66.73 +field ' c' [explicit solve]: current solution: 9.617982e+01, current residual:5.806485e+01 +field 'mu' [explicit solve]: current solution: 4.852556e+00, current residual:2.930101e+00 +wall time: 0.000679016s + +time increment:66740 time: 66.74 +field ' c' [explicit solve]: current solution: 9.617985e+01, current residual:5.806487e+01 +field 'mu' [explicit solve]: current solution: 4.852499e+00, current residual:2.930064e+00 +wall time: 0.000601053s + +time increment:66750 time: 66.75 +field ' c' [explicit solve]: current solution: 9.617989e+01, current residual:5.806488e+01 +field 'mu' [explicit solve]: current solution: 4.852442e+00, current residual:2.930028e+00 +wall time: 0.000696898s + +time increment:66760 time: 66.76 +field ' c' [explicit solve]: current solution: 9.617992e+01, current residual:5.806490e+01 +field 'mu' [explicit solve]: current solution: 4.852385e+00, current residual:2.929992e+00 +wall time: 0.000766993s + +time increment:66770 time: 66.77 +field ' c' [explicit solve]: current solution: 9.617995e+01, current residual:5.806491e+01 +field 'mu' [explicit solve]: current solution: 4.852327e+00, current residual:2.929955e+00 +wall time: 0.000662088s + +time increment:66780 time: 66.78 +field ' c' [explicit solve]: current solution: 9.617998e+01, current residual:5.806493e+01 +field 'mu' [explicit solve]: current solution: 4.852270e+00, current residual:2.929919e+00 +wall time: 0.000602961s + +time increment:66790 time: 66.79 +field ' c' [explicit solve]: current solution: 9.618001e+01, current residual:5.806495e+01 +field 'mu' [explicit solve]: current solution: 4.852213e+00, current residual:2.929883e+00 +wall time: 0.000664949s + +time increment:66800 time: 66.8 +field ' c' [explicit solve]: current solution: 9.618005e+01, current residual:5.806496e+01 +field 'mu' [explicit solve]: current solution: 4.852156e+00, current residual:2.929847e+00 +wall time: 0.000683784s + +time increment:66810 time: 66.81 +field ' c' [explicit solve]: current solution: 9.618008e+01, current residual:5.806498e+01 +field 'mu' [explicit solve]: current solution: 4.852099e+00, current residual:2.929810e+00 +wall time: 0.000668049s + +time increment:66820 time: 66.82 +field ' c' [explicit solve]: current solution: 9.618011e+01, current residual:5.806500e+01 +field 'mu' [explicit solve]: current solution: 4.852041e+00, current residual:2.929774e+00 +wall time: 0.000751972s + +time increment:66830 time: 66.83 +field ' c' [explicit solve]: current solution: 9.618014e+01, current residual:5.806501e+01 +field 'mu' [explicit solve]: current solution: 4.851984e+00, current residual:2.929738e+00 +wall time: 0.000700951s + +time increment:66840 time: 66.84 +field ' c' [explicit solve]: current solution: 9.618017e+01, current residual:5.806503e+01 +field 'mu' [explicit solve]: current solution: 4.851927e+00, current residual:2.929702e+00 +wall time: 0.00077796s + +time increment:66850 time: 66.85 +field ' c' [explicit solve]: current solution: 9.618021e+01, current residual:5.806504e+01 +field 'mu' [explicit solve]: current solution: 4.851870e+00, current residual:2.929665e+00 +wall time: 0.000580788s + +time increment:66860 time: 66.86 +field ' c' [explicit solve]: current solution: 9.618024e+01, current residual:5.806506e+01 +field 'mu' [explicit solve]: current solution: 4.851813e+00, current residual:2.929629e+00 +wall time: 0.000583887s + +time increment:66870 time: 66.87 +field ' c' [explicit solve]: current solution: 9.618027e+01, current residual:5.806508e+01 +field 'mu' [explicit solve]: current solution: 4.851755e+00, current residual:2.929593e+00 +wall time: 0.00057292s + +time increment:66880 time: 66.88 +field ' c' [explicit solve]: current solution: 9.618030e+01, current residual:5.806509e+01 +field 'mu' [explicit solve]: current solution: 4.851698e+00, current residual:2.929557e+00 +wall time: 0.000562906s + +time increment:66890 time: 66.89 +field ' c' [explicit solve]: current solution: 9.618034e+01, current residual:5.806511e+01 +field 'mu' [explicit solve]: current solution: 4.851641e+00, current residual:2.929520e+00 +wall time: 0.000569105s + +time increment:66900 time: 66.9 +field ' c' [explicit solve]: current solution: 9.618037e+01, current residual:5.806513e+01 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[explicit solve]: current solution: 4.851355e+00, current residual:2.929339e+00 +wall time: 0.000714779s + +time increment:66950 time: 66.95 +field ' c' [explicit solve]: current solution: 9.618053e+01, current residual:5.806521e+01 +field 'mu' [explicit solve]: current solution: 4.851298e+00, current residual:2.929303e+00 +wall time: 0.000683069s + +time increment:66960 time: 66.96 +field ' c' [explicit solve]: current solution: 9.618056e+01, current residual:5.806522e+01 +field 'mu' [explicit solve]: current solution: 4.851241e+00, current residual:2.929267e+00 +wall time: 0.000585079s + +time increment:66970 time: 66.97 +field ' c' [explicit solve]: current solution: 9.618059e+01, current residual:5.806524e+01 +field 'mu' [explicit solve]: current solution: 4.851184e+00, current residual:2.929231e+00 +wall time: 0.000603199s + +time increment:66980 time: 66.98 +field ' c' [explicit solve]: current solution: 9.618063e+01, current residual:5.806526e+01 +field 'mu' [explicit solve]: current solution: 4.851127e+00, current residual:2.929194e+00 +wall time: 0.000562906s + +time increment:66990 time: 66.99 +field ' c' [explicit solve]: current solution: 9.618066e+01, current residual:5.806527e+01 +field 'mu' [explicit solve]: current solution: 4.851070e+00, current residual:2.929158e+00 +wall time: 0.00061512s + +time increment:67000 time: 67 +field ' c' [explicit solve]: current solution: 9.618069e+01, current residual:5.806529e+01 +field 'mu' [explicit solve]: current solution: 4.851013e+00, current residual:2.929122e+00 +wall time: 0.000633001s + +time increment:67010 time: 67.01 +field ' c' [explicit solve]: current solution: 9.618072e+01, current residual:5.806530e+01 +field 'mu' [explicit solve]: current solution: 4.850955e+00, current residual:2.929086e+00 +wall time: 0.000628948s + +time increment:67020 time: 67.02 +field ' c' [explicit solve]: current solution: 9.618076e+01, current residual:5.806532e+01 +field 'mu' [explicit solve]: current solution: 4.850898e+00, current residual:2.929050e+00 +wall time: 0.000669003s + +time increment:67030 time: 67.03 +field ' c' [explicit solve]: current solution: 9.618079e+01, current residual:5.806534e+01 +field 'mu' [explicit solve]: current solution: 4.850841e+00, current residual:2.929013e+00 +wall time: 0.000756979s + +time increment:67040 time: 67.04 +field ' c' [explicit solve]: current solution: 9.618082e+01, current residual:5.806535e+01 +field 'mu' [explicit solve]: current solution: 4.850784e+00, current residual:2.928977e+00 +wall time: 0.000687122s + +time increment:67050 time: 67.05 +field ' c' [explicit solve]: current solution: 9.618085e+01, current residual:5.806537e+01 +field 'mu' [explicit solve]: current solution: 4.850727e+00, current residual:2.928941e+00 +wall time: 0.000687838s + +time increment:67060 time: 67.06 +field ' c' [explicit solve]: current solution: 9.618089e+01, current residual:5.806539e+01 +field 'mu' [explicit solve]: current solution: 4.850670e+00, current residual:2.928905e+00 +wall time: 0.000687838s + +time increment:67070 time: 67.07 +field ' c' [explicit solve]: current solution: 9.618092e+01, current residual:5.806540e+01 +field 'mu' [explicit solve]: current solution: 4.850613e+00, current residual:2.928869e+00 +wall time: 0.000709057s + +time increment:67080 time: 67.08 +field ' c' [explicit solve]: current solution: 9.618095e+01, current residual:5.806542e+01 +field 'mu' [explicit solve]: current solution: 4.850556e+00, current residual:2.928832e+00 +wall time: 0.000667095s + +time increment:67090 time: 67.09 +field ' c' [explicit solve]: current solution: 9.618098e+01, current residual:5.806544e+01 +field 'mu' [explicit solve]: current solution: 4.850499e+00, current residual:2.928796e+00 +wall time: 0.000714064s + +time increment:67100 time: 67.1 +field ' c' [explicit solve]: current solution: 9.618102e+01, current residual:5.806545e+01 +field 'mu' [explicit solve]: current solution: 4.850442e+00, current residual:2.928760e+00 +wall time: 0.000787973s + +time increment:67110 time: 67.11 +field ' c' [explicit solve]: current solution: 9.618105e+01, current residual:5.806547e+01 +field 'mu' [explicit solve]: current solution: 4.850385e+00, current residual:2.928724e+00 +wall time: 0.000680923s + +time increment:67120 time: 67.12 +field ' c' [explicit solve]: current solution: 9.618108e+01, current residual:5.806549e+01 +field 'mu' [explicit solve]: current solution: 4.850328e+00, current residual:2.928688e+00 +wall time: 0.000737906s + +time increment:67130 time: 67.13 +field ' c' [explicit solve]: current solution: 9.618111e+01, current residual:5.806550e+01 +field 'mu' [explicit solve]: current solution: 4.850271e+00, current residual:2.928652e+00 +wall time: 0.000654936s + +time increment:67140 time: 67.14 +field ' c' [explicit solve]: current solution: 9.618115e+01, current residual:5.806552e+01 +field 'mu' [explicit solve]: current solution: 4.850214e+00, current residual:2.928615e+00 +wall time: 0.000687122s + +time increment:67150 time: 67.15 +field ' c' [explicit solve]: current solution: 9.618118e+01, current residual:5.806553e+01 +field 'mu' [explicit solve]: current solution: 4.850156e+00, current residual:2.928579e+00 +wall time: 0.000667095s + +time increment:67160 time: 67.16 +field ' c' [explicit solve]: current solution: 9.618121e+01, current residual:5.806555e+01 +field 'mu' [explicit solve]: current solution: 4.850099e+00, current residual:2.928543e+00 +wall time: 0.000763893s + +time increment:67170 time: 67.17 +field ' c' [explicit solve]: current solution: 9.618124e+01, current residual:5.806557e+01 +field 'mu' [explicit solve]: current solution: 4.850042e+00, current residual:2.928507e+00 +wall time: 0.000673056s + +time increment:67180 time: 67.18 +field ' c' [explicit solve]: current solution: 9.618128e+01, current residual:5.806558e+01 +field 'mu' [explicit solve]: current solution: 4.849985e+00, current residual:2.928471e+00 +wall time: 0.000653028s + +time increment:67190 time: 67.19 +field ' c' [explicit solve]: current solution: 9.618131e+01, current residual:5.806560e+01 +field 'mu' [explicit solve]: current solution: 4.849928e+00, current residual:2.928435e+00 +wall time: 0.000699997s + +time increment:67200 time: 67.2 +field ' c' [explicit solve]: current solution: 9.618134e+01, current residual:5.806562e+01 +field 'mu' [explicit solve]: current solution: 4.849871e+00, current residual:2.928399e+00 +wall time: 0.000683784s + +time increment:67210 time: 67.21 +field ' c' [explicit solve]: current solution: 9.618137e+01, current residual:5.806563e+01 +field 'mu' [explicit solve]: current solution: 4.849814e+00, current residual:2.928362e+00 +wall time: 0.00067997s + +time increment:67220 time: 67.22 +field ' c' [explicit solve]: current solution: 9.618141e+01, current residual:5.806565e+01 +field 'mu' [explicit solve]: current solution: 4.849757e+00, current residual:2.928326e+00 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0.000711918s + +time increment:67270 time: 67.27 +field ' c' [explicit solve]: current solution: 9.618157e+01, current residual:5.806573e+01 +field 'mu' [explicit solve]: current solution: 4.849472e+00, current residual:2.928146e+00 +wall time: 0.00077486s + +time increment:67280 time: 67.28 +field ' c' [explicit solve]: current solution: 9.618160e+01, current residual:5.806575e+01 +field 'mu' [explicit solve]: current solution: 4.849415e+00, current residual:2.928110e+00 +wall time: 0.000695944s + +time increment:67290 time: 67.29 +field ' c' [explicit solve]: current solution: 9.618163e+01, current residual:5.806577e+01 +field 'mu' [explicit solve]: current solution: 4.849358e+00, current residual:2.928074e+00 +wall time: 0.000648022s + +time increment:67300 time: 67.3 +field ' c' [explicit solve]: current solution: 9.618167e+01, current residual:5.806578e+01 +field 'mu' [explicit solve]: current solution: 4.849301e+00, current residual:2.928037e+00 +wall time: 0.000787973s + +time increment:67310 time: 67.31 +field ' c' [explicit solve]: current solution: 9.618170e+01, current residual:5.806580e+01 +field 'mu' [explicit solve]: current solution: 4.849245e+00, current residual:2.928001e+00 +wall time: 0.000656843s + +time increment:67320 time: 67.32 +field ' c' [explicit solve]: current solution: 9.618173e+01, current residual:5.806582e+01 +field 'mu' [explicit solve]: current solution: 4.849188e+00, current residual:2.927965e+00 +wall time: 0.000653028s + +time increment:67330 time: 67.33 +field ' c' [explicit solve]: current solution: 9.618177e+01, current residual:5.806583e+01 +field 'mu' [explicit solve]: current solution: 4.849131e+00, current residual:2.927929e+00 +wall time: 0.000648975s + +time increment:67340 time: 67.34 +field ' c' [explicit solve]: current solution: 9.618180e+01, current residual:5.806585e+01 +field 'mu' [explicit solve]: current solution: 4.849074e+00, current residual:2.927893e+00 +wall time: 0.000684023s + +time increment:67350 time: 67.35 +field ' c' [explicit solve]: current solution: 9.618183e+01, current residual:5.806587e+01 +field 'mu' [explicit solve]: current solution: 4.849017e+00, current residual:2.927857e+00 +wall time: 0.000720978s + +time increment:67360 time: 67.36 +field ' c' [explicit solve]: current solution: 9.618186e+01, current residual:5.806588e+01 +field 'mu' [explicit solve]: current solution: 4.848960e+00, current residual:2.927821e+00 +wall time: 0.000622988s + +time increment:67370 time: 67.37 +field ' c' [explicit solve]: current solution: 9.618190e+01, current residual:5.806590e+01 +field 'mu' [explicit solve]: current solution: 4.848903e+00, current residual:2.927785e+00 +wall time: 0.000681162s + +time increment:67380 time: 67.38 +field ' c' [explicit solve]: current solution: 9.618193e+01, current residual:5.806592e+01 +field 'mu' [explicit solve]: current solution: 4.848846e+00, current residual:2.927749e+00 +wall time: 0.000688076s + +time increment:67390 time: 67.39 +field ' c' [explicit solve]: current solution: 9.618196e+01, current residual:5.806593e+01 +field 'mu' [explicit solve]: current solution: 4.848789e+00, current residual:2.927713e+00 +wall time: 0.000647068s + +time increment:67400 time: 67.4 +field ' c' [explicit solve]: current solution: 9.618199e+01, current residual:5.806595e+01 +field 'mu' [explicit solve]: current solution: 4.848732e+00, current residual:2.927676e+00 +wall time: 0.000692844s + +time increment:67410 time: 67.41 +field ' c' [explicit solve]: current solution: 9.618203e+01, current residual:5.806597e+01 +field 'mu' [explicit solve]: current solution: 4.848675e+00, current residual:2.927640e+00 +wall time: 0.000723124s + +time increment:67420 time: 67.42 +field ' c' [explicit solve]: current solution: 9.618206e+01, current residual:5.806598e+01 +field 'mu' [explicit solve]: current solution: 4.848618e+00, current residual:2.927604e+00 +wall time: 0.000643015s + +time increment:67430 time: 67.43 +field ' c' [explicit solve]: current solution: 9.618209e+01, current residual:5.806600e+01 +field 'mu' [explicit solve]: current solution: 4.848561e+00, current residual:2.927568e+00 +wall time: 0.000625849s + +time increment:67440 time: 67.44 +field ' c' [explicit solve]: current solution: 9.618213e+01, current residual:5.806602e+01 +field 'mu' [explicit solve]: current solution: 4.848504e+00, current residual:2.927532e+00 +wall time: 0.000570059s + +time increment:67450 time: 67.45 +field ' c' [explicit solve]: current solution: 9.618216e+01, current residual:5.806603e+01 +field 'mu' [explicit solve]: current solution: 4.848448e+00, current residual:2.927496e+00 +wall time: 0.000573874s + +time increment:67460 time: 67.46 +field ' c' [explicit solve]: current solution: 9.618219e+01, current residual:5.806605e+01 +field 'mu' [explicit solve]: current solution: 4.848391e+00, current residual:2.927460e+00 +wall time: 0.000573874s + +time increment:67470 time: 67.47 +field ' c' [explicit solve]: current solution: 9.618222e+01, current residual:5.806607e+01 +field 'mu' [explicit solve]: current solution: 4.848334e+00, current residual:2.927424e+00 +wall time: 0.000575066s + +time increment:67480 time: 67.48 +field ' c' [explicit solve]: current solution: 9.618226e+01, current residual:5.806608e+01 +field 'mu' [explicit solve]: current solution: 4.848277e+00, current residual:2.927388e+00 +wall time: 0.000588894s + +time increment:67490 time: 67.49 +field ' c' [explicit solve]: current solution: 9.618229e+01, current residual:5.806610e+01 +field 'mu' [explicit solve]: current solution: 4.848220e+00, current residual:2.927352e+00 +wall time: 0.000599146s + +time increment:67500 time: 67.5 +field ' c' [explicit solve]: current solution: 9.618232e+01, current residual:5.806612e+01 +field 'mu' [explicit solve]: current solution: 4.848163e+00, current residual:2.927316e+00 +wall time: 0.000552893s + +time increment:67510 time: 67.51 +field ' c' [explicit solve]: current solution: 9.618236e+01, current residual:5.806613e+01 +field 'mu' [explicit solve]: current solution: 4.848106e+00, current residual:2.927280e+00 +wall time: 0.000583887s + +time increment:67520 time: 67.52 +field ' c' [explicit solve]: current solution: 9.618239e+01, current residual:5.806615e+01 +field 'mu' [explicit solve]: current solution: 4.848049e+00, current residual:2.927244e+00 +wall time: 0.000602961s + +time increment:67530 time: 67.53 +field ' c' [explicit solve]: current solution: 9.618242e+01, current residual:5.806617e+01 +field 'mu' [explicit solve]: current solution: 4.847993e+00, current residual:2.927208e+00 +wall time: 0.000650167s + +time increment:67540 time: 67.54 +field ' c' [explicit solve]: current solution: 9.618246e+01, current residual:5.806618e+01 +field 'mu' [explicit solve]: current solution: 4.847936e+00, current residual:2.927172e+00 +wall time: 0.000644922s + +time increment:67550 time: 67.55 +field ' c' [explicit solve]: current solution: 9.618249e+01, current residual:5.806620e+01 +field 'mu' [explicit solve]: current solution: 4.847879e+00, current residual:2.927136e+00 +wall time: 0.000610113s + +time increment:67560 time: 67.56 +field ' c' [explicit solve]: current solution: 9.618252e+01, current residual:5.806622e+01 +field 'mu' [explicit solve]: current solution: 4.847822e+00, current residual:2.927100e+00 +wall time: 0.000716925s + +time increment:67570 time: 67.57 +field ' c' [explicit solve]: current solution: 9.618255e+01, current residual:5.806623e+01 +field 'mu' [explicit solve]: current solution: 4.847765e+00, current residual:2.927064e+00 +wall time: 0.000593901s + +time increment:67580 time: 67.58 +field ' c' [explicit solve]: current solution: 9.618259e+01, current residual:5.806625e+01 +field 'mu' [explicit solve]: current solution: 4.847708e+00, current residual:2.927028e+00 +wall time: 0.000574827s + +time increment:67590 time: 67.59 +field ' c' [explicit solve]: current solution: 9.618262e+01, current residual:5.806627e+01 +field 'mu' [explicit solve]: current solution: 4.847651e+00, current residual:2.926992e+00 +wall time: 0.00058198s + +time increment:67600 time: 67.6 +field ' c' [explicit solve]: current solution: 9.618265e+01, current residual:5.806628e+01 +field 'mu' [explicit solve]: current solution: 4.847595e+00, current residual:2.926956e+00 +wall time: 0.000596046s + +time increment:67610 time: 67.61 +field ' c' [explicit solve]: current solution: 9.618269e+01, current residual:5.806630e+01 +field 'mu' [explicit solve]: current solution: 4.847538e+00, current residual:2.926920e+00 +wall time: 0.000601053s + +time increment:67620 time: 67.62 +field ' c' [explicit solve]: current solution: 9.618272e+01, current residual:5.806632e+01 +field 'mu' [explicit solve]: current solution: 4.847481e+00, current residual:2.926884e+00 +wall time: 0.000650883s + +time increment:67630 time: 67.63 +field ' c' [explicit solve]: current solution: 9.618275e+01, current residual:5.806633e+01 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[explicit solve]: current solution: 4.847197e+00, current residual:2.926704e+00 +wall time: 0.000738859s + +time increment:67680 time: 67.68 +field ' c' [explicit solve]: current solution: 9.618292e+01, current residual:5.806642e+01 +field 'mu' [explicit solve]: current solution: 4.847140e+00, current residual:2.926668e+00 +wall time: 0.000662804s + +time increment:67690 time: 67.69 +field ' c' [explicit solve]: current solution: 9.618295e+01, current residual:5.806643e+01 +field 'mu' [explicit solve]: current solution: 4.847083e+00, current residual:2.926632e+00 +wall time: 0.000588894s + +time increment:67700 time: 67.7 +field ' c' [explicit solve]: current solution: 9.618298e+01, current residual:5.806645e+01 +field 'mu' [explicit solve]: current solution: 4.847027e+00, current residual:2.926596e+00 +wall time: 0.000591993s + +time increment:67710 time: 67.71 +field ' c' [explicit solve]: current solution: 9.618302e+01, current residual:5.806647e+01 +field 'mu' [explicit solve]: current solution: 4.846970e+00, current residual:2.926560e+00 +wall time: 0.000691891s + +time increment:67720 time: 67.72 +field ' c' [explicit solve]: current solution: 9.618305e+01, current residual:5.806649e+01 +field 'mu' [explicit solve]: current solution: 4.846913e+00, current residual:2.926524e+00 +wall time: 0.000557899s + +time increment:67730 time: 67.73 +field ' c' [explicit solve]: current solution: 9.618308e+01, current residual:5.806650e+01 +field 'mu' [explicit solve]: current solution: 4.846856e+00, current residual:2.926488e+00 +wall time: 0.000585079s + +time increment:67740 time: 67.74 +field ' c' [explicit solve]: current solution: 9.618312e+01, current residual:5.806652e+01 +field 'mu' [explicit solve]: current solution: 4.846800e+00, current residual:2.926452e+00 +wall time: 0.000641108s + +time increment:67750 time: 67.75 +field ' c' [explicit solve]: current solution: 9.618315e+01, current residual:5.806654e+01 +field 'mu' [explicit solve]: current solution: 4.846743e+00, current residual:2.926416e+00 +wall time: 0.000581026s + +time increment:67760 time: 67.76 +field ' c' [explicit solve]: current solution: 9.618318e+01, current residual:5.806655e+01 +field 'mu' [explicit solve]: current solution: 4.846686e+00, current residual:2.926380e+00 +wall time: 0.000649929s + +time increment:67770 time: 67.77 +field ' c' [explicit solve]: current solution: 9.618322e+01, current residual:5.806657e+01 +field 'mu' [explicit solve]: current solution: 4.846629e+00, current residual:2.926344e+00 +wall time: 0.000683069s + +time increment:67780 time: 67.78 +field ' c' [explicit solve]: current solution: 9.618325e+01, current residual:5.806659e+01 +field 'mu' [explicit solve]: current solution: 4.846573e+00, current residual:2.926308e+00 +wall time: 0.000586033s + +time increment:67790 time: 67.79 +field ' c' [explicit solve]: current solution: 9.618328e+01, current residual:5.806660e+01 +field 'mu' [explicit solve]: current solution: 4.846516e+00, current residual:2.926272e+00 +wall time: 0.000648975s + +time increment:67800 time: 67.8 +field ' c' [explicit solve]: current solution: 9.618332e+01, current residual:5.806662e+01 +field 'mu' [explicit solve]: current solution: 4.846459e+00, current residual:2.926236e+00 +wall time: 0.000626087s + +time increment:67810 time: 67.81 +field ' c' [explicit solve]: current solution: 9.618335e+01, current residual:5.806664e+01 +field 'mu' [explicit solve]: current solution: 4.846402e+00, current residual:2.926200e+00 +wall time: 0.000643969s + +time increment:67820 time: 67.82 +field ' c' [explicit solve]: current solution: 9.618338e+01, current residual:5.806665e+01 +field 'mu' [explicit solve]: current solution: 4.846346e+00, current residual:2.926164e+00 +wall time: 0.000697851s + +time increment:67830 time: 67.83 +field ' c' [explicit solve]: current solution: 9.618342e+01, current residual:5.806667e+01 +field 'mu' [explicit solve]: current solution: 4.846289e+00, current residual:2.926128e+00 +wall time: 0.000582933s + +time increment:67840 time: 67.84 +field ' c' [explicit solve]: current solution: 9.618345e+01, current residual:5.806669e+01 +field 'mu' [explicit solve]: current solution: 4.846232e+00, current residual:2.926092e+00 +wall time: 0.000566006s + +time increment:67850 time: 67.85 +field ' c' [explicit solve]: current solution: 9.618348e+01, current residual:5.806671e+01 +field 'mu' [explicit solve]: current solution: 4.846175e+00, current residual:2.926056e+00 +wall time: 0.000691175s + +time increment:67860 time: 67.86 +field ' c' [explicit solve]: current solution: 9.618352e+01, current residual:5.806672e+01 +field 'mu' [explicit solve]: current solution: 4.846119e+00, current residual:2.926020e+00 +wall time: 0.000658989s + +time increment:67870 time: 67.87 +field ' c' [explicit solve]: current solution: 9.618355e+01, current residual:5.806674e+01 +field 'mu' [explicit solve]: current solution: 4.846062e+00, current residual:2.925984e+00 +wall time: 0.000617981s + +time increment:67880 time: 67.88 +field ' c' [explicit solve]: current solution: 9.618358e+01, current residual:5.806676e+01 +field 'mu' [explicit solve]: current solution: 4.846005e+00, current residual:2.925948e+00 +wall time: 0.000575066s + +time increment:67890 time: 67.89 +field ' c' [explicit solve]: current solution: 9.618362e+01, current residual:5.806677e+01 +field 'mu' [explicit solve]: current solution: 4.845949e+00, current residual:2.925912e+00 +wall time: 0.000570059s + +time increment:67900 time: 67.9 +field ' c' [explicit solve]: current solution: 9.618365e+01, current residual:5.806679e+01 +field 'mu' [explicit solve]: current solution: 4.845892e+00, current residual:2.925877e+00 +wall time: 0.000594139s + +time increment:67910 time: 67.91 +field ' c' [explicit solve]: current solution: 9.618368e+01, current residual:5.806681e+01 +field 'mu' [explicit solve]: current solution: 4.845835e+00, current residual:2.925841e+00 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0.000706196s + +time increment:67960 time: 67.96 +field ' c' [explicit solve]: current solution: 9.618385e+01, current residual:5.806689e+01 +field 'mu' [explicit solve]: current solution: 4.845552e+00, current residual:2.925661e+00 +wall time: 0.000741005s + +time increment:67970 time: 67.97 +field ' c' [explicit solve]: current solution: 9.618389e+01, current residual:5.806691e+01 +field 'mu' [explicit solve]: current solution: 4.845495e+00, current residual:2.925625e+00 +wall time: 0.000731945s + +time increment:67980 time: 67.98 +field ' c' [explicit solve]: current solution: 9.618392e+01, current residual:5.806693e+01 +field 'mu' [explicit solve]: current solution: 4.845438e+00, current residual:2.925589e+00 +wall time: 0.00064683s + +time increment:67990 time: 67.99 +field ' c' [explicit solve]: current solution: 9.618395e+01, current residual:5.806694e+01 +field 'mu' [explicit solve]: current solution: 4.845382e+00, current residual:2.925553e+00 +wall time: 0.000689983s + +time increment:68000 time: 68 +field ' c' [explicit solve]: current solution: 9.618399e+01, current residual:5.806696e+01 +field 'mu' [explicit solve]: current solution: 4.845325e+00, current residual:2.925517e+00 +wall time: 0.000885963s + +time increment:68010 time: 68.01 +field ' c' [explicit solve]: current solution: 9.618402e+01, current residual:5.806698e+01 +field 'mu' [explicit solve]: current solution: 4.845269e+00, current residual:2.925482e+00 +wall time: 0.000730038s + +time increment:68020 time: 68.02 +field ' c' [explicit solve]: current solution: 9.618405e+01, current residual:5.806700e+01 +field 'mu' [explicit solve]: current solution: 4.845212e+00, current residual:2.925446e+00 +wall time: 0.000577211s + +time increment:68030 time: 68.03 +field ' c' [explicit solve]: current solution: 9.618409e+01, current residual:5.806701e+01 +field 'mu' [explicit solve]: current solution: 4.845155e+00, current residual:2.925410e+00 +wall time: 0.000566959s + +time increment:68040 time: 68.04 +field ' c' [explicit solve]: current solution: 9.618412e+01, current residual:5.806703e+01 +field 'mu' [explicit solve]: current solution: 4.845099e+00, current residual:2.925374e+00 +wall time: 0.000560999s + +time increment:68050 time: 68.05 +field ' c' [explicit solve]: current solution: 9.618415e+01, current residual:5.806705e+01 +field 'mu' [explicit solve]: current solution: 4.845042e+00, current residual:2.925338e+00 +wall time: 0.000706911s + +time increment:68060 time: 68.06 +field ' c' [explicit solve]: current solution: 9.618419e+01, current residual:5.806706e+01 +field 'mu' [explicit solve]: current solution: 4.844985e+00, current residual:2.925302e+00 +wall time: 0.000800848s + +time increment:68070 time: 68.07 +field ' c' [explicit solve]: current solution: 9.618422e+01, current residual:5.806708e+01 +field 'mu' [explicit solve]: current solution: 4.844929e+00, current residual:2.925266e+00 +wall time: 0.000679016s + +time increment:68080 time: 68.08 +field ' c' [explicit solve]: current solution: 9.618425e+01, current residual:5.806710e+01 +field 'mu' [explicit solve]: current solution: 4.844872e+00, current residual:2.925230e+00 +wall time: 0.000731945s + +time increment:68090 time: 68.09 +field ' c' [explicit solve]: current solution: 9.618429e+01, current residual:5.806712e+01 +field 'mu' [explicit solve]: current solution: 4.844816e+00, current residual:2.925195e+00 +wall time: 0.000675917s + +time increment:68100 time: 68.1 +field ' c' [explicit solve]: current solution: 9.618432e+01, current residual:5.806713e+01 +field 'mu' [explicit solve]: current solution: 4.844759e+00, current residual:2.925159e+00 +wall time: 0.000677109s + +time increment:68110 time: 68.11 +field ' c' [explicit solve]: current solution: 9.618436e+01, current residual:5.806715e+01 +field 'mu' [explicit solve]: current solution: 4.844702e+00, current residual:2.925123e+00 +wall time: 0.000735998s + +time increment:68120 time: 68.12 +field ' c' [explicit solve]: current solution: 9.618439e+01, current residual:5.806717e+01 +field 'mu' [explicit solve]: current solution: 4.844646e+00, current residual:2.925087e+00 +wall time: 0.00069499s + +time increment:68130 time: 68.13 +field ' c' [explicit solve]: current solution: 9.618442e+01, current residual:5.806718e+01 +field 'mu' [explicit solve]: current solution: 4.844589e+00, current residual:2.925051e+00 +wall time: 0.000705004s + +time increment:68140 time: 68.14 +field ' c' [explicit solve]: current solution: 9.618446e+01, current residual:5.806720e+01 +field 'mu' [explicit solve]: current solution: 4.844532e+00, current residual:2.925015e+00 +wall time: 0.000667095s + +time increment:68150 time: 68.15 +field ' c' [explicit solve]: current solution: 9.618449e+01, current residual:5.806722e+01 +field 'mu' [explicit solve]: current solution: 4.844476e+00, current residual:2.924979e+00 +wall time: 0.000654936s + +time increment:68160 time: 68.16 +field ' c' [explicit solve]: current solution: 9.618452e+01, current residual:5.806724e+01 +field 'mu' [explicit solve]: current solution: 4.844419e+00, current residual:2.924943e+00 +wall time: 0.00073719s + +time increment:68170 time: 68.17 +field ' c' [explicit solve]: current solution: 9.618456e+01, current residual:5.806725e+01 +field 'mu' [explicit solve]: current solution: 4.844363e+00, current residual:2.924908e+00 +wall time: 0.000638962s + +time increment:68180 time: 68.18 +field ' c' [explicit solve]: current solution: 9.618459e+01, current residual:5.806727e+01 +field 'mu' [explicit solve]: current solution: 4.844306e+00, current residual:2.924872e+00 +wall time: 0.000736952s + +time increment:68190 time: 68.19 +field ' c' [explicit solve]: current solution: 9.618462e+01, current residual:5.806729e+01 +field 'mu' [explicit solve]: current solution: 4.844250e+00, current residual:2.924836e+00 +wall time: 0.000774145s + +time increment:68200 time: 68.2 +field ' c' [explicit solve]: current solution: 9.618466e+01, current residual:5.806730e+01 +field 'mu' [explicit solve]: current solution: 4.844193e+00, current residual:2.924800e+00 +wall time: 0.000649214s + +time increment:68210 time: 68.21 +field ' c' [explicit solve]: current solution: 9.618469e+01, current residual:5.806732e+01 +field 'mu' [explicit solve]: current solution: 4.844136e+00, current residual:2.924764e+00 +wall time: 0.000765085s + +time increment:68220 time: 68.22 +field ' c' [explicit solve]: current solution: 9.618473e+01, current residual:5.806734e+01 +field 'mu' [explicit solve]: current solution: 4.844080e+00, current residual:2.924728e+00 +wall time: 0.000643969s + +time increment:68230 time: 68.23 +field ' c' [explicit solve]: current solution: 9.618476e+01, current residual:5.806736e+01 +field 'mu' [explicit solve]: current solution: 4.844023e+00, current residual:2.924693e+00 +wall time: 0.000738144s + +time increment:68240 time: 68.24 +field ' c' [explicit solve]: current solution: 9.618479e+01, current residual:5.806737e+01 +field 'mu' [explicit solve]: current solution: 4.843967e+00, current residual:2.924657e+00 +wall time: 0.000683069s + +time increment:68250 time: 68.25 +field ' c' [explicit solve]: current solution: 9.618483e+01, current residual:5.806739e+01 +field 'mu' [explicit solve]: current solution: 4.843910e+00, current residual:2.924621e+00 +wall time: 0.000715017s + +time increment:68260 time: 68.26 +field ' c' [explicit solve]: current solution: 9.618486e+01, current residual:5.806741e+01 +field 'mu' [explicit solve]: current solution: 4.843854e+00, current residual:2.924585e+00 +wall time: 0.000673771s + +time increment:68270 time: 68.27 +field ' c' [explicit solve]: current solution: 9.618490e+01, current residual:5.806743e+01 +field 'mu' [explicit solve]: current solution: 4.843797e+00, current residual:2.924549e+00 +wall time: 0.000643015s + +time increment:68280 time: 68.28 +field ' c' [explicit solve]: current solution: 9.618493e+01, current residual:5.806744e+01 +field 'mu' [explicit solve]: current solution: 4.843741e+00, current residual:2.924513e+00 +wall time: 0.000751019s + +time increment:68290 time: 68.29 +field ' c' [explicit solve]: current solution: 9.618496e+01, current residual:5.806746e+01 +field 'mu' [explicit solve]: current solution: 4.843684e+00, current residual:2.924478e+00 +wall time: 0.00071907s + +time increment:68300 time: 68.3 +field ' c' [explicit solve]: current solution: 9.618500e+01, current residual:5.806748e+01 +field 'mu' [explicit solve]: current solution: 4.843628e+00, current residual:2.924442e+00 +wall time: 0.000644922s + +time increment:68310 time: 68.31 +field ' c' [explicit solve]: current solution: 9.618503e+01, current residual:5.806750e+01 +field 'mu' [explicit solve]: current solution: 4.843571e+00, current residual:2.924406e+00 +wall time: 0.000655174s + +time increment:68320 time: 68.32 +field ' c' [explicit solve]: current solution: 9.618506e+01, current residual:5.806751e+01 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solve]: current solution: 4.843288e+00, current residual:2.924227e+00 +wall time: 0.000708103s + +time increment:68370 time: 68.37 +field ' c' [explicit solve]: current solution: 9.618523e+01, current residual:5.806760e+01 +field 'mu' [explicit solve]: current solution: 4.843232e+00, current residual:2.924191e+00 +wall time: 0.000729084s + +time increment:68380 time: 68.38 +field ' c' [explicit solve]: current solution: 9.618527e+01, current residual:5.806762e+01 +field 'mu' [explicit solve]: current solution: 4.843175e+00, current residual:2.924155e+00 +wall time: 0.000707865s + +time increment:68390 time: 68.39 +field ' c' [explicit solve]: current solution: 9.618530e+01, current residual:5.806763e+01 +field 'mu' [explicit solve]: current solution: 4.843119e+00, current residual:2.924120e+00 +wall time: 0.000659943s + +time increment:68400 time: 68.4 +field ' c' [explicit solve]: current solution: 9.618534e+01, current residual:5.806765e+01 +field 'mu' [explicit solve]: current solution: 4.843062e+00, current residual:2.924084e+00 +wall time: 0.00071907s + +time increment:68410 time: 68.41 +field ' c' [explicit solve]: current solution: 9.618537e+01, current residual:5.806767e+01 +field 'mu' [explicit solve]: current solution: 4.843006e+00, current residual:2.924048e+00 +wall time: 0.000639915s + +time increment:68420 time: 68.42 +field ' c' [explicit solve]: current solution: 9.618540e+01, current residual:5.806769e+01 +field 'mu' [explicit solve]: current solution: 4.842950e+00, current residual:2.924012e+00 +wall time: 0.000632048s + +time increment:68430 time: 68.43 +field ' c' [explicit solve]: current solution: 9.618544e+01, current residual:5.806770e+01 +field 'mu' [explicit solve]: current solution: 4.842893e+00, current residual:2.923976e+00 +wall time: 0.000694036s + +time increment:68440 time: 68.44 +field ' c' [explicit solve]: current solution: 9.618547e+01, current residual:5.806772e+01 +field 'mu' [explicit solve]: current solution: 4.842837e+00, current residual:2.923941e+00 +wall time: 0.000555992s + +time increment:68450 time: 68.45 +field ' c' [explicit solve]: current solution: 9.618551e+01, current residual:5.806774e+01 +field 'mu' [explicit solve]: current solution: 4.842780e+00, current residual:2.923905e+00 +wall time: 0.000604153s + +time increment:68460 time: 68.46 +field ' c' [explicit solve]: current solution: 9.618554e+01, current residual:5.806776e+01 +field 'mu' [explicit solve]: current solution: 4.842724e+00, current residual:2.923869e+00 +wall time: 0.000636101s + +time increment:68470 time: 68.47 +field ' c' [explicit solve]: current solution: 9.618557e+01, current residual:5.806777e+01 +field 'mu' [explicit solve]: current solution: 4.842667e+00, current residual:2.923833e+00 +wall time: 0.000727892s + +time increment:68480 time: 68.48 +field ' c' [explicit solve]: current solution: 9.618561e+01, current residual:5.806779e+01 +field 'mu' [explicit solve]: current solution: 4.842611e+00, current residual:2.923798e+00 +wall time: 0.000928879s + +time increment:68490 time: 68.49 +field ' c' [explicit solve]: current solution: 9.618564e+01, current residual:5.806781e+01 +field 'mu' [explicit solve]: current solution: 4.842554e+00, current residual:2.923762e+00 +wall time: 0.000683069s + +time increment:68500 time: 68.5 +field ' c' [explicit solve]: current solution: 9.618568e+01, current residual:5.806783e+01 +field 'mu' [explicit solve]: current solution: 4.842498e+00, current residual:2.923726e+00 +wall time: 0.000653982s + +time increment:68510 time: 68.51 +field ' c' [explicit solve]: current solution: 9.618571e+01, current residual:5.806784e+01 +field 'mu' [explicit solve]: current solution: 4.842441e+00, current residual:2.923690e+00 +wall time: 0.000657082s + +time increment:68520 time: 68.52 +field ' c' [explicit solve]: current solution: 9.618574e+01, current residual:5.806786e+01 +field 'mu' [explicit solve]: current solution: 4.842385e+00, current residual:2.923655e+00 +wall time: 0.000699997s + +time increment:68530 time: 68.53 +field ' c' [explicit solve]: current solution: 9.618578e+01, current residual:5.806788e+01 +field 'mu' [explicit solve]: current solution: 4.842328e+00, current residual:2.923619e+00 +wall time: 0.000657082s + +time increment:68540 time: 68.54 +field ' c' [explicit solve]: current solution: 9.618581e+01, current residual:5.806790e+01 +field 'mu' [explicit solve]: current solution: 4.842272e+00, current residual:2.923583e+00 +wall time: 0.000637054s + +time increment:68550 time: 68.55 +field ' c' [explicit solve]: current solution: 9.618585e+01, current residual:5.806791e+01 +field 'mu' [explicit solve]: current solution: 4.842216e+00, current residual:2.923547e+00 +wall time: 0.000669003s + +time increment:68560 time: 68.56 +field ' c' [explicit solve]: current solution: 9.618588e+01, current residual:5.806793e+01 +field 'mu' [explicit solve]: current solution: 4.842159e+00, current residual:2.923512e+00 +wall time: 0.000751972s + +time increment:68570 time: 68.57 +field ' c' [explicit solve]: current solution: 9.618592e+01, current residual:5.806795e+01 +field 'mu' [explicit solve]: current solution: 4.842103e+00, current residual:2.923476e+00 +wall time: 0.000560999s + +time increment:68580 time: 68.58 +field ' c' [explicit solve]: current solution: 9.618595e+01, current residual:5.806797e+01 +field 'mu' [explicit solve]: current solution: 4.842046e+00, current residual:2.923440e+00 +wall time: 0.000573874s + +time increment:68590 time: 68.59 +field ' c' [explicit solve]: current solution: 9.618598e+01, current residual:5.806798e+01 +field 'mu' [explicit solve]: current solution: 4.841990e+00, current residual:2.923404e+00 +wall time: 0.000727892s + +time increment:68600 time: 68.6 +field ' c' [explicit solve]: current solution: 9.618602e+01, current residual:5.806800e+01 +field 'mu' [explicit solve]: current solution: 4.841933e+00, current residual:2.923369e+00 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0.000580788s + +time increment:68650 time: 68.65 +field ' c' [explicit solve]: current solution: 9.618619e+01, current residual:5.806809e+01 +field 'mu' [explicit solve]: current solution: 4.841651e+00, current residual:2.923190e+00 +wall time: 0.00057888s + +time increment:68660 time: 68.66 +field ' c' [explicit solve]: current solution: 9.618622e+01, current residual:5.806811e+01 +field 'mu' [explicit solve]: current solution: 4.841595e+00, current residual:2.923154e+00 +wall time: 0.000580072s + +time increment:68670 time: 68.67 +field ' c' [explicit solve]: current solution: 9.618626e+01, current residual:5.806812e+01 +field 'mu' [explicit solve]: current solution: 4.841539e+00, current residual:2.923119e+00 +wall time: 0.000666142s + +time increment:68680 time: 68.68 +field ' c' [explicit solve]: current solution: 9.618629e+01, current residual:5.806814e+01 +field 'mu' [explicit solve]: current solution: 4.841482e+00, current residual:2.923083e+00 +wall time: 0.000574827s + +time increment:68690 time: 68.69 +field ' c' [explicit solve]: current solution: 9.618633e+01, current residual:5.806816e+01 +field 'mu' [explicit solve]: current solution: 4.841426e+00, current residual:2.923047e+00 +wall time: 0.000687838s + +time increment:68700 time: 68.7 +field ' c' [explicit solve]: current solution: 9.618636e+01, current residual:5.806818e+01 +field 'mu' [explicit solve]: current solution: 4.841370e+00, current residual:2.923011e+00 +wall time: 0.000631809s + +time increment:68710 time: 68.71 +field ' c' [explicit solve]: current solution: 9.618639e+01, current residual:5.806819e+01 +field 'mu' [explicit solve]: current solution: 4.841313e+00, current residual:2.922976e+00 +wall time: 0.000622988s + +time increment:68720 time: 68.72 +field ' c' [explicit solve]: current solution: 9.618643e+01, current residual:5.806821e+01 +field 'mu' [explicit solve]: current solution: 4.841257e+00, current residual:2.922940e+00 +wall time: 0.000622988s + +time increment:68730 time: 68.73 +field ' c' [explicit solve]: current solution: 9.618646e+01, current residual:5.806823e+01 +field 'mu' [explicit solve]: current solution: 4.841200e+00, current residual:2.922904e+00 +wall time: 0.000646114s + +time increment:68740 time: 68.74 +field ' c' [explicit solve]: current solution: 9.618650e+01, current residual:5.806825e+01 +field 'mu' [explicit solve]: current solution: 4.841144e+00, current residual:2.922869e+00 +wall time: 0.000605106s + +time increment:68750 time: 68.75 +field ' c' [explicit solve]: current solution: 9.618653e+01, current residual:5.806826e+01 +field 'mu' [explicit solve]: current solution: 4.841088e+00, current residual:2.922833e+00 +wall time: 0.000622988s + +time increment:68760 time: 68.76 +field ' c' [explicit solve]: current solution: 9.618657e+01, current residual:5.806828e+01 +field 'mu' [explicit solve]: current solution: 4.841031e+00, current residual:2.922797e+00 +wall time: 0.000575066s + +time increment:68770 time: 68.77 +field ' c' [explicit solve]: current solution: 9.618660e+01, current residual:5.806830e+01 +field 'mu' [explicit solve]: current solution: 4.840975e+00, current residual:2.922762e+00 +wall time: 0.000611782s + +time increment:68780 time: 68.78 +field ' c' [explicit solve]: current solution: 9.618664e+01, current residual:5.806832e+01 +field 'mu' [explicit solve]: current solution: 4.840919e+00, current residual:2.922726e+00 +wall time: 0.000602007s + +time increment:68790 time: 68.79 +field ' c' [explicit solve]: current solution: 9.618667e+01, current residual:5.806834e+01 +field 'mu' [explicit solve]: current solution: 4.840862e+00, current residual:2.922690e+00 +wall time: 0.00060606s + +time increment:68800 time: 68.8 +field ' c' [explicit solve]: current solution: 9.618670e+01, current residual:5.806835e+01 +field 'mu' [explicit solve]: current solution: 4.840806e+00, current residual:2.922655e+00 +wall time: 0.000622034s + +time increment:68810 time: 68.81 +field ' c' [explicit solve]: current solution: 9.618674e+01, current residual:5.806837e+01 +field 'mu' [explicit solve]: current solution: 4.840750e+00, current residual:2.922619e+00 +wall time: 0.000689983s + +time increment:68820 time: 68.82 +field ' c' [explicit solve]: current solution: 9.618677e+01, current residual:5.806839e+01 +field 'mu' [explicit solve]: current solution: 4.840693e+00, current residual:2.922583e+00 +wall time: 0.000566006s + +time increment:68830 time: 68.83 +field ' c' [explicit solve]: current solution: 9.618681e+01, current residual:5.806841e+01 +field 'mu' [explicit solve]: current solution: 4.840637e+00, current residual:2.922547e+00 +wall time: 0.000664949s + +time increment:68840 time: 68.84 +field ' c' [explicit solve]: current solution: 9.618684e+01, current residual:5.806842e+01 +field 'mu' [explicit solve]: current solution: 4.840581e+00, current residual:2.922512e+00 +wall time: 0.000668049s + +time increment:68850 time: 68.85 +field ' c' [explicit solve]: current solution: 9.618688e+01, current residual:5.806844e+01 +field 'mu' [explicit solve]: current solution: 4.840524e+00, current residual:2.922476e+00 +wall time: 0.000638008s + +time increment:68860 time: 68.86 +field ' c' [explicit solve]: current solution: 9.618691e+01, current residual:5.806846e+01 +field 'mu' [explicit solve]: current solution: 4.840468e+00, current residual:2.922440e+00 +wall time: 0.000714064s + +time increment:68870 time: 68.87 +field ' c' [explicit solve]: current solution: 9.618695e+01, current residual:5.806848e+01 +field 'mu' [explicit solve]: current solution: 4.840412e+00, current residual:2.922405e+00 +wall time: 0.000669956s + +time increment:68880 time: 68.88 +field ' c' [explicit solve]: current solution: 9.618698e+01, current residual:5.806849e+01 +field 'mu' [explicit solve]: current solution: 4.840355e+00, current residual:2.922369e+00 +wall time: 0.000576019s + +time increment:68890 time: 68.89 +field ' c' [explicit solve]: current solution: 9.618701e+01, current residual:5.806851e+01 +field 'mu' [explicit solve]: current solution: 4.840299e+00, current residual:2.922333e+00 +wall time: 0.000637054s + +time increment:68900 time: 68.9 +field ' c' [explicit solve]: current solution: 9.618705e+01, current residual:5.806853e+01 +field 'mu' [explicit solve]: current solution: 4.840243e+00, current residual:2.922298e+00 +wall time: 0.000633955s + +time increment:68910 time: 68.91 +field ' c' [explicit solve]: current solution: 9.618708e+01, current residual:5.806855e+01 +field 'mu' [explicit solve]: current solution: 4.840187e+00, current residual:2.922262e+00 +wall time: 0.000665903s + +time increment:68920 time: 68.92 +field ' c' [explicit solve]: current solution: 9.618712e+01, current residual:5.806857e+01 +field 'mu' [explicit solve]: current solution: 4.840130e+00, current residual:2.922227e+00 +wall time: 0.000557184s + +time increment:68930 time: 68.93 +field ' c' [explicit solve]: current solution: 9.618715e+01, current residual:5.806858e+01 +field 'mu' [explicit solve]: current solution: 4.840074e+00, current residual:2.922191e+00 +wall time: 0.000566006s + +time increment:68940 time: 68.94 +field ' c' [explicit solve]: current solution: 9.618719e+01, current residual:5.806860e+01 +field 'mu' [explicit solve]: current solution: 4.840018e+00, current residual:2.922155e+00 +wall time: 0.000675917s + +time increment:68950 time: 68.95 +field ' c' [explicit solve]: current solution: 9.618722e+01, current residual:5.806862e+01 +field 'mu' [explicit solve]: current solution: 4.839961e+00, current residual:2.922120e+00 +wall time: 0.00067091s + +time increment:68960 time: 68.96 +field ' c' [explicit solve]: current solution: 9.618726e+01, current residual:5.806864e+01 +field 'mu' [explicit solve]: current solution: 4.839905e+00, current residual:2.922084e+00 +wall time: 0.000658989s + +time increment:68970 time: 68.97 +field ' c' [explicit solve]: current solution: 9.618729e+01, current residual:5.806865e+01 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solve]: current solution: 4.839624e+00, current residual:2.921906e+00 +wall time: 0.000578165s + +time increment:69020 time: 69.02 +field ' c' [explicit solve]: current solution: 9.618746e+01, current residual:5.806874e+01 +field 'mu' [explicit solve]: current solution: 4.839567e+00, current residual:2.921870e+00 +wall time: 0.000563145s + +time increment:69030 time: 69.03 +field ' c' [explicit solve]: current solution: 9.618750e+01, current residual:5.806876e+01 +field 'mu' [explicit solve]: current solution: 4.839511e+00, current residual:2.921834e+00 +wall time: 0.000607014s + +time increment:69040 time: 69.04 +field ' c' [explicit solve]: current solution: 9.618753e+01, current residual:5.806878e+01 +field 'mu' [explicit solve]: current solution: 4.839455e+00, current residual:2.921799e+00 +wall time: 0.000727177s + +time increment:69050 time: 69.05 +field ' c' [explicit solve]: current solution: 9.618757e+01, current residual:5.806880e+01 +field 'mu' [explicit solve]: current solution: 4.839399e+00, current residual:2.921763e+00 +wall time: 0.000572205s + +time increment:69060 time: 69.06 +field ' c' [explicit solve]: current solution: 9.618760e+01, current residual:5.806882e+01 +field 'mu' [explicit solve]: current solution: 4.839342e+00, current residual:2.921728e+00 +wall time: 0.000659943s + +time increment:69070 time: 69.07 +field ' c' [explicit solve]: current solution: 9.618764e+01, current residual:5.806883e+01 +field 'mu' [explicit solve]: current solution: 4.839286e+00, current residual:2.921692e+00 +wall time: 0.000654936s + +time increment:69080 time: 69.08 +field ' c' [explicit solve]: current solution: 9.618767e+01, current residual:5.806885e+01 +field 'mu' [explicit solve]: current solution: 4.839230e+00, current residual:2.921656e+00 +wall time: 0.000687838s + +time increment:69090 time: 69.09 +field ' c' [explicit solve]: current solution: 9.618771e+01, current residual:5.806887e+01 +field 'mu' [explicit solve]: current solution: 4.839174e+00, current residual:2.921621e+00 +wall time: 0.000566959s + +time increment:69100 time: 69.1 +field ' c' [explicit solve]: current solution: 9.618774e+01, current residual:5.806889e+01 +field 'mu' [explicit solve]: current solution: 4.839118e+00, current residual:2.921585e+00 +wall time: 0.00063014s + +time increment:69110 time: 69.11 +field ' c' [explicit solve]: current solution: 9.618778e+01, current residual:5.806890e+01 +field 'mu' [explicit solve]: current solution: 4.839061e+00, current residual:2.921550e+00 +wall time: 0.000583887s + +time increment:69120 time: 69.12 +field ' c' [explicit solve]: current solution: 9.618781e+01, current residual:5.806892e+01 +field 'mu' [explicit solve]: current solution: 4.839005e+00, current residual:2.921514e+00 +wall time: 0.000720978s + +time increment:69130 time: 69.13 +field ' c' [explicit solve]: current solution: 9.618785e+01, current residual:5.806894e+01 +field 'mu' [explicit solve]: current solution: 4.838949e+00, current residual:2.921478e+00 +wall time: 0.000679016s + +time increment:69140 time: 69.14 +field ' c' [explicit solve]: current solution: 9.618788e+01, current residual:5.806896e+01 +field 'mu' [explicit solve]: current solution: 4.838893e+00, current residual:2.921443e+00 +wall time: 0.000654936s + +time increment:69150 time: 69.15 +field ' c' [explicit solve]: current solution: 9.618792e+01, current residual:5.806898e+01 +field 'mu' [explicit solve]: current solution: 4.838836e+00, current residual:2.921407e+00 +wall time: 0.000673056s + +time increment:69160 time: 69.16 +field ' c' [explicit solve]: current solution: 9.618795e+01, current residual:5.806899e+01 +field 'mu' [explicit solve]: current solution: 4.838780e+00, current residual:2.921372e+00 +wall time: 0.000618935s + +time increment:69170 time: 69.17 +field ' c' [explicit solve]: current solution: 9.618799e+01, current residual:5.806901e+01 +field 'mu' [explicit solve]: current solution: 4.838724e+00, current residual:2.921336e+00 +wall time: 0.000731945s + +time increment:69180 time: 69.18 +field ' c' [explicit solve]: current solution: 9.618802e+01, current residual:5.806903e+01 +field 'mu' [explicit solve]: current solution: 4.838668e+00, current residual:2.921300e+00 +wall time: 0.000632048s + +time increment:69190 time: 69.19 +field ' c' [explicit solve]: current solution: 9.618806e+01, current residual:5.806905e+01 +field 'mu' [explicit solve]: current solution: 4.838612e+00, current residual:2.921265e+00 +wall time: 0.00071907s + +time increment:69200 time: 69.2 +field ' c' [explicit solve]: current solution: 9.618809e+01, current residual:5.806907e+01 +field 'mu' [explicit solve]: current solution: 4.838555e+00, current residual:2.921229e+00 +wall time: 0.000650167s + +time increment:69210 time: 69.21 +field ' c' [explicit solve]: current solution: 9.618812e+01, current residual:5.806908e+01 +field 'mu' [explicit solve]: current solution: 4.838499e+00, current residual:2.921194e+00 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0.00067687s + +time increment:69260 time: 69.26 +field ' c' [explicit solve]: current solution: 9.618830e+01, current residual:5.806917e+01 +field 'mu' [explicit solve]: current solution: 4.838218e+00, current residual:2.921016e+00 +wall time: 0.000656128s + +time increment:69270 time: 69.27 +field ' c' [explicit solve]: current solution: 9.618833e+01, current residual:5.806919e+01 +field 'mu' [explicit solve]: current solution: 4.838162e+00, current residual:2.920980e+00 +wall time: 0.000738144s + +time increment:69280 time: 69.28 +field ' c' [explicit solve]: current solution: 9.618837e+01, current residual:5.806921e+01 +field 'mu' [explicit solve]: current solution: 4.838106e+00, current residual:2.920945e+00 +wall time: 0.000564098s + +time increment:69290 time: 69.29 +field ' c' [explicit solve]: current solution: 9.618840e+01, current residual:5.806923e+01 +field 'mu' [explicit solve]: current solution: 4.838050e+00, current residual:2.920909e+00 +wall time: 0.000672102s + +time increment:69300 time: 69.3 +field ' c' [explicit solve]: current solution: 9.618844e+01, current residual:5.806925e+01 +field 'mu' [explicit solve]: current solution: 4.837994e+00, current residual:2.920873e+00 +wall time: 0.000771046s + +time increment:69310 time: 69.31 +field ' c' [explicit solve]: current solution: 9.618847e+01, current residual:5.806926e+01 +field 'mu' [explicit solve]: current solution: 4.837937e+00, current residual:2.920838e+00 +wall time: 0.000715971s + +time increment:69320 time: 69.32 +field ' c' [explicit solve]: current solution: 9.618851e+01, current residual:5.806928e+01 +field 'mu' [explicit solve]: current solution: 4.837881e+00, current residual:2.920802e+00 +wall time: 0.00068593s + +time increment:69330 time: 69.33 +field ' c' [explicit solve]: current solution: 9.618854e+01, current residual:5.806930e+01 +field 'mu' [explicit solve]: current solution: 4.837825e+00, current residual:2.920767e+00 +wall time: 0.000675917s + +time increment:69340 time: 69.34 +field ' c' [explicit solve]: current solution: 9.618858e+01, current residual:5.806932e+01 +field 'mu' [explicit solve]: current solution: 4.837769e+00, current residual:2.920731e+00 +wall time: 0.000742197s + +time increment:69350 time: 69.35 +field ' c' [explicit solve]: current solution: 9.618861e+01, current residual:5.806934e+01 +field 'mu' [explicit solve]: current solution: 4.837713e+00, current residual:2.920696e+00 +wall time: 0.000687122s + +time increment:69360 time: 69.36 +field ' c' [explicit solve]: current solution: 9.618865e+01, current residual:5.806935e+01 +field 'mu' [explicit solve]: current solution: 4.837657e+00, current residual:2.920660e+00 +wall time: 0.000583887s + +time increment:69370 time: 69.37 +field ' c' [explicit solve]: current solution: 9.618868e+01, current residual:5.806937e+01 +field 'mu' [explicit solve]: current solution: 4.837601e+00, current residual:2.920625e+00 +wall time: 0.000794172s + +time increment:69380 time: 69.38 +field ' c' [explicit solve]: current solution: 9.618872e+01, current residual:5.806939e+01 +field 'mu' [explicit solve]: current solution: 4.837544e+00, current residual:2.920589e+00 +wall time: 0.000721931s + +time increment:69390 time: 69.39 +field ' c' [explicit solve]: current solution: 9.618875e+01, current residual:5.806941e+01 +field 'mu' [explicit solve]: current solution: 4.837488e+00, current residual:2.920554e+00 +wall time: 0.00075388s + +time increment:69400 time: 69.4 +field ' c' [explicit solve]: current solution: 9.618879e+01, current residual:5.806943e+01 +field 'mu' [explicit solve]: current solution: 4.837432e+00, current residual:2.920518e+00 +wall time: 0.000639915s + +time increment:69410 time: 69.41 +field ' c' [explicit solve]: current solution: 9.618882e+01, current residual:5.806944e+01 +field 'mu' [explicit solve]: current solution: 4.837376e+00, current residual:2.920482e+00 +wall time: 0.000734091s + +time increment:69420 time: 69.42 +field ' c' [explicit solve]: current solution: 9.618886e+01, current residual:5.806946e+01 +field 'mu' [explicit solve]: current solution: 4.837320e+00, current residual:2.920447e+00 +wall time: 0.000707865s + +time increment:69430 time: 69.43 +field ' c' [explicit solve]: current solution: 9.618889e+01, current residual:5.806948e+01 +field 'mu' [explicit solve]: current solution: 4.837264e+00, current residual:2.920411e+00 +wall time: 0.000663042s + +time increment:69440 time: 69.44 +field ' c' [explicit solve]: current solution: 9.618893e+01, current residual:5.806950e+01 +field 'mu' [explicit solve]: current solution: 4.837208e+00, current residual:2.920376e+00 +wall time: 0.000650883s + +time increment:69450 time: 69.45 +field ' c' [explicit solve]: current solution: 9.618896e+01, current residual:5.806952e+01 +field 'mu' [explicit solve]: current solution: 4.837151e+00, current residual:2.920340e+00 +wall time: 0.000710011s + +time increment:69460 time: 69.46 +field ' c' [explicit solve]: current solution: 9.618900e+01, current residual:5.806954e+01 +field 'mu' [explicit solve]: current solution: 4.837095e+00, current residual:2.920305e+00 +wall time: 0.000737906s + +time increment:69470 time: 69.47 +field ' c' [explicit solve]: current solution: 9.618903e+01, current residual:5.806955e+01 +field 'mu' [explicit solve]: current solution: 4.837039e+00, current residual:2.920269e+00 +wall time: 0.000701904s + +time increment:69480 time: 69.48 +field ' c' [explicit solve]: current solution: 9.618907e+01, current residual:5.806957e+01 +field 'mu' [explicit solve]: current solution: 4.836983e+00, current residual:2.920234e+00 +wall time: 0.000830173s + +time increment:69490 time: 69.49 +field ' c' [explicit solve]: current solution: 9.618911e+01, current residual:5.806959e+01 +field 'mu' [explicit solve]: current solution: 4.836927e+00, current residual:2.920198e+00 +wall time: 0.000725031s + +time increment:69500 time: 69.5 +field ' c' [explicit solve]: current solution: 9.618914e+01, current residual:5.806961e+01 +field 'mu' [explicit solve]: current solution: 4.836871e+00, current residual:2.920163e+00 +wall time: 0.000717878s + +time increment:69510 time: 69.51 +field ' c' [explicit solve]: current solution: 9.618918e+01, current residual:5.806963e+01 +field 'mu' [explicit solve]: current solution: 4.836815e+00, current residual:2.920127e+00 +wall time: 0.000692844s + +time increment:69520 time: 69.52 +field ' c' [explicit solve]: current solution: 9.618921e+01, current residual:5.806964e+01 +field 'mu' [explicit solve]: current solution: 4.836759e+00, current residual:2.920092e+00 +wall time: 0.000693083s + +time increment:69530 time: 69.53 +field ' c' [explicit solve]: current solution: 9.618925e+01, current residual:5.806966e+01 +field 'mu' [explicit solve]: current solution: 4.836703e+00, current residual:2.920056e+00 +wall time: 0.000701904s + +time increment:69540 time: 69.54 +field ' c' [explicit solve]: current solution: 9.618928e+01, current residual:5.806968e+01 +field 'mu' [explicit solve]: current solution: 4.836647e+00, current residual:2.920021e+00 +wall time: 0.00064683s + +time increment:69550 time: 69.55 +field ' c' [explicit solve]: current solution: 9.618932e+01, current residual:5.806970e+01 +field 'mu' [explicit solve]: current solution: 4.836590e+00, current residual:2.919985e+00 +wall time: 0.00068593s + +time increment:69560 time: 69.56 +field ' c' [explicit solve]: current solution: 9.618935e+01, current residual:5.806972e+01 +field 'mu' [explicit solve]: current solution: 4.836534e+00, current residual:2.919950e+00 +wall time: 0.000763893s + +time increment:69570 time: 69.57 +field ' c' [explicit solve]: current solution: 9.618939e+01, current residual:5.806974e+01 +field 'mu' [explicit solve]: current solution: 4.836478e+00, current residual:2.919914e+00 +wall time: 0.000710011s + +time increment:69580 time: 69.58 +field ' c' [explicit solve]: current solution: 9.618942e+01, current residual:5.806975e+01 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solve]: current solution: 4.836198e+00, current residual:2.919737e+00 +wall time: 0.000674963s + +time increment:69630 time: 69.63 +field ' c' [explicit solve]: current solution: 9.618960e+01, current residual:5.806984e+01 +field 'mu' [explicit solve]: current solution: 4.836142e+00, current residual:2.919701e+00 +wall time: 0.000638008s + +time increment:69640 time: 69.64 +field ' c' [explicit solve]: current solution: 9.618963e+01, current residual:5.806986e+01 +field 'mu' [explicit solve]: current solution: 4.836086e+00, current residual:2.919666e+00 +wall time: 0.00071311s + +time increment:69650 time: 69.65 +field ' c' [explicit solve]: current solution: 9.618967e+01, current residual:5.806988e+01 +field 'mu' [explicit solve]: current solution: 4.836030e+00, current residual:2.919630e+00 +wall time: 0.000614882s + +time increment:69660 time: 69.66 +field ' c' [explicit solve]: current solution: 9.618970e+01, current residual:5.806990e+01 +field 'mu' [explicit solve]: current solution: 4.835974e+00, current residual:2.919595e+00 +wall time: 0.000629902s + +time increment:69670 time: 69.67 +field ' c' [explicit solve]: current solution: 9.618974e+01, current residual:5.806992e+01 +field 'mu' [explicit solve]: current solution: 4.835918e+00, current residual:2.919559e+00 +wall time: 0.000643015s + +time increment:69680 time: 69.68 +field ' c' [explicit solve]: current solution: 9.618978e+01, current residual:5.806994e+01 +field 'mu' [explicit solve]: current solution: 4.835862e+00, current residual:2.919524e+00 +wall time: 0.000643969s + +time increment:69690 time: 69.69 +field ' c' [explicit solve]: current solution: 9.618981e+01, current residual:5.806995e+01 +field 'mu' [explicit solve]: current solution: 4.835806e+00, current residual:2.919488e+00 +wall time: 0.000641108s + +time increment:69700 time: 69.7 +field ' c' [explicit solve]: current solution: 9.618985e+01, current residual:5.806997e+01 +field 'mu' [explicit solve]: current solution: 4.835750e+00, current residual:2.919453e+00 +wall time: 0.000654936s + +time increment:69710 time: 69.71 +field ' c' [explicit solve]: current solution: 9.618988e+01, current residual:5.806999e+01 +field 'mu' [explicit solve]: current solution: 4.835694e+00, current residual:2.919417e+00 +wall time: 0.000597s + +time increment:69720 time: 69.72 +field ' c' [explicit solve]: current solution: 9.618992e+01, current residual:5.807001e+01 +field 'mu' [explicit solve]: current solution: 4.835638e+00, current residual:2.919382e+00 +wall time: 0.000698805s + +time increment:69730 time: 69.73 +field ' c' [explicit solve]: current solution: 9.618995e+01, current residual:5.807003e+01 +field 'mu' [explicit solve]: current solution: 4.835581e+00, current residual:2.919347e+00 +wall time: 0.00066185s + +time increment:69740 time: 69.74 +field ' c' [explicit solve]: current solution: 9.618999e+01, current residual:5.807005e+01 +field 'mu' [explicit solve]: current solution: 4.835525e+00, current residual:2.919311e+00 +wall time: 0.00066185s + +time increment:69750 time: 69.75 +field ' c' [explicit solve]: current solution: 9.619002e+01, current residual:5.807006e+01 +field 'mu' [explicit solve]: current solution: 4.835469e+00, current residual:2.919276e+00 +wall time: 0.000703096s + +time increment:69760 time: 69.76 +field ' c' [explicit solve]: current solution: 9.619006e+01, current residual:5.807008e+01 +field 'mu' [explicit solve]: current solution: 4.835413e+00, current residual:2.919240e+00 +wall time: 0.000643015s + +time increment:69770 time: 69.77 +field ' c' [explicit solve]: current solution: 9.619009e+01, current residual:5.807010e+01 +field 'mu' [explicit solve]: current solution: 4.835357e+00, current residual:2.919205e+00 +wall time: 0.000694036s + +time increment:69780 time: 69.78 +field ' c' [explicit solve]: current solution: 9.619013e+01, current residual:5.807012e+01 +field 'mu' [explicit solve]: current solution: 4.835301e+00, current residual:2.919169e+00 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0.000588894s + +time increment:69830 time: 69.83 +field ' c' [explicit solve]: current solution: 9.619031e+01, current residual:5.807021e+01 +field 'mu' [explicit solve]: current solution: 4.835021e+00, current residual:2.918992e+00 +wall time: 0.000559092s + +time increment:69840 time: 69.84 +field ' c' [explicit solve]: current solution: 9.619034e+01, current residual:5.807023e+01 +field 'mu' [explicit solve]: current solution: 4.834965e+00, current residual:2.918957e+00 +wall time: 0.000632048s + +time increment:69850 time: 69.85 +field ' c' [explicit solve]: current solution: 9.619038e+01, current residual:5.807025e+01 +field 'mu' [explicit solve]: current solution: 4.834909e+00, current residual:2.918921e+00 +wall time: 0.000620842s + +time increment:69860 time: 69.86 +field ' c' [explicit solve]: current solution: 9.619041e+01, current residual:5.807027e+01 +field 'mu' [explicit solve]: current solution: 4.834853e+00, current residual:2.918886e+00 +wall time: 0.00066185s + +time increment:69870 time: 69.87 +field ' c' [explicit solve]: current solution: 9.619045e+01, current residual:5.807028e+01 +field 'mu' [explicit solve]: current solution: 4.834797e+00, current residual:2.918850e+00 +wall time: 0.000581026s + +time increment:69880 time: 69.88 +field ' c' [explicit solve]: current solution: 9.619048e+01, current residual:5.807030e+01 +field 'mu' [explicit solve]: current solution: 4.834741e+00, current residual:2.918815e+00 +wall time: 0.00056982s + +time increment:69890 time: 69.89 +field ' c' [explicit solve]: current solution: 9.619052e+01, current residual:5.807032e+01 +field 'mu' [explicit solve]: current solution: 4.834685e+00, current residual:2.918779e+00 +wall time: 0.000705004s + +time increment:69900 time: 69.9 +field ' c' [explicit solve]: current solution: 9.619056e+01, current residual:5.807034e+01 +field 'mu' [explicit solve]: current solution: 4.834629e+00, current residual:2.918744e+00 +wall time: 0.0006392s + +time increment:69910 time: 69.91 +field ' c' [explicit solve]: current solution: 9.619059e+01, current residual:5.807036e+01 +field 'mu' [explicit solve]: current solution: 4.834573e+00, current residual:2.918709e+00 +wall time: 0.000701189s + +time increment:69920 time: 69.92 +field ' c' [explicit solve]: current solution: 9.619063e+01, current residual:5.807038e+01 +field 'mu' [explicit solve]: current solution: 4.834517e+00, current residual:2.918673e+00 +wall time: 0.000686884s + +time increment:69930 time: 69.93 +field ' c' [explicit solve]: current solution: 9.619066e+01, current residual:5.807040e+01 +field 'mu' [explicit solve]: current solution: 4.834461e+00, current residual:2.918638e+00 +wall time: 0.000662088s + +time increment:69940 time: 69.94 +field ' c' [explicit solve]: current solution: 9.619070e+01, current residual:5.807041e+01 +field 'mu' [explicit solve]: current solution: 4.834406e+00, current residual:2.918602e+00 +wall time: 0.000571966s + +time increment:69950 time: 69.95 +field ' c' [explicit solve]: current solution: 9.619073e+01, current residual:5.807043e+01 +field 'mu' [explicit solve]: current solution: 4.834350e+00, current residual:2.918567e+00 +wall time: 0.000598907s + +time increment:69960 time: 69.96 +field ' c' [explicit solve]: current solution: 9.619077e+01, current residual:5.807045e+01 +field 'mu' [explicit solve]: current solution: 4.834294e+00, current residual:2.918531e+00 +wall time: 0.000786066s + +time increment:69970 time: 69.97 +field ' c' [explicit solve]: current solution: 9.619080e+01, current residual:5.807047e+01 +field 'mu' [explicit solve]: current solution: 4.834238e+00, current residual:2.918496e+00 +wall time: 0.000651836s + +time increment:69980 time: 69.98 +field ' c' [explicit solve]: current solution: 9.619084e+01, current residual:5.807049e+01 +field 'mu' [explicit solve]: current solution: 4.834182e+00, current residual:2.918461e+00 +wall time: 0.000602007s + +time increment:69990 time: 69.99 +field ' c' [explicit solve]: current solution: 9.619088e+01, current residual:5.807051e+01 +field 'mu' [explicit solve]: current solution: 4.834126e+00, current residual:2.918425e+00 +wall time: 0.000568867s + +time increment:70000 time: 70 +field ' c' [explicit solve]: current solution: 9.619091e+01, current residual:5.807052e+01 +field 'mu' [explicit solve]: current solution: 4.834070e+00, current residual:2.918390e+00 +wall time: 0.000699043s +fields: 2 +fields: 2 +fields: 2 +fields: 2 +Output written to:solution-070000.pvtu + +Energy: 234.747 +Energy Components: 112.122 122.625 0 + +time increment:70010 time: 70.01 +field ' c' [explicit solve]: current solution: 9.619095e+01, current residual:5.807054e+01 +field 'mu' [explicit solve]: current solution: 4.834014e+00, current residual:2.918354e+00 +wall time: 0.000643969s + +time increment:70020 time: 70.02 +field ' c' [explicit solve]: current solution: 9.619098e+01, current residual:5.807056e+01 +field 'mu' [explicit solve]: current solution: 4.833958e+00, current residual:2.918319e+00 +wall time: 0.000633001s + +time increment:70030 time: 70.03 +field ' c' [explicit solve]: current solution: 9.619102e+01, current residual:5.807058e+01 +field 'mu' [explicit solve]: current solution: 4.833902e+00, current residual:2.918284e+00 +wall time: 0.000664949s + +time increment:70040 time: 70.04 +field ' c' [explicit solve]: current solution: 9.619105e+01, current residual:5.807060e+01 +field 'mu' [explicit solve]: current solution: 4.833846e+00, current residual:2.918248e+00 +wall time: 0.000659943s + +time increment:70050 time: 70.05 +field ' c' [explicit solve]: current solution: 9.619109e+01, current residual:5.807062e+01 +field 'mu' [explicit solve]: current solution: 4.833790e+00, current residual:2.918213e+00 +wall time: 0.000727892s + +time increment:70060 time: 70.06 +field ' c' [explicit solve]: current solution: 9.619113e+01, current residual:5.807064e+01 +field 'mu' [explicit solve]: current solution: 4.833734e+00, current residual:2.918177e+00 +wall time: 0.000648975s + +time increment:70070 time: 70.07 +field ' c' [explicit solve]: current solution: 9.619116e+01, current residual:5.807065e+01 +field 'mu' [explicit solve]: current solution: 4.833678e+00, current residual:2.918142e+00 +wall time: 0.000730991s + +time increment:70080 time: 70.08 +field ' c' [explicit solve]: current solution: 9.619120e+01, current residual:5.807067e+01 +field 'mu' [explicit solve]: current solution: 4.833622e+00, current residual:2.918107e+00 +wall time: 0.000679016s + +time increment:70090 time: 70.09 +field ' c' [explicit solve]: current solution: 9.619123e+01, current residual:5.807069e+01 +field 'mu' [explicit solve]: current solution: 4.833566e+00, current residual:2.918071e+00 +wall time: 0.000757933s + +time increment:70100 time: 70.1 +field ' c' [explicit solve]: current solution: 9.619127e+01, current residual:5.807071e+01 +field 'mu' [explicit solve]: current solution: 4.833510e+00, current residual:2.918036e+00 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0.000670195s + +time increment:70150 time: 70.15 +field ' c' [explicit solve]: current solution: 9.619145e+01, current residual:5.807080e+01 +field 'mu' [explicit solve]: current solution: 4.833231e+00, current residual:2.917859e+00 +wall time: 0.000730991s + +time increment:70160 time: 70.16 +field ' c' [explicit solve]: current solution: 9.619148e+01, current residual:5.807082e+01 +field 'mu' [explicit solve]: current solution: 4.833175e+00, current residual:2.917824e+00 +wall time: 0.000667095s + +time increment:70170 time: 70.17 +field ' c' [explicit solve]: current solution: 9.619152e+01, current residual:5.807084e+01 +field 'mu' [explicit solve]: current solution: 4.833119e+00, current residual:2.917788e+00 +wall time: 0.000652075s + +time increment:70180 time: 70.18 +field ' c' [explicit solve]: current solution: 9.619156e+01, current residual:5.807086e+01 +field 'mu' [explicit solve]: current solution: 4.833063e+00, current residual:2.917753e+00 +wall time: 0.000648022s + +time increment:70190 time: 70.19 +field ' c' [explicit solve]: current solution: 9.619159e+01, current residual:5.807088e+01 +field 'mu' [explicit solve]: current solution: 4.833007e+00, current residual:2.917717e+00 +wall time: 0.000679016s + +time increment:70200 time: 70.2 +field ' c' [explicit solve]: current solution: 9.619163e+01, current residual:5.807090e+01 +field 'mu' [explicit solve]: current solution: 4.832951e+00, current residual:2.917682e+00 +wall time: 0.000677824s + +time increment:70210 time: 70.21 +field ' c' [explicit solve]: current solution: 9.619166e+01, current residual:5.807091e+01 +field 'mu' [explicit solve]: current solution: 4.832895e+00, current residual:2.917647e+00 +wall time: 0.000717878s + +time increment:70220 time: 70.22 +field ' c' [explicit solve]: current solution: 9.619170e+01, current residual:5.807093e+01 +field 'mu' [explicit solve]: current solution: 4.832839e+00, current residual:2.917611e+00 +wall time: 0.000704765s + +time increment:70230 time: 70.23 +field ' c' [explicit solve]: current solution: 9.619173e+01, current residual:5.807095e+01 +field 'mu' [explicit solve]: current solution: 4.832784e+00, current residual:2.917576e+00 +wall time: 0.000679016s + +time increment:70240 time: 70.24 +field ' c' [explicit solve]: current solution: 9.619177e+01, current residual:5.807097e+01 +field 'mu' [explicit solve]: current solution: 4.832728e+00, current residual:2.917541e+00 +wall time: 0.000684023s + +time increment:70250 time: 70.25 +field ' c' [explicit solve]: current solution: 9.619181e+01, current residual:5.807099e+01 +field 'mu' [explicit solve]: current solution: 4.832672e+00, current residual:2.917505e+00 +wall time: 0.000663996s + +time increment:70260 time: 70.26 +field ' c' [explicit solve]: current solution: 9.619184e+01, current residual:5.807101e+01 +field 'mu' [explicit solve]: current solution: 4.832616e+00, current residual:2.917470e+00 +wall time: 0.000730038s + +time increment:70270 time: 70.27 +field ' c' [explicit solve]: current solution: 9.619188e+01, current residual:5.807103e+01 +field 'mu' [explicit solve]: current solution: 4.832560e+00, current residual:2.917435e+00 +wall time: 0.000674009s + +time increment:70280 time: 70.28 +field ' c' [explicit solve]: current solution: 9.619191e+01, current residual:5.807105e+01 +field 'mu' [explicit solve]: current solution: 4.832504e+00, current residual:2.917399e+00 +wall time: 0.000758886s + +time increment:70290 time: 70.29 +field ' c' [explicit solve]: current solution: 9.619195e+01, current residual:5.807106e+01 +field 'mu' [explicit solve]: current solution: 4.832448e+00, current residual:2.917364e+00 +wall time: 0.000653028s + +time increment:70300 time: 70.3 +field ' c' [explicit solve]: current solution: 9.619199e+01, current residual:5.807108e+01 +field 'mu' [explicit solve]: current solution: 4.832392e+00, current residual:2.917329e+00 +wall time: 0.000603914s + +time increment:70310 time: 70.31 +field ' c' [explicit solve]: current solution: 9.619202e+01, current residual:5.807110e+01 +field 'mu' [explicit solve]: current solution: 4.832337e+00, current residual:2.917293e+00 +wall time: 0.000581026s + +time increment:70320 time: 70.32 +field ' c' [explicit solve]: current solution: 9.619206e+01, current residual:5.807112e+01 +field 'mu' [explicit solve]: current solution: 4.832281e+00, current residual:2.917258e+00 +wall time: 0.000653028s + +time increment:70330 time: 70.33 +field ' c' [explicit solve]: current solution: 9.619209e+01, current residual:5.807114e+01 +field 'mu' [explicit solve]: current solution: 4.832225e+00, current residual:2.917222e+00 +wall time: 0.000566959s + +time increment:70340 time: 70.34 +field ' c' [explicit solve]: current solution: 9.619213e+01, current residual:5.807116e+01 +field 'mu' [explicit solve]: current solution: 4.832169e+00, current residual:2.917187e+00 +wall time: 0.000674009s + +time increment:70350 time: 70.35 +field ' c' [explicit solve]: current solution: 9.619217e+01, current residual:5.807118e+01 +field 'mu' [explicit solve]: current solution: 4.832113e+00, current residual:2.917152e+00 +wall time: 0.00056386s + +time increment:70360 time: 70.36 +field ' c' [explicit solve]: current solution: 9.619220e+01, current residual:5.807120e+01 +field 'mu' [explicit solve]: current solution: 4.832057e+00, current residual:2.917116e+00 +wall time: 0.000618935s + +time increment:70370 time: 70.37 +field ' c' [explicit solve]: current solution: 9.619224e+01, current residual:5.807121e+01 +field 'mu' [explicit solve]: current solution: 4.832001e+00, current residual:2.917081e+00 +wall time: 0.000574112s + +time increment:70380 time: 70.38 +field ' c' [explicit solve]: current solution: 9.619227e+01, current residual:5.807123e+01 +field 'mu' [explicit solve]: current solution: 4.831946e+00, current residual:2.917046e+00 +wall time: 0.000565052s + +time increment:70390 time: 70.39 +field ' c' [explicit solve]: current solution: 9.619231e+01, current residual:5.807125e+01 +field 'mu' [explicit solve]: current solution: 4.831890e+00, current residual:2.917010e+00 +wall time: 0.000628948s + +time increment:70400 time: 70.4 +field ' c' [explicit solve]: current solution: 9.619235e+01, current residual:5.807127e+01 +field 'mu' [explicit solve]: current solution: 4.831834e+00, current residual:2.916975e+00 +wall time: 0.000661135s + +time increment:70410 time: 70.41 +field ' c' [explicit solve]: current solution: 9.619238e+01, current residual:5.807129e+01 +field 'mu' [explicit solve]: current solution: 4.831778e+00, current residual:2.916940e+00 +wall time: 0.000574112s + +time increment:70420 time: 70.42 +field ' c' [explicit solve]: current solution: 9.619242e+01, current residual:5.807131e+01 +field 'mu' [explicit solve]: current solution: 4.831722e+00, current residual:2.916905e+00 +wall time: 0.000622988s + +time increment:70430 time: 70.43 +field ' c' [explicit solve]: current solution: 9.619246e+01, current residual:5.807133e+01 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[explicit solve]: current solution: 4.831443e+00, current residual:2.916728e+00 +wall time: 0.00059104s + +time increment:70480 time: 70.48 +field ' c' [explicit solve]: current solution: 9.619264e+01, current residual:5.807142e+01 +field 'mu' [explicit solve]: current solution: 4.831387e+00, current residual:2.916693e+00 +wall time: 0.000580788s + +time increment:70490 time: 70.49 +field ' c' [explicit solve]: current solution: 9.619267e+01, current residual:5.807144e+01 +field 'mu' [explicit solve]: current solution: 4.831331e+00, current residual:2.916657e+00 +wall time: 0.000613928s + +time increment:70500 time: 70.5 +field ' c' [explicit solve]: current solution: 9.619271e+01, current residual:5.807146e+01 +field 'mu' [explicit solve]: current solution: 4.831276e+00, current residual:2.916622e+00 +wall time: 0.000649929s + +time increment:70510 time: 70.51 +field ' c' [explicit solve]: current solution: 9.619274e+01, current residual:5.807148e+01 +field 'mu' [explicit solve]: current solution: 4.831220e+00, current residual:2.916587e+00 +wall time: 0.000639915s + +time increment:70520 time: 70.52 +field ' c' [explicit solve]: current solution: 9.619278e+01, current residual:5.807150e+01 +field 'mu' [explicit solve]: current solution: 4.831164e+00, current residual:2.916551e+00 +wall time: 0.000631809s + +time increment:70530 time: 70.53 +field ' c' [explicit solve]: current solution: 9.619282e+01, current residual:5.807151e+01 +field 'mu' [explicit solve]: current solution: 4.831108e+00, current residual:2.916516e+00 +wall time: 0.00065589s + +time increment:70540 time: 70.54 +field ' c' [explicit solve]: current solution: 9.619285e+01, current residual:5.807153e+01 +field 'mu' [explicit solve]: current solution: 4.831052e+00, current residual:2.916481e+00 +wall time: 0.000566006s + +time increment:70550 time: 70.55 +field ' c' [explicit solve]: current solution: 9.619289e+01, current residual:5.807155e+01 +field 'mu' [explicit solve]: current solution: 4.830996e+00, current residual:2.916445e+00 +wall time: 0.000607014s + +time increment:70560 time: 70.56 +field ' c' [explicit solve]: current solution: 9.619293e+01, current residual:5.807157e+01 +field 'mu' [explicit solve]: current solution: 4.830941e+00, current residual:2.916410e+00 +wall time: 0.000569105s + +time increment:70570 time: 70.57 +field ' c' [explicit solve]: current solution: 9.619296e+01, current residual:5.807159e+01 +field 'mu' [explicit solve]: current solution: 4.830885e+00, current residual:2.916375e+00 +wall time: 0.000647068s + +time increment:70580 time: 70.58 +field ' c' [explicit solve]: current solution: 9.619300e+01, current residual:5.807161e+01 +field 'mu' [explicit solve]: current solution: 4.830829e+00, current residual:2.916340e+00 +wall time: 0.00060606s + +time increment:70590 time: 70.59 +field ' c' [explicit solve]: current solution: 9.619303e+01, current residual:5.807163e+01 +field 'mu' [explicit solve]: current solution: 4.830773e+00, current residual:2.916304e+00 +wall time: 0.00082612s + +time increment:70600 time: 70.6 +field ' c' [explicit solve]: current solution: 9.619307e+01, current residual:5.807165e+01 +field 'mu' [explicit solve]: current solution: 4.830717e+00, current residual:2.916269e+00 +wall time: 0.000649929s + +time increment:70610 time: 70.61 +field ' c' [explicit solve]: current solution: 9.619311e+01, current residual:5.807167e+01 +field 'mu' [explicit solve]: current solution: 4.830662e+00, current residual:2.916234e+00 +wall time: 0.000705957s + +time increment:70620 time: 70.62 +field ' c' [explicit solve]: current solution: 9.619314e+01, current residual:5.807168e+01 +field 'mu' [explicit solve]: current solution: 4.830606e+00, current residual:2.916198e+00 +wall time: 0.000566006s + +time increment:70630 time: 70.63 +field ' c' [explicit solve]: current solution: 9.619318e+01, current residual:5.807170e+01 +field 'mu' [explicit solve]: current solution: 4.830550e+00, current residual:2.916163e+00 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increment:70720 time: 70.72 +field ' c' [explicit solve]: current solution: 9.619351e+01, current residual:5.807187e+01 +field 'mu' [explicit solve]: current solution: 4.830048e+00, current residual:2.915846e+00 +wall time: 0.000651121s + +time increment:70730 time: 70.73 +field ' c' [explicit solve]: current solution: 9.619354e+01, current residual:5.807189e+01 +field 'mu' [explicit solve]: current solution: 4.829992e+00, current residual:2.915810e+00 +wall time: 0.000589132s + +time increment:70740 time: 70.74 +field ' c' [explicit solve]: current solution: 9.619358e+01, current residual:5.807191e+01 +field 'mu' [explicit solve]: current solution: 4.829937e+00, current residual:2.915775e+00 +wall time: 0.000700951s + +time increment:70750 time: 70.75 +field ' c' [explicit solve]: current solution: 9.619362e+01, current residual:5.807193e+01 +field 'mu' [explicit solve]: current solution: 4.829881e+00, current residual:2.915740e+00 +wall time: 0.000707865s + +time increment:70760 time: 70.76 +field ' c' [explicit solve]: current solution: 9.619365e+01, current residual:5.807195e+01 +field 'mu' [explicit solve]: current solution: 4.829825e+00, current residual:2.915705e+00 +wall time: 0.00070405s + +time increment:70770 time: 70.77 +field ' c' [explicit solve]: current solution: 9.619369e+01, current residual:5.807197e+01 +field 'mu' [explicit solve]: current solution: 4.829769e+00, current residual:2.915669e+00 +wall time: 0.00058794s + +time increment:70780 time: 70.78 +field ' c' [explicit solve]: current solution: 9.619372e+01, current residual:5.807199e+01 +field 'mu' [explicit solve]: current solution: 4.829714e+00, current residual:2.915634e+00 +wall time: 0.000597s + +time increment:70790 time: 70.79 +field ' c' [explicit solve]: current solution: 9.619376e+01, current residual:5.807201e+01 +field 'mu' [explicit solve]: current solution: 4.829658e+00, current residual:2.915599e+00 +wall time: 0.000607967s + +time increment:70800 time: 70.8 +field ' c' [explicit solve]: current solution: 9.619380e+01, current residual:5.807203e+01 +field 'mu' [explicit solve]: current solution: 4.829602e+00, current residual:2.915564e+00 +wall time: 0.000566006s + +time increment:70810 time: 70.81 +field ' c' [explicit solve]: current solution: 9.619383e+01, current residual:5.807204e+01 +field 'mu' [explicit solve]: current solution: 4.829546e+00, current residual:2.915528e+00 +wall time: 0.000689983s + +time increment:70820 time: 70.82 +field ' c' [explicit solve]: current solution: 9.619387e+01, current residual:5.807206e+01 +field 'mu' [explicit solve]: current solution: 4.829491e+00, current residual:2.915493e+00 +wall time: 0.000577211s + +time increment:70830 time: 70.83 +field ' c' [explicit solve]: current solution: 9.619391e+01, current residual:5.807208e+01 +field 'mu' [explicit solve]: current solution: 4.829435e+00, current residual:2.915458e+00 +wall time: 0.000681877s + +time increment:70840 time: 70.84 +field ' c' [explicit solve]: 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residual:5.807225e+01 +field 'mu' [explicit solve]: current solution: 4.828933e+00, current residual:2.915141e+00 +wall time: 0.000828028s + +time increment:70930 time: 70.93 +field ' c' [explicit solve]: current solution: 9.619427e+01, current residual:5.807227e+01 +field 'mu' [explicit solve]: current solution: 4.828877e+00, current residual:2.915105e+00 +wall time: 0.00067997s + +time increment:70940 time: 70.94 +field ' c' [explicit solve]: current solution: 9.619431e+01, current residual:5.807229e+01 +field 'mu' [explicit solve]: current solution: 4.828822e+00, current residual:2.915070e+00 +wall time: 0.000673056s + +time increment:70950 time: 70.95 +field ' c' [explicit solve]: current solution: 9.619435e+01, current residual:5.807231e+01 +field 'mu' [explicit solve]: current solution: 4.828766e+00, current residual:2.915035e+00 +wall time: 0.000721931s + +time increment:70960 time: 70.96 +field ' c' [explicit solve]: current solution: 9.619438e+01, current residual:5.807233e+01 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solve]: current solution: 4.828487e+00, current residual:2.914859e+00 +wall time: 0.000710964s + +time increment:71010 time: 71.01 +field ' c' [explicit solve]: current solution: 9.619457e+01, current residual:5.807243e+01 +field 'mu' [explicit solve]: current solution: 4.828432e+00, current residual:2.914824e+00 +wall time: 0.000693083s + +time increment:71020 time: 71.02 +field ' c' [explicit solve]: current solution: 9.619460e+01, current residual:5.807245e+01 +field 'mu' [explicit solve]: current solution: 4.828376e+00, current residual:2.914789e+00 +wall time: 0.000633001s + +time increment:71030 time: 71.03 +field ' c' [explicit solve]: current solution: 9.619464e+01, current residual:5.807246e+01 +field 'mu' [explicit solve]: current solution: 4.828320e+00, current residual:2.914753e+00 +wall time: 0.00070715s + +time increment:71040 time: 71.04 +field ' c' [explicit solve]: current solution: 9.619468e+01, current residual:5.807248e+01 +field 'mu' [explicit solve]: current solution: 4.828265e+00, current residual:2.914718e+00 +wall time: 0.00067091s + +time increment:71050 time: 71.05 +field ' c' [explicit solve]: current solution: 9.619471e+01, current residual:5.807250e+01 +field 'mu' [explicit solve]: current solution: 4.828209e+00, current residual:2.914683e+00 +wall time: 0.000686169s + +time increment:71060 time: 71.06 +field ' c' [explicit solve]: current solution: 9.619475e+01, current residual:5.807252e+01 +field 'mu' [explicit solve]: current solution: 4.828153e+00, current residual:2.914648e+00 +wall time: 0.000653982s + +time increment:71070 time: 71.07 +field ' c' [explicit solve]: current solution: 9.619479e+01, current residual:5.807254e+01 +field 'mu' [explicit solve]: current solution: 4.828098e+00, current residual:2.914612e+00 +wall time: 0.000684023s + +time increment:71080 time: 71.08 +field ' c' [explicit solve]: current solution: 9.619482e+01, current residual:5.807256e+01 +field 'mu' [explicit solve]: current solution: 4.828042e+00, current residual:2.914577e+00 +wall time: 0.000864983s + +time increment:71090 time: 71.09 +field ' c' [explicit solve]: current solution: 9.619486e+01, current residual:5.807258e+01 +field 'mu' [explicit solve]: current solution: 4.827986e+00, current residual:2.914542e+00 +wall time: 0.000688076s + +time increment:71100 time: 71.1 +field ' c' [explicit solve]: current solution: 9.619490e+01, current residual:5.807260e+01 +field 'mu' [explicit solve]: current solution: 4.827931e+00, current residual:2.914507e+00 +wall time: 0.000773191s + +time increment:71110 time: 71.11 +field ' c' [explicit solve]: current solution: 9.619493e+01, current residual:5.807262e+01 +field 'mu' [explicit solve]: current solution: 4.827875e+00, current residual:2.914472e+00 +wall time: 0.000748873s + +time increment:71120 time: 71.12 +field ' c' [explicit solve]: current solution: 9.619497e+01, current residual:5.807264e+01 +field 'mu' [explicit solve]: current solution: 4.827819e+00, current residual:2.914436e+00 +wall time: 0.000681877s + +time increment:71130 time: 71.13 +field ' c' [explicit solve]: current solution: 9.619501e+01, current residual:5.807266e+01 +field 'mu' [explicit solve]: current solution: 4.827764e+00, current residual:2.914401e+00 +wall time: 0.000657082s + +time increment:71140 time: 71.14 +field ' c' [explicit solve]: current solution: 9.619504e+01, current residual:5.807268e+01 +field 'mu' [explicit solve]: current solution: 4.827708e+00, current residual:2.914366e+00 +wall time: 0.00067091s + +time increment:71150 time: 71.15 +field ' c' [explicit solve]: current solution: 9.619508e+01, current residual:5.807269e+01 +field 'mu' [explicit solve]: current solution: 4.827652e+00, current residual:2.914331e+00 +wall time: 0.000699997s + +time increment:71160 time: 71.16 +field ' c' [explicit solve]: current solution: 9.619512e+01, current residual:5.807271e+01 +field 'mu' [explicit solve]: current solution: 4.827596e+00, current residual:2.914296e+00 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0.000684977s + +time increment:71210 time: 71.21 +field ' c' [explicit solve]: current solution: 9.619530e+01, current residual:5.807281e+01 +field 'mu' [explicit solve]: current solution: 4.827318e+00, current residual:2.914120e+00 +wall time: 0.000642061s + +time increment:71220 time: 71.22 +field ' c' [explicit solve]: current solution: 9.619534e+01, current residual:5.807283e+01 +field 'mu' [explicit solve]: current solution: 4.827263e+00, current residual:2.914085e+00 +wall time: 0.000684023s + +time increment:71230 time: 71.23 +field ' c' [explicit solve]: current solution: 9.619537e+01, current residual:5.807285e+01 +field 'mu' [explicit solve]: current solution: 4.827207e+00, current residual:2.914049e+00 +wall time: 0.000658035s + +time increment:71240 time: 71.24 +field ' c' [explicit solve]: current solution: 9.619541e+01, current residual:5.807287e+01 +field 'mu' [explicit solve]: current solution: 4.827151e+00, current residual:2.914014e+00 +wall time: 0.000638008s + +time 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time: 71.29 +field ' c' [explicit solve]: current solution: 9.619560e+01, current residual:5.807296e+01 +field 'mu' [explicit solve]: current solution: 4.826873e+00, current residual:2.913838e+00 +wall time: 0.000659943s + +time increment:71300 time: 71.3 +field ' c' [explicit solve]: current solution: 9.619563e+01, current residual:5.807298e+01 +field 'mu' [explicit solve]: current solution: 4.826817e+00, current residual:2.913803e+00 +wall time: 0.000663996s + +time increment:71310 time: 71.31 +field ' c' [explicit solve]: current solution: 9.619567e+01, current residual:5.807300e+01 +field 'mu' [explicit solve]: current solution: 4.826762e+00, current residual:2.913768e+00 +wall time: 0.000658035s + +time increment:71320 time: 71.32 +field ' c' [explicit solve]: current solution: 9.619571e+01, current residual:5.807302e+01 +field 'mu' [explicit solve]: current solution: 4.826706e+00, current residual:2.913733e+00 +wall time: 0.000641823s + +time increment:71330 time: 71.33 +field ' 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increment:71860 time: 71.86 +field ' c' [explicit solve]: current solution: 9.619771e+01, current residual:5.807408e+01 +field 'mu' [explicit solve]: current solution: 4.823704e+00, current residual:2.911836e+00 +wall time: 0.000694036s + +time increment:71870 time: 71.87 +field ' c' [explicit solve]: current solution: 9.619775e+01, current residual:5.807409e+01 +field 'mu' [explicit solve]: current solution: 4.823649e+00, current residual:2.911801e+00 +wall time: 0.000674009s + +time increment:71880 time: 71.88 +field ' c' [explicit solve]: current solution: 9.619779e+01, current residual:5.807411e+01 +field 'mu' [explicit solve]: current solution: 4.823593e+00, current residual:2.911766e+00 +wall time: 0.00071907s + +time increment:71890 time: 71.89 +field ' c' [explicit solve]: current solution: 9.619783e+01, current residual:5.807413e+01 +field 'mu' [explicit solve]: current solution: 4.823538e+00, current residual:2.911731e+00 +wall time: 0.00073719s + +time increment:71900 time: 71.9 +field ' c' [explicit solve]: current solution: 9.619786e+01, current residual:5.807415e+01 +field 'mu' [explicit solve]: current solution: 4.823482e+00, current residual:2.911696e+00 +wall time: 0.000732183s + +time increment:71910 time: 71.91 +field ' c' [explicit solve]: current solution: 9.619790e+01, current residual:5.807417e+01 +field 'mu' [explicit solve]: current solution: 4.823426e+00, current residual:2.911661e+00 +wall time: 0.000681162s + +time increment:71920 time: 71.92 +field ' c' [explicit solve]: current solution: 9.619794e+01, current residual:5.807419e+01 +field 'mu' [explicit solve]: current solution: 4.823371e+00, current residual:2.911626e+00 +wall time: 0.000663042s + +time increment:71930 time: 71.93 +field ' c' [explicit solve]: current solution: 9.619798e+01, current residual:5.807421e+01 +field 'mu' [explicit solve]: current solution: 4.823315e+00, current residual:2.911591e+00 +wall time: 0.000658989s + +time increment:71940 time: 71.94 +field ' c' [explicit solve]: current solution: 9.619801e+01, current residual:5.807423e+01 +field 'mu' [explicit solve]: current solution: 4.823260e+00, current residual:2.911556e+00 +wall time: 0.000680923s + +time increment:71950 time: 71.95 +field ' c' [explicit solve]: current solution: 9.619805e+01, current residual:5.807425e+01 +field 'mu' [explicit solve]: current solution: 4.823204e+00, current residual:2.911521e+00 +wall time: 0.000643015s + +time increment:71960 time: 71.96 +field ' c' [explicit solve]: current solution: 9.619809e+01, current residual:5.807427e+01 +field 'mu' [explicit solve]: current solution: 4.823149e+00, current residual:2.911486e+00 +wall time: 0.000642061s + +time increment:71970 time: 71.97 +field ' c' [explicit solve]: current solution: 9.619813e+01, current residual:5.807429e+01 +field 'mu' [explicit solve]: current solution: 4.823093e+00, current residual:2.911451e+00 +wall time: 0.000676155s + +time increment:71980 time: 71.98 +field ' c' [explicit solve]: current solution: 9.619816e+01, current residual:5.807431e+01 +field 'mu' [explicit solve]: current solution: 4.823038e+00, current residual:2.911415e+00 +wall time: 0.000635862s + +time increment:71990 time: 71.99 +field ' c' [explicit solve]: current solution: 9.619820e+01, current residual:5.807433e+01 +field 'mu' [explicit solve]: current solution: 4.822982e+00, current residual:2.911380e+00 +wall time: 0.000720978s + +time increment:72000 time: 72 +field ' c' [explicit solve]: current solution: 9.619824e+01, current residual:5.807435e+01 +field 'mu' [explicit solve]: current solution: 4.822927e+00, current residual:2.911345e+00 +wall time: 0.000684977s + +time increment:72010 time: 72.01 +field ' c' [explicit solve]: current solution: 9.619828e+01, current residual:5.807437e+01 +field 'mu' [explicit solve]: current solution: 4.822871e+00, current residual:2.911310e+00 +wall time: 0.000705957s + +time increment:72020 time: 72.02 +field ' c' [explicit solve]: current solution: 9.619832e+01, current residual:5.807439e+01 +field 'mu' [explicit solve]: current solution: 4.822816e+00, current residual:2.911275e+00 +wall time: 0.000623941s + +time increment:72030 time: 72.03 +field ' c' [explicit solve]: current solution: 9.619835e+01, current residual:5.807441e+01 +field 'mu' [explicit solve]: current solution: 4.822760e+00, current residual:2.911240e+00 +wall time: 0.000730991s + +time increment:72040 time: 72.04 +field ' c' [explicit solve]: current solution: 9.619839e+01, current residual:5.807443e+01 +field 'mu' [explicit solve]: current solution: 4.822705e+00, current residual:2.911205e+00 +wall time: 0.000689983s + +time increment:72050 time: 72.05 +field ' c' [explicit solve]: current solution: 9.619843e+01, current residual:5.807445e+01 +field 'mu' [explicit solve]: current solution: 4.822649e+00, current residual:2.911170e+00 +wall time: 0.000689983s + +time increment:72060 time: 72.06 +field ' c' [explicit solve]: current solution: 9.619847e+01, current residual:5.807447e+01 +field 'mu' [explicit solve]: current solution: 4.822594e+00, current residual:2.911135e+00 +wall time: 0.000861168s + +time increment:72070 time: 72.07 +field ' c' [explicit solve]: current solution: 9.619850e+01, current residual:5.807449e+01 +field 'mu' [explicit solve]: current solution: 4.822538e+00, current residual:2.911100e+00 +wall time: 0.000684023s + +time increment:72080 time: 72.08 +field ' c' [explicit solve]: current solution: 9.619854e+01, current residual:5.807451e+01 +field 'mu' [explicit solve]: current solution: 4.822483e+00, current residual:2.911065e+00 +wall time: 0.000647068s + +time increment:72090 time: 72.09 +field ' c' [explicit solve]: current solution: 9.619858e+01, current residual:5.807453e+01 +field 'mu' [explicit solve]: current solution: 4.822427e+00, current residual:2.911030e+00 +wall time: 0.000645161s + +time increment:72100 time: 72.1 +field ' c' [explicit solve]: current solution: 9.619862e+01, current residual:5.807455e+01 +field 'mu' [explicit solve]: current solution: 4.822372e+00, current residual:2.910995e+00 +wall time: 0.000677109s + +time increment:72110 time: 72.11 +field ' c' [explicit solve]: current solution: 9.619865e+01, current residual:5.807457e+01 +field 'mu' [explicit solve]: current solution: 4.822316e+00, current residual:2.910960e+00 +wall time: 0.000735998s + +time increment:72120 time: 72.12 +field ' c' [explicit solve]: current solution: 9.619869e+01, current residual:5.807459e+01 +field 'mu' [explicit solve]: current solution: 4.822261e+00, current residual:2.910925e+00 +wall time: 0.000571012s + +time increment:72130 time: 72.13 +field ' c' [explicit solve]: current solution: 9.619873e+01, current residual:5.807461e+01 +field 'mu' [explicit solve]: current solution: 4.822205e+00, current residual:2.910890e+00 +wall time: 0.000653982s + +time increment:72140 time: 72.14 +field ' c' [explicit solve]: current solution: 9.619877e+01, current residual:5.807463e+01 +field 'mu' [explicit solve]: current solution: 4.822150e+00, current residual:2.910855e+00 +wall time: 0.000657082s + +time increment:72150 time: 72.15 +field ' c' [explicit solve]: current solution: 9.619880e+01, current residual:5.807465e+01 +field 'mu' [explicit solve]: current solution: 4.822094e+00, current residual:2.910820e+00 +wall time: 0.000607967s + +time increment:72160 time: 72.16 +field ' c' [explicit solve]: current solution: 9.619884e+01, current residual:5.807467e+01 +field 'mu' [explicit solve]: current solution: 4.822039e+00, current residual:2.910785e+00 +wall time: 0.000577927s + +time increment:72170 time: 72.17 +field ' c' [explicit solve]: current solution: 9.619888e+01, current residual:5.807469e+01 +field 'mu' [explicit solve]: current solution: 4.821983e+00, current residual:2.910750e+00 +wall time: 0.000643015s + +time increment:72180 time: 72.18 +field ' c' [explicit solve]: current solution: 9.619892e+01, current residual:5.807471e+01 +field 'mu' [explicit solve]: current solution: 4.821928e+00, current residual:2.910714e+00 +wall time: 0.000545979s + +time increment:72190 time: 72.19 +field ' c' [explicit solve]: current solution: 9.619896e+01, current residual:5.807473e+01 +field 'mu' [explicit solve]: current solution: 4.821872e+00, current residual:2.910679e+00 +wall time: 0.000576973s + +time increment:72200 time: 72.2 +field ' c' [explicit solve]: current solution: 9.619899e+01, current residual:5.807475e+01 +field 'mu' [explicit solve]: current solution: 4.821817e+00, current residual:2.910644e+00 +wall time: 0.000569105s + +time increment:72210 time: 72.21 +field ' c' [explicit solve]: current solution: 9.619903e+01, current residual:5.807477e+01 +field 'mu' [explicit solve]: current solution: 4.821761e+00, current residual:2.910609e+00 +wall time: 0.000651121s + +time increment:72220 time: 72.22 +field ' c' [explicit solve]: current solution: 9.619907e+01, current residual:5.807479e+01 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[explicit solve]: current solution: 4.821484e+00, current residual:2.910434e+00 +wall time: 0.00069499s + +time increment:72270 time: 72.27 +field ' c' [explicit solve]: current solution: 9.619926e+01, current residual:5.807489e+01 +field 'mu' [explicit solve]: current solution: 4.821428e+00, current residual:2.910399e+00 +wall time: 0.000574827s + +time increment:72280 time: 72.28 +field ' c' [explicit solve]: current solution: 9.619930e+01, current residual:5.807491e+01 +field 'mu' [explicit solve]: current solution: 4.821373e+00, current residual:2.910364e+00 +wall time: 0.000550985s + +time increment:72290 time: 72.29 +field ' c' [explicit solve]: current solution: 9.619933e+01, current residual:5.807493e+01 +field 'mu' [explicit solve]: current solution: 4.821317e+00, current residual:2.910329e+00 +wall time: 0.000599146s + +time increment:72300 time: 72.3 +field ' c' [explicit solve]: current solution: 9.619937e+01, current residual:5.807495e+01 +field 'mu' [explicit solve]: current solution: 4.821262e+00, current residual:2.910294e+00 +wall time: 0.000672102s + +time increment:72310 time: 72.31 +field ' c' [explicit solve]: current solution: 9.619941e+01, current residual:5.807497e+01 +field 'mu' [explicit solve]: current solution: 4.821206e+00, current residual:2.910259e+00 +wall time: 0.000657797s + +time increment:72320 time: 72.32 +field ' c' [explicit solve]: current solution: 9.619945e+01, current residual:5.807499e+01 +field 'mu' [explicit solve]: current solution: 4.821151e+00, current residual:2.910224e+00 +wall time: 0.000690937s + +time increment:72330 time: 72.33 +field ' c' [explicit solve]: current solution: 9.619948e+01, current residual:5.807501e+01 +field 'mu' [explicit solve]: current solution: 4.821095e+00, current residual:2.910189e+00 +wall time: 0.000658989s + +time increment:72340 time: 72.34 +field ' c' [explicit solve]: current solution: 9.619952e+01, current residual:5.807503e+01 +field 'mu' [explicit solve]: current solution: 4.821040e+00, current residual:2.910154e+00 +wall time: 0.000677824s + +time increment:72350 time: 72.35 +field ' c' [explicit solve]: current solution: 9.619956e+01, current residual:5.807505e+01 +field 'mu' [explicit solve]: current solution: 4.820984e+00, current residual:2.910119e+00 +wall time: 0.000577211s + +time increment:72360 time: 72.36 +field ' c' [explicit solve]: current solution: 9.619960e+01, current residual:5.807507e+01 +field 'mu' [explicit solve]: current solution: 4.820929e+00, current residual:2.910084e+00 +wall time: 0.000649929s + +time increment:72370 time: 72.37 +field ' c' [explicit solve]: current solution: 9.619964e+01, current residual:5.807508e+01 +field 'mu' [explicit solve]: current solution: 4.820873e+00, current residual:2.910049e+00 +wall time: 0.000738859s + +time increment:72380 time: 72.38 +field ' c' [explicit solve]: current solution: 9.619967e+01, current residual:5.807510e+01 +field 'mu' [explicit solve]: current solution: 4.820818e+00, current residual:2.910014e+00 +wall time: 0.000695944s + +time increment:72390 time: 72.39 +field ' c' [explicit solve]: current solution: 9.619971e+01, current residual:5.807512e+01 +field 'mu' [explicit solve]: current solution: 4.820763e+00, current residual:2.909979e+00 +wall time: 0.000772953s + +time increment:72400 time: 72.4 +field ' c' [explicit solve]: current solution: 9.619975e+01, current residual:5.807514e+01 +field 'mu' [explicit solve]: current solution: 4.820707e+00, current residual:2.909944e+00 +wall time: 0.000682831s + +time increment:72410 time: 72.41 +field ' c' [explicit solve]: current solution: 9.619979e+01, current residual:5.807516e+01 +field 'mu' [explicit solve]: current solution: 4.820652e+00, current residual:2.909909e+00 +wall time: 0.000680208s + +time increment:72420 time: 72.42 +field ' c' [explicit solve]: current solution: 9.619983e+01, current residual:5.807518e+01 +field 'mu' [explicit solve]: current solution: 4.820596e+00, current residual:2.909874e+00 +wall time: 0.000691175s + +time increment:72430 time: 72.43 +field ' c' [explicit solve]: current solution: 9.619986e+01, current residual:5.807520e+01 +field 'mu' [explicit solve]: current solution: 4.820541e+00, current residual:2.909839e+00 +wall time: 0.000638962s + +time increment:72440 time: 72.44 +field ' c' [explicit solve]: current solution: 9.619990e+01, current residual:5.807522e+01 +field 'mu' [explicit solve]: current solution: 4.820485e+00, current residual:2.909804e+00 +wall time: 0.000566006s + +time increment:72450 time: 72.45 +field ' c' [explicit solve]: current solution: 9.619994e+01, current residual:5.807524e+01 +field 'mu' [explicit solve]: current solution: 4.820430e+00, current residual:2.909769e+00 +wall time: 0.000607967s + +time increment:72460 time: 72.46 +field ' c' [explicit solve]: current solution: 9.619998e+01, current residual:5.807526e+01 +field 'mu' [explicit solve]: current solution: 4.820374e+00, current residual:2.909734e+00 +wall time: 0.000649214s + +time increment:72470 time: 72.47 +field ' c' [explicit solve]: current solution: 9.620001e+01, current residual:5.807528e+01 +field 'mu' [explicit solve]: current solution: 4.820319e+00, current residual:2.909699e+00 +wall time: 0.000589132s + +time increment:72480 time: 72.48 +field ' c' [explicit solve]: current solution: 9.620005e+01, current residual:5.807530e+01 +field 'mu' [explicit solve]: current solution: 4.820263e+00, current residual:2.909664e+00 +wall time: 0.000639915s + +time increment:72490 time: 72.49 +field ' c' [explicit solve]: current solution: 9.620009e+01, current residual:5.807532e+01 +field 'mu' [explicit solve]: current solution: 4.820208e+00, current residual:2.909629e+00 +wall time: 0.000772953s + +time increment:72500 time: 72.5 +field ' c' [explicit solve]: current solution: 9.620013e+01, current residual:5.807534e+01 +field 'mu' [explicit solve]: current solution: 4.820152e+00, current residual:2.909594e+00 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0.000566006s + +time increment:72550 time: 72.55 +field ' c' [explicit solve]: current solution: 9.620032e+01, current residual:5.807544e+01 +field 'mu' [explicit solve]: current solution: 4.819875e+00, current residual:2.909419e+00 +wall time: 0.00150323s + +time increment:72560 time: 72.56 +field ' c' [explicit solve]: current solution: 9.620036e+01, current residual:5.807547e+01 +field 'mu' [explicit solve]: current solution: 4.819820e+00, current residual:2.909384e+00 +wall time: 0.0008111s + +time increment:72570 time: 72.57 +field ' c' [explicit solve]: current solution: 9.620040e+01, current residual:5.807549e+01 +field 'mu' [explicit solve]: current solution: 4.819764e+00, current residual:2.909349e+00 +wall time: 0.000717878s + +time increment:72580 time: 72.58 +field ' c' [explicit solve]: current solution: 9.620043e+01, current residual:5.807551e+01 +field 'mu' [explicit solve]: current solution: 4.819709e+00, current residual:2.909314e+00 +wall time: 0.000671864s + +time increment:72590 time: 72.59 +field ' c' [explicit solve]: current solution: 9.620047e+01, current residual:5.807553e+01 +field 'mu' [explicit solve]: current solution: 4.819653e+00, current residual:2.909279e+00 +wall time: 0.000751019s + +time increment:72600 time: 72.6 +field ' c' [explicit solve]: current solution: 9.620051e+01, current residual:5.807555e+01 +field 'mu' [explicit solve]: current solution: 4.819598e+00, current residual:2.909244e+00 +wall time: 0.000638008s + +time increment:72610 time: 72.61 +field ' c' [explicit solve]: current solution: 9.620055e+01, current residual:5.807557e+01 +field 'mu' [explicit solve]: current solution: 4.819543e+00, current residual:2.909209e+00 +wall time: 0.000639915s + +time increment:72620 time: 72.62 +field ' c' [explicit solve]: current solution: 9.620059e+01, current residual:5.807559e+01 +field 'mu' [explicit solve]: current solution: 4.819487e+00, current residual:2.909174e+00 +wall time: 0.000672102s + +time increment:72630 time: 72.63 +field ' c' [explicit solve]: current solution: 9.620062e+01, current residual:5.807561e+01 +field 'mu' [explicit solve]: current solution: 4.819432e+00, current residual:2.909139e+00 +wall time: 0.000713825s + +time increment:72640 time: 72.64 +field ' c' [explicit solve]: current solution: 9.620066e+01, current residual:5.807563e+01 +field 'mu' [explicit solve]: current solution: 4.819376e+00, current residual:2.909104e+00 +wall time: 0.000707865s + +time increment:72650 time: 72.65 +field ' c' [explicit solve]: current solution: 9.620070e+01, current residual:5.807565e+01 +field 'mu' [explicit solve]: current solution: 4.819321e+00, current residual:2.909069e+00 +wall time: 0.000602007s + +time increment:72660 time: 72.66 +field ' c' [explicit solve]: current solution: 9.620074e+01, current residual:5.807567e+01 +field 'mu' [explicit solve]: current solution: 4.819265e+00, current residual:2.909034e+00 +wall time: 0.00059104s + +time increment:72670 time: 72.67 +field ' c' [explicit solve]: current solution: 9.620078e+01, current residual:5.807569e+01 +field 'mu' [explicit solve]: current solution: 4.819210e+00, current residual:2.908999e+00 +wall time: 0.000601053s + +time increment:72680 time: 72.68 +field ' c' [explicit solve]: current solution: 9.620081e+01, current residual:5.807571e+01 +field 'mu' [explicit solve]: current solution: 4.819155e+00, current residual:2.908964e+00 +wall time: 0.0006001s + +time increment:72690 time: 72.69 +field ' c' [explicit solve]: current solution: 9.620085e+01, current residual:5.807573e+01 +field 'mu' [explicit solve]: current solution: 4.819099e+00, current residual:2.908929e+00 +wall time: 0.000565052s + +time increment:72700 time: 72.7 +field ' c' [explicit solve]: current solution: 9.620089e+01, current residual:5.807575e+01 +field 'mu' [explicit solve]: current solution: 4.819044e+00, current residual:2.908894e+00 +wall time: 0.000581026s + +time increment:72710 time: 72.71 +field ' c' [explicit solve]: current solution: 9.620093e+01, current residual:5.807577e+01 +field 'mu' [explicit solve]: current solution: 4.818988e+00, current residual:2.908859e+00 +wall time: 0.000571012s + +time increment:72720 time: 72.72 +field ' c' [explicit solve]: current solution: 9.620097e+01, current residual:5.807579e+01 +field 'mu' [explicit solve]: current solution: 4.818933e+00, current residual:2.908824e+00 +wall time: 0.000566959s + +time increment:72730 time: 72.73 +field ' c' [explicit solve]: current solution: 9.620100e+01, current residual:5.807581e+01 +field 'mu' [explicit solve]: current solution: 4.818877e+00, current residual:2.908789e+00 +wall time: 0.000564098s + +time increment:72740 time: 72.74 +field ' c' [explicit solve]: current solution: 9.620104e+01, current residual:5.807583e+01 +field 'mu' [explicit solve]: current solution: 4.818822e+00, current residual:2.908754e+00 +wall time: 0.000611067s + +time increment:72750 time: 72.75 +field ' c' [explicit solve]: current solution: 9.620108e+01, current residual:5.807585e+01 +field 'mu' [explicit solve]: current solution: 4.818767e+00, current residual:2.908719e+00 +wall time: 0.000591993s + +time increment:72760 time: 72.76 +field ' c' [explicit solve]: current solution: 9.620112e+01, current residual:5.807587e+01 +field 'mu' [explicit solve]: current solution: 4.818711e+00, current residual:2.908684e+00 +wall time: 0.000575066s + +time increment:72770 time: 72.77 +field ' c' [explicit solve]: current solution: 9.620116e+01, current residual:5.807589e+01 +field 'mu' [explicit solve]: current solution: 4.818656e+00, current residual:2.908650e+00 +wall time: 0.000568867s + +time increment:72780 time: 72.78 +field ' c' [explicit solve]: current solution: 9.620120e+01, current residual:5.807591e+01 +field 'mu' [explicit solve]: current solution: 4.818600e+00, current residual:2.908615e+00 +wall time: 0.000619173s + +time increment:72790 time: 72.79 +field ' c' [explicit solve]: current solution: 9.620123e+01, current residual:5.807593e+01 +field 'mu' [explicit solve]: current solution: 4.818545e+00, current residual:2.908580e+00 +wall time: 0.00060606s + +time increment:72800 time: 72.8 +field ' c' [explicit solve]: current solution: 9.620127e+01, current residual:5.807595e+01 +field 'mu' [explicit solve]: current solution: 4.818489e+00, current residual:2.908545e+00 +wall time: 0.000702143s + +time increment:72810 time: 72.81 +field ' c' [explicit solve]: current solution: 9.620131e+01, current residual:5.807597e+01 +field 'mu' [explicit solve]: current solution: 4.818434e+00, current residual:2.908510e+00 +wall time: 0.000603199s + +time increment:72820 time: 72.82 +field ' c' [explicit solve]: current solution: 9.620135e+01, current residual:5.807599e+01 +field 'mu' [explicit solve]: current solution: 4.818379e+00, current residual:2.908475e+00 +wall time: 0.00078702s + +time increment:72830 time: 72.83 +field ' c' [explicit solve]: current solution: 9.620139e+01, current residual:5.807601e+01 +field 'mu' [explicit solve]: current solution: 4.818323e+00, current residual:2.908440e+00 +wall time: 0.000643015s + +time increment:72840 time: 72.84 +field ' c' [explicit solve]: current solution: 9.620143e+01, current residual:5.807603e+01 +field 'mu' [explicit solve]: current solution: 4.818268e+00, current residual:2.908405e+00 +wall time: 0.000658989s + +time increment:72850 time: 72.85 +field ' c' [explicit solve]: current solution: 9.620146e+01, current residual:5.807605e+01 +field 'mu' [explicit solve]: current solution: 4.818212e+00, current residual:2.908370e+00 +wall time: 0.00069499s + +time increment:72860 time: 72.86 +field ' c' [explicit solve]: current solution: 9.620150e+01, current residual:5.807607e+01 +field 'mu' [explicit solve]: current solution: 4.818157e+00, current residual:2.908335e+00 +wall time: 0.000680923s + +time increment:72870 time: 72.87 +field ' c' [explicit solve]: current solution: 9.620154e+01, current residual:5.807609e+01 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solve]: current solution: 4.817880e+00, current residual:2.908160e+00 +wall time: 0.000732899s + +time increment:72920 time: 72.92 +field ' c' [explicit solve]: current solution: 9.620173e+01, current residual:5.807619e+01 +field 'mu' [explicit solve]: current solution: 4.817825e+00, current residual:2.908125e+00 +wall time: 0.000641108s + +time increment:72930 time: 72.93 +field ' c' [explicit solve]: current solution: 9.620177e+01, current residual:5.807621e+01 +field 'mu' [explicit solve]: current solution: 4.817769e+00, current residual:2.908090e+00 +wall time: 0.000679016s + +time increment:72940 time: 72.94 +field ' c' [explicit solve]: current solution: 9.620181e+01, current residual:5.807623e+01 +field 'mu' [explicit solve]: current solution: 4.817714e+00, current residual:2.908055e+00 +wall time: 0.000771999s + +time increment:72950 time: 72.95 +field ' c' [explicit solve]: current solution: 9.620185e+01, current residual:5.807625e+01 +field 'mu' [explicit solve]: current solution: 4.817658e+00, current residual:2.908020e+00 +wall time: 0.000649929s + +time increment:72960 time: 72.96 +field ' c' [explicit solve]: current solution: 9.620189e+01, current residual:5.807627e+01 +field 'mu' [explicit solve]: current solution: 4.817603e+00, current residual:2.907985e+00 +wall time: 0.000628948s + +time increment:72970 time: 72.97 +field ' c' [explicit solve]: current solution: 9.620192e+01, current residual:5.807629e+01 +field 'mu' [explicit solve]: current solution: 4.817548e+00, current residual:2.907950e+00 +wall time: 0.000638008s + +time increment:72980 time: 72.98 +field ' c' [explicit solve]: current solution: 9.620196e+01, current residual:5.807631e+01 +field 'mu' [explicit solve]: current solution: 4.817492e+00, current residual:2.907916e+00 +wall time: 0.000698805s + +time increment:72990 time: 72.99 +field ' c' [explicit solve]: current solution: 9.620200e+01, current residual:5.807633e+01 +field 'mu' [explicit solve]: current solution: 4.817437e+00, current residual:2.907881e+00 +wall time: 0.000632048s + +time increment:73000 time: 73 +field ' c' [explicit solve]: current solution: 9.620204e+01, current residual:5.807635e+01 +field 'mu' [explicit solve]: current solution: 4.817381e+00, current residual:2.907846e+00 +wall time: 0.000765085s + +time increment:73010 time: 73.01 +field ' c' [explicit solve]: current solution: 9.620208e+01, current residual:5.807637e+01 +field 'mu' [explicit solve]: current solution: 4.817326e+00, current residual:2.907811e+00 +wall time: 0.00067997s + +time increment:73020 time: 73.02 +field ' c' [explicit solve]: current solution: 9.620212e+01, current residual:5.807639e+01 +field 'mu' [explicit solve]: current solution: 4.817271e+00, current residual:2.907776e+00 +wall time: 0.000684977s + +time increment:73030 time: 73.03 +field ' c' [explicit solve]: current solution: 9.620215e+01, current residual:5.807641e+01 +field 'mu' [explicit solve]: current solution: 4.817215e+00, current residual:2.907741e+00 +wall time: 0.000716925s + +time increment:73040 time: 73.04 +field ' c' [explicit solve]: current solution: 9.620219e+01, current residual:5.807643e+01 +field 'mu' [explicit solve]: current solution: 4.817160e+00, current residual:2.907706e+00 +wall time: 0.000738144s + +time increment:73050 time: 73.05 +field ' c' [explicit solve]: current solution: 9.620223e+01, current residual:5.807645e+01 +field 'mu' [explicit solve]: current solution: 4.817104e+00, current residual:2.907671e+00 +wall time: 0.000714779s + +time increment:73060 time: 73.06 +field ' c' [explicit solve]: current solution: 9.620227e+01, current residual:5.807647e+01 +field 'mu' [explicit solve]: current solution: 4.817049e+00, current residual:2.907636e+00 +wall time: 0.000652075s + +time increment:73070 time: 73.07 +field ' c' [explicit solve]: current solution: 9.620231e+01, current residual:5.807650e+01 +field 'mu' [explicit solve]: current solution: 4.816994e+00, current residual:2.907601e+00 +wall time: 0.000663996s + +time increment:73080 time: 73.08 +field ' c' [explicit solve]: current solution: 9.620235e+01, current residual:5.807652e+01 +field 'mu' [explicit solve]: current solution: 4.816938e+00, current residual:2.907566e+00 +wall time: 0.000725031s + +time increment:73090 time: 73.09 +field ' c' [explicit solve]: current solution: 9.620239e+01, current residual:5.807654e+01 +field 'mu' [explicit solve]: current solution: 4.816883e+00, current residual:2.907531e+00 +wall time: 0.000749111s + +time increment:73100 time: 73.1 +field ' c' [explicit solve]: current solution: 9.620242e+01, current residual:5.807656e+01 +field 'mu' [explicit solve]: current solution: 4.816827e+00, current residual:2.907496e+00 +wall time: 0.000696898s + +time increment:73110 time: 73.11 +field ' c' [explicit solve]: current solution: 9.620246e+01, current residual:5.807658e+01 +field 'mu' [explicit solve]: current solution: 4.816772e+00, current residual:2.907461e+00 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0.000694036s + +time increment:73160 time: 73.16 +field ' c' [explicit solve]: current solution: 9.620265e+01, current residual:5.807668e+01 +field 'mu' [explicit solve]: current solution: 4.816495e+00, current residual:2.907287e+00 +wall time: 0.000735044s + +time increment:73170 time: 73.17 +field ' c' [explicit solve]: current solution: 9.620269e+01, current residual:5.807670e+01 +field 'mu' [explicit solve]: current solution: 4.816440e+00, current residual:2.907252e+00 +wall time: 0.000699043s + +time increment:73180 time: 73.18 +field ' c' [explicit solve]: current solution: 9.620273e+01, current residual:5.807672e+01 +field 'mu' [explicit solve]: current solution: 4.816384e+00, current residual:2.907217e+00 +wall time: 0.000703096s + +time increment:73190 time: 73.19 +field ' c' [explicit solve]: current solution: 9.620277e+01, current residual:5.807674e+01 +field 'mu' [explicit solve]: current solution: 4.816329e+00, current residual:2.907182e+00 +wall time: 0.000568867s + +time increment:73200 time: 73.2 +field ' c' [explicit solve]: current solution: 9.620281e+01, current residual:5.807676e+01 +field 'mu' [explicit solve]: current solution: 4.816274e+00, current residual:2.907147e+00 +wall time: 0.000595808s + +time increment:73210 time: 73.21 +field ' c' [explicit solve]: current solution: 9.620285e+01, current residual:5.807678e+01 +field 'mu' [explicit solve]: current solution: 4.816218e+00, current residual:2.907112e+00 +wall time: 0.000617981s + +time increment:73220 time: 73.22 +field ' c' [explicit solve]: current solution: 9.620289e+01, current residual:5.807680e+01 +field 'mu' [explicit solve]: current solution: 4.816163e+00, current residual:2.907077e+00 +wall time: 0.000637054s + +time increment:73230 time: 73.23 +field ' c' [explicit solve]: current solution: 9.620292e+01, current residual:5.807682e+01 +field 'mu' [explicit solve]: current solution: 4.816107e+00, current residual:2.907042e+00 +wall time: 0.000565052s + +time increment:73240 time: 73.24 +field ' c' [explicit solve]: current solution: 9.620296e+01, current residual:5.807684e+01 +field 'mu' [explicit solve]: current solution: 4.816052e+00, current residual:2.907007e+00 +wall time: 0.000747919s + +time increment:73250 time: 73.25 +field ' c' [explicit solve]: current solution: 9.620300e+01, current residual:5.807686e+01 +field 'mu' [explicit solve]: current solution: 4.815997e+00, current residual:2.906973e+00 +wall time: 0.000631809s + +time increment:73260 time: 73.26 +field ' c' [explicit solve]: current solution: 9.620304e+01, current residual:5.807688e+01 +field 'mu' [explicit solve]: current solution: 4.815941e+00, current residual:2.906938e+00 +wall time: 0.000571012s + +time increment:73270 time: 73.27 +field ' c' [explicit solve]: current solution: 9.620308e+01, current residual:5.807690e+01 +field 'mu' [explicit solve]: current solution: 4.815886e+00, current residual:2.906903e+00 +wall time: 0.000644922s + +time increment:73280 time: 73.28 +field ' c' [explicit solve]: current solution: 9.620312e+01, current residual:5.807692e+01 +field 'mu' [explicit solve]: current solution: 4.815831e+00, current residual:2.906868e+00 +wall time: 0.000632048s + +time increment:73290 time: 73.29 +field ' c' [explicit solve]: current solution: 9.620316e+01, current residual:5.807694e+01 +field 'mu' [explicit solve]: current solution: 4.815775e+00, current residual:2.906833e+00 +wall time: 0.000602007s + +time increment:73300 time: 73.3 +field ' c' [explicit solve]: current solution: 9.620319e+01, current residual:5.807696e+01 +field 'mu' [explicit solve]: current solution: 4.815720e+00, current residual:2.906798e+00 +wall time: 0.000604153s + +time increment:73310 time: 73.31 +field ' c' [explicit solve]: current solution: 9.620323e+01, current residual:5.807698e+01 +field 'mu' [explicit solve]: current solution: 4.815665e+00, current residual:2.906763e+00 +wall time: 0.000592947s + +time increment:73320 time: 73.32 +field ' c' [explicit solve]: current solution: 9.620327e+01, current residual:5.807701e+01 +field 'mu' [explicit solve]: current solution: 4.815609e+00, current residual:2.906728e+00 +wall time: 0.000597s + +time increment:73330 time: 73.33 +field ' c' [explicit solve]: current solution: 9.620331e+01, current residual:5.807703e+01 +field 'mu' [explicit solve]: current solution: 4.815554e+00, current residual:2.906693e+00 +wall time: 0.000627041s + +time increment:73340 time: 73.34 +field ' c' [explicit solve]: current solution: 9.620335e+01, current residual:5.807705e+01 +field 'mu' [explicit solve]: current solution: 4.815498e+00, current residual:2.906658e+00 +wall time: 0.000566006s + +time increment:73350 time: 73.35 +field ' c' [explicit solve]: current solution: 9.620339e+01, current residual:5.807707e+01 +field 'mu' [explicit solve]: current solution: 4.815443e+00, current residual:2.906623e+00 +wall time: 0.000655174s + +time increment:73360 time: 73.36 +field ' c' [explicit solve]: current solution: 9.620343e+01, current residual:5.807709e+01 +field 'mu' [explicit solve]: current solution: 4.815388e+00, current residual:2.906589e+00 +wall time: 0.000658035s + +time increment:73370 time: 73.37 +field ' c' [explicit solve]: current solution: 9.620347e+01, current residual:5.807711e+01 +field 'mu' [explicit solve]: current solution: 4.815332e+00, current residual:2.906554e+00 +wall time: 0.000555038s + +time increment:73380 time: 73.38 +field ' c' [explicit solve]: current solution: 9.620350e+01, current residual:5.807713e+01 +field 'mu' [explicit solve]: current solution: 4.815277e+00, current residual:2.906519e+00 +wall time: 0.000589132s + +time increment:73390 time: 73.39 +field ' c' [explicit solve]: current solution: 9.620354e+01, current residual:5.807715e+01 +field 'mu' [explicit solve]: current solution: 4.815222e+00, current residual:2.906484e+00 +wall time: 0.000623941s + +time increment:73400 time: 73.4 +field ' c' [explicit solve]: current solution: 9.620358e+01, current residual:5.807717e+01 +field 'mu' [explicit solve]: current solution: 4.815166e+00, current residual:2.906449e+00 +wall time: 0.000643015s + +time increment:73410 time: 73.41 +field ' c' [explicit solve]: current solution: 9.620362e+01, current residual:5.807719e+01 +field 'mu' [explicit solve]: current solution: 4.815111e+00, current residual:2.906414e+00 +wall time: 0.000625134s + +time increment:73420 time: 73.42 +field ' c' [explicit solve]: current solution: 9.620366e+01, current residual:5.807721e+01 +field 'mu' [explicit solve]: current solution: 4.815056e+00, current residual:2.906379e+00 +wall time: 0.00059104s + +time increment:73430 time: 73.43 +field ' c' [explicit solve]: current solution: 9.620370e+01, current residual:5.807723e+01 +field 'mu' [explicit solve]: current solution: 4.815000e+00, current residual:2.906344e+00 +wall time: 0.000633001s + +time increment:73440 time: 73.44 +field ' c' [explicit solve]: current solution: 9.620374e+01, current residual:5.807725e+01 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solve]: current solution: 4.814723e+00, current residual:2.906170e+00 +wall time: 0.000598192s + +time increment:73490 time: 73.49 +field ' c' [explicit solve]: current solution: 9.620393e+01, current residual:5.807735e+01 +field 'mu' [explicit solve]: current solution: 4.814668e+00, current residual:2.906135e+00 +wall time: 0.000627995s + +time increment:73500 time: 73.5 +field ' c' [explicit solve]: current solution: 9.620397e+01, current residual:5.807737e+01 +field 'mu' [explicit solve]: current solution: 4.814613e+00, current residual:2.906100e+00 +wall time: 0.000571966s + +time increment:73510 time: 73.51 +field ' c' [explicit solve]: current solution: 9.620401e+01, current residual:5.807740e+01 +field 'mu' [explicit solve]: current solution: 4.814557e+00, current residual:2.906065e+00 +wall time: 0.000591993s + +time increment:73520 time: 73.52 +field ' c' [explicit solve]: current solution: 9.620405e+01, current residual:5.807742e+01 +field 'mu' [explicit solve]: current solution: 4.814502e+00, current residual:2.906030e+00 +wall time: 0.000598907s + +time increment:73530 time: 73.53 +field ' c' [explicit solve]: current solution: 9.620409e+01, current residual:5.807744e+01 +field 'mu' [explicit solve]: current solution: 4.814447e+00, current residual:2.905995e+00 +wall time: 0.000620127s + +time increment:73540 time: 73.54 +field ' c' [explicit solve]: current solution: 9.620412e+01, current residual:5.807746e+01 +field 'mu' [explicit solve]: current solution: 4.814391e+00, current residual:2.905961e+00 +wall time: 0.000780821s + +time increment:73550 time: 73.55 +field ' c' [explicit solve]: current solution: 9.620416e+01, current residual:5.807748e+01 +field 'mu' [explicit solve]: current solution: 4.814336e+00, current residual:2.905926e+00 +wall time: 0.000745058s + +time increment:73560 time: 73.56 +field ' c' [explicit solve]: current solution: 9.620420e+01, current residual:5.807750e+01 +field 'mu' [explicit solve]: current solution: 4.814281e+00, current residual:2.905891e+00 +wall time: 0.00058794s + +time increment:73570 time: 73.57 +field ' c' [explicit solve]: current solution: 9.620424e+01, current residual:5.807752e+01 +field 'mu' [explicit solve]: current solution: 4.814225e+00, current residual:2.905856e+00 +wall time: 0.000674963s + +time increment:73580 time: 73.58 +field ' c' [explicit solve]: current solution: 9.620428e+01, current residual:5.807754e+01 +field 'mu' [explicit solve]: current solution: 4.814170e+00, current residual:2.905821e+00 +wall time: 0.000576019s + +time increment:73590 time: 73.59 +field ' c' [explicit solve]: current solution: 9.620432e+01, current residual:5.807756e+01 +field 'mu' [explicit solve]: current solution: 4.814114e+00, current residual:2.905786e+00 +wall time: 0.00058794s + +time increment:73600 time: 73.6 +field ' c' [explicit solve]: current solution: 9.620436e+01, current residual:5.807758e+01 +field 'mu' [explicit solve]: current solution: 4.814059e+00, current residual:2.905751e+00 +wall time: 0.0006001s + +time increment:73610 time: 73.61 +field ' c' [explicit solve]: current solution: 9.620440e+01, current residual:5.807760e+01 +field 'mu' [explicit solve]: current solution: 4.814004e+00, current residual:2.905716e+00 +wall time: 0.000649929s + +time increment:73620 time: 73.62 +field ' c' [explicit solve]: current solution: 9.620444e+01, current residual:5.807762e+01 +field 'mu' [explicit solve]: current solution: 4.813948e+00, current residual:2.905681e+00 +wall time: 0.000566959s + +time increment:73630 time: 73.63 +field ' c' [explicit solve]: current solution: 9.620447e+01, current residual:5.807764e+01 +field 'mu' [explicit solve]: current solution: 4.813893e+00, current residual:2.905647e+00 +wall time: 0.000569105s + +time increment:73640 time: 73.64 +field ' c' [explicit solve]: current solution: 9.620451e+01, current residual:5.807766e+01 +field 'mu' [explicit solve]: current solution: 4.813838e+00, current residual:2.905612e+00 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0.000648022s + +time increment:73690 time: 73.69 +field ' c' [explicit solve]: current solution: 9.620471e+01, current residual:5.807777e+01 +field 'mu' [explicit solve]: current solution: 4.813561e+00, current residual:2.905437e+00 +wall time: 0.000622034s + +time increment:73700 time: 73.7 +field ' c' [explicit solve]: current solution: 9.620475e+01, current residual:5.807779e+01 +field 'mu' [explicit solve]: current solution: 4.813506e+00, current residual:2.905402e+00 +wall time: 0.000709057s + +time increment:73710 time: 73.71 +field ' c' [explicit solve]: current solution: 9.620479e+01, current residual:5.807781e+01 +field 'mu' [explicit solve]: current solution: 4.813450e+00, current residual:2.905368e+00 +wall time: 0.000562191s + +time increment:73720 time: 73.72 +field ' c' [explicit solve]: current solution: 9.620483e+01, current residual:5.807783e+01 +field 'mu' [explicit solve]: current solution: 4.813395e+00, current residual:2.905333e+00 +wall time: 0.000679016s + +time increment:73730 time: 73.73 +field ' c' [explicit solve]: current solution: 9.620486e+01, current residual:5.807785e+01 +field 'mu' [explicit solve]: current solution: 4.813340e+00, current residual:2.905298e+00 +wall time: 0.000612974s + +time increment:73740 time: 73.74 +field ' c' [explicit solve]: current solution: 9.620490e+01, current residual:5.807787e+01 +field 'mu' [explicit solve]: current solution: 4.813284e+00, current residual:2.905263e+00 +wall time: 0.000555038s + +time increment:73750 time: 73.75 +field ' c' [explicit solve]: current solution: 9.620494e+01, current residual:5.807789e+01 +field 'mu' [explicit solve]: current solution: 4.813229e+00, current residual:2.905228e+00 +wall time: 0.00062108s + +time increment:73760 time: 73.76 +field ' c' [explicit solve]: current solution: 9.620498e+01, current residual:5.807791e+01 +field 'mu' [explicit solve]: current solution: 4.813174e+00, current residual:2.905193e+00 +wall time: 0.000616074s + +time increment:73770 time: 73.77 +field ' c' [explicit solve]: current solution: 9.620502e+01, current residual:5.807793e+01 +field 'mu' [explicit solve]: current solution: 4.813118e+00, current residual:2.905158e+00 +wall time: 0.000586033s + +time increment:73780 time: 73.78 +field ' c' [explicit solve]: current solution: 9.620506e+01, current residual:5.807795e+01 +field 'mu' [explicit solve]: current solution: 4.813063e+00, current residual:2.905124e+00 +wall time: 0.000651121s + +time increment:73790 time: 73.79 +field ' c' [explicit solve]: current solution: 9.620510e+01, current residual:5.807797e+01 +field 'mu' [explicit solve]: current solution: 4.813008e+00, current residual:2.905089e+00 +wall time: 0.000588894s + +time increment:73800 time: 73.8 +field ' c' [explicit solve]: current solution: 9.620514e+01, current residual:5.807799e+01 +field 'mu' [explicit solve]: current solution: 4.812952e+00, current residual:2.905054e+00 +wall time: 0.000693083s + +time increment:73810 time: 73.81 +field ' c' [explicit solve]: current solution: 9.620518e+01, current residual:5.807801e+01 +field 'mu' [explicit solve]: current solution: 4.812897e+00, current residual:2.905019e+00 +wall time: 0.000741959s + +time increment:73820 time: 73.82 +field ' c' [explicit solve]: current solution: 9.620522e+01, current residual:5.807804e+01 +field 'mu' [explicit solve]: current solution: 4.812842e+00, current residual:2.904984e+00 +wall time: 0.000656843s + +time increment:73830 time: 73.83 +field ' c' [explicit solve]: current solution: 9.620525e+01, current residual:5.807806e+01 +field 'mu' [explicit solve]: current solution: 4.812786e+00, current residual:2.904949e+00 +wall time: 0.000617027s + +time increment:73840 time: 73.84 +field ' c' [explicit solve]: current solution: 9.620529e+01, current residual:5.807808e+01 +field 'mu' [explicit solve]: current solution: 4.812731e+00, current residual:2.904914e+00 +wall time: 0.00058794s + +time increment:73850 time: 73.85 +field ' c' [explicit solve]: current solution: 9.620533e+01, current residual:5.807810e+01 +field 'mu' [explicit solve]: current solution: 4.812676e+00, current residual:2.904880e+00 +wall time: 0.000711918s + +time increment:73860 time: 73.86 +field ' c' [explicit solve]: current solution: 9.620537e+01, current residual:5.807812e+01 +field 'mu' [explicit solve]: current solution: 4.812620e+00, current residual:2.904845e+00 +wall time: 0.000712156s + +time increment:73870 time: 73.87 +field ' c' [explicit solve]: current solution: 9.620541e+01, current residual:5.807814e+01 +field 'mu' [explicit solve]: current solution: 4.812565e+00, current residual:2.904810e+00 +wall time: 0.000679016s + +time increment:73880 time: 73.88 +field ' c' [explicit solve]: current solution: 9.620545e+01, current residual:5.807816e+01 +field 'mu' [explicit solve]: current solution: 4.812510e+00, current residual:2.904775e+00 +wall time: 0.000725985s + +time increment:73890 time: 73.89 +field ' c' [explicit solve]: current solution: 9.620549e+01, current residual:5.807818e+01 +field 'mu' [explicit solve]: current solution: 4.812454e+00, current residual:2.904740e+00 +wall time: 0.000727177s + +time increment:73900 time: 73.9 +field ' c' [explicit solve]: current solution: 9.620553e+01, current residual:5.807820e+01 +field 'mu' [explicit solve]: current solution: 4.812399e+00, current residual:2.904705e+00 +wall time: 0.000643969s + +time increment:73910 time: 73.91 +field ' c' [explicit solve]: current solution: 9.620557e+01, current residual:5.807822e+01 +field 'mu' [explicit solve]: current solution: 4.812344e+00, current residual:2.904670e+00 +wall time: 0.000716925s + +time increment:73920 time: 73.92 +field ' c' [explicit solve]: current solution: 9.620561e+01, current residual:5.807824e+01 +field 'mu' [explicit solve]: current solution: 4.812288e+00, current residual:2.904636e+00 +wall time: 0.000628948s + +time increment:73930 time: 73.93 +field ' c' [explicit solve]: current solution: 9.620565e+01, current residual:5.807826e+01 +field 'mu' [explicit solve]: current solution: 4.812233e+00, current residual:2.904601e+00 +wall time: 0.000787973s + +time increment:73940 time: 73.94 +field ' c' [explicit solve]: current solution: 9.620569e+01, current residual:5.807828e+01 +field 'mu' [explicit solve]: current solution: 4.812178e+00, current residual:2.904566e+00 +wall time: 0.00073719s + +time increment:73950 time: 73.95 +field ' c' [explicit solve]: current solution: 9.620572e+01, current residual:5.807831e+01 +field 'mu' [explicit solve]: current solution: 4.812122e+00, current residual:2.904531e+00 +wall time: 0.00069499s + +time increment:73960 time: 73.96 +field ' c' [explicit solve]: current solution: 9.620576e+01, current residual:5.807833e+01 +field 'mu' [explicit solve]: current solution: 4.812067e+00, current residual:2.904496e+00 +wall time: 0.000637054s + +time increment:73970 time: 73.97 +field ' c' [explicit solve]: current solution: 9.620580e+01, current residual:5.807835e+01 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solve]: current solution: 4.811790e+00, current residual:2.904322e+00 +wall time: 0.000645876s + +time increment:74020 time: 74.02 +field ' c' [explicit solve]: current solution: 9.620600e+01, current residual:5.807845e+01 +field 'mu' [explicit solve]: current solution: 4.811735e+00, current residual:2.904287e+00 +wall time: 0.000691175s + +time increment:74030 time: 74.03 +field ' c' [explicit solve]: current solution: 9.620604e+01, current residual:5.807847e+01 +field 'mu' [explicit solve]: current solution: 4.811680e+00, current residual:2.904252e+00 +wall time: 0.000630856s + +time increment:74040 time: 74.04 +field ' c' [explicit solve]: current solution: 9.620608e+01, current residual:5.807849e+01 +field 'mu' [explicit solve]: current solution: 4.811624e+00, current residual:2.904217e+00 +wall time: 0.000827074s + +time increment:74050 time: 74.05 +field ' c' [explicit solve]: current solution: 9.620612e+01, current residual:5.807851e+01 +field 'mu' [explicit solve]: current solution: 4.811569e+00, current residual:2.904183e+00 +wall time: 0.000692844s + +time increment:74060 time: 74.06 +field ' c' [explicit solve]: current solution: 9.620616e+01, current residual:5.807853e+01 +field 'mu' [explicit solve]: current solution: 4.811514e+00, current residual:2.904148e+00 +wall time: 0.000699997s + +time increment:74070 time: 74.07 +field ' c' [explicit solve]: current solution: 9.620619e+01, current residual:5.807856e+01 +field 'mu' [explicit solve]: current solution: 4.811458e+00, current residual:2.904113e+00 +wall time: 0.00066185s + +time increment:74080 time: 74.08 +field ' c' [explicit solve]: current solution: 9.620623e+01, current residual:5.807858e+01 +field 'mu' [explicit solve]: current solution: 4.811403e+00, current residual:2.904078e+00 +wall time: 0.000691891s + +time increment:74090 time: 74.09 +field ' c' [explicit solve]: current solution: 9.620627e+01, current residual:5.807860e+01 +field 'mu' [explicit solve]: current solution: 4.811348e+00, current residual:2.904043e+00 +wall time: 0.00068903s + +time increment:74100 time: 74.1 +field ' c' [explicit solve]: current solution: 9.620631e+01, current residual:5.807862e+01 +field 'mu' [explicit solve]: current solution: 4.811292e+00, current residual:2.904008e+00 +wall time: 0.000690937s + +time increment:74110 time: 74.11 +field ' c' [explicit solve]: current solution: 9.620635e+01, current residual:5.807864e+01 +field 'mu' [explicit solve]: current solution: 4.811237e+00, current residual:2.903974e+00 +wall time: 0.000648975s + +time increment:74120 time: 74.12 +field ' c' [explicit solve]: current solution: 9.620639e+01, current residual:5.807866e+01 +field 'mu' [explicit solve]: current solution: 4.811182e+00, current residual:2.903939e+00 +wall time: 0.000727892s + +time increment:74130 time: 74.13 +field ' c' [explicit solve]: current solution: 9.620643e+01, current residual:5.807868e+01 +field 'mu' [explicit solve]: current solution: 4.811126e+00, current residual:2.903904e+00 +wall time: 0.000643969s + +time increment:74140 time: 74.14 +field ' c' [explicit solve]: current solution: 9.620647e+01, current residual:5.807870e+01 +field 'mu' [explicit solve]: current solution: 4.811071e+00, current residual:2.903869e+00 +wall time: 0.000660896s + +time increment:74150 time: 74.15 +field ' c' [explicit solve]: current solution: 9.620651e+01, current residual:5.807872e+01 +field 'mu' [explicit solve]: current solution: 4.811016e+00, current residual:2.903834e+00 +wall time: 0.000721931s + +time increment:74160 time: 74.16 +field ' c' [explicit solve]: current solution: 9.620655e+01, current residual:5.807874e+01 +field 'mu' [explicit solve]: current solution: 4.810960e+00, current residual:2.903799e+00 +wall time: 0.00071907s + +time increment:74170 time: 74.17 +field ' c' [explicit solve]: current solution: 9.620659e+01, current residual:5.807876e+01 +field 'mu' [explicit solve]: current solution: 4.810905e+00, current residual:2.903765e+00 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0.000636101s + +time increment:74220 time: 74.22 +field ' c' [explicit solve]: current solution: 9.620678e+01, current residual:5.807887e+01 +field 'mu' [explicit solve]: current solution: 4.810629e+00, current residual:2.903590e+00 +wall time: 0.000643015s + +time increment:74230 time: 74.23 +field ' c' [explicit solve]: current solution: 9.620682e+01, current residual:5.807889e+01 +field 'mu' [explicit solve]: current solution: 4.810573e+00, current residual:2.903556e+00 +wall time: 0.000643969s + +time increment:74240 time: 74.24 +field ' c' [explicit solve]: current solution: 9.620686e+01, current residual:5.807891e+01 +field 'mu' [explicit solve]: current solution: 4.810518e+00, current residual:2.903521e+00 +wall time: 0.000653982s + +time increment:74250 time: 74.25 +field ' c' [explicit solve]: current solution: 9.620690e+01, current residual:5.807893e+01 +field 'mu' [explicit solve]: current solution: 4.810463e+00, current residual:2.903486e+00 +wall time: 0.000661135s + +time increment:74260 time: 74.26 +field ' c' [explicit solve]: current solution: 9.620694e+01, current residual:5.807895e+01 +field 'mu' [explicit solve]: current solution: 4.810407e+00, current residual:2.903451e+00 +wall time: 0.000574827s + +time increment:74270 time: 74.27 +field ' c' [explicit solve]: current solution: 9.620698e+01, current residual:5.807897e+01 +field 'mu' [explicit solve]: current solution: 4.810352e+00, current residual:2.903416e+00 +wall time: 0.000561953s + +time increment:74280 time: 74.28 +field ' c' [explicit solve]: current solution: 9.620702e+01, current residual:5.807899e+01 +field 'mu' [explicit solve]: current solution: 4.810297e+00, current residual:2.903381e+00 +wall time: 0.000695944s + +time increment:74290 time: 74.29 +field ' c' [explicit solve]: current solution: 9.620706e+01, current residual:5.807902e+01 +field 'mu' [explicit solve]: current solution: 4.810241e+00, current residual:2.903347e+00 +wall time: 0.000677109s + +time increment:74300 time: 74.3 +field ' c' [explicit solve]: current solution: 9.620710e+01, current residual:5.807904e+01 +field 'mu' [explicit solve]: current solution: 4.810186e+00, current residual:2.903312e+00 +wall time: 0.000681877s + +time increment:74310 time: 74.31 +field ' c' [explicit solve]: current solution: 9.620714e+01, current residual:5.807906e+01 +field 'mu' [explicit solve]: current solution: 4.810131e+00, current residual:2.903277e+00 +wall time: 0.000675917s + +time increment:74320 time: 74.32 +field ' c' [explicit solve]: current solution: 9.620718e+01, current residual:5.807908e+01 +field 'mu' [explicit solve]: current solution: 4.810075e+00, current residual:2.903242e+00 +wall time: 0.000585079s + +time increment:74330 time: 74.33 +field ' c' [explicit solve]: current solution: 9.620722e+01, current residual:5.807910e+01 +field 'mu' [explicit solve]: current solution: 4.810020e+00, current residual:2.903207e+00 +wall time: 0.000567913s + +time increment:74340 time: 74.34 +field ' c' [explicit solve]: current solution: 9.620726e+01, current residual:5.807912e+01 +field 'mu' [explicit solve]: current solution: 4.809965e+00, current residual:2.903172e+00 +wall time: 0.000622034s + +time increment:74350 time: 74.35 +field ' c' [explicit solve]: current solution: 9.620730e+01, current residual:5.807914e+01 +field 'mu' [explicit solve]: current solution: 4.809909e+00, current residual:2.903138e+00 +wall time: 0.000576019s + +time increment:74360 time: 74.36 +field ' c' [explicit solve]: current solution: 9.620734e+01, current residual:5.807916e+01 +field 'mu' [explicit solve]: current solution: 4.809854e+00, current residual:2.903103e+00 +wall time: 0.000677109s + +time increment:74370 time: 74.37 +field ' c' [explicit solve]: current solution: 9.620738e+01, current residual:5.807918e+01 +field 'mu' [explicit solve]: current solution: 4.809799e+00, current residual:2.903068e+00 +wall time: 0.000731945s + +time increment:74380 time: 74.38 +field ' c' [explicit solve]: current solution: 9.620742e+01, current residual:5.807920e+01 +field 'mu' [explicit solve]: current solution: 4.809743e+00, current residual:2.903033e+00 +wall time: 0.000595093s + +time increment:74390 time: 74.39 +field ' c' [explicit solve]: current solution: 9.620745e+01, current residual:5.807923e+01 +field 'mu' [explicit solve]: current solution: 4.809688e+00, current residual:2.902998e+00 +wall time: 0.000590086s + +time increment:74400 time: 74.4 +field ' c' [explicit solve]: current solution: 9.620749e+01, current residual:5.807925e+01 +field 'mu' [explicit solve]: current solution: 4.809633e+00, current residual:2.902964e+00 +wall time: 0.000613928s + +time increment:74410 time: 74.41 +field ' c' [explicit solve]: current solution: 9.620753e+01, current residual:5.807927e+01 +field 'mu' [explicit solve]: current solution: 4.809578e+00, current residual:2.902929e+00 +wall time: 0.000607014s + +time increment:74420 time: 74.42 +field ' c' [explicit solve]: current solution: 9.620757e+01, current residual:5.807929e+01 +field 'mu' [explicit solve]: current solution: 4.809522e+00, current residual:2.902894e+00 +wall time: 0.000659943s + +time increment:74430 time: 74.43 +field ' c' [explicit solve]: current solution: 9.620761e+01, current residual:5.807931e+01 +field 'mu' [explicit solve]: current solution: 4.809467e+00, current residual:2.902859e+00 +wall time: 0.000609159s + +time increment:74440 time: 74.44 +field ' c' [explicit solve]: current solution: 9.620765e+01, current residual:5.807933e+01 +field 'mu' [explicit solve]: current solution: 4.809412e+00, current residual:2.902824e+00 +wall time: 0.000569105s + +time increment:74450 time: 74.45 +field ' c' [explicit solve]: current solution: 9.620769e+01, current residual:5.807935e+01 +field 'mu' [explicit solve]: current solution: 4.809356e+00, current residual:2.902790e+00 +wall time: 0.000633001s + +time increment:74460 time: 74.46 +field ' c' [explicit solve]: current solution: 9.620773e+01, current residual:5.807937e+01 +field 'mu' [explicit solve]: current solution: 4.809301e+00, current residual:2.902755e+00 +wall time: 0.000723124s + +time increment:74470 time: 74.47 +field ' c' [explicit solve]: current solution: 9.620777e+01, current residual:5.807939e+01 +field 'mu' [explicit solve]: current solution: 4.809246e+00, current residual:2.902720e+00 +wall time: 0.00058794s + +time increment:74480 time: 74.48 +field ' c' [explicit solve]: current solution: 9.620781e+01, current residual:5.807942e+01 +field 'mu' [explicit solve]: current solution: 4.809190e+00, current residual:2.902685e+00 +wall time: 0.000646114s + +time increment:74490 time: 74.49 +field ' c' [explicit solve]: current solution: 9.620785e+01, current residual:5.807944e+01 +field 'mu' [explicit solve]: current solution: 4.809135e+00, current residual:2.902650e+00 +wall time: 0.000565052s + +time increment:74500 time: 74.5 +field ' c' [explicit solve]: current solution: 9.620789e+01, current residual:5.807946e+01 +field 'mu' [explicit solve]: current solution: 4.809080e+00, current residual:2.902615e+00 +wall time: 0.000590086s + +time increment:74510 time: 74.51 +field ' c' [explicit solve]: current solution: 9.620793e+01, current residual:5.807948e+01 +field 'mu' [explicit solve]: current solution: 4.809024e+00, current residual:2.902581e+00 +wall time: 0.000660896s + +time increment:74520 time: 74.52 +field ' c' [explicit solve]: current solution: 9.620797e+01, current residual:5.807950e+01 +field 'mu' [explicit solve]: current solution: 4.808969e+00, current residual:2.902546e+00 +wall time: 0.000712156s + +time increment:74530 time: 74.53 +field ' c' [explicit solve]: current solution: 9.620801e+01, current residual:5.807952e+01 +field 'mu' [explicit solve]: current solution: 4.808914e+00, current residual:2.902511e+00 +wall time: 0.000611067s + +time increment:74540 time: 74.54 +field ' c' [explicit solve]: current solution: 9.620805e+01, current residual:5.807954e+01 +field 'mu' [explicit solve]: current solution: 4.808858e+00, current residual:2.902476e+00 +wall time: 0.000617981s + +time increment:74550 time: 74.55 +field ' c' [explicit solve]: current solution: 9.620809e+01, current residual:5.807956e+01 +field 'mu' [explicit solve]: current solution: 4.808803e+00, current residual:2.902441e+00 +wall time: 0.000767946s + +time increment:74560 time: 74.56 +field ' c' [explicit solve]: current solution: 9.620813e+01, current residual:5.807958e+01 +field 'mu' [explicit solve]: current solution: 4.808748e+00, current residual:2.902407e+00 +wall time: 0.000639915s + +time increment:74570 time: 74.57 +field ' c' [explicit solve]: current solution: 9.620817e+01, current residual:5.807961e+01 +field 'mu' [explicit solve]: current solution: 4.808693e+00, current residual:2.902372e+00 +wall time: 0.00069809s + +time increment:74580 time: 74.58 +field ' c' [explicit solve]: current solution: 9.620821e+01, current residual:5.807963e+01 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solve]: current solution: 4.808416e+00, current residual:2.902198e+00 +wall time: 0.000629187s + +time increment:74630 time: 74.63 +field ' c' [explicit solve]: current solution: 9.620840e+01, current residual:5.807973e+01 +field 'mu' [explicit solve]: current solution: 4.808361e+00, current residual:2.902163e+00 +wall time: 0.000662804s + +time increment:74640 time: 74.64 +field ' c' [explicit solve]: current solution: 9.620844e+01, current residual:5.807975e+01 +field 'mu' [explicit solve]: current solution: 4.808305e+00, current residual:2.902128e+00 +wall time: 0.000699997s + +time increment:74650 time: 74.65 +field ' c' [explicit solve]: current solution: 9.620848e+01, current residual:5.807977e+01 +field 'mu' [explicit solve]: current solution: 4.808250e+00, current residual:2.902093e+00 +wall time: 0.000622034s + +time increment:74660 time: 74.66 +field ' c' [explicit solve]: current solution: 9.620852e+01, current residual:5.807980e+01 +field 'mu' [explicit solve]: current solution: 4.808195e+00, current residual:2.902059e+00 +wall time: 0.000562906s + +time increment:74670 time: 74.67 +field ' c' [explicit solve]: current solution: 9.620856e+01, current residual:5.807982e+01 +field 'mu' [explicit solve]: current solution: 4.808139e+00, current residual:2.902024e+00 +wall time: 0.000592947s + +time increment:74680 time: 74.68 +field ' c' [explicit solve]: current solution: 9.620860e+01, current residual:5.807984e+01 +field 'mu' [explicit solve]: current solution: 4.808084e+00, current residual:2.901989e+00 +wall time: 0.000586987s + +time increment:74690 time: 74.69 +field ' c' [explicit solve]: current solution: 9.620864e+01, current residual:5.807986e+01 +field 'mu' [explicit solve]: current solution: 4.808029e+00, current residual:2.901954e+00 +wall time: 0.000563145s + +time increment:74700 time: 74.7 +field ' c' [explicit solve]: current solution: 9.620868e+01, current residual:5.807988e+01 +field 'mu' [explicit solve]: current solution: 4.807973e+00, current residual:2.901919e+00 +wall time: 0.000659943s + +time increment:74710 time: 74.71 +field ' c' [explicit solve]: current solution: 9.620872e+01, current residual:5.807990e+01 +field 'mu' [explicit solve]: current solution: 4.807918e+00, current residual:2.901885e+00 +wall time: 0.000566959s + +time increment:74720 time: 74.72 +field ' c' [explicit solve]: current solution: 9.620876e+01, current residual:5.807992e+01 +field 'mu' [explicit solve]: current solution: 4.807863e+00, current residual:2.901850e+00 +wall time: 0.000591993s + +time increment:74730 time: 74.73 +field ' c' [explicit solve]: current solution: 9.620880e+01, current residual:5.807994e+01 +field 'mu' [explicit solve]: current solution: 4.807808e+00, current residual:2.901815e+00 +wall time: 0.000565052s + +time increment:74740 time: 74.74 +field ' c' [explicit solve]: current solution: 9.620884e+01, current residual:5.807996e+01 +field 'mu' [explicit solve]: current solution: 4.807752e+00, current residual:2.901780e+00 +wall time: 0.000642061s + +time increment:74750 time: 74.75 +field ' c' [explicit solve]: current solution: 9.620888e+01, current residual:5.807999e+01 +field 'mu' [explicit solve]: current solution: 4.807697e+00, current residual:2.901745e+00 +wall time: 0.00063014s + +time increment:74760 time: 74.76 +field ' c' [explicit solve]: current solution: 9.620892e+01, current residual:5.808001e+01 +field 'mu' [explicit solve]: current solution: 4.807642e+00, current residual:2.901711e+00 +wall time: 0.000607967s + +time increment:74770 time: 74.77 +field ' c' [explicit solve]: current solution: 9.620896e+01, current residual:5.808003e+01 +field 'mu' [explicit solve]: current solution: 4.807586e+00, current residual:2.901676e+00 +wall time: 0.000705957s + +time increment:74780 time: 74.78 +field ' c' [explicit solve]: current solution: 9.620900e+01, current residual:5.808005e+01 +field 'mu' [explicit solve]: current solution: 4.807531e+00, current residual:2.901641e+00 +wall time: 0.000562906s + +time increment:74790 time: 74.79 +field ' c' [explicit solve]: current solution: 9.620904e+01, current residual:5.808007e+01 +field 'mu' [explicit solve]: current solution: 4.807476e+00, current residual:2.901606e+00 +wall time: 0.00060606s + +time increment:74800 time: 74.8 +field ' c' [explicit solve]: current solution: 9.620908e+01, current residual:5.808009e+01 +field 'mu' [explicit solve]: current solution: 4.807420e+00, current residual:2.901571e+00 +wall time: 0.000674963s + +time increment:74810 time: 74.81 +field ' c' [explicit solve]: current solution: 9.620912e+01, current residual:5.808011e+01 +field 'mu' [explicit solve]: current solution: 4.807365e+00, current residual:2.901537e+00 +wall time: 0.000675201s + +time increment:74820 time: 74.82 +field ' c' [explicit solve]: current solution: 9.620916e+01, current residual:5.808013e+01 +field 'mu' [explicit solve]: current solution: 4.807310e+00, current residual:2.901502e+00 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0.000625849s + +time increment:74870 time: 74.87 +field ' c' [explicit solve]: current solution: 9.620936e+01, current residual:5.808024e+01 +field 'mu' [explicit solve]: current solution: 4.807033e+00, current residual:2.901328e+00 +wall time: 0.000616074s + +time increment:74880 time: 74.88 +field ' c' [explicit solve]: current solution: 9.620940e+01, current residual:5.808026e+01 +field 'mu' [explicit solve]: current solution: 4.806978e+00, current residual:2.901293e+00 +wall time: 0.000638008s + +time increment:74890 time: 74.89 +field ' c' [explicit solve]: current solution: 9.620944e+01, current residual:5.808028e+01 +field 'mu' [explicit solve]: current solution: 4.806923e+00, current residual:2.901258e+00 +wall time: 0.000555992s + +time increment:74900 time: 74.9 +field ' c' [explicit solve]: current solution: 9.620948e+01, current residual:5.808030e+01 +field 'mu' [explicit solve]: current solution: 4.806867e+00, current residual:2.901224e+00 +wall time: 0.000684977s + +time increment:74910 time: 74.91 +field ' c' [explicit solve]: current solution: 9.620952e+01, current residual:5.808033e+01 +field 'mu' [explicit solve]: current solution: 4.806812e+00, current residual:2.901189e+00 +wall time: 0.000589132s + +time increment:74920 time: 74.92 +field ' c' [explicit solve]: current solution: 9.620956e+01, current residual:5.808035e+01 +field 'mu' [explicit solve]: current solution: 4.806757e+00, current residual:2.901154e+00 +wall time: 0.00065589s + +time increment:74930 time: 74.93 +field ' c' [explicit solve]: current solution: 9.620960e+01, current residual:5.808037e+01 +field 'mu' [explicit solve]: current solution: 4.806701e+00, current residual:2.901119e+00 +wall time: 0.000680208s + +time increment:74940 time: 74.94 +field ' c' [explicit solve]: current solution: 9.620964e+01, current residual:5.808039e+01 +field 'mu' [explicit solve]: current solution: 4.806646e+00, current residual:2.901084e+00 +wall time: 0.000641823s + +time increment:74950 time: 74.95 +field ' c' [explicit solve]: current solution: 9.620968e+01, current residual:5.808041e+01 +field 'mu' [explicit solve]: current solution: 4.806591e+00, current residual:2.901050e+00 +wall time: 0.000696898s + +time increment:74960 time: 74.96 +field ' c' [explicit solve]: current solution: 9.620972e+01, current residual:5.808043e+01 +field 'mu' [explicit solve]: current solution: 4.806535e+00, current residual:2.901015e+00 +wall time: 0.00068593s + +time increment:74970 time: 74.97 +field ' c' [explicit solve]: current solution: 9.620976e+01, current residual:5.808045e+01 +field 'mu' [explicit solve]: current solution: 4.806480e+00, current residual:2.900980e+00 +wall time: 0.000696898s + +time increment:74980 time: 74.98 +field ' c' [explicit solve]: current solution: 9.620980e+01, current residual:5.808048e+01 +field 'mu' [explicit solve]: current solution: 4.806425e+00, current residual:2.900945e+00 +wall time: 0.000747919s + +time increment:74990 time: 74.99 +field ' c' [explicit solve]: current solution: 9.620984e+01, current residual:5.808050e+01 +field 'mu' [explicit solve]: current solution: 4.806370e+00, current residual:2.900911e+00 +wall time: 0.000648975s + +time increment:75000 time: 75 +field ' c' [explicit solve]: current solution: 9.620988e+01, current residual:5.808052e+01 +field 'mu' [explicit solve]: current solution: 4.806314e+00, current residual:2.900876e+00 +wall time: 0.000715971s + +time increment:75010 time: 75.01 +field ' c' [explicit solve]: current solution: 9.620992e+01, current residual:5.808054e+01 +field 'mu' [explicit solve]: current solution: 4.806259e+00, current residual:2.900841e+00 +wall time: 0.000686884s + +time increment:75020 time: 75.02 +field ' c' [explicit solve]: current solution: 9.620996e+01, current residual:5.808056e+01 +field 'mu' [explicit solve]: current solution: 4.806204e+00, current residual:2.900806e+00 +wall time: 0.00069499s + +time increment:75030 time: 75.03 +field ' c' [explicit solve]: current solution: 9.621000e+01, current residual:5.808058e+01 +field 'mu' [explicit solve]: current solution: 4.806148e+00, current residual:2.900771e+00 +wall time: 0.000665903s + +time increment:75040 time: 75.04 +field ' c' [explicit solve]: current solution: 9.621004e+01, current residual:5.808060e+01 +field 'mu' [explicit solve]: current solution: 4.806093e+00, current residual:2.900737e+00 +wall time: 0.000694036s + +time increment:75050 time: 75.05 +field ' c' [explicit solve]: current solution: 9.621008e+01, current residual:5.808062e+01 +field 'mu' [explicit solve]: current solution: 4.806038e+00, current residual:2.900702e+00 +wall time: 0.000751972s + +time increment:75060 time: 75.06 +field ' c' [explicit solve]: current solution: 9.621012e+01, current residual:5.808065e+01 +field 'mu' [explicit solve]: current solution: 4.805982e+00, current residual:2.900667e+00 +wall time: 0.000750065s + +time increment:75070 time: 75.07 +field ' c' [explicit solve]: current solution: 9.621016e+01, current residual:5.808067e+01 +field 'mu' [explicit solve]: current solution: 4.805927e+00, current residual:2.900632e+00 +wall time: 0.000761032s + +time increment:75080 time: 75.08 +field ' c' [explicit solve]: current solution: 9.621020e+01, current residual:5.808069e+01 +field 'mu' [explicit solve]: current solution: 4.805872e+00, current residual:2.900597e+00 +wall time: 0.000741959s + +time increment:75090 time: 75.09 +field ' c' [explicit solve]: current solution: 9.621024e+01, current residual:5.808071e+01 +field 'mu' [explicit solve]: current solution: 4.805816e+00, current residual:2.900563e+00 +wall time: 0.000745058s + +time increment:75100 time: 75.1 +field ' c' [explicit solve]: current solution: 9.621028e+01, current residual:5.808073e+01 +field 'mu' [explicit solve]: current solution: 4.805761e+00, current residual:2.900528e+00 +wall time: 0.000714064s + +time increment:75110 time: 75.11 +field ' c' [explicit solve]: current solution: 9.621032e+01, current residual:5.808075e+01 +field 'mu' [explicit solve]: current solution: 4.805706e+00, current residual:2.900493e+00 +wall time: 0.00069809s + +time increment:75120 time: 75.12 +field ' c' [explicit solve]: current solution: 9.621036e+01, current residual:5.808077e+01 +field 'mu' [explicit solve]: current solution: 4.805650e+00, current residual:2.900458e+00 +wall time: 0.000705957s + +time increment:75130 time: 75.13 +field ' c' [explicit solve]: current solution: 9.621040e+01, current residual:5.808080e+01 +field 'mu' [explicit solve]: current solution: 4.805595e+00, current residual:2.900424e+00 +wall time: 0.000691175s + +time increment:75140 time: 75.14 +field ' c' [explicit solve]: current solution: 9.621044e+01, current residual:5.808082e+01 +field 'mu' [explicit solve]: current solution: 4.805540e+00, current residual:2.900389e+00 +wall time: 0.000620127s + +time increment:75150 time: 75.15 +field ' c' [explicit solve]: current solution: 9.621048e+01, current residual:5.808084e+01 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[explicit solve]: current solution: 4.805263e+00, current residual:2.900215e+00 +wall time: 0.000670195s + +time increment:75200 time: 75.2 +field ' c' [explicit solve]: current solution: 9.621068e+01, current residual:5.808095e+01 +field 'mu' [explicit solve]: current solution: 4.805208e+00, current residual:2.900180e+00 +wall time: 0.000674009s + +time increment:75210 time: 75.21 +field ' c' [explicit solve]: current solution: 9.621072e+01, current residual:5.808097e+01 +field 'mu' [explicit solve]: current solution: 4.805153e+00, current residual:2.900145e+00 +wall time: 0.000725031s + +time increment:75220 time: 75.22 +field ' c' [explicit solve]: current solution: 9.621076e+01, current residual:5.808099e+01 +field 'mu' [explicit solve]: current solution: 4.805097e+00, current residual:2.900111e+00 +wall time: 0.000668049s + +time increment:75230 time: 75.23 +field ' c' [explicit solve]: current solution: 9.621080e+01, current residual:5.808101e+01 +field 'mu' [explicit solve]: current solution: 4.805042e+00, current residual:2.900076e+00 +wall time: 0.000706196s + +time increment:75240 time: 75.24 +field ' c' [explicit solve]: current solution: 9.621084e+01, current residual:5.808103e+01 +field 'mu' [explicit solve]: current solution: 4.804987e+00, current residual:2.900041e+00 +wall time: 0.000766039s + +time increment:75250 time: 75.25 +field ' c' [explicit solve]: current solution: 9.621088e+01, current residual:5.808105e+01 +field 'mu' [explicit solve]: current solution: 4.804931e+00, current residual:2.900006e+00 +wall time: 0.000733137s + +time increment:75260 time: 75.26 +field ' c' [explicit solve]: current solution: 9.621092e+01, current residual:5.808107e+01 +field 'mu' [explicit solve]: current solution: 4.804876e+00, current residual:2.899972e+00 +wall time: 0.000669003s + +time increment:75270 time: 75.27 +field ' c' [explicit solve]: current solution: 9.621096e+01, current residual:5.808110e+01 +field 'mu' [explicit solve]: current solution: 4.804821e+00, current residual:2.899937e+00 +wall time: 0.000686884s + +time increment:75280 time: 75.28 +field ' c' [explicit solve]: current solution: 9.621100e+01, current residual:5.808112e+01 +field 'mu' [explicit solve]: current solution: 4.804765e+00, current residual:2.899902e+00 +wall time: 0.000639915s + +time increment:75290 time: 75.29 +field ' c' [explicit solve]: current solution: 9.621104e+01, current residual:5.808114e+01 +field 'mu' [explicit solve]: current solution: 4.804710e+00, current residual:2.899867e+00 +wall time: 0.000656843s + +time increment:75300 time: 75.3 +field ' c' [explicit solve]: current solution: 9.621108e+01, current residual:5.808116e+01 +field 'mu' [explicit solve]: current solution: 4.804655e+00, current residual:2.899833e+00 +wall time: 0.000577211s + +time increment:75310 time: 75.31 +field ' c' [explicit solve]: current solution: 9.621112e+01, current residual:5.808118e+01 +field 'mu' [explicit solve]: current solution: 4.804600e+00, current residual:2.899798e+00 +wall time: 0.000597s + +time increment:75320 time: 75.32 +field ' c' [explicit solve]: current solution: 9.621116e+01, current residual:5.808120e+01 +field 'mu' [explicit solve]: current solution: 4.804544e+00, current residual:2.899763e+00 +wall time: 0.000653028s + +time increment:75330 time: 75.33 +field ' c' [explicit solve]: current solution: 9.621120e+01, current residual:5.808122e+01 +field 'mu' [explicit solve]: current solution: 4.804489e+00, current residual:2.899728e+00 +wall time: 0.000690937s + +time increment:75340 time: 75.34 +field ' c' [explicit solve]: current solution: 9.621124e+01, current residual:5.808125e+01 +field 'mu' [explicit solve]: current solution: 4.804434e+00, current residual:2.899693e+00 +wall time: 0.000696898s + +time increment:75350 time: 75.35 +field ' c' [explicit solve]: current solution: 9.621128e+01, current residual:5.808127e+01 +field 'mu' [explicit solve]: current solution: 4.804378e+00, current residual:2.899659e+00 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0.000618935s + +time increment:75400 time: 75.4 +field ' c' [explicit solve]: current solution: 9.621148e+01, current residual:5.808137e+01 +field 'mu' [explicit solve]: current solution: 4.804102e+00, current residual:2.899485e+00 +wall time: 0.000584126s + +time increment:75410 time: 75.41 +field ' c' [explicit solve]: current solution: 9.621152e+01, current residual:5.808140e+01 +field 'mu' [explicit solve]: current solution: 4.804046e+00, current residual:2.899450e+00 +wall time: 0.00056982s + +time increment:75420 time: 75.42 +field ' c' [explicit solve]: current solution: 9.621156e+01, current residual:5.808142e+01 +field 'mu' [explicit solve]: current solution: 4.803991e+00, current residual:2.899415e+00 +wall time: 0.000740051s + +time increment:75430 time: 75.43 +field ' c' [explicit solve]: current solution: 9.621160e+01, current residual:5.808144e+01 +field 'mu' [explicit solve]: current solution: 4.803936e+00, current residual:2.899381e+00 +wall time: 0.000560045s + +time increment:75440 time: 75.44 +field ' c' [explicit solve]: current solution: 9.621164e+01, current residual:5.808146e+01 +field 'mu' [explicit solve]: current solution: 4.803880e+00, current residual:2.899346e+00 +wall time: 0.000562906s + +time increment:75450 time: 75.45 +field ' c' [explicit solve]: current solution: 9.621168e+01, current residual:5.808148e+01 +field 'mu' [explicit solve]: current solution: 4.803825e+00, current residual:2.899311e+00 +wall time: 0.000617027s + +time increment:75460 time: 75.46 +field ' c' [explicit solve]: current solution: 9.621172e+01, current residual:5.808150e+01 +field 'mu' [explicit solve]: current solution: 4.803770e+00, current residual:2.899276e+00 +wall time: 0.000682116s + +time increment:75470 time: 75.47 +field ' c' [explicit solve]: current solution: 9.621176e+01, current residual:5.808153e+01 +field 'mu' [explicit solve]: current solution: 4.803714e+00, current residual:2.899242e+00 +wall time: 0.000696898s + +time increment:75480 time: 75.48 +field ' c' [explicit solve]: current solution: 9.621180e+01, current residual:5.808155e+01 +field 'mu' [explicit solve]: current solution: 4.803659e+00, current residual:2.899207e+00 +wall time: 0.000604868s + +time increment:75490 time: 75.49 +field ' c' [explicit solve]: current solution: 9.621184e+01, current residual:5.808157e+01 +field 'mu' [explicit solve]: current solution: 4.803604e+00, current residual:2.899172e+00 +wall time: 0.000575781s + +time increment:75500 time: 75.5 +field ' c' [explicit solve]: current solution: 9.621188e+01, current residual:5.808159e+01 +field 'mu' [explicit solve]: current solution: 4.803549e+00, current residual:2.899137e+00 +wall time: 0.000627995s + +time increment:75510 time: 75.51 +field ' c' [explicit solve]: current solution: 9.621192e+01, current residual:5.808161e+01 +field 'mu' [explicit solve]: current solution: 4.803493e+00, current residual:2.899103e+00 +wall time: 0.000565052s + +time increment:75520 time: 75.52 +field ' c' [explicit solve]: current solution: 9.621196e+01, current residual:5.808163e+01 +field 'mu' [explicit solve]: current solution: 4.803438e+00, current residual:2.899068e+00 +wall time: 0.000693083s + +time increment:75530 time: 75.53 +field ' c' [explicit solve]: current solution: 9.621200e+01, current residual:5.808165e+01 +field 'mu' [explicit solve]: current solution: 4.803383e+00, current residual:2.899033e+00 +wall time: 0.000591993s + +time increment:75540 time: 75.54 +field ' c' [explicit solve]: current solution: 9.621205e+01, current residual:5.808168e+01 +field 'mu' [explicit solve]: current solution: 4.803327e+00, current residual:2.898998e+00 +wall time: 0.000633001s + +time increment:75550 time: 75.55 +field ' c' [explicit solve]: current solution: 9.621209e+01, current residual:5.808170e+01 +field 'mu' [explicit solve]: current solution: 4.803272e+00, current residual:2.898963e+00 +wall time: 0.000678062s + +time increment:75560 time: 75.56 +field ' c' [explicit solve]: current solution: 9.621213e+01, current residual:5.808172e+01 +field 'mu' [explicit solve]: current solution: 4.803217e+00, current residual:2.898929e+00 +wall time: 0.000778913s + +time increment:75570 time: 75.57 +field ' c' [explicit solve]: current solution: 9.621217e+01, current residual:5.808174e+01 +field 'mu' [explicit solve]: current solution: 4.803161e+00, current residual:2.898894e+00 +wall time: 0.000682116s + +time increment:75580 time: 75.58 +field ' c' [explicit solve]: current solution: 9.621221e+01, current residual:5.808176e+01 +field 'mu' [explicit solve]: current solution: 4.803106e+00, current residual:2.898859e+00 +wall time: 0.000681162s + +time increment:75590 time: 75.59 +field ' c' [explicit solve]: current solution: 9.621225e+01, current residual:5.808178e+01 +field 'mu' [explicit solve]: current solution: 4.803051e+00, current residual:2.898824e+00 +wall time: 0.000721216s + +time increment:75600 time: 75.6 +field ' c' [explicit solve]: current solution: 9.621229e+01, current residual:5.808181e+01 +field 'mu' [explicit solve]: current solution: 4.802995e+00, current residual:2.898790e+00 +wall time: 0.000718117s + +time increment:75610 time: 75.61 +field ' c' [explicit solve]: current solution: 9.621233e+01, current residual:5.808183e+01 +field 'mu' [explicit solve]: current solution: 4.802940e+00, current residual:2.898755e+00 +wall time: 0.000668049s + +time increment:75620 time: 75.62 +field ' c' [explicit solve]: current solution: 9.621237e+01, current residual:5.808185e+01 +field 'mu' [explicit solve]: current solution: 4.802885e+00, current residual:2.898720e+00 +wall time: 0.000670195s + +time increment:75630 time: 75.63 +field ' c' [explicit solve]: current solution: 9.621241e+01, current residual:5.808187e+01 +field 'mu' [explicit solve]: current solution: 4.802829e+00, current residual:2.898685e+00 +wall time: 0.000695944s + +time increment:75640 time: 75.64 +field ' c' [explicit solve]: current solution: 9.621245e+01, 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residual:5.808198e+01 +field 'mu' [explicit solve]: current solution: 4.802553e+00, current residual:2.898512e+00 +wall time: 0.000725985s + +time increment:75690 time: 75.69 +field ' c' [explicit solve]: current solution: 9.621265e+01, current residual:5.808200e+01 +field 'mu' [explicit solve]: current solution: 4.802497e+00, current residual:2.898477e+00 +wall time: 0.000712156s + +time increment:75700 time: 75.7 +field ' c' [explicit solve]: current solution: 9.621269e+01, current residual:5.808202e+01 +field 'mu' [explicit solve]: current solution: 4.802442e+00, current residual:2.898442e+00 +wall time: 0.000646114s + +time increment:75710 time: 75.71 +field ' c' [explicit solve]: current solution: 9.621273e+01, current residual:5.808204e+01 +field 'mu' [explicit solve]: current solution: 4.802387e+00, current residual:2.898407e+00 +wall time: 0.000651836s + +time increment:75720 time: 75.72 +field ' c' [explicit solve]: current solution: 9.621277e+01, current residual:5.808207e+01 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solve]: current solution: 4.802110e+00, current residual:2.898234e+00 +wall time: 0.000666142s + +time increment:75770 time: 75.77 +field ' c' [explicit solve]: current solution: 9.621297e+01, current residual:5.808217e+01 +field 'mu' [explicit solve]: current solution: 4.802055e+00, current residual:2.898199e+00 +wall time: 0.000720024s + +time increment:75780 time: 75.78 +field ' c' [explicit solve]: current solution: 9.621301e+01, current residual:5.808220e+01 +field 'mu' [explicit solve]: current solution: 4.801999e+00, current residual:2.898164e+00 +wall time: 0.000668049s + +time increment:75790 time: 75.79 +field ' c' [explicit solve]: current solution: 9.621306e+01, current residual:5.808222e+01 +field 'mu' [explicit solve]: current solution: 4.801944e+00, current residual:2.898129e+00 +wall time: 0.000648975s + +time increment:75800 time: 75.8 +field ' c' [explicit solve]: current solution: 9.621310e+01, current residual:5.808224e+01 +field 'mu' [explicit solve]: current solution: 4.801889e+00, current residual:2.898095e+00 +wall time: 0.000772953s + +time increment:75810 time: 75.81 +field ' c' [explicit solve]: current solution: 9.621314e+01, current residual:5.808226e+01 +field 'mu' [explicit solve]: current solution: 4.801833e+00, current residual:2.898060e+00 +wall time: 0.000672102s + +time increment:75820 time: 75.82 +field ' c' [explicit solve]: current solution: 9.621318e+01, current residual:5.808228e+01 +field 'mu' [explicit solve]: current solution: 4.801778e+00, current residual:2.898025e+00 +wall time: 0.000743866s + +time increment:75830 time: 75.83 +field ' c' [explicit solve]: current solution: 9.621322e+01, current residual:5.808230e+01 +field 'mu' [explicit solve]: current solution: 4.801723e+00, current residual:2.897990e+00 +wall time: 0.000761986s + +time increment:75840 time: 75.84 +field ' c' [explicit solve]: current solution: 9.621326e+01, current residual:5.808233e+01 +field 'mu' [explicit solve]: current solution: 4.801667e+00, current residual:2.897956e+00 +wall time: 0.000715017s + +time increment:75850 time: 75.85 +field ' c' [explicit solve]: current solution: 9.621330e+01, current residual:5.808235e+01 +field 'mu' [explicit solve]: current solution: 4.801612e+00, current residual:2.897921e+00 +wall time: 0.000695944s + +time increment:75860 time: 75.86 +field ' c' [explicit solve]: current solution: 9.621334e+01, current residual:5.808237e+01 +field 'mu' [explicit solve]: current solution: 4.801557e+00, current residual:2.897886e+00 +wall time: 0.000702858s + +time increment:75870 time: 75.87 +field ' c' [explicit solve]: current solution: 9.621338e+01, current residual:5.808239e+01 +field 'mu' [explicit solve]: current solution: 4.801501e+00, current residual:2.897851e+00 +wall time: 0.00068903s + +time increment:75880 time: 75.88 +field ' c' [explicit solve]: current solution: 9.621342e+01, current residual:5.808241e+01 +field 'mu' [explicit solve]: current solution: 4.801446e+00, current residual:2.897817e+00 +wall time: 0.000710011s + +time increment:75890 time: 75.89 +field ' c' [explicit solve]: current solution: 9.621346e+01, current residual:5.808243e+01 +field 'mu' [explicit solve]: current solution: 4.801391e+00, current residual:2.897782e+00 +wall time: 0.000675917s + +time increment:75900 time: 75.9 +field ' c' [explicit solve]: current solution: 9.621350e+01, current residual:5.808246e+01 +field 'mu' [explicit solve]: current solution: 4.801335e+00, current residual:2.897747e+00 +wall time: 0.00069499s + +time increment:75910 time: 75.91 +field ' c' [explicit solve]: current solution: 9.621354e+01, current residual:5.808248e+01 +field 'mu' [explicit solve]: current solution: 4.801280e+00, current residual:2.897712e+00 +wall time: 0.000756979s + +time increment:75920 time: 75.92 +field ' c' [explicit solve]: current solution: 9.621358e+01, current residual:5.808250e+01 +field 'mu' [explicit solve]: current solution: 4.801225e+00, current residual:2.897678e+00 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0.000706911s + +time increment:75970 time: 75.97 +field ' c' [explicit solve]: current solution: 9.621379e+01, current residual:5.808261e+01 +field 'mu' [explicit solve]: current solution: 4.800948e+00, current residual:2.897504e+00 +wall time: 0.000712872s + +time increment:75980 time: 75.98 +field ' c' [explicit solve]: current solution: 9.621383e+01, current residual:5.808263e+01 +field 'mu' [explicit solve]: current solution: 4.800893e+00, current residual:2.897469e+00 +wall time: 0.000726938s + +time increment:75990 time: 75.99 +field ' c' [explicit solve]: current solution: 9.621387e+01, current residual:5.808265e+01 +field 'mu' [explicit solve]: current solution: 4.800837e+00, current residual:2.897435e+00 +wall time: 0.00070405s + +time increment:76000 time: 76 +field ' c' [explicit solve]: current solution: 9.621391e+01, current residual:5.808267e+01 +field 'mu' [explicit solve]: current solution: 4.800782e+00, current residual:2.897400e+00 +wall time: 0.000659943s + +time increment:76010 time: 76.01 +field ' c' [explicit solve]: current solution: 9.621395e+01, current residual:5.808270e+01 +field 'mu' [explicit solve]: current solution: 4.800727e+00, current residual:2.897365e+00 +wall time: 0.000624895s + +time increment:76020 time: 76.02 +field ' c' [explicit solve]: current solution: 9.621399e+01, current residual:5.808272e+01 +field 'mu' [explicit solve]: current solution: 4.800671e+00, current residual:2.897330e+00 +wall time: 0.000669956s + +time increment:76030 time: 76.03 +field ' c' [explicit solve]: current solution: 9.621403e+01, current residual:5.808274e+01 +field 'mu' [explicit solve]: current solution: 4.800616e+00, current residual:2.897296e+00 +wall time: 0.000668049s + +time increment:76040 time: 76.04 +field ' c' [explicit solve]: current solution: 9.621407e+01, current residual:5.808276e+01 +field 'mu' [explicit solve]: current solution: 4.800561e+00, current residual:2.897261e+00 +wall time: 0.000681877s + +time increment:76050 time: 76.05 +field ' c' [explicit solve]: current solution: 9.621411e+01, current residual:5.808278e+01 +field 'mu' [explicit solve]: current solution: 4.800505e+00, current residual:2.897226e+00 +wall time: 0.000685215s + +time increment:76060 time: 76.06 +field ' c' [explicit solve]: current solution: 9.621415e+01, current residual:5.808281e+01 +field 'mu' [explicit solve]: current solution: 4.800450e+00, current residual:2.897191e+00 +wall time: 0.000691891s + +time increment:76070 time: 76.07 +field ' c' [explicit solve]: current solution: 9.621419e+01, current residual:5.808283e+01 +field 'mu' [explicit solve]: current solution: 4.800395e+00, current residual:2.897157e+00 +wall time: 0.000703096s + +time increment:76080 time: 76.08 +field ' c' [explicit solve]: current solution: 9.621423e+01, current residual:5.808285e+01 +field 'mu' [explicit solve]: current solution: 4.800339e+00, current residual:2.897122e+00 +wall time: 0.000656843s + +time increment:76090 time: 76.09 +field ' c' [explicit solve]: current solution: 9.621427e+01, current residual:5.808287e+01 +field 'mu' [explicit solve]: current solution: 4.800284e+00, current residual:2.897087e+00 +wall time: 0.000682116s + +time increment:76100 time: 76.1 +field ' c' [explicit solve]: current solution: 9.621431e+01, current residual:5.808289e+01 +field 'mu' [explicit solve]: current solution: 4.800229e+00, current residual:2.897052e+00 +wall time: 0.000678062s + +time increment:76110 time: 76.11 +field ' c' [explicit solve]: current solution: 9.621435e+01, current residual:5.808291e+01 +field 'mu' [explicit solve]: current solution: 4.800173e+00, current residual:2.897018e+00 +wall time: 0.000705004s + +time increment:76120 time: 76.12 +field ' c' [explicit solve]: current solution: 9.621440e+01, current residual:5.808294e+01 +field 'mu' [explicit solve]: current solution: 4.800118e+00, current residual:2.896983e+00 +wall time: 0.000631094s + +time increment:76130 time: 76.13 +field ' c' [explicit solve]: current solution: 9.621444e+01, current residual:5.808296e+01 +field 'mu' [explicit solve]: current solution: 4.800063e+00, current residual:2.896948e+00 +wall time: 0.000691891s + +time increment:76140 time: 76.14 +field ' c' [explicit solve]: current solution: 9.621448e+01, current residual:5.808298e+01 +field 'mu' [explicit solve]: current solution: 4.800007e+00, current residual:2.896913e+00 +wall time: 0.000677109s + +time increment:76150 time: 76.15 +field ' c' [explicit solve]: current solution: 9.621452e+01, current residual:5.808300e+01 +field 'mu' [explicit solve]: current solution: 4.799952e+00, current residual:2.896879e+00 +wall time: 0.000770092s + +time increment:76160 time: 76.16 +field ' c' [explicit solve]: current solution: 9.621456e+01, current residual:5.808302e+01 +field 'mu' [explicit solve]: current solution: 4.799897e+00, current residual:2.896844e+00 +wall time: 0.000699043s + +time increment:76170 time: 76.17 +field ' c' [explicit solve]: current solution: 9.621460e+01, current residual:5.808305e+01 +field 'mu' [explicit solve]: current solution: 4.799841e+00, current residual:2.896809e+00 +wall time: 0.000637054s + +time increment:76180 time: 76.18 +field ' c' [explicit solve]: current solution: 9.621464e+01, current residual:5.808307e+01 +field 'mu' [explicit solve]: current solution: 4.799786e+00, current residual:2.896774e+00 +wall time: 0.000674009s + +time increment:76190 time: 76.19 +field ' c' [explicit solve]: current solution: 9.621468e+01, current residual:5.808309e+01 +field 'mu' [explicit solve]: current solution: 4.799731e+00, current residual:2.896740e+00 +wall time: 0.000690937s + +time increment:76200 time: 76.2 +field ' c' [explicit solve]: current solution: 9.621472e+01, current residual:5.808311e+01 +field 'mu' [explicit solve]: current solution: 4.799675e+00, current residual:2.896705e+00 +wall time: 0.00062418s + +time increment:76210 time: 76.21 +field ' c' [explicit solve]: current solution: 9.621476e+01, current residual:5.808313e+01 +field 'mu' [explicit solve]: current solution: 4.799620e+00, current residual:2.896670e+00 +wall time: 0.000560045s + +time increment:76220 time: 76.22 +field ' c' [explicit solve]: current solution: 9.621480e+01, current residual:5.808316e+01 +field 'mu' [explicit solve]: current solution: 4.799565e+00, current residual:2.896635e+00 +wall time: 0.000560045s + +time increment:76230 time: 76.23 +field ' c' [explicit solve]: current solution: 9.621484e+01, current residual:5.808318e+01 +field 'mu' [explicit solve]: current solution: 4.799509e+00, current residual:2.896601e+00 +wall time: 0.000624895s + +time increment:76240 time: 76.24 +field ' c' [explicit solve]: current solution: 9.621488e+01, current residual:5.808320e+01 +field 'mu' [explicit solve]: current solution: 4.799454e+00, current residual:2.896566e+00 +wall time: 0.000604868s + +time increment:76250 time: 76.25 +field ' c' [explicit solve]: current solution: 9.621492e+01, current residual:5.808322e+01 +field 'mu' [explicit solve]: current solution: 4.799398e+00, current residual:2.896531e+00 +wall time: 0.000568151s + +time increment:76260 time: 76.26 +field ' c' [explicit solve]: current solution: 9.621497e+01, current residual:5.808324e+01 +field 'mu' [explicit solve]: current solution: 4.799343e+00, current residual:2.896497e+00 +wall time: 0.000694036s + +time increment:76270 time: 76.27 +field ' c' [explicit solve]: current solution: 9.621501e+01, current residual:5.808326e+01 +field 'mu' [explicit solve]: current solution: 4.799288e+00, current residual:2.896462e+00 +wall time: 0.000602007s + +time increment:76280 time: 76.28 +field ' c' [explicit solve]: current solution: 9.621505e+01, current residual:5.808329e+01 +field 'mu' [explicit solve]: current solution: 4.799232e+00, current residual:2.896427e+00 +wall time: 0.000568867s + +time increment:76290 time: 76.29 +field ' c' [explicit solve]: current solution: 9.621509e+01, current residual:5.808331e+01 +field 'mu' [explicit solve]: current solution: 4.799177e+00, current residual:2.896392e+00 +wall time: 0.000566006s + +time increment:76300 time: 76.3 +field ' c' [explicit solve]: current solution: 9.621513e+01, current residual:5.808333e+01 +field 'mu' [explicit solve]: current solution: 4.799122e+00, current residual:2.896358e+00 +wall time: 0.000611067s + +time increment:76310 time: 76.31 +field ' c' [explicit solve]: current solution: 9.621517e+01, current residual:5.808335e+01 +field 'mu' [explicit solve]: current solution: 4.799066e+00, current residual:2.896323e+00 +wall time: 0.000573874s + +time increment:76320 time: 76.32 +field ' c' [explicit solve]: current solution: 9.621521e+01, current residual:5.808337e+01 +field 'mu' [explicit solve]: current solution: 4.799011e+00, current residual:2.896288e+00 +wall time: 0.000722885s + +time increment:76330 time: 76.33 +field ' c' [explicit solve]: current solution: 9.621525e+01, current residual:5.808340e+01 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solve]: current solution: 4.798734e+00, current residual:2.896114e+00 +wall time: 0.000560045s + +time increment:76380 time: 76.38 +field ' c' [explicit solve]: current solution: 9.621546e+01, current residual:5.808351e+01 +field 'mu' [explicit solve]: current solution: 4.798679e+00, current residual:2.896080e+00 +wall time: 0.000689983s + +time increment:76390 time: 76.39 +field ' c' [explicit solve]: current solution: 9.621550e+01, current residual:5.808353e+01 +field 'mu' [explicit solve]: current solution: 4.798623e+00, current residual:2.896045e+00 +wall time: 0.000597954s + +time increment:76400 time: 76.4 +field ' c' [explicit solve]: current solution: 9.621554e+01, current residual:5.808355e+01 +field 'mu' [explicit solve]: current solution: 4.798568e+00, current residual:2.896010e+00 +wall time: 0.000623941s + +time increment:76410 time: 76.41 +field ' c' [explicit solve]: current solution: 9.621558e+01, current residual:5.808357e+01 +field 'mu' [explicit solve]: current solution: 4.798513e+00, current residual:2.895976e+00 +wall time: 0.000608921s + +time increment:76420 time: 76.42 +field ' c' [explicit solve]: current solution: 9.621562e+01, current residual:5.808359e+01 +field 'mu' [explicit solve]: current solution: 4.798457e+00, current residual:2.895941e+00 +wall time: 0.000642061s + +time increment:76430 time: 76.43 +field ' c' [explicit solve]: current solution: 9.621566e+01, current residual:5.808362e+01 +field 'mu' [explicit solve]: current solution: 4.798402e+00, current residual:2.895906e+00 +wall time: 0.00059104s + +time increment:76440 time: 76.44 +field ' c' [explicit solve]: current solution: 9.621570e+01, current residual:5.808364e+01 +field 'mu' [explicit solve]: current solution: 4.798347e+00, current residual:2.895871e+00 +wall time: 0.000640869s + +time increment:76450 time: 76.45 +field ' c' [explicit solve]: current solution: 9.621574e+01, current residual:5.808366e+01 +field 'mu' [explicit solve]: current solution: 4.798291e+00, current residual:2.895837e+00 +wall time: 0.000691891s + +time increment:76460 time: 76.46 +field ' c' [explicit solve]: current solution: 9.621578e+01, current residual:5.808368e+01 +field 'mu' [explicit solve]: current solution: 4.798236e+00, current residual:2.895802e+00 +wall time: 0.000701189s + +time increment:76470 time: 76.47 +field ' c' [explicit solve]: current solution: 9.621582e+01, current residual:5.808370e+01 +field 'mu' [explicit solve]: current solution: 4.798180e+00, current residual:2.895767e+00 +wall time: 0.000674009s + +time increment:76480 time: 76.48 +field ' c' [explicit solve]: current solution: 9.621586e+01, current residual:5.808373e+01 +field 'mu' [explicit solve]: current solution: 4.798125e+00, current residual:2.895732e+00 +wall time: 0.000639915s + +time increment:76490 time: 76.49 +field ' c' [explicit solve]: current solution: 9.621591e+01, current residual:5.808375e+01 +field 'mu' [explicit solve]: current solution: 4.798070e+00, current residual:2.895698e+00 +wall time: 0.00057292s + +time increment:76500 time: 76.5 +field ' c' [explicit solve]: current solution: 9.621595e+01, current residual:5.808377e+01 +field 'mu' [explicit solve]: current solution: 4.798014e+00, current residual:2.895663e+00 +wall time: 0.000619173s + +time increment:76510 time: 76.51 +field ' c' [explicit solve]: current solution: 9.621599e+01, current residual:5.808379e+01 +field 'mu' [explicit solve]: current solution: 4.797959e+00, current residual:2.895628e+00 +wall time: 0.000880003s + +time increment:76520 time: 76.52 +field ' c' [explicit solve]: current solution: 9.621603e+01, current residual:5.808381e+01 +field 'mu' [explicit solve]: current solution: 4.797904e+00, current residual:2.895593e+00 +wall time: 0.000735998s + +time increment:76530 time: 76.53 +field ' c' [explicit solve]: current solution: 9.621607e+01, current residual:5.808384e+01 +field 'mu' [explicit solve]: current solution: 4.797848e+00, current residual:2.895559e+00 +wall time: 0.000616074s + +time increment:76540 time: 76.54 +field ' c' [explicit solve]: current solution: 9.621611e+01, current residual:5.808386e+01 +field 'mu' [explicit solve]: current solution: 4.797793e+00, current residual:2.895524e+00 +wall time: 0.000674963s + +time increment:76550 time: 76.55 +field ' c' [explicit solve]: current solution: 9.621615e+01, current residual:5.808388e+01 +field 'mu' [explicit solve]: current solution: 4.797738e+00, current residual:2.895489e+00 +wall time: 0.000557899s + +time increment:76560 time: 76.56 +field ' c' [explicit solve]: current solution: 9.621619e+01, current residual:5.808390e+01 +field 'mu' [explicit solve]: current solution: 4.797682e+00, current residual:2.895455e+00 +wall time: 0.000589848s + +time increment:76570 time: 76.57 +field ' c' [explicit solve]: current solution: 9.621623e+01, current residual:5.808392e+01 +field 'mu' [explicit solve]: current solution: 4.797627e+00, current residual:2.895420e+00 +wall time: 0.00061202s + +time increment:76580 time: 76.58 +field ' c' [explicit solve]: current solution: 9.621627e+01, current residual:5.808395e+01 +field 'mu' [explicit solve]: current solution: 4.797571e+00, current residual:2.895385e+00 +wall time: 0.00058794s + +time increment:76590 time: 76.59 +field ' c' [explicit solve]: current solution: 9.621632e+01, current residual:5.808397e+01 +field 'mu' [explicit solve]: current solution: 4.797516e+00, current residual:2.895350e+00 +wall time: 0.000653028s + +time increment:76600 time: 76.6 +field ' c' [explicit solve]: current solution: 9.621636e+01, current residual:5.808399e+01 +field 'mu' [explicit solve]: current solution: 4.797461e+00, current residual:2.895316e+00 +wall time: 0.000658989s + +time increment:76610 time: 76.61 +field ' c' [explicit solve]: current solution: 9.621640e+01, current residual:5.808401e+01 +field 'mu' [explicit solve]: current solution: 4.797405e+00, current residual:2.895281e+00 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0.000597954s + +time increment:76660 time: 76.66 +field ' c' [explicit solve]: current solution: 9.621660e+01, current residual:5.808412e+01 +field 'mu' [explicit solve]: current solution: 4.797128e+00, current residual:2.895107e+00 +wall time: 0.000729084s + +time increment:76670 time: 76.67 +field ' c' [explicit solve]: current solution: 9.621664e+01, current residual:5.808414e+01 +field 'mu' [explicit solve]: current solution: 4.797073e+00, current residual:2.895072e+00 +wall time: 0.00057292s + +time increment:76680 time: 76.68 +field ' c' [explicit solve]: current solution: 9.621669e+01, current residual:5.808417e+01 +field 'mu' [explicit solve]: current solution: 4.797018e+00, current residual:2.895038e+00 +wall time: 0.000617981s + +time increment:76690 time: 76.69 +field ' c' [explicit solve]: current solution: 9.621673e+01, current residual:5.808419e+01 +field 'mu' [explicit solve]: current solution: 4.796962e+00, current residual:2.895003e+00 +wall time: 0.00077486s + +time increment:76700 time: 76.7 +field ' c' [explicit solve]: current solution: 9.621677e+01, current residual:5.808421e+01 +field 'mu' [explicit solve]: current solution: 4.796907e+00, current residual:2.894968e+00 +wall time: 0.000574112s + +time increment:76710 time: 76.71 +field ' c' [explicit solve]: current solution: 9.621681e+01, current residual:5.808423e+01 +field 'mu' [explicit solve]: current solution: 4.796851e+00, current residual:2.894934e+00 +wall time: 0.000571012s + +time increment:76720 time: 76.72 +field ' c' [explicit solve]: current solution: 9.621685e+01, current residual:5.808426e+01 +field 'mu' [explicit solve]: current solution: 4.796796e+00, current residual:2.894899e+00 +wall time: 0.000720024s + +time increment:76730 time: 76.73 +field ' c' [explicit solve]: current solution: 9.621689e+01, current residual:5.808428e+01 +field 'mu' [explicit solve]: current solution: 4.796741e+00, current residual:2.894864e+00 +wall time: 0.00059104s + +time increment:76740 time: 76.74 +field ' c' [explicit solve]: current solution: 9.621693e+01, current residual:5.808430e+01 +field 'mu' [explicit solve]: current solution: 4.796685e+00, current residual:2.894829e+00 +wall time: 0.000609159s + +time increment:76750 time: 76.75 +field ' c' [explicit solve]: current solution: 9.621697e+01, current residual:5.808432e+01 +field 'mu' [explicit solve]: current solution: 4.796630e+00, current residual:2.894795e+00 +wall time: 0.000613928s + +time increment:76760 time: 76.76 +field ' c' [explicit solve]: current solution: 9.621701e+01, current residual:5.808434e+01 +field 'mu' [explicit solve]: current solution: 4.796574e+00, current residual:2.894760e+00 +wall time: 0.000662804s + +time increment:76770 time: 76.77 +field ' c' [explicit solve]: current solution: 9.621705e+01, current residual:5.808437e+01 +field 'mu' [explicit solve]: current solution: 4.796519e+00, current residual:2.894725e+00 +wall time: 0.000648975s + +time increment:76780 time: 76.78 +field ' c' [explicit solve]: current solution: 9.621710e+01, current residual:5.808439e+01 +field 'mu' [explicit solve]: current solution: 4.796464e+00, current residual:2.894690e+00 +wall time: 0.000653028s + +time increment:76790 time: 76.79 +field ' c' [explicit solve]: current solution: 9.621714e+01, current residual:5.808441e+01 +field 'mu' [explicit solve]: current solution: 4.796408e+00, current residual:2.894656e+00 +wall time: 0.00073123s + +time increment:76800 time: 76.8 +field ' c' [explicit solve]: current solution: 9.621718e+01, current residual:5.808443e+01 +field 'mu' [explicit solve]: current solution: 4.796353e+00, current residual:2.894621e+00 +wall time: 0.000571966s + +time increment:76810 time: 76.81 +field ' c' [explicit solve]: current solution: 9.621722e+01, current residual:5.808445e+01 +field 'mu' [explicit solve]: current solution: 4.796297e+00, current residual:2.894586e+00 +wall time: 0.000759125s + +time increment:76820 time: 76.82 +field ' c' [explicit solve]: current solution: 9.621726e+01, current residual:5.808448e+01 +field 'mu' [explicit solve]: current solution: 4.796242e+00, current residual:2.894551e+00 +wall time: 0.000745058s + +time increment:76830 time: 76.83 +field ' c' [explicit solve]: current solution: 9.621730e+01, current residual:5.808450e+01 +field 'mu' [explicit solve]: current solution: 4.796187e+00, current residual:2.894517e+00 +wall time: 0.000752926s + +time increment:76840 time: 76.84 +field ' c' [explicit solve]: current solution: 9.621734e+01, current residual:5.808452e+01 +field 'mu' [explicit solve]: current solution: 4.796131e+00, current residual:2.894482e+00 +wall time: 0.000729084s + +time increment:76850 time: 76.85 +field ' c' [explicit solve]: current solution: 9.621738e+01, current residual:5.808454e+01 +field 'mu' [explicit solve]: current solution: 4.796076e+00, current residual:2.894447e+00 +wall time: 0.000682116s + +time increment:76860 time: 76.86 +field ' c' [explicit solve]: current solution: 9.621743e+01, current residual:5.808457e+01 +field 'mu' [explicit solve]: current solution: 4.796020e+00, current residual:2.894413e+00 +wall time: 0.000720978s + +time increment:76870 time: 76.87 +field ' c' [explicit solve]: current solution: 9.621747e+01, current residual:5.808459e+01 +field 'mu' [explicit solve]: current solution: 4.795965e+00, current residual:2.894378e+00 +wall time: 0.000642061s + +time increment:76880 time: 76.88 +field ' c' [explicit solve]: current solution: 9.621751e+01, current residual:5.808461e+01 +field 'mu' [explicit solve]: current solution: 4.795910e+00, current residual:2.894343e+00 +wall time: 0.000751972s + +time increment:76890 time: 76.89 +field ' c' [explicit solve]: current solution: 9.621755e+01, current residual:5.808463e+01 +field 'mu' [explicit solve]: current solution: 4.795854e+00, current residual:2.894308e+00 +wall time: 0.000696898s + +time increment:76900 time: 76.9 +field ' c' [explicit solve]: current solution: 9.621759e+01, current residual:5.808465e+01 +field 'mu' [explicit solve]: current solution: 4.795799e+00, current residual:2.894274e+00 +wall time: 0.000702143s + +time increment:76910 time: 76.91 +field ' c' [explicit solve]: current solution: 9.621763e+01, current residual:5.808468e+01 +field 'mu' [explicit solve]: current solution: 4.795743e+00, current residual:2.894239e+00 +wall time: 0.000763893s + +time increment:76920 time: 76.92 +field ' c' [explicit solve]: current solution: 9.621767e+01, current residual:5.808470e+01 +field 'mu' [explicit solve]: current solution: 4.795688e+00, current residual:2.894204e+00 +wall time: 0.000668049s + +time increment:76930 time: 76.93 +field ' c' [explicit solve]: current solution: 9.621771e+01, current residual:5.808472e+01 +field 'mu' [explicit solve]: current solution: 4.795633e+00, current residual:2.894169e+00 +wall time: 0.000665903s + +time increment:76940 time: 76.94 +field ' c' [explicit solve]: current solution: 9.621776e+01, current residual:5.808474e+01 +field 'mu' [explicit solve]: current solution: 4.795577e+00, current residual:2.894135e+00 +wall time: 0.000670195s + +time increment:76950 time: 76.95 +field ' c' [explicit solve]: current solution: 9.621780e+01, current residual:5.808477e+01 +field 'mu' [explicit solve]: current solution: 4.795522e+00, current residual:2.894100e+00 +wall time: 0.00068903s + +time increment:76960 time: 76.96 +field ' c' [explicit solve]: current solution: 9.621784e+01, current residual:5.808479e+01 +field 'mu' [explicit solve]: current solution: 4.795466e+00, current residual:2.894065e+00 +wall time: 0.00067687s + +time increment:76970 time: 76.97 +field ' c' [explicit solve]: current solution: 9.621788e+01, current residual:5.808481e+01 +field 'mu' [explicit solve]: current solution: 4.795411e+00, current residual:2.894031e+00 +wall time: 0.000709057s + +time increment:76980 time: 76.98 +field ' c' [explicit solve]: current solution: 9.621792e+01, current residual:5.808483e+01 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solve]: current solution: 4.795134e+00, current residual:2.893857e+00 +wall time: 0.000720024s + +time increment:77030 time: 77.03 +field ' c' [explicit solve]: current solution: 9.621813e+01, current residual:5.808494e+01 +field 'mu' [explicit solve]: current solution: 4.795079e+00, current residual:2.893822e+00 +wall time: 0.00101709s + +time increment:77040 time: 77.04 +field ' c' [explicit solve]: current solution: 9.621817e+01, current residual:5.808497e+01 +field 'mu' [explicit solve]: current solution: 4.795023e+00, current residual:2.893787e+00 +wall time: 0.000677109s + +time increment:77050 time: 77.05 +field ' c' [explicit solve]: current solution: 9.621821e+01, current residual:5.808499e+01 +field 'mu' [explicit solve]: current solution: 4.794968e+00, current residual:2.893753e+00 +wall time: 0.000766039s + +time increment:77060 time: 77.06 +field ' c' [explicit solve]: current solution: 9.621825e+01, current residual:5.808501e+01 +field 'mu' [explicit solve]: current solution: 4.794912e+00, current residual:2.893718e+00 +wall time: 0.000681162s + +time increment:77070 time: 77.07 +field ' c' [explicit solve]: current solution: 9.621829e+01, current residual:5.808503e+01 +field 'mu' [explicit solve]: current solution: 4.794857e+00, current residual:2.893683e+00 +wall time: 0.000763893s + +time increment:77080 time: 77.08 +field ' c' [explicit solve]: current solution: 9.621833e+01, current residual:5.808505e+01 +field 'mu' [explicit solve]: current solution: 4.794801e+00, current residual:2.893649e+00 +wall time: 0.00070405s + +time increment:77090 time: 77.09 +field ' c' [explicit solve]: current solution: 9.621837e+01, current residual:5.808508e+01 +field 'mu' [explicit solve]: current solution: 4.794746e+00, current residual:2.893614e+00 +wall time: 0.000682116s + +time increment:77100 time: 77.1 +field ' c' [explicit solve]: current solution: 9.621842e+01, current residual:5.808510e+01 +field 'mu' [explicit solve]: current solution: 4.794691e+00, current residual:2.893579e+00 +wall time: 0.000718117s + +time increment:77110 time: 77.11 +field ' c' [explicit solve]: current solution: 9.621846e+01, current residual:5.808512e+01 +field 'mu' [explicit solve]: current solution: 4.794635e+00, current residual:2.893544e+00 +wall time: 0.000678778s + +time increment:77120 time: 77.12 +field ' c' [explicit solve]: current solution: 9.621850e+01, current residual:5.808514e+01 +field 'mu' [explicit solve]: current solution: 4.794580e+00, current residual:2.893510e+00 +wall time: 0.000652075s + +time increment:77130 time: 77.13 +field ' c' [explicit solve]: current solution: 9.621854e+01, current residual:5.808517e+01 +field 'mu' [explicit solve]: current solution: 4.794524e+00, current residual:2.893475e+00 +wall time: 0.00067687s + +time increment:77140 time: 77.14 +field ' c' [explicit solve]: current solution: 9.621858e+01, current residual:5.808519e+01 +field 'mu' [explicit solve]: current solution: 4.794469e+00, current residual:2.893440e+00 +wall time: 0.000696182s + +time increment:77150 time: 77.15 +field ' c' [explicit solve]: current solution: 9.621862e+01, current residual:5.808521e+01 +field 'mu' [explicit solve]: current solution: 4.794413e+00, current residual:2.893405e+00 +wall time: 0.00067997s + +time increment:77160 time: 77.16 +field ' c' [explicit solve]: current solution: 9.621866e+01, current residual:5.808523e+01 +field 'mu' [explicit solve]: current solution: 4.794358e+00, current residual:2.893371e+00 +wall time: 0.000684023s + +time increment:77170 time: 77.17 +field ' c' [explicit solve]: current solution: 9.621871e+01, current residual:5.808526e+01 +field 'mu' [explicit solve]: current solution: 4.794303e+00, current residual:2.893336e+00 +wall time: 0.000715971s + +time increment:77180 time: 77.18 +field ' c' [explicit solve]: current solution: 9.621875e+01, current residual:5.808528e+01 +field 'mu' [explicit solve]: current solution: 4.794247e+00, current residual:2.893301e+00 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0.000663996s + +time increment:77230 time: 77.23 +field ' c' [explicit solve]: current solution: 9.621895e+01, current residual:5.808539e+01 +field 'mu' [explicit solve]: current solution: 4.793970e+00, current residual:2.893128e+00 +wall time: 0.000756979s + +time increment:77240 time: 77.24 +field ' c' [explicit solve]: current solution: 9.621899e+01, current residual:5.808541e+01 +field 'mu' [explicit solve]: current solution: 4.793915e+00, current residual:2.893093e+00 +wall time: 0.000564098s + +time increment:77250 time: 77.25 +field ' c' [explicit solve]: current solution: 9.621904e+01, current residual:5.808543e+01 +field 'mu' [explicit solve]: current solution: 4.793859e+00, current residual:2.893058e+00 +wall time: 0.000669956s + +time increment:77260 time: 77.26 +field ' c' [explicit solve]: current solution: 9.621908e+01, current residual:5.808546e+01 +field 'mu' [explicit solve]: current solution: 4.793804e+00, current residual:2.893023e+00 +wall time: 0.000559092s + +time increment:77270 time: 77.27 +field ' c' [explicit solve]: current solution: 9.621912e+01, current residual:5.808548e+01 +field 'mu' [explicit solve]: current solution: 4.793748e+00, current residual:2.892989e+00 +wall time: 0.000566959s + +time increment:77280 time: 77.28 +field ' c' [explicit solve]: current solution: 9.621916e+01, current residual:5.808550e+01 +field 'mu' [explicit solve]: current solution: 4.793693e+00, current residual:2.892954e+00 +wall time: 0.000672102s + +time increment:77290 time: 77.29 +field ' c' [explicit solve]: current solution: 9.621920e+01, current residual:5.808552e+01 +field 'mu' [explicit solve]: current solution: 4.793637e+00, current residual:2.892919e+00 +wall time: 0.000609875s + +time increment:77300 time: 77.3 +field ' c' [explicit solve]: current solution: 9.621924e+01, current residual:5.808555e+01 +field 'mu' [explicit solve]: current solution: 4.793582e+00, current residual:2.892884e+00 +wall time: 0.000590086s + +time increment:77310 time: 77.31 +field ' c' [explicit solve]: current solution: 9.621928e+01, current residual:5.808557e+01 +field 'mu' [explicit solve]: current solution: 4.793527e+00, current residual:2.892850e+00 +wall time: 0.000646114s + +time increment:77320 time: 77.32 +field ' c' [explicit solve]: current solution: 9.621933e+01, current residual:5.808559e+01 +field 'mu' [explicit solve]: current solution: 4.793471e+00, current residual:2.892815e+00 +wall time: 0.000649929s + +time increment:77330 time: 77.33 +field ' c' [explicit solve]: current solution: 9.621937e+01, current residual:5.808561e+01 +field 'mu' [explicit solve]: current solution: 4.793416e+00, current residual:2.892780e+00 +wall time: 0.000566006s + +time increment:77340 time: 77.34 +field ' c' [explicit solve]: current solution: 9.621941e+01, current residual:5.808564e+01 +field 'mu' [explicit solve]: current solution: 4.793360e+00, current residual:2.892746e+00 +wall time: 0.00078702s + +time increment:77350 time: 77.35 +field ' c' [explicit solve]: current solution: 9.621945e+01, current residual:5.808566e+01 +field 'mu' [explicit solve]: current solution: 4.793305e+00, current residual:2.892711e+00 +wall time: 0.000617981s + +time increment:77360 time: 77.36 +field ' c' [explicit solve]: current solution: 9.621949e+01, current residual:5.808568e+01 +field 'mu' [explicit solve]: current solution: 4.793249e+00, current residual:2.892676e+00 +wall time: 0.000701189s + +time increment:77370 time: 77.37 +field ' c' [explicit solve]: current solution: 9.621953e+01, current residual:5.808570e+01 +field 'mu' [explicit solve]: current solution: 4.793194e+00, current residual:2.892641e+00 +wall time: 0.000631094s + +time increment:77380 time: 77.38 +field ' c' [explicit solve]: current solution: 9.621958e+01, current residual:5.808572e+01 +field 'mu' [explicit solve]: current solution: 4.793138e+00, current residual:2.892607e+00 +wall time: 0.000648022s + +time increment:77390 time: 77.39 +field ' c' [explicit solve]: current solution: 9.621962e+01, current residual:5.808575e+01 +field 'mu' [explicit solve]: current solution: 4.793083e+00, current residual:2.892572e+00 +wall time: 0.000592947s + +time increment:77400 time: 77.4 +field ' c' [explicit solve]: current solution: 9.621966e+01, current residual:5.808577e+01 +field 'mu' [explicit solve]: current solution: 4.793027e+00, current residual:2.892537e+00 +wall time: 0.00056982s + +time increment:77410 time: 77.41 +field ' c' [explicit solve]: current solution: 9.621970e+01, current residual:5.808579e+01 +field 'mu' [explicit solve]: current solution: 4.792972e+00, current residual:2.892502e+00 +wall time: 0.00068903s + +time increment:77420 time: 77.42 +field ' c' [explicit solve]: current solution: 9.621974e+01, current residual:5.808581e+01 +field 'mu' [explicit solve]: current solution: 4.792917e+00, current residual:2.892468e+00 +wall time: 0.000566006s + +time increment:77430 time: 77.43 +field ' c' [explicit solve]: current solution: 9.621978e+01, current residual:5.808584e+01 +field 'mu' [explicit solve]: current solution: 4.792861e+00, current residual:2.892433e+00 +wall time: 0.00056386s + +time increment:77440 time: 77.44 +field ' c' [explicit solve]: current solution: 9.621982e+01, current residual:5.808586e+01 +field 'mu' [explicit solve]: current solution: 4.792806e+00, current residual:2.892398e+00 +wall time: 0.000643969s + +time increment:77450 time: 77.45 +field ' c' [explicit solve]: current solution: 9.621987e+01, current residual:5.808588e+01 +field 'mu' [explicit solve]: current solution: 4.792750e+00, current residual:2.892364e+00 +wall time: 0.000617981s + +time increment:77460 time: 77.46 +field ' c' [explicit solve]: current solution: 9.621991e+01, current residual:5.808590e+01 +field 'mu' [explicit solve]: current solution: 4.792695e+00, current residual:2.892329e+00 +wall time: 0.000581026s + +time increment:77470 time: 77.47 +field ' c' [explicit solve]: current solution: 9.621995e+01, current residual:5.808593e+01 +field 'mu' [explicit solve]: current solution: 4.792639e+00, current residual:2.892294e+00 +wall time: 0.000586033s + +time increment:77480 time: 77.48 +field ' c' [explicit solve]: current solution: 9.621999e+01, current residual:5.808595e+01 +field 'mu' [explicit solve]: current solution: 4.792584e+00, current residual:2.892259e+00 +wall time: 0.000653028s + +time increment:77490 time: 77.49 +field ' c' [explicit solve]: current solution: 9.622003e+01, current residual:5.808597e+01 +field 'mu' [explicit solve]: current solution: 4.792528e+00, current residual:2.892225e+00 +wall time: 0.000614882s + +time increment:77500 time: 77.5 +field ' c' [explicit solve]: current solution: 9.622007e+01, current residual:5.808599e+01 +field 'mu' [explicit solve]: current solution: 4.792473e+00, current residual:2.892190e+00 +wall time: 0.00069809s + +time increment:77510 time: 77.51 +field ' c' [explicit solve]: current solution: 9.622012e+01, current residual:5.808602e+01 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[explicit solve]: current solution: 4.792196e+00, current residual:2.892016e+00 +wall time: 0.000746012s + +time increment:77560 time: 77.56 +field ' c' [explicit solve]: current solution: 9.622032e+01, current residual:5.808613e+01 +field 'mu' [explicit solve]: current solution: 4.792140e+00, current residual:2.891981e+00 +wall time: 0.000632048s + +time increment:77570 time: 77.57 +field ' c' [explicit solve]: current solution: 9.622037e+01, current residual:5.808615e+01 +field 'mu' [explicit solve]: current solution: 4.792085e+00, current residual:2.891947e+00 +wall time: 0.000693083s + +time increment:77580 time: 77.58 +field ' c' [explicit solve]: current solution: 9.622041e+01, current residual:5.808617e+01 +field 'mu' [explicit solve]: current solution: 4.792029e+00, current residual:2.891912e+00 +wall time: 0.000657082s + +time increment:77590 time: 77.59 +field ' c' [explicit solve]: current solution: 9.622045e+01, current residual:5.808620e+01 +field 'mu' [explicit solve]: current solution: 4.791974e+00, current residual:2.891877e+00 +wall time: 0.000691175s + +time increment:77600 time: 77.6 +field ' c' [explicit solve]: current solution: 9.622049e+01, current residual:5.808622e+01 +field 'mu' [explicit solve]: current solution: 4.791918e+00, current residual:2.891843e+00 +wall time: 0.00070715s + +time increment:77610 time: 77.61 +field ' c' [explicit solve]: current solution: 9.622053e+01, current residual:5.808624e+01 +field 'mu' [explicit solve]: current solution: 4.791863e+00, current residual:2.891808e+00 +wall time: 0.000691175s + +time increment:77620 time: 77.62 +field ' c' [explicit solve]: current solution: 9.622057e+01, current residual:5.808626e+01 +field 'mu' [explicit solve]: current solution: 4.791807e+00, current residual:2.891773e+00 +wall time: 0.000710964s + +time increment:77630 time: 77.63 +field ' c' [explicit solve]: current solution: 9.622061e+01, current residual:5.808629e+01 +field 'mu' [explicit solve]: current solution: 4.791752e+00, current residual:2.891738e+00 +wall time: 0.000653982s + +time increment:77640 time: 77.64 +field ' c' [explicit solve]: current solution: 9.622066e+01, current residual:5.808631e+01 +field 'mu' [explicit solve]: current solution: 4.791696e+00, current residual:2.891704e+00 +wall time: 0.000693083s + +time increment:77650 time: 77.65 +field ' c' [explicit solve]: current solution: 9.622070e+01, current residual:5.808633e+01 +field 'mu' [explicit solve]: current solution: 4.791641e+00, current residual:2.891669e+00 +wall time: 0.000664949s + +time increment:77660 time: 77.66 +field ' c' [explicit solve]: current solution: 9.622074e+01, current residual:5.808635e+01 +field 'mu' [explicit solve]: current solution: 4.791585e+00, current residual:2.891634e+00 +wall time: 0.000620127s + +time increment:77670 time: 77.67 +field ' c' [explicit solve]: current solution: 9.622078e+01, current residual:5.808638e+01 +field 'mu' [explicit solve]: current solution: 4.791530e+00, current residual:2.891599e+00 +wall time: 0.000693083s + +time increment:77680 time: 77.68 +field ' c' [explicit solve]: current solution: 9.622082e+01, current residual:5.808640e+01 +field 'mu' [explicit solve]: current solution: 4.791474e+00, current residual:2.891565e+00 +wall time: 0.00068903s + +time increment:77690 time: 77.69 +field ' c' [explicit solve]: current solution: 9.622086e+01, current residual:5.808642e+01 +field 'mu' [explicit solve]: current solution: 4.791419e+00, current residual:2.891530e+00 +wall time: 0.000689983s + +time increment:77700 time: 77.7 +field ' c' [explicit solve]: current solution: 9.622091e+01, current residual:5.808644e+01 +field 'mu' [explicit solve]: current solution: 4.791363e+00, current residual:2.891495e+00 +wall time: 0.000711203s + +time increment:77710 time: 77.71 +field ' c' [explicit solve]: current solution: 9.622095e+01, current residual:5.808647e+01 +field 'mu' [explicit solve]: current solution: 4.791308e+00, current residual:2.891461e+00 +wall time: 0.000739098s + +time increment:77720 time: 77.72 +field ' c' [explicit solve]: current solution: 9.622099e+01, current residual:5.808649e+01 +field 'mu' [explicit solve]: current solution: 4.791252e+00, current residual:2.891426e+00 +wall time: 0.000694036s + +time increment:77730 time: 77.73 +field ' c' [explicit solve]: current solution: 9.622103e+01, current residual:5.808651e+01 +field 'mu' [explicit solve]: current solution: 4.791197e+00, current residual:2.891391e+00 +wall time: 0.000689983s + +time increment:77740 time: 77.74 +field ' c' [explicit solve]: current solution: 9.622107e+01, current residual:5.808653e+01 +field 'mu' [explicit solve]: current solution: 4.791141e+00, current residual:2.891356e+00 +wall time: 0.000716925s + +time increment:77750 time: 77.75 +field ' c' [explicit solve]: current solution: 9.622112e+01, current residual:5.808656e+01 +field 'mu' [explicit solve]: current solution: 4.791086e+00, current residual:2.891322e+00 +wall time: 0.000757933s + +time increment:77760 time: 77.76 +field ' c' [explicit solve]: current solution: 9.622116e+01, current residual:5.808658e+01 +field 'mu' [explicit solve]: current solution: 4.791030e+00, current residual:2.891287e+00 +wall time: 0.000638008s + +time increment:77770 time: 77.77 +field ' c' [explicit solve]: current solution: 9.622120e+01, current residual:5.808660e+01 +field 'mu' [explicit solve]: current solution: 4.790975e+00, current residual:2.891252e+00 +wall time: 0.000705957s + +time increment:77780 time: 77.78 +field ' c' [explicit solve]: current solution: 9.622124e+01, current residual:5.808662e+01 +field 'mu' [explicit solve]: current solution: 4.790919e+00, current residual:2.891217e+00 +wall time: 0.00068903s + +time increment:77790 time: 77.79 +field ' c' [explicit solve]: current solution: 9.622128e+01, current residual:5.808665e+01 +field 'mu' [explicit solve]: current solution: 4.790864e+00, current residual:2.891183e+00 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0.000653028s + +time increment:77840 time: 77.84 +field ' c' [explicit solve]: current solution: 9.622149e+01, current residual:5.808676e+01 +field 'mu' [explicit solve]: current solution: 4.790586e+00, current residual:2.891009e+00 +wall time: 0.000695944s + +time increment:77850 time: 77.85 +field ' c' [explicit solve]: current solution: 9.622153e+01, current residual:5.808678e+01 +field 'mu' [explicit solve]: current solution: 4.790531e+00, current residual:2.890974e+00 +wall time: 0.000731945s + +time increment:77860 time: 77.86 +field ' c' [explicit solve]: current solution: 9.622157e+01, current residual:5.808680e+01 +field 'mu' [explicit solve]: current solution: 4.790475e+00, current residual:2.890939e+00 +wall time: 0.000597s + +time increment:77870 time: 77.87 +field ' c' [explicit solve]: current solution: 9.622162e+01, current residual:5.808683e+01 +field 'mu' [explicit solve]: current solution: 4.790420e+00, current residual:2.890905e+00 +wall time: 0.000643015s + +time increment:77880 time: 77.88 +field ' c' [explicit solve]: current solution: 9.622166e+01, current residual:5.808685e+01 +field 'mu' [explicit solve]: current solution: 4.790364e+00, current residual:2.890870e+00 +wall time: 0.000623941s + +time increment:77890 time: 77.89 +field ' c' [explicit solve]: current solution: 9.622170e+01, current residual:5.808687e+01 +field 'mu' [explicit solve]: current solution: 4.790309e+00, current residual:2.890835e+00 +wall time: 0.000600815s + +time increment:77900 time: 77.9 +field ' c' [explicit solve]: current solution: 9.622174e+01, current residual:5.808690e+01 +field 'mu' [explicit solve]: current solution: 4.790253e+00, current residual:2.890801e+00 +wall time: 0.000563145s + +time increment:77910 time: 77.91 +field ' c' [explicit solve]: current solution: 9.622178e+01, current residual:5.808692e+01 +field 'mu' [explicit solve]: current solution: 4.790198e+00, current residual:2.890766e+00 +wall time: 0.00059104s + +time increment:77920 time: 77.92 +field ' c' [explicit solve]: current solution: 9.622183e+01, current residual:5.808694e+01 +field 'mu' [explicit solve]: current solution: 4.790142e+00, current residual:2.890731e+00 +wall time: 0.00065589s + +time increment:77930 time: 77.93 +field ' c' [explicit solve]: current solution: 9.622187e+01, current residual:5.808696e+01 +field 'mu' [explicit solve]: current solution: 4.790087e+00, current residual:2.890696e+00 +wall time: 0.00056982s + +time increment:77940 time: 77.94 +field ' c' [explicit solve]: current solution: 9.622191e+01, current residual:5.808699e+01 +field 'mu' [explicit solve]: current solution: 4.790031e+00, current residual:2.890662e+00 +wall time: 0.00068593s + +time increment:77950 time: 77.95 +field ' c' [explicit solve]: current solution: 9.622195e+01, current residual:5.808701e+01 +field 'mu' [explicit solve]: current solution: 4.789976e+00, current residual:2.890627e+00 +wall time: 0.000593901s + +time increment:77960 time: 77.96 +field ' c' [explicit solve]: current solution: 9.622199e+01, current residual:5.808703e+01 +field 'mu' [explicit solve]: current solution: 4.789920e+00, current residual:2.890592e+00 +wall time: 0.000579834s + +time increment:77970 time: 77.97 +field ' c' [explicit solve]: current solution: 9.622203e+01, current residual:5.808705e+01 +field 'mu' [explicit solve]: current solution: 4.789865e+00, current residual:2.890557e+00 +wall time: 0.00064683s + +time increment:77980 time: 77.98 +field ' c' [explicit solve]: current solution: 9.622208e+01, current residual:5.808708e+01 +field 'mu' [explicit solve]: current solution: 4.789809e+00, current residual:2.890523e+00 +wall time: 0.000653028s + +time increment:77990 time: 77.99 +field ' c' [explicit solve]: current solution: 9.622212e+01, current residual:5.808710e+01 +field 'mu' [explicit solve]: current solution: 4.789754e+00, current residual:2.890488e+00 +wall time: 0.000767946s + +time increment:78000 time: 78 +field ' c' [explicit solve]: current solution: 9.622216e+01, current residual:5.808712e+01 +field 'mu' [explicit solve]: current solution: 4.789698e+00, current residual:2.890453e+00 +wall time: 0.00071907s + +time increment:78010 time: 78.01 +field ' c' [explicit solve]: current solution: 9.622220e+01, current residual:5.808714e+01 +field 'mu' [explicit solve]: current solution: 4.789643e+00, current residual:2.890418e+00 +wall time: 0.000667095s + +time increment:78020 time: 78.02 +field ' c' [explicit solve]: current solution: 9.622224e+01, current residual:5.808717e+01 +field 'mu' [explicit solve]: current solution: 4.789587e+00, current residual:2.890384e+00 +wall time: 0.000602961s + +time increment:78030 time: 78.03 +field ' c' [explicit solve]: current solution: 9.622229e+01, current residual:5.808719e+01 +field 'mu' [explicit solve]: current solution: 4.789532e+00, current residual:2.890349e+00 +wall time: 0.000566006s + +time increment:78040 time: 78.04 +field ' c' [explicit solve]: current solution: 9.622233e+01, current residual:5.808721e+01 +field 'mu' [explicit solve]: current solution: 4.789476e+00, current residual:2.890314e+00 +wall time: 0.000682831s + +time increment:78050 time: 78.05 +field ' c' [explicit solve]: current solution: 9.622237e+01, current residual:5.808723e+01 +field 'mu' [explicit solve]: current solution: 4.789420e+00, current residual:2.890279e+00 +wall time: 0.000567913s + +time increment:78060 time: 78.06 +field ' c' [explicit solve]: current solution: 9.622241e+01, current residual:5.808726e+01 +field 'mu' [explicit solve]: current solution: 4.789365e+00, current residual:2.890245e+00 +wall time: 0.000626802s + +time increment:78070 time: 78.07 +field ' c' [explicit solve]: current solution: 9.622245e+01, current residual:5.808728e+01 +field 'mu' [explicit solve]: current solution: 4.789309e+00, current residual:2.890210e+00 +wall time: 0.000582933s + +time increment:78080 time: 78.08 +field ' c' [explicit solve]: current solution: 9.622250e+01, current residual:5.808730e+01 +field 'mu' [explicit solve]: current solution: 4.789254e+00, current residual:2.890175e+00 +wall time: 0.000576973s + +time increment:78090 time: 78.09 +field ' c' [explicit solve]: current solution: 9.622254e+01, current residual:5.808733e+01 +field 'mu' [explicit solve]: current solution: 4.789198e+00, current residual:2.890141e+00 +wall time: 0.00059104s + +time increment:78100 time: 78.1 +field ' c' [explicit solve]: current solution: 9.622258e+01, current residual:5.808735e+01 +field 'mu' [explicit solve]: current solution: 4.789143e+00, current residual:2.890106e+00 +wall time: 0.000591993s + +time increment:78110 time: 78.11 +field ' c' [explicit solve]: current solution: 9.622262e+01, current residual:5.808737e+01 +field 'mu' [explicit solve]: current solution: 4.789087e+00, current residual:2.890071e+00 +wall time: 0.000573874s + +time increment:78120 time: 78.12 +field ' c' [explicit solve]: current solution: 9.622266e+01, current residual:5.808739e+01 +field 'mu' [explicit solve]: current solution: 4.789032e+00, current residual:2.890036e+00 +wall time: 0.000629902s + +time increment:78130 time: 78.13 +field ' c' [explicit solve]: current solution: 9.622270e+01, current residual:5.808742e+01 +field 'mu' [explicit solve]: current solution: 4.788976e+00, current residual:2.890002e+00 +wall time: 0.00057292s + +time increment:78140 time: 78.14 +field ' c' [explicit solve]: current solution: 9.622275e+01, current residual:5.808744e+01 +field 'mu' [explicit solve]: current solution: 4.788921e+00, current residual:2.889967e+00 +wall time: 0.000556946s + +time increment:78150 time: 78.15 +field ' c' [explicit solve]: current solution: 9.622279e+01, current residual:5.808746e+01 +field 'mu' [explicit solve]: current solution: 4.788865e+00, current residual:2.889932e+00 +wall time: 0.000573874s + +time increment:78160 time: 78.16 +field ' c' [explicit solve]: current solution: 9.622283e+01, current residual:5.808748e+01 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solve]: current solution: 4.788587e+00, current residual:2.889758e+00 +wall time: 0.00067091s + +time increment:78210 time: 78.21 +field ' c' [explicit solve]: current solution: 9.622304e+01, current residual:5.808760e+01 +field 'mu' [explicit solve]: current solution: 4.788532e+00, current residual:2.889724e+00 +wall time: 0.000678778s + +time increment:78220 time: 78.22 +field ' c' [explicit solve]: current solution: 9.622308e+01, current residual:5.808762e+01 +field 'mu' [explicit solve]: current solution: 4.788476e+00, current residual:2.889689e+00 +wall time: 0.000663996s + +time increment:78230 time: 78.23 +field ' c' [explicit solve]: current solution: 9.622312e+01, current residual:5.808764e+01 +field 'mu' [explicit solve]: current solution: 4.788421e+00, current residual:2.889654e+00 +wall time: 0.000734806s + +time increment:78240 time: 78.24 +field ' c' [explicit solve]: current solution: 9.622317e+01, current residual:5.808767e+01 +field 'mu' [explicit solve]: current solution: 4.788365e+00, current residual:2.889619e+00 +wall time: 0.000775814s + +time increment:78250 time: 78.25 +field ' c' [explicit solve]: current solution: 9.622321e+01, current residual:5.808769e+01 +field 'mu' [explicit solve]: current solution: 4.788310e+00, current residual:2.889585e+00 +wall time: 0.000693083s + +time increment:78260 time: 78.26 +field ' c' [explicit solve]: current solution: 9.622325e+01, current residual:5.808771e+01 +field 'mu' [explicit solve]: current solution: 4.788254e+00, current residual:2.889550e+00 +wall time: 0.000659943s + +time increment:78270 time: 78.27 +field ' c' [explicit solve]: current solution: 9.622329e+01, current residual:5.808773e+01 +field 'mu' [explicit solve]: current solution: 4.788198e+00, current residual:2.889515e+00 +wall time: 0.000707865s + +time increment:78280 time: 78.28 +field ' c' [explicit solve]: current solution: 9.622333e+01, current residual:5.808776e+01 +field 'mu' [explicit solve]: current solution: 4.788143e+00, current residual:2.889480e+00 +wall time: 0.00067091s + +time increment:78290 time: 78.29 +field ' c' [explicit solve]: current solution: 9.622338e+01, current residual:5.808778e+01 +field 'mu' [explicit solve]: current solution: 4.788087e+00, current residual:2.889446e+00 +wall time: 0.000760078s + +time increment:78300 time: 78.3 +field ' c' [explicit solve]: current solution: 9.622342e+01, current residual:5.808780e+01 +field 'mu' [explicit solve]: current solution: 4.788032e+00, current residual:2.889411e+00 +wall time: 0.000766039s + +time increment:78310 time: 78.31 +field ' c' [explicit solve]: current solution: 9.622346e+01, current residual:5.808783e+01 +field 'mu' [explicit solve]: current solution: 4.787976e+00, current residual:2.889376e+00 +wall time: 0.00074482s + +time increment:78320 time: 78.32 +field ' c' [explicit solve]: current solution: 9.622350e+01, current residual:5.808785e+01 +field 'mu' [explicit solve]: current solution: 4.787921e+00, current residual:2.889341e+00 +wall time: 0.000784159s + +time increment:78330 time: 78.33 +field ' c' [explicit solve]: current solution: 9.622354e+01, current residual:5.808787e+01 +field 'mu' [explicit solve]: current solution: 4.787865e+00, current residual:2.889307e+00 +wall time: 0.000659943s + +time increment:78340 time: 78.34 +field ' c' [explicit solve]: current solution: 9.622359e+01, current residual:5.808789e+01 +field 'mu' [explicit solve]: current solution: 4.787809e+00, current residual:2.889272e+00 +wall time: 0.000664949s + +time increment:78350 time: 78.35 +field ' c' [explicit solve]: current solution: 9.622363e+01, current residual:5.808792e+01 +field 'mu' [explicit solve]: current solution: 4.787754e+00, current residual:2.889237e+00 +wall time: 0.000636101s + +time increment:78360 time: 78.36 +field ' c' [explicit solve]: current solution: 9.622367e+01, current residual:5.808794e+01 +field 'mu' [explicit solve]: current solution: 4.787698e+00, current residual:2.889202e+00 +wall time: 0.000684977s + +time increment:78370 time: 78.37 +field ' c' [explicit solve]: current solution: 9.622371e+01, current residual:5.808796e+01 +field 'mu' [explicit solve]: current solution: 4.787643e+00, current residual:2.889168e+00 +wall time: 0.000708818s + +time increment:78380 time: 78.38 +field ' c' [explicit solve]: current solution: 9.622376e+01, current residual:5.808799e+01 +field 'mu' [explicit solve]: current solution: 4.787587e+00, current residual:2.889133e+00 +wall time: 0.000644922s + +time increment:78390 time: 78.39 +field ' c' [explicit solve]: current solution: 9.622380e+01, current residual:5.808801e+01 +field 'mu' [explicit solve]: current solution: 4.787532e+00, current residual:2.889098e+00 +wall time: 0.000679016s + +time increment:78400 time: 78.4 +field ' c' [explicit solve]: current solution: 9.622384e+01, current residual:5.808803e+01 +field 'mu' [explicit solve]: current solution: 4.787476e+00, current residual:2.889063e+00 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0.000680208s + +time increment:78450 time: 78.45 +field ' c' [explicit solve]: current solution: 9.622405e+01, current residual:5.808814e+01 +field 'mu' [explicit solve]: current solution: 4.787198e+00, current residual:2.888890e+00 +wall time: 0.000653028s + +time increment:78460 time: 78.46 +field ' c' [explicit solve]: current solution: 9.622409e+01, current residual:5.808817e+01 +field 'mu' [explicit solve]: current solution: 4.787142e+00, current residual:2.888855e+00 +wall time: 0.00066185s + +time increment:78470 time: 78.47 +field ' c' [explicit solve]: current solution: 9.622413e+01, current residual:5.808819e+01 +field 'mu' [explicit solve]: current solution: 4.787087e+00, current residual:2.888820e+00 +wall time: 0.00120115s + +time increment:78480 time: 78.48 +field ' c' [explicit solve]: current solution: 9.622418e+01, current residual:5.808821e+01 +field 'mu' [explicit solve]: current solution: 4.787031e+00, current residual:2.888785e+00 +wall time: 0.000788927s + +time increment:78490 time: 78.49 +field ' c' [explicit solve]: current solution: 9.622422e+01, current residual:5.808824e+01 +field 'mu' [explicit solve]: current solution: 4.786976e+00, current residual:2.888751e+00 +wall time: 0.000666857s + +time increment:78500 time: 78.5 +field ' c' [explicit solve]: current solution: 9.622426e+01, current residual:5.808826e+01 +field 'mu' [explicit solve]: current solution: 4.786920e+00, current residual:2.888716e+00 +wall time: 0.000682116s + +time increment:78510 time: 78.51 +field ' c' [explicit solve]: current solution: 9.622430e+01, current residual:5.808828e+01 +field 'mu' [explicit solve]: current solution: 4.786864e+00, current residual:2.888681e+00 +wall time: 0.00070405s + +time increment:78520 time: 78.52 +field ' c' [explicit solve]: current solution: 9.622434e+01, current residual:5.808830e+01 +field 'mu' [explicit solve]: current solution: 4.786809e+00, current residual:2.888646e+00 +wall time: 0.000669003s + +time increment:78530 time: 78.53 +field ' c' [explicit solve]: current solution: 9.622439e+01, current residual:5.808833e+01 +field 'mu' [explicit solve]: current solution: 4.786753e+00, current residual:2.888612e+00 +wall time: 0.00067997s + +time increment:78540 time: 78.54 +field ' c' [explicit solve]: current solution: 9.622443e+01, current residual:5.808835e+01 +field 'mu' [explicit solve]: current solution: 4.786698e+00, current residual:2.888577e+00 +wall time: 0.00065589s + +time increment:78550 time: 78.55 +field ' c' [explicit solve]: current solution: 9.622447e+01, current residual:5.808837e+01 +field 'mu' [explicit solve]: current solution: 4.786642e+00, current residual:2.888542e+00 +wall time: 0.000657082s + +time increment:78560 time: 78.56 +field ' c' [explicit solve]: current solution: 9.622451e+01, current residual:5.808840e+01 +field 'mu' [explicit solve]: current solution: 4.786586e+00, current residual:2.888507e+00 +wall time: 0.000657082s + +time increment:78570 time: 78.57 +field ' c' [explicit solve]: current solution: 9.622456e+01, current residual:5.808842e+01 +field 'mu' [explicit solve]: current solution: 4.786531e+00, current residual:2.888473e+00 +wall time: 0.000569105s + +time increment:78580 time: 78.58 +field ' c' [explicit solve]: current solution: 9.622460e+01, current residual:5.808844e+01 +field 'mu' [explicit solve]: current solution: 4.786475e+00, current residual:2.888438e+00 +wall time: 0.000622034s + +time increment:78590 time: 78.59 +field ' c' [explicit solve]: current solution: 9.622464e+01, current residual:5.808846e+01 +field 'mu' [explicit solve]: current solution: 4.786420e+00, current residual:2.888403e+00 +wall time: 0.000643015s + +time increment:78600 time: 78.6 +field ' c' [explicit solve]: current solution: 9.622468e+01, current residual:5.808849e+01 +field 'mu' [explicit solve]: current solution: 4.786364e+00, current residual:2.888368e+00 +wall time: 0.000590086s + +time increment:78610 time: 78.61 +field ' c' [explicit solve]: current solution: 9.622472e+01, current residual:5.808851e+01 +field 'mu' [explicit solve]: current solution: 4.786308e+00, current residual:2.888334e+00 +wall time: 0.000580072s + +time increment:78620 time: 78.62 +field ' c' [explicit solve]: current solution: 9.622477e+01, current residual:5.808853e+01 +field 'mu' [explicit solve]: current solution: 4.786253e+00, current residual:2.888299e+00 +wall time: 0.000638008s + +time increment:78630 time: 78.63 +field ' c' [explicit solve]: current solution: 9.622481e+01, current residual:5.808856e+01 +field 'mu' [explicit solve]: current solution: 4.786197e+00, current residual:2.888264e+00 +wall time: 0.000669956s + +time increment:78640 time: 78.64 +field ' c' [explicit solve]: current solution: 9.622485e+01, current residual:5.808858e+01 +field 'mu' [explicit solve]: current solution: 4.786142e+00, current residual:2.888229e+00 +wall time: 0.000648975s + +time increment:78650 time: 78.65 +field ' c' [explicit solve]: current solution: 9.622489e+01, current residual:5.808860e+01 +field 'mu' [explicit solve]: current solution: 4.786086e+00, current residual:2.888195e+00 +wall time: 0.00068903s + +time increment:78660 time: 78.66 +field ' c' [explicit solve]: current solution: 9.622494e+01, current residual:5.808863e+01 +field 'mu' [explicit solve]: current solution: 4.786030e+00, current residual:2.888160e+00 +wall time: 0.000573158s + +time increment:78670 time: 78.67 +field ' c' [explicit solve]: current solution: 9.622498e+01, current residual:5.808865e+01 +field 'mu' [explicit solve]: current solution: 4.785975e+00, current residual:2.888125e+00 +wall time: 0.00061202s + +time increment:78680 time: 78.68 +field ' c' [explicit solve]: current solution: 9.622502e+01, current residual:5.808867e+01 +field 'mu' [explicit solve]: current solution: 4.785919e+00, current residual:2.888090e+00 +wall time: 0.000638008s + +time increment:78690 time: 78.69 +field ' c' [explicit solve]: current solution: 9.622506e+01, current residual:5.808869e+01 +field 'mu' [explicit solve]: current solution: 4.785863e+00, current residual:2.888056e+00 +wall time: 0.000658989s + +time increment:78700 time: 78.7 +field ' c' [explicit solve]: current solution: 9.622510e+01, current residual:5.808872e+01 +field 'mu' [explicit solve]: current solution: 4.785808e+00, current residual:2.888021e+00 +wall time: 0.000626087s + +time increment:78710 time: 78.71 +field ' c' [explicit solve]: current solution: 9.622515e+01, current residual:5.808874e+01 +field 'mu' [explicit solve]: current solution: 4.785752e+00, current residual:2.887986e+00 +wall time: 0.000638962s + +time increment:78720 time: 78.72 +field ' c' [explicit solve]: current solution: 9.622519e+01, current residual:5.808876e+01 +field 'mu' [explicit solve]: current solution: 4.785697e+00, current residual:2.887951e+00 +wall time: 0.000570059s + +time increment:78730 time: 78.73 +field ' c' [explicit solve]: current solution: 9.622523e+01, current residual:5.808879e+01 +field 'mu' [explicit solve]: current solution: 4.785641e+00, current residual:2.887917e+00 +wall time: 0.000569105s + +time increment:78740 time: 78.74 +field ' c' [explicit solve]: current solution: 9.622527e+01, current residual:5.808881e+01 +field 'mu' [explicit solve]: current solution: 4.785585e+00, current residual:2.887882e+00 +wall time: 0.000701904s + +time increment:78750 time: 78.75 +field ' c' [explicit solve]: current solution: 9.622532e+01, current residual:5.808883e+01 +field 'mu' [explicit solve]: current solution: 4.785530e+00, current residual:2.887847e+00 +wall time: 0.000565052s + +time increment:78760 time: 78.76 +field ' c' [explicit solve]: current solution: 9.622536e+01, current residual:5.808885e+01 +field 'mu' [explicit solve]: current solution: 4.785474e+00, current residual:2.887812e+00 +wall time: 0.000576019s + +time increment:78770 time: 78.77 +field ' c' [explicit solve]: current solution: 9.622540e+01, current residual:5.808888e+01 +field 'mu' [explicit solve]: current solution: 4.785418e+00, current residual:2.887778e+00 +wall time: 0.000571966s + +time increment:78780 time: 78.78 +field ' c' [explicit solve]: current solution: 9.622544e+01, current residual:5.808890e+01 +field 'mu' [explicit solve]: current solution: 4.785363e+00, current residual:2.887743e+00 +wall time: 0.000565052s + +time increment:78790 time: 78.79 +field ' c' [explicit solve]: current solution: 9.622549e+01, current residual:5.808892e+01 +field 'mu' [explicit solve]: current solution: 4.785307e+00, current residual:2.887708e+00 +wall time: 0.000592947s + +time increment:78800 time: 78.8 +field ' c' [explicit solve]: current solution: 9.622553e+01, current residual:5.808895e+01 +field 'mu' [explicit solve]: current solution: 4.785251e+00, current residual:2.887673e+00 +wall time: 0.000650883s + +time increment:78810 time: 78.81 +field ' c' [explicit solve]: current solution: 9.622557e+01, current residual:5.808897e+01 +field 'mu' [explicit solve]: current solution: 4.785196e+00, current residual:2.887639e+00 +wall time: 0.000599146s + +time increment:78820 time: 78.82 +field ' c' [explicit solve]: current solution: 9.622561e+01, current residual:5.808899e+01 +field 'mu' [explicit solve]: current solution: 4.785140e+00, current residual:2.887604e+00 +wall time: 0.00059104s + +time increment:78830 time: 78.83 +field ' c' [explicit solve]: current solution: 9.622565e+01, current residual:5.808902e+01 +field 'mu' [explicit solve]: current solution: 4.785085e+00, current residual:2.887569e+00 +wall time: 0.000576019s + +time increment:78840 time: 78.84 +field ' c' [explicit solve]: current solution: 9.622570e+01, current residual:5.808904e+01 +field 'mu' [explicit solve]: current solution: 4.785029e+00, current residual:2.887534e+00 +wall time: 0.000633001s + +time increment:78850 time: 78.85 +field ' c' [explicit solve]: current solution: 9.622574e+01, current residual:5.808906e+01 +field 'mu' [explicit solve]: current solution: 4.784973e+00, current residual:2.887500e+00 +wall time: 0.000742912s + +time increment:78860 time: 78.86 +field ' c' [explicit solve]: current solution: 9.622578e+01, current residual:5.808908e+01 +field 'mu' [explicit solve]: current solution: 4.784918e+00, current residual:2.887465e+00 +wall time: 0.000614882s + +time increment:78870 time: 78.87 +field ' c' [explicit solve]: current solution: 9.622582e+01, current residual:5.808911e+01 +field 'mu' [explicit solve]: current solution: 4.784862e+00, current residual:2.887430e+00 +wall time: 0.000561953s + +time increment:78880 time: 78.88 +field ' c' [explicit solve]: current solution: 9.622587e+01, current residual:5.808913e+01 +field 'mu' [explicit solve]: current solution: 4.784806e+00, current residual:2.887395e+00 +wall time: 0.000674009s + +time increment:78890 time: 78.89 +field ' c' [explicit solve]: current solution: 9.622591e+01, current residual:5.808915e+01 +field 'mu' [explicit solve]: current solution: 4.784751e+00, current residual:2.887360e+00 +wall time: 0.000562906s + +time increment:78900 time: 78.9 +field ' c' [explicit solve]: current solution: 9.622595e+01, current residual:5.808918e+01 +field 'mu' [explicit solve]: current solution: 4.784695e+00, current residual:2.887326e+00 +wall time: 0.000646114s + +time increment:78910 time: 78.91 +field ' c' [explicit solve]: current solution: 9.622599e+01, current residual:5.808920e+01 +field 'mu' [explicit solve]: current solution: 4.784639e+00, current residual:2.887291e+00 +wall time: 0.000591993s + +time increment:78920 time: 78.92 +field ' c' [explicit solve]: current solution: 9.622604e+01, current residual:5.808922e+01 +field 'mu' [explicit solve]: current solution: 4.784584e+00, current residual:2.887256e+00 +wall time: 0.000696898s + +time increment:78930 time: 78.93 +field ' c' [explicit solve]: current solution: 9.622608e+01, current residual:5.808925e+01 +field 'mu' [explicit solve]: current solution: 4.784528e+00, current residual:2.887221e+00 +wall time: 0.000628948s + +time increment:78940 time: 78.94 +field ' c' [explicit solve]: current solution: 9.622612e+01, current residual:5.808927e+01 +field 'mu' [explicit solve]: current solution: 4.784472e+00, current residual:2.887187e+00 +wall time: 0.000663042s + +time increment:78950 time: 78.95 +field ' c' [explicit solve]: current solution: 9.622616e+01, current residual:5.808929e+01 +field 'mu' [explicit solve]: current solution: 4.784417e+00, current residual:2.887152e+00 +wall time: 0.000670195s + +time increment:78960 time: 78.96 +field ' c' [explicit solve]: current solution: 9.622621e+01, current residual:5.808931e+01 +field 'mu' [explicit solve]: current solution: 4.784361e+00, current residual:2.887117e+00 +wall time: 0.000570059s + +time increment:78970 time: 78.97 +field ' c' [explicit solve]: current solution: 9.622625e+01, current residual:5.808934e+01 +field 'mu' [explicit solve]: current solution: 4.784305e+00, current residual:2.887082e+00 +wall time: 0.000627041s + +time increment:78980 time: 78.98 +field ' c' [explicit solve]: current solution: 9.622629e+01, current residual:5.808936e+01 +field 'mu' [explicit solve]: current solution: 4.784250e+00, current residual:2.887048e+00 +wall time: 0.000663042s + +time increment:78990 time: 78.99 +field ' c' [explicit solve]: current solution: 9.622633e+01, current residual:5.808938e+01 +field 'mu' [explicit solve]: current solution: 4.784194e+00, current residual:2.887013e+00 +wall time: 0.000658035s + +time increment:79000 time: 79 +field ' c' [explicit solve]: current solution: 9.622637e+01, current residual:5.808941e+01 +field 'mu' [explicit solve]: current solution: 4.784138e+00, current residual:2.886978e+00 +wall time: 0.000663996s + +time increment:79010 time: 79.01 +field ' c' [explicit solve]: current solution: 9.622642e+01, current residual:5.808943e+01 +field 'mu' [explicit solve]: current solution: 4.784083e+00, current residual:2.886943e+00 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0.000605106s + +time increment:79060 time: 79.06 +field ' c' [explicit solve]: current solution: 9.622663e+01, current residual:5.808954e+01 +field 'mu' [explicit solve]: current solution: 4.783804e+00, current residual:2.886769e+00 +wall time: 0.000558138s + +time increment:79070 time: 79.07 +field ' c' [explicit solve]: current solution: 9.622667e+01, current residual:5.808957e+01 +field 'mu' [explicit solve]: current solution: 4.783749e+00, current residual:2.886735e+00 +wall time: 0.000699997s + +time increment:79080 time: 79.08 +field ' c' [explicit solve]: current solution: 9.622671e+01, current residual:5.808959e+01 +field 'mu' [explicit solve]: current solution: 4.783693e+00, current residual:2.886700e+00 +wall time: 0.000650883s + +time increment:79090 time: 79.09 +field ' c' [explicit solve]: current solution: 9.622676e+01, current residual:5.808961e+01 +field 'mu' [explicit solve]: current solution: 4.783637e+00, current residual:2.886665e+00 +wall time: 0.000657082s + +time increment:79100 time: 79.1 +field ' c' [explicit solve]: current solution: 9.622680e+01, current residual:5.808964e+01 +field 'mu' [explicit solve]: current solution: 4.783581e+00, current residual:2.886630e+00 +wall time: 0.00059104s + +time increment:79110 time: 79.11 +field ' c' [explicit solve]: current solution: 9.622684e+01, current residual:5.808966e+01 +field 'mu' [explicit solve]: current solution: 4.783526e+00, current residual:2.886596e+00 +wall time: 0.000629902s + +time increment:79120 time: 79.12 +field ' c' [explicit solve]: current solution: 9.622688e+01, current residual:5.808968e+01 +field 'mu' [explicit solve]: current solution: 4.783470e+00, current residual:2.886561e+00 +wall time: 0.000733852s + +time increment:79130 time: 79.13 +field ' c' [explicit solve]: current solution: 9.622693e+01, current residual:5.808971e+01 +field 'mu' [explicit solve]: current solution: 4.783414e+00, current residual:2.886526e+00 +wall time: 0.000677824s + +time increment:79140 time: 79.14 +field ' c' [explicit solve]: current solution: 9.622697e+01, current residual:5.808973e+01 +field 'mu' [explicit solve]: current solution: 4.783359e+00, current residual:2.886491e+00 +wall time: 0.000681877s + +time increment:79150 time: 79.15 +field ' c' [explicit solve]: current solution: 9.622701e+01, current residual:5.808975e+01 +field 'mu' [explicit solve]: current solution: 4.783303e+00, current residual:2.886456e+00 +wall time: 0.000672102s + +time increment:79160 time: 79.16 +field ' c' [explicit solve]: current solution: 9.622705e+01, current residual:5.808978e+01 +field 'mu' [explicit solve]: current solution: 4.783247e+00, current residual:2.886422e+00 +wall time: 0.00069809s + +time increment:79170 time: 79.17 +field ' c' [explicit solve]: current solution: 9.622710e+01, current residual:5.808980e+01 +field 'mu' [explicit solve]: current solution: 4.783192e+00, current residual:2.886387e+00 +wall time: 0.000653982s + +time increment:79180 time: 79.18 +field ' c' [explicit solve]: current solution: 9.622714e+01, current residual:5.808982e+01 +field 'mu' [explicit solve]: current solution: 4.783136e+00, current residual:2.886352e+00 +wall time: 0.000668049s + +time increment:79190 time: 79.19 +field ' c' [explicit solve]: current solution: 9.622718e+01, current residual:5.808985e+01 +field 'mu' [explicit solve]: current solution: 4.783080e+00, current residual:2.886317e+00 +wall time: 0.000673056s + +time increment:79200 time: 79.2 +field ' c' [explicit solve]: current solution: 9.622722e+01, current residual:5.808987e+01 +field 'mu' [explicit solve]: current solution: 4.783024e+00, current residual:2.886283e+00 +wall time: 0.000669003s + +time increment:79210 time: 79.21 +field ' c' [explicit solve]: current solution: 9.622727e+01, current residual:5.808989e+01 +field 'mu' [explicit solve]: current solution: 4.782969e+00, current residual:2.886248e+00 +wall time: 0.000680208s + +time increment:79220 time: 79.22 +field ' c' [explicit solve]: current solution: 9.622731e+01, current residual:5.808991e+01 +field 'mu' [explicit solve]: current solution: 4.782913e+00, current residual:2.886213e+00 +wall time: 0.000668049s + +time increment:79230 time: 79.23 +field ' c' [explicit solve]: current solution: 9.622735e+01, current residual:5.808994e+01 +field 'mu' [explicit solve]: current solution: 4.782857e+00, current residual:2.886178e+00 +wall time: 0.000675917s + +time increment:79240 time: 79.24 +field ' c' [explicit solve]: current solution: 9.622739e+01, current residual:5.808996e+01 +field 'mu' [explicit solve]: current solution: 4.782802e+00, current residual:2.886144e+00 +wall time: 0.000716925s + +time increment:79250 time: 79.25 +field ' c' [explicit solve]: current solution: 9.622744e+01, current residual:5.808998e+01 +field 'mu' [explicit solve]: current solution: 4.782746e+00, current residual:2.886109e+00 +wall time: 0.000642061s + +time increment:79260 time: 79.26 +field ' c' [explicit solve]: current solution: 9.622748e+01, current residual:5.809001e+01 +field 'mu' [explicit solve]: current solution: 4.782690e+00, current residual:2.886074e+00 +wall time: 0.000638008s + +time increment:79270 time: 79.27 +field ' c' [explicit solve]: current solution: 9.622752e+01, current residual:5.809003e+01 +field 'mu' [explicit solve]: current solution: 4.782634e+00, current residual:2.886039e+00 +wall time: 0.000639915s + +time increment:79280 time: 79.28 +field ' c' [explicit solve]: current solution: 9.622756e+01, current residual:5.809005e+01 +field 'mu' [explicit solve]: current solution: 4.782579e+00, current residual:2.886004e+00 +wall time: 0.000675917s + +time increment:79290 time: 79.29 +field ' c' [explicit solve]: current solution: 9.622761e+01, current residual:5.809008e+01 +field 'mu' [explicit solve]: current solution: 4.782523e+00, current residual:2.885970e+00 +wall time: 0.000718832s + +time increment:79300 time: 79.3 +field ' c' [explicit solve]: current solution: 9.622765e+01, current residual:5.809010e+01 +field 'mu' [explicit solve]: current solution: 4.782467e+00, current residual:2.885935e+00 +wall time: 0.000654936s + +time increment:79310 time: 79.31 +field ' c' [explicit solve]: current solution: 9.622769e+01, current residual:5.809012e+01 +field 'mu' [explicit solve]: current solution: 4.782412e+00, current residual:2.885900e+00 +wall time: 0.000665903s + +time increment:79320 time: 79.32 +field ' c' [explicit solve]: current solution: 9.622774e+01, current residual:5.809015e+01 +field 'mu' [explicit solve]: current solution: 4.782356e+00, current residual:2.885865e+00 +wall time: 0.000637054s + +time increment:79330 time: 79.33 +field ' c' [explicit solve]: current solution: 9.622778e+01, current residual:5.809017e+01 +field 'mu' [explicit solve]: current solution: 4.782300e+00, current residual:2.885830e+00 +wall time: 0.000705957s + +time increment:79340 time: 79.34 +field ' c' [explicit solve]: current solution: 9.622782e+01, current residual:5.809019e+01 +field 'mu' [explicit solve]: current solution: 4.782244e+00, current residual:2.885796e+00 +wall time: 0.000649929s + +time increment:79350 time: 79.35 +field ' c' [explicit solve]: current solution: 9.622786e+01, current residual:5.809022e+01 +field 'mu' [explicit solve]: current solution: 4.782189e+00, current residual:2.885761e+00 +wall time: 0.00070405s + +time increment:79360 time: 79.36 +field ' c' [explicit solve]: current solution: 9.622791e+01, current residual:5.809024e+01 +field 'mu' [explicit solve]: current solution: 4.782133e+00, current residual:2.885726e+00 +wall time: 0.000693083s + +time increment:79370 time: 79.37 +field ' c' [explicit solve]: current solution: 9.622795e+01, current residual:5.809026e+01 +field 'mu' [explicit solve]: current solution: 4.782077e+00, current residual:2.885691e+00 +wall time: 0.00068593s + +time increment:79380 time: 79.38 +field ' c' [explicit solve]: current solution: 9.622799e+01, current residual:5.809028e+01 +field 'mu' [explicit solve]: current solution: 4.782021e+00, current residual:2.885657e+00 +wall time: 0.000597s + +time increment:79390 time: 79.39 +field ' c' [explicit solve]: current solution: 9.622803e+01, current residual:5.809031e+01 +field 'mu' [explicit solve]: current solution: 4.781966e+00, current residual:2.885622e+00 +wall time: 0.00067997s + +time increment:79400 time: 79.4 +field ' c' [explicit solve]: current solution: 9.622808e+01, current residual:5.809033e+01 +field 'mu' [explicit solve]: current solution: 4.781910e+00, current residual:2.885587e+00 +wall time: 0.000652075s + +time increment:79410 time: 79.41 +field ' c' [explicit solve]: current solution: 9.622812e+01, current residual:5.809035e+01 +field 'mu' [explicit solve]: current solution: 4.781854e+00, current residual:2.885552e+00 +wall time: 0.00070405s + +time increment:79420 time: 79.42 +field ' c' [explicit solve]: current solution: 9.622816e+01, current residual:5.809038e+01 +field 'mu' [explicit solve]: current solution: 4.781798e+00, current residual:2.885517e+00 +wall time: 0.000720978s + +time increment:79430 time: 79.43 +field ' c' [explicit solve]: current solution: 9.622820e+01, current residual:5.809040e+01 +field 'mu' [explicit solve]: current solution: 4.781743e+00, current residual:2.885483e+00 +wall time: 0.000678062s + +time increment:79440 time: 79.44 +field ' c' [explicit solve]: current solution: 9.622825e+01, current residual:5.809042e+01 +field 'mu' [explicit solve]: current solution: 4.781687e+00, current residual:2.885448e+00 +wall time: 0.00069809s + +time increment:79450 time: 79.45 +field ' c' [explicit solve]: current solution: 9.622829e+01, current residual:5.809045e+01 +field 'mu' [explicit solve]: current solution: 4.781631e+00, current residual:2.885413e+00 +wall time: 0.000695944s + +time increment:79460 time: 79.46 +field ' c' [explicit solve]: current solution: 9.622833e+01, current residual:5.809047e+01 +field 'mu' [explicit solve]: current solution: 4.781575e+00, current residual:2.885378e+00 +wall time: 0.000674963s + +time increment:79470 time: 79.47 +field ' c' [explicit solve]: current solution: 9.622837e+01, current residual:5.809049e+01 +field 'mu' [explicit solve]: current solution: 4.781520e+00, current residual:2.885344e+00 +wall time: 0.000668049s + +time increment:79480 time: 79.48 +field ' c' [explicit solve]: current solution: 9.622842e+01, current residual:5.809052e+01 +field 'mu' [explicit solve]: current solution: 4.781464e+00, current residual:2.885309e+00 +wall time: 0.000692129s + +time increment:79490 time: 79.49 +field ' c' [explicit solve]: current solution: 9.622846e+01, current residual:5.809054e+01 +field 'mu' [explicit solve]: current solution: 4.781408e+00, current residual:2.885274e+00 +wall time: 0.000773907s + +time increment:79500 time: 79.5 +field ' c' [explicit solve]: current solution: 9.622850e+01, current residual:5.809056e+01 +field 'mu' [explicit solve]: current solution: 4.781352e+00, current residual:2.885239e+00 +wall time: 0.00069499s + +time increment:79510 time: 79.51 +field ' c' [explicit solve]: current solution: 9.622855e+01, current residual:5.809059e+01 +field 'mu' [explicit solve]: current solution: 4.781297e+00, current residual:2.885204e+00 +wall time: 0.000777006s + +time increment:79520 time: 79.52 +field ' c' [explicit solve]: current solution: 9.622859e+01, current residual:5.809061e+01 +field 'mu' [explicit solve]: current solution: 4.781241e+00, current residual:2.885170e+00 +wall time: 0.000591993s + +time increment:79530 time: 79.53 +field ' c' [explicit solve]: current solution: 9.622863e+01, current residual:5.809063e+01 +field 'mu' [explicit solve]: current solution: 4.781185e+00, current residual:2.885135e+00 +wall time: 0.000623941s + +time increment:79540 time: 79.54 +field ' c' [explicit solve]: current solution: 9.622867e+01, current residual:5.809066e+01 +field 'mu' [explicit solve]: current solution: 4.781129e+00, current residual:2.885100e+00 +wall time: 0.000624895s + +time increment:79550 time: 79.55 +field ' c' [explicit solve]: current solution: 9.622872e+01, current residual:5.809068e+01 +field 'mu' [explicit solve]: current solution: 4.781074e+00, current residual:2.885065e+00 +wall time: 0.000560045s + +time increment:79560 time: 79.56 +field ' c' [explicit solve]: current solution: 9.622876e+01, current residual:5.809070e+01 +field 'mu' [explicit solve]: current solution: 4.781018e+00, current residual:2.885030e+00 +wall time: 0.000555992s + +time increment:79570 time: 79.57 +field ' c' [explicit solve]: current solution: 9.622880e+01, current residual:5.809073e+01 +field 'mu' [explicit solve]: current solution: 4.780962e+00, current residual:2.884996e+00 +wall time: 0.000573874s + +time increment:79580 time: 79.58 +field ' c' [explicit solve]: current solution: 9.622884e+01, current residual:5.809075e+01 +field 'mu' [explicit solve]: current solution: 4.780906e+00, current residual:2.884961e+00 +wall time: 0.000646114s + +time increment:79590 time: 79.59 +field ' c' [explicit solve]: current solution: 9.622889e+01, current residual:5.809077e+01 +field 'mu' [explicit solve]: current solution: 4.780850e+00, current residual:2.884926e+00 +wall time: 0.000622034s + +time increment:79600 time: 79.6 +field ' c' [explicit solve]: current solution: 9.622893e+01, current residual:5.809080e+01 +field 'mu' [explicit solve]: current solution: 4.780795e+00, current residual:2.884891e+00 +wall time: 0.000643015s + +time increment:79610 time: 79.61 +field ' c' [explicit solve]: current solution: 9.622897e+01, current residual:5.809082e+01 +field 'mu' [explicit solve]: current solution: 4.780739e+00, current residual:2.884856e+00 +wall time: 0.000678062s + +time increment:79620 time: 79.62 +field ' c' [explicit solve]: current solution: 9.622902e+01, current residual:5.809084e+01 +field 'mu' [explicit solve]: current solution: 4.780683e+00, current residual:2.884822e+00 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0.000653982s + +time increment:79670 time: 79.67 +field ' c' [explicit solve]: current solution: 9.622923e+01, current residual:5.809096e+01 +field 'mu' [explicit solve]: current solution: 4.780404e+00, current residual:2.884648e+00 +wall time: 0.000586033s + +time increment:79680 time: 79.68 +field ' c' [explicit solve]: current solution: 9.622927e+01, current residual:5.809098e+01 +field 'mu' [explicit solve]: current solution: 4.780348e+00, current residual:2.884613e+00 +wall time: 0.000566959s + +time increment:79690 time: 79.69 +field ' c' [explicit solve]: current solution: 9.622931e+01, current residual:5.809101e+01 +field 'mu' [explicit solve]: current solution: 4.780293e+00, current residual:2.884578e+00 +wall time: 0.000629902s + +time increment:79700 time: 79.7 +field ' c' [explicit solve]: current solution: 9.622936e+01, current residual:5.809103e+01 +field 'mu' [explicit solve]: current solution: 4.780237e+00, current residual:2.884543e+00 +wall time: 0.000560999s + +time increment:79710 time: 79.71 +field ' c' [explicit solve]: current solution: 9.622940e+01, current residual:5.809105e+01 +field 'mu' [explicit solve]: current solution: 4.780181e+00, current residual:2.884508e+00 +wall time: 0.000642776s + +time increment:79720 time: 79.72 +field ' c' [explicit solve]: current solution: 9.622944e+01, current residual:5.809108e+01 +field 'mu' [explicit solve]: current solution: 4.780125e+00, current residual:2.884474e+00 +wall time: 0.000710011s + +time increment:79730 time: 79.73 +field ' c' [explicit solve]: current solution: 9.622949e+01, current residual:5.809110e+01 +field 'mu' [explicit solve]: current solution: 4.780069e+00, current residual:2.884439e+00 +wall time: 0.000663996s + +time increment:79740 time: 79.74 +field ' c' [explicit solve]: current solution: 9.622953e+01, current residual:5.809112e+01 +field 'mu' [explicit solve]: current solution: 4.780014e+00, current residual:2.884404e+00 +wall time: 0.000625849s + +time increment:79750 time: 79.75 +field ' c' [explicit solve]: current solution: 9.622957e+01, current residual:5.809115e+01 +field 'mu' [explicit solve]: current solution: 4.779958e+00, current residual:2.884369e+00 +wall time: 0.00065589s + +time increment:79760 time: 79.76 +field ' c' [explicit solve]: current solution: 9.622961e+01, current residual:5.809117e+01 +field 'mu' [explicit solve]: current solution: 4.779902e+00, current residual:2.884334e+00 +wall time: 0.000616074s + +time increment:79770 time: 79.77 +field ' c' [explicit solve]: current solution: 9.622966e+01, current residual:5.809119e+01 +field 'mu' [explicit solve]: current solution: 4.779846e+00, current residual:2.884300e+00 +wall time: 0.000643969s + +time increment:79780 time: 79.78 +field ' c' [explicit solve]: current solution: 9.622970e+01, current residual:5.809122e+01 +field 'mu' [explicit solve]: current solution: 4.779790e+00, current residual:2.884265e+00 +wall time: 0.000667095s + +time increment:79790 time: 79.79 +field ' c' [explicit solve]: current solution: 9.622974e+01, current residual:5.809124e+01 +field 'mu' [explicit solve]: current solution: 4.779735e+00, current residual:2.884230e+00 +wall time: 0.000643969s + +time increment:79800 time: 79.8 +field ' c' [explicit solve]: current solution: 9.622979e+01, current residual:5.809126e+01 +field 'mu' [explicit solve]: current solution: 4.779679e+00, current residual:2.884195e+00 +wall time: 0.000691891s + +time increment:79810 time: 79.81 +field ' c' [explicit solve]: current solution: 9.622983e+01, current residual:5.809129e+01 +field 'mu' [explicit solve]: current solution: 4.779623e+00, current residual:2.884160e+00 +wall time: 0.000681877s + +time increment:79820 time: 79.82 +field ' c' [explicit solve]: current solution: 9.622987e+01, current residual:5.809131e+01 +field 'mu' [explicit solve]: current solution: 4.779567e+00, current residual:2.884126e+00 +wall time: 0.000575066s + +time increment:79830 time: 79.83 +field ' c' [explicit solve]: current solution: 9.622991e+01, current residual:5.809133e+01 +field 'mu' [explicit solve]: current solution: 4.779511e+00, current residual:2.884091e+00 +wall time: 0.000568867s + +time increment:79840 time: 79.84 +field ' c' [explicit solve]: current solution: 9.622996e+01, current residual:5.809136e+01 +field 'mu' [explicit solve]: current solution: 4.779455e+00, current residual:2.884056e+00 +wall time: 0.000698805s + +time increment:79850 time: 79.85 +field ' c' [explicit solve]: current solution: 9.623000e+01, current residual:5.809138e+01 +field 'mu' [explicit solve]: current solution: 4.779400e+00, current residual:2.884021e+00 +wall time: 0.000589848s + +time increment:79860 time: 79.86 +field ' c' [explicit solve]: current solution: 9.623004e+01, current residual:5.809140e+01 +field 'mu' [explicit solve]: current solution: 4.779344e+00, current residual:2.883986e+00 +wall time: 0.000593901s + +time increment:79870 time: 79.87 +field ' c' [explicit solve]: current solution: 9.623009e+01, current residual:5.809143e+01 +field 'mu' [explicit solve]: current solution: 4.779288e+00, current residual:2.883952e+00 +wall time: 0.000560999s + +time increment:79880 time: 79.88 +field ' c' [explicit solve]: current solution: 9.623013e+01, current residual:5.809145e+01 +field 'mu' [explicit solve]: current solution: 4.779232e+00, current residual:2.883917e+00 +wall time: 0.000702858s + +time increment:79890 time: 79.89 +field ' c' [explicit solve]: current solution: 9.623017e+01, current residual:5.809147e+01 +field 'mu' [explicit solve]: current solution: 4.779176e+00, current residual:2.883882e+00 +wall time: 0.000674963s + +time increment:79900 time: 79.9 +field ' c' [explicit solve]: current solution: 9.623021e+01, current residual:5.809150e+01 +field 'mu' [explicit solve]: current solution: 4.779120e+00, current residual:2.883847e+00 +wall time: 0.000566006s + +time increment:79910 time: 79.91 +field ' c' [explicit solve]: current solution: 9.623026e+01, current residual:5.809152e+01 +field 'mu' [explicit solve]: current solution: 4.779065e+00, current residual:2.883812e+00 +wall time: 0.000574112s + +time increment:79920 time: 79.92 +field ' c' [explicit solve]: current solution: 9.623030e+01, current residual:5.809154e+01 +field 'mu' [explicit solve]: current solution: 4.779009e+00, current residual:2.883778e+00 +wall time: 0.000617981s + +time increment:79930 time: 79.93 +field ' c' [explicit solve]: current solution: 9.623034e+01, current residual:5.809157e+01 +field 'mu' [explicit solve]: current solution: 4.778953e+00, current residual:2.883743e+00 +wall time: 0.000640154s + +time increment:79940 time: 79.94 +field ' c' [explicit solve]: current solution: 9.623039e+01, current residual:5.809159e+01 +field 'mu' [explicit solve]: current solution: 4.778897e+00, current residual:2.883708e+00 +wall time: 0.000682116s + +time increment:79950 time: 79.95 +field ' c' [explicit solve]: current solution: 9.623043e+01, current residual:5.809161e+01 +field 'mu' [explicit solve]: current solution: 4.778841e+00, current residual:2.883673e+00 +wall time: 0.000639915s + +time increment:79960 time: 79.96 +field ' c' [explicit solve]: current solution: 9.623047e+01, current residual:5.809164e+01 +field 'mu' [explicit solve]: current solution: 4.778785e+00, current residual:2.883638e+00 +wall time: 0.000559092s + +time increment:79970 time: 79.97 +field ' c' [explicit solve]: current solution: 9.623051e+01, current residual:5.809166e+01 +field 'mu' [explicit solve]: current solution: 4.778730e+00, current residual:2.883603e+00 +wall time: 0.000579119s + +time increment:79980 time: 79.98 +field ' c' [explicit solve]: current solution: 9.623056e+01, current residual:5.809168e+01 +field 'mu' [explicit solve]: current solution: 4.778674e+00, current residual:2.883569e+00 +wall time: 0.000819921s + +time increment:79990 time: 79.99 +field ' c' [explicit solve]: current solution: 9.623060e+01, current residual:5.809171e+01 +field 'mu' [explicit solve]: current solution: 4.778618e+00, current residual:2.883534e+00 +wall time: 0.000678062s + +time increment:80000 time: 80 +field ' c' [explicit solve]: current solution: 9.623064e+01, current residual:5.809173e+01 +field 'mu' [explicit solve]: current solution: 4.778562e+00, current residual:2.883499e+00 +wall time: 0.000661135s +fields: 2 +fields: 2 +fields: 2 +fields: 2 +Output written to:solution-080000.pvtu + +Energy: 232.153 +Energy Components: 110.766 121.387 0 + +time increment:80010 time: 80.01 +field ' c' [explicit solve]: current solution: 9.623069e+01, current residual:5.809175e+01 +field 'mu' [explicit solve]: current solution: 4.778506e+00, current residual:2.883464e+00 +wall time: 0.000669956s + +time increment:80020 time: 80.02 +field ' c' [explicit solve]: current solution: 9.623073e+01, current residual:5.809178e+01 +field 'mu' [explicit solve]: current solution: 4.778450e+00, current residual:2.883429e+00 +wall time: 0.000581026s + +time increment:80030 time: 80.03 +field ' c' [explicit solve]: current solution: 9.623077e+01, current residual:5.809180e+01 +field 'mu' [explicit solve]: current solution: 4.778394e+00, current residual:2.883395e+00 +wall time: 0.000669003s + +time increment:80040 time: 80.04 +field ' c' [explicit solve]: current solution: 9.623081e+01, current residual:5.809182e+01 +field 'mu' [explicit solve]: current solution: 4.778339e+00, current residual:2.883360e+00 +wall time: 0.000691891s + +time increment:80050 time: 80.05 +field ' c' [explicit solve]: current solution: 9.623086e+01, current residual:5.809185e+01 +field 'mu' [explicit solve]: current solution: 4.778283e+00, current residual:2.883325e+00 +wall time: 0.000694036s + +time increment:80060 time: 80.06 +field ' c' [explicit solve]: current solution: 9.623090e+01, current residual:5.809187e+01 +field 'mu' [explicit solve]: current solution: 4.778227e+00, current residual:2.883290e+00 +wall time: 0.000686169s + +time increment:80070 time: 80.07 +field ' c' [explicit solve]: current solution: 9.623094e+01, current residual:5.809189e+01 +field 'mu' [explicit solve]: current solution: 4.778171e+00, current residual:2.883255e+00 +wall time: 0.000827074s + +time increment:80080 time: 80.08 +field ' c' [explicit solve]: current solution: 9.623099e+01, current residual:5.809192e+01 +field 'mu' [explicit solve]: current solution: 4.778115e+00, current residual:2.883221e+00 +wall time: 0.000792027s + +time increment:80090 time: 80.09 +field ' c' [explicit solve]: current solution: 9.623103e+01, current residual:5.809194e+01 +field 'mu' [explicit solve]: current solution: 4.778059e+00, current residual:2.883186e+00 +wall time: 0.000733137s + +time increment:80100 time: 80.1 +field ' c' [explicit solve]: current solution: 9.623107e+01, current residual:5.809196e+01 +field 'mu' [explicit solve]: current solution: 4.778003e+00, current residual:2.883151e+00 +wall time: 0.000644922s + +time increment:80110 time: 80.11 +field ' c' [explicit solve]: current solution: 9.623112e+01, current residual:5.809199e+01 +field 'mu' [explicit solve]: current solution: 4.777947e+00, current residual:2.883116e+00 +wall time: 0.000659943s + +time increment:80120 time: 80.12 +field ' c' [explicit solve]: current solution: 9.623116e+01, current residual:5.809201e+01 +field 'mu' [explicit solve]: current solution: 4.777892e+00, current residual:2.883081e+00 +wall time: 0.000663996s + +time increment:80130 time: 80.13 +field ' c' [explicit solve]: current solution: 9.623120e+01, current residual:5.809203e+01 +field 'mu' [explicit solve]: current solution: 4.777836e+00, current residual:2.883046e+00 +wall time: 0.000708103s + +time increment:80140 time: 80.14 +field ' c' [explicit solve]: current solution: 9.623124e+01, current residual:5.809206e+01 +field 'mu' [explicit solve]: current solution: 4.777780e+00, current residual:2.883012e+00 +wall time: 0.000668049s + +time increment:80150 time: 80.15 +field ' c' [explicit solve]: current solution: 9.623129e+01, current residual:5.809208e+01 +field 'mu' [explicit solve]: current solution: 4.777724e+00, current residual:2.882977e+00 +wall time: 0.000693083s + +time increment:80160 time: 80.16 +field ' c' [explicit solve]: current solution: 9.623133e+01, current residual:5.809210e+01 +field 'mu' [explicit solve]: current solution: 4.777668e+00, current residual:2.882942e+00 +wall time: 0.000683784s + +time increment:80170 time: 80.17 +field ' c' [explicit solve]: current solution: 9.623137e+01, current residual:5.809213e+01 +field 'mu' [explicit solve]: current solution: 4.777612e+00, current residual:2.882907e+00 +wall time: 0.000674009s + +time increment:80180 time: 80.18 +field ' c' [explicit solve]: current solution: 9.623142e+01, current residual:5.809215e+01 +field 'mu' [explicit solve]: current solution: 4.777556e+00, current residual:2.882872e+00 +wall time: 0.000715017s + +time increment:80190 time: 80.19 +field ' c' [explicit solve]: current solution: 9.623146e+01, current residual:5.809217e+01 +field 'mu' [explicit solve]: current solution: 4.777500e+00, current residual:2.882837e+00 +wall time: 0.000684023s + +time increment:80200 time: 80.2 +field ' c' [explicit solve]: current solution: 9.623150e+01, current residual:5.809220e+01 +field 'mu' [explicit solve]: current solution: 4.777444e+00, current residual:2.882803e+00 +wall time: 0.000674009s + +time increment:80210 time: 80.21 +field ' c' [explicit solve]: current solution: 9.623155e+01, current residual:5.809222e+01 +field 'mu' [explicit solve]: current solution: 4.777389e+00, current residual:2.882768e+00 +wall time: 0.000671148s + +time increment:80220 time: 80.22 +field ' c' [explicit solve]: current solution: 9.623159e+01, current residual:5.809225e+01 +field 'mu' [explicit solve]: current solution: 4.777333e+00, current residual:2.882733e+00 +wall time: 0.000639915s + +time increment:80230 time: 80.23 +field ' c' [explicit solve]: current solution: 9.623163e+01, current residual:5.809227e+01 +field 'mu' [explicit solve]: current solution: 4.777277e+00, current residual:2.882698e+00 +wall time: 0.000642061s + +time increment:80240 time: 80.24 +field ' c' [explicit solve]: current solution: 9.623168e+01, current residual:5.809229e+01 +field 'mu' [explicit solve]: current solution: 4.777221e+00, current residual:2.882663e+00 +wall time: 0.000706911s + +time increment:80250 time: 80.25 +field ' c' [explicit solve]: current solution: 9.623172e+01, current residual:5.809232e+01 +field 'mu' [explicit solve]: current solution: 4.777165e+00, current residual:2.882628e+00 +wall time: 0.000697136s + +time increment:80260 time: 80.26 +field ' c' [explicit solve]: current solution: 9.623176e+01, current residual:5.809234e+01 +field 'mu' [explicit solve]: current solution: 4.777109e+00, current residual:2.882594e+00 +wall time: 0.000715017s + +time increment:80270 time: 80.27 +field ' c' [explicit solve]: current solution: 9.623180e+01, current residual:5.809236e+01 +field 'mu' [explicit solve]: current solution: 4.777053e+00, current residual:2.882559e+00 +wall time: 0.000618935s + +time increment:80280 time: 80.28 +field ' c' [explicit solve]: current solution: 9.623185e+01, current residual:5.809239e+01 +field 'mu' [explicit solve]: current solution: 4.776997e+00, current residual:2.882524e+00 +wall time: 0.000718832s + +time increment:80290 time: 80.29 +field ' c' [explicit solve]: current solution: 9.623189e+01, current residual:5.809241e+01 +field 'mu' [explicit solve]: current solution: 4.776941e+00, current residual:2.882489e+00 +wall time: 0.00070405s + +time increment:80300 time: 80.3 +field ' c' [explicit solve]: current solution: 9.623193e+01, current residual:5.809243e+01 +field 'mu' [explicit solve]: current solution: 4.776885e+00, current residual:2.882454e+00 +wall time: 0.000715017s + +time increment:80310 time: 80.31 +field ' c' [explicit solve]: current solution: 9.623198e+01, current residual:5.809246e+01 +field 'mu' [explicit solve]: current solution: 4.776830e+00, current residual:2.882419e+00 +wall time: 0.000570059s + +time increment:80320 time: 80.32 +field ' c' [explicit solve]: current solution: 9.623202e+01, current residual:5.809248e+01 +field 'mu' [explicit solve]: current solution: 4.776774e+00, current residual:2.882385e+00 +wall time: 0.000650883s + +time increment:80330 time: 80.33 +field ' c' [explicit solve]: current solution: 9.623206e+01, current residual:5.809250e+01 +field 'mu' [explicit solve]: current solution: 4.776718e+00, current residual:2.882350e+00 +wall time: 0.000559092s + +time increment:80340 time: 80.34 +field ' c' [explicit solve]: current solution: 9.623211e+01, current residual:5.809253e+01 +field 'mu' [explicit solve]: current solution: 4.776662e+00, current residual:2.882315e+00 +wall time: 0.000609159s + +time increment:80350 time: 80.35 +field ' c' [explicit solve]: current solution: 9.623215e+01, current residual:5.809255e+01 +field 'mu' [explicit solve]: current solution: 4.776606e+00, current residual:2.882280e+00 +wall time: 0.000623941s + +time increment:80360 time: 80.36 +field ' c' [explicit solve]: current solution: 9.623219e+01, current residual:5.809257e+01 +field 'mu' [explicit solve]: current solution: 4.776550e+00, current residual:2.882245e+00 +wall time: 0.000589132s + +time increment:80370 time: 80.37 +field ' c' [explicit solve]: current solution: 9.623224e+01, current residual:5.809260e+01 +field 'mu' [explicit solve]: current solution: 4.776494e+00, current residual:2.882210e+00 +wall time: 0.000752211s + +time increment:80380 time: 80.38 +field ' c' [explicit solve]: current solution: 9.623228e+01, current residual:5.809262e+01 +field 'mu' [explicit solve]: current solution: 4.776438e+00, current residual:2.882176e+00 +wall time: 0.00081706s + +time increment:80390 time: 80.39 +field ' c' [explicit solve]: current solution: 9.623232e+01, current residual:5.809265e+01 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solve]: current solution: 4.776158e+00, current residual:2.882001e+00 +wall time: 0.000654936s + +time increment:80440 time: 80.44 +field ' c' [explicit solve]: current solution: 9.623254e+01, current residual:5.809276e+01 +field 'mu' [explicit solve]: current solution: 4.776102e+00, current residual:2.881967e+00 +wall time: 0.000673056s + +time increment:80450 time: 80.45 +field ' c' [explicit solve]: current solution: 9.623258e+01, current residual:5.809279e+01 +field 'mu' [explicit solve]: current solution: 4.776046e+00, current residual:2.881932e+00 +wall time: 0.000599146s + +time increment:80460 time: 80.46 +field ' c' [explicit solve]: current solution: 9.623262e+01, current residual:5.809281e+01 +field 'mu' [explicit solve]: current solution: 4.775991e+00, current residual:2.881897e+00 +wall time: 0.000571966s + +time increment:80470 time: 80.47 +field ' c' [explicit solve]: current solution: 9.623267e+01, current residual:5.809283e+01 +field 'mu' [explicit solve]: current solution: 4.775935e+00, current residual:2.881862e+00 +wall time: 0.000583887s + +time increment:80480 time: 80.48 +field ' c' [explicit solve]: current solution: 9.623271e+01, current residual:5.809286e+01 +field 'mu' [explicit solve]: current solution: 4.775879e+00, current residual:2.881827e+00 +wall time: 0.000564814s + +time increment:80490 time: 80.49 +field ' c' [explicit solve]: current solution: 9.623275e+01, current residual:5.809288e+01 +field 'mu' [explicit solve]: current solution: 4.775823e+00, current residual:2.881792e+00 +wall time: 0.000637054s + +time increment:80500 time: 80.5 +field ' c' [explicit solve]: current solution: 9.623280e+01, current residual:5.809290e+01 +field 'mu' [explicit solve]: current solution: 4.775767e+00, current residual:2.881758e+00 +wall time: 0.000650883s + +time increment:80510 time: 80.51 +field ' c' [explicit solve]: current solution: 9.623284e+01, current residual:5.809293e+01 +field 'mu' [explicit solve]: current solution: 4.775711e+00, current residual:2.881723e+00 +wall time: 0.000583887s + +time increment:80520 time: 80.52 +field ' c' [explicit solve]: current solution: 9.623288e+01, current residual:5.809295e+01 +field 'mu' [explicit solve]: current solution: 4.775655e+00, current residual:2.881688e+00 +wall time: 0.00060606s + +time increment:80530 time: 80.53 +field ' c' [explicit solve]: current solution: 9.623293e+01, current residual:5.809298e+01 +field 'mu' [explicit solve]: current solution: 4.775599e+00, current residual:2.881653e+00 +wall time: 0.0006001s + +time increment:80540 time: 80.54 +field ' c' [explicit solve]: current solution: 9.623297e+01, current residual:5.809300e+01 +field 'mu' [explicit solve]: current solution: 4.775543e+00, current residual:2.881618e+00 +wall time: 0.000636101s + +time increment:80550 time: 80.55 +field ' c' [explicit solve]: current solution: 9.623301e+01, current residual:5.809302e+01 +field 'mu' [explicit solve]: current solution: 4.775487e+00, current residual:2.881583e+00 +wall time: 0.000637054s + +time increment:80560 time: 80.56 +field ' c' [explicit solve]: current solution: 9.623306e+01, current residual:5.809305e+01 +field 'mu' [explicit solve]: current solution: 4.775431e+00, current residual:2.881548e+00 +wall time: 0.000570059s + +time increment:80570 time: 80.57 +field ' c' [explicit solve]: current solution: 9.623310e+01, current residual:5.809307e+01 +field 'mu' [explicit solve]: current solution: 4.775375e+00, current residual:2.881514e+00 +wall time: 0.000657082s + +time increment:80580 time: 80.58 +field ' c' [explicit solve]: current solution: 9.623314e+01, current residual:5.809309e+01 +field 'mu' [explicit solve]: current solution: 4.775319e+00, current residual:2.881479e+00 +wall time: 0.000575066s + +time increment:80590 time: 80.59 +field ' c' [explicit solve]: current solution: 9.623318e+01, current residual:5.809312e+01 +field 'mu' [explicit solve]: current solution: 4.775263e+00, current residual:2.881444e+00 +wall time: 0.00060606s + +time increment:80600 time: 80.6 +field ' c' [explicit solve]: current solution: 9.623323e+01, current residual:5.809314e+01 +field 'mu' [explicit solve]: current solution: 4.775207e+00, current residual:2.881409e+00 +wall time: 0.000847101s + +time increment:80610 time: 80.61 +field ' c' [explicit solve]: current solution: 9.623327e+01, current residual:5.809316e+01 +field 'mu' [explicit solve]: current solution: 4.775151e+00, current residual:2.881374e+00 +wall time: 0.00059104s + +time increment:80620 time: 80.62 +field ' c' [explicit solve]: current solution: 9.623331e+01, current residual:5.809319e+01 +field 'mu' [explicit solve]: current solution: 4.775095e+00, current residual:2.881339e+00 +wall time: 0.000705004s + +time increment:80630 time: 80.63 +field ' c' [explicit solve]: current solution: 9.623336e+01, current residual:5.809321e+01 +field 'mu' [explicit solve]: current solution: 4.775039e+00, current residual:2.881305e+00 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0.00063014s + +time increment:80680 time: 80.68 +field ' c' [explicit solve]: current solution: 9.623357e+01, current residual:5.809333e+01 +field 'mu' [explicit solve]: current solution: 4.774759e+00, current residual:2.881130e+00 +wall time: 0.000593901s + +time increment:80690 time: 80.69 +field ' c' [explicit solve]: current solution: 9.623362e+01, current residual:5.809335e+01 +field 'mu' [explicit solve]: current solution: 4.774703e+00, current residual:2.881095e+00 +wall time: 0.000676155s + +time increment:80700 time: 80.7 +field ' c' [explicit solve]: current solution: 9.623366e+01, current residual:5.809338e+01 +field 'mu' [explicit solve]: current solution: 4.774647e+00, current residual:2.881061e+00 +wall time: 0.000609159s + +time increment:80710 time: 80.71 +field ' c' [explicit solve]: current solution: 9.623370e+01, current residual:5.809340e+01 +field 'mu' [explicit solve]: current solution: 4.774591e+00, current residual:2.881026e+00 +wall time: 0.000695944s + +time increment:80720 time: 80.72 +field ' c' [explicit solve]: current solution: 9.623375e+01, current residual:5.809342e+01 +field 'mu' [explicit solve]: current solution: 4.774535e+00, current residual:2.880991e+00 +wall time: 0.000690222s + +time increment:80730 time: 80.73 +field ' c' [explicit solve]: current solution: 9.623379e+01, current residual:5.809345e+01 +field 'mu' [explicit solve]: current solution: 4.774479e+00, current residual:2.880956e+00 +wall time: 0.000571012s + +time increment:80740 time: 80.74 +field ' c' [explicit solve]: current solution: 9.623383e+01, current residual:5.809347e+01 +field 'mu' [explicit solve]: current solution: 4.774423e+00, current residual:2.880921e+00 +wall time: 0.000660181s + +time increment:80750 time: 80.75 +field ' c' [explicit solve]: current solution: 9.623388e+01, current residual:5.809349e+01 +field 'mu' [explicit solve]: current solution: 4.774367e+00, current residual:2.880886e+00 +wall time: 0.000598907s + +time increment:80760 time: 80.76 +field ' c' [explicit solve]: current solution: 9.623392e+01, current residual:5.809352e+01 +field 'mu' [explicit solve]: current solution: 4.774311e+00, current residual:2.880851e+00 +wall time: 0.000664949s + +time increment:80770 time: 80.77 +field ' c' [explicit solve]: current solution: 9.623396e+01, current residual:5.809354e+01 +field 'mu' [explicit solve]: current solution: 4.774255e+00, current residual:2.880816e+00 +wall time: 0.000777006s + +time increment:80780 time: 80.78 +field ' c' [explicit solve]: current solution: 9.623401e+01, current residual:5.809357e+01 +field 'mu' [explicit solve]: current solution: 4.774199e+00, current residual:2.880782e+00 +wall time: 0.000721931s + +time increment:80790 time: 80.79 +field ' c' [explicit solve]: current solution: 9.623405e+01, current residual:5.809359e+01 +field 'mu' [explicit solve]: current solution: 4.774143e+00, current residual:2.880747e+00 +wall time: 0.000614882s + +time increment:80800 time: 80.8 +field ' c' [explicit solve]: current solution: 9.623409e+01, current residual:5.809361e+01 +field 'mu' [explicit solve]: current solution: 4.774087e+00, current residual:2.880712e+00 +wall time: 0.000597954s + +time increment:80810 time: 80.81 +field ' c' [explicit solve]: current solution: 9.623414e+01, current residual:5.809364e+01 +field 'mu' [explicit solve]: current solution: 4.774031e+00, current residual:2.880677e+00 +wall time: 0.000619888s + +time increment:80820 time: 80.82 +field ' c' [explicit solve]: current solution: 9.623418e+01, current residual:5.809366e+01 +field 'mu' [explicit solve]: current solution: 4.773975e+00, current residual:2.880642e+00 +wall time: 0.000636101s + +time increment:80830 time: 80.83 +field ' c' [explicit solve]: current solution: 9.623422e+01, current residual:5.809368e+01 +field 'mu' [explicit solve]: current solution: 4.773919e+00, current residual:2.880607e+00 +wall time: 0.000710011s + +time increment:80840 time: 80.84 +field ' c' [explicit solve]: current solution: 9.623427e+01, current residual:5.809371e+01 +field 'mu' [explicit solve]: current solution: 4.773863e+00, current residual:2.880572e+00 +wall time: 0.000581026s + +time increment:80850 time: 80.85 +field ' c' [explicit solve]: current solution: 9.623431e+01, current residual:5.809373e+01 +field 'mu' [explicit solve]: current solution: 4.773807e+00, current residual:2.880538e+00 +wall time: 0.000633955s + +time increment:80860 time: 80.86 +field ' c' [explicit solve]: current solution: 9.623435e+01, current residual:5.809376e+01 +field 'mu' [explicit solve]: current solution: 4.773751e+00, current residual:2.880503e+00 +wall time: 0.000715971s + +time increment:80870 time: 80.87 +field ' c' [explicit solve]: current solution: 9.623440e+01, current residual:5.809378e+01 +field 'mu' [explicit solve]: current solution: 4.773695e+00, current residual:2.880468e+00 +wall time: 0.00067091s + +time increment:80880 time: 80.88 +field ' c' [explicit solve]: current solution: 9.623444e+01, current residual:5.809380e+01 +field 'mu' [explicit solve]: current solution: 4.773639e+00, current residual:2.880433e+00 +wall time: 0.000616074s + +time increment:80890 time: 80.89 +field ' c' [explicit solve]: current solution: 9.623448e+01, current residual:5.809383e+01 +field 'mu' [explicit solve]: current solution: 4.773583e+00, current residual:2.880398e+00 +wall time: 0.000580072s + +time increment:80900 time: 80.9 +field ' c' [explicit solve]: current solution: 9.623453e+01, current residual:5.809385e+01 +field 'mu' [explicit solve]: current solution: 4.773527e+00, current residual:2.880363e+00 +wall time: 0.000679016s + +time increment:80910 time: 80.91 +field ' c' [explicit solve]: current solution: 9.623457e+01, current residual:5.809387e+01 +field 'mu' [explicit solve]: current solution: 4.773471e+00, current residual:2.880328e+00 +wall time: 0.000823975s + +time increment:80920 time: 80.92 +field ' c' [explicit solve]: current solution: 9.623461e+01, current residual:5.809390e+01 +field 'mu' [explicit solve]: current solution: 4.773415e+00, current residual:2.880293e+00 +wall time: 0.000653028s + +time increment:80930 time: 80.93 +field ' c' [explicit solve]: current solution: 9.623466e+01, current residual:5.809392e+01 +field 'mu' [explicit solve]: current solution: 4.773359e+00, current residual:2.880259e+00 +wall time: 0.000702143s + +time increment:80940 time: 80.94 +field ' c' [explicit solve]: current solution: 9.623470e+01, current residual:5.809395e+01 +field 'mu' [explicit solve]: current solution: 4.773303e+00, current residual:2.880224e+00 +wall time: 0.000699997s + +time increment:80950 time: 80.95 +field ' c' [explicit solve]: current solution: 9.623474e+01, current residual:5.809397e+01 +field 'mu' [explicit solve]: current solution: 4.773246e+00, current residual:2.880189e+00 +wall time: 0.00056386s + +time increment:80960 time: 80.96 +field ' c' [explicit solve]: current solution: 9.623479e+01, current residual:5.809399e+01 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solve]: current solution: 4.772966e+00, current residual:2.880014e+00 +wall time: 0.000716925s + +time increment:81010 time: 81.01 +field ' c' [explicit solve]: current solution: 9.623500e+01, current residual:5.809411e+01 +field 'mu' [explicit solve]: current solution: 4.772910e+00, current residual:2.879980e+00 +wall time: 0.000645876s + +time increment:81020 time: 81.02 +field ' c' [explicit solve]: current solution: 9.623505e+01, current residual:5.809414e+01 +field 'mu' [explicit solve]: current solution: 4.772854e+00, current residual:2.879945e+00 +wall time: 0.000648975s + +time increment:81030 time: 81.03 +field ' c' [explicit solve]: current solution: 9.623509e+01, current residual:5.809416e+01 +field 'mu' [explicit solve]: current solution: 4.772798e+00, current residual:2.879910e+00 +wall time: 0.000675917s + +time increment:81040 time: 81.04 +field ' c' [explicit solve]: current solution: 9.623513e+01, current residual:5.809418e+01 +field 'mu' [explicit solve]: current solution: 4.772742e+00, current residual:2.879875e+00 +wall time: 0.000668049s + +time increment:81050 time: 81.05 +field ' c' [explicit solve]: current solution: 9.623518e+01, current residual:5.809421e+01 +field 'mu' [explicit solve]: current solution: 4.772686e+00, current residual:2.879840e+00 +wall time: 0.00067687s + +time increment:81060 time: 81.06 +field ' c' [explicit solve]: current solution: 9.623522e+01, current residual:5.809423e+01 +field 'mu' [explicit solve]: current solution: 4.772630e+00, current residual:2.879805e+00 +wall time: 0.00070405s + +time increment:81070 time: 81.07 +field ' c' [explicit solve]: current solution: 9.623526e+01, current residual:5.809425e+01 +field 'mu' [explicit solve]: current solution: 4.772574e+00, current residual:2.879770e+00 +wall time: 0.000679016s + +time increment:81080 time: 81.08 +field ' c' [explicit solve]: current solution: 9.623531e+01, current residual:5.809428e+01 +field 'mu' [explicit solve]: current solution: 4.772518e+00, current residual:2.879735e+00 +wall time: 0.000998974s + +time increment:81090 time: 81.09 +field ' c' [explicit solve]: current solution: 9.623535e+01, current residual:5.809430e+01 +field 'mu' [explicit solve]: current solution: 4.772462e+00, current residual:2.879701e+00 +wall time: 0.000813007s + +time increment:81100 time: 81.1 +field ' c' [explicit solve]: current solution: 9.623540e+01, current residual:5.809433e+01 +field 'mu' [explicit solve]: current solution: 4.772405e+00, current residual:2.879666e+00 +wall time: 0.000648022s + +time increment:81110 time: 81.11 +field ' c' [explicit solve]: current solution: 9.623544e+01, current residual:5.809435e+01 +field 'mu' [explicit solve]: current solution: 4.772349e+00, current residual:2.879631e+00 +wall time: 0.000680923s + +time increment:81120 time: 81.12 +field ' c' [explicit solve]: current solution: 9.623548e+01, current residual:5.809437e+01 +field 'mu' [explicit solve]: current solution: 4.772293e+00, current residual:2.879596e+00 +wall time: 0.000771999s + +time increment:81130 time: 81.13 +field ' c' [explicit solve]: current solution: 9.623553e+01, current residual:5.809440e+01 +field 'mu' [explicit solve]: current solution: 4.772237e+00, current residual:2.879561e+00 +wall time: 0.00067687s + +time increment:81140 time: 81.14 +field ' c' [explicit solve]: current solution: 9.623557e+01, current residual:5.809442e+01 +field 'mu' [explicit solve]: current solution: 4.772181e+00, current residual:2.879526e+00 +wall time: 0.000649929s + +time increment:81150 time: 81.15 +field ' c' [explicit solve]: current solution: 9.623561e+01, current residual:5.809444e+01 +field 'mu' [explicit solve]: current solution: 4.772125e+00, current residual:2.879491e+00 +wall time: 0.000680208s + +time increment:81160 time: 81.16 +field ' c' [explicit solve]: current solution: 9.623566e+01, current residual:5.809447e+01 +field 'mu' [explicit solve]: current solution: 4.772069e+00, current residual:2.879456e+00 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increment:81250 time: 81.25 +field ' c' [explicit solve]: current solution: 9.623605e+01, current residual:5.809468e+01 +field 'mu' [explicit solve]: current solution: 4.771564e+00, current residual:2.879142e+00 +wall time: 0.000684977s + +time increment:81260 time: 81.26 +field ' c' [explicit solve]: current solution: 9.623609e+01, current residual:5.809471e+01 +field 'mu' [explicit solve]: current solution: 4.771508e+00, current residual:2.879107e+00 +wall time: 0.000564814s + +time increment:81270 time: 81.27 +field ' c' [explicit solve]: current solution: 9.623613e+01, current residual:5.809473e+01 +field 'mu' [explicit solve]: current solution: 4.771452e+00, current residual:2.879072e+00 +wall time: 0.000697136s + +time increment:81280 time: 81.28 +field ' c' [explicit solve]: current solution: 9.623618e+01, current residual:5.809475e+01 +field 'mu' [explicit solve]: current solution: 4.771396e+00, current residual:2.879038e+00 +wall time: 0.000692844s + +time increment:81290 time: 81.29 +field ' c' [explicit solve]: current solution: 9.623622e+01, current residual:5.809478e+01 +field 'mu' [explicit solve]: current solution: 4.771339e+00, current residual:2.879003e+00 +wall time: 0.000571966s + +time increment:81300 time: 81.3 +field ' c' [explicit solve]: current solution: 9.623627e+01, current residual:5.809480e+01 +field 'mu' [explicit solve]: current solution: 4.771283e+00, current residual:2.878968e+00 +wall time: 0.000586987s + +time increment:81310 time: 81.31 +field ' c' [explicit solve]: current solution: 9.623631e+01, current residual:5.809483e+01 +field 'mu' [explicit solve]: current solution: 4.771227e+00, current residual:2.878933e+00 +wall time: 0.000566959s + +time increment:81320 time: 81.32 +field ' c' [explicit solve]: current solution: 9.623635e+01, current residual:5.809485e+01 +field 'mu' [explicit solve]: current solution: 4.771171e+00, current residual:2.878898e+00 +wall time: 0.000629187s + +time increment:81330 time: 81.33 +field ' 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current solution: 9.623657e+01, current residual:5.809497e+01 +field 'mu' [explicit solve]: current solution: 4.770890e+00, current residual:2.878723e+00 +wall time: 0.000710964s + +time increment:81380 time: 81.38 +field ' c' [explicit solve]: current solution: 9.623661e+01, current residual:5.809499e+01 +field 'mu' [explicit solve]: current solution: 4.770834e+00, current residual:2.878688e+00 +wall time: 0.00067997s + +time increment:81390 time: 81.39 +field ' c' [explicit solve]: current solution: 9.623666e+01, current residual:5.809502e+01 +field 'mu' [explicit solve]: current solution: 4.770778e+00, current residual:2.878654e+00 +wall time: 0.000694036s + +time increment:81400 time: 81.4 +field ' c' [explicit solve]: current solution: 9.623670e+01, current residual:5.809504e+01 +field 'mu' [explicit solve]: current solution: 4.770722e+00, current residual:2.878619e+00 +wall time: 0.000664949s + +time increment:81410 time: 81.41 +field ' c' [explicit solve]: current solution: 9.623674e+01, current residual:5.809506e+01 +field 'mu' [explicit solve]: current solution: 4.770666e+00, current residual:2.878584e+00 +wall time: 0.000679016s + +time increment:81420 time: 81.42 +field ' c' [explicit solve]: current solution: 9.623679e+01, current residual:5.809509e+01 +field 'mu' [explicit solve]: current solution: 4.770610e+00, current residual:2.878549e+00 +wall time: 0.000658035s + +time increment:81430 time: 81.43 +field ' c' [explicit solve]: current solution: 9.623683e+01, current residual:5.809511e+01 +field 'mu' [explicit solve]: current solution: 4.770553e+00, current residual:2.878514e+00 +wall time: 0.000648022s + +time increment:81440 time: 81.44 +field ' c' [explicit solve]: current solution: 9.623688e+01, current residual:5.809514e+01 +field 'mu' [explicit solve]: current solution: 4.770497e+00, current residual:2.878479e+00 +wall time: 0.000571966s + +time increment:81450 time: 81.45 +field ' c' [explicit solve]: current solution: 9.623692e+01, 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4.769317e+00, current residual:2.877746e+00 +wall time: 0.000680923s + +time increment:81660 time: 81.66 +field ' c' [explicit solve]: current solution: 9.623784e+01, current residual:5.809566e+01 +field 'mu' [explicit solve]: current solution: 4.769261e+00, current residual:2.877711e+00 +wall time: 0.000665903s + +time increment:81670 time: 81.67 +field ' c' [explicit solve]: current solution: 9.623788e+01, current residual:5.809569e+01 +field 'mu' [explicit solve]: current solution: 4.769205e+00, current residual:2.877676e+00 +wall time: 0.000715971s + +time increment:81680 time: 81.68 +field ' c' [explicit solve]: current solution: 9.623792e+01, current residual:5.809571e+01 +field 'mu' [explicit solve]: current solution: 4.769149e+00, current residual:2.877641e+00 +wall time: 0.000736952s + +time increment:81690 time: 81.69 +field ' c' [explicit solve]: current solution: 9.623797e+01, current residual:5.809573e+01 +field 'mu' [explicit solve]: current solution: 4.769092e+00, 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residual:2.877466e+00 +wall time: 0.000692844s + +time increment:81740 time: 81.74 +field ' c' [explicit solve]: current solution: 9.623819e+01, current residual:5.809585e+01 +field 'mu' [explicit solve]: current solution: 4.768811e+00, current residual:2.877431e+00 +wall time: 0.000692129s + +time increment:81750 time: 81.75 +field ' c' [explicit solve]: current solution: 9.623823e+01, current residual:5.809588e+01 +field 'mu' [explicit solve]: current solution: 4.768755e+00, current residual:2.877396e+00 +wall time: 0.000673056s + +time increment:81760 time: 81.76 +field ' c' [explicit solve]: current solution: 9.623827e+01, current residual:5.809590e+01 +field 'mu' [explicit solve]: current solution: 4.768699e+00, current residual:2.877361e+00 +wall time: 0.00067997s + +time increment:81770 time: 81.77 +field ' c' [explicit solve]: current solution: 9.623832e+01, current residual:5.809593e+01 +field 'mu' [explicit solve]: current solution: 4.768643e+00, current residual:2.877327e+00 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0.00068903s + +time increment:81820 time: 81.82 +field ' c' [explicit solve]: current solution: 9.623854e+01, current residual:5.809605e+01 +field 'mu' [explicit solve]: current solution: 4.768361e+00, current residual:2.877152e+00 +wall time: 0.000667095s + +time increment:81830 time: 81.83 +field ' c' [explicit solve]: current solution: 9.623858e+01, current residual:5.809607e+01 +field 'mu' [explicit solve]: current solution: 4.768305e+00, current residual:2.877117e+00 +wall time: 0.000694036s + +time increment:81840 time: 81.84 +field ' c' [explicit solve]: current solution: 9.623862e+01, current residual:5.809609e+01 +field 'mu' [explicit solve]: current solution: 4.768249e+00, current residual:2.877082e+00 +wall time: 0.000678062s + +time increment:81850 time: 81.85 +field ' c' [explicit solve]: current solution: 9.623867e+01, current residual:5.809612e+01 +field 'mu' [explicit solve]: current solution: 4.768193e+00, current residual:2.877047e+00 +wall time: 0.000674009s + +time increment:81860 time: 81.86 +field ' c' [explicit solve]: current solution: 9.623871e+01, current residual:5.809614e+01 +field 'mu' [explicit solve]: current solution: 4.768136e+00, current residual:2.877012e+00 +wall time: 0.000720978s + +time increment:81870 time: 81.87 +field ' c' [explicit solve]: current solution: 9.623875e+01, current residual:5.809617e+01 +field 'mu' [explicit solve]: current solution: 4.768080e+00, current residual:2.876977e+00 +wall time: 0.000661135s + +time increment:81880 time: 81.88 +field ' c' [explicit solve]: current solution: 9.623880e+01, current residual:5.809619e+01 +field 'mu' [explicit solve]: current solution: 4.768024e+00, current residual:2.876942e+00 +wall time: 0.000656128s + +time increment:81890 time: 81.89 +field ' c' [explicit solve]: current solution: 9.623884e+01, current residual:5.809621e+01 +field 'mu' [explicit solve]: current solution: 4.767967e+00, current residual:2.876907e+00 +wall time: 0.000633001s + +time increment:81900 time: 81.9 +field ' c' [explicit solve]: current solution: 9.623889e+01, current residual:5.809624e+01 +field 'mu' [explicit solve]: current solution: 4.767911e+00, current residual:2.876872e+00 +wall time: 0.000573874s + +time increment:81910 time: 81.91 +field ' c' [explicit solve]: current solution: 9.623893e+01, current residual:5.809626e+01 +field 'mu' [explicit solve]: current solution: 4.767855e+00, current residual:2.876837e+00 +wall time: 0.000625849s + +time increment:81920 time: 81.92 +field ' c' [explicit solve]: current solution: 9.623897e+01, current residual:5.809629e+01 +field 'mu' [explicit solve]: current solution: 4.767799e+00, current residual:2.876802e+00 +wall time: 0.000597s + +time increment:81930 time: 81.93 +field ' c' [explicit solve]: current solution: 9.623902e+01, current residual:5.809631e+01 +field 'mu' [explicit solve]: current solution: 4.767742e+00, current residual:2.876767e+00 +wall time: 0.000640154s + +time increment:81940 time: 81.94 +field ' c' [explicit solve]: current solution: 9.623906e+01, current residual:5.809633e+01 +field 'mu' [explicit solve]: current solution: 4.767686e+00, current residual:2.876732e+00 +wall time: 0.000588179s + +time increment:81950 time: 81.95 +field ' c' [explicit solve]: current solution: 9.623910e+01, current residual:5.809636e+01 +field 'mu' [explicit solve]: current solution: 4.767630e+00, current residual:2.876697e+00 +wall time: 0.000714064s + +time increment:81960 time: 81.96 +field ' c' [explicit solve]: current solution: 9.623915e+01, current residual:5.809638e+01 +field 'mu' [explicit solve]: current solution: 4.767574e+00, current residual:2.876663e+00 +wall time: 0.000631094s + +time increment:81970 time: 81.97 +field ' c' [explicit solve]: current solution: 9.623919e+01, current residual:5.809641e+01 +field 'mu' [explicit solve]: current solution: 4.767517e+00, current residual:2.876628e+00 +wall time: 0.000577927s + +time increment:81980 time: 81.98 +field ' c' [explicit solve]: current solution: 9.623924e+01, current residual:5.809643e+01 +field 'mu' [explicit solve]: current solution: 4.767461e+00, current residual:2.876593e+00 +wall time: 0.000720024s + +time increment:81990 time: 81.99 +field ' c' [explicit solve]: current solution: 9.623928e+01, current residual:5.809646e+01 +field 'mu' [explicit solve]: current solution: 4.767405e+00, current residual:2.876558e+00 +wall time: 0.000594139s + +time increment:82000 time: 82 +field ' c' [explicit solve]: current solution: 9.623932e+01, current residual:5.809648e+01 +field 'mu' [explicit solve]: current solution: 4.767348e+00, current residual:2.876523e+00 +wall time: 0.000585079s + +time increment:82010 time: 82.01 +field ' c' [explicit solve]: current solution: 9.623937e+01, current residual:5.809650e+01 +field 'mu' [explicit solve]: current solution: 4.767292e+00, current residual:2.876488e+00 +wall time: 0.000596046s + +time increment:82020 time: 82.02 +field ' c' [explicit solve]: current solution: 9.623941e+01, current residual:5.809653e+01 +field 'mu' [explicit solve]: current solution: 4.767236e+00, current residual:2.876453e+00 +wall time: 0.000643969s + +time increment:82030 time: 82.03 +field ' c' [explicit solve]: current solution: 9.623946e+01, current residual:5.809655e+01 +field 'mu' [explicit solve]: current solution: 4.767179e+00, current residual:2.876418e+00 +wall time: 0.000679016s + +time increment:82040 time: 82.04 +field ' c' [explicit solve]: current solution: 9.623950e+01, current residual:5.809658e+01 +field 'mu' [explicit solve]: current solution: 4.767123e+00, current residual:2.876383e+00 +wall time: 0.000683069s + +time increment:82050 time: 82.05 +field ' c' [explicit solve]: current solution: 9.623954e+01, current residual:5.809660e+01 +field 'mu' [explicit solve]: current solution: 4.767067e+00, current residual:2.876348e+00 +wall time: 0.000653982s + +time increment:82060 time: 82.06 +field ' c' [explicit solve]: current solution: 9.623959e+01, current residual:5.809662e+01 +field 'mu' [explicit solve]: current solution: 4.767011e+00, current residual:2.876313e+00 +wall time: 0.000668049s + +time increment:82070 time: 82.07 +field ' c' [explicit solve]: current solution: 9.623963e+01, current residual:5.809665e+01 +field 'mu' [explicit solve]: current solution: 4.766954e+00, current residual:2.876278e+00 +wall time: 0.000583172s + +time increment:82080 time: 82.08 +field ' c' [explicit solve]: current solution: 9.623968e+01, current residual:5.809667e+01 +field 'mu' [explicit solve]: current solution: 4.766898e+00, current residual:2.876243e+00 +wall time: 0.000627041s + +time increment:82090 time: 82.09 +field ' c' [explicit solve]: current solution: 9.623972e+01, current residual:5.809670e+01 +field 'mu' [explicit solve]: current solution: 4.766842e+00, current residual:2.876208e+00 +wall time: 0.000730038s + +time increment:82100 time: 82.1 +field ' c' [explicit solve]: current solution: 9.623976e+01, current residual:5.809672e+01 +field 'mu' [explicit solve]: current solution: 4.766785e+00, current residual:2.876173e+00 +wall time: 0.000695944s + +time increment:82110 time: 82.11 +field ' c' [explicit solve]: current solution: 9.623981e+01, current residual:5.809674e+01 +field 'mu' [explicit solve]: current solution: 4.766729e+00, current residual:2.876138e+00 +wall time: 0.000631094s + +time increment:82120 time: 82.12 +field ' c' [explicit solve]: current solution: 9.623985e+01, current residual:5.809677e+01 +field 'mu' [explicit solve]: current solution: 4.766673e+00, current residual:2.876103e+00 +wall time: 0.000590086s + +time increment:82130 time: 82.13 +field ' c' [explicit solve]: current solution: 9.623989e+01, current residual:5.809679e+01 +field 'mu' [explicit solve]: current solution: 4.766616e+00, current residual:2.876068e+00 +wall time: 0.000568867s + +time increment:82140 time: 82.14 +field ' c' [explicit solve]: current solution: 9.623994e+01, current residual:5.809682e+01 +field 'mu' [explicit solve]: current solution: 4.766560e+00, current residual:2.876033e+00 +wall time: 0.000608206s + +time increment:82150 time: 82.15 +field ' c' [explicit solve]: current solution: 9.623998e+01, current residual:5.809684e+01 +field 'mu' [explicit solve]: current solution: 4.766504e+00, current residual:2.875998e+00 +wall time: 0.000710964s + +time increment:82160 time: 82.16 +field ' c' [explicit solve]: current solution: 9.624003e+01, current residual:5.809686e+01 +field 'mu' [explicit solve]: current solution: 4.766447e+00, current residual:2.875963e+00 +wall time: 0.00075984s + +time increment:82170 time: 82.17 +field ' c' [explicit solve]: current solution: 9.624007e+01, current residual:5.809689e+01 +field 'mu' [explicit solve]: current solution: 4.766391e+00, current residual:2.875928e+00 +wall time: 0.000633001s + +time increment:82180 time: 82.18 +field ' c' [explicit solve]: current solution: 9.624011e+01, current residual:5.809691e+01 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solve]: current solution: 4.766109e+00, current residual:2.875753e+00 +wall time: 0.000568867s + +time increment:82230 time: 82.23 +field ' c' [explicit solve]: current solution: 9.624033e+01, current residual:5.809703e+01 +field 'mu' [explicit solve]: current solution: 4.766053e+00, current residual:2.875718e+00 +wall time: 0.000688076s + +time increment:82240 time: 82.24 +field ' c' [explicit solve]: current solution: 9.624038e+01, current residual:5.809706e+01 +field 'mu' [explicit solve]: current solution: 4.765997e+00, current residual:2.875683e+00 +wall time: 0.00060606s + +time increment:82250 time: 82.25 +field ' c' [explicit solve]: current solution: 9.624042e+01, current residual:5.809708e+01 +field 'mu' [explicit solve]: current solution: 4.765940e+00, current residual:2.875648e+00 +wall time: 0.000678062s + +time increment:82260 time: 82.26 +field ' c' [explicit solve]: current solution: 9.624047e+01, current residual:5.809711e+01 +field 'mu' [explicit solve]: current solution: 4.765884e+00, current residual:2.875614e+00 +wall time: 0.000558853s + +time increment:82270 time: 82.27 +field ' c' [explicit solve]: current solution: 9.624051e+01, current residual:5.809713e+01 +field 'mu' [explicit solve]: current solution: 4.765828e+00, current residual:2.875579e+00 +wall time: 0.000622034s + +time increment:82280 time: 82.28 +field ' c' [explicit solve]: current solution: 9.624055e+01, current residual:5.809715e+01 +field 'mu' [explicit solve]: current solution: 4.765771e+00, current residual:2.875544e+00 +wall time: 0.00071907s + +time increment:82290 time: 82.29 +field ' c' [explicit solve]: current solution: 9.624060e+01, current residual:5.809718e+01 +field 'mu' [explicit solve]: current solution: 4.765715e+00, current residual:2.875509e+00 +wall time: 0.000694036s + +time increment:82300 time: 82.3 +field ' c' [explicit solve]: current solution: 9.624064e+01, current residual:5.809720e+01 +field 'mu' [explicit solve]: current solution: 4.765658e+00, current residual:2.875474e+00 +wall time: 0.000560999s + +time increment:82310 time: 82.31 +field ' c' [explicit solve]: current solution: 9.624069e+01, current residual:5.809723e+01 +field 'mu' [explicit solve]: current solution: 4.765602e+00, current residual:2.875439e+00 +wall time: 0.000597s + +time increment:82320 time: 82.32 +field ' c' [explicit solve]: current solution: 9.624073e+01, current residual:5.809725e+01 +field 'mu' [explicit solve]: current solution: 4.765546e+00, current residual:2.875404e+00 +wall time: 0.000720024s + +time increment:82330 time: 82.33 +field ' c' [explicit solve]: current solution: 9.624077e+01, current residual:5.809728e+01 +field 'mu' [explicit solve]: current solution: 4.765489e+00, current residual:2.875369e+00 +wall time: 0.000628948s + +time increment:82340 time: 82.34 +field ' c' [explicit solve]: current solution: 9.624082e+01, current residual:5.809730e+01 +field 'mu' [explicit solve]: current solution: 4.765433e+00, current residual:2.875334e+00 +wall time: 0.000663042s + +time increment:82350 time: 82.35 +field ' c' [explicit solve]: current solution: 9.624086e+01, current residual:5.809732e+01 +field 'mu' [explicit solve]: current solution: 4.765377e+00, current residual:2.875299e+00 +wall time: 0.00067687s + +time increment:82360 time: 82.36 +field ' c' [explicit solve]: current solution: 9.624091e+01, current residual:5.809735e+01 +field 'mu' [explicit solve]: current solution: 4.765320e+00, current residual:2.875264e+00 +wall time: 0.000652075s + +time increment:82370 time: 82.37 +field ' c' [explicit solve]: current solution: 9.624095e+01, current residual:5.809737e+01 +field 'mu' [explicit solve]: current solution: 4.765264e+00, current residual:2.875229e+00 +wall time: 0.000711918s + +time increment:82380 time: 82.38 +field ' c' [explicit solve]: current solution: 9.624099e+01, current residual:5.809740e+01 +field 'mu' [explicit solve]: current solution: 4.765207e+00, current residual:2.875194e+00 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0.000677109s + +time increment:82430 time: 82.43 +field ' c' [explicit solve]: current solution: 9.624121e+01, current residual:5.809752e+01 +field 'mu' [explicit solve]: current solution: 4.764925e+00, current residual:2.875019e+00 +wall time: 0.000641108s + +time increment:82440 time: 82.44 +field ' c' [explicit solve]: current solution: 9.624126e+01, current residual:5.809754e+01 +field 'mu' [explicit solve]: current solution: 4.764869e+00, current residual:2.874984e+00 +wall time: 0.000671864s + +time increment:82450 time: 82.45 +field ' c' [explicit solve]: current solution: 9.624130e+01, current residual:5.809757e+01 +field 'mu' [explicit solve]: current solution: 4.764813e+00, current residual:2.874949e+00 +wall time: 0.000619173s + +time increment:82460 time: 82.46 +field ' c' [explicit solve]: current solution: 9.624135e+01, current residual:5.809759e+01 +field 'mu' [explicit solve]: current solution: 4.764756e+00, current residual:2.874914e+00 +wall time: 0.000668049s + +time increment:82470 time: 82.47 +field ' c' [explicit solve]: current solution: 9.624139e+01, current residual:5.809761e+01 +field 'mu' [explicit solve]: current solution: 4.764700e+00, current residual:2.874879e+00 +wall time: 0.000645876s + +time increment:82480 time: 82.48 +field ' c' [explicit solve]: current solution: 9.624143e+01, current residual:5.809764e+01 +field 'mu' [explicit solve]: current solution: 4.764643e+00, current residual:2.874844e+00 +wall time: 0.000687838s + +time increment:82490 time: 82.49 +field ' c' [explicit solve]: current solution: 9.624148e+01, current residual:5.809766e+01 +field 'mu' [explicit solve]: current solution: 4.764587e+00, current residual:2.874809e+00 +wall time: 0.00065589s + +time increment:82500 time: 82.5 +field ' c' [explicit solve]: current solution: 9.624152e+01, current residual:5.809769e+01 +field 'mu' [explicit solve]: current solution: 4.764531e+00, current residual:2.874774e+00 +wall time: 0.000708103s + +time increment:82510 time: 82.51 +field ' c' [explicit solve]: current solution: 9.624157e+01, current residual:5.809771e+01 +field 'mu' [explicit solve]: current solution: 4.764474e+00, current residual:2.874739e+00 +wall time: 0.000660181s + +time increment:82520 time: 82.52 +field ' c' [explicit solve]: current solution: 9.624161e+01, current residual:5.809774e+01 +field 'mu' [explicit solve]: current solution: 4.764418e+00, current residual:2.874704e+00 +wall time: 0.000729084s + +time increment:82530 time: 82.53 +field ' c' [explicit solve]: current solution: 9.624165e+01, current residual:5.809776e+01 +field 'mu' [explicit solve]: current solution: 4.764361e+00, current residual:2.874669e+00 +wall time: 0.000670195s + +time increment:82540 time: 82.54 +field ' c' [explicit solve]: current solution: 9.624170e+01, current residual:5.809778e+01 +field 'mu' [explicit solve]: current solution: 4.764305e+00, current residual:2.874634e+00 +wall time: 0.000649929s + +time increment:82550 time: 82.55 +field ' c' [explicit solve]: current solution: 9.624174e+01, current residual:5.809781e+01 +field 'mu' [explicit solve]: current solution: 4.764248e+00, current residual:2.874599e+00 +wall time: 0.000662088s + +time increment:82560 time: 82.56 +field ' c' [explicit solve]: current solution: 9.624179e+01, current residual:5.809783e+01 +field 'mu' [explicit solve]: current solution: 4.764192e+00, current residual:2.874564e+00 +wall time: 0.000669956s + +time increment:82570 time: 82.57 +field ' c' [explicit solve]: current solution: 9.624183e+01, current residual:5.809786e+01 +field 'mu' [explicit solve]: current solution: 4.764136e+00, current residual:2.874529e+00 +wall time: 0.000736952s + +time increment:82580 time: 82.58 +field ' c' [explicit solve]: current solution: 9.624187e+01, current residual:5.809788e+01 +field 'mu' [explicit solve]: current solution: 4.764079e+00, current residual:2.874494e+00 +wall time: 0.000695944s + +time increment:82590 time: 82.59 +field ' c' [explicit solve]: current solution: 9.624192e+01, current residual:5.809790e+01 +field 'mu' [explicit solve]: current solution: 4.764023e+00, current residual:2.874459e+00 +wall time: 0.000757933s + +time increment:82600 time: 82.6 +field ' c' [explicit solve]: current solution: 9.624196e+01, current residual:5.809793e+01 +field 'mu' [explicit solve]: current solution: 4.763966e+00, current residual:2.874424e+00 +wall time: 0.000779867s + +time increment:82610 time: 82.61 +field ' c' [explicit solve]: current solution: 9.624201e+01, current residual:5.809795e+01 +field 'mu' [explicit solve]: current solution: 4.763910e+00, current residual:2.874389e+00 +wall time: 0.000758171s + +time increment:82620 time: 82.62 +field ' c' [explicit solve]: current solution: 9.624205e+01, current residual:5.809798e+01 +field 'mu' [explicit solve]: current solution: 4.763853e+00, current residual:2.874354e+00 +wall time: 0.000658035s + +time increment:82630 time: 82.63 +field ' c' [explicit solve]: current solution: 9.624209e+01, current residual:5.809800e+01 +field 'mu' [explicit solve]: current solution: 4.763797e+00, current residual:2.874319e+00 +wall time: 0.000836849s + +time increment:82640 time: 82.64 +field ' c' [explicit solve]: current solution: 9.624214e+01, current residual:5.809803e+01 +field 'mu' [explicit solve]: current solution: 4.763740e+00, current residual:2.874284e+00 +wall time: 0.000633001s + +time increment:82650 time: 82.65 +field ' c' [explicit solve]: current solution: 9.624218e+01, current residual:5.809805e+01 +field 'mu' [explicit solve]: current solution: 4.763684e+00, current residual:2.874249e+00 +wall time: 0.000703096s + +time increment:82660 time: 82.66 +field ' c' [explicit solve]: current solution: 9.624223e+01, current residual:5.809807e+01 +field 'mu' [explicit solve]: current solution: 4.763627e+00, current residual:2.874214e+00 +wall time: 0.000653982s + +time increment:82670 time: 82.67 +field ' c' [explicit solve]: current solution: 9.624227e+01, current residual:5.809810e+01 +field 'mu' [explicit solve]: current solution: 4.763571e+00, current residual:2.874179e+00 +wall time: 0.000652075s + +time increment:82680 time: 82.68 +field ' c' [explicit solve]: current solution: 9.624232e+01, current residual:5.809812e+01 +field 'mu' [explicit solve]: current solution: 4.763515e+00, current residual:2.874144e+00 +wall time: 0.000664949s + +time increment:82690 time: 82.69 +field ' c' [explicit solve]: current solution: 9.624236e+01, current residual:5.809815e+01 +field 'mu' [explicit solve]: current solution: 4.763458e+00, current residual:2.874109e+00 +wall time: 0.000679016s + +time increment:82700 time: 82.7 +field ' c' [explicit solve]: current solution: 9.624240e+01, current residual:5.809817e+01 +field 'mu' [explicit solve]: current solution: 4.763402e+00, current residual:2.874074e+00 +wall time: 0.000648975s + +time increment:82710 time: 82.71 +field ' c' [explicit solve]: current solution: 9.624245e+01, current residual:5.809820e+01 +field 'mu' [explicit solve]: current solution: 4.763345e+00, current residual:2.874038e+00 +wall time: 0.000654936s + +time increment:82720 time: 82.72 +field ' c' [explicit solve]: current solution: 9.624249e+01, current residual:5.809822e+01 +field 'mu' [explicit solve]: current solution: 4.763289e+00, current residual:2.874003e+00 +wall time: 0.000687838s + +time increment:82730 time: 82.73 +field ' c' [explicit solve]: current solution: 9.624254e+01, current residual:5.809824e+01 +field 'mu' [explicit solve]: current solution: 4.763232e+00, current residual:2.873968e+00 +wall time: 0.00071907s + +time increment:82740 time: 82.74 +field ' c' [explicit solve]: current solution: 9.624258e+01, current residual:5.809827e+01 +field 'mu' [explicit solve]: current solution: 4.763176e+00, current residual:2.873933e+00 +wall time: 0.000658035s + +time increment:82750 time: 82.75 +field ' c' [explicit solve]: current solution: 9.624262e+01, current residual:5.809829e+01 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solve]: current solution: 4.762893e+00, current residual:2.873758e+00 +wall time: 0.000741005s + +time increment:82800 time: 82.8 +field ' c' [explicit solve]: current solution: 9.624285e+01, current residual:5.809841e+01 +field 'mu' [explicit solve]: current solution: 4.762837e+00, current residual:2.873723e+00 +wall time: 0.000641823s + +time increment:82810 time: 82.81 +field ' c' [explicit solve]: current solution: 9.624289e+01, current residual:5.809844e+01 +field 'mu' [explicit solve]: current solution: 4.762780e+00, current residual:2.873688e+00 +wall time: 0.000649929s + +time increment:82820 time: 82.82 +field ' c' [explicit solve]: current solution: 9.624293e+01, current residual:5.809846e+01 +field 'mu' [explicit solve]: current solution: 4.762724e+00, current residual:2.873653e+00 +wall time: 0.000571966s + +time increment:82830 time: 82.83 +field ' c' [explicit solve]: current solution: 9.624298e+01, current residual:5.809849e+01 +field 'mu' [explicit solve]: current solution: 4.762667e+00, current residual:2.873618e+00 +wall time: 0.000571966s + +time increment:82840 time: 82.84 +field ' c' [explicit solve]: current solution: 9.624302e+01, current residual:5.809851e+01 +field 'mu' [explicit solve]: current solution: 4.762611e+00, current residual:2.873583e+00 +wall time: 0.000679016s + +time increment:82850 time: 82.85 +field ' c' [explicit solve]: current solution: 9.624307e+01, current residual:5.809854e+01 +field 'mu' [explicit solve]: current solution: 4.762554e+00, current residual:2.873548e+00 +wall time: 0.000679016s + +time increment:82860 time: 82.86 +field ' c' [explicit solve]: current solution: 9.624311e+01, current residual:5.809856e+01 +field 'mu' [explicit solve]: current solution: 4.762498e+00, current residual:2.873513e+00 +wall time: 0.000628948s + +time increment:82870 time: 82.87 +field ' c' [explicit solve]: current solution: 9.624315e+01, current residual:5.809859e+01 +field 'mu' [explicit solve]: current solution: 4.762441e+00, current residual:2.873478e+00 +wall time: 0.000597954s + +time increment:82880 time: 82.88 +field ' c' [explicit solve]: current solution: 9.624320e+01, current residual:5.809861e+01 +field 'mu' [explicit solve]: current solution: 4.762385e+00, current residual:2.873443e+00 +wall time: 0.000565767s + +time increment:82890 time: 82.89 +field ' c' [explicit solve]: current solution: 9.624324e+01, current residual:5.809863e+01 +field 'mu' [explicit solve]: current solution: 4.762328e+00, current residual:2.873408e+00 +wall time: 0.000627995s + +time increment:82900 time: 82.9 +field ' c' [explicit solve]: current solution: 9.624329e+01, current residual:5.809866e+01 +field 'mu' [explicit solve]: current solution: 4.762272e+00, current residual:2.873373e+00 +wall time: 0.000643015s + +time increment:82910 time: 82.91 +field ' c' [explicit solve]: current solution: 9.624333e+01, current residual:5.809868e+01 +field 'mu' [explicit solve]: current solution: 4.762215e+00, current residual:2.873338e+00 +wall time: 0.000647068s + +time increment:82920 time: 82.92 +field ' c' [explicit solve]: current solution: 9.624338e+01, current residual:5.809871e+01 +field 'mu' [explicit solve]: current solution: 4.762159e+00, current residual:2.873303e+00 +wall time: 0.000556946s + +time increment:82930 time: 82.93 +field ' c' [explicit solve]: current solution: 9.624342e+01, current residual:5.809873e+01 +field 'mu' [explicit solve]: current solution: 4.762102e+00, current residual:2.873268e+00 +wall time: 0.000598907s + +time increment:82940 time: 82.94 +field ' c' [explicit solve]: current solution: 9.624346e+01, current residual:5.809876e+01 +field 'mu' [explicit solve]: current solution: 4.762046e+00, current residual:2.873233e+00 +wall time: 0.000627995s + +time increment:82950 time: 82.95 +field ' c' [explicit solve]: current solution: 9.624351e+01, current residual:5.809878e+01 +field 'mu' [explicit solve]: current solution: 4.761989e+00, current residual:2.873198e+00 +wall time: 0.000571012s + +time increment:82960 time: 82.96 +field ' c' [explicit solve]: current solution: 9.624355e+01, current residual:5.809880e+01 +field 'mu' [explicit solve]: current solution: 4.761933e+00, current residual:2.873163e+00 +wall time: 0.000740051s + +time increment:82970 time: 82.97 +field ' c' [explicit solve]: current solution: 9.624360e+01, current residual:5.809883e+01 +field 'mu' [explicit solve]: current solution: 4.761876e+00, current residual:2.873128e+00 +wall time: 0.000664949s + +time increment:82980 time: 82.98 +field ' c' [explicit solve]: current solution: 9.624364e+01, current residual:5.809885e+01 +field 'mu' [explicit solve]: current solution: 4.761819e+00, current residual:2.873093e+00 +wall time: 0.000622034s + +time increment:82990 time: 82.99 +field ' c' [explicit solve]: current solution: 9.624369e+01, current residual:5.809888e+01 +field 'mu' [explicit solve]: current solution: 4.761763e+00, current residual:2.873058e+00 +wall time: 0.000566959s + +time increment:83000 time: 83 +field ' c' [explicit solve]: current solution: 9.624373e+01, current residual:5.809890e+01 +field 'mu' [explicit solve]: current solution: 4.761706e+00, current residual:2.873022e+00 +wall time: 0.000597s + +time increment:83010 time: 83.01 +field ' c' [explicit solve]: current solution: 9.624377e+01, current residual:5.809893e+01 +field 'mu' [explicit solve]: current solution: 4.761650e+00, current residual:2.872987e+00 +wall time: 0.000607014s + +time increment:83020 time: 83.02 +field ' c' [explicit solve]: current solution: 9.624382e+01, current residual:5.809895e+01 +field 'mu' [explicit solve]: current solution: 4.761593e+00, current residual:2.872952e+00 +wall time: 0.000672817s + +time increment:83030 time: 83.03 +field ' c' [explicit solve]: current solution: 9.624386e+01, current residual:5.809897e+01 +field 'mu' [explicit solve]: current solution: 4.761537e+00, current residual:2.872917e+00 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0.000697136s + +time increment:83080 time: 83.08 +field ' c' [explicit solve]: current solution: 9.624408e+01, current residual:5.809910e+01 +field 'mu' [explicit solve]: current solution: 4.761254e+00, current residual:2.872742e+00 +wall time: 0.000625849s + +time increment:83090 time: 83.09 +field ' c' [explicit solve]: current solution: 9.624413e+01, current residual:5.809912e+01 +field 'mu' [explicit solve]: current solution: 4.761197e+00, current residual:2.872707e+00 +wall time: 0.000715017s + +time increment:83100 time: 83.1 +field ' c' [explicit solve]: current solution: 9.624417e+01, current residual:5.809915e+01 +field 'mu' [explicit solve]: current solution: 4.761141e+00, current residual:2.872672e+00 +wall time: 0.000594139s + +time increment:83110 time: 83.11 +field ' c' [explicit solve]: current solution: 9.624422e+01, current residual:5.809917e+01 +field 'mu' [explicit solve]: current solution: 4.761084e+00, current residual:2.872637e+00 +wall time: 0.000582218s + +time increment:83120 time: 83.12 +field ' c' [explicit solve]: current solution: 9.624426e+01, current residual:5.809919e+01 +field 'mu' [explicit solve]: current solution: 4.761028e+00, current residual:2.872602e+00 +wall time: 0.000573158s + +time increment:83130 time: 83.13 +field ' c' [explicit solve]: current solution: 9.624431e+01, current residual:5.809922e+01 +field 'mu' [explicit solve]: current solution: 4.760971e+00, current residual:2.872567e+00 +wall time: 0.000563145s + +time increment:83140 time: 83.14 +field ' c' [explicit solve]: current solution: 9.624435e+01, current residual:5.809924e+01 +field 'mu' [explicit solve]: current solution: 4.760914e+00, current residual:2.872532e+00 +wall time: 0.000576019s + +time increment:83150 time: 83.15 +field ' c' [explicit solve]: current solution: 9.624439e+01, current residual:5.809927e+01 +field 'mu' [explicit solve]: current solution: 4.760858e+00, current residual:2.872497e+00 +wall time: 0.000566006s + +time increment:83160 time: 83.16 +field ' c' [explicit solve]: current solution: 9.624444e+01, current residual:5.809929e+01 +field 'mu' [explicit solve]: current solution: 4.760801e+00, current residual:2.872462e+00 +wall time: 0.000665903s + +time increment:83170 time: 83.17 +field ' c' [explicit solve]: current solution: 9.624448e+01, current residual:5.809932e+01 +field 'mu' [explicit solve]: current solution: 4.760745e+00, current residual:2.872427e+00 +wall time: 0.00059104s + +time increment:83180 time: 83.18 +field ' c' [explicit solve]: current solution: 9.624453e+01, current residual:5.809934e+01 +field 'mu' [explicit solve]: current solution: 4.760688e+00, current residual:2.872391e+00 +wall time: 0.00071907s + +time increment:83190 time: 83.19 +field ' c' [explicit solve]: current solution: 9.624457e+01, current residual:5.809937e+01 +field 'mu' [explicit solve]: current solution: 4.760631e+00, current residual:2.872356e+00 +wall time: 0.000560045s + +time increment:83200 time: 83.2 +field ' c' [explicit solve]: current solution: 9.624462e+01, current residual:5.809939e+01 +field 'mu' [explicit solve]: current solution: 4.760575e+00, current residual:2.872321e+00 +wall time: 0.000569105s + +time increment:83210 time: 83.21 +field ' c' [explicit solve]: current solution: 9.624466e+01, current residual:5.809941e+01 +field 'mu' [explicit solve]: current solution: 4.760518e+00, current residual:2.872286e+00 +wall time: 0.000623941s + +time increment:83220 time: 83.22 +field ' c' [explicit solve]: current solution: 9.624470e+01, current residual:5.809944e+01 +field 'mu' [explicit solve]: current solution: 4.760462e+00, current residual:2.872251e+00 +wall time: 0.000696898s + +time increment:83230 time: 83.23 +field ' c' [explicit solve]: current solution: 9.624475e+01, current residual:5.809946e+01 +field 'mu' [explicit solve]: current solution: 4.760405e+00, current residual:2.872216e+00 +wall time: 0.000562906s + +time increment:83240 time: 83.24 +field ' c' [explicit solve]: current solution: 9.624479e+01, current residual:5.809949e+01 +field 'mu' [explicit solve]: current solution: 4.760348e+00, current residual:2.872181e+00 +wall time: 0.000697136s + +time increment:83250 time: 83.25 +field ' c' [explicit solve]: current solution: 9.624484e+01, current residual:5.809951e+01 +field 'mu' [explicit solve]: current solution: 4.760292e+00, current residual:2.872146e+00 +wall time: 0.00068903s + +time increment:83260 time: 83.26 +field ' c' [explicit solve]: current solution: 9.624488e+01, current residual:5.809954e+01 +field 'mu' [explicit solve]: current solution: 4.760235e+00, current residual:2.872111e+00 +wall time: 0.000669956s + +time increment:83270 time: 83.27 +field ' c' [explicit solve]: current solution: 9.624493e+01, current residual:5.809956e+01 +field 'mu' [explicit solve]: current solution: 4.760179e+00, current residual:2.872076e+00 +wall time: 0.0006392s + +time increment:83280 time: 83.28 +field ' c' [explicit solve]: current solution: 9.624497e+01, current residual:5.809959e+01 +field 'mu' [explicit solve]: current solution: 4.760122e+00, current residual:2.872041e+00 +wall time: 0.000591993s + +time increment:83290 time: 83.29 +field ' c' [explicit solve]: current solution: 9.624501e+01, current residual:5.809961e+01 +field 'mu' [explicit solve]: current solution: 4.760065e+00, current residual:2.872006e+00 +wall time: 0.000564098s + +time increment:83300 time: 83.3 +field ' c' [explicit solve]: current solution: 9.624506e+01, current residual:5.809963e+01 +field 'mu' [explicit solve]: current solution: 4.760009e+00, current residual:2.871971e+00 +wall time: 0.00058198s + +time increment:83310 time: 83.31 +field ' c' [explicit solve]: current solution: 9.624510e+01, current residual:5.809966e+01 +field 'mu' [explicit solve]: current solution: 4.759952e+00, current residual:2.871935e+00 +wall time: 0.000567913s + +time increment:83320 time: 83.32 +field ' c' [explicit solve]: current solution: 9.624515e+01, current residual:5.809968e+01 +field 'mu' [explicit solve]: current solution: 4.759895e+00, current residual:2.871900e+00 +wall time: 0.000616074s + +time increment:83330 time: 83.33 +field ' c' [explicit solve]: current solution: 9.624519e+01, current residual:5.809971e+01 +field 'mu' [explicit solve]: current solution: 4.759839e+00, current residual:2.871865e+00 +wall time: 0.000626087s + +time increment:83340 time: 83.34 +field ' c' [explicit solve]: current solution: 9.624524e+01, current residual:5.809973e+01 +field 'mu' [explicit solve]: current solution: 4.759782e+00, current residual:2.871830e+00 +wall time: 0.000555992s + +time increment:83350 time: 83.35 +field ' c' [explicit solve]: current solution: 9.624528e+01, current residual:5.809976e+01 +field 'mu' [explicit solve]: current solution: 4.759726e+00, current residual:2.871795e+00 +wall time: 0.000570059s + +time increment:83360 time: 83.36 +field ' c' [explicit solve]: current solution: 9.624533e+01, current residual:5.809978e+01 +field 'mu' [explicit solve]: current solution: 4.759669e+00, current residual:2.871760e+00 +wall time: 0.000566959s + +time increment:83370 time: 83.37 +field ' c' [explicit solve]: current solution: 9.624537e+01, current residual:5.809981e+01 +field 'mu' [explicit solve]: current solution: 4.759612e+00, current residual:2.871725e+00 +wall time: 0.000583172s + +time increment:83380 time: 83.38 +field ' c' [explicit solve]: current solution: 9.624541e+01, current residual:5.809983e+01 +field 'mu' [explicit solve]: current solution: 4.759556e+00, current residual:2.871690e+00 +wall time: 0.000684977s + +time increment:83390 time: 83.39 +field ' c' [explicit solve]: current solution: 9.624546e+01, current residual:5.809985e+01 +field 'mu' [explicit solve]: current solution: 4.759499e+00, current residual:2.871655e+00 +wall time: 0.00057292s + +time increment:83400 time: 83.4 +field ' c' [explicit solve]: current solution: 9.624550e+01, current residual:5.809988e+01 +field 'mu' [explicit solve]: current solution: 4.759442e+00, current residual:2.871620e+00 +wall time: 0.00058794s + +time increment:83410 time: 83.41 +field ' c' [explicit solve]: current solution: 9.624555e+01, current residual:5.809990e+01 +field 'mu' [explicit solve]: current solution: 4.759386e+00, current residual:2.871585e+00 +wall time: 0.000634193s + +time increment:83420 time: 83.42 +field ' c' [explicit solve]: current solution: 9.624559e+01, current residual:5.809993e+01 +field 'mu' [explicit solve]: current solution: 4.759329e+00, current residual:2.871550e+00 +wall time: 0.000607014s + +time increment:83430 time: 83.43 +field ' c' [explicit solve]: current solution: 9.624564e+01, current residual:5.809995e+01 +field 'mu' [explicit solve]: current solution: 4.759272e+00, current residual:2.871514e+00 +wall time: 0.000580072s + +time increment:83440 time: 83.44 +field ' c' [explicit solve]: current solution: 9.624568e+01, current residual:5.809998e+01 +field 'mu' [explicit solve]: current solution: 4.759216e+00, current residual:2.871479e+00 +wall time: 0.0006392s + +time increment:83450 time: 83.45 +field ' c' [explicit solve]: current solution: 9.624572e+01, current residual:5.810000e+01 +field 'mu' [explicit solve]: current solution: 4.759159e+00, current residual:2.871444e+00 +wall time: 0.000591993s + +time increment:83460 time: 83.46 +field ' c' [explicit solve]: current solution: 9.624577e+01, current residual:5.810003e+01 +field 'mu' [explicit solve]: current solution: 4.759102e+00, current residual:2.871409e+00 +wall time: 0.000561953s + +time increment:83470 time: 83.47 +field ' c' [explicit solve]: current solution: 9.624581e+01, current residual:5.810005e+01 +field 'mu' [explicit solve]: current solution: 4.759046e+00, current residual:2.871374e+00 +wall time: 0.000657082s + +time increment:83480 time: 83.48 +field ' c' [explicit solve]: current solution: 9.624586e+01, current residual:5.810008e+01 +field 'mu' [explicit solve]: current solution: 4.758989e+00, current residual:2.871339e+00 +wall time: 0.000699997s + +time increment:83490 time: 83.49 +field ' c' [explicit solve]: current solution: 9.624590e+01, current residual:5.810010e+01 +field 'mu' [explicit solve]: current solution: 4.758932e+00, current residual:2.871304e+00 +wall time: 0.000743151s + +time increment:83500 time: 83.5 +field ' c' [explicit solve]: current solution: 9.624595e+01, current residual:5.810012e+01 +field 'mu' [explicit solve]: current solution: 4.758876e+00, current residual:2.871269e+00 +wall time: 0.000682831s + +time increment:83510 time: 83.51 +field ' c' [explicit solve]: current solution: 9.624599e+01, current residual:5.810015e+01 +field 'mu' [explicit solve]: current solution: 4.758819e+00, current residual:2.871234e+00 +wall time: 0.000588179s + +time increment:83520 time: 83.52 +field ' c' [explicit solve]: current solution: 9.624604e+01, current residual:5.810017e+01 +field 'mu' [explicit solve]: current solution: 4.758762e+00, current residual:2.871199e+00 +wall time: 0.000600815s + +time increment:83530 time: 83.53 +field ' c' [explicit solve]: current solution: 9.624608e+01, current residual:5.810020e+01 +field 'mu' [explicit solve]: current solution: 4.758706e+00, current residual:2.871164e+00 +wall time: 0.000681877s + +time increment:83540 time: 83.54 +field ' c' [explicit solve]: current solution: 9.624612e+01, current residual:5.810022e+01 +field 'mu' [explicit solve]: current solution: 4.758649e+00, current residual:2.871128e+00 +wall time: 0.00069499s + +time increment:83550 time: 83.55 +field ' c' [explicit solve]: current solution: 9.624617e+01, current residual:5.810025e+01 +field 'mu' [explicit solve]: current solution: 4.758592e+00, current residual:2.871093e+00 +wall time: 0.000645161s + +time increment:83560 time: 83.56 +field ' c' [explicit solve]: current solution: 9.624621e+01, current residual:5.810027e+01 +field 'mu' [explicit solve]: current solution: 4.758535e+00, current residual:2.871058e+00 +wall time: 0.000643969s + +time increment:83570 time: 83.57 +field ' c' [explicit solve]: current solution: 9.624626e+01, current residual:5.810030e+01 +field 'mu' [explicit solve]: current solution: 4.758479e+00, current residual:2.871023e+00 +wall time: 0.000715971s + +time increment:83580 time: 83.58 +field ' c' [explicit solve]: current solution: 9.624630e+01, current residual:5.810032e+01 +field 'mu' [explicit solve]: current solution: 4.758422e+00, current residual:2.870988e+00 +wall time: 0.000637054s + +time increment:83590 time: 83.59 +field ' c' [explicit solve]: current solution: 9.624635e+01, current residual:5.810034e+01 +field 'mu' [explicit solve]: current solution: 4.758365e+00, current residual:2.870953e+00 +wall time: 0.000658989s + +time increment:83600 time: 83.6 +field ' c' [explicit solve]: current solution: 9.624639e+01, current residual:5.810037e+01 +field 'mu' [explicit solve]: current solution: 4.758309e+00, current residual:2.870918e+00 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0.000640154s + +time increment:83650 time: 83.65 +field ' c' [explicit solve]: current solution: 9.624661e+01, current residual:5.810049e+01 +field 'mu' [explicit solve]: current solution: 4.758025e+00, current residual:2.870742e+00 +wall time: 0.000688076s + +time increment:83660 time: 83.66 +field ' c' [explicit solve]: current solution: 9.624666e+01, current residual:5.810052e+01 +field 'mu' [explicit solve]: current solution: 4.757968e+00, current residual:2.870707e+00 +wall time: 0.000658035s + +time increment:83670 time: 83.67 +field ' c' [explicit solve]: current solution: 9.624670e+01, current residual:5.810054e+01 +field 'mu' [explicit solve]: current solution: 4.757912e+00, current residual:2.870672e+00 +wall time: 0.000737906s + +time increment:83680 time: 83.68 +field ' c' [explicit solve]: current solution: 9.624675e+01, current residual:5.810057e+01 +field 'mu' [explicit solve]: current solution: 4.757855e+00, current residual:2.870637e+00 +wall time: 0.000710011s + +time increment:83690 time: 83.69 +field ' c' [explicit solve]: current solution: 9.624679e+01, current residual:5.810059e+01 +field 'mu' [explicit solve]: current solution: 4.757798e+00, current residual:2.870602e+00 +wall time: 0.000834942s + +time increment:83700 time: 83.7 +field ' c' [explicit solve]: current solution: 9.624684e+01, current residual:5.810062e+01 +field 'mu' [explicit solve]: current solution: 4.757741e+00, current residual:2.870567e+00 +wall time: 0.000662088s + +time increment:83710 time: 83.71 +field ' c' [explicit solve]: current solution: 9.624688e+01, current residual:5.810064e+01 +field 'mu' [explicit solve]: current solution: 4.757685e+00, current residual:2.870532e+00 +wall time: 0.000721931s + +time increment:83720 time: 83.72 +field ' c' [explicit solve]: current solution: 9.624693e+01, current residual:5.810066e+01 +field 'mu' [explicit solve]: current solution: 4.757628e+00, current residual:2.870496e+00 +wall time: 0.000679016s + +time increment:83730 time: 83.73 +field ' c' [explicit solve]: current solution: 9.624697e+01, current residual:5.810069e+01 +field 'mu' [explicit solve]: current solution: 4.757571e+00, current residual:2.870461e+00 +wall time: 0.00069809s + +time increment:83740 time: 83.74 +field ' c' [explicit solve]: current solution: 9.624701e+01, current residual:5.810071e+01 +field 'mu' [explicit solve]: current solution: 4.757515e+00, current residual:2.870426e+00 +wall time: 0.000670195s + +time increment:83750 time: 83.75 +field ' c' [explicit solve]: current solution: 9.624706e+01, current residual:5.810074e+01 +field 'mu' [explicit solve]: current solution: 4.757458e+00, current residual:2.870391e+00 +wall time: 0.000678062s + +time increment:83760 time: 83.76 +field ' c' [explicit solve]: current solution: 9.624710e+01, current residual:5.810076e+01 +field 'mu' [explicit solve]: current solution: 4.757401e+00, current residual:2.870356e+00 +wall time: 0.000680923s + +time increment:83770 time: 83.77 +field ' c' [explicit solve]: current solution: 9.624715e+01, current residual:5.810079e+01 +field 'mu' [explicit solve]: current solution: 4.757344e+00, current residual:2.870321e+00 +wall time: 0.000680923s + +time increment:83780 time: 83.78 +field ' c' [explicit solve]: current solution: 9.624719e+01, current residual:5.810081e+01 +field 'mu' [explicit solve]: current solution: 4.757288e+00, current residual:2.870286e+00 +wall time: 0.000684023s + +time increment:83790 time: 83.79 +field ' c' [explicit solve]: current solution: 9.624724e+01, current residual:5.810084e+01 +field 'mu' [explicit solve]: current solution: 4.757231e+00, current residual:2.870251e+00 +wall time: 0.000636101s + +time increment:83800 time: 83.8 +field ' c' [explicit solve]: current solution: 9.624728e+01, current residual:5.810086e+01 +field 'mu' [explicit solve]: current solution: 4.757174e+00, current residual:2.870215e+00 +wall time: 0.000709057s + +time increment:83810 time: 83.81 +field ' c' [explicit solve]: current solution: 9.624733e+01, current residual:5.810089e+01 +field 'mu' [explicit solve]: current solution: 4.757117e+00, current residual:2.870180e+00 +wall time: 0.000696898s + +time increment:83820 time: 83.82 +field ' c' [explicit solve]: current solution: 9.624737e+01, current residual:5.810091e+01 +field 'mu' [explicit solve]: current solution: 4.757060e+00, current residual:2.870145e+00 +wall time: 0.00064683s + +time increment:83830 time: 83.83 +field ' c' [explicit solve]: current solution: 9.624742e+01, current residual:5.810093e+01 +field 'mu' [explicit solve]: current solution: 4.757004e+00, current residual:2.870110e+00 +wall time: 0.00075984s + +time increment:83840 time: 83.84 +field ' c' [explicit solve]: current solution: 9.624746e+01, current residual:5.810096e+01 +field 'mu' [explicit solve]: current solution: 4.756947e+00, current residual:2.870075e+00 +wall time: 0.000654936s + +time increment:83850 time: 83.85 +field ' c' [explicit solve]: current solution: 9.624750e+01, current residual:5.810098e+01 +field 'mu' [explicit solve]: current solution: 4.756890e+00, current residual:2.870040e+00 +wall time: 0.000687122s + +time increment:83860 time: 83.86 +field ' c' [explicit solve]: current solution: 9.624755e+01, current residual:5.810101e+01 +field 'mu' [explicit solve]: current solution: 4.756833e+00, current residual:2.870005e+00 +wall time: 0.000677109s + +time increment:83870 time: 83.87 +field ' c' [explicit solve]: current solution: 9.624759e+01, current residual:5.810103e+01 +field 'mu' [explicit solve]: current solution: 4.756777e+00, current residual:2.869969e+00 +wall time: 0.000664949s + +time increment:83880 time: 83.88 +field ' c' [explicit solve]: current solution: 9.624764e+01, current residual:5.810106e+01 +field 'mu' [explicit solve]: current solution: 4.756720e+00, current residual:2.869934e+00 +wall time: 0.000751972s + +time increment:83890 time: 83.89 +field ' c' [explicit solve]: current solution: 9.624768e+01, 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residual:5.810118e+01 +field 'mu' [explicit solve]: current solution: 4.756436e+00, current residual:2.869759e+00 +wall time: 0.000602961s + +time increment:83940 time: 83.94 +field ' c' [explicit solve]: current solution: 9.624791e+01, current residual:5.810121e+01 +field 'mu' [explicit solve]: current solution: 4.756379e+00, current residual:2.869724e+00 +wall time: 0.000576019s + +time increment:83950 time: 83.95 +field ' c' [explicit solve]: current solution: 9.624795e+01, current residual:5.810123e+01 +field 'mu' [explicit solve]: current solution: 4.756322e+00, current residual:2.869688e+00 +wall time: 0.000653982s + +time increment:83960 time: 83.96 +field ' c' [explicit solve]: current solution: 9.624800e+01, current residual:5.810125e+01 +field 'mu' [explicit solve]: current solution: 4.756265e+00, current residual:2.869653e+00 +wall time: 0.00068903s + +time increment:83970 time: 83.97 +field ' c' [explicit solve]: current solution: 9.624804e+01, current residual:5.810128e+01 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solve]: current solution: 4.755981e+00, current residual:2.869477e+00 +wall time: 0.00068593s + +time increment:84020 time: 84.02 +field ' c' [explicit solve]: current solution: 9.624826e+01, current residual:5.810140e+01 +field 'mu' [explicit solve]: current solution: 4.755925e+00, current residual:2.869442e+00 +wall time: 0.000682116s + +time increment:84030 time: 84.03 +field ' c' [explicit solve]: current solution: 9.624831e+01, current residual:5.810143e+01 +field 'mu' [explicit solve]: current solution: 4.755868e+00, current residual:2.869407e+00 +wall time: 0.000573874s + +time increment:84040 time: 84.04 +field ' c' [explicit solve]: current solution: 9.624835e+01, current residual:5.810145e+01 +field 'mu' [explicit solve]: current solution: 4.755811e+00, current residual:2.869372e+00 +wall time: 0.000615835s + +time increment:84050 time: 84.05 +field ' c' [explicit solve]: current solution: 9.624840e+01, current residual:5.810148e+01 +field 'mu' [explicit solve]: current solution: 4.755754e+00, current residual:2.869337e+00 +wall time: 0.000626087s + +time increment:84060 time: 84.06 +field ' c' [explicit solve]: current solution: 9.624844e+01, current residual:5.810150e+01 +field 'mu' [explicit solve]: current solution: 4.755697e+00, current residual:2.869302e+00 +wall time: 0.000560045s + +time increment:84070 time: 84.07 +field ' c' [explicit solve]: current solution: 9.624849e+01, current residual:5.810153e+01 +field 'mu' [explicit solve]: current solution: 4.755640e+00, current residual:2.869267e+00 +wall time: 0.000775814s + +time increment:84080 time: 84.08 +field ' c' [explicit solve]: current solution: 9.624853e+01, current residual:5.810155e+01 +field 'mu' [explicit solve]: current solution: 4.755584e+00, current residual:2.869231e+00 +wall time: 0.000664949s + +time increment:84090 time: 84.09 +field ' c' [explicit solve]: current solution: 9.624858e+01, current residual:5.810158e+01 +field 'mu' [explicit solve]: current solution: 4.755527e+00, current residual:2.869196e+00 +wall time: 0.000654936s + +time increment:84100 time: 84.1 +field ' c' [explicit solve]: current solution: 9.624862e+01, current residual:5.810160e+01 +field 'mu' [explicit solve]: current solution: 4.755470e+00, current residual:2.869161e+00 +wall time: 0.000663042s + +time increment:84110 time: 84.11 +field ' c' [explicit solve]: current solution: 9.624867e+01, current residual:5.810162e+01 +field 'mu' [explicit solve]: current solution: 4.755413e+00, current residual:2.869126e+00 +wall time: 0.000701904s + +time increment:84120 time: 84.12 +field ' c' [explicit solve]: current solution: 9.624871e+01, current residual:5.810165e+01 +field 'mu' [explicit solve]: current solution: 4.755356e+00, current residual:2.869091e+00 +wall time: 0.000669956s + +time increment:84130 time: 84.13 +field ' c' [explicit solve]: current solution: 9.624875e+01, current residual:5.810167e+01 +field 'mu' [explicit solve]: current solution: 4.755299e+00, current residual:2.869056e+00 +wall time: 0.000635147s + +time increment:84140 time: 84.14 +field ' c' [explicit solve]: current solution: 9.624880e+01, current residual:5.810170e+01 +field 'mu' [explicit solve]: current solution: 4.755243e+00, current residual:2.869020e+00 +wall time: 0.000669003s + +time increment:84150 time: 84.15 +field ' c' [explicit solve]: current solution: 9.624884e+01, current residual:5.810172e+01 +field 'mu' [explicit solve]: current solution: 4.755186e+00, current residual:2.868985e+00 +wall time: 0.000635147s + +time increment:84160 time: 84.16 +field ' c' [explicit solve]: current solution: 9.624889e+01, current residual:5.810175e+01 +field 'mu' [explicit solve]: current solution: 4.755129e+00, current residual:2.868950e+00 +wall time: 0.000681877s + +time increment:84170 time: 84.17 +field ' c' [explicit solve]: current solution: 9.624893e+01, current residual:5.810177e+01 +field 'mu' [explicit solve]: current solution: 4.755072e+00, current residual:2.868915e+00 +wall time: 0.000622988s + +time increment:84180 time: 84.18 +field ' c' [explicit solve]: current solution: 9.624898e+01, current residual:5.810180e+01 +field 'mu' [explicit solve]: current solution: 4.755015e+00, current residual:2.868880e+00 +wall time: 0.00068593s + +time increment:84190 time: 84.19 +field ' c' [explicit solve]: current solution: 9.624902e+01, current residual:5.810182e+01 +field 'mu' [explicit solve]: current solution: 4.754958e+00, current residual:2.868845e+00 +wall time: 0.000658989s + +time increment:84200 time: 84.2 +field ' c' [explicit solve]: current solution: 9.624907e+01, current residual:5.810185e+01 +field 'mu' [explicit solve]: current solution: 4.754901e+00, current residual:2.868809e+00 +wall time: 0.000657082s + +time increment:84210 time: 84.21 +field ' c' [explicit solve]: current solution: 9.624911e+01, current residual:5.810187e+01 +field 'mu' [explicit solve]: current solution: 4.754844e+00, current residual:2.868774e+00 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0.000670195s + +time increment:84260 time: 84.26 +field ' c' [explicit solve]: current solution: 9.624934e+01, current residual:5.810200e+01 +field 'mu' [explicit solve]: current solution: 4.754560e+00, current residual:2.868598e+00 +wall time: 0.000650883s + +time increment:84270 time: 84.27 +field ' c' [explicit solve]: current solution: 9.624938e+01, current residual:5.810202e+01 +field 'mu' [explicit solve]: current solution: 4.754503e+00, current residual:2.868563e+00 +wall time: 0.000698805s + +time increment:84280 time: 84.28 +field ' c' [explicit solve]: current solution: 9.624942e+01, current residual:5.810204e+01 +field 'mu' [explicit solve]: current solution: 4.754446e+00, current residual:2.868528e+00 +wall time: 0.000696898s + +time increment:84290 time: 84.29 +field ' c' [explicit solve]: current solution: 9.624947e+01, current residual:5.810207e+01 +field 'mu' [explicit solve]: current solution: 4.754389e+00, current residual:2.868493e+00 +wall time: 0.000663996s + +time increment:84300 time: 84.3 +field ' c' [explicit solve]: current solution: 9.624951e+01, current residual:5.810209e+01 +field 'mu' [explicit solve]: current solution: 4.754332e+00, current residual:2.868458e+00 +wall time: 0.000664949s + +time increment:84310 time: 84.31 +field ' c' [explicit solve]: current solution: 9.624956e+01, current residual:5.810212e+01 +field 'mu' [explicit solve]: current solution: 4.754276e+00, current residual:2.868422e+00 +wall time: 0.000671864s + +time increment:84320 time: 84.32 +field ' c' [explicit solve]: current solution: 9.624960e+01, current residual:5.810214e+01 +field 'mu' [explicit solve]: current solution: 4.754219e+00, current residual:2.868387e+00 +wall time: 0.000695944s + +time increment:84330 time: 84.33 +field ' c' [explicit solve]: current solution: 9.624965e+01, current residual:5.810217e+01 +field 'mu' [explicit solve]: current solution: 4.754162e+00, current residual:2.868352e+00 +wall time: 0.000652075s + +time increment:84340 time: 84.34 +field ' c' [explicit solve]: current solution: 9.624969e+01, current residual:5.810219e+01 +field 'mu' [explicit solve]: current solution: 4.754105e+00, current residual:2.868317e+00 +wall time: 0.000683069s + +time increment:84350 time: 84.35 +field ' c' [explicit solve]: current solution: 9.624974e+01, current residual:5.810222e+01 +field 'mu' [explicit solve]: current solution: 4.754048e+00, current residual:2.868282e+00 +wall time: 0.000642061s + +time increment:84360 time: 84.36 +field ' c' [explicit solve]: current solution: 9.624978e+01, current residual:5.810224e+01 +field 'mu' [explicit solve]: current solution: 4.753991e+00, current residual:2.868247e+00 +wall time: 0.000672102s + +time increment:84370 time: 84.37 +field ' c' [explicit solve]: current solution: 9.624983e+01, current residual:5.810227e+01 +field 'mu' [explicit solve]: current solution: 4.753934e+00, current residual:2.868211e+00 +wall time: 0.000688791s + +time increment:84380 time: 84.38 +field ' c' [explicit solve]: current solution: 9.624987e+01, current residual:5.810229e+01 +field 'mu' [explicit solve]: current solution: 4.753877e+00, current residual:2.868176e+00 +wall time: 0.000605106s + +time increment:84390 time: 84.39 +field ' c' [explicit solve]: current solution: 9.624992e+01, current residual:5.810232e+01 +field 'mu' [explicit solve]: current solution: 4.753820e+00, current residual:2.868141e+00 +wall time: 0.000617981s + +time increment:84400 time: 84.4 +field ' c' [explicit solve]: current solution: 9.624996e+01, current residual:5.810234e+01 +field 'mu' [explicit solve]: current solution: 4.753763e+00, current residual:2.868106e+00 +wall time: 0.000586033s + +time increment:84410 time: 84.41 +field ' c' [explicit solve]: current solution: 9.625001e+01, current residual:5.810237e+01 +field 'mu' [explicit solve]: current solution: 4.753706e+00, current residual:2.868071e+00 +wall time: 0.000565052s + +time increment:84420 time: 84.42 +field ' c' [explicit solve]: current solution: 9.625005e+01, current residual:5.810239e+01 +field 'mu' [explicit solve]: current solution: 4.753649e+00, current residual:2.868035e+00 +wall time: 0.000673056s + +time increment:84430 time: 84.43 +field ' c' [explicit solve]: current solution: 9.625010e+01, current residual:5.810242e+01 +field 'mu' [explicit solve]: current solution: 4.753593e+00, current residual:2.868000e+00 +wall time: 0.000654221s + +time increment:84440 time: 84.44 +field ' c' [explicit solve]: current solution: 9.625014e+01, current residual:5.810244e+01 +field 'mu' [explicit solve]: current solution: 4.753536e+00, current residual:2.867965e+00 +wall time: 0.000622988s + +time increment:84450 time: 84.45 +field ' c' [explicit solve]: current solution: 9.625019e+01, current residual:5.810247e+01 +field 'mu' [explicit solve]: current solution: 4.753479e+00, current residual:2.867930e+00 +wall time: 0.000635147s + +time increment:84460 time: 84.46 +field ' c' [explicit solve]: current solution: 9.625023e+01, current residual:5.810249e+01 +field 'mu' [explicit solve]: current solution: 4.753422e+00, current residual:2.867895e+00 +wall time: 0.000672102s + +time increment:84470 time: 84.47 +field ' c' [explicit solve]: current solution: 9.625028e+01, current residual:5.810252e+01 +field 'mu' [explicit solve]: current solution: 4.753365e+00, current residual:2.867859e+00 +wall time: 0.000565052s + +time increment:84480 time: 84.48 +field ' c' [explicit solve]: current solution: 9.625032e+01, current residual:5.810254e+01 +field 'mu' [explicit solve]: current solution: 4.753308e+00, current residual:2.867824e+00 +wall time: 0.000678062s + +time increment:84490 time: 84.49 +field ' c' [explicit solve]: current solution: 9.625037e+01, current residual:5.810256e+01 +field 'mu' [explicit solve]: current solution: 4.753251e+00, current residual:2.867789e+00 +wall time: 0.000683069s + +time increment:84500 time: 84.5 +field ' c' [explicit solve]: current solution: 9.625041e+01, current residual:5.810259e+01 +field 'mu' [explicit solve]: current solution: 4.753194e+00, current residual:2.867754e+00 +wall time: 0.000714064s + +time increment:84510 time: 84.51 +field ' c' [explicit solve]: current solution: 9.625045e+01, current residual:5.810261e+01 +field 'mu' [explicit solve]: current solution: 4.753137e+00, current residual:2.867719e+00 +wall time: 0.000587225s + +time increment:84520 time: 84.52 +field ' c' [explicit solve]: current solution: 9.625050e+01, current residual:5.810264e+01 +field 'mu' [explicit solve]: current solution: 4.753080e+00, current residual:2.867683e+00 +wall time: 0.000631809s + +time increment:84530 time: 84.53 +field ' c' [explicit solve]: current solution: 9.625054e+01, current residual:5.810266e+01 +field 'mu' [explicit solve]: current solution: 4.753023e+00, current residual:2.867648e+00 +wall time: 0.000658035s + +time increment:84540 time: 84.54 +field ' c' [explicit solve]: current solution: 9.625059e+01, current residual:5.810269e+01 +field 'mu' [explicit solve]: current solution: 4.752966e+00, current residual:2.867613e+00 +wall time: 0.000571012s + +time increment:84550 time: 84.55 +field ' c' [explicit solve]: current solution: 9.625063e+01, current residual:5.810271e+01 +field 'mu' [explicit solve]: current solution: 4.752909e+00, current residual:2.867578e+00 +wall time: 0.000605106s + +time increment:84560 time: 84.56 +field ' c' [explicit solve]: current solution: 9.625068e+01, current residual:5.810274e+01 +field 'mu' [explicit solve]: current solution: 4.752852e+00, current residual:2.867543e+00 +wall time: 0.000694036s + +time increment:84570 time: 84.57 +field ' c' [explicit solve]: current solution: 9.625072e+01, current residual:5.810276e+01 +field 'mu' [explicit solve]: current solution: 4.752795e+00, current residual:2.867507e+00 +wall time: 0.000716925s + +time increment:84580 time: 84.58 +field ' c' [explicit solve]: current solution: 9.625077e+01, current residual:5.810279e+01 +field 'mu' [explicit solve]: current solution: 4.752738e+00, current residual:2.867472e+00 +wall time: 0.000762939s + +time increment:84590 time: 84.59 +field ' c' [explicit solve]: current solution: 9.625081e+01, current residual:5.810281e+01 +field 'mu' [explicit solve]: current solution: 4.752681e+00, current residual:2.867437e+00 +wall time: 0.000674009s + +time increment:84600 time: 84.6 +field ' c' [explicit solve]: current solution: 9.625086e+01, current residual:5.810284e+01 +field 'mu' [explicit solve]: current solution: 4.752624e+00, current residual:2.867402e+00 +wall time: 0.00069499s + +time increment:84610 time: 84.61 +field ' c' [explicit solve]: current solution: 9.625090e+01, current residual:5.810286e+01 +field 'mu' [explicit solve]: current solution: 4.752567e+00, current residual:2.867366e+00 +wall time: 0.000706911s + +time increment:84620 time: 84.62 +field ' c' [explicit solve]: current solution: 9.625095e+01, current residual:5.810289e+01 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solve]: current solution: 4.752282e+00, current residual:2.867190e+00 +wall time: 0.000725985s + +time increment:84670 time: 84.67 +field ' c' [explicit solve]: current solution: 9.625117e+01, current residual:5.810301e+01 +field 'mu' [explicit solve]: current solution: 4.752225e+00, current residual:2.867155e+00 +wall time: 0.000674963s + +time increment:84680 time: 84.68 +field ' c' [explicit solve]: current solution: 9.625122e+01, current residual:5.810304e+01 +field 'mu' [explicit solve]: current solution: 4.752168e+00, current residual:2.867120e+00 +wall time: 0.000648975s + +time increment:84690 time: 84.69 +field ' c' [explicit solve]: current solution: 9.625126e+01, current residual:5.810306e+01 +field 'mu' [explicit solve]: current solution: 4.752111e+00, current residual:2.867085e+00 +wall time: 0.000662088s + +time increment:84700 time: 84.7 +field ' c' [explicit solve]: current solution: 9.625131e+01, current residual:5.810309e+01 +field 'mu' [explicit solve]: current solution: 4.752054e+00, current residual:2.867049e+00 +wall time: 0.00067687s + +time increment:84710 time: 84.71 +field ' c' [explicit solve]: current solution: 9.625135e+01, current residual:5.810311e+01 +field 'mu' [explicit solve]: current solution: 4.751997e+00, current residual:2.867014e+00 +wall time: 0.000656843s + +time increment:84720 time: 84.72 +field ' c' [explicit solve]: current solution: 9.625140e+01, current residual:5.810314e+01 +field 'mu' [explicit solve]: current solution: 4.751940e+00, current residual:2.866979e+00 +wall time: 0.000729084s + +time increment:84730 time: 84.73 +field ' c' [explicit solve]: current solution: 9.625144e+01, current residual:5.810316e+01 +field 'mu' [explicit solve]: current solution: 4.751883e+00, current residual:2.866944e+00 +wall time: 0.000671864s + +time increment:84740 time: 84.74 +field ' c' [explicit solve]: current solution: 9.625149e+01, current residual:5.810319e+01 +field 'mu' [explicit solve]: current solution: 4.751826e+00, current residual:2.866909e+00 +wall time: 0.000672102s + +time increment:84750 time: 84.75 +field ' c' [explicit solve]: current solution: 9.625153e+01, current residual:5.810321e+01 +field 'mu' [explicit solve]: current solution: 4.751769e+00, current residual:2.866873e+00 +wall time: 0.000641823s + +time increment:84760 time: 84.76 +field ' c' [explicit solve]: current solution: 9.625158e+01, current residual:5.810324e+01 +field 'mu' [explicit solve]: current solution: 4.751712e+00, current residual:2.866838e+00 +wall time: 0.000736952s + +time increment:84770 time: 84.77 +field ' c' [explicit solve]: current solution: 9.625162e+01, current residual:5.810326e+01 +field 'mu' [explicit solve]: current solution: 4.751655e+00, current residual:2.866803e+00 +wall time: 0.000656843s + +time increment:84780 time: 84.78 +field ' c' [explicit solve]: current solution: 9.625167e+01, current residual:5.810328e+01 +field 'mu' [explicit solve]: current solution: 4.751598e+00, current residual:2.866768e+00 +wall time: 0.00067687s + +time increment:84790 time: 84.79 +field ' c' [explicit solve]: current solution: 9.625171e+01, current residual:5.810331e+01 +field 'mu' [explicit solve]: current solution: 4.751541e+00, current residual:2.866732e+00 +wall time: 0.000663042s + +time increment:84800 time: 84.8 +field ' c' [explicit solve]: current solution: 9.625176e+01, current residual:5.810333e+01 +field 'mu' [explicit solve]: current solution: 4.751484e+00, current residual:2.866697e+00 +wall time: 0.000679016s + +time increment:84810 time: 84.81 +field ' c' [explicit solve]: current solution: 9.625180e+01, current residual:5.810336e+01 +field 'mu' [explicit solve]: current solution: 4.751427e+00, current residual:2.866662e+00 +wall time: 0.000716925s + +time increment:84820 time: 84.82 +field ' c' [explicit solve]: current solution: 9.625185e+01, current residual:5.810338e+01 +field 'mu' [explicit solve]: current solution: 4.751370e+00, current residual:2.866627e+00 +wall time: 0.000689983s + +time increment:84830 time: 84.83 +field ' c' [explicit solve]: current solution: 9.625189e+01, current residual:5.810341e+01 +field 'mu' [explicit solve]: current solution: 4.751313e+00, current residual:2.866591e+00 +wall time: 0.000674009s + +time increment:84840 time: 84.84 +field ' c' [explicit solve]: current solution: 9.625194e+01, current residual:5.810343e+01 +field 'mu' [explicit solve]: current solution: 4.751256e+00, current residual:2.866556e+00 +wall time: 0.000679016s + +time increment:84850 time: 84.85 +field ' c' [explicit solve]: current solution: 9.625198e+01, current residual:5.810346e+01 +field 'mu' [explicit solve]: current solution: 4.751199e+00, current residual:2.866521e+00 +wall time: 0.000647068s + +time increment:84860 time: 84.86 +field ' c' [explicit solve]: current solution: 9.625202e+01, current residual:5.810348e+01 +field 'mu' [explicit solve]: current solution: 4.751142e+00, current residual:2.866486e+00 +wall time: 0.000656843s + +time increment:84870 time: 84.87 +field ' c' [explicit solve]: current solution: 9.625207e+01, current residual:5.810351e+01 +field 'mu' [explicit solve]: current solution: 4.751085e+00, current residual:2.866450e+00 +wall time: 0.000677824s + +time increment:84880 time: 84.88 +field ' c' [explicit solve]: current solution: 9.625211e+01, current residual:5.810353e+01 +field 'mu' [explicit solve]: current solution: 4.751028e+00, current residual:2.866415e+00 +wall time: 0.000641823s + +time increment:84890 time: 84.89 +field ' c' [explicit solve]: current solution: 9.625216e+01, current residual:5.810356e+01 +field 'mu' [explicit solve]: current solution: 4.750970e+00, current residual:2.866380e+00 +wall time: 0.000686884s + +time increment:84900 time: 84.9 +field ' c' [explicit solve]: current solution: 9.625220e+01, current residual:5.810358e+01 +field 'mu' [explicit solve]: current solution: 4.750913e+00, current residual:2.866345e+00 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increment:84990 time: 84.99 +field ' c' [explicit solve]: current solution: 9.625261e+01, current residual:5.810381e+01 +field 'mu' [explicit solve]: current solution: 4.750400e+00, current residual:2.866027e+00 +wall time: 0.000591993s + +time increment:85000 time: 85 +field ' c' [explicit solve]: current solution: 9.625265e+01, current residual:5.810383e+01 +field 'mu' [explicit solve]: current solution: 4.750343e+00, current residual:2.865992e+00 +wall time: 0.000631809s + +time increment:85010 time: 85.01 +field ' c' [explicit solve]: current solution: 9.625270e+01, current residual:5.810386e+01 +field 'mu' [explicit solve]: current solution: 4.750285e+00, current residual:2.865957e+00 +wall time: 0.000618935s + +time increment:85020 time: 85.02 +field ' c' [explicit solve]: current solution: 9.625274e+01, current residual:5.810388e+01 +field 'mu' [explicit solve]: current solution: 4.750228e+00, current residual:2.865922e+00 +wall time: 0.000595093s + +time increment:85030 time: 85.03 +field ' c' [explicit solve]: current solution: 9.625279e+01, current residual:5.810391e+01 +field 'mu' [explicit solve]: current solution: 4.750171e+00, current residual:2.865886e+00 +wall time: 0.000669956s + +time increment:85040 time: 85.04 +field ' c' [explicit solve]: current solution: 9.625283e+01, current residual:5.810393e+01 +field 'mu' [explicit solve]: current solution: 4.750114e+00, current residual:2.865851e+00 +wall time: 0.000576019s + +time increment:85050 time: 85.05 +field ' c' [explicit solve]: current solution: 9.625288e+01, current residual:5.810396e+01 +field 'mu' [explicit solve]: current solution: 4.750057e+00, current residual:2.865816e+00 +wall time: 0.000586033s + +time increment:85060 time: 85.06 +field ' c' [explicit solve]: current solution: 9.625292e+01, current residual:5.810398e+01 +field 'mu' [explicit solve]: current solution: 4.750000e+00, current residual:2.865781e+00 +wall time: 0.000739813s + +time increment:85070 time: 85.07 +field ' c' [explicit solve]: current solution: 9.625297e+01, current residual:5.810401e+01 +field 'mu' [explicit solve]: current solution: 4.749943e+00, current residual:2.865745e+00 +wall time: 0.000847101s + +time increment:85080 time: 85.08 +field ' c' [explicit solve]: current solution: 9.625301e+01, current residual:5.810403e+01 +field 'mu' [explicit solve]: current solution: 4.749886e+00, current residual:2.865710e+00 +wall time: 0.000681877s + +time increment:85090 time: 85.09 +field ' c' [explicit solve]: current solution: 9.625306e+01, current residual:5.810406e+01 +field 'mu' [explicit solve]: current solution: 4.749829e+00, current residual:2.865675e+00 +wall time: 0.000649929s + +time increment:85100 time: 85.1 +field ' c' [explicit solve]: current solution: 9.625310e+01, current residual:5.810408e+01 +field 'mu' [explicit solve]: current solution: 4.749771e+00, current residual:2.865640e+00 +wall time: 0.000571012s + +time increment:85110 time: 85.11 +field ' c' [explicit solve]: current solution: 9.625315e+01, current residual:5.810411e+01 +field 'mu' [explicit solve]: current solution: 4.749714e+00, current residual:2.865604e+00 +wall time: 0.000565052s + +time increment:85120 time: 85.12 +field ' c' [explicit solve]: current solution: 9.625319e+01, current residual:5.810413e+01 +field 'mu' [explicit solve]: current solution: 4.749657e+00, current residual:2.865569e+00 +wall time: 0.000583887s + +time increment:85130 time: 85.13 +field ' c' [explicit solve]: current solution: 9.625324e+01, current residual:5.810416e+01 +field 'mu' [explicit solve]: current solution: 4.749600e+00, current residual:2.865534e+00 +wall time: 0.000629902s + +time increment:85140 time: 85.14 +field ' c' [explicit solve]: current solution: 9.625328e+01, current residual:5.810418e+01 +field 'mu' [explicit solve]: current solution: 4.749543e+00, current residual:2.865498e+00 +wall time: 0.000555038s + +time increment:85150 time: 85.15 +field ' c' [explicit solve]: current solution: 9.625333e+01, current residual:5.810421e+01 +field 'mu' [explicit solve]: current solution: 4.749486e+00, current residual:2.865463e+00 +wall time: 0.000699997s + +time increment:85160 time: 85.16 +field ' c' [explicit solve]: current solution: 9.625337e+01, current residual:5.810423e+01 +field 'mu' [explicit solve]: current solution: 4.749429e+00, current residual:2.865428e+00 +wall time: 0.00056982s + +time increment:85170 time: 85.17 +field ' c' [explicit solve]: current solution: 9.625342e+01, current residual:5.810426e+01 +field 'mu' [explicit solve]: current solution: 4.749371e+00, current residual:2.865393e+00 +wall time: 0.000651121s + +time increment:85180 time: 85.18 +field ' c' [explicit solve]: current solution: 9.625346e+01, current residual:5.810428e+01 +field 'mu' [explicit solve]: current solution: 4.749314e+00, current residual:2.865357e+00 +wall time: 0.000631094s + +time increment:85190 time: 85.19 +field ' c' [explicit solve]: current solution: 9.625351e+01, current residual:5.810431e+01 +field 'mu' [explicit solve]: current solution: 4.749257e+00, current residual:2.865322e+00 +wall time: 0.000656128s + +time increment:85200 time: 85.2 +field ' c' [explicit solve]: current solution: 9.625355e+01, current residual:5.810433e+01 +field 'mu' [explicit solve]: current solution: 4.749200e+00, current residual:2.865287e+00 +wall time: 0.000590086s + +time increment:85210 time: 85.21 +field ' c' [explicit solve]: current solution: 9.625360e+01, current residual:5.810436e+01 +field 'mu' [explicit solve]: current solution: 4.749143e+00, current residual:2.865251e+00 +wall time: 0.000619888s + +time increment:85220 time: 85.22 +field ' c' [explicit solve]: current solution: 9.625364e+01, current residual:5.810438e+01 +field 'mu' [explicit solve]: current solution: 4.749086e+00, current residual:2.865216e+00 +wall time: 0.000581026s + +time increment:85230 time: 85.23 +field ' c' [explicit solve]: current solution: 9.625369e+01, current residual:5.810441e+01 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[explicit solve]: current solution: 4.748800e+00, current residual:2.865040e+00 +wall time: 0.000679016s + +time increment:85280 time: 85.28 +field ' c' [explicit solve]: current solution: 9.625391e+01, current residual:5.810453e+01 +field 'mu' [explicit solve]: current solution: 4.748743e+00, current residual:2.865004e+00 +wall time: 0.000709057s + +time increment:85290 time: 85.29 +field ' c' [explicit solve]: current solution: 9.625396e+01, current residual:5.810456e+01 +field 'mu' [explicit solve]: current solution: 4.748685e+00, current residual:2.864969e+00 +wall time: 0.000600815s + +time increment:85300 time: 85.3 +field ' c' [explicit solve]: current solution: 9.625400e+01, current residual:5.810458e+01 +field 'mu' [explicit solve]: current solution: 4.748628e+00, current residual:2.864934e+00 +wall time: 0.000671864s + +time increment:85310 time: 85.31 +field ' c' [explicit solve]: current solution: 9.625405e+01, current residual:5.810461e+01 +field 'mu' [explicit solve]: current solution: 4.748571e+00, current residual:2.864899e+00 +wall time: 0.000649929s + +time increment:85320 time: 85.32 +field ' c' [explicit solve]: current solution: 9.625409e+01, current residual:5.810463e+01 +field 'mu' [explicit solve]: current solution: 4.748514e+00, current residual:2.864863e+00 +wall time: 0.000648975s + +time increment:85330 time: 85.33 +field ' c' [explicit solve]: current solution: 9.625414e+01, current residual:5.810466e+01 +field 'mu' [explicit solve]: current solution: 4.748457e+00, current residual:2.864828e+00 +wall time: 0.000653982s + +time increment:85340 time: 85.34 +field ' c' [explicit solve]: current solution: 9.625419e+01, current residual:5.810468e+01 +field 'mu' [explicit solve]: current solution: 4.748400e+00, current residual:2.864793e+00 +wall time: 0.000753164s + +time increment:85350 time: 85.35 +field ' c' [explicit solve]: current solution: 9.625423e+01, current residual:5.810471e+01 +field 'mu' [explicit solve]: current solution: 4.748342e+00, current residual:2.864757e+00 +wall time: 0.000640869s + +time increment:85360 time: 85.36 +field ' c' [explicit solve]: current solution: 9.625428e+01, current residual:5.810473e+01 +field 'mu' [explicit solve]: current solution: 4.748285e+00, current residual:2.864722e+00 +wall time: 0.000609875s + +time increment:85370 time: 85.37 +field ' c' [explicit solve]: current solution: 9.625432e+01, current residual:5.810476e+01 +field 'mu' [explicit solve]: current solution: 4.748228e+00, current residual:2.864687e+00 +wall time: 0.000693083s + +time increment:85380 time: 85.38 +field ' c' [explicit solve]: current solution: 9.625437e+01, current residual:5.810478e+01 +field 'mu' [explicit solve]: current solution: 4.748171e+00, current residual:2.864651e+00 +wall time: 0.000767946s + +time increment:85390 time: 85.39 +field ' c' [explicit solve]: current solution: 9.625441e+01, current residual:5.810481e+01 +field 'mu' [explicit solve]: current solution: 4.748114e+00, current residual:2.864616e+00 +wall time: 0.000720978s + +time increment:85400 time: 85.4 +field ' c' [explicit solve]: current solution: 9.625446e+01, current residual:5.810483e+01 +field 'mu' [explicit solve]: current solution: 4.748056e+00, current residual:2.864581e+00 +wall time: 0.000756979s + +time increment:85410 time: 85.41 +field ' c' [explicit solve]: current solution: 9.625450e+01, current residual:5.810486e+01 +field 'mu' [explicit solve]: current solution: 4.747999e+00, current residual:2.864546e+00 +wall time: 0.000745058s + +time increment:85420 time: 85.42 +field ' c' [explicit solve]: current solution: 9.625455e+01, current residual:5.810488e+01 +field 'mu' [explicit solve]: current solution: 4.747942e+00, current residual:2.864510e+00 +wall time: 0.00072217s + +time increment:85430 time: 85.43 +field ' c' [explicit solve]: current solution: 9.625459e+01, current residual:5.810491e+01 +field 'mu' [explicit solve]: current solution: 4.747885e+00, current residual:2.864475e+00 +wall time: 0.000691175s + +time increment:85440 time: 85.44 +field ' c' [explicit solve]: current solution: 9.625464e+01, current residual:5.810493e+01 +field 'mu' [explicit solve]: current solution: 4.747827e+00, current residual:2.864440e+00 +wall time: 0.000663996s + +time increment:85450 time: 85.45 +field ' c' [explicit solve]: current solution: 9.625468e+01, current residual:5.810496e+01 +field 'mu' [explicit solve]: current solution: 4.747770e+00, current residual:2.864404e+00 +wall time: 0.000643969s + +time increment:85460 time: 85.46 +field ' c' [explicit solve]: current solution: 9.625473e+01, current residual:5.810498e+01 +field 'mu' [explicit solve]: current solution: 4.747713e+00, current residual:2.864369e+00 +wall time: 0.000664949s + +time increment:85470 time: 85.47 +field ' c' [explicit solve]: current solution: 9.625477e+01, current residual:5.810501e+01 +field 'mu' [explicit solve]: current solution: 4.747656e+00, current residual:2.864334e+00 +wall time: 0.00066185s + +time increment:85480 time: 85.48 +field ' c' [explicit solve]: current solution: 9.625482e+01, current residual:5.810503e+01 +field 'mu' [explicit solve]: current solution: 4.747598e+00, current residual:2.864298e+00 +wall time: 0.000684023s + +time increment:85490 time: 85.49 +field ' c' [explicit solve]: current solution: 9.625486e+01, current residual:5.810506e+01 +field 'mu' [explicit solve]: current solution: 4.747541e+00, current residual:2.864263e+00 +wall time: 0.000617981s + +time increment:85500 time: 85.5 +field ' c' [explicit solve]: current solution: 9.625491e+01, current residual:5.810508e+01 +field 'mu' [explicit solve]: current solution: 4.747484e+00, current residual:2.864228e+00 +wall time: 0.000668049s + +time increment:85510 time: 85.51 +field ' c' [explicit solve]: current solution: 9.625495e+01, current residual:5.810511e+01 +field 'mu' [explicit solve]: current solution: 4.747427e+00, current residual:2.864192e+00 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0.000638008s + +time increment:85560 time: 85.56 +field ' c' [explicit solve]: current solution: 9.625518e+01, current residual:5.810523e+01 +field 'mu' [explicit solve]: current solution: 4.747141e+00, current residual:2.864016e+00 +wall time: 0.000921965s + +time increment:85570 time: 85.57 +field ' c' [explicit solve]: current solution: 9.625522e+01, current residual:5.810526e+01 +field 'mu' [explicit solve]: current solution: 4.747083e+00, current residual:2.863981e+00 +wall time: 0.000699043s + +time increment:85580 time: 85.58 +field ' c' [explicit solve]: current solution: 9.625527e+01, current residual:5.810528e+01 +field 'mu' [explicit solve]: current solution: 4.747026e+00, current residual:2.863945e+00 +wall time: 0.00069809s + +time increment:85590 time: 85.59 +field ' c' [explicit solve]: current solution: 9.625531e+01, current residual:5.810531e+01 +field 'mu' [explicit solve]: current solution: 4.746969e+00, current residual:2.863910e+00 +wall time: 0.000705957s + +time increment:85600 time: 85.6 +field ' c' [explicit solve]: current solution: 9.625536e+01, current residual:5.810533e+01 +field 'mu' [explicit solve]: current solution: 4.746911e+00, current residual:2.863875e+00 +wall time: 0.000756025s + +time increment:85610 time: 85.61 +field ' c' [explicit solve]: current solution: 9.625540e+01, current residual:5.810536e+01 +field 'mu' [explicit solve]: current solution: 4.746854e+00, current residual:2.863839e+00 +wall time: 0.000663996s + +time increment:85620 time: 85.62 +field ' c' [explicit solve]: current solution: 9.625545e+01, current residual:5.810538e+01 +field 'mu' [explicit solve]: current solution: 4.746797e+00, current residual:2.863804e+00 +wall time: 0.000709057s + +time increment:85630 time: 85.63 +field ' c' [explicit solve]: current solution: 9.625549e+01, current residual:5.810541e+01 +field 'mu' [explicit solve]: current solution: 4.746740e+00, current residual:2.863769e+00 +wall time: 0.000782967s + +time increment:85640 time: 85.64 +field ' c' [explicit solve]: current solution: 9.625554e+01, current residual:5.810543e+01 +field 'mu' [explicit solve]: current solution: 4.746682e+00, current residual:2.863733e+00 +wall time: 0.000705004s + +time increment:85650 time: 85.65 +field ' c' [explicit solve]: current solution: 9.625558e+01, current residual:5.810546e+01 +field 'mu' [explicit solve]: current solution: 4.746625e+00, current residual:2.863698e+00 +wall time: 0.000605106s + +time increment:85660 time: 85.66 +field ' c' [explicit solve]: current solution: 9.625563e+01, current residual:5.810548e+01 +field 'mu' [explicit solve]: current solution: 4.746568e+00, current residual:2.863663e+00 +wall time: 0.000627995s + +time increment:85670 time: 85.67 +field ' c' [explicit solve]: current solution: 9.625567e+01, current residual:5.810551e+01 +field 'mu' [explicit solve]: current solution: 4.746510e+00, current residual:2.863627e+00 +wall time: 0.000665903s + +time increment:85680 time: 85.68 +field ' c' [explicit solve]: current solution: 9.625572e+01, current residual:5.810553e+01 +field 'mu' [explicit solve]: current solution: 4.746453e+00, current residual:2.863592e+00 +wall time: 0.000676155s + +time increment:85690 time: 85.69 +field ' c' [explicit solve]: current solution: 9.625576e+01, current residual:5.810556e+01 +field 'mu' [explicit solve]: current solution: 4.746396e+00, current residual:2.863557e+00 +wall time: 0.000691891s + +time increment:85700 time: 85.7 +field ' c' [explicit solve]: current solution: 9.625581e+01, current residual:5.810558e+01 +field 'mu' [explicit solve]: current solution: 4.746339e+00, current residual:2.863521e+00 +wall time: 0.000730991s + +time increment:85710 time: 85.71 +field ' c' [explicit solve]: current solution: 9.625586e+01, current residual:5.810561e+01 +field 'mu' [explicit solve]: current solution: 4.746281e+00, current residual:2.863486e+00 +wall time: 0.000706196s + +time increment:85720 time: 85.72 +field ' c' [explicit solve]: current solution: 9.625590e+01, current residual:5.810563e+01 +field 'mu' [explicit solve]: current solution: 4.746224e+00, current residual:2.863450e+00 +wall time: 0.000688076s + +time increment:85730 time: 85.73 +field ' c' [explicit solve]: current solution: 9.625595e+01, current residual:5.810566e+01 +field 'mu' [explicit solve]: current solution: 4.746167e+00, current residual:2.863415e+00 +wall time: 0.000662804s + +time increment:85740 time: 85.74 +field ' c' [explicit solve]: current solution: 9.625599e+01, current residual:5.810568e+01 +field 'mu' [explicit solve]: current solution: 4.746109e+00, current residual:2.863380e+00 +wall time: 0.00065279s + +time increment:85750 time: 85.75 +field ' c' [explicit solve]: current solution: 9.625604e+01, current residual:5.810571e+01 +field 'mu' [explicit solve]: current solution: 4.746052e+00, current residual:2.863344e+00 +wall time: 0.00070405s + +time increment:85760 time: 85.76 +field ' c' [explicit solve]: current solution: 9.625608e+01, current residual:5.810573e+01 +field 'mu' [explicit solve]: current solution: 4.745995e+00, current residual:2.863309e+00 +wall time: 0.000581026s + +time increment:85770 time: 85.77 +field ' c' [explicit solve]: current solution: 9.625613e+01, current residual:5.810576e+01 +field 'mu' [explicit solve]: current solution: 4.745937e+00, current residual:2.863274e+00 +wall time: 0.000624895s + +time increment:85780 time: 85.78 +field ' c' [explicit solve]: current solution: 9.625617e+01, current residual:5.810578e+01 +field 'mu' [explicit solve]: current solution: 4.745880e+00, current residual:2.863238e+00 +wall time: 0.000603914s + +time increment:85790 time: 85.79 +field ' c' [explicit solve]: current solution: 9.625622e+01, current residual:5.810581e+01 +field 'mu' [explicit solve]: current solution: 4.745823e+00, current residual:2.863203e+00 +wall time: 0.000694036s + +time increment:85800 time: 85.8 +field ' c' [explicit solve]: current solution: 9.625626e+01, current residual:5.810583e+01 +field 'mu' [explicit solve]: current solution: 4.745765e+00, current residual:2.863168e+00 +wall time: 0.000670195s + +time increment:85810 time: 85.81 +field ' c' [explicit solve]: current solution: 9.625631e+01, current residual:5.810586e+01 +field 'mu' [explicit solve]: current solution: 4.745708e+00, current residual:2.863132e+00 +wall time: 0.000567913s + +time increment:85820 time: 85.82 +field ' c' [explicit solve]: current solution: 9.625635e+01, current residual:5.810588e+01 +field 'mu' [explicit solve]: current solution: 4.745651e+00, current residual:2.863097e+00 +wall time: 0.000565052s + +time increment:85830 time: 85.83 +field ' c' [explicit solve]: current solution: 9.625640e+01, current residual:5.810591e+01 +field 'mu' [explicit solve]: current solution: 4.745593e+00, current residual:2.863062e+00 +wall time: 0.000660181s + +time increment:85840 time: 85.84 +field ' c' [explicit solve]: current solution: 9.625644e+01, current residual:5.810593e+01 +field 'mu' [explicit solve]: current solution: 4.745536e+00, current residual:2.863026e+00 +wall time: 0.000601053s + +time increment:85850 time: 85.85 +field ' c' [explicit solve]: current solution: 9.625649e+01, current residual:5.810596e+01 +field 'mu' [explicit solve]: current solution: 4.745479e+00, current residual:2.862991e+00 +wall time: 0.000698805s + +time increment:85860 time: 85.86 +field ' c' [explicit solve]: current solution: 9.625653e+01, current residual:5.810598e+01 +field 'mu' [explicit solve]: current solution: 4.745421e+00, current residual:2.862956e+00 +wall time: 0.000625849s + +time increment:85870 time: 85.87 +field ' c' [explicit solve]: current solution: 9.625658e+01, current residual:5.810601e+01 +field 'mu' [explicit solve]: current solution: 4.745364e+00, current residual:2.862920e+00 +wall time: 0.000713825s + +time increment:85880 time: 85.88 +field ' c' [explicit solve]: current solution: 9.625662e+01, current residual:5.810604e+01 +field 'mu' [explicit solve]: current solution: 4.745307e+00, current residual:2.862885e+00 +wall time: 0.000577927s + +time increment:85890 time: 85.89 +field ' c' [explicit solve]: current solution: 9.625667e+01, current residual:5.810606e+01 +field 'mu' [explicit solve]: current solution: 4.745249e+00, current residual:2.862849e+00 +wall time: 0.000577927s + +time increment:85900 time: 85.9 +field ' c' [explicit solve]: current solution: 9.625671e+01, current residual:5.810609e+01 +field 'mu' [explicit solve]: current solution: 4.745192e+00, current residual:2.862814e+00 +wall time: 0.000567198s + +time increment:85910 time: 85.91 +field ' c' [explicit solve]: current solution: 9.625676e+01, current residual:5.810611e+01 +field 'mu' [explicit solve]: current solution: 4.745135e+00, current residual:2.862779e+00 +wall time: 0.00066781s + +time increment:85920 time: 85.92 +field ' c' [explicit solve]: current solution: 9.625681e+01, current residual:5.810614e+01 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[explicit solve]: current solution: 4.744848e+00, current residual:2.862602e+00 +wall time: 0.000579119s + +time increment:85970 time: 85.97 +field ' c' [explicit solve]: current solution: 9.625703e+01, current residual:5.810626e+01 +field 'mu' [explicit solve]: current solution: 4.744790e+00, current residual:2.862567e+00 +wall time: 0.000581026s + +time increment:85980 time: 85.98 +field ' c' [explicit solve]: current solution: 9.625708e+01, current residual:5.810629e+01 +field 'mu' [explicit solve]: current solution: 4.744733e+00, current residual:2.862531e+00 +wall time: 0.000617027s + +time increment:85990 time: 85.99 +field ' c' [explicit solve]: current solution: 9.625712e+01, current residual:5.810631e+01 +field 'mu' [explicit solve]: current solution: 4.744676e+00, current residual:2.862496e+00 +wall time: 0.000641108s + +time increment:86000 time: 86 +field ' c' [explicit solve]: current solution: 9.625717e+01, current residual:5.810634e+01 +field 'mu' [explicit solve]: current solution: 4.744618e+00, current residual:2.862460e+00 +wall time: 0.00067091s + +time increment:86010 time: 86.01 +field ' c' [explicit solve]: current solution: 9.625721e+01, current residual:5.810636e+01 +field 'mu' [explicit solve]: current solution: 4.744561e+00, current residual:2.862425e+00 +wall time: 0.000586033s + +time increment:86020 time: 86.02 +field ' c' [explicit solve]: current solution: 9.625726e+01, current residual:5.810639e+01 +field 'mu' [explicit solve]: current solution: 4.744503e+00, current residual:2.862390e+00 +wall time: 0.000655174s + +time increment:86030 time: 86.03 +field ' c' [explicit solve]: current solution: 9.625730e+01, current residual:5.810641e+01 +field 'mu' [explicit solve]: current solution: 4.744446e+00, current residual:2.862354e+00 +wall time: 0.000652075s + +time increment:86040 time: 86.04 +field ' c' [explicit solve]: current solution: 9.625735e+01, current residual:5.810644e+01 +field 'mu' [explicit solve]: current solution: 4.744389e+00, current residual:2.862319e+00 +wall time: 0.000574827s + +time increment:86050 time: 86.05 +field ' c' [explicit solve]: current solution: 9.625739e+01, current residual:5.810646e+01 +field 'mu' [explicit solve]: current solution: 4.744331e+00, current residual:2.862284e+00 +wall time: 0.000677109s + +time increment:86060 time: 86.06 +field ' c' [explicit solve]: current solution: 9.625744e+01, current residual:5.810649e+01 +field 'mu' [explicit solve]: current solution: 4.744274e+00, current residual:2.862248e+00 +wall time: 0.000585794s + +time increment:86070 time: 86.07 +field ' c' [explicit solve]: current solution: 9.625748e+01, current residual:5.810651e+01 +field 'mu' [explicit solve]: current solution: 4.744216e+00, current residual:2.862213e+00 +wall time: 0.000648022s + +time increment:86080 time: 86.08 +field ' c' [explicit solve]: current solution: 9.625753e+01, current residual:5.810654e+01 +field 'mu' [explicit solve]: current solution: 4.744159e+00, current residual:2.862177e+00 +wall time: 0.000574112s + +time increment:86090 time: 86.09 +field ' c' [explicit solve]: current solution: 9.625758e+01, current residual:5.810656e+01 +field 'mu' [explicit solve]: current solution: 4.744102e+00, current residual:2.862142e+00 +wall time: 0.000578165s + +time increment:86100 time: 86.1 +field ' c' [explicit solve]: current solution: 9.625762e+01, current residual:5.810659e+01 +field 'mu' [explicit solve]: current solution: 4.744044e+00, current residual:2.862107e+00 +wall time: 0.000724077s + +time increment:86110 time: 86.11 +field ' c' [explicit solve]: current solution: 9.625767e+01, current residual:5.810661e+01 +field 'mu' [explicit solve]: current solution: 4.743987e+00, current residual:2.862071e+00 +wall time: 0.000693083s + +time increment:86120 time: 86.12 +field ' c' [explicit solve]: current solution: 9.625771e+01, current residual:5.810664e+01 +field 'mu' [explicit solve]: current solution: 4.743929e+00, current residual:2.862036e+00 +wall time: 0.0007689s + +time increment:86130 time: 86.13 +field ' c' [explicit solve]: current solution: 9.625776e+01, current residual:5.810666e+01 +field 'mu' [explicit solve]: current solution: 4.743872e+00, current residual:2.862000e+00 +wall time: 0.000690937s + +time increment:86140 time: 86.14 +field ' c' [explicit solve]: current solution: 9.625780e+01, current residual:5.810669e+01 +field 'mu' [explicit solve]: current solution: 4.743814e+00, current residual:2.861965e+00 +wall time: 0.000643015s + +time increment:86150 time: 86.15 +field ' c' [explicit solve]: current solution: 9.625785e+01, current residual:5.810671e+01 +field 'mu' [explicit solve]: current solution: 4.743757e+00, current residual:2.861930e+00 +wall time: 0.000690937s + +time increment:86160 time: 86.16 +field ' c' [explicit solve]: current solution: 9.625789e+01, current residual:5.810674e+01 +field 'mu' [explicit solve]: current solution: 4.743700e+00, current residual:2.861894e+00 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0.000656128s + +time increment:86210 time: 86.21 +field ' c' [explicit solve]: current solution: 9.625812e+01, current residual:5.810687e+01 +field 'mu' [explicit solve]: current solution: 4.743412e+00, current residual:2.861717e+00 +wall time: 0.000686169s + +time increment:86220 time: 86.22 +field ' c' [explicit solve]: current solution: 9.625816e+01, current residual:5.810689e+01 +field 'mu' [explicit solve]: current solution: 4.743355e+00, current residual:2.861682e+00 +wall time: 0.000693083s + +time increment:86230 time: 86.23 +field ' c' [explicit solve]: current solution: 9.625821e+01, current residual:5.810692e+01 +field 'mu' [explicit solve]: current solution: 4.743297e+00, current residual:2.861646e+00 +wall time: 0.000699043s + +time increment:86240 time: 86.24 +field ' c' [explicit solve]: current solution: 9.625826e+01, current residual:5.810694e+01 +field 'mu' [explicit solve]: current solution: 4.743240e+00, current residual:2.861611e+00 +wall time: 0.000679016s + +time increment:86250 time: 86.25 +field ' c' [explicit solve]: current solution: 9.625830e+01, current residual:5.810697e+01 +field 'mu' [explicit solve]: current solution: 4.743183e+00, current residual:2.861576e+00 +wall time: 0.000669003s + +time increment:86260 time: 86.26 +field ' c' [explicit solve]: current solution: 9.625835e+01, current residual:5.810699e+01 +field 'mu' [explicit solve]: current solution: 4.743125e+00, current residual:2.861540e+00 +wall time: 0.000645161s + +time increment:86270 time: 86.27 +field ' c' [explicit solve]: current solution: 9.625839e+01, current residual:5.810702e+01 +field 'mu' [explicit solve]: current solution: 4.743068e+00, current residual:2.861505e+00 +wall time: 0.000721931s + +time increment:86280 time: 86.28 +field ' c' [explicit solve]: current solution: 9.625844e+01, current residual:5.810704e+01 +field 'mu' [explicit solve]: current solution: 4.743010e+00, current residual:2.861469e+00 +wall time: 0.000709057s + +time increment:86290 time: 86.29 +field ' c' [explicit solve]: current solution: 9.625848e+01, current residual:5.810707e+01 +field 'mu' [explicit solve]: current solution: 4.742953e+00, current residual:2.861434e+00 +wall time: 0.00065279s + +time increment:86300 time: 86.3 +field ' c' [explicit solve]: current solution: 9.625853e+01, current residual:5.810709e+01 +field 'mu' [explicit solve]: current solution: 4.742895e+00, current residual:2.861399e+00 +wall time: 0.000663996s + +time increment:86310 time: 86.31 +field ' c' [explicit solve]: current solution: 9.625857e+01, current residual:5.810712e+01 +field 'mu' [explicit solve]: current solution: 4.742838e+00, current residual:2.861363e+00 +wall time: 0.000658989s + +time increment:86320 time: 86.32 +field ' c' [explicit solve]: current solution: 9.625862e+01, current residual:5.810714e+01 +field 'mu' [explicit solve]: current solution: 4.742780e+00, current residual:2.861328e+00 +wall time: 0.000687838s + +time increment:86330 time: 86.33 +field ' c' [explicit solve]: current solution: 9.625866e+01, current residual:5.810717e+01 +field 'mu' [explicit solve]: current solution: 4.742723e+00, current residual:2.861292e+00 +wall time: 0.000746012s + +time increment:86340 time: 86.34 +field ' c' [explicit solve]: current solution: 9.625871e+01, current residual:5.810719e+01 +field 'mu' [explicit solve]: current solution: 4.742665e+00, current residual:2.861257e+00 +wall time: 0.00067687s + +time increment:86350 time: 86.35 +field ' c' [explicit solve]: current solution: 9.625875e+01, current residual:5.810722e+01 +field 'mu' [explicit solve]: current solution: 4.742608e+00, current residual:2.861221e+00 +wall time: 0.000741005s + +time increment:86360 time: 86.36 +field ' c' [explicit solve]: current solution: 9.625880e+01, current residual:5.810724e+01 +field 'mu' [explicit solve]: current solution: 4.742550e+00, current residual:2.861186e+00 +wall time: 0.000684023s + +time increment:86370 time: 86.37 +field ' c' [explicit solve]: current solution: 9.625884e+01, current residual:5.810727e+01 +field 'mu' [explicit solve]: current solution: 4.742493e+00, current residual:2.861151e+00 +wall time: 0.000692844s + +time increment:86380 time: 86.38 +field ' c' [explicit solve]: current solution: 9.625889e+01, current residual:5.810729e+01 +field 'mu' [explicit solve]: current solution: 4.742435e+00, current residual:2.861115e+00 +wall time: 0.000643969s + +time increment:86390 time: 86.39 +field ' c' [explicit solve]: current solution: 9.625894e+01, current residual:5.810732e+01 +field 'mu' [explicit solve]: current solution: 4.742378e+00, current residual:2.861080e+00 +wall time: 0.000649929s + +time increment:86400 time: 86.4 +field ' c' [explicit solve]: current solution: 9.625898e+01, current residual:5.810735e+01 +field 'mu' [explicit solve]: current solution: 4.742320e+00, current residual:2.861044e+00 +wall time: 0.000689983s + +time increment:86410 time: 86.41 +field ' c' [explicit solve]: current solution: 9.625903e+01, current residual:5.810737e+01 +field 'mu' [explicit solve]: current solution: 4.742263e+00, current residual:2.861009e+00 +wall time: 0.00068903s + +time increment:86420 time: 86.42 +field ' c' [explicit solve]: current solution: 9.625907e+01, current residual:5.810740e+01 +field 'mu' [explicit solve]: current solution: 4.742205e+00, current residual:2.860974e+00 +wall time: 0.000695944s + +time increment:86430 time: 86.43 +field ' c' [explicit solve]: current solution: 9.625912e+01, current residual:5.810742e+01 +field 'mu' [explicit solve]: current solution: 4.742148e+00, current residual:2.860938e+00 +wall time: 0.000686884s + +time increment:86440 time: 86.44 +field ' c' [explicit solve]: current solution: 9.625916e+01, current residual:5.810745e+01 +field 'mu' [explicit solve]: current solution: 4.742090e+00, current residual:2.860903e+00 +wall time: 0.000690937s + +time increment:86450 time: 86.45 +field ' c' [explicit solve]: current solution: 9.625921e+01, current residual:5.810747e+01 +field 'mu' [explicit solve]: current solution: 4.742033e+00, current residual:2.860867e+00 +wall time: 0.00068593s + +time increment:86460 time: 86.46 +field ' c' [explicit solve]: current solution: 9.625925e+01, current residual:5.810750e+01 +field 'mu' [explicit solve]: current solution: 4.741975e+00, current residual:2.860832e+00 +wall time: 0.000586987s + +time increment:86470 time: 86.47 +field ' c' [explicit solve]: current solution: 9.625930e+01, current residual:5.810752e+01 +field 'mu' [explicit solve]: current solution: 4.741918e+00, current residual:2.860796e+00 +wall time: 0.000571012s + +time increment:86480 time: 86.48 +field ' c' [explicit solve]: current solution: 9.625934e+01, current residual:5.810755e+01 +field 'mu' [explicit solve]: current solution: 4.741860e+00, current residual:2.860761e+00 +wall time: 0.000571966s + +time increment:86490 time: 86.49 +field ' c' [explicit solve]: current solution: 9.625939e+01, current residual:5.810757e+01 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solve]: current solution: 4.741572e+00, current residual:2.860584e+00 +wall time: 0.000771999s + +time increment:86540 time: 86.54 +field ' c' [explicit solve]: current solution: 9.625962e+01, current residual:5.810770e+01 +field 'mu' [explicit solve]: current solution: 4.741515e+00, current residual:2.860548e+00 +wall time: 0.00057292s + +time increment:86550 time: 86.55 +field ' c' [explicit solve]: current solution: 9.625966e+01, current residual:5.810772e+01 +field 'mu' [explicit solve]: current solution: 4.741457e+00, current residual:2.860513e+00 +wall time: 0.000577927s + +time increment:86560 time: 86.56 +field ' c' [explicit solve]: current solution: 9.625971e+01, current residual:5.810775e+01 +field 'mu' [explicit solve]: current solution: 4.741400e+00, current residual:2.860477e+00 +wall time: 0.000782967s + +time increment:86570 time: 86.57 +field ' c' [explicit solve]: current solution: 9.625975e+01, current residual:5.810778e+01 +field 'mu' [explicit solve]: current solution: 4.741342e+00, current residual:2.860442e+00 +wall time: 0.000700951s + +time increment:86580 time: 86.58 +field ' c' [explicit solve]: current solution: 9.625980e+01, current residual:5.810780e+01 +field 'mu' [explicit solve]: current solution: 4.741285e+00, current residual:2.860407e+00 +wall time: 0.000689983s + +time increment:86590 time: 86.59 +field ' c' [explicit solve]: current solution: 9.625984e+01, current residual:5.810783e+01 +field 'mu' [explicit solve]: current solution: 4.741227e+00, current residual:2.860371e+00 +wall time: 0.000584126s + +time increment:86600 time: 86.6 +field ' c' [explicit solve]: current solution: 9.625989e+01, current residual:5.810785e+01 +field 'mu' [explicit solve]: current solution: 4.741169e+00, current residual:2.860336e+00 +wall time: 0.000585079s + +time increment:86610 time: 86.61 +field ' c' [explicit solve]: current solution: 9.625994e+01, current residual:5.810788e+01 +field 'mu' [explicit solve]: current solution: 4.741112e+00, current residual:2.860300e+00 +wall time: 0.000597954s + +time increment:86620 time: 86.62 +field ' c' [explicit solve]: current solution: 9.625998e+01, current residual:5.810790e+01 +field 'mu' [explicit solve]: current solution: 4.741054e+00, current residual:2.860265e+00 +wall time: 0.000718117s + +time increment:86630 time: 86.63 +field ' c' [explicit solve]: current solution: 9.626003e+01, current residual:5.810793e+01 +field 'mu' [explicit solve]: current solution: 4.740997e+00, current residual:2.860229e+00 +wall time: 0.000622034s + +time increment:86640 time: 86.64 +field ' c' [explicit solve]: current solution: 9.626007e+01, current residual:5.810795e+01 +field 'mu' [explicit solve]: current solution: 4.740939e+00, current residual:2.860194e+00 +wall time: 0.000627995s + +time increment:86650 time: 86.65 +field ' c' [explicit solve]: current solution: 9.626012e+01, current residual:5.810798e+01 +field 'mu' [explicit solve]: current solution: 4.740882e+00, current residual:2.860158e+00 +wall time: 0.000657797s + +time increment:86660 time: 86.66 +field ' c' [explicit solve]: current solution: 9.626016e+01, current residual:5.810800e+01 +field 'mu' [explicit solve]: current solution: 4.740824e+00, current residual:2.860123e+00 +wall time: 0.000730991s + +time increment:86670 time: 86.67 +field ' c' [explicit solve]: current solution: 9.626021e+01, current residual:5.810803e+01 +field 'mu' [explicit solve]: current solution: 4.740766e+00, current residual:2.860087e+00 +wall time: 0.000638962s + +time increment:86680 time: 86.68 +field ' c' [explicit solve]: current solution: 9.626025e+01, current residual:5.810805e+01 +field 'mu' [explicit solve]: current solution: 4.740709e+00, current residual:2.860052e+00 +wall time: 0.000566006s + +time increment:86690 time: 86.69 +field ' c' [explicit solve]: current solution: 9.626030e+01, current residual:5.810808e+01 +field 'mu' [explicit solve]: current solution: 4.740651e+00, current residual:2.860017e+00 +wall time: 0.000676155s + +time increment:86700 time: 86.7 +field ' c' [explicit solve]: current solution: 9.626034e+01, current residual:5.810810e+01 +field 'mu' [explicit solve]: current solution: 4.740594e+00, current residual:2.859981e+00 +wall time: 0.000643015s + +time increment:86710 time: 86.71 +field ' c' [explicit solve]: current solution: 9.626039e+01, current residual:5.810813e+01 +field 'mu' [explicit solve]: current solution: 4.740536e+00, current residual:2.859946e+00 +wall time: 0.000556946s + +time increment:86720 time: 86.72 +field ' c' [explicit solve]: current solution: 9.626044e+01, current residual:5.810816e+01 +field 'mu' [explicit solve]: current solution: 4.740478e+00, current residual:2.859910e+00 +wall time: 0.000577927s + +time increment:86730 time: 86.73 +field ' c' [explicit solve]: current solution: 9.626048e+01, current residual:5.810818e+01 +field 'mu' [explicit solve]: current solution: 4.740421e+00, current residual:2.859875e+00 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0.000607967s + +time increment:86780 time: 86.78 +field ' c' [explicit solve]: current solution: 9.626071e+01, current residual:5.810831e+01 +field 'mu' [explicit solve]: current solution: 4.740133e+00, current residual:2.859697e+00 +wall time: 0.000684977s + +time increment:86790 time: 86.79 +field ' c' [explicit solve]: current solution: 9.626075e+01, current residual:5.810833e+01 +field 'mu' [explicit solve]: current solution: 4.740075e+00, current residual:2.859662e+00 +wall time: 0.000598907s + +time increment:86800 time: 86.8 +field ' c' [explicit solve]: current solution: 9.626080e+01, current residual:5.810836e+01 +field 'mu' [explicit solve]: current solution: 4.740017e+00, current residual:2.859626e+00 +wall time: 0.000578165s + +time increment:86810 time: 86.81 +field ' c' [explicit solve]: current solution: 9.626085e+01, current residual:5.810838e+01 +field 'mu' [explicit solve]: current solution: 4.739960e+00, current residual:2.859591e+00 +wall time: 0.000660896s + +time increment:86820 time: 86.82 +field ' c' [explicit solve]: current solution: 9.626089e+01, current residual:5.810841e+01 +field 'mu' [explicit solve]: current solution: 4.739902e+00, current residual:2.859555e+00 +wall time: 0.000603914s + +time increment:86830 time: 86.83 +field ' c' [explicit solve]: current solution: 9.626094e+01, current residual:5.810843e+01 +field 'mu' [explicit solve]: current solution: 4.739845e+00, current residual:2.859520e+00 +wall time: 0.000627995s + +time increment:86840 time: 86.84 +field ' c' [explicit solve]: current solution: 9.626098e+01, current residual:5.810846e+01 +field 'mu' [explicit solve]: current solution: 4.739787e+00, current residual:2.859484e+00 +wall time: 0.000613213s + +time increment:86850 time: 86.85 +field ' c' [explicit solve]: current solution: 9.626103e+01, current residual:5.810848e+01 +field 'mu' [explicit solve]: current solution: 4.739729e+00, current residual:2.859449e+00 +wall time: 0.000735044s + +time increment:86860 time: 86.86 +field ' c' [explicit solve]: current solution: 9.626107e+01, current residual:5.810851e+01 +field 'mu' [explicit solve]: current solution: 4.739672e+00, current residual:2.859414e+00 +wall time: 0.000741005s + +time increment:86870 time: 86.87 +field ' c' [explicit solve]: current solution: 9.626112e+01, current residual:5.810854e+01 +field 'mu' [explicit solve]: current solution: 4.739614e+00, current residual:2.859378e+00 +wall time: 0.00066185s + +time increment:86880 time: 86.88 +field ' c' [explicit solve]: current solution: 9.626116e+01, current residual:5.810856e+01 +field 'mu' [explicit solve]: current solution: 4.739556e+00, current residual:2.859343e+00 +wall time: 0.000688076s + +time increment:86890 time: 86.89 +field ' c' [explicit solve]: current solution: 9.626121e+01, current residual:5.810859e+01 +field 'mu' [explicit solve]: current solution: 4.739499e+00, current residual:2.859307e+00 +wall time: 0.000672102s + +time increment:86900 time: 86.9 +field ' 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current solution: 9.626144e+01, current residual:5.810871e+01 +field 'mu' [explicit solve]: current solution: 4.739210e+00, current residual:2.859130e+00 +wall time: 0.000646114s + +time increment:86950 time: 86.95 +field ' c' [explicit solve]: current solution: 9.626148e+01, current residual:5.810874e+01 +field 'mu' [explicit solve]: current solution: 4.739153e+00, current residual:2.859094e+00 +wall time: 0.000584841s + +time increment:86960 time: 86.96 +field ' c' [explicit solve]: current solution: 9.626153e+01, current residual:5.810876e+01 +field 'mu' [explicit solve]: current solution: 4.739095e+00, current residual:2.859059e+00 +wall time: 0.000579119s + +time increment:86970 time: 86.97 +field ' c' [explicit solve]: current solution: 9.626157e+01, current residual:5.810879e+01 +field 'mu' [explicit solve]: current solution: 4.739037e+00, current residual:2.859023e+00 +wall time: 0.000737906s + +time increment:86980 time: 86.98 +field ' c' [explicit solve]: current solution: 9.626162e+01, current residual:5.810881e+01 +field 'mu' [explicit solve]: current solution: 4.738980e+00, current residual:2.858988e+00 +wall time: 0.000563145s + +time increment:86990 time: 86.99 +field ' c' [explicit solve]: current solution: 9.626167e+01, current residual:5.810884e+01 +field 'mu' [explicit solve]: current solution: 4.738922e+00, current residual:2.858952e+00 +wall time: 0.000622988s + +time increment:87000 time: 87 +field ' c' [explicit solve]: current solution: 9.626171e+01, current residual:5.810887e+01 +field 'mu' [explicit solve]: current solution: 4.738864e+00, current residual:2.858917e+00 +wall time: 0.000653982s + +time increment:87010 time: 87.01 +field ' c' [explicit solve]: current solution: 9.626176e+01, current residual:5.810889e+01 +field 'mu' [explicit solve]: current solution: 4.738807e+00, current residual:2.858881e+00 +wall time: 0.000564098s + +time increment:87020 time: 87.02 +field ' c' [explicit solve]: current solution: 9.626180e+01, current residual:5.810892e+01 +field 'mu' [explicit solve]: current solution: 4.738749e+00, current residual:2.858846e+00 +wall time: 0.000577927s + +time increment:87030 time: 87.03 +field ' c' [explicit solve]: current solution: 9.626185e+01, current residual:5.810894e+01 +field 'mu' [explicit solve]: current solution: 4.738691e+00, current residual:2.858810e+00 +wall time: 0.000708103s + +time increment:87040 time: 87.04 +field ' c' [explicit solve]: current solution: 9.626189e+01, current residual:5.810897e+01 +field 'mu' [explicit solve]: current solution: 4.738634e+00, current residual:2.858775e+00 +wall time: 0.000595808s + +time increment:87050 time: 87.05 +field ' c' [explicit solve]: current solution: 9.626194e+01, current residual:5.810899e+01 +field 'mu' [explicit solve]: current solution: 4.738576e+00, current residual:2.858739e+00 +wall time: 0.000577211s + +time increment:87060 time: 87.06 +field ' c' [explicit solve]: current solution: 9.626198e+01, current residual:5.810902e+01 +field 'mu' [explicit solve]: current solution: 4.738518e+00, current residual:2.858704e+00 +wall time: 0.000766993s + +time increment:87070 time: 87.07 +field ' c' [explicit solve]: current solution: 9.626203e+01, current residual:5.810904e+01 +field 'mu' [explicit solve]: current solution: 4.738460e+00, current residual:2.858668e+00 +wall time: 0.000740051s + +time increment:87080 time: 87.08 +field ' c' [explicit solve]: current solution: 9.626208e+01, current residual:5.810907e+01 +field 'mu' [explicit solve]: current solution: 4.738403e+00, current residual:2.858633e+00 +wall time: 0.000767231s + +time increment:87090 time: 87.09 +field ' c' [explicit solve]: current solution: 9.626212e+01, current residual:5.810909e+01 +field 'mu' [explicit solve]: current solution: 4.738345e+00, current residual:2.858597e+00 +wall time: 0.000728846s + +time increment:87100 time: 87.1 +field ' c' [explicit solve]: current solution: 9.626217e+01, current residual:5.810912e+01 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[explicit solve]: current solution: 4.738056e+00, current residual:2.858420e+00 +wall time: 0.000782967s + +time increment:87150 time: 87.15 +field ' c' [explicit solve]: current solution: 9.626239e+01, current residual:5.810925e+01 +field 'mu' [explicit solve]: current solution: 4.737999e+00, current residual:2.858384e+00 +wall time: 0.000830173s + +time increment:87160 time: 87.16 +field ' c' [explicit solve]: current solution: 9.626244e+01, current residual:5.810927e+01 +field 'mu' [explicit solve]: current solution: 4.737941e+00, current residual:2.858349e+00 +wall time: 0.000669003s + +time increment:87170 time: 87.17 +field ' c' [explicit solve]: current solution: 9.626249e+01, current residual:5.810930e+01 +field 'mu' [explicit solve]: current solution: 4.737883e+00, current residual:2.858313e+00 +wall time: 0.000654936s + +time increment:87180 time: 87.18 +field ' c' [explicit solve]: current solution: 9.626253e+01, current residual:5.810932e+01 +field 'mu' [explicit solve]: 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4.737594e+00, current residual:2.858135e+00 +wall time: 0.000667095s + +time increment:87230 time: 87.23 +field ' c' [explicit solve]: current solution: 9.626276e+01, current residual:5.810945e+01 +field 'mu' [explicit solve]: current solution: 4.737537e+00, current residual:2.858100e+00 +wall time: 0.000629187s + +time increment:87240 time: 87.24 +field ' c' [explicit solve]: current solution: 9.626281e+01, current residual:5.810948e+01 +field 'mu' [explicit solve]: current solution: 4.737479e+00, current residual:2.858064e+00 +wall time: 0.000763893s + +time increment:87250 time: 87.25 +field ' c' [explicit solve]: current solution: 9.626285e+01, current residual:5.810950e+01 +field 'mu' [explicit solve]: current solution: 4.737421e+00, current residual:2.858029e+00 +wall time: 0.000714064s + +time increment:87260 time: 87.26 +field ' c' [explicit solve]: current solution: 9.626290e+01, current residual:5.810953e+01 +field 'mu' [explicit solve]: current solution: 4.737363e+00, 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residual:2.857851e+00 +wall time: 0.000662088s + +time increment:87310 time: 87.31 +field ' c' [explicit solve]: current solution: 9.626312e+01, current residual:5.810965e+01 +field 'mu' [explicit solve]: current solution: 4.737075e+00, current residual:2.857816e+00 +wall time: 0.000658035s + +time increment:87320 time: 87.32 +field ' c' [explicit solve]: current solution: 9.626317e+01, current residual:5.810968e+01 +field 'mu' [explicit solve]: current solution: 4.737017e+00, current residual:2.857780e+00 +wall time: 0.000671864s + +time increment:87330 time: 87.33 +field ' c' [explicit solve]: current solution: 9.626322e+01, current residual:5.810970e+01 +field 'mu' [explicit solve]: current solution: 4.736959e+00, current residual:2.857745e+00 +wall time: 0.000669003s + +time increment:87340 time: 87.34 +field ' c' [explicit solve]: current solution: 9.626326e+01, current residual:5.810973e+01 +field 'mu' [explicit solve]: current solution: 4.736901e+00, current residual:2.857709e+00 +wall time: 0.000649929s + +time increment:87350 time: 87.35 +field ' c' [explicit solve]: current solution: 9.626331e+01, current residual:5.810976e+01 +field 'mu' [explicit solve]: current solution: 4.736843e+00, current residual:2.857673e+00 +wall time: 0.000599146s + +time increment:87360 time: 87.36 +field ' c' [explicit solve]: current solution: 9.626335e+01, current residual:5.810978e+01 +field 'mu' [explicit solve]: current solution: 4.736786e+00, current residual:2.857638e+00 +wall time: 0.000683069s + +time increment:87370 time: 87.37 +field ' c' [explicit solve]: current solution: 9.626340e+01, current residual:5.810981e+01 +field 'mu' [explicit solve]: current solution: 4.736728e+00, current residual:2.857602e+00 +wall time: 0.000640869s + +time increment:87380 time: 87.38 +field ' c' [explicit solve]: current solution: 9.626344e+01, current residual:5.810983e+01 +field 'mu' [explicit solve]: current solution: 4.736670e+00, current 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0.000656128s + +time increment:87470 time: 87.47 +field ' c' [explicit solve]: current solution: 9.626386e+01, current residual:5.811006e+01 +field 'mu' [explicit solve]: current solution: 4.736150e+00, current residual:2.857247e+00 +wall time: 0.000683069s + +time increment:87480 time: 87.48 +field ' c' [explicit solve]: current solution: 9.626390e+01, current residual:5.811009e+01 +field 'mu' [explicit solve]: current solution: 4.736092e+00, current residual:2.857211e+00 +wall time: 0.000676155s + +time increment:87490 time: 87.49 +field ' c' [explicit solve]: current solution: 9.626395e+01, current residual:5.811011e+01 +field 'mu' [explicit solve]: current solution: 4.736034e+00, current residual:2.857176e+00 +wall time: 0.000617027s + +time increment:87500 time: 87.5 +field ' c' [explicit solve]: current solution: 9.626399e+01, current residual:5.811014e+01 +field 'mu' [explicit solve]: current solution: 4.735976e+00, current residual:2.857140e+00 +wall time: 0.000602961s + +time increment:87510 time: 87.51 +field ' c' [explicit solve]: current solution: 9.626404e+01, current residual:5.811016e+01 +field 'mu' [explicit solve]: current solution: 4.735918e+00, current residual:2.857105e+00 +wall time: 0.000576019s + +time increment:87520 time: 87.52 +field ' c' [explicit solve]: current solution: 9.626408e+01, current residual:5.811019e+01 +field 'mu' [explicit solve]: current solution: 4.735860e+00, current residual:2.857069e+00 +wall time: 0.000745058s + +time increment:87530 time: 87.53 +field ' c' [explicit solve]: current solution: 9.626413e+01, current residual:5.811021e+01 +field 'mu' [explicit solve]: current solution: 4.735803e+00, current residual:2.857033e+00 +wall time: 0.000654936s + +time increment:87540 time: 87.54 +field ' c' [explicit solve]: current solution: 9.626418e+01, current residual:5.811024e+01 +field 'mu' [explicit solve]: current solution: 4.735745e+00, current residual:2.856998e+00 +wall time: 0.000652075s + +time increment:87550 time: 87.55 +field ' c' [explicit solve]: current solution: 9.626422e+01, current residual:5.811027e+01 +field 'mu' [explicit solve]: current solution: 4.735687e+00, current residual:2.856962e+00 +wall time: 0.000645876s + +time increment:87560 time: 87.56 +field ' c' [explicit solve]: current solution: 9.626427e+01, current residual:5.811029e+01 +field 'mu' [explicit solve]: current solution: 4.735629e+00, current residual:2.856927e+00 +wall time: 0.000699997s + +time increment:87570 time: 87.57 +field ' c' [explicit solve]: current solution: 9.626431e+01, current residual:5.811032e+01 +field 'mu' [explicit solve]: current solution: 4.735571e+00, current residual:2.856891e+00 +wall time: 0.000648022s + +time increment:87580 time: 87.58 +field ' c' [explicit solve]: current solution: 9.626436e+01, current residual:5.811034e+01 +field 'mu' [explicit solve]: current solution: 4.735513e+00, current residual:2.856856e+00 +wall time: 0.000643969s + +time increment:87590 time: 87.59 +field ' c' [explicit solve]: current solution: 9.626440e+01, current residual:5.811037e+01 +field 'mu' [explicit solve]: current solution: 4.735455e+00, current residual:2.856820e+00 +wall time: 0.000656843s + +time increment:87600 time: 87.6 +field ' c' [explicit solve]: current solution: 9.626445e+01, current residual:5.811039e+01 +field 'mu' [explicit solve]: current solution: 4.735398e+00, current residual:2.856785e+00 +wall time: 0.000598907s + +time increment:87610 time: 87.61 +field ' c' [explicit solve]: current solution: 9.626450e+01, current residual:5.811042e+01 +field 'mu' [explicit solve]: current solution: 4.735340e+00, current residual:2.856749e+00 +wall time: 0.000560045s + +time increment:87620 time: 87.62 +field ' c' [explicit solve]: current solution: 9.626454e+01, current residual:5.811044e+01 +field 'mu' [explicit solve]: current solution: 4.735282e+00, current residual:2.856713e+00 +wall time: 0.000571012s + +time increment:87630 time: 87.63 +field ' c' [explicit solve]: current solution: 9.626459e+01, current residual:5.811047e+01 +field 'mu' [explicit solve]: current solution: 4.735224e+00, current residual:2.856678e+00 +wall time: 0.000628948s + +time increment:87640 time: 87.64 +field ' c' [explicit solve]: current solution: 9.626463e+01, current residual:5.811050e+01 +field 'mu' [explicit solve]: current solution: 4.735166e+00, current residual:2.856642e+00 +wall time: 0.000580072s + +time increment:87650 time: 87.65 +field ' c' [explicit solve]: current solution: 9.626468e+01, current residual:5.811052e+01 +field 'mu' [explicit solve]: current solution: 4.735108e+00, current residual:2.856607e+00 +wall time: 0.000573158s + +time increment:87660 time: 87.66 +field ' c' [explicit solve]: current solution: 9.626472e+01, current residual:5.811055e+01 +field 'mu' [explicit solve]: current solution: 4.735050e+00, current residual:2.856571e+00 +wall time: 0.000607014s + +time increment:87670 time: 87.67 +field ' c' [explicit solve]: current solution: 9.626477e+01, current residual:5.811057e+01 +field 'mu' [explicit solve]: current solution: 4.734992e+00, current residual:2.856535e+00 +wall time: 0.000573874s + +time increment:87680 time: 87.68 +field ' c' [explicit solve]: current solution: 9.626482e+01, current residual:5.811060e+01 +field 'mu' [explicit solve]: current solution: 4.734935e+00, current residual:2.856500e+00 +wall time: 0.000663042s + +time increment:87690 time: 87.69 +field ' c' [explicit solve]: current solution: 9.626486e+01, current residual:5.811062e+01 +field 'mu' [explicit solve]: current solution: 4.734877e+00, current residual:2.856464e+00 +wall time: 0.000569105s + +time increment:87700 time: 87.7 +field ' c' [explicit solve]: current solution: 9.626491e+01, current residual:5.811065e+01 +field 'mu' [explicit solve]: current solution: 4.734819e+00, current residual:2.856429e+00 +wall time: 0.000643969s + +time increment:87710 time: 87.71 +field ' c' [explicit solve]: current solution: 9.626495e+01, current residual:5.811067e+01 +field 'mu' [explicit solve]: current solution: 4.734761e+00, current residual:2.856393e+00 +wall time: 0.00067687s + +time increment:87720 time: 87.72 +field ' c' [explicit solve]: current solution: 9.626500e+01, current residual:5.811070e+01 +field 'mu' [explicit solve]: current solution: 4.734703e+00, current residual:2.856358e+00 +wall time: 0.000612974s + +time increment:87730 time: 87.73 +field ' c' [explicit solve]: current solution: 9.626504e+01, current residual:5.811072e+01 +field 'mu' [explicit solve]: current solution: 4.734645e+00, current residual:2.856322e+00 +wall time: 0.000599146s + +time increment:87740 time: 87.74 +field ' c' [explicit solve]: current solution: 9.626509e+01, current residual:5.811075e+01 +field 'mu' [explicit solve]: current solution: 4.734587e+00, current residual:2.856286e+00 +wall time: 0.000653982s + +time increment:87750 time: 87.75 +field ' c' [explicit solve]: current solution: 9.626514e+01, current residual:5.811078e+01 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solve]: current solution: 4.734298e+00, current residual:2.856108e+00 +wall time: 0.000598907s + +time increment:87800 time: 87.8 +field ' c' [explicit solve]: current solution: 9.626537e+01, current residual:5.811090e+01 +field 'mu' [explicit solve]: current solution: 4.734240e+00, current residual:2.856073e+00 +wall time: 0.000620842s + +time increment:87810 time: 87.81 +field ' c' [explicit solve]: current solution: 9.626541e+01, current residual:5.811093e+01 +field 'mu' [explicit solve]: current solution: 4.734182e+00, current residual:2.856037e+00 +wall time: 0.000642061s + +time increment:87820 time: 87.82 +field ' c' [explicit solve]: current solution: 9.626546e+01, current residual:5.811096e+01 +field 'mu' [explicit solve]: current solution: 4.734124e+00, current residual:2.856002e+00 +wall time: 0.000674963s + +time increment:87830 time: 87.83 +field ' c' [explicit solve]: current solution: 9.626550e+01, current residual:5.811098e+01 +field 'mu' [explicit solve]: current solution: 4.734066e+00, current residual:2.855966e+00 +wall time: 0.000617027s + +time increment:87840 time: 87.84 +field ' c' [explicit solve]: current solution: 9.626555e+01, current residual:5.811101e+01 +field 'mu' [explicit solve]: current solution: 4.734008e+00, current residual:2.855930e+00 +wall time: 0.00061202s + +time increment:87850 time: 87.85 +field ' c' [explicit solve]: current solution: 9.626559e+01, current residual:5.811103e+01 +field 'mu' [explicit solve]: current solution: 4.733950e+00, current residual:2.855895e+00 +wall time: 0.000576973s + +time increment:87860 time: 87.86 +field ' c' [explicit solve]: current solution: 9.626564e+01, current residual:5.811106e+01 +field 'mu' [explicit solve]: current solution: 4.733892e+00, current residual:2.855859e+00 +wall time: 0.000634909s + +time increment:87870 time: 87.87 +field ' c' [explicit solve]: current solution: 9.626569e+01, current residual:5.811108e+01 +field 'mu' [explicit solve]: current solution: 4.733834e+00, current residual:2.855824e+00 +wall time: 0.000560045s + +time increment:87880 time: 87.88 +field ' c' [explicit solve]: current solution: 9.626573e+01, current residual:5.811111e+01 +field 'mu' [explicit solve]: current solution: 4.733776e+00, current residual:2.855788e+00 +wall time: 0.000665903s + +time increment:87890 time: 87.89 +field ' c' [explicit solve]: current solution: 9.626578e+01, current residual:5.811113e+01 +field 'mu' [explicit solve]: current solution: 4.733718e+00, current residual:2.855752e+00 +wall time: 0.000671148s + +time increment:87900 time: 87.9 +field ' c' [explicit solve]: current solution: 9.626582e+01, current residual:5.811116e+01 +field 'mu' [explicit solve]: current solution: 4.733660e+00, current residual:2.855717e+00 +wall time: 0.000720978s + +time increment:87910 time: 87.91 +field ' c' [explicit solve]: current solution: 9.626587e+01, current residual:5.811119e+01 +field 'mu' [explicit solve]: current solution: 4.733602e+00, current residual:2.855681e+00 +wall time: 0.000595093s + +time increment:87920 time: 87.92 +field ' c' [explicit solve]: current solution: 9.626591e+01, current residual:5.811121e+01 +field 'mu' [explicit solve]: current solution: 4.733544e+00, current residual:2.855645e+00 +wall time: 0.000584126s + +time increment:87930 time: 87.93 +field ' c' [explicit solve]: current solution: 9.626596e+01, current residual:5.811124e+01 +field 'mu' [explicit solve]: current solution: 4.733486e+00, current residual:2.855610e+00 +wall time: 0.000580072s + +time increment:87940 time: 87.94 +field ' c' [explicit solve]: current solution: 9.626601e+01, current residual:5.811126e+01 +field 'mu' [explicit solve]: current solution: 4.733428e+00, current residual:2.855574e+00 +wall time: 0.00057292s + +time increment:87950 time: 87.95 +field ' c' [explicit solve]: current solution: 9.626605e+01, current residual:5.811129e+01 +field 'mu' [explicit solve]: current solution: 4.733370e+00, current residual:2.855539e+00 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0.000627041s + +time increment:88000 time: 88 +field ' c' [explicit solve]: current solution: 9.626628e+01, current residual:5.811142e+01 +field 'mu' [explicit solve]: current solution: 4.733080e+00, current residual:2.855361e+00 +wall time: 0.000571966s + +time increment:88010 time: 88.01 +field ' c' [explicit solve]: current solution: 9.626633e+01, current residual:5.811144e+01 +field 'mu' [explicit solve]: current solution: 4.733022e+00, current residual:2.855325e+00 +wall time: 0.000566006s + +time increment:88020 time: 88.02 +field ' c' [explicit solve]: current solution: 9.626637e+01, current residual:5.811147e+01 +field 'mu' [explicit solve]: current solution: 4.732964e+00, current residual:2.855289e+00 +wall time: 0.00069809s + +time increment:88030 time: 88.03 +field ' c' [explicit solve]: current solution: 9.626642e+01, current residual:5.811149e+01 +field 'mu' [explicit solve]: current solution: 4.732906e+00, current residual:2.855254e+00 +wall time: 0.00064683s + +time increment:88040 time: 88.04 +field ' c' [explicit solve]: current solution: 9.626646e+01, current residual:5.811152e+01 +field 'mu' [explicit solve]: current solution: 4.732848e+00, current residual:2.855218e+00 +wall time: 0.000662088s + +time increment:88050 time: 88.05 +field ' c' [explicit solve]: current solution: 9.626651e+01, current residual:5.811154e+01 +field 'mu' [explicit solve]: current solution: 4.732790e+00, current residual:2.855182e+00 +wall time: 0.000661135s + +time increment:88060 time: 88.06 +field ' c' [explicit solve]: current solution: 9.626656e+01, current residual:5.811157e+01 +field 'mu' [explicit solve]: current solution: 4.732732e+00, current residual:2.855147e+00 +wall time: 0.000714064s + +time increment:88070 time: 88.07 +field ' c' [explicit solve]: current solution: 9.626660e+01, current residual:5.811159e+01 +field 'mu' [explicit solve]: current solution: 4.732674e+00, current residual:2.855111e+00 +wall time: 0.000717878s + +time increment:88080 time: 88.08 +field ' c' [explicit solve]: current solution: 9.626665e+01, current residual:5.811162e+01 +field 'mu' [explicit solve]: current solution: 4.732616e+00, current residual:2.855075e+00 +wall time: 0.000637054s + +time increment:88090 time: 88.09 +field ' c' [explicit solve]: current solution: 9.626669e+01, current residual:5.811165e+01 +field 'mu' [explicit solve]: current solution: 4.732558e+00, current residual:2.855040e+00 +wall time: 0.000646114s + +time increment:88100 time: 88.1 +field ' c' [explicit solve]: current solution: 9.626674e+01, current residual:5.811167e+01 +field 'mu' [explicit solve]: current solution: 4.732500e+00, current residual:2.855004e+00 +wall time: 0.000656843s + +time increment:88110 time: 88.11 +field ' c' [explicit solve]: current solution: 9.626679e+01, current residual:5.811170e+01 +field 'mu' [explicit solve]: current solution: 4.732442e+00, current residual:2.854969e+00 +wall time: 0.000667095s + +time increment:88120 time: 88.12 +field ' c' [explicit solve]: current solution: 9.626683e+01, current residual:5.811172e+01 +field 'mu' [explicit solve]: current solution: 4.732384e+00, current residual:2.854933e+00 +wall time: 0.000747919s + +time increment:88130 time: 88.13 +field ' c' [explicit solve]: current solution: 9.626688e+01, current residual:5.811175e+01 +field 'mu' [explicit solve]: current solution: 4.732326e+00, current residual:2.854897e+00 +wall time: 0.000703096s + +time increment:88140 time: 88.14 +field ' c' [explicit solve]: current solution: 9.626692e+01, current residual:5.811177e+01 +field 'mu' [explicit solve]: current solution: 4.732268e+00, current residual:2.854862e+00 +wall time: 0.000715017s + +time increment:88150 time: 88.15 +field ' c' [explicit solve]: current solution: 9.626697e+01, current residual:5.811180e+01 +field 'mu' [explicit solve]: current solution: 4.732210e+00, current residual:2.854826e+00 +wall time: 0.000669003s + +time increment:88160 time: 88.16 +field ' c' [explicit solve]: current solution: 9.626702e+01, current residual:5.811183e+01 +field 'mu' [explicit solve]: current solution: 4.732152e+00, current residual:2.854790e+00 +wall time: 0.000684977s + +time increment:88170 time: 88.17 +field ' c' [explicit solve]: current solution: 9.626706e+01, current residual:5.811185e+01 +field 'mu' [explicit solve]: current solution: 4.732094e+00, current residual:2.854755e+00 +wall time: 0.000645161s + +time increment:88180 time: 88.18 +field ' c' [explicit solve]: current solution: 9.626711e+01, current residual:5.811188e+01 +field 'mu' [explicit solve]: current solution: 4.732036e+00, current residual:2.854719e+00 +wall time: 0.000737906s + +time increment:88190 time: 88.19 +field ' c' [explicit solve]: current solution: 9.626715e+01, current residual:5.811190e+01 +field 'mu' [explicit solve]: current solution: 4.731978e+00, current residual:2.854683e+00 +wall time: 0.000701904s + +time increment:88200 time: 88.2 +field ' c' [explicit solve]: current solution: 9.626720e+01, current residual:5.811193e+01 +field 'mu' [explicit solve]: current solution: 4.731920e+00, current residual:2.854648e+00 +wall time: 0.000653982s + +time increment:88210 time: 88.21 +field ' c' [explicit solve]: current solution: 9.626724e+01, current residual:5.811195e+01 +field 'mu' [explicit solve]: current solution: 4.731862e+00, current residual:2.854612e+00 +wall time: 0.000658035s + +time increment:88220 time: 88.22 +field ' c' [explicit solve]: current solution: 9.626729e+01, current residual:5.811198e+01 +field 'mu' [explicit solve]: current solution: 4.731804e+00, current residual:2.854576e+00 +wall time: 0.00068593s + +time increment:88230 time: 88.23 +field ' c' [explicit solve]: current solution: 9.626734e+01, current residual:5.811201e+01 +field 'mu' [explicit solve]: current solution: 4.731746e+00, current residual:2.854541e+00 +wall time: 0.000677824s + +time increment:88240 time: 88.24 +field ' c' [explicit solve]: current solution: 9.626738e+01, current residual:5.811203e+01 +field 'mu' [explicit solve]: current solution: 4.731688e+00, current residual:2.854505e+00 +wall time: 0.000699997s + +time increment:88250 time: 88.25 +field ' c' [explicit solve]: current solution: 9.626743e+01, current residual:5.811206e+01 +field 'mu' [explicit solve]: current solution: 4.731630e+00, current residual:2.854470e+00 +wall time: 0.000705957s + +time increment:88260 time: 88.26 +field ' c' [explicit solve]: current solution: 9.626747e+01, current residual:5.811208e+01 +field 'mu' [explicit solve]: current solution: 4.731572e+00, current residual:2.854434e+00 +wall time: 0.000632048s + +time increment:88270 time: 88.27 +field ' c' [explicit solve]: current solution: 9.626752e+01, current residual:5.811211e+01 +field 'mu' [explicit solve]: current solution: 4.731514e+00, current residual:2.854398e+00 +wall time: 0.000683069s + +time increment:88280 time: 88.28 +field ' c' [explicit solve]: current solution: 9.626757e+01, current residual:5.811213e+01 +field 'mu' [explicit solve]: current solution: 4.731456e+00, current residual:2.854363e+00 +wall time: 0.000683069s + +time increment:88290 time: 88.29 +field ' c' [explicit solve]: current solution: 9.626761e+01, current residual:5.811216e+01 +field 'mu' [explicit solve]: current solution: 4.731397e+00, current residual:2.854327e+00 +wall time: 0.000658989s + +time increment:88300 time: 88.3 +field ' c' [explicit solve]: current solution: 9.626766e+01, current residual:5.811218e+01 +field 'mu' [explicit solve]: current solution: 4.731339e+00, current residual:2.854291e+00 +wall time: 0.000678062s + +time increment:88310 time: 88.31 +field ' c' [explicit solve]: current solution: 9.626770e+01, current residual:5.811221e+01 +field 'mu' [explicit solve]: current solution: 4.731281e+00, current residual:2.854256e+00 +wall time: 0.00072217s + +time increment:88320 time: 88.32 +field ' c' [explicit solve]: current solution: 9.626775e+01, current residual:5.811224e+01 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[explicit solve]: current solution: 4.730991e+00, current residual:2.854077e+00 +wall time: 0.000665903s + +time increment:88370 time: 88.37 +field ' c' [explicit solve]: current solution: 9.626798e+01, current residual:5.811236e+01 +field 'mu' [explicit solve]: current solution: 4.730933e+00, current residual:2.854042e+00 +wall time: 0.000654936s + +time increment:88380 time: 88.38 +field ' c' [explicit solve]: current solution: 9.626803e+01, current residual:5.811239e+01 +field 'mu' [explicit solve]: current solution: 4.730875e+00, current residual:2.854006e+00 +wall time: 0.000672102s + +time increment:88390 time: 88.39 +field ' c' [explicit solve]: current solution: 9.626807e+01, current residual:5.811242e+01 +field 'mu' [explicit solve]: current solution: 4.730816e+00, current residual:2.853970e+00 +wall time: 0.000716925s + +time increment:88400 time: 88.4 +field ' c' [explicit solve]: current solution: 9.626812e+01, current residual:5.811244e+01 +field 'mu' [explicit solve]: current solution: 4.730758e+00, current residual:2.853935e+00 +wall time: 0.000677109s + +time increment:88410 time: 88.41 +field ' c' [explicit solve]: current solution: 9.626816e+01, current residual:5.811247e+01 +field 'mu' [explicit solve]: current solution: 4.730700e+00, current residual:2.853899e+00 +wall time: 0.000566959s + +time increment:88420 time: 88.42 +field ' c' [explicit solve]: current solution: 9.626821e+01, current residual:5.811249e+01 +field 'mu' [explicit solve]: current solution: 4.730642e+00, current residual:2.853863e+00 +wall time: 0.000582933s + +time increment:88430 time: 88.43 +field ' c' [explicit solve]: current solution: 9.626825e+01, current residual:5.811252e+01 +field 'mu' [explicit solve]: current solution: 4.730584e+00, current residual:2.853828e+00 +wall time: 0.00058198s + +time increment:88440 time: 88.44 +field ' c' [explicit solve]: current solution: 9.626830e+01, current residual:5.811254e+01 +field 'mu' [explicit solve]: current solution: 4.730526e+00, current residual:2.853792e+00 +wall time: 0.000619888s + +time increment:88450 time: 88.45 +field ' c' [explicit solve]: current solution: 9.626835e+01, current residual:5.811257e+01 +field 'mu' [explicit solve]: current solution: 4.730468e+00, current residual:2.853756e+00 +wall time: 0.000558853s + +time increment:88460 time: 88.46 +field ' c' [explicit solve]: current solution: 9.626839e+01, current residual:5.811260e+01 +field 'mu' [explicit solve]: current solution: 4.730410e+00, current residual:2.853720e+00 +wall time: 0.000679016s + +time increment:88470 time: 88.47 +field ' c' [explicit solve]: current solution: 9.626844e+01, current residual:5.811262e+01 +field 'mu' [explicit solve]: current solution: 4.730352e+00, current residual:2.853685e+00 +wall time: 0.000562906s + +time increment:88480 time: 88.48 +field ' c' [explicit solve]: current solution: 9.626848e+01, current residual:5.811265e+01 +field 'mu' [explicit solve]: current solution: 4.730293e+00, current residual:2.853649e+00 +wall time: 0.00056386s + +time increment:88490 time: 88.49 +field ' c' [explicit solve]: current solution: 9.626853e+01, current residual:5.811267e+01 +field 'mu' [explicit solve]: current solution: 4.730235e+00, current residual:2.853613e+00 +wall time: 0.000564098s + +time increment:88500 time: 88.5 +field ' c' [explicit solve]: current solution: 9.626858e+01, current residual:5.811270e+01 +field 'mu' [explicit solve]: current solution: 4.730177e+00, current residual:2.853578e+00 +wall time: 0.000571012s + +time increment:88510 time: 88.51 +field ' c' [explicit solve]: current solution: 9.626862e+01, current residual:5.811272e+01 +field 'mu' [explicit solve]: current solution: 4.730119e+00, current residual:2.853542e+00 +wall time: 0.000658035s + +time increment:88520 time: 88.52 +field ' c' [explicit solve]: current solution: 9.626867e+01, current residual:5.811275e+01 +field 'mu' [explicit solve]: current solution: 4.730061e+00, current residual:2.853506e+00 +wall time: 0.000560999s + +time increment:88530 time: 88.53 +field ' c' [explicit solve]: current solution: 9.626871e+01, current residual:5.811278e+01 +field 'mu' [explicit solve]: current solution: 4.730003e+00, current residual:2.853471e+00 +wall time: 0.000580072s + +time increment:88540 time: 88.54 +field ' c' [explicit solve]: current solution: 9.626876e+01, current residual:5.811280e+01 +field 'mu' [explicit solve]: current solution: 4.729945e+00, current residual:2.853435e+00 +wall time: 0.000565052s + +time increment:88550 time: 88.55 +field ' c' [explicit solve]: current solution: 9.626881e+01, current residual:5.811283e+01 +field 'mu' [explicit solve]: current solution: 4.729886e+00, current residual:2.853399e+00 +wall time: 0.00073123s + +time increment:88560 time: 88.56 +field ' c' [explicit solve]: current solution: 9.626885e+01, current residual:5.811285e+01 +field 'mu' [explicit solve]: current solution: 4.729828e+00, current residual:2.853364e+00 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0.000660896s + +time increment:88610 time: 88.61 +field ' c' [explicit solve]: current solution: 9.626908e+01, current residual:5.811298e+01 +field 'mu' [explicit solve]: current solution: 4.729537e+00, current residual:2.853185e+00 +wall time: 0.000747919s + +time increment:88620 time: 88.62 +field ' c' [explicit solve]: current solution: 9.626913e+01, current residual:5.811301e+01 +field 'mu' [explicit solve]: current solution: 4.729479e+00, current residual:2.853149e+00 +wall time: 0.000700951s + +time increment:88630 time: 88.63 +field ' c' [explicit solve]: current solution: 9.626917e+01, current residual:5.811303e+01 +field 'mu' [explicit solve]: current solution: 4.729421e+00, current residual:2.853114e+00 +wall time: 0.000752926s + +time increment:88640 time: 88.64 +field ' c' [explicit solve]: current solution: 9.626922e+01, current residual:5.811306e+01 +field 'mu' [explicit solve]: current solution: 4.729363e+00, current residual:2.853078e+00 +wall time: 0.000672817s + +time increment:88650 time: 88.65 +field ' c' [explicit solve]: current solution: 9.626927e+01, current residual:5.811308e+01 +field 'mu' [explicit solve]: current solution: 4.729305e+00, current residual:2.853042e+00 +wall time: 0.000756979s + +time increment:88660 time: 88.66 +field ' c' [explicit solve]: current solution: 9.626931e+01, current residual:5.811311e+01 +field 'mu' [explicit solve]: current solution: 4.729246e+00, current residual:2.853007e+00 +wall time: 0.000643015s + +time increment:88670 time: 88.67 +field ' c' [explicit solve]: current solution: 9.626936e+01, current residual:5.811314e+01 +field 'mu' [explicit solve]: current solution: 4.729188e+00, current residual:2.852971e+00 +wall time: 0.000612974s + +time increment:88680 time: 88.68 +field ' c' [explicit solve]: current solution: 9.626940e+01, current residual:5.811316e+01 +field 'mu' [explicit solve]: current solution: 4.729130e+00, current residual:2.852935e+00 +wall time: 0.000644922s + +time increment:88690 time: 88.69 +field ' c' [explicit solve]: current solution: 9.626945e+01, current residual:5.811319e+01 +field 'mu' [explicit solve]: current solution: 4.729072e+00, current residual:2.852899e+00 +wall time: 0.000716925s + +time increment:88700 time: 88.7 +field ' c' [explicit solve]: current solution: 9.626950e+01, current residual:5.811321e+01 +field 'mu' [explicit solve]: current solution: 4.729014e+00, current residual:2.852864e+00 +wall time: 0.00070405s + +time increment:88710 time: 88.71 +field ' c' [explicit solve]: current solution: 9.626954e+01, current residual:5.811324e+01 +field 'mu' [explicit solve]: current solution: 4.728955e+00, current residual:2.852828e+00 +wall time: 0.000658989s + +time increment:88720 time: 88.72 +field ' c' [explicit solve]: current solution: 9.626959e+01, current residual:5.811326e+01 +field 'mu' [explicit solve]: current solution: 4.728897e+00, current residual:2.852792e+00 +wall time: 0.000629902s + +time increment:88730 time: 88.73 +field ' c' [explicit solve]: current solution: 9.626963e+01, current residual:5.811329e+01 +field 'mu' [explicit solve]: current solution: 4.728839e+00, current residual:2.852757e+00 +wall time: 0.000740051s + +time increment:88740 time: 88.74 +field ' c' [explicit solve]: current solution: 9.626968e+01, current residual:5.811332e+01 +field 'mu' [explicit solve]: current solution: 4.728781e+00, current residual:2.852721e+00 +wall time: 0.000647068s + +time increment:88750 time: 88.75 +field ' c' [explicit solve]: current solution: 9.626973e+01, current residual:5.811334e+01 +field 'mu' [explicit solve]: current solution: 4.728723e+00, current residual:2.852685e+00 +wall time: 0.000635147s + +time increment:88760 time: 88.76 +field ' c' [explicit solve]: current solution: 9.626977e+01, current residual:5.811337e+01 +field 'mu' [explicit solve]: current solution: 4.728664e+00, current residual:2.852649e+00 +wall time: 0.000677109s + +time increment:88770 time: 88.77 +field ' c' [explicit solve]: current solution: 9.626982e+01, current residual:5.811339e+01 +field 'mu' [explicit solve]: current solution: 4.728606e+00, current residual:2.852614e+00 +wall time: 0.000688076s + +time increment:88780 time: 88.78 +field ' c' [explicit solve]: current solution: 9.626986e+01, current residual:5.811342e+01 +field 'mu' [explicit solve]: current solution: 4.728548e+00, current residual:2.852578e+00 +wall time: 0.000643969s + +time increment:88790 time: 88.79 +field ' c' [explicit solve]: current solution: 9.626991e+01, current residual:5.811345e+01 +field 'mu' [explicit solve]: current solution: 4.728490e+00, current residual:2.852542e+00 +wall time: 0.000668049s + +time increment:88800 time: 88.8 +field ' c' [explicit solve]: current solution: 9.626996e+01, current residual:5.811347e+01 +field 'mu' [explicit solve]: current solution: 4.728431e+00, current residual:2.852506e+00 +wall time: 0.000642061s + +time increment:88810 time: 88.81 +field ' c' [explicit solve]: current solution: 9.627000e+01, current residual:5.811350e+01 +field 'mu' [explicit solve]: current solution: 4.728373e+00, current residual:2.852471e+00 +wall time: 0.000654936s + +time increment:88820 time: 88.82 +field ' c' [explicit solve]: current solution: 9.627005e+01, current residual:5.811352e+01 +field 'mu' [explicit solve]: current solution: 4.728315e+00, current residual:2.852435e+00 +wall time: 0.000652075s + +time increment:88830 time: 88.83 +field ' c' [explicit solve]: current solution: 9.627009e+01, current residual:5.811355e+01 +field 'mu' [explicit solve]: current solution: 4.728257e+00, current residual:2.852399e+00 +wall time: 0.000664949s + +time increment:88840 time: 88.84 +field ' c' [explicit solve]: current solution: 9.627014e+01, current residual:5.811357e+01 +field 'mu' [explicit solve]: current solution: 4.728199e+00, current residual:2.852364e+00 +wall time: 0.00070405s + +time increment:88850 time: 88.85 +field ' c' [explicit solve]: current solution: 9.627019e+01, current residual:5.811360e+01 +field 'mu' [explicit solve]: current solution: 4.728140e+00, current residual:2.852328e+00 +wall time: 0.000624895s + +time increment:88860 time: 88.86 +field ' c' [explicit solve]: current solution: 9.627023e+01, current residual:5.811363e+01 +field 'mu' [explicit solve]: current solution: 4.728082e+00, current residual:2.852292e+00 +wall time: 0.000666142s + +time increment:88870 time: 88.87 +field ' c' [explicit solve]: current solution: 9.627028e+01, current residual:5.811365e+01 +field 'mu' [explicit solve]: current solution: 4.728024e+00, current residual:2.852256e+00 +wall time: 0.00058198s + +time increment:88880 time: 88.88 +field ' c' [explicit solve]: current solution: 9.627032e+01, current residual:5.811368e+01 +field 'mu' [explicit solve]: current solution: 4.727966e+00, current residual:2.852221e+00 +wall time: 0.00057292s + +time increment:88890 time: 88.89 +field ' c' [explicit solve]: current solution: 9.627037e+01, current residual:5.811370e+01 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solve]: current solution: 4.727674e+00, current residual:2.852042e+00 +wall time: 0.000725985s + +time increment:88940 time: 88.94 +field ' c' [explicit solve]: current solution: 9.627060e+01, current residual:5.811383e+01 +field 'mu' [explicit solve]: current solution: 4.727616e+00, current residual:2.852006e+00 +wall time: 0.000567913s + +time increment:88950 time: 88.95 +field ' c' [explicit solve]: current solution: 9.627065e+01, current residual:5.811386e+01 +field 'mu' [explicit solve]: current solution: 4.727558e+00, current residual:2.851970e+00 +wall time: 0.000693083s + +time increment:88960 time: 88.96 +field ' c' [explicit solve]: current solution: 9.627069e+01, current residual:5.811388e+01 +field 'mu' [explicit solve]: current solution: 4.727499e+00, current residual:2.851935e+00 +wall time: 0.000571966s + +time increment:88970 time: 88.97 +field ' c' [explicit solve]: current solution: 9.627074e+01, current residual:5.811391e+01 +field 'mu' [explicit solve]: current solution: 4.727441e+00, current residual:2.851899e+00 +wall time: 0.000595093s + +time increment:88980 time: 88.98 +field ' c' [explicit solve]: current solution: 9.627079e+01, current residual:5.811394e+01 +field 'mu' [explicit solve]: current solution: 4.727383e+00, current residual:2.851863e+00 +wall time: 0.000605106s + +time increment:88990 time: 88.99 +field ' c' [explicit solve]: current solution: 9.627083e+01, current residual:5.811396e+01 +field 'mu' [explicit solve]: current solution: 4.727325e+00, current residual:2.851827e+00 +wall time: 0.0007689s + +time increment:89000 time: 89 +field ' c' [explicit solve]: current solution: 9.627088e+01, current residual:5.811399e+01 +field 'mu' [explicit solve]: current solution: 4.727266e+00, current residual:2.851792e+00 +wall time: 0.000627041s + +time increment:89010 time: 89.01 +field ' c' [explicit solve]: current solution: 9.627092e+01, current residual:5.811401e+01 +field 'mu' [explicit solve]: current solution: 4.727208e+00, current residual:2.851756e+00 +wall time: 0.000674009s + +time increment:89020 time: 89.02 +field ' c' [explicit solve]: current solution: 9.627097e+01, current residual:5.811404e+01 +field 'mu' [explicit solve]: current solution: 4.727150e+00, current residual:2.851720e+00 +wall time: 0.000673056s + +time increment:89030 time: 89.03 +field ' c' [explicit solve]: current solution: 9.627102e+01, current residual:5.811406e+01 +field 'mu' [explicit solve]: current solution: 4.727091e+00, current residual:2.851684e+00 +wall time: 0.000689983s + +time increment:89040 time: 89.04 +field ' c' [explicit solve]: current solution: 9.627106e+01, current residual:5.811409e+01 +field 'mu' [explicit solve]: current solution: 4.727033e+00, current residual:2.851649e+00 +wall time: 0.000721931s + +time increment:89050 time: 89.05 +field ' c' [explicit solve]: current solution: 9.627111e+01, current residual:5.811412e+01 +field 'mu' [explicit solve]: current solution: 4.726975e+00, current residual:2.851613e+00 +wall time: 0.000697851s + +time increment:89060 time: 89.06 +field ' c' [explicit solve]: current solution: 9.627115e+01, current residual:5.811414e+01 +field 'mu' [explicit solve]: current solution: 4.726916e+00, current residual:2.851577e+00 +wall time: 0.000668049s + +time increment:89070 time: 89.07 +field ' c' [explicit solve]: current solution: 9.627120e+01, current residual:5.811417e+01 +field 'mu' [explicit solve]: current solution: 4.726858e+00, current residual:2.851541e+00 +wall time: 0.000673056s + +time increment:89080 time: 89.08 +field ' c' [explicit solve]: current solution: 9.627125e+01, current residual:5.811419e+01 +field 'mu' [explicit solve]: current solution: 4.726800e+00, current residual:2.851506e+00 +wall time: 0.000716925s + +time increment:89090 time: 89.09 +field ' c' [explicit solve]: current solution: 9.627129e+01, current residual:5.811422e+01 +field 'mu' [explicit solve]: current solution: 4.726741e+00, current residual:2.851470e+00 +wall time: 0.000699997s + +time increment:89100 time: 89.1 +field ' c' [explicit solve]: current solution: 9.627134e+01, current residual:5.811424e+01 +field 'mu' [explicit solve]: current solution: 4.726683e+00, current residual:2.851434e+00 +wall time: 0.000681877s + +time increment:89110 time: 89.11 +field ' c' [explicit solve]: current solution: 9.627138e+01, current residual:5.811427e+01 +field 'mu' [explicit solve]: current solution: 4.726625e+00, current residual:2.851398e+00 +wall time: 0.000630856s + +time increment:89120 time: 89.12 +field ' c' [explicit solve]: current solution: 9.627143e+01, current residual:5.811430e+01 +field 'mu' [explicit solve]: current solution: 4.726566e+00, current residual:2.851363e+00 +wall time: 0.000607967s + +time increment:89130 time: 89.13 +field ' c' [explicit solve]: current solution: 9.627148e+01, current residual:5.811432e+01 +field 'mu' [explicit solve]: current solution: 4.726508e+00, current residual:2.851327e+00 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0.000716209s + +time increment:89180 time: 89.18 +field ' c' [explicit solve]: current solution: 9.627171e+01, current residual:5.811445e+01 +field 'mu' [explicit solve]: current solution: 4.726216e+00, current residual:2.851148e+00 +wall time: 0.000653982s + +time increment:89190 time: 89.19 +field ' c' [explicit solve]: current solution: 9.627175e+01, current residual:5.811448e+01 +field 'mu' [explicit solve]: current solution: 4.726158e+00, current residual:2.851112e+00 +wall time: 0.000633001s + +time increment:89200 time: 89.2 +field ' c' [explicit solve]: current solution: 9.627180e+01, current residual:5.811450e+01 +field 'mu' [explicit solve]: current solution: 4.726100e+00, current residual:2.851076e+00 +wall time: 0.000663042s + +time increment:89210 time: 89.21 +field ' c' [explicit solve]: current solution: 9.627185e+01, current residual:5.811453e+01 +field 'mu' [explicit solve]: current solution: 4.726041e+00, current residual:2.851041e+00 +wall time: 0.000679016s + +time increment:89220 time: 89.22 +field ' c' [explicit solve]: current solution: 9.627189e+01, current residual:5.811456e+01 +field 'mu' [explicit solve]: current solution: 4.725983e+00, current residual:2.851005e+00 +wall time: 0.000712872s + +time increment:89230 time: 89.23 +field ' c' [explicit solve]: current solution: 9.627194e+01, current residual:5.811458e+01 +field 'mu' [explicit solve]: current solution: 4.725925e+00, current residual:2.850969e+00 +wall time: 0.000679016s + +time increment:89240 time: 89.24 +field ' c' [explicit solve]: current solution: 9.627198e+01, current residual:5.811461e+01 +field 'mu' [explicit solve]: current solution: 4.725866e+00, current residual:2.850933e+00 +wall time: 0.000674009s + +time increment:89250 time: 89.25 +field ' c' [explicit solve]: current solution: 9.627203e+01, current residual:5.811463e+01 +field 'mu' [explicit solve]: current solution: 4.725808e+00, current residual:2.850897e+00 +wall time: 0.000730991s + +time increment:89260 time: 89.26 +field ' c' [explicit solve]: current solution: 9.627208e+01, current residual:5.811466e+01 +field 'mu' [explicit solve]: current solution: 4.725750e+00, current residual:2.850862e+00 +wall time: 0.00070405s + +time increment:89270 time: 89.27 +field ' c' [explicit solve]: current solution: 9.627212e+01, current residual:5.811468e+01 +field 'mu' [explicit solve]: current solution: 4.725691e+00, current residual:2.850826e+00 +wall time: 0.000797033s + +time increment:89280 time: 89.28 +field ' c' [explicit solve]: current solution: 9.627217e+01, current residual:5.811471e+01 +field 'mu' [explicit solve]: current solution: 4.725633e+00, current residual:2.850790e+00 +wall time: 0.00072813s + +time increment:89290 time: 89.29 +field ' c' [explicit solve]: current solution: 9.627221e+01, current residual:5.811474e+01 +field 'mu' [explicit solve]: current solution: 4.725574e+00, current residual:2.850754e+00 +wall time: 0.000699043s + +time increment:89300 time: 89.3 +field ' c' [explicit solve]: current solution: 9.627226e+01, current residual:5.811476e+01 +field 'mu' [explicit solve]: current solution: 4.725516e+00, current residual:2.850719e+00 +wall time: 0.000656843s + +time increment:89310 time: 89.31 +field ' c' [explicit solve]: current solution: 9.627231e+01, current residual:5.811479e+01 +field 'mu' [explicit solve]: current solution: 4.725458e+00, current residual:2.850683e+00 +wall time: 0.000693083s + +time increment:89320 time: 89.32 +field ' c' [explicit solve]: current solution: 9.627235e+01, current residual:5.811481e+01 +field 'mu' [explicit solve]: current solution: 4.725399e+00, current residual:2.850647e+00 +wall time: 0.000702143s + +time increment:89330 time: 89.33 +field ' c' [explicit solve]: current solution: 9.627240e+01, current residual:5.811484e+01 +field 'mu' [explicit solve]: current solution: 4.725341e+00, current residual:2.850611e+00 +wall time: 0.000684023s + +time increment:89340 time: 89.34 +field ' c' [explicit solve]: current solution: 9.627245e+01, current residual:5.811487e+01 +field 'mu' [explicit solve]: current solution: 4.725282e+00, current residual:2.850575e+00 +wall time: 0.000687122s + +time increment:89350 time: 89.35 +field ' c' [explicit solve]: current solution: 9.627249e+01, current residual:5.811489e+01 +field 'mu' [explicit solve]: current solution: 4.725224e+00, current residual:2.850540e+00 +wall time: 0.000693083s + +time increment:89360 time: 89.36 +field ' c' [explicit solve]: current solution: 9.627254e+01, current residual:5.811492e+01 +field 'mu' [explicit solve]: current solution: 4.725166e+00, current residual:2.850504e+00 +wall time: 0.000694036s + +time increment:89370 time: 89.37 +field ' c' [explicit solve]: current solution: 9.627258e+01, current residual:5.811494e+01 +field 'mu' [explicit solve]: current solution: 4.725107e+00, current residual:2.850468e+00 +wall time: 0.000618935s + +time increment:89380 time: 89.38 +field ' c' [explicit solve]: current solution: 9.627263e+01, current residual:5.811497e+01 +field 'mu' [explicit solve]: current solution: 4.725049e+00, current residual:2.850432e+00 +wall time: 0.000575066s + +time increment:89390 time: 89.39 +field ' c' [explicit solve]: current solution: 9.627268e+01, current residual:5.811499e+01 +field 'mu' [explicit solve]: current solution: 4.724990e+00, current residual:2.850396e+00 +wall time: 0.000663996s + +time increment:89400 time: 89.4 +field ' c' [explicit solve]: current solution: 9.627272e+01, current residual:5.811502e+01 +field 'mu' [explicit solve]: current solution: 4.724932e+00, current residual:2.850361e+00 +wall time: 0.000560999s + +time increment:89410 time: 89.41 +field ' c' [explicit solve]: current solution: 9.627277e+01, current residual:5.811505e+01 +field 'mu' [explicit solve]: current solution: 4.724874e+00, current residual:2.850325e+00 +wall time: 0.000647068s + +time increment:89420 time: 89.42 +field ' c' [explicit solve]: current solution: 9.627281e+01, current residual:5.811507e+01 +field 'mu' [explicit solve]: current solution: 4.724815e+00, current residual:2.850289e+00 +wall time: 0.00056982s + +time increment:89430 time: 89.43 +field ' c' [explicit solve]: current solution: 9.627286e+01, current residual:5.811510e+01 +field 'mu' [explicit solve]: current solution: 4.724757e+00, current residual:2.850253e+00 +wall time: 0.000573158s + +time increment:89440 time: 89.44 +field ' c' [explicit solve]: current solution: 9.627291e+01, current residual:5.811512e+01 +field 'mu' [explicit solve]: current solution: 4.724698e+00, current residual:2.850217e+00 +wall time: 0.000673056s + +time increment:89450 time: 89.45 +field ' c' [explicit solve]: current solution: 9.627295e+01, current residual:5.811515e+01 +field 'mu' [explicit solve]: current solution: 4.724640e+00, current residual:2.850182e+00 +wall time: 0.000622988s + +time increment:89460 time: 89.46 +field ' c' [explicit solve]: current solution: 9.627300e+01, current residual:5.811518e+01 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solve]: current solution: 4.724348e+00, current residual:2.850002e+00 +wall time: 0.000663042s + +time increment:89510 time: 89.51 +field ' c' [explicit solve]: current solution: 9.627323e+01, current residual:5.811531e+01 +field 'mu' [explicit solve]: current solution: 4.724289e+00, current residual:2.849967e+00 +wall time: 0.000683069s + +time increment:89520 time: 89.52 +field ' c' [explicit solve]: current solution: 9.627328e+01, current residual:5.811533e+01 +field 'mu' [explicit solve]: current solution: 4.724231e+00, current residual:2.849931e+00 +wall time: 0.000648975s + +time increment:89530 time: 89.53 +field ' c' [explicit solve]: current solution: 9.627332e+01, current residual:5.811536e+01 +field 'mu' [explicit solve]: current solution: 4.724172e+00, current residual:2.849895e+00 +wall time: 0.000557899s + +time increment:89540 time: 89.54 +field ' c' [explicit solve]: current solution: 9.627337e+01, current residual:5.811538e+01 +field 'mu' [explicit solve]: current solution: 4.724114e+00, current residual:2.849859e+00 +wall time: 0.000641823s + +time increment:89550 time: 89.55 +field ' c' [explicit solve]: current solution: 9.627341e+01, current residual:5.811541e+01 +field 'mu' [explicit solve]: current solution: 4.724055e+00, current residual:2.849823e+00 +wall time: 0.00067687s + +time increment:89560 time: 89.56 +field ' c' [explicit solve]: current solution: 9.627346e+01, current residual:5.811544e+01 +field 'mu' [explicit solve]: current solution: 4.723997e+00, current residual:2.849788e+00 +wall time: 0.000667095s + +time increment:89570 time: 89.57 +field ' c' [explicit solve]: current solution: 9.627351e+01, current residual:5.811546e+01 +field 'mu' [explicit solve]: current solution: 4.723939e+00, current residual:2.849752e+00 +wall time: 0.000684977s + +time increment:89580 time: 89.58 +field ' c' [explicit solve]: current solution: 9.627355e+01, current residual:5.811549e+01 +field 'mu' [explicit solve]: current solution: 4.723880e+00, current residual:2.849716e+00 +wall time: 0.000702858s + +time increment:89590 time: 89.59 +field ' c' [explicit solve]: current solution: 9.627360e+01, current residual:5.811551e+01 +field 'mu' [explicit solve]: current solution: 4.723822e+00, current residual:2.849680e+00 +wall time: 0.00056386s + +time increment:89600 time: 89.6 +field ' c' [explicit solve]: current solution: 9.627365e+01, current residual:5.811554e+01 +field 'mu' [explicit solve]: current solution: 4.723763e+00, current residual:2.849644e+00 +wall time: 0.000675201s + +time increment:89610 time: 89.61 +field ' c' [explicit solve]: current solution: 9.627369e+01, current residual:5.811556e+01 +field 'mu' [explicit solve]: current solution: 4.723705e+00, current residual:2.849608e+00 +wall time: 0.00058198s + +time increment:89620 time: 89.62 +field ' c' [explicit solve]: current solution: 9.627374e+01, current residual:5.811559e+01 +field 'mu' [explicit solve]: current solution: 4.723646e+00, current residual:2.849573e+00 +wall time: 0.000566959s + +time increment:89630 time: 89.63 +field ' c' [explicit solve]: current solution: 9.627378e+01, current residual:5.811562e+01 +field 'mu' [explicit solve]: current solution: 4.723588e+00, current residual:2.849537e+00 +wall time: 0.000674963s + +time increment:89640 time: 89.64 +field ' c' [explicit solve]: current solution: 9.627383e+01, current residual:5.811564e+01 +field 'mu' [explicit solve]: current solution: 4.723529e+00, current residual:2.849501e+00 +wall time: 0.000684977s + +time increment:89650 time: 89.65 +field ' c' [explicit solve]: current solution: 9.627388e+01, current residual:5.811567e+01 +field 'mu' [explicit solve]: current solution: 4.723471e+00, current residual:2.849465e+00 +wall time: 0.000678062s + +time increment:89660 time: 89.66 +field ' c' [explicit solve]: current solution: 9.627392e+01, current residual:5.811569e+01 +field 'mu' [explicit solve]: current solution: 4.723412e+00, current residual:2.849429e+00 +wall time: 0.000608921s + +time increment:89670 time: 89.67 +field ' c' [explicit solve]: current solution: 9.627397e+01, current residual:5.811572e+01 +field 'mu' [explicit solve]: current solution: 4.723354e+00, current residual:2.849393e+00 +wall time: 0.000627995s + +time increment:89680 time: 89.68 +field ' c' [explicit solve]: current solution: 9.627402e+01, current residual:5.811575e+01 +field 'mu' [explicit solve]: current solution: 4.723295e+00, current residual:2.849358e+00 +wall time: 0.000607967s + +time increment:89690 time: 89.69 +field ' c' [explicit solve]: current solution: 9.627406e+01, current residual:5.811577e+01 +field 'mu' [explicit solve]: current solution: 4.723237e+00, current residual:2.849322e+00 +wall time: 0.000632048s + +time increment:89700 time: 89.7 +field ' c' [explicit solve]: current solution: 9.627411e+01, current residual:5.811580e+01 +field 'mu' [explicit solve]: current solution: 4.723178e+00, current residual:2.849286e+00 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0.000607967s + +time increment:89750 time: 89.75 +field ' c' [explicit solve]: current solution: 9.627434e+01, current residual:5.811593e+01 +field 'mu' [explicit solve]: current solution: 4.722886e+00, current residual:2.849107e+00 +wall time: 0.000651121s + +time increment:89760 time: 89.76 +field ' c' [explicit solve]: current solution: 9.627439e+01, current residual:5.811595e+01 +field 'mu' [explicit solve]: current solution: 4.722827e+00, current residual:2.849071e+00 +wall time: 0.000571966s + +time increment:89770 time: 89.77 +field ' c' [explicit solve]: current solution: 9.627443e+01, current residual:5.811598e+01 +field 'mu' [explicit solve]: current solution: 4.722769e+00, current residual:2.849035e+00 +wall time: 0.000597954s + +time increment:89780 time: 89.78 +field ' c' [explicit solve]: current solution: 9.627448e+01, current residual:5.811601e+01 +field 'mu' [explicit solve]: current solution: 4.722710e+00, current residual:2.848999e+00 +wall time: 0.000687122s + +time increment:89790 time: 89.79 +field ' c' [explicit solve]: current solution: 9.627452e+01, current residual:5.811603e+01 +field 'mu' [explicit solve]: current solution: 4.722652e+00, current residual:2.848963e+00 +wall time: 0.000565052s + +time increment:89800 time: 89.8 +field ' c' [explicit solve]: current solution: 9.627457e+01, current residual:5.811606e+01 +field 'mu' [explicit solve]: current solution: 4.722593e+00, current residual:2.848927e+00 +wall time: 0.000657082s + +time increment:89810 time: 89.81 +field ' c' [explicit solve]: current solution: 9.627462e+01, current residual:5.811608e+01 +field 'mu' [explicit solve]: current solution: 4.722535e+00, current residual:2.848892e+00 +wall time: 0.000651836s + +time increment:89820 time: 89.82 +field ' c' [explicit solve]: current solution: 9.627466e+01, current residual:5.811611e+01 +field 'mu' [explicit solve]: current solution: 4.722476e+00, current residual:2.848856e+00 +wall time: 0.000565052s + +time increment:89830 time: 89.83 +field ' c' [explicit solve]: current solution: 9.627471e+01, current residual:5.811614e+01 +field 'mu' [explicit solve]: current solution: 4.722417e+00, current residual:2.848820e+00 +wall time: 0.000572205s + +time increment:89840 time: 89.84 +field ' c' [explicit solve]: current solution: 9.627476e+01, current residual:5.811616e+01 +field 'mu' [explicit solve]: current solution: 4.722359e+00, current residual:2.848784e+00 +wall time: 0.000555038s + +time increment:89850 time: 89.85 +field ' c' [explicit solve]: current solution: 9.627480e+01, current residual:5.811619e+01 +field 'mu' [explicit solve]: current solution: 4.722300e+00, current residual:2.848748e+00 +wall time: 0.000643969s + +time increment:89860 time: 89.86 +field ' c' [explicit solve]: current solution: 9.627485e+01, current residual:5.811621e+01 +field 'mu' [explicit solve]: current solution: 4.722242e+00, current residual:2.848712e+00 +wall time: 0.000633955s + +time increment:89870 time: 89.87 +field ' c' [explicit solve]: current solution: 9.627489e+01, current residual:5.811624e+01 +field 'mu' [explicit solve]: current solution: 4.722183e+00, current residual:2.848676e+00 +wall time: 0.000710011s + +time increment:89880 time: 89.88 +field ' c' [explicit solve]: current solution: 9.627494e+01, current residual:5.811627e+01 +field 'mu' [explicit solve]: current solution: 4.722125e+00, current residual:2.848641e+00 +wall time: 0.000751019s + +time increment:89890 time: 89.89 +field ' c' [explicit solve]: current solution: 9.627499e+01, current residual:5.811629e+01 +field 'mu' [explicit solve]: current solution: 4.722066e+00, current residual:2.848605e+00 +wall time: 0.000718117s + +time increment:89900 time: 89.9 +field ' c' [explicit solve]: current solution: 9.627503e+01, current residual:5.811632e+01 +field 'mu' [explicit solve]: current solution: 4.722008e+00, current residual:2.848569e+00 +wall time: 0.000666142s + +time increment:89910 time: 89.91 +field ' c' [explicit solve]: current solution: 9.627508e+01, current residual:5.811634e+01 +field 'mu' [explicit solve]: current solution: 4.721949e+00, current residual:2.848533e+00 +wall time: 0.000746012s + +time increment:89920 time: 89.92 +field ' c' [explicit solve]: current solution: 9.627513e+01, current residual:5.811637e+01 +field 'mu' [explicit solve]: current solution: 4.721890e+00, current residual:2.848497e+00 +wall time: 0.000634909s + +time increment:89930 time: 89.93 +field ' c' [explicit solve]: current solution: 9.627517e+01, current residual:5.811640e+01 +field 'mu' [explicit solve]: current solution: 4.721832e+00, current residual:2.848461e+00 +wall time: 0.000707865s + +time increment:89940 time: 89.94 +field ' c' [explicit solve]: current solution: 9.627522e+01, current residual:5.811642e+01 +field 'mu' [explicit solve]: current solution: 4.721773e+00, current residual:2.848425e+00 +wall time: 0.000680923s + +time increment:89950 time: 89.95 +field ' c' [explicit solve]: current solution: 9.627526e+01, current residual:5.811645e+01 +field 'mu' [explicit solve]: current solution: 4.721715e+00, current residual:2.848389e+00 +wall time: 0.000707865s + +time increment:89960 time: 89.96 +field ' c' [explicit solve]: current solution: 9.627531e+01, current residual:5.811647e+01 +field 'mu' [explicit solve]: current solution: 4.721656e+00, current residual:2.848354e+00 +wall time: 0.000700951s + +time increment:89970 time: 89.97 +field ' c' [explicit solve]: current solution: 9.627536e+01, current residual:5.811650e+01 +field 'mu' [explicit solve]: current solution: 4.721598e+00, current residual:2.848318e+00 +wall time: 0.000657082s + +time increment:89980 time: 89.98 +field ' c' [explicit solve]: current solution: 9.627540e+01, current residual:5.811653e+01 +field 'mu' [explicit solve]: current solution: 4.721539e+00, current residual:2.848282e+00 +wall time: 0.000678062s + +time increment:89990 time: 89.99 +field ' c' [explicit solve]: current solution: 9.627545e+01, current residual:5.811655e+01 +field 'mu' [explicit solve]: current solution: 4.721480e+00, current residual:2.848246e+00 +wall time: 0.000637054s + +time increment:90000 time: 90 +field ' c' [explicit solve]: current solution: 9.627550e+01, current residual:5.811658e+01 +field 'mu' [explicit solve]: current solution: 4.721422e+00, current residual:2.848210e+00 +wall time: 0.000653028s +fields: 2 +fields: 2 +fields: 2 +fields: 2 +Output written to:solution-090000.pvtu + +Energy: 229.743 +Energy Components: 109.51 120.233 0 + +time increment:90010 time: 90.01 +field ' c' [explicit solve]: current solution: 9.627554e+01, current residual:5.811660e+01 +field 'mu' [explicit solve]: current solution: 4.721363e+00, current residual:2.848174e+00 +wall time: 0.000643015s + +time increment:90020 time: 90.02 +field ' c' [explicit solve]: current solution: 9.627559e+01, current residual:5.811663e+01 +field 'mu' [explicit solve]: current solution: 4.721305e+00, current residual:2.848138e+00 +wall time: 0.000624895s + +time increment:90030 time: 90.03 +field ' c' [explicit solve]: current solution: 9.627563e+01, current residual:5.811666e+01 +field 'mu' [explicit solve]: current solution: 4.721246e+00, current residual:2.848102e+00 +wall time: 0.000560045s + +time increment:90040 time: 90.04 +field ' c' [explicit solve]: current solution: 9.627568e+01, current residual:5.811668e+01 +field 'mu' [explicit solve]: current solution: 4.721187e+00, current residual:2.848066e+00 +wall time: 0.00068903s + +time increment:90050 time: 90.05 +field ' c' [explicit solve]: current solution: 9.627573e+01, current residual:5.811671e+01 +field 'mu' [explicit solve]: current solution: 4.721129e+00, current residual:2.848031e+00 +wall time: 0.000648022s + +time increment:90060 time: 90.06 +field ' c' [explicit solve]: current solution: 9.627577e+01, current residual:5.811673e+01 +field 'mu' [explicit solve]: current solution: 4.721070e+00, current residual:2.847995e+00 +wall time: 0.000604868s + 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time: 90.11 +field ' c' [explicit solve]: current solution: 9.627601e+01, current residual:5.811686e+01 +field 'mu' [explicit solve]: current solution: 4.720777e+00, current residual:2.847815e+00 +wall time: 0.000647068s + +time increment:90120 time: 90.12 +field ' c' [explicit solve]: current solution: 9.627605e+01, current residual:5.811689e+01 +field 'mu' [explicit solve]: current solution: 4.720718e+00, current residual:2.847779e+00 +wall time: 0.000588894s + +time increment:90130 time: 90.13 +field ' c' [explicit solve]: current solution: 9.627610e+01, current residual:5.811692e+01 +field 'mu' [explicit solve]: current solution: 4.720660e+00, current residual:2.847743e+00 +wall time: 0.000605822s + +time increment:90140 time: 90.14 +field ' c' [explicit solve]: current solution: 9.627614e+01, current residual:5.811694e+01 +field 'mu' [explicit solve]: current solution: 4.720601e+00, current residual:2.847708e+00 +wall time: 0.000668049s + +time increment:90150 time: 90.15 +field ' 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9.627656e+01, current residual:5.811718e+01 +field 'mu' [explicit solve]: current solution: 4.720073e+00, current residual:2.847384e+00 +wall time: 0.00065589s + +time increment:90240 time: 90.24 +field ' c' [explicit solve]: current solution: 9.627661e+01, current residual:5.811720e+01 +field 'mu' [explicit solve]: current solution: 4.720015e+00, current residual:2.847349e+00 +wall time: 0.000681877s + +time increment:90250 time: 90.25 +field ' c' [explicit solve]: current solution: 9.627665e+01, current residual:5.811723e+01 +field 'mu' [explicit solve]: current solution: 4.719956e+00, current residual:2.847313e+00 +wall time: 0.000663996s + +time increment:90260 time: 90.26 +field ' c' [explicit solve]: current solution: 9.627670e+01, current residual:5.811725e+01 +field 'mu' [explicit solve]: current solution: 4.719897e+00, current residual:2.847277e+00 +wall time: 0.000736952s + +time increment:90270 time: 90.27 +field ' c' [explicit solve]: current solution: 9.627675e+01, current residual:5.811728e+01 +field 'mu' [explicit solve]: current solution: 4.719839e+00, current residual:2.847241e+00 +wall time: 0.000688076s + +time increment:90280 time: 90.28 +field ' c' [explicit solve]: current solution: 9.627679e+01, current residual:5.811731e+01 +field 'mu' [explicit solve]: current solution: 4.719780e+00, current residual:2.847205e+00 +wall time: 0.000663996s + +time increment:90290 time: 90.29 +field ' c' [explicit solve]: current solution: 9.627684e+01, current residual:5.811733e+01 +field 'mu' [explicit solve]: current solution: 4.719721e+00, current residual:2.847169e+00 +wall time: 0.000599861s + +time increment:90300 time: 90.3 +field ' c' [explicit solve]: current solution: 9.627689e+01, current residual:5.811736e+01 +field 'mu' [explicit solve]: current solution: 4.719663e+00, current residual:2.847133e+00 +wall time: 0.00068593s + +time increment:90310 time: 90.31 +field ' c' [explicit solve]: current solution: 9.627693e+01, current residual:5.811738e+01 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solution: 4.719134e+00, current residual:2.846810e+00 +wall time: 0.00065589s + +time increment:90400 time: 90.4 +field ' c' [explicit solve]: current solution: 9.627735e+01, current residual:5.811762e+01 +field 'mu' [explicit solve]: current solution: 4.719075e+00, current residual:2.846774e+00 +wall time: 0.000630856s + +time increment:90410 time: 90.41 +field ' c' [explicit solve]: current solution: 9.627740e+01, current residual:5.811764e+01 +field 'mu' [explicit solve]: current solution: 4.719017e+00, current residual:2.846738e+00 +wall time: 0.00069499s + +time increment:90420 time: 90.42 +field ' c' [explicit solve]: current solution: 9.627744e+01, current residual:5.811767e+01 +field 'mu' [explicit solve]: current solution: 4.718958e+00, current residual:2.846702e+00 +wall time: 0.000699997s + +time increment:90430 time: 90.43 +field ' c' [explicit solve]: current solution: 9.627749e+01, current residual:5.811770e+01 +field 'mu' [explicit solve]: current solution: 4.718899e+00, 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residual:2.846522e+00 +wall time: 0.000672102s + +time increment:90480 time: 90.48 +field ' c' [explicit solve]: current solution: 9.627772e+01, current residual:5.811783e+01 +field 'mu' [explicit solve]: current solution: 4.718606e+00, current residual:2.846486e+00 +wall time: 0.000700951s + +time increment:90490 time: 90.49 +field ' c' [explicit solve]: current solution: 9.627777e+01, current residual:5.811785e+01 +field 'mu' [explicit solve]: current solution: 4.718547e+00, current residual:2.846450e+00 +wall time: 0.000635862s + +time increment:90500 time: 90.5 +field ' c' [explicit solve]: current solution: 9.627781e+01, current residual:5.811788e+01 +field 'mu' [explicit solve]: current solution: 4.718488e+00, current residual:2.846414e+00 +wall time: 0.00067687s + +time increment:90510 time: 90.51 +field ' c' [explicit solve]: current solution: 9.627786e+01, current residual:5.811791e+01 +field 'mu' [explicit solve]: current solution: 4.718429e+00, current residual:2.846378e+00 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0.000684977s + +time increment:90560 time: 90.56 +field ' c' [explicit solve]: current solution: 9.627809e+01, current residual:5.811804e+01 +field 'mu' [explicit solve]: current solution: 4.718136e+00, current residual:2.846199e+00 +wall time: 0.000648022s + +time increment:90570 time: 90.57 +field ' c' [explicit solve]: current solution: 9.627814e+01, current residual:5.811806e+01 +field 'mu' [explicit solve]: current solution: 4.718077e+00, current residual:2.846163e+00 +wall time: 0.000673056s + +time increment:90580 time: 90.58 +field ' c' [explicit solve]: current solution: 9.627818e+01, current residual:5.811809e+01 +field 'mu' [explicit solve]: current solution: 4.718018e+00, current residual:2.846127e+00 +wall time: 0.000675917s + +time increment:90590 time: 90.59 +field ' c' [explicit solve]: current solution: 9.627823e+01, current residual:5.811811e+01 +field 'mu' [explicit solve]: current solution: 4.717959e+00, current residual:2.846091e+00 +wall time: 0.000679016s + +time increment:90600 time: 90.6 +field ' c' [explicit solve]: current solution: 9.627828e+01, current residual:5.811814e+01 +field 'mu' [explicit solve]: current solution: 4.717901e+00, current residual:2.846055e+00 +wall time: 0.00070405s + +time increment:90610 time: 90.61 +field ' c' [explicit solve]: current solution: 9.627832e+01, current residual:5.811817e+01 +field 'mu' [explicit solve]: current solution: 4.717842e+00, current residual:2.846019e+00 +wall time: 0.000597954s + +time increment:90620 time: 90.62 +field ' c' [explicit solve]: current solution: 9.627837e+01, current residual:5.811819e+01 +field 'mu' [explicit solve]: current solution: 4.717783e+00, current residual:2.845983e+00 +wall time: 0.000559807s + +time increment:90630 time: 90.63 +field ' c' [explicit solve]: current solution: 9.627842e+01, current residual:5.811822e+01 +field 'mu' [explicit solve]: current solution: 4.717724e+00, current residual:2.845947e+00 +wall time: 0.000642061s + +time increment:90640 time: 90.64 +field ' c' [explicit solve]: current solution: 9.627846e+01, current residual:5.811824e+01 +field 'mu' [explicit solve]: current solution: 4.717665e+00, current residual:2.845911e+00 +wall time: 0.000568151s + +time increment:90650 time: 90.65 +field ' c' [explicit solve]: current solution: 9.627851e+01, current residual:5.811827e+01 +field 'mu' [explicit solve]: current solution: 4.717607e+00, current residual:2.845875e+00 +wall time: 0.000746012s + +time increment:90660 time: 90.66 +field ' c' [explicit solve]: current solution: 9.627856e+01, current residual:5.811830e+01 +field 'mu' [explicit solve]: current solution: 4.717548e+00, current residual:2.845839e+00 +wall time: 0.000665188s + +time increment:90670 time: 90.67 +field ' c' [explicit solve]: current solution: 9.627860e+01, current residual:5.811832e+01 +field 'mu' [explicit solve]: current solution: 4.717489e+00, current residual:2.845803e+00 +wall time: 0.000617981s + +time increment:90680 time: 90.68 +field ' c' [explicit solve]: current solution: 9.627865e+01, current residual:5.811835e+01 +field 'mu' [explicit solve]: current solution: 4.717430e+00, current residual:2.845767e+00 +wall time: 0.000627041s + +time increment:90690 time: 90.69 +field ' c' [explicit solve]: current solution: 9.627870e+01, current residual:5.811837e+01 +field 'mu' [explicit solve]: current solution: 4.717371e+00, current residual:2.845731e+00 +wall time: 0.000694036s + +time increment:90700 time: 90.7 +field ' c' [explicit solve]: current solution: 9.627874e+01, current residual:5.811840e+01 +field 'mu' [explicit solve]: current solution: 4.717313e+00, current residual:2.845695e+00 +wall time: 0.000634193s + +time increment:90710 time: 90.71 +field ' c' [explicit solve]: current solution: 9.627879e+01, current residual:5.811843e+01 +field 'mu' [explicit solve]: current solution: 4.717254e+00, current residual:2.845659e+00 +wall time: 0.000649929s + +time increment:90720 time: 90.72 +field ' c' [explicit solve]: current solution: 9.627883e+01, current residual:5.811845e+01 +field 'mu' [explicit solve]: current solution: 4.717195e+00, current residual:2.845623e+00 +wall time: 0.00067687s + +time increment:90730 time: 90.73 +field ' c' [explicit solve]: current solution: 9.627888e+01, current residual:5.811848e+01 +field 'mu' [explicit solve]: current solution: 4.717136e+00, current residual:2.845587e+00 +wall time: 0.00059104s + +time increment:90740 time: 90.74 +field ' c' [explicit solve]: current solution: 9.627893e+01, current residual:5.811851e+01 +field 'mu' [explicit solve]: current solution: 4.717077e+00, current residual:2.845551e+00 +wall time: 0.00061512s + +time increment:90750 time: 90.75 +field ' c' [explicit solve]: current solution: 9.627897e+01, current residual:5.811853e+01 +field 'mu' [explicit solve]: current solution: 4.717019e+00, current residual:2.845515e+00 +wall time: 0.000674963s + +time increment:90760 time: 90.76 +field ' c' [explicit solve]: current solution: 9.627902e+01, current residual:5.811856e+01 +field 'mu' [explicit solve]: current solution: 4.716960e+00, current residual:2.845479e+00 +wall time: 0.000642061s + +time increment:90770 time: 90.77 +field ' c' [explicit solve]: current solution: 9.627907e+01, current residual:5.811858e+01 +field 'mu' [explicit solve]: current solution: 4.716901e+00, current residual:2.845443e+00 +wall time: 0.000679016s + +time increment:90780 time: 90.78 +field ' c' [explicit solve]: current solution: 9.627911e+01, current residual:5.811861e+01 +field 'mu' [explicit solve]: current solution: 4.716842e+00, current residual:2.845407e+00 +wall time: 0.000586987s + +time increment:90790 time: 90.79 +field ' c' [explicit solve]: current solution: 9.627916e+01, current residual:5.811864e+01 +field 'mu' [explicit solve]: current solution: 4.716783e+00, current residual:2.845371e+00 +wall time: 0.000597954s + +time increment:90800 time: 90.8 +field ' c' [explicit solve]: current solution: 9.627921e+01, current residual:5.811866e+01 +field 'mu' [explicit solve]: current solution: 4.716724e+00, current residual:2.845335e+00 +wall time: 0.000761986s + +time increment:90810 time: 90.81 +field ' c' [explicit solve]: current solution: 9.627925e+01, current residual:5.811869e+01 +field 'mu' [explicit solve]: current solution: 4.716666e+00, current residual:2.845299e+00 +wall time: 0.000594139s + +time increment:90820 time: 90.82 +field ' c' [explicit solve]: current solution: 9.627930e+01, current residual:5.811871e+01 +field 'mu' [explicit solve]: current solution: 4.716607e+00, current residual:2.845263e+00 +wall time: 0.000653028s + +time increment:90830 time: 90.83 +field ' c' [explicit solve]: current solution: 9.627935e+01, current residual:5.811874e+01 +field 'mu' [explicit solve]: current solution: 4.716548e+00, current residual:2.845227e+00 +wall time: 0.00062108s + +time increment:90840 time: 90.84 +field ' c' [explicit solve]: current solution: 9.627939e+01, current residual:5.811877e+01 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[explicit solve]: current solution: 4.716254e+00, current residual:2.845048e+00 +wall time: 0.000620127s + +time increment:90890 time: 90.89 +field ' c' [explicit solve]: current solution: 9.627962e+01, current residual:5.811890e+01 +field 'mu' [explicit solve]: current solution: 4.716195e+00, current residual:2.845012e+00 +wall time: 0.000652075s + +time increment:90900 time: 90.9 +field ' c' [explicit solve]: current solution: 9.627967e+01, current residual:5.811892e+01 +field 'mu' [explicit solve]: current solution: 4.716136e+00, current residual:2.844976e+00 +wall time: 0.000562906s + +time increment:90910 time: 90.91 +field ' c' [explicit solve]: current solution: 9.627972e+01, current residual:5.811895e+01 +field 'mu' [explicit solve]: current solution: 4.716077e+00, current residual:2.844940e+00 +wall time: 0.000667095s + +time increment:90920 time: 90.92 +field ' c' [explicit solve]: current solution: 9.627976e+01, current residual:5.811898e+01 +field 'mu' [explicit solve]: current solution: 4.716018e+00, current residual:2.844904e+00 +wall time: 0.000701904s + +time increment:90930 time: 90.93 +field ' c' [explicit solve]: current solution: 9.627981e+01, current residual:5.811900e+01 +field 'mu' [explicit solve]: current solution: 4.715959e+00, current residual:2.844868e+00 +wall time: 0.000571966s + +time increment:90940 time: 90.94 +field ' c' [explicit solve]: current solution: 9.627986e+01, current residual:5.811903e+01 +field 'mu' [explicit solve]: current solution: 4.715900e+00, current residual:2.844832e+00 +wall time: 0.000580072s + +time increment:90950 time: 90.95 +field ' c' [explicit solve]: current solution: 9.627990e+01, current residual:5.811905e+01 +field 'mu' [explicit solve]: current solution: 4.715842e+00, current residual:2.844796e+00 +wall time: 0.000580072s + +time increment:90960 time: 90.96 +field ' c' [explicit solve]: current solution: 9.627995e+01, current residual:5.811908e+01 +field 'mu' [explicit solve]: current solution: 4.715783e+00, current residual:2.844760e+00 +wall time: 0.00068903s + +time increment:90970 time: 90.97 +field ' c' [explicit solve]: current solution: 9.628000e+01, current residual:5.811911e+01 +field 'mu' [explicit solve]: current solution: 4.715724e+00, current residual:2.844724e+00 +wall time: 0.000672102s + +time increment:90980 time: 90.98 +field ' c' [explicit solve]: current solution: 9.628004e+01, current residual:5.811913e+01 +field 'mu' [explicit solve]: current solution: 4.715665e+00, current residual:2.844688e+00 +wall time: 0.000680923s + +time increment:90990 time: 90.99 +field ' c' [explicit solve]: current solution: 9.628009e+01, current residual:5.811916e+01 +field 'mu' [explicit solve]: current solution: 4.715606e+00, current residual:2.844652e+00 +wall time: 0.000576019s + +time increment:91000 time: 91 +field ' c' [explicit solve]: current solution: 9.628014e+01, current residual:5.811919e+01 +field 'mu' [explicit solve]: current solution: 4.715547e+00, current residual:2.844616e+00 +wall time: 0.000764132s + +time increment:91010 time: 91.01 +field ' c' [explicit solve]: current solution: 9.628018e+01, current residual:5.811921e+01 +field 'mu' [explicit solve]: current solution: 4.715488e+00, current residual:2.844579e+00 +wall time: 0.000648975s + +time increment:91020 time: 91.02 +field ' c' [explicit solve]: current solution: 9.628023e+01, current residual:5.811924e+01 +field 'mu' [explicit solve]: current solution: 4.715429e+00, current residual:2.844543e+00 +wall time: 0.000678062s + +time increment:91030 time: 91.03 +field ' c' [explicit solve]: current solution: 9.628027e+01, current residual:5.811926e+01 +field 'mu' [explicit solve]: current solution: 4.715370e+00, current residual:2.844507e+00 +wall time: 0.000669003s + +time increment:91040 time: 91.04 +field ' c' [explicit solve]: current solution: 9.628032e+01, current residual:5.811929e+01 +field 'mu' [explicit solve]: current solution: 4.715312e+00, current residual:2.844471e+00 +wall time: 0.000752926s + +time increment:91050 time: 91.05 +field ' c' [explicit solve]: current solution: 9.628037e+01, current residual:5.811932e+01 +field 'mu' [explicit solve]: current solution: 4.715253e+00, current residual:2.844435e+00 +wall time: 0.000560999s + +time increment:91060 time: 91.06 +field ' c' [explicit solve]: current solution: 9.628041e+01, current residual:5.811934e+01 +field 'mu' [explicit solve]: current solution: 4.715194e+00, current residual:2.844399e+00 +wall time: 0.000571966s + +time increment:91070 time: 91.07 +field ' c' [explicit solve]: current solution: 9.628046e+01, current residual:5.811937e+01 +field 'mu' [explicit solve]: current solution: 4.715135e+00, current residual:2.844363e+00 +wall time: 0.000715017s + +time increment:91080 time: 91.08 +field ' c' [explicit solve]: current solution: 9.628051e+01, current residual:5.811939e+01 +field 'mu' [explicit solve]: current solution: 4.715076e+00, current residual:2.844327e+00 +wall time: 0.000750065s + +time increment:91090 time: 91.09 +field ' c' [explicit solve]: current solution: 9.628055e+01, current residual:5.811942e+01 +field 'mu' [explicit solve]: current solution: 4.715017e+00, current residual:2.844291e+00 +wall time: 0.000622988s + +time increment:91100 time: 91.1 +field ' c' [explicit solve]: current solution: 9.628060e+01, current residual:5.811945e+01 +field 'mu' [explicit solve]: current solution: 4.714958e+00, current residual:2.844255e+00 +wall time: 0.000567913s + +time increment:91110 time: 91.11 +field ' c' [explicit solve]: current solution: 9.628065e+01, current residual:5.811947e+01 +field 'mu' [explicit solve]: current solution: 4.714899e+00, current residual:2.844219e+00 +wall time: 0.000653028s + +time increment:91120 time: 91.12 +field ' c' [explicit solve]: current solution: 9.628069e+01, current residual:5.811950e+01 +field 'mu' [explicit solve]: current solution: 4.714840e+00, current residual:2.844183e+00 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0.000732899s + +time increment:91170 time: 91.17 +field ' c' [explicit solve]: current solution: 9.628093e+01, current residual:5.811963e+01 +field 'mu' [explicit solve]: current solution: 4.714546e+00, current residual:2.844003e+00 +wall time: 0.000566959s + +time increment:91180 time: 91.18 +field ' c' [explicit solve]: current solution: 9.628097e+01, current residual:5.811966e+01 +field 'mu' [explicit solve]: current solution: 4.714487e+00, current residual:2.843967e+00 +wall time: 0.000591993s + +time increment:91190 time: 91.19 +field ' c' [explicit solve]: current solution: 9.628102e+01, current residual:5.811968e+01 +field 'mu' [explicit solve]: current solution: 4.714428e+00, current residual:2.843931e+00 +wall time: 0.000657082s + +time increment:91200 time: 91.2 +field ' c' [explicit solve]: current solution: 9.628107e+01, current residual:5.811971e+01 +field 'mu' [explicit solve]: current solution: 4.714369e+00, current residual:2.843895e+00 +wall time: 0.000566959s + +time increment:91210 time: 91.21 +field ' c' [explicit solve]: current solution: 9.628111e+01, current residual:5.811974e+01 +field 'mu' [explicit solve]: current solution: 4.714310e+00, current residual:2.843859e+00 +wall time: 0.000594139s + +time increment:91220 time: 91.22 +field ' c' [explicit solve]: current solution: 9.628116e+01, current residual:5.811976e+01 +field 'mu' [explicit solve]: current solution: 4.714251e+00, current residual:2.843823e+00 +wall time: 0.000584126s + +time increment:91230 time: 91.23 +field ' c' [explicit solve]: current solution: 9.628121e+01, current residual:5.811979e+01 +field 'mu' [explicit solve]: current solution: 4.714192e+00, current residual:2.843787e+00 +wall time: 0.000614882s + +time increment:91240 time: 91.24 +field ' c' [explicit solve]: current solution: 9.628125e+01, current residual:5.811981e+01 +field 'mu' [explicit solve]: current solution: 4.714133e+00, current residual:2.843751e+00 +wall time: 0.00066185s + +time increment:91250 time: 91.25 +field ' c' [explicit solve]: current solution: 9.628130e+01, current residual:5.811984e+01 +field 'mu' [explicit solve]: current solution: 4.714074e+00, current residual:2.843715e+00 +wall time: 0.000697851s + +time increment:91260 time: 91.26 +field ' c' [explicit solve]: current solution: 9.628134e+01, current residual:5.811987e+01 +field 'mu' [explicit solve]: current solution: 4.714015e+00, current residual:2.843679e+00 +wall time: 0.000646114s + +time increment:91270 time: 91.27 +field ' c' [explicit solve]: current solution: 9.628139e+01, current residual:5.811989e+01 +field 'mu' [explicit solve]: current solution: 4.713956e+00, current residual:2.843643e+00 +wall time: 0.000675917s + +time increment:91280 time: 91.28 +field ' c' [explicit solve]: current solution: 9.628144e+01, current residual:5.811992e+01 +field 'mu' [explicit solve]: current solution: 4.713897e+00, current residual:2.843607e+00 +wall time: 0.000721931s + +time increment:91290 time: 91.29 +field ' c' [explicit solve]: current solution: 9.628148e+01, current residual:5.811994e+01 +field 'mu' [explicit solve]: current solution: 4.713838e+00, current residual:2.843571e+00 +wall time: 0.000721216s + +time increment:91300 time: 91.3 +field ' c' [explicit solve]: current solution: 9.628153e+01, current residual:5.811997e+01 +field 'mu' [explicit solve]: current solution: 4.713779e+00, current residual:2.843535e+00 +wall time: 0.000792027s + +time increment:91310 time: 91.31 +field ' c' [explicit solve]: current solution: 9.628158e+01, current residual:5.812000e+01 +field 'mu' [explicit solve]: current solution: 4.713720e+00, current residual:2.843499e+00 +wall time: 0.000679016s + +time increment:91320 time: 91.32 +field ' c' [explicit solve]: current solution: 9.628162e+01, current residual:5.812002e+01 +field 'mu' [explicit solve]: current solution: 4.713661e+00, current residual:2.843463e+00 +wall time: 0.000683069s + +time increment:91330 time: 91.33 +field ' c' [explicit solve]: current solution: 9.628167e+01, current residual:5.812005e+01 +field 'mu' [explicit solve]: current solution: 4.713602e+00, current residual:2.843427e+00 +wall time: 0.000837088s + +time increment:91340 time: 91.34 +field ' c' [explicit solve]: current solution: 9.628172e+01, current residual:5.812008e+01 +field 'mu' [explicit solve]: current solution: 4.713543e+00, current residual:2.843390e+00 +wall time: 0.000705004s + +time increment:91350 time: 91.35 +field ' c' [explicit solve]: current solution: 9.628176e+01, current residual:5.812010e+01 +field 'mu' [explicit solve]: current solution: 4.713484e+00, current residual:2.843354e+00 +wall time: 0.000720978s + +time increment:91360 time: 91.36 +field ' c' [explicit solve]: current solution: 9.628181e+01, current residual:5.812013e+01 +field 'mu' [explicit solve]: current solution: 4.713425e+00, current residual:2.843318e+00 +wall time: 0.000749111s + +time increment:91370 time: 91.37 +field ' c' [explicit solve]: current solution: 9.628186e+01, current residual:5.812015e+01 +field 'mu' [explicit solve]: current solution: 4.713366e+00, current residual:2.843282e+00 +wall time: 0.000702143s + +time increment:91380 time: 91.38 +field ' c' [explicit solve]: current solution: 9.628190e+01, current residual:5.812018e+01 +field 'mu' [explicit solve]: current solution: 4.713307e+00, current residual:2.843246e+00 +wall time: 0.000678062s + +time increment:91390 time: 91.39 +field ' c' [explicit solve]: current solution: 9.628195e+01, current residual:5.812021e+01 +field 'mu' [explicit solve]: current solution: 4.713248e+00, current residual:2.843210e+00 +wall time: 0.000648975s + +time increment:91400 time: 91.4 +field ' c' [explicit solve]: current solution: 9.628200e+01, current residual:5.812023e+01 +field 'mu' [explicit solve]: current solution: 4.713189e+00, current residual:2.843174e+00 +wall time: 0.00067997s + +time increment:91410 time: 91.41 +field ' c' [explicit solve]: current solution: 9.628204e+01, current residual:5.812026e+01 +field 'mu' [explicit solve]: current solution: 4.713130e+00, current residual:2.843138e+00 +wall time: 0.000684977s + +time increment:91420 time: 91.42 +field ' c' [explicit solve]: current solution: 9.628209e+01, current residual:5.812029e+01 +field 'mu' [explicit solve]: current solution: 4.713071e+00, current residual:2.843102e+00 +wall time: 0.000658989s + +time increment:91430 time: 91.43 +field ' c' [explicit solve]: current solution: 9.628214e+01, current residual:5.812031e+01 +field 'mu' [explicit solve]: current solution: 4.713012e+00, current residual:2.843066e+00 +wall time: 0.000684977s + +time increment:91440 time: 91.44 +field ' c' [explicit solve]: current solution: 9.628218e+01, current residual:5.812034e+01 +field 'mu' [explicit solve]: current solution: 4.712953e+00, current residual:2.843030e+00 +wall time: 0.000737906s + +time increment:91450 time: 91.45 +field ' c' [explicit solve]: current solution: 9.628223e+01, current residual:5.812036e+01 +field 'mu' [explicit solve]: current solution: 4.712894e+00, current residual:2.842994e+00 +wall time: 0.000667095s + +time increment:91460 time: 91.46 +field ' c' [explicit solve]: current solution: 9.628228e+01, current residual:5.812039e+01 +field 'mu' [explicit solve]: current solution: 4.712835e+00, current residual:2.842958e+00 +wall time: 0.000663996s + +time increment:91470 time: 91.47 +field ' c' [explicit solve]: current solution: 9.628232e+01, current residual:5.812042e+01 +field 'mu' [explicit solve]: current solution: 4.712776e+00, current residual:2.842922e+00 +wall time: 0.000674963s + +time increment:91480 time: 91.48 +field ' c' [explicit solve]: current solution: 9.628237e+01, current residual:5.812044e+01 +field 'mu' [explicit solve]: current solution: 4.712717e+00, current residual:2.842886e+00 +wall time: 0.000693083s + +time increment:91490 time: 91.49 +field ' c' [explicit solve]: current solution: 9.628242e+01, current residual:5.812047e+01 +field 'mu' [explicit solve]: current solution: 4.712658e+00, current residual:2.842850e+00 +wall time: 0.000715017s + +time increment:91500 time: 91.5 +field ' c' [explicit solve]: current solution: 9.628246e+01, current residual:5.812050e+01 +field 'mu' [explicit solve]: current solution: 4.712599e+00, current residual:2.842813e+00 +wall time: 0.000645876s + +time increment:91510 time: 91.51 +field ' c' [explicit solve]: current solution: 9.628251e+01, current residual:5.812052e+01 +field 'mu' [explicit solve]: current solution: 4.712540e+00, current residual:2.842777e+00 +wall time: 0.000662088s + +time increment:91520 time: 91.52 +field ' c' [explicit solve]: current solution: 9.628256e+01, current residual:5.812055e+01 +field 'mu' [explicit solve]: current solution: 4.712481e+00, current residual:2.842741e+00 +wall time: 0.000669956s + +time increment:91530 time: 91.53 +field ' c' [explicit solve]: current solution: 9.628260e+01, current residual:5.812057e+01 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[explicit solve]: current solution: 4.712185e+00, current residual:2.842561e+00 +wall time: 0.000715017s + +time increment:91580 time: 91.58 +field ' c' [explicit solve]: current solution: 9.628283e+01, current residual:5.812071e+01 +field 'mu' [explicit solve]: current solution: 4.712126e+00, current residual:2.842525e+00 +wall time: 0.0006392s + +time increment:91590 time: 91.59 +field ' c' [explicit solve]: current solution: 9.628288e+01, current residual:5.812073e+01 +field 'mu' [explicit solve]: current solution: 4.712067e+00, current residual:2.842489e+00 +wall time: 0.000649929s + +time increment:91600 time: 91.6 +field ' c' [explicit solve]: current solution: 9.628293e+01, current residual:5.812076e+01 +field 'mu' [explicit solve]: current solution: 4.712008e+00, current residual:2.842453e+00 +wall time: 0.000673056s + +time increment:91610 time: 91.61 +field ' c' [explicit solve]: current solution: 9.628297e+01, current residual:5.812078e+01 +field 'mu' [explicit solve]: current solution: 4.711949e+00, current residual:2.842417e+00 +wall time: 0.000590086s + +time increment:91620 time: 91.62 +field ' c' [explicit solve]: current solution: 9.628302e+01, current residual:5.812081e+01 +field 'mu' [explicit solve]: current solution: 4.711890e+00, current residual:2.842380e+00 +wall time: 0.000607014s + +time increment:91630 time: 91.63 +field ' c' [explicit solve]: current solution: 9.628307e+01, current residual:5.812084e+01 +field 'mu' [explicit solve]: current solution: 4.711831e+00, current residual:2.842344e+00 +wall time: 0.00056982s + +time increment:91640 time: 91.64 +field ' c' [explicit solve]: current solution: 9.628311e+01, current residual:5.812086e+01 +field 'mu' [explicit solve]: current solution: 4.711772e+00, current residual:2.842308e+00 +wall time: 0.000570059s + +time increment:91650 time: 91.65 +field ' c' [explicit solve]: current solution: 9.628316e+01, current residual:5.812089e+01 +field 'mu' [explicit solve]: current solution: 4.711713e+00, current residual:2.842272e+00 +wall time: 0.000726938s + +time increment:91660 time: 91.66 +field ' c' [explicit solve]: current solution: 9.628321e+01, current residual:5.812092e+01 +field 'mu' [explicit solve]: current solution: 4.711654e+00, current residual:2.842236e+00 +wall time: 0.00070715s + +time increment:91670 time: 91.67 +field ' c' [explicit solve]: current solution: 9.628325e+01, current residual:5.812094e+01 +field 'mu' [explicit solve]: current solution: 4.711595e+00, current residual:2.842200e+00 +wall time: 0.000555992s + +time increment:91680 time: 91.68 +field ' c' [explicit solve]: current solution: 9.628330e+01, current residual:5.812097e+01 +field 'mu' [explicit solve]: current solution: 4.711536e+00, current residual:2.842164e+00 +wall time: 0.000609159s + +time increment:91690 time: 91.69 +field ' c' [explicit solve]: current solution: 9.628335e+01, current residual:5.812099e+01 +field 'mu' [explicit solve]: current solution: 4.711476e+00, current residual:2.842128e+00 +wall time: 0.00059104s + +time increment:91700 time: 91.7 +field ' c' [explicit solve]: current solution: 9.628339e+01, current residual:5.812102e+01 +field 'mu' [explicit solve]: current solution: 4.711417e+00, current residual:2.842092e+00 +wall time: 0.00057292s + +time increment:91710 time: 91.71 +field ' c' [explicit solve]: current solution: 9.628344e+01, current residual:5.812105e+01 +field 'mu' [explicit solve]: current solution: 4.711358e+00, current residual:2.842056e+00 +wall time: 0.000687122s + +time increment:91720 time: 91.72 +field ' c' [explicit solve]: current solution: 9.628349e+01, current residual:5.812107e+01 +field 'mu' [explicit solve]: current solution: 4.711299e+00, current residual:2.842020e+00 +wall time: 0.000652075s + +time increment:91730 time: 91.73 +field ' c' [explicit solve]: current solution: 9.628353e+01, current residual:5.812110e+01 +field 'mu' [explicit solve]: current solution: 4.711240e+00, current residual:2.841983e+00 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0.00058794s + +time increment:91780 time: 91.78 +field ' c' [explicit solve]: current solution: 9.628377e+01, current residual:5.812123e+01 +field 'mu' [explicit solve]: current solution: 4.710944e+00, current residual:2.841803e+00 +wall time: 0.000710964s + +time increment:91790 time: 91.79 +field ' c' [explicit solve]: current solution: 9.628381e+01, current residual:5.812126e+01 +field 'mu' [explicit solve]: current solution: 4.710885e+00, current residual:2.841767e+00 +wall time: 0.000644922s + +time increment:91800 time: 91.8 +field ' c' [explicit solve]: current solution: 9.628386e+01, current residual:5.812128e+01 +field 'mu' [explicit solve]: current solution: 4.710826e+00, current residual:2.841731e+00 +wall time: 0.000654936s + +time increment:91810 time: 91.81 +field ' c' [explicit solve]: current solution: 9.628391e+01, current residual:5.812131e+01 +field 'mu' [explicit solve]: current solution: 4.710767e+00, current residual:2.841695e+00 +wall time: 0.000675917s + +time increment:91820 time: 91.82 +field ' c' [explicit solve]: current solution: 9.628395e+01, current residual:5.812134e+01 +field 'mu' [explicit solve]: current solution: 4.710708e+00, current residual:2.841658e+00 +wall time: 0.000612974s + +time increment:91830 time: 91.83 +field ' c' [explicit solve]: current solution: 9.628400e+01, current residual:5.812136e+01 +field 'mu' [explicit solve]: current solution: 4.710649e+00, current residual:2.841622e+00 +wall time: 0.000767946s + +time increment:91840 time: 91.84 +field ' c' [explicit solve]: current solution: 9.628405e+01, current residual:5.812139e+01 +field 'mu' [explicit solve]: current solution: 4.710590e+00, current residual:2.841586e+00 +wall time: 0.000639915s + +time increment:91850 time: 91.85 +field ' c' [explicit solve]: current solution: 9.628409e+01, current residual:5.812142e+01 +field 'mu' [explicit solve]: current solution: 4.710531e+00, current residual:2.841550e+00 +wall time: 0.000657082s + +time increment:91860 time: 91.86 +field ' c' [explicit solve]: current solution: 9.628414e+01, current residual:5.812144e+01 +field 'mu' [explicit solve]: current solution: 4.710471e+00, current residual:2.841514e+00 +wall time: 0.00066185s + +time increment:91870 time: 91.87 +field ' c' [explicit solve]: current solution: 9.628419e+01, current residual:5.812147e+01 +field 'mu' [explicit solve]: current solution: 4.710412e+00, current residual:2.841478e+00 +wall time: 0.000641108s + +time increment:91880 time: 91.88 +field ' c' [explicit solve]: current solution: 9.628423e+01, current residual:5.812149e+01 +field 'mu' [explicit solve]: current solution: 4.710353e+00, current residual:2.841442e+00 +wall time: 0.00068903s + +time increment:91890 time: 91.89 +field ' c' [explicit solve]: current solution: 9.628428e+01, current residual:5.812152e+01 +field 'mu' [explicit solve]: current solution: 4.710294e+00, current residual:2.841406e+00 +wall time: 0.000587225s + +time increment:91900 time: 91.9 +field ' c' [explicit solve]: current solution: 9.628433e+01, current residual:5.812155e+01 +field 'mu' [explicit solve]: current solution: 4.710235e+00, current residual:2.841370e+00 +wall time: 0.000676155s + +time increment:91910 time: 91.91 +field ' c' [explicit solve]: current solution: 9.628437e+01, current residual:5.812157e+01 +field 'mu' [explicit solve]: current solution: 4.710176e+00, current residual:2.841333e+00 +wall time: 0.000592947s + +time increment:91920 time: 91.92 +field ' c' [explicit solve]: current solution: 9.628442e+01, current residual:5.812160e+01 +field 'mu' [explicit solve]: current solution: 4.710116e+00, current residual:2.841297e+00 +wall time: 0.000557899s + +time increment:91930 time: 91.93 +field ' c' [explicit solve]: current solution: 9.628447e+01, current residual:5.812163e+01 +field 'mu' [explicit solve]: current solution: 4.710057e+00, current residual:2.841261e+00 +wall time: 0.000663042s + +time increment:91940 time: 91.94 +field ' c' [explicit solve]: current solution: 9.628451e+01, current residual:5.812165e+01 +field 'mu' [explicit solve]: current solution: 4.709998e+00, current residual:2.841225e+00 +wall time: 0.000648975s + +time increment:91950 time: 91.95 +field ' c' [explicit solve]: current solution: 9.628456e+01, current residual:5.812168e+01 +field 'mu' [explicit solve]: current solution: 4.709939e+00, current residual:2.841189e+00 +wall time: 0.000573874s + +time increment:91960 time: 91.96 +field ' c' [explicit solve]: current solution: 9.628461e+01, current residual:5.812170e+01 +field 'mu' [explicit solve]: current solution: 4.709880e+00, current residual:2.841153e+00 +wall time: 0.00058794s + +time increment:91970 time: 91.97 +field ' c' [explicit solve]: current solution: 9.628465e+01, current residual:5.812173e+01 +field 'mu' [explicit solve]: current solution: 4.709821e+00, current residual:2.841117e+00 +wall time: 0.000638008s + +time increment:91980 time: 91.98 +field ' c' [explicit solve]: current solution: 9.628470e+01, current residual:5.812176e+01 +field 'mu' [explicit solve]: current solution: 4.709761e+00, current residual:2.841080e+00 +wall time: 0.000566006s + +time increment:91990 time: 91.99 +field ' c' [explicit solve]: current solution: 9.628475e+01, current residual:5.812178e+01 +field 'mu' [explicit solve]: current solution: 4.709702e+00, current residual:2.841044e+00 +wall time: 0.000617981s + +time increment:92000 time: 92 +field ' c' [explicit solve]: current solution: 9.628479e+01, current residual:5.812181e+01 +field 'mu' [explicit solve]: current solution: 4.709643e+00, current residual:2.841008e+00 +wall time: 0.000585079s + +time increment:92010 time: 92.01 +field ' c' [explicit solve]: current solution: 9.628484e+01, current residual:5.812184e+01 +field 'mu' [explicit solve]: current solution: 4.709584e+00, current residual:2.840972e+00 +wall time: 0.000663042s + +time increment:92020 time: 92.02 +field ' c' [explicit solve]: current solution: 9.628489e+01, current residual:5.812186e+01 +field 'mu' [explicit solve]: current solution: 4.709525e+00, current residual:2.840936e+00 +wall time: 0.000566006s + +time increment:92030 time: 92.03 +field ' c' [explicit solve]: current solution: 9.628493e+01, current residual:5.812189e+01 +field 'mu' [explicit solve]: current solution: 4.709465e+00, current residual:2.840900e+00 +wall time: 0.000718117s + +time increment:92040 time: 92.04 +field ' c' [explicit solve]: current solution: 9.628498e+01, current residual:5.812192e+01 +field 'mu' [explicit solve]: current solution: 4.709406e+00, current residual:2.840864e+00 +wall time: 0.000574112s + +time increment:92050 time: 92.05 +field ' c' [explicit solve]: current solution: 9.628503e+01, current residual:5.812194e+01 +field 'mu' [explicit solve]: current solution: 4.709347e+00, current residual:2.840828e+00 +wall time: 0.000594139s + +time increment:92060 time: 92.06 +field ' c' [explicit solve]: current solution: 9.628507e+01, current residual:5.812197e+01 +field 'mu' [explicit solve]: current solution: 4.709288e+00, current residual:2.840791e+00 +wall time: 0.000686169s + +time increment:92070 time: 92.07 +field ' c' [explicit solve]: current solution: 9.628512e+01, current residual:5.812199e+01 +field 'mu' [explicit solve]: current solution: 4.709229e+00, current residual:2.840755e+00 +wall time: 0.000580072s + +time increment:92080 time: 92.08 +field ' c' [explicit solve]: current solution: 9.628517e+01, current residual:5.812202e+01 +field 'mu' [explicit solve]: current solution: 4.709169e+00, current residual:2.840719e+00 +wall time: 0.000610113s + +time increment:92090 time: 92.09 +field ' c' [explicit solve]: current solution: 9.628521e+01, current residual:5.812205e+01 +field 'mu' [explicit solve]: current solution: 4.709110e+00, current residual:2.840683e+00 +wall time: 0.000633955s + +time increment:92100 time: 92.1 +field ' c' [explicit solve]: current solution: 9.628526e+01, current residual:5.812207e+01 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[explicit solve]: current solution: 4.708814e+00, current residual:2.840502e+00 +wall time: 0.000730991s + +time increment:92150 time: 92.15 +field ' c' [explicit solve]: current solution: 9.628549e+01, current residual:5.812221e+01 +field 'mu' [explicit solve]: current solution: 4.708755e+00, current residual:2.840466e+00 +wall time: 0.000699997s + +time increment:92160 time: 92.16 +field ' c' [explicit solve]: current solution: 9.628554e+01, current residual:5.812223e+01 +field 'mu' [explicit solve]: current solution: 4.708696e+00, current residual:2.840430e+00 +wall time: 0.000852108s + +time increment:92170 time: 92.17 +field ' c' [explicit solve]: current solution: 9.628559e+01, current residual:5.812226e+01 +field 'mu' [explicit solve]: current solution: 4.708636e+00, current residual:2.840394e+00 +wall time: 0.000671148s + +time increment:92180 time: 92.18 +field ' c' [explicit solve]: current solution: 9.628563e+01, current residual:5.812228e+01 +field 'mu' [explicit solve]: current solution: 4.708577e+00, current residual:2.840358e+00 +wall time: 0.000682116s + +time increment:92190 time: 92.19 +field ' c' [explicit solve]: current solution: 9.628568e+01, current residual:5.812231e+01 +field 'mu' [explicit solve]: current solution: 4.708518e+00, current residual:2.840321e+00 +wall time: 0.000712156s + +time increment:92200 time: 92.2 +field ' c' [explicit solve]: current solution: 9.628573e+01, current residual:5.812234e+01 +field 'mu' [explicit solve]: current solution: 4.708459e+00, current residual:2.840285e+00 +wall time: 0.00064683s + +time increment:92210 time: 92.21 +field ' c' [explicit solve]: current solution: 9.628577e+01, current residual:5.812236e+01 +field 'mu' [explicit solve]: current solution: 4.708399e+00, current residual:2.840249e+00 +wall time: 0.000746012s + +time increment:92220 time: 92.22 +field ' c' [explicit solve]: current solution: 9.628582e+01, current residual:5.812239e+01 +field 'mu' [explicit solve]: current solution: 4.708340e+00, current residual:2.840213e+00 +wall time: 0.000709057s + +time increment:92230 time: 92.23 +field ' c' [explicit solve]: current solution: 9.628587e+01, current residual:5.812242e+01 +field 'mu' [explicit solve]: current solution: 4.708281e+00, current residual:2.840177e+00 +wall time: 0.000795841s + +time increment:92240 time: 92.24 +field ' c' [explicit solve]: current solution: 9.628591e+01, current residual:5.812244e+01 +field 'mu' [explicit solve]: current solution: 4.708222e+00, current residual:2.840141e+00 +wall time: 0.000658989s + +time increment:92250 time: 92.25 +field ' c' [explicit solve]: current solution: 9.628596e+01, current residual:5.812247e+01 +field 'mu' [explicit solve]: current solution: 4.708162e+00, current residual:2.840104e+00 +wall time: 0.000677109s + +time increment:92260 time: 92.26 +field ' c' [explicit solve]: current solution: 9.628601e+01, current residual:5.812250e+01 +field 'mu' [explicit solve]: current solution: 4.708103e+00, current residual:2.840068e+00 +wall time: 0.000718117s + +time increment:92270 time: 92.27 +field ' c' [explicit solve]: current solution: 9.628605e+01, current residual:5.812252e+01 +field 'mu' [explicit solve]: current solution: 4.708044e+00, current residual:2.840032e+00 +wall time: 0.000660896s + +time increment:92280 time: 92.28 +field ' c' [explicit solve]: current solution: 9.628610e+01, current residual:5.812255e+01 +field 'mu' [explicit solve]: current solution: 4.707985e+00, current residual:2.839996e+00 +wall time: 0.000663042s + +time increment:92290 time: 92.29 +field ' c' [explicit solve]: current solution: 9.628615e+01, current residual:5.812257e+01 +field 'mu' [explicit solve]: current solution: 4.707925e+00, current residual:2.839960e+00 +wall time: 0.000854969s + +time increment:92300 time: 92.3 +field ' c' [explicit solve]: current solution: 9.628619e+01, current residual:5.812260e+01 +field 'mu' [explicit solve]: current solution: 4.707866e+00, current residual:2.839924e+00 +wall time: 0.000689983s + +time increment:92310 time: 92.31 +field ' c' [explicit solve]: current solution: 9.628624e+01, current residual:5.812263e+01 +field 'mu' [explicit solve]: current solution: 4.707807e+00, current residual:2.839887e+00 +wall time: 0.000736952s + +time increment:92320 time: 92.32 +field ' c' [explicit solve]: current solution: 9.628629e+01, current residual:5.812265e+01 +field 'mu' [explicit solve]: current solution: 4.707748e+00, current residual:2.839851e+00 +wall time: 0.000797033s + +time increment:92330 time: 92.33 +field ' c' [explicit solve]: current solution: 9.628633e+01, current residual:5.812268e+01 +field 'mu' [explicit solve]: current solution: 4.707688e+00, current residual:2.839815e+00 +wall time: 0.000648022s + +time increment:92340 time: 92.34 +field ' c' [explicit solve]: current solution: 9.628638e+01, current residual:5.812271e+01 +field 'mu' [explicit solve]: current solution: 4.707629e+00, current residual:2.839779e+00 +wall time: 0.000727892s + +time increment:92350 time: 92.35 +field ' c' [explicit solve]: current solution: 9.628643e+01, current residual:5.812273e+01 +field 'mu' [explicit solve]: current solution: 4.707570e+00, current residual:2.839743e+00 +wall time: 0.000665188s + +time increment:92360 time: 92.36 +field ' c' [explicit solve]: current solution: 9.628647e+01, current residual:5.812276e+01 +field 'mu' [explicit solve]: current solution: 4.707510e+00, current residual:2.839707e+00 +wall time: 0.000694036s + +time increment:92370 time: 92.37 +field ' c' [explicit solve]: current solution: 9.628652e+01, current residual:5.812279e+01 +field 'mu' [explicit solve]: current solution: 4.707451e+00, current residual:2.839670e+00 +wall time: 0.000757933s + +time increment:92380 time: 92.38 +field ' c' [explicit solve]: current solution: 9.628657e+01, current residual:5.812281e+01 +field 'mu' [explicit solve]: current solution: 4.707392e+00, current residual:2.839634e+00 +wall time: 0.000679016s + +time increment:92390 time: 92.39 +field ' c' [explicit solve]: current solution: 9.628661e+01, current residual:5.812284e+01 +field 'mu' [explicit solve]: current solution: 4.707333e+00, current residual:2.839598e+00 +wall time: 0.000653982s + +time increment:92400 time: 92.4 +field ' c' [explicit solve]: current solution: 9.628666e+01, current residual:5.812286e+01 +field 'mu' [explicit solve]: current solution: 4.707273e+00, current residual:2.839562e+00 +wall time: 0.000744104s + +time increment:92410 time: 92.41 +field ' c' [explicit solve]: current solution: 9.628671e+01, current residual:5.812289e+01 +field 'mu' [explicit solve]: current solution: 4.707214e+00, current residual:2.839526e+00 +wall time: 0.000715017s + +time increment:92420 time: 92.42 +field ' c' [explicit solve]: current solution: 9.628675e+01, current residual:5.812292e+01 +field 'mu' [explicit solve]: current solution: 4.707155e+00, current residual:2.839489e+00 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0.00069499s + +time increment:92470 time: 92.47 +field ' c' [explicit solve]: current solution: 9.628699e+01, current residual:5.812305e+01 +field 'mu' [explicit solve]: current solution: 4.706858e+00, current residual:2.839308e+00 +wall time: 0.000759125s + +time increment:92480 time: 92.48 +field ' c' [explicit solve]: current solution: 9.628704e+01, current residual:5.812308e+01 +field 'mu' [explicit solve]: current solution: 4.706799e+00, current residual:2.839272e+00 +wall time: 0.000774145s + +time increment:92490 time: 92.49 +field ' c' [explicit solve]: current solution: 9.628708e+01, current residual:5.812310e+01 +field 'mu' [explicit solve]: current solution: 4.706739e+00, current residual:2.839236e+00 +wall time: 0.000568151s + +time increment:92500 time: 92.5 +field ' c' [explicit solve]: current solution: 9.628713e+01, current residual:5.812313e+01 +field 'mu' [explicit solve]: current solution: 4.706680e+00, current residual:2.839200e+00 +wall time: 0.000665903s + +time increment:92510 time: 92.51 +field ' c' [explicit solve]: current solution: 9.628718e+01, current residual:5.812315e+01 +field 'mu' [explicit solve]: current solution: 4.706621e+00, current residual:2.839164e+00 +wall time: 0.000641823s + +time increment:92520 time: 92.52 +field ' c' [explicit solve]: current solution: 9.628722e+01, current residual:5.812318e+01 +field 'mu' [explicit solve]: current solution: 4.706561e+00, current residual:2.839127e+00 +wall time: 0.000684977s + +time increment:92530 time: 92.53 +field ' c' [explicit solve]: current solution: 9.628727e+01, current residual:5.812321e+01 +field 'mu' [explicit solve]: current solution: 4.706502e+00, current residual:2.839091e+00 +wall time: 0.000684977s + +time increment:92540 time: 92.54 +field ' c' [explicit solve]: current solution: 9.628732e+01, current residual:5.812323e+01 +field 'mu' [explicit solve]: current solution: 4.706443e+00, current residual:2.839055e+00 +wall time: 0.000563145s + +time increment:92550 time: 92.55 +field ' c' [explicit solve]: current solution: 9.628736e+01, current residual:5.812326e+01 +field 'mu' [explicit solve]: current solution: 4.706383e+00, current residual:2.839019e+00 +wall time: 0.00056386s + +time increment:92560 time: 92.56 +field ' c' [explicit solve]: current solution: 9.628741e+01, current residual:5.812329e+01 +field 'mu' [explicit solve]: current solution: 4.706324e+00, current residual:2.838983e+00 +wall time: 0.00071907s + +time increment:92570 time: 92.57 +field ' c' [explicit solve]: current solution: 9.628746e+01, current residual:5.812331e+01 +field 'mu' [explicit solve]: current solution: 4.706265e+00, current residual:2.838946e+00 +wall time: 0.000642061s + +time increment:92580 time: 92.58 +field ' c' [explicit solve]: current solution: 9.628750e+01, current residual:5.812334e+01 +field 'mu' [explicit solve]: current solution: 4.706205e+00, current residual:2.838910e+00 +wall time: 0.00067687s + +time increment:92590 time: 92.59 +field ' c' [explicit solve]: current solution: 9.628755e+01, current residual:5.812337e+01 +field 'mu' [explicit solve]: current solution: 4.706146e+00, current residual:2.838874e+00 +wall time: 0.000579119s + +time increment:92600 time: 92.6 +field ' c' [explicit solve]: current solution: 9.628760e+01, current residual:5.812339e+01 +field 'mu' [explicit solve]: current solution: 4.706087e+00, current residual:2.838838e+00 +wall time: 0.000565052s + +time increment:92610 time: 92.61 +field ' c' [explicit solve]: current solution: 9.628764e+01, current residual:5.812342e+01 +field 'mu' [explicit solve]: current solution: 4.706027e+00, current residual:2.838802e+00 +wall time: 0.000656843s + +time increment:92620 time: 92.62 +field ' c' [explicit solve]: current solution: 9.628769e+01, current residual:5.812345e+01 +field 'mu' [explicit solve]: current solution: 4.705968e+00, current residual:2.838765e+00 +wall time: 0.000727177s + +time increment:92630 time: 92.63 +field ' c' [explicit solve]: current solution: 9.628774e+01, current residual:5.812347e+01 +field 'mu' [explicit solve]: current solution: 4.705909e+00, current residual:2.838729e+00 +wall time: 0.000682831s + +time increment:92640 time: 92.64 +field ' c' [explicit solve]: current solution: 9.628778e+01, current residual:5.812350e+01 +field 'mu' [explicit solve]: current solution: 4.705849e+00, current residual:2.838693e+00 +wall time: 0.000620127s + +time increment:92650 time: 92.65 +field ' c' [explicit solve]: current solution: 9.628783e+01, current residual:5.812352e+01 +field 'mu' [explicit solve]: current solution: 4.705790e+00, current residual:2.838657e+00 +wall time: 0.000813961s + +time increment:92660 time: 92.66 +field ' c' [explicit solve]: current solution: 9.628788e+01, current residual:5.812355e+01 +field 'mu' [explicit solve]: current solution: 4.705730e+00, current residual:2.838621e+00 +wall time: 0.000808001s + +time increment:92670 time: 92.67 +field ' c' [explicit solve]: current solution: 9.628792e+01, current residual:5.812358e+01 +field 'mu' [explicit solve]: current solution: 4.705671e+00, current residual:2.838584e+00 +wall time: 0.00069809s + +time increment:92680 time: 92.68 +field ' c' [explicit solve]: current solution: 9.628797e+01, current residual:5.812360e+01 +field 'mu' [explicit solve]: current solution: 4.705612e+00, current residual:2.838548e+00 +wall time: 0.00071907s + +time increment:92690 time: 92.69 +field ' c' [explicit solve]: current solution: 9.628802e+01, current residual:5.812363e+01 +field 'mu' [explicit solve]: current solution: 4.705552e+00, current residual:2.838512e+00 +wall time: 0.000709057s + +time increment:92700 time: 92.7 +field ' c' [explicit solve]: current solution: 9.628806e+01, current residual:5.812366e+01 +field 'mu' [explicit solve]: current solution: 4.705493e+00, current residual:2.838476e+00 +wall time: 0.000694036s + +time increment:92710 time: 92.71 +field ' c' [explicit solve]: current solution: 9.628811e+01, current residual:5.812368e+01 +field 'mu' [explicit solve]: current solution: 4.705433e+00, current residual:2.838439e+00 +wall time: 0.000632048s + +time increment:92720 time: 92.72 +field ' c' [explicit solve]: current solution: 9.628816e+01, current residual:5.812371e+01 +field 'mu' [explicit solve]: current solution: 4.705374e+00, current residual:2.838403e+00 +wall time: 0.00067687s + +time increment:92730 time: 92.73 +field ' c' [explicit solve]: current solution: 9.628820e+01, current residual:5.812374e+01 +field 'mu' [explicit solve]: current solution: 4.705315e+00, current residual:2.838367e+00 +wall time: 0.000618935s + +time increment:92740 time: 92.74 +field ' c' [explicit solve]: current solution: 9.628825e+01, current residual:5.812376e+01 +field 'mu' [explicit solve]: current solution: 4.705255e+00, current residual:2.838331e+00 +wall time: 0.000696182s + +time increment:92750 time: 92.75 +field ' c' [explicit solve]: current solution: 9.628830e+01, current residual:5.812379e+01 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[explicit solve]: current solution: 4.704958e+00, current residual:2.838150e+00 +wall time: 0.000706911s + +time increment:92800 time: 92.8 +field ' c' [explicit solve]: current solution: 9.628853e+01, current residual:5.812392e+01 +field 'mu' [explicit solve]: current solution: 4.704899e+00, current residual:2.838113e+00 +wall time: 0.000675917s + +time increment:92810 time: 92.81 +field ' c' [explicit solve]: current solution: 9.628858e+01, current residual:5.812395e+01 +field 'mu' [explicit solve]: current solution: 4.704839e+00, current residual:2.838077e+00 +wall time: 0.000663996s + +time increment:92820 time: 92.82 +field ' c' [explicit solve]: current solution: 9.628863e+01, current residual:5.812397e+01 +field 'mu' [explicit solve]: current solution: 4.704780e+00, current residual:2.838041e+00 +wall time: 0.000747919s + +time increment:92830 time: 92.83 +field ' c' [explicit solve]: current solution: 9.628867e+01, current residual:5.812400e+01 +field 'mu' [explicit solve]: current solution: 4.704721e+00, current residual:2.838005e+00 +wall time: 0.000702143s + +time increment:92840 time: 92.84 +field ' c' [explicit solve]: current solution: 9.628872e+01, current residual:5.812403e+01 +field 'mu' [explicit solve]: current solution: 4.704661e+00, current residual:2.837968e+00 +wall time: 0.000777006s + +time increment:92850 time: 92.85 +field ' c' [explicit solve]: current solution: 9.628877e+01, current residual:5.812405e+01 +field 'mu' [explicit solve]: current solution: 4.704602e+00, current residual:2.837932e+00 +wall time: 0.000733137s + +time increment:92860 time: 92.86 +field ' c' [explicit solve]: current solution: 9.628881e+01, current residual:5.812408e+01 +field 'mu' [explicit solve]: current solution: 4.704542e+00, current residual:2.837896e+00 +wall time: 0.000788927s + +time increment:92870 time: 92.87 +field ' c' [explicit solve]: current solution: 9.628886e+01, current residual:5.812411e+01 +field 'mu' [explicit solve]: current solution: 4.704483e+00, current residual:2.837860e+00 +wall time: 0.00071907s + +time increment:92880 time: 92.88 +field ' c' [explicit solve]: current solution: 9.628891e+01, current residual:5.812413e+01 +field 'mu' [explicit solve]: current solution: 4.704423e+00, current residual:2.837823e+00 +wall time: 0.000742912s + +time increment:92890 time: 92.89 +field ' c' [explicit solve]: current solution: 9.628895e+01, current residual:5.812416e+01 +field 'mu' [explicit solve]: current solution: 4.704364e+00, current residual:2.837787e+00 +wall time: 0.000782967s + +time increment:92900 time: 92.9 +field ' c' [explicit solve]: current solution: 9.628900e+01, current residual:5.812419e+01 +field 'mu' [explicit solve]: current solution: 4.704305e+00, current residual:2.837751e+00 +wall time: 0.000710964s + +time increment:92910 time: 92.91 +field ' c' [explicit solve]: current solution: 9.628905e+01, current residual:5.812421e+01 +field 'mu' [explicit solve]: current solution: 4.704245e+00, current residual:2.837715e+00 +wall time: 0.000682831s + +time increment:92920 time: 92.92 +field ' c' [explicit solve]: current solution: 9.628909e+01, current residual:5.812424e+01 +field 'mu' [explicit solve]: current solution: 4.704186e+00, current residual:2.837678e+00 +wall time: 0.000854969s + +time increment:92930 time: 92.93 +field ' c' [explicit solve]: current solution: 9.628914e+01, current residual:5.812426e+01 +field 'mu' [explicit solve]: current solution: 4.704126e+00, current residual:2.837642e+00 +wall time: 0.000649929s + +time increment:92940 time: 92.94 +field ' c' [explicit solve]: current solution: 9.628919e+01, current residual:5.812429e+01 +field 'mu' [explicit solve]: current solution: 4.704067e+00, current residual:2.837606e+00 +wall time: 0.000720978s + +time increment:92950 time: 92.95 +field ' c' [explicit solve]: current solution: 9.628923e+01, current residual:5.812432e+01 +field 'mu' [explicit solve]: current solution: 4.704007e+00, current residual:2.837570e+00 +wall time: 0.000644922s + +time increment:92960 time: 92.96 +field ' c' [explicit solve]: current solution: 9.628928e+01, current residual:5.812434e+01 +field 'mu' [explicit solve]: current solution: 4.703948e+00, current residual:2.837533e+00 +wall time: 0.000687838s + +time increment:92970 time: 92.97 +field ' c' [explicit solve]: current solution: 9.628933e+01, current residual:5.812437e+01 +field 'mu' [explicit solve]: current solution: 4.703888e+00, current residual:2.837497e+00 +wall time: 0.000710011s + +time increment:92980 time: 92.98 +field ' c' [explicit solve]: current solution: 9.628937e+01, current residual:5.812440e+01 +field 'mu' [explicit solve]: current solution: 4.703829e+00, current residual:2.837461e+00 +wall time: 0.0006392s + +time increment:92990 time: 92.99 +field ' c' [explicit solve]: current solution: 9.628942e+01, current residual:5.812442e+01 +field 'mu' [explicit solve]: current solution: 4.703769e+00, current residual:2.837425e+00 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increment:93080 time: 93.08 +field ' c' [explicit solve]: current solution: 9.628984e+01, current residual:5.812466e+01 +field 'mu' [explicit solve]: current solution: 4.703234e+00, current residual:2.837098e+00 +wall time: 0.000651836s + +time increment:93090 time: 93.09 +field ' c' [explicit solve]: current solution: 9.628989e+01, current residual:5.812469e+01 +field 'mu' [explicit solve]: current solution: 4.703175e+00, current residual:2.837062e+00 +wall time: 0.00065279s + +time increment:93100 time: 93.1 +field ' c' [explicit solve]: current solution: 9.628994e+01, current residual:5.812471e+01 +field 'mu' [explicit solve]: current solution: 4.703115e+00, current residual:2.837026e+00 +wall time: 0.000678062s + +time increment:93110 time: 93.11 +field ' c' [explicit solve]: current solution: 9.628998e+01, current residual:5.812474e+01 +field 'mu' [explicit solve]: current solution: 4.703056e+00, current residual:2.836989e+00 +wall time: 0.000654936s + +time increment:93120 time: 93.12 +field ' c' [explicit solve]: current solution: 9.629003e+01, current residual:5.812477e+01 +field 'mu' [explicit solve]: current solution: 4.702996e+00, current residual:2.836953e+00 +wall time: 0.000616074s + +time increment:93130 time: 93.13 +field ' c' [explicit solve]: current solution: 9.629008e+01, current residual:5.812479e+01 +field 'mu' [explicit solve]: current solution: 4.702937e+00, current residual:2.836917e+00 +wall time: 0.000664949s + +time increment:93140 time: 93.14 +field ' c' [explicit solve]: current solution: 9.629012e+01, current residual:5.812482e+01 +field 'mu' [explicit solve]: current solution: 4.702877e+00, current residual:2.836881e+00 +wall time: 0.000658035s + +time increment:93150 time: 93.15 +field ' c' [explicit solve]: current solution: 9.629017e+01, current residual:5.812485e+01 +field 'mu' [explicit solve]: current solution: 4.702818e+00, current residual:2.836844e+00 +wall time: 0.000634909s + +time increment:93160 time: 93.16 +field ' c' [explicit solve]: current solution: 9.629022e+01, current residual:5.812487e+01 +field 'mu' [explicit solve]: current solution: 4.702758e+00, current residual:2.836808e+00 +wall time: 0.000695944s + +time increment:93170 time: 93.17 +field ' c' [explicit solve]: current solution: 9.629026e+01, current residual:5.812490e+01 +field 'mu' [explicit solve]: current solution: 4.702698e+00, current residual:2.836772e+00 +wall time: 0.000575066s + +time increment:93180 time: 93.18 +field ' c' [explicit solve]: current solution: 9.629031e+01, current residual:5.812493e+01 +field 'mu' [explicit solve]: current solution: 4.702639e+00, current residual:2.836736e+00 +wall time: 0.000576973s + +time increment:93190 time: 93.19 +field ' c' [explicit solve]: current solution: 9.629036e+01, current residual:5.812495e+01 +field 'mu' [explicit solve]: current solution: 4.702579e+00, current residual:2.836699e+00 +wall time: 0.000673056s + +time increment:93200 time: 93.2 +field ' c' [explicit solve]: current solution: 9.629040e+01, current residual:5.812498e+01 +field 'mu' [explicit solve]: current solution: 4.702520e+00, current residual:2.836663e+00 +wall time: 0.000570059s + +time increment:93210 time: 93.21 +field ' c' [explicit solve]: current solution: 9.629045e+01, current residual:5.812501e+01 +field 'mu' [explicit solve]: current solution: 4.702460e+00, current residual:2.836627e+00 +wall time: 0.000581026s + +time increment:93220 time: 93.22 +field ' c' [explicit solve]: current solution: 9.629050e+01, current residual:5.812503e+01 +field 'mu' [explicit solve]: current solution: 4.702401e+00, current residual:2.836590e+00 +wall time: 0.000571012s + +time increment:93230 time: 93.23 +field ' c' [explicit solve]: current solution: 9.629055e+01, current residual:5.812506e+01 +field 'mu' [explicit solve]: current solution: 4.702341e+00, current residual:2.836554e+00 +wall time: 0.000597954s + +time increment:93240 time: 93.24 +field ' c' [explicit solve]: current solution: 9.629059e+01, current residual:5.812509e+01 +field 'mu' [explicit solve]: current solution: 4.702282e+00, current residual:2.836518e+00 +wall time: 0.000570059s + +time increment:93250 time: 93.25 +field ' c' [explicit solve]: current solution: 9.629064e+01, current residual:5.812511e+01 +field 'mu' [explicit solve]: current solution: 4.702222e+00, current residual:2.836482e+00 +wall time: 0.000590086s + +time increment:93260 time: 93.26 +field ' c' [explicit solve]: current solution: 9.629069e+01, current residual:5.812514e+01 +field 'mu' [explicit solve]: current solution: 4.702163e+00, current residual:2.836445e+00 +wall time: 0.000573158s + +time increment:93270 time: 93.27 +field ' c' [explicit solve]: current solution: 9.629073e+01, current residual:5.812517e+01 +field 'mu' [explicit solve]: current solution: 4.702103e+00, current residual:2.836409e+00 +wall time: 0.000569105s + +time increment:93280 time: 93.28 +field ' c' [explicit solve]: current solution: 9.629078e+01, current residual:5.812519e+01 +field 'mu' [explicit solve]: current solution: 4.702044e+00, current residual:2.836373e+00 +wall time: 0.00062418s + +time increment:93290 time: 93.29 +field ' c' [explicit solve]: current solution: 9.629083e+01, current residual:5.812522e+01 +field 'mu' [explicit solve]: current solution: 4.701984e+00, current residual:2.836336e+00 +wall time: 0.000626087s + +time increment:93300 time: 93.3 +field ' c' [explicit solve]: current solution: 9.629087e+01, current residual:5.812524e+01 +field 'mu' [explicit solve]: current solution: 4.701924e+00, current residual:2.836300e+00 +wall time: 0.000644922s + +time increment:93310 time: 93.31 +field ' c' [explicit solve]: current solution: 9.629092e+01, current residual:5.812527e+01 +field 'mu' [explicit solve]: current solution: 4.701865e+00, current residual:2.836264e+00 +wall time: 0.000566006s + +time increment:93320 time: 93.32 +field ' c' [explicit solve]: current solution: 9.629097e+01, current residual:5.812530e+01 +field 'mu' [explicit solve]: current solution: 4.701805e+00, current residual:2.836227e+00 +wall time: 0.000618219s + +time increment:93330 time: 93.33 +field ' c' [explicit solve]: current solution: 9.629101e+01, current residual:5.812532e+01 +field 'mu' [explicit solve]: current solution: 4.701746e+00, current residual:2.836191e+00 +wall time: 0.000648975s + +time increment:93340 time: 93.34 +field ' c' [explicit solve]: current solution: 9.629106e+01, current residual:5.812535e+01 +field 'mu' [explicit solve]: current solution: 4.701686e+00, current residual:2.836155e+00 +wall time: 0.000579834s + +time increment:93350 time: 93.35 +field ' c' [explicit solve]: current solution: 9.629111e+01, current residual:5.812538e+01 +field 'mu' [explicit solve]: current solution: 4.701627e+00, current residual:2.836119e+00 +wall time: 0.000560999s + +time increment:93360 time: 93.36 +field ' c' [explicit solve]: current solution: 9.629115e+01, current residual:5.812540e+01 +field 'mu' [explicit solve]: current solution: 4.701567e+00, current residual:2.836082e+00 +wall time: 0.000577927s + +time increment:93370 time: 93.37 +field ' c' [explicit solve]: current solution: 9.629120e+01, current residual:5.812543e+01 +field 'mu' [explicit solve]: current solution: 4.701507e+00, current residual:2.836046e+00 +wall time: 0.000627041s + +time increment:93380 time: 93.38 +field ' c' [explicit solve]: current solution: 9.629125e+01, current residual:5.812546e+01 +field 'mu' [explicit solve]: current solution: 4.701448e+00, current residual:2.836010e+00 +wall time: 0.000566006s + +time increment:93390 time: 93.39 +field ' c' [explicit solve]: current solution: 9.629129e+01, current residual:5.812548e+01 +field 'mu' [explicit solve]: current solution: 4.701388e+00, current residual:2.835973e+00 +wall time: 0.000617981s + +time increment:93400 time: 93.4 +field ' c' [explicit solve]: current solution: 9.629134e+01, current residual:5.812551e+01 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[explicit solve]: current solution: 4.701090e+00, current residual:2.835792e+00 +wall time: 0.000576019s + +time increment:93450 time: 93.45 +field ' c' [explicit solve]: current solution: 9.629158e+01, current residual:5.812564e+01 +field 'mu' [explicit solve]: current solution: 4.701031e+00, current residual:2.835755e+00 +wall time: 0.000674009s + +time increment:93460 time: 93.46 +field ' c' [explicit solve]: current solution: 9.629162e+01, current residual:5.812567e+01 +field 'mu' [explicit solve]: current solution: 4.700971e+00, current residual:2.835719e+00 +wall time: 0.000620842s + +time increment:93470 time: 93.47 +field ' c' [explicit solve]: current solution: 9.629167e+01, current residual:5.812570e+01 +field 'mu' [explicit solve]: current solution: 4.700912e+00, current residual:2.835683e+00 +wall time: 0.000635862s + +time increment:93480 time: 93.48 +field ' c' [explicit solve]: current solution: 9.629172e+01, current residual:5.812572e+01 +field 'mu' [explicit solve]: 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93.73 +field ' c' [explicit solve]: current solution: 9.629289e+01, current residual:5.812639e+01 +field 'mu' [explicit solve]: current solution: 4.699361e+00, current residual:2.834738e+00 +wall time: 0.000579119s + +time increment:93740 time: 93.74 +field ' c' [explicit solve]: current solution: 9.629294e+01, current residual:5.812641e+01 +field 'mu' [explicit solve]: current solution: 4.699301e+00, current residual:2.834702e+00 +wall time: 0.000617981s + +time increment:93750 time: 93.75 +field ' c' [explicit solve]: current solution: 9.629298e+01, current residual:5.812644e+01 +field 'mu' [explicit solve]: current solution: 4.699242e+00, current residual:2.834665e+00 +wall time: 0.000715971s + +time increment:93760 time: 93.76 +field ' c' [explicit solve]: current solution: 9.629303e+01, current residual:5.812647e+01 +field 'mu' [explicit solve]: current solution: 4.699182e+00, current residual:2.834629e+00 +wall time: 0.000558138s + +time increment:93770 time: 93.77 +field ' c' [explicit solve]: current solution: 9.629308e+01, current residual:5.812649e+01 +field 'mu' [explicit solve]: current solution: 4.699122e+00, current residual:2.834593e+00 +wall time: 0.000683069s + +time increment:93780 time: 93.78 +field ' c' [explicit solve]: current solution: 9.629312e+01, current residual:5.812652e+01 +field 'mu' [explicit solve]: current solution: 4.699063e+00, current residual:2.834556e+00 +wall time: 0.000631094s + +time increment:93790 time: 93.79 +field ' c' [explicit solve]: current solution: 9.629317e+01, current residual:5.812655e+01 +field 'mu' [explicit solve]: current solution: 4.699003e+00, current residual:2.834520e+00 +wall time: 0.000553131s + +time increment:93800 time: 93.8 +field ' c' [explicit solve]: current solution: 9.629322e+01, current residual:5.812657e+01 +field 'mu' [explicit solve]: current solution: 4.698943e+00, current residual:2.834484e+00 +wall time: 0.000603199s + +time increment:93810 time: 93.81 +field ' c' [explicit solve]: 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residual:5.812702e+01 +field 'mu' [explicit solve]: current solution: 4.697928e+00, current residual:2.833865e+00 +wall time: 0.000849962s + +time increment:93980 time: 93.98 +field ' c' [explicit solve]: current solution: 9.629406e+01, current residual:5.812705e+01 +field 'mu' [explicit solve]: current solution: 4.697868e+00, current residual:2.833829e+00 +wall time: 0.000694036s + +time increment:93990 time: 93.99 +field ' c' [explicit solve]: current solution: 9.629411e+01, current residual:5.812708e+01 +field 'mu' [explicit solve]: current solution: 4.697808e+00, current residual:2.833793e+00 +wall time: 0.000727892s + +time increment:94000 time: 94 +field ' c' [explicit solve]: current solution: 9.629416e+01, current residual:5.812710e+01 +field 'mu' [explicit solve]: current solution: 4.697749e+00, current residual:2.833756e+00 +wall time: 0.000676155s + +time increment:94010 time: 94.01 +field ' c' [explicit solve]: current solution: 9.629420e+01, current residual:5.812713e+01 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solve]: current solution: 4.697450e+00, current residual:2.833574e+00 +wall time: 0.000741005s + +time increment:94060 time: 94.06 +field ' c' [explicit solve]: current solution: 9.629444e+01, current residual:5.812726e+01 +field 'mu' [explicit solve]: current solution: 4.697390e+00, current residual:2.833538e+00 +wall time: 0.000627041s + +time increment:94070 time: 94.07 +field ' c' [explicit solve]: current solution: 9.629449e+01, current residual:5.812729e+01 +field 'mu' [explicit solve]: current solution: 4.697330e+00, current residual:2.833502e+00 +wall time: 0.000683069s + +time increment:94080 time: 94.08 +field ' c' [explicit solve]: current solution: 9.629453e+01, current residual:5.812732e+01 +field 'mu' [explicit solve]: current solution: 4.697271e+00, current residual:2.833465e+00 +wall time: 0.000665903s + +time increment:94090 time: 94.09 +field ' c' [explicit solve]: current solution: 9.629458e+01, current residual:5.812734e+01 +field 'mu' [explicit solve]: current solution: 4.697211e+00, current residual:2.833429e+00 +wall time: 0.000670195s + +time increment:94100 time: 94.1 +field ' c' [explicit solve]: current solution: 9.629463e+01, current residual:5.812737e+01 +field 'mu' [explicit solve]: current solution: 4.697151e+00, current residual:2.833392e+00 +wall time: 0.000668049s + +time increment:94110 time: 94.11 +field ' c' [explicit solve]: current solution: 9.629467e+01, current residual:5.812740e+01 +field 'mu' [explicit solve]: current solution: 4.697091e+00, current residual:2.833356e+00 +wall time: 0.000637054s + +time increment:94120 time: 94.12 +field ' c' [explicit solve]: current solution: 9.629472e+01, current residual:5.812742e+01 +field 'mu' [explicit solve]: current solution: 4.697031e+00, current residual:2.833320e+00 +wall time: 0.000869036s + +time increment:94130 time: 94.13 +field ' c' [explicit solve]: current solution: 9.629477e+01, current residual:5.812745e+01 +field 'mu' [explicit solve]: current solution: 4.696972e+00, current residual:2.833283e+00 +wall time: 0.000730991s + +time increment:94140 time: 94.14 +field ' c' [explicit solve]: current solution: 9.629481e+01, current residual:5.812748e+01 +field 'mu' [explicit solve]: current solution: 4.696912e+00, current residual:2.833247e+00 +wall time: 0.00075388s + +time increment:94150 time: 94.15 +field ' c' [explicit solve]: current solution: 9.629486e+01, current residual:5.812750e+01 +field 'mu' [explicit solve]: current solution: 4.696852e+00, current residual:2.833211e+00 +wall time: 0.000692129s + +time increment:94160 time: 94.16 +field ' c' [explicit solve]: current solution: 9.629491e+01, current residual:5.812753e+01 +field 'mu' [explicit solve]: current solution: 4.696792e+00, current residual:2.833174e+00 +wall time: 0.00067091s + +time increment:94170 time: 94.17 +field ' c' [explicit solve]: current solution: 9.629496e+01, current residual:5.812756e+01 +field 'mu' [explicit solve]: current solution: 4.696733e+00, current residual:2.833138e+00 +wall time: 0.000660896s + +time increment:94180 time: 94.18 +field ' c' [explicit solve]: current solution: 9.629500e+01, current residual:5.812758e+01 +field 'mu' [explicit solve]: current solution: 4.696673e+00, current residual:2.833101e+00 +wall time: 0.000693083s + +time increment:94190 time: 94.19 +field ' c' [explicit solve]: current solution: 9.629505e+01, current residual:5.812761e+01 +field 'mu' [explicit solve]: current solution: 4.696613e+00, current residual:2.833065e+00 +wall time: 0.000628948s + +time increment:94200 time: 94.2 +field ' c' [explicit solve]: current solution: 9.629510e+01, current residual:5.812764e+01 +field 'mu' [explicit solve]: current solution: 4.696553e+00, current residual:2.833028e+00 +wall time: 0.000761032s + +time increment:94210 time: 94.21 +field ' c' [explicit solve]: current solution: 9.629514e+01, current residual:5.812766e+01 +field 'mu' [explicit solve]: current solution: 4.696493e+00, current residual:2.832992e+00 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0.000703096s + +time increment:94260 time: 94.26 +field ' c' [explicit solve]: current solution: 9.629538e+01, current residual:5.812780e+01 +field 'mu' [explicit solve]: current solution: 4.696194e+00, current residual:2.832810e+00 +wall time: 0.000581026s + +time increment:94270 time: 94.27 +field ' c' [explicit solve]: current solution: 9.629543e+01, current residual:5.812782e+01 +field 'mu' [explicit solve]: current solution: 4.696134e+00, current residual:2.832774e+00 +wall time: 0.00070405s + +time increment:94280 time: 94.28 +field ' c' [explicit solve]: current solution: 9.629547e+01, current residual:5.812785e+01 +field 'mu' [explicit solve]: current solution: 4.696075e+00, current residual:2.832737e+00 +wall time: 0.000581026s + +time increment:94290 time: 94.29 +field ' c' [explicit solve]: current solution: 9.629552e+01, current residual:5.812788e+01 +field 'mu' [explicit solve]: current solution: 4.696015e+00, current residual:2.832701e+00 +wall time: 0.000637054s + +time increment:94300 time: 94.3 +field ' c' [explicit solve]: current solution: 9.629557e+01, current residual:5.812790e+01 +field 'mu' [explicit solve]: current solution: 4.695955e+00, current residual:2.832664e+00 +wall time: 0.000571012s + +time increment:94310 time: 94.31 +field ' c' [explicit solve]: current solution: 9.629561e+01, current residual:5.812793e+01 +field 'mu' [explicit solve]: current solution: 4.695895e+00, current residual:2.832628e+00 +wall time: 0.000576973s + +time increment:94320 time: 94.32 +field ' c' [explicit solve]: current solution: 9.629566e+01, current residual:5.812796e+01 +field 'mu' [explicit solve]: current solution: 4.695835e+00, current residual:2.832592e+00 +wall time: 0.000664949s + +time increment:94330 time: 94.33 +field ' c' [explicit solve]: current solution: 9.629571e+01, current residual:5.812798e+01 +field 'mu' [explicit solve]: current solution: 4.695775e+00, current residual:2.832555e+00 +wall time: 0.000632048s + +time increment:94340 time: 94.34 +field ' c' [explicit solve]: current solution: 9.629575e+01, current residual:5.812801e+01 +field 'mu' [explicit solve]: current solution: 4.695716e+00, current residual:2.832519e+00 +wall time: 0.000645876s + +time increment:94350 time: 94.35 +field ' c' [explicit solve]: current solution: 9.629580e+01, current residual:5.812804e+01 +field 'mu' [explicit solve]: current solution: 4.695656e+00, current residual:2.832482e+00 +wall time: 0.000571012s + +time increment:94360 time: 94.36 +field ' c' [explicit solve]: current solution: 9.629585e+01, current residual:5.812806e+01 +field 'mu' [explicit solve]: current solution: 4.695596e+00, current residual:2.832446e+00 +wall time: 0.000555992s + +time increment:94370 time: 94.37 +field ' c' [explicit solve]: current solution: 9.629590e+01, current residual:5.812809e+01 +field 'mu' [explicit solve]: current solution: 4.695536e+00, current residual:2.832409e+00 +wall time: 0.000677824s + +time increment:94380 time: 94.38 +field ' c' [explicit solve]: current solution: 9.629594e+01, current residual:5.812812e+01 +field 'mu' [explicit solve]: current solution: 4.695476e+00, current residual:2.832373e+00 +wall time: 0.000634909s + +time increment:94390 time: 94.39 +field ' c' [explicit solve]: current solution: 9.629599e+01, current residual:5.812814e+01 +field 'mu' [explicit solve]: current solution: 4.695416e+00, current residual:2.832337e+00 +wall time: 0.00056386s + +time increment:94400 time: 94.4 +field ' c' [explicit solve]: current solution: 9.629604e+01, current residual:5.812817e+01 +field 'mu' [explicit solve]: current solution: 4.695357e+00, current residual:2.832300e+00 +wall time: 0.00064683s + +time increment:94410 time: 94.41 +field ' c' [explicit solve]: current solution: 9.629608e+01, current residual:5.812820e+01 +field 'mu' [explicit solve]: current solution: 4.695297e+00, current residual:2.832264e+00 +wall time: 0.00062108s + +time increment:94420 time: 94.42 +field ' c' [explicit solve]: current solution: 9.629613e+01, current residual:5.812822e+01 +field 'mu' [explicit solve]: current solution: 4.695237e+00, current residual:2.832227e+00 +wall time: 0.000674009s + +time increment:94430 time: 94.43 +field ' c' [explicit solve]: current solution: 9.629618e+01, current residual:5.812825e+01 +field 'mu' [explicit solve]: current solution: 4.695177e+00, current residual:2.832191e+00 +wall time: 0.000568867s + +time increment:94440 time: 94.44 +field ' c' [explicit solve]: current solution: 9.629622e+01, current residual:5.812828e+01 +field 'mu' [explicit solve]: current solution: 4.695117e+00, current residual:2.832154e+00 +wall time: 0.000626087s + +time increment:94450 time: 94.45 +field ' c' [explicit solve]: current solution: 9.629627e+01, current residual:5.812830e+01 +field 'mu' [explicit solve]: current solution: 4.695057e+00, current residual:2.832118e+00 +wall time: 0.000667095s + +time increment:94460 time: 94.46 +field ' c' [explicit solve]: current solution: 9.629632e+01, current residual:5.812833e+01 +field 'mu' [explicit solve]: current solution: 4.694997e+00, current residual:2.832082e+00 +wall time: 0.000576973s + +time increment:94470 time: 94.47 +field ' c' [explicit solve]: current solution: 9.629637e+01, current residual:5.812836e+01 +field 'mu' [explicit solve]: current solution: 4.694937e+00, current residual:2.832045e+00 +wall time: 0.000638962s + +time increment:94480 time: 94.48 +field ' c' [explicit solve]: current solution: 9.629641e+01, current residual:5.812838e+01 +field 'mu' [explicit solve]: current solution: 4.694878e+00, current residual:2.832009e+00 +wall time: 0.00068593s + +time increment:94490 time: 94.49 +field ' c' [explicit solve]: current solution: 9.629646e+01, current residual:5.812841e+01 +field 'mu' [explicit solve]: current solution: 4.694818e+00, current residual:2.831972e+00 +wall time: 0.000563145s + +time increment:94500 time: 94.5 +field ' c' [explicit solve]: current solution: 9.629651e+01, current residual:5.812844e+01 +field 'mu' [explicit solve]: current solution: 4.694758e+00, current residual:2.831936e+00 +wall time: 0.000767946s + +time increment:94510 time: 94.51 +field ' c' [explicit solve]: current solution: 9.629655e+01, current residual:5.812846e+01 +field 'mu' [explicit solve]: current solution: 4.694698e+00, current residual:2.831899e+00 +wall time: 0.000616789s + +time increment:94520 time: 94.52 +field ' c' [explicit solve]: current solution: 9.629660e+01, current residual:5.812849e+01 +field 'mu' [explicit solve]: current solution: 4.694638e+00, current residual:2.831863e+00 +wall time: 0.000680923s + +time increment:94530 time: 94.53 +field ' c' [explicit solve]: current solution: 9.629665e+01, current residual:5.812852e+01 +field 'mu' [explicit solve]: current solution: 4.694578e+00, current residual:2.831827e+00 +wall time: 0.000723124s + +time increment:94540 time: 94.54 +field ' c' [explicit solve]: current solution: 9.629669e+01, current residual:5.812854e+01 +field 'mu' [explicit solve]: current solution: 4.694518e+00, current residual:2.831790e+00 +wall time: 0.000576973s + +time increment:94550 time: 94.55 +field ' c' [explicit solve]: current solution: 9.629674e+01, current residual:5.812857e+01 +field 'mu' [explicit solve]: current solution: 4.694458e+00, current residual:2.831754e+00 +wall time: 0.000597s + +time increment:94560 time: 94.56 +field ' c' [explicit solve]: current solution: 9.629679e+01, current residual:5.812860e+01 +field 'mu' [explicit solve]: current solution: 4.694398e+00, current residual:2.831717e+00 +wall time: 0.000560045s + +time increment:94570 time: 94.57 +field ' c' [explicit solve]: current solution: 9.629684e+01, current residual:5.812862e+01 +field 'mu' [explicit solve]: current solution: 4.694339e+00, current residual:2.831681e+00 +wall time: 0.000652075s + +time increment:94580 time: 94.58 +field ' c' [explicit solve]: current solution: 9.629688e+01, current residual:5.812865e+01 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solve]: current solution: 4.694039e+00, current residual:2.831499e+00 +wall time: 0.000671864s + +time increment:94630 time: 94.63 +field ' c' [explicit solve]: current solution: 9.629712e+01, current residual:5.812878e+01 +field 'mu' [explicit solve]: current solution: 4.693979e+00, current residual:2.831462e+00 +wall time: 0.000679016s + +time increment:94640 time: 94.64 +field ' c' [explicit solve]: current solution: 9.629717e+01, current residual:5.812881e+01 +field 'mu' [explicit solve]: current solution: 4.693919e+00, current residual:2.831426e+00 +wall time: 0.000675917s + +time increment:94650 time: 94.65 +field ' c' [explicit solve]: current solution: 9.629721e+01, current residual:5.812884e+01 +field 'mu' [explicit solve]: current solution: 4.693859e+00, current residual:2.831389e+00 +wall time: 0.000705957s + +time increment:94660 time: 94.66 +field ' c' [explicit solve]: current solution: 9.629726e+01, current residual:5.812886e+01 +field 'mu' [explicit solve]: current solution: 4.693799e+00, current residual:2.831353e+00 +wall time: 0.000684023s + +time increment:94670 time: 94.67 +field ' c' [explicit solve]: current solution: 9.629731e+01, current residual:5.812889e+01 +field 'mu' [explicit solve]: current solution: 4.693739e+00, current residual:2.831316e+00 +wall time: 0.000667095s + +time increment:94680 time: 94.68 +field ' c' [explicit solve]: current solution: 9.629735e+01, current residual:5.812892e+01 +field 'mu' [explicit solve]: current solution: 4.693679e+00, current residual:2.831280e+00 +wall time: 0.000652075s + +time increment:94690 time: 94.69 +field ' c' [explicit solve]: current solution: 9.629740e+01, current residual:5.812894e+01 +field 'mu' [explicit solve]: current solution: 4.693620e+00, current residual:2.831243e+00 +wall time: 0.000652075s + +time increment:94700 time: 94.7 +field ' c' [explicit solve]: current solution: 9.629745e+01, current residual:5.812897e+01 +field 'mu' [explicit solve]: current solution: 4.693560e+00, current residual:2.831207e+00 +wall time: 0.000641108s + +time increment:94710 time: 94.71 +field ' c' [explicit solve]: current solution: 9.629749e+01, current residual:5.812900e+01 +field 'mu' [explicit solve]: current solution: 4.693500e+00, current residual:2.831170e+00 +wall time: 0.000665903s + +time increment:94720 time: 94.72 +field ' c' [explicit solve]: current solution: 9.629754e+01, current residual:5.812902e+01 +field 'mu' [explicit solve]: current solution: 4.693440e+00, current residual:2.831134e+00 +wall time: 0.000558138s + +time increment:94730 time: 94.73 +field ' c' [explicit solve]: current solution: 9.629759e+01, current residual:5.812905e+01 +field 'mu' [explicit solve]: current solution: 4.693380e+00, current residual:2.831097e+00 +wall time: 0.000560045s + +time increment:94740 time: 94.74 +field ' c' [explicit solve]: current solution: 9.629764e+01, current residual:5.812908e+01 +field 'mu' [explicit solve]: current solution: 4.693320e+00, current residual:2.831061e+00 +wall time: 0.000565052s + +time increment:94750 time: 94.75 +field ' c' [explicit solve]: current solution: 9.629768e+01, current residual:5.812910e+01 +field 'mu' [explicit solve]: current solution: 4.693260e+00, current residual:2.831025e+00 +wall time: 0.000593901s + +time increment:94760 time: 94.76 +field ' c' [explicit solve]: current solution: 9.629773e+01, current residual:5.812913e+01 +field 'mu' [explicit solve]: current solution: 4.693200e+00, current residual:2.830988e+00 +wall time: 0.000632048s + +time increment:94770 time: 94.77 +field ' c' [explicit solve]: current solution: 9.629778e+01, current residual:5.812916e+01 +field 'mu' [explicit solve]: current solution: 4.693140e+00, current residual:2.830952e+00 +wall time: 0.000570059s + +time increment:94780 time: 94.78 +field ' c' [explicit solve]: current solution: 9.629782e+01, current residual:5.812918e+01 +field 'mu' [explicit solve]: current solution: 4.693080e+00, current residual:2.830915e+00 +wall time: 0.000683069s + +time increment:94790 time: 94.79 +field ' c' [explicit solve]: current solution: 9.629787e+01, current residual:5.812921e+01 +field 'mu' [explicit solve]: current solution: 4.693020e+00, current residual:2.830879e+00 +wall time: 0.000634909s + +time increment:94800 time: 94.8 +field ' c' [explicit solve]: current solution: 9.629792e+01, current residual:5.812924e+01 +field 'mu' [explicit solve]: current solution: 4.692960e+00, current residual:2.830842e+00 +wall time: 0.000560045s + +time increment:94810 time: 94.81 +field ' c' [explicit solve]: current solution: 9.629797e+01, current residual:5.812926e+01 +field 'mu' [explicit solve]: current solution: 4.692900e+00, current residual:2.830806e+00 +wall time: 0.000707865s + +time increment:94820 time: 94.82 +field ' c' [explicit solve]: current solution: 9.629801e+01, current residual:5.812929e+01 +field 'mu' [explicit solve]: current solution: 4.692840e+00, current residual:2.830769e+00 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0.000574112s + +time increment:94870 time: 94.87 +field ' c' [explicit solve]: current solution: 9.629825e+01, current residual:5.812942e+01 +field 'mu' [explicit solve]: current solution: 4.692540e+00, current residual:2.830587e+00 +wall time: 0.00056386s + +time increment:94880 time: 94.88 +field ' c' [explicit solve]: current solution: 9.629829e+01, current residual:5.812945e+01 +field 'mu' [explicit solve]: current solution: 4.692480e+00, current residual:2.830550e+00 +wall time: 0.000668049s + +time increment:94890 time: 94.89 +field ' c' [explicit solve]: current solution: 9.629834e+01, current residual:5.812948e+01 +field 'mu' [explicit solve]: current solution: 4.692420e+00, current residual:2.830514e+00 +wall time: 0.000711203s + +time increment:94900 time: 94.9 +field ' c' [explicit solve]: current solution: 9.629839e+01, current residual:5.812950e+01 +field 'mu' [explicit solve]: current solution: 4.692360e+00, current residual:2.830477e+00 +wall time: 0.000682116s + +time increment:94910 time: 94.91 +field ' c' [explicit solve]: current solution: 9.629844e+01, current residual:5.812953e+01 +field 'mu' [explicit solve]: current solution: 4.692300e+00, current residual:2.830441e+00 +wall time: 0.000679016s + +time increment:94920 time: 94.92 +field ' c' [explicit solve]: current solution: 9.629848e+01, current residual:5.812956e+01 +field 'mu' [explicit solve]: current solution: 4.692240e+00, current residual:2.830404e+00 +wall time: 0.000680923s + +time increment:94930 time: 94.93 +field ' c' [explicit solve]: current solution: 9.629853e+01, current residual:5.812958e+01 +field 'mu' [explicit solve]: current solution: 4.692180e+00, current residual:2.830368e+00 +wall time: 0.000653982s + +time increment:94940 time: 94.94 +field ' c' [explicit solve]: current solution: 9.629858e+01, current residual:5.812961e+01 +field 'mu' [explicit solve]: current solution: 4.692120e+00, current residual:2.830331e+00 +wall time: 0.000666857s + +time increment:94950 time: 94.95 +field ' c' [explicit solve]: current solution: 9.629862e+01, current residual:5.812964e+01 +field 'mu' [explicit solve]: current solution: 4.692060e+00, current residual:2.830295e+00 +wall time: 0.000695944s + +time increment:94960 time: 94.96 +field ' c' [explicit solve]: current solution: 9.629867e+01, current residual:5.812966e+01 +field 'mu' [explicit solve]: current solution: 4.692000e+00, current residual:2.830258e+00 +wall time: 0.000662088s + +time increment:94970 time: 94.97 +field ' c' [explicit solve]: current solution: 9.629872e+01, current residual:5.812969e+01 +field 'mu' [explicit solve]: current solution: 4.691940e+00, current residual:2.830222e+00 +wall time: 0.000637054s + +time increment:94980 time: 94.98 +field ' c' [explicit solve]: current solution: 9.629877e+01, current residual:5.812972e+01 +field 'mu' [explicit solve]: current solution: 4.691880e+00, current residual:2.830185e+00 +wall time: 0.000669003s + +time increment:94990 time: 94.99 +field ' c' [explicit solve]: current solution: 9.629881e+01, current residual:5.812974e+01 +field 'mu' [explicit solve]: current solution: 4.691820e+00, current residual:2.830149e+00 +wall time: 0.000633001s + +time increment:95000 time: 95 +field ' c' [explicit solve]: current solution: 9.629886e+01, current residual:5.812977e+01 +field 'mu' [explicit solve]: current solution: 4.691760e+00, current residual:2.830112e+00 +wall time: 0.000745058s + +time increment:95010 time: 95.01 +field ' c' [explicit solve]: current solution: 9.629891e+01, current residual:5.812980e+01 +field 'mu' [explicit solve]: current solution: 4.691700e+00, current residual:2.830076e+00 +wall time: 0.000682116s + +time increment:95020 time: 95.02 +field ' c' [explicit solve]: current solution: 9.629895e+01, current residual:5.812982e+01 +field 'mu' [explicit solve]: current solution: 4.691640e+00, current residual:2.830039e+00 +wall time: 0.000647068s + +time increment:95030 time: 95.03 +field ' c' [explicit solve]: current solution: 9.629900e+01, current residual:5.812985e+01 +field 'mu' [explicit solve]: current solution: 4.691580e+00, current residual:2.830003e+00 +wall time: 0.000720024s + +time increment:95040 time: 95.04 +field ' c' [explicit solve]: current solution: 9.629905e+01, current residual:5.812988e+01 +field 'mu' [explicit solve]: current solution: 4.691520e+00, current residual:2.829966e+00 +wall time: 0.000643015s + +time increment:95050 time: 95.05 +field ' c' [explicit solve]: current solution: 9.629910e+01, current residual:5.812991e+01 +field 'mu' [explicit solve]: current solution: 4.691460e+00, current residual:2.829930e+00 +wall time: 0.000746012s + +time increment:95060 time: 95.06 +field ' c' [explicit solve]: current solution: 9.629914e+01, current residual:5.812993e+01 +field 'mu' [explicit solve]: current solution: 4.691400e+00, current residual:2.829893e+00 +wall time: 0.00067091s + +time increment:95070 time: 95.07 +field ' c' [explicit solve]: current solution: 9.629919e+01, current residual:5.812996e+01 +field 'mu' [explicit solve]: current solution: 4.691340e+00, current residual:2.829857e+00 +wall time: 0.000627041s + +time increment:95080 time: 95.08 +field ' c' [explicit solve]: current solution: 9.629924e+01, current residual:5.812999e+01 +field 'mu' [explicit solve]: current solution: 4.691280e+00, current residual:2.829820e+00 +wall time: 0.000653982s + +time increment:95090 time: 95.09 +field ' c' [explicit solve]: current solution: 9.629928e+01, current residual:5.813001e+01 +field 'mu' [explicit solve]: current solution: 4.691220e+00, current residual:2.829784e+00 +wall time: 0.000674963s + +time increment:95100 time: 95.1 +field ' c' [explicit solve]: current solution: 9.629933e+01, current residual:5.813004e+01 +field 'mu' [explicit solve]: current solution: 4.691160e+00, current residual:2.829747e+00 +wall time: 0.000690937s + +time increment:95110 time: 95.11 +field ' c' [explicit solve]: current solution: 9.629938e+01, current residual:5.813007e+01 +field 'mu' [explicit solve]: current solution: 4.691100e+00, current residual:2.829711e+00 +wall time: 0.000715017s + +time increment:95120 time: 95.12 +field ' c' [explicit solve]: current solution: 9.629942e+01, current residual:5.813009e+01 +field 'mu' [explicit solve]: current solution: 4.691040e+00, current residual:2.829674e+00 +wall time: 0.000687122s + +time increment:95130 time: 95.13 +field ' c' [explicit solve]: current solution: 9.629947e+01, current residual:5.813012e+01 +field 'mu' [explicit solve]: current solution: 4.690980e+00, current residual:2.829638e+00 +wall time: 0.000763893s + +time increment:95140 time: 95.14 +field ' c' [explicit solve]: current solution: 9.629952e+01, current residual:5.813015e+01 +field 'mu' [explicit solve]: current solution: 4.690920e+00, current residual:2.829601e+00 +wall time: 0.000711203s + +time increment:95150 time: 95.15 +field ' c' [explicit solve]: current solution: 9.629957e+01, current residual:5.813017e+01 +field 'mu' [explicit solve]: current solution: 4.690860e+00, current residual:2.829565e+00 +wall time: 0.000697851s + +time increment:95160 time: 95.16 +field ' c' [explicit solve]: current solution: 9.629961e+01, current residual:5.813020e+01 +field 'mu' [explicit solve]: current solution: 4.690800e+00, current residual:2.829528e+00 +wall time: 0.000643969s + +time increment:95170 time: 95.17 +field ' c' [explicit solve]: current solution: 9.629966e+01, current residual:5.813023e+01 +field 'mu' [explicit solve]: current solution: 4.690740e+00, current residual:2.829492e+00 +wall time: 0.000707865s + +time increment:95180 time: 95.18 +field ' c' [explicit solve]: current solution: 9.629971e+01, current residual:5.813025e+01 +field 'mu' [explicit solve]: current solution: 4.690680e+00, current residual:2.829455e+00 +wall time: 0.000740051s + +time increment:95190 time: 95.19 +field ' c' [explicit solve]: current solution: 9.629975e+01, current residual:5.813028e+01 +field 'mu' [explicit solve]: current solution: 4.690619e+00, current residual:2.829419e+00 +wall time: 0.000688791s + +time increment:95200 time: 95.2 +field ' c' [explicit solve]: current solution: 9.629980e+01, current residual:5.813031e+01 +field 'mu' [explicit solve]: current solution: 4.690559e+00, current residual:2.829382e+00 +wall time: 0.000710011s + +time increment:95210 time: 95.21 +field ' c' [explicit solve]: current solution: 9.629985e+01, current residual:5.813033e+01 +field 'mu' [explicit solve]: current solution: 4.690499e+00, current residual:2.829346e+00 +wall time: 0.000645161s + +time increment:95220 time: 95.22 +field ' c' [explicit solve]: current solution: 9.629990e+01, current residual:5.813036e+01 +field 'mu' [explicit solve]: current solution: 4.690439e+00, current residual:2.829309e+00 +wall time: 0.000684977s + +time increment:95230 time: 95.23 +field ' c' [explicit solve]: current solution: 9.629994e+01, current residual:5.813039e+01 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solve]: current solution: 4.690139e+00, current residual:2.829127e+00 +wall time: 0.000680923s + +time increment:95280 time: 95.28 +field ' c' [explicit solve]: current solution: 9.630018e+01, current residual:5.813052e+01 +field 'mu' [explicit solve]: current solution: 4.690079e+00, current residual:2.829090e+00 +wall time: 0.000658989s + +time increment:95290 time: 95.29 +field ' c' [explicit solve]: current solution: 9.630023e+01, current residual:5.813055e+01 +field 'mu' [explicit solve]: current solution: 4.690019e+00, current residual:2.829054e+00 +wall time: 0.000567913s + +time increment:95300 time: 95.3 +field ' c' [explicit solve]: current solution: 9.630027e+01, current residual:5.813057e+01 +field 'mu' [explicit solve]: current solution: 4.689959e+00, current residual:2.829017e+00 +wall time: 0.000576973s + +time increment:95310 time: 95.31 +field ' c' [explicit solve]: current solution: 9.630032e+01, current residual:5.813060e+01 +field 'mu' [explicit solve]: current solution: 4.689899e+00, current residual:2.828981e+00 +wall time: 0.000658035s + +time increment:95320 time: 95.32 +field ' c' [explicit solve]: current solution: 9.630037e+01, current residual:5.813063e+01 +field 'mu' [explicit solve]: current solution: 4.689838e+00, current residual:2.828944e+00 +wall time: 0.000653028s + +time increment:95330 time: 95.33 +field ' c' [explicit solve]: current solution: 9.630041e+01, current residual:5.813065e+01 +field 'mu' [explicit solve]: current solution: 4.689778e+00, current residual:2.828907e+00 +wall time: 0.00059104s + +time increment:95340 time: 95.34 +field ' c' [explicit solve]: current solution: 9.630046e+01, current residual:5.813068e+01 +field 'mu' [explicit solve]: current solution: 4.689718e+00, current residual:2.828871e+00 +wall time: 0.000597954s + +time increment:95350 time: 95.35 +field ' c' [explicit solve]: current solution: 9.630051e+01, current residual:5.813071e+01 +field 'mu' [explicit solve]: current solution: 4.689658e+00, current residual:2.828834e+00 +wall time: 0.000619888s + +time increment:95360 time: 95.36 +field ' c' [explicit solve]: current solution: 9.630056e+01, current residual:5.813073e+01 +field 'mu' [explicit solve]: current solution: 4.689598e+00, current residual:2.828798e+00 +wall time: 0.000571966s + +time increment:95370 time: 95.37 +field ' c' [explicit solve]: current solution: 9.630060e+01, current residual:5.813076e+01 +field 'mu' [explicit solve]: current solution: 4.689538e+00, current residual:2.828761e+00 +wall time: 0.000652075s + +time increment:95380 time: 95.38 +field ' c' [explicit solve]: current solution: 9.630065e+01, current residual:5.813079e+01 +field 'mu' [explicit solve]: current solution: 4.689478e+00, current residual:2.828725e+00 +wall time: 0.000649929s + +time increment:95390 time: 95.39 +field ' c' [explicit solve]: current solution: 9.630070e+01, current residual:5.813081e+01 +field 'mu' [explicit solve]: current solution: 4.689418e+00, current residual:2.828688e+00 +wall time: 0.000699043s + +time increment:95400 time: 95.4 +field ' c' [explicit solve]: current solution: 9.630074e+01, current residual:5.813084e+01 +field 'mu' [explicit solve]: current solution: 4.689358e+00, current residual:2.828652e+00 +wall time: 0.00057292s + +time increment:95410 time: 95.41 +field ' c' [explicit solve]: current solution: 9.630079e+01, current residual:5.813087e+01 +field 'mu' [explicit solve]: current solution: 4.689297e+00, current residual:2.828615e+00 +wall time: 0.000615835s + +time increment:95420 time: 95.42 +field ' c' [explicit solve]: current solution: 9.630084e+01, current residual:5.813090e+01 +field 'mu' [explicit solve]: current solution: 4.689237e+00, current residual:2.828579e+00 +wall time: 0.000607967s + +time increment:95430 time: 95.43 +field ' c' [explicit solve]: current solution: 9.630089e+01, current residual:5.813092e+01 +field 'mu' [explicit solve]: current solution: 4.689177e+00, current residual:2.828542e+00 +wall time: 0.000607967s + +time increment:95440 time: 95.44 +field ' c' [explicit solve]: current solution: 9.630093e+01, current residual:5.813095e+01 +field 'mu' [explicit solve]: current solution: 4.689117e+00, current residual:2.828506e+00 +wall time: 0.000740051s + +time increment:95450 time: 95.45 +field ' c' [explicit solve]: current solution: 9.630098e+01, current residual:5.813098e+01 +field 'mu' [explicit solve]: current solution: 4.689057e+00, current residual:2.828469e+00 +wall time: 0.000695944s + +time increment:95460 time: 95.46 +field ' c' [explicit solve]: current solution: 9.630103e+01, current residual:5.813100e+01 +field 'mu' [explicit solve]: current solution: 4.688997e+00, current residual:2.828432e+00 +wall time: 0.000716209s + +time increment:95470 time: 95.47 +field ' c' [explicit solve]: current solution: 9.630107e+01, current residual:5.813103e+01 +field 'mu' [explicit solve]: current solution: 4.688937e+00, current residual:2.828396e+00 +wall time: 0.000653028s + +time increment:95480 time: 95.48 +field ' c' [explicit solve]: current solution: 9.630112e+01, current residual:5.813106e+01 +field 'mu' [explicit solve]: current solution: 4.688876e+00, current residual:2.828359e+00 +wall time: 0.000637054s + +time increment:95490 time: 95.49 +field ' c' [explicit solve]: current solution: 9.630117e+01, current residual:5.813108e+01 +field 'mu' [explicit solve]: current solution: 4.688816e+00, current residual:2.828323e+00 +wall time: 0.000570059s + +time increment:95500 time: 95.5 +field ' c' [explicit solve]: current solution: 9.630122e+01, current residual:5.813111e+01 +field 'mu' [explicit solve]: current solution: 4.688756e+00, current residual:2.828286e+00 +wall time: 0.000564098s + +time increment:95510 time: 95.51 +field ' c' [explicit solve]: current solution: 9.630126e+01, current residual:5.813114e+01 +field 'mu' [explicit solve]: current solution: 4.688696e+00, current residual:2.828250e+00 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0.000642061s + +time increment:95560 time: 95.56 +field ' c' [explicit solve]: current solution: 9.630150e+01, current residual:5.813127e+01 +field 'mu' [explicit solve]: current solution: 4.688395e+00, current residual:2.828067e+00 +wall time: 0.000630856s + +time increment:95570 time: 95.57 +field ' c' [explicit solve]: current solution: 9.630155e+01, current residual:5.813130e+01 +field 'mu' [explicit solve]: current solution: 4.688335e+00, current residual:2.828030e+00 +wall time: 0.000648975s + +time increment:95580 time: 95.58 +field ' c' [explicit solve]: current solution: 9.630159e+01, current residual:5.813132e+01 +field 'mu' [explicit solve]: current solution: 4.688275e+00, current residual:2.827994e+00 +wall time: 0.000553846s + +time increment:95590 time: 95.59 +field ' c' [explicit solve]: current solution: 9.630164e+01, current residual:5.813135e+01 +field 'mu' [explicit solve]: current solution: 4.688215e+00, current residual:2.827957e+00 +wall time: 0.000595093s + +time increment:95600 time: 95.6 +field ' c' [explicit solve]: current solution: 9.630169e+01, current residual:5.813138e+01 +field 'mu' [explicit solve]: current solution: 4.688155e+00, current residual:2.827921e+00 +wall time: 0.000582218s + +time increment:95610 time: 95.61 +field ' c' [explicit solve]: current solution: 9.630173e+01, current residual:5.813140e+01 +field 'mu' [explicit solve]: current solution: 4.688094e+00, current residual:2.827884e+00 +wall time: 0.000567913s + +time increment:95620 time: 95.62 +field ' c' [explicit solve]: current solution: 9.630178e+01, current residual:5.813143e+01 +field 'mu' [explicit solve]: current solution: 4.688034e+00, current residual:2.827848e+00 +wall time: 0.000568867s + +time increment:95630 time: 95.63 +field ' c' [explicit solve]: current solution: 9.630183e+01, current residual:5.813146e+01 +field 'mu' [explicit solve]: current solution: 4.687974e+00, current residual:2.827811e+00 +wall time: 0.000798941s + +time increment:95640 time: 95.64 +field ' c' [explicit solve]: current solution: 9.630188e+01, current residual:5.813148e+01 +field 'mu' [explicit solve]: current solution: 4.687914e+00, current residual:2.827774e+00 +wall time: 0.000570059s + +time increment:95650 time: 95.65 +field ' c' [explicit solve]: current solution: 9.630192e+01, current residual:5.813151e+01 +field 'mu' [explicit solve]: current solution: 4.687854e+00, current residual:2.827738e+00 +wall time: 0.000571012s + +time increment:95660 time: 95.66 +field ' c' [explicit solve]: current solution: 9.630197e+01, current residual:5.813154e+01 +field 'mu' [explicit solve]: current solution: 4.687794e+00, current residual:2.827701e+00 +wall time: 0.000570059s + +time increment:95670 time: 95.67 +field ' c' [explicit solve]: current solution: 9.630202e+01, current residual:5.813157e+01 +field 'mu' [explicit solve]: current solution: 4.687733e+00, current residual:2.827665e+00 +wall time: 0.000563145s + +time increment:95680 time: 95.68 +field ' c' [explicit solve]: current solution: 9.630206e+01, current residual:5.813159e+01 +field 'mu' [explicit solve]: current solution: 4.687673e+00, current residual:2.827628e+00 +wall time: 0.000637054s + +time increment:95690 time: 95.69 +field ' c' [explicit solve]: current solution: 9.630211e+01, current residual:5.813162e+01 +field 'mu' [explicit solve]: current solution: 4.687613e+00, current residual:2.827592e+00 +wall time: 0.00058794s + +time increment:95700 time: 95.7 +field ' c' [explicit solve]: current solution: 9.630216e+01, current residual:5.813165e+01 +field 'mu' [explicit solve]: current solution: 4.687553e+00, current residual:2.827555e+00 +wall time: 0.0005548s + +time increment:95710 time: 95.71 +field ' c' [explicit solve]: current solution: 9.630221e+01, current residual:5.813167e+01 +field 'mu' [explicit solve]: current solution: 4.687493e+00, current residual:2.827518e+00 +wall time: 0.000653982s + +time increment:95720 time: 95.72 +field ' c' [explicit solve]: current solution: 9.630225e+01, current residual:5.813170e+01 +field 'mu' [explicit solve]: current solution: 4.687432e+00, current residual:2.827482e+00 +wall time: 0.000597954s + +time increment:95730 time: 95.73 +field ' c' [explicit solve]: current solution: 9.630230e+01, current residual:5.813173e+01 +field 'mu' [explicit solve]: current solution: 4.687372e+00, current residual:2.827445e+00 +wall time: 0.000605106s + +time increment:95740 time: 95.74 +field ' c' [explicit solve]: current solution: 9.630235e+01, current residual:5.813175e+01 +field 'mu' [explicit solve]: current solution: 4.687312e+00, current residual:2.827409e+00 +wall time: 0.00067997s + +time increment:95750 time: 95.75 +field ' c' [explicit solve]: current solution: 9.630239e+01, current residual:5.813178e+01 +field 'mu' [explicit solve]: current solution: 4.687252e+00, current residual:2.827372e+00 +wall time: 0.000580072s + +time increment:95760 time: 95.76 +field ' c' [explicit solve]: current solution: 9.630244e+01, current residual:5.813181e+01 +field 'mu' [explicit solve]: current solution: 4.687192e+00, current residual:2.827336e+00 +wall time: 0.000571966s + +time increment:95770 time: 95.77 +field ' c' [explicit solve]: current solution: 9.630249e+01, current residual:5.813183e+01 +field 'mu' [explicit solve]: current solution: 4.687131e+00, current residual:2.827299e+00 +wall time: 0.000596046s + +time increment:95780 time: 95.78 +field ' c' [explicit solve]: current solution: 9.630254e+01, current residual:5.813186e+01 +field 'mu' [explicit solve]: current solution: 4.687071e+00, current residual:2.827262e+00 +wall time: 0.000642061s + +time increment:95790 time: 95.79 +field ' c' [explicit solve]: current solution: 9.630258e+01, current residual:5.813189e+01 +field 'mu' [explicit solve]: current solution: 4.687011e+00, current residual:2.827226e+00 +wall time: 0.000634909s + +time increment:95800 time: 95.8 +field ' c' [explicit solve]: current solution: 9.630263e+01, current residual:5.813191e+01 +field 'mu' [explicit solve]: current solution: 4.686951e+00, current residual:2.827189e+00 +wall time: 0.000622988s + +time increment:95810 time: 95.81 +field ' c' [explicit solve]: current solution: 9.630268e+01, current residual:5.813194e+01 +field 'mu' [explicit solve]: current solution: 4.686890e+00, current residual:2.827153e+00 +wall time: 0.000600815s + +time increment:95820 time: 95.82 +field ' c' [explicit solve]: current solution: 9.630272e+01, current residual:5.813197e+01 +field 'mu' [explicit solve]: current solution: 4.686830e+00, current residual:2.827116e+00 +wall time: 0.00070405s + +time increment:95830 time: 95.83 +field ' c' [explicit solve]: current solution: 9.630277e+01, current residual:5.813199e+01 +field 'mu' [explicit solve]: current solution: 4.686770e+00, current residual:2.827079e+00 +wall time: 0.000561953s + +time increment:95840 time: 95.84 +field ' c' [explicit solve]: current solution: 9.630282e+01, current residual:5.813202e+01 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[explicit solve]: current solution: 4.686469e+00, current residual:2.826896e+00 +wall time: 0.000608921s + +time increment:95890 time: 95.89 +field ' c' [explicit solve]: current solution: 9.630305e+01, current residual:5.813216e+01 +field 'mu' [explicit solve]: current solution: 4.686409e+00, current residual:2.826860e+00 +wall time: 0.000620127s + +time increment:95900 time: 95.9 +field ' c' [explicit solve]: current solution: 9.630310e+01, current residual:5.813218e+01 +field 'mu' [explicit solve]: current solution: 4.686348e+00, current residual:2.826823e+00 +wall time: 0.000586033s + +time increment:95910 time: 95.91 +field ' c' [explicit solve]: current solution: 9.630315e+01, current residual:5.813221e+01 +field 'mu' [explicit solve]: current solution: 4.686288e+00, current residual:2.826787e+00 +wall time: 0.000576973s + +time increment:95920 time: 95.92 +field ' c' [explicit solve]: current solution: 9.630320e+01, current residual:5.813224e+01 +field 'mu' [explicit solve]: current solution: 4.686228e+00, current residual:2.826750e+00 +wall time: 0.00063777s + +time increment:95930 time: 95.93 +field ' c' [explicit solve]: current solution: 9.630324e+01, current residual:5.813226e+01 +field 'mu' [explicit solve]: current solution: 4.686168e+00, current residual:2.826713e+00 +wall time: 0.000645161s + +time increment:95940 time: 95.94 +field ' c' [explicit solve]: current solution: 9.630329e+01, current residual:5.813229e+01 +field 'mu' [explicit solve]: current solution: 4.686107e+00, current residual:2.826677e+00 +wall time: 0.000674009s + +time increment:95950 time: 95.95 +field ' c' [explicit solve]: current solution: 9.630334e+01, current residual:5.813232e+01 +field 'mu' [explicit solve]: current solution: 4.686047e+00, current residual:2.826640e+00 +wall time: 0.000680923s + +time increment:95960 time: 95.96 +field ' c' [explicit solve]: current solution: 9.630338e+01, current residual:5.813234e+01 +field 'mu' [explicit solve]: current solution: 4.685987e+00, current residual:2.826604e+00 +wall time: 0.000640154s + +time increment:95970 time: 95.97 +field ' c' [explicit solve]: current solution: 9.630343e+01, current residual:5.813237e+01 +field 'mu' [explicit solve]: current solution: 4.685927e+00, current residual:2.826567e+00 +wall time: 0.000666142s + +time increment:95980 time: 95.98 +field ' c' [explicit solve]: current solution: 9.630348e+01, current residual:5.813240e+01 +field 'mu' [explicit solve]: current solution: 4.685866e+00, current residual:2.826530e+00 +wall time: 0.00069499s + +time increment:95990 time: 95.99 +field ' c' [explicit solve]: current solution: 9.630353e+01, current residual:5.813242e+01 +field 'mu' [explicit solve]: current solution: 4.685806e+00, current residual:2.826494e+00 +wall time: 0.000658035s + +time increment:96000 time: 96 +field ' c' [explicit solve]: current solution: 9.630357e+01, current residual:5.813245e+01 +field 'mu' [explicit solve]: current solution: 4.685746e+00, current residual:2.826457e+00 +wall time: 0.000668049s + +time increment:96010 time: 96.01 +field ' c' [explicit solve]: current solution: 9.630362e+01, current residual:5.813248e+01 +field 'mu' [explicit solve]: current solution: 4.685686e+00, current residual:2.826421e+00 +wall time: 0.000679016s + +time increment:96020 time: 96.02 +field ' c' [explicit solve]: current solution: 9.630367e+01, current residual:5.813250e+01 +field 'mu' [explicit solve]: current solution: 4.685625e+00, current residual:2.826384e+00 +wall time: 0.000724077s + +time increment:96030 time: 96.03 +field ' c' [explicit solve]: current solution: 9.630372e+01, current residual:5.813253e+01 +field 'mu' [explicit solve]: current solution: 4.685565e+00, current residual:2.826347e+00 +wall time: 0.00064683s + +time increment:96040 time: 96.04 +field ' c' [explicit solve]: current solution: 9.630376e+01, current residual:5.813256e+01 +field 'mu' [explicit solve]: current solution: 4.685505e+00, current residual:2.826311e+00 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0.00071311s + +time increment:96090 time: 96.09 +field ' c' [explicit solve]: current solution: 9.630400e+01, current residual:5.813269e+01 +field 'mu' [explicit solve]: current solution: 4.685203e+00, current residual:2.826128e+00 +wall time: 0.000662088s + +time increment:96100 time: 96.1 +field ' c' [explicit solve]: current solution: 9.630405e+01, current residual:5.813272e+01 +field 'mu' [explicit solve]: current solution: 4.685143e+00, current residual:2.826091e+00 +wall time: 0.000677109s + +time increment:96110 time: 96.11 +field ' c' [explicit solve]: current solution: 9.630409e+01, current residual:5.813275e+01 +field 'mu' [explicit solve]: current solution: 4.685083e+00, current residual:2.826055e+00 +wall time: 0.000666857s + +time increment:96120 time: 96.12 +field ' c' [explicit solve]: current solution: 9.630414e+01, current residual:5.813277e+01 +field 'mu' [explicit solve]: current solution: 4.685022e+00, current residual:2.826018e+00 +wall time: 0.000660896s + +time increment:96130 time: 96.13 +field ' c' [explicit solve]: current solution: 9.630419e+01, current residual:5.813280e+01 +field 'mu' [explicit solve]: current solution: 4.684962e+00, current residual:2.825981e+00 +wall time: 0.000671148s + +time increment:96140 time: 96.14 +field ' c' [explicit solve]: current solution: 9.630423e+01, current residual:5.813283e+01 +field 'mu' [explicit solve]: current solution: 4.684902e+00, current residual:2.825945e+00 +wall time: 0.000679016s + +time increment:96150 time: 96.15 +field ' c' [explicit solve]: current solution: 9.630428e+01, current residual:5.813285e+01 +field 'mu' [explicit solve]: current solution: 4.684841e+00, current residual:2.825908e+00 +wall time: 0.000668049s + +time increment:96160 time: 96.16 +field ' c' [explicit solve]: current solution: 9.630433e+01, current residual:5.813288e+01 +field 'mu' [explicit solve]: current solution: 4.684781e+00, current residual:2.825871e+00 +wall time: 0.000683069s + +time increment:96170 time: 96.17 +field ' c' [explicit solve]: current solution: 9.630438e+01, current residual:5.813291e+01 +field 'mu' [explicit solve]: current solution: 4.684721e+00, current residual:2.825835e+00 +wall time: 0.000663996s + +time increment:96180 time: 96.18 +field ' c' [explicit solve]: current solution: 9.630442e+01, current residual:5.813293e+01 +field 'mu' [explicit solve]: current solution: 4.684661e+00, current residual:2.825798e+00 +wall time: 0.000725031s + +time increment:96190 time: 96.19 +field ' c' [explicit solve]: current solution: 9.630447e+01, current residual:5.813296e+01 +field 'mu' [explicit solve]: current solution: 4.684600e+00, current residual:2.825762e+00 +wall time: 0.000695944s + +time increment:96200 time: 96.2 +field ' c' [explicit solve]: current solution: 9.630452e+01, current residual:5.813299e+01 +field 'mu' [explicit solve]: current solution: 4.684540e+00, current residual:2.825725e+00 +wall time: 0.000703096s + +time increment:96210 time: 96.21 +field ' c' [explicit solve]: current solution: 9.630456e+01, current residual:5.813301e+01 +field 'mu' [explicit solve]: current solution: 4.684480e+00, current residual:2.825688e+00 +wall time: 0.000663996s + +time increment:96220 time: 96.22 +field ' c' [explicit solve]: current solution: 9.630461e+01, current residual:5.813304e+01 +field 'mu' [explicit solve]: current solution: 4.684419e+00, current residual:2.825652e+00 +wall time: 0.000649214s + +time increment:96230 time: 96.23 +field ' c' [explicit solve]: current solution: 9.630466e+01, current residual:5.813307e+01 +field 'mu' [explicit solve]: current solution: 4.684359e+00, current residual:2.825615e+00 +wall time: 0.000761032s + +time increment:96240 time: 96.24 +field ' c' [explicit solve]: current solution: 9.630471e+01, current residual:5.813310e+01 +field 'mu' [explicit solve]: current solution: 4.684299e+00, current residual:2.825578e+00 +wall time: 0.000654936s + +time increment:96250 time: 96.25 +field ' c' [explicit solve]: current solution: 9.630475e+01, current residual:5.813312e+01 +field 'mu' [explicit solve]: current solution: 4.684238e+00, current residual:2.825542e+00 +wall time: 0.000622034s + +time increment:96260 time: 96.26 +field ' c' [explicit solve]: current solution: 9.630480e+01, current residual:5.813315e+01 +field 'mu' [explicit solve]: current solution: 4.684178e+00, current residual:2.825505e+00 +wall time: 0.000663996s + +time increment:96270 time: 96.27 +field ' c' [explicit solve]: current solution: 9.630485e+01, current residual:5.813318e+01 +field 'mu' [explicit solve]: current solution: 4.684118e+00, current residual:2.825468e+00 +wall time: 0.000624895s + +time increment:96280 time: 96.28 +field ' c' [explicit solve]: current solution: 9.630490e+01, current residual:5.813320e+01 +field 'mu' [explicit solve]: current solution: 4.684057e+00, current residual:2.825432e+00 +wall time: 0.000724077s + +time increment:96290 time: 96.29 +field ' c' [explicit solve]: current solution: 9.630494e+01, current residual:5.813323e+01 +field 'mu' [explicit solve]: current solution: 4.683997e+00, current residual:2.825395e+00 +wall time: 0.000675917s + +time increment:96300 time: 96.3 +field ' c' [explicit solve]: current solution: 9.630499e+01, current residual:5.813326e+01 +field 'mu' [explicit solve]: current solution: 4.683937e+00, current residual:2.825359e+00 +wall time: 0.000571012s + +time increment:96310 time: 96.31 +field ' c' [explicit solve]: current solution: 9.630504e+01, current residual:5.813328e+01 +field 'mu' [explicit solve]: current solution: 4.683876e+00, current residual:2.825322e+00 +wall time: 0.00060606s + +time increment:96320 time: 96.32 +field ' c' [explicit solve]: current solution: 9.630508e+01, current residual:5.813331e+01 +field 'mu' [explicit solve]: current solution: 4.683816e+00, current residual:2.825285e+00 +wall time: 0.000608921s + +time increment:96330 time: 96.33 +field ' c' [explicit solve]: current solution: 9.630513e+01, current residual:5.813334e+01 +field 'mu' [explicit solve]: current solution: 4.683756e+00, current residual:2.825249e+00 +wall time: 0.000578165s + +time increment:96340 time: 96.34 +field ' c' [explicit solve]: current solution: 9.630518e+01, current residual:5.813336e+01 +field 'mu' [explicit solve]: current solution: 4.683695e+00, current residual:2.825212e+00 +wall time: 0.000560999s + +time increment:96350 time: 96.35 +field ' c' [explicit solve]: current solution: 9.630523e+01, current residual:5.813339e+01 +field 'mu' [explicit solve]: current solution: 4.683635e+00, current residual:2.825175e+00 +wall time: 0.000696182s + +time increment:96360 time: 96.36 +field ' c' [explicit solve]: current solution: 9.630527e+01, current residual:5.813342e+01 +field 'mu' [explicit solve]: current solution: 4.683574e+00, current residual:2.825139e+00 +wall time: 0.000564098s + +time increment:96370 time: 96.37 +field ' c' [explicit solve]: current solution: 9.630532e+01, current residual:5.813344e+01 +field 'mu' [explicit solve]: current solution: 4.683514e+00, current residual:2.825102e+00 +wall time: 0.000667095s + +time increment:96380 time: 96.38 +field ' c' [explicit solve]: current solution: 9.630537e+01, current residual:5.813347e+01 +field 'mu' [explicit solve]: current solution: 4.683454e+00, current residual:2.825065e+00 +wall time: 0.000662088s + +time increment:96390 time: 96.39 +field ' c' [explicit solve]: current solution: 9.630541e+01, current residual:5.813350e+01 +field 'mu' [explicit solve]: current solution: 4.683393e+00, current residual:2.825029e+00 +wall time: 0.000557899s + +time increment:96400 time: 96.4 +field ' c' [explicit solve]: current solution: 9.630546e+01, current residual:5.813353e+01 +field 'mu' [explicit solve]: current solution: 4.683333e+00, current residual:2.824992e+00 +wall time: 0.000616789s + +time increment:96410 time: 96.41 +field ' c' [explicit solve]: current solution: 9.630551e+01, current residual:5.813355e+01 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[explicit solve]: current solution: 4.683031e+00, current residual:2.824809e+00 +wall time: 0.000645876s + +time increment:96460 time: 96.46 +field ' c' [explicit solve]: current solution: 9.630575e+01, current residual:5.813369e+01 +field 'mu' [explicit solve]: current solution: 4.682971e+00, current residual:2.824772e+00 +wall time: 0.000579119s + +time increment:96470 time: 96.47 +field ' c' [explicit solve]: current solution: 9.630579e+01, current residual:5.813371e+01 +field 'mu' [explicit solve]: current solution: 4.682910e+00, current residual:2.824736e+00 +wall time: 0.000625849s + +time increment:96480 time: 96.48 +field ' c' [explicit solve]: current solution: 9.630584e+01, current residual:5.813374e+01 +field 'mu' [explicit solve]: current solution: 4.682850e+00, current residual:2.824699e+00 +wall time: 0.000602007s + +time increment:96490 time: 96.49 +field ' c' [explicit solve]: current solution: 9.630589e+01, current residual:5.813377e+01 +field 'mu' [explicit solve]: current solution: 4.682790e+00, current residual:2.824662e+00 +wall time: 0.000651121s + +time increment:96500 time: 96.5 +field ' c' [explicit solve]: current solution: 9.630593e+01, current residual:5.813379e+01 +field 'mu' [explicit solve]: current solution: 4.682729e+00, current residual:2.824626e+00 +wall time: 0.000556946s + +time increment:96510 time: 96.51 +field ' c' [explicit solve]: current solution: 9.630598e+01, current residual:5.813382e+01 +field 'mu' [explicit solve]: current solution: 4.682669e+00, current residual:2.824589e+00 +wall time: 0.00060606s + +time increment:96520 time: 96.52 +field ' c' [explicit solve]: current solution: 9.630603e+01, current residual:5.813385e+01 +field 'mu' [explicit solve]: current solution: 4.682608e+00, current residual:2.824552e+00 +wall time: 0.000607967s + +time increment:96530 time: 96.53 +field ' c' [explicit solve]: current solution: 9.630608e+01, current residual:5.813387e+01 +field 'mu' [explicit solve]: current solution: 4.682548e+00, current residual:2.824516e+00 +wall time: 0.00069499s + +time increment:96540 time: 96.54 +field ' c' [explicit solve]: current solution: 9.630612e+01, current residual:5.813390e+01 +field 'mu' [explicit solve]: current solution: 4.682488e+00, current residual:2.824479e+00 +wall time: 0.000557184s + +time increment:96550 time: 96.55 +field ' c' [explicit solve]: current solution: 9.630617e+01, current residual:5.813393e+01 +field 'mu' [explicit solve]: current solution: 4.682427e+00, current residual:2.824442e+00 +wall time: 0.000712156s + +time increment:96560 time: 96.56 +field ' c' [explicit solve]: current solution: 9.630622e+01, current residual:5.813396e+01 +field 'mu' [explicit solve]: current solution: 4.682367e+00, current residual:2.824406e+00 +wall time: 0.000589848s + +time increment:96570 time: 96.57 +field ' c' [explicit solve]: current solution: 9.630627e+01, current residual:5.813398e+01 +field 'mu' [explicit solve]: current solution: 4.682306e+00, current residual:2.824369e+00 +wall time: 0.000570059s + +time increment:96580 time: 96.58 +field ' c' [explicit solve]: current solution: 9.630631e+01, current residual:5.813401e+01 +field 'mu' [explicit solve]: current solution: 4.682246e+00, current residual:2.824332e+00 +wall time: 0.000573874s + +time increment:96590 time: 96.59 +field ' c' [explicit solve]: current solution: 9.630636e+01, current residual:5.813404e+01 +field 'mu' [explicit solve]: current solution: 4.682186e+00, current residual:2.824296e+00 +wall time: 0.000654936s + +time increment:96600 time: 96.6 +field ' c' [explicit solve]: current solution: 9.630641e+01, current residual:5.813406e+01 +field 'mu' [explicit solve]: current solution: 4.682125e+00, current residual:2.824259e+00 +wall time: 0.000667095s + +time increment:96610 time: 96.61 +field ' c' [explicit solve]: current solution: 9.630645e+01, current residual:5.813409e+01 +field 'mu' [explicit solve]: current solution: 4.682065e+00, current residual:2.824222e+00 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0.000642061s + +time increment:96660 time: 96.66 +field ' c' [explicit solve]: current solution: 9.630669e+01, current residual:5.813422e+01 +field 'mu' [explicit solve]: current solution: 4.681763e+00, current residual:2.824039e+00 +wall time: 0.000679016s + +time increment:96670 time: 96.67 +field ' c' [explicit solve]: current solution: 9.630674e+01, current residual:5.813425e+01 +field 'mu' [explicit solve]: current solution: 4.681702e+00, current residual:2.824002e+00 +wall time: 0.00064683s + +time increment:96680 time: 96.68 +field ' c' [explicit solve]: current solution: 9.630678e+01, current residual:5.813428e+01 +field 'mu' [explicit solve]: current solution: 4.681642e+00, current residual:2.823966e+00 +wall time: 0.000720024s + +time increment:96690 time: 96.69 +field ' c' [explicit solve]: current solution: 9.630683e+01, current residual:5.813431e+01 +field 'mu' [explicit solve]: current solution: 4.681581e+00, current residual:2.823929e+00 +wall time: 0.000595093s + +time increment:96700 time: 96.7 +field ' c' [explicit solve]: current solution: 9.630688e+01, current residual:5.813433e+01 +field 'mu' [explicit solve]: current solution: 4.681521e+00, current residual:2.823892e+00 +wall time: 0.000569105s + +time increment:96710 time: 96.71 +field ' c' [explicit solve]: current solution: 9.630693e+01, current residual:5.813436e+01 +field 'mu' [explicit solve]: current solution: 4.681460e+00, current residual:2.823855e+00 +wall time: 0.000750065s + +time increment:96720 time: 96.72 +field ' c' [explicit solve]: current solution: 9.630697e+01, current residual:5.813439e+01 +field 'mu' [explicit solve]: current solution: 4.681400e+00, current residual:2.823819e+00 +wall time: 0.000652075s + +time increment:96730 time: 96.73 +field ' c' [explicit solve]: current solution: 9.630702e+01, current residual:5.813441e+01 +field 'mu' [explicit solve]: current solution: 4.681339e+00, current residual:2.823782e+00 +wall time: 0.000581026s + +time increment:96740 time: 96.74 +field ' c' [explicit solve]: current solution: 9.630707e+01, current residual:5.813444e+01 +field 'mu' [explicit solve]: current solution: 4.681279e+00, current residual:2.823745e+00 +wall time: 0.000643969s + +time increment:96750 time: 96.75 +field ' c' [explicit solve]: current solution: 9.630712e+01, current residual:5.813447e+01 +field 'mu' [explicit solve]: current solution: 4.681219e+00, current residual:2.823709e+00 +wall time: 0.000653028s + +time increment:96760 time: 96.76 +field ' c' [explicit solve]: current solution: 9.630716e+01, current residual:5.813449e+01 +field 'mu' [explicit solve]: current solution: 4.681158e+00, current residual:2.823672e+00 +wall time: 0.000555992s + +time increment:96770 time: 96.77 +field ' c' [explicit solve]: current solution: 9.630721e+01, current residual:5.813452e+01 +field 'mu' [explicit solve]: current solution: 4.681098e+00, current residual:2.823635e+00 +wall time: 0.000560999s + +time increment:96780 time: 96.78 +field ' c' [explicit solve]: current solution: 9.630726e+01, current residual:5.813455e+01 +field 'mu' [explicit solve]: current solution: 4.681037e+00, current residual:2.823599e+00 +wall time: 0.000579119s + +time increment:96790 time: 96.79 +field ' c' [explicit solve]: current solution: 9.630730e+01, current residual:5.813457e+01 +field 'mu' [explicit solve]: current solution: 4.680977e+00, current residual:2.823562e+00 +wall time: 0.000639915s + +time increment:96800 time: 96.8 +field ' c' [explicit solve]: current solution: 9.630735e+01, current residual:5.813460e+01 +field 'mu' [explicit solve]: current solution: 4.680916e+00, current residual:2.823525e+00 +wall time: 0.000564098s + +time increment:96810 time: 96.81 +field ' c' [explicit solve]: current solution: 9.630740e+01, current residual:5.813463e+01 +field 'mu' [explicit solve]: current solution: 4.680856e+00, current residual:2.823489e+00 +wall time: 0.000680923s + +time increment:96820 time: 96.82 +field ' c' [explicit solve]: current solution: 9.630745e+01, current residual:5.813466e+01 +field 'mu' [explicit solve]: current solution: 4.680795e+00, current residual:2.823452e+00 +wall time: 0.000715971s + +time increment:96830 time: 96.83 +field ' c' [explicit solve]: current solution: 9.630749e+01, current residual:5.813468e+01 +field 'mu' [explicit solve]: current solution: 4.680735e+00, current residual:2.823415e+00 +wall time: 0.000691891s + +time increment:96840 time: 96.84 +field ' c' [explicit solve]: current solution: 9.630754e+01, current residual:5.813471e+01 +field 'mu' [explicit solve]: current solution: 4.680674e+00, current residual:2.823379e+00 +wall time: 0.000675201s + +time increment:96850 time: 96.85 +field ' c' [explicit solve]: current solution: 9.630759e+01, current residual:5.813474e+01 +field 'mu' [explicit solve]: current solution: 4.680614e+00, current residual:2.823342e+00 +wall time: 0.00067687s + +time increment:96860 time: 96.86 +field ' c' [explicit solve]: current solution: 9.630764e+01, current residual:5.813476e+01 +field 'mu' [explicit solve]: current solution: 4.680553e+00, current residual:2.823305e+00 +wall time: 0.000640154s + +time increment:96870 time: 96.87 +field ' c' [explicit solve]: current solution: 9.630768e+01, current residual:5.813479e+01 +field 'mu' [explicit solve]: current solution: 4.680493e+00, current residual:2.823268e+00 +wall time: 0.000656843s + +time increment:96880 time: 96.88 +field ' c' [explicit solve]: current solution: 9.630773e+01, current residual:5.813482e+01 +field 'mu' [explicit solve]: current solution: 4.680432e+00, current residual:2.823232e+00 +wall time: 0.000746965s + +time increment:96890 time: 96.89 +field ' c' [explicit solve]: current solution: 9.630778e+01, current residual:5.813484e+01 +field 'mu' [explicit solve]: current solution: 4.680372e+00, current residual:2.823195e+00 +wall time: 0.000690937s + +time increment:96900 time: 96.9 +field ' c' [explicit solve]: current solution: 9.630782e+01, current residual:5.813487e+01 +field 'mu' [explicit solve]: current solution: 4.680312e+00, current residual:2.823158e+00 +wall time: 0.000663996s + +time increment:96910 time: 96.91 +field ' c' [explicit solve]: current solution: 9.630787e+01, current residual:5.813490e+01 +field 'mu' [explicit solve]: current solution: 4.680251e+00, current residual:2.823122e+00 +wall time: 0.000697136s + +time increment:96920 time: 96.92 +field ' c' [explicit solve]: current solution: 9.630792e+01, current residual:5.813492e+01 +field 'mu' [explicit solve]: current solution: 4.680191e+00, current residual:2.823085e+00 +wall time: 0.000642061s + +time increment:96930 time: 96.93 +field ' c' [explicit solve]: current solution: 9.630797e+01, current residual:5.813495e+01 +field 'mu' [explicit solve]: current solution: 4.680130e+00, current residual:2.823048e+00 +wall time: 0.000761986s + +time increment:96940 time: 96.94 +field ' c' [explicit solve]: current solution: 9.630801e+01, current residual:5.813498e+01 +field 'mu' [explicit solve]: current solution: 4.680070e+00, current residual:2.823012e+00 +wall time: 0.000659943s + +time increment:96950 time: 96.95 +field ' c' [explicit solve]: current solution: 9.630806e+01, current residual:5.813501e+01 +field 'mu' [explicit solve]: current solution: 4.680009e+00, current residual:2.822975e+00 +wall time: 0.00079608s + +time increment:96960 time: 96.96 +field ' c' [explicit solve]: current solution: 9.630811e+01, current residual:5.813503e+01 +field 'mu' [explicit solve]: current solution: 4.679949e+00, current residual:2.822938e+00 +wall time: 0.00067997s + +time increment:96970 time: 96.97 +field ' c' [explicit solve]: current solution: 9.630816e+01, current residual:5.813506e+01 +field 'mu' [explicit solve]: current solution: 4.679888e+00, current residual:2.822901e+00 +wall time: 0.000789165s + +time increment:96980 time: 96.98 +field ' c' [explicit solve]: current solution: 9.630820e+01, current residual:5.813509e+01 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solve]: current solution: 4.679585e+00, current residual:2.822718e+00 +wall time: 0.000661135s + +time increment:97030 time: 97.03 +field ' c' [explicit solve]: current solution: 9.630844e+01, current residual:5.813522e+01 +field 'mu' [explicit solve]: current solution: 4.679525e+00, current residual:2.822681e+00 +wall time: 0.000658035s + +time increment:97040 time: 97.04 +field ' c' [explicit solve]: current solution: 9.630849e+01, current residual:5.813525e+01 +field 'mu' [explicit solve]: current solution: 4.679464e+00, current residual:2.822644e+00 +wall time: 0.000695944s + +time increment:97050 time: 97.05 +field ' c' [explicit solve]: current solution: 9.630853e+01, current residual:5.813528e+01 +field 'mu' [explicit solve]: current solution: 4.679404e+00, current residual:2.822608e+00 +wall time: 0.00067091s + +time increment:97060 time: 97.06 +field ' c' [explicit solve]: current solution: 9.630858e+01, current residual:5.813530e+01 +field 'mu' [explicit solve]: current solution: 4.679343e+00, current residual:2.822571e+00 +wall time: 0.000694036s + +time increment:97070 time: 97.07 +field ' c' [explicit solve]: current solution: 9.630863e+01, current residual:5.813533e+01 +field 'mu' [explicit solve]: current solution: 4.679283e+00, current residual:2.822534e+00 +wall time: 0.000721931s + +time increment:97080 time: 97.08 +field ' c' [explicit solve]: current solution: 9.630868e+01, current residual:5.813536e+01 +field 'mu' [explicit solve]: current solution: 4.679222e+00, current residual:2.822498e+00 +wall time: 0.000706911s + +time increment:97090 time: 97.09 +field ' c' [explicit solve]: current solution: 9.630872e+01, current residual:5.813538e+01 +field 'mu' [explicit solve]: current solution: 4.679162e+00, current residual:2.822461e+00 +wall time: 0.000857115s + +time increment:97100 time: 97.1 +field ' c' [explicit solve]: current solution: 9.630877e+01, current residual:5.813541e+01 +field 'mu' [explicit solve]: current solution: 4.679101e+00, current residual:2.822424e+00 +wall time: 0.000669003s + +time increment:97110 time: 97.11 +field ' c' [explicit solve]: current solution: 9.630882e+01, current residual:5.813544e+01 +field 'mu' [explicit solve]: current solution: 4.679041e+00, current residual:2.822387e+00 +wall time: 0.000650883s + +time increment:97120 time: 97.12 +field ' c' [explicit solve]: current solution: 9.630887e+01, current residual:5.813546e+01 +field 'mu' [explicit solve]: current solution: 4.678980e+00, current residual:2.822351e+00 +wall time: 0.000678062s + +time increment:97130 time: 97.13 +field ' c' [explicit solve]: current solution: 9.630891e+01, current residual:5.813549e+01 +field 'mu' [explicit solve]: current solution: 4.678920e+00, current residual:2.822314e+00 +wall time: 0.000661135s + +time increment:97140 time: 97.14 +field ' c' [explicit solve]: current solution: 9.630896e+01, current residual:5.813552e+01 +field 'mu' [explicit solve]: current solution: 4.678859e+00, current residual:2.822277e+00 +wall time: 0.000622988s + +time increment:97150 time: 97.15 +field ' c' [explicit solve]: current solution: 9.630901e+01, current residual:5.813554e+01 +field 'mu' [explicit solve]: current solution: 4.678799e+00, current residual:2.822240e+00 +wall time: 0.000727892s + +time increment:97160 time: 97.16 +field ' c' [explicit solve]: current solution: 9.630905e+01, current residual:5.813557e+01 +field 'mu' [explicit solve]: current solution: 4.678738e+00, current residual:2.822204e+00 +wall time: 0.000669003s + +time increment:97170 time: 97.17 +field ' c' [explicit solve]: current solution: 9.630910e+01, current residual:5.813560e+01 +field 'mu' [explicit solve]: current solution: 4.678677e+00, current residual:2.822167e+00 +wall time: 0.000696182s + +time increment:97180 time: 97.18 +field ' c' [explicit solve]: current solution: 9.630915e+01, current residual:5.813563e+01 +field 'mu' [explicit solve]: current solution: 4.678617e+00, current residual:2.822130e+00 +wall time: 0.000759125s + +time increment:97190 time: 97.19 +field ' c' [explicit solve]: current solution: 9.630920e+01, current residual:5.813565e+01 +field 'mu' [explicit solve]: current solution: 4.678556e+00, current residual:2.822094e+00 +wall time: 0.00068593s + +time increment:97200 time: 97.2 +field ' c' [explicit solve]: current solution: 9.630924e+01, current residual:5.813568e+01 +field 'mu' [explicit solve]: current solution: 4.678496e+00, current residual:2.822057e+00 +wall time: 0.000760078s + +time increment:97210 time: 97.21 +field ' c' [explicit solve]: current solution: 9.630929e+01, current residual:5.813571e+01 +field 'mu' [explicit solve]: current solution: 4.678435e+00, current residual:2.822020e+00 +wall time: 0.000691891s + +time increment:97220 time: 97.22 +field ' c' [explicit solve]: current solution: 9.630934e+01, current residual:5.813573e+01 +field 'mu' [explicit solve]: current solution: 4.678375e+00, current residual:2.821983e+00 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0.00067997s + +time increment:97270 time: 97.27 +field ' c' [explicit solve]: current solution: 9.630957e+01, current residual:5.813587e+01 +field 'mu' [explicit solve]: current solution: 4.678072e+00, current residual:2.821800e+00 +wall time: 0.000684023s + +time increment:97280 time: 97.28 +field ' c' [explicit solve]: current solution: 9.630962e+01, current residual:5.813590e+01 +field 'mu' [explicit solve]: current solution: 4.678011e+00, current residual:2.821763e+00 +wall time: 0.000683069s + +time increment:97290 time: 97.29 +field ' c' [explicit solve]: current solution: 9.630967e+01, current residual:5.813592e+01 +field 'mu' [explicit solve]: current solution: 4.677951e+00, current residual:2.821726e+00 +wall time: 0.000772953s + +time increment:97300 time: 97.3 +field ' c' [explicit solve]: current solution: 9.630972e+01, current residual:5.813595e+01 +field 'mu' [explicit solve]: current solution: 4.677890e+00, current residual:2.821689e+00 +wall time: 0.000757933s + +time increment:97310 time: 97.31 +field ' c' [explicit solve]: current solution: 9.630976e+01, current residual:5.813598e+01 +field 'mu' [explicit solve]: current solution: 4.677829e+00, current residual:2.821653e+00 +wall time: 0.000754118s + +time increment:97320 time: 97.32 +field ' c' [explicit solve]: current solution: 9.630981e+01, current residual:5.813600e+01 +field 'mu' [explicit solve]: current solution: 4.677769e+00, current residual:2.821616e+00 +wall time: 0.000784874s + +time increment:97330 time: 97.33 +field ' c' [explicit solve]: current solution: 9.630986e+01, current residual:5.813603e+01 +field 'mu' [explicit solve]: current solution: 4.677708e+00, current residual:2.821579e+00 +wall time: 0.000713825s + +time increment:97340 time: 97.34 +field ' c' [explicit solve]: current solution: 9.630991e+01, current residual:5.813606e+01 +field 'mu' [explicit solve]: current solution: 4.677648e+00, current residual:2.821542e+00 +wall time: 0.000762939s + +time increment:97350 time: 97.35 +field ' c' [explicit solve]: current solution: 9.630995e+01, current residual:5.813608e+01 +field 'mu' [explicit solve]: current solution: 4.677587e+00, current residual:2.821506e+00 +wall time: 0.000745773s + +time increment:97360 time: 97.36 +field ' c' [explicit solve]: current solution: 9.631000e+01, current residual:5.813611e+01 +field 'mu' [explicit solve]: current solution: 4.677527e+00, current residual:2.821469e+00 +wall time: 0.000709772s + +time increment:97370 time: 97.37 +field ' c' [explicit solve]: current solution: 9.631005e+01, current residual:5.813614e+01 +field 'mu' [explicit solve]: current solution: 4.677466e+00, current residual:2.821432e+00 +wall time: 0.000819921s + +time increment:97380 time: 97.38 +field ' c' [explicit solve]: current solution: 9.631009e+01, current residual:5.813617e+01 +field 'mu' [explicit solve]: current solution: 4.677405e+00, current residual:2.821395e+00 +wall time: 0.000814915s + +time increment:97390 time: 97.39 +field ' c' [explicit solve]: current solution: 9.631014e+01, current residual:5.813619e+01 +field 'mu' [explicit solve]: current solution: 4.677345e+00, current residual:2.821359e+00 +wall time: 0.000828981s + +time increment:97400 time: 97.4 +field ' c' [explicit solve]: current solution: 9.631019e+01, current residual:5.813622e+01 +field 'mu' [explicit solve]: current solution: 4.677284e+00, current residual:2.821322e+00 +wall time: 0.000732899s + +time increment:97410 time: 97.41 +field ' c' [explicit solve]: current solution: 9.631024e+01, current residual:5.813625e+01 +field 'mu' [explicit solve]: current solution: 4.677224e+00, current residual:2.821285e+00 +wall time: 0.000761032s + +time increment:97420 time: 97.42 +field ' c' [explicit solve]: current solution: 9.631028e+01, current residual:5.813627e+01 +field 'mu' [explicit solve]: current solution: 4.677163e+00, current residual:2.821248e+00 +wall time: 0.000772953s + +time increment:97430 time: 97.43 +field ' c' [explicit solve]: current solution: 9.631033e+01, current residual:5.813630e+01 +field 'mu' [explicit solve]: current solution: 4.677102e+00, current residual:2.821212e+00 +wall time: 0.000674963s + +time increment:97440 time: 97.44 +field ' c' [explicit solve]: current solution: 9.631038e+01, current residual:5.813633e+01 +field 'mu' [explicit solve]: current solution: 4.677042e+00, current residual:2.821175e+00 +wall time: 0.000747919s + +time increment:97450 time: 97.45 +field ' c' [explicit solve]: current solution: 9.631043e+01, current residual:5.813635e+01 +field 'mu' [explicit solve]: current solution: 4.676981e+00, current residual:2.821138e+00 +wall time: 0.000717878s + +time increment:97460 time: 97.46 +field ' c' [explicit solve]: current solution: 9.631047e+01, current residual:5.813638e+01 +field 'mu' [explicit solve]: current solution: 4.676920e+00, current residual:2.821101e+00 +wall time: 0.000784874s + +time increment:97470 time: 97.47 +field ' c' [explicit solve]: current solution: 9.631052e+01, current residual:5.813641e+01 +field 'mu' [explicit solve]: current solution: 4.676860e+00, current residual:2.821065e+00 +wall time: 0.000752926s + +time increment:97480 time: 97.48 +field ' c' [explicit solve]: current solution: 9.631057e+01, current residual:5.813644e+01 +field 'mu' [explicit solve]: current solution: 4.676799e+00, current residual:2.821028e+00 +wall time: 0.000738859s + +time increment:97490 time: 97.49 +field ' c' [explicit solve]: current solution: 9.631062e+01, current residual:5.813646e+01 +field 'mu' [explicit solve]: current solution: 4.676739e+00, current residual:2.820991e+00 +wall time: 0.000825167s + +time increment:97500 time: 97.5 +field ' c' [explicit solve]: current solution: 9.631066e+01, current residual:5.813649e+01 +field 'mu' [explicit solve]: current solution: 4.676678e+00, current residual:2.820954e+00 +wall time: 0.0007689s + +time increment:97510 time: 97.51 +field ' c' [explicit solve]: current solution: 9.631071e+01, current residual:5.813652e+01 +field 'mu' [explicit solve]: current solution: 4.676617e+00, current residual:2.820918e+00 +wall time: 0.000691175s + +time increment:97520 time: 97.52 +field ' c' [explicit solve]: current solution: 9.631076e+01, current residual:5.813654e+01 +field 'mu' [explicit solve]: current solution: 4.676557e+00, current residual:2.820881e+00 +wall time: 0.000735998s + +time increment:97530 time: 97.53 +field ' c' [explicit solve]: current solution: 9.631080e+01, current residual:5.813657e+01 +field 'mu' [explicit solve]: current solution: 4.676496e+00, current residual:2.820844e+00 +wall time: 0.000687122s + +time increment:97540 time: 97.54 +field ' c' [explicit solve]: current solution: 9.631085e+01, current residual:5.813660e+01 +field 'mu' [explicit solve]: current solution: 4.676435e+00, current residual:2.820807e+00 +wall time: 0.00085783s + +time increment:97550 time: 97.55 +field ' c' [explicit solve]: current solution: 9.631090e+01, current residual:5.813662e+01 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[explicit solve]: current solution: 4.676132e+00, current residual:2.820623e+00 +wall time: 0.000763893s + +time increment:97600 time: 97.6 +field ' c' [explicit solve]: current solution: 9.631114e+01, current residual:5.813676e+01 +field 'mu' [explicit solve]: current solution: 4.676072e+00, current residual:2.820587e+00 +wall time: 0.000792027s + +time increment:97610 time: 97.61 +field ' c' [explicit solve]: current solution: 9.631118e+01, current residual:5.813679e+01 +field 'mu' [explicit solve]: current solution: 4.676011e+00, current residual:2.820550e+00 +wall time: 0.000684023s + +time increment:97620 time: 97.62 +field ' c' [explicit solve]: current solution: 9.631123e+01, current residual:5.813681e+01 +field 'mu' [explicit solve]: current solution: 4.675950e+00, current residual:2.820513e+00 +wall time: 0.000741959s + +time increment:97630 time: 97.63 +field ' c' [explicit solve]: current solution: 9.631128e+01, current residual:5.813684e+01 +field 'mu' [explicit solve]: current solution: 4.675890e+00, current residual:2.820476e+00 +wall time: 0.000837088s + +time increment:97640 time: 97.64 +field ' c' [explicit solve]: current solution: 9.631133e+01, current residual:5.813687e+01 +field 'mu' [explicit solve]: current solution: 4.675829e+00, current residual:2.820440e+00 +wall time: 0.000791073s + +time increment:97650 time: 97.65 +field ' c' [explicit solve]: current solution: 9.631137e+01, current residual:5.813689e+01 +field 'mu' [explicit solve]: current solution: 4.675768e+00, current residual:2.820403e+00 +wall time: 0.000741005s + +time increment:97660 time: 97.66 +field ' c' [explicit solve]: current solution: 9.631142e+01, current residual:5.813692e+01 +field 'mu' [explicit solve]: current solution: 4.675708e+00, current residual:2.820366e+00 +wall time: 0.000784159s + +time increment:97670 time: 97.67 +field ' c' [explicit solve]: current solution: 9.631147e+01, current residual:5.813695e+01 +field 'mu' [explicit solve]: current solution: 4.675647e+00, current residual:2.820329e+00 +wall time: 0.000770092s + +time increment:97680 time: 97.68 +field ' c' [explicit solve]: current solution: 9.631151e+01, current residual:5.813698e+01 +field 'mu' [explicit solve]: current solution: 4.675586e+00, current residual:2.820293e+00 +wall time: 0.000787973s + +time increment:97690 time: 97.69 +field ' c' [explicit solve]: current solution: 9.631156e+01, current residual:5.813700e+01 +field 'mu' [explicit solve]: current solution: 4.675526e+00, current residual:2.820256e+00 +wall time: 0.000702858s + +time increment:97700 time: 97.7 +field ' c' [explicit solve]: current solution: 9.631161e+01, current residual:5.813703e+01 +field 'mu' [explicit solve]: current solution: 4.675465e+00, current residual:2.820219e+00 +wall time: 0.000691175s + +time increment:97710 time: 97.71 +field ' c' [explicit solve]: current solution: 9.631166e+01, current residual:5.813706e+01 +field 'mu' [explicit solve]: current solution: 4.675404e+00, current residual:2.820182e+00 +wall time: 0.000725031s + +time increment:97720 time: 97.72 +field ' c' [explicit solve]: current solution: 9.631170e+01, current residual:5.813708e+01 +field 'mu' [explicit solve]: current solution: 4.675344e+00, current residual:2.820145e+00 +wall time: 0.000646114s + +time increment:97730 time: 97.73 +field ' c' [explicit solve]: current solution: 9.631175e+01, current residual:5.813711e+01 +field 'mu' [explicit solve]: current solution: 4.675283e+00, current residual:2.820109e+00 +wall time: 0.000712156s + +time increment:97740 time: 97.74 +field ' c' [explicit solve]: current solution: 9.631180e+01, current residual:5.813714e+01 +field 'mu' [explicit solve]: current solution: 4.675222e+00, current residual:2.820072e+00 +wall time: 0.000722885s + +time increment:97750 time: 97.75 +field ' c' [explicit solve]: current solution: 9.631185e+01, current residual:5.813716e+01 +field 'mu' [explicit solve]: current solution: 4.675162e+00, current residual:2.820035e+00 +wall time: 0.000691891s + +time increment:97760 time: 97.76 +field ' c' [explicit solve]: current solution: 9.631189e+01, current residual:5.813719e+01 +field 'mu' [explicit solve]: current solution: 4.675101e+00, current residual:2.819998e+00 +wall time: 0.000748873s + +time increment:97770 time: 97.77 +field ' c' [explicit solve]: current solution: 9.631194e+01, current residual:5.813722e+01 +field 'mu' [explicit solve]: current solution: 4.675040e+00, current residual:2.819961e+00 +wall time: 0.000632048s + +time increment:97780 time: 97.78 +field ' c' [explicit solve]: current solution: 9.631199e+01, current residual:5.813725e+01 +field 'mu' [explicit solve]: current solution: 4.674979e+00, current residual:2.819925e+00 +wall time: 0.000705957s + +time increment:97790 time: 97.79 +field ' c' [explicit solve]: current solution: 9.631204e+01, current residual:5.813727e+01 +field 'mu' [explicit solve]: current solution: 4.674919e+00, current residual:2.819888e+00 +wall time: 0.000730038s + +time increment:97800 time: 97.8 +field ' c' [explicit solve]: current solution: 9.631208e+01, current residual:5.813730e+01 +field 'mu' [explicit solve]: current solution: 4.674858e+00, current residual:2.819851e+00 +wall time: 0.000675917s + +time increment:97810 time: 97.81 +field ' c' [explicit solve]: current solution: 9.631213e+01, current residual:5.813733e+01 +field 'mu' [explicit solve]: current solution: 4.674797e+00, current residual:2.819814e+00 +wall time: 0.000706911s + +time increment:97820 time: 97.82 +field ' c' [explicit solve]: current solution: 9.631218e+01, current residual:5.813735e+01 +field 'mu' [explicit solve]: current solution: 4.674737e+00, current residual:2.819778e+00 +wall time: 0.000831842s + +time increment:97830 time: 97.83 +field ' c' [explicit solve]: current solution: 9.631222e+01, current residual:5.813738e+01 +field 'mu' [explicit solve]: current solution: 4.674676e+00, current residual:2.819741e+00 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0.000721931s + +time increment:97880 time: 97.88 +field ' c' [explicit solve]: current solution: 9.631246e+01, current residual:5.813752e+01 +field 'mu' [explicit solve]: current solution: 4.674372e+00, current residual:2.819557e+00 +wall time: 0.000813961s + +time increment:97890 time: 97.89 +field ' c' [explicit solve]: current solution: 9.631251e+01, current residual:5.813754e+01 +field 'mu' [explicit solve]: current solution: 4.674312e+00, current residual:2.819520e+00 +wall time: 0.000674009s + +time increment:97900 time: 97.9 +field ' c' [explicit solve]: current solution: 9.631256e+01, current residual:5.813757e+01 +field 'mu' [explicit solve]: current solution: 4.674251e+00, current residual:2.819483e+00 +wall time: 0.000648975s + +time increment:97910 time: 97.91 +field ' c' [explicit solve]: current solution: 9.631260e+01, current residual:5.813760e+01 +field 'mu' [explicit solve]: current solution: 4.674190e+00, current residual:2.819446e+00 +wall time: 0.000759125s + +time increment:97920 time: 97.92 +field ' c' [explicit solve]: current solution: 9.631265e+01, current residual:5.813762e+01 +field 'mu' [explicit solve]: current solution: 4.674130e+00, current residual:2.819410e+00 +wall time: 0.000627041s + +time increment:97930 time: 97.93 +field ' c' [explicit solve]: current solution: 9.631270e+01, current residual:5.813765e+01 +field 'mu' [explicit solve]: current solution: 4.674069e+00, current residual:2.819373e+00 +wall time: 0.000710964s + +time increment:97940 time: 97.94 +field ' c' [explicit solve]: current solution: 9.631275e+01, current residual:5.813768e+01 +field 'mu' [explicit solve]: current solution: 4.674008e+00, current residual:2.819336e+00 +wall time: 0.000669003s + +time increment:97950 time: 97.95 +field ' c' [explicit solve]: current solution: 9.631279e+01, current residual:5.813771e+01 +field 'mu' [explicit solve]: current solution: 4.673947e+00, current residual:2.819299e+00 +wall time: 0.00061512s + +time increment:97960 time: 97.96 +field ' c' [explicit solve]: current solution: 9.631284e+01, current residual:5.813773e+01 +field 'mu' [explicit solve]: current solution: 4.673887e+00, current residual:2.819262e+00 +wall time: 0.000724077s + +time increment:97970 time: 97.97 +field ' c' [explicit solve]: current solution: 9.631289e+01, current residual:5.813776e+01 +field 'mu' [explicit solve]: current solution: 4.673826e+00, current residual:2.819226e+00 +wall time: 0.000663996s + +time increment:97980 time: 97.98 +field ' c' [explicit solve]: current solution: 9.631294e+01, current residual:5.813779e+01 +field 'mu' [explicit solve]: current solution: 4.673765e+00, current residual:2.819189e+00 +wall time: 0.000659943s + +time increment:97990 time: 97.99 +field ' c' [explicit solve]: current solution: 9.631298e+01, current residual:5.813781e+01 +field 'mu' [explicit solve]: current solution: 4.673704e+00, current residual:2.819152e+00 +wall time: 0.000641823s + +time increment:98000 time: 98 +field ' c' [explicit solve]: current solution: 9.631303e+01, current residual:5.813784e+01 +field 'mu' [explicit solve]: current solution: 4.673644e+00, current residual:2.819115e+00 +wall time: 0.000611067s + +time increment:98010 time: 98.01 +field ' c' [explicit solve]: current solution: 9.631308e+01, current residual:5.813787e+01 +field 'mu' [explicit solve]: current solution: 4.673583e+00, current residual:2.819078e+00 +wall time: 0.000720024s + +time increment:98020 time: 98.02 +field ' c' [explicit solve]: current solution: 9.631312e+01, current residual:5.813789e+01 +field 'mu' [explicit solve]: current solution: 4.673522e+00, current residual:2.819041e+00 +wall time: 0.000667095s + +time increment:98030 time: 98.03 +field ' c' [explicit solve]: current solution: 9.631317e+01, current residual:5.813792e+01 +field 'mu' [explicit solve]: current solution: 4.673461e+00, current residual:2.819005e+00 +wall time: 0.000592947s + +time increment:98040 time: 98.04 +field ' c' [explicit solve]: current solution: 9.631322e+01, current residual:5.813795e+01 +field 'mu' [explicit solve]: current solution: 4.673401e+00, current residual:2.818968e+00 +wall time: 0.000715017s + +time increment:98050 time: 98.05 +field ' c' [explicit solve]: current solution: 9.631327e+01, current residual:5.813798e+01 +field 'mu' [explicit solve]: current solution: 4.673340e+00, current residual:2.818931e+00 +wall time: 0.000829935s + +time increment:98060 time: 98.06 +field ' c' [explicit solve]: current solution: 9.631331e+01, current residual:5.813800e+01 +field 'mu' [explicit solve]: current solution: 4.673279e+00, current residual:2.818894e+00 +wall time: 0.000660181s + +time increment:98070 time: 98.07 +field ' c' [explicit solve]: current solution: 9.631336e+01, current residual:5.813803e+01 +field 'mu' [explicit solve]: current solution: 4.673218e+00, current residual:2.818857e+00 +wall time: 0.000588894s + +time increment:98080 time: 98.08 +field ' c' [explicit solve]: current solution: 9.631341e+01, current residual:5.813806e+01 +field 'mu' [explicit solve]: current solution: 4.673158e+00, current residual:2.818821e+00 +wall time: 0.000773907s + +time increment:98090 time: 98.09 +field ' c' [explicit solve]: current solution: 9.631346e+01, current residual:5.813808e+01 +field 'mu' [explicit solve]: current solution: 4.673097e+00, current residual:2.818784e+00 +wall time: 0.000642061s + +time increment:98100 time: 98.1 +field ' c' [explicit solve]: current solution: 9.631350e+01, current residual:5.813811e+01 +field 'mu' [explicit solve]: current solution: 4.673036e+00, current residual:2.818747e+00 +wall time: 0.00070715s + +time increment:98110 time: 98.11 +field ' c' [explicit solve]: current solution: 9.631355e+01, current residual:5.813814e+01 +field 'mu' [explicit solve]: current solution: 4.672975e+00, current residual:2.818710e+00 +wall time: 0.000643969s + +time increment:98120 time: 98.12 +field ' c' [explicit solve]: current solution: 9.631360e+01, current residual:5.813816e+01 +field 'mu' [explicit solve]: current solution: 4.672915e+00, current residual:2.818673e+00 +wall time: 0.00061202s + +time increment:98130 time: 98.13 +field ' c' [explicit solve]: current solution: 9.631365e+01, current residual:5.813819e+01 +field 'mu' [explicit solve]: current solution: 4.672854e+00, current residual:2.818636e+00 +wall time: 0.000699043s + +time increment:98140 time: 98.14 +field ' c' [explicit solve]: current solution: 9.631369e+01, current residual:5.813822e+01 +field 'mu' [explicit solve]: current solution: 4.672793e+00, current residual:2.818600e+00 +wall time: 0.000618935s + +time increment:98150 time: 98.15 +field ' c' [explicit solve]: current solution: 9.631374e+01, current residual:5.813825e+01 +field 'mu' [explicit solve]: current solution: 4.672732e+00, current residual:2.818563e+00 +wall time: 0.000560045s + +time increment:98160 time: 98.16 +field ' c' [explicit solve]: current solution: 9.631379e+01, current residual:5.813827e+01 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solve]: current solution: 4.672428e+00, current residual:2.818379e+00 +wall time: 0.000561953s + +time increment:98210 time: 98.21 +field ' c' [explicit solve]: current solution: 9.631402e+01, current residual:5.813841e+01 +field 'mu' [explicit solve]: current solution: 4.672368e+00, current residual:2.818342e+00 +wall time: 0.000677109s + +time increment:98220 time: 98.22 +field ' c' [explicit solve]: current solution: 9.631407e+01, current residual:5.813844e+01 +field 'mu' [explicit solve]: current solution: 4.672307e+00, current residual:2.818305e+00 +wall time: 0.000602961s + +time increment:98230 time: 98.23 +field ' c' [explicit solve]: current solution: 9.631412e+01, current residual:5.813846e+01 +field 'mu' [explicit solve]: current solution: 4.672246e+00, current residual:2.818268e+00 +wall time: 0.000607014s + +time increment:98240 time: 98.24 +field ' c' [explicit solve]: current solution: 9.631417e+01, current residual:5.813849e+01 +field 'mu' [explicit solve]: current solution: 4.672185e+00, current residual:2.818231e+00 +wall time: 0.000658035s + +time increment:98250 time: 98.25 +field ' c' [explicit solve]: current solution: 9.631421e+01, current residual:5.813852e+01 +field 'mu' [explicit solve]: current solution: 4.672124e+00, current residual:2.818195e+00 +wall time: 0.000692844s + +time increment:98260 time: 98.26 +field ' c' [explicit solve]: current solution: 9.631426e+01, current residual:5.813854e+01 +field 'mu' [explicit solve]: current solution: 4.672064e+00, current residual:2.818158e+00 +wall time: 0.000680208s + +time increment:98270 time: 98.27 +field ' c' [explicit solve]: current solution: 9.631431e+01, current residual:5.813857e+01 +field 'mu' [explicit solve]: current solution: 4.672003e+00, current residual:2.818121e+00 +wall time: 0.000637054s + +time increment:98280 time: 98.28 +field ' c' [explicit solve]: current solution: 9.631436e+01, current residual:5.813860e+01 +field 'mu' [explicit solve]: current solution: 4.671942e+00, current residual:2.818084e+00 +wall time: 0.000622988s + +time increment:98290 time: 98.29 +field ' c' [explicit solve]: current solution: 9.631440e+01, current residual:5.813863e+01 +field 'mu' [explicit solve]: current solution: 4.671881e+00, current residual:2.818047e+00 +wall time: 0.000567913s + +time increment:98300 time: 98.3 +field ' c' [explicit solve]: current solution: 9.631445e+01, current residual:5.813865e+01 +field 'mu' [explicit solve]: current solution: 4.671820e+00, current residual:2.818010e+00 +wall time: 0.000718117s + +time increment:98310 time: 98.31 +field ' c' [explicit solve]: current solution: 9.631450e+01, current residual:5.813868e+01 +field 'mu' [explicit solve]: current solution: 4.671760e+00, current residual:2.817974e+00 +wall time: 0.000652075s + +time increment:98320 time: 98.32 +field ' c' [explicit solve]: current solution: 9.631455e+01, current residual:5.813871e+01 +field 'mu' [explicit solve]: current solution: 4.671699e+00, current residual:2.817937e+00 +wall time: 0.000677109s + +time increment:98330 time: 98.33 +field ' c' [explicit solve]: current solution: 9.631459e+01, current residual:5.813873e+01 +field 'mu' [explicit solve]: current solution: 4.671638e+00, current residual:2.817900e+00 +wall time: 0.000564098s + +time increment:98340 time: 98.34 +field ' c' [explicit solve]: current solution: 9.631464e+01, current residual:5.813876e+01 +field 'mu' [explicit solve]: current solution: 4.671577e+00, current residual:2.817863e+00 +wall time: 0.000602007s + +time increment:98350 time: 98.35 +field ' c' [explicit solve]: current solution: 9.631469e+01, current residual:5.813879e+01 +field 'mu' [explicit solve]: current solution: 4.671516e+00, current residual:2.817826e+00 +wall time: 0.000678778s + +time increment:98360 time: 98.36 +field ' c' [explicit solve]: current solution: 9.631474e+01, current residual:5.813881e+01 +field 'mu' [explicit solve]: current solution: 4.671456e+00, current residual:2.817789e+00 +wall time: 0.000724077s + +time increment:98370 time: 98.37 +field ' c' [explicit solve]: current solution: 9.631478e+01, current residual:5.813884e+01 +field 'mu' [explicit solve]: current solution: 4.671395e+00, current residual:2.817752e+00 +wall time: 0.000797033s + +time increment:98380 time: 98.38 +field ' c' [explicit solve]: current solution: 9.631483e+01, current residual:5.813887e+01 +field 'mu' [explicit solve]: current solution: 4.671334e+00, current residual:2.817716e+00 +wall time: 0.000707865s + +time increment:98390 time: 98.39 +field ' c' [explicit solve]: current solution: 9.631488e+01, current residual:5.813890e+01 +field 'mu' [explicit solve]: current solution: 4.671273e+00, current residual:2.817679e+00 +wall time: 0.000694036s + +time increment:98400 time: 98.4 +field ' c' [explicit solve]: current solution: 9.631493e+01, current residual:5.813892e+01 +field 'mu' [explicit solve]: current solution: 4.671212e+00, current residual:2.817642e+00 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0.000700951s + +time increment:98450 time: 98.45 +field ' c' [explicit solve]: current solution: 9.631516e+01, current residual:5.813906e+01 +field 'mu' [explicit solve]: current solution: 4.670908e+00, current residual:2.817458e+00 +wall time: 0.000790834s + +time increment:98460 time: 98.46 +field ' c' [explicit solve]: current solution: 9.631521e+01, current residual:5.813909e+01 +field 'mu' [explicit solve]: current solution: 4.670847e+00, current residual:2.817421e+00 +wall time: 0.000682116s + +time increment:98470 time: 98.47 +field ' c' [explicit solve]: current solution: 9.631526e+01, current residual:5.813911e+01 +field 'mu' [explicit solve]: current solution: 4.670786e+00, current residual:2.817384e+00 +wall time: 0.000746012s + +time increment:98480 time: 98.48 +field ' c' [explicit solve]: current solution: 9.631530e+01, current residual:5.813914e+01 +field 'mu' [explicit solve]: current solution: 4.670726e+00, current residual:2.817347e+00 +wall time: 0.000672102s + +time increment:98490 time: 98.49 +field ' c' [explicit solve]: current solution: 9.631535e+01, current residual:5.813917e+01 +field 'mu' [explicit solve]: current solution: 4.670665e+00, current residual:2.817310e+00 +wall time: 0.000791073s + +time increment:98500 time: 98.5 +field ' c' [explicit solve]: current solution: 9.631540e+01, current residual:5.813919e+01 +field 'mu' [explicit solve]: current solution: 4.670604e+00, current residual:2.817273e+00 +wall time: 0.000687122s + +time increment:98510 time: 98.51 +field ' c' [explicit solve]: current solution: 9.631545e+01, current residual:5.813922e+01 +field 'mu' [explicit solve]: current solution: 4.670543e+00, current residual:2.817237e+00 +wall time: 0.000677109s + +time increment:98520 time: 98.52 +field ' c' [explicit solve]: current solution: 9.631549e+01, current residual:5.813925e+01 +field 'mu' [explicit solve]: current solution: 4.670482e+00, current residual:2.817200e+00 +wall time: 0.00072217s + +time increment:98530 time: 98.53 +field ' c' [explicit solve]: current solution: 9.631554e+01, current residual:5.813928e+01 +field 'mu' [explicit solve]: current solution: 4.670421e+00, current residual:2.817163e+00 +wall time: 0.000697851s + +time increment:98540 time: 98.54 +field ' c' [explicit solve]: current solution: 9.631559e+01, current residual:5.813930e+01 +field 'mu' [explicit solve]: current solution: 4.670361e+00, current residual:2.817126e+00 +wall time: 0.000736952s + +time increment:98550 time: 98.55 +field ' c' [explicit solve]: current solution: 9.631564e+01, current residual:5.813933e+01 +field 'mu' [explicit solve]: current solution: 4.670300e+00, current residual:2.817089e+00 +wall time: 0.000699043s + +time increment:98560 time: 98.56 +field ' c' [explicit solve]: current solution: 9.631568e+01, current residual:5.813936e+01 +field 'mu' [explicit solve]: current solution: 4.670239e+00, current residual:2.817052e+00 +wall time: 0.000760078s + +time increment:98570 time: 98.57 +field ' c' [explicit solve]: current solution: 9.631573e+01, current residual:5.813938e+01 +field 'mu' [explicit solve]: current solution: 4.670178e+00, current residual:2.817015e+00 +wall time: 0.000741959s + +time increment:98580 time: 98.58 +field ' c' [explicit solve]: current solution: 9.631578e+01, current residual:5.813941e+01 +field 'mu' [explicit solve]: current solution: 4.670117e+00, current residual:2.816979e+00 +wall time: 0.000699997s + +time increment:98590 time: 98.59 +field ' c' [explicit solve]: current solution: 9.631583e+01, current residual:5.813944e+01 +field 'mu' [explicit solve]: current solution: 4.670056e+00, current residual:2.816942e+00 +wall time: 0.000715017s + +time increment:98600 time: 98.6 +field ' c' [explicit solve]: current solution: 9.631587e+01, current residual:5.813946e+01 +field 'mu' [explicit solve]: current solution: 4.669995e+00, current residual:2.816905e+00 +wall time: 0.000746012s + +time increment:98610 time: 98.61 +field ' c' [explicit solve]: current solution: 9.631592e+01, current residual:5.813949e+01 +field 'mu' [explicit solve]: current solution: 4.669934e+00, current residual:2.816868e+00 +wall time: 0.000647783s + +time increment:98620 time: 98.62 +field ' c' [explicit solve]: current solution: 9.631597e+01, current residual:5.813952e+01 +field 'mu' [explicit solve]: current solution: 4.669874e+00, current residual:2.816831e+00 +wall time: 0.000630856s + +time increment:98630 time: 98.63 +field ' c' [explicit solve]: current solution: 9.631602e+01, current residual:5.813955e+01 +field 'mu' [explicit solve]: current solution: 4.669813e+00, current residual:2.816794e+00 +wall time: 0.000777006s + +time increment:98640 time: 98.64 +field ' c' [explicit solve]: current solution: 9.631606e+01, current residual:5.813957e+01 +field 'mu' [explicit solve]: current solution: 4.669752e+00, current residual:2.816757e+00 +wall time: 0.000736952s + +time increment:98650 time: 98.65 +field ' c' [explicit solve]: current solution: 9.631611e+01, current residual:5.813960e+01 +field 'mu' [explicit solve]: current solution: 4.669691e+00, current residual:2.816720e+00 +wall time: 0.000674963s + +time increment:98660 time: 98.66 +field ' c' [explicit solve]: current solution: 9.631616e+01, current residual:5.813963e+01 +field 'mu' [explicit solve]: current solution: 4.669630e+00, current residual:2.816684e+00 +wall time: 0.000699997s + +time increment:98670 time: 98.67 +field ' c' [explicit solve]: current solution: 9.631620e+01, current residual:5.813965e+01 +field 'mu' [explicit solve]: current solution: 4.669569e+00, current residual:2.816647e+00 +wall time: 0.000645161s + +time increment:98680 time: 98.68 +field ' c' [explicit solve]: current solution: 9.631625e+01, current residual:5.813968e+01 +field 'mu' [explicit solve]: current solution: 4.669508e+00, current residual:2.816610e+00 +wall time: 0.000741005s + +time increment:98690 time: 98.69 +field ' c' [explicit solve]: current solution: 9.631630e+01, current residual:5.813971e+01 +field 'mu' [explicit solve]: current solution: 4.669447e+00, current residual:2.816573e+00 +wall time: 0.000741005s + +time increment:98700 time: 98.7 +field ' c' [explicit solve]: current solution: 9.631635e+01, current residual:5.813974e+01 +field 'mu' [explicit solve]: current solution: 4.669387e+00, current residual:2.816536e+00 +wall time: 0.000730038s + +time increment:98710 time: 98.71 +field ' c' [explicit solve]: current solution: 9.631639e+01, current residual:5.813976e+01 +field 'mu' [explicit solve]: current solution: 4.669326e+00, current residual:2.816499e+00 +wall time: 0.000628948s + +time increment:98720 time: 98.72 +field ' c' [explicit solve]: current solution: 9.631644e+01, current residual:5.813979e+01 +field 'mu' [explicit solve]: current solution: 4.669265e+00, current residual:2.816462e+00 +wall time: 0.000737906s + +time increment:98730 time: 98.73 +field ' c' [explicit solve]: current solution: 9.631649e+01, current residual:5.813982e+01 +field 'mu' [explicit solve]: current solution: 4.669204e+00, current residual:2.816425e+00 +wall time: 0.000682831s + +time increment:98740 time: 98.74 +field ' c' [explicit solve]: current solution: 9.631654e+01, current residual:5.813984e+01 +field 'mu' [explicit solve]: current solution: 4.669143e+00, current residual:2.816389e+00 +wall time: 0.000709057s + +time increment:98750 time: 98.75 +field ' c' [explicit solve]: current solution: 9.631658e+01, current residual:5.813987e+01 +field 'mu' [explicit solve]: current solution: 4.669082e+00, current residual:2.816352e+00 +wall time: 0.000664949s + +time increment:98760 time: 98.76 +field ' c' [explicit solve]: current solution: 9.631663e+01, current residual:5.813990e+01 +field 'mu' [explicit solve]: current solution: 4.669021e+00, current residual:2.816315e+00 +wall time: 0.000579834s + +time increment:98770 time: 98.77 +field ' c' [explicit solve]: current solution: 9.631668e+01, current residual:5.813993e+01 +field 'mu' [explicit solve]: current solution: 4.668960e+00, current residual:2.816278e+00 +wall time: 0.000677824s + +time increment:98780 time: 98.78 +field ' c' [explicit solve]: current solution: 9.631673e+01, current residual:5.813995e+01 +field 'mu' [explicit solve]: current solution: 4.668899e+00, current residual:2.816241e+00 +wall time: 0.000710011s + +time increment:98790 time: 98.79 +field ' c' [explicit solve]: current solution: 9.631677e+01, current residual:5.813998e+01 +field 'mu' [explicit solve]: current solution: 4.668838e+00, current residual:2.816204e+00 +wall time: 0.000575066s + +time increment:98800 time: 98.8 +field ' c' [explicit solve]: current solution: 9.631682e+01, current residual:5.814001e+01 +field 'mu' [explicit solve]: current solution: 4.668777e+00, current residual:2.816167e+00 +wall time: 0.00061202s + +time increment:98810 time: 98.81 +field ' c' [explicit solve]: current solution: 9.631687e+01, current residual:5.814003e+01 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[explicit solve]: current solution: 4.668473e+00, current residual:2.815983e+00 +wall time: 0.000636101s + +time increment:98860 time: 98.86 +field ' c' [explicit solve]: current solution: 9.631711e+01, current residual:5.814017e+01 +field 'mu' [explicit solve]: current solution: 4.668412e+00, current residual:2.815946e+00 +wall time: 0.000594139s + +time increment:98870 time: 98.87 +field ' c' [explicit solve]: current solution: 9.631715e+01, current residual:5.814020e+01 +field 'mu' [explicit solve]: current solution: 4.668351e+00, current residual:2.815909e+00 +wall time: 0.000651121s + +time increment:98880 time: 98.88 +field ' c' [explicit solve]: current solution: 9.631720e+01, current residual:5.814022e+01 +field 'mu' [explicit solve]: current solution: 4.668290e+00, current residual:2.815872e+00 +wall time: 0.000659943s + +time increment:98890 time: 98.89 +field ' c' [explicit solve]: current solution: 9.631725e+01, current residual:5.814025e+01 +field 'mu' [explicit solve]: current solution: 4.668229e+00, current residual:2.815835e+00 +wall time: 0.000597s + +time increment:98900 time: 98.9 +field ' c' [explicit solve]: current solution: 9.631730e+01, current residual:5.814028e+01 +field 'mu' [explicit solve]: current solution: 4.668168e+00, current residual:2.815798e+00 +wall time: 0.00068903s + +time increment:98910 time: 98.91 +field ' c' [explicit solve]: current solution: 9.631734e+01, current residual:5.814031e+01 +field 'mu' [explicit solve]: current solution: 4.668107e+00, current residual:2.815762e+00 +wall time: 0.000646114s + +time increment:98920 time: 98.92 +field ' c' [explicit solve]: current solution: 9.631739e+01, current residual:5.814033e+01 +field 'mu' [explicit solve]: current solution: 4.668046e+00, current residual:2.815725e+00 +wall time: 0.000643015s + +time increment:98930 time: 98.93 +field ' c' [explicit solve]: current solution: 9.631744e+01, current residual:5.814036e+01 +field 'mu' [explicit solve]: current solution: 4.667985e+00, current residual:2.815688e+00 +wall time: 0.000650167s + +time increment:98940 time: 98.94 +field ' c' [explicit solve]: current solution: 9.631749e+01, current residual:5.814039e+01 +field 'mu' [explicit solve]: current solution: 4.667925e+00, current residual:2.815651e+00 +wall time: 0.000666857s + +time increment:98950 time: 98.95 +field ' c' [explicit solve]: current solution: 9.631753e+01, current residual:5.814041e+01 +field 'mu' [explicit solve]: current solution: 4.667864e+00, current residual:2.815614e+00 +wall time: 0.000705004s + +time increment:98960 time: 98.96 +field ' c' [explicit solve]: current solution: 9.631758e+01, current residual:5.814044e+01 +field 'mu' [explicit solve]: current solution: 4.667803e+00, current residual:2.815577e+00 +wall time: 0.000653028s + +time increment:98970 time: 98.97 +field ' c' [explicit solve]: current solution: 9.631763e+01, current residual:5.814047e+01 +field 'mu' [explicit solve]: current solution: 4.667742e+00, current residual:2.815540e+00 +wall time: 0.000647783s + +time increment:98980 time: 98.98 +field ' c' [explicit solve]: current solution: 9.631767e+01, current residual:5.814050e+01 +field 'mu' [explicit solve]: current solution: 4.667681e+00, current residual:2.815503e+00 +wall time: 0.000735998s + +time increment:98990 time: 98.99 +field ' c' [explicit solve]: current solution: 9.631772e+01, current residual:5.814052e+01 +field 'mu' [explicit solve]: current solution: 4.667620e+00, current residual:2.815466e+00 +wall time: 0.000684977s + +time increment:99000 time: 99 +field ' c' [explicit solve]: current solution: 9.631777e+01, current residual:5.814055e+01 +field 'mu' [explicit solve]: current solution: 4.667559e+00, current residual:2.815429e+00 +wall time: 0.000716925s + +time increment:99010 time: 99.01 +field ' c' [explicit solve]: current solution: 9.631782e+01, current residual:5.814058e+01 +field 'mu' [explicit solve]: current solution: 4.667498e+00, current residual:2.815393e+00 +wall time: 0.000775814s + +time increment:99020 time: 99.02 +field ' c' [explicit solve]: current solution: 9.631786e+01, current residual:5.814060e+01 +field 'mu' [explicit solve]: current solution: 4.667437e+00, current residual:2.815356e+00 +wall time: 0.000679016s + +time increment:99030 time: 99.03 +field ' c' [explicit solve]: current solution: 9.631791e+01, current residual:5.814063e+01 +field 'mu' [explicit solve]: current solution: 4.667376e+00, current residual:2.815319e+00 +wall time: 0.00070405s + +time increment:99040 time: 99.04 +field ' c' [explicit solve]: current solution: 9.631796e+01, current residual:5.814066e+01 +field 'mu' [explicit solve]: current solution: 4.667315e+00, current residual:2.815282e+00 +wall time: 0.000659943s + +time increment:99050 time: 99.05 +field ' c' [explicit solve]: current solution: 9.631801e+01, current residual:5.814068e+01 +field 'mu' [explicit solve]: current solution: 4.667254e+00, current residual:2.815245e+00 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0.00066185s + +time increment:99100 time: 99.1 +field ' c' [explicit solve]: current solution: 9.631824e+01, current residual:5.814082e+01 +field 'mu' [explicit solve]: current solution: 4.666949e+00, current residual:2.815060e+00 +wall time: 0.000731945s + +time increment:99110 time: 99.11 +field ' c' [explicit solve]: current solution: 9.631829e+01, current residual:5.814085e+01 +field 'mu' [explicit solve]: current solution: 4.666888e+00, current residual:2.815023e+00 +wall time: 0.000684023s + +time increment:99120 time: 99.12 +field ' c' [explicit solve]: current solution: 9.631834e+01, current residual:5.814087e+01 +field 'mu' [explicit solve]: current solution: 4.666827e+00, current residual:2.814987e+00 +wall time: 0.000684023s + +time increment:99130 time: 99.13 +field ' c' [explicit solve]: current solution: 9.631839e+01, current residual:5.814090e+01 +field 'mu' [explicit solve]: current solution: 4.666766e+00, current residual:2.814950e+00 +wall time: 0.000708103s + +time increment:99140 time: 99.14 +field ' c' [explicit solve]: current solution: 9.631843e+01, current residual:5.814093e+01 +field 'mu' [explicit solve]: current solution: 4.666705e+00, current residual:2.814913e+00 +wall time: 0.00068903s + +time increment:99150 time: 99.15 +field ' c' [explicit solve]: current solution: 9.631848e+01, current residual:5.814096e+01 +field 'mu' [explicit solve]: current solution: 4.666644e+00, current residual:2.814876e+00 +wall time: 0.000691891s + +time increment:99160 time: 99.16 +field ' c' [explicit solve]: current solution: 9.631853e+01, current residual:5.814098e+01 +field 'mu' [explicit solve]: current solution: 4.666583e+00, current residual:2.814839e+00 +wall time: 0.000681162s + +time increment:99170 time: 99.17 +field ' c' [explicit solve]: current solution: 9.631858e+01, current residual:5.814101e+01 +field 'mu' [explicit solve]: current solution: 4.666522e+00, current residual:2.814802e+00 +wall time: 0.000659943s + +time increment:99180 time: 99.18 +field ' c' [explicit solve]: current solution: 9.631862e+01, current residual:5.814104e+01 +field 'mu' [explicit solve]: current solution: 4.666461e+00, current residual:2.814765e+00 +wall time: 0.000668049s + +time increment:99190 time: 99.19 +field ' c' [explicit solve]: current solution: 9.631867e+01, current residual:5.814106e+01 +field 'mu' [explicit solve]: current solution: 4.666400e+00, current residual:2.814728e+00 +wall time: 0.000674009s + +time increment:99200 time: 99.2 +field ' c' [explicit solve]: current solution: 9.631872e+01, current residual:5.814109e+01 +field 'mu' [explicit solve]: current solution: 4.666339e+00, current residual:2.814691e+00 +wall time: 0.00077796s + +time increment:99210 time: 99.21 +field ' c' [explicit solve]: current solution: 9.631877e+01, current residual:5.814112e+01 +field 'mu' [explicit solve]: current solution: 4.666278e+00, current residual:2.814654e+00 +wall time: 0.000676155s + +time increment:99220 time: 99.22 +field ' c' [explicit solve]: current solution: 9.631881e+01, current residual:5.814115e+01 +field 'mu' [explicit solve]: current solution: 4.666217e+00, current residual:2.814617e+00 +wall time: 0.000669003s + +time increment:99230 time: 99.23 +field ' c' [explicit solve]: current solution: 9.631886e+01, current residual:5.814117e+01 +field 'mu' [explicit solve]: current solution: 4.666156e+00, current residual:2.814580e+00 +wall time: 0.00070405s + +time increment:99240 time: 99.24 +field ' c' [explicit solve]: current solution: 9.631891e+01, current residual:5.814120e+01 +field 'mu' [explicit solve]: current solution: 4.666095e+00, current residual:2.814544e+00 +wall time: 0.000739098s + +time increment:99250 time: 99.25 +field ' c' [explicit solve]: current solution: 9.631896e+01, current residual:5.814123e+01 +field 'mu' [explicit solve]: current solution: 4.666034e+00, current residual:2.814507e+00 +wall time: 0.000639915s + +time increment:99260 time: 99.26 +field ' c' [explicit solve]: current solution: 9.631900e+01, current residual:5.814125e+01 +field 'mu' [explicit solve]: current solution: 4.665973e+00, current residual:2.814470e+00 +wall time: 0.00067997s + +time increment:99270 time: 99.27 +field ' c' [explicit solve]: current solution: 9.631905e+01, current residual:5.814128e+01 +field 'mu' [explicit solve]: current solution: 4.665912e+00, current residual:2.814433e+00 +wall time: 0.000771999s + +time increment:99280 time: 99.28 +field ' c' [explicit solve]: current solution: 9.631910e+01, current residual:5.814131e+01 +field 'mu' [explicit solve]: current solution: 4.665851e+00, current residual:2.814396e+00 +wall time: 0.000697136s + +time increment:99290 time: 99.29 +field ' c' [explicit solve]: current solution: 9.631915e+01, current residual:5.814134e+01 +field 'mu' [explicit solve]: current solution: 4.665790e+00, current residual:2.814359e+00 +wall time: 0.000746965s + +time increment:99300 time: 99.3 +field ' c' [explicit solve]: current solution: 9.631919e+01, current residual:5.814136e+01 +field 'mu' [explicit solve]: current solution: 4.665729e+00, current residual:2.814322e+00 +wall time: 0.000681162s + +time increment:99310 time: 99.31 +field ' c' [explicit solve]: current solution: 9.631924e+01, current residual:5.814139e+01 +field 'mu' [explicit solve]: current solution: 4.665668e+00, current residual:2.814285e+00 +wall time: 0.000715017s + +time increment:99320 time: 99.32 +field ' c' [explicit solve]: current solution: 9.631929e+01, current residual:5.814142e+01 +field 'mu' [explicit solve]: current solution: 4.665607e+00, current residual:2.814248e+00 +wall time: 0.000617981s + +time increment:99330 time: 99.33 +field ' c' [explicit solve]: current solution: 9.631934e+01, current residual:5.814145e+01 +field 'mu' [explicit solve]: current solution: 4.665546e+00, current residual:2.814211e+00 +wall time: 0.00071907s + +time increment:99340 time: 99.34 +field ' c' [explicit solve]: current solution: 9.631938e+01, current residual:5.814147e+01 +field 'mu' [explicit solve]: current solution: 4.665485e+00, current residual:2.814174e+00 +wall time: 0.000618935s + +time increment:99350 time: 99.35 +field ' c' [explicit solve]: current solution: 9.631943e+01, current residual:5.814150e+01 +field 'mu' [explicit solve]: current solution: 4.665424e+00, current residual:2.814137e+00 +wall time: 0.000648975s + +time increment:99360 time: 99.36 +field ' c' [explicit solve]: current solution: 9.631948e+01, current residual:5.814153e+01 +field 'mu' [explicit solve]: current solution: 4.665363e+00, current residual:2.814100e+00 +wall time: 0.000616074s + +time increment:99370 time: 99.37 +field ' c' [explicit solve]: current solution: 9.631952e+01, current residual:5.814155e+01 +field 'mu' [explicit solve]: current solution: 4.665302e+00, current residual:2.814063e+00 +wall time: 0.000718117s + +time increment:99380 time: 99.38 +field ' c' [explicit solve]: current solution: 9.631957e+01, current residual:5.814158e+01 +field 'mu' [explicit solve]: current solution: 4.665241e+00, current residual:2.814027e+00 +wall time: 0.00066185s + +time increment:99390 time: 99.39 +field ' c' [explicit solve]: current solution: 9.631962e+01, current residual:5.814161e+01 +field 'mu' [explicit solve]: current solution: 4.665180e+00, current residual:2.813990e+00 +wall time: 0.000715971s + +time increment:99400 time: 99.4 +field ' c' [explicit solve]: current solution: 9.631967e+01, current residual:5.814164e+01 +field 'mu' [explicit solve]: current solution: 4.665119e+00, current residual:2.813953e+00 +wall time: 0.000665903s + +time increment:99410 time: 99.41 +field ' c' [explicit solve]: current solution: 9.631971e+01, current residual:5.814166e+01 +field 'mu' [explicit solve]: current solution: 4.665058e+00, current residual:2.813916e+00 +wall time: 0.00071907s + +time increment:99420 time: 99.42 +field ' c' [explicit solve]: current solution: 9.631976e+01, current residual:5.814169e+01 +field 'mu' [explicit solve]: current solution: 4.664997e+00, current residual:2.813879e+00 +wall time: 0.00069499s + +time increment:99430 time: 99.43 +field ' c' [explicit solve]: current solution: 9.631981e+01, current residual:5.814172e+01 +field 'mu' [explicit solve]: current solution: 4.664936e+00, current residual:2.813842e+00 +wall time: 0.000715971s + +time increment:99440 time: 99.44 +field ' c' [explicit solve]: current solution: 9.631986e+01, current residual:5.814174e+01 +field 'mu' [explicit solve]: current solution: 4.664875e+00, current residual:2.813805e+00 +wall time: 0.000643015s + +time increment:99450 time: 99.45 +field ' c' [explicit solve]: current solution: 9.631990e+01, current residual:5.814177e+01 +field 'mu' [explicit solve]: current solution: 4.664814e+00, current residual:2.813768e+00 +wall time: 0.000629902s + +time increment:99460 time: 99.46 +field ' c' [explicit solve]: current solution: 9.631995e+01, current residual:5.814180e+01 +field 'mu' [explicit solve]: current solution: 4.664753e+00, current residual:2.813731e+00 +wall time: 0.000700951s + +time increment:99470 time: 99.47 +field ' c' [explicit solve]: current solution: 9.632000e+01, current residual:5.814183e+01 +field 'mu' [explicit solve]: current solution: 4.664692e+00, current residual:2.813694e+00 +wall time: 0.000620127s + +time increment:99480 time: 99.48 +field ' c' [explicit solve]: current solution: 9.632005e+01, current residual:5.814185e+01 +field 'mu' [explicit solve]: current solution: 4.664631e+00, current residual:2.813657e+00 +wall time: 0.000726938s + +time increment:99490 time: 99.49 +field ' c' [explicit solve]: current solution: 9.632009e+01, current residual:5.814188e+01 +field 'mu' [explicit solve]: current solution: 4.664570e+00, current residual:2.813620e+00 +wall time: 0.000669003s + +time increment:99500 time: 99.5 +field ' c' [explicit solve]: current solution: 9.632014e+01, current residual:5.814191e+01 +field 'mu' [explicit solve]: current solution: 4.664509e+00, current residual:2.813583e+00 +wall time: 0.000784159s + +time increment:99510 time: 99.51 +field ' c' [explicit solve]: current solution: 9.632019e+01, current residual:5.814193e+01 +field 'mu' [explicit solve]: current solution: 4.664448e+00, current residual:2.813546e+00 +wall time: 0.000804901s + +time increment:99520 time: 99.52 +field ' c' [explicit solve]: current solution: 9.632024e+01, current residual:5.814196e+01 +field 'mu' [explicit solve]: current solution: 4.664386e+00, current residual:2.813509e+00 +wall time: 0.000787973s + +time increment:99530 time: 99.53 +field ' c' [explicit solve]: current solution: 9.632028e+01, current residual:5.814199e+01 +field 'mu' [explicit solve]: current solution: 4.664325e+00, current residual:2.813472e+00 +wall time: 0.000671864s + +time increment:99540 time: 99.54 +field ' c' [explicit solve]: current solution: 9.632033e+01, current residual:5.814202e+01 +field 'mu' [explicit solve]: current solution: 4.664264e+00, current residual:2.813435e+00 +wall time: 0.000767946s + +time increment:99550 time: 99.55 +field ' c' [explicit solve]: current solution: 9.632038e+01, current residual:5.814204e+01 +field 'mu' [explicit solve]: current solution: 4.664203e+00, current residual:2.813399e+00 +wall time: 0.000671864s + +time increment:99560 time: 99.56 +field ' c' [explicit solve]: current solution: 9.632043e+01, current residual:5.814207e+01 +field 'mu' [explicit solve]: current solution: 4.664142e+00, current residual:2.813362e+00 +wall time: 0.000688791s + +time increment:99570 time: 99.57 +field ' c' [explicit solve]: current solution: 9.632047e+01, current residual:5.814210e+01 +field 'mu' [explicit solve]: current solution: 4.664081e+00, current residual:2.813325e+00 +wall time: 0.00087595s + +time increment:99580 time: 99.58 +field ' c' [explicit solve]: current solution: 9.632052e+01, current residual:5.814212e+01 +field 'mu' [explicit solve]: current solution: 4.664020e+00, current residual:2.813288e+00 +wall time: 0.000775099s + +time increment:99590 time: 99.59 +field ' c' [explicit solve]: current solution: 9.632057e+01, current residual:5.814215e+01 +field 'mu' [explicit solve]: current solution: 4.663959e+00, current residual:2.813251e+00 +wall time: 0.000735044s + +time increment:99600 time: 99.6 +field ' c' [explicit solve]: current solution: 9.632062e+01, current residual:5.814218e+01 +field 'mu' [explicit solve]: current solution: 4.663898e+00, current residual:2.813214e+00 +wall time: 0.000765085s + +time increment:99610 time: 99.61 +field ' c' [explicit solve]: current solution: 9.632066e+01, current residual:5.814221e+01 +field 'mu' [explicit solve]: current solution: 4.663837e+00, current residual:2.813177e+00 +wall time: 0.000684023s + +time increment:99620 time: 99.62 +field ' c' [explicit solve]: current solution: 9.632071e+01, current residual:5.814223e+01 +field 'mu' [explicit solve]: current solution: 4.663776e+00, current residual:2.813140e+00 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0.000643969s + +time increment:99670 time: 99.67 +field ' c' [explicit solve]: current solution: 9.632095e+01, current residual:5.814237e+01 +field 'mu' [explicit solve]: current solution: 4.663470e+00, current residual:2.812955e+00 +wall time: 0.000751972s + +time increment:99680 time: 99.68 +field ' c' [explicit solve]: current solution: 9.632100e+01, current residual:5.814240e+01 +field 'mu' [explicit solve]: current solution: 4.663409e+00, current residual:2.812918e+00 +wall time: 0.000688076s + +time increment:99690 time: 99.69 +field ' c' [explicit solve]: current solution: 9.632104e+01, current residual:5.814242e+01 +field 'mu' [explicit solve]: current solution: 4.663348e+00, current residual:2.812881e+00 +wall time: 0.00069809s + +time increment:99700 time: 99.7 +field ' c' [explicit solve]: current solution: 9.632109e+01, current residual:5.814245e+01 +field 'mu' [explicit solve]: current solution: 4.663287e+00, current residual:2.812844e+00 +wall time: 0.000595093s + +time increment:99710 time: 99.71 +field ' c' [explicit solve]: current solution: 9.632114e+01, current residual:5.814248e+01 +field 'mu' [explicit solve]: current solution: 4.663226e+00, current residual:2.812807e+00 +wall time: 0.000746012s + +time increment:99720 time: 99.72 +field ' c' [explicit solve]: current solution: 9.632119e+01, current residual:5.814250e+01 +field 'mu' [explicit solve]: current solution: 4.663165e+00, current residual:2.812770e+00 +wall time: 0.000738144s + +time increment:99730 time: 99.73 +field ' c' [explicit solve]: current solution: 9.632123e+01, current residual:5.814253e+01 +field 'mu' [explicit solve]: current solution: 4.663104e+00, current residual:2.812733e+00 +wall time: 0.000696182s + +time increment:99740 time: 99.74 +field ' c' [explicit solve]: current solution: 9.632128e+01, current residual:5.814256e+01 +field 'mu' [explicit solve]: current solution: 4.663043e+00, current residual:2.812696e+00 +wall time: 0.000756025s + +time increment:99750 time: 99.75 +field ' c' [explicit solve]: current solution: 9.632133e+01, current residual:5.814259e+01 +field 'mu' [explicit solve]: current solution: 4.662982e+00, current residual:2.812659e+00 +wall time: 0.000647068s + +time increment:99760 time: 99.76 +field ' c' [explicit solve]: current solution: 9.632138e+01, current residual:5.814261e+01 +field 'mu' [explicit solve]: current solution: 4.662920e+00, current residual:2.812622e+00 +wall time: 0.000710011s + +time increment:99770 time: 99.77 +field ' c' [explicit solve]: current solution: 9.632142e+01, current residual:5.814264e+01 +field 'mu' [explicit solve]: current solution: 4.662859e+00, current residual:2.812585e+00 +wall time: 0.000701904s + +time increment:99780 time: 99.78 +field ' c' [explicit solve]: current solution: 9.632147e+01, current residual:5.814267e+01 +field 'mu' [explicit solve]: current solution: 4.662798e+00, current residual:2.812548e+00 +wall time: 0.000622034s + +time increment:99790 time: 99.79 +field ' c' [explicit solve]: current solution: 9.632152e+01, current residual:5.814270e+01 +field 'mu' [explicit solve]: current solution: 4.662737e+00, current residual:2.812512e+00 +wall time: 0.000689983s + +time increment:99800 time: 99.8 +field ' c' [explicit solve]: current solution: 9.632157e+01, current residual:5.814272e+01 +field 'mu' [explicit solve]: current solution: 4.662676e+00, current residual:2.812475e+00 +wall time: 0.000691891s + +time increment:99810 time: 99.81 +field ' c' [explicit solve]: current solution: 9.632161e+01, current residual:5.814275e+01 +field 'mu' [explicit solve]: current solution: 4.662615e+00, current residual:2.812438e+00 +wall time: 0.000901937s + +time increment:99820 time: 99.82 +field ' c' [explicit solve]: current solution: 9.632166e+01, current residual:5.814278e+01 +field 'mu' [explicit solve]: current solution: 4.662554e+00, current residual:2.812401e+00 +wall time: 0.000689983s + +time increment:99830 time: 99.83 +field ' c' [explicit solve]: current solution: 9.632171e+01, current residual:5.814280e+01 +field 'mu' [explicit solve]: current solution: 4.662493e+00, current residual:2.812364e+00 +wall time: 0.000644922s + +time increment:99840 time: 99.84 +field ' c' [explicit solve]: current solution: 9.632176e+01, current residual:5.814283e+01 +field 'mu' [explicit solve]: current solution: 4.662432e+00, current residual:2.812327e+00 +wall time: 0.000726938s + +time increment:99850 time: 99.85 +field ' c' [explicit solve]: current solution: 9.632180e+01, current residual:5.814286e+01 +field 'mu' [explicit solve]: current solution: 4.662370e+00, current residual:2.812290e+00 +wall time: 0.000691891s + +time increment:99860 time: 99.86 +field ' c' [explicit solve]: current solution: 9.632185e+01, current residual:5.814289e+01 +field 'mu' [explicit solve]: current solution: 4.662309e+00, current residual:2.812253e+00 +wall time: 0.000568151s + +time increment:99870 time: 99.87 +field ' c' [explicit solve]: current solution: 9.632190e+01, current residual:5.814291e+01 +field 'mu' [explicit solve]: current solution: 4.662248e+00, current residual:2.812216e+00 +wall time: 0.00056982s + +time increment:99880 time: 99.88 +field ' c' [explicit solve]: current solution: 9.632195e+01, current residual:5.814294e+01 +field 'mu' [explicit solve]: current solution: 4.662187e+00, current residual:2.812179e+00 +wall time: 0.000599861s + +time increment:99890 time: 99.89 +field ' c' [explicit solve]: current solution: 9.632199e+01, current residual:5.814297e+01 +field 'mu' [explicit solve]: current solution: 4.662126e+00, current residual:2.812142e+00 +wall time: 0.000571966s + +time increment:99900 time: 99.9 +field ' c' [explicit solve]: current solution: 9.632204e+01, current residual:5.814299e+01 +field 'mu' [explicit solve]: current solution: 4.662065e+00, current residual:2.812105e+00 +wall time: 0.000597s + +time increment:99910 time: 99.91 +field ' c' [explicit solve]: current solution: 9.632209e+01, current residual:5.814302e+01 +field 'mu' [explicit solve]: current solution: 4.662004e+00, current residual:2.812068e+00 +wall time: 0.000684977s + +time increment:99920 time: 99.92 +field ' c' [explicit solve]: current solution: 9.632214e+01, current residual:5.814305e+01 +field 'mu' [explicit solve]: current solution: 4.661943e+00, current residual:2.812031e+00 +wall time: 0.000644207s + +time increment:99930 time: 99.93 +field ' c' [explicit solve]: current solution: 9.632218e+01, current residual:5.814308e+01 +field 'mu' [explicit solve]: current solution: 4.661881e+00, current residual:2.811994e+00 +wall time: 0.000715017s + +time increment:99940 time: 99.94 +field ' c' [explicit solve]: current solution: 9.632223e+01, current residual:5.814310e+01 +field 'mu' [explicit solve]: current solution: 4.661820e+00, current residual:2.811957e+00 +wall time: 0.000666142s + +time increment:99950 time: 99.95 +field ' c' [explicit solve]: current solution: 9.632228e+01, current residual:5.814313e+01 +field 'mu' [explicit solve]: current solution: 4.661759e+00, current residual:2.811920e+00 +wall time: 0.000650883s + +time increment:99960 time: 99.96 +field ' c' [explicit solve]: current solution: 9.632233e+01, current residual:5.814316e+01 +field 'mu' [explicit solve]: current solution: 4.661698e+00, current residual:2.811883e+00 +wall time: 0.000638008s + +time increment:99970 time: 99.97 +field ' c' [explicit solve]: current solution: 9.632237e+01, current residual:5.814318e+01 +field 'mu' [explicit solve]: current solution: 4.661637e+00, current residual:2.811846e+00 +wall time: 0.000628948s + +time increment:99980 time: 99.98 +field ' c' [explicit solve]: current solution: 9.632242e+01, current residual:5.814321e+01 +field 'mu' [explicit solve]: current solution: 4.661576e+00, current residual:2.811809e+00 +wall time: 0.000931978s + +time increment:99990 time: 99.99 +field ' c' [explicit solve]: current solution: 9.632247e+01, current residual:5.814324e+01 +field 'mu' [explicit solve]: current solution: 4.661515e+00, current residual:2.811772e+00 +wall time: 0.000612974s + +time increment:100000 time: 100 +field ' c' [explicit solve]: current solution: 9.632252e+01, current residual:5.814327e+01 +field 'mu' [explicit solve]: current solution: 4.661453e+00, current residual:2.811735e+00 +wall time: 0.000592947s +fields: 2 +fields: 2 +fields: 2 +fields: 2 +Output written to:solution-100000.pvtu + +Energy: 227.489 +Energy Components: 108.355 119.134 0 + + ++---------------------------------------------+------------+------------+ +| Total wallclock time elapsed since start | 70.1s | | +| | | | +| Section | no. calls | wall time | % of total | ++---------------------------------+-----------+------------+------------+ +| matrixFreePDE: AMR | 100000 | 0.384s | 0.55% | +| matrixFreePDE: computeEnergy | 11 | 0.71s | 1% | +| matrixFreePDE: computeRHS | 100000 | 41s | 59% | +| matrixFreePDE: initialization | 1 | 0.047s | 0.067% | +| matrixFreePDE: output | 11 | 1.97s | 2.8% | +| matrixFreePDE: solve | 1 | 70s | 1e+02% | +| matrixFreePDE: solveIncrements | 100000 | 60.7s | 87% | ++---------------------------------+-----------+------------+------------+ + diff --git a/applications/cahnHilliard/parameters.h b/applications/cahnHilliard/parameters.h index 2b282e08e..89c2b26d6 100644 --- a/applications/cahnHilliard/parameters.h +++ b/applications/cahnHilliard/parameters.h @@ -56,8 +56,11 @@ // outputCondition is "LIST" #define outputList {} +// Status is printed to the screen every skipPrintSteps +#define skipPrintSteps 10 + // ================================================================================= // Set the flag determining if the total free energy is calculated for each output // ================================================================================= -#define calcEnergy false +#define calcEnergy true diff --git a/applications/cahnHilliardWithAdaptivity/main.cc b/applications/cahnHilliardWithAdaptivity/main.cc index 2b20cf223..b5bb0809e 100644 --- a/applications/cahnHilliardWithAdaptivity/main.cc +++ b/applications/cahnHilliardWithAdaptivity/main.cc @@ -20,7 +20,7 @@ int main (int argc, char **argv) problem.setBCs(); problem.buildFields(); - problem.init (); + problem.init (); problem.solve(); } catch (std::exception &exc) diff --git a/applications/cahnHilliardWithAdaptivity/parameters.h b/applications/cahnHilliardWithAdaptivity/parameters.h index 61c080a46..191ddb976 100644 --- a/applications/cahnHilliardWithAdaptivity/parameters.h +++ b/applications/cahnHilliardWithAdaptivity/parameters.h @@ -78,6 +78,8 @@ // outputCondition is "LIST" #define outputList {} +// Status is printed to the screen every skipPrintSteps +#define skipPrintSteps 10 // ================================================================================= // Set the flag determining if the total free energy is calculated for each output diff --git a/applications/coupledCahnHilliardAllenCahn/parameters.h b/applications/coupledCahnHilliardAllenCahn/parameters.h index f33af9925..a18095f3b 100644 --- a/applications/coupledCahnHilliardAllenCahn/parameters.h +++ b/applications/coupledCahnHilliardAllenCahn/parameters.h @@ -56,6 +56,8 @@ // outputCondition is "LIST" #define outputList {} +// Status is printed to the screen every skipPrintSteps +#define skipPrintSteps 10 // ================================================================================= // Set the flag determining if the total free energy is calculated for each output diff --git a/applications/fickianDiffusion/parameters.h b/applications/fickianDiffusion/parameters.h index e2b5988d3..111b72043 100644 --- a/applications/fickianDiffusion/parameters.h +++ b/applications/fickianDiffusion/parameters.h @@ -56,6 +56,8 @@ // outputCondition is "LIST" #define outputList {} +// Status is printed to the screen every skipPrintSteps +#define skipPrintSteps 10 // ================================================================================= // Set the flag determining if the total free energy is calculated for each output diff --git a/applications/grainGrowth/parameters.h b/applications/grainGrowth/parameters.h index 0f7725ec9..9b593385a 100644 --- a/applications/grainGrowth/parameters.h +++ b/applications/grainGrowth/parameters.h @@ -81,6 +81,8 @@ // outputCondition is "LIST" #define outputList {} +// Status is printed to the screen every skipPrintSteps +#define skipPrintSteps 10 // ================================================================================= // Set the flag determining if the total free energy is calculated for each output diff --git a/applications/mechanics/ICs_and_BCs.h b/applications/mechanics/ICs_and_BCs.h index cc18ed1d8..b93163225 100644 --- a/applications/mechanics/ICs_and_BCs.h +++ b/applications/mechanics/ICs_and_BCs.h @@ -83,7 +83,7 @@ void generalizedProblem::setBCs(){ // Face numbering: starts at zero with the minimum of the first direction, one for the maximum of the first direction // two for the minimum of the second direction, etc. - inputBCs(4,0,"DIRICHLET",-0.1, "DIRICHLET",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0); + inputBCs(4,0,"DIRICHLET",-1.0, "DIRICHLET",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0); inputBCs(4,1,"DIRICHLET",0.0, "DIRICHLET",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0); inputBCs(4,2,"DIRICHLET",0.0, "DIRICHLET",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0); diff --git a/applications/precipitateEvolution/parameters.h b/applications/precipitateEvolution/parameters.h index cd145c191..b7895f124 100644 --- a/applications/precipitateEvolution/parameters.h +++ b/applications/precipitateEvolution/parameters.h @@ -54,7 +54,7 @@ // The size of the time step #define timeStep 4.0e-4 #define timeFinal 100.0 -#define timeIncrements 5000 +#define timeIncrements 10 //5000 // ================================================================================= // Set the elliptic solver parameters @@ -90,6 +90,9 @@ // outputCondition is "LIST" #define outputList {} +// Status is printed to the screen every skipPrintSteps +#define skipPrintSteps 10 + // ================================================================================= // Set the flag determining if the total free energy is calculated for each output // ================================================================================= diff --git a/applications/precipitateEvolution_pfunction/parameters.h b/applications/precipitateEvolution_pfunction/parameters.h index 4275bb190..f0c001edc 100644 --- a/applications/precipitateEvolution_pfunction/parameters.h +++ b/applications/precipitateEvolution_pfunction/parameters.h @@ -90,6 +90,9 @@ // outputCondition is "LIST" #define outputList {} +// Status is printed to the screen every skipPrintSteps +#define skipPrintSteps 10 + // ================================================================================= // Set the flag determining if the total free energy is calculated for each output // ================================================================================= diff --git a/applications/singlePrecipitateKKS/parameters.h b/applications/singlePrecipitateKKS/parameters.h index 46af760be..3b77d5aa9 100644 --- a/applications/singlePrecipitateKKS/parameters.h +++ b/applications/singlePrecipitateKKS/parameters.h @@ -94,7 +94,7 @@ #define outputList {} // Status is printed to the screen every skipPrintSteps -#define skipPrintSteps 1 //1000 +#define skipPrintSteps 1000 // ================================================================================= // Set the flag determining if the total free energy is calculated for each output diff --git a/html/____deprecated_demos_2__cahn_hilliard_with_adaptivity_2main_8cc.html b/html/____deprecated_demos_2__cahn_hilliard_with_adaptivity_2main_8cc.html index 73f4cc0dd..16707ee6c 100644 --- a/html/____deprecated_demos_2__cahn_hilliard_with_adaptivity_2main_8cc.html +++ b/html/____deprecated_demos_2__cahn_hilliard_with_adaptivity_2main_8cc.html @@ -94,7 +94,7 @@
#include "../../../include/dealIIheaders.h"
#include "parameters.h"
-#include "residuals.h"
+#include "residuals.h"
#include "../../../src/models/diffusion/CH.h"
#include "ICs_and_BCs.h"
diff --git a/html/____deprecated_demos_2__cahn_hilliard_with_adaptivity_2parameters_8h.html b/html/____deprecated_demos_2__cahn_hilliard_with_adaptivity_2parameters_8h.html index f36545900..365473d8a 100644 --- a/html/____deprecated_demos_2__cahn_hilliard_with_adaptivity_2parameters_8h.html +++ b/html/____deprecated_demos_2__cahn_hilliard_with_adaptivity_2parameters_8h.html @@ -123,7 +123,7 @@ - + @@ -188,7 +188,7 @@

Macro Definition Documentation

 
#define hAdaptivity   true
 
#define maxRefinementLevel   (refineFactor)
#define maxRefinementLevel   (refineFactor)
 
#define minRefinementLevel   (0)
 
- +
#define maxRefinementLevel   (refineFactor)#define maxRefinementLevel   (refineFactor)
diff --git a/html/____deprecated_demos_2__mechanics_with_adaptivity_2parameters_8h.html b/html/____deprecated_demos_2__mechanics_with_adaptivity_2parameters_8h.html index 771ef740c..0c0dd12b0 100644 --- a/html/____deprecated_demos_2__mechanics_with_adaptivity_2parameters_8h.html +++ b/html/____deprecated_demos_2__mechanics_with_adaptivity_2parameters_8h.html @@ -129,7 +129,7 @@   #define hAdaptivity   true   -#define maxRefinementLevel   (refineFactor+1) +#define maxRefinementLevel   (refineFactor+1)   #define minRefinementLevel   (0)   @@ -206,7 +206,7 @@

Macro Definition Documentation

- +
#define maxRefinementLevel   (refineFactor+1)#define maxRefinementLevel   (refineFactor+1)
diff --git a/html/_c_h_8h_source.html b/html/_c_h_8h_source.html index 14343dab2..545722960 100644 --- a/html/_c_h_8h_source.html +++ b/html/_c_h_8h_source.html @@ -163,8 +163,8 @@
71  scalargradType cx = cVals.get_gradient(q);
72 
73  //submit values
-
74  muVals.submit_value(rmuV,q); muVals.submit_gradient(rmuxV,q);
-
75  cVals.submit_value(rcV,q); cVals.submit_gradient(rcxV,q);
+
74  muVals.submit_value(rmuV,q); muVals.submit_gradient(rmuxV,q);
+
75  cVals.submit_value(rcV,q); cVals.submit_gradient(rcxV,q);
76  }
77 
78  //integrate values
@@ -176,29 +176,29 @@
84 #ifndef hAdaptivity
85 //adaptive refinement control
86 template <int dim>
-
87 void CahnHilliardProblem<dim>::adaptiveRefine(unsigned int currentIncrement){
+
87 void CahnHilliardProblem<dim>::adaptiveRefine(unsigned int currentIncrement){
88 }
89 
90 //adaptive refinement criterion
91 template <int dim>
-
92 void CahnHilliardProblem<dim>::adaptiveRefineCriterion(){
+
92 void CahnHilliardProblem<dim>::adaptiveRefineCriterion(){
93 }
94 #endif
95 
96 #endif
+
rcxV
#define rcxV
Definition: residuals.h:12
+
rmuV
#define rmuV
Definition: residuals.h:9
CahnHilliardProblem::applyInitialConditions
void applyInitialConditions()
Definition: ICs_and_BCs.h:47
MatrixFreePDE
Definition: matrixFreePDE.h:85
scalargradType
dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
Definition: matrixFreePDE.h:51
-
CahnHilliardProblem::adaptiveRefine
void adaptiveRefine(unsigned int currentIncrement)
Definition: CH.h:87
-
rcV
#define rcV
Definition: residuals.h:11
-
rmuV
#define rmuV
Definition: residuals.h:9
-
CahnHilliardProblem::adaptiveRefineCriterion
void adaptiveRefineCriterion()
Definition: CH.h:92
-
rmuxV
#define rmuxV
Definition: residuals.h:10
+
CahnHilliardProblem::adaptiveRefine
void adaptiveRefine(unsigned int currentIncrement)
Definition: main.cc:13
+
rmuxV
#define rmuxV
Definition: residuals.h:10
+
CahnHilliardProblem::adaptiveRefineCriterion
void adaptiveRefineCriterion()
Definition: main.cc:21
CahnHilliardProblem::getRHS
void getRHS(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range) const
Definition: CH.h:47
typeScalar
dealii::FEEvaluation< problemDIM, finiteElementDegree, finiteElementDegree+1, 1, double > typeScalar
Definition: matrixFreePDE.h:27
-
rcxV
#define rcxV
Definition: residuals.h:12
+
rcV
#define rcV
Definition: residuals.h:11
CahnHilliardProblem::CahnHilliardProblem
CahnHilliardProblem()
Definition: CH.h:32
-
MatrixFreePDE::currentIncrement
unsigned int currentIncrement
Definition: matrixFreePDE.h:255
+
MatrixFreePDE::currentIncrement
unsigned int currentIncrement
Definition: matrixFreePDE.h:268
scalarvalueType
dealii::VectorizedArray< double > scalarvalueType
Definition: matrixFreePDE.h:34
CahnHilliardProblem
Definition: CH.h:10
diff --git a/html/_c_h_a_c__anisotropy_2_i_cs__and___b_cs_8h_source.html b/html/_c_h_a_c__anisotropy_2_i_cs__and___b_cs_8h_source.html index e34382112..87e3f0a7a 100644 --- a/html/_c_h_a_c__anisotropy_2_i_cs__and___b_cs_8h_source.html +++ b/html/_c_h_a_c__anisotropy_2_i_cs__and___b_cs_8h_source.html @@ -127,90 +127,86 @@
35 #endif
36  }
37  else {
-
38  r=p.distance(Point<dim>(spanX/3.0,spanY/3.0));
-
39  scalar_IC = 0.5*(1.0-std::tanh((r-spanX/5.0)/(3*dx)));
-
40  r=p.distance(Point<dim>(3.0*spanX/4.0,3.0*spanY/4.0));
-
41  scalar_IC += 0.5*(1.0-std::tanh((r-spanX/12.0)/(3*dx)));
-
42 #if problemDIM==1
-
43  r=p[0];
-
44  scalar_IC = 0.5*(1-std::tanh((r-spanX/2.0)/6.0));
-
45 #elif problemDIM==2
-
46  r=p.distance(Point<dim>(spanX/2.0,spanY/2.0));
-
47  scalar_IC = 0.5*(1.0-std::tanh((r-spanX/4.0)/4.0));
-
48 #elif problemDIM==3
-
49  r=p.distance(Point<dim>(spanX/2.0,spanY/2.0,spanZ/2.0));
-
50  scalar_IC = 0.5*(1.0-std::tanh((r-spanX/4.0)/4.0));
-
51 #endif
-
52  }
-
53 
-
54  // =====================================================================
-
55  return scalar_IC;
-
56  }
-
57 };
-
58 
-
59 template <int dim>
-
60 class InitialConditionVec : public Function<dim>
-
61 {
-
62 public:
-
63  unsigned int index;
-
64  //Vector<double> values;
-
65  InitialConditionVec (const unsigned int _index) : Function<dim>(dim), index(_index) {
-
66  std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1);
-
67  }
-
68  void vector_value (const Point<dim> &p,Vector<double> &vector_IC) const
-
69  {
-
70  // =====================================================================
-
71  // ENTER THE INITIAL CONDITIONS HERE FOR VECTOR FIELDS
-
72  // =====================================================================
-
73  // Enter the function describing conditions for the fields at point "p".
-
74  // Use "if" statements to set the initial condition for each variable
-
75  // according to its variable index.
-
76 
+
38 #if problemDIM==1
+
39  r=p[0];
+
40  scalar_IC = 0.5*(1-std::tanh((r-spanX/2.0)/6.0));
+
41 #elif problemDIM==2
+
42  r=p.distance(Point<dim>(spanX/2.0,spanY/2.0));
+
43  scalar_IC = 0.5*(1.0-std::tanh((r-spanX/4.0)/4.0));
+
44 #elif problemDIM==3
+
45  r=p.distance(Point<dim>(spanX/2.0,spanY/2.0,spanZ/2.0));
+
46  scalar_IC = 0.5*(1.0-std::tanh((r-spanX/4.0)/4.0));
+
47 #endif
+
48  }
+
49 
+
50  // =====================================================================
+
51  return scalar_IC;
+
52  }
+
53 };
+
54 
+
55 template <int dim>
+
56 class InitialConditionVec : public Function<dim>
+
57 {
+
58 public:
+
59  unsigned int index;
+
60  //Vector<double> values;
+
61  InitialConditionVec (const unsigned int _index) : Function<dim>(dim), index(_index) {
+
62  std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1);
+
63  }
+
64  void vector_value (const Point<dim> &p,Vector<double> &vector_IC) const
+
65  {
+
66  // =====================================================================
+
67  // ENTER THE INITIAL CONDITIONS HERE FOR VECTOR FIELDS
+
68  // =====================================================================
+
69  // Enter the function describing conditions for the fields at point "p".
+
70  // Use "if" statements to set the initial condition for each variable
+
71  // according to its variable index.
+
72 
+
73 
+
74  // =====================================================================
+
75  }
+
76 };
77 
-
78  // =====================================================================
-
79  }
-
80 };
-
81 
-
82 template <int dim>
-
83 void generalizedProblem<dim>::setBCs(){
-
84 
-
85  // =====================================================================
-
86  // ENTER THE BOUNDARY CONDITIONS HERE
-
87  // =====================================================================
-
88  // This function sets the BCs for the problem variables
-
89  // The function "inputBCs" should be called for each component of
-
90  // each variable and should be in numerical order. Four input arguments
-
91  // set the same BC on the entire boundary. Two plus two times the
-
92  // number of dimensions inputs sets separate BCs on each face of the domain.
-
93  // Inputs to "inputBCs":
-
94  // First input: variable number
-
95  // Second input: component number
-
96  // Third input: BC type (options are "ZERO_DERIVATIVE" and "DIRICHLET")
-
97  // Fourth input: BC value (ignored unless the BC type is "DIRICHLET")
-
98  // Odd inputs after the third: BC type
-
99  // Even inputs after the third: BC value
-
100  // Face numbering: starts at zero with the minimum of the first direction, one for the maximum of the first direction
-
101  // two for the minimum of the second direction, etc.
-
102 
-
103  inputBCs(0,0,"ZERO_DERIVATIVE",0);
-
104  inputBCs(1,0,"ZERO_DERIVATIVE",0);
-
105 
-
106 }
-
107 
-
108 
-
InitialConditionVec::index
unsigned int index
Definition: ICs_and_BCs.h:46
-
InitialConditionVec::vector_value
void vector_value(const Point< dim > &p, Vector< double > &vector_IC) const
Definition: ICs_and_BCs.h:68
+
78 template <int dim>
+
79 void generalizedProblem<dim>::setBCs(){
+
80 
+
81  // =====================================================================
+
82  // ENTER THE BOUNDARY CONDITIONS HERE
+
83  // =====================================================================
+
84  // This function sets the BCs for the problem variables
+
85  // The function "inputBCs" should be called for each component of
+
86  // each variable and should be in numerical order. Four input arguments
+
87  // set the same BC on the entire boundary. Two plus two times the
+
88  // number of dimensions inputs sets separate BCs on each face of the domain.
+
89  // Inputs to "inputBCs":
+
90  // First input: variable number
+
91  // Second input: component number
+
92  // Third input: BC type (options are "ZERO_DERIVATIVE", "DIRICHLET", and "PERIODIC")
+
93  // Fourth input: BC value (ignored unless the BC type is "DIRICHLET")
+
94  // Odd inputs after the third: BC type
+
95  // Even inputs after the third: BC value
+
96  // Face numbering: starts at zero with the minimum of the first direction, one for the maximum of the first direction
+
97  // two for the minimum of the second direction, etc.
+
98 
+
99  inputBCs(0,0,"ZERO_DERIVATIVE",0);
+
100  inputBCs(1,0,"ZERO_DERIVATIVE",0);
+
101 
+
102 }
+
103 
+
104 
+
InitialConditionVec::index
unsigned int index
Definition: ICs_and_BCs.h:47
+
InitialConditionVec::vector_value
void vector_value(const Point< dim > &p, Vector< double > &vector_IC) const
Definition: ICs_and_BCs.h:64
spanX
#define spanX
Definition: defaultValues.h:12
InitialCondition::InitialCondition
InitialCondition(const unsigned int _index)
Definition: ICs_and_BCs.h:7
spanY
#define spanY
Definition: defaultValues.h:17
InitialCondition::value
double value(const Point< dim > &p, const unsigned int component=0) const
Definition: ICs_and_BCs.h:10
InitialCondition::index
unsigned int index
Definition: ICs_and_BCs.h:5
refineFactor
#define refineFactor
Definition: defaultValues.h:27
-
generalizedProblem::setBCs
void setBCs()
Definition: ICs_and_BCs.h:66
-
InitialConditionVec::InitialConditionVec
InitialConditionVec(const unsigned int _index)
Definition: ICs_and_BCs.h:65
+
generalizedProblem::setBCs
void setBCs()
Definition: ICs_and_BCs.h:67
+
InitialConditionVec::InitialConditionVec
InitialConditionVec(const unsigned int _index)
Definition: ICs_and_BCs.h:61
subdivisionsX
#define subdivisionsX
Definition: parameters.h:14
InitialCondition
Definition: ICs_and_BCs.h:2
-
InitialConditionVec
Definition: ICs_and_BCs.h:43
+
InitialConditionVec
Definition: ICs_and_BCs.h:44
spanZ
#define spanZ
Definition: defaultValues.h:22
InitialCondition::values
Vector< double > values
Definition: ICs_and_BCs.h:6
diff --git a/html/_c_h_a_c__anisotropy_2equations_8h.html b/html/_c_h_a_c__anisotropy_2equations_8h.html index f5a189776..e569d2124 100644 --- a/html/_c_h_a_c__anisotropy_2equations_8h.html +++ b/html/_c_h_a_c__anisotropy_2equations_8h.html @@ -147,11 +147,11 @@   #define rcV   ( c )   -#define rcxV   (constV(-McV*timeStep)*(cx*((1.0-hV)*faccV+hV*fbccV)+nx*hnV*(fbcV-facV))) +#define rcxV   (constV(-McV*timeStep)*(cx*((1.0-hV)*faccV+hV*fbccV)+nx*hnV*(fbcV-facV)))   -#define rnV   (n-constV(timeStep*MnV)*(fbV-faV)*hnV) +#define rnV   (n-constV(timeStep*MnV)*(fbV-faV)*hnV)   -#define rnxV   (constV(timeStep*MnV)*(-aniso)) +#define rnxV   (constV(timeStep*MnV)*(-aniso))  

Macro Definition Documentation

@@ -424,7 +424,7 @@

Macro Definition Documentation

- +
#define rcxV   (constV(-McV*timeStep)*(cx*((1.0-hV)*faccV+hV*fbccV)+nx*hnV*(fbcV-facV)))#define rcxV   (constV(-McV*timeStep)*(cx*((1.0-hV)*faccV+hV*fbccV)+nx*hnV*(fbcV-facV)))
@@ -436,7 +436,7 @@

Macro Definition Documentation

- +
#define rnV   (n-constV(timeStep*MnV)*(fbV-faV)*hnV)#define rnV   (n-constV(timeStep*MnV)*(fbV-faV)*hnV)
@@ -448,7 +448,7 @@

Macro Definition Documentation

- +
#define rnxV   (constV(timeStep*MnV)*(-aniso))#define rnxV   (constV(timeStep*MnV)*(-aniso))
diff --git a/html/_c_h_a_c__anisotropy_2equations_8h_source.html b/html/_c_h_a_c__anisotropy_2equations_8h_source.html index eb1ddc831..b72cb4346 100644 --- a/html/_c_h_a_c__anisotropy_2equations_8h_source.html +++ b/html/_c_h_a_c__anisotropy_2equations_8h_source.html @@ -175,8 +175,8 @@
83 // each residual equation. The index for each variable in these lists corresponds to
84 // the order it is defined at the top of this file (starting at 0).
85 template <int dim>
-
86 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim>> & modelVariablesList,
-
87  std::vector<modelResidual<dim>> & modelResidualsList,
+
86 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim> > & modelVariablesList,
+
87  std::vector<modelResidual<dim> > & modelResidualsList,
88  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
89 
90 //c
@@ -235,7 +235,7 @@
143 // that the correct residual is being submitted. The index of the field being solved
144 // can be accessed by "this->currentFieldIndex".
145 template <int dim>
-
146 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim>> & modelVarList,
+
146 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim> > & modelVarList,
147  modelResidual<dim> & modelRes, dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
148 
149 }
@@ -252,7 +252,7 @@
160 // density are added to the "energy_components" variable (index 0: chemical energy,
161 // index 1: gradient energy, index 2: elastic energy).
162 template <int dim>
-
163 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim>> & modelVarList,
+
163 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim> > & modelVarList,
164  const dealii::VectorizedArray<double> & JxW_value,
165  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) {
166 
@@ -286,13 +286,12 @@
194 }
195 assembler_lock.release ();
196 }
-
generalizedProblem::energyDensity
void energyDensity(const std::vector< modelVariable< dim >> &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
Definition: equations.h:106
modelResidual
Definition: model_variables.h:25
problemDIM
#define problemDIM
Definition: defaultValues.h:7
gamma
#define gamma
Definition: equations.h:56
+
generalizedProblem::residualRHS
void residualRHS(const std::vector< modelVariable< dim > > &modelVarList, std::vector< modelResidual< dim > > &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:58
rnV
#define rnV
Definition: equations.h:72
scalargradType
dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
Definition: matrixFreePDE.h:51
-
generalizedProblem::residualLHS
void residualLHS(const std::vector< modelVariable< dim >> &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:89
constV
#define constV(a)
Definition: matrixFreePDE.h:67
faV
#define faV
Definition: equations.h:37
rcxV
#define rcxV
Definition: equations.h:71
@@ -300,12 +299,13 @@
rcV
#define rcV
Definition: equations.h:70
rnxV
#define rnxV
Definition: equations.h:73
hV
#define hV
Definition: equations.h:43
+
generalizedProblem::residualLHS
void residualLHS(const std::vector< modelVariable< dim > > &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:89
fbV
#define fbV
Definition: equations.h:40
gammany
#define gammany
Definition: equations.h:61
gammanz
#define gammanz
Definition: equations.h:62
modelVariable
Definition: model_variables.h:4
scalarvalueType
dealii::VectorizedArray< double > scalarvalueType
Definition: matrixFreePDE.h:34
-
generalizedProblem::residualRHS
void residualRHS(const std::vector< modelVariable< dim >> &modelVarList, std::vector< modelResidual< dim >> &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:58
+
generalizedProblem::energyDensity
void energyDensity(const std::vector< modelVariable< dim > > &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
Definition: equations.h:106
diff --git a/html/_c_h_a_c__anisotropy_2parameters_8h.html b/html/_c_h_a_c__anisotropy_2parameters_8h.html index f7035687c..622d22d26 100644 --- a/html/_c_h_a_c__anisotropy_2parameters_8h.html +++ b/html/_c_h_a_c__anisotropy_2parameters_8h.html @@ -123,8 +123,14 @@   #define writeOutput   true   -#define skipOutputSteps   2000 -  +#define outputCondition   "EQUAL_SPACING" +  +#define numOutputs   10 +  +#define outputList   {} +  +#define skipPrintSteps   10 +  #define calcEnergy   true   @@ -151,6 +157,42 @@

Macro Definition Documentation

+
+ + +
+
+ + + + +
#define numOutputs   10
+
+ +
+
+ +
+
+ + + + +
#define outputCondition   "EQUAL_SPACING"
+
+ +
+
+ +
+
+ + + + +
#define outputList   {}
+
+
@@ -177,12 +219,12 @@

Macro Definition Documentation

- +
- +
#define skipOutputSteps   2000#define skipPrintSteps   10
diff --git a/html/_c_h_a_c__anisotropy_2parameters_8h_source.html b/html/_c_h_a_c__anisotropy_2parameters_8h_source.html index e3749016c..cd791c1fa 100644 --- a/html/_c_h_a_c__anisotropy_2parameters_8h_source.html +++ b/html/_c_h_a_c__anisotropy_2parameters_8h_source.html @@ -136,13 +136,25 @@
44 // Each field in the problem will be output is writeOutput is set to "true"
45 #define writeOutput true
46 
-
47 // Output files are written every skipOutputSteps time steps
-
48 #define skipOutputSteps 2000
-
49 
-
50 // =================================================================================
-
51 // Set the flag determining if the total free energy is calculated for each output
-
52 // =================================================================================
-
53 #define calcEnergy true
+
47 // Type of spacing between outputs ("EQUAL_SPACING", "LOG_SPACING", "N_PER_DECADE",
+
48 // or "LIST")
+
49 #define outputCondition "EQUAL_SPACING"
+
50 
+
51 // Number of times the program outputs the fields (total number for "EQUAL_SPACING"
+
52 // and "LOG_SPACING", number per decade for "N_PER_DECADE", ignored for "LIST")
+
53 #define numOutputs 10
+
54 
+
55 // User-defined list of time steps where the program should output. Only used if
+
56 // outputCondition is "LIST"
+
57 #define outputList {}
+
58 
+
59 // Status is printed to the screen every skipPrintSteps
+
60 #define skipPrintSteps 10
+
61 
+
62 // =================================================================================
+
63 // Set the flag determining if the total free energy is calculated for each output
+
64 // =================================================================================
+
65 #define calcEnergy true
diff --git a/html/_c_h_a_c__anisotropy_regularized_2_i_cs__and___b_cs_8h_source.html b/html/_c_h_a_c__anisotropy_regularized_2_i_cs__and___b_cs_8h_source.html index e891437cd..1c96f236b 100644 --- a/html/_c_h_a_c__anisotropy_regularized_2_i_cs__and___b_cs_8h_source.html +++ b/html/_c_h_a_c__anisotropy_regularized_2_i_cs__and___b_cs_8h_source.html @@ -127,92 +127,88 @@
35 #endif
36  }
37  if (index == 1) {
-
38  r=p.distance(Point<dim>(spanX/3.0,spanY/3.0));
-
39  scalar_IC = 0.5*(1.0-std::tanh((r-spanX/5.0)/(3*dx)));
-
40  r=p.distance(Point<dim>(3.0*spanX/4.0,3.0*spanY/4.0));
-
41  scalar_IC += 0.5*(1.0-std::tanh((r-spanX/12.0)/(3*dx)));
-
42 #if problemDIM==1
-
43  r=p[0];
-
44  scalar_IC = 0.5*(1-std::tanh((r-spanX/2.0)/6.0));
-
45 #elif problemDIM==2
-
46  r=p.distance(Point<dim>(spanX/2.0,spanY/2.0));
-
47  scalar_IC = 0.5*(1.0-std::tanh((r-spanX/4.0)/4.0));
-
48 #elif problemDIM==3
-
49  r=p.distance(Point<dim>(spanX/2.0,spanY/2.0,spanZ/2.0));
-
50  scalar_IC = 0.5*(1.0-std::tanh((r-spanX/4.0)/4.0));
-
51 #endif
-
52  }
-
53  if (index == 2) scalar_IC = 0.0;
-
54  // =====================================================================
-
55  return scalar_IC;
-
56  }
-
57 };
-
58 
-
59 template <int dim>
-
60 class InitialConditionVec : public Function<dim>
-
61 {
-
62 public:
-
63  unsigned int index;
-
64  //Vector<double> values;
-
65  InitialConditionVec (const unsigned int _index) : Function<dim>(dim), index(_index) {
-
66  std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1);
-
67  }
-
68  void vector_value (const Point<dim> &p,Vector<double> &vector_IC) const
-
69  {
-
70  // =====================================================================
-
71  // ENTER THE INITIAL CONDITIONS HERE FOR VECTOR FIELDS
-
72  // =====================================================================
-
73  // Enter the function describing conditions for the fields at point "p".
-
74  // Use "if" statements to set the initial condition for each variable
-
75  // according to its variable index.
-
76 
+
38 #if problemDIM==1
+
39  r=p[0];
+
40  scalar_IC = 0.5*(1-std::tanh((r-spanX/2.0)/6.0));
+
41 #elif problemDIM==2
+
42  r=p.distance(Point<dim>(spanX/2.0,spanY/2.0));
+
43  scalar_IC = 0.5*(1.0-std::tanh((r-spanX/4.0)/4.0));
+
44 #elif problemDIM==3
+
45  r=p.distance(Point<dim>(spanX/2.0,spanY/2.0,spanZ/2.0));
+
46  scalar_IC = 0.5*(1.0-std::tanh((r-spanX/4.0)/4.0));
+
47 #endif
+
48  }
+
49  if (index == 2) scalar_IC = 0.0;
+
50  // =====================================================================
+
51  return scalar_IC;
+
52  }
+
53 };
+
54 
+
55 template <int dim>
+
56 class InitialConditionVec : public Function<dim>
+
57 {
+
58 public:
+
59  unsigned int index;
+
60  //Vector<double> values;
+
61  InitialConditionVec (const unsigned int _index) : Function<dim>(dim), index(_index) {
+
62  std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1);
+
63  }
+
64  void vector_value (const Point<dim> &p,Vector<double> &vector_IC) const
+
65  {
+
66  // =====================================================================
+
67  // ENTER THE INITIAL CONDITIONS HERE FOR VECTOR FIELDS
+
68  // =====================================================================
+
69  // Enter the function describing conditions for the fields at point "p".
+
70  // Use "if" statements to set the initial condition for each variable
+
71  // according to its variable index.
+
72 
+
73 
+
74  // =====================================================================
+
75  }
+
76 };
77 
-
78  // =====================================================================
-
79  }
-
80 };
+
78 
+
79 template <int dim>
+
80 void generalizedProblem<dim>::setBCs(){
81 
-
82 
-
83 template <int dim>
-
84 void generalizedProblem<dim>::setBCs(){
-
85 
-
86  // =====================================================================
-
87  // ENTER THE BOUNDARY CONDITIONS HERE
-
88  // =====================================================================
-
89  // This function sets the BCs for the problem variables
-
90  // The function "inputBCs" should be called for each component of
-
91  // each variable and should be in numerical order. Four input arguments
-
92  // set the same BC on the entire boundary. Two plus two times the
-
93  // number of dimensions inputs sets separate BCs on each face of the domain.
-
94  // Inputs to "inputBCs":
-
95  // First input: variable number
-
96  // Second input: component number
-
97  // Third input: BC type (options are "ZERO_DERIVATIVE" and "DIRICHLET")
-
98  // Fourth input: BC value (ignored unless the BC type is "DIRICHLET")
-
99  // Odd inputs after the third: BC type
-
100  // Even inputs after the third: BC value
-
101  // Face numbering: starts at zero with the minimum of the first direction, one for the maximum of the first direction
-
102  // two for the minimum of the second direction, etc.
+
82  // =====================================================================
+
83  // ENTER THE BOUNDARY CONDITIONS HERE
+
84  // =====================================================================
+
85  // This function sets the BCs for the problem variables
+
86  // The function "inputBCs" should be called for each component of
+
87  // each variable and should be in numerical order. Four input arguments
+
88  // set the same BC on the entire boundary. Two plus two times the
+
89  // number of dimensions inputs sets separate BCs on each face of the domain.
+
90  // Inputs to "inputBCs":
+
91  // First input: variable number
+
92  // Second input: component number
+
93  // Third input: BC type (options are "ZERO_DERIVATIVE", "DIRICHLET", and "PERIODIC")
+
94  // Fourth input: BC value (ignored unless the BC type is "DIRICHLET")
+
95  // Odd inputs after the third: BC type
+
96  // Even inputs after the third: BC value
+
97  // Face numbering: starts at zero with the minimum of the first direction, one for the maximum of the first direction
+
98  // two for the minimum of the second direction, etc.
+
99 
+
100  inputBCs(0,0,"ZERO_DERIVATIVE",0);
+
101  inputBCs(1,0,"ZERO_DERIVATIVE",0);
+
102  inputBCs(2,0,"ZERO_DERIVATIVE",0);
103 
-
104  inputBCs(0,0,"ZERO_DERIVATIVE",0);
-
105  inputBCs(1,0,"ZERO_DERIVATIVE",0);
-
106  inputBCs(2,0,"ZERO_DERIVATIVE",0);
-
107 
-
108 }
-
109 
-
110 
-
InitialConditionVec::index
unsigned int index
Definition: ICs_and_BCs.h:46
-
InitialConditionVec::vector_value
void vector_value(const Point< dim > &p, Vector< double > &vector_IC) const
Definition: ICs_and_BCs.h:68
+
104 }
+
105 
+
106 
+
InitialConditionVec::index
unsigned int index
Definition: ICs_and_BCs.h:47
+
InitialConditionVec::vector_value
void vector_value(const Point< dim > &p, Vector< double > &vector_IC) const
Definition: ICs_and_BCs.h:64
spanX
#define spanX
Definition: defaultValues.h:12
InitialCondition::InitialCondition
InitialCondition(const unsigned int _index)
Definition: ICs_and_BCs.h:7
spanY
#define spanY
Definition: defaultValues.h:17
InitialCondition::value
double value(const Point< dim > &p, const unsigned int component=0) const
Definition: ICs_and_BCs.h:10
InitialCondition::index
unsigned int index
Definition: ICs_and_BCs.h:5
refineFactor
#define refineFactor
Definition: defaultValues.h:27
-
generalizedProblem::setBCs
void setBCs()
Definition: ICs_and_BCs.h:66
-
InitialConditionVec::InitialConditionVec
InitialConditionVec(const unsigned int _index)
Definition: ICs_and_BCs.h:65
+
generalizedProblem::setBCs
void setBCs()
Definition: ICs_and_BCs.h:67
+
InitialConditionVec::InitialConditionVec
InitialConditionVec(const unsigned int _index)
Definition: ICs_and_BCs.h:61
subdivisionsX
#define subdivisionsX
Definition: parameters.h:14
InitialCondition
Definition: ICs_and_BCs.h:2
-
InitialConditionVec
Definition: ICs_and_BCs.h:43
+
InitialConditionVec
Definition: ICs_and_BCs.h:44
spanZ
#define spanZ
Definition: defaultValues.h:22
InitialCondition::values
Vector< double > values
Definition: ICs_and_BCs.h:6
diff --git a/html/_c_h_a_c__anisotropy_regularized_2equations_8h.html b/html/_c_h_a_c__anisotropy_regularized_2equations_8h.html index fb766c920..a7b8d7999 100644 --- a/html/_c_h_a_c__anisotropy_regularized_2equations_8h.html +++ b/html/_c_h_a_c__anisotropy_regularized_2equations_8h.html @@ -153,11 +153,11 @@   #define rcV   ( c )   -#define rcxV   (constV(-McV*timeStep)*(cx*((1.0-hV)*faccV+hV*fbccV)+nx*hnV*(fbcV-facV))) +#define rcxV   (constV(-McV*timeStep)*(cx*((1.0-hV)*faccV+hV*fbccV)+nx*hnV*(fbcV-facV)))   -#define rnV   (n-constV(timeStep*MnV)*(fbV-faV)*hnV) +#define rnV   (n-constV(timeStep*MnV)*(fbV-faV)*hnV)   -#define rnxV   (constV(timeStep*MnV)*(-aniso+constV(delta2)*biharmx)) +#define rnxV   (constV(timeStep*MnV)*(-aniso+constV(delta2)*biharmx))  

Macro Definition Documentation

@@ -466,7 +466,7 @@

Macro Definition Documentation

- +
#define rcxV   (constV(-McV*timeStep)*(cx*((1.0-hV)*faccV+hV*fbccV)+nx*hnV*(fbcV-facV)))#define rcxV   (constV(-McV*timeStep)*(cx*((1.0-hV)*faccV+hV*fbccV)+nx*hnV*(fbcV-facV)))
@@ -478,7 +478,7 @@

Macro Definition Documentation

- +
#define rnV   (n-constV(timeStep*MnV)*(fbV-faV)*hnV)#define rnV   (n-constV(timeStep*MnV)*(fbV-faV)*hnV)
@@ -490,7 +490,7 @@

Macro Definition Documentation

- +
#define rnxV   (constV(timeStep*MnV)*(-aniso+constV(delta2)*biharmx))#define rnxV   (constV(timeStep*MnV)*(-aniso+constV(delta2)*biharmx))
diff --git a/html/_c_h_a_c__anisotropy_regularized_2equations_8h_source.html b/html/_c_h_a_c__anisotropy_regularized_2equations_8h_source.html index 156ff9f08..2bd9f8917 100644 --- a/html/_c_h_a_c__anisotropy_regularized_2equations_8h_source.html +++ b/html/_c_h_a_c__anisotropy_regularized_2equations_8h_source.html @@ -179,130 +179,130 @@
87 // "modelResidualsList", a list of the value and gradient terms of the residual for
88 // each residual equation. The index for each variable in these lists corresponds to
89 // the order it is defined at the top of this file (starting at 0).template <int dim>
-
90 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim>> & modelVariablesList,
-
91  std::vector<modelResidual<dim>> & modelResidualsList,
-
92  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
-
93 
-
94 //c
-
95 scalarvalueType c = modelVariablesList[0].scalarValue;
-
96 scalargradType cx = modelVariablesList[0].scalarGrad;
-
97 
-
98 //n
-
99 scalarvalueType n = modelVariablesList[1].scalarValue;
-
100 scalargradType nx = modelVariablesList[1].scalarGrad;
-
101 
-
102 //biharm
-
103 scalargradType biharmx = modelVariablesList[2].scalarGrad;
-
104 
-
105 // anisotropy code
-
106 scalarvalueType normgradn = std::sqrt(nx.norm_square());
-
107 scalargradType normal = nx/(normgradn+constV(1.0e-16));
-
108 scalarvalueType gamma_scl = gamma;
-
109 scalargradType aniso;
-
110 #if problemDIM==1
-
111  aniso = gamma_scl*gamma_scl*nx
-
112 #else
-
113  scalargradType dgammadnorm;
-
114  dgammadnorm[0]=gammanx;
-
115  dgammadnorm[1]=gammany;
-
116 #if problemDIM>2
-
117  dgammadnorm[2]=gammanz;
-
118 #endif
-
119  for (unsigned int i=0; i<problemDIM; ++i){
-
120  for (unsigned int j=0; j<problemDIM; ++j){
-
121  aniso[i] += -normal[i]*normal[j]*dgammadnorm[j];
-
122  if (i==j) aniso[i] +=dgammadnorm[j];
-
123  }
-
124  }
-
125  aniso = gamma_scl*(aniso*normgradn+gamma_scl*nx);
-
126 #endif
-
127 // end anisotropy code
-
128 
-
129 modelResidualsList[0].scalarValueResidual = rcV;
-
130 modelResidualsList[0].scalarGradResidual = rcxV;
-
131 
-
132 modelResidualsList[1].scalarValueResidual = rnV;
-
133 modelResidualsList[1].scalarGradResidual = rnxV;
-
134 
-
135 modelResidualsList[2].scalarGradResidual = rbiharmxV;
-
136 }
-
137 
-
138 // =================================================================================
-
139 // residualLHS (needed only if at least one equation is elliptic)
-
140 // =================================================================================
-
141 // This function calculates the residual equations for the iterative solver for
-
142 // elliptic equations.for each variable. It takes "modelVariablesList" as an input,
-
143 // which is a list of the value and derivatives of each of the variables at a
-
144 // specific quadrature point. The (x,y,z) location of that quadrature point is given
-
145 // by "q_point_loc". The function outputs "modelRes", the value and gradient terms of
-
146 // for the left-hand-side of the residual equation for the iterative solver. The
-
147 // index for each variable in these lists corresponds to the order it is defined at
-
148 // the top of this file (starting at 0), not counting variables that have
-
149 // "need_val_LHS", "need_grad_LHS", and "need_hess_LHS" all set to "false". If there
-
150 // are multiple elliptic equations, conditional statements should be used to ensure
-
151 // that the correct residual is being submitted. The index of the field being solved
-
152 // can be accessed by "this->currentFieldIndex".
-
153 template <int dim>
-
154 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim>> & modelVarList,
-
155  modelResidual<dim> & modelRes, dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
-
156 
-
157 }
-
158 
-
159 // =================================================================================
-
160 // energyDensity (needed only if calcEnergy == true)
-
161 // =================================================================================
-
162 // This function integrates the free energy density across the computational domain.
-
163 // It takes "modelVariablesList" as an input, which is a list of the value and
-
164 // derivatives of each of the variables at a specific quadrature point. It also
-
165 // takes the mapped quadrature weight, "JxW_value", as an input. The (x,y,z) location
-
166 // of the quadrature point is given by "q_point_loc". The weighted value of the
-
167 // energy density is added to "energy" variable and the components of the energy
-
168 // density are added to the "energy_components" variable (index 0: chemical energy,
-
169 // index 1: gradient energy, index 2: elastic energy).
-
170 template <int dim>
-
171 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim>> & modelVarList,
-
172  const dealii::VectorizedArray<double> & JxW_value,
-
173  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) {
-
174 
-
175 scalarvalueType total_energy_density = constV(0.0);
-
176 
-
177 //c
-
178 scalarvalueType c = modelVarList[0].scalarValue;
-
179 scalargradType cx = modelVarList[0].scalarGrad;
-
180 
-
181 //n1
-
182 scalarvalueType n = modelVarList[1].scalarValue;
-
183 scalargradType nx = modelVarList[1].scalarGrad;
-
184 
-
185 //biharm
-
186 scalarvalueType biharm = modelVarList[2].scalarValue;
-
187 
-
188 scalarvalueType f_chem = (constV(1.0)-hV)*faV + hV*fbV;
-
189 
-
190 scalargradType normal = nx/(std::sqrt(nx.norm_square())+constV(1.0e-16));
-
191 scalarvalueType f_grad = constV(0.5)*gamma*gamma*nx*nx;
-
192 scalarvalueType f_reg = constV(0.5)*delta2*biharm*biharm;
-
193 
-
194 total_energy_density = f_chem + f_grad + f_reg;
-
195 
-
196 assembler_lock.acquire ();
-
197 for (unsigned i=0; i<c.n_array_elements;i++){
-
198  // For some reason, some of the values in this loop
-
199  if (c[i] > 1.0e-10){
-
200  this->energy+=total_energy_density[i]*JxW_value[i];
-
201  this->energy_components[0]+= f_chem[i]*JxW_value[i];
-
202  this->energy_components[1]+= f_grad[i]*JxW_value[i];
-
203  this->energy_components[2]+= f_reg[i]*JxW_value[i];
-
204  }
-
205 }
-
206 assembler_lock.release ();
-
207 }
-
generalizedProblem::energyDensity
void energyDensity(const std::vector< modelVariable< dim >> &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
Definition: equations.h:106
+
90 template <int dim>
+
91 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim> > & modelVariablesList,
+
92  std::vector<modelResidual<dim> > & modelResidualsList,
+
93  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
+
94 
+
95 //c
+
96 scalarvalueType c = modelVariablesList[0].scalarValue;
+
97 scalargradType cx = modelVariablesList[0].scalarGrad;
+
98 
+
99 //n
+
100 scalarvalueType n = modelVariablesList[1].scalarValue;
+
101 scalargradType nx = modelVariablesList[1].scalarGrad;
+
102 
+
103 //biharm
+
104 scalargradType biharmx = modelVariablesList[2].scalarGrad;
+
105 
+
106 // anisotropy code
+
107 scalarvalueType normgradn = std::sqrt(nx.norm_square());
+
108 scalargradType normal = nx/(normgradn+constV(1.0e-16));
+
109 scalarvalueType gamma_scl = gamma;
+
110 scalargradType aniso;
+
111 #if problemDIM==1
+
112  aniso = gamma_scl*gamma_scl*nx
+
113 #else
+
114  scalargradType dgammadnorm;
+
115  dgammadnorm[0]=gammanx;
+
116  dgammadnorm[1]=gammany;
+
117 #if problemDIM>2
+
118  dgammadnorm[2]=gammanz;
+
119 #endif
+
120  for (unsigned int i=0; i<problemDIM; ++i){
+
121  for (unsigned int j=0; j<problemDIM; ++j){
+
122  aniso[i] += -normal[i]*normal[j]*dgammadnorm[j];
+
123  if (i==j) aniso[i] +=dgammadnorm[j];
+
124  }
+
125  }
+
126  aniso = gamma_scl*(aniso*normgradn+gamma_scl*nx);
+
127 #endif
+
128 // end anisotropy code
+
129 
+
130 modelResidualsList[0].scalarValueResidual = rcV;
+
131 modelResidualsList[0].scalarGradResidual = rcxV;
+
132 
+
133 modelResidualsList[1].scalarValueResidual = rnV;
+
134 modelResidualsList[1].scalarGradResidual = rnxV;
+
135 
+
136 modelResidualsList[2].scalarGradResidual = rbiharmxV;
+
137 }
+
138 
+
139 // =================================================================================
+
140 // residualLHS (needed only if at least one equation is elliptic)
+
141 // =================================================================================
+
142 // This function calculates the residual equations for the iterative solver for
+
143 // elliptic equations.for each variable. It takes "modelVariablesList" as an input,
+
144 // which is a list of the value and derivatives of each of the variables at a
+
145 // specific quadrature point. The (x,y,z) location of that quadrature point is given
+
146 // by "q_point_loc". The function outputs "modelRes", the value and gradient terms of
+
147 // for the left-hand-side of the residual equation for the iterative solver. The
+
148 // index for each variable in these lists corresponds to the order it is defined at
+
149 // the top of this file (starting at 0), not counting variables that have
+
150 // "need_val_LHS", "need_grad_LHS", and "need_hess_LHS" all set to "false". If there
+
151 // are multiple elliptic equations, conditional statements should be used to ensure
+
152 // that the correct residual is being submitted. The index of the field being solved
+
153 // can be accessed by "this->currentFieldIndex".
+
154 template <int dim>
+
155 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim> > & modelVarList,
+
156  modelResidual<dim> & modelRes, dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
+
157 
+
158 }
+
159 
+
160 // =================================================================================
+
161 // energyDensity (needed only if calcEnergy == true)
+
162 // =================================================================================
+
163 // This function integrates the free energy density across the computational domain.
+
164 // It takes "modelVariablesList" as an input, which is a list of the value and
+
165 // derivatives of each of the variables at a specific quadrature point. It also
+
166 // takes the mapped quadrature weight, "JxW_value", as an input. The (x,y,z) location
+
167 // of the quadrature point is given by "q_point_loc". The weighted value of the
+
168 // energy density is added to "energy" variable and the components of the energy
+
169 // density are added to the "energy_components" variable (index 0: chemical energy,
+
170 // index 1: gradient energy, index 2: elastic energy).
+
171 template <int dim>
+
172 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim> > & modelVarList,
+
173  const dealii::VectorizedArray<double> & JxW_value,
+
174  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) {
+
175 
+
176 scalarvalueType total_energy_density = constV(0.0);
+
177 
+
178 //c
+
179 scalarvalueType c = modelVarList[0].scalarValue;
+
180 scalargradType cx = modelVarList[0].scalarGrad;
+
181 
+
182 //n1
+
183 scalarvalueType n = modelVarList[1].scalarValue;
+
184 scalargradType nx = modelVarList[1].scalarGrad;
+
185 
+
186 //biharm
+
187 scalarvalueType biharm = modelVarList[2].scalarValue;
+
188 
+
189 scalarvalueType f_chem = (constV(1.0)-hV)*faV + hV*fbV;
+
190 
+
191 scalargradType normal = nx/(std::sqrt(nx.norm_square())+constV(1.0e-16));
+
192 scalarvalueType f_grad = constV(0.5)*gamma*gamma*nx*nx;
+
193 scalarvalueType f_reg = constV(0.5)*delta2*biharm*biharm;
+
194 
+
195 total_energy_density = f_chem + f_grad + f_reg;
+
196 
+
197 assembler_lock.acquire ();
+
198 for (unsigned i=0; i<c.n_array_elements;i++){
+
199  // For some reason, some of the values in this loop
+
200  if (c[i] > 1.0e-10){
+
201  this->energy+=total_energy_density[i]*JxW_value[i];
+
202  this->energy_components[0]+= f_chem[i]*JxW_value[i];
+
203  this->energy_components[1]+= f_grad[i]*JxW_value[i];
+
204  this->energy_components[2]+= f_reg[i]*JxW_value[i];
+
205  }
+
206 }
+
207 assembler_lock.release ();
+
208 }
modelResidual
Definition: model_variables.h:25
problemDIM
#define problemDIM
Definition: defaultValues.h:7
+
generalizedProblem::residualRHS
void residualRHS(const std::vector< modelVariable< dim > > &modelVarList, std::vector< modelResidual< dim > > &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:58
hV
#define hV
Definition: equations.h:43
scalargradType
dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
Definition: matrixFreePDE.h:51
-
generalizedProblem::residualLHS
void residualLHS(const std::vector< modelVariable< dim >> &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:89
constV
#define constV(a)
Definition: matrixFreePDE.h:67
rcV
#define rcV
Definition: equations.h:75
faV
#define faV
Definition: equations.h:37
@@ -311,14 +311,15 @@
rnV
#define rnV
Definition: equations.h:77
fbV
#define fbV
Definition: equations.h:40
rcxV
#define rcxV
Definition: equations.h:76
+
generalizedProblem::residualLHS
void residualLHS(const std::vector< modelVariable< dim > > &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:89
rnxV
#define rnxV
Definition: equations.h:78
gammanx
#define gammanx
Definition: equations.h:62
gammany
#define gammany
Definition: equations.h:63
modelVariable
Definition: model_variables.h:4
scalarvalueType
dealii::VectorizedArray< double > scalarvalueType
Definition: matrixFreePDE.h:34
-
generalizedProblem::residualRHS
void residualRHS(const std::vector< modelVariable< dim >> &modelVarList, std::vector< modelResidual< dim >> &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:58
rbiharmxV
#define rbiharmxV
Definition: equations.h:74
delta2
#define delta2
Definition: equations.h:48
+
generalizedProblem::energyDensity
void energyDensity(const std::vector< modelVariable< dim > > &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
Definition: equations.h:106
diff --git a/html/_c_h_a_c__anisotropy_regularized_2parameters_8h.html b/html/_c_h_a_c__anisotropy_regularized_2parameters_8h.html index 314398f55..343abcf19 100644 --- a/html/_c_h_a_c__anisotropy_regularized_2parameters_8h.html +++ b/html/_c_h_a_c__anisotropy_regularized_2parameters_8h.html @@ -123,8 +123,14 @@   #define writeOutput   true   -#define skipOutputSteps   2000 -  +#define outputCondition   "EQUAL_SPACING" +  +#define numOutputs   10 +  +#define outputList   {} +  +#define skipPrintSteps   10 +  #define calcEnergy   true   @@ -151,6 +157,42 @@

Macro Definition Documentation

+
+ + +
+
+ + + + +
#define numOutputs   10
+
+ +
+
+ +
+
+ + + + +
#define outputCondition   "EQUAL_SPACING"
+
+ +
+
+ +
+
+ + + + +
#define outputList   {}
+
+
@@ -177,12 +219,12 @@

Macro Definition Documentation

- +
- +
#define skipOutputSteps   2000#define skipPrintSteps   10
diff --git a/html/_c_h_a_c__anisotropy_regularized_2parameters_8h_source.html b/html/_c_h_a_c__anisotropy_regularized_2parameters_8h_source.html index 7a07e0468..eb59dfa83 100644 --- a/html/_c_h_a_c__anisotropy_regularized_2parameters_8h_source.html +++ b/html/_c_h_a_c__anisotropy_regularized_2parameters_8h_source.html @@ -136,13 +136,25 @@
44 // Each field in the problem will be output is writeOutput is set to "true"
45 #define writeOutput true
46 
-
47 // Output files are written every skipOutputSteps time steps
-
48 #define skipOutputSteps 2000
-
49 
-
50 // =================================================================================
-
51 // Set the flag determining if the total free energy is calculated for each output
-
52 // =================================================================================
-
53 #define calcEnergy true
+
47 // Type of spacing between outputs ("EQUAL_SPACING", "LOG_SPACING", "N_PER_DECADE",
+
48 // or "LIST")
+
49 #define outputCondition "EQUAL_SPACING"
+
50 
+
51 // Number of times the program outputs the fields (total number for "EQUAL_SPACING"
+
52 // and "LOG_SPACING", number per decade for "N_PER_DECADE", ignored for "LIST")
+
53 #define numOutputs 10
+
54 
+
55 // User-defined list of time steps where the program should output. Only used if
+
56 // outputCondition is "LIST"
+
57 #define outputList {}
+
58 
+
59 // Status is printed to the screen every skipPrintSteps
+
60 #define skipPrintSteps 10
+
61 
+
62 // =================================================================================
+
63 // Set the flag determining if the total free energy is calculated for each output
+
64 // =================================================================================
+
65 #define calcEnergy true
diff --git a/html/_c_hi_ma_d__benchmark1a_2_i_cs__and___b_cs_8h.html b/html/_c_hi_ma_d__benchmark1a_2_i_cs__and___b_cs_8h.html new file mode 100644 index 000000000..aa958e878 --- /dev/null +++ b/html/_c_hi_ma_d__benchmark1a_2_i_cs__and___b_cs_8h.html @@ -0,0 +1,113 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/CHiMaD_benchmark1a/ICs_and_BCs.h File Reference + + + + + + + + + + +
+
+ + + + + + +
+
PRISMS PhaseField +  1.0 +
+
+
+ + + + + + +
+
+ + +
+ +
+ + +
+
+
+Classes
+
+
ICs_and_BCs.h File Reference
+
+
+ +

Go to the source code of this file.

+ + + + + + +

+Classes

class  InitialCondition< dim >
 
class  InitialConditionVec< dim >
 
+
+ +
+Generated by   +doxygen + 1.8.10 +
+ + diff --git a/html/_c_hi_ma_d__benchmark1a_2_i_cs__and___b_cs_8h_source.html b/html/_c_hi_ma_d__benchmark1a_2_i_cs__and___b_cs_8h_source.html new file mode 100644 index 000000000..264f0af4b --- /dev/null +++ b/html/_c_hi_ma_d__benchmark1a_2_i_cs__and___b_cs_8h_source.html @@ -0,0 +1,200 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/CHiMaD_benchmark1a/ICs_and_BCs.h Source File + + + + + + + + + + +
+
+ + + + + + +
+
PRISMS PhaseField +  1.0 +
+
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+ +
+ + +
+
+
+
ICs_and_BCs.h
+
+
+Go to the documentation of this file.
1 template <int dim>
+
2 class InitialCondition : public Function<dim>
+
3 {
+
4 public:
+
5  unsigned int index;
+
6  Vector<double> values;
+
7  InitialCondition (const unsigned int _index) : Function<dim>(1), index(_index) {
+
8  std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1);
+
9  }
+
10  double value (const Point<dim> &p, const unsigned int component = 0) const
+
11  {
+
12  double scalar_IC = 0;
+
13  // =====================================================================
+
14  // ENTER THE INITIAL CONDITIONS HERE FOR SCALAR FIELDS
+
15  // =====================================================================
+
16  // Enter the function describing conditions for the fields at point "p".
+
17  // Use "if" statements to set the initial condition for each variable
+
18  // according to its variable index.
+
19 
+
20  if (index == 0){
+
21  double epsilon=0.01;
+
22  double c0 = 0.5;
+
23  double dx=spanX/((double) subdivisionsX)/std::pow(2.0,refineFactor);
+
24  scalar_IC = c0+epsilon*(std::cos(0.105*p[0])*std::cos(0.11*p[1])+
+
25  std::pow(std::cos(0.13*p[0])*std::cos(0.087*p[1]),2.0)+
+
26  std::cos(0.025*p[0]-0.15*p[1])*std::cos(0.07*p[0]-0.02*p[1]));
+
27 
+
28  }
+
29  else {
+
30  scalar_IC = 0.0;
+
31  }
+
32 
+
33  // =====================================================================
+
34  return scalar_IC;
+
35  }
+
36 };
+
37 
+
38 template <int dim>
+
39 class InitialConditionVec : public Function<dim>
+
40 {
+
41 public:
+
42  unsigned int index;
+
43  //Vector<double> values;
+
44  InitialConditionVec (const unsigned int _index) : Function<dim>(dim), index(_index) {
+
45  std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1);
+
46  }
+
47  void vector_value (const Point<dim> &p,Vector<double> &vector_IC) const
+
48  {
+
49  // =====================================================================
+
50  // ENTER THE INITIAL CONDITIONS HERE FOR VECTOR FIELDS
+
51  // =====================================================================
+
52  // Enter the function describing conditions for the fields at point "p".
+
53  // Use "if" statements to set the initial condition for each variable
+
54  // according to its variable index.
+
55 
+
56 
+
57  // =====================================================================
+
58  }
+
59 };
+
60 
+
61 
+
62 template <int dim>
+
63 void generalizedProblem<dim>::setBCs(){
+
64 
+
65  // =====================================================================
+
66  // ENTER THE BOUNDARY CONDITIONS HERE
+
67  // =====================================================================
+
68  // This function sets the BCs for the problem variables
+
69  // The function "inputBCs" should be called for each component of
+
70  // each variable and should be in numerical order. Four input arguments
+
71  // set the same BC on the entire boundary. Two plus two times the
+
72  // number of dimensions inputs sets separate BCs on each face of the domain.
+
73  // Inputs to "inputBCs":
+
74  // First input: variable number
+
75  // Second input: component number
+
76  // Third input: BC type (options are "ZERO_DERIVATIVE", "DIRICHLET", and "PERIODIC")
+
77  // Fourth input: BC value (ignored unless the BC type is "DIRICHLET")
+
78  // Odd inputs after the third: BC type
+
79  // Even inputs after the third: BC value
+
80  // Face numbering: starts at zero with the minimum of the first direction, one for the maximum of the first direction
+
81  // two for the minimum of the second direction, etc.
+
82 
+
83  inputBCs(0,0,"ZERO_DERIVATIVE",0);
+
84  inputBCs(1,0,"ZERO_DERIVATIVE",0);
+
85 
+
86 }
+
InitialConditionVec::index
unsigned int index
Definition: ICs_and_BCs.h:47
+
InitialConditionVec::vector_value
void vector_value(const Point< dim > &p, Vector< double > &vector_IC) const
Definition: ICs_and_BCs.h:47
+
spanX
#define spanX
Definition: defaultValues.h:12
+
InitialCondition::InitialCondition
InitialCondition(const unsigned int _index)
Definition: ICs_and_BCs.h:7
+
InitialCondition::value
double value(const Point< dim > &p, const unsigned int component=0) const
Definition: ICs_and_BCs.h:10
+
InitialCondition::index
unsigned int index
Definition: ICs_and_BCs.h:5
+
refineFactor
#define refineFactor
Definition: defaultValues.h:27
+
generalizedProblem::setBCs
void setBCs()
Definition: ICs_and_BCs.h:67
+
InitialConditionVec::InitialConditionVec
InitialConditionVec(const unsigned int _index)
Definition: ICs_and_BCs.h:44
+
subdivisionsX
#define subdivisionsX
Definition: parameters.h:14
+
InitialCondition
Definition: ICs_and_BCs.h:2
+
InitialConditionVec
Definition: ICs_and_BCs.h:44
+
InitialCondition::values
Vector< double > values
Definition: ICs_and_BCs.h:6
+
+ +
+Generated by   +doxygen + 1.8.10 +
+ + diff --git a/html/_c_hi_ma_d__benchmark1a_2equations_8h.html b/html/_c_hi_ma_d__benchmark1a_2equations_8h.html new file mode 100644 index 000000000..eb89973db --- /dev/null +++ b/html/_c_hi_ma_d__benchmark1a_2equations_8h.html @@ -0,0 +1,348 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/CHiMaD_benchmark1a/equations.h File Reference + + + + + + + + + + +
+
+ + + + + + +
+
PRISMS PhaseField +  1.0 +
+
+
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+Macros
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equations.h File Reference
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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Macros

#define num_var   2
 
#define variable_name   {"c", "mu"}
 
#define variable_type   {"SCALAR", "SCALAR"}
 
#define variable_eq_type   {"PARABOLIC", "PARABOLIC"}
 
#define need_val   {true, true}
 
#define need_grad   {true, true}
 
#define need_hess   {false, false}
 
#define need_val_residual   {true, true}
 
#define need_grad_residual   {true, true}
 
#define McV   5.0
 
#define KcV   2.0
 
#define fV   (5.0*(c-0.3)*(c-0.3)*(c-0.7)*(c-0.7))
 
#define fcV   (5.0*(2.0*(c-0.3)*(c-0.7)*(c-0.7)+2.0*(c-0.3)*(c-0.3)*(c-0.7)))
 
#define rmuV   (fcV)
 
#define rmuxV   (constV(KcV)*cx)
 
#define rcV   (c)
 
#define rcxV   (constV(-McV*timeStep)*mux)
 
+

Macro Definition Documentation

+ +
+
+ + + + +
#define fcV   (5.0*(2.0*(c-0.3)*(c-0.7)*(c-0.7)+2.0*(c-0.3)*(c-0.3)*(c-0.7)))
+
+ +
+
+ +
+
+ + + + +
#define fV   (5.0*(c-0.3)*(c-0.3)*(c-0.7)*(c-0.7))
+
+ +
+
+ +
+
+ + + + +
#define KcV   2.0
+
+ +
+
+ +
+
+ + + + +
#define McV   5.0
+
+ +
+
+ +
+
+ + + + +
#define need_grad   {true, true}
+
+ +
+
+ +
+
+ + + + +
#define need_grad_residual   {true, true}
+
+ +
+
+ +
+
+ + + + +
#define need_hess   {false, false}
+
+ +
+
+ +
+
+ + + + +
#define need_val   {true, true}
+
+ +
+
+ +
+
+ + + + +
#define need_val_residual   {true, true}
+
+ +
+
+ +
+
+ + + + +
#define num_var   2
+
+ +
+
+ +
+
+ + + + +
#define rcV   (c)
+
+ +
+
+ +
+
+ + + + +
#define rcxV   (constV(-McV*timeStep)*mux)
+
+ +
+
+ +
+
+ + + + +
#define rmuV   (fcV)
+
+ +
+
+ +
+
+ + + + +
#define rmuxV   (constV(KcV)*cx)
+
+ +
+
+ +
+
+ + + + +
#define variable_eq_type   {"PARABOLIC", "PARABOLIC"}
+
+ +
+
+ +
+
+ + + + +
#define variable_name   {"c", "mu"}
+
+ +
+
+ +
+
+ + + + +
#define variable_type   {"SCALAR", "SCALAR"}
+
+ +
+
+
+ +
+Generated by   +doxygen + 1.8.10 +
+ + diff --git a/html/_c_hi_ma_d__benchmark1a_2equations_8h_source.html b/html/_c_hi_ma_d__benchmark1a_2equations_8h_source.html new file mode 100644 index 000000000..965e4be75 --- /dev/null +++ b/html/_c_hi_ma_d__benchmark1a_2equations_8h_source.html @@ -0,0 +1,274 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/CHiMaD_benchmark1a/equations.h Source File + + + + + + + + + + +
+
+ + + + + + +
+
PRISMS PhaseField +  1.0 +
+
+
+ + + + + + +
+
+ + +
+ +
+ + +
+
+
+
equations.h
+
+
+Go to the documentation of this file.
1 // List of variables and residual equations for the Cahn-Hilliard example application
+
2 
+
3 // =================================================================================
+
4 // Define the variables in the model
+
5 // =================================================================================
+
6 // The number of variables
+
7 #define num_var 2
+
8 
+
9 // The names of the variables, whether they are scalars or vectors and whether the
+
10 // governing eqn for the variable is parabolic or elliptic
+
11 #define variable_name {"c", "mu"}
+
12 #define variable_type {"SCALAR", "SCALAR"}
+
13 #define variable_eq_type {"PARABOLIC", "PARABOLIC"}
+
14 
+
15 // Flags for whether the value, gradient, and Hessian are needed in the residual eqns
+
16 #define need_val {true, true}
+
17 #define need_grad {true, true}
+
18 #define need_hess {false, false}
+
19 
+
20 // Flags for whether the residual equation has a term multiplied by the test function
+
21 // (need_val_residual) and/or the gradient of the test function (need_grad_residual)
+
22 #define need_val_residual {true, true}
+
23 #define need_grad_residual {true, true}
+
24 
+
25 // =================================================================================
+
26 // Define the model parameters and the residual equations
+
27 // =================================================================================
+
28 // Parameters in the residual equations and expressions for the residual equations
+
29 // can be set here. For simple cases, the entire residual equation can be written
+
30 // here. For more complex cases with loops or conditional statements, residual
+
31 // equations (or parts of residual equations) can be written below in "residualRHS".
+
32 
+
33 // Mobility
+
34 #define McV 5.0
+
35 
+
36 // Gradient energy coefficient
+
37 #define KcV 2.0
+
38 
+
39 // The free energy and its derivative
+
40 #define fV (5.0*(c-0.3)*(c-0.3)*(c-0.7)*(c-0.7))
+
41 #define fcV (5.0*(2.0*(c-0.3)*(c-0.7)*(c-0.7)+2.0*(c-0.3)*(c-0.3)*(c-0.7)))
+
42 
+
43 // Residual equations
+
44 #define rmuV (fcV)
+
45 #define rmuxV (constV(KcV)*cx)
+
46 #define rcV (c)
+
47 #define rcxV (constV(-McV*timeStep)*mux)
+
48 
+
49 
+
50 // =================================================================================
+
51 // residualRHS
+
52 // =================================================================================
+
53 // This function calculates the residual equations for each variable. It takes
+
54 // "modelVariablesList" as an input, which is a list of the value and derivatives of
+
55 // each of the variables at a specific quadrature point. The (x,y,z) location of
+
56 // that quadrature point is given by "q_point_loc". The function outputs
+
57 // "modelResidualsList", a list of the value and gradient terms of the residual for
+
58 // each residual equation. The index for each variable in these lists corresponds to
+
59 // the order it is defined at the top of this file (starting at 0).
+
60 template <int dim>
+
61 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim> > & modelVariablesList,
+
62  std::vector<modelResidual<dim> > & modelResidualsList,
+
63  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
+
64 
+
65 // The concentration and its derivatives (names here should match those in the macros above)
+
66 scalarvalueType c = modelVariablesList[0].scalarValue;
+
67 scalargradType cx = modelVariablesList[0].scalarGrad;
+
68 
+
69 // The chemical potential and its derivatives (names here should match those in the macros above)
+
70 scalarvalueType mu = modelVariablesList[1].scalarValue;
+
71 scalargradType mux = modelVariablesList[1].scalarGrad;
+
72 
+
73 // Residuals for the equation to evolve the concentration (names here should match those in the macros above)
+
74 modelResidualsList[0].scalarValueResidual = rcV;
+
75 modelResidualsList[0].scalarGradResidual = rcxV;
+
76 
+
77 // Residuals for the equation to evolve the chemical potential (names here should match those in the macros above)
+
78 modelResidualsList[1].scalarValueResidual = rmuV;
+
79 modelResidualsList[1].scalarGradResidual = rmuxV;
+
80 
+
81 }
+
82 
+
83 // =================================================================================
+
84 // residualLHS (needed only if at least one equation is elliptic)
+
85 // =================================================================================
+
86 // This function calculates the residual equations for the iterative solver for
+
87 // elliptic equations.for each variable. It takes "modelVariablesList" as an input,
+
88 // which is a list of the value and derivatives of each of the variables at a
+
89 // specific quadrature point. The (x,y,z) location of that quadrature point is given
+
90 // by "q_point_loc". The function outputs "modelRes", the value and gradient terms of
+
91 // for the left-hand-side of the residual equation for the iterative solver. The
+
92 // index for each variable in these lists corresponds to the order it is defined at
+
93 // the top of this file (starting at 0), not counting variables that have
+
94 // "need_val_LHS", "need_grad_LHS", and "need_hess_LHS" all set to "false". If there
+
95 // are multiple elliptic equations, conditional statements should be used to ensure
+
96 // that the correct residual is being submitted. The index of the field being solved
+
97 // can be accessed by "this->currentFieldIndex".
+
98 template <int dim>
+
99 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim> > & modelVarList,
+
100  modelResidual<dim> & modelRes,
+
101  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
+
102 }
+
103 
+
104 // =================================================================================
+
105 // energyDensity (needed only if calcEnergy == true)
+
106 // =================================================================================
+
107 // This function integrates the free energy density across the computational domain.
+
108 // It takes "modelVariablesList" as an input, which is a list of the value and
+
109 // derivatives of each of the variables at a specific quadrature point. It also
+
110 // takes the mapped quadrature weight, "JxW_value", as an input. The (x,y,z) location
+
111 // of the quadrature point is given by "q_point_loc". The weighted value of the
+
112 // energy density is added to "energy" variable and the components of the energy
+
113 // density are added to the "energy_components" variable (index 0: chemical energy,
+
114 // index 1: gradient energy, index 2: elastic energy).
+
115 template <int dim>
+
116 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim> > & modelVariablesList,
+
117  const dealii::VectorizedArray<double> & JxW_value,
+
118  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) {
+
119 scalarvalueType total_energy_density = constV(0.0);
+
120 
+
121 // The concentration and its derivatives (names here should match those in the macros above)
+
122 scalarvalueType c = modelVariablesList[0].scalarValue;
+
123 scalargradType cx = modelVariablesList[0].scalarGrad;
+
124 
+
125 // The chemical potential and its derivatives (names here should match those in the macros above)
+
126 scalarvalueType mu = modelVariablesList[1].scalarValue;
+
127 scalargradType mux = modelVariablesList[1].scalarGrad;
+
128 
+
129 // The homogenous free energy
+
130 scalarvalueType f_chem = fV;
+
131 
+
132 // The gradient free energy
+
133 scalarvalueType f_grad = constV(0.0);
+
134 
+
135 for (int i=0; i<dim; i++){
+
136  for (int j=0; j<dim; j++){
+
137  f_grad += constV(0.5*KcV)*cx[i]*cx[j];
+
138  }
+
139 }
+
140 
+
141 // The total free energy
+
142 total_energy_density = f_chem + f_grad;
+
143 
+
144 // Loop to step through each element of the vectorized arrays. Working with deal.ii
+
145 // developers to see if there is a more elegant way to do this.
+
146 assembler_lock.acquire ();
+
147 for (unsigned i=0; i<c.n_array_elements;i++){
+
148  if (c[i] > 1.0e-10){
+
149  this->energy+=total_energy_density[i]*JxW_value[i];
+
150  this->energy_components[0]+= f_chem[i]*JxW_value[i];
+
151  this->energy_components[1]+= f_grad[i]*JxW_value[i];
+
152  }
+
153 }
+
154 assembler_lock.release ();
+
155 }
+
156 
+
157 
+
158 
+
159 
+
modelResidual
Definition: model_variables.h:25
+
generalizedProblem::residualRHS
void residualRHS(const std::vector< modelVariable< dim > > &modelVarList, std::vector< modelResidual< dim > > &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:58
+
KcV
#define KcV
Definition: equations.h:37
+
fV
#define fV
Definition: equations.h:40
+
rcxV
#define rcxV
Definition: equations.h:47
+
scalargradType
dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
Definition: matrixFreePDE.h:51
+
constV
#define constV(a)
Definition: matrixFreePDE.h:67
+
rmuV
#define rmuV
Definition: equations.h:44
+
generalizedProblem::residualLHS
void residualLHS(const std::vector< modelVariable< dim > > &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:89
+
modelVariable
Definition: model_variables.h:4
+
scalarvalueType
dealii::VectorizedArray< double > scalarvalueType
Definition: matrixFreePDE.h:34
+
rcV
#define rcV
Definition: equations.h:46
+
rmuxV
#define rmuxV
Definition: equations.h:45
+
generalizedProblem::energyDensity
void energyDensity(const std::vector< modelVariable< dim > > &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
Definition: equations.h:106
+
+ +
+Generated by   +doxygen + 1.8.10 +
+ + diff --git a/html/_c_hi_ma_d__benchmark1a_2main_8cc.html b/html/_c_hi_ma_d__benchmark1a_2main_8cc.html new file mode 100644 index 000000000..a25ac2178 --- /dev/null +++ b/html/_c_hi_ma_d__benchmark1a_2main_8cc.html @@ -0,0 +1,142 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/CHiMaD_benchmark1a/main.cc File Reference + + + + + + + + + + +
+
+ + + + + + +
+
PRISMS PhaseField +  1.0 +
+
+
+ + + + + + +
+
+ + +
+ +
+ + +
+
+
+Functions
+
+
main.cc File Reference
+
+
+
#include "../../include/dealIIheaders.h"
+#include "parameters.h"
+#include "../../src/models/coupled/generalized_model.h"
+#include "equations.h"
+#include "ICs_and_BCs.h"
+#include "../../src/models/coupled/generalized_model_functions.h"
+
+ + + +

+Functions

int main (int argc, char **argv)
 
+

Function Documentation

+ +
+
+ + + + + + + + + + + + + + + + + + +
int main (int argc,
char ** argv 
)
+
+ +
+
+
+ +
+Generated by   +doxygen + 1.8.10 +
+ + diff --git a/html/_c_hi_ma_d__benchmark1a_2parameters_8h.html b/html/_c_hi_ma_d__benchmark1a_2parameters_8h.html new file mode 100644 index 000000000..a9f9a4342 --- /dev/null +++ b/html/_c_hi_ma_d__benchmark1a_2parameters_8h.html @@ -0,0 +1,362 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/CHiMaD_benchmark1a/parameters.h File Reference + + + + + + + + + + +
+
+ + + + + + +
+
PRISMS PhaseField +  1.0 +
+
+
+ + + + + + +
+
+ + +
+ +
+ + +
+
+
+Macros
+
+
parameters.h File Reference
+
+
+ +

Go to the source code of this file.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Macros

#define problemDIM   2
 
#define spanX   200.0
 
#define spanY   200.0
 
#define spanZ   100.0
 
#define subdivisionsX   1
 
#define subdivisionsY   1
 
#define subdivisionsZ   1
 
#define refineFactor   7
 
#define finiteElementDegree   1
 
#define timeStep   5.0e-3
 
#define timeFinal   10000.0
 
#define timeIncrements   2000000
 
#define writeOutput   true
 
#define outputCondition   "LIST"
 
#define numOutputs   10
 
#define outputList   {0,200,1000,2000,4000,20000,40000,100000,200000,400000,600000,2000000}
 
#define skipPrintSteps   10
 
#define calcEnergy   true
 
+

Macro Definition Documentation

+ +
+
+ + + + +
#define calcEnergy   true
+
+ +
+
+ +
+
+ + + + +
#define finiteElementDegree   1
+
+ +
+
+ +
+
+ + + + +
#define numOutputs   10
+
+ +
+
+ +
+
+ + + + +
#define outputCondition   "LIST"
+
+ +
+
+ +
+
+ + + + +
#define outputList   {0,200,1000,2000,4000,20000,40000,100000,200000,400000,600000,2000000}
+
+ +
+
+ +
+
+ + + + +
#define problemDIM   2
+
+ +
+
+ +
+
+ + + + +
#define refineFactor   7
+
+ +
+
+ +
+
+ + + + +
#define skipPrintSteps   10
+
+ +
+
+ +
+
+ + + + +
#define spanX   200.0
+
+ +
+
+ +
+
+ + + + +
#define spanY   200.0
+
+ +
+
+ +
+
+ + + + +
#define spanZ   100.0
+
+ +
+
+ +
+
+ + + + +
#define subdivisionsX   1
+
+ +
+
+ +
+
+ + + + +
#define subdivisionsY   1
+
+ +
+
+ +
+
+ + + + +
#define subdivisionsZ   1
+
+ +
+
+ +
+
+ + + + +
#define timeFinal   10000.0
+
+ +
+
+ +
+
+ + + + +
#define timeIncrements   2000000
+
+ +
+
+ +
+
+ + + + +
#define timeStep   5.0e-3
+
+ +
+
+ +
+
+ + + + +
#define writeOutput   true
+
+ +
+
+
+ +
+Generated by   +doxygen + 1.8.10 +
+ + diff --git a/html/_c_hi_ma_d__benchmark1a_2parameters_8h_source.html b/html/_c_hi_ma_d__benchmark1a_2parameters_8h_source.html new file mode 100644 index 000000000..ad680ebab --- /dev/null +++ b/html/_c_hi_ma_d__benchmark1a_2parameters_8h_source.html @@ -0,0 +1,167 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/CHiMaD_benchmark1a/parameters.h Source File + + + + + + + + + + +
+
+ + + + + + +
+
PRISMS PhaseField +  1.0 +
+
+
+ + + + + + +
+
+ + +
+ +
+ + +
+
+
+
parameters.h
+
+
+Go to the documentation of this file.
1 // Parameter list for the Cahn-Hilliard example application
+
2 // All strictly numerical parameters should be set in this file
+
3 
+
4 // =================================================================================
+
5 // Set the number of dimensions (1, 2, or 3 for a 1D, 2D, or 3D calculation)
+
6 // =================================================================================
+
7 #define problemDIM 2
+
8 
+
9 // =================================================================================
+
10 // Set the length of the domain in all three dimensions
+
11 // =================================================================================
+
12 // Each axes spans from zero to the specified length
+
13 #define spanX 200.0
+
14 #define spanY 200.0
+
15 #define spanZ 100.0
+
16 
+
17 // =================================================================================
+
18 // Set the element parameters
+
19 // =================================================================================
+
20 // The number of elements in each direction is 2^(refineFactor) * subdivisions
+
21 // For optimal performance, use refineFactor primarily to determine the element size
+
22 #define subdivisionsX 1
+
23 #define subdivisionsY 1
+
24 #define subdivisionsZ 1
+
25 #define refineFactor 7
+
26 
+
27 // Set the polynomial degree of the element (suggested values: 1 or 2)
+
28 #define finiteElementDegree 1
+
29 
+
30 // =================================================================================
+
31 // Set the time step parameters
+
32 // =================================================================================
+
33 // The size of the time step
+
34 #define timeStep 5.0e-3
+
35 
+
36 // The simulation ends when either timeFinal is reached or the number of time steps
+
37 // equals timeIncrements
+
38 #define timeFinal 10000.0
+
39 #define timeIncrements 2000000
+
40 
+
41 // =================================================================================
+
42 // Set the output parameters
+
43 // =================================================================================
+
44 // Each field in the problem will be output is writeOutput is set to "true"
+
45 #define writeOutput true
+
46 
+
47 // Type of spacing between outputs ("EQUAL_SPACING", "LOG_SPACING", "N_PER_DECADE",
+
48 // or "LIST")
+
49 #define outputCondition "LIST"
+
50 
+
51 // Number of times the program outputs the fields (total number for "EQUAL_SPACING"
+
52 // and "LOG_SPACING", number per decade for "N_PER_DECADE", ignored for "LIST")
+
53 #define numOutputs 10
+
54 
+
55 // User-defined list of time steps where the program should output. Only used if
+
56 // outputCondition is "LIST"
+
57 #define outputList {0,200,1000,2000,4000,20000,40000,100000,200000,400000,600000,2000000}
+
58 
+
59 // Status is printed to the screen every skipPrintSteps
+
60 #define skipPrintSteps 10
+
61 
+
62 // =================================================================================
+
63 // Set the flag determining if the total free energy is calculated for each output
+
64 // =================================================================================
+
65 #define calcEnergy true
+
66 
+
+ +
+Generated by   +doxygen + 1.8.10 +
+ + diff --git a/html/_c_hi_ma_d__benchmark2a_2_i_cs__and___b_cs_8h.html b/html/_c_hi_ma_d__benchmark2a_2_i_cs__and___b_cs_8h.html new file mode 100644 index 000000000..7d412049a --- /dev/null +++ b/html/_c_hi_ma_d__benchmark2a_2_i_cs__and___b_cs_8h.html @@ -0,0 +1,113 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/CHiMaD_benchmark2a/ICs_and_BCs.h File Reference + + + + + + + + + + +
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+ + + + + + +
+
PRISMS PhaseField +  1.0 +
+
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+Classes
+
+
ICs_and_BCs.h File Reference
+
+
+ +

Go to the source code of this file.

+ + + + + + +

+Classes

class  InitialCondition< dim >
 
class  InitialConditionVec< dim >
 
+
+ +
+Generated by   +doxygen + 1.8.10 +
+ + diff --git a/html/_c_hi_ma_d__benchmark2a_2_i_cs__and___b_cs_8h_source.html b/html/_c_hi_ma_d__benchmark2a_2_i_cs__and___b_cs_8h_source.html new file mode 100644 index 000000000..e149d7840 --- /dev/null +++ b/html/_c_hi_ma_d__benchmark2a_2_i_cs__and___b_cs_8h_source.html @@ -0,0 +1,239 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/CHiMaD_benchmark2a/ICs_and_BCs.h Source File + + + + + + + + + + +
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PRISMS PhaseField +  1.0 +
+
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+
+ + +
+ +
+ + +
+
+
+
ICs_and_BCs.h
+
+
+Go to the documentation of this file.
1 template <int dim>
+
2 class InitialCondition : public Function<dim>
+
3 {
+
4 public:
+
5  unsigned int index;
+
6  Vector<double> values;
+
7  InitialCondition (const unsigned int _index) : Function<dim>(1), index(_index) {
+
8  std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1);
+
9  }
+
10  double value (const Point<dim> &p, const unsigned int component = 0) const
+
11  {
+
12  double scalar_IC = 0;
+
13 
+
14  // =====================================================================
+
15  // ENTER THE INITIAL CONDITIONS HERE FOR SCALAR FIELDS
+
16  // =====================================================================
+
17  // Enter the function describing conditions for the fields at point "p".
+
18  // Use "if" statements to set the initial condition for each variable
+
19  // according to its variable index.
+
20 
+
21  //Constants for initial conditions equations
+
22  double c_0 = 0.5;
+
23  double epsilon_c = 0.05;
+
24  double epsilon_n = 0.1;
+
25  double psi = 1.5;
+
26 
+
27  double dx=spanX/((double) subdivisionsX)/std::pow(2.0,refineFactor);
+
28  double x=p[0];
+
29  double y=p[1];
+
30 
+
31  // Initial condition for the concentration field
+
32  if (index == 0){
+
33  if (dim == 2){
+
34  scalar_IC = std::cos(0.105*x)*std::cos(0.11*y);
+
35  scalar_IC += std::cos(0.13*x)*std::cos(0.087*y)*std::cos(0.13*x)*std::cos(0.087*y);
+
36  scalar_IC += std::cos(0.025*x-0.15*y)*std::cos(0.07*x-0.02*y);
+
37  scalar_IC = c_0 + epsilon_c*scalar_IC;
+
38  }
+
39  else if (dim == 3) {
+
40  }
+
41 
+
42  }
+
43  // Initial condition for the chemical potential field
+
44  if (index == 1){
+
45  if (dim == 2){
+
46  scalar_IC = 0.0;
+
47  }
+
48  else if (dim == 3) {
+
49  }
+
50  }
+
51  // Initial condition for order parameters
+
52  if (index >= 2){
+
53  double j = ((double) index)-1.0;
+
54  if (dim == 2){
+
55  double term1;
+
56  double term2;
+
57  term1 = std::cos(0.01*j*x-4.0)*std::cos((0.007+0.01*j)*y);
+
58  term1 += std::cos((0.11+0.01*j)*x)*std::cos((0.11+0.01*j)*y);
+
59  term2 = std::cos((0.046+0.001*j)*x + (0.0405+0.001*j)*y)*std::cos((0.031+0.001*j)*x - (0.004+0.001*j)*y);
+
60  term2 = psi*term2*term2;
+
61  scalar_IC = term1 + term2;
+
62  scalar_IC = epsilon_n*(scalar_IC*scalar_IC);
+
63  }
+
64  else if (dim == 3) {
+
65  }
+
66  }
+
67 
+
68  // =====================================================================
+
69  return scalar_IC;
+
70  }
+
71 };
+
72 
+
73 template <int dim>
+
74 class InitialConditionVec : public Function<dim>
+
75 {
+
76 public:
+
77  unsigned int index;
+
78  //Vector<double> values;
+
79  InitialConditionVec (const unsigned int _index) : Function<dim>(dim), index(_index) {
+
80  std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1);
+
81  }
+
82  void vector_value (const Point<dim> &p,Vector<double> &vector_IC) const
+
83  {
+
84  // =====================================================================
+
85  // ENTER THE INITIAL CONDITIONS HERE FOR VECTOR FIELDS
+
86  // =====================================================================
+
87  // Enter the function describing conditions for the fields at point "p".
+
88  // Use "if" statements to set the initial condition for each variable
+
89  // according to its variable index.
+
90 
+
91 
+
92  // =====================================================================
+
93  }
+
94 };
+
95 
+
96 template <int dim>
+
97 void generalizedProblem<dim>::setBCs(){
+
98 
+
99  // =====================================================================
+
100  // ENTER THE BOUNDARY CONDITIONS HERE
+
101  // =====================================================================
+
102  // This function sets the BCs for the problem variables
+
103  // The function "inputBCs" should be called for each component of
+
104  // each variable and should be in numerical order. Four input arguments
+
105  // set the same BC on the entire boundary. Two plus two times the
+
106  // number of dimensions inputs sets separate BCs on each face of the domain.
+
107  // Inputs to "inputBCs":
+
108  // First input: variable number
+
109  // Second input: component number
+
110  // Third input: BC type (options are "ZERO_DERIVATIVE", "DIRICHLET", and "PERIODIC")
+
111  // Fourth input: BC value (ignored unless the BC type is "DIRICHLET")
+
112  // Odd inputs after the third: BC type
+
113  // Even inputs after the third: BC value
+
114  // Face numbering: starts at zero with the minimum of the first direction, one for the maximum of the first direction
+
115  // two for the minimum of the second direction, etc.
+
116 
+
117  inputBCs(0,0,"ZERO_DERIVATIVE",0);
+
118  inputBCs(1,0,"ZERO_DERIVATIVE",0);
+
119  inputBCs(2,0,"ZERO_DERIVATIVE",0);
+
120  inputBCs(3,0,"ZERO_DERIVATIVE",0);
+
121  inputBCs(4,0,"ZERO_DERIVATIVE",0);
+
122  inputBCs(5,0,"ZERO_DERIVATIVE",0);
+
123 }
+
124 
+
125 
+
InitialConditionVec::index
unsigned int index
Definition: ICs_and_BCs.h:47
+
InitialConditionVec::vector_value
void vector_value(const Point< dim > &p, Vector< double > &vector_IC) const
Definition: ICs_and_BCs.h:82
+
spanX
#define spanX
Definition: defaultValues.h:12
+
InitialCondition::InitialCondition
InitialCondition(const unsigned int _index)
Definition: ICs_and_BCs.h:7
+
InitialCondition::value
double value(const Point< dim > &p, const unsigned int component=0) const
Definition: ICs_and_BCs.h:10
+
InitialCondition::index
unsigned int index
Definition: ICs_and_BCs.h:5
+
refineFactor
#define refineFactor
Definition: defaultValues.h:27
+
generalizedProblem::setBCs
void setBCs()
Definition: ICs_and_BCs.h:67
+
InitialConditionVec::InitialConditionVec
InitialConditionVec(const unsigned int _index)
Definition: ICs_and_BCs.h:79
+
subdivisionsX
#define subdivisionsX
Definition: parameters.h:14
+
InitialCondition
Definition: ICs_and_BCs.h:2
+
InitialConditionVec
Definition: ICs_and_BCs.h:44
+
InitialCondition::values
Vector< double > values
Definition: ICs_and_BCs.h:6
+
+ +
+Generated by   +doxygen + 1.8.10 +
+ + diff --git a/html/_c_hi_ma_d__benchmark2a_2equations_8h.html b/html/_c_hi_ma_d__benchmark2a_2equations_8h.html new file mode 100644 index 000000000..1be1c0cf1 --- /dev/null +++ b/html/_c_hi_ma_d__benchmark2a_2equations_8h.html @@ -0,0 +1,740 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/CHiMaD_benchmark2a/equations.h File Reference + + + + + + + + + + +
+
+ + + + + + +
+
PRISMS PhaseField +  1.0 +
+
+
+ + + + + + +
+
+ + +
+ +
+ + +
+
+
+Macros
+
+
equations.h File Reference
+
+
+ +

Go to the source code of this file.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Macros

#define num_var   6
 
#define variable_name   {"c", "mu", "n1", "n2", "n3", "n4"}
 
#define variable_type   {"SCALAR","SCALAR","SCALAR","SCALAR","SCALAR","SCALAR"}
 
#define variable_eq_type   {"PARABOLIC","PARABOLIC","PARABOLIC","PARABOLIC","PARABOLIC","PARABOLIC"}
 
#define need_val   {true, false, true, true, true, true}
 
#define need_grad   {true, true, true, true, true, true}
 
#define need_hess   {false, false, false, false, false, false}
 
#define need_val_residual   {true, true, true, true, true, true}
 
#define need_grad_residual   {true, true, true, true, true, true}
 
#define McV   constV(5.0)
 
#define MnV   constV(5.0)
 
#define KnV   constV(3.0)
 
#define KcV   constV(3.0)
 
#define wV   constV(1.0)
 
#define alpha   constV(5.0)
 
#define faV   (constV(2.0)*(c-constV(0.3))*(c-constV(0.3)))
 
#define facV   (constV(4.0)*(c-constV(0.3)))
 
#define faccV   (constV(4.0))
 
#define fbV   (constV(2.0)*(c-constV(0.7))*(c-constV(0.7)))
 
#define fbcV   (constV(4.0)*(c-constV(0.7)))
 
#define fbccV   (constV(4.0))
 
#define hV   ( n1*n1*n1*(constV(6.0)*n1*n1-constV(15.0)*n1+constV(10.0)) + n2*n2*n2*(constV(6.0)*n2*n2-constV(15.0)*n2+constV(10.0)) + n3*n3*n3*(constV(6.0)*n3*n3-constV(15.0)*n3+constV(10.0)) + n4*n4*n4*(constV(6.0)*n4*n4-constV(15.0)*n4+constV(10.0)) )
 
#define hn1V   ( n1*n1*(constV(30.0)*n1*n1-constV(60.0)*n1+constV(30.0)) )
 
#define hn2V   ( n2*n2*(constV(30.0)*n2*n2-constV(60.0)*n2+constV(30.0)) )
 
#define hn3V   ( n3*n3*(constV(30.0)*n3*n3-constV(60.0)*n3+constV(30.0)) )
 
#define hn4V   ( n4*n4*(constV(30.0)*n4*n4-constV(60.0)*n4+constV(30.0)) )
 
#define gdwV   ( n1*n1*(constV(1.0)-n1)*(constV(1.0)-n1) + n2*n2*(constV(1.0)-n2)*(constV(1.0)-n2) +n3*n3*(constV(1.0)-n3)*(constV(1.0)-n3) + n4*n4*(constV(1.0)-n4)*(constV(1.0)-n4) )
 
#define gintV   ( alpha*(n1*n1*n2*n2 + n1*n1*n3*n3+ n1*n1*n4*n4 + n2*n2*n3*n3 + n2*n2*n4*n4 + n3*n3*n4*n4) )
 
#define gV   ( gdwV + gintV )
 
#define dgn1V   ( constV(2.0)*n1*(constV(1.0)-n1)*(constV(1.0)-constV(2.0)*n1) + constV(2.0)*alpha*n1*(n2*n2+n3*n3+n4*n4) )
 
#define dgn2V   ( constV(2.0)*n2*(constV(1.0)-n2)*(constV(1.0)-constV(2.0)*n2) + constV(2.0)*alpha*n2*(n1*n1+n3*n3+n4*n4) )
 
#define dgn3V   ( constV(2.0)*n3*(constV(1.0)-n3)*(constV(1.0)-constV(2.0)*n3) + constV(2.0)*alpha*n3*(n1*n1+n2*n2+n4*n4) )
 
#define dgn4V   ( constV(2.0)*n4*(constV(1.0)-n4)*(constV(1.0)-constV(2.0)*n4) + constV(2.0)*alpha*n4*(n1*n1+n2*n2+n3*n3) )
 
#define rn1V   ( n1 - constV(timeStep)*MnV*((fbV-faV)*hn1V + wV*dgn1V) )
 
#define rn2V   ( n2 - constV(timeStep)*MnV*((fbV-faV)*hn2V + wV*dgn2V) )
 
#define rn3V   ( n3 - constV(timeStep)*MnV*((fbV-faV)*hn3V + wV*dgn3V) )
 
#define rn4V   ( n4 - constV(timeStep)*MnV*((fbV-faV)*hn4V + wV*dgn4V) )
 
#define rn1xV   ( constV(-timeStep)*KnV*MnV*n1x )
 
#define rn2xV   ( constV(-timeStep)*KnV*MnV*n2x )
 
#define rn3xV   ( constV(-timeStep)*KnV*MnV*n3x )
 
#define rn4xV   ( constV(-timeStep)*KnV*MnV*n4x )
 
#define rmuV   ( (constV(1.0)-hV)*facV+hV*fbcV )
 
#define rmuxV   ( KcV*cx )
 
#define rcV   ( c )
 
#define rcxV   ( constV(-timeStep)*McV*mux )
 
+

Macro Definition Documentation

+ +
+
+ + + + +
#define alpha   constV(5.0)
+
+ +
+
+ +
+
+ + + + +
#define dgn1V   ( constV(2.0)*n1*(constV(1.0)-n1)*(constV(1.0)-constV(2.0)*n1) + constV(2.0)*alpha*n1*(n2*n2+n3*n3+n4*n4) )
+
+ +
+
+ +
+
+ + + + +
#define dgn2V   ( constV(2.0)*n2*(constV(1.0)-n2)*(constV(1.0)-constV(2.0)*n2) + constV(2.0)*alpha*n2*(n1*n1+n3*n3+n4*n4) )
+
+ +
+
+ +
+
+ + + + +
#define dgn3V   ( constV(2.0)*n3*(constV(1.0)-n3)*(constV(1.0)-constV(2.0)*n3) + constV(2.0)*alpha*n3*(n1*n1+n2*n2+n4*n4) )
+
+ +
+
+ +
+
+ + + + +
#define dgn4V   ( constV(2.0)*n4*(constV(1.0)-n4)*(constV(1.0)-constV(2.0)*n4) + constV(2.0)*alpha*n4*(n1*n1+n2*n2+n3*n3) )
+
+ +
+
+ +
+
+ + + + +
#define faccV   (constV(4.0))
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+ +
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+ +
+
+ + + + +
#define facV   (constV(4.0)*(c-constV(0.3)))
+
+ +
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+ +
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+ + + + +
#define faV   (constV(2.0)*(c-constV(0.3))*(c-constV(0.3)))
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+ +
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+ +
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+ + + + +
#define fbccV   (constV(4.0))
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+ + + + +
#define fbcV   (constV(4.0)*(c-constV(0.7)))
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+ + + + +
#define fbV   (constV(2.0)*(c-constV(0.7))*(c-constV(0.7)))
+
+ +
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+ +
+
+ + + + +
#define gdwV   ( n1*n1*(constV(1.0)-n1)*(constV(1.0)-n1) + n2*n2*(constV(1.0)-n2)*(constV(1.0)-n2) +n3*n3*(constV(1.0)-n3)*(constV(1.0)-n3) + n4*n4*(constV(1.0)-n4)*(constV(1.0)-n4) )
+
+ +
+
+ +
+
+ + + + +
#define gintV   ( alpha*(n1*n1*n2*n2 + n1*n1*n3*n3+ n1*n1*n4*n4 + n2*n2*n3*n3 + n2*n2*n4*n4 + n3*n3*n4*n4) )
+
+ +
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+ + + + +
#define gV   ( gdwV + gintV )
+
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+ + + + +
#define hn1V   ( n1*n1*(constV(30.0)*n1*n1-constV(60.0)*n1+constV(30.0)) )
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+ +
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+ +
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+ + + + +
#define hn2V   ( n2*n2*(constV(30.0)*n2*n2-constV(60.0)*n2+constV(30.0)) )
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+ +
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+ + + + +
#define hn3V   ( n3*n3*(constV(30.0)*n3*n3-constV(60.0)*n3+constV(30.0)) )
+
+ +
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#define hn4V   ( n4*n4*(constV(30.0)*n4*n4-constV(60.0)*n4+constV(30.0)) )
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+ +
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#define hV   ( n1*n1*n1*(constV(6.0)*n1*n1-constV(15.0)*n1+constV(10.0)) + n2*n2*n2*(constV(6.0)*n2*n2-constV(15.0)*n2+constV(10.0)) + n3*n3*n3*(constV(6.0)*n3*n3-constV(15.0)*n3+constV(10.0)) + n4*n4*n4*(constV(6.0)*n4*n4-constV(15.0)*n4+constV(10.0)) )
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#define KcV   constV(3.0)
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#define KnV   constV(3.0)
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#define McV   constV(5.0)
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#define MnV   constV(5.0)
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#define need_grad   {true, true, true, true, true, true}
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#define need_grad_residual   {true, true, true, true, true, true}
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#define need_hess   {false, false, false, false, false, false}
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#define need_val   {true, false, true, true, true, true}
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#define need_val_residual   {true, true, true, true, true, true}
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#define num_var   6
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#define rcV   ( c )
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#define rcxV   ( constV(-timeStep)*McV*mux )
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#define rmuV   ( (constV(1.0)-hV)*facV+hV*fbcV )
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#define rmuxV   ( KcV*cx )
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#define rn1V   ( n1 - constV(timeStep)*MnV*((fbV-faV)*hn1V + wV*dgn1V) )
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#define rn1xV   ( constV(-timeStep)*KnV*MnV*n1x )
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#define rn2V   ( n2 - constV(timeStep)*MnV*((fbV-faV)*hn2V + wV*dgn2V) )
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#define rn2xV   ( constV(-timeStep)*KnV*MnV*n2x )
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#define rn3V   ( n3 - constV(timeStep)*MnV*((fbV-faV)*hn3V + wV*dgn3V) )
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#define rn3xV   ( constV(-timeStep)*KnV*MnV*n3x )
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#define rn4V   ( n4 - constV(timeStep)*MnV*((fbV-faV)*hn4V + wV*dgn4V) )
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#define rn4xV   ( constV(-timeStep)*KnV*MnV*n4x )
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#define variable_eq_type   {"PARABOLIC","PARABOLIC","PARABOLIC","PARABOLIC","PARABOLIC","PARABOLIC"}
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#define variable_name   {"c", "mu", "n1", "n2", "n3", "n4"}
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#define variable_type   {"SCALAR","SCALAR","SCALAR","SCALAR","SCALAR","SCALAR"}
+
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+ +
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+ + + + +
#define wV   constV(1.0)
+
+ +
+
+
+ +
+Generated by   +doxygen + 1.8.10 +
+ + diff --git a/html/_c_hi_ma_d__benchmark2a_2equations_8h_source.html b/html/_c_hi_ma_d__benchmark2a_2equations_8h_source.html new file mode 100644 index 000000000..99332ea7a --- /dev/null +++ b/html/_c_hi_ma_d__benchmark2a_2equations_8h_source.html @@ -0,0 +1,351 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/CHiMaD_benchmark2a/equations.h Source File + + + + + + + + + + +
+
+ + + + + + +
+
PRISMS PhaseField +  1.0 +
+
+
+ + + + + + +
+
+ + +
+ +
+ + +
+
+
+
equations.h
+
+
+Go to the documentation of this file.
1 // List of variables and residual equations for the coupled Allen-Cahn/Cahn-Hilliard example application
+
2 
+
3 // =================================================================================
+
4 // Define the variables in the model
+
5 // =================================================================================
+
6 // The number of variables
+
7 #define num_var 6
+
8 
+
9 // The names of the variables, whether they are scalars or vectors and whether the
+
10 // governing eqn for the variable is parabolic or elliptic
+
11 #define variable_name {"c", "mu", "n1", "n2", "n3", "n4"}
+
12 #define variable_type {"SCALAR","SCALAR","SCALAR","SCALAR","SCALAR","SCALAR"}
+
13 #define variable_eq_type {"PARABOLIC","PARABOLIC","PARABOLIC","PARABOLIC","PARABOLIC","PARABOLIC"}
+
14 
+
15 // Flags for whether the value, gradient, and Hessian are needed in the residual eqns
+
16 #define need_val {true, false, true, true, true, true}
+
17 #define need_grad {true, true, true, true, true, true}
+
18 #define need_hess {false, false, false, false, false, false}
+
19 
+
20 // Flags for whether the residual equation has a term multiplied by the test function
+
21 // (need_val_residual) and/or the gradient of the test function (need_grad_residual)
+
22 #define need_val_residual {true, true, true, true, true, true}
+
23 #define need_grad_residual {true, true, true, true, true, true}
+
24 
+
25 // =================================================================================
+
26 // Define the model parameters and the residual equations
+
27 // =================================================================================
+
28 // Parameters in the residual equations and expressions for the residual equations
+
29 // can be set here. For simple cases, the entire residual equation can be written
+
30 // here. For more complex cases with loops or conditional statements, residual
+
31 // equations (or parts of residual equations) can be written below in "residualRHS".
+
32 
+
33 // Cahn-Hilliard mobility
+
34 #define McV constV(5.0)
+
35 
+
36 // Allen-Cahn mobility
+
37 #define MnV constV(5.0)
+
38 
+
39 // Allen-Cahn gradient energy coefficient
+
40 #define KnV constV(3.0)
+
41 
+
42 // Cahn-Hilliard gradient energy coefficient
+
43 #define KcV constV(3.0)
+
44 
+
45 //Common coefficient for the double well and structural order parameter interaction terms
+
46 #define wV constV(1.0)
+
47 
+
48 //Coefficient order parameter for the structural interaction term only
+
49 #define alpha constV(5.0)
+
50 
+
51 // Free energy for each phase and their first and second derivatives
+
52 #define faV (constV(2.0)*(c-constV(0.3))*(c-constV(0.3)))
+
53 #define facV (constV(4.0)*(c-constV(0.3)))
+
54 #define faccV (constV(4.0))
+
55 #define fbV (constV(2.0)*(c-constV(0.7))*(c-constV(0.7)))
+
56 #define fbcV (constV(4.0)*(c-constV(0.7)))
+
57 #define fbccV (constV(4.0))
+
58 
+
59 // Interpolation function and its derivatives
+
60 #define hV ( n1*n1*n1*(constV(6.0)*n1*n1-constV(15.0)*n1+constV(10.0)) + n2*n2*n2*(constV(6.0)*n2*n2-constV(15.0)*n2+constV(10.0)) + n3*n3*n3*(constV(6.0)*n3*n3-constV(15.0)*n3+constV(10.0)) + n4*n4*n4*(constV(6.0)*n4*n4-constV(15.0)*n4+constV(10.0)) )
+
61 #define hn1V ( n1*n1*(constV(30.0)*n1*n1-constV(60.0)*n1+constV(30.0)) )
+
62 #define hn2V ( n2*n2*(constV(30.0)*n2*n2-constV(60.0)*n2+constV(30.0)) )
+
63 #define hn3V ( n3*n3*(constV(30.0)*n3*n3-constV(60.0)*n3+constV(30.0)) )
+
64 #define hn4V ( n4*n4*(constV(30.0)*n4*n4-constV(60.0)*n4+constV(30.0)) )
+
65 
+
66 //Combined double-well and interaction functions (function g) and its derivatives
+
67 //Double-well part
+
68 #define gdwV ( n1*n1*(constV(1.0)-n1)*(constV(1.0)-n1) + n2*n2*(constV(1.0)-n2)*(constV(1.0)-n2) +n3*n3*(constV(1.0)-n3)*(constV(1.0)-n3) + n4*n4*(constV(1.0)-n4)*(constV(1.0)-n4) )
+
69 //Interaction part
+
70 #define gintV ( alpha*(n1*n1*n2*n2 + n1*n1*n3*n3+ n1*n1*n4*n4 + n2*n2*n3*n3 + n2*n2*n4*n4 + n3*n3*n4*n4) )
+
71 //Combined function (g)
+
72 #define gV ( gdwV + gintV )
+
73 //Derivatives
+
74 #define dgn1V ( constV(2.0)*n1*(constV(1.0)-n1)*(constV(1.0)-constV(2.0)*n1) + constV(2.0)*alpha*n1*(n2*n2+n3*n3+n4*n4) )
+
75 #define dgn2V ( constV(2.0)*n2*(constV(1.0)-n2)*(constV(1.0)-constV(2.0)*n2) + constV(2.0)*alpha*n2*(n1*n1+n3*n3+n4*n4) )
+
76 #define dgn3V ( constV(2.0)*n3*(constV(1.0)-n3)*(constV(1.0)-constV(2.0)*n3) + constV(2.0)*alpha*n3*(n1*n1+n2*n2+n4*n4) )
+
77 #define dgn4V ( constV(2.0)*n4*(constV(1.0)-n4)*(constV(1.0)-constV(2.0)*n4) + constV(2.0)*alpha*n4*(n1*n1+n2*n2+n3*n3) )
+
78 
+
79 //CHANGES FOR CHAC BENCHMARK PROBLEM UP TO THIS POINT
+
80 
+
81 // Residual equations
+
82 #define rn1V ( n1 - constV(timeStep)*MnV*((fbV-faV)*hn1V + wV*dgn1V) )
+
83 #define rn2V ( n2 - constV(timeStep)*MnV*((fbV-faV)*hn2V + wV*dgn2V) )
+
84 #define rn3V ( n3 - constV(timeStep)*MnV*((fbV-faV)*hn3V + wV*dgn3V) )
+
85 #define rn4V ( n4 - constV(timeStep)*MnV*((fbV-faV)*hn4V + wV*dgn4V) )
+
86 #define rn1xV ( constV(-timeStep)*KnV*MnV*n1x )
+
87 #define rn2xV ( constV(-timeStep)*KnV*MnV*n2x )
+
88 #define rn3xV ( constV(-timeStep)*KnV*MnV*n3x )
+
89 #define rn4xV ( constV(-timeStep)*KnV*MnV*n4x )
+
90 #define rmuV ( (constV(1.0)-hV)*facV+hV*fbcV )
+
91 #define rmuxV ( KcV*cx )
+
92 #define rcV ( c )
+
93 #define rcxV ( constV(-timeStep)*McV*mux )
+
94 
+
95 
+
96 // =================================================================================
+
97 // residualRHS
+
98 // =================================================================================
+
99 // This function calculates the residual equations for each variable. It takes
+
100 // "modelVariablesList" as an input, which is a list of the value and derivatives of
+
101 // each of the variables at a specific quadrature point. The (x,y,z) location of
+
102 // that quadrature point is given by "q_point_loc". The function outputs
+
103 // "modelResidualsList", a list of the value and gradient terms of the residual for
+
104 // each residual equation. The index for each variable in these lists corresponds to
+
105 // the order it is defined at the top of this file (starting at 0).
+
106 template <int dim>
+
107 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim> > & modelVariablesList,
+
108  std::vector<modelResidual<dim> > & modelResidualsList,
+
109  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
+
110 
+
111 // The concentration and its derivatives (names here should match those in the macros above)
+
112 scalarvalueType c = modelVariablesList[0].scalarValue;
+
113 scalargradType cx = modelVariablesList[0].scalarGrad;
+
114 
+
115 // The chemical potential and its derivatives (names here should match those in the macros above)
+
116 scalargradType mux = modelVariablesList[1].scalarGrad;
+
117 
+
118 // The order parameters and their derivatives (names here should match those in the macros above)
+
119 scalarvalueType n1 = modelVariablesList[2].scalarValue;
+
120 scalargradType n1x = modelVariablesList[2].scalarGrad;
+
121 scalarvalueType n2 = modelVariablesList[3].scalarValue;
+
122 scalargradType n2x = modelVariablesList[3].scalarGrad;
+
123 scalarvalueType n3 = modelVariablesList[4].scalarValue;
+
124 scalargradType n3x = modelVariablesList[4].scalarGrad;
+
125 scalarvalueType n4 = modelVariablesList[5].scalarValue;
+
126 scalargradType n4x = modelVariablesList[5].scalarGrad;
+
127 
+
128 // Residuals for the equation to evolve the concentration (names here should match those in the macros above)
+
129 modelResidualsList[0].scalarValueResidual = rcV;
+
130 modelResidualsList[0].scalarGradResidual = rcxV;
+
131 modelResidualsList[1].scalarValueResidual = rmuV;
+
132 modelResidualsList[1].scalarGradResidual = rmuxV;
+
133 
+
134 // Residuals for the equation to evolve the order parameters (names here should match those in the macros above)
+
135 modelResidualsList[2].scalarValueResidual = rn1V;
+
136 modelResidualsList[2].scalarGradResidual = rn1xV;
+
137 modelResidualsList[3].scalarValueResidual = rn2V;
+
138 modelResidualsList[3].scalarGradResidual = rn2xV;
+
139 modelResidualsList[4].scalarValueResidual = rn3V;
+
140 modelResidualsList[4].scalarGradResidual = rn3xV;
+
141 modelResidualsList[5].scalarValueResidual = rn4V;
+
142 modelResidualsList[5].scalarGradResidual = rn4xV;
+
143 
+
144 }
+
145 
+
146 // =================================================================================
+
147 // residualLHS (needed only if at least one equation is elliptic)
+
148 // =================================================================================
+
149 // This function calculates the residual equations for the iterative solver for
+
150 // elliptic equations.for each variable. It takes "modelVariablesList" as an input,
+
151 // which is a list of the value and derivatives of each of the variables at a
+
152 // specific quadrature point. The (x,y,z) location of that quadrature point is given
+
153 // by "q_point_loc". The function outputs "modelRes", the value and gradient terms of
+
154 // for the left-hand-side of the residual equation for the iterative solver. The
+
155 // index for each variable in these lists corresponds to the order it is defined at
+
156 // the top of this file (starting at 0), not counting variables that have
+
157 // "need_val_LHS", "need_grad_LHS", and "need_hess_LHS" all set to "false". If there
+
158 // are multiple elliptic equations, conditional statements should be used to ensure
+
159 // that the correct residual is being submitted. The index of the field being solved
+
160 // can be accessed by "this->currentFieldIndex".
+
161 template <int dim>
+
162 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim> > & modelVariablesList,
+
163  modelResidual<dim> & modelRes,
+
164  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
+
165 
+
166 }
+
167 
+
168 // =================================================================================
+
169 // energyDensity (needed only if calcEnergy == true)
+
170 // =================================================================================
+
171 // This function integrates the free energy density across the computational domain.
+
172 // It takes "modelVariablesList" as an input, which is a list of the value and
+
173 // derivatives of each of the variables at a specific quadrature point. It also
+
174 // takes the mapped quadrature weight, "JxW_value", as an input. The (x,y,z) location
+
175 // of the quadrature point is given by "q_point_loc". The weighted value of the
+
176 // energy density is added to "energy" variable and the components of the energy
+
177 // density are added to the "energy_components" variable (index 0: chemical energy,
+
178 // index 1: gradient energy, index 2: elastic energy).
+
179 template <int dim>
+
180 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim> > & modelVariablesList,
+
181  const dealii::VectorizedArray<double> & JxW_value,
+
182  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) {
+
183 
+
184 // The concentration and its derivatives (names here should match those in the macros above)
+
185 scalarvalueType c = modelVariablesList[0].scalarValue;
+
186 scalargradType cx = modelVariablesList[0].scalarGrad;
+
187 
+
188 // The order parameter and its derivatives (names here should match those in the macros above)
+
189 scalarvalueType n1 = modelVariablesList[2].scalarValue;
+
190 scalargradType n1x = modelVariablesList[2].scalarGrad;
+
191 scalarvalueType n2 = modelVariablesList[3].scalarValue;
+
192 scalargradType n2x = modelVariablesList[3].scalarGrad;
+
193 scalarvalueType n3 = modelVariablesList[4].scalarValue;
+
194 scalargradType n3x = modelVariablesList[4].scalarGrad;
+
195 scalarvalueType n4 = modelVariablesList[5].scalarValue;
+
196 scalargradType n4x = modelVariablesList[5].scalarGrad;
+
197 
+
198 // The homogenous free energy
+
199 scalarvalueType f_chem = (constV(1.0)-hV)*faV + hV*fbV + wV*gV;
+
200 
+
201 // The gradient free energy
+
202 scalarvalueType f_grad = constV(0.5)*KnV*(n1x*n1x+n2x*n2x+n3x*n3x+n4x*n4x) + constV(0.5)*KcV*cx*cx;
+
203 
+
204 // The total free energy
+
205 scalarvalueType total_energy_density;
+
206 total_energy_density = f_chem + f_grad;
+
207 
+
208 // Loop to step through each element of the vectorized arrays. Working with deal.ii
+
209 // developers to see if there is a more elegant way to do this.
+
210 assembler_lock.acquire ();
+
211 for (unsigned i=0; i<c.n_array_elements;i++){
+
212  if (c[i] > 1.0e-10){
+
213  this->energy+=total_energy_density[i]*JxW_value[i];
+
214  this->energy_components[0]+= f_chem[i]*JxW_value[i];
+
215  this->energy_components[1]+= f_grad[i]*JxW_value[i];
+
216  }
+
217 }
+
218 assembler_lock.release ();
+
219 }
+
220 
+
221 
+
222 
+
223 
+
faV
#define faV
Definition: equations.h:52
+
modelResidual
Definition: model_variables.h:25
+
rcxV
#define rcxV
Definition: equations.h:93
+
generalizedProblem::residualRHS
void residualRHS(const std::vector< modelVariable< dim > > &modelVarList, std::vector< modelResidual< dim > > &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:58
+
rn2V
#define rn2V
Definition: equations.h:83
+
rn3xV
#define rn3xV
Definition: equations.h:88
+
wV
#define wV
Definition: equations.h:46
+
scalargradType
dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
Definition: matrixFreePDE.h:51
+
constV
#define constV(a)
Definition: matrixFreePDE.h:67
+
rn1V
#define rn1V
Definition: equations.h:82
+
KnV
#define KnV
Definition: equations.h:40
+
rn3V
#define rn3V
Definition: equations.h:84
+
gV
#define gV
Definition: equations.h:72
+
rn2xV
#define rn2xV
Definition: equations.h:87
+
rmuV
#define rmuV
Definition: equations.h:90
+
rn1xV
#define rn1xV
Definition: equations.h:86
+
rcV
#define rcV
Definition: equations.h:92
+
fbV
#define fbV
Definition: equations.h:55
+
generalizedProblem::residualLHS
void residualLHS(const std::vector< modelVariable< dim > > &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:89
+
rmuxV
#define rmuxV
Definition: equations.h:91
+
rn4xV
#define rn4xV
Definition: equations.h:89
+
modelVariable
Definition: model_variables.h:4
+
KcV
#define KcV
Definition: equations.h:43
+
hV
#define hV
Definition: equations.h:60
+
rn4V
#define rn4V
Definition: equations.h:85
+
scalarvalueType
dealii::VectorizedArray< double > scalarvalueType
Definition: matrixFreePDE.h:34
+
generalizedProblem::energyDensity
void energyDensity(const std::vector< modelVariable< dim > > &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
Definition: equations.h:106
+
+ +
+Generated by   +doxygen + 1.8.10 +
+ + diff --git a/html/_c_hi_ma_d__benchmark2a_2main_8cc.html b/html/_c_hi_ma_d__benchmark2a_2main_8cc.html new file mode 100644 index 000000000..501566437 --- /dev/null +++ b/html/_c_hi_ma_d__benchmark2a_2main_8cc.html @@ -0,0 +1,142 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/CHiMaD_benchmark2a/main.cc File Reference + + + + + + + + + + +
+
+ + + + + + +
+
PRISMS PhaseField +  1.0 +
+
+
+ + + + + + +
+
+ + +
+ +
+ + +
+
+
+Functions
+
+
main.cc File Reference
+
+
+
#include "../../include/dealIIheaders.h"
+#include "parameters.h"
+#include "../../src/models/coupled/generalized_model.h"
+#include "equations.h"
+#include "ICs_and_BCs.h"
+#include "../../src/models/coupled/generalized_model_functions.h"
+
+ + + +

+Functions

int main (int argc, char **argv)
 
+

Function Documentation

+ +
+
+ + + + + + + + + + + + + + + + + + +
int main (int argc,
char ** argv 
)
+
+ +
+
+
+ +
+Generated by   +doxygen + 1.8.10 +
+ + diff --git a/html/_c_hi_ma_d__benchmark2a_2parameters_8h.html b/html/_c_hi_ma_d__benchmark2a_2parameters_8h.html new file mode 100644 index 000000000..5f3b284e0 --- /dev/null +++ b/html/_c_hi_ma_d__benchmark2a_2parameters_8h.html @@ -0,0 +1,460 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/CHiMaD_benchmark2a/parameters.h File Reference + + + + + + + + + + +
+
+ + + + + + +
+
PRISMS PhaseField +  1.0 +
+
+
+ + + + + + +
+
+ + +
+ +
+ + +
+
+
+Macros
+
+
parameters.h File Reference
+
+
+ +

Go to the source code of this file.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Macros

#define problemDIM   2
 
#define spanX   200.0
 
#define spanY   200.0
 
#define spanZ   200.0
 
#define subdivisionsX   3
 
#define subdivisionsY   3
 
#define subdivisionsZ   3
 
#define refineFactor   6
 
#define finiteElementDegree   1
 
#define hAdaptivity   false
 
#define maxRefinementLevel   (refineFactor)
 
#define minRefinementLevel   (0)
 
#define refineCriterionFields   {0,2,3,4,5}
 
#define refineWindowMax   {0.65,0.999,0.999,0.999,0.999}
 
#define refineWindowMin   {0.35,0.001,0.001,0.001,0.001}
 
#define skipRemeshingSteps   100
 
#define timeStep   (1.0e-3)
 
#define timeFinal   10000.0
 
#define timeIncrements   20000
 
#define writeOutput   true
 
#define outputCondition   "LIST"
 
#define numOutputs   10
 
#define outputList   {0, 1000, 5000, 10000, 20000}
 
#define skipPrintSteps   1000
 
#define calcEnergy   true
 
+

Macro Definition Documentation

+ +
+
+ + + + +
#define calcEnergy   true
+
+ +
+
+ +
+
+ + + + +
#define finiteElementDegree   1
+
+ +
+
+ +
+
+ + + + +
#define hAdaptivity   false
+
+ +
+
+ +
+
+ + + + +
#define maxRefinementLevel   (refineFactor)
+
+ +
+
+ +
+
+ + + + +
#define minRefinementLevel   (0)
+
+ +
+
+ +
+
+ + + + +
#define numOutputs   10
+
+ +
+
+ +
+
+ + + + +
#define outputCondition   "LIST"
+
+ +
+
+ +
+
+ + + + +
#define outputList   {0, 1000, 5000, 10000, 20000}
+
+ +
+
+ +
+
+ + + + +
#define problemDIM   2
+
+ +
+
+ +
+
+ + + + +
#define refineCriterionFields   {0,2,3,4,5}
+
+ +
+
+ +
+
+ + + + +
#define refineFactor   6
+
+ +
+
+ +
+
+ + + + +
#define refineWindowMax   {0.65,0.999,0.999,0.999,0.999}
+
+ +
+
+ +
+
+ + + + +
#define refineWindowMin   {0.35,0.001,0.001,0.001,0.001}
+
+ +
+
+ +
+
+ + + + +
#define skipPrintSteps   1000
+
+ +
+
+ +
+
+ + + + +
#define skipRemeshingSteps   100
+
+ +
+
+ +
+
+ + + + +
#define spanX   200.0
+
+ +
+
+ +
+
+ + + + +
#define spanY   200.0
+
+ +
+
+ +
+
+ + + + +
#define spanZ   200.0
+
+ +
+
+ +
+
+ + + + +
#define subdivisionsX   3
+
+ +
+
+ +
+
+ + + + +
#define subdivisionsY   3
+
+ +
+
+ +
+
+ + + + +
#define subdivisionsZ   3
+
+ +
+
+ +
+
+ + + + +
#define timeFinal   10000.0
+
+ +
+
+ +
+
+ + + + +
#define timeIncrements   20000
+
+ +
+
+ +
+
+ + + + +
#define timeStep   (1.0e-3)
+
+ +
+
+ +
+
+ + + + +
#define writeOutput   true
+
+ +
+
+
+ +
+Generated by   +doxygen + 1.8.10 +
+ + diff --git a/html/_c_hi_ma_d__benchmark2a_2parameters_8h_source.html b/html/_c_hi_ma_d__benchmark2a_2parameters_8h_source.html new file mode 100644 index 000000000..9c22b8b79 --- /dev/null +++ b/html/_c_hi_ma_d__benchmark2a_2parameters_8h_source.html @@ -0,0 +1,193 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/CHiMaD_benchmark2a/parameters.h Source File + + + + + + + + + + +
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+ + + + + + +
+
PRISMS PhaseField +  1.0 +
+
+
+ + + + + + +
+
+ + +
+ +
+ + +
+
+
+
parameters.h
+
+
+Go to the documentation of this file.
1 // Parameter list for the coupled Allen-Cahn/Cahn-Hilliard example application
+
2 // All strictly numerical parameters should be set in this file
+
3 
+
4 // =================================================================================
+
5 // Set the number of dimensions (1, 2, or 3 for a 1D, 2D, or 3D calculation)
+
6 // =================================================================================
+
7 #define problemDIM 2
+
8 
+
9 // =================================================================================
+
10 // Set the length of the domain in all three dimensions
+
11 // =================================================================================
+
12 // Each axes spans from zero to the specified length
+
13 #define spanX 200.0
+
14 #define spanY 200.0
+
15 #define spanZ 200.0
+
16 
+
17 //#define t_domain true
+
18 
+
19 // =================================================================================
+
20 // Set the element parameters
+
21 // =================================================================================
+
22 // The number of elements in each direction is 2^(refineFactor) * subdivisions
+
23 // For optimal performance, use refineFactor primarily to determine the element size
+
24 #define subdivisionsX 3
+
25 #define subdivisionsY 3
+
26 #define subdivisionsZ 3
+
27 #define refineFactor 6
+
28 
+
29 // Set the polynomial degree of the element (suggested values: 1 or 2)
+
30 #define finiteElementDegree 1
+
31 
+
32 // =================================================================================
+
33 // Set the adaptive mesh refinement parameters
+
34 // =================================================================================
+
35 // Set the flag determining if adaptive meshing is activated
+
36 #define hAdaptivity false
+
37 
+
38 // Set the maximum and minimum level of refinement
+
39 #define maxRefinementLevel (refineFactor)
+
40 #define minRefinementLevel (0)
+
41 
+
42 // Set the fields used to determine the refinement. Fields determined by the order
+
43 // declared in "equations.h", starting at zero
+
44 #define refineCriterionFields {0,2,3,4,5}
+
45 
+
46 // Set the maximum and minimum value of the fields where the mesh should be refined
+
47 #define refineWindowMax {0.65,0.999,0.999,0.999,0.999}
+
48 #define refineWindowMin {0.35,0.001,0.001,0.001,0.001}
+
49 
+
50 // Set the number of time steps between remeshing operations
+
51 #define skipRemeshingSteps 100
+
52 
+
53 // =================================================================================
+
54 // Set the time step parameters
+
55 // =================================================================================
+
56 // The size of the time step
+
57 #define timeStep (1.0e-3)
+
58 
+
59 // The simulation ends when either timeFinal is reached or the number of time steps
+
60 // equals timeIncrements
+
61 #define timeFinal 10000.0
+
62 #define timeIncrements 20000
+
63 
+
64 // =================================================================================
+
65 // Set the output parameters
+
66 // =================================================================================
+
67 // Each field in the problem will be output is writeOutput is set to "true"
+
68 #define writeOutput true
+
69 
+
70 // Type of spacing between outputs ("EQUAL_SPACING", "LOG_SPACING", "N_PER_DECADE",
+
71 // or "LIST")
+
72 #define outputCondition "LIST"
+
73 
+
74 // Number of times the program outputs the fields (total number for "EQUAL_SPACING"
+
75 // and "LOG_SPACING", number per decade for "N_PER_DECADE", ignored for "LIST")
+
76 #define numOutputs 10
+
77 
+
78 // User-defined list of time steps where the program should output. Only used if
+
79 // outputCondition is "LIST"
+
80 #define outputList {0, 1000, 5000, 10000, 20000}
+
81 
+
82 #define skipPrintSteps 1000
+
83 
+
84 // =================================================================================
+
85 // Set the flag determining if the total free energy is calculated for each output
+
86 // =================================================================================
+
87 #define calcEnergy true
+
88 
+
89 
+
90 
+
91 
+
92 
+
+ +
+Generated by   +doxygen + 1.8.10 +
+ + diff --git a/html/_fickian_8h_source.html b/html/_fickian_8h_source.html index c39f00a33..ba2855878 100644 --- a/html/_fickian_8h_source.html +++ b/html/_fickian_8h_source.html @@ -160,8 +160,8 @@
68  scalargradType cx = cVals.get_gradient(q);
69 
70  //submit residual value
-
71  cVals.submit_value(rcV,q);
-
72  cVals.submit_gradient(rcxV,q);
+
71  cVals.submit_value(rcV,q);
+
72  cVals.submit_gradient(rcxV,q);
73  }
74 
75  //integrate values
@@ -170,14 +170,14 @@
78 }
79 
80 #endif
+
rcxV
#define rcxV
Definition: residuals.h:12
MatrixFreePDE
Definition: matrixFreePDE.h:85
scalargradType
dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
Definition: matrixFreePDE.h:51
-
rcV
#define rcV
Definition: residuals.h:11
FickianProblem::FickianProblem
FickianProblem()
Definition: Fickian.h:28
FickianProblem
Definition: Fickian.h:10
FickianProblem::applyInitialConditions
void applyInitialConditions()
typeScalar
dealii::FEEvaluation< problemDIM, finiteElementDegree, finiteElementDegree+1, 1, double > typeScalar
Definition: matrixFreePDE.h:27
-
rcxV
#define rcxV
Definition: residuals.h:12
+
rcV
#define rcV
Definition: residuals.h:11
FickianProblem::getRHS
void getRHS(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range) const
Definition: Fickian.h:44
scalarvalueType
dealii::VectorizedArray< double > scalarvalueType
Definition: matrixFreePDE.h:34
diff --git a/html/_p_library_8cc.html b/html/_p_library_8cc.html index 365fdf394..3fa376af9 100644 --- a/html/_p_library_8cc.html +++ b/html/_p_library_8cc.html @@ -4,7 +4,7 @@ -PRISMS PhaseField: phaseField_dev/applications/_precipitateEvolution_pfunction/PLibrary/PLibrary.cc File Reference +PRISMS PhaseField: phaseField_dev/applications/precipitateEvolution_pfunction/PLibrary/PLibrary.cc File Reference @@ -82,7 +82,7 @@
@@ -93,15 +93,15 @@
PLibrary.cc File Reference
-
#include "../../_precipitateEvolution_pfunction/PLibrary/PLibrary.hh"
-#include <cstring>
+
#include <cstring>
#include <stdexcept>
-#include "../../_precipitateEvolution_pfunction/PLibrary/pfunct_faV.hh"
-#include "../../_precipitateEvolution_pfunction/PLibrary/pfunct_fbV.hh"
-#include "../../_precipitateEvolution_pfunction/PLibrary/pfunct_McV.hh"
-#include "../../_precipitateEvolution_pfunction/PLibrary/pfunct_Mn1V.hh"
-#include "../../_precipitateEvolution_pfunction/PLibrary/pfunct_Mn2V.hh"
-#include "../../_precipitateEvolution_pfunction/PLibrary/pfunct_Mn3V.hh"
+#include "pfunct_faV.hh"
+#include "pfunct_fbV.hh"
+#include "pfunct_McV.hh"
+#include "pfunct_Mn1V.hh"
+#include "pfunct_Mn2V.hh"
+#include "pfunct_Mn3V.hh"
+#include "PLibrary.hh"
diff --git a/html/allen_cahn_2_i_cs__and___b_cs_8h_source.html b/html/allen_cahn_2_i_cs__and___b_cs_8h_source.html index 6de5bea76..b351d885e 100644 --- a/html/allen_cahn_2_i_cs__and___b_cs_8h_source.html +++ b/html/allen_cahn_2_i_cs__and___b_cs_8h_source.html @@ -124,76 +124,77 @@
32  if (dist < rad[i]){
33  scalar_IC = 1.0;
34  }
-
35  };
-
36 
-
37  // =====================================================================
-
38  return scalar_IC;
-
39  }
-
40 };
-
41 
-
42 template <int dim>
-
43 class InitialConditionVec : public Function<dim>
-
44 {
-
45 public:
-
46  unsigned int index;
-
47  //Vector<double> values;
-
48  InitialConditionVec (const unsigned int _index) : Function<dim>(dim), index(_index) {
-
49  std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1);
-
50  }
-
51  void vector_value (const Point<dim> &p,Vector<double> &vector_IC) const
-
52  {
-
53  // =====================================================================
-
54  // ENTER THE INITIAL CONDITIONS HERE FOR VECTOR FIELDS
-
55  // =====================================================================
-
56  // Enter the function describing conditions for the fields at point "p".
-
57  // Use "if" statements to set the initial condition for each variable
-
58  // according to its variable index.
-
59 
+
35 
+
36  };
+
37 
+
38  // =====================================================================
+
39  return scalar_IC;
+
40  }
+
41 };
+
42 
+
43 template <int dim>
+
44 class InitialConditionVec : public Function<dim>
+
45 {
+
46 public:
+
47  unsigned int index;
+
48  //Vector<double> values;
+
49  InitialConditionVec (const unsigned int _index) : Function<dim>(dim), index(_index) {
+
50  std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1);
+
51  }
+
52  void vector_value (const Point<dim> &p,Vector<double> &vector_IC) const
+
53  {
+
54  // =====================================================================
+
55  // ENTER THE INITIAL CONDITIONS HERE FOR VECTOR FIELDS
+
56  // =====================================================================
+
57  // Enter the function describing conditions for the fields at point "p".
+
58  // Use "if" statements to set the initial condition for each variable
+
59  // according to its variable index.
60 
-
61  // =====================================================================
-
62  }
-
63 };
-
64 
-
65 template <int dim>
-
66 void generalizedProblem<dim>::setBCs(){
-
67  // =====================================================================
-
68  // ENTER THE BOUNDARY CONDITIONS HERE
-
69  // =====================================================================
-
70  // This function sets the BCs for the problem variables
-
71  // The function "inputBCs" should be called for each component of
-
72  // each variable and should be in numerical order. Four input arguments
-
73  // set the same BC on the entire boundary. Two plus two times the
-
74  // number of dimensions inputs sets separate BCs on each face of the domain.
-
75  // Inputs to "inputBCs":
-
76  // First input: variable number
-
77  // Second input: component number
-
78  // Third input: BC type (options are "ZERO_DERIVATIVE" and "DIRICHLET")
-
79  // Fourth input: BC value (ignored unless the BC type is "DIRICHLET")
-
80  // Odd inputs after the third: BC type
-
81  // Even inputs after the third: BC value
-
82  // Face numbering: starts at zero with the minimum of the first direction, one for the maximum of the first direction
-
83  // two for the minimum of the second direction, etc.
-
84 
-
85  inputBCs(0,0,"ZERO_DERIVATIVE",0);
-
86 
-
87  // =====================================================================
-
88 
-
89 }
-
90 
+
61 
+
62  // =====================================================================
+
63  }
+
64 };
+
65 
+
66 template <int dim>
+
67 void generalizedProblem<dim>::setBCs(){
+
68  // =====================================================================
+
69  // ENTER THE BOUNDARY CONDITIONS HERE
+
70  // =====================================================================
+
71  // This function sets the BCs for the problem variables
+
72  // The function "inputBCs" should be called for each component of
+
73  // each variable and should be in numerical order. Four input arguments
+
74  // set the same BC on the entire boundary. Two plus two times the
+
75  // number of dimensions inputs sets separate BCs on each face of the domain.
+
76  // Inputs to "inputBCs":
+
77  // First input: variable number
+
78  // Second input: component number
+
79  // Third input: BC type (options are "ZERO_DERIVATIVE", "DIRICHLET", and "PERIODIC")
+
80  // Fourth input: BC value (ignored unless the BC type is "DIRICHLET")
+
81  // Odd inputs after the third: BC type
+
82  // Even inputs after the third: BC value
+
83  // Face numbering: starts at zero with the minimum of the first direction, one for the maximum of the first direction
+
84  // two for the minimum of the second direction, etc.
+
85 
+
86  inputBCs(0,0,"ZERO_DERIVATIVE",0);
+
87 
+
88  // =====================================================================
+
89 
+
90 }
91 
-
InitialConditionVec::index
unsigned int index
Definition: ICs_and_BCs.h:46
-
InitialConditionVec::vector_value
void vector_value(const Point< dim > &p, Vector< double > &vector_IC) const
Definition: ICs_and_BCs.h:51
+
92 
+
InitialConditionVec::index
unsigned int index
Definition: ICs_and_BCs.h:47
+
InitialConditionVec::vector_value
void vector_value(const Point< dim > &p, Vector< double > &vector_IC) const
Definition: ICs_and_BCs.h:52
spanX
#define spanX
Definition: defaultValues.h:12
InitialCondition::InitialCondition
InitialCondition(const unsigned int _index)
Definition: ICs_and_BCs.h:7
spanY
#define spanY
Definition: defaultValues.h:17
InitialCondition::value
double value(const Point< dim > &p, const unsigned int component=0) const
Definition: ICs_and_BCs.h:10
InitialCondition::index
unsigned int index
Definition: ICs_and_BCs.h:5
refineFactor
#define refineFactor
Definition: defaultValues.h:27
-
generalizedProblem::setBCs
void setBCs()
Definition: ICs_and_BCs.h:66
-
InitialConditionVec::InitialConditionVec
InitialConditionVec(const unsigned int _index)
Definition: ICs_and_BCs.h:48
+
generalizedProblem::setBCs
void setBCs()
Definition: ICs_and_BCs.h:67
+
InitialConditionVec::InitialConditionVec
InitialConditionVec(const unsigned int _index)
Definition: ICs_and_BCs.h:49
subdivisionsX
#define subdivisionsX
Definition: parameters.h:14
InitialCondition
Definition: ICs_and_BCs.h:2
-
InitialConditionVec
Definition: ICs_and_BCs.h:43
+
InitialConditionVec
Definition: ICs_and_BCs.h:44
spanZ
#define spanZ
Definition: defaultValues.h:22
InitialCondition::values
Vector< double > values
Definition: ICs_and_BCs.h:6
diff --git a/html/allen_cahn_2equations_8h.html b/html/allen_cahn_2equations_8h.html index 388da031f..b364fbbed 100644 --- a/html/allen_cahn_2equations_8h.html +++ b/html/allen_cahn_2equations_8h.html @@ -123,9 +123,9 @@ - + - +

Namespaces

 
#define fnV   (4.0*n*(n-1.0)*(n-0.5))
 
#define rnV   (n-constV(timeStep*MnV)*fnV)
#define rnV   (n-constV(timeStep*MnV)*fnV)
 
#define rnxV   (constV(-timeStep*KnV*MnV)*nx)
#define rnxV   (constV(-timeStep*KnV*MnV)*nx)
 

Macro Definition Documentation

@@ -254,7 +254,7 @@

Macro Definition Documentation

- +
#define rnV   (n-constV(timeStep*MnV)*fnV)#define rnV   (n-constV(timeStep*MnV)*fnV)
@@ -266,7 +266,7 @@

Macro Definition Documentation

- +
#define rnxV   (constV(-timeStep*KnV*MnV)*nx)#define rnxV   (constV(-timeStep*KnV*MnV)*nx)
diff --git a/html/allen_cahn_2equations_8h_source.html b/html/allen_cahn_2equations_8h_source.html index 0e92860b7..6c1b7fe67 100644 --- a/html/allen_cahn_2equations_8h_source.html +++ b/html/allen_cahn_2equations_8h_source.html @@ -147,8 +147,8 @@
55 // each residual equation. The index for each variable in these lists corresponds to
56 // the order it is defined at the top of this file (starting at 0).
57 template <int dim>
-
58 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim>> & modelVariablesList,
-
59  std::vector<modelResidual<dim>> & modelResidualsList,
+
58 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim> > & modelVariablesList,
+
59  std::vector<modelResidual<dim> > & modelResidualsList,
60  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
61 
62 // The order parameter and its derivatives (names here should match those in the macros above)
@@ -178,7 +178,7 @@
86 // that the correct residual is being submitted. The index of the field being solved
87 // can be accessed by "this->currentFieldIndex".
88 template <int dim>
-
89 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim>> & modelVarList,
+
89 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim> > & modelVarList,
90  modelResidual<dim> & modelRes,
91  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
92 }
@@ -195,7 +195,7 @@
103 // density are added to the "energy_components" variable (index 0: chemical energy,
104 // index 1: gradient energy, index 2: elastic energy).
105 template <int dim>
-
106 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim>> & modelVarList,
+
106 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim> > & modelVarList,
107  const dealii::VectorizedArray<double> & JxW_value,
108  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) {
109 scalarvalueType total_energy_density = constV(0.0);
@@ -236,18 +236,18 @@
144 
145 
146 
-
generalizedProblem::energyDensity
void energyDensity(const std::vector< modelVariable< dim >> &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
Definition: equations.h:106
modelResidual
Definition: model_variables.h:25
+
generalizedProblem::residualRHS
void residualRHS(const std::vector< modelVariable< dim > > &modelVarList, std::vector< modelResidual< dim > > &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:58
scalargradType
dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
Definition: matrixFreePDE.h:51
-
generalizedProblem::residualLHS
void residualLHS(const std::vector< modelVariable< dim >> &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:89
constV
#define constV(a)
Definition: matrixFreePDE.h:67
KnV
#define KnV
Definition: equations.h:37
fV
#define fV
Definition: equations.h:40
+
generalizedProblem::residualLHS
void residualLHS(const std::vector< modelVariable< dim > > &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:89
modelVariable
Definition: model_variables.h:4
rnV
#define rnV
Definition: equations.h:44
scalarvalueType
dealii::VectorizedArray< double > scalarvalueType
Definition: matrixFreePDE.h:34
-
generalizedProblem::residualRHS
void residualRHS(const std::vector< modelVariable< dim >> &modelVarList, std::vector< modelResidual< dim >> &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:58
rnxV
#define rnxV
Definition: equations.h:45
+
generalizedProblem::energyDensity
void energyDensity(const std::vector< modelVariable< dim > > &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
Definition: equations.h:106
diff --git a/html/allen_cahn_2parameters_8h.html b/html/allen_cahn_2parameters_8h.html index 77290a090..1b57376e5 100644 --- a/html/allen_cahn_2parameters_8h.html +++ b/html/allen_cahn_2parameters_8h.html @@ -123,8 +123,14 @@   #define writeOutput   true   -#define skipOutputSteps   1000 -  +#define outputCondition   "EQUAL_SPACING" +  +#define numOutputs   10 +  +#define outputList   {} +  +#define skipPrintSteps   10 +  #define calcEnergy   true   @@ -151,6 +157,42 @@

Macro Definition Documentation

+
+
+ +
+
+ + + + +
#define numOutputs   10
+
+ +
+
+ +
+
+ + + + +
#define outputCondition   "EQUAL_SPACING"
+
+ +
+
+ +
+
+ + + + +
#define outputList   {}
+
+
@@ -177,12 +219,12 @@

Macro Definition Documentation

- +
- +
#define skipOutputSteps   1000#define skipPrintSteps   10
diff --git a/html/allen_cahn_2parameters_8h_source.html b/html/allen_cahn_2parameters_8h_source.html index 24c945595..d082d683e 100644 --- a/html/allen_cahn_2parameters_8h_source.html +++ b/html/allen_cahn_2parameters_8h_source.html @@ -136,20 +136,32 @@
44 // Each field in the problem will be output is writeOutput is set to "true"
45 #define writeOutput true
46 
-
47 // Output files are written every skipOutputSteps time steps
-
48 #define skipOutputSteps 1000
-
49 
-
50 // =================================================================================
-
51 // Set the flag determining if the total free energy is calculated for each output
-
52 // =================================================================================
-
53 #define calcEnergy true
+
47 // Type of spacing between outputs ("EQUAL_SPACING", "LOG_SPACING", "N_PER_DECADE",
+
48 // or "LIST")
+
49 #define outputCondition "EQUAL_SPACING"
+
50 
+
51 // Number of times the program outputs the fields (total number for "EQUAL_SPACING"
+
52 // and "LOG_SPACING", number per decade for "N_PER_DECADE", ignored for "LIST")
+
53 #define numOutputs 10
54 
-
55 
-
56 
-
57 
+
55 // User-defined list of time steps where the program should output. Only used if
+
56 // outputCondition is "LIST"
+
57 #define outputList {}
58 
-
59 
-
60 
+
59 // Status is printed to the screen every skipPrintSteps
+
60 #define skipPrintSteps 10
+
61 
+
62 // =================================================================================
+
63 // Set the flag determining if the total free energy is calculated for each output
+
64 // =================================================================================
+
65 #define calcEnergy true
+
66 
+
67 
+
68 
+
69 
+
70 
+
71 
+
72 
diff --git a/html/allen_cahn__pfield_2_i_cs__and___b_cs_8h.html b/html/allen_cahn__pfield_2_i_cs__and___b_cs_8h.html new file mode 100644 index 000000000..607ecded4 --- /dev/null +++ b/html/allen_cahn__pfield_2_i_cs__and___b_cs_8h.html @@ -0,0 +1,188 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/allenCahn_pfield/ICs_and_BCs.h File Reference + + + + + + + + + + +
+
+ + + + + + +
+
PRISMS PhaseField +  1.0 +
+
+
+ + + + + + +
+
+ + +
+ +
+ + +
+
+
+Classes | +Macros
+
+
ICs_and_BCs.h File Reference
+
+
+ +

Go to the source code of this file.

+ + + + + + +

+Classes

class  InitialCondition< dim >
 
class  InitialConditionVec< dim >
 
+ + + + + + + + + + + +

+Macros

#define enablePFields   true
 
#define loadICs   {true}
 
#define loadSerialFile   {false}
 
#define loadFileName   {"parallel_input"}
 
#define loadFieldName   {"n"}
 
+

Macro Definition Documentation

+ +
+
+ + + + +
#define enablePFields   true
+
+ +
+
+ +
+
+ + + + +
#define loadFieldName   {"n"}
+
+ +
+
+ +
+
+ + + + +
#define loadFileName   {"parallel_input"}
+
+ +
+
+ +
+
+ + + + +
#define loadICs   {true}
+
+ +
+
+ +
+
+ + + + +
#define loadSerialFile   {false}
+
+ +
+
+
+ +
+Generated by   +doxygen + 1.8.10 +
+ + diff --git a/html/allen_cahn__pfield_2_i_cs__and___b_cs_8h_source.html b/html/allen_cahn__pfield_2_i_cs__and___b_cs_8h_source.html new file mode 100644 index 000000000..1179b936e --- /dev/null +++ b/html/allen_cahn__pfield_2_i_cs__and___b_cs_8h_source.html @@ -0,0 +1,233 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/allenCahn_pfield/ICs_and_BCs.h Source File + + + + + + + + + + +
+
+ + + + + + +
+
PRISMS PhaseField +  1.0 +
+
+
+ + + + + + +
+
+ + +
+ +
+ + +
+
+
+
ICs_and_BCs.h
+
+
+Go to the documentation of this file.
1 // Initial conditions and boundary conditions
+
2 
+
3 // =================================================================================
+
4 // Determine what (if any) files to load for the initial conditions
+
5 // =================================================================================
+
6 // Set whether PFields are enables (requires PRISMS IntegrationTools)
+
7 #define enablePFields true
+
8 
+
9 // Set whether the initial conditions are loaded from file for each variable
+
10 #define loadICs {true}
+
11 
+
12 // Set whether the file is serial or a series of parallel files
+
13 #define loadSerialFile {false}
+
14 
+
15 // Set the name of the input files
+
16 #define loadFileName {"parallel_input"}
+
17 
+
18 // Set the field names in the input files
+
19 #define loadFieldName {"n"}
+
20 
+
21 // =================================================================================
+
22 
+
23 template <int dim>
+
24 class InitialCondition : public Function<dim>
+
25 {
+
26 public:
+
27  unsigned int index;
+
28  Vector<double> values;
+
29 
+
30 
+
31  InitialCondition (const unsigned int _index) : Function<dim>(1), index(_index) {
+
32  std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1);
+
33 
+
34  }
+
35  double value (const Point<dim> &p, const unsigned int component = 0) const
+
36  {
+
37  double scalar_IC;
+
38  // =====================================================================
+
39  // ENTER THE INITIAL CONDITIONS HERE FOR SCALAR FIELDS
+
40  // =====================================================================
+
41  // Enter the function describing conditions for the fields at point "p".
+
42  // Use "if" statements to set the initial condition for each variable
+
43  // according to its variable index.
+
44 
+
45  double dx=spanX/((double) subdivisionsX)/std::pow(2.0,refineFactor);
+
46  double x_loc[12] = {0.1, 0.8, 0.5, 0.4, 0.3, 0.8, 0.9, 0.0, 0.1, 0.5, 1, 0.7};
+
47  double y_loc[12] = {0.3, 0.7, 0.2, 0.4, 0.9, 0.1, 0.5, 0.1, 0.6, 0.6, 1, 0.95};
+
48  double rad[12] = {12, 14, 19, 16, 11, 12, 17, 15, 20, 10, 11, 14};
+
49  double dist;
+
50  scalar_IC = 0;
+
51  for (unsigned int i=0; i<12; i++){
+
52  #if problemDIM == 2
+
53  dist = p.distance(Point<dim>(x_loc[i]*spanX,y_loc[i]*spanY));
+
54  #elif problemDIM == 3
+
55  dist = p.distance(Point<dim>(x_loc[i]*spanX,y_loc[i]*spanY,0.5*spanZ));
+
56  #endif
+
57  if (dist < rad[i]){
+
58  scalar_IC = 1.0;
+
59  }
+
60 
+
61  }
+
62 
+
63  // =====================================================================
+
64  return scalar_IC;
+
65  }
+
66 };
+
67 
+
68 template <int dim>
+
69 class InitialConditionVec : public Function<dim>
+
70 {
+
71 public:
+
72  unsigned int index;
+
73  //Vector<double> values;
+
74  InitialConditionVec (const unsigned int _index) : Function<dim>(dim), index(_index) {
+
75  std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1);
+
76  }
+
77  void vector_value (const Point<dim> &p,Vector<double> &vector_IC) const
+
78  {
+
79  // =====================================================================
+
80  // ENTER THE INITIAL CONDITIONS HERE FOR VECTOR FIELDS
+
81  // =====================================================================
+
82  // Enter the function describing conditions for the fields at point "p".
+
83  // Use "if" statements to set the initial condition for each variable
+
84  // according to its variable index.
+
85 
+
86 
+
87  // =====================================================================
+
88  }
+
89 };
+
90 
+
91 template <int dim>
+
92 void generalizedProblem<dim>::setBCs(){
+
93  // =====================================================================
+
94  // ENTER THE BOUNDARY CONDITIONS HERE
+
95  // =====================================================================
+
96  // This function sets the BCs for the problem variables
+
97  // The function "inputBCs" should be called for each component of
+
98  // each variable and should be in numerical order. Four input arguments
+
99  // set the same BC on the entire boundary. Two plus two times the
+
100  // number of dimensions inputs sets separate BCs on each face of the domain.
+
101  // Inputs to "inputBCs":
+
102  // First input: variable number
+
103  // Second input: component number
+
104  // Third input: BC type (options are "ZERO_DERIVATIVE", "DIRICHLET", and "PERIODIC")
+
105  // Fourth input: BC value (ignored unless the BC type is "DIRICHLET")
+
106  // Odd inputs after the third: BC type
+
107  // Even inputs after the third: BC value
+
108  // Face numbering: starts at zero with the minimum of the first direction, one for the maximum of the first direction
+
109  // two for the minimum of the second direction, etc.
+
110 
+
111  inputBCs(0,0,"ZERO_DERIVATIVE",0);
+
112 
+
113  // =====================================================================
+
114 
+
115 }
+
116 
+
117 
+
InitialConditionVec::index
unsigned int index
Definition: ICs_and_BCs.h:47
+
InitialConditionVec::vector_value
void vector_value(const Point< dim > &p, Vector< double > &vector_IC) const
Definition: ICs_and_BCs.h:77
+
spanX
#define spanX
Definition: defaultValues.h:12
+
InitialCondition::InitialCondition
InitialCondition(const unsigned int _index)
Definition: ICs_and_BCs.h:31
+
spanY
#define spanY
Definition: defaultValues.h:17
+
InitialCondition::value
double value(const Point< dim > &p, const unsigned int component=0) const
Definition: ICs_and_BCs.h:35
+
InitialCondition::index
unsigned int index
Definition: ICs_and_BCs.h:5
+
refineFactor
#define refineFactor
Definition: defaultValues.h:27
+
generalizedProblem::setBCs
void setBCs()
Definition: ICs_and_BCs.h:67
+
InitialConditionVec::InitialConditionVec
InitialConditionVec(const unsigned int _index)
Definition: ICs_and_BCs.h:74
+
subdivisionsX
#define subdivisionsX
Definition: parameters.h:14
+
InitialCondition
Definition: ICs_and_BCs.h:2
+
InitialConditionVec
Definition: ICs_and_BCs.h:44
+
spanZ
#define spanZ
Definition: defaultValues.h:22
+
InitialCondition::values
Vector< double > values
Definition: ICs_and_BCs.h:6
+
+ +
+Generated by   +doxygen + 1.8.10 +
+ + diff --git a/html/allen_cahn__pfield_2equations_8h.html b/html/allen_cahn__pfield_2equations_8h.html new file mode 100644 index 000000000..f8c8989d1 --- /dev/null +++ b/html/allen_cahn__pfield_2equations_8h.html @@ -0,0 +1,320 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/allenCahn_pfield/equations.h File Reference + + + + + + + + + + +
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PRISMS PhaseField +  1.0 +
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equations.h File Reference
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Go to the source code of this file.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Macros

#define num_var   1
 
#define variable_name   {"n"}
 
#define variable_type   {"SCALAR"}
 
#define variable_eq_type   {"PARABOLIC"}
 
#define need_val   {true}
 
#define need_grad   {true}
 
#define need_hess   {false}
 
#define need_val_residual   {true}
 
#define need_grad_residual   {true}
 
#define MnV   1.0
 
#define KnV   4.0
 
#define fV   (n*n*n*n - 2.0*n*n*n + n*n)
 
#define fnV   (4.0*n*(n-1.0)*(n-0.5))
 
#define rnV   (n-constV(timeStep*MnV)*fnV)
 
#define rnxV   (constV(-timeStep*KnV*MnV)*nx)
 
+

Macro Definition Documentation

+ +
+
+ + + + +
#define fnV   (4.0*n*(n-1.0)*(n-0.5))
+
+ +
+
+ +
+
+ + + + +
#define fV   (n*n*n*n - 2.0*n*n*n + n*n)
+
+ +
+
+ +
+
+ + + + +
#define KnV   4.0
+
+ +
+
+ +
+
+ + + + +
#define MnV   1.0
+
+ +
+
+ +
+
+ + + + +
#define need_grad   {true}
+
+ +
+
+ +
+
+ + + + +
#define need_grad_residual   {true}
+
+ +
+
+ +
+
+ + + + +
#define need_hess   {false}
+
+ +
+
+ +
+
+ + + + +
#define need_val   {true}
+
+ +
+
+ +
+
+ + + + +
#define need_val_residual   {true}
+
+ +
+
+ +
+
+ + + + +
#define num_var   1
+
+ +
+
+ +
+
+ + + + +
#define rnV   (n-constV(timeStep*MnV)*fnV)
+
+ +
+
+ +
+
+ + + + +
#define rnxV   (constV(-timeStep*KnV*MnV)*nx)
+
+ +
+
+ +
+
+ + + + +
#define variable_eq_type   {"PARABOLIC"}
+
+ +
+
+ +
+
+ + + + +
#define variable_name   {"n"}
+
+ +
+
+ +
+
+ + + + +
#define variable_type   {"SCALAR"}
+
+ +
+
+
+ +
+Generated by   +doxygen + 1.8.10 +
+ + diff --git a/html/allen_cahn__pfield_2equations_8h_source.html b/html/allen_cahn__pfield_2equations_8h_source.html new file mode 100644 index 000000000..01b2e2902 --- /dev/null +++ b/html/allen_cahn__pfield_2equations_8h_source.html @@ -0,0 +1,259 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/allenCahn_pfield/equations.h Source File + + + + + + + + + + +
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+ + + + + + +
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PRISMS PhaseField +  1.0 +
+
+
+ + + + + + +
+
+ + +
+ +
+ + +
+
+
+
equations.h
+
+
+Go to the documentation of this file.
1 // List of variables and residual equations for the coupled Allen-Cahn example application
+
2 
+
3 // =================================================================================
+
4 // Define the variables in the model
+
5 // =================================================================================
+
6 // The number of variables
+
7 #define num_var 1
+
8 
+
9 // The names of the variables, whether they are scalars or vectors and whether the
+
10 // governing eqn for the variable is parabolic or elliptic
+
11 #define variable_name {"n"}
+
12 #define variable_type {"SCALAR"}
+
13 #define variable_eq_type {"PARABOLIC"}
+
14 
+
15 // Flags for whether the value, gradient, and Hessian are needed in the residual eqns
+
16 #define need_val {true}
+
17 #define need_grad {true}
+
18 #define need_hess {false}
+
19 
+
20 // Flags for whether the residual equation has a term multiplied by the test function
+
21 // (need_val_residual) and/or the gradient of the test function (need_grad_residual)
+
22 #define need_val_residual {true}
+
23 #define need_grad_residual {true}
+
24 
+
25 // =================================================================================
+
26 // Define the model parameters and the residual equations
+
27 // =================================================================================
+
28 // Parameters in the residual equations and expressions for the residual equations
+
29 // can be set here. For simple cases, the entire residual equation can be written
+
30 // here. For more complex cases with loops or conditional statements, residual
+
31 // equations (or parts of residual equations) can be written below in "residualRHS".
+
32 
+
33 // Mobility
+
34 #define MnV 1.0
+
35 
+
36 // Gradient energy coefficient
+
37 #define KnV 4.0
+
38 
+
39 // Free energy and its derivative
+
40 #define fV (n*n*n*n - 2.0*n*n*n + n*n)
+
41 #define fnV (4.0*n*(n-1.0)*(n-0.5))
+
42 
+
43 // Residual equations
+
44 #define rnV (n-constV(timeStep*MnV)*fnV)
+
45 #define rnxV (constV(-timeStep*KnV*MnV)*nx)
+
46 
+
47 // =================================================================================
+
48 // residualRHS
+
49 // =================================================================================
+
50 // This function calculates the residual equations for each variable. It takes
+
51 // "modelVariablesList" as an input, which is a list of the value and derivatives of
+
52 // each of the variables at a specific quadrature point. The (x,y,z) location of
+
53 // that quadrature point is given by "q_point_loc". The function outputs
+
54 // "modelResidualsList", a list of the value and gradient terms of the residual for
+
55 // each residual equation. The index for each variable in these lists corresponds to
+
56 // the order it is defined at the top of this file (starting at 0).
+
57 template <int dim>
+
58 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim>> & modelVariablesList,
+
59  std::vector<modelResidual<dim>> & modelResidualsList,
+
60  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
+
61 
+
62 // The order parameter and its derivatives (names here should match those in the macros above)
+
63 scalarvalueType n = modelVariablesList[0].scalarValue;
+
64 scalargradType nx = modelVariablesList[0].scalarGrad;
+
65 
+
66 
+
67 // Residuals for the equation to evolve the order parameter (names here should match those in the macros above)
+
68 modelResidualsList[0].scalarValueResidual = rnV;
+
69 modelResidualsList[0].scalarGradResidual = rnxV;
+
70 
+
71 }
+
72 
+
73 // =================================================================================
+
74 // residualLHS (needed only if at least one equation is elliptic)
+
75 // =================================================================================
+
76 // This function calculates the residual equations for the iterative solver for
+
77 // elliptic equations.for each variable. It takes "modelVariablesList" as an input,
+
78 // which is a list of the value and derivatives of each of the variables at a
+
79 // specific quadrature point. The (x,y,z) location of that quadrature point is given
+
80 // by "q_point_loc". The function outputs "modelRes", the value and gradient terms of
+
81 // for the left-hand-side of the residual equation for the iterative solver. The
+
82 // index for each variable in these lists corresponds to the order it is defined at
+
83 // the top of this file (starting at 0), not counting variables that have
+
84 // "need_val_LHS", "need_grad_LHS", and "need_hess_LHS" all set to "false". If there
+
85 // are multiple elliptic equations, conditional statements should be used to ensure
+
86 // that the correct residual is being submitted. The index of the field being solved
+
87 // can be accessed by "this->currentFieldIndex".
+
88 template <int dim>
+
89 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim>> & modelVarList,
+
90  modelResidual<dim> & modelRes,
+
91  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
+
92 }
+
93 
+
94 // =================================================================================
+
95 // energyDensity (needed only if calcEnergy == true)
+
96 // =================================================================================
+
97 // This function integrates the free energy density across the computational domain.
+
98 // It takes "modelVariablesList" as an input, which is a list of the value and
+
99 // derivatives of each of the variables at a specific quadrature point. It also
+
100 // takes the mapped quadrature weight, "JxW_value", as an input. The (x,y,z) location
+
101 // of the quadrature point is given by "q_point_loc". The weighted value of the
+
102 // energy density is added to "energy" variable and the components of the energy
+
103 // density are added to the "energy_components" variable (index 0: chemical energy,
+
104 // index 1: gradient energy, index 2: elastic energy).
+
105 template <int dim>
+
106 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim>> & modelVarList,
+
107  const dealii::VectorizedArray<double> & JxW_value,
+
108  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) {
+
109 scalarvalueType total_energy_density = constV(0.0);
+
110 
+
111 // The order parameter and its derivatives (names here should match those in the macros above)
+
112 scalarvalueType n = modelVarList[0].scalarValue;
+
113 scalargradType nx = modelVarList[0].scalarGrad;
+
114 
+
115 // The homogenous free energy
+
116 scalarvalueType f_chem = fV;
+
117 
+
118 // The gradient free energy
+
119 scalarvalueType f_grad = constV(0.0);
+
120 
+
121 for (int i=0; i<dim; i++){
+
122  for (int j=0; j<dim; j++){
+
123  f_grad += constV(0.5*KnV)*nx[i]*nx[j];
+
124  }
+
125 }
+
126 
+
127 // The total free energy
+
128 total_energy_density = f_chem + f_grad;
+
129 
+
130 // Loop to step through each element of the vectorized arrays. Working with deal.ii
+
131 // developers to see if there is a more elegant way to do this.
+
132 assembler_lock.acquire ();
+
133 for (unsigned i=0; i<n.n_array_elements;i++){
+
134  // For some reason, some of the values in this loop
+
135  if (n[i] > 1.0e-10){
+
136  this->energy+=total_energy_density[i]*JxW_value[i];
+
137  this->energy_components[0]+= f_chem[i]*JxW_value[i];
+
138  this->energy_components[1]+= f_grad[i]*JxW_value[i];
+
139  }
+
140 }
+
141 assembler_lock.release ();
+
142 }
+
143 
+
144 
+
145 
+
146 
+
rnxV
#define rnxV
Definition: equations.h:45
+
modelResidual
Definition: model_variables.h:25
+
generalizedProblem::residualRHS
void residualRHS(const std::vector< modelVariable< dim > > &modelVarList, std::vector< modelResidual< dim > > &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:58
+
rnV
#define rnV
Definition: equations.h:44
+
scalargradType
dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
Definition: matrixFreePDE.h:51
+
constV
#define constV(a)
Definition: matrixFreePDE.h:67
+
KnV
#define KnV
Definition: equations.h:37
+
fV
#define fV
Definition: equations.h:40
+
generalizedProblem::residualLHS
void residualLHS(const std::vector< modelVariable< dim > > &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:89
+
modelVariable
Definition: model_variables.h:4
+
scalarvalueType
dealii::VectorizedArray< double > scalarvalueType
Definition: matrixFreePDE.h:34
+
generalizedProblem::energyDensity
void energyDensity(const std::vector< modelVariable< dim > > &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
Definition: equations.h:106
+
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+Functions
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main.cc File Reference
+
+
+
#include "IntegrationTools/PField.hh"
+#include "../../include/dealIIheaders.h"
+#include "parameters.h"
+#include "../../src/models/coupled/generalized_model.h"
+#include "equations.h"
+#include "ICs_and_BCs.h"
+#include "../../src/models/coupled/generalized_model_functions.h"
+
+ + + +

+Functions

int main (int argc, char **argv)
 
+

Function Documentation

+ +
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int main (int argc,
char ** argv 
)
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+ + diff --git a/html/allen_cahn__pfield_2parameters_8h.html b/html/allen_cahn__pfield_2parameters_8h.html new file mode 100644 index 000000000..30014d9a4 --- /dev/null +++ b/html/allen_cahn__pfield_2parameters_8h.html @@ -0,0 +1,376 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/allenCahn_pfield/parameters.h File Reference + + + + + + + + + + +
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PRISMS PhaseField +  1.0 +
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parameters.h File Reference
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Go to the source code of this file.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Macros

#define problemDIM   2
 
#define spanX   100.0
 
#define spanY   100.0
 
#define spanZ   100.0
 
#define subdivisionsX   1
 
#define subdivisionsY   1
 
#define subdivisionsZ   1
 
#define refineFactor   7
 
#define finiteElementDegree   1
 
#define timeStep   1.0e-3
 
#define timeFinal   20.0
 
#define timeIncrements   20000
 
#define writeOutput   true
 
#define outputFileType   "vtk"
 
#define outputCondition   "EQUAL_SPACING"
 
#define numOutputs   10
 
#define outputList   {}
 
#define skipPrintSteps   10
 
#define calcEnergy   true
 
+

Macro Definition Documentation

+ +
+
+ + + + +
#define calcEnergy   true
+
+ +
+
+ +
+
+ + + + +
#define finiteElementDegree   1
+
+ +
+
+ +
+
+ + + + +
#define numOutputs   10
+
+ +
+
+ +
+
+ + + + +
#define outputCondition   "EQUAL_SPACING"
+
+ +
+
+ +
+
+ + + + +
#define outputFileType   "vtk"
+
+ +
+
+ +
+
+ + + + +
#define outputList   {}
+
+ +
+
+ +
+
+ + + + +
#define problemDIM   2
+
+ +
+
+ +
+
+ + + + +
#define refineFactor   7
+
+ +
+
+ +
+
+ + + + +
#define skipPrintSteps   10
+
+ +
+
+ +
+
+ + + + +
#define spanX   100.0
+
+ +
+
+ +
+
+ + + + +
#define spanY   100.0
+
+ +
+
+ +
+
+ + + + +
#define spanZ   100.0
+
+ +
+
+ +
+
+ + + + +
#define subdivisionsX   1
+
+ +
+
+ +
+
+ + + + +
#define subdivisionsY   1
+
+ +
+
+ +
+
+ + + + +
#define subdivisionsZ   1
+
+ +
+
+ +
+
+ + + + +
#define timeFinal   20.0
+
+ +
+
+ +
+
+ + + + +
#define timeIncrements   20000
+
+ +
+
+ +
+
+ + + + +
#define timeStep   1.0e-3
+
+ +
+
+ +
+
+ + + + +
#define writeOutput   true
+
+ +
+
+
+ +
+Generated by   +doxygen + 1.8.10 +
+ + diff --git a/html/allen_cahn__pfield_2parameters_8h_source.html b/html/allen_cahn__pfield_2parameters_8h_source.html new file mode 100644 index 000000000..e0cca7b36 --- /dev/null +++ b/html/allen_cahn__pfield_2parameters_8h_source.html @@ -0,0 +1,177 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/allenCahn_pfield/parameters.h Source File + + + + + + + + + + +
+
+ + + + + + +
+
PRISMS PhaseField +  1.0 +
+
+
+ + + + + + +
+
+ + +
+ +
+ + +
+
+
+
parameters.h
+
+
+Go to the documentation of this file.
1 // Parameter list for the Allen-Cahn example application
+
2 // All strictly numerical parameters should be set in this file
+
3 
+
4 // =================================================================================
+
5 // Set the number of dimensions (1, 2, or 3 for a 1D, 2D, or 3D calculation)
+
6 // =================================================================================
+
7 #define problemDIM 2
+
8 
+
9 // =================================================================================
+
10 // Set the length of the domain in all three dimensions
+
11 // =================================================================================
+
12 // Each axes spans from zero to the specified length
+
13 #define spanX 100.0
+
14 #define spanY 100.0
+
15 #define spanZ 100.0
+
16 
+
17 // =================================================================================
+
18 // Set the element parameters
+
19 // =================================================================================
+
20 // The number of elements in each direction is 2^(refineFactor) * subdivisions
+
21 // For optimal performance, use refineFactor primarily to determine the element size
+
22 #define subdivisionsX 1
+
23 #define subdivisionsY 1
+
24 #define subdivisionsZ 1
+
25 #define refineFactor 7
+
26 
+
27 // Set the polynomial degree of the element (suggested values: 1 or 2)
+
28 #define finiteElementDegree 1
+
29 
+
30 // =================================================================================
+
31 // Set the time step parameters
+
32 // =================================================================================
+
33 // The size of the time step
+
34 #define timeStep 1.0e-3
+
35 
+
36 // The simulation ends when either timeFinal is reached or the number of time steps
+
37 // equals timeIncrements
+
38 #define timeFinal 20.0
+
39 #define timeIncrements 20000
+
40 
+
41 // =================================================================================
+
42 // Set the output parameters
+
43 // =================================================================================
+
44 // Each field in the problem will be output is writeOutput is set to "true"
+
45 #define writeOutput true
+
46 
+
47 // Set the output file type from list of available deal.II output formats (common
+
48 // (choices are write_vtu and write_vtk)
+
49 #define outputFileType "vtk"
+
50 
+
51 // Type of spacing between outputs ("EQUAL_SPACING", "LOG_SPACING", "N_PER_DECADE",
+
52 // or "LIST")
+
53 #define outputCondition "EQUAL_SPACING"
+
54 
+
55 // Number of times the program outputs the fields (total number for "EQUAL_SPACING"
+
56 // and "LOG_SPACING", number per decade for "N_PER_DECADE", ignored for "LIST")
+
57 #define numOutputs 10
+
58 
+
59 // User-defined list of time steps where the program should output. Only used if
+
60 // outputCondition is "LIST"
+
61 #define outputList {}
+
62 
+
63 // Status is printed to the screen every skipPrintSteps
+
64 #define skipPrintSteps 10
+
65 
+
66 // =================================================================================
+
67 // Set the flag determining if the total free energy is calculated for each output
+
68 // =================================================================================
+
69 #define calcEnergy true
+
70 
+
71 
+
72 
+
73 
+
74 
+
75 
+
76 
+
+ +
+Generated by   +doxygen + 1.8.10 +
+ + diff --git a/html/annotated.html b/html/annotated.html index 7974dc746..4a0a17a88 100644 --- a/html/annotated.html +++ b/html/annotated.html @@ -89,19 +89,20 @@
Here are the classes, structs, unions and interfaces with brief descriptions:
- - - - - - - - - - - - - +
[detail level 12]
 CAllenCahnProblem
 CBCFunction
 CCahnHilliardProblem
 CCoupledCahnHilliardMechanicsProblem
 CCoupledCHACMechanicsProblem
 CCoupledCHACProblem
 CFickianProblem
 CField
 CgeneralizedProblem
 CInitialCondition
 CInitialConditionC
 CInitialConditionN
+ + + + + + + + + + + + + diff --git a/html/cahn_hilliard_2_i_cs__and___b_cs_8h_source.html b/html/cahn_hilliard_2_i_cs__and___b_cs_8h_source.html index f77953410..791454293 100644 --- a/html/cahn_hilliard_2_i_cs__and___b_cs_8h_source.html +++ b/html/cahn_hilliard_2_i_cs__and___b_cs_8h_source.html @@ -174,7 +174,7 @@
82  // Inputs to "inputBCs":
83  // First input: variable number
84  // Second input: component number
-
85  // Third input: BC type (options are "ZERO_DERIVATIVE" and "DIRICHLET")
+
85  // Third input: BC type (options are "ZERO_DERIVATIVE", "DIRICHLET", and "PERIODIC")
86  // Fourth input: BC value (ignored unless the BC type is "DIRICHLET")
87  // Odd inputs after the third: BC type
88  // Even inputs after the third: BC value
@@ -185,7 +185,7 @@
93  inputBCs(1,0,"ZERO_DERIVATIVE",0);
94 
95 }
-
InitialConditionVec::index
unsigned int index
Definition: ICs_and_BCs.h:46
+
InitialConditionVec::index
unsigned int index
Definition: ICs_and_BCs.h:47
InitialConditionVec::vector_value
void vector_value(const Point< dim > &p, Vector< double > &vector_IC) const
Definition: ICs_and_BCs.h:56
spanX
#define spanX
Definition: defaultValues.h:12
InitialCondition::InitialCondition
InitialCondition(const unsigned int _index)
Definition: ICs_and_BCs.h:7
@@ -193,11 +193,11 @@
InitialCondition::value
double value(const Point< dim > &p, const unsigned int component=0) const
Definition: ICs_and_BCs.h:10
InitialCondition::index
unsigned int index
Definition: ICs_and_BCs.h:5
refineFactor
#define refineFactor
Definition: defaultValues.h:27
-
generalizedProblem::setBCs
void setBCs()
Definition: ICs_and_BCs.h:66
+
generalizedProblem::setBCs
void setBCs()
Definition: ICs_and_BCs.h:67
InitialConditionVec::InitialConditionVec
InitialConditionVec(const unsigned int _index)
Definition: ICs_and_BCs.h:53
subdivisionsX
#define subdivisionsX
Definition: parameters.h:14
InitialCondition
Definition: ICs_and_BCs.h:2
-
InitialConditionVec
Definition: ICs_and_BCs.h:43
+
InitialConditionVec
Definition: ICs_and_BCs.h:44
spanZ
#define spanZ
Definition: defaultValues.h:22
InitialCondition::values
Vector< double > values
Definition: ICs_and_BCs.h:6
diff --git a/html/cahn_hilliard_2equations_8h.html b/html/cahn_hilliard_2equations_8h.html index cee68ab8c..14c90a10e 100644 --- a/html/cahn_hilliard_2equations_8h.html +++ b/html/cahn_hilliard_2equations_8h.html @@ -123,13 +123,13 @@ - + - + - +
 ►NPFunctions
 CAllenCahnProblem
 CBCFunction
 CCahnHilliardProblem
 CCoupledCahnHilliardMechanicsProblem
 CCoupledCHACMechanicsProblem
 CCoupledCHACProblem
 CFickianProblem
 CField
 CgeneralizedProblem
 CInitialCondition
 CInitialConditionC
 CInitialConditionVec
 CMatrixFreePDE
 CMechanicsProblem
 
#define fcV   (4.0*c*(c-1.0)*(c-0.5))
 
#define rmuV   (fcV)
#define rmuV   (fcV)
 
#define rmuxV   (constV(KcV)*cx)
#define rmuxV   (constV(KcV)*cx)
 
#define rcV   (c)
 
#define rcxV   (constV(-McV*timeStep)*mux)
#define rcxV   (constV(-McV*timeStep)*mux)
 

Macro Definition Documentation

@@ -270,7 +270,7 @@

Macro Definition Documentation

- +
#define rcxV   (constV(-McV*timeStep)*mux)#define rcxV   (constV(-McV*timeStep)*mux)
@@ -282,7 +282,7 @@

Macro Definition Documentation

- +
#define rmuV   (fcV)#define rmuV   (fcV)
@@ -294,7 +294,7 @@

Macro Definition Documentation

- +
#define rmuxV   (constV(KcV)*cx)#define rmuxV   (constV(KcV)*cx)
diff --git a/html/cahn_hilliard_2equations_8h_source.html b/html/cahn_hilliard_2equations_8h_source.html index 2707e470c..edf911e09 100644 --- a/html/cahn_hilliard_2equations_8h_source.html +++ b/html/cahn_hilliard_2equations_8h_source.html @@ -150,8 +150,8 @@
58 // each residual equation. The index for each variable in these lists corresponds to
59 // the order it is defined at the top of this file (starting at 0).
60 template <int dim>
-
61 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim>> & modelVariablesList,
-
62  std::vector<modelResidual<dim>> & modelResidualsList,
+
61 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim> > & modelVariablesList,
+
62  std::vector<modelResidual<dim> > & modelResidualsList,
63  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
64 
65 // The concentration and its derivatives (names here should match those in the macros above)
@@ -188,7 +188,7 @@
96 // that the correct residual is being submitted. The index of the field being solved
97 // can be accessed by "this->currentFieldIndex".
98 template <int dim>
-
99 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim>> & modelVarList,
+
99 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim> > & modelVarList,
100  modelResidual<dim> & modelRes,
101  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
102 }
@@ -205,7 +205,7 @@
113 // density are added to the "energy_components" variable (index 0: chemical energy,
114 // index 1: gradient energy, index 2: elastic energy).
115 template <int dim>
-
116 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim>> & modelVariablesList,
+
116 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim> > & modelVariablesList,
117  const dealii::VectorizedArray<double> & JxW_value,
118  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) {
119 scalarvalueType total_energy_density = constV(0.0);
@@ -250,19 +250,19 @@
158 
159 
rmuxV
#define rmuxV
Definition: equations.h:45
-
generalizedProblem::energyDensity
void energyDensity(const std::vector< modelVariable< dim >> &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
Definition: equations.h:106
modelResidual
Definition: model_variables.h:25
+
generalizedProblem::residualRHS
void residualRHS(const std::vector< modelVariable< dim > > &modelVarList, std::vector< modelResidual< dim > > &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:58
scalargradType
dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
Definition: matrixFreePDE.h:51
-
generalizedProblem::residualLHS
void residualLHS(const std::vector< modelVariable< dim >> &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:89
constV
#define constV(a)
Definition: matrixFreePDE.h:67
KcV
#define KcV
Definition: equations.h:37
rcxV
#define rcxV
Definition: equations.h:47
+
generalizedProblem::residualLHS
void residualLHS(const std::vector< modelVariable< dim > > &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:89
rmuV
#define rmuV
Definition: equations.h:44
modelVariable
Definition: model_variables.h:4
fV
#define fV
Definition: equations.h:40
rcV
#define rcV
Definition: equations.h:46
scalarvalueType
dealii::VectorizedArray< double > scalarvalueType
Definition: matrixFreePDE.h:34
-
generalizedProblem::residualRHS
void residualRHS(const std::vector< modelVariable< dim >> &modelVarList, std::vector< modelResidual< dim >> &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:58
+
generalizedProblem::energyDensity
void energyDensity(const std::vector< modelVariable< dim > > &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
Definition: equations.h:106
diff --git a/html/cahn_hilliard_2parameters_8h.html b/html/cahn_hilliard_2parameters_8h.html index f5fc75ab3..f694804bb 100644 --- a/html/cahn_hilliard_2parameters_8h.html +++ b/html/cahn_hilliard_2parameters_8h.html @@ -123,8 +123,14 @@   #define writeOutput   true   -#define skipOutputSteps   1000 -  +#define outputCondition   "EQUAL_SPACING" +  +#define numOutputs   10 +  +#define outputList   {} +  +#define skipPrintSteps   10 +  #define calcEnergy   false   @@ -151,6 +157,42 @@

Macro Definition Documentation

+
+
+ +
+
+ + + + +
#define numOutputs   10
+
+ +
+
+ +
+
+ + + + +
#define outputCondition   "EQUAL_SPACING"
+
+ +
+
+ +
+
+ + + + +
#define outputList   {}
+
+
@@ -177,12 +219,12 @@

Macro Definition Documentation

- +
- +
#define skipOutputSteps   1000#define skipPrintSteps   10
diff --git a/html/cahn_hilliard_2parameters_8h_source.html b/html/cahn_hilliard_2parameters_8h_source.html index 21419a93b..2058d5482 100644 --- a/html/cahn_hilliard_2parameters_8h_source.html +++ b/html/cahn_hilliard_2parameters_8h_source.html @@ -136,14 +136,26 @@
44 // Each field in the problem will be output is writeOutput is set to "true"
45 #define writeOutput true
46 
-
47 // Output files are written every skipOutputSteps time steps
-
48 #define skipOutputSteps 1000
-
49 
-
50 // =================================================================================
-
51 // Set the flag determining if the total free energy is calculated for each output
-
52 // =================================================================================
-
53 #define calcEnergy false
+
47 // Type of spacing between outputs ("EQUAL_SPACING", "LOG_SPACING", "N_PER_DECADE",
+
48 // or "LIST")
+
49 #define outputCondition "EQUAL_SPACING"
+
50 
+
51 // Number of times the program outputs the fields (total number for "EQUAL_SPACING"
+
52 // and "LOG_SPACING", number per decade for "N_PER_DECADE", ignored for "LIST")
+
53 #define numOutputs 10
54 
+
55 // User-defined list of time steps where the program should output. Only used if
+
56 // outputCondition is "LIST"
+
57 #define outputList {}
+
58 
+
59 // Status is printed to the screen every skipPrintSteps
+
60 #define skipPrintSteps 10
+
61 
+
62 // =================================================================================
+
63 // Set the flag determining if the total free energy is calculated for each output
+
64 // =================================================================================
+
65 #define calcEnergy false
+
66 
diff --git a/html/cahn_hilliard_with_adaptivity_2_i_cs__and___b_cs_8h_source.html b/html/cahn_hilliard_with_adaptivity_2_i_cs__and___b_cs_8h_source.html index 2f9999fe9..8ffd417b4 100644 --- a/html/cahn_hilliard_with_adaptivity_2_i_cs__and___b_cs_8h_source.html +++ b/html/cahn_hilliard_with_adaptivity_2_i_cs__and___b_cs_8h_source.html @@ -172,7 +172,7 @@
80  // Inputs to "inputBCs":
81  // First input: variable number
82  // Second input: component number
-
83  // Third input: BC type (options are "ZERO_DERIVATIVE" and "DIRICHLET")
+
83  // Third input: BC type (options are "ZERO_DERIVATIVE", "DIRICHLET", and "PERIODIC")
84  // Fourth input: BC value (ignored unless the BC type is "DIRICHLET")
85  // Odd inputs after the third: BC type
86  // Even inputs after the third: BC value
@@ -185,7 +185,7 @@
93  // =====================================================================
94 
95 }
-
InitialConditionVec::index
unsigned int index
Definition: ICs_and_BCs.h:46
+
InitialConditionVec::index
unsigned int index
Definition: ICs_and_BCs.h:47
InitialConditionVec::vector_value
void vector_value(const Point< dim > &p, Vector< double > &vector_IC) const
Definition: ICs_and_BCs.h:56
spanX
#define spanX
Definition: defaultValues.h:12
InitialCondition::InitialCondition
InitialCondition(const unsigned int _index)
Definition: ICs_and_BCs.h:7
@@ -193,11 +193,11 @@
InitialCondition::value
double value(const Point< dim > &p, const unsigned int component=0) const
Definition: ICs_and_BCs.h:10
InitialCondition::index
unsigned int index
Definition: ICs_and_BCs.h:5
refineFactor
#define refineFactor
Definition: defaultValues.h:27
-
generalizedProblem::setBCs
void setBCs()
Definition: ICs_and_BCs.h:66
+
generalizedProblem::setBCs
void setBCs()
Definition: ICs_and_BCs.h:67
InitialConditionVec::InitialConditionVec
InitialConditionVec(const unsigned int _index)
Definition: ICs_and_BCs.h:53
subdivisionsX
#define subdivisionsX
Definition: parameters.h:14
InitialCondition
Definition: ICs_and_BCs.h:2
-
InitialConditionVec
Definition: ICs_and_BCs.h:43
+
InitialConditionVec
Definition: ICs_and_BCs.h:44
InitialCondition::values
Vector< double > values
Definition: ICs_and_BCs.h:6
diff --git a/html/cahn_hilliard_with_adaptivity_2equations_8h.html b/html/cahn_hilliard_with_adaptivity_2equations_8h.html index a3b3acaf1..6a608388b 100644 --- a/html/cahn_hilliard_with_adaptivity_2equations_8h.html +++ b/html/cahn_hilliard_with_adaptivity_2equations_8h.html @@ -123,13 +123,13 @@   #define fcV   (4.0*c*(c-1.0)*(c-0.5))   -#define rmuV   (fcV) +#define rmuV   (fcV)   -#define rmuxV   (constV(KcV)*cx) +#define rmuxV   (constV(KcV)*cx)   #define rcV   (c)   -#define rcxV   (constV(-McV*timeStep)*mux) +#define rcxV   (constV(-McV*timeStep)*mux)  

Macro Definition Documentation

@@ -270,7 +270,7 @@

Macro Definition Documentation

- +
#define rcxV   (constV(-McV*timeStep)*mux)#define rcxV   (constV(-McV*timeStep)*mux)
@@ -282,7 +282,7 @@

Macro Definition Documentation

- +
#define rmuV   (fcV)#define rmuV   (fcV)
@@ -294,7 +294,7 @@

Macro Definition Documentation

- +
#define rmuxV   (constV(KcV)*cx)#define rmuxV   (constV(KcV)*cx)
diff --git a/html/cahn_hilliard_with_adaptivity_2equations_8h_source.html b/html/cahn_hilliard_with_adaptivity_2equations_8h_source.html index b7fcfa28b..93e67758f 100644 --- a/html/cahn_hilliard_with_adaptivity_2equations_8h_source.html +++ b/html/cahn_hilliard_with_adaptivity_2equations_8h_source.html @@ -149,8 +149,8 @@
57 // each residual equation. The index for each variable in these lists corresponds to
58 // the order it is defined at the top of this file (starting at 0).
59 template <int dim>
-
60 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim>> & modelVariablesList,
-
61  std::vector<modelResidual<dim>> & modelResidualsList,
+
60 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim> > & modelVariablesList,
+
61  std::vector<modelResidual<dim> > & modelResidualsList,
62  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
63 
64 // The concentration and its derivatives (names here should match those in the macros above)
@@ -187,7 +187,7 @@
95 // that the correct residual is being submitted. The index of the field being solved
96 // can be accessed by "this->currentFieldIndex".
97 template <int dim>
-
98 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim>> & modelVarList,
+
98 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim> > & modelVarList,
99  modelResidual<dim> & modelRes,
100  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
101 }
@@ -204,7 +204,7 @@
112 // density are added to the "energy_components" variable (index 0: chemical energy,
113 // index 1: gradient energy, index 2: elastic energy).
114 template <int dim>
-
115 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim>> & modelVariablesList,
+
115 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim> > & modelVariablesList,
116  const dealii::VectorizedArray<double> & JxW_value,
117  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) {
118 scalarvalueType total_energy_density = constV(0.0);
@@ -247,20 +247,20 @@
155 
156 
157 
-
generalizedProblem::energyDensity
void energyDensity(const std::vector< modelVariable< dim >> &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
Definition: equations.h:106
modelResidual
Definition: model_variables.h:25
+
generalizedProblem::residualRHS
void residualRHS(const std::vector< modelVariable< dim > > &modelVarList, std::vector< modelResidual< dim > > &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:58
scalargradType
dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
Definition: matrixFreePDE.h:51
-
generalizedProblem::residualLHS
void residualLHS(const std::vector< modelVariable< dim >> &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:89
constV
#define constV(a)
Definition: matrixFreePDE.h:67
fV
#define fV
Definition: equations.h:40
rmuV
#define rmuV
Definition: equations.h:44
rcV
#define rcV
Definition: equations.h:46
rmuxV
#define rmuxV
Definition: equations.h:45
+
generalizedProblem::residualLHS
void residualLHS(const std::vector< modelVariable< dim > > &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:89
KcV
#define KcV
Definition: equations.h:37
rcxV
#define rcxV
Definition: equations.h:47
modelVariable
Definition: model_variables.h:4
scalarvalueType
dealii::VectorizedArray< double > scalarvalueType
Definition: matrixFreePDE.h:34
-
generalizedProblem::residualRHS
void residualRHS(const std::vector< modelVariable< dim >> &modelVarList, std::vector< modelResidual< dim >> &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
Definition: equations.h:58
+
generalizedProblem::energyDensity
void energyDensity(const std::vector< modelVariable< dim > > &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
Definition: equations.h:106
diff --git a/html/cahn_hilliard_with_adaptivity_2parameters_8h.html b/html/cahn_hilliard_with_adaptivity_2parameters_8h.html index 8db61724c..2b7199490 100644 --- a/html/cahn_hilliard_with_adaptivity_2parameters_8h.html +++ b/html/cahn_hilliard_with_adaptivity_2parameters_8h.html @@ -117,9 +117,9 @@   #define hAdaptivity   true   -#define maxRefinementLevel   (refineFactor) +#define maxRefinementLevel   (refineFactor)   -#define minRefinementLevel   (refineFactor-1) +#define minRefinementLevel   (refineFactor-1)   #define refineCriterionFields   {0}   @@ -137,8 +137,14 @@   #define writeOutput   true   -#define skipOutputSteps   1000 -  +#define outputCondition   "EQUAL_SPACING" +  +#define numOutputs   10 +  +#define outputList   {} +  +#define skipPrintSteps   10 +  #define calcEnergy   false   @@ -184,7 +190,7 @@

Macro Definition Documentation

- +
#define maxRefinementLevel   (refineFactor)#define maxRefinementLevel   (refineFactor)
@@ -196,7 +202,43 @@

Macro Definition Documentation

- + + +
#define minRefinementLevel   (refineFactor-1)#define minRefinementLevel   (refineFactor-1)
+
+ +
+
+ +
+
+ + + + +
#define numOutputs   10
+
+ +
+
+ +
+
+ + + + +
#define outputCondition   "EQUAL_SPACING"
+
+ +
+
+ +
+
+ + +
#define outputList   {}
@@ -263,12 +305,12 @@

Macro Definition Documentation

- +
- +
#define skipOutputSteps   1000#define skipPrintSteps   10
diff --git a/html/cahn_hilliard_with_adaptivity_2parameters_8h_source.html b/html/cahn_hilliard_with_adaptivity_2parameters_8h_source.html index b1f9f03ab..33bb6620e 100644 --- a/html/cahn_hilliard_with_adaptivity_2parameters_8h_source.html +++ b/html/cahn_hilliard_with_adaptivity_2parameters_8h_source.html @@ -158,18 +158,30 @@
66 // Each field in the problem will be output is writeOutput is set to "true"
67 #define writeOutput true
68 
-
69 // Output files are written every skipOutputSteps time steps
-
70 #define skipOutputSteps 1000
-
71 
-
72 // =================================================================================
-
73 // Set the flag determining if the total free energy is calculated for each output
-
74 // =================================================================================
-
75 #define calcEnergy false
+
69 // Type of spacing between outputs ("EQUAL_SPACING", "LOG_SPACING", "N_PER_DECADE",
+
70 // or "LIST")
+
71 #define outputCondition "EQUAL_SPACING"
+
72 
+
73 // Number of times the program outputs the fields (total number for "EQUAL_SPACING"
+
74 // and "LOG_SPACING", number per decade for "N_PER_DECADE", ignored for "LIST")
+
75 #define numOutputs 10
76 
-
77 
-
78 
-
79 
+
77 // User-defined list of time steps where the program should output. Only used if
+
78 // outputCondition is "LIST"
+
79 #define outputList {}
80 
+
81 // Status is printed to the screen every skipPrintSteps
+
82 #define skipPrintSteps 10
+
83 
+
84 // =================================================================================
+
85 // Set the flag determining if the total free energy is calculated for each output
+
86 // =================================================================================
+
87 #define calcEnergy false
+
88 
+
89 
+
90 
+
91 
+
92 
diff --git a/html/class_allen_cahn_problem-members.html b/html/class_allen_cahn_problem-members.html index bea9bb4c7..54695be49 100644 --- a/html/class_allen_cahn_problem-members.html +++ b/html/class_allen_cahn_problem-members.html @@ -101,15 +101,15 @@ computeInvM()MatrixFreePDE< dim >protected computeRHS()MatrixFreePDE< dim >protected computing_timerMatrixFreePDE< dim >mutableprotected - constraintsHangingNodesSetMatrixFreePDE< dim >protected - constraintsHangingNodesSet2MatrixFreePDE< dim >protected - constraintsSetMatrixFreePDE< dim >protected - constraintsSet2MatrixFreePDE< dim >protected + constraintsDirichletSetMatrixFreePDE< dim >protected + constraintsDirichletSet_nonconstMatrixFreePDE< dim >protected + constraintsOtherSetMatrixFreePDE< dim >protected + constraintsOtherSet_nonconstMatrixFreePDE< dim >protected currentFieldIndexMatrixFreePDE< dim >protected currentIncrementMatrixFreePDE< dim >protected currentTimeMatrixFreePDE< dim >protected dofHandlersSetMatrixFreePDE< dim >protected - dofHandlersSet2MatrixFreePDE< dim >protected + dofHandlersSet_nonconstMatrixFreePDE< dim >protected dtValueMatrixFreePDE< dim >protected dUMatrixFreePDE< dim >protected ellipticFieldIndexMatrixFreePDE< dim >protected @@ -119,9 +119,11 @@ fieldsMatrixFreePDE< dim > finalTimeMatrixFreePDE< dim >protected freeEnergyValuesMatrixFreePDE< dim >protected - getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)MatrixFreePDE< dim >protectedvirtual - getFieldIndex(std::string _name)MatrixFreePDE< dim >protected - getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const MatrixFreePDE< dim >protectedvirtual + getComponentsWithRigidBodyModes(std::vector< int > &)MatrixFreePDE< dim >protectedvirtual + getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)MatrixFreePDE< dim >protectedvirtual + getFieldIndex(std::string _name)MatrixFreePDE< dim >protected + getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const MatrixFreePDE< dim >protectedvirtual + getOutputTimeSteps(std::string outputSpacingType, unsigned int numberOfOutputs, std::vector< unsigned int > &userGivenTimeStepList, std::vector< unsigned int > &timeStepList)MatrixFreePDE< dim >protected getRHS(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range) const AllenCahnProblem< dim >privatevirtual init(unsigned int iter=0)MatrixFreePDE< dim > initForTests()MatrixFreePDE< dim > @@ -129,7 +131,7 @@ isEllipticBVPMatrixFreePDE< dim >protected isTimeDependentBVPMatrixFreePDE< dim >protected locally_relevant_dofsSetMatrixFreePDE< dim >protected - locally_relevant_dofsSet2MatrixFreePDE< dim >protected + locally_relevant_dofsSet_nonconstMatrixFreePDE< dim >protected markBoundaries()MatrixFreePDE< dim >protectedvirtual matrixFreeObjectMatrixFreePDE< dim >protected MatrixFreePDE()MatrixFreePDE< dim > @@ -137,21 +139,26 @@ num_quadrature_pointsMatrixFreePDE< dim >protected outputFreeEnergy(std::vector< double > &freeEnergyValues)MatrixFreePDE< dim >protected outputResults()MatrixFreePDE< dim >protected - parabolicFieldIndexMatrixFreePDE< dim >protected - pcoutMatrixFreePDE< dim >protected - refineGrid()MatrixFreePDE< dim >protected - refineMesh(unsigned int _currentIncrement)MatrixFreePDE< dim >protected + outputTimeStepListMatrixFreePDE< dim >protected + parabolicFieldIndexMatrixFreePDE< dim >protected + pcoutMatrixFreePDE< dim >protected + refineGrid()MatrixFreePDE< dim >protected + refineMesh(unsigned int _currentIncrement)MatrixFreePDE< dim >protected + reinit(unsigned int iter=0)MatrixFreePDE< dim > residualSetMatrixFreePDE< dim >protected - shiftConcentration()MatrixFreePDE< dim > - soltransSetMatrixFreePDE< dim >protected - solutionSetMatrixFreePDE< dim >protected - solve()MatrixFreePDE< dim > - solveIncrement()MatrixFreePDE< dim >protected - totalIncrementsMatrixFreePDE< dim >protected - triangulationMatrixFreePDE< dim >protected - valuesDirichletSetMatrixFreePDE< dim >protected - vmult(vectorType &dst, const vectorType &src) const MatrixFreePDE< dim > - ~MatrixFreePDE()MatrixFreePDE< dim > + setPeriodicity()MatrixFreePDE< dim >protectedvirtual + setPeriodicityConstraints(ConstraintMatrix *, DoFHandler< dim > *)MatrixFreePDE< dim >protectedvirtual + setRigidBodyModeConstraints(std::vector< int >, ConstraintMatrix *, DoFHandler< dim > *)MatrixFreePDE< dim >protectedvirtual + shiftConcentration()MatrixFreePDE< dim > + soltransSetMatrixFreePDE< dim >protected + solutionSetMatrixFreePDE< dim >protected + solve()MatrixFreePDE< dim > + solveIncrement()MatrixFreePDE< dim >protected + totalIncrementsMatrixFreePDE< dim >protected + triangulationMatrixFreePDE< dim >protected + valuesDirichletSetMatrixFreePDE< dim >protected + vmult(vectorType &dst, const vectorType &src) const MatrixFreePDE< dim > + ~MatrixFreePDE()MatrixFreePDE< dim >
diff --git a/html/class_allen_cahn_problem.html b/html/class_allen_cahn_problem.html index a6824430a..026824607 100644 --- a/html/class_allen_cahn_problem.html +++ b/html/class_allen_cahn_problem.html @@ -115,6 +115,8 @@   void init (unsigned int iter=0)   +void reinit (unsigned int iter=0) +  void initForTests ()   void solve () @@ -141,6 +143,8 @@   void outputResults ()   +void getOutputTimeSteps (std::string outputSpacingType, unsigned int numberOfOutputs, std::vector< unsigned int > &userGivenTimeStepList, std::vector< unsigned int > &timeStepList) +  void computeInvM ()   void computeRHS () @@ -159,6 +163,14 @@   virtual void applyDirichletBCs ()   +virtual void setPeriodicity () +  +virtual void setPeriodicityConstraints (ConstraintMatrix *, DoFHandler< dim > *) +  +virtual void getComponentsWithRigidBodyModes (std::vector< int > &) +  +virtual void setRigidBodyModeConstraints (std::vector< int >, ConstraintMatrix *, DoFHandler< dim > *) +  virtual void modifySolutionFields ()   void computeEnergy () @@ -172,26 +184,28 @@ void computeIntegral (double &integratedField)   - Protected Attributes inherited from MatrixFreePDE< dim > +std::vector< unsigned int > outputTimeStepList +  parallel::distributed::Triangulation< dim > triangulation   std::vector< FESystem< dim > * > FESet   -std::vector< const ConstraintMatrix * > constraintsSet -  -std::vector< const ConstraintMatrix * > constraintsHangingNodesSet -  +std::vector< const ConstraintMatrix * > constraintsDirichletSet +  +std::vector< const ConstraintMatrix * > constraintsOtherSet +  std::vector< const DoFHandler< dim > * > dofHandlersSet   std::vector< const IndexSet * > locally_relevant_dofsSet   -std::vector< ConstraintMatrix * > constraintsSet2 -  -std::vector< ConstraintMatrix * > constraintsHangingNodesSet2 -  -std::vector< DoFHandler< dim > * > dofHandlersSet2 -  -std::vector< IndexSet * > locally_relevant_dofsSet2 -  +std::vector< ConstraintMatrix * > constraintsDirichletSet_nonconst +  +std::vector< ConstraintMatrix * > constraintsOtherSet_nonconst +  +std::vector< DoFHandler< dim > * > dofHandlersSet_nonconst +  +std::vector< IndexSet * > locally_relevant_dofsSet_nonconst +  std::vector< vectorType * > solutionSet   std::vector< vectorType * > residualSet diff --git a/html/class_cahn_hilliard_problem-members.html b/html/class_cahn_hilliard_problem-members.html index 646f25470..bc007b529 100644 --- a/html/class_cahn_hilliard_problem-members.html +++ b/html/class_cahn_hilliard_problem-members.html @@ -101,15 +101,15 @@ computeInvM()MatrixFreePDE< dim >protected computeRHS()MatrixFreePDE< dim >protected computing_timerMatrixFreePDE< dim >mutableprotected - constraintsHangingNodesSetMatrixFreePDE< dim >protected - constraintsHangingNodesSet2MatrixFreePDE< dim >protected - constraintsSetMatrixFreePDE< dim >protected - constraintsSet2MatrixFreePDE< dim >protected + constraintsDirichletSetMatrixFreePDE< dim >protected + constraintsDirichletSet_nonconstMatrixFreePDE< dim >protected + constraintsOtherSetMatrixFreePDE< dim >protected + constraintsOtherSet_nonconstMatrixFreePDE< dim >protected currentFieldIndexMatrixFreePDE< dim >protected currentIncrementMatrixFreePDE< dim >protected currentTimeMatrixFreePDE< dim >protected dofHandlersSetMatrixFreePDE< dim >protected - dofHandlersSet2MatrixFreePDE< dim >protected + dofHandlersSet_nonconstMatrixFreePDE< dim >protected dtValueMatrixFreePDE< dim >protected dUMatrixFreePDE< dim >protected ellipticFieldIndexMatrixFreePDE< dim >protected @@ -119,9 +119,11 @@ fieldsMatrixFreePDE< dim > finalTimeMatrixFreePDE< dim >protected freeEnergyValuesMatrixFreePDE< dim >protected - getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)MatrixFreePDE< dim >protectedvirtual - getFieldIndex(std::string _name)MatrixFreePDE< dim >protected - getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const MatrixFreePDE< dim >protectedvirtual + getComponentsWithRigidBodyModes(std::vector< int > &)MatrixFreePDE< dim >protectedvirtual + getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)MatrixFreePDE< dim >protectedvirtual + getFieldIndex(std::string _name)MatrixFreePDE< dim >protected + getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const MatrixFreePDE< dim >protectedvirtual + getOutputTimeSteps(std::string outputSpacingType, unsigned int numberOfOutputs, std::vector< unsigned int > &userGivenTimeStepList, std::vector< unsigned int > &timeStepList)MatrixFreePDE< dim >protected getRHS(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range) const CahnHilliardProblem< dim >privatevirtual init(unsigned int iter=0)MatrixFreePDE< dim > initForTests()MatrixFreePDE< dim > @@ -129,7 +131,7 @@ isEllipticBVPMatrixFreePDE< dim >protected isTimeDependentBVPMatrixFreePDE< dim >protected locally_relevant_dofsSetMatrixFreePDE< dim >protected - locally_relevant_dofsSet2MatrixFreePDE< dim >protected + locally_relevant_dofsSet_nonconstMatrixFreePDE< dim >protected markBoundaries()MatrixFreePDE< dim >protectedvirtual matrixFreeObjectMatrixFreePDE< dim >protected MatrixFreePDE()MatrixFreePDE< dim > @@ -137,21 +139,26 @@ num_quadrature_pointsMatrixFreePDE< dim >protected outputFreeEnergy(std::vector< double > &freeEnergyValues)MatrixFreePDE< dim >protected outputResults()MatrixFreePDE< dim >protected - parabolicFieldIndexMatrixFreePDE< dim >protected - pcoutMatrixFreePDE< dim >protected - refineGrid()MatrixFreePDE< dim >protected - refineMesh(unsigned int _currentIncrement)MatrixFreePDE< dim >protected + outputTimeStepListMatrixFreePDE< dim >protected + parabolicFieldIndexMatrixFreePDE< dim >protected + pcoutMatrixFreePDE< dim >protected + refineGrid()MatrixFreePDE< dim >protected + refineMesh(unsigned int _currentIncrement)MatrixFreePDE< dim >protected + reinit(unsigned int iter=0)MatrixFreePDE< dim > residualSetMatrixFreePDE< dim >protected - shiftConcentration()MatrixFreePDE< dim > - soltransSetMatrixFreePDE< dim >protected - solutionSetMatrixFreePDE< dim >protected - solve()MatrixFreePDE< dim > - solveIncrement()MatrixFreePDE< dim >protected - totalIncrementsMatrixFreePDE< dim >protected - triangulationMatrixFreePDE< dim >protected - valuesDirichletSetMatrixFreePDE< dim >protected - vmult(vectorType &dst, const vectorType &src) const MatrixFreePDE< dim > - ~MatrixFreePDE()MatrixFreePDE< dim > + setPeriodicity()MatrixFreePDE< dim >protectedvirtual + setPeriodicityConstraints(ConstraintMatrix *, DoFHandler< dim > *)MatrixFreePDE< dim >protectedvirtual + setRigidBodyModeConstraints(std::vector< int >, ConstraintMatrix *, DoFHandler< dim > *)MatrixFreePDE< dim >protectedvirtual + shiftConcentration()MatrixFreePDE< dim > + soltransSetMatrixFreePDE< dim >protected + solutionSetMatrixFreePDE< dim >protected + solve()MatrixFreePDE< dim > + solveIncrement()MatrixFreePDE< dim >protected + totalIncrementsMatrixFreePDE< dim >protected + triangulationMatrixFreePDE< dim >protected + valuesDirichletSetMatrixFreePDE< dim >protected + vmult(vectorType &dst, const vectorType &src) const MatrixFreePDE< dim > + ~MatrixFreePDE()MatrixFreePDE< dim >
diff --git a/html/class_cahn_hilliard_problem.html b/html/class_cahn_hilliard_problem.html index 367e6f077..52bb5f0e0 100644 --- a/html/class_cahn_hilliard_problem.html +++ b/html/class_cahn_hilliard_problem.html @@ -115,6 +115,8 @@   void init (unsigned int iter=0)   +void reinit (unsigned int iter=0) +  void initForTests ()   void solve () @@ -145,6 +147,8 @@   void outputResults ()   +void getOutputTimeSteps (std::string outputSpacingType, unsigned int numberOfOutputs, std::vector< unsigned int > &userGivenTimeStepList, std::vector< unsigned int > &timeStepList) +  void computeInvM ()   void computeRHS () @@ -159,6 +163,14 @@   virtual void applyDirichletBCs ()   +virtual void setPeriodicity () +  +virtual void setPeriodicityConstraints (ConstraintMatrix *, DoFHandler< dim > *) +  +virtual void getComponentsWithRigidBodyModes (std::vector< int > &) +  +virtual void setRigidBodyModeConstraints (std::vector< int >, ConstraintMatrix *, DoFHandler< dim > *) +  virtual void modifySolutionFields ()   void computeEnergy () @@ -172,26 +184,28 @@ void computeIntegral (double &integratedField)   - Protected Attributes inherited from MatrixFreePDE< dim > +std::vector< unsigned int > outputTimeStepList +  parallel::distributed::Triangulation< dim > triangulation   std::vector< FESystem< dim > * > FESet   -std::vector< const ConstraintMatrix * > constraintsSet -  -std::vector< const ConstraintMatrix * > constraintsHangingNodesSet -  +std::vector< const ConstraintMatrix * > constraintsDirichletSet +  +std::vector< const ConstraintMatrix * > constraintsOtherSet +  std::vector< const DoFHandler< dim > * > dofHandlersSet   std::vector< const IndexSet * > locally_relevant_dofsSet   -std::vector< ConstraintMatrix * > constraintsSet2 -  -std::vector< ConstraintMatrix * > constraintsHangingNodesSet2 -  -std::vector< DoFHandler< dim > * > dofHandlersSet2 -  -std::vector< IndexSet * > locally_relevant_dofsSet2 -  +std::vector< ConstraintMatrix * > constraintsDirichletSet_nonconst +  +std::vector< ConstraintMatrix * > constraintsOtherSet_nonconst +  +std::vector< DoFHandler< dim > * > dofHandlersSet_nonconst +  +std::vector< IndexSet * > locally_relevant_dofsSet_nonconst +  std::vector< vectorType * > solutionSet   std::vector< vectorType * > residualSet diff --git a/html/class_coupled_c_h_a_c_mechanics_problem-members.html b/html/class_coupled_c_h_a_c_mechanics_problem-members.html index ab1817a83..892c06ba2 100644 --- a/html/class_coupled_c_h_a_c_mechanics_problem-members.html +++ b/html/class_coupled_c_h_a_c_mechanics_problem-members.html @@ -108,16 +108,16 @@ computeInvM()MatrixFreePDE< dim >protected computeRHS()MatrixFreePDE< dim >protected computing_timerMatrixFreePDE< dim >mutableprotected - constraintsHangingNodesSetMatrixFreePDE< dim >protected - constraintsHangingNodesSet2MatrixFreePDE< dim >protected - constraintsSetMatrixFreePDE< dim >protected - constraintsSet2MatrixFreePDE< dim >protected + constraintsDirichletSetMatrixFreePDE< dim >protected + constraintsDirichletSet_nonconstMatrixFreePDE< dim >protected + constraintsOtherSetMatrixFreePDE< dim >protected + constraintsOtherSet_nonconstMatrixFreePDE< dim >protected CoupledCHACMechanicsProblem()CoupledCHACMechanicsProblem< dim > currentFieldIndexMatrixFreePDE< dim >protected currentIncrementMatrixFreePDE< dim >protected currentTimeMatrixFreePDE< dim >protected dofHandlersSetMatrixFreePDE< dim >protected - dofHandlersSet2MatrixFreePDE< dim >protected + dofHandlersSet_nonconstMatrixFreePDE< dim >protected dtValueMatrixFreePDE< dim >protected dUMatrixFreePDE< dim >protected ellipticFieldIndexMatrixFreePDE< dim >protected @@ -127,9 +127,11 @@ fieldsMatrixFreePDE< dim > finalTimeMatrixFreePDE< dim >protected freeEnergyValuesMatrixFreePDE< dim >protected - getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)CoupledCHACMechanicsProblem< dim >privatevirtual - getFieldIndex(std::string _name)MatrixFreePDE< dim >protected - getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const CoupledCHACMechanicsProblem< dim >privatevirtual + getComponentsWithRigidBodyModes(std::vector< int > &)MatrixFreePDE< dim >protectedvirtual + getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)CoupledCHACMechanicsProblem< dim >privatevirtual + getFieldIndex(std::string _name)MatrixFreePDE< dim >protected + getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const CoupledCHACMechanicsProblem< dim >privatevirtual + getOutputTimeSteps(std::string outputSpacingType, unsigned int numberOfOutputs, std::vector< unsigned int > &userGivenTimeStepList, std::vector< unsigned int > &timeStepList)MatrixFreePDE< dim >protected getRHS(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range) const CoupledCHACMechanicsProblem< dim >privatevirtual init(unsigned int iter=0)MatrixFreePDE< dim > initForTests()MatrixFreePDE< dim > @@ -137,7 +139,7 @@ isEllipticBVPMatrixFreePDE< dim >protected isTimeDependentBVPMatrixFreePDE< dim >protected locally_relevant_dofsSetMatrixFreePDE< dim >protected - locally_relevant_dofsSet2MatrixFreePDE< dim >protected + locally_relevant_dofsSet_nonconstMatrixFreePDE< dim >protected markBoundaries()CoupledCHACMechanicsProblem< dim >privatevirtual matrixFreeObjectMatrixFreePDE< dim >protected MatrixFreePDE()MatrixFreePDE< dim > @@ -145,21 +147,26 @@ num_quadrature_pointsMatrixFreePDE< dim >protected outputFreeEnergy(std::vector< double > &freeEnergyValues)MatrixFreePDE< dim >protected outputResults()MatrixFreePDE< dim >protected - parabolicFieldIndexMatrixFreePDE< dim >protected - pcoutMatrixFreePDE< dim >protected - refineGrid()MatrixFreePDE< dim >protected - refineMesh(unsigned int _currentIncrement)MatrixFreePDE< dim >protected + outputTimeStepListMatrixFreePDE< dim >protected + parabolicFieldIndexMatrixFreePDE< dim >protected + pcoutMatrixFreePDE< dim >protected + refineGrid()MatrixFreePDE< dim >protected + refineMesh(unsigned int _currentIncrement)MatrixFreePDE< dim >protected + reinit(unsigned int iter=0)MatrixFreePDE< dim > residualSetMatrixFreePDE< dim >protected - shiftConcentration()CoupledCHACMechanicsProblem< dim > - soltransSetMatrixFreePDE< dim >protected - solutionSetMatrixFreePDE< dim >protected - solve()MatrixFreePDE< dim > - solveIncrement()MatrixFreePDE< dim >protected - totalIncrementsMatrixFreePDE< dim >protected - triangulationMatrixFreePDE< dim >protected - valuesDirichletSetMatrixFreePDE< dim >protected - vmult(vectorType &dst, const vectorType &src) const MatrixFreePDE< dim > - ~MatrixFreePDE()MatrixFreePDE< dim > + setPeriodicity()MatrixFreePDE< dim >protectedvirtual + setPeriodicityConstraints(ConstraintMatrix *, DoFHandler< dim > *)MatrixFreePDE< dim >protectedvirtual + setRigidBodyModeConstraints(std::vector< int >, ConstraintMatrix *, DoFHandler< dim > *)MatrixFreePDE< dim >protectedvirtual + shiftConcentration()CoupledCHACMechanicsProblem< dim > + soltransSetMatrixFreePDE< dim >protected + solutionSetMatrixFreePDE< dim >protected + solve()MatrixFreePDE< dim > + solveIncrement()MatrixFreePDE< dim >protected + totalIncrementsMatrixFreePDE< dim >protected + triangulationMatrixFreePDE< dim >protected + valuesDirichletSetMatrixFreePDE< dim >protected + vmult(vectorType &dst, const vectorType &src) const MatrixFreePDE< dim > + ~MatrixFreePDE()MatrixFreePDE< dim >
diff --git a/html/class_coupled_c_h_a_c_mechanics_problem.html b/html/class_coupled_c_h_a_c_mechanics_problem.html index 770c925ed..1d37e03fd 100644 --- a/html/class_coupled_c_h_a_c_mechanics_problem.html +++ b/html/class_coupled_c_h_a_c_mechanics_problem.html @@ -118,6 +118,8 @@   void init (unsigned int iter=0)   +void reinit (unsigned int iter=0) +  void initForTests ()   void solve () @@ -179,6 +181,8 @@   void outputResults ()   +void getOutputTimeSteps (std::string outputSpacingType, unsigned int numberOfOutputs, std::vector< unsigned int > &userGivenTimeStepList, std::vector< unsigned int > &timeStepList) +  void computeInvM ()   void computeRHS () @@ -187,6 +191,14 @@   void refineMesh (unsigned int _currentIncrement)   +virtual void setPeriodicity () +  +virtual void setPeriodicityConstraints (ConstraintMatrix *, DoFHandler< dim > *) +  +virtual void getComponentsWithRigidBodyModes (std::vector< int > &) +  +virtual void setRigidBodyModeConstraints (std::vector< int >, ConstraintMatrix *, DoFHandler< dim > *) +  void computeEnergy ()   unsigned int getFieldIndex (std::string _name) @@ -196,26 +208,28 @@ void computeIntegral (double &integratedField)   - Protected Attributes inherited from MatrixFreePDE< dim > +std::vector< unsigned int > outputTimeStepList +  parallel::distributed::Triangulation< dim > triangulation   std::vector< FESystem< dim > * > FESet   -std::vector< const ConstraintMatrix * > constraintsSet -  -std::vector< const ConstraintMatrix * > constraintsHangingNodesSet -  +std::vector< const ConstraintMatrix * > constraintsDirichletSet +  +std::vector< const ConstraintMatrix * > constraintsOtherSet +  std::vector< const DoFHandler< dim > * > dofHandlersSet   std::vector< const IndexSet * > locally_relevant_dofsSet   -std::vector< ConstraintMatrix * > constraintsSet2 -  -std::vector< ConstraintMatrix * > constraintsHangingNodesSet2 -  -std::vector< DoFHandler< dim > * > dofHandlersSet2 -  -std::vector< IndexSet * > locally_relevant_dofsSet2 -  +std::vector< ConstraintMatrix * > constraintsDirichletSet_nonconst +  +std::vector< ConstraintMatrix * > constraintsOtherSet_nonconst +  +std::vector< DoFHandler< dim > * > dofHandlersSet_nonconst +  +std::vector< IndexSet * > locally_relevant_dofsSet_nonconst +  std::vector< vectorType * > solutionSet   std::vector< vectorType * > residualSet @@ -775,9 +789,8 @@

Member Data Documentation

-
The documentation for this class was generated from the following files:
    +
    The documentation for this class was generated from the following file: diff --git a/html/class_coupled_c_h_a_c_problem-members.html b/html/class_coupled_c_h_a_c_problem-members.html index a8120b811..906808eed 100644 --- a/html/class_coupled_c_h_a_c_problem-members.html +++ b/html/class_coupled_c_h_a_c_problem-members.html @@ -101,17 +101,17 @@ computeInvM()MatrixFreePDE< dim >protected computeRHS()MatrixFreePDE< dim >protected computing_timerMatrixFreePDE< dim >mutableprotected - constraintsHangingNodesSetMatrixFreePDE< dim >protected - constraintsHangingNodesSet2MatrixFreePDE< dim >protected - constraintsSetMatrixFreePDE< dim >protected - constraintsSet2MatrixFreePDE< dim >protected + constraintsDirichletSetMatrixFreePDE< dim >protected + constraintsDirichletSet_nonconstMatrixFreePDE< dim >protected + constraintsOtherSetMatrixFreePDE< dim >protected + constraintsOtherSet_nonconstMatrixFreePDE< dim >protected CoupledCHACProblem()CoupledCHACProblem< dim > CoupledCHACProblem()CoupledCHACProblem< dim > currentFieldIndexMatrixFreePDE< dim >protected currentIncrementMatrixFreePDE< dim >protected currentTimeMatrixFreePDE< dim >protected dofHandlersSetMatrixFreePDE< dim >protected - dofHandlersSet2MatrixFreePDE< dim >protected + dofHandlersSet_nonconstMatrixFreePDE< dim >protected dtValueMatrixFreePDE< dim >protected dUMatrixFreePDE< dim >protected ellipticFieldIndexMatrixFreePDE< dim >protected @@ -121,9 +121,11 @@ fieldsMatrixFreePDE< dim > finalTimeMatrixFreePDE< dim >protected freeEnergyValuesMatrixFreePDE< dim >protected - getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)MatrixFreePDE< dim >protectedvirtual - getFieldIndex(std::string _name)MatrixFreePDE< dim >protected - getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const MatrixFreePDE< dim >protectedvirtual + getComponentsWithRigidBodyModes(std::vector< int > &)MatrixFreePDE< dim >protectedvirtual + getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)MatrixFreePDE< dim >protectedvirtual + getFieldIndex(std::string _name)MatrixFreePDE< dim >protected + getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const MatrixFreePDE< dim >protectedvirtual + getOutputTimeSteps(std::string outputSpacingType, unsigned int numberOfOutputs, std::vector< unsigned int > &userGivenTimeStepList, std::vector< unsigned int > &timeStepList)MatrixFreePDE< dim >protected getRHS(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range) const CoupledCHACProblem< dim >privatevirtual getRHS(std::map< std::string, typeScalar * > valsScalar, std::map< std::string, typeVector * > valsVector, unsigned int q) const CoupledCHACProblem< dim >private init(unsigned int iter=0)MatrixFreePDE< dim > @@ -132,7 +134,7 @@ isEllipticBVPMatrixFreePDE< dim >protected isTimeDependentBVPMatrixFreePDE< dim >protected locally_relevant_dofsSetMatrixFreePDE< dim >protected - locally_relevant_dofsSet2MatrixFreePDE< dim >protected + locally_relevant_dofsSet_nonconstMatrixFreePDE< dim >protected markBoundaries()MatrixFreePDE< dim >protectedvirtual matrixFreeObjectMatrixFreePDE< dim >protected MatrixFreePDE()MatrixFreePDE< dim > @@ -141,21 +143,26 @@ num_quadrature_pointsMatrixFreePDE< dim >protected outputFreeEnergy(std::vector< double > &freeEnergyValues)MatrixFreePDE< dim >protected outputResults()MatrixFreePDE< dim >protected - parabolicFieldIndexMatrixFreePDE< dim >protected - pcoutMatrixFreePDE< dim >protected - refineGrid()MatrixFreePDE< dim >protected - refineMesh(unsigned int _currentIncrement)MatrixFreePDE< dim >protected + outputTimeStepListMatrixFreePDE< dim >protected + parabolicFieldIndexMatrixFreePDE< dim >protected + pcoutMatrixFreePDE< dim >protected + refineGrid()MatrixFreePDE< dim >protected + refineMesh(unsigned int _currentIncrement)MatrixFreePDE< dim >protected + reinit(unsigned int iter=0)MatrixFreePDE< dim > residualSetMatrixFreePDE< dim >protected - shiftConcentration()MatrixFreePDE< dim > - soltransSetMatrixFreePDE< dim >protected - solutionSetMatrixFreePDE< dim >protected - solve()MatrixFreePDE< dim > - solveIncrement()MatrixFreePDE< dim >protected - totalIncrementsMatrixFreePDE< dim >protected - triangulationMatrixFreePDE< dim >protected - valuesDirichletSetMatrixFreePDE< dim >protected - vmult(vectorType &dst, const vectorType &src) const MatrixFreePDE< dim > - ~MatrixFreePDE()MatrixFreePDE< dim > + setPeriodicity()MatrixFreePDE< dim >protectedvirtual + setPeriodicityConstraints(ConstraintMatrix *, DoFHandler< dim > *)MatrixFreePDE< dim >protectedvirtual + setRigidBodyModeConstraints(std::vector< int >, ConstraintMatrix *, DoFHandler< dim > *)MatrixFreePDE< dim >protectedvirtual + shiftConcentration()MatrixFreePDE< dim > + soltransSetMatrixFreePDE< dim >protected + solutionSetMatrixFreePDE< dim >protected + solve()MatrixFreePDE< dim > + solveIncrement()MatrixFreePDE< dim >protected + totalIncrementsMatrixFreePDE< dim >protected + triangulationMatrixFreePDE< dim >protected + valuesDirichletSetMatrixFreePDE< dim >protected + vmult(vectorType &dst, const vectorType &src) const MatrixFreePDE< dim > + ~MatrixFreePDE()MatrixFreePDE< dim >
    diff --git a/html/class_coupled_c_h_a_c_problem.html b/html/class_coupled_c_h_a_c_problem.html index 5d8125bf1..4d1448544 100644 --- a/html/class_coupled_c_h_a_c_problem.html +++ b/html/class_coupled_c_h_a_c_problem.html @@ -118,6 +118,8 @@   void init (unsigned int iter=0)   +void reinit (unsigned int iter=0) +  void initForTests ()   void solve () @@ -152,6 +154,8 @@   void outputResults ()   +void getOutputTimeSteps (std::string outputSpacingType, unsigned int numberOfOutputs, std::vector< unsigned int > &userGivenTimeStepList, std::vector< unsigned int > &timeStepList) +  void computeInvM ()   void computeRHS () @@ -170,6 +174,14 @@   virtual void applyDirichletBCs ()   +virtual void setPeriodicity () +  +virtual void setPeriodicityConstraints (ConstraintMatrix *, DoFHandler< dim > *) +  +virtual void getComponentsWithRigidBodyModes (std::vector< int > &) +  +virtual void setRigidBodyModeConstraints (std::vector< int >, ConstraintMatrix *, DoFHandler< dim > *) +  void computeEnergy ()   virtual void getEnergy (const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range) @@ -181,26 +193,28 @@ void computeIntegral (double &integratedField)   - Protected Attributes inherited from MatrixFreePDE< dim > +std::vector< unsigned int > outputTimeStepList +  parallel::distributed::Triangulation< dim > triangulation   std::vector< FESystem< dim > * > FESet   -std::vector< const ConstraintMatrix * > constraintsSet -  -std::vector< const ConstraintMatrix * > constraintsHangingNodesSet -  +std::vector< const ConstraintMatrix * > constraintsDirichletSet +  +std::vector< const ConstraintMatrix * > constraintsOtherSet +  std::vector< const DoFHandler< dim > * > dofHandlersSet   std::vector< const IndexSet * > locally_relevant_dofsSet   -std::vector< ConstraintMatrix * > constraintsSet2 -  -std::vector< ConstraintMatrix * > constraintsHangingNodesSet2 -  -std::vector< DoFHandler< dim > * > dofHandlersSet2 -  -std::vector< IndexSet * > locally_relevant_dofsSet2 -  +std::vector< ConstraintMatrix * > constraintsDirichletSet_nonconst +  +std::vector< ConstraintMatrix * > constraintsOtherSet_nonconst +  +std::vector< DoFHandler< dim > * > dofHandlersSet_nonconst +  +std::vector< IndexSet * > locally_relevant_dofsSet_nonconst +  std::vector< vectorType * > solutionSet   std::vector< vectorType * > residualSet diff --git a/html/class_coupled_cahn_hilliard_mechanics_problem-members.html b/html/class_coupled_cahn_hilliard_mechanics_problem-members.html index b8b3d15a3..1be5fb76c 100644 --- a/html/class_coupled_cahn_hilliard_mechanics_problem-members.html +++ b/html/class_coupled_cahn_hilliard_mechanics_problem-members.html @@ -102,16 +102,16 @@ computeInvM()MatrixFreePDE< dim >protected computeRHS()MatrixFreePDE< dim >protected computing_timerMatrixFreePDE< dim >mutableprotected - constraintsHangingNodesSetMatrixFreePDE< dim >protected - constraintsHangingNodesSet2MatrixFreePDE< dim >protected - constraintsSetMatrixFreePDE< dim >protected - constraintsSet2MatrixFreePDE< dim >protected + constraintsDirichletSetMatrixFreePDE< dim >protected + constraintsDirichletSet_nonconstMatrixFreePDE< dim >protected + constraintsOtherSetMatrixFreePDE< dim >protected + constraintsOtherSet_nonconstMatrixFreePDE< dim >protected CoupledCahnHilliardMechanicsProblem()CoupledCahnHilliardMechanicsProblem< dim > currentFieldIndexMatrixFreePDE< dim >protected currentIncrementMatrixFreePDE< dim >protected currentTimeMatrixFreePDE< dim >protected dofHandlersSetMatrixFreePDE< dim >protected - dofHandlersSet2MatrixFreePDE< dim >protected + dofHandlersSet_nonconstMatrixFreePDE< dim >protected dtValueMatrixFreePDE< dim >protected dUMatrixFreePDE< dim >protected ellipticFieldIndexMatrixFreePDE< dim >protected @@ -121,9 +121,11 @@ fieldsMatrixFreePDE< dim > finalTimeMatrixFreePDE< dim >protected freeEnergyValuesMatrixFreePDE< dim >protected - getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)CoupledCahnHilliardMechanicsProblem< dim >privatevirtual - getFieldIndex(std::string _name)MatrixFreePDE< dim >protected - getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const CoupledCahnHilliardMechanicsProblem< dim >privatevirtual + getComponentsWithRigidBodyModes(std::vector< int > &)MatrixFreePDE< dim >protectedvirtual + getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)CoupledCahnHilliardMechanicsProblem< dim >privatevirtual + getFieldIndex(std::string _name)MatrixFreePDE< dim >protected + getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const CoupledCahnHilliardMechanicsProblem< dim >privatevirtual + getOutputTimeSteps(std::string outputSpacingType, unsigned int numberOfOutputs, std::vector< unsigned int > &userGivenTimeStepList, std::vector< unsigned int > &timeStepList)MatrixFreePDE< dim >protected getRHS(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range) const CoupledCahnHilliardMechanicsProblem< dim >privatevirtual init(unsigned int iter=0)MatrixFreePDE< dim > initForTests()MatrixFreePDE< dim > @@ -131,7 +133,7 @@ isEllipticBVPMatrixFreePDE< dim >protected isTimeDependentBVPMatrixFreePDE< dim >protected locally_relevant_dofsSetMatrixFreePDE< dim >protected - locally_relevant_dofsSet2MatrixFreePDE< dim >protected + locally_relevant_dofsSet_nonconstMatrixFreePDE< dim >protected markBoundaries()MatrixFreePDE< dim >protectedvirtual matrixFreeObjectMatrixFreePDE< dim >protected MatrixFreePDE()MatrixFreePDE< dim > @@ -139,21 +141,26 @@ num_quadrature_pointsMatrixFreePDE< dim >protected outputFreeEnergy(std::vector< double > &freeEnergyValues)MatrixFreePDE< dim >protected outputResults()MatrixFreePDE< dim >protected - parabolicFieldIndexMatrixFreePDE< dim >protected - pcoutMatrixFreePDE< dim >protected - refineGrid()MatrixFreePDE< dim >protected - refineMesh(unsigned int _currentIncrement)MatrixFreePDE< dim >protected + outputTimeStepListMatrixFreePDE< dim >protected + parabolicFieldIndexMatrixFreePDE< dim >protected + pcoutMatrixFreePDE< dim >protected + refineGrid()MatrixFreePDE< dim >protected + refineMesh(unsigned int _currentIncrement)MatrixFreePDE< dim >protected + reinit(unsigned int iter=0)MatrixFreePDE< dim > residualSetMatrixFreePDE< dim >protected - shiftConcentration()MatrixFreePDE< dim > - soltransSetMatrixFreePDE< dim >protected - solutionSetMatrixFreePDE< dim >protected - solve()MatrixFreePDE< dim > - solveIncrement()MatrixFreePDE< dim >protected - totalIncrementsMatrixFreePDE< dim >protected - triangulationMatrixFreePDE< dim >protected - valuesDirichletSetMatrixFreePDE< dim >protected - vmult(vectorType &dst, const vectorType &src) const MatrixFreePDE< dim > - ~MatrixFreePDE()MatrixFreePDE< dim > + setPeriodicity()MatrixFreePDE< dim >protectedvirtual + setPeriodicityConstraints(ConstraintMatrix *, DoFHandler< dim > *)MatrixFreePDE< dim >protectedvirtual + setRigidBodyModeConstraints(std::vector< int >, ConstraintMatrix *, DoFHandler< dim > *)MatrixFreePDE< dim >protectedvirtual + shiftConcentration()MatrixFreePDE< dim > + soltransSetMatrixFreePDE< dim >protected + solutionSetMatrixFreePDE< dim >protected + solve()MatrixFreePDE< dim > + solveIncrement()MatrixFreePDE< dim >protected + totalIncrementsMatrixFreePDE< dim >protected + triangulationMatrixFreePDE< dim >protected + valuesDirichletSetMatrixFreePDE< dim >protected + vmult(vectorType &dst, const vectorType &src) const MatrixFreePDE< dim > + ~MatrixFreePDE()MatrixFreePDE< dim >
    diff --git a/html/class_coupled_cahn_hilliard_mechanics_problem.html b/html/class_coupled_cahn_hilliard_mechanics_problem.html index ded240a51..c91393ab8 100644 --- a/html/class_coupled_cahn_hilliard_mechanics_problem.html +++ b/html/class_coupled_cahn_hilliard_mechanics_problem.html @@ -116,6 +116,8 @@   void init (unsigned int iter=0)   +void reinit (unsigned int iter=0) +  void initForTests ()   void solve () @@ -155,6 +157,8 @@   void outputResults ()   +void getOutputTimeSteps (std::string outputSpacingType, unsigned int numberOfOutputs, std::vector< unsigned int > &userGivenTimeStepList, std::vector< unsigned int > &timeStepList) +  void computeInvM ()   void computeRHS () @@ -169,6 +173,14 @@   virtual void markBoundaries ()   +virtual void setPeriodicity () +  +virtual void setPeriodicityConstraints (ConstraintMatrix *, DoFHandler< dim > *) +  +virtual void getComponentsWithRigidBodyModes (std::vector< int > &) +  +virtual void setRigidBodyModeConstraints (std::vector< int >, ConstraintMatrix *, DoFHandler< dim > *) +  virtual void modifySolutionFields ()   void computeEnergy () @@ -180,26 +192,28 @@ void computeIntegral (double &integratedField)   - Protected Attributes inherited from MatrixFreePDE< dim > +std::vector< unsigned int > outputTimeStepList +  parallel::distributed::Triangulation< dim > triangulation   std::vector< FESystem< dim > * > FESet   -std::vector< const ConstraintMatrix * > constraintsSet -  -std::vector< const ConstraintMatrix * > constraintsHangingNodesSet -  +std::vector< const ConstraintMatrix * > constraintsDirichletSet +  +std::vector< const ConstraintMatrix * > constraintsOtherSet +  std::vector< const DoFHandler< dim > * > dofHandlersSet   std::vector< const IndexSet * > locally_relevant_dofsSet   -std::vector< ConstraintMatrix * > constraintsSet2 -  -std::vector< ConstraintMatrix * > constraintsHangingNodesSet2 -  -std::vector< DoFHandler< dim > * > dofHandlersSet2 -  -std::vector< IndexSet * > locally_relevant_dofsSet2 -  +std::vector< ConstraintMatrix * > constraintsDirichletSet_nonconst +  +std::vector< ConstraintMatrix * > constraintsOtherSet_nonconst +  +std::vector< DoFHandler< dim > * > dofHandlersSet_nonconst +  +std::vector< IndexSet * > locally_relevant_dofsSet_nonconst +  std::vector< vectorType * > solutionSet   std::vector< vectorType * > residualSet diff --git a/html/class_fickian_problem-members.html b/html/class_fickian_problem-members.html index 517f35a62..11dbff07d 100644 --- a/html/class_fickian_problem-members.html +++ b/html/class_fickian_problem-members.html @@ -100,15 +100,15 @@ computeInvM()MatrixFreePDE< dim >protected computeRHS()MatrixFreePDE< dim >protected computing_timerMatrixFreePDE< dim >mutableprotected - constraintsHangingNodesSetMatrixFreePDE< dim >protected - constraintsHangingNodesSet2MatrixFreePDE< dim >protected - constraintsSetMatrixFreePDE< dim >protected - constraintsSet2MatrixFreePDE< dim >protected + constraintsDirichletSetMatrixFreePDE< dim >protected + constraintsDirichletSet_nonconstMatrixFreePDE< dim >protected + constraintsOtherSetMatrixFreePDE< dim >protected + constraintsOtherSet_nonconstMatrixFreePDE< dim >protected currentFieldIndexMatrixFreePDE< dim >protected currentIncrementMatrixFreePDE< dim >protected currentTimeMatrixFreePDE< dim >protected dofHandlersSetMatrixFreePDE< dim >protected - dofHandlersSet2MatrixFreePDE< dim >protected + dofHandlersSet_nonconstMatrixFreePDE< dim >protected dtValueMatrixFreePDE< dim >protected dUMatrixFreePDE< dim >protected ellipticFieldIndexMatrixFreePDE< dim >protected @@ -119,9 +119,11 @@ fieldsMatrixFreePDE< dim > finalTimeMatrixFreePDE< dim >protected freeEnergyValuesMatrixFreePDE< dim >protected - getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)MatrixFreePDE< dim >protectedvirtual - getFieldIndex(std::string _name)MatrixFreePDE< dim >protected - getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const MatrixFreePDE< dim >protectedvirtual + getComponentsWithRigidBodyModes(std::vector< int > &)MatrixFreePDE< dim >protectedvirtual + getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)MatrixFreePDE< dim >protectedvirtual + getFieldIndex(std::string _name)MatrixFreePDE< dim >protected + getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const MatrixFreePDE< dim >protectedvirtual + getOutputTimeSteps(std::string outputSpacingType, unsigned int numberOfOutputs, std::vector< unsigned int > &userGivenTimeStepList, std::vector< unsigned int > &timeStepList)MatrixFreePDE< dim >protected getRHS(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range) const FickianProblem< dim >privatevirtual init(unsigned int iter=0)MatrixFreePDE< dim > initForTests()MatrixFreePDE< dim > @@ -129,7 +131,7 @@ isEllipticBVPMatrixFreePDE< dim >protected isTimeDependentBVPMatrixFreePDE< dim >protected locally_relevant_dofsSetMatrixFreePDE< dim >protected - locally_relevant_dofsSet2MatrixFreePDE< dim >protected + locally_relevant_dofsSet_nonconstMatrixFreePDE< dim >protected markBoundaries()MatrixFreePDE< dim >protectedvirtual matrixFreeObjectMatrixFreePDE< dim >protected MatrixFreePDE()MatrixFreePDE< dim > @@ -137,21 +139,26 @@ num_quadrature_pointsMatrixFreePDE< dim >protected outputFreeEnergy(std::vector< double > &freeEnergyValues)MatrixFreePDE< dim >protected outputResults()MatrixFreePDE< dim >protected - parabolicFieldIndexMatrixFreePDE< dim >protected - pcoutMatrixFreePDE< dim >protected - refineGrid()MatrixFreePDE< dim >protected - refineMesh(unsigned int _currentIncrement)MatrixFreePDE< dim >protected + outputTimeStepListMatrixFreePDE< dim >protected + parabolicFieldIndexMatrixFreePDE< dim >protected + pcoutMatrixFreePDE< dim >protected + refineGrid()MatrixFreePDE< dim >protected + refineMesh(unsigned int _currentIncrement)MatrixFreePDE< dim >protected + reinit(unsigned int iter=0)MatrixFreePDE< dim > residualSetMatrixFreePDE< dim >protected - shiftConcentration()MatrixFreePDE< dim > - soltransSetMatrixFreePDE< dim >protected - solutionSetMatrixFreePDE< dim >protected - solve()MatrixFreePDE< dim > - solveIncrement()MatrixFreePDE< dim >protected - totalIncrementsMatrixFreePDE< dim >protected - triangulationMatrixFreePDE< dim >protected - valuesDirichletSetMatrixFreePDE< dim >protected - vmult(vectorType &dst, const vectorType &src) const MatrixFreePDE< dim > - ~MatrixFreePDE()MatrixFreePDE< dim > + setPeriodicity()MatrixFreePDE< dim >protectedvirtual + setPeriodicityConstraints(ConstraintMatrix *, DoFHandler< dim > *)MatrixFreePDE< dim >protectedvirtual + setRigidBodyModeConstraints(std::vector< int >, ConstraintMatrix *, DoFHandler< dim > *)MatrixFreePDE< dim >protectedvirtual + shiftConcentration()MatrixFreePDE< dim > + soltransSetMatrixFreePDE< dim >protected + solutionSetMatrixFreePDE< dim >protected + solve()MatrixFreePDE< dim > + solveIncrement()MatrixFreePDE< dim >protected + totalIncrementsMatrixFreePDE< dim >protected + triangulationMatrixFreePDE< dim >protected + valuesDirichletSetMatrixFreePDE< dim >protected + vmult(vectorType &dst, const vectorType &src) const MatrixFreePDE< dim > + ~MatrixFreePDE()MatrixFreePDE< dim >
    diff --git a/html/class_fickian_problem.html b/html/class_fickian_problem.html index ff939940a..085fbc4ed 100644 --- a/html/class_fickian_problem.html +++ b/html/class_fickian_problem.html @@ -115,6 +115,8 @@   void init (unsigned int iter=0)   +void reinit (unsigned int iter=0) +  void initForTests ()   void solve () @@ -141,6 +143,8 @@   void outputResults ()   +void getOutputTimeSteps (std::string outputSpacingType, unsigned int numberOfOutputs, std::vector< unsigned int > &userGivenTimeStepList, std::vector< unsigned int > &timeStepList) +  void computeInvM ()   void computeRHS () @@ -159,6 +163,14 @@   virtual void applyDirichletBCs ()   +virtual void setPeriodicity () +  +virtual void setPeriodicityConstraints (ConstraintMatrix *, DoFHandler< dim > *) +  +virtual void getComponentsWithRigidBodyModes (std::vector< int > &) +  +virtual void setRigidBodyModeConstraints (std::vector< int >, ConstraintMatrix *, DoFHandler< dim > *) +  virtual void modifySolutionFields ()   void computeEnergy () @@ -172,26 +184,28 @@ void computeIntegral (double &integratedField)   - Protected Attributes inherited from MatrixFreePDE< dim > +std::vector< unsigned int > outputTimeStepList +  parallel::distributed::Triangulation< dim > triangulation   std::vector< FESystem< dim > * > FESet   -std::vector< const ConstraintMatrix * > constraintsSet -  -std::vector< const ConstraintMatrix * > constraintsHangingNodesSet -  +std::vector< const ConstraintMatrix * > constraintsDirichletSet +  +std::vector< const ConstraintMatrix * > constraintsOtherSet +  std::vector< const DoFHandler< dim > * > dofHandlersSet   std::vector< const IndexSet * > locally_relevant_dofsSet   -std::vector< ConstraintMatrix * > constraintsSet2 -  -std::vector< ConstraintMatrix * > constraintsHangingNodesSet2 -  -std::vector< DoFHandler< dim > * > dofHandlersSet2 -  -std::vector< IndexSet * > locally_relevant_dofsSet2 -  +std::vector< ConstraintMatrix * > constraintsDirichletSet_nonconst +  +std::vector< ConstraintMatrix * > constraintsOtherSet_nonconst +  +std::vector< DoFHandler< dim > * > dofHandlersSet_nonconst +  +std::vector< IndexSet * > locally_relevant_dofsSet_nonconst +  std::vector< vectorType * > solutionSet   std::vector< vectorType * > residualSet diff --git a/html/class_initial_condition-members.html b/html/class_initial_condition-members.html index a68389fea..0424c4adf 100644 --- a/html/class_initial_condition-members.html +++ b/html/class_initial_condition-members.html @@ -103,6 +103,16 @@ InitialCondition(const unsigned int _index)InitialCondition< dim >inline InitialCondition(const unsigned int _index)InitialCondition< dim >inline InitialCondition(const unsigned int _index)InitialCondition< dim >inline + InitialCondition(const unsigned int _index)InitialCondition< dim >inline + InitialCondition(const unsigned int _index)InitialCondition< dim >inline + InitialCondition(const unsigned int _index)InitialCondition< dim >inline + InitialCondition(const unsigned int _index)InitialCondition< dim >inline + InitialCondition(const unsigned int _index)InitialCondition< dim >inline + value(const Point< dim > &p, const unsigned int component=0) const InitialCondition< dim >inline + value(const Point< dim > &p, const unsigned int component=0) const InitialCondition< dim >inline + value(const Point< dim > &p, const unsigned int component=0) const InitialCondition< dim >inline + value(const Point< dim > &p, const unsigned int component=0) const InitialCondition< dim >inline + value(const Point< dim > &p, const unsigned int component=0) const InitialCondition< dim >inline value(const Point< dim > &p, const unsigned int component=0) const InitialCondition< dim >inline value(const Point< dim > &p, const unsigned int component=0) const InitialCondition< dim >inline value(const Point< dim > &p, const unsigned int component=0) const InitialCondition< dim >inline diff --git a/html/class_initial_condition.html b/html/class_initial_condition.html index 085620989..aca1ae19b 100644 --- a/html/class_initial_condition.html +++ b/html/class_initial_condition.html @@ -149,6 +149,26 @@   double value (const Point< dim > &p, const unsigned int component=0) const   + InitialCondition (const unsigned int _index) +  +double value (const Point< dim > &p, const unsigned int component=0) const +  + InitialCondition (const unsigned int _index) +  +double value (const Point< dim > &p, const unsigned int component=0) const +  + InitialCondition (const unsigned int _index) +  +double value (const Point< dim > &p, const unsigned int component=0) const +  + InitialCondition (const unsigned int _index) +  +double value (const Point< dim > &p, const unsigned int component=0) const +  + InitialCondition (const unsigned int _index) +  +double value (const Point< dim > &p, const unsigned int component=0) const +  @@ -420,6 +440,126 @@

    Constructor & Destructor Documentation

    Public Attributes

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    InitialCondition (const unsigned int _index)
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    InitialCondition (const unsigned int _index)
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    Member Function Documentation

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    Member Function Documentation

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    double value (const Point< dim > & p,
    const unsigned int component = 0 
    ) const
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    +
    + + + + + +
    + + + + + + + + + + + + + + + + + + +
    double value (const Point< dim > & p,
    const unsigned int component = 0 
    ) const
    +     		+inline
    +
    + +
    +
    + +
    +
    + + + + + +
    + + + + + + + + + + + + + + + + + + +
    double value (const Point< dim > & p,
    const unsigned int component = 0 
    ) const
    +     		+inline
    +
    + +
    +
    + +
    +
    + + + + + +
    + + + + + + + + + + + + + + + + + + +
    double value (const Point< dim > & p,
    const unsigned int component = 0 
    ) const
    +     		+inline
    +
    + +
    +
    + +
    +
    + + + + + +
    + + + + + + + + + + + + + + + + + + +
    double value (const Point< dim > & p,
    const unsigned int component = 0 
    ) const
    +     		+inline
    +
    +

    Member Data Documentation

    diff --git a/html/class_initial_condition.png b/html/class_initial_condition.png index eae70da04..88ccafa6f 100644 Binary files a/html/class_initial_condition.png and b/html/class_initial_condition.png differ diff --git a/html/class_initial_condition_c-members.html b/html/class_initial_condition_c-members.html index 14824d80b..7d4386da2 100644 --- a/html/class_initial_condition_c-members.html +++ b/html/class_initial_condition_c-members.html @@ -92,8 +92,6 @@

    This is the complete list of members for InitialConditionC< dim >, including all inherited members.

    - -
    InitialConditionC()InitialConditionC< dim >inline
    InitialConditionC()InitialConditionC< dim >inline
    value(const Point< dim > &p, const unsigned int component=0) const InitialConditionC< dim >inline
    value(const Point< dim > &p, const unsigned int component=0) const InitialConditionC< dim >inline
    diff --git a/html/class_initial_condition_c.html b/html/class_initial_condition_c.html index a112026be..25238f6d3 100644 --- a/html/class_initial_condition_c.html +++ b/html/class_initial_condition_c.html @@ -108,10 +108,6 @@   double value (const Point< dim > &p, const unsigned int component=0) const   - InitialConditionC () -  -double value (const Point< dim > &p, const unsigned int component=0) const - 

    Constructor & Destructor Documentation

    @@ -135,29 +131,6 @@

    Constructor & Destructor Documentation

    -
    - - -
    -
    - - - - - -
    - - - - - - - -
    InitialConditionC ()
    -     		-inline
    -
    -

    Member Function Documentation

    @@ -193,40 +166,6 @@

    Member Function Documentation

    -
    - - -
    -
    - - - - - -
    - - - - - - - - - - - - - - - - - - -
    double value (const Point< dim > & p,
    const unsigned int component = 0 
    ) const
    -     		-inline
    -
    -
    The documentation for this class was generated from the following file:
      diff --git a/html/class_initial_condition_c.png b/html/class_initial_condition_c.png index 4f67a0680..ded6133d3 100644 Binary files a/html/class_initial_condition_c.png and b/html/class_initial_condition_c.png differ diff --git a/html/class_initial_condition_vec-members.html b/html/class_initial_condition_vec-members.html index aca4d48ee..4b8373a4f 100644 --- a/html/class_initial_condition_vec-members.html +++ b/html/class_initial_condition_vec-members.html @@ -103,6 +103,16 @@ InitialConditionVec(const unsigned int _index)InitialConditionVec< dim >inline InitialConditionVec(const unsigned int _index)InitialConditionVec< dim >inline InitialConditionVec(const unsigned int _index)InitialConditionVec< dim >inline + InitialConditionVec(const unsigned int _index)InitialConditionVec< dim >inline + InitialConditionVec(const unsigned int _index)InitialConditionVec< dim >inline + InitialConditionVec(const unsigned int _index)InitialConditionVec< dim >inline + InitialConditionVec(const unsigned int _index)InitialConditionVec< dim >inline + InitialConditionVec(const unsigned int _index)InitialConditionVec< dim >inline + vector_value(const Point< dim > &p, Vector< double > &vector_IC) const InitialConditionVec< dim >inline + vector_value(const Point< dim > &p, Vector< double > &vector_IC) const InitialConditionVec< dim >inline + vector_value(const Point< dim > &p, Vector< double > &vector_IC) const InitialConditionVec< dim >inline + vector_value(const Point< dim > &p, Vector< double > &vector_IC) const InitialConditionVec< dim >inline + vector_value(const Point< dim > &p, Vector< double > &vector_IC) const InitialConditionVec< dim >inline vector_value(const Point< dim > &p, Vector< double > &vector_IC) const InitialConditionVec< dim >inline vector_value(const Point< dim > &p, Vector< double > &vector_IC) const InitialConditionVec< dim >inline vector_value(const Point< dim > &p, Vector< double > &vector_IC) const InitialConditionVec< dim >inline diff --git a/html/class_initial_condition_vec.html b/html/class_initial_condition_vec.html index 73ba16a65..b258d1c4d 100644 --- a/html/class_initial_condition_vec.html +++ b/html/class_initial_condition_vec.html @@ -149,6 +149,26 @@   void vector_value (const Point< dim > &p, Vector< double > &vector_IC) const   + InitialConditionVec (const unsigned int _index) +  +void vector_value (const Point< dim > &p, Vector< double > &vector_IC) const +  + InitialConditionVec (const unsigned int _index) +  +void vector_value (const Point< dim > &p, Vector< double > &vector_IC) const +  + InitialConditionVec (const unsigned int _index) +  +void vector_value (const Point< dim > &p, Vector< double > &vector_IC) const +  + InitialConditionVec (const unsigned int _index) +  +void vector_value (const Point< dim > &p, Vector< double > &vector_IC) const +  + InitialConditionVec (const unsigned int _index) +  +void vector_value (const Point< dim > &p, Vector< double > &vector_IC) const +  @@ -418,6 +438,126 @@

      Constructor & Destructor Documentation

      Public Attributes

      +
      + + +
      +
      + + + + + +
      + + + + + + + + +
      InitialConditionVec (const unsigned int _index)
      +       		+inline
      +
      + +
      +
      + +
      +
      + + + + + +
      + + + + + + + + +
      InitialConditionVec (const unsigned int _index)
      +       		+inline
      +
      + +
      +
      + +
      +
      + + + + + +
      + + + + + + + + +
      InitialConditionVec (const unsigned int _index)
      +       		+inline
      +
      + +
      +
      + +
      +
      + + + + + +
      + + + + + + + + +
      InitialConditionVec (const unsigned int _index)
      +       		+inline
      +
      + +
      +
      + +
      +
      + + + + + +
      + + + + + + + + +
      InitialConditionVec (const unsigned int _index)
      +       		+inline
      +
      +

      Member Function Documentation

      @@ -793,6 +933,176 @@

      Member Function Documentation

      +
      + + +
      +
      + + + + + +
      + + + + + + + + + + + + + + + + + + +
      void vector_value (const Point< dim > & p,
      Vector< double > & vector_IC 
      ) const
      +       		+inline
      +
      + +
      +
      + +
      +
      + + + + + +
      + + + + + + + + + + + + + + + + + + +
      void vector_value (const Point< dim > & p,
      Vector< double > & vector_IC 
      ) const
      +       		+inline
      +
      + +
      +
      + +
      +
      + + + + + +
      + + + + + + + + + + + + + + + + + + +
      void vector_value (const Point< dim > & p,
      Vector< double > & vector_IC 
      ) const
      +       		+inline
      +
      + +
      +
      + +
      +
      + + + + + +
      + + + + + + + + + + + + + + + + + + +
      void vector_value (const Point< dim > & p,
      Vector< double > & vector_IC 
      ) const
      +       		+inline
      +
      + +
      +
      + +
      +
      + + + + + +
      + + + + + + + + + + + + + + + + + + +
      void vector_value (const Point< dim > & p,
      Vector< double > & vector_IC 
      ) const
      +       		+inline
      +
      +

      Member Data Documentation

      diff --git a/html/class_initial_condition_vec.png b/html/class_initial_condition_vec.png index 502187c70..71959ff21 100644 Binary files a/html/class_initial_condition_vec.png and b/html/class_initial_condition_vec.png differ diff --git a/html/class_matrix_free_p_d_e-members.html b/html/class_matrix_free_p_d_e-members.html index fb6f70c86..4f5975fbb 100644 --- a/html/class_matrix_free_p_d_e-members.html +++ b/html/class_matrix_free_p_d_e-members.html @@ -100,15 +100,15 @@ computeInvM()MatrixFreePDE< dim >protected computeRHS()MatrixFreePDE< dim >protected computing_timerMatrixFreePDE< dim >mutableprotected - constraintsHangingNodesSetMatrixFreePDE< dim >protected - constraintsHangingNodesSet2MatrixFreePDE< dim >protected - constraintsSetMatrixFreePDE< dim >protected - constraintsSet2MatrixFreePDE< dim >protected + constraintsDirichletSetMatrixFreePDE< dim >protected + constraintsDirichletSet_nonconstMatrixFreePDE< dim >protected + constraintsOtherSetMatrixFreePDE< dim >protected + constraintsOtherSet_nonconstMatrixFreePDE< dim >protected currentFieldIndexMatrixFreePDE< dim >protected currentIncrementMatrixFreePDE< dim >protected currentTimeMatrixFreePDE< dim >protected dofHandlersSetMatrixFreePDE< dim >protected - dofHandlersSet2MatrixFreePDE< dim >protected + dofHandlersSet_nonconstMatrixFreePDE< dim >protected dtValueMatrixFreePDE< dim >protected dUMatrixFreePDE< dim >protected ellipticFieldIndexMatrixFreePDE< dim >protected @@ -118,9 +118,11 @@ fieldsMatrixFreePDE< dim > finalTimeMatrixFreePDE< dim >protected freeEnergyValuesMatrixFreePDE< dim >protected - getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)MatrixFreePDE< dim >protectedvirtual - getFieldIndex(std::string _name)MatrixFreePDE< dim >protected - getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const MatrixFreePDE< dim >protectedvirtual + getComponentsWithRigidBodyModes(std::vector< int > &)MatrixFreePDE< dim >protectedvirtual + getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)MatrixFreePDE< dim >protectedvirtual + getFieldIndex(std::string _name)MatrixFreePDE< dim >protected + getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const MatrixFreePDE< dim >protectedvirtual + getOutputTimeSteps(std::string outputSpacingType, unsigned int numberOfOutputs, std::vector< unsigned int > &userGivenTimeStepList, std::vector< unsigned int > &timeStepList)MatrixFreePDE< dim >protected getRHS(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range) const =0MatrixFreePDE< dim >protectedpure virtual init(unsigned int iter=0)MatrixFreePDE< dim > initForTests()MatrixFreePDE< dim > @@ -128,7 +130,7 @@ isEllipticBVPMatrixFreePDE< dim >protected isTimeDependentBVPMatrixFreePDE< dim >protected locally_relevant_dofsSetMatrixFreePDE< dim >protected - locally_relevant_dofsSet2MatrixFreePDE< dim >protected + locally_relevant_dofsSet_nonconstMatrixFreePDE< dim >protected markBoundaries()MatrixFreePDE< dim >protectedvirtual matrixFreeObjectMatrixFreePDE< dim >protected MatrixFreePDE()MatrixFreePDE< dim > @@ -136,21 +138,26 @@ num_quadrature_pointsMatrixFreePDE< dim >protected outputFreeEnergy(std::vector< double > &freeEnergyValues)MatrixFreePDE< dim >protected outputResults()MatrixFreePDE< dim >protected - parabolicFieldIndexMatrixFreePDE< dim >protected - pcoutMatrixFreePDE< dim >protected - refineGrid()MatrixFreePDE< dim >protected - refineMesh(unsigned int _currentIncrement)MatrixFreePDE< dim >protected + outputTimeStepListMatrixFreePDE< dim >protected + parabolicFieldIndexMatrixFreePDE< dim >protected + pcoutMatrixFreePDE< dim >protected + refineGrid()MatrixFreePDE< dim >protected + refineMesh(unsigned int _currentIncrement)MatrixFreePDE< dim >protected + reinit(unsigned int iter=0)MatrixFreePDE< dim > residualSetMatrixFreePDE< dim >protected - shiftConcentration()MatrixFreePDE< dim > - soltransSetMatrixFreePDE< dim >protected - solutionSetMatrixFreePDE< dim >protected - solve()MatrixFreePDE< dim > - solveIncrement()MatrixFreePDE< dim >protected - totalIncrementsMatrixFreePDE< dim >protected - triangulationMatrixFreePDE< dim >protected - valuesDirichletSetMatrixFreePDE< dim >protected - vmult(vectorType &dst, const vectorType &src) const MatrixFreePDE< dim > - ~MatrixFreePDE()MatrixFreePDE< dim > + setPeriodicity()MatrixFreePDE< dim >protectedvirtual + setPeriodicityConstraints(ConstraintMatrix *, DoFHandler< dim > *)MatrixFreePDE< dim >protectedvirtual + setRigidBodyModeConstraints(std::vector< int >, ConstraintMatrix *, DoFHandler< dim > *)MatrixFreePDE< dim >protectedvirtual + shiftConcentration()MatrixFreePDE< dim > + soltransSetMatrixFreePDE< dim >protected + solutionSetMatrixFreePDE< dim >protected + solve()MatrixFreePDE< dim > + solveIncrement()MatrixFreePDE< dim >protected + totalIncrementsMatrixFreePDE< dim >protected + triangulationMatrixFreePDE< dim >protected + valuesDirichletSetMatrixFreePDE< dim >protected + vmult(vectorType &dst, const vectorType &src) const MatrixFreePDE< dim > + ~MatrixFreePDE()MatrixFreePDE< dim >
      diff --git a/html/class_matrix_free_p_d_e.html b/html/class_matrix_free_p_d_e.html index f6bd68374..792c6449c 100644 --- a/html/class_matrix_free_p_d_e.html +++ b/html/class_matrix_free_p_d_e.html @@ -122,6 +122,8 @@   void init (unsigned int iter=0)   +void reinit (unsigned int iter=0) +  void initForTests ()   void solve () @@ -142,6 +144,8 @@   void outputResults ()   +void getOutputTimeSteps (std::string outputSpacingType, unsigned int numberOfOutputs, std::vector< unsigned int > &userGivenTimeStepList, std::vector< unsigned int > &timeStepList) +  void computeInvM ()   void computeRHS () @@ -162,6 +166,14 @@   virtual void applyDirichletBCs ()   +virtual void setPeriodicity () +  +virtual void setPeriodicityConstraints (ConstraintMatrix *, DoFHandler< dim > *) +  +virtual void getComponentsWithRigidBodyModes (std::vector< int > &) +  +virtual void setRigidBodyModeConstraints (std::vector< int >, ConstraintMatrix *, DoFHandler< dim > *) +  virtual void applyInitialConditions ()   virtual void modifySolutionFields () @@ -179,26 +191,28 @@ + + - - - - + + + + - - - - - - - - + + + + + + + + @@ -477,6 +491,32 @@

      Member Function Documentation

      Protected Attributes

      std::vector< unsigned int > outputTimeStepList
       
      parallel::distributed::Triangulation< dim > triangulation
       
      std::vector< FESystem< dim > * > FESet
       
      std::vector< const ConstraintMatrix * > constraintsSet
       
      std::vector< const ConstraintMatrix * > constraintsHangingNodesSet
       
      std::vector< const ConstraintMatrix * > constraintsDirichletSet
       
      std::vector< const ConstraintMatrix * > constraintsOtherSet
       
      std::vector< const DoFHandler< dim > * > dofHandlersSet
       
      std::vector< const IndexSet * > locally_relevant_dofsSet
       
      std::vector< ConstraintMatrix * > constraintsSet2
       
      std::vector< ConstraintMatrix * > constraintsHangingNodesSet2
       
      std::vector< DoFHandler< dim > * > dofHandlersSet2
       
      std::vector< IndexSet * > locally_relevant_dofsSet2
       
      std::vector< ConstraintMatrix * > constraintsDirichletSet_nonconst
       
      std::vector< ConstraintMatrix * > constraintsOtherSet_nonconst
       
      std::vector< DoFHandler< dim > * > dofHandlersSet_nonconst
       
      std::vector< IndexSet * > locally_relevant_dofsSet_nonconst
       
      std::vector< vectorType * > solutionSet
       
      std::vector< vectorType * > residualSet
      +
      + + +
      +
      + + + + + +
      + + + + + + + + +
      void getComponentsWithRigidBodyModes (std::vector< int > & rigidBodyModeComponents)
      +       		+protectedvirtual
      +
      + +

      Reimplemented in generalizedProblem< dim >.

      +
      @@ -597,6 +637,52 @@

      Member Function Documentation

      Reimplemented in generalizedProblem< dim >, CoupledCHACMechanicsProblem< dim >, CoupledCahnHilliardMechanicsProblem< dim >, and MechanicsProblem< dim >.

      + + + +
      +
      + + + + + +
      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      void getOutputTimeSteps (std::string outputSpacingType,
      unsigned int numberOfOutputs,
      std::vector< unsigned int > & userGivenTimeStepList,
      std::vector< unsigned int > & timeStepList 
      )
      +       		+protected
      +
      +
      @@ -823,6 +909,123 @@

      Member Function Documentation

      +
      + + +
      +
      + + + + + + + + +
      void reinit (unsigned int iter = 0)
      +
      + +
      +
      + +
      +
      + + + + + +
      + + + + + + + +
      void setPeriodicity ()
      +       		+protectedvirtual
      +
      + +

      Reimplemented in generalizedProblem< dim >.

      + +
      +
      + +
      +
      + + + + + +
      + + + + + + + + + + + + + + + + + + +
      void setPeriodicityConstraints (ConstraintMatrix * constraints,
      DoFHandler< dim > * dof_handler 
      )
      +       		+protectedvirtual
      +
      + +

      Reimplemented in generalizedProblem< dim >.

      + +
      +
      + +
      +
      + + + + + +
      + + + + + + + + + + + + + + + + + + + + + + + + +
      void setRigidBodyModeConstraints (std::vector< int > rigidBodyModeComponents,
      ConstraintMatrix * constraints,
      DoFHandler< dim > * dof_handler 
      )
      +       		+protectedvirtual
      +
      +
      @@ -929,7 +1132,7 @@

      Member Data Documentation

      - +
      @@ -937,7 +1140,7 @@

      Member Data Documentation

      @@ -949,7 +1152,7 @@

      Member Data Documentation

      - +
      - +
      std::vector<const ConstraintMatrix*> constraintsHangingNodesSetstd::vector<const ConstraintMatrix*> constraintsDirichletSet
      @@ -957,7 +1160,7 @@

      Member Data Documentation

      @@ -969,7 +1172,7 @@

      Member Data Documentation

      - +
      - +
      std::vector<ConstraintMatrix*> constraintsHangingNodesSet2std::vector<ConstraintMatrix*> constraintsDirichletSet_nonconst
      @@ -977,7 +1180,7 @@

      Member Data Documentation

      @@ -989,7 +1192,7 @@

      Member Data Documentation

      - +
      - +
      std::vector<const ConstraintMatrix*> constraintsSetstd::vector<const ConstraintMatrix*> constraintsOtherSet
      @@ -997,7 +1200,7 @@

      Member Data Documentation

      @@ -1089,7 +1292,7 @@

      Member Data Documentation

      - +
      - +
      std::vector<ConstraintMatrix*> constraintsSet2std::vector<ConstraintMatrix*> constraintsOtherSet_nonconst
      @@ -1097,7 +1300,7 @@

      Member Data Documentation

      @@ -1362,7 +1565,7 @@

      Member Data Documentation

      - +
      - +
      std::vector<DoFHandler<dim>*> dofHandlersSet2std::vector<DoFHandler<dim>*> dofHandlersSet_nonconst
      @@ -1370,7 +1573,7 @@

      Member Data Documentation

      @@ -1420,6 +1623,26 @@

      Member Data Documentation

      - +
      std::vector<IndexSet*> locally_relevant_dofsSet2std::vector<IndexSet*> locally_relevant_dofsSet_nonconst
      +
      +
      + +
      +
      + + + + + +
      + + + + +
      std::vector<unsigned int> outputTimeStepList
      +       		+protected
      +
      +
      @@ -1588,6 +1811,7 @@

      Member Data Documentation

    • phaseField_dev/src/matrixfree/calcFreeEnergy.cc
    • phaseField_dev/src/matrixfree/computeLHS.cc
    • phaseField_dev/src/matrixfree/computeRHS.cc
      • +
    • phaseField_dev/src/matrixfree/getOutputTimeSteps.cc
    • phaseField_dev/src/matrixfree/init.cc
    • phaseField_dev/src/matrixfree/initForTests.cc
    • phaseField_dev/src/matrixfree/initialConditions.cc
      • @@ -1598,6 +1822,7 @@

      Member Data Documentation

    • phaseField_dev/src/matrixfree/modifyFields.cc
    • phaseField_dev/src/matrixfree/outputResults.cc
    • phaseField_dev/src/matrixfree/refine.cc
      • +
    • phaseField_dev/src/matrixfree/reinit.cc
    • phaseField_dev/src/matrixfree/solve.cc
    • phaseField_dev/src/matrixfree/solveIncrement.cc
    • phaseField_dev/src/matrixfree/utilities.cc
      • diff --git a/html/class_mechanics_problem-members.html b/html/class_mechanics_problem-members.html index 17125a3f7..9a5b639aa 100644 --- a/html/class_mechanics_problem-members.html +++ b/html/class_mechanics_problem-members.html @@ -101,15 +101,15 @@ computeInvM()MatrixFreePDE< dim >protected computeRHS()MatrixFreePDE< dim >protected computing_timerMatrixFreePDE< dim >mutableprotected - constraintsHangingNodesSetMatrixFreePDE< dim >protected - constraintsHangingNodesSet2MatrixFreePDE< dim >protected - constraintsSetMatrixFreePDE< dim >protected - constraintsSet2MatrixFreePDE< dim >protected + constraintsDirichletSetMatrixFreePDE< dim >protected + constraintsDirichletSet_nonconstMatrixFreePDE< dim >protected + constraintsOtherSetMatrixFreePDE< dim >protected + constraintsOtherSet_nonconstMatrixFreePDE< dim >protected currentFieldIndexMatrixFreePDE< dim >protected currentIncrementMatrixFreePDE< dim >protected currentTimeMatrixFreePDE< dim >protected dofHandlersSetMatrixFreePDE< dim >protected - dofHandlersSet2MatrixFreePDE< dim >protected + dofHandlersSet_nonconstMatrixFreePDE< dim >protected dtValueMatrixFreePDE< dim >protected dUMatrixFreePDE< dim >protected ellipticFieldIndexMatrixFreePDE< dim >protected @@ -119,9 +119,11 @@ fieldsMatrixFreePDE< dim > finalTimeMatrixFreePDE< dim >protected freeEnergyValuesMatrixFreePDE< dim >protected - getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)MatrixFreePDE< dim >protectedvirtual - getFieldIndex(std::string _name)MatrixFreePDE< dim >protected - getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const MechanicsProblem< dim >privatevirtual + getComponentsWithRigidBodyModes(std::vector< int > &)MatrixFreePDE< dim >protectedvirtual + getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)MatrixFreePDE< dim >protectedvirtual + getFieldIndex(std::string _name)MatrixFreePDE< dim >protected + getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const MechanicsProblem< dim >privatevirtual + getOutputTimeSteps(std::string outputSpacingType, unsigned int numberOfOutputs, std::vector< unsigned int > &userGivenTimeStepList, std::vector< unsigned int > &timeStepList)MatrixFreePDE< dim >protected getRHS(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range) const MechanicsProblem< dim >privatevirtual init(unsigned int iter=0)MatrixFreePDE< dim > initForTests()MatrixFreePDE< dim > @@ -129,7 +131,7 @@ isEllipticBVPMatrixFreePDE< dim >protected isTimeDependentBVPMatrixFreePDE< dim >protected locally_relevant_dofsSetMatrixFreePDE< dim >protected - locally_relevant_dofsSet2MatrixFreePDE< dim >protected + locally_relevant_dofsSet_nonconstMatrixFreePDE< dim >protected markBoundaries()MechanicsProblem< dim >privatevirtual matrixFreeObjectMatrixFreePDE< dim >protected MatrixFreePDE()MatrixFreePDE< dim > @@ -138,21 +140,26 @@ num_quadrature_pointsMatrixFreePDE< dim >protected outputFreeEnergy(std::vector< double > &freeEnergyValues)MatrixFreePDE< dim >protected outputResults()MatrixFreePDE< dim >protected - parabolicFieldIndexMatrixFreePDE< dim >protected - pcoutMatrixFreePDE< dim >protected - refineGrid()MatrixFreePDE< dim >protected - refineMesh(unsigned int _currentIncrement)MatrixFreePDE< dim >protected + outputTimeStepListMatrixFreePDE< dim >protected + parabolicFieldIndexMatrixFreePDE< dim >protected + pcoutMatrixFreePDE< dim >protected + refineGrid()MatrixFreePDE< dim >protected + refineMesh(unsigned int _currentIncrement)MatrixFreePDE< dim >protected + reinit(unsigned int iter=0)MatrixFreePDE< dim > residualSetMatrixFreePDE< dim >protected - shiftConcentration()MatrixFreePDE< dim > - soltransSetMatrixFreePDE< dim >protected - solutionSetMatrixFreePDE< dim >protected - solve()MatrixFreePDE< dim > - solveIncrement()MatrixFreePDE< dim >protected - totalIncrementsMatrixFreePDE< dim >protected - triangulationMatrixFreePDE< dim >protected - valuesDirichletSetMatrixFreePDE< dim >protected - vmult(vectorType &dst, const vectorType &src) const MatrixFreePDE< dim > - ~MatrixFreePDE()MatrixFreePDE< dim > + setPeriodicity()MatrixFreePDE< dim >protectedvirtual + setPeriodicityConstraints(ConstraintMatrix *, DoFHandler< dim > *)MatrixFreePDE< dim >protectedvirtual + setRigidBodyModeConstraints(std::vector< int >, ConstraintMatrix *, DoFHandler< dim > *)MatrixFreePDE< dim >protectedvirtual + shiftConcentration()MatrixFreePDE< dim > + soltransSetMatrixFreePDE< dim >protected + solutionSetMatrixFreePDE< dim >protected + solve()MatrixFreePDE< dim > + solveIncrement()MatrixFreePDE< dim >protected + totalIncrementsMatrixFreePDE< dim >protected + triangulationMatrixFreePDE< dim >protected + valuesDirichletSetMatrixFreePDE< dim >protected + vmult(vectorType &dst, const vectorType &src) const MatrixFreePDE< dim > + ~MatrixFreePDE()MatrixFreePDE< dim >
      diff --git a/html/class_mechanics_problem.html b/html/class_mechanics_problem.html index 34d098806..53337d6b3 100644 --- a/html/class_mechanics_problem.html +++ b/html/class_mechanics_problem.html @@ -116,6 +116,8 @@   void init (unsigned int iter=0)   +void reinit (unsigned int iter=0) +  void initForTests ()   void solve () @@ -155,6 +157,8 @@   void outputResults ()   +void getOutputTimeSteps (std::string outputSpacingType, unsigned int numberOfOutputs, std::vector< unsigned int > &userGivenTimeStepList, std::vector< unsigned int > &timeStepList) +  void computeInvM ()   void computeRHS () @@ -163,6 +167,14 @@   void refineMesh (unsigned int _currentIncrement)   +virtual void setPeriodicity () +  +virtual void setPeriodicityConstraints (ConstraintMatrix *, DoFHandler< dim > *) +  +virtual void getComponentsWithRigidBodyModes (std::vector< int > &) +  +virtual void setRigidBodyModeConstraints (std::vector< int >, ConstraintMatrix *, DoFHandler< dim > *) +  virtual void applyInitialConditions ()   virtual void modifySolutionFields () @@ -178,26 +190,28 @@ void computeIntegral (double &integratedField)   - Protected Attributes inherited from MatrixFreePDE< dim > +std::vector< unsigned int > outputTimeStepList +  parallel::distributed::Triangulation< dim > triangulation   std::vector< FESystem< dim > * > FESet   -std::vector< const ConstraintMatrix * > constraintsSet -  -std::vector< const ConstraintMatrix * > constraintsHangingNodesSet -  +std::vector< const ConstraintMatrix * > constraintsDirichletSet +  +std::vector< const ConstraintMatrix * > constraintsOtherSet +  std::vector< const DoFHandler< dim > * > dofHandlersSet   std::vector< const IndexSet * > locally_relevant_dofsSet   -std::vector< ConstraintMatrix * > constraintsSet2 -  -std::vector< ConstraintMatrix * > constraintsHangingNodesSet2 -  -std::vector< DoFHandler< dim > * > dofHandlersSet2 -  -std::vector< IndexSet * > locally_relevant_dofsSet2 -  +std::vector< ConstraintMatrix * > constraintsDirichletSet_nonconst +  +std::vector< ConstraintMatrix * > constraintsOtherSet_nonconst +  +std::vector< DoFHandler< dim > * > dofHandlersSet_nonconst +  +std::vector< IndexSet * > locally_relevant_dofsSet_nonconst +  std::vector< vectorType * > solutionSet   std::vector< vectorType * > residualSet diff --git a/html/class_p_functions_1_1p_function-members.html b/html/class_p_functions_1_1p_function-members.html new file mode 100644 index 000000000..e81c59514 --- /dev/null +++ b/html/class_p_functions_1_1p_function-members.html @@ -0,0 +1,111 @@ + + + + + + +PRISMS PhaseField: Member List + + + + + + + + + + +
      +
      + + + + + + +
      +
      PRISMS PhaseField +  1.0 +
      +
      +
      + + + + + + +
      +
      + + +
      + +
      + + +
      +
      +
      +
      pFunction Member List
      +
      +
      + +

      This is the complete list of members for pFunction, including all inherited members.

      + + + + + + +
      funpFunctionprivate
      grad(scalarvalueType, unsigned int)pFunction
      hess(scalarvalueType, unsigned int, unsigned int)pFunction
      pFunction(std::string function_name)pFunction
      val(scalarvalueType)pFunction
      + +
      +Generated by   +doxygen + 1.8.10 +
      + + diff --git a/html/class_p_functions_1_1p_function.html b/html/class_p_functions_1_1p_function.html new file mode 100644 index 000000000..6ce42b7fc --- /dev/null +++ b/html/class_p_functions_1_1p_function.html @@ -0,0 +1,242 @@ + + + + + + +PRISMS PhaseField: pFunction Class Reference + + + + + + + + + + +
      +
      + + + + + + +
      +
      PRISMS PhaseField +  1.0 +
      +
      +
      + + + + + + +
      +
      + + +
      + +
      + + +
      +
      +
      +Public Member Functions | +Private Attributes | +List of all members
      +
      +
      pFunction Class Reference
      +
      +
      + +

      #include <pFunction.h>

      + + + + + + + + + + +

      +Public Member Functions

       pFunction (std::string function_name)
       
      scalarvalueType val (scalarvalueType)
       
      scalarvalueType grad (scalarvalueType, unsigned int)
       
      scalarvalueType hess (scalarvalueType, unsigned int, unsigned int)
       
      + + + +

      +Private Attributes

      PRISMS::PFunction< double *, double > fun
       
      +

      Constructor & Destructor Documentation

      + +
      +
      + + + + + + + + +
      pFunction (std::string function_name)
      +
      + +
      +
      +

      Member Function Documentation

      + +
      +
      + + + + + + + + + + + + + + + + + + +
      scalarvalueType grad (scalarvalueType var,
      unsigned int dir 
      )
      +
      + +
      +
      + +
      +
      + + + + + + + + + + + + + + + + + + + + + + + + +
      scalarvalueType hess (scalarvalueType var,
      unsigned int dir1,
      unsigned int dir2 
      )
      +
      + +
      +
      + +
      +
      + + + + + + + + +
      scalarvalueType val (scalarvalueType var)
      +
      + +
      +
      +

      Member Data Documentation

      + +
      +
      + + + + + +
      + + + + +
      PRISMS::PFunction<double*, double> fun
      +       		+private
      +
      + +
      +
      +
      The documentation for this class was generated from the following file: +
      + +
      +Generated by   +doxygen + 1.8.10 +
      + + diff --git a/html/classes.html b/html/classes.html index 221f37b68..c390f5eb7 100644 --- a/html/classes.html +++ b/html/classes.html @@ -88,31 +88,32 @@
      Class Index
      -
      A | B | C | F | G | I | M | N | V
      +
      A | B | C | F | G | I | M | N | P | V
      - - + + + - - + + + + - - + +
        A  
      		CoupledCahnHilliardMechanicsProblem   InitialConditionC   
        m  
      -      		variable_info   
      CoupledCHACMechanicsProblem   InitialConditionN   vectorBCFunction   
      AllenCahnProblem   CoupledCHACProblem   InitialConditionVec   modelResidual   
        v  
      +
      CoupledCHACMechanicsProblem   InitialConditionVec   
      AllenCahnProblem   CoupledCHACProblem   
        M  
      +      		modelResidual   varBCs   
        B  
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      -
        M  
      -      		modelVariable   
        n  
      -
      BCFunction   FickianProblem   MatrixFreePDE   
      modelVariable   variable_info   
      MatrixFreePDE   
        n  
      +      		vectorBCFunction   
      BCFunction   FickianProblem   MechanicsProblem   
        C  
      -      		Field   MechanicsProblem   nucleus   
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        g  
      +      		nucleus   
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      +
        p  
      CahnHilliardProblem   
      InitialCondition   generalizedProblem   varBCs   
      CahnHilliardProblem   generalizedProblem   
      InitialCondition   pFunction (PFunctions)   
      -
      A | B | C | F | G | I | M | N | V
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      diff --git a/html/classgeneralized_problem-members.html b/html/classgeneralized_problem-members.html index 73d06c4ef..70e599a04 100644 --- a/html/classgeneralized_problem-members.html +++ b/html/classgeneralized_problem-members.html @@ -106,29 +106,31 @@ computeInvM()MatrixFreePDE< dim >protected computeRHS()MatrixFreePDE< dim >protected computing_timerMatrixFreePDE< dim >mutableprotected - constraintsHangingNodesSetMatrixFreePDE< dim >protected - constraintsHangingNodesSet2MatrixFreePDE< dim >protected - constraintsSetMatrixFreePDE< dim >protected - constraintsSet2MatrixFreePDE< dim >protected + constraintsDirichletSetMatrixFreePDE< dim >protected + constraintsDirichletSet_nonconstMatrixFreePDE< dim >protected + constraintsOtherSetMatrixFreePDE< dim >protected + constraintsOtherSet_nonconstMatrixFreePDE< dim >protected currentFieldIndexMatrixFreePDE< dim >protected currentIncrementMatrixFreePDE< dim >protected currentTimeMatrixFreePDE< dim >protected dofHandlersSetMatrixFreePDE< dim >protected - dofHandlersSet2MatrixFreePDE< dim >protected + dofHandlersSet_nonconstMatrixFreePDE< dim >protected dtValueMatrixFreePDE< dim >protected dUMatrixFreePDE< dim >protected ellipticFieldIndexMatrixFreePDE< dim >protected energyMatrixFreePDE< dim >protected energy_componentsMatrixFreePDE< dim >protected - energyDensity(const std::vector< modelVariable< dim >> &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)generalizedProblem< dim >private + energyDensity(const std::vector< modelVariable< dim > > &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)generalizedProblem< dim >private FESetMatrixFreePDE< dim >protected fieldsMatrixFreePDE< dim > finalTimeMatrixFreePDE< dim >protected freeEnergyValuesMatrixFreePDE< dim >protected generalizedProblem()generalizedProblem< dim > - getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)generalizedProblem< dim >privatevirtual - getFieldIndex(std::string _name)MatrixFreePDE< dim >protected - getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const generalizedProblem< dim >privatevirtual + getComponentsWithRigidBodyModes(std::vector< int > &)generalizedProblem< dim >privatevirtual + getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)generalizedProblem< dim >privatevirtual + getFieldIndex(std::string _name)MatrixFreePDE< dim >protected + getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const generalizedProblem< dim >privatevirtual + getOutputTimeSteps(std::string outputSpacingType, unsigned int numberOfOutputs, std::vector< unsigned int > &userGivenTimeStepList, std::vector< unsigned int > &timeStepList)MatrixFreePDE< dim >protected getRHS(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range) const generalizedProblem< dim >privatevirtual gradient_residualgeneralizedProblem< dim >private gradient_residual_LHSgeneralizedProblem< dim >private @@ -142,7 +144,7 @@ isEllipticBVPMatrixFreePDE< dim >protected isTimeDependentBVPMatrixFreePDE< dim >protected locally_relevant_dofsSetMatrixFreePDE< dim >protected - locally_relevant_dofsSet2MatrixFreePDE< dim >protected + locally_relevant_dofsSet_nonconstMatrixFreePDE< dim >protected markBoundaries()generalizedProblem< dim >privatevirtual matrixFreeObjectMatrixFreePDE< dim >protected MatrixFreePDE()MatrixFreePDE< dim > @@ -157,32 +159,37 @@ num_var_LHSgeneralizedProblem< dim >private outputFreeEnergy(std::vector< double > &freeEnergyValues)MatrixFreePDE< dim >protected outputResults()MatrixFreePDE< dim >protected - parabolicFieldIndexMatrixFreePDE< dim >protected - pcoutMatrixFreePDE< dim >protected - refineGrid()MatrixFreePDE< dim >protected - refineMesh(unsigned int _currentIncrement)MatrixFreePDE< dim >protected - residualLHS(const std::vector< modelVariable< dim >> &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const generalizedProblem< dim >private - residualRHS(const std::vector< modelVariable< dim >> &modelVarList, std::vector< modelResidual< dim >> &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const generalizedProblem< dim >private + outputTimeStepListMatrixFreePDE< dim >protected + parabolicFieldIndexMatrixFreePDE< dim >protected + pcoutMatrixFreePDE< dim >protected + refineGrid()MatrixFreePDE< dim >protected + refineMesh(unsigned int _currentIncrement)MatrixFreePDE< dim >protected + reinit(unsigned int iter=0)MatrixFreePDE< dim > + residualLHS(const std::vector< modelVariable< dim > > &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const generalizedProblem< dim >private + residualRHS(const std::vector< modelVariable< dim > > &modelVarList, std::vector< modelResidual< dim > > &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const generalizedProblem< dim >private residualSetMatrixFreePDE< dim >protected resInfoListRHSgeneralizedProblem< dim >private setBCs()generalizedProblem< dim > - shiftConcentration()generalizedProblem< dim > - soltransSetMatrixFreePDE< dim >protected - solutionSetMatrixFreePDE< dim >protected - solve()MatrixFreePDE< dim > - solveIncrement()MatrixFreePDE< dim >protected - totalIncrementsMatrixFreePDE< dim >protected - triangulationMatrixFreePDE< dim >protected - value_residualgeneralizedProblem< dim >private - value_residual_LHSgeneralizedProblem< dim >private - valuesDirichletSetMatrixFreePDE< dim >protected - var_eq_typegeneralizedProblem< dim >private - var_namegeneralizedProblem< dim >private - var_typegeneralizedProblem< dim >private - varInfoListLHSgeneralizedProblem< dim >private - varInfoListRHSgeneralizedProblem< dim >private - vmult(vectorType &dst, const vectorType &src) const MatrixFreePDE< dim > - ~MatrixFreePDE()MatrixFreePDE< dim > + setPeriodicity()generalizedProblem< dim >privatevirtual + setPeriodicityConstraints(ConstraintMatrix *, DoFHandler< dim > *)generalizedProblem< dim >privatevirtual + setRigidBodyModeConstraints(std::vector< int >, ConstraintMatrix *, DoFHandler< dim > *)MatrixFreePDE< dim >protectedvirtual + shiftConcentration()generalizedProblem< dim > + soltransSetMatrixFreePDE< dim >protected + solutionSetMatrixFreePDE< dim >protected + solve()MatrixFreePDE< dim > + solveIncrement()MatrixFreePDE< dim >protected + totalIncrementsMatrixFreePDE< dim >protected + triangulationMatrixFreePDE< dim >protected + value_residualgeneralizedProblem< dim >private + value_residual_LHSgeneralizedProblem< dim >private + valuesDirichletSetMatrixFreePDE< dim >protected + var_eq_typegeneralizedProblem< dim >private + var_namegeneralizedProblem< dim >private + var_typegeneralizedProblem< dim >private + varInfoListLHSgeneralizedProblem< dim >private + varInfoListRHSgeneralizedProblem< dim >private + vmult(vectorType &dst, const vectorType &src) const MatrixFreePDE< dim > + ~MatrixFreePDE()MatrixFreePDE< dim >
      diff --git a/html/classgeneralized_problem.html b/html/classgeneralized_problem.html index 4941c490f..3a0679454 100644 --- a/html/classgeneralized_problem.html +++ b/html/classgeneralized_problem.html @@ -132,6 +132,8 @@   void init (unsigned int iter=0)   +void reinit (unsigned int iter=0) +  void initForTests ()   void solve () @@ -165,14 +167,20 @@   void markBoundaries ()   +void setPeriodicity () +  +void setPeriodicityConstraints (ConstraintMatrix *, DoFHandler< dim > *) +  +void getComponentsWithRigidBodyModes (std::vector< int > &) +  void getEnergy (const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)   -void residualRHS (const std::vector< modelVariable< dim >> &modelVarList, std::vector< modelResidual< dim >> &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const -  -void residualLHS (const std::vector< modelVariable< dim >> &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const -  -void energyDensity (const std::vector< modelVariable< dim >> &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) -  +void residualRHS (const std::vector< modelVariable< dim > > &modelVarList, std::vector< modelResidual< dim > > &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const +  +void residualLHS (const std::vector< modelVariable< dim > > &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const +  +void energyDensity (const std::vector< modelVariable< dim > > &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) +  void adaptiveRefine (unsigned int currentIncrement)   void adaptiveRefineCriterion () @@ -233,6 +241,8 @@   void outputResults ()   +void getOutputTimeSteps (std::string outputSpacingType, unsigned int numberOfOutputs, std::vector< unsigned int > &userGivenTimeStepList, std::vector< unsigned int > &timeStepList) +  void computeInvM ()   void computeRHS () @@ -241,6 +251,8 @@   void refineMesh (unsigned int _currentIncrement)   +virtual void setRigidBodyModeConstraints (std::vector< int >, ConstraintMatrix *, DoFHandler< dim > *) +  void computeEnergy ()   unsigned int getFieldIndex (std::string _name) @@ -250,26 +262,28 @@ void computeIntegral (double &integratedField)   - Protected Attributes inherited from MatrixFreePDE< dim > +std::vector< unsigned int > outputTimeStepList +  parallel::distributed::Triangulation< dim > triangulation   std::vector< FESystem< dim > * > FESet   -std::vector< const ConstraintMatrix * > constraintsSet -  -std::vector< const ConstraintMatrix * > constraintsHangingNodesSet -  +std::vector< const ConstraintMatrix * > constraintsDirichletSet +  +std::vector< const ConstraintMatrix * > constraintsOtherSet +  std::vector< const DoFHandler< dim > * > dofHandlersSet   std::vector< const IndexSet * > locally_relevant_dofsSet   -std::vector< ConstraintMatrix * > constraintsSet2 -  -std::vector< ConstraintMatrix * > constraintsHangingNodesSet2 -  -std::vector< DoFHandler< dim > * > dofHandlersSet2 -  -std::vector< IndexSet * > locally_relevant_dofsSet2 -  +std::vector< ConstraintMatrix * > constraintsDirichletSet_nonconst +  +std::vector< ConstraintMatrix * > constraintsOtherSet_nonconst +  +std::vector< DoFHandler< dim > * > dofHandlersSet_nonconst +  +std::vector< IndexSet * > locally_relevant_dofsSet_nonconst +  std::vector< vectorType * > solutionSet   std::vector< vectorType * > residualSet @@ -474,7 +488,7 @@

      Member Function Documentation

      - +
      @@ -484,7 +498,7 @@

      Member Function Documentation

      - + @@ -512,6 +526,32 @@

      Member Function Documentation

      void energyDensity (const std::vector< modelVariable< dim >> & const std::vector< modelVariable< dim > > &  modelVarList,
      +
      +
      + +
      +
      + + + + + +
      + + + + + + + + +
      void getComponentsWithRigidBodyModes (std::vector< int > & rigidBodyModeComponents)
      +       		+privatevirtual
      +
      + +

      Reimplemented from MatrixFreePDE< dim >.

      +
      @@ -968,7 +1008,7 @@

      Member Function Documentation

      - +
      @@ -978,7 +1018,7 @@

      Member Function Documentation

      - + @@ -1008,7 +1048,7 @@

      Member Function Documentation

      - +
      void residualLHS (const std::vector< modelVariable< dim >> & const std::vector< modelVariable< dim > > &  modelVarList,
      @@ -1018,13 +1058,13 @@

      Member Function Documentation

      - + - + @@ -1061,6 +1101,67 @@

      Member Function Documentation

      void residualRHS (const std::vector< modelVariable< dim >> & const std::vector< modelVariable< dim > > &  modelVarList,
      std::vector< modelResidual< dim >> & std::vector< modelResidual< dim > > &  modelResidualsList,
      +
      +
      + +
      +
      + + + + + +
      + + + + + + + +
      void setPeriodicity ()
      +       		+privatevirtual
      +
      + +

      Reimplemented from MatrixFreePDE< dim >.

      + +
      +
      + +
      +
      + + + + + +
      + + + + + + + + + + + + + + + + + + +
      void setPeriodicityConstraints (ConstraintMatrix * constraints,
      DoFHandler< dim > * dof_handler 
      )
      +       		+privatevirtual
      +
      + +

      Reimplemented from MatrixFreePDE< dim >.

      +
      diff --git a/html/classvector_b_c_function-members.html b/html/classvector_b_c_function-members.html index 20ecb4110..80e5e1424 100644 --- a/html/classvector_b_c_function-members.html +++ b/html/classvector_b_c_function-members.html @@ -93,7 +93,7 @@ - +
      BC_valuesvectorBCFunction< dim >private
      vector_value(const Point< dim > &p, Vector< double > &values) const vectorBCFunction< dim >virtual
      vector_value_list(const std::vector< Point< dim >> &points, std::vector< Vector< double >> &value_list) const vectorBCFunction< dim >virtual
      vector_value_list(const std::vector< Point< dim > > &points, std::vector< Vector< double > > &value_list) const vectorBCFunction< dim >virtual
      vectorBCFunction(const std::vector< double > BC_values)vectorBCFunction< dim >
      diff --git a/html/classvector_b_c_function.html b/html/classvector_b_c_function.html index 5c9956e7c..7950c5d17 100644 --- a/html/classvector_b_c_function.html +++ b/html/classvector_b_c_function.html @@ -109,8 +109,8 @@   virtual void vector_value (const Point< dim > &p, Vector< double > &values) const   -virtual void vector_value_list (const std::vector< Point< dim >> &points, std::vector< Vector< double >> &value_list) const -  +virtual void vector_value_list (const std::vector< Point< dim > > &points, std::vector< Vector< double > > &value_list) const +  @@ -169,7 +169,7 @@

      Member Function Documentation

      - +

      Private Attributes

      @@ -179,13 +179,13 @@

      Member Function Documentation

      - + - + diff --git a/html/coupled_c_h_a_c2_8h_source.html b/html/coupled_c_h_a_c2_8h_source.html index 0ad22fd74..409d5b38a 100644 --- a/html/coupled_c_h_a_c2_8h_source.html +++ b/html/coupled_c_h_a_c2_8h_source.html @@ -164,21 +164,21 @@
      72  //concentration field
      73  else if (this->fields[this->currentFieldIndex].name.compare("c")==0){
      74  //compute residuals
      -
      75  valsScalar["c"]->submit_value(rcV,q);
      -
      76  valsScalar["c"]->submit_gradient(rcxV,q);
      +
      75  valsScalar["c"]->submit_value(rcV,q);
      +
      76  valsScalar["c"]->submit_gradient(rcxV,q);
      77  }
      78 }
      79 
      80 #endif
      +
      rcxV
      #define rcxV
      Definition: residuals.h:12
      MatrixFreePDE
      Definition: matrixFreePDE.h:85
      scalargradType
      dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
      Definition: matrixFreePDE.h:51
      CoupledCHACProblem::modifySolutionFields
      void modifySolutionFields()
      -
      rcV
      #define rcV
      Definition: residuals.h:11
      CoupledCHACProblem::getRHS
      void getRHS(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range) const
      Definition: coupledCHAC.h:50
      -
      rcxV
      #define rcxV
      Definition: residuals.h:12
      CoupledCHACProblem::CoupledCHACProblem
      CoupledCHACProblem()
      Definition: coupledCHAC.h:31
      CoupledCHACProblem
      Definition: coupledCHAC.h:10
      CoupledCHACProblem::applyInitialConditions
      void applyInitialConditions()
      +
      rcV
      #define rcV
      Definition: residuals.h:11
      rnV
      #define rnV
      Definition: equations.h:44
      scalarvalueType
      dealii::VectorizedArray< double > scalarvalueType
      Definition: matrixFreePDE.h:34
      rnxV
      #define rnxV
      Definition: equations.h:45
      diff --git a/html/coupled_c_h_a_c_8h_source.html b/html/coupled_c_h_a_c_8h_source.html index 8c7fbe853..0cd431f11 100644 --- a/html/coupled_c_h_a_c_8h_source.html +++ b/html/coupled_c_h_a_c_8h_source.html @@ -167,7 +167,7 @@
      75 
      76  //submit values
      77  nVals.submit_value(rnV,q); nVals.submit_gradient(rnxV,q);
      -
      78  cVals.submit_value(rcV,q); cVals.submit_gradient(rcxV,q);
      +
      78  cVals.submit_value(rcV,q); cVals.submit_gradient(rcxV,q);
      79  }
      80 
      81  //integrate values
      @@ -178,16 +178,16 @@
      86 
      87 
      88 #endif
      +
      rcxV
      #define rcxV
      Definition: residuals.h:12
      MatrixFreePDE
      Definition: matrixFreePDE.h:85
      scalargradType
      dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
      Definition: matrixFreePDE.h:51
      CoupledCHACProblem::modifySolutionFields
      void modifySolutionFields()
      -
      rcV
      #define rcV
      Definition: residuals.h:11
      CoupledCHACProblem::getRHS
      void getRHS(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range) const
      Definition: coupledCHAC.h:50
      typeScalar
      dealii::FEEvaluation< problemDIM, finiteElementDegree, finiteElementDegree+1, 1, double > typeScalar
      Definition: matrixFreePDE.h:27
      -
      rcxV
      #define rcxV
      Definition: residuals.h:12
      CoupledCHACProblem::CoupledCHACProblem
      CoupledCHACProblem()
      Definition: coupledCHAC.h:31
      CoupledCHACProblem
      Definition: coupledCHAC.h:10
      CoupledCHACProblem::applyInitialConditions
      void applyInitialConditions()
      +
      rcV
      #define rcV
      Definition: residuals.h:11
      rnV
      #define rnV
      Definition: equations.h:44
      scalarvalueType
      dealii::VectorizedArray< double > scalarvalueType
      Definition: matrixFreePDE.h:34
      rnxV
      #define rnxV
      Definition: equations.h:45
      diff --git a/html/coupled_c_h_a_c_mechanics_8h.html b/html/coupled_c_h_a_c_mechanics_8h.html index 10a212d98..fd7336a1b 100644 --- a/html/coupled_c_h_a_c_mechanics_8h.html +++ b/html/coupled_c_h_a_c_mechanics_8h.html @@ -108,17 +108,17 @@
      void vector_value_list (const std::vector< Point< dim >> & const std::vector< Point< dim > > &  points,
      std::vector< Vector< double >> & std::vector< Vector< double > > &  value_list 
      - + - + - + - + - + - + @@ -152,7 +152,7 @@

      Macro Definition Documentation

      Macros

      #define h1strainV   h1V
      #define h1strainV   h1V
       
      #define h2strainV   h2V
      #define h2strainV   h2V
       
      #define h3strainV   h3V
      #define h3strainV   h3V
       
      #define hn1strainV   hn1V
      #define hn1strainV   hn1V
       
      #define hn2strainV   hn2V
      #define hn2strainV   hn2V
       
      #define hn3strainV   hn3V
      #define hn3strainV   hn3V
       
      #define W   0.0
       
      - +
      #define h1strainV   h1V#define h1strainV   h1V
      @@ -164,7 +164,7 @@

      Macro Definition Documentation

      - +
      #define h2strainV   h2V#define h2strainV   h2V
      @@ -176,7 +176,7 @@

      Macro Definition Documentation

      - +
      #define h3strainV   h3V#define h3strainV   h3V
      @@ -188,7 +188,7 @@

      Macro Definition Documentation

      - +
      #define hn1strainV   hn1V#define hn1strainV   hn1V
      @@ -200,7 +200,7 @@

      Macro Definition Documentation

      - +
      #define hn2strainV   hn2V#define hn2strainV   hn2V
      @@ -212,7 +212,7 @@

      Macro Definition Documentation

      - +
      #define hn3strainV   hn3V#define hn3strainV   hn3V
      diff --git a/html/coupled_c_h_a_c_mechanics_8h_source.html b/html/coupled_c_h_a_c_mechanics_8h_source.html index 6717a52a3..7e5797887 100644 --- a/html/coupled_c_h_a_c_mechanics_8h_source.html +++ b/html/coupled_c_h_a_c_mechanics_8h_source.html @@ -164,12 +164,12 @@
      72 {
      73  //initialize elasticity matrix
      74 #if defined(MaterialModelV) && defined(MaterialConstantsV)
      -
      75  if (n_dependent_stiffness == true){
      +
      75  if (n_dependent_stiffness == true){
      76  double materialConstants[]=MaterialConstantsV;
      77  getCIJMatrix<dim>(MaterialModelV, materialConstants, CIJ_alpha, this->pcout);
      78 
      -
      79  double materialConstantsBeta[]=MaterialConstantsBetaV;
      -
      80  getCIJMatrix<dim>(MaterialModelBetaV, materialConstantsBeta, CIJ_beta, this->pcout);
      +
      79  double materialConstantsBeta[]=MaterialConstantsBetaV;
      +
      80  getCIJMatrix<dim>(MaterialModelBetaV, materialConstantsBeta, CIJ_beta, this->pcout);
      81 
      82  for (unsigned int i=0; i<2*dim-1+dim/3; i++){
      83  for (unsigned int j=0; j<2*dim-1+dim/3; j++){
      @@ -191,7 +191,7 @@
      99 c_dependent_misfit = false;
      100 for (unsigned int i=0; i<dim; i++){
      101  for (unsigned int j=0; j<dim; j++){
      -
      102  if ((std::abs(sfts_linear1[i][j])>1.0e-12)||(std::abs(sfts_linear2[i][j])>1.0e-12)||(std::abs(sfts_linear3[i][j])>1.0e-12)){
      +
      102  if ((std::abs(sfts_linear1[i][j])>1.0e-12)||(std::abs(sfts_linear2[i][j])>1.0e-12)||(std::abs(sfts_linear3[i][j])>1.0e-12)){
      103  c_dependent_misfit = true;
      104  }
      105  }
      @@ -250,7 +250,7 @@
      158  typeScalar n3Vals(data,3);
      159  #endif
      160 
      -
      161  typeVector uVals(data, num_sop+1);
      +
      161  typeVector uVals(data, num_sop+1);
      162 
      163  //loop over cells
      164  for (unsigned int cell=cell_range.first; cell<cell_range.second; ++cell){
      @@ -267,7 +267,7 @@
      175  #endif
      176 
      177  //initialize u field
      -
      178  uVals.reinit(cell); uVals.read_dof_values_plain(*src[num_sop+1]);
      +
      178  uVals.reinit(cell); uVals.read_dof_values_plain(*src[num_sop+1]);
      179 
      180  if (c_dependent_misfit == true){
      181  uVals.evaluate(false, true, true);
      @@ -321,18 +321,18 @@
      229  for (unsigned int i=0; i<dim; i++){
      230  for (unsigned int j=0; j<dim; j++){
      231  // Polynomial fits for the stress-free transformation strains, of the form: sfts = a_p * c + b_p
      -
      232  sfts1[i][j] = constV(sfts_linear1[i][j])*c + constV(sfts_const1[i][j]);
      -
      233  sfts1c[i][j] = constV(sfts_linear1[i][j]);
      +
      232  sfts1[i][j] = constV(sfts_linear1[i][j])*c + constV(sfts_const1[i][j]);
      +
      233  sfts1c[i][j] = constV(sfts_linear1[i][j]);
      234  sfts1cc[i][j] = constV(0.0);
      235 
      236  // Polynomial fits for the stress-free transformation strains, of the form: sfts = a_p * c + b_p
      -
      237  sfts2[i][j] = constV(sfts_linear2[i][j])*c + constV(sfts_const2[i][j]);
      -
      238  sfts2c[i][j] = constV(sfts_linear1[i][j]);
      +
      237  sfts2[i][j] = constV(sfts_linear2[i][j])*c + constV(sfts_const2[i][j]);
      +
      238  sfts2c[i][j] = constV(sfts_linear1[i][j]);
      239  sfts2cc[i][j] = constV(0.0);
      240 
      241  // Polynomial fits for the stress-free transformation strains, of the form: sfts = a_p * c + b_p
      -
      242  sfts3[i][j] = constV(sfts_linear3[i][j])*c + constV(sfts_const3[i][j]);
      -
      243  sfts3c[i][j] = constV(sfts_linear3[i][j]);
      +
      242  sfts3[i][j] = constV(sfts_linear3[i][j])*c + constV(sfts_const3[i][j]);
      +
      243  sfts3c[i][j] = constV(sfts_linear3[i][j]);
      244  sfts3cc[i][j] = constV(0.0);
      245  }
      246  }
      @@ -353,9 +353,9 @@
      261  // Compute stress tensor (which is equal to the residual, Rux)
      262  dealii::VectorizedArray<double> CIJ_combined[2*dim-1+dim/3][2*dim-1+dim/3];
      263 
      -
      264  if (n_dependent_stiffness == true){
      +
      264  if (n_dependent_stiffness == true){
      265  dealii::VectorizedArray<double> sum_hV;
      -
      266  sum_hV = h1V+h2V+h3V;
      +
      266  sum_hV = h1V+h2V+h3V;
      267  for (unsigned int i=0; i<2*dim-1+dim/3; i++){
      268  for (unsigned int j=0; j<2*dim-1+dim/3; j++){
      269  CIJ_combined[i][j] = constV(CIJ_alpha(i,j))*(constV(1.0)-sum_hV) + constV(CIJ_beta(i,j))*sum_hV;
      @@ -400,7 +400,7 @@
      308  dealii::VectorizedArray<double> heterMechAC3=constV(0.0);
      309  dealii::VectorizedArray<double> S2[dim][dim];
      310 
      -
      311  if (n_dependent_stiffness == true){
      +
      311  if (n_dependent_stiffness == true){
      312  computeStress<dim>(CIJ_diff, E2, S2);
      313  for (unsigned int i=0; i<dim; i++){
      314  for (unsigned int j=0; j<dim; j++){
      @@ -408,10 +408,10 @@
      316  }
      317  }
      318  // Aside from HnpV, heterMechAC1, heterMechAC2, and heterMechAC3 are equal
      -
      319  heterMechAC2 = 0.5*hn2V*heterMechAC1;
      -
      320  heterMechAC3 = 0.5*hn3V*heterMechAC1;
      +
      319  heterMechAC2 = 0.5*hn2V*heterMechAC1;
      +
      320  heterMechAC3 = 0.5*hn3V*heterMechAC1;
      321 
      -
      322  heterMechAC1 = 0.5*hn1V*heterMechAC1;
      +
      322  heterMechAC1 = 0.5*hn1V*heterMechAC1;
      323  }
      324 
      325  // compute the stress term in the gradient of the concentration chemical potential, grad_mu_el = [C*(E-E0)*E0c]x, must be a vector with length dim
      @@ -427,7 +427,7 @@
      335  }
      336  }
      337 
      -
      338  if (n_dependent_stiffness == true){
      +
      338  if (n_dependent_stiffness == true){
      339  computeStress<dim>(CIJ_combined, E3, S3);
      340  }
      341  else{
      @@ -450,8 +450,8 @@
      358  grad_mu_el[k]+= - S[i][j] * (sfts1c[i][j]*hn1strainV*n1x[k] + sfts2c[i][j]*hn2strainV*n2x[k] + sfts3c[i][j]*hn3strainV*n3x[k]
      359  + (sfts1cc[i][j]*h1strainV + sfts2cc[i][j]*h2strainV + sfts3cc[i][j]*h3strainV)*cx[k]);
      360 
      -
      361  if (n_dependent_stiffness == true){
      -
      362  grad_mu_el[k]+= - S2[i][j] * (sfts1c[i][j]*hn1V*n1x[k] + sfts2c[i][j]*hn2V*n2x[k] + sfts3c[i][j]*hn3V*n3x[k]);
      +
      361  if (n_dependent_stiffness == true){
      +
      362  grad_mu_el[k]+= - S2[i][j] * (sfts1c[i][j]*hn1V*n1x[k] + sfts2c[i][j]*hn2V*n2x[k] + sfts3c[i][j]*hn3V*n3x[k]);
      363 
      364  }
      365  }
      @@ -467,20 +467,20 @@
      375  Knx2[a]=0.0;
      376  Knx3[a]=0.0;
      377  for (unsigned int b=0; b<dim; b++){
      -
      378  Knx1[a]+=constV(Kn1[a][b])*n1x[b];
      -
      379  Knx2[a]+=constV(Kn2[a][b])*n2x[b];
      -
      380  Knx3[a]+=constV(Kn3[a][b])*n3x[b];
      +
      378  Knx1[a]+=constV(Kn1[a][b])*n1x[b];
      +
      379  Knx2[a]+=constV(Kn2[a][b])*n2x[b];
      +
      380  Knx3[a]+=constV(Kn3[a][b])*n3x[b];
      381  }
      382  }
      383 
      384  //submit values
      -
      385  cVals.submit_value(rcV,q); cVals.submit_gradient(rcxV,q);
      -
      386  n1Vals.submit_value(rn1V,q); n1Vals.submit_gradient(rn1xV,q);
      +
      385  cVals.submit_value(rcV,q); cVals.submit_gradient(rcxV,q);
      +
      386  n1Vals.submit_value(rn1V,q); n1Vals.submit_gradient(rn1xV,q);
      387  #if num_sop>1
      -
      388  n2Vals.submit_value(rn2V,q); n2Vals.submit_gradient(rn2xV,q);
      +
      388  n2Vals.submit_value(rn2V,q); n2Vals.submit_gradient(rn2xV,q);
      389  #endif
      390  #if num_sop>2
      -
      391  n3Vals.submit_value(rn3V,q); n3Vals.submit_gradient(rn3xV,q);
      +
      391  n3Vals.submit_value(rn3V,q); n3Vals.submit_gradient(rn3xV,q);
      392  #endif
      393  uVals.submit_gradient(Rux,q);
      394 
      @@ -495,7 +495,7 @@
      403  #if num_sop>2
      404  n3Vals.integrate(true, true); n3Vals.distribute_local_to_global(*dst[3]);
      405  #endif
      -
      406  uVals.integrate(false, true); uVals.distribute_local_to_global(*dst[1+num_sop]);
      +
      406  uVals.integrate(false, true); uVals.distribute_local_to_global(*dst[1+num_sop]);
      407  }
      408 }
      409 
      @@ -513,7 +513,7 @@
      421  typeScalar n3Vals(data,3);
      422  #endif
      423 
      -
      424  typeVector uVals(data, num_sop+1);
      +
      424  typeVector uVals(data, num_sop+1);
      425 
      426  //loop over cells
      427  for (unsigned int cell=cell_range.first; cell<cell_range.second; ++cell){
      @@ -555,9 +555,9 @@
      463  E = constV(0.5)*(ux + transpose(ux));
      464 
      465  // Compute stress tensor (which is equal to the residual, Rux)
      -
      466  if (n_dependent_stiffness == true){
      +
      466  if (n_dependent_stiffness == true){
      467  dealii::Tensor<2, 2*dim-1+dim/3, dealii::VectorizedArray<double> > CIJ_combined;
      -
      468  CIJ_combined = CIJ_alpha_tensor*(constV(1.0)-h1V-h2V-h3V) + CIJ_beta_tensor*(h1V+h2V+h3V);
      +
      468  CIJ_combined = CIJ_alpha_tensor*(constV(1.0)-h1V-h2V-h3V) + CIJ_beta_tensor*(h1V+h2V+h3V);
      469 
      470  computeStress<dim>(CIJ_combined, E, Rux);
      471  }
      @@ -592,7 +592,7 @@
      500  typeScalar n2Vals(data,2);
      501  #endif
      502 
      -
      503  typeVector uVals(data, num_sop+1);
      +
      503  typeVector uVals(data, num_sop+1);
      504 
      505  //loop over cells
      506  for (unsigned int cell=cell_range.first; cell<cell_range.second; ++cell){
      @@ -609,7 +609,7 @@
      517  #endif
      518 
      519  //initialize u field
      -
      520  uVals.reinit(cell); uVals.read_dof_values_plain(*src[num_sop+1]);
      +
      520  uVals.reinit(cell); uVals.read_dof_values_plain(*src[num_sop+1]);
      521  uVals.evaluate(false, true, false);
      522 
      523  dealii::AlignedVector<dealii::VectorizedArray<double> > JxW(cVals.n_q_points);
      @@ -649,26 +649,26 @@
      557 
      558  scalarvalueType total_energy_density = constV(0.0);
      559 
      -
      560  scalarvalueType f_chem = (constV(1.0)-(h1V+h2V+h3V))*faV + (h1V+h2V+h3V)*fbV + W*fbarrierV;
      +
      560  scalarvalueType f_chem = (constV(1.0)-(h1V+h2V+h3V))*faV + (h1V+h2V+h3V)*fbV + W*fbarrierV;
      561 
      562  scalarvalueType f_grad = constV(0.0);
      563 
      564  for (int i=0; i<dim; i++){
      565  for (int j=0; j<dim; j++){
      -
      566  f_grad += constV(0.5*Kn1[i][j])*n1x[i]*n1x[j];
      +
      566  f_grad += constV(0.5*Kn1[i][j])*n1x[i]*n1x[j];
      567  }
      568  }
      569 #if num_sop>1
      570  for (int i=0; i<dim; i++){
      571  for (int j=0; j<dim; j++){
      -
      572  f_grad += constV(0.5*Kn2[i][j])*n2x[i]*n2x[j];
      +
      572  f_grad += constV(0.5*Kn2[i][j])*n2x[i]*n2x[j];
      573  }
      574  }
      575 #endif
      576 #if num_sop>2
      577  for (int i=0; i<dim; i++){
      578  for (int j=0; j<dim; j++){
      -
      579  f_grad += constV(0.5*Kn3[i][j])*n3x[i]*n3x[j];
      +
      579  f_grad += constV(0.5*Kn3[i][j])*n3x[i]*n3x[j];
      580  }
      581  }
      582 #endif
      @@ -680,18 +680,18 @@
      588  for (unsigned int i=0; i<dim; i++){
      589  for (unsigned int j=0; j<dim; j++){
      590  // Polynomial fits for the stress-free transformation strains, of the form: sfts = a_p * c + b_p
      -
      591  sfts1[i][j] = constV(sfts_linear1[i][j])*c + constV(sfts_const1[i][j]);
      -
      592  sfts1c[i][j] = constV(sfts_linear1[i][j]);
      +
      591  sfts1[i][j] = constV(sfts_linear1[i][j])*c + constV(sfts_const1[i][j]);
      +
      592  sfts1c[i][j] = constV(sfts_linear1[i][j]);
      593  sfts1cc[i][j] = constV(0.0);
      594 
      595  // Polynomial fits for the stress-free transformation strains, of the form: sfts = a_p * c + b_p
      -
      596  sfts2[i][j] = constV(sfts_linear2[i][j])*c + constV(sfts_const2[i][j]);
      -
      597  sfts2c[i][j] = constV(sfts_linear1[i][j]);
      +
      596  sfts2[i][j] = constV(sfts_linear2[i][j])*c + constV(sfts_const2[i][j]);
      +
      597  sfts2c[i][j] = constV(sfts_linear1[i][j]);
      598  sfts2cc[i][j] = constV(0.0);
      599 
      600  // Polynomial fits for the stress-free transformation strains, of the form: sfts = a_p * c + b_p
      -
      601  sfts3[i][j] = constV(sfts_linear3[i][j])*c + constV(sfts_const3[i][j]);
      -
      602  sfts3c[i][j] = constV(sfts_linear3[i][j]);
      +
      601  sfts3[i][j] = constV(sfts_linear3[i][j])*c + constV(sfts_const3[i][j]);
      +
      602  sfts3c[i][j] = constV(sfts_linear3[i][j]);
      603  sfts3cc[i][j] = constV(0.0);
      604  }
      605  }
      @@ -711,9 +711,9 @@
      619  //S=C*(E-E0)
      620  dealii::VectorizedArray<double> CIJ_combined[2*dim-1+dim/3][2*dim-1+dim/3];
      621 
      -
      622  if (n_dependent_stiffness == true){
      +
      622  if (n_dependent_stiffness == true){
      623  dealii::VectorizedArray<double> sum_hV;
      -
      624  sum_hV = h1V+h2V+h3V;
      +
      624  sum_hV = h1V+h2V+h3V;
      625  for (unsigned int i=0; i<2*dim-1+dim/3; i++){
      626  for (unsigned int j=0; j<2*dim-1+dim/3; j++){
      627  CIJ_combined[i][j] = constV(CIJ_alpha(i,j))*(constV(1.0)-sum_hV) + constV(CIJ_beta(i,j))*sum_hV;
      @@ -793,7 +793,7 @@
      701  nuclei.clear();
      702 
      703 #ifndef c_matrix
      -
      704  double c_matrix = 1.0e-6;
      +
      704  double c_matrix = 1.0e-6;
      705 #endif
      706 
      707  //populate localNuclei vector
      @@ -1043,92 +1043,90 @@
      951 }
      952 
      953 #endif
      -
      Kn1
      double Kn1[3][3]
      Definition: residuals.h:11
      +
      rcxV
      #define rcxV
      Definition: residuals.h:12
      CoupledCHACMechanicsProblem::CIJ_alpha_tensor
      dealii::Tensor< 2, 2 *dim-1+dim/3, dealii::VectorizedArray< double > > CIJ_alpha_tensor
      Definition: coupledCHACMechanics.h:27
      -
      hn1V
      #define hn1V
      Definition: residuals.h:58
      -
      MatrixFreePDE::pcout
      ConditionalOStream pcout
      Definition: matrixFreePDE.h:258
      -
      faV
      #define faV
      Definition: residuals.h:43
      -
      hn3V
      #define hn3V
      Definition: residuals.h:60
      +
      MatrixFreePDE::pcout
      ConditionalOStream pcout
      Definition: matrixFreePDE.h:271
      nucleus::seededTime
      double seededTime
      Definition: coupledCHACMechanics.h:667
      +
      hn2V
      #define hn2V
      Definition: equations.h:62
      +
      sfts_linear1
      double sfts_linear1[3][3]
      Definition: equations.h:67
      MatrixFreePDE
      Definition: matrixFreePDE.h:85
      -
      CoupledCHACMechanicsProblem::markBoundaries
      void markBoundaries()
      Definition: ICs_and_BCs.h:79
      +
      nucleus::seedingTime
      double seedingTime
      Definition: coupledCHACMechanics.h:667
      -
      rn1V
      #define rn1V
      Definition: residuals.h:67
      finiteElementDegree
      #define finiteElementDegree
      Definition: defaultValues.h:32
      +
      rn2V
      #define rn2V
      Definition: equations.h:83
      W
      #define W
      -
      MaterialConstantsBetaV
      #define MaterialConstantsBetaV
      Definition: residuals.h:24
      +
      rn3xV
      #define rn3xV
      Definition: equations.h:88
      hn1strainV
      #define hn1strainV
      scalargradType
      dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
      Definition: matrixFreePDE.h:51
      -
      sfts_const1
      double sfts_const1[3][3]
      Definition: residuals.h:29
      -
      MaterialModelBetaV
      #define MaterialModelBetaV
      Definition: residuals.h:23
      CoupledCHACMechanicsProblem::c_dependent_misfit
      bool c_dependent_misfit
      Definition: coupledCHACMechanics.h:54
      -
      rn3V
      #define rn3V
      Definition: residuals.h:69
      hn2strainV
      #define hn2strainV
      -
      rcV
      #define rcV
      Definition: residuals.h:11
      -
      h1V
      #define h1V
      Definition: residuals.h:55
      +
      h2V
      #define h2V
      Definition: equations.h:84
      +
      hn3V
      #define hn3V
      Definition: equations.h:63
      nucleus::center
      dealii::Point< problemDIM > center
      Definition: coupledCHACMechanics.h:665
      constV
      #define constV(a)
      Definition: matrixFreePDE.h:67
      +
      faV
      #define faV
      Definition: equations.h:37
      nuclei
      std::vector< nucleus > nuclei
      Definition: coupledCHACMechanics.h:670
      spanX
      #define spanX
      Definition: defaultValues.h:12
      +
      rn1V
      #define rn1V
      Definition: equations.h:82
      +
      sfts_const2
      double sfts_const2[3][3]
      Definition: equations.h:71
      +
      c_matrix
      #define c_matrix
      +
      Kn3
      double Kn3[3][3]
      Definition: equations.h:56
      CoupledCHACMechanicsProblem::CIJ
      Table< 2, double > CIJ
      Definition: coupledCHACMechanics.h:22
      -
      rn1xV
      #define rn1xV
      Definition: residuals.h:70
      vectorType
      dealii::parallel::distributed::Vector< double > vectorType
      Definition: matrixFreePDE.h:23
      CoupledCHACMechanicsProblem::modifySolutionFields
      void modifySolutionFields()
      Definition: coupledCHACMechanics.h:674
      -
      num_sop
      #define num_sop
      Definition: parameters.h:20
      spanY
      #define spanY
      Definition: defaultValues.h:17
      h1strainV
      #define h1strainV
      -
      sfts_linear1
      double sfts_linear1[3][3]
      Definition: residuals.h:28
      +
      h3V
      #define h3V
      Definition: equations.h:85
      localNuclei
      std::vector< nucleus > localNuclei
      Definition: coupledCHACMechanics.h:670
      -
      Kn3
      double Kn3[3][3]
      Definition: residuals.h:13
      +
      Kn1
      double Kn1[3][3]
      Definition: equations.h:54
      +
      rn3V
      #define rn3V
      Definition: equations.h:84
      hn3strainV
      #define hn3strainV
      CoupledCHACMechanicsProblem::getRHS
      void getRHS(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range) const
      Definition: coupledCHACMechanics.h:144
      -
      CoupledCHACMechanicsProblem::applyDirichletBCs
      void applyDirichletBCs()
      Definition: ICs_and_BCs.h:70
      -
      sfts_linear3
      double sfts_linear3[3][3]
      Definition: residuals.h:34
      +
      refineFactor
      #define refineFactor
      Definition: defaultValues.h:27
      h2strainV
      #define h2strainV
      +
      Kn2
      double Kn2[3][3]
      Definition: equations.h:55
      +
      rn2xV
      #define rn2xV
      Definition: equations.h:87
      CoupledCHACMechanicsProblem::assembler_lock
      Threads::Mutex assembler_lock
      Definition: coupledCHACMechanics.h:60
      CoupledCHACMechanicsProblem::computeIntegral
      void computeIntegral(double &integratedField)
      Definition: coupledCHACMechanics.h:917
      -
      rn2xV
      #define rn2xV
      Definition: residuals.h:71
      nucleus::radius
      double radius
      Definition: coupledCHACMechanics.h:666
      CoupledCHACMechanicsProblem::adaptiveRefine
      void adaptiveRefine(unsigned int currentIncrement)
      CoupledCHACMechanicsProblem::CIJ_alpha
      Table< 2, double > CIJ_alpha
      Definition: coupledCHACMechanics.h:23
      CoupledCHACMechanicsProblem::getEnergy
      void getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)
      Definition: coupledCHACMechanics.h:487
      -
      Kn2
      double Kn2[3][3]
      Definition: residuals.h:12
      +
      rn1xV
      #define rn1xV
      Definition: equations.h:86
      CoupledCHACMechanicsProblem::CIJ_beta_tensor
      dealii::Tensor< 2, 2 *dim-1+dim/3, dealii::VectorizedArray< double > > CIJ_beta_tensor
      Definition: coupledCHACMechanics.h:27
      CoupledCHACMechanicsProblem::CoupledCHACMechanicsProblem
      CoupledCHACMechanicsProblem()
      Definition: coupledCHACMechanics.h:70
      +
      n_dependent_stiffness
      #define n_dependent_stiffness
      Definition: equations.h:59
      +
      sfts_linear2
      double sfts_linear2[3][3]
      Definition: equations.h:70
      typeScalar
      dealii::FEEvaluation< problemDIM, finiteElementDegree, finiteElementDegree+1, 1, double > typeScalar
      Definition: matrixFreePDE.h:27
      -
      rcxV
      #define rcxV
      Definition: residuals.h:12
      subdivisionsX
      #define subdivisionsX
      Definition: parameters.h:14
      MaterialModelV
      #define MaterialModelV
      Definition: parameters.h:26
      -
      CoupledCHACMechanicsProblem::applyInitialConditions
      void applyInitialConditions()
      Definition: ICs_and_BCs.h:50
      -
      h2V
      #define h2V
      Definition: residuals.h:56
      -
      hn2V
      #define hn2V
      Definition: residuals.h:59
      -
      sfts_const2
      double sfts_const2[3][3]
      Definition: residuals.h:32
      +
      timeStep
      #define timeStep
      nucleus::index
      unsigned int index
      Definition: coupledCHACMechanics.h:664
      +
      fbV
      #define fbV
      Definition: equations.h:40
      +
      sfts_const1
      double sfts_const1[3][3]
      Definition: equations.h:68
      CoupledCHACMechanicsProblem::getLHS
      void getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const
      Definition: coupledCHACMechanics.h:411
      CoupledCHACMechanicsProblem
      Definition: coupledCHACMechanics.h:13
      vectorhessType
      dealii::Tensor< 3, problemDIM, dealii::VectorizedArray< double > > vectorhessType
      Definition: matrixFreePDE.h:60
      +
      rcV
      #define rcV
      Definition: residuals.h:11
      timeIncrements
      #define timeIncrements
      MaterialConstantsV
      #define MaterialConstantsV
      Definition: parameters.h:27
      h3strainV
      #define h3strainV
      vectorgradType
      dealii::Tensor< 2, problemDIM, dealii::VectorizedArray< double > > vectorgradType
      Definition: matrixFreePDE.h:57
      -
      sfts_const3
      double sfts_const3[3][3]
      Definition: residuals.h:35
      +
      hn1V
      #define hn1V
      Definition: equations.h:61
      +
      h1V
      #define h1V
      Definition: equations.h:83
      fbarrierV
      #define fbarrierV
      -
      MatrixFreePDE::currentIncrement
      unsigned int currentIncrement
      Definition: matrixFreePDE.h:255
      -
      n_dependent_stiffness
      #define n_dependent_stiffness
      Definition: residuals.h:16
      +
      MatrixFreePDE::currentIncrement
      unsigned int currentIncrement
      Definition: matrixFreePDE.h:268
      +
      sfts_const3
      double sfts_const3[3][3]
      Definition: equations.h:74
      scalarvalueType
      dealii::VectorizedArray< double > scalarvalueType
      Definition: matrixFreePDE.h:34
      -
      fbV
      #define fbV
      Definition: residuals.h:51
      -
      h3V
      #define h3V
      Definition: residuals.h:57
      -
      sfts_linear2
      double sfts_linear2[3][3]
      Definition: residuals.h:31
      +
      sfts_linear3
      double sfts_linear3[3][3]
      Definition: equations.h:73
      nucleus
      Definition: coupledCHACMechanics.h:663
      CoupledCHACMechanicsProblem::CIJ_beta
      Table< 2, double > CIJ_beta
      Definition: coupledCHACMechanics.h:24
      -
      rn2V
      #define rn2V
      Definition: residuals.h:68
      CoupledCHACMechanicsProblem::CIJ_diff
      Table< 2, double > CIJ_diff
      Definition: coupledCHACMechanics.h:25
      typeVector
      dealii::FEEvaluation< problemDIM, finiteElementDegree, finiteElementDegree+1, problemDIM, double > typeVector
      Definition: matrixFreePDE.h:30
      -
      rn3xV
      #define rn3xV
      Definition: residuals.h:72
      diff --git a/html/coupled_c_h_mechanics_8h_source.html b/html/coupled_c_h_mechanics_8h_source.html index 153d2a017..bb80feaaf 100644 --- a/html/coupled_c_h_mechanics_8h_source.html +++ b/html/coupled_c_h_mechanics_8h_source.html @@ -229,8 +229,8 @@
      137  }
      138 
      139  //submit values
      -
      140  muVals.submit_value(rmuV,q); muVals.submit_gradient(rmuxV,q);
      -
      141  cVals.submit_value(rcV,q); cVals.submit_gradient(rcxV,q);
      +
      140  muVals.submit_value(rmuV,q); muVals.submit_gradient(rmuxV,q);
      +
      141  cVals.submit_value(rcV,q); cVals.submit_gradient(rcxV,q);
      142  uVals.submit_gradient(Rux,q);
      143  }
      144 
      @@ -323,7 +323,7 @@
      231  total_energy_density = c*c*c*c - constV(2.0)*c*c*c + c*c;
      232 
      233  for (unsigned int i=1; i<dim; i++){
      -
      234  total_energy_density += KcV*constV(0.5)*cx[i]*cx[i];
      +
      234  total_energy_density += KcV*constV(0.5)*cx[i]*cx[i];
      235  }
      236 
      237  assembler_lock.acquire ();
      @@ -337,29 +337,29 @@
      245 
      246 
      247 #endif
      -
      MatrixFreePDE::pcout
      ConditionalOStream pcout
      Definition: matrixFreePDE.h:258
      +
      rcxV
      #define rcxV
      Definition: residuals.h:12
      +
      MatrixFreePDE::pcout
      ConditionalOStream pcout
      Definition: matrixFreePDE.h:271
      +
      rmuV
      #define rmuV
      Definition: residuals.h:9
      CoupledCahnHilliardMechanicsProblem::getEnergy
      void getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)
      Definition: coupledCHMechanics.h:199
      MatrixFreePDE
      Definition: matrixFreePDE.h:85
      CoupledCahnHilliardMechanicsProblem::CIJ
      Table< 2, double > CIJ
      Definition: coupledCHMechanics.h:20
      CoupledCahnHilliardMechanicsProblem::getRHS
      void getRHS(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range) const
      Definition: coupledCHMechanics.h:82
      scalargradType
      dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
      Definition: matrixFreePDE.h:51
      -
      rcV
      #define rcV
      Definition: residuals.h:11
      +
      rmuxV
      #define rmuxV
      Definition: residuals.h:10
      constV
      #define constV(a)
      Definition: matrixFreePDE.h:67
      -
      rmuV
      #define rmuV
      Definition: residuals.h:9
      vectorType
      dealii::parallel::distributed::Vector< double > vectorType
      Definition: matrixFreePDE.h:23
      CoupledCahnHilliardMechanicsProblem::CoupledCahnHilliardMechanicsProblem
      CoupledCahnHilliardMechanicsProblem()
      Definition: coupledCHMechanics.h:50
      -
      rmuxV
      #define rmuxV
      Definition: residuals.h:10
      CoupledCahnHilliardMechanicsProblem
      Definition: coupledCHMechanics.h:13
      typeScalar
      dealii::FEEvaluation< problemDIM, finiteElementDegree, finiteElementDegree+1, 1, double > typeScalar
      Definition: matrixFreePDE.h:27
      -
      rcxV
      #define rcxV
      Definition: residuals.h:12
      MaterialModelV
      #define MaterialModelV
      Definition: parameters.h:26
      CoupledCahnHilliardMechanicsProblem::getLHS
      void getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const
      Definition: coupledCHMechanics.h:153
      -
      KcV
      #define KcV
      Definition: residuals.h:3
      CoupledCahnHilliardMechanicsProblem::assembler_lock
      Threads::Mutex assembler_lock
      Definition: coupledCHMechanics.h:45
      +
      rcV
      #define rcV
      Definition: residuals.h:11
      MaterialConstantsV
      #define MaterialConstantsV
      Definition: parameters.h:27
      vectorgradType
      dealii::Tensor< 2, problemDIM, dealii::VectorizedArray< double > > vectorgradType
      Definition: matrixFreePDE.h:57
      +
      KcV
      #define KcV
      Definition: residuals.h:3
      scalarvalueType
      dealii::VectorizedArray< double > scalarvalueType
      Definition: matrixFreePDE.h:34
      typeVector
      dealii::FEEvaluation< problemDIM, finiteElementDegree, finiteElementDegree+1, problemDIM, double > typeVector
      Definition: matrixFreePDE.h:30
      diff --git a/html/coupled_cahn_hilliard_allen_cahn_2_i_cs__and___b_cs_8h_source.html b/html/coupled_cahn_hilliard_allen_cahn_2_i_cs__and___b_cs_8h_source.html index 2e07434c9..3738bf477 100644 --- a/html/coupled_cahn_hilliard_allen_cahn_2_i_cs__and___b_cs_8h_source.html +++ b/html/coupled_cahn_hilliard_allen_cahn_2_i_cs__and___b_cs_8h_source.html @@ -186,7 +186,7 @@
      94  // Inputs to "inputBCs":
      95  // First input: variable number
      96  // Second input: component number
      -
      97  // Third input: BC type (options are "ZERO_DERIVATIVE" and "DIRICHLET")
      +
      97  // Third input: BC type (options are "ZERO_DERIVATIVE", "DIRICHLET", and "PERIODIC")
      98  // Fourth input: BC value (ignored unless the BC type is "DIRICHLET")
      99  // Odd inputs after the third: BC type
      100  // Even inputs after the third: BC value
      @@ -199,7 +199,7 @@
      107 }
      108 
      109 
      -
      InitialConditionVec::index
      unsigned int index
      Definition: ICs_and_BCs.h:46
      +
      InitialConditionVec::index
      unsigned int index
      Definition: ICs_and_BCs.h:47
      InitialConditionVec::vector_value
      void vector_value(const Point< dim > &p, Vector< double > &vector_IC) const
      Definition: ICs_and_BCs.h:69
      spanX
      #define spanX
      Definition: defaultValues.h:12
      InitialCondition::InitialCondition
      InitialCondition(const unsigned int _index)
      Definition: ICs_and_BCs.h:7
      @@ -207,11 +207,11 @@
      InitialCondition::value
      double value(const Point< dim > &p, const unsigned int component=0) const
      Definition: ICs_and_BCs.h:10
      InitialCondition::index
      unsigned int index
      Definition: ICs_and_BCs.h:5
      refineFactor
      #define refineFactor
      Definition: defaultValues.h:27
      -
      generalizedProblem::setBCs
      void setBCs()
      Definition: ICs_and_BCs.h:66
      +
      generalizedProblem::setBCs
      void setBCs()
      Definition: ICs_and_BCs.h:67
      InitialConditionVec::InitialConditionVec
      InitialConditionVec(const unsigned int _index)
      Definition: ICs_and_BCs.h:66
      subdivisionsX
      #define subdivisionsX
      Definition: parameters.h:14
      InitialCondition
      Definition: ICs_and_BCs.h:2
      -
      InitialConditionVec
      Definition: ICs_and_BCs.h:43
      +
      InitialConditionVec
      Definition: ICs_and_BCs.h:44
      spanZ
      #define spanZ
      Definition: defaultValues.h:22
      InitialCondition::values
      Vector< double > values
      Definition: ICs_and_BCs.h:6
      diff --git a/html/coupled_cahn_hilliard_allen_cahn_2equations_8h.html b/html/coupled_cahn_hilliard_allen_cahn_2equations_8h.html index 11c921bdc..15dfcf168 100644 --- a/html/coupled_cahn_hilliard_allen_cahn_2equations_8h.html +++ b/html/coupled_cahn_hilliard_allen_cahn_2equations_8h.html @@ -137,15 +137,15 @@   #define hnV   (30.0*n*n-60.0*n*n*n+30.0*n*n*n*n)   -#define muxV   ( cx*((1.0-hV)*faccV+hV*fbccV) + nx*((fbcV-facV)*hnV) ) +#define muxV   ( cx*((1.0-hV)*faccV+hV*fbccV) + nx*((fbcV-facV)*hnV) )   #define rcV   (c)   -#define rcxV   (constV(-McV*timeStep)*muxV) +#define rcxV   (constV(-McV*timeStep)*muxV)   -#define rnV   (n-constV(timeStep*MnV)*(fbV-faV)*hnV) +#define rnV   (n-constV(timeStep*MnV)*(fbV-faV)*hnV)   -#define rnxV   (constV(-timeStep*KnV*MnV)*nx) +#define rnxV   (constV(-timeStep*KnV*MnV)*nx)  

      Macro Definition Documentation

      @@ -286,7 +286,7 @@

      Macro Definition Documentation

      - +
      #define muxV   ( cx*((1.0-hV)*faccV+hV*fbccV) + nx*((fbcV-facV)*hnV) )#define muxV   ( cx*((1.0-hV)*faccV+hV*fbccV) + nx*((fbcV-facV)*hnV) )
      @@ -382,7 +382,7 @@

      Macro Definition Documentation

      - +
      #define rcxV   (constV(-McV*timeStep)*muxV)#define rcxV   (constV(-McV*timeStep)*muxV)
      @@ -394,7 +394,7 @@

      Macro Definition Documentation

      - +
      #define rnV   (n-constV(timeStep*MnV)*(fbV-faV)*hnV)#define rnV   (n-constV(timeStep*MnV)*(fbV-faV)*hnV)
      @@ -406,7 +406,7 @@

      Macro Definition Documentation

      - +
      #define rnxV   (constV(-timeStep*KnV*MnV)*nx)#define rnxV   (constV(-timeStep*KnV*MnV)*nx)
      diff --git a/html/coupled_cahn_hilliard_allen_cahn_2equations_8h_source.html b/html/coupled_cahn_hilliard_allen_cahn_2equations_8h_source.html index 2523dd02e..a63f7b984 100644 --- a/html/coupled_cahn_hilliard_allen_cahn_2equations_8h_source.html +++ b/html/coupled_cahn_hilliard_allen_cahn_2equations_8h_source.html @@ -161,8 +161,8 @@
      69 // each residual equation. The index for each variable in these lists corresponds to
      70 // the order it is defined at the top of this file (starting at 0).
      71 template <int dim>
      -
      72 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim>> & modelVariablesList,
      -
      73  std::vector<modelResidual<dim>> & modelResidualsList,
      +
      72 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim> > & modelVariablesList,
      +
      73  std::vector<modelResidual<dim> > & modelResidualsList,
      74  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
      75 
      76 // The concentration and its derivatives (names here should match those in the macros above)
      @@ -199,7 +199,7 @@
      107 // that the correct residual is being submitted. The index of the field being solved
      108 // can be accessed by "this->currentFieldIndex".
      109 template <int dim>
      -
      110 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim>> & modelVariablesList,
      +
      110 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim> > & modelVariablesList,
      111  modelResidual<dim> & modelRes,
      112  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
      113 
      @@ -217,7 +217,7 @@
      125 // density are added to the "energy_components" variable (index 0: chemical energy,
      126 // index 1: gradient energy, index 2: elastic energy).
      127 template <int dim>
      -
      128 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim>> & modelVariablesList,
      +
      128 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim> > & modelVariablesList,
      129  const dealii::VectorizedArray<double> & JxW_value,
      130  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) {
      131 
      @@ -255,22 +255,22 @@
      163 
      164 
      165 
      -
      generalizedProblem::energyDensity
      void energyDensity(const std::vector< modelVariable< dim >> &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
      Definition: equations.h:106
      modelResidual
      Definition: model_variables.h:25
      faV
      #define faV
      Definition: equations.h:43
      +
      generalizedProblem::residualRHS
      void residualRHS(const std::vector< modelVariable< dim > > &modelVarList, std::vector< modelResidual< dim > > &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
      Definition: equations.h:58
      scalargradType
      dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
      Definition: matrixFreePDE.h:51
      -
      generalizedProblem::residualLHS
      void residualLHS(const std::vector< modelVariable< dim >> &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
      Definition: equations.h:89
      constV
      #define constV(a)
      Definition: matrixFreePDE.h:67
      rcV
      #define rcV
      Definition: equations.h:56
      rnxV
      #define rnxV
      Definition: equations.h:59
      KnV
      #define KnV
      Definition: equations.h:40
      rcxV
      #define rcxV
      Definition: equations.h:57
      +
      generalizedProblem::residualLHS
      void residualLHS(const std::vector< modelVariable< dim > > &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
      Definition: equations.h:89
      rnV
      #define rnV
      Definition: equations.h:58
      modelVariable
      Definition: model_variables.h:4
      fbV
      #define fbV
      Definition: equations.h:46
      hV
      #define hV
      Definition: equations.h:51
      scalarvalueType
      dealii::VectorizedArray< double > scalarvalueType
      Definition: matrixFreePDE.h:34
      -
      generalizedProblem::residualRHS
      void residualRHS(const std::vector< modelVariable< dim >> &modelVarList, std::vector< modelResidual< dim >> &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
      Definition: equations.h:58
      +
      generalizedProblem::energyDensity
      void energyDensity(const std::vector< modelVariable< dim > > &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
      Definition: equations.h:106
      diff --git a/html/coupled_cahn_hilliard_allen_cahn_2parameters_8h.html b/html/coupled_cahn_hilliard_allen_cahn_2parameters_8h.html index 5f01a57eb..5c0a6b5f3 100644 --- a/html/coupled_cahn_hilliard_allen_cahn_2parameters_8h.html +++ b/html/coupled_cahn_hilliard_allen_cahn_2parameters_8h.html @@ -123,8 +123,14 @@   #define writeOutput   true   -#define skipOutputSteps   1000 -  +#define outputCondition   "EQUAL_SPACING" +  +#define numOutputs   10 +  +#define outputList   {} +  +#define skipPrintSteps   10 +  #define calcEnergy   true   @@ -151,6 +157,42 @@

      Macro Definition Documentation

      +
      +
      + +
      +
      + + + + +
      #define numOutputs   10
      +
      + +
      +
      + +
      +
      + + + + +
      #define outputCondition   "EQUAL_SPACING"
      +
      + +
      +
      + +
      +
      + + + + +
      #define outputList   {}
      +
      +
      @@ -177,12 +219,12 @@

      Macro Definition Documentation

      - +
      - +
      #define skipOutputSteps   1000#define skipPrintSteps   10
      diff --git a/html/coupled_cahn_hilliard_allen_cahn_2parameters_8h_source.html b/html/coupled_cahn_hilliard_allen_cahn_2parameters_8h_source.html index d370cd8d9..ad66df6cb 100644 --- a/html/coupled_cahn_hilliard_allen_cahn_2parameters_8h_source.html +++ b/html/coupled_cahn_hilliard_allen_cahn_2parameters_8h_source.html @@ -136,18 +136,30 @@
      44 // Each field in the problem will be output is writeOutput is set to "true"
      45 #define writeOutput true
      46 
      -
      47 // Output files are written every skipOutputSteps time steps
      -
      48 #define skipOutputSteps 1000
      -
      49 
      -
      50 // =================================================================================
      -
      51 // Set the flag determining if the total free energy is calculated for each output
      -
      52 // =================================================================================
      -
      53 #define calcEnergy true
      +
      47 // Type of spacing between outputs ("EQUAL_SPACING", "LOG_SPACING", "N_PER_DECADE",
      +
      48 // or "LIST")
      +
      49 #define outputCondition "EQUAL_SPACING"
      +
      50 
      +
      51 // Number of times the program outputs the fields (total number for "EQUAL_SPACING"
      +
      52 // and "LOG_SPACING", number per decade for "N_PER_DECADE", ignored for "LIST")
      +
      53 #define numOutputs 10
      54 
      -
      55 
      -
      56 
      -
      57 
      +
      55 // User-defined list of time steps where the program should output. Only used if
      +
      56 // outputCondition is "LIST"
      +
      57 #define outputList {}
      58 
      +
      59 // Status is printed to the screen every skipPrintSteps
      +
      60 #define skipPrintSteps 10
      +
      61 
      +
      62 // =================================================================================
      +
      63 // Set the flag determining if the total free energy is calculated for each output
      +
      64 // =================================================================================
      +
      65 #define calcEnergy true
      +
      66 
      +
      67 
      +
      68 
      +
      69 
      +
      70 
      diff --git a/html/deal_i_iheaders_8h.html b/html/deal_i_iheaders_8h.html index 38b252d9f..b0745feb5 100644 --- a/html/deal_i_iheaders_8h.html +++ b/html/deal_i_iheaders_8h.html @@ -107,6 +107,7 @@ #include <deal.II/grid/tria_accessor.h>
      #include <deal.II/grid/tria_iterator.h>
      #include <deal.II/grid/grid_generator.h>
      +#include <deal.II/grid/grid_tools.h>
      #include <deal.II/dofs/dof_tools.h>
      #include <deal.II/dofs/dof_handler.h>
      #include <deal.II/numerics/data_out.h>
      diff --git a/html/deal_i_iheaders_8h_source.html b/html/deal_i_iheaders_8h_source.html index 403611fc0..f7743b0e3 100644 --- a/html/deal_i_iheaders_8h_source.html +++ b/html/deal_i_iheaders_8h_source.html @@ -108,20 +108,21 @@
      16 #include <deal.II/grid/tria_accessor.h>
      17 #include <deal.II/grid/tria_iterator.h>
      18 #include <deal.II/grid/grid_generator.h>
      -
      19 #include <deal.II/dofs/dof_tools.h>
      -
      20 #include <deal.II/dofs/dof_handler.h>
      -
      21 #include <deal.II/numerics/data_out.h>
      -
      22 #include <deal.II/numerics/vector_tools.h>
      -
      23 #include <deal.II/lac/parallel_vector.h>
      -
      24 #include <deal.II/matrix_free/matrix_free.h>
      -
      25 #include <deal.II/matrix_free/fe_evaluation.h>
      -
      26 #include <deal.II/base/config.h>
      -
      27 #include <deal.II/base/exceptions.h>
      -
      28 #include <deal.II/distributed/tria.h>
      -
      29 #include <deal.II/distributed/solution_transfer.h>
      -
      30 #include <deal.II/numerics/error_estimator.h>
      -
      31 #include <deal.II/distributed/grid_refinement.h>
      -
      32 
      +
      19 #include <deal.II/grid/grid_tools.h>
      +
      20 #include <deal.II/dofs/dof_tools.h>
      +
      21 #include <deal.II/dofs/dof_handler.h>
      +
      22 #include <deal.II/numerics/data_out.h>
      +
      23 #include <deal.II/numerics/vector_tools.h>
      +
      24 #include <deal.II/lac/parallel_vector.h>
      +
      25 #include <deal.II/matrix_free/matrix_free.h>
      +
      26 #include <deal.II/matrix_free/fe_evaluation.h>
      +
      27 #include <deal.II/base/config.h>
      +
      28 #include <deal.II/base/exceptions.h>
      +
      29 #include <deal.II/distributed/tria.h>
      +
      30 #include <deal.II/distributed/solution_transfer.h>
      +
      31 #include <deal.II/numerics/error_estimator.h>
      +
      32 #include <deal.II/distributed/grid_refinement.h>
      +
      33 
      diff --git a/html/default_values_8h.html b/html/default_values_8h.html index ee027525f..d39402064 100644 --- a/html/default_values_8h.html +++ b/html/default_values_8h.html @@ -123,6 +123,18 @@   #define skipImplicitSolves   1   +#define outputCondition   "EQUAL_SPACING" +  +#define outputList   {0} +  +#define outputFileType   "vtu" +  +#define numOutputs   1 +  +#define skipPrintSteps   1 +  +#define hAdaptivity   false + 

      Macro Definition Documentation

      @@ -135,6 +147,18 @@

      Macro Definition Documentation

      +
      + + +
      +
      + + + + +
      #define hAdaptivity   false
      +
      +
      @@ -159,6 +183,54 @@

      Macro Definition Documentation

      +
      + + +
      +
      + + + + +
      #define numOutputs   1
      +
      + +
      +
      + +
      +
      + + + + +
      #define outputCondition   "EQUAL_SPACING"
      +
      + +
      +
      + +
      +
      + + + + +
      #define outputFileType   "vtu"
      +
      + +
      +
      + +
      +
      + + + + +
      #define outputList   {0}
      +
      +
      @@ -207,6 +279,18 @@

      Macro Definition Documentation

      +
      + + +
      +
      + + + + +
      #define skipPrintSteps   1
      +
      +
      diff --git a/html/default_values_8h_source.html b/html/default_values_8h_source.html index c754f9ec1..e4731d8d0 100644 --- a/html/default_values_8h_source.html +++ b/html/default_values_8h_source.html @@ -159,7 +159,36 @@
      67 #define skipImplicitSolves 1
      68 #endif
      69 
      -
      70 #endif
      +
      70 //number of implicit solves to skip. None are skipped if value is 1, which is the default.
      +
      71 #ifndef outputCondition
      +
      72 #define outputCondition "EQUAL_SPACING"
      +
      73 #endif
      +
      74 
      +
      75 #ifndef outputList
      +
      76 #define outputList {0}
      +
      77 #endif
      +
      78 
      +
      79 #ifndef outputFileType
      +
      80 #define outputFileType "vtu"
      +
      81 #endif
      +
      82 
      +
      83 #ifndef numOutputs
      +
      84 #define numOutputs 1
      +
      85 #endif
      +
      86 
      +
      87 //#ifndef enablePFields
      +
      88 //#define enablePFields false
      +
      89 //#endif
      +
      90 
      +
      91 #ifndef skipPrintSteps
      +
      92 #define skipPrintSteps 1
      +
      93 #endif
      +
      94 
      +
      95 #ifndef hAdaptivity
      +
      96 #define hAdaptivity false
      +
      97 #endif
      +
      98 
      +
      99 #endif
      diff --git a/html/dir_00903e9e694c49742a3bab55e4d86531.html b/html/dir_00903e9e694c49742a3bab55e4d86531.html new file mode 100644 index 000000000..b1df73d07 --- /dev/null +++ b/html/dir_00903e9e694c49742a3bab55e4d86531.html @@ -0,0 +1,107 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/allenCahn_pfield Directory Reference + + + + + + + + + + +
      +
      + + + + + + +
      +
      PRISMS PhaseField +  1.0 +
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      +
      allenCahn_pfield Directory Reference
      +
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      +Files

      file  equations.h [code]
       
      file  ICs_and_BCs.h [code]
       
      file  main.cc
       
      file  parameters.h [code]
       
      +
      + +
      +Generated by   +doxygen + 1.8.10 +
      + + diff --git a/html/dir_1366dd5926958b394178a0b7be98ccde.html b/html/dir_1366dd5926958b394178a0b7be98ccde.html new file mode 100644 index 000000000..ffdc367ea --- /dev/null +++ b/html/dir_1366dd5926958b394178a0b7be98ccde.html @@ -0,0 +1,101 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/src/pFunction Directory Reference + + + + + + + + + + +
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      file  equations.h [code]
       
      file  ICs_and_BCs.h [code]
       
      file  main.cc
       
      file  parameters.h [code]
       
      +
      + +
      +Generated by   +doxygen + 1.8.10 +
      + + diff --git a/html/dir_596cafc87c409051734ff25d3f3a8536.html b/html/dir_596cafc87c409051734ff25d3f3a8536.html new file mode 100644 index 000000000..2ce779095 --- /dev/null +++ b/html/dir_596cafc87c409051734ff25d3f3a8536.html @@ -0,0 +1,107 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/CHiMaD_benchmark1a Directory Reference + + + + + + + + + + +
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      file  equations.h [code]
       
      file  ICs_and_BCs.h [code]
       
      file  main.cc
       
      file  parameters.h [code]
       
      +
      + +
      +Generated by   +doxygen + 1.8.10 +
      + + diff --git a/html/dir_6f4c8ec7a166ba926b367e6a31522088.html b/html/dir_6f4c8ec7a166ba926b367e6a31522088.html new file mode 100644 index 000000000..908549993 --- /dev/null +++ b/html/dir_6f4c8ec7a166ba926b367e6a31522088.html @@ -0,0 +1,107 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/CHiMaD_benchmark2a Directory Reference + + + + + + + + + + +
      +
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      CHiMaD_benchmark2a Directory Reference
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      +Files

      file  equations.h [code]
       
      file  ICs_and_BCs.h [code]
       
      file  main.cc
       
      file  parameters.h [code]
       
      +
      + +
      +Generated by   +doxygen + 1.8.10 +
      + + diff --git a/html/dir_7baae37d69621177578e4543a43f7d3c.html b/html/dir_7baae37d69621177578e4543a43f7d3c.html index 6f5396d4e..e4333d3eb 100644 --- a/html/dir_7baae37d69621177578e4543a43f7d3c.html +++ b/html/dir_7baae37d69621177578e4543a43f7d3c.html @@ -93,7 +93,7 @@   file  parameters.h [code]   -file  residuals.h [code] +file  residuals.h [code]   diff --git a/html/dir_8b2ccb0ee04f4745dfaa8718adc0ecb6.html b/html/dir_8b2ccb0ee04f4745dfaa8718adc0ecb6.html new file mode 100644 index 000000000..efc68781e --- /dev/null +++ b/html/dir_8b2ccb0ee04f4745dfaa8718adc0ecb6.html @@ -0,0 +1,112 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/precipitateEvolution_pfunction Directory Reference + + + + + + + + + + +
      +
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      +
      precipitateEvolution_pfunction Directory Reference
      +
      +
      + + + + +

      +Directories

      directory  PLibrary
       
      + + + + + + + + + +

      +Files

      file  equations.h [code]
       
      file  ICs_and_BCs.h [code]
       
      file  main.cc
       
      file  parameters.h [code]
       
      +
      + +
      +Generated by   +doxygen + 1.8.10 +
      + + diff --git a/html/dir_da2bc9e5bb0cc4a7e846a35a317e0152.html b/html/dir_da2bc9e5bb0cc4a7e846a35a317e0152.html index cf22fb6f8..990e3ff47 100644 --- a/html/dir_da2bc9e5bb0cc4a7e846a35a317e0152.html +++ b/html/dir_da2bc9e5bb0cc4a7e846a35a317e0152.html @@ -91,6 +91,8 @@   directory  models   +directory  pFunction +  diff --git a/html/dir_e88e8cad8d3a8e4d610d48bf31089907.html b/html/dir_e88e8cad8d3a8e4d610d48bf31089907.html index f4806c166..e5ddc8db2 100644 --- a/html/dir_e88e8cad8d3a8e4d610d48bf31089907.html +++ b/html/dir_e88e8cad8d3a8e4d610d48bf31089907.html @@ -89,10 +89,10 @@ Directories directory  __deprecatedDemos   -directory  _precipitateEvolution_pfunction -  directory  allenCahn   +directory  allenCahn_pfield +  directory  cahnHilliard   directory  cahnHilliardWithAdaptivity @@ -101,6 +101,10 @@   directory  CHAC_anisotropyRegularized   +directory  CHiMaD_benchmark1a +  +directory  CHiMaD_benchmark2a +  directory  coupledCahnHilliardAllenCahn   directory  eshelbyInclusion @@ -113,6 +117,10 @@   directory  precipitateEvolution   +directory  precipitateEvolution_pfunction +  +directory  singlePrecipitateKKS +  diff --git a/html/dir_ec60aa9c3ba5c26f7ff21ab62051f263.html b/html/dir_ec60aa9c3ba5c26f7ff21ab62051f263.html index 349156f6e..698a402f7 100644 --- a/html/dir_ec60aa9c3ba5c26f7ff21ab62051f263.html +++ b/html/dir_ec60aa9c3ba5c26f7ff21ab62051f263.html @@ -95,6 +95,8 @@   file  computeRHS.cc   +file  getOutputTimeSteps.cc +  file  init.cc   file  initForTests.cc @@ -115,6 +117,8 @@   file  refine.cc   +file  reinit.cc +  file  solve.cc   file  solveIncrement.cc diff --git a/html/eshelby_inclusion_2_i_cs__and___b_cs_8h_source.html b/html/eshelby_inclusion_2_i_cs__and___b_cs_8h_source.html index 752d86844..c31fbbae9 100644 --- a/html/eshelby_inclusion_2_i_cs__and___b_cs_8h_source.html +++ b/html/eshelby_inclusion_2_i_cs__and___b_cs_8h_source.html @@ -155,7 +155,7 @@
      63  // Inputs to "inputBCs":
      64  // First input: variable number
      65  // Second input: component number
      -
      66  // Third input: BC type (options are "ZERO_DERIVATIVE" and "DIRICHLET")
      +
      66  // Third input: BC type (options are "ZERO_DERIVATIVE", "DIRICHLET", and "PERIODIC")
      67  // Fourth input: BC value (ignored unless the BC type is "DIRICHLET")
      68  // Odd inputs after the third: BC type
      69  // Even inputs after the third: BC value
      @@ -169,15 +169,15 @@
      77 }
      78 
      79 
      -
      InitialConditionVec::index
      unsigned int index
      Definition: ICs_and_BCs.h:46
      +
      InitialConditionVec::index
      unsigned int index
      Definition: ICs_and_BCs.h:47
      InitialConditionVec::vector_value
      void vector_value(const Point< dim > &p, Vector< double > &vector_IC) const
      Definition: ICs_and_BCs.h:35
      InitialCondition::InitialCondition
      InitialCondition(const unsigned int _index)
      Definition: ICs_and_BCs.h:7
      InitialCondition::value
      double value(const Point< dim > &p, const unsigned int component=0) const
      Definition: ICs_and_BCs.h:10
      InitialCondition::index
      unsigned int index
      Definition: ICs_and_BCs.h:5
      -
      generalizedProblem::setBCs
      void setBCs()
      Definition: ICs_and_BCs.h:66
      +
      generalizedProblem::setBCs
      void setBCs()
      Definition: ICs_and_BCs.h:67
      InitialConditionVec::InitialConditionVec
      InitialConditionVec(const unsigned int _index)
      Definition: ICs_and_BCs.h:32
      InitialCondition
      Definition: ICs_and_BCs.h:2
      -
      InitialConditionVec
      Definition: ICs_and_BCs.h:43
      +
      InitialConditionVec
      Definition: ICs_and_BCs.h:44
      InitialCondition::values
      Vector< double > values
      Definition: ICs_and_BCs.h:6
      diff --git a/html/eshelby_inclusion_2equations_8h_source.html b/html/eshelby_inclusion_2equations_8h_source.html index 48487a4ea..c5823f201 100644 --- a/html/eshelby_inclusion_2equations_8h_source.html +++ b/html/eshelby_inclusion_2equations_8h_source.html @@ -150,8 +150,8 @@
      58 // each residual equation. The index for each variable in these lists corresponds to
      59 // the order it is defined at the top of this file (starting at 0).
      60 template <int dim>
      -
      61 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim>> & modelVariablesList,
      -
      62  std::vector<modelResidual<dim>> & modelResidualsList,
      +
      61 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim> > & modelVariablesList,
      +
      62  std::vector<modelResidual<dim> > & modelResidualsList,
      63  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
      64 
      65 //u
      @@ -223,7 +223,7 @@
      131 // that the correct residual is being submitted. The index of the field being solved
      132 // can be accessed by "this->currentFieldIndex".
      133 template <int dim>
      -
      134 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim>> & modelVarList,
      +
      134 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim> > & modelVarList,
      135  modelResidual<dim> & modelRes,
      136  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
      137 
      @@ -265,7 +265,7 @@
      173 // density are added to the "energy_components" variable (index 0: chemical energy,
      174 // index 1: gradient energy, index 2: elastic energy).
      175 template <int dim>
      -
      176 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim>> & modelVarList, const dealii::VectorizedArray<double> & JxW_value, dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) {
      +
      176 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim> > & modelVarList, const dealii::VectorizedArray<double> & JxW_value, dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) {
      177 
      178  //u
      179  vectorgradType ux = modelVarList[0].vectorGrad;
      @@ -328,15 +328,15 @@
      236 
      237 
      238 
      -
      generalizedProblem::energyDensity
      void energyDensity(const std::vector< modelVariable< dim >> &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
      Definition: equations.h:106
      modelResidual
      Definition: model_variables.h:25
      -
      generalizedProblem::residualLHS
      void residualLHS(const std::vector< modelVariable< dim >> &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
      Definition: equations.h:89
      +
      generalizedProblem::residualRHS
      void residualRHS(const std::vector< modelVariable< dim > > &modelVarList, std::vector< modelResidual< dim > > &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
      Definition: equations.h:58
      constV
      #define constV(a)
      Definition: matrixFreePDE.h:67
      modelResidual::vectorGradResidual
      vectorgradType vectorGradResidual
      Definition: model_variables.h:33
      +
      generalizedProblem::residualLHS
      void residualLHS(const std::vector< modelVariable< dim > > &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
      Definition: equations.h:89
      modelVariable
      Definition: model_variables.h:4
      vectorgradType
      dealii::Tensor< 2, problemDIM, dealii::VectorizedArray< double > > vectorgradType
      Definition: matrixFreePDE.h:57
      scalarvalueType
      dealii::VectorizedArray< double > scalarvalueType
      Definition: matrixFreePDE.h:34
      -
      generalizedProblem::residualRHS
      void residualRHS(const std::vector< modelVariable< dim >> &modelVarList, std::vector< modelResidual< dim >> &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
      Definition: equations.h:58
      +
      generalizedProblem::energyDensity
      void energyDensity(const std::vector< modelVariable< dim > > &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
      Definition: equations.h:106
      diff --git a/html/fickian_diffusion_2_i_cs__and___b_cs_8h_source.html b/html/fickian_diffusion_2_i_cs__and___b_cs_8h_source.html index c584cb5d1..ab57ecf92 100644 --- a/html/fickian_diffusion_2_i_cs__and___b_cs_8h_source.html +++ b/html/fickian_diffusion_2_i_cs__and___b_cs_8h_source.html @@ -153,7 +153,7 @@
      61  // Inputs to "inputBCs":
      62  // First input: variable number
      63  // Second input: component number
      -
      64  // Third input: BC type (options are "ZERO_DERIVATIVE" and "DIRICHLET")
      +
      64  // Third input: BC type (options are "ZERO_DERIVATIVE", "DIRICHLET", and "PERIODIC")
      65  // Fourth input: BC value (ignored unless the BC type is "DIRICHLET")
      66  // Odd inputs after the third: BC type
      67  // Even inputs after the third: BC value
      @@ -167,15 +167,15 @@
      75 }
      76 
      77 
      -
      InitialConditionVec::index
      unsigned int index
      Definition: ICs_and_BCs.h:46
      +
      InitialConditionVec::index
      unsigned int index
      Definition: ICs_and_BCs.h:47
      InitialConditionVec::vector_value
      void vector_value(const Point< dim > &p, Vector< double > &vector_IC) const
      Definition: ICs_and_BCs.h:36
      InitialCondition::InitialCondition
      InitialCondition(const unsigned int _index)
      Definition: ICs_and_BCs.h:7
      InitialCondition::value
      double value(const Point< dim > &p, const unsigned int component=0) const
      Definition: ICs_and_BCs.h:10
      InitialCondition::index
      unsigned int index
      Definition: ICs_and_BCs.h:5
      -
      generalizedProblem::setBCs
      void setBCs()
      Definition: ICs_and_BCs.h:66
      +
      generalizedProblem::setBCs
      void setBCs()
      Definition: ICs_and_BCs.h:67
      InitialConditionVec::InitialConditionVec
      InitialConditionVec(const unsigned int _index)
      Definition: ICs_and_BCs.h:33
      InitialCondition
      Definition: ICs_and_BCs.h:2
      -
      InitialConditionVec
      Definition: ICs_and_BCs.h:43
      +
      InitialConditionVec
      Definition: ICs_and_BCs.h:44
      InitialCondition::values
      Vector< double > values
      Definition: ICs_and_BCs.h:6
      diff --git a/html/fickian_diffusion_2equations_8h.html b/html/fickian_diffusion_2equations_8h.html index d4edf4b3d..639cb4f03 100644 --- a/html/fickian_diffusion_2equations_8h.html +++ b/html/fickian_diffusion_2equations_8h.html @@ -117,13 +117,13 @@   #define DcV   0.01   -#define exp1   (std::sqrt(x*x+y*y)<spanX/10) +#define exp1   (std::sqrt(x*x+y*y)<spanX/10)   -#define exp2   (t<timeFinal/4) +#define exp2   (t<timeFinal/4)   -#define rcV   (c + timeStep*subdomain(exp1 && exp2, constV(1.0)) ) +#define rcV   (c + timeStep*subdomain(exp1 && exp2, constV(1.0)) )   -#define rcxV   (constV(-DcV*timeStep)*cx) +#define rcxV   (constV(-DcV*timeStep)*cx)  

      Macro Definition Documentation

      @@ -144,7 +144,7 @@

      Macro Definition Documentation

      - +
      #define exp1   (std::sqrt(x*x+y*y)<spanX/10)#define exp1   (std::sqrt(x*x+y*y)<spanX/10)
      @@ -156,7 +156,7 @@

      Macro Definition Documentation

      - +
      #define exp2   (t<timeFinal/4)#define exp2   (t<timeFinal/4)
      @@ -240,7 +240,7 @@

      Macro Definition Documentation

      - +
      #define rcV   (c + timeStep*subdomain(exp1 && exp2, constV(1.0)) )#define rcV   (c + timeStep*subdomain(exp1 && exp2, constV(1.0)) )
      @@ -252,7 +252,7 @@

      Macro Definition Documentation

      - +
      #define rcxV   (constV(-DcV*timeStep)*cx)#define rcxV   (constV(-DcV*timeStep)*cx)
      diff --git a/html/fickian_diffusion_2equations_8h_source.html b/html/fickian_diffusion_2equations_8h_source.html index 33e04336b..4397d33c8 100644 --- a/html/fickian_diffusion_2equations_8h_source.html +++ b/html/fickian_diffusion_2equations_8h_source.html @@ -148,8 +148,8 @@
      56 // each residual equation. The index for each variable in these lists corresponds to
      57 // the order it is defined at the top of this file (starting at 0).
      58 template <int dim>
      -
      59 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim>> & modelVariablesList,
      -
      60  std::vector<modelResidual<dim>> & modelResidualsList,
      +
      59 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim> > & modelVariablesList,
      +
      60  std::vector<modelResidual<dim> > & modelResidualsList,
      61  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
      62 
      63 
      @@ -181,7 +181,7 @@
      89 // that the correct residual is being submitted. The index of the field being solved
      90 // can be accessed by "this->currentFieldIndex".
      91 template <int dim>
      -
      92 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim>> & modelVarList,
      +
      92 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim> > & modelVarList,
      93  modelResidual<dim> & modelRes,
      94  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
      95 
      @@ -199,7 +199,7 @@
      107 // density are added to the "energy_components" variable (index 0: chemical energy,
      108 // index 1: gradient energy, index 2: elastic energy).
      109 template <int dim>
      -
      110 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim>> & modelVarList,
      +
      110 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim> > & modelVarList,
      111  const dealii::VectorizedArray<double> & JxW_value,
      112  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) {
      113 
      @@ -209,15 +209,15 @@
      117 
      118 
      119 
      -
      generalizedProblem::energyDensity
      void energyDensity(const std::vector< modelVariable< dim >> &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
      Definition: equations.h:106
      modelResidual
      Definition: model_variables.h:25
      +
      generalizedProblem::residualRHS
      void residualRHS(const std::vector< modelVariable< dim > > &modelVarList, std::vector< modelResidual< dim > > &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
      Definition: equations.h:58
      scalargradType
      dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
      Definition: matrixFreePDE.h:51
      -
      generalizedProblem::residualLHS
      void residualLHS(const std::vector< modelVariable< dim >> &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
      Definition: equations.h:89
      rcxV
      #define rcxV
      Definition: equations.h:46
      rcV
      #define rcV
      Definition: equations.h:45
      +
      generalizedProblem::residualLHS
      void residualLHS(const std::vector< modelVariable< dim > > &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
      Definition: equations.h:89
      modelVariable
      Definition: model_variables.h:4
      scalarvalueType
      dealii::VectorizedArray< double > scalarvalueType
      Definition: matrixFreePDE.h:34
      -
      generalizedProblem::residualRHS
      void residualRHS(const std::vector< modelVariable< dim >> &modelVarList, std::vector< modelResidual< dim >> &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
      Definition: equations.h:58
      +
      generalizedProblem::energyDensity
      void energyDensity(const std::vector< modelVariable< dim > > &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
      Definition: equations.h:106
      diff --git a/html/fickian_diffusion_2parameters_8h.html b/html/fickian_diffusion_2parameters_8h.html index ddd81c200..c9acf7936 100644 --- a/html/fickian_diffusion_2parameters_8h.html +++ b/html/fickian_diffusion_2parameters_8h.html @@ -123,8 +123,14 @@   #define writeOutput   true   -#define skipOutputSteps   100 -  +#define outputCondition   "EQUAL_SPACING" +  +#define numOutputs   10 +  +#define outputList   {} +  +#define skipPrintSteps   10 +  #define calcEnergy   false   @@ -151,6 +157,42 @@

      Macro Definition Documentation

      +
      +
      + +
      +
      + + + + +
      #define numOutputs   10
      +
      + +
      +
      + +
      +
      + + + + +
      #define outputCondition   "EQUAL_SPACING"
      +
      + +
      +
      + +
      +
      + + + + +
      #define outputList   {}
      +
      +
      @@ -177,12 +219,12 @@

      Macro Definition Documentation

      - +
      - +
      #define skipOutputSteps   100#define skipPrintSteps   10
      diff --git a/html/fickian_diffusion_2parameters_8h_source.html b/html/fickian_diffusion_2parameters_8h_source.html index 18f8010de..8d1a0bc6a 100644 --- a/html/fickian_diffusion_2parameters_8h_source.html +++ b/html/fickian_diffusion_2parameters_8h_source.html @@ -136,21 +136,33 @@
      44 // Each field in the problem will be output is writeOutput is set to "true"
      45 #define writeOutput true
      46 
      -
      47 // Output files are written every skipOutputSteps time steps
      -
      48 #define skipOutputSteps 100
      -
      49 
      -
      50 // =================================================================================
      -
      51 // Set the flag determining if the total free energy is calculated for each output
      -
      52 // =================================================================================
      -
      53 #define calcEnergy false
      +
      47 // Type of spacing between outputs ("EQUAL_SPACING", "LOG_SPACING", "N_PER_DECADE",
      +
      48 // or "LIST")
      +
      49 #define outputCondition "EQUAL_SPACING"
      +
      50 
      +
      51 // Number of times the program outputs the fields (total number for "EQUAL_SPACING"
      +
      52 // and "LOG_SPACING", number per decade for "N_PER_DECADE", ignored for "LIST")
      +
      53 #define numOutputs 10
      54 
      -
      55 
      -
      56 
      -
      57 
      +
      55 // User-defined list of time steps where the program should output. Only used if
      +
      56 // outputCondition is "LIST"
      +
      57 #define outputList {}
      58 
      -
      59 
      -
      60 
      +
      59 // Status is printed to the screen every skipPrintSteps
      +
      60 #define skipPrintSteps 10
      61 
      +
      62 // =================================================================================
      +
      63 // Set the flag determining if the total free energy is calculated for each output
      +
      64 // =================================================================================
      +
      65 #define calcEnergy false
      +
      66 
      +
      67 
      +
      68 
      +
      69 
      +
      70 
      +
      71 
      +
      72 
      +
      73 
      diff --git a/html/files.html b/html/files.html index 1fe4c7b82..fac1dd914 100644 --- a/html/files.html +++ b/html/files.html @@ -95,24 +95,21 @@  ICs_and_BCs.h  main.cc  parameters.h - residuals.h + residuals.h  ► _mechanicsWithAdaptivity  ICs_and_BCs.h  main.cc  parameters.h - ► _precipitateEvolution_pfunction - ► PLibrary - PExtern.cc - PLibrary.cc - ICs_and_BCs.h - main.cc - parameters.h - residuals.h - ► allenCahn - equations.h - ICs_and_BCs.h - main.cc - parameters.h + ► allenCahn + equations.h + ICs_and_BCs.h + main.cc + parameters.h + ► allenCahn_pfield + equations.h + ICs_and_BCs.h + main.cc + parameters.h  ► cahnHilliard  equations.h  ICs_and_BCs.h @@ -133,36 +130,58 @@  ICs_and_BCs.h  main.cc  parameters.h - ► coupledCahnHilliardAllenCahn - equations.h - ICs_and_BCs.h - main.cc - parameters.h - ► eshelbyInclusion - equations.h - ICs_and_BCs.h - main.cc - parameters.h - ► fickianDiffusion - equations.h - ICs_and_BCs.h - main.cc - parameters.h - ► grainGrowth - equations.h - ICs_and_BCs.h - main.cc - parameters.h - ► mechanics - equations.h - ICs_and_BCs.h - main.cc - parameters.h - ► precipitateEvolution - equations.h - ICs_and_BCs.h - main.cc - parameters.h + ► CHiMaD_benchmark1a + equations.h + ICs_and_BCs.h + main.cc + parameters.h + ► CHiMaD_benchmark2a + equations.h + ICs_and_BCs.h + main.cc + parameters.h + ► coupledCahnHilliardAllenCahn + equations.h + ICs_and_BCs.h + main.cc + parameters.h + ► eshelbyInclusion + equations.h + ICs_and_BCs.h + main.cc + parameters.h + ► fickianDiffusion + equations.h + ICs_and_BCs.h + main.cc + parameters.h + ► grainGrowth + equations.h + ICs_and_BCs.h + main.cc + parameters.h + ► mechanics + equations.h + ICs_and_BCs.h + main.cc + parameters.h + ► precipitateEvolution + equations.h + ICs_and_BCs.h + main.cc + parameters.h + ► precipitateEvolution_pfunction + ► PLibrary + PLibrary.cc + equations.h + ICs_and_BCs.h + main.cc + parameters.h + ► singlePrecipitateKKS + equations.h + ICs_and_BCs.h + main.cc + parameters.h  ► include  dealIIheaders.h  defaultValues.h @@ -175,19 +194,21 @@  calcFreeEnergy.cc  computeLHS.cc  computeRHS.cc - init.cc - initForTests.cc - initialConditions.cc - integrate_and_shift_field.cc - invM.cc - markBoundaries.cc - matrixFreePDE.cc - modifyFields.cc - outputResults.cc - refine.cc - solve.cc - solveIncrement.cc - utilities.cc + getOutputTimeSteps.cc + init.cc + initForTests.cc + initialConditions.cc + integrate_and_shift_field.cc + invM.cc + markBoundaries.cc + matrixFreePDE.cc + modifyFields.cc + outputResults.cc + refine.cc + reinit.cc + solve.cc + solveIncrement.cc + utilities.cc  ► models  ► coupled  coupledCHACMechanics.h @@ -204,6 +225,8 @@  anisotropy.h  computeStress.h  mechanics.h + ► pFunction + pFunction.h diff --git a/html/functions.html b/html/functions.html index 37783e388..9a9918cc2 100644 --- a/html/functions.html +++ b/html/functions.html @@ -84,6 +84,7 @@
    • e
    • f
    • g
      • +
    • h
    • i
    • l
    • m
      • diff --git a/html/functions_b.html b/html/functions_b.html index 0834db78c..d201844e4 100644 --- a/html/functions_b.html +++ b/html/functions_b.html @@ -84,6 +84,7 @@
    • e
    • f
    • g
      • +
    • h
    • i
    • l
    • m
      • diff --git a/html/functions_c.html b/html/functions_c.html index 68eadd8dc..15f4dd92d 100644 --- a/html/functions_c.html +++ b/html/functions_c.html @@ -84,6 +84,7 @@
    • e
    • f
    • g
      • +
    • h
    • i
    • l
    • m
      • @@ -169,17 +170,17 @@

      - c -

      • computing_timer : MatrixFreePDE< dim >
        • -
      • constraintsHangingNodesSet -: MatrixFreePDE< dim > +
      • constraintsDirichletSet +: MatrixFreePDE< dim >
        • -
      • constraintsHangingNodesSet2 -: MatrixFreePDE< dim > +
      • constraintsDirichletSet_nonconst +: MatrixFreePDE< dim >
        • -
      • constraintsSet -: MatrixFreePDE< dim > +
      • constraintsOtherSet +: MatrixFreePDE< dim >
        • -
      • constraintsSet2 -: MatrixFreePDE< dim > +
      • constraintsOtherSet_nonconst +: MatrixFreePDE< dim >
      • CoupledCahnHilliardMechanicsProblem() : CoupledCahnHilliardMechanicsProblem< dim > diff --git a/html/functions_d.html b/html/functions_d.html index 65e308fe2..514537586 100644 --- a/html/functions_d.html +++ b/html/functions_d.html @@ -84,6 +84,7 @@
      • e
      • f
      • g
        • +
      • h
      • i
      • l
      • m
        • @@ -119,8 +120,8 @@

        - d -

        • dofHandlersSet : MatrixFreePDE< dim >
          • -
        • dofHandlersSet2 -: MatrixFreePDE< dim > +
        • dofHandlersSet_nonconst +: MatrixFreePDE< dim >
        • dtValue : MatrixFreePDE< dim > diff --git a/html/functions_e.html b/html/functions_e.html index 79239f1c3..e7d119854 100644 --- a/html/functions_e.html +++ b/html/functions_e.html @@ -84,6 +84,7 @@
        • e
        • f
        • g
          • +
        • h
        • i
        • l
        • m
          • @@ -126,7 +127,7 @@

          - e -

          diff --git a/html/functions_f.html b/html/functions_f.html index 25cd9cb47..c286a28b0 100644 --- a/html/functions_f.html +++ b/html/functions_f.html @@ -84,6 +84,7 @@
        • e
        • f
        • g
          • +
        • h
        • i
        • l
        • m
          • @@ -137,6 +138,9 @@

          - f -

          diff --git a/html/functions_func.html b/html/functions_func.html index 9839c26e5..7411ab13f 100644 --- a/html/functions_func.html +++ b/html/functions_func.html @@ -83,9 +83,11 @@
        • e
        • f
        • g
          • +
        • h
        • i
        • m
        • o
          • +
        • p
        • r
        • s
        • v
          • @@ -190,7 +192,7 @@

          - c -

            - e -

            @@ -209,6 +211,10 @@

            - g -

            • generalizedProblem() : generalizedProblem< dim >
              • +
            • getComponentsWithRigidBodyModes() +: generalizedProblem< dim > +, MatrixFreePDE< dim > +
            • getEnergy() : CoupledCahnHilliardMechanicsProblem< dim > , CoupledCHACMechanicsProblem< dim > @@ -225,17 +231,30 @@

              - g -

              + + +

              - h -

              @@ -252,14 +271,11 @@

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              +

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              • refineGrid() : MatrixFreePDE< dim > @@ -309,11 +332,14 @@

                - r -

                @@ -322,6 +348,17 @@

                - s -

                • setBCs() : generalizedProblem< dim >
                  • +
                • setPeriodicity() +: generalizedProblem< dim > +, MatrixFreePDE< dim > +
                  • +
                • setPeriodicityConstraints() +: generalizedProblem< dim > +, MatrixFreePDE< dim > +
                  • +
                • setRigidBodyModeConstraints() +: MatrixFreePDE< dim > +
                • shiftConcentration() : CoupledCHACMechanicsProblem< dim > , generalizedProblem< dim > @@ -337,10 +374,12 @@

                  - s -

                    - v -

                      +
                    • val() +: pFunction +
                    • value() : InitialCondition< dim > , InitialConditionC< dim > -, InitialConditionN< dim >
                    • vector_value() : BCFunction< dim > @@ -348,7 +387,7 @@

                      - v -

                        , vectorBCFunction< dim >
                      • vector_value_list() -: vectorBCFunction< dim > +: vectorBCFunction< dim >
                      • vectorBCFunction() : vectorBCFunction< dim > diff --git a/html/functions_g.html b/html/functions_g.html index 38c2ccf4e..b4286316c 100644 --- a/html/functions_g.html +++ b/html/functions_g.html @@ -84,6 +84,7 @@
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                        • generalizedProblem() : generalizedProblem< dim >
                          • +
                        • getComponentsWithRigidBodyModes() +: generalizedProblem< dim > +, MatrixFreePDE< dim > +
                        • getEnergy() : CoupledCahnHilliardMechanicsProblem< dim > , CoupledCHACMechanicsProblem< dim > @@ -135,12 +140,15 @@

                          - g -

                            , MatrixFreePDE< dim > , MechanicsProblem< dim > +
                          • getOutputTimeSteps() +: MatrixFreePDE< dim > +
                          • getRHS() : AllenCahnProblem< dim > , CahnHilliardProblem< dim > , CoupledCahnHilliardMechanicsProblem< dim > , CoupledCHACMechanicsProblem< dim > -, CoupledCHACProblem< dim > +, CoupledCHACProblem< dim > , FickianProblem< dim > , generalizedProblem< dim > , MatrixFreePDE< dim > @@ -152,6 +160,9 @@

                            - g -

                            • global_var_index : variable_info< dim >
                              • +
                            • grad() +: pFunction +
                            • gradient_residual : generalizedProblem< dim >
                              • diff --git a/html/functions_h.html b/html/functions_h.html new file mode 100644 index 000000000..4374b9818 --- /dev/null +++ b/html/functions_h.html @@ -0,0 +1,132 @@ + + + + + + +PRISMS PhaseField: Class Members + + + + + + + + + + +
                              +
                              + + + + + + +
                              +
                              PRISMS PhaseField +  1.0 +
                              +
                              +
                              + + + + + + + +
                              + +
                              +
                              + + +
                              + +
                              + +
                              +
                              Here is a list of all class members with links to the classes they belong to:
                              + +

                              - h -

                              +
                              + +
                              +Generated by   +doxygen + 1.8.10 +
                              + + diff --git a/html/functions_i.html b/html/functions_i.html index 6089abb6b..c2b7826cd 100644 --- a/html/functions_i.html +++ b/html/functions_i.html @@ -84,6 +84,7 @@
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                              • index : Field< dim > , InitialCondition< dim > -, InitialConditionN< dim > , InitialConditionVec< dim > , nucleus
                                • @@ -138,14 +138,11 @@

                                - i -

                                • InitialConditionC() : InitialConditionC< dim >
                                  • -
                                • InitialConditionN() -: InitialConditionN< dim > -
                                • InitialConditionVec() : InitialConditionVec< dim >
                                • inputBCs() -: generalizedProblem< dim > +: generalizedProblem< dim >
                                • invM : MatrixFreePDE< dim > diff --git a/html/functions_l.html b/html/functions_l.html index b8ac6e34e..e432b530b 100644 --- a/html/functions_l.html +++ b/html/functions_l.html @@ -84,6 +84,7 @@
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                                  diff --git a/html/functions_p.html b/html/functions_p.html index 8beb8764e..48b83626a 100644 --- a/html/functions_p.html +++ b/html/functions_p.html @@ -84,6 +84,7 @@
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                                  diff --git a/html/functions_r.html b/html/functions_r.html index 32643c113..1a100c432 100644 --- a/html/functions_r.html +++ b/html/functions_r.html @@ -84,6 +84,7 @@
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                                  - r -

                                  • refineMesh() : MatrixFreePDE< dim >
                                    • +
                                  • reinit() +: MatrixFreePDE< dim > +
                                  • residualLHS() -: generalizedProblem< dim > +: generalizedProblem< dim >
                                  • residualRHS() -: generalizedProblem< dim > +: generalizedProblem< dim >
                                  • residualSet : MatrixFreePDE< dim > diff --git a/html/functions_s.html b/html/functions_s.html index 7f7e295e5..c42c81eb1 100644 --- a/html/functions_s.html +++ b/html/functions_s.html @@ -84,6 +84,7 @@
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                                    • setBCs() : generalizedProblem< dim >
                                      • +
                                    • setPeriodicity() +: generalizedProblem< dim > +, MatrixFreePDE< dim > +
                                      • +
                                    • setPeriodicityConstraints() +: generalizedProblem< dim > +, MatrixFreePDE< dim > +
                                      • +
                                    • setRigidBodyModeConstraints() +: MatrixFreePDE< dim > +
                                    • shiftConcentration() : CoupledCHACMechanicsProblem< dim > , generalizedProblem< dim > diff --git a/html/functions_t.html b/html/functions_t.html index de91be1b0..4856a9c50 100644 --- a/html/functions_t.html +++ b/html/functions_t.html @@ -84,6 +84,7 @@
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                                      • @@ -116,10 +117,12 @@
                                      Here is a list of all class members with links to the classes they belong to:

                                      - v -

                                        +
                                      • val() +: pFunction +
                                      • value() : InitialCondition< dim > , InitialConditionC< dim > -, InitialConditionN< dim >
                                      • value_residual : generalizedProblem< dim > @@ -160,7 +163,7 @@

                                        - v -

                                          , vectorBCFunction< dim >
                                        • vector_value_list() -: vectorBCFunction< dim > +: vectorBCFunction< dim >
                                        • vectorBCFunction() : vectorBCFunction< dim > diff --git a/html/functions_vars.html b/html/functions_vars.html index 899102d43..830a2ba00 100644 --- a/html/functions_vars.html +++ b/html/functions_vars.html @@ -88,6 +88,7 @@
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                                          • @@ -169,17 +170,17 @@

                                          - c -

                                          • computing_timer : MatrixFreePDE< dim >
                                            • -
                                          • constraintsHangingNodesSet -: MatrixFreePDE< dim > +
                                          • constraintsDirichletSet +: MatrixFreePDE< dim >
                                            • -
                                          • constraintsHangingNodesSet2 -: MatrixFreePDE< dim > +
                                          • constraintsDirichletSet_nonconst +: MatrixFreePDE< dim >
                                            • -
                                          • constraintsSet -: MatrixFreePDE< dim > +
                                          • constraintsOtherSet +: MatrixFreePDE< dim >
                                            • -
                                          • constraintsSet2 -: MatrixFreePDE< dim > +
                                          • constraintsOtherSet_nonconst +: MatrixFreePDE< dim >
                                          • currentFieldIndex : MatrixFreePDE< dim > @@ -197,8 +198,8 @@

                                            - d -

                                            • dofHandlersSet : MatrixFreePDE< dim >
                                              • -
                                            • dofHandlersSet2 -: MatrixFreePDE< dim > +
                                            • dofHandlersSet_nonconst +: MatrixFreePDE< dim >
                                            • dtValue : MatrixFreePDE< dim > @@ -238,6 +239,9 @@

                                              - f -

                                              @@ -261,7 +265,6 @@

                                              - i -

                                              • index : Field< dim > , InitialCondition< dim > -, InitialConditionN< dim > , InitialConditionVec< dim > , nucleus
                                                • @@ -287,8 +290,8 @@

                                                - l -

                                                @@ -334,6 +337,13 @@

                                                - n -

                                                +

                                                - o -

                                                + +

                                                - p -

                                                • parabolicFieldIndex : MatrixFreePDE< dim > diff --git a/html/functions_~.html b/html/functions_~.html index 1665641ab..1ff901671 100644 --- a/html/functions_~.html +++ b/html/functions_~.html @@ -84,6 +84,7 @@
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                                                  • diff --git a/html/generalized__model_8h_source.html b/html/generalized__model_8h_source.html index 2059d8c53..49743d14f 100644 --- a/html/generalized__model_8h_source.html +++ b/html/generalized__model_8h_source.html @@ -113,163 +113,173 @@
                                                  21 class generalizedProblem: public MatrixFreePDE<dim>
                                                  22 {
                                                  23  public:
                                                  -
                                                  24  generalizedProblem();
                                                  -
                                                  25 
                                                  -
                                                  26  void shiftConcentration();
                                                  -
                                                  27 
                                                  -
                                                  28  void setBCs();
                                                  -
                                                  29 
                                                  -
                                                  30  void buildFields();
                                                  -
                                                  31 
                                                  -
                                                  32  void inputBCs(int var, int component, std::string BC_type_dim1_min, double BC_value_dim1_min,
                                                  -
                                                  33  std::string BC_type_dim1_max, double BC_value_dim1_max, std::string BC_type_dim2_min, double BC_value_dim2_min,
                                                  -
                                                  34  std::string BC_type_dim2_max, double BC_value_dim2_max,std::string BC_type_dim3_min, double BC_value_dim3_min,
                                                  -
                                                  35  std::string BC_type_dim3_max, double BC_value_dim3_max);
                                                  -
                                                  36 
                                                  -
                                                  37  void inputBCs(int var, int component, std::string BC_type_dim1_min, double BC_value_dim1_min,
                                                  -
                                                  38  std::string BC_type_dim1_max, double BC_value_dim1_max, std::string BC_type_dim2_min, double BC_value_dim2_min,
                                                  -
                                                  39  std::string BC_type_dim2_max, double BC_value_dim2_max);
                                                  -
                                                  40 
                                                  -
                                                  41  void inputBCs(int var, int component, std::string BC_type_dim1_min, double BC_value_dim1_min,
                                                  -
                                                  42  std::string BC_type_dim1_max, double BC_value_dim1_max);
                                                  -
                                                  43 
                                                  -
                                                  44  void inputBCs(int var, int component, std::string BC_type, double BC_value);
                                                  -
                                                  45 
                                                  -
                                                  46  // Boundary condition object
                                                  -
                                                  47  std::vector<varBCs<dim>> BC_list;
                                                  -
                                                  48 
                                                  -
                                                  49  private:
                                                  -
                                                  50 
                                                  -
                                                  51  // Declare vectors to put the information about the variables into
                                                  -
                                                  52  std::vector<std::string> var_name;
                                                  -
                                                  53  std::vector<std::string> var_type;
                                                  -
                                                  54  std::vector<std::string> var_eq_type;
                                                  -
                                                  55 
                                                  -
                                                  56  std::vector<bool> need_value;
                                                  -
                                                  57  std::vector<bool> need_gradient;
                                                  -
                                                  58  std::vector<bool> need_hessian;
                                                  -
                                                  59  std::vector<bool> value_residual;
                                                  -
                                                  60  std::vector<bool> gradient_residual;
                                                  -
                                                  61 
                                                  -
                                                  62  std::vector<bool> need_value_LHS;
                                                  -
                                                  63  std::vector<bool> need_gradient_LHS;
                                                  -
                                                  64  std::vector<bool> need_hessian_LHS;
                                                  -
                                                  65  std::vector<bool> value_residual_LHS;
                                                  -
                                                  66  std::vector<bool> gradient_residual_LHS;
                                                  -
                                                  67 
                                                  -
                                                  68  // Elasticity matrix variables
                                                  -
                                                  69  const static unsigned int CIJ_tensor_size = 2*dim-1+dim/3;
                                                  -
                                                  70  std::vector<dealii::Tensor<2, CIJ_tensor_size, dealii::VectorizedArray<double> > > CIJ_list;
                                                  -
                                                  71 
                                                  -
                                                  72  bool c_dependent_misfit;
                                                  -
                                                  73 
                                                  -
                                                  74  Threads::Mutex assembler_lock;
                                                  -
                                                  75 
                                                  -
                                                  76  // Variables needed to calculate the LHS
                                                  -
                                                  77  std::vector<variable_info<dim>> varInfoListRHS;
                                                  -
                                                  78  std::vector<variable_info<dim>> resInfoListRHS;
                                                  -
                                                  79 
                                                  -
                                                  80  // Variables needed to calculate the LHS
                                                  -
                                                  81  unsigned int num_var_LHS;
                                                  -
                                                  82  std::vector<variable_info<dim>> varInfoListLHS;
                                                  -
                                                  83 
                                                  -
                                                  84  //RHS implementation for explicit solve
                                                  -
                                                  85  void getRHS(const MatrixFree<dim,double> &data,
                                                  -
                                                  86  std::vector<vectorType*> &dst,
                                                  -
                                                  87  const std::vector<vectorType*> &src,
                                                  -
                                                  88  const std::pair<unsigned int,unsigned int> &cell_range) const;
                                                  -
                                                  89 
                                                  -
                                                  90  //LHS implementation for implicit solve
                                                  -
                                                  91  void getLHS(const MatrixFree<dim,double> &data,
                                                  -
                                                  92  vectorType &dst,
                                                  -
                                                  93  const vectorType &src,
                                                  -
                                                  94  const std::pair<unsigned int,unsigned int> &cell_range) const;
                                                  -
                                                  95 
                                                  -
                                                  96  //method to apply initial conditions
                                                  -
                                                  97  void applyInitialConditions();
                                                  -
                                                  98 
                                                  -
                                                  99  //methods to apply dirichlet BC's on displacement
                                                  -
                                                  100  void applyDirichletBCs();
                                                  -
                                                  101 
                                                  -
                                                  102  // method to modify the fields for nucleation
                                                  -
                                                  103  void modifySolutionFields();
                                                  -
                                                  104 
                                                  -
                                                  105  void computeIntegral(double& integratedField);
                                                  -
                                                  106 
                                                  -
                                                  107  void markBoundaries();
                                                  -
                                                  108 
                                                  -
                                                  109  void getEnergy(const MatrixFree<dim,double> &data,
                                                  -
                                                  110  std::vector<vectorType*> &dst,
                                                  -
                                                  111  const std::vector<vectorType*> &src,
                                                  -
                                                  112  const std::pair<unsigned int,unsigned int> &cell_range);
                                                  -
                                                  113 
                                                  +
                                                  24 
                                                  +
                                                  25  generalizedProblem();
                                                  +
                                                  26 
                                                  +
                                                  27  void shiftConcentration();
                                                  +
                                                  28 
                                                  +
                                                  29  void setBCs();
                                                  +
                                                  30 
                                                  +
                                                  31  void buildFields();
                                                  +
                                                  32 
                                                  +
                                                  33  void inputBCs(int var, int component, std::string BC_type_dim1_min, double BC_value_dim1_min,
                                                  +
                                                  34  std::string BC_type_dim1_max, double BC_value_dim1_max, std::string BC_type_dim2_min, double BC_value_dim2_min,
                                                  +
                                                  35  std::string BC_type_dim2_max, double BC_value_dim2_max,std::string BC_type_dim3_min, double BC_value_dim3_min,
                                                  +
                                                  36  std::string BC_type_dim3_max, double BC_value_dim3_max);
                                                  +
                                                  37 
                                                  +
                                                  38  void inputBCs(int var, int component, std::string BC_type_dim1_min, double BC_value_dim1_min,
                                                  +
                                                  39  std::string BC_type_dim1_max, double BC_value_dim1_max, std::string BC_type_dim2_min, double BC_value_dim2_min,
                                                  +
                                                  40  std::string BC_type_dim2_max, double BC_value_dim2_max);
                                                  +
                                                  41 
                                                  +
                                                  42  void inputBCs(int var, int component, std::string BC_type_dim1_min, double BC_value_dim1_min,
                                                  +
                                                  43  std::string BC_type_dim1_max, double BC_value_dim1_max);
                                                  +
                                                  44 
                                                  +
                                                  45  void inputBCs(int var, int component, std::string BC_type, double BC_value);
                                                  +
                                                  46 
                                                  +
                                                  47  // Boundary condition object
                                                  +
                                                  48  std::vector<varBCs<dim> > BC_list;
                                                  +
                                                  49 
                                                  +
                                                  50  private:
                                                  +
                                                  51 
                                                  +
                                                  52  // Declare vectors to put the information about the variables into
                                                  +
                                                  53  std::vector<std::string> var_name;
                                                  +
                                                  54  std::vector<std::string> var_type;
                                                  +
                                                  55  std::vector<std::string> var_eq_type;
                                                  +
                                                  56 
                                                  +
                                                  57  std::vector<bool> need_value;
                                                  +
                                                  58  std::vector<bool> need_gradient;
                                                  +
                                                  59  std::vector<bool> need_hessian;
                                                  +
                                                  60  std::vector<bool> value_residual;
                                                  +
                                                  61  std::vector<bool> gradient_residual;
                                                  +
                                                  62 
                                                  +
                                                  63  std::vector<bool> need_value_LHS;
                                                  +
                                                  64  std::vector<bool> need_gradient_LHS;
                                                  +
                                                  65  std::vector<bool> need_hessian_LHS;
                                                  +
                                                  66  std::vector<bool> value_residual_LHS;
                                                  +
                                                  67  std::vector<bool> gradient_residual_LHS;
                                                  +
                                                  68 
                                                  +
                                                  69  // Elasticity matrix variables
                                                  +
                                                  70  const static unsigned int CIJ_tensor_size = 2*dim-1+dim/3;
                                                  +
                                                  71  std::vector<dealii::Tensor<2, CIJ_tensor_size, dealii::VectorizedArray<double> > > CIJ_list;
                                                  +
                                                  72 
                                                  +
                                                  73  bool c_dependent_misfit;
                                                  +
                                                  74 
                                                  +
                                                  75  Threads::Mutex assembler_lock;
                                                  +
                                                  76 
                                                  +
                                                  77  // Variables needed to calculate the LHS
                                                  +
                                                  78  std::vector<variable_info<dim> > varInfoListRHS;
                                                  +
                                                  79  std::vector<variable_info<dim> > resInfoListRHS;
                                                  +
                                                  80 
                                                  +
                                                  81  // Variables needed to calculate the LHS
                                                  +
                                                  82  unsigned int num_var_LHS;
                                                  +
                                                  83  std::vector<variable_info<dim> > varInfoListLHS;
                                                  +
                                                  84 
                                                  +
                                                  85  //RHS implementation for explicit solve
                                                  +
                                                  86  void getRHS(const MatrixFree<dim,double> &data,
                                                  +
                                                  87  std::vector<vectorType*> &dst,
                                                  +
                                                  88  const std::vector<vectorType*> &src,
                                                  +
                                                  89  const std::pair<unsigned int,unsigned int> &cell_range) const;
                                                  +
                                                  90 
                                                  +
                                                  91  //LHS implementation for implicit solve
                                                  +
                                                  92  void getLHS(const MatrixFree<dim,double> &data,
                                                  +
                                                  93  vectorType &dst,
                                                  +
                                                  94  const vectorType &src,
                                                  +
                                                  95  const std::pair<unsigned int,unsigned int> &cell_range) const;
                                                  +
                                                  96 
                                                  +
                                                  97  //method to apply initial conditions
                                                  +
                                                  98  void applyInitialConditions();
                                                  +
                                                  99 
                                                  +
                                                  100  //methods to apply dirichlet BC's on displacement
                                                  +
                                                  101  void applyDirichletBCs();
                                                  +
                                                  102 
                                                  +
                                                  103  // method to modify the fields for nucleation
                                                  +
                                                  104  void modifySolutionFields();
                                                  +
                                                  105 
                                                  +
                                                  106  void computeIntegral(double& integratedField);
                                                  +
                                                  107 
                                                  +
                                                  108  void markBoundaries();
                                                  +
                                                  109 
                                                  +
                                                  110  void setPeriodicity();
                                                  +
                                                  111  void setPeriodicityConstraints(ConstraintMatrix*, DoFHandler<dim>*);
                                                  +
                                                  112  void getComponentsWithRigidBodyModes(std::vector<int> &);
                                                  +
                                                  113  //void setRigidBodyModeConstraints( std::vector<int>, ConstraintMatrix*, DoFHandler<dim>*);
                                                  114 
                                                  -
                                                  115  void residualRHS(const std::vector<modelVariable<dim>> & modelVarList,
                                                  -
                                                  116  std::vector<modelResidual<dim>> & modelResidualsList,
                                                  -
                                                  117  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const;
                                                  -
                                                  118 
                                                  -
                                                  119  void residualLHS(const std::vector<modelVariable<dim>> & modelVarList,
                                                  -
                                                  120  modelResidual<dim> & modelRes,
                                                  -
                                                  121  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const;
                                                  -
                                                  122 
                                                  -
                                                  123  void energyDensity(const std::vector<modelVariable<dim>> & modelVarList, const dealii::VectorizedArray<double> & JxW_value,
                                                  -
                                                  124  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc);
                                                  +
                                                  115 
                                                  +
                                                  116  void getEnergy(const MatrixFree<dim,double> &data,
                                                  +
                                                  117  std::vector<vectorType*> &dst,
                                                  +
                                                  118  const std::vector<vectorType*> &src,
                                                  +
                                                  119  const std::pair<unsigned int,unsigned int> &cell_range);
                                                  +
                                                  120 
                                                  +
                                                  121 
                                                  +
                                                  122  void residualRHS(const std::vector<modelVariable<dim> > & modelVarList,
                                                  +
                                                  123  std::vector<modelResidual<dim> > & modelResidualsList,
                                                  +
                                                  124  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const;
                                                  125 
                                                  -
                                                  126  //AMR methods
                                                  -
                                                  127  void adaptiveRefine(unsigned int currentIncrement);
                                                  -
                                                  128  void adaptiveRefineCriterion();
                                                  +
                                                  126  void residualLHS(const std::vector<modelVariable<dim> > & modelVarList,
                                                  +
                                                  127  modelResidual<dim> & modelRes,
                                                  +
                                                  128  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const;
                                                  129 
                                                  -
                                                  130 };
                                                  -
                                                  131 
                                                  -
                                                  generalizedProblem::energyDensity
                                                  void energyDensity(const std::vector< modelVariable< dim >> &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
                                                  Definition: equations.h:106
                                                  +
                                                  130  void energyDensity(const std::vector<modelVariable<dim> > & modelVarList, const dealii::VectorizedArray<double> & JxW_value,
                                                  +
                                                  131  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc);
                                                  +
                                                  132 
                                                  +
                                                  133  //AMR methods
                                                  +
                                                  134  void adaptiveRefine(unsigned int currentIncrement);
                                                  +
                                                  135  void adaptiveRefineCriterion();
                                                  +
                                                  136 
                                                  +
                                                  137 };
                                                  +
                                                  138 
                                                  modelResidual
                                                  Definition: model_variables.h:25
                                                  -
                                                  generalizedProblem::var_type
                                                  std::vector< std::string > var_type
                                                  Definition: generalized_model.h:53
                                                  -
                                                  generalizedProblem::varInfoListLHS
                                                  std::vector< variable_info< dim > > varInfoListLHS
                                                  Definition: generalized_model.h:82
                                                  -
                                                  generalizedProblem::gradient_residual_LHS
                                                  std::vector< bool > gradient_residual_LHS
                                                  Definition: generalized_model.h:66
                                                  -
                                                  generalizedProblem::var_name
                                                  std::vector< std::string > var_name
                                                  Definition: generalized_model.h:52
                                                  +
                                                  generalizedProblem::var_type
                                                  std::vector< std::string > var_type
                                                  Definition: generalized_model.h:54
                                                  +
                                                  generalizedProblem::residualRHS
                                                  void residualRHS(const std::vector< modelVariable< dim > > &modelVarList, std::vector< modelResidual< dim > > &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
                                                  Definition: equations.h:58
                                                  +
                                                  generalizedProblem::varInfoListLHS
                                                  std::vector< variable_info< dim > > varInfoListLHS
                                                  Definition: generalized_model.h:83
                                                  +
                                                  generalizedProblem::gradient_residual_LHS
                                                  std::vector< bool > gradient_residual_LHS
                                                  Definition: generalized_model.h:67
                                                  +
                                                  generalizedProblem::var_name
                                                  std::vector< std::string > var_name
                                                  Definition: generalized_model.h:53
                                                  MatrixFreePDE
                                                  Definition: matrixFreePDE.h:85
                                                  -
                                                  generalizedProblem::need_value
                                                  std::vector< bool > need_value
                                                  Definition: generalized_model.h:56
                                                  +
                                                  generalizedProblem::need_value
                                                  std::vector< bool > need_value
                                                  Definition: generalized_model.h:57
                                                  generalizedProblem
                                                  Definition: generalized_model.h:21
                                                  -
                                                  generalizedProblem::adaptiveRefine
                                                  void adaptiveRefine(unsigned int currentIncrement)
                                                  Definition: generalized_model_functions.h:596
                                                  -
                                                  generalizedProblem::buildFields
                                                  void buildFields()
                                                  Definition: generalized_model_functions.h:674
                                                  -
                                                  generalizedProblem::getRHS
                                                  void getRHS(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range) const
                                                  Definition: generalized_model_functions.h:185
                                                  -
                                                  generalizedProblem::getEnergy
                                                  void getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)
                                                  Definition: generalized_model_functions.h:450
                                                  -
                                                  generalizedProblem::CIJ_list
                                                  std::vector< dealii::Tensor< 2, CIJ_tensor_size, dealii::VectorizedArray< double > > > CIJ_list
                                                  Definition: generalized_model.h:70
                                                  -
                                                  generalizedProblem::need_hessian_LHS
                                                  std::vector< bool > need_hessian_LHS
                                                  Definition: generalized_model.h:64
                                                  -
                                                  generalizedProblem::residualLHS
                                                  void residualLHS(const std::vector< modelVariable< dim >> &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
                                                  Definition: equations.h:89
                                                  +
                                                  generalizedProblem::adaptiveRefine
                                                  void adaptiveRefine(unsigned int currentIncrement)
                                                  Definition: generalized_model_functions.h:617
                                                  +
                                                  generalizedProblem::buildFields
                                                  void buildFields()
                                                  Definition: generalized_model_functions.h:705
                                                  +
                                                  generalizedProblem::getRHS
                                                  void getRHS(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range) const
                                                  Definition: generalized_model_functions.h:206
                                                  +
                                                  generalizedProblem::getEnergy
                                                  void getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)
                                                  Definition: generalized_model_functions.h:471
                                                  +
                                                  generalizedProblem::CIJ_list
                                                  std::vector< dealii::Tensor< 2, CIJ_tensor_size, dealii::VectorizedArray< double > > > CIJ_list
                                                  Definition: generalized_model.h:71
                                                  +
                                                  generalizedProblem::need_hessian_LHS
                                                  std::vector< bool > need_hessian_LHS
                                                  Definition: generalized_model.h:65
                                                  varBCs
                                                  Definition: generalized_model.h:12
                                                  -
                                                  generalizedProblem::need_hessian
                                                  std::vector< bool > need_hessian
                                                  Definition: generalized_model.h:58
                                                  +
                                                  generalizedProblem::need_hessian
                                                  std::vector< bool > need_hessian
                                                  Definition: generalized_model.h:59
                                                  generalizedProblem::generalizedProblem
                                                  generalizedProblem()
                                                  Definition: generalized_model_functions.h:6
                                                  varBCs::var_BC_val
                                                  std::vector< double > var_BC_val
                                                  Definition: generalized_model.h:17
                                                  -
                                                  generalizedProblem::markBoundaries
                                                  void markBoundaries()
                                                  Definition: generalized_model_functions.h:823
                                                  +
                                                  generalizedProblem::setPeriodicity
                                                  void setPeriodicity()
                                                  Definition: generalized_model_functions.h:1133
                                                  +
                                                  generalizedProblem::markBoundaries
                                                  void markBoundaries()
                                                  Definition: generalized_model_functions.h:977
                                                  vectorType
                                                  dealii::parallel::distributed::Vector< double > vectorType
                                                  Definition: matrixFreePDE.h:23
                                                  -
                                                  generalizedProblem::BC_list
                                                  std::vector< varBCs< dim > > BC_list
                                                  Definition: generalized_model.h:47
                                                  -
                                                  generalizedProblem::value_residual
                                                  std::vector< bool > value_residual
                                                  Definition: generalized_model.h:59
                                                  +
                                                  generalizedProblem::BC_list
                                                  std::vector< varBCs< dim > > BC_list
                                                  Definition: generalized_model.h:48
                                                  +
                                                  generalizedProblem::value_residual
                                                  std::vector< bool > value_residual
                                                  Definition: generalized_model.h:60
                                                  varBCs::var_BC_type
                                                  std::vector< std::string > var_BC_type
                                                  Definition: generalized_model.h:16
                                                  -
                                                  generalizedProblem::applyDirichletBCs
                                                  void applyDirichletBCs()
                                                  Definition: generalized_model_functions.h:766
                                                  -
                                                  generalizedProblem::setBCs
                                                  void setBCs()
                                                  Definition: ICs_and_BCs.h:66
                                                  -
                                                  generalizedProblem::c_dependent_misfit
                                                  bool c_dependent_misfit
                                                  Definition: generalized_model.h:72
                                                  -
                                                  generalizedProblem::computeIntegral
                                                  void computeIntegral(double &integratedField)
                                                  Definition: generalized_model_functions.h:554
                                                  +
                                                  generalizedProblem::applyDirichletBCs
                                                  void applyDirichletBCs()
                                                  Definition: generalized_model_functions.h:920
                                                  +
                                                  generalizedProblem::setBCs
                                                  void setBCs()
                                                  Definition: ICs_and_BCs.h:67
                                                  +
                                                  generalizedProblem::c_dependent_misfit
                                                  bool c_dependent_misfit
                                                  Definition: generalized_model.h:73
                                                  +
                                                  generalizedProblem::computeIntegral
                                                  void computeIntegral(double &integratedField)
                                                  Definition: generalized_model_functions.h:575
                                                  -
                                                  generalizedProblem::inputBCs
                                                  void inputBCs(int var, int component, std::string BC_type_dim1_min, double BC_value_dim1_min, std::string BC_type_dim1_max, double BC_value_dim1_max, std::string BC_type_dim2_min, double BC_value_dim2_min, std::string BC_type_dim2_max, double BC_value_dim2_max, std::string BC_type_dim3_min, double BC_value_dim3_min, std::string BC_type_dim3_max, double BC_value_dim3_max)
                                                  Definition: generalized_model_functions.h:853
                                                  -
                                                  generalizedProblem::CIJ_tensor_size
                                                  static const unsigned int CIJ_tensor_size
                                                  Definition: generalized_model.h:69
                                                  -
                                                  generalizedProblem::value_residual_LHS
                                                  std::vector< bool > value_residual_LHS
                                                  Definition: generalized_model.h:65
                                                  -
                                                  generalizedProblem::num_var_LHS
                                                  unsigned int num_var_LHS
                                                  Definition: generalized_model.h:81
                                                  -
                                                  generalizedProblem::assembler_lock
                                                  Threads::Mutex assembler_lock
                                                  Definition: generalized_model.h:74
                                                  -
                                                  generalizedProblem::applyInitialConditions
                                                  void applyInitialConditions()
                                                  Definition: generalized_model_functions.h:711
                                                  -
                                                  generalizedProblem::varInfoListRHS
                                                  std::vector< variable_info< dim > > varInfoListRHS
                                                  Definition: generalized_model.h:77
                                                  -
                                                  generalizedProblem::adaptiveRefineCriterion
                                                  void adaptiveRefineCriterion()
                                                  Definition: generalized_model_functions.h:606
                                                  -
                                                  generalizedProblem::resInfoListRHS
                                                  std::vector< variable_info< dim > > resInfoListRHS
                                                  Definition: generalized_model.h:78
                                                  -
                                                  generalizedProblem::var_eq_type
                                                  std::vector< std::string > var_eq_type
                                                  Definition: generalized_model.h:54
                                                  +
                                                  generalizedProblem::inputBCs
                                                  void inputBCs(int var, int component, std::string BC_type_dim1_min, double BC_value_dim1_min, std::string BC_type_dim1_max, double BC_value_dim1_max, std::string BC_type_dim2_min, double BC_value_dim2_min, std::string BC_type_dim2_max, double BC_value_dim2_max, std::string BC_type_dim3_min, double BC_value_dim3_min, std::string BC_type_dim3_max, double BC_value_dim3_max)
                                                  Definition: generalized_model_functions.h:1007
                                                  +
                                                  generalizedProblem::CIJ_tensor_size
                                                  static const unsigned int CIJ_tensor_size
                                                  Definition: generalized_model.h:70
                                                  +
                                                  generalizedProblem::getComponentsWithRigidBodyModes
                                                  void getComponentsWithRigidBodyModes(std::vector< int > &)
                                                  Definition: generalized_model_functions.h:1168
                                                  +
                                                  generalizedProblem::value_residual_LHS
                                                  std::vector< bool > value_residual_LHS
                                                  Definition: generalized_model.h:66
                                                  +
                                                  generalizedProblem::num_var_LHS
                                                  unsigned int num_var_LHS
                                                  Definition: generalized_model.h:82
                                                  +
                                                  generalizedProblem::assembler_lock
                                                  Threads::Mutex assembler_lock
                                                  Definition: generalized_model.h:75
                                                  +
                                                  generalizedProblem::applyInitialConditions
                                                  void applyInitialConditions()
                                                  Definition: generalized_model_functions.h:777
                                                  +
                                                  generalizedProblem::residualLHS
                                                  void residualLHS(const std::vector< modelVariable< dim > > &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
                                                  Definition: equations.h:89
                                                  +
                                                  generalizedProblem::setPeriodicityConstraints
                                                  void setPeriodicityConstraints(ConstraintMatrix *, DoFHandler< dim > *)
                                                  Definition: generalized_model_functions.h:1154
                                                  +
                                                  generalizedProblem::varInfoListRHS
                                                  std::vector< variable_info< dim > > varInfoListRHS
                                                  Definition: generalized_model.h:78
                                                  +
                                                  generalizedProblem::adaptiveRefineCriterion
                                                  void adaptiveRefineCriterion()
                                                  Definition: generalized_model_functions.h:637
                                                  +
                                                  generalizedProblem::resInfoListRHS
                                                  std::vector< variable_info< dim > > resInfoListRHS
                                                  Definition: generalized_model.h:79
                                                  +
                                                  generalizedProblem::var_eq_type
                                                  std::vector< std::string > var_eq_type
                                                  Definition: generalized_model.h:55
                                                  modelVariable
                                                  Definition: model_variables.h:4
                                                  -
                                                  generalizedProblem::modifySolutionFields
                                                  void modifySolutionFields()
                                                  Definition: generalized_model_functions.h:940
                                                  -
                                                  generalizedProblem::gradient_residual
                                                  std::vector< bool > gradient_residual
                                                  Definition: generalized_model.h:60
                                                  -
                                                  generalizedProblem::need_value_LHS
                                                  std::vector< bool > need_value_LHS
                                                  Definition: generalized_model.h:62
                                                  -
                                                  MatrixFreePDE::currentIncrement
                                                  unsigned int currentIncrement
                                                  Definition: matrixFreePDE.h:255
                                                  -
                                                  generalizedProblem::getLHS
                                                  void getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const
                                                  Definition: generalized_model_functions.h:311
                                                  -
                                                  generalizedProblem::need_gradient_LHS
                                                  std::vector< bool > need_gradient_LHS
                                                  Definition: generalized_model.h:63
                                                  -
                                                  generalizedProblem::residualRHS
                                                  void residualRHS(const std::vector< modelVariable< dim >> &modelVarList, std::vector< modelResidual< dim >> &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
                                                  Definition: equations.h:58
                                                  -
                                                  generalizedProblem::need_gradient
                                                  std::vector< bool > need_gradient
                                                  Definition: generalized_model.h:57
                                                  +
                                                  generalizedProblem::modifySolutionFields
                                                  void modifySolutionFields()
                                                  Definition: generalized_model_functions.h:1252
                                                  +
                                                  generalizedProblem::gradient_residual
                                                  std::vector< bool > gradient_residual
                                                  Definition: generalized_model.h:61
                                                  +
                                                  generalizedProblem::need_value_LHS
                                                  std::vector< bool > need_value_LHS
                                                  Definition: generalized_model.h:63
                                                  +
                                                  MatrixFreePDE::currentIncrement
                                                  unsigned int currentIncrement
                                                  Definition: matrixFreePDE.h:268
                                                  +
                                                  generalizedProblem::getLHS
                                                  void getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const
                                                  Definition: generalized_model_functions.h:332
                                                  +
                                                  generalizedProblem::need_gradient_LHS
                                                  std::vector< bool > need_gradient_LHS
                                                  Definition: generalized_model.h:64
                                                  +
                                                  generalizedProblem::need_gradient
                                                  std::vector< bool > need_gradient
                                                  Definition: generalized_model.h:58
                                                  +
                                                  generalizedProblem::energyDensity
                                                  void energyDensity(const std::vector< modelVariable< dim > > &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
                                                  Definition: equations.h:106
                                                  diff --git a/html/generalized__model__functions_8h.html b/html/generalized__model__functions_8h.html index 6b86c1a65..64ccd79fa 100644 --- a/html/generalized__model__functions_8h.html +++ b/html/generalized__model__functions_8h.html @@ -112,6 +112,16 @@   #define nucleation_occurs   false   +#define enablePFields   false +  +#define loadICs   {} +  +#define loadSerialFile   {} +  +#define loadFileName   {} +  +#define loadFieldName   {} +  @@ -121,6 +131,66 @@

                                                  Variables

                                                   

                                                  Macro Definition Documentation

                                                  + +
                                                  +
                                                  + + + + +
                                                  #define enablePFields   false
                                                  +
                                                  + +
                                                  +
                                                  + +
                                                  +
                                                  + + + + +
                                                  #define loadFieldName   {}
                                                  +
                                                  + +
                                                  +
                                                  + +
                                                  +
                                                  + + + + +
                                                  #define loadFileName   {}
                                                  +
                                                  + +
                                                  +
                                                  + +
                                                  +
                                                  + + + + +
                                                  #define loadICs   {}
                                                  +
                                                  + +
                                                  +
                                                  + +
                                                  +
                                                  + + + + +
                                                  #define loadSerialFile   {}
                                                  +
                                                  + +
                                                  +
                                                  diff --git a/html/generalized__model__functions_8h_source.html b/html/generalized__model__functions_8h_source.html index a81ded2c0..04bf68cc3 100644 --- a/html/generalized__model__functions_8h_source.html +++ b/html/generalized__model__functions_8h_source.html @@ -97,1283 +97,1609 @@
                                                  5 template <int dim>
                                                  6 generalizedProblem<dim>::generalizedProblem(): MatrixFreePDE<dim>()
                                                  7 {
                                                  -
                                                  8 #ifndef timeIncrements
                                                  -
                                                  9 #define timeIncrements 1
                                                  -
                                                  10 #endif
                                                  -
                                                  11 #ifndef timeStep
                                                  -
                                                  12 #define timeStep 0.0
                                                  -
                                                  13 #endif
                                                  -
                                                  14 
                                                  -
                                                  15  var_name = variable_name;
                                                  -
                                                  16  var_type = variable_type;
                                                  -
                                                  17  var_eq_type = variable_eq_type;
                                                  -
                                                  18 
                                                  -
                                                  19  need_value = need_val;
                                                  -
                                                  20  need_gradient = need_grad;
                                                  -
                                                  21  need_hessian = need_hess;
                                                  -
                                                  22  value_residual = need_val_residual;
                                                  -
                                                  23  gradient_residual = need_grad_residual;
                                                  -
                                                  24 
                                                  -
                                                  25  #ifdef need_val_LHS
                                                  -
                                                  26  need_value_LHS = need_val_LHS;
                                                  -
                                                  27  #else
                                                  -
                                                  28  for (unsigned int i=0; i<num_var; i++)
                                                  -
                                                  29  need_value_LHS.push_back(false);
                                                  -
                                                  30  #endif
                                                  -
                                                  31  #ifdef need_grad_LHS
                                                  -
                                                  32  need_gradient_LHS = need_grad_LHS;
                                                  -
                                                  33  #else
                                                  -
                                                  34  for (unsigned int i=0; i<num_var; i++)
                                                  -
                                                  35  need_gradient_LHS.push_back(false);
                                                  -
                                                  36  #endif
                                                  -
                                                  37  #ifdef need_hess_LHS
                                                  -
                                                  38  need_hessian_LHS = need_hess_LHS;
                                                  -
                                                  39  #else
                                                  -
                                                  40  for (unsigned int i=0; i<num_var; i++)
                                                  -
                                                  41  need_hessian_LHS.push_back(false);
                                                  -
                                                  42  #endif
                                                  -
                                                  43  #ifdef need_val_residual_LHS
                                                  -
                                                  44  value_residual_LHS = need_val_residual_LHS;
                                                  -
                                                  45  #else
                                                  -
                                                  46  for (unsigned int i=0; i<num_var; i++)
                                                  -
                                                  47  value_residual_LHS.push_back(false);
                                                  -
                                                  48  #endif
                                                  -
                                                  49  #ifdef need_grad_residual_LHS
                                                  -
                                                  50  gradient_residual_LHS = need_grad_residual_LHS;
                                                  -
                                                  51  #else
                                                  -
                                                  52  for (unsigned int i=0; i<num_var; i++)
                                                  -
                                                  53  gradient_residual_LHS.push_back(false);
                                                  -
                                                  54  #endif
                                                  -
                                                  55 
                                                  -
                                                  56 
                                                  -
                                                  57 // initialize CIJ vector
                                                  -
                                                  58 #if defined(MaterialModels) && defined(MaterialConstants)
                                                  -
                                                  59  std::vector<std::vector<double> > temp_mat_consts = MaterialConstants;
                                                  -
                                                  60  std::vector<std::string> temp_mat_models = MaterialModels;
                                                  -
                                                  61  elasticityModel mat_model;
                                                  -
                                                  62 
                                                  -
                                                  63  dealii::Tensor<2, CIJ_tensor_size, dealii::VectorizedArray<double> > CIJ_temp;
                                                  -
                                                  64  for (unsigned int mater_num=0; mater_num < temp_mat_consts.size(); mater_num++){
                                                  -
                                                  65  if (temp_mat_models[mater_num] == "ISOTROPIC"){
                                                  -
                                                  66  mat_model = ISOTROPIC;
                                                  -
                                                  67  }
                                                  -
                                                  68  else if (temp_mat_models[mater_num] == "TRANSVERSE"){
                                                  -
                                                  69  mat_model = TRANSVERSE;
                                                  -
                                                  70  }
                                                  -
                                                  71  else if (temp_mat_models[mater_num] == "ORTHOTROPIC"){
                                                  -
                                                  72  mat_model = ORTHOTROPIC;
                                                  -
                                                  73  }
                                                  -
                                                  74  else if (temp_mat_models[mater_num] == "ANISOTROPIC"){
                                                  -
                                                  75  mat_model = ANISOTROPIC;
                                                  -
                                                  76  }
                                                  -
                                                  77  else {
                                                  -
                                                  78  // Should change to an exception
                                                  -
                                                  79  std::cout << "Elastic material model is invalid, please use ISOTROPIC, TRANSVERSE, ORTHOTROPIC, or ANISOTROPIC" << std::endl;
                                                  -
                                                  80  }
                                                  -
                                                  81 
                                                  -
                                                  82  getCIJMatrix<dim>(mat_model, temp_mat_consts[mater_num], CIJ_temp, this->pcout);
                                                  -
                                                  83  CIJ_list.push_back(CIJ_temp);
                                                  -
                                                  84  }
                                                  -
                                                  85 #endif
                                                  -
                                                  86 
                                                  -
                                                  87 
                                                  -
                                                  88 // I should probably get rid of this or move it, since it is only relevant to the precipitate case
                                                  -
                                                  89 c_dependent_misfit = false;
                                                  -
                                                  90 #if defined(sfts_linear1) && defined(sfts_linear2) && defined(sfts_linear3)
                                                  -
                                                  91 for (unsigned int i=0; i<dim; i++){
                                                  -
                                                  92  for (unsigned int j=0; j<dim; j++){
                                                  -
                                                  93  if ((std::abs(sfts_linear1[i][j])>1.0e-12)||(std::abs(sfts_linear2[i][j])>1.0e-12)||(std::abs(sfts_linear3[i][j])>1.0e-12)){
                                                  -
                                                  94  c_dependent_misfit = true;
                                                  -
                                                  95  }
                                                  -
                                                  96  }
                                                  -
                                                  97 }
                                                  -
                                                  98 #endif
                                                  -
                                                  99 
                                                  -
                                                  100 
                                                  -
                                                  101 // If the LHS variable attributes aren't defined
                                                  +
                                                  8 
                                                  +
                                                  9 #ifndef timeIncrements
                                                  +
                                                  10 #define timeIncrements 1
                                                  +
                                                  11 #endif
                                                  +
                                                  12 #ifndef timeStep
                                                  +
                                                  13 #define timeStep 0.0
                                                  +
                                                  14 #endif
                                                  +
                                                  15 
                                                  +
                                                  16  //initialize time step variables
                                                  +
                                                  17  #ifdef timeStep
                                                  +
                                                  18  this->dtValue=timeStep;
                                                  +
                                                  19  #endif
                                                  +
                                                  20  #ifdef timeFinal
                                                  +
                                                  21  this->finalTime=timeFinal;
                                                  +
                                                  22  #else
                                                  +
                                                  23  this->finalTime = 0.0;
                                                  +
                                                  24  #endif
                                                  +
                                                  25 
                                                  +
                                                  26 
                                                  +
                                                  27  // Determine the maximum number of time steps
                                                  +
                                                  28  if (std::ceil(this->finalTime/timeStep) < timeIncrements){
                                                  +
                                                  29  this->totalIncrements = std::ceil(this->finalTime/timeStep);
                                                  +
                                                  30  }
                                                  +
                                                  31  else {
                                                  +
                                                  32  this->totalIncrements = timeIncrements;
                                                  +
                                                  33  }
                                                  +
                                                  34 
                                                  +
                                                  35 
                                                  +
                                                  36  var_name = variable_name;
                                                  +
                                                  37  var_type = variable_type;
                                                  +
                                                  38  var_eq_type = variable_eq_type;
                                                  +
                                                  39 
                                                  +
                                                  40  need_value = need_val;
                                                  +
                                                  41  need_gradient = need_grad;
                                                  +
                                                  42  need_hessian = need_hess;
                                                  +
                                                  43  value_residual = need_val_residual;
                                                  +
                                                  44  gradient_residual = need_grad_residual;
                                                  +
                                                  45 
                                                  +
                                                  46  #ifdef need_val_LHS
                                                  +
                                                  47  need_value_LHS = need_val_LHS;
                                                  +
                                                  48  #else
                                                  +
                                                  49  for (unsigned int i=0; i<num_var; i++)
                                                  +
                                                  50  need_value_LHS.push_back(false);
                                                  +
                                                  51  #endif
                                                  +
                                                  52  #ifdef need_grad_LHS
                                                  +
                                                  53  need_gradient_LHS = need_grad_LHS;
                                                  +
                                                  54  #else
                                                  +
                                                  55  for (unsigned int i=0; i<num_var; i++)
                                                  +
                                                  56  need_gradient_LHS.push_back(false);
                                                  +
                                                  57  #endif
                                                  +
                                                  58  #ifdef need_hess_LHS
                                                  +
                                                  59  need_hessian_LHS = need_hess_LHS;
                                                  +
                                                  60  #else
                                                  +
                                                  61  for (unsigned int i=0; i<num_var; i++)
                                                  +
                                                  62  need_hessian_LHS.push_back(false);
                                                  +
                                                  63  #endif
                                                  +
                                                  64  #ifdef need_val_residual_LHS
                                                  +
                                                  65  value_residual_LHS = need_val_residual_LHS;
                                                  +
                                                  66  #else
                                                  +
                                                  67  for (unsigned int i=0; i<num_var; i++)
                                                  +
                                                  68  value_residual_LHS.push_back(false);
                                                  +
                                                  69  #endif
                                                  +
                                                  70  #ifdef need_grad_residual_LHS
                                                  +
                                                  71  gradient_residual_LHS = need_grad_residual_LHS;
                                                  +
                                                  72  #else
                                                  +
                                                  73  for (unsigned int i=0; i<num_var; i++)
                                                  +
                                                  74  gradient_residual_LHS.push_back(false);
                                                  +
                                                  75  #endif
                                                  +
                                                  76 
                                                  +
                                                  77 
                                                  +
                                                  78 // initialize CIJ vector
                                                  +
                                                  79 #if defined(MaterialModels) && defined(MaterialConstants)
                                                  +
                                                  80  std::vector<std::vector<double> > temp_mat_consts = MaterialConstants;
                                                  +
                                                  81  std::vector<std::string> temp_mat_models = MaterialModels;
                                                  +
                                                  82  elasticityModel mat_model;
                                                  +
                                                  83 
                                                  +
                                                  84  dealii::Tensor<2, CIJ_tensor_size, dealii::VectorizedArray<double> > CIJ_temp;
                                                  +
                                                  85  for (unsigned int mater_num=0; mater_num < temp_mat_consts.size(); mater_num++){
                                                  +
                                                  86  if (temp_mat_models[mater_num] == "ISOTROPIC"){
                                                  +
                                                  87  mat_model = ISOTROPIC;
                                                  +
                                                  88  }
                                                  +
                                                  89  else if (temp_mat_models[mater_num] == "TRANSVERSE"){
                                                  +
                                                  90  mat_model = TRANSVERSE;
                                                  +
                                                  91  }
                                                  +
                                                  92  else if (temp_mat_models[mater_num] == "ORTHOTROPIC"){
                                                  +
                                                  93  mat_model = ORTHOTROPIC;
                                                  +
                                                  94  }
                                                  +
                                                  95  else if (temp_mat_models[mater_num] == "ANISOTROPIC"){
                                                  +
                                                  96  mat_model = ANISOTROPIC;
                                                  +
                                                  97  }
                                                  +
                                                  98  else {
                                                  +
                                                  99  // Should change to an exception
                                                  +
                                                  100  std::cout << "Elastic material model is invalid, please use ISOTROPIC, TRANSVERSE, ORTHOTROPIC, or ANISOTROPIC" << std::endl;
                                                  +
                                                  101  }
                                                  102 
                                                  -
                                                  103 // If nucleation isn't specifically turned on, set nucleation_occurs to false
                                                  -
                                                  104 #ifndef nucleation_occurs
                                                  -
                                                  105  #define nucleation_occurs false
                                                  +
                                                  103  getCIJMatrix<dim>(mat_model, temp_mat_consts[mater_num], CIJ_temp, this->pcout);
                                                  +
                                                  104  CIJ_list.push_back(CIJ_temp);
                                                  +
                                                  105  }
                                                  106 #endif
                                                  107 
                                                  -
                                                  108 // Load variable information for calculating the RHS
                                                  -
                                                  109 varInfoListRHS.reserve(num_var);
                                                  -
                                                  110 unsigned int field_number = 0;
                                                  -
                                                  111 unsigned int scalar_var_index = 0;
                                                  -
                                                  112 unsigned int vector_var_index = 0;
                                                  -
                                                  113 for (unsigned int i=0; i<num_var; i++){
                                                  -
                                                  114  variable_info<dim> varInfo;
                                                  -
                                                  115  if (need_value[i] or need_gradient[i] or need_hessian[i]){
                                                  -
                                                  116  varInfo.global_var_index = i;
                                                  -
                                                  117  varInfo.global_field_index = field_number;
                                                  -
                                                  118  if (var_type[i] == "SCALAR"){
                                                  -
                                                  119  varInfo.is_scalar = true;
                                                  -
                                                  120  varInfo.scalar_or_vector_index = scalar_var_index;
                                                  -
                                                  121  scalar_var_index++;
                                                  -
                                                  122  }
                                                  -
                                                  123  else {
                                                  -
                                                  124  varInfo.is_scalar = false;
                                                  -
                                                  125  varInfo.scalar_or_vector_index = vector_var_index;
                                                  -
                                                  126  vector_var_index++;
                                                  -
                                                  127  }
                                                  -
                                                  128  varInfoListRHS.push_back(varInfo);
                                                  -
                                                  129  }
                                                  -
                                                  130 
                                                  -
                                                  131  if (var_type[i] == "SCALAR"){
                                                  -
                                                  132  field_number++;
                                                  -
                                                  133  }
                                                  -
                                                  134  else {
                                                  -
                                                  135  field_number+=dim;
                                                  -
                                                  136  }
                                                  -
                                                  137 }
                                                  -
                                                  138 
                                                  -
                                                  139 // Load variable information for calculating the LHS
                                                  -
                                                  140 num_var_LHS = 0;
                                                  -
                                                  141 for (unsigned int i=0; i<num_var; i++){
                                                  -
                                                  142  if (need_value_LHS[i] or need_gradient_LHS[i] or need_hessian_LHS[i]){
                                                  -
                                                  143  num_var_LHS++;
                                                  -
                                                  144  }
                                                  -
                                                  145 }
                                                  -
                                                  146 
                                                  -
                                                  147 varInfoListLHS.reserve(num_var_LHS);
                                                  -
                                                  148 field_number = 0;
                                                  -
                                                  149 scalar_var_index = 0;
                                                  -
                                                  150 vector_var_index = 0;
                                                  -
                                                  151 for (unsigned int i=0; i<num_var; i++){
                                                  -
                                                  152  variable_info<dim> varInfo;
                                                  -
                                                  153  if (need_value_LHS[i] or need_gradient_LHS[i] or need_hessian_LHS[i]){
                                                  -
                                                  154  varInfo.global_var_index = i;
                                                  -
                                                  155  varInfo.global_field_index = field_number;
                                                  -
                                                  156  if (var_type[i] == "SCALAR"){
                                                  -
                                                  157  varInfo.is_scalar = true;
                                                  -
                                                  158  varInfo.scalar_or_vector_index = scalar_var_index;
                                                  -
                                                  159  scalar_var_index++;
                                                  -
                                                  160  }
                                                  -
                                                  161  else {
                                                  -
                                                  162  varInfo.is_scalar = false;
                                                  -
                                                  163  varInfo.scalar_or_vector_index = vector_var_index;
                                                  -
                                                  164  vector_var_index++;
                                                  -
                                                  165  }
                                                  -
                                                  166  varInfoListLHS.push_back(varInfo);
                                                  -
                                                  167  }
                                                  -
                                                  168 
                                                  -
                                                  169  if (var_type[i] == "SCALAR"){
                                                  -
                                                  170  field_number++;
                                                  -
                                                  171  }
                                                  -
                                                  172  else {
                                                  -
                                                  173  field_number+=dim;
                                                  -
                                                  174  }
                                                  -
                                                  175 }
                                                  -
                                                  176 
                                                  -
                                                  177 }
                                                  -
                                                  178 
                                                  -
                                                  179 
                                                  -
                                                  180 // =====================================================================
                                                  -
                                                  181 // RESIDUAL CONSTRUCTION FUNCTIONS (RHS, LHS, ENERGY DENSITY)
                                                  -
                                                  182 // =====================================================================
                                                  -
                                                  183 
                                                  -
                                                  184 template <int dim>
                                                  -
                                                  185 void generalizedProblem<dim>::getRHS(const MatrixFree<dim,double> &data,
                                                  -
                                                  186  std::vector<vectorType*> &dst,
                                                  -
                                                  187  const std::vector<vectorType*> &src,
                                                  -
                                                  188  const std::pair<unsigned int,unsigned int> &cell_range) const{
                                                  +
                                                  108 
                                                  +
                                                  109 // I should probably get rid of this or move it, since it is only relevant to the precipitate case
                                                  +
                                                  110 c_dependent_misfit = false;
                                                  +
                                                  111 #if defined(sfts_linear1) && defined(sfts_linear2) && defined(sfts_linear3)
                                                  +
                                                  112 for (unsigned int i=0; i<dim; i++){
                                                  +
                                                  113  for (unsigned int j=0; j<dim; j++){
                                                  +
                                                  114  if ((std::abs(sfts_linear1[i][j])>1.0e-12)||(std::abs(sfts_linear2[i][j])>1.0e-12)||(std::abs(sfts_linear3[i][j])>1.0e-12)){
                                                  +
                                                  115  c_dependent_misfit = true;
                                                  +
                                                  116  }
                                                  +
                                                  117  }
                                                  +
                                                  118 }
                                                  +
                                                  119 #endif
                                                  +
                                                  120 
                                                  +
                                                  121 
                                                  +
                                                  122 // If the LHS variable attributes aren't defined
                                                  +
                                                  123 
                                                  +
                                                  124 // If nucleation isn't specifically turned on, set nucleation_occurs to false
                                                  +
                                                  125 #ifndef nucleation_occurs
                                                  +
                                                  126  #define nucleation_occurs false
                                                  +
                                                  127 #endif
                                                  +
                                                  128 
                                                  +
                                                  129 // Load variable information for calculating the RHS
                                                  +
                                                  130 varInfoListRHS.reserve(num_var);
                                                  +
                                                  131 unsigned int field_number = 0;
                                                  +
                                                  132 unsigned int scalar_var_index = 0;
                                                  +
                                                  133 unsigned int vector_var_index = 0;
                                                  +
                                                  134 for (unsigned int i=0; i<num_var; i++){
                                                  +
                                                  135  variable_info<dim> varInfo;
                                                  +
                                                  136  if (need_value[i] or need_gradient[i] or need_hessian[i]){
                                                  +
                                                  137  varInfo.global_var_index = i;
                                                  +
                                                  138  varInfo.global_field_index = field_number;
                                                  +
                                                  139  if (var_type[i] == "SCALAR"){
                                                  +
                                                  140  varInfo.is_scalar = true;
                                                  +
                                                  141  varInfo.scalar_or_vector_index = scalar_var_index;
                                                  +
                                                  142  scalar_var_index++;
                                                  +
                                                  143  }
                                                  +
                                                  144  else {
                                                  +
                                                  145  varInfo.is_scalar = false;
                                                  +
                                                  146  varInfo.scalar_or_vector_index = vector_var_index;
                                                  +
                                                  147  vector_var_index++;
                                                  +
                                                  148  }
                                                  +
                                                  149  varInfoListRHS.push_back(varInfo);
                                                  +
                                                  150  }
                                                  +
                                                  151 
                                                  +
                                                  152  if (var_type[i] == "SCALAR"){
                                                  +
                                                  153  field_number++;
                                                  +
                                                  154  }
                                                  +
                                                  155  else {
                                                  +
                                                  156  field_number+=dim;
                                                  +
                                                  157  }
                                                  +
                                                  158 }
                                                  +
                                                  159 
                                                  +
                                                  160 // Load variable information for calculating the LHS
                                                  +
                                                  161 num_var_LHS = 0;
                                                  +
                                                  162 for (unsigned int i=0; i<num_var; i++){
                                                  +
                                                  163  if (need_value_LHS[i] or need_gradient_LHS[i] or need_hessian_LHS[i]){
                                                  +
                                                  164  num_var_LHS++;
                                                  +
                                                  165  }
                                                  +
                                                  166 }
                                                  +
                                                  167 
                                                  +
                                                  168 varInfoListLHS.reserve(num_var_LHS);
                                                  +
                                                  169 field_number = 0;
                                                  +
                                                  170 scalar_var_index = 0;
                                                  +
                                                  171 vector_var_index = 0;
                                                  +
                                                  172 for (unsigned int i=0; i<num_var; i++){
                                                  +
                                                  173  variable_info<dim> varInfo;
                                                  +
                                                  174  if (need_value_LHS[i] or need_gradient_LHS[i] or need_hessian_LHS[i]){
                                                  +
                                                  175  varInfo.global_var_index = i;
                                                  +
                                                  176  varInfo.global_field_index = field_number;
                                                  +
                                                  177  if (var_type[i] == "SCALAR"){
                                                  +
                                                  178  varInfo.is_scalar = true;
                                                  +
                                                  179  varInfo.scalar_or_vector_index = scalar_var_index;
                                                  +
                                                  180  scalar_var_index++;
                                                  +
                                                  181  }
                                                  +
                                                  182  else {
                                                  +
                                                  183  varInfo.is_scalar = false;
                                                  +
                                                  184  varInfo.scalar_or_vector_index = vector_var_index;
                                                  +
                                                  185  vector_var_index++;
                                                  +
                                                  186  }
                                                  +
                                                  187  varInfoListLHS.push_back(varInfo);
                                                  +
                                                  188  }
                                                  189 
                                                  -
                                                  190 
                                                  -
                                                  191  //initialize FEEvaulation objects
                                                  -
                                                  192  std::vector<typeScalar> scalar_vars;
                                                  -
                                                  193  std::vector<typeVector> vector_vars;
                                                  -
                                                  194 
                                                  -
                                                  195  for (unsigned int i=0; i<num_var; i++){
                                                  -
                                                  196  if (varInfoListRHS[i].is_scalar){
                                                  -
                                                  197  typeScalar var(data, i);
                                                  -
                                                  198  scalar_vars.push_back(var);
                                                  -
                                                  199  }
                                                  -
                                                  200  else {
                                                  -
                                                  201  typeVector var(data, i);
                                                  -
                                                  202  vector_vars.push_back(var);
                                                  -
                                                  203  }
                                                  -
                                                  204  }
                                                  -
                                                  205 
                                                  -
                                                  206  std::vector<modelVariable<dim> > modelVarList;
                                                  -
                                                  207  std::vector<modelResidual<dim> > modelResidualsList;
                                                  -
                                                  208  modelVarList.reserve(num_var);
                                                  -
                                                  209  modelResidualsList.reserve(num_var);
                                                  +
                                                  190  if (var_type[i] == "SCALAR"){
                                                  +
                                                  191  field_number++;
                                                  +
                                                  192  }
                                                  +
                                                  193  else {
                                                  +
                                                  194  field_number+=dim;
                                                  +
                                                  195  }
                                                  +
                                                  196 }
                                                  +
                                                  197 
                                                  +
                                                  198 }
                                                  +
                                                  199 
                                                  +
                                                  200 
                                                  +
                                                  201 // =====================================================================
                                                  +
                                                  202 // RESIDUAL CONSTRUCTION FUNCTIONS (RHS, LHS, ENERGY DENSITY)
                                                  +
                                                  203 // =====================================================================
                                                  +
                                                  204 
                                                  +
                                                  205 template <int dim>
                                                  +
                                                  206 void generalizedProblem<dim>::getRHS(const MatrixFree<dim,double> &data,
                                                  +
                                                  207  std::vector<vectorType*> &dst,
                                                  +
                                                  208  const std::vector<vectorType*> &src,
                                                  +
                                                  209  const std::pair<unsigned int,unsigned int> &cell_range) const{
                                                  210 
                                                  -
                                                  211  //loop over cells
                                                  -
                                                  212  for (unsigned int cell=cell_range.first; cell<cell_range.second; ++cell){
                                                  -
                                                  213 
                                                  -
                                                  214  // Initialize, read DOFs, and set evaulation flags for each variable
                                                  -
                                                  215  for (unsigned int i=0; i<num_var; i++){
                                                  -
                                                  216  if (varInfoListRHS[i].is_scalar) {
                                                  -
                                                  217  scalar_vars[varInfoListRHS[i].scalar_or_vector_index].reinit(cell);
                                                  -
                                                  218  scalar_vars[varInfoListRHS[i].scalar_or_vector_index].read_dof_values_plain(*src[varInfoListRHS[i].global_field_index]);
                                                  -
                                                  219  scalar_vars[varInfoListRHS[i].scalar_or_vector_index].evaluate(need_value[i], need_gradient[i], need_hessian[i]);
                                                  -
                                                  220  }
                                                  -
                                                  221  else {
                                                  -
                                                  222  vector_vars[varInfoListRHS[i].scalar_or_vector_index].reinit(cell);
                                                  -
                                                  223  vector_vars[varInfoListRHS[i].scalar_or_vector_index].read_dof_values_plain(*src[varInfoListRHS[i].global_field_index]);
                                                  -
                                                  224  vector_vars[varInfoListRHS[i].scalar_or_vector_index].evaluate(need_value[i], need_gradient[i], need_hessian[i]);
                                                  -
                                                  225  }
                                                  -
                                                  226  }
                                                  -
                                                  227 
                                                  -
                                                  228  unsigned int num_q_points;
                                                  -
                                                  229  if (scalar_vars.size() > 0){
                                                  -
                                                  230  num_q_points = scalar_vars[0].n_q_points;
                                                  -
                                                  231  }
                                                  -
                                                  232  else {
                                                  -
                                                  233  num_q_points = vector_vars[0].n_q_points;
                                                  -
                                                  234  }
                                                  -
                                                  235 
                                                  -
                                                  236  //loop over quadrature points
                                                  -
                                                  237  for (unsigned int q=0; q<num_q_points; ++q){
                                                  -
                                                  238 
                                                  -
                                                  239  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc;
                                                  -
                                                  240  if (scalar_vars.size() > 0){
                                                  -
                                                  241  q_point_loc = scalar_vars[0].quadrature_point(q);
                                                  -
                                                  242  }
                                                  -
                                                  243  else {
                                                  -
                                                  244  q_point_loc = vector_vars[0].quadrature_point(q);
                                                  -
                                                  245  }
                                                  -
                                                  246 
                                                  -
                                                  247  for (unsigned int i=0; i<num_var; i++){
                                                  -
                                                  248  if (varInfoListRHS[i].is_scalar) {
                                                  -
                                                  249  if (need_value[i]){
                                                  -
                                                  250  modelVarList[i].scalarValue = scalar_vars[varInfoListRHS[i].scalar_or_vector_index].get_value(q);
                                                  -
                                                  251  }
                                                  -
                                                  252  if (need_gradient[i]){
                                                  -
                                                  253  modelVarList[i].scalarGrad = scalar_vars[varInfoListRHS[i].scalar_or_vector_index].get_gradient(q);
                                                  -
                                                  254  }
                                                  -
                                                  255  if (need_hessian[i]){
                                                  -
                                                  256  modelVarList[i].scalarHess = scalar_vars[varInfoListRHS[i].scalar_or_vector_index].get_hessian(q);
                                                  -
                                                  257  }
                                                  -
                                                  258  }
                                                  -
                                                  259  else {
                                                  -
                                                  260  if (need_value[i]){
                                                  -
                                                  261  modelVarList[i].vectorValue = vector_vars[varInfoListRHS[i].scalar_or_vector_index].get_value(q);
                                                  -
                                                  262  }
                                                  -
                                                  263  if (need_gradient[i]){
                                                  -
                                                  264  modelVarList[i].vectorGrad = vector_vars[varInfoListRHS[i].scalar_or_vector_index].get_gradient(q);
                                                  -
                                                  265  }
                                                  -
                                                  266  if (need_hessian[i]){
                                                  -
                                                  267  modelVarList[i].vectorHess = vector_vars[varInfoListRHS[i].scalar_or_vector_index].get_hessian(q);
                                                  -
                                                  268  }
                                                  -
                                                  269  }
                                                  -
                                                  270  }
                                                  -
                                                  271 
                                                  -
                                                  272  // Calculate the residuals
                                                  -
                                                  273  residualRHS(modelVarList,modelResidualsList,q_point_loc);
                                                  -
                                                  274 
                                                  -
                                                  275  // Submit values
                                                  -
                                                  276  for (unsigned int i=0; i<num_var; i++){
                                                  -
                                                  277  if (varInfoListRHS[i].is_scalar) {
                                                  -
                                                  278  if (value_residual[i] == true){
                                                  -
                                                  279  scalar_vars[varInfoListRHS[i].scalar_or_vector_index].submit_value(modelResidualsList[i].scalarValueResidual,q);
                                                  -
                                                  280  }
                                                  -
                                                  281  if (gradient_residual[i] == true){
                                                  -
                                                  282  scalar_vars[varInfoListRHS[i].scalar_or_vector_index].submit_gradient(modelResidualsList[i].scalarGradResidual,q);
                                                  +
                                                  211 
                                                  +
                                                  212  //initialize FEEvaulation objects
                                                  +
                                                  213  std::vector<typeScalar> scalar_vars;
                                                  +
                                                  214  std::vector<typeVector> vector_vars;
                                                  +
                                                  215 
                                                  +
                                                  216  for (unsigned int i=0; i<num_var; i++){
                                                  +
                                                  217  if (varInfoListRHS[i].is_scalar){
                                                  +
                                                  218  typeScalar var(data, i);
                                                  +
                                                  219  scalar_vars.push_back(var);
                                                  +
                                                  220  }
                                                  +
                                                  221  else {
                                                  +
                                                  222  typeVector var(data, i);
                                                  +
                                                  223  vector_vars.push_back(var);
                                                  +
                                                  224  }
                                                  +
                                                  225  }
                                                  +
                                                  226 
                                                  +
                                                  227  std::vector<modelVariable<dim> > modelVarList;
                                                  +
                                                  228  std::vector<modelResidual<dim> > modelResidualsList;
                                                  +
                                                  229  modelVarList.reserve(num_var);
                                                  +
                                                  230  modelResidualsList.reserve(num_var);
                                                  +
                                                  231 
                                                  +
                                                  232  //loop over cells
                                                  +
                                                  233  for (unsigned int cell=cell_range.first; cell<cell_range.second; ++cell){
                                                  +
                                                  234 
                                                  +
                                                  235  // Initialize, read DOFs, and set evaulation flags for each variable
                                                  +
                                                  236  for (unsigned int i=0; i<num_var; i++){
                                                  +
                                                  237  if (varInfoListRHS[i].is_scalar) {
                                                  +
                                                  238  scalar_vars[varInfoListRHS[i].scalar_or_vector_index].reinit(cell);
                                                  +
                                                  239  scalar_vars[varInfoListRHS[i].scalar_or_vector_index].read_dof_values_plain(*src[varInfoListRHS[i].global_field_index]);
                                                  +
                                                  240  scalar_vars[varInfoListRHS[i].scalar_or_vector_index].evaluate(need_value[i], need_gradient[i], need_hessian[i]);
                                                  +
                                                  241  }
                                                  +
                                                  242  else {
                                                  +
                                                  243  vector_vars[varInfoListRHS[i].scalar_or_vector_index].reinit(cell);
                                                  +
                                                  244  vector_vars[varInfoListRHS[i].scalar_or_vector_index].read_dof_values_plain(*src[varInfoListRHS[i].global_field_index]);
                                                  +
                                                  245  vector_vars[varInfoListRHS[i].scalar_or_vector_index].evaluate(need_value[i], need_gradient[i], need_hessian[i]);
                                                  +
                                                  246  }
                                                  +
                                                  247  }
                                                  +
                                                  248 
                                                  +
                                                  249  unsigned int num_q_points;
                                                  +
                                                  250  if (scalar_vars.size() > 0){
                                                  +
                                                  251  num_q_points = scalar_vars[0].n_q_points;
                                                  +
                                                  252  }
                                                  +
                                                  253  else {
                                                  +
                                                  254  num_q_points = vector_vars[0].n_q_points;
                                                  +
                                                  255  }
                                                  +
                                                  256 
                                                  +
                                                  257  //loop over quadrature points
                                                  +
                                                  258  for (unsigned int q=0; q<num_q_points; ++q){
                                                  +
                                                  259 
                                                  +
                                                  260  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc;
                                                  +
                                                  261  if (scalar_vars.size() > 0){
                                                  +
                                                  262  q_point_loc = scalar_vars[0].quadrature_point(q);
                                                  +
                                                  263  }
                                                  +
                                                  264  else {
                                                  +
                                                  265  q_point_loc = vector_vars[0].quadrature_point(q);
                                                  +
                                                  266  }
                                                  +
                                                  267 
                                                  +
                                                  268  for (unsigned int i=0; i<num_var; i++){
                                                  +
                                                  269  if (varInfoListRHS[i].is_scalar) {
                                                  +
                                                  270  if (need_value[i]){
                                                  +
                                                  271  modelVarList[i].scalarValue = scalar_vars[varInfoListRHS[i].scalar_or_vector_index].get_value(q);
                                                  +
                                                  272  }
                                                  +
                                                  273  if (need_gradient[i]){
                                                  +
                                                  274  modelVarList[i].scalarGrad = scalar_vars[varInfoListRHS[i].scalar_or_vector_index].get_gradient(q);
                                                  +
                                                  275  }
                                                  +
                                                  276  if (need_hessian[i]){
                                                  +
                                                  277  modelVarList[i].scalarHess = scalar_vars[varInfoListRHS[i].scalar_or_vector_index].get_hessian(q);
                                                  +
                                                  278  }
                                                  +
                                                  279  }
                                                  +
                                                  280  else {
                                                  +
                                                  281  if (need_value[i]){
                                                  +
                                                  282  modelVarList[i].vectorValue = vector_vars[varInfoListRHS[i].scalar_or_vector_index].get_value(q);
                                                  283  }
                                                  -
                                                  284  }
                                                  -
                                                  285  else {
                                                  -
                                                  286  if (value_residual[i] == true){
                                                  -
                                                  287  vector_vars[varInfoListRHS[i].scalar_or_vector_index].submit_value(modelResidualsList[i].vectorValueResidual,q);
                                                  -
                                                  288  }
                                                  -
                                                  289  if (gradient_residual[i] == true){
                                                  -
                                                  290  vector_vars[varInfoListRHS[i].scalar_or_vector_index].submit_gradient(modelResidualsList[i].vectorGradResidual,q);
                                                  -
                                                  291  }
                                                  -
                                                  292  }
                                                  -
                                                  293  }
                                                  -
                                                  294 
                                                  -
                                                  295  }
                                                  -
                                                  296 
                                                  -
                                                  297  for (unsigned int i=0; i<num_var; i++){
                                                  -
                                                  298  if (varInfoListRHS[i].is_scalar) {
                                                  -
                                                  299  scalar_vars[varInfoListRHS[i].scalar_or_vector_index].integrate(value_residual[i], gradient_residual[i]);
                                                  -
                                                  300  scalar_vars[varInfoListRHS[i].scalar_or_vector_index].distribute_local_to_global(*dst[varInfoListRHS[i].global_field_index]);
                                                  -
                                                  301  }
                                                  -
                                                  302  else {
                                                  -
                                                  303  vector_vars[varInfoListRHS[i].scalar_or_vector_index].integrate(value_residual[i], gradient_residual[i]);
                                                  -
                                                  304  vector_vars[varInfoListRHS[i].scalar_or_vector_index].distribute_local_to_global(*dst[varInfoListRHS[i].global_field_index]);
                                                  -
                                                  305  }
                                                  -
                                                  306  }
                                                  -
                                                  307  }
                                                  -
                                                  308 }
                                                  -
                                                  309 
                                                  -
                                                  310 template <int dim>
                                                  -
                                                  311 void generalizedProblem<dim>::getLHS(const MatrixFree<dim,double> &data,
                                                  -
                                                  312  vectorType &dst,
                                                  -
                                                  313  const vectorType &src,
                                                  -
                                                  314  const std::pair<unsigned int,unsigned int> &cell_range) const{
                                                  +
                                                  284  if (need_gradient[i]){
                                                  +
                                                  285  modelVarList[i].vectorGrad = vector_vars[varInfoListRHS[i].scalar_or_vector_index].get_gradient(q);
                                                  +
                                                  286  }
                                                  +
                                                  287  if (need_hessian[i]){
                                                  +
                                                  288  modelVarList[i].vectorHess = vector_vars[varInfoListRHS[i].scalar_or_vector_index].get_hessian(q);
                                                  +
                                                  289  }
                                                  +
                                                  290  }
                                                  +
                                                  291  }
                                                  +
                                                  292 
                                                  +
                                                  293  // Calculate the residuals
                                                  +
                                                  294  residualRHS(modelVarList,modelResidualsList,q_point_loc);
                                                  +
                                                  295 
                                                  +
                                                  296  // Submit values
                                                  +
                                                  297  for (unsigned int i=0; i<num_var; i++){
                                                  +
                                                  298  if (varInfoListRHS[i].is_scalar) {
                                                  +
                                                  299  if (value_residual[i] == true){
                                                  +
                                                  300  scalar_vars[varInfoListRHS[i].scalar_or_vector_index].submit_value(modelResidualsList[i].scalarValueResidual,q);
                                                  +
                                                  301  }
                                                  +
                                                  302  if (gradient_residual[i] == true){
                                                  +
                                                  303  scalar_vars[varInfoListRHS[i].scalar_or_vector_index].submit_gradient(modelResidualsList[i].scalarGradResidual,q);
                                                  +
                                                  304  }
                                                  +
                                                  305  }
                                                  +
                                                  306  else {
                                                  +
                                                  307  if (value_residual[i] == true){
                                                  +
                                                  308  vector_vars[varInfoListRHS[i].scalar_or_vector_index].submit_value(modelResidualsList[i].vectorValueResidual,q);
                                                  +
                                                  309  }
                                                  +
                                                  310  if (gradient_residual[i] == true){
                                                  +
                                                  311  vector_vars[varInfoListRHS[i].scalar_or_vector_index].submit_gradient(modelResidualsList[i].vectorGradResidual,q);
                                                  +
                                                  312  }
                                                  +
                                                  313  }
                                                  +
                                                  314  }
                                                  315 
                                                  -
                                                  316  variable_info<dim> resInfoLHS;
                                                  -
                                                  317  for (unsigned int i=0; i<num_var_LHS; i++){
                                                  -
                                                  318  if (MatrixFreePDE<dim>::currentFieldIndex == varInfoListLHS[i].global_field_index){
                                                  -
                                                  319  resInfoLHS = varInfoListLHS[i];
                                                  -
                                                  320  }
                                                  -
                                                  321  }
                                                  -
                                                  322 
                                                  -
                                                  323  //initialize FEEvaulation objects
                                                  -
                                                  324  std::vector<typeScalar> scalar_vars;
                                                  -
                                                  325  std::vector<typeVector> vector_vars;
                                                  -
                                                  326 
                                                  -
                                                  327  for (unsigned int i=0; i<num_var_LHS; i++){
                                                  -
                                                  328  if (varInfoListLHS[i].is_scalar){
                                                  -
                                                  329  typeScalar var(data, varInfoListLHS[i].global_field_index);
                                                  -
                                                  330  scalar_vars.push_back(var);
                                                  -
                                                  331  }
                                                  -
                                                  332  else {
                                                  -
                                                  333  typeVector var(data, varInfoListLHS[i].global_field_index);
                                                  -
                                                  334  vector_vars.push_back(var);
                                                  -
                                                  335  }
                                                  -
                                                  336  }
                                                  -
                                                  337 
                                                  -
                                                  338  std::vector<modelVariable<dim> > modelVarList;
                                                  -
                                                  339  modelVarList.reserve(num_var_LHS);
                                                  -
                                                  340  modelResidual<dim> modelRes;
                                                  -
                                                  341 
                                                  -
                                                  342  //loop over cells
                                                  -
                                                  343  for (unsigned int cell=cell_range.first; cell<cell_range.second; ++cell){
                                                  -
                                                  344 
                                                  -
                                                  345  // Initialize, read DOFs, and set evaulation flags for each variable
                                                  -
                                                  346  for (unsigned int i=0; i<num_var_LHS; i++){
                                                  -
                                                  347  if (varInfoListLHS[i].is_scalar) {
                                                  -
                                                  348  scalar_vars[varInfoListLHS[i].scalar_or_vector_index].reinit(cell);
                                                  -
                                                  349  if ( varInfoListLHS[i].global_field_index == resInfoLHS.global_field_index ){
                                                  -
                                                  350  scalar_vars[varInfoListLHS[i].scalar_or_vector_index].read_dof_values_plain(src);
                                                  -
                                                  351  }
                                                  -
                                                  352  else{
                                                  -
                                                  353  scalar_vars[varInfoListLHS[i].scalar_or_vector_index].read_dof_values_plain(*MatrixFreePDE<dim>::solutionSet[varInfoListLHS[i].global_field_index]);
                                                  -
                                                  354  }
                                                  -
                                                  355  scalar_vars[varInfoListLHS[i].scalar_or_vector_index].evaluate(need_value_LHS[varInfoListLHS[i].global_var_index], need_gradient_LHS[varInfoListLHS[i].global_var_index], need_hessian_LHS[varInfoListLHS[i].global_var_index]);
                                                  -
                                                  356  }
                                                  -
                                                  357  else {
                                                  -
                                                  358  vector_vars[varInfoListLHS[i].scalar_or_vector_index].reinit(cell);
                                                  -
                                                  359  if ( varInfoListLHS[i].global_field_index == resInfoLHS.global_field_index ){
                                                  -
                                                  360  vector_vars[varInfoListLHS[i].scalar_or_vector_index].read_dof_values_plain(src);
                                                  -
                                                  361  }
                                                  -
                                                  362  else {
                                                  -
                                                  363  vector_vars[varInfoListLHS[i].scalar_or_vector_index].read_dof_values_plain(*MatrixFreePDE<dim>::solutionSet[varInfoListLHS[i].global_field_index]);
                                                  -
                                                  364  }
                                                  -
                                                  365  vector_vars[varInfoListLHS[i].scalar_or_vector_index].evaluate(need_value_LHS[varInfoListLHS[i].global_var_index], need_gradient_LHS[varInfoListLHS[i].global_var_index], need_hessian_LHS[varInfoListLHS[i].global_var_index]);
                                                  -
                                                  366  }
                                                  -
                                                  367  }
                                                  -
                                                  368 
                                                  -
                                                  369  unsigned int num_q_points;
                                                  -
                                                  370  if (scalar_vars.size() > 0){
                                                  -
                                                  371  num_q_points = scalar_vars[0].n_q_points;
                                                  -
                                                  372  }
                                                  -
                                                  373  else {
                                                  -
                                                  374  num_q_points = vector_vars[0].n_q_points;
                                                  -
                                                  375  }
                                                  -
                                                  376 
                                                  -
                                                  377  //loop over quadrature points
                                                  -
                                                  378  for (unsigned int q=0; q<num_q_points; ++q){
                                                  -
                                                  379  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc;
                                                  -
                                                  380  if (scalar_vars.size() > 0){
                                                  -
                                                  381  q_point_loc = scalar_vars[0].quadrature_point(q);
                                                  -
                                                  382  }
                                                  -
                                                  383  else {
                                                  -
                                                  384  q_point_loc = vector_vars[0].quadrature_point(q);
                                                  -
                                                  385  }
                                                  -
                                                  386 
                                                  -
                                                  387  for (unsigned int i=0; i<num_var_LHS; i++){
                                                  -
                                                  388  if (varInfoListLHS[i].is_scalar) {
                                                  -
                                                  389  if (need_value_LHS[varInfoListLHS[i].global_var_index]){
                                                  -
                                                  390  modelVarList[i].scalarValue = scalar_vars[varInfoListLHS[i].scalar_or_vector_index].get_value(q);
                                                  -
                                                  391  }
                                                  -
                                                  392  if (need_gradient_LHS[varInfoListLHS[i].global_var_index]){
                                                  -
                                                  393  modelVarList[i].scalarGrad = scalar_vars[varInfoListLHS[i].scalar_or_vector_index].get_gradient(q);
                                                  -
                                                  394  }
                                                  -
                                                  395  if (need_hessian_LHS[varInfoListLHS[i].global_var_index]){
                                                  -
                                                  396  modelVarList[i].scalarHess = scalar_vars[varInfoListLHS[i].scalar_or_vector_index].get_hessian(q);
                                                  -
                                                  397  }
                                                  -
                                                  398  }
                                                  -
                                                  399  else {
                                                  -
                                                  400  if (need_value_LHS[varInfoListLHS[i].global_var_index]){
                                                  -
                                                  401  modelVarList[i].vectorValue = vector_vars[varInfoListLHS[i].scalar_or_vector_index].get_value(q);
                                                  -
                                                  402  }
                                                  -
                                                  403  if (need_gradient_LHS[varInfoListLHS[i].global_var_index]){
                                                  -
                                                  404  modelVarList[i].vectorGrad = vector_vars[varInfoListLHS[i].scalar_or_vector_index].get_gradient(q);
                                                  -
                                                  405  }
                                                  -
                                                  406  if (need_hessian_LHS[varInfoListLHS[i].global_var_index]){
                                                  -
                                                  407  modelVarList[i].vectorHess = vector_vars[varInfoListLHS[i].scalar_or_vector_index].get_hessian(q);
                                                  -
                                                  408  }
                                                  -
                                                  409  }
                                                  -
                                                  410  }
                                                  -
                                                  411 
                                                  -
                                                  412  // Calculate the residuals
                                                  -
                                                  413  residualLHS(modelVarList,modelRes,q_point_loc);
                                                  -
                                                  414 
                                                  -
                                                  415  // Submit values
                                                  -
                                                  416  if (resInfoLHS.is_scalar){
                                                  -
                                                  417  if (value_residual[resInfoLHS.global_var_index]){
                                                  -
                                                  418  scalar_vars[resInfoLHS.scalar_or_vector_index].submit_value(modelRes.scalarValueResidual,q);
                                                  +
                                                  316  }
                                                  +
                                                  317 
                                                  +
                                                  318  for (unsigned int i=0; i<num_var; i++){
                                                  +
                                                  319  if (varInfoListRHS[i].is_scalar) {
                                                  +
                                                  320  scalar_vars[varInfoListRHS[i].scalar_or_vector_index].integrate(value_residual[i], gradient_residual[i]);
                                                  +
                                                  321  scalar_vars[varInfoListRHS[i].scalar_or_vector_index].distribute_local_to_global(*dst[varInfoListRHS[i].global_field_index]);
                                                  +
                                                  322  }
                                                  +
                                                  323  else {
                                                  +
                                                  324  vector_vars[varInfoListRHS[i].scalar_or_vector_index].integrate(value_residual[i], gradient_residual[i]);
                                                  +
                                                  325  vector_vars[varInfoListRHS[i].scalar_or_vector_index].distribute_local_to_global(*dst[varInfoListRHS[i].global_field_index]);
                                                  +
                                                  326  }
                                                  +
                                                  327  }
                                                  +
                                                  328  }
                                                  +
                                                  329 }
                                                  +
                                                  330 
                                                  +
                                                  331 template <int dim>
                                                  +
                                                  332 void generalizedProblem<dim>::getLHS(const MatrixFree<dim,double> &data,
                                                  +
                                                  333  vectorType &dst,
                                                  +
                                                  334  const vectorType &src,
                                                  +
                                                  335  const std::pair<unsigned int,unsigned int> &cell_range) const{
                                                  +
                                                  336 
                                                  +
                                                  337  variable_info<dim> resInfoLHS;
                                                  +
                                                  338  for (unsigned int i=0; i<num_var_LHS; i++){
                                                  +
                                                  339  if (MatrixFreePDE<dim>::currentFieldIndex == varInfoListLHS[i].global_field_index){
                                                  +
                                                  340  resInfoLHS = varInfoListLHS[i];
                                                  +
                                                  341  }
                                                  +
                                                  342  }
                                                  +
                                                  343 
                                                  +
                                                  344  //initialize FEEvaulation objects
                                                  +
                                                  345  std::vector<typeScalar> scalar_vars;
                                                  +
                                                  346  std::vector<typeVector> vector_vars;
                                                  +
                                                  347 
                                                  +
                                                  348  for (unsigned int i=0; i<num_var_LHS; i++){
                                                  +
                                                  349  if (varInfoListLHS[i].is_scalar){
                                                  +
                                                  350  typeScalar var(data, varInfoListLHS[i].global_field_index);
                                                  +
                                                  351  scalar_vars.push_back(var);
                                                  +
                                                  352  }
                                                  +
                                                  353  else {
                                                  +
                                                  354  typeVector var(data, varInfoListLHS[i].global_field_index);
                                                  +
                                                  355  vector_vars.push_back(var);
                                                  +
                                                  356  }
                                                  +
                                                  357  }
                                                  +
                                                  358 
                                                  +
                                                  359  std::vector<modelVariable<dim> > modelVarList;
                                                  +
                                                  360  modelVarList.reserve(num_var_LHS);
                                                  +
                                                  361  modelResidual<dim> modelRes;
                                                  +
                                                  362 
                                                  +
                                                  363  //loop over cells
                                                  +
                                                  364  for (unsigned int cell=cell_range.first; cell<cell_range.second; ++cell){
                                                  +
                                                  365 
                                                  +
                                                  366  // Initialize, read DOFs, and set evaulation flags for each variable
                                                  +
                                                  367  for (unsigned int i=0; i<num_var_LHS; i++){
                                                  +
                                                  368  if (varInfoListLHS[i].is_scalar) {
                                                  +
                                                  369  scalar_vars[varInfoListLHS[i].scalar_or_vector_index].reinit(cell);
                                                  +
                                                  370  if ( varInfoListLHS[i].global_field_index == resInfoLHS.global_field_index ){
                                                  +
                                                  371  scalar_vars[varInfoListLHS[i].scalar_or_vector_index].read_dof_values_plain(src);
                                                  +
                                                  372  }
                                                  +
                                                  373  else{
                                                  +
                                                  374  scalar_vars[varInfoListLHS[i].scalar_or_vector_index].read_dof_values_plain(*MatrixFreePDE<dim>::solutionSet[varInfoListLHS[i].global_field_index]);
                                                  +
                                                  375  }
                                                  +
                                                  376  scalar_vars[varInfoListLHS[i].scalar_or_vector_index].evaluate(need_value_LHS[varInfoListLHS[i].global_var_index], need_gradient_LHS[varInfoListLHS[i].global_var_index], need_hessian_LHS[varInfoListLHS[i].global_var_index]);
                                                  +
                                                  377  }
                                                  +
                                                  378  else {
                                                  +
                                                  379  vector_vars[varInfoListLHS[i].scalar_or_vector_index].reinit(cell);
                                                  +
                                                  380  if ( varInfoListLHS[i].global_field_index == resInfoLHS.global_field_index ){
                                                  +
                                                  381  vector_vars[varInfoListLHS[i].scalar_or_vector_index].read_dof_values_plain(src);
                                                  +
                                                  382  }
                                                  +
                                                  383  else {
                                                  +
                                                  384  vector_vars[varInfoListLHS[i].scalar_or_vector_index].read_dof_values_plain(*MatrixFreePDE<dim>::solutionSet[varInfoListLHS[i].global_field_index]);
                                                  +
                                                  385  }
                                                  +
                                                  386  vector_vars[varInfoListLHS[i].scalar_or_vector_index].evaluate(need_value_LHS[varInfoListLHS[i].global_var_index], need_gradient_LHS[varInfoListLHS[i].global_var_index], need_hessian_LHS[varInfoListLHS[i].global_var_index]);
                                                  +
                                                  387  }
                                                  +
                                                  388  }
                                                  +
                                                  389 
                                                  +
                                                  390  unsigned int num_q_points;
                                                  +
                                                  391  if (scalar_vars.size() > 0){
                                                  +
                                                  392  num_q_points = scalar_vars[0].n_q_points;
                                                  +
                                                  393  }
                                                  +
                                                  394  else {
                                                  +
                                                  395  num_q_points = vector_vars[0].n_q_points;
                                                  +
                                                  396  }
                                                  +
                                                  397 
                                                  +
                                                  398  //loop over quadrature points
                                                  +
                                                  399  for (unsigned int q=0; q<num_q_points; ++q){
                                                  +
                                                  400  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc;
                                                  +
                                                  401  if (scalar_vars.size() > 0){
                                                  +
                                                  402  q_point_loc = scalar_vars[0].quadrature_point(q);
                                                  +
                                                  403  }
                                                  +
                                                  404  else {
                                                  +
                                                  405  q_point_loc = vector_vars[0].quadrature_point(q);
                                                  +
                                                  406  }
                                                  +
                                                  407 
                                                  +
                                                  408  for (unsigned int i=0; i<num_var_LHS; i++){
                                                  +
                                                  409  if (varInfoListLHS[i].is_scalar) {
                                                  +
                                                  410  if (need_value_LHS[varInfoListLHS[i].global_var_index]){
                                                  +
                                                  411  modelVarList[i].scalarValue = scalar_vars[varInfoListLHS[i].scalar_or_vector_index].get_value(q);
                                                  +
                                                  412  }
                                                  +
                                                  413  if (need_gradient_LHS[varInfoListLHS[i].global_var_index]){
                                                  +
                                                  414  modelVarList[i].scalarGrad = scalar_vars[varInfoListLHS[i].scalar_or_vector_index].get_gradient(q);
                                                  +
                                                  415  }
                                                  +
                                                  416  if (need_hessian_LHS[varInfoListLHS[i].global_var_index]){
                                                  +
                                                  417  modelVarList[i].scalarHess = scalar_vars[varInfoListLHS[i].scalar_or_vector_index].get_hessian(q);
                                                  +
                                                  418  }
                                                  419  }
                                                  -
                                                  420  if (gradient_residual[resInfoLHS.global_var_index]){
                                                  -
                                                  421  scalar_vars[resInfoLHS.scalar_or_vector_index].submit_gradient(modelRes.scalarGradResidual,q);
                                                  -
                                                  422  }
                                                  -
                                                  423  }
                                                  -
                                                  424  else {
                                                  -
                                                  425  if (value_residual[resInfoLHS.global_var_index]){
                                                  -
                                                  426  vector_vars[resInfoLHS.scalar_or_vector_index].submit_value(modelRes.vectorValueResidual,q);
                                                  -
                                                  427  }
                                                  -
                                                  428  if (gradient_residual[resInfoLHS.global_var_index]){
                                                  -
                                                  429  vector_vars[resInfoLHS.scalar_or_vector_index].submit_gradient(modelRes.vectorGradResidual,q);
                                                  +
                                                  420  else {
                                                  +
                                                  421  if (need_value_LHS[varInfoListLHS[i].global_var_index]){
                                                  +
                                                  422  modelVarList[i].vectorValue = vector_vars[varInfoListLHS[i].scalar_or_vector_index].get_value(q);
                                                  +
                                                  423  }
                                                  +
                                                  424  if (need_gradient_LHS[varInfoListLHS[i].global_var_index]){
                                                  +
                                                  425  modelVarList[i].vectorGrad = vector_vars[varInfoListLHS[i].scalar_or_vector_index].get_gradient(q);
                                                  +
                                                  426  }
                                                  +
                                                  427  if (need_hessian_LHS[varInfoListLHS[i].global_var_index]){
                                                  +
                                                  428  modelVarList[i].vectorHess = vector_vars[varInfoListLHS[i].scalar_or_vector_index].get_hessian(q);
                                                  +
                                                  429  }
                                                  430  }
                                                  431  }
                                                  432 
                                                  -
                                                  433  }
                                                  -
                                                  434 
                                                  -
                                                  435  //integrate
                                                  -
                                                  436  if (resInfoLHS.is_scalar) {
                                                  -
                                                  437  scalar_vars[resInfoLHS.scalar_or_vector_index].integrate(value_residual[resInfoLHS.global_var_index], gradient_residual[resInfoLHS.global_var_index]);
                                                  -
                                                  438  scalar_vars[resInfoLHS.scalar_or_vector_index].distribute_local_to_global(dst);
                                                  -
                                                  439  }
                                                  -
                                                  440  else {
                                                  -
                                                  441  vector_vars[resInfoLHS.scalar_or_vector_index].integrate(value_residual[resInfoLHS.global_var_index], gradient_residual[resInfoLHS.global_var_index]);
                                                  -
                                                  442  vector_vars[resInfoLHS.scalar_or_vector_index].distribute_local_to_global(dst);
                                                  -
                                                  443  }
                                                  -
                                                  444  }
                                                  -
                                                  445 
                                                  -
                                                  446 }
                                                  -
                                                  447 
                                                  -
                                                  448 // Calculate the free energy
                                                  -
                                                  449 template <int dim>
                                                  -
                                                  450 void generalizedProblem<dim>::getEnergy(const MatrixFree<dim,double> &data,
                                                  -
                                                  451  std::vector<vectorType*> &dst,
                                                  -
                                                  452  const std::vector<vectorType*> &src,
                                                  -
                                                  453  const std::pair<unsigned int,unsigned int> &cell_range) {
                                                  -
                                                  454 
                                                  -
                                                  455  //initialize FEEvaulation objects
                                                  -
                                                  456  std::vector<typeScalar> scalar_vars;
                                                  -
                                                  457  std::vector<typeVector> vector_vars;
                                                  -
                                                  458 
                                                  -
                                                  459  for (unsigned int i=0; i<num_var; i++){
                                                  -
                                                  460  if (varInfoListRHS[i].is_scalar){
                                                  -
                                                  461  typeScalar var(data, i);
                                                  -
                                                  462  scalar_vars.push_back(var);
                                                  -
                                                  463  }
                                                  -
                                                  464  else {
                                                  -
                                                  465  typeVector var(data, i);
                                                  -
                                                  466  vector_vars.push_back(var);
                                                  -
                                                  467  }
                                                  -
                                                  468  }
                                                  -
                                                  469 
                                                  -
                                                  470  std::vector<modelVariable<dim> > modelVarList;
                                                  -
                                                  471  std::vector<modelResidual<dim> > modelResidualsList;
                                                  -
                                                  472  modelVarList.reserve(num_var);
                                                  -
                                                  473  modelResidualsList.reserve(num_var);
                                                  -
                                                  474 
                                                  -
                                                  475  //loop over cells
                                                  -
                                                  476  for (unsigned int cell=cell_range.first; cell<cell_range.second; ++cell){
                                                  -
                                                  477 
                                                  -
                                                  478  // Initialize, read DOFs, and set evaulation flags for each variable
                                                  -
                                                  479  for (unsigned int i=0; i<num_var; i++){
                                                  -
                                                  480  if (varInfoListRHS[i].is_scalar) {
                                                  -
                                                  481  scalar_vars[varInfoListRHS[i].scalar_or_vector_index].reinit(cell);
                                                  -
                                                  482  scalar_vars[varInfoListRHS[i].scalar_or_vector_index].read_dof_values_plain(*src[varInfoListRHS[i].global_field_index]);
                                                  -
                                                  483  scalar_vars[varInfoListRHS[i].scalar_or_vector_index].evaluate(need_value[i], need_gradient[i], need_hessian[i]);
                                                  -
                                                  484  }
                                                  -
                                                  485  else {
                                                  -
                                                  486  vector_vars[varInfoListRHS[i].scalar_or_vector_index].reinit(cell);
                                                  -
                                                  487  vector_vars[varInfoListRHS[i].scalar_or_vector_index].read_dof_values_plain(*src[varInfoListRHS[i].global_field_index]);
                                                  -
                                                  488  vector_vars[varInfoListRHS[i].scalar_or_vector_index].evaluate(need_value[i], need_gradient[i], need_hessian[i]);
                                                  -
                                                  489  }
                                                  -
                                                  490  }
                                                  -
                                                  491 
                                                  -
                                                  492 
                                                  -
                                                  493  unsigned int num_q_points;
                                                  -
                                                  494  if (scalar_vars.size() > 0){
                                                  -
                                                  495  num_q_points = scalar_vars[0].n_q_points;
                                                  -
                                                  496  }
                                                  -
                                                  497  else {
                                                  -
                                                  498  num_q_points = vector_vars[0].n_q_points;
                                                  -
                                                  499  }
                                                  -
                                                  500 
                                                  -
                                                  501  dealii::AlignedVector<dealii::VectorizedArray<double> > JxW(num_q_points);
                                                  -
                                                  502 
                                                  -
                                                  503  if (scalar_vars.size() > 0){
                                                  -
                                                  504  scalar_vars[0].fill_JxW_values(JxW);
                                                  -
                                                  505  }
                                                  -
                                                  506  else {
                                                  -
                                                  507  vector_vars[0].fill_JxW_values(JxW);
                                                  -
                                                  508  }
                                                  -
                                                  509 
                                                  -
                                                  510  //loop over quadrature points
                                                  -
                                                  511  for (unsigned int q=0; q<num_q_points; ++q){
                                                  -
                                                  512  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc;
                                                  -
                                                  513  if (scalar_vars.size() > 0){
                                                  -
                                                  514  q_point_loc = scalar_vars[0].quadrature_point(q);
                                                  -
                                                  515  }
                                                  -
                                                  516  else {
                                                  -
                                                  517  q_point_loc = vector_vars[0].quadrature_point(q);
                                                  -
                                                  518  }
                                                  -
                                                  519 
                                                  -
                                                  520  for (unsigned int i=0; i<num_var; i++){
                                                  -
                                                  521  if (varInfoListRHS[i].is_scalar) {
                                                  -
                                                  522  if (need_value[i]){
                                                  -
                                                  523  modelVarList[i].scalarValue = scalar_vars[varInfoListRHS[i].scalar_or_vector_index].get_value(q);
                                                  -
                                                  524  }
                                                  -
                                                  525  if (need_gradient[i]){
                                                  -
                                                  526  modelVarList[i].scalarGrad = scalar_vars[varInfoListRHS[i].scalar_or_vector_index].get_gradient(q);
                                                  -
                                                  527  }
                                                  -
                                                  528  if (need_hessian[i]){
                                                  -
                                                  529  modelVarList[i].scalarHess = scalar_vars[varInfoListRHS[i].scalar_or_vector_index].get_hessian(q);
                                                  -
                                                  530  }
                                                  -
                                                  531  }
                                                  -
                                                  532  else {
                                                  -
                                                  533  if (need_value[i]){
                                                  -
                                                  534  modelVarList[i].vectorValue = vector_vars[varInfoListRHS[i].scalar_or_vector_index].get_value(q);
                                                  -
                                                  535  }
                                                  -
                                                  536  if (need_gradient[i]){
                                                  -
                                                  537  modelVarList[i].vectorGrad = vector_vars[varInfoListRHS[i].scalar_or_vector_index].get_gradient(q);
                                                  -
                                                  538  }
                                                  -
                                                  539  if (need_hessian[i]){
                                                  -
                                                  540  modelVarList[i].vectorHess = vector_vars[varInfoListRHS[i].scalar_or_vector_index].get_hessian(q);
                                                  -
                                                  541  }
                                                  -
                                                  542  }
                                                  -
                                                  543  }
                                                  -
                                                  544 
                                                  -
                                                  545  // Calculate the energy density
                                                  -
                                                  546  energyDensity(modelVarList,JxW[q],q_point_loc);
                                                  -
                                                  547  }
                                                  -
                                                  548  }
                                                  -
                                                  549 
                                                  -
                                                  550 }
                                                  -
                                                  551 
                                                  -
                                                  552 //compute the integral of one of the fields
                                                  -
                                                  553 template <int dim>
                                                  -
                                                  554 void generalizedProblem<dim>::computeIntegral(double& integratedField){
                                                  -
                                                  555  QGauss<dim> quadrature_formula(finiteElementDegree+1);
                                                  -
                                                  556  FE_Q<dim> FE (QGaussLobatto<1>(finiteElementDegree+1));
                                                  -
                                                  557  FEValues<dim> fe_values (FE, quadrature_formula, update_values | update_JxW_values | update_quadrature_points);
                                                  -
                                                  558  const unsigned int dofs_per_cell = FE.dofs_per_cell;
                                                  -
                                                  559  const unsigned int n_q_points = quadrature_formula.size();
                                                  -
                                                  560  std::vector<double> cVal(n_q_points);
                                                  -
                                                  561 
                                                  -
                                                  562  typename DoFHandler<dim>::active_cell_iterator cell= this->dofHandlersSet[0]->begin_active(), endc = this->dofHandlersSet[0]->end();
                                                  -
                                                  563 
                                                  -
                                                  564  double value = 0.0;
                                                  +
                                                  433  // Calculate the residuals
                                                  +
                                                  434  residualLHS(modelVarList,modelRes,q_point_loc);
                                                  +
                                                  435 
                                                  +
                                                  436  // Submit values
                                                  +
                                                  437  if (resInfoLHS.is_scalar){
                                                  +
                                                  438  if (value_residual[resInfoLHS.global_var_index]){
                                                  +
                                                  439  scalar_vars[resInfoLHS.scalar_or_vector_index].submit_value(modelRes.scalarValueResidual,q);
                                                  +
                                                  440  }
                                                  +
                                                  441  if (gradient_residual[resInfoLHS.global_var_index]){
                                                  +
                                                  442  scalar_vars[resInfoLHS.scalar_or_vector_index].submit_gradient(modelRes.scalarGradResidual,q);
                                                  +
                                                  443  }
                                                  +
                                                  444  }
                                                  +
                                                  445  else {
                                                  +
                                                  446  if (value_residual[resInfoLHS.global_var_index]){
                                                  +
                                                  447  vector_vars[resInfoLHS.scalar_or_vector_index].submit_value(modelRes.vectorValueResidual,q);
                                                  +
                                                  448  }
                                                  +
                                                  449  if (gradient_residual[resInfoLHS.global_var_index]){
                                                  +
                                                  450  vector_vars[resInfoLHS.scalar_or_vector_index].submit_gradient(modelRes.vectorGradResidual,q);
                                                  +
                                                  451  }
                                                  +
                                                  452  }
                                                  +
                                                  453 
                                                  +
                                                  454  }
                                                  +
                                                  455 
                                                  +
                                                  456  //integrate
                                                  +
                                                  457  if (resInfoLHS.is_scalar) {
                                                  +
                                                  458  scalar_vars[resInfoLHS.scalar_or_vector_index].integrate(value_residual[resInfoLHS.global_var_index], gradient_residual[resInfoLHS.global_var_index]);
                                                  +
                                                  459  scalar_vars[resInfoLHS.scalar_or_vector_index].distribute_local_to_global(dst);
                                                  +
                                                  460  }
                                                  +
                                                  461  else {
                                                  +
                                                  462  vector_vars[resInfoLHS.scalar_or_vector_index].integrate(value_residual[resInfoLHS.global_var_index], gradient_residual[resInfoLHS.global_var_index]);
                                                  +
                                                  463  vector_vars[resInfoLHS.scalar_or_vector_index].distribute_local_to_global(dst);
                                                  +
                                                  464  }
                                                  +
                                                  465  }
                                                  +
                                                  466 
                                                  +
                                                  467 }
                                                  +
                                                  468 
                                                  +
                                                  469 // Calculate the free energy
                                                  +
                                                  470 template <int dim>
                                                  +
                                                  471 void generalizedProblem<dim>::getEnergy(const MatrixFree<dim,double> &data,
                                                  +
                                                  472  std::vector<vectorType*> &dst,
                                                  +
                                                  473  const std::vector<vectorType*> &src,
                                                  +
                                                  474  const std::pair<unsigned int,unsigned int> &cell_range) {
                                                  +
                                                  475 
                                                  +
                                                  476  //initialize FEEvaulation objects
                                                  +
                                                  477  std::vector<typeScalar> scalar_vars;
                                                  +
                                                  478  std::vector<typeVector> vector_vars;
                                                  +
                                                  479 
                                                  +
                                                  480  for (unsigned int i=0; i<num_var; i++){
                                                  +
                                                  481  if (varInfoListRHS[i].is_scalar){
                                                  +
                                                  482  typeScalar var(data, i);
                                                  +
                                                  483  scalar_vars.push_back(var);
                                                  +
                                                  484  }
                                                  +
                                                  485  else {
                                                  +
                                                  486  typeVector var(data, i);
                                                  +
                                                  487  vector_vars.push_back(var);
                                                  +
                                                  488  }
                                                  +
                                                  489  }
                                                  +
                                                  490 
                                                  +
                                                  491  std::vector<modelVariable<dim> > modelVarList;
                                                  +
                                                  492  std::vector<modelResidual<dim> > modelResidualsList;
                                                  +
                                                  493  modelVarList.reserve(num_var);
                                                  +
                                                  494  modelResidualsList.reserve(num_var);
                                                  +
                                                  495 
                                                  +
                                                  496  //loop over cells
                                                  +
                                                  497  for (unsigned int cell=cell_range.first; cell<cell_range.second; ++cell){
                                                  +
                                                  498 
                                                  +
                                                  499  // Initialize, read DOFs, and set evaulation flags for each variable
                                                  +
                                                  500  for (unsigned int i=0; i<num_var; i++){
                                                  +
                                                  501  if (varInfoListRHS[i].is_scalar) {
                                                  +
                                                  502  scalar_vars[varInfoListRHS[i].scalar_or_vector_index].reinit(cell);
                                                  +
                                                  503  scalar_vars[varInfoListRHS[i].scalar_or_vector_index].read_dof_values_plain(*src[varInfoListRHS[i].global_field_index]);
                                                  +
                                                  504  scalar_vars[varInfoListRHS[i].scalar_or_vector_index].evaluate(need_value[i], need_gradient[i], need_hessian[i]);
                                                  +
                                                  505  }
                                                  +
                                                  506  else {
                                                  +
                                                  507  vector_vars[varInfoListRHS[i].scalar_or_vector_index].reinit(cell);
                                                  +
                                                  508  vector_vars[varInfoListRHS[i].scalar_or_vector_index].read_dof_values_plain(*src[varInfoListRHS[i].global_field_index]);
                                                  +
                                                  509  vector_vars[varInfoListRHS[i].scalar_or_vector_index].evaluate(need_value[i], need_gradient[i], need_hessian[i]);
                                                  +
                                                  510  }
                                                  +
                                                  511  }
                                                  +
                                                  512 
                                                  +
                                                  513 
                                                  +
                                                  514  unsigned int num_q_points;
                                                  +
                                                  515  if (scalar_vars.size() > 0){
                                                  +
                                                  516  num_q_points = scalar_vars[0].n_q_points;
                                                  +
                                                  517  }
                                                  +
                                                  518  else {
                                                  +
                                                  519  num_q_points = vector_vars[0].n_q_points;
                                                  +
                                                  520  }
                                                  +
                                                  521 
                                                  +
                                                  522  dealii::AlignedVector<dealii::VectorizedArray<double> > JxW(num_q_points);
                                                  +
                                                  523 
                                                  +
                                                  524  if (scalar_vars.size() > 0){
                                                  +
                                                  525  scalar_vars[0].fill_JxW_values(JxW);
                                                  +
                                                  526  }
                                                  +
                                                  527  else {
                                                  +
                                                  528  vector_vars[0].fill_JxW_values(JxW);
                                                  +
                                                  529  }
                                                  +
                                                  530 
                                                  +
                                                  531  //loop over quadrature points
                                                  +
                                                  532  for (unsigned int q=0; q<num_q_points; ++q){
                                                  +
                                                  533  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc;
                                                  +
                                                  534  if (scalar_vars.size() > 0){
                                                  +
                                                  535  q_point_loc = scalar_vars[0].quadrature_point(q);
                                                  +
                                                  536  }
                                                  +
                                                  537  else {
                                                  +
                                                  538  q_point_loc = vector_vars[0].quadrature_point(q);
                                                  +
                                                  539  }
                                                  +
                                                  540 
                                                  +
                                                  541  for (unsigned int i=0; i<num_var; i++){
                                                  +
                                                  542  if (varInfoListRHS[i].is_scalar) {
                                                  +
                                                  543  if (need_value[i]){
                                                  +
                                                  544  modelVarList[i].scalarValue = scalar_vars[varInfoListRHS[i].scalar_or_vector_index].get_value(q);
                                                  +
                                                  545  }
                                                  +
                                                  546  if (need_gradient[i]){
                                                  +
                                                  547  modelVarList[i].scalarGrad = scalar_vars[varInfoListRHS[i].scalar_or_vector_index].get_gradient(q);
                                                  +
                                                  548  }
                                                  +
                                                  549  if (need_hessian[i]){
                                                  +
                                                  550  modelVarList[i].scalarHess = scalar_vars[varInfoListRHS[i].scalar_or_vector_index].get_hessian(q);
                                                  +
                                                  551  }
                                                  +
                                                  552  }
                                                  +
                                                  553  else {
                                                  +
                                                  554  if (need_value[i]){
                                                  +
                                                  555  modelVarList[i].vectorValue = vector_vars[varInfoListRHS[i].scalar_or_vector_index].get_value(q);
                                                  +
                                                  556  }
                                                  +
                                                  557  if (need_gradient[i]){
                                                  +
                                                  558  modelVarList[i].vectorGrad = vector_vars[varInfoListRHS[i].scalar_or_vector_index].get_gradient(q);
                                                  +
                                                  559  }
                                                  +
                                                  560  if (need_hessian[i]){
                                                  +
                                                  561  modelVarList[i].vectorHess = vector_vars[varInfoListRHS[i].scalar_or_vector_index].get_hessian(q);
                                                  +
                                                  562  }
                                                  +
                                                  563  }
                                                  +
                                                  564  }
                                                  565 
                                                  -
                                                  566  unsigned int fieldIndex;
                                                  -
                                                  567  fieldIndex=this->getFieldIndex("c");
                                                  -
                                                  568 
                                                  -
                                                  569  for (; cell!=endc; ++cell) {
                                                  -
                                                  570  if (cell->is_locally_owned()){
                                                  -
                                                  571  fe_values.reinit (cell);
                                                  +
                                                  566  // Calculate the energy density
                                                  +
                                                  567  energyDensity(modelVarList,JxW[q],q_point_loc);
                                                  +
                                                  568  }
                                                  +
                                                  569  }
                                                  +
                                                  570 
                                                  +
                                                  571 }
                                                  572 
                                                  -
                                                  573  fe_values.get_function_values(*this->solutionSet[fieldIndex], cVal);
                                                  -
                                                  574 
                                                  -
                                                  575  for (unsigned int q=0; q<n_q_points; ++q){
                                                  -
                                                  576  value+=(cVal[q])*fe_values.JxW(q);
                                                  -
                                                  577  }
                                                  -
                                                  578  }
                                                  -
                                                  579  }
                                                  -
                                                  580 
                                                  -
                                                  581  value=Utilities::MPI::sum(value, MPI_COMM_WORLD);
                                                  +
                                                  573 //compute the integral of one of the fields
                                                  +
                                                  574 template <int dim>
                                                  +
                                                  575 void generalizedProblem<dim>::computeIntegral(double& integratedField){
                                                  +
                                                  576  QGauss<dim> quadrature_formula(finiteElementDegree+1);
                                                  +
                                                  577  FE_Q<dim> FE (QGaussLobatto<1>(finiteElementDegree+1));
                                                  +
                                                  578  FEValues<dim> fe_values (FE, quadrature_formula, update_values | update_JxW_values | update_quadrature_points);
                                                  +
                                                  579  const unsigned int dofs_per_cell = FE.dofs_per_cell;
                                                  +
                                                  580  const unsigned int n_q_points = quadrature_formula.size();
                                                  +
                                                  581  std::vector<double> cVal(n_q_points);
                                                  582 
                                                  -
                                                  583  if (Utilities::MPI::this_mpi_process(MPI_COMM_WORLD) == 0){
                                                  -
                                                  584  std::cout<<"Integrated field: "<<value<<std::endl;
                                                  -
                                                  585  }
                                                  +
                                                  583  typename DoFHandler<dim>::active_cell_iterator cell= this->dofHandlersSet[0]->begin_active(), endc = this->dofHandlersSet[0]->end();
                                                  +
                                                  584 
                                                  +
                                                  585  double value = 0.0;
                                                  586 
                                                  -
                                                  587  integratedField = value;
                                                  -
                                                  588 }
                                                  +
                                                  587  unsigned int fieldIndex;
                                                  +
                                                  588  fieldIndex=this->getFieldIndex("c");
                                                  589 
                                                  -
                                                  590 // =====================================================================
                                                  -
                                                  591 // ADAPTIVE MESHING FUNCTIONS
                                                  -
                                                  592 // =====================================================================
                                                  +
                                                  590  for (; cell!=endc; ++cell) {
                                                  +
                                                  591  if (cell->is_locally_owned()){
                                                  +
                                                  592  fe_values.reinit (cell);
                                                  593 
                                                  -
                                                  594 //adaptive refinement control
                                                  -
                                                  595 template <int dim>
                                                  -
                                                  596 void generalizedProblem<dim>::adaptiveRefine(unsigned int currentIncrement){
                                                  -
                                                  597  #if hAdaptivity == true
                                                  -
                                                  598  if ((currentIncrement>0) && (currentIncrement%skipRemeshingSteps==0)){
                                                  -
                                                  599  this->refineMesh(currentIncrement);
                                                  -
                                                  600  }
                                                  -
                                                  601  #endif
                                                  -
                                                  602 }
                                                  +
                                                  594  fe_values.get_function_values(*this->solutionSet[fieldIndex], cVal);
                                                  +
                                                  595 
                                                  +
                                                  596  for (unsigned int q=0; q<n_q_points; ++q){
                                                  +
                                                  597  value+=(cVal[q])*fe_values.JxW(q);
                                                  +
                                                  598  }
                                                  +
                                                  599  }
                                                  +
                                                  600  }
                                                  +
                                                  601 
                                                  +
                                                  602  value=Utilities::MPI::sum(value, MPI_COMM_WORLD);
                                                  603 
                                                  -
                                                  604 //adaptive refinement criterion
                                                  -
                                                  605 template <int dim>
                                                  -
                                                  606 void generalizedProblem<dim>::adaptiveRefineCriterion(){
                                                  -
                                                  607 #if hAdaptivity == true
                                                  -
                                                  608  //Custom defined estimation criterion
                                                  -
                                                  609  std::vector<int> refine_criterion_fields = refineCriterionFields;
                                                  -
                                                  610  std::vector<double> refine_window_max = refineWindowMax;
                                                  -
                                                  611  std::vector<double> refine_window_min = refineWindowMin;
                                                  -
                                                  612  std::vector<std::vector<double> > errorOutV;
                                                  -
                                                  613 
                                                  +
                                                  604  if (Utilities::MPI::this_mpi_process(MPI_COMM_WORLD) == 0){
                                                  +
                                                  605  std::cout<<"Integrated field: "<<value<<std::endl;
                                                  +
                                                  606  }
                                                  +
                                                  607 
                                                  +
                                                  608  integratedField = value;
                                                  +
                                                  609 }
                                                  +
                                                  610 
                                                  +
                                                  611 // =====================================================================
                                                  +
                                                  612 // ADAPTIVE MESHING FUNCTIONS
                                                  +
                                                  613 // =====================================================================
                                                  614 
                                                  -
                                                  615  QGauss<dim> quadrature(finiteElementDegree+1);
                                                  -
                                                  616  FEValues<dim> fe_values (*this->FESet[refine_criterion_fields[0]], quadrature, update_values);
                                                  -
                                                  617  const unsigned int num_quad_points = quadrature.size();
                                                  -
                                                  618 
                                                  -
                                                  619  std::vector<double> errorOut(num_quad_points);
                                                  -
                                                  620 
                                                  -
                                                  621  typename DoFHandler<dim>::active_cell_iterator cell = this->dofHandlersSet2[refine_criterion_fields[0]]->begin_active(), endc = this->dofHandlersSet2[refine_criterion_fields[0]]->end();
                                                  -
                                                  622 
                                                  -
                                                  623  for (;cell!=endc; ++cell){
                                                  -
                                                  624  if (cell->is_locally_owned()){
                                                  -
                                                  625  fe_values.reinit (cell);
                                                  -
                                                  626 
                                                  -
                                                  627  for (unsigned int field_index=0; field_index<refine_criterion_fields.size(); field_index++){
                                                  -
                                                  628  fe_values.get_function_values(*this->solutionSet[refine_criterion_fields[field_index]], errorOut);
                                                  -
                                                  629  errorOutV.push_back(errorOut);
                                                  -
                                                  630  }
                                                  -
                                                  631 
                                                  -
                                                  632  bool mark_refine = false;
                                                  -
                                                  633 
                                                  -
                                                  634  for (unsigned int q_point=0; q_point<num_quad_points; ++q_point){
                                                  -
                                                  635  for (unsigned int field_index=0; field_index<refine_criterion_fields.size(); field_index++){
                                                  -
                                                  636  if ((errorOutV[field_index][q_point]>refine_window_min[field_index]) && (errorOutV[field_index][q_point]<refine_window_max[field_index])){
                                                  -
                                                  637  mark_refine = true;
                                                  -
                                                  638  break;
                                                  -
                                                  639  }
                                                  -
                                                  640  }
                                                  -
                                                  641  }
                                                  -
                                                  642 
                                                  -
                                                  643  errorOutV.clear();
                                                  +
                                                  615 //adaptive refinement control
                                                  +
                                                  616 template <int dim>
                                                  +
                                                  617 void generalizedProblem<dim>::adaptiveRefine(unsigned int currentIncrement){
                                                  +
                                                  618  #if hAdaptivity == true
                                                  +
                                                  619  //if ((currentIncrement>0) && (currentIncrement%skipRemeshingSteps==0)){
                                                  +
                                                  620 // if ( (currentIncrement == 2) || (currentIncrement%skipRemeshingSteps==0) ){
                                                  +
                                                  621 // this->refineMesh(currentIncrement);
                                                  +
                                                  622 // }
                                                  +
                                                  623 
                                                  +
                                                  624  if ( (currentIncrement == 0) ){
                                                  +
                                                  625  for (unsigned int remesh_index=0; remesh_index < (maxRefinementLevel-minRefinementLevel); remesh_index++){
                                                  +
                                                  626  this->refineMesh(currentIncrement);
                                                  +
                                                  627  }
                                                  +
                                                  628  }
                                                  +
                                                  629  else if ( (currentIncrement%skipRemeshingSteps==0) ){
                                                  +
                                                  630  this->refineMesh(currentIncrement);
                                                  +
                                                  631  }
                                                  +
                                                  632  #endif
                                                  +
                                                  633 }
                                                  +
                                                  634 
                                                  +
                                                  635 //adaptive refinement criterion
                                                  +
                                                  636 template <int dim>
                                                  +
                                                  637 void generalizedProblem<dim>::adaptiveRefineCriterion(){
                                                  +
                                                  638 #if hAdaptivity == true
                                                  +
                                                  639  //Custom defined estimation criterion
                                                  +
                                                  640  std::vector<int> refine_criterion_fields = refineCriterionFields;
                                                  +
                                                  641  std::vector<double> refine_window_max = refineWindowMax;
                                                  +
                                                  642  std::vector<double> refine_window_min = refineWindowMin;
                                                  +
                                                  643  std::vector<std::vector<double> > errorOutV;
                                                  644 
                                                  -
                                                  645 // fe_values.get_function_values(*this->solutionSet[refine_criterion_fields[0]], errorOut);
                                                  -
                                                  646 //
                                                  -
                                                  647 // bool mark_refine = false;
                                                  -
                                                  648 //
                                                  -
                                                  649 // for (unsigned int q_point=0; q_point<num_quad_points; ++q_point){
                                                  -
                                                  650 // if ((errorOut[q_point]>refine_window_min[0]) && (errorOut[q_point]<refine_window_max[0])){
                                                  -
                                                  651 // mark_refine = true;
                                                  -
                                                  652 // break;
                                                  -
                                                  653 // }
                                                  -
                                                  654 // }
                                                  -
                                                  655 
                                                  -
                                                  656 
                                                  -
                                                  657  if ( (mark_refine == true) ){
                                                  -
                                                  658  cell->set_refine_flag();
                                                  -
                                                  659  }
                                                  -
                                                  660  else {
                                                  -
                                                  661  cell->set_coarsen_flag();
                                                  -
                                                  662  }
                                                  -
                                                  663  }
                                                  -
                                                  664  }
                                                  -
                                                  665 #endif
                                                  -
                                                  666 }
                                                  -
                                                  667 
                                                  -
                                                  668 
                                                  -
                                                  669 // =====================================================================
                                                  -
                                                  670 // FUNCTION TO BUILD THE VECTOR OF FIELDS
                                                  -
                                                  671 // =====================================================================
                                                  -
                                                  672 
                                                  -
                                                  673 template <int dim>
                                                  -
                                                  674 void generalizedProblem<dim>::buildFields(){
                                                  -
                                                  675  // Build each of the fields in the system
                                                  -
                                                  676  for (unsigned int i=0; i<num_var; i++){
                                                  -
                                                  677  if (var_type[i] == "SCALAR"){
                                                  -
                                                  678  if (var_eq_type[i] == "ELLIPTIC"){
                                                  -
                                                  679  this->fields.push_back(Field<problemDIM>(SCALAR, ELLIPTIC, var_name[i]));
                                                  -
                                                  680  }
                                                  -
                                                  681  else if (var_eq_type[i] == "PARABOLIC"){
                                                  -
                                                  682  this->fields.push_back(Field<problemDIM>(SCALAR, PARABOLIC, var_name[i]));
                                                  -
                                                  683  }
                                                  -
                                                  684  else{
                                                  -
                                                  685  // Need to change to throw an exception
                                                  -
                                                  686  std::cerr << "Error: Equation type must be ELLIPTIC or PARABOLIC " << std::endl;
                                                  -
                                                  687  }
                                                  -
                                                  688  }
                                                  -
                                                  689  else if (var_type[i] == "VECTOR"){
                                                  -
                                                  690  if (var_eq_type[i] == "ELLIPTIC"){
                                                  -
                                                  691  this->fields.push_back(Field<problemDIM>(VECTOR, ELLIPTIC, var_name[i]));
                                                  -
                                                  692  }
                                                  -
                                                  693  else if (var_eq_type[i] == "PARABOLIC"){
                                                  -
                                                  694  this->fields.push_back(Field<problemDIM>(VECTOR, PARABOLIC, var_name[i]));
                                                  -
                                                  695  }
                                                  -
                                                  696  else{
                                                  -
                                                  697  // Need to change to throw an exception
                                                  -
                                                  698  std::cerr << "Error: Variable type must be SCALAR or VECTOR " << std::endl;
                                                  -
                                                  699  }
                                                  -
                                                  700  }
                                                  -
                                                  701  }
                                                  -
                                                  702 
                                                  -
                                                  703 }
                                                  -
                                                  704 
                                                  -
                                                  705 // =====================================================================
                                                  -
                                                  706 // INITIAL CONDITION FUNCTIONS
                                                  -
                                                  707 // =====================================================================
                                                  -
                                                  708 
                                                  -
                                                  709 //apply initial conditions
                                                  -
                                                  710 template <int dim>
                                                  -
                                                  711 void generalizedProblem<dim>::applyInitialConditions()
                                                  -
                                                  712 {
                                                  -
                                                  713 
                                                  -
                                                  714 unsigned int fieldIndex = 0;
                                                  -
                                                  715 
                                                  -
                                                  716 for (unsigned int var_index=0; var_index < num_var; var_index++){
                                                  -
                                                  717 
                                                  -
                                                  718  if (var_type[var_index] == "SCALAR"){
                                                  -
                                                  719  VectorTools::interpolate (*this->dofHandlersSet[fieldIndex], InitialCondition<dim>(var_index), *this->solutionSet[fieldIndex]);
                                                  -
                                                  720  fieldIndex++;
                                                  -
                                                  721  }
                                                  -
                                                  722  else {
                                                  -
                                                  723  VectorTools::interpolate (*this->dofHandlersSet[fieldIndex], InitialConditionVec<dim>(var_index), *this->solutionSet[fieldIndex]);
                                                  -
                                                  724  fieldIndex += dim;
                                                  -
                                                  725  }
                                                  -
                                                  726 }
                                                  -
                                                  727 }
                                                  -
                                                  728 
                                                  -
                                                  729 // =====================================================================
                                                  -
                                                  730 // BOUNDARY CONDITION FUNCTIONS
                                                  -
                                                  731 // =====================================================================
                                                  -
                                                  732 
                                                  -
                                                  733 template <int dim>
                                                  -
                                                  734 class vectorBCFunction : public Function<dim,double>
                                                  -
                                                  735 {
                                                  -
                                                  736 public:
                                                  -
                                                  737  vectorBCFunction(const std::vector<double> BC_values);
                                                  -
                                                  738  virtual void vector_value (const Point<dim> &p, Vector<double> &values) const;
                                                  +
                                                  645 
                                                  +
                                                  646  QGauss<dim> quadrature(finiteElementDegree+1);
                                                  +
                                                  647  FEValues<dim> fe_values (*this->FESet[refine_criterion_fields[0]], quadrature, update_values);
                                                  +
                                                  648  const unsigned int num_quad_points = quadrature.size();
                                                  +
                                                  649 
                                                  +
                                                  650  std::vector<double> errorOut(num_quad_points);
                                                  +
                                                  651 
                                                  +
                                                  652  typename DoFHandler<dim>::active_cell_iterator cell = this->dofHandlersSet_nonconst[refine_criterion_fields[0]]->begin_active(), endc = this->dofHandlersSet_nonconst[refine_criterion_fields[0]]->end();
                                                  +
                                                  653 
                                                  +
                                                  654  for (;cell!=endc; ++cell){
                                                  +
                                                  655  if (cell->is_locally_owned()){
                                                  +
                                                  656  fe_values.reinit (cell);
                                                  +
                                                  657 
                                                  +
                                                  658  for (unsigned int field_index=0; field_index<refine_criterion_fields.size(); field_index++){
                                                  +
                                                  659  fe_values.get_function_values(*this->solutionSet[refine_criterion_fields[field_index]], errorOut);
                                                  +
                                                  660  errorOutV.push_back(errorOut);
                                                  +
                                                  661  }
                                                  +
                                                  662 
                                                  +
                                                  663  bool mark_refine = false;
                                                  +
                                                  664 
                                                  +
                                                  665  for (unsigned int q_point=0; q_point<num_quad_points; ++q_point){
                                                  +
                                                  666  for (unsigned int field_index=0; field_index<refine_criterion_fields.size(); field_index++){
                                                  +
                                                  667  if ((errorOutV[field_index][q_point]>refine_window_min[field_index]) && (errorOutV[field_index][q_point]<refine_window_max[field_index])){
                                                  +
                                                  668  mark_refine = true;
                                                  +
                                                  669  break;
                                                  +
                                                  670  }
                                                  +
                                                  671  }
                                                  +
                                                  672  }
                                                  +
                                                  673 
                                                  +
                                                  674  errorOutV.clear();
                                                  +
                                                  675 
                                                  +
                                                  676 // fe_values.get_function_values(*this->solutionSet[refine_criterion_fields[0]], errorOut);
                                                  +
                                                  677 //
                                                  +
                                                  678 // bool mark_refine = false;
                                                  +
                                                  679 //
                                                  +
                                                  680 // for (unsigned int q_point=0; q_point<num_quad_points; ++q_point){
                                                  +
                                                  681 // if ((errorOut[q_point]>refine_window_min[0]) && (errorOut[q_point]<refine_window_max[0])){
                                                  +
                                                  682 // mark_refine = true;
                                                  +
                                                  683 // break;
                                                  +
                                                  684 // }
                                                  +
                                                  685 // }
                                                  +
                                                  686 
                                                  +
                                                  687 
                                                  +
                                                  688  if ( (mark_refine == true) ){
                                                  +
                                                  689  cell->set_refine_flag();
                                                  +
                                                  690  }
                                                  +
                                                  691  else {
                                                  +
                                                  692  cell->set_coarsen_flag();
                                                  +
                                                  693  }
                                                  +
                                                  694  }
                                                  +
                                                  695  }
                                                  +
                                                  696 #endif
                                                  +
                                                  697 }
                                                  +
                                                  698 
                                                  +
                                                  699 
                                                  +
                                                  700 // =====================================================================
                                                  +
                                                  701 // FUNCTION TO BUILD THE VECTOR OF FIELDS
                                                  +
                                                  702 // =====================================================================
                                                  +
                                                  703 
                                                  +
                                                  704 template <int dim>
                                                  +
                                                  705 void generalizedProblem<dim>::buildFields(){
                                                  +
                                                  706  // Build each of the fields in the system
                                                  +
                                                  707  for (unsigned int i=0; i<num_var; i++){
                                                  +
                                                  708  if (var_type[i] == "SCALAR"){
                                                  +
                                                  709  if (var_eq_type[i] == "ELLIPTIC"){
                                                  +
                                                  710  this->fields.push_back(Field<problemDIM>(SCALAR, ELLIPTIC, var_name[i]));
                                                  +
                                                  711  }
                                                  +
                                                  712  else if (var_eq_type[i] == "PARABOLIC"){
                                                  +
                                                  713  this->fields.push_back(Field<problemDIM>(SCALAR, PARABOLIC, var_name[i]));
                                                  +
                                                  714  }
                                                  +
                                                  715  else{
                                                  +
                                                  716  // Need to change to throw an exception
                                                  +
                                                  717  std::cerr << "Error: Equation type must be ELLIPTIC or PARABOLIC " << std::endl;
                                                  +
                                                  718  }
                                                  +
                                                  719  }
                                                  +
                                                  720  else if (var_type[i] == "VECTOR"){
                                                  +
                                                  721  if (var_eq_type[i] == "ELLIPTIC"){
                                                  +
                                                  722  this->fields.push_back(Field<problemDIM>(VECTOR, ELLIPTIC, var_name[i]));
                                                  +
                                                  723  }
                                                  +
                                                  724  else if (var_eq_type[i] == "PARABOLIC"){
                                                  +
                                                  725  this->fields.push_back(Field<problemDIM>(VECTOR, PARABOLIC, var_name[i]));
                                                  +
                                                  726  }
                                                  +
                                                  727  else{
                                                  +
                                                  728  // Need to change to throw an exception
                                                  +
                                                  729  std::cerr << "Error: Variable type must be SCALAR or VECTOR " << std::endl;
                                                  +
                                                  730  }
                                                  +
                                                  731  }
                                                  +
                                                  732  }
                                                  +
                                                  733 
                                                  +
                                                  734 }
                                                  +
                                                  735 
                                                  +
                                                  736 // =====================================================================
                                                  +
                                                  737 // INITIAL CONDITION FUNCTIONS
                                                  +
                                                  738 // =====================================================================
                                                  739 
                                                  -
                                                  740  virtual void vector_value_list (const std::vector<Point<dim>> &points, std::vector<Vector<double>> &value_list) const;
                                                  -
                                                  741 
                                                  -
                                                  742 private:
                                                  -
                                                  743  const std::vector<double> BC_values;
                                                  -
                                                  744 };
                                                  -
                                                  745 
                                                  -
                                                  746 template <int dim>
                                                  -
                                                  747 vectorBCFunction<dim>::vectorBCFunction(std::vector<double> input_values) : Function<dim>(dim), BC_values (input_values) {}
                                                  -
                                                  748 
                                                  -
                                                  749 template <int dim>
                                                  -
                                                  750 void vectorBCFunction<dim>::vector_value(const Point<dim> &p, Vector<double> &values) const {
                                                  -
                                                  751 
                                                  -
                                                  752  for (unsigned int i=0; i<dim; i++) {
                                                  -
                                                  753  values(i) = BC_values[i];
                                                  -
                                                  754  }
                                                  -
                                                  755 }
                                                  +
                                                  740 #ifdef enablePFields
                                                  +
                                                  741 #if enablePFields == true
                                                  +
                                                  742 template <int dim>
                                                  +
                                                  743 class InitialConditionPField : public Function<dim>
                                                  +
                                                  744 {
                                                  +
                                                  745 public:
                                                  +
                                                  746  unsigned int index;
                                                  +
                                                  747  Vector<double> values;
                                                  +
                                                  748  typedef PRISMS::PField<double*, double, 2> ScalarField2D;
                                                  +
                                                  749  ScalarField2D &inputField;
                                                  +
                                                  750 
                                                  +
                                                  751  InitialConditionPField (const unsigned int _index, ScalarField2D &_inputField) : Function<dim>(1), index(_index), inputField(_inputField) {}
                                                  +
                                                  752 
                                                  +
                                                  753  double value (const Point<dim> &p, const unsigned int component = 0) const
                                                  +
                                                  754  {
                                                  +
                                                  755  double scalar_IC;
                                                  756 
                                                  -
                                                  757 template <int dim>
                                                  -
                                                  758 void vectorBCFunction<dim>::vector_value_list (const std::vector<Point<dim>> &points, std::vector<Vector<double>> &value_list) const{
                                                  -
                                                  759  const unsigned int n_points = points.size();
                                                  -
                                                  760  for (unsigned int p=0; p<n_points; ++p)
                                                  -
                                                  761  vectorBCFunction<dim>::vector_value(points[p],value_list[p]);
                                                  -
                                                  762 }
                                                  +
                                                  757  double coord[dim];
                                                  +
                                                  758  for (unsigned int i = 0; i < dim; i++){
                                                  +
                                                  759  coord[i] = p(i);
                                                  +
                                                  760  }
                                                  +
                                                  761 
                                                  +
                                                  762  scalar_IC = inputField(coord);
                                                  763 
                                                  -
                                                  764 //apply Dirchlet BC function
                                                  -
                                                  765 template <int dim>
                                                  -
                                                  766 void generalizedProblem<dim>::applyDirichletBCs(){
                                                  -
                                                  767  // First, get the variable index of the current field
                                                  -
                                                  768  unsigned int var_index;
                                                  -
                                                  769  unsigned int field_number = 0;
                                                  -
                                                  770  for (unsigned int i=0; i<num_var; i++){
                                                  -
                                                  771 
                                                  -
                                                  772  if (field_number == this->currentFieldIndex){
                                                  -
                                                  773  var_index = i;
                                                  -
                                                  774  }
                                                  -
                                                  775 
                                                  -
                                                  776  if (var_type[i] == "SCALAR"){
                                                  -
                                                  777  field_number++;
                                                  -
                                                  778  }
                                                  -
                                                  779  else {
                                                  -
                                                  780  field_number+=dim;
                                                  -
                                                  781  }
                                                  -
                                                  782  }
                                                  -
                                                  783 
                                                  -
                                                  784  if (var_type[var_index] == "SCALAR"){
                                                  -
                                                  785  for (unsigned int direction = 0; direction < 2*dim; direction++){
                                                  -
                                                  786  if (BC_list[this->currentFieldIndex].var_BC_type[direction] == "DIRICHLET"){
                                                  -
                                                  787  VectorTools::interpolate_boundary_values (*this->dofHandlersSet[this->currentFieldIndex],\
                                                  -
                                                  788  direction, ConstantFunction<dim>(BC_list[this->currentFieldIndex].var_BC_val[direction],1), *(ConstraintMatrix*) \
                                                  -
                                                  789  this->constraintsSet[this->currentFieldIndex]);
                                                  -
                                                  790  }
                                                  -
                                                  791  }
                                                  -
                                                  792  }
                                                  -
                                                  793  else {
                                                  -
                                                  794  for (unsigned int direction = 0; direction < 2*dim; direction++){
                                                  -
                                                  795 
                                                  -
                                                  796  std::vector<double> BC_values;
                                                  -
                                                  797  for (unsigned int component=0; component < dim; component++){
                                                  -
                                                  798  BC_values.push_back(BC_list[this->currentFieldIndex+component].var_BC_val[direction]);
                                                  -
                                                  799  }
                                                  -
                                                  800 
                                                  -
                                                  801  std::vector<bool> mask;
                                                  -
                                                  802  for (unsigned int component=0; component < dim; component++){
                                                  -
                                                  803  if (BC_list[this->currentFieldIndex+component].var_BC_type[direction] == "DIRICHLET"){
                                                  -
                                                  804  mask.push_back(true);
                                                  -
                                                  805  }
                                                  -
                                                  806  else {
                                                  -
                                                  807  mask.push_back(false);
                                                  -
                                                  808  }
                                                  -
                                                  809  }
                                                  -
                                                  810 
                                                  -
                                                  811 
                                                  -
                                                  812  VectorTools::interpolate_boundary_values (*this->dofHandlersSet[this->currentFieldIndex],\
                                                  -
                                                  813  direction, vectorBCFunction<dim>(BC_values), *(ConstraintMatrix*) \
                                                  -
                                                  814  this->constraintsSet[this->currentFieldIndex],mask);
                                                  -
                                                  815 
                                                  -
                                                  816 
                                                  -
                                                  817  }
                                                  -
                                                  818  }
                                                  -
                                                  819 }
                                                  -
                                                  820 
                                                  -
                                                  821 //methods to mark boundaries
                                                  -
                                                  822 template <int dim>
                                                  -
                                                  823 void generalizedProblem<dim>::markBoundaries(){
                                                  -
                                                  824 
                                                  -
                                                  825  std::vector<double> domain_size;
                                                  -
                                                  826  domain_size.push_back(spanX);
                                                  -
                                                  827  domain_size.push_back(spanY);
                                                  -
                                                  828  domain_size.push_back(spanZ);
                                                  -
                                                  829 
                                                  -
                                                  830  typename Triangulation<dim>::cell_iterator
                                                  -
                                                  831  cell = MatrixFreePDE<dim>::triangulation.begin (),
                                                  -
                                                  832  endc = MatrixFreePDE<dim>::triangulation.end();
                                                  -
                                                  833 
                                                  -
                                                  834  for (; cell!=endc; ++cell){
                                                  -
                                                  835 
                                                  -
                                                  836  // Mark all of the faces
                                                  -
                                                  837  for (unsigned int face_number=0; face_number<GeometryInfo<dim>::faces_per_cell;++face_number){
                                                  -
                                                  838  for (unsigned int i=0; i<dim; i++){
                                                  -
                                                  839  if ( std::fabs(cell->face(face_number)->center()(i) - (0)) < 1e-12 ){
                                                  -
                                                  840  cell->face(face_number)->set_boundary_id (2*i);
                                                  -
                                                  841  }
                                                  -
                                                  842  else if (std::fabs(cell->face(face_number)->center()(i) - (domain_size[i])) < 1e-12){
                                                  -
                                                  843  cell->face(face_number)->set_boundary_id (2*i+1);
                                                  -
                                                  844  }
                                                  -
                                                  845 
                                                  -
                                                  846  }
                                                  -
                                                  847  }
                                                  -
                                                  848  }
                                                  -
                                                  849 }
                                                  -
                                                  850 
                                                  -
                                                  851 // Input the boundary conditions for each face individually for 3D domains
                                                  -
                                                  852 template <int dim>
                                                  -
                                                  853 void generalizedProblem<dim>::inputBCs(int var, int component, std::string BC_type_dim1_min, double BC_value_dim1_min,
                                                  -
                                                  854  std::string BC_type_dim1_max, double BC_value_dim1_max, std::string BC_type_dim2_min, double BC_value_dim2_min,
                                                  -
                                                  855  std::string BC_type_dim2_max, double BC_value_dim2_max,std::string BC_type_dim3_min, double BC_value_dim3_min,
                                                  -
                                                  856  std::string BC_type_dim3_max, double BC_value_dim3_max){
                                                  -
                                                  857 
                                                  -
                                                  858  varBCs<dim> newBC;
                                                  -
                                                  859  newBC.var_BC_type.push_back(BC_type_dim1_min);
                                                  -
                                                  860  newBC.var_BC_type.push_back(BC_type_dim1_max);
                                                  -
                                                  861  newBC.var_BC_type.push_back(BC_type_dim2_min);
                                                  -
                                                  862  newBC.var_BC_type.push_back(BC_type_dim2_max);
                                                  -
                                                  863  newBC.var_BC_type.push_back(BC_type_dim3_min);
                                                  -
                                                  864  newBC.var_BC_type.push_back(BC_type_dim3_max);
                                                  -
                                                  865 
                                                  -
                                                  866  newBC.var_BC_val.push_back(BC_value_dim1_min);
                                                  -
                                                  867  newBC.var_BC_val.push_back(BC_value_dim1_max);
                                                  -
                                                  868  newBC.var_BC_val.push_back(BC_value_dim2_min);
                                                  -
                                                  869  newBC.var_BC_val.push_back(BC_value_dim2_max);
                                                  -
                                                  870  newBC.var_BC_val.push_back(BC_value_dim3_min);
                                                  -
                                                  871  newBC.var_BC_val.push_back(BC_value_dim3_max);
                                                  -
                                                  872 
                                                  -
                                                  873  BC_list.push_back(newBC);
                                                  -
                                                  874 }
                                                  -
                                                  875 
                                                  -
                                                  876 // Input the boundary conditions for each face individually for 2D domains
                                                  -
                                                  877 template <int dim>
                                                  -
                                                  878 void generalizedProblem<dim>::inputBCs(int var, int component, std::string BC_type_dim1_min, double BC_value_dim1_min,
                                                  -
                                                  879  std::string BC_type_dim1_max, double BC_value_dim1_max, std::string BC_type_dim2_min, double BC_value_dim2_min,
                                                  -
                                                  880  std::string BC_type_dim2_max, double BC_value_dim2_max){
                                                  +
                                                  764  return scalar_IC;
                                                  +
                                                  765  }
                                                  +
                                                  766 };
                                                  +
                                                  767 
                                                  +
                                                  768 #endif
                                                  +
                                                  769 #else
                                                  +
                                                  770 #define enablePFields false
                                                  +
                                                  771 #endif
                                                  +
                                                  772 
                                                  +
                                                  773 // =================================================================================
                                                  +
                                                  774 
                                                  +
                                                  775 //apply initial conditions
                                                  +
                                                  776 template <int dim>
                                                  +
                                                  777 void generalizedProblem<dim>::applyInitialConditions()
                                                  +
                                                  778 {
                                                  +
                                                  779 
                                                  +
                                                  780 
                                                  +
                                                  781 #ifndef loadICs
                                                  +
                                                  782 #define loadICs {}
                                                  +
                                                  783 #endif
                                                  +
                                                  784 
                                                  +
                                                  785 #ifndef loadSerialFile
                                                  +
                                                  786 #define loadSerialFile {}
                                                  +
                                                  787 #endif
                                                  +
                                                  788 
                                                  +
                                                  789 #ifndef loadFileName
                                                  +
                                                  790 #define loadFileName {}
                                                  +
                                                  791 #endif
                                                  +
                                                  792 
                                                  +
                                                  793 #ifndef loadFieldName
                                                  +
                                                  794 #define loadFieldName {}
                                                  +
                                                  795 #endif
                                                  +
                                                  796 
                                                  +
                                                  797 std::vector<bool> load_ICs = loadICs;
                                                  +
                                                  798 std::vector<bool> load_serial_file = loadSerialFile;
                                                  +
                                                  799 std::vector<std::string> load_file_name = loadFileName;
                                                  +
                                                  800 std::vector<std::string> load_field_name = loadFieldName;
                                                  +
                                                  801 
                                                  +
                                                  802 // If load_ICs is empty, it should be set to false for each variable
                                                  +
                                                  803 if (load_ICs.size() == 0){
                                                  +
                                                  804  for (unsigned int i=0; i<num_var; i++){
                                                  +
                                                  805  load_ICs.push_back(false);
                                                  +
                                                  806  }
                                                  +
                                                  807 }
                                                  +
                                                  808 
                                                  +
                                                  809 unsigned int fieldIndex = 0;
                                                  +
                                                  810 for (unsigned int var_index=0; var_index < num_var; var_index++){
                                                  +
                                                  811  if (load_ICs[var_index] == false){
                                                  +
                                                  812  if (var_type[var_index] == "SCALAR"){
                                                  +
                                                  813  VectorTools::interpolate (*this->dofHandlersSet[fieldIndex], InitialCondition<dim>(var_index), *this->solutionSet[fieldIndex]);
                                                  +
                                                  814  fieldIndex++;
                                                  +
                                                  815  }
                                                  +
                                                  816  else {
                                                  +
                                                  817  VectorTools::interpolate (*this->dofHandlersSet[fieldIndex], InitialConditionVec<dim>(var_index), *this->solutionSet[fieldIndex]);
                                                  +
                                                  818  fieldIndex += dim;
                                                  +
                                                  819  }
                                                  +
                                                  820  }
                                                  +
                                                  821  else{
                                                  +
                                                  822  #if enablePFields == true
                                                  +
                                                  823  // Declare the PField types and containers
                                                  +
                                                  824  typedef PRISMS::PField<double*, double, 2> ScalarField2D;
                                                  +
                                                  825  typedef PRISMS::Body<double*, 2> Body2D;
                                                  +
                                                  826  Body2D body;
                                                  +
                                                  827 
                                                  +
                                                  828  // Create the filename of the the file to be loaded
                                                  +
                                                  829  std::string filename;
                                                  +
                                                  830  if (load_serial_file[var_index] == true){
                                                  +
                                                  831  filename = load_file_name[var_index] + ".vtk";
                                                  +
                                                  832  }
                                                  +
                                                  833  else {
                                                  +
                                                  834  int proc_num = Utilities::MPI::this_mpi_process(MPI_COMM_WORLD);
                                                  +
                                                  835  std::ostringstream conversion;
                                                  +
                                                  836  conversion << proc_num;
                                                  +
                                                  837  filename = load_file_name[var_index] + "." + conversion.str() + ".vtk";
                                                  +
                                                  838  }
                                                  +
                                                  839 
                                                  +
                                                  840  // Load the data from the file using a PField
                                                  +
                                                  841  body.read_vtk(filename);
                                                  +
                                                  842  ScalarField2D &conc = body.find_scalar_field(load_field_name[var_index]);
                                                  +
                                                  843  if (var_type[var_index] == "SCALAR"){
                                                  +
                                                  844  VectorTools::interpolate (*this->dofHandlersSet[fieldIndex], InitialConditionPField<dim>(var_index,conc), *this->solutionSet[fieldIndex]);
                                                  +
                                                  845  fieldIndex++;
                                                  +
                                                  846  }
                                                  +
                                                  847  else {
                                                  +
                                                  848  std::cout << "PRISMS-PF Error: Cannot load vector fields. Loading initial conditions from file is currently limited to scalar fields" << std::endl;
                                                  +
                                                  849  }
                                                  +
                                                  850  #else
                                                  +
                                                  851  std::cout << "PRISMS-PF Error: The parameter \"enablePFields\" must be set to true to load initial conditions from file." << std::endl;
                                                  +
                                                  852  #endif
                                                  +
                                                  853  }
                                                  +
                                                  854 }
                                                  +
                                                  855 }
                                                  +
                                                  856 
                                                  +
                                                  857 // =================================================================================
                                                  +
                                                  858 
                                                  +
                                                  859 // I don't think vector fields are implemented in PFields yet
                                                  +
                                                  860 //template <int dim>
                                                  +
                                                  861 //class InitialConditionPFieldVec : public Function<dim>
                                                  +
                                                  862 //{
                                                  +
                                                  863 //public:
                                                  +
                                                  864 // unsigned int index;
                                                  +
                                                  865 // Vector<double> values;
                                                  +
                                                  866 // typedef PRISMS::PField<double*, double, 2> ScalarField2D;
                                                  +
                                                  867 // ScalarField2D &inputField;
                                                  +
                                                  868 //
                                                  +
                                                  869 // InitialConditionPFieldVec (const unsigned int _index, ScalarField2D &_inputField) : Function<dim>(1), index(_index), inputField(_inputField) {}
                                                  +
                                                  870 //
                                                  +
                                                  871 // void vector_value (const Point<dim> &p,Vector<double> &vector_IC) const
                                                  +
                                                  872 // {
                                                  +
                                                  873 // double coord[dim];
                                                  +
                                                  874 // for (unsigned int i = 0; i < dim; i++){
                                                  +
                                                  875 // coord[i] = p(i);
                                                  +
                                                  876 // }
                                                  +
                                                  877 //
                                                  +
                                                  878 // vector_IC = inputField(coord);
                                                  +
                                                  879 // }
                                                  +
                                                  880 //};
                                                  881 
                                                  -
                                                  882  varBCs<dim> newBC;
                                                  -
                                                  883  newBC.var_BC_type.push_back(BC_type_dim1_min);
                                                  -
                                                  884  newBC.var_BC_type.push_back(BC_type_dim1_max);
                                                  -
                                                  885  newBC.var_BC_type.push_back(BC_type_dim2_min);
                                                  -
                                                  886  newBC.var_BC_type.push_back(BC_type_dim2_max);
                                                  -
                                                  887 
                                                  -
                                                  888  newBC.var_BC_val.push_back(BC_value_dim1_min);
                                                  -
                                                  889  newBC.var_BC_val.push_back(BC_value_dim1_max);
                                                  -
                                                  890  newBC.var_BC_val.push_back(BC_value_dim2_min);
                                                  -
                                                  891  newBC.var_BC_val.push_back(BC_value_dim2_max);
                                                  -
                                                  892 
                                                  -
                                                  893  BC_list.push_back(newBC);
                                                  -
                                                  894 }
                                                  +
                                                  882 
                                                  +
                                                  883 // =====================================================================
                                                  +
                                                  884 // BOUNDARY CONDITION FUNCTIONS
                                                  +
                                                  885 // =====================================================================
                                                  +
                                                  886 
                                                  +
                                                  887 template <int dim>
                                                  +
                                                  888 class vectorBCFunction : public Function<dim,double>
                                                  +
                                                  889 {
                                                  +
                                                  890 public:
                                                  +
                                                  891  vectorBCFunction(const std::vector<double> BC_values);
                                                  +
                                                  892  virtual void vector_value (const Point<dim> &p, Vector<double> &values) const;
                                                  +
                                                  893 
                                                  +
                                                  894  virtual void vector_value_list (const std::vector<Point<dim> > &points, std::vector<Vector<double> > &value_list) const;
                                                  895 
                                                  -
                                                  896 // Input the boundary conditions for each face individually for 1D domains
                                                  -
                                                  897 template <int dim>
                                                  -
                                                  898 void generalizedProblem<dim>::inputBCs(int var, int component, std::string BC_type_dim1_min, double BC_value_dim1_min,
                                                  -
                                                  899  std::string BC_type_dim1_max, double BC_value_dim1_max){
                                                  -
                                                  900 
                                                  -
                                                  901  varBCs<dim> newBC;
                                                  -
                                                  902  newBC.var_BC_type.push_back(BC_type_dim1_min);
                                                  -
                                                  903  newBC.var_BC_type.push_back(BC_type_dim1_max);
                                                  -
                                                  904 
                                                  -
                                                  905  newBC.var_BC_val.push_back(BC_value_dim1_min);
                                                  -
                                                  906  newBC.var_BC_val.push_back(BC_value_dim1_max);
                                                  -
                                                  907 
                                                  -
                                                  908  BC_list.push_back(newBC);
                                                  +
                                                  896 private:
                                                  +
                                                  897  const std::vector<double> BC_values;
                                                  +
                                                  898 };
                                                  +
                                                  899 
                                                  +
                                                  900 template <int dim>
                                                  +
                                                  901 vectorBCFunction<dim>::vectorBCFunction(std::vector<double> input_values) : Function<dim>(dim), BC_values (input_values) {}
                                                  +
                                                  902 
                                                  +
                                                  903 template <int dim>
                                                  +
                                                  904 void vectorBCFunction<dim>::vector_value(const Point<dim> &p, Vector<double> &values) const {
                                                  +
                                                  905 
                                                  +
                                                  906  for (unsigned int i=0; i<dim; i++) {
                                                  +
                                                  907  values(i) = BC_values[i];
                                                  +
                                                  908  }
                                                  909 }
                                                  910 
                                                  -
                                                  911 // Input the boundary conditions when all faces have the same boundary condition
                                                  -
                                                  912 template <int dim>
                                                  -
                                                  913 void generalizedProblem<dim>::inputBCs(int var, int component, std::string BC_type, double BC_value){
                                                  -
                                                  914 
                                                  -
                                                  915  varBCs<dim> newBC;
                                                  -
                                                  916  for (unsigned int face=0; face<(dim*2); face++){
                                                  -
                                                  917  newBC.var_BC_type.push_back(BC_type);
                                                  -
                                                  918  newBC.var_BC_val.push_back(BC_value);
                                                  -
                                                  919  }
                                                  -
                                                  920 
                                                  -
                                                  921  BC_list.push_back(newBC);
                                                  -
                                                  922 }
                                                  -
                                                  923 
                                                  -
                                                  924 // =====================================================================
                                                  -
                                                  925 // NUCLEATION FUNCTIONS
                                                  -
                                                  926 // =====================================================================
                                                  -
                                                  927 
                                                  -
                                                  928 //structure representing each nucleus
                                                  -
                                                  929 struct nucleus{
                                                  -
                                                  930  unsigned int index;
                                                  -
                                                  931  dealii::Point<problemDIM> center;
                                                  -
                                                  932  double radius;
                                                  -
                                                  933  double seededTime, seedingTime;
                                                  -
                                                  934 };
                                                  -
                                                  935 //vector of all nucleus seeded in the problem
                                                  -
                                                  936 std::vector<nucleus> nuclei, localNuclei;
                                                  +
                                                  911 template <int dim>
                                                  +
                                                  912 void vectorBCFunction<dim>::vector_value_list (const std::vector<Point<dim> > &points, std::vector<Vector<double> > &value_list) const{
                                                  +
                                                  913  const unsigned int n_points = points.size();
                                                  +
                                                  914  for (unsigned int p=0; p<n_points; ++p)
                                                  +
                                                  915  vectorBCFunction<dim>::vector_value(points[p],value_list[p]);
                                                  +
                                                  916 }
                                                  +
                                                  917 
                                                  +
                                                  918 //apply Dirchlet BC function
                                                  +
                                                  919 template <int dim>
                                                  +
                                                  920 void generalizedProblem<dim>::applyDirichletBCs(){
                                                  +
                                                  921  // First, get the variable index of the current field
                                                  +
                                                  922  unsigned int var_index;
                                                  +
                                                  923  unsigned int field_number = 0;
                                                  +
                                                  924  for (unsigned int i=0; i<num_var; i++){
                                                  +
                                                  925 
                                                  +
                                                  926  if (field_number == this->currentFieldIndex){
                                                  +
                                                  927  var_index = i;
                                                  +
                                                  928  }
                                                  +
                                                  929 
                                                  +
                                                  930  if (var_type[i] == "SCALAR"){
                                                  +
                                                  931  field_number++;
                                                  +
                                                  932  }
                                                  +
                                                  933  else {
                                                  +
                                                  934  field_number+=dim;
                                                  +
                                                  935  }
                                                  +
                                                  936  }
                                                  937 
                                                  -
                                                  938 //nucleation model implementation
                                                  -
                                                  939 template <int dim>
                                                  -
                                                  940 void generalizedProblem<dim>::modifySolutionFields()
                                                  -
                                                  941 {
                                                  -
                                                  942  //current time
                                                  -
                                                  943  double t=this->currentTime;
                                                  -
                                                  944  unsigned int inc=this->currentIncrement;
                                                  -
                                                  945  double dx=spanX/( (double)subdivisionsX )/std::pow(2.0,refineFactor);
                                                  -
                                                  946  double rand_val;
                                                  -
                                                  947  int count = 0;
                                                  -
                                                  948  //nucleation parameters
                                                  -
                                                  949  double nRadius = 2.5; //spanX/20.0;
                                                  -
                                                  950  double minDistBetwenNuclei=4*nRadius;
                                                  -
                                                  951 
                                                  -
                                                  952  unsigned int maxNumberNuclei=5; // doesn't do anything currently
                                                  -
                                                  953 
                                                  -
                                                  954  //get the list of node points in the domain
                                                  -
                                                  955  std::map<dealii::types::global_dof_index, dealii::Point<dim> > support_points;
                                                  -
                                                  956  dealii::DoFTools::map_dofs_to_support_points (dealii::MappingQ1<dim>(), *this->dofHandlersSet[0], support_points);
                                                  -
                                                  957  //fields
                                                  -
                                                  958  vectorType* n1=this->solutionSet[this->getFieldIndex("n1")];
                                                  -
                                                  959  vectorType* n2=this->solutionSet[this->getFieldIndex("n2")];
                                                  -
                                                  960  vectorType* n3=this->solutionSet[this->getFieldIndex("n3")];
                                                  -
                                                  961  vectorType* c=this->solutionSet[this->getFieldIndex("c")];
                                                  -
                                                  962  const double k1 = 0.0001; // nucleation probability constant
                                                  -
                                                  963  const double k2 = 1.0; // nucleation probability constant
                                                  -
                                                  964  const double c0 = 0.300; // baseline concentration?
                                                  -
                                                  965  double J = 0.0;
                                                  -
                                                  966  //delete the previous entries in the nuclei vector (old nucleus are still retained in the localNuclei vector)
                                                  -
                                                  967  nuclei.clear();
                                                  -
                                                  968 
                                                  -
                                                  969 #ifndef c_matrix
                                                  -
                                                  970  double c_matrix = 1.0e-6;
                                                  -
                                                  971 #endif
                                                  -
                                                  972 
                                                  -
                                                  973  //populate localNuclei vector
                                                  -
                                                  974  if (inc <= timeIncrements){
                                                  -
                                                  975  nucleus* temp;
                                                  -
                                                  976  //add nuclei based on concentration field values
                                                  -
                                                  977  //loop over all points in the domain
                                                  -
                                                  978  for (typename std::map<dealii::types::global_dof_index, dealii::Point<dim> >::iterator it=support_points.begin(); it!=support_points.end(); ++it){
                                                  -
                                                  979  unsigned int dof=it->first;
                                                  -
                                                  980  //set only local owned values of the parallel vector (eventually turn this into a separate function for each order parameter)
                                                  -
                                                  981  if (n1->locally_owned_elements().is_element(dof)){
                                                  -
                                                  982  dealii::Point<dim> nodePoint=it->second;
                                                  -
                                                  983  double n1Value=(*n1)(dof);
                                                  -
                                                  984  double n2Value=(*n2)(dof);
                                                  -
                                                  985  double n3Value=(*n3)(dof);
                                                  -
                                                  986  double cValue=(*c)(dof);
                                                  +
                                                  938  if (var_type[var_index] == "SCALAR"){
                                                  +
                                                  939  for (unsigned int direction = 0; direction < 2*dim; direction++){
                                                  +
                                                  940  if (BC_list[this->currentFieldIndex].var_BC_type[direction] == "DIRICHLET"){
                                                  +
                                                  941  VectorTools::interpolate_boundary_values (*this->dofHandlersSet[this->currentFieldIndex],\
                                                  +
                                                  942  direction, ConstantFunction<dim>(BC_list[this->currentFieldIndex].var_BC_val[direction],1), *(ConstraintMatrix*) \
                                                  +
                                                  943  this->constraintsDirichletSet[this->currentFieldIndex]);
                                                  +
                                                  944  }
                                                  +
                                                  945  }
                                                  +
                                                  946  }
                                                  +
                                                  947  else {
                                                  +
                                                  948  for (unsigned int direction = 0; direction < 2*dim; direction++){
                                                  +
                                                  949 
                                                  +
                                                  950  std::vector<double> BC_values;
                                                  +
                                                  951  for (unsigned int component=0; component < dim; component++){
                                                  +
                                                  952  BC_values.push_back(BC_list[this->currentFieldIndex+component].var_BC_val[direction]);
                                                  +
                                                  953  }
                                                  +
                                                  954 
                                                  +
                                                  955  std::vector<bool> mask;
                                                  +
                                                  956  for (unsigned int component=0; component < dim; component++){
                                                  +
                                                  957  if (BC_list[this->currentFieldIndex+component].var_BC_type[direction] == "DIRICHLET"){
                                                  +
                                                  958  mask.push_back(true);
                                                  +
                                                  959  }
                                                  +
                                                  960  else {
                                                  +
                                                  961  mask.push_back(false);
                                                  +
                                                  962  }
                                                  +
                                                  963  }
                                                  +
                                                  964 
                                                  +
                                                  965 
                                                  +
                                                  966  VectorTools::interpolate_boundary_values (*this->dofHandlersSet[this->currentFieldIndex],\
                                                  +
                                                  967  direction, vectorBCFunction<dim>(BC_values), *(ConstraintMatrix*) \
                                                  +
                                                  968  this->constraintsDirichletSet[this->currentFieldIndex],mask);
                                                  +
                                                  969 
                                                  +
                                                  970 
                                                  +
                                                  971  }
                                                  +
                                                  972  }
                                                  +
                                                  973 }
                                                  +
                                                  974 
                                                  +
                                                  975 //methods to mark boundaries
                                                  +
                                                  976 template <int dim>
                                                  +
                                                  977 void generalizedProblem<dim>::markBoundaries(){
                                                  +
                                                  978 
                                                  +
                                                  979  std::vector<double> domain_size;
                                                  +
                                                  980  domain_size.push_back(spanX);
                                                  +
                                                  981  domain_size.push_back(spanY);
                                                  +
                                                  982  domain_size.push_back(spanZ);
                                                  +
                                                  983 
                                                  +
                                                  984  typename Triangulation<dim>::cell_iterator
                                                  +
                                                  985  cell = MatrixFreePDE<dim>::triangulation.begin (),
                                                  +
                                                  986  endc = MatrixFreePDE<dim>::triangulation.end();
                                                  987 
                                                  -
                                                  988  rand_val = (double)rand()/(RAND_MAX);
                                                  +
                                                  988  for (; cell!=endc; ++cell){
                                                  989 
                                                  -
                                                  990  if ((t > 1000000000*timeStep) || (n1Value+n2Value+n3Value > 1.0e-6) || (cValue <= 0.0)) {
                                                  -
                                                  991  J = 0;
                                                  -
                                                  992  }
                                                  -
                                                  993  else{
                                                  -
                                                  994  J = cValue/c_matrix * dx*dx/((double)spanX * (double)spanY) * 0.01; // Only true in 2D!
                                                  -
                                                  995  }
                                                  -
                                                  996 
                                                  -
                                                  997  if (rand_val <= J){
                                                  -
                                                  998  bool isClose=false;
                                                  -
                                                  999  for (std::vector<nucleus>::iterator thisNuclei=localNuclei.begin(); thisNuclei!=localNuclei.end(); ++thisNuclei){
                                                  -
                                                  1000  if (thisNuclei->center.distance(nodePoint)<minDistBetwenNuclei){
                                                  -
                                                  1001  isClose=true;
                                                  -
                                                  1002  }
                                                  -
                                                  1003  }
                                                  +
                                                  990  // Mark all of the faces
                                                  +
                                                  991  for (unsigned int face_number=0; face_number<GeometryInfo<dim>::faces_per_cell;++face_number){
                                                  +
                                                  992  for (unsigned int i=0; i<dim; i++){
                                                  +
                                                  993  if ( std::fabs(cell->face(face_number)->center()(i) - (0)) < 1e-12 ){
                                                  +
                                                  994  cell->face(face_number)->set_boundary_id (2*i);
                                                  +
                                                  995  }
                                                  +
                                                  996  else if (std::fabs(cell->face(face_number)->center()(i) - (domain_size[i])) < 1e-12){
                                                  +
                                                  997  cell->face(face_number)->set_boundary_id (2*i+1);
                                                  +
                                                  998  }
                                                  +
                                                  999 
                                                  +
                                                  1000  }
                                                  +
                                                  1001  }
                                                  +
                                                  1002  }
                                                  +
                                                  1003 }
                                                  1004 
                                                  -
                                                  1005  if (!isClose){
                                                  -
                                                  1006  temp = new nucleus;
                                                  -
                                                  1007  temp->index=localNuclei.size();
                                                  -
                                                  1008  temp->center=nodePoint;
                                                  -
                                                  1009  temp->radius=nRadius;
                                                  -
                                                  1010  temp->seededTime=t;
                                                  -
                                                  1011  temp->seedingTime = 10000.0*timeStep;
                                                  -
                                                  1012  localNuclei.push_back(*temp);
                                                  -
                                                  1013  }
                                                  -
                                                  1014  }
                                                  -
                                                  1015  }
                                                  -
                                                  1016  }
                                                  -
                                                  1017 
                                                  -
                                                  1018 
                                                  -
                                                  1019  //filter nuclei by comparing with other processors
                                                  -
                                                  1020  int numProcs=Utilities::MPI::n_mpi_processes(MPI_COMM_WORLD);
                                                  -
                                                  1021  int thisProc=Utilities::MPI::this_mpi_process(MPI_COMM_WORLD);
                                                  -
                                                  1022  std::vector<int> numNucleiInProcs(numProcs, 0);
                                                  -
                                                  1023  //send nuclei information to processor 0
                                                  -
                                                  1024  int numNuclei=localNuclei.size();
                                                  -
                                                  1025  //send information about number of nuclei to processor 0
                                                  -
                                                  1026  if (thisProc!=0){
                                                  -
                                                  1027  MPI_Send(&numNuclei, 1, MPI_INT, 0, 0, MPI_COMM_WORLD);
                                                  -
                                                  1028  }
                                                  -
                                                  1029  else{
                                                  -
                                                  1030  numNucleiInProcs[0]=numNuclei;
                                                  -
                                                  1031  for (int proc=1; proc<numProcs; proc++){
                                                  -
                                                  1032  MPI_Recv(&numNucleiInProcs[proc], 1, MPI_INT, proc, 0, MPI_COMM_WORLD, MPI_STATUS_IGNORE);
                                                  -
                                                  1033  }
                                                  -
                                                  1034  }
                                                  -
                                                  1035  MPI_Barrier(MPI_COMM_WORLD);
                                                  -
                                                  1036  //filter nuclei in processor zero
                                                  -
                                                  1037  //receive nuclei info from all processors
                                                  -
                                                  1038  if (thisProc!=0){
                                                  -
                                                  1039  if (numNuclei>0){
                                                  -
                                                  1040  std::vector<double> tempData((dim+3)*numNuclei);
                                                  -
                                                  1041  unsigned int i=0;
                                                  -
                                                  1042  for (std::vector<nucleus>::iterator thisNuclei=localNuclei.begin(); thisNuclei!=localNuclei.end(); ++thisNuclei){
                                                  -
                                                  1043  tempData[i*(dim+3)]=thisNuclei->radius;
                                                  -
                                                  1044  tempData[i*(dim+3)+1]=thisNuclei->seededTime;
                                                  -
                                                  1045  tempData[i*(dim+3)+2]=thisNuclei->seedingTime;
                                                  -
                                                  1046  for (unsigned int j=0; j<dim; j++) tempData[i*(dim+3)+3+j]=thisNuclei->center[j];
                                                  -
                                                  1047  i++;
                                                  -
                                                  1048  }
                                                  -
                                                  1049  MPI_Send(&tempData[0], numNuclei*(dim+3), MPI_DOUBLE, 0, 1, MPI_COMM_WORLD);
                                                  -
                                                  1050  }
                                                  -
                                                  1051  }
                                                  -
                                                  1052  else{
                                                  -
                                                  1053  //temporary array to store all the nuclei
                                                  -
                                                  1054  std::vector<std::vector<double>*> tempNuceli(numProcs);
                                                  -
                                                  1055  for (int proc=0; proc<numProcs; proc++) {
                                                  -
                                                  1056  std::vector<double>* temp=new std::vector<double>(numNucleiInProcs[proc]*(dim+3));
                                                  -
                                                  1057  if (numNucleiInProcs[proc]>0){
                                                  -
                                                  1058  if (proc==0){
                                                  -
                                                  1059  unsigned int i=0;
                                                  -
                                                  1060  for (std::vector<nucleus>::iterator thisNuclei=localNuclei.begin(); thisNuclei!=localNuclei.end(); ++thisNuclei){
                                                  -
                                                  1061  (*temp)[i*(dim+3)]=thisNuclei->radius;
                                                  -
                                                  1062  (*temp)[i*(dim+3)+1]=thisNuclei->seededTime;
                                                  -
                                                  1063  (*temp)[i*(dim+3)+2]=thisNuclei->seedingTime;
                                                  -
                                                  1064  for (unsigned int j=0; j<dim; j++) (*temp)[i*(dim+3)+3+j]=thisNuclei->center[j];
                                                  -
                                                  1065  i++;
                                                  -
                                                  1066  }
                                                  -
                                                  1067  }
                                                  -
                                                  1068  else{
                                                  -
                                                  1069  MPI_Recv(&((*temp)[0]), numNucleiInProcs[proc]*(dim+3), MPI_DOUBLE, proc, 1, MPI_COMM_WORLD, MPI_STATUS_IGNORE);
                                                  -
                                                  1070  }
                                                  -
                                                  1071  tempNuceli[proc]=temp;
                                                  -
                                                  1072  }
                                                  -
                                                  1073  }
                                                  -
                                                  1074 
                                                  -
                                                  1075  //filter the nuclei and add to nuclei vector in processor zero
                                                  -
                                                  1076  for (int proc1=0; proc1<numProcs; proc1++) {
                                                  -
                                                  1077  for (int i1=0; i1<numNucleiInProcs[proc1]; i1++){
                                                  -
                                                  1078  double rad1=(*tempNuceli[proc1])[i1*(dim+3)];
                                                  -
                                                  1079  double time1=(*tempNuceli[proc1])[i1*(dim+3)+1];
                                                  -
                                                  1080  double seedingTime1=(*tempNuceli[proc1])[i1*(dim+3)+2];
                                                  -
                                                  1081  dealii::Point<dim> center1;
                                                  -
                                                  1082  for (unsigned int j1=0; j1<dim; j1++) {
                                                  -
                                                  1083  center1[j1]=(*tempNuceli[proc1])[i1*(dim+3)+3+j1];
                                                  -
                                                  1084  }
                                                  -
                                                  1085  bool addNuclei=true;
                                                  -
                                                  1086  //check if this nuceli present in any other processor
                                                  -
                                                  1087  for (int proc2=0; proc2<numProcs; proc2++) {
                                                  -
                                                  1088  if (proc1!=proc2){
                                                  -
                                                  1089  for (int i2=0; i2<numNucleiInProcs[proc2]; i2++){
                                                  -
                                                  1090  double rad2=(*tempNuceli[proc2])[i2*(dim+3)];
                                                  -
                                                  1091  double time2=(*tempNuceli[proc2])[i2*(dim+3)+1];
                                                  -
                                                  1092  dealii::Point<dim> center2;
                                                  -
                                                  1093  for (unsigned int j2=0; j2<dim; j2++) center2(j2)=(*tempNuceli[proc2])[i2*(dim+3)+3+j2];
                                                  -
                                                  1094  if ((center1.distance(center2)<=minDistBetwenNuclei) && (time1>=time2)){
                                                  -
                                                  1095  addNuclei=false;
                                                  -
                                                  1096  break;
                                                  -
                                                  1097  }
                                                  -
                                                  1098  }
                                                  -
                                                  1099  if (!addNuclei) {break;}
                                                  -
                                                  1100  }
                                                  -
                                                  1101  }
                                                  -
                                                  1102  if (addNuclei){
                                                  -
                                                  1103  temp = new nucleus;
                                                  -
                                                  1104  temp->index=nuclei.size();
                                                  -
                                                  1105  temp->radius=rad1;
                                                  -
                                                  1106  temp->seededTime=time1;
                                                  -
                                                  1107  temp->seedingTime=seedingTime1;
                                                  -
                                                  1108  temp->center=center1;
                                                  -
                                                  1109  nuclei.push_back(*temp);
                                                  -
                                                  1110  }
                                                  -
                                                  1111  }
                                                  -
                                                  1112  }
                                                  -
                                                  1113  }
                                                  -
                                                  1114  MPI_Barrier(MPI_COMM_WORLD);
                                                  -
                                                  1115 
                                                  -
                                                  1116  //disperse nuclei to all other processors
                                                  -
                                                  1117  unsigned int numGlobalNuclei;
                                                  -
                                                  1118  if (Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)==0) {numGlobalNuclei=nuclei.size();}
                                                  -
                                                  1119  MPI_Bcast(&numGlobalNuclei, 1, MPI_INT, 0, MPI_COMM_WORLD);
                                                  -
                                                  1120  this->pcout << "total number of nuclei currently seeded : " << numGlobalNuclei << std::endl;
                                                  -
                                                  1121  MPI_Barrier(MPI_COMM_WORLD);
                                                  -
                                                  1122  //
                                                  -
                                                  1123  std::vector<double> temp2(numGlobalNuclei*(dim+3));
                                                  -
                                                  1124  if (Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)==0){
                                                  -
                                                  1125  unsigned int i=0;
                                                  -
                                                  1126  for (std::vector<nucleus>::iterator thisNuclei=nuclei.begin(); thisNuclei!=nuclei.end(); ++thisNuclei){
                                                  -
                                                  1127  temp2[i*(dim+3)]=thisNuclei->radius;
                                                  -
                                                  1128  temp2[i*(dim+3)+1]=thisNuclei->seededTime;
                                                  -
                                                  1129  temp2[i*(dim+3)+2]=thisNuclei->seedingTime;
                                                  -
                                                  1130  for (unsigned int j=0; j<dim; j++) temp2[i*(dim+3)+3+j]=thisNuclei->center[j];
                                                  -
                                                  1131  i++;
                                                  -
                                                  1132  }
                                                  -
                                                  1133  }
                                                  -
                                                  1134  MPI_Bcast(&temp2[0], numGlobalNuclei*(dim+3), MPI_DOUBLE, 0, MPI_COMM_WORLD);
                                                  -
                                                  1135  MPI_Barrier(MPI_COMM_WORLD);
                                                  -
                                                  1136  //receive all nuclei
                                                  -
                                                  1137  if (Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)!=0){
                                                  -
                                                  1138  for(unsigned int i=0; i<numGlobalNuclei; i++){
                                                  -
                                                  1139  temp = new nucleus;
                                                  -
                                                  1140  temp->index=nuclei.size();
                                                  -
                                                  1141  temp->radius=temp2[i*(dim+3)];
                                                  -
                                                  1142  temp->seededTime=temp2[i*(dim+3)+1];
                                                  -
                                                  1143  temp->seedingTime=temp2[i*(dim+3)+2];
                                                  -
                                                  1144  dealii::Point<dim> tempCenter;
                                                  -
                                                  1145  for (unsigned int j=0; j<dim; j++) tempCenter[j]=temp2[i*(dim+3)+3+j];
                                                  -
                                                  1146  temp->center=tempCenter;
                                                  -
                                                  1147  nuclei.push_back(*temp);
                                                  -
                                                  1148  }
                                                  -
                                                  1149  }
                                                  -
                                                  1150  }
                                                  +
                                                  1005 // Input the boundary conditions for each face individually for 3D domains
                                                  +
                                                  1006 template <int dim>
                                                  +
                                                  1007 void generalizedProblem<dim>::inputBCs(int var, int component, std::string BC_type_dim1_min, double BC_value_dim1_min,
                                                  +
                                                  1008  std::string BC_type_dim1_max, double BC_value_dim1_max, std::string BC_type_dim2_min, double BC_value_dim2_min,
                                                  +
                                                  1009  std::string BC_type_dim2_max, double BC_value_dim2_max,std::string BC_type_dim3_min, double BC_value_dim3_min,
                                                  +
                                                  1010  std::string BC_type_dim3_max, double BC_value_dim3_max){
                                                  +
                                                  1011 
                                                  +
                                                  1012  // Validate input
                                                  +
                                                  1013  try{
                                                  +
                                                  1014  if ((BC_type_dim1_min == "PERIODIC") && (BC_type_dim1_max != "PERIODIC")){
                                                  +
                                                  1015  throw 0;
                                                  +
                                                  1016  }
                                                  +
                                                  1017  if ((BC_type_dim2_min == "PERIODIC") && (BC_type_dim2_max != "PERIODIC")){
                                                  +
                                                  1018  throw 0;
                                                  +
                                                  1019  }
                                                  +
                                                  1020  if ((BC_type_dim3_min == "PERIODIC") && (BC_type_dim3_max != "PERIODIC")){
                                                  +
                                                  1021  throw 0;
                                                  +
                                                  1022  }
                                                  +
                                                  1023  }
                                                  +
                                                  1024  catch (int e){
                                                  +
                                                  1025  if (e == 0){
                                                  +
                                                  1026  std::cout << "Error: For periodic BCs, both faces for a given direction must be set as periodic. "
                                                  +
                                                  1027  "Please check the BCs that are set in ICs_and_BCs.h." << std::endl;
                                                  +
                                                  1028  }
                                                  +
                                                  1029  abort();
                                                  +
                                                  1030  }
                                                  +
                                                  1031 
                                                  +
                                                  1032  varBCs<dim> newBC;
                                                  +
                                                  1033  newBC.var_BC_type.push_back(BC_type_dim1_min);
                                                  +
                                                  1034  newBC.var_BC_type.push_back(BC_type_dim1_max);
                                                  +
                                                  1035  newBC.var_BC_type.push_back(BC_type_dim2_min);
                                                  +
                                                  1036  newBC.var_BC_type.push_back(BC_type_dim2_max);
                                                  +
                                                  1037  newBC.var_BC_type.push_back(BC_type_dim3_min);
                                                  +
                                                  1038  newBC.var_BC_type.push_back(BC_type_dim3_max);
                                                  +
                                                  1039 
                                                  +
                                                  1040  newBC.var_BC_val.push_back(BC_value_dim1_min);
                                                  +
                                                  1041  newBC.var_BC_val.push_back(BC_value_dim1_max);
                                                  +
                                                  1042  newBC.var_BC_val.push_back(BC_value_dim2_min);
                                                  +
                                                  1043  newBC.var_BC_val.push_back(BC_value_dim2_max);
                                                  +
                                                  1044  newBC.var_BC_val.push_back(BC_value_dim3_min);
                                                  +
                                                  1045  newBC.var_BC_val.push_back(BC_value_dim3_max);
                                                  +
                                                  1046 
                                                  +
                                                  1047  BC_list.push_back(newBC);
                                                  +
                                                  1048 }
                                                  +
                                                  1049 
                                                  +
                                                  1050 // Input the boundary conditions for each face individually for 2D domains
                                                  +
                                                  1051 template <int dim>
                                                  +
                                                  1052 void generalizedProblem<dim>::inputBCs(int var, int component, std::string BC_type_dim1_min, double BC_value_dim1_min,
                                                  +
                                                  1053  std::string BC_type_dim1_max, double BC_value_dim1_max, std::string BC_type_dim2_min, double BC_value_dim2_min,
                                                  +
                                                  1054  std::string BC_type_dim2_max, double BC_value_dim2_max){
                                                  +
                                                  1055 
                                                  +
                                                  1056  // Validate input
                                                  +
                                                  1057  try{
                                                  +
                                                  1058  if ((BC_type_dim1_min == "PERIODIC") && (BC_type_dim1_max != "PERIODIC")){
                                                  +
                                                  1059  throw 0;
                                                  +
                                                  1060  }
                                                  +
                                                  1061  if ((BC_type_dim2_min == "PERIODIC") && (BC_type_dim2_max != "PERIODIC")){
                                                  +
                                                  1062  throw 0;
                                                  +
                                                  1063  }
                                                  +
                                                  1064  }
                                                  +
                                                  1065  catch (int e){
                                                  +
                                                  1066  if (e == 0){
                                                  +
                                                  1067  std::cout << "Error: For periodic BCs, both faces for a given direction must be set as periodic. "
                                                  +
                                                  1068  "Please check the BCs that are set in ICs_and_BCs.h." << std::endl;
                                                  +
                                                  1069  }
                                                  +
                                                  1070  abort();
                                                  +
                                                  1071  }
                                                  +
                                                  1072 
                                                  +
                                                  1073  varBCs<dim> newBC;
                                                  +
                                                  1074  newBC.var_BC_type.push_back(BC_type_dim1_min);
                                                  +
                                                  1075  newBC.var_BC_type.push_back(BC_type_dim1_max);
                                                  +
                                                  1076  newBC.var_BC_type.push_back(BC_type_dim2_min);
                                                  +
                                                  1077  newBC.var_BC_type.push_back(BC_type_dim2_max);
                                                  +
                                                  1078 
                                                  +
                                                  1079  newBC.var_BC_val.push_back(BC_value_dim1_min);
                                                  +
                                                  1080  newBC.var_BC_val.push_back(BC_value_dim1_max);
                                                  +
                                                  1081  newBC.var_BC_val.push_back(BC_value_dim2_min);
                                                  +
                                                  1082  newBC.var_BC_val.push_back(BC_value_dim2_max);
                                                  +
                                                  1083 
                                                  +
                                                  1084  BC_list.push_back(newBC);
                                                  +
                                                  1085 }
                                                  +
                                                  1086 
                                                  +
                                                  1087 // Input the boundary conditions for each face individually for 1D domains
                                                  +
                                                  1088 template <int dim>
                                                  +
                                                  1089 void generalizedProblem<dim>::inputBCs(int var, int component, std::string BC_type_dim1_min, double BC_value_dim1_min,
                                                  +
                                                  1090  std::string BC_type_dim1_max, double BC_value_dim1_max){
                                                  +
                                                  1091 
                                                  +
                                                  1092  // Validate input
                                                  +
                                                  1093  try{
                                                  +
                                                  1094  if ((BC_type_dim1_min == "PERIODIC") && (BC_type_dim1_max != "PERIODIC")){
                                                  +
                                                  1095  throw 0;
                                                  +
                                                  1096  }
                                                  +
                                                  1097 
                                                  +
                                                  1098  }
                                                  +
                                                  1099  catch (int e){
                                                  +
                                                  1100  if (e == 0){
                                                  +
                                                  1101  std::cout << "Error: For periodic BCs, both faces for a given direction must be set as periodic. "
                                                  +
                                                  1102  "Please check the BCs that are set in ICs_and_BCs.h." << std::endl;
                                                  +
                                                  1103  }
                                                  +
                                                  1104  abort();
                                                  +
                                                  1105  }
                                                  +
                                                  1106 
                                                  +
                                                  1107 
                                                  +
                                                  1108  varBCs<dim> newBC;
                                                  +
                                                  1109  newBC.var_BC_type.push_back(BC_type_dim1_min);
                                                  +
                                                  1110  newBC.var_BC_type.push_back(BC_type_dim1_max);
                                                  +
                                                  1111 
                                                  +
                                                  1112  newBC.var_BC_val.push_back(BC_value_dim1_min);
                                                  +
                                                  1113  newBC.var_BC_val.push_back(BC_value_dim1_max);
                                                  +
                                                  1114 
                                                  +
                                                  1115  BC_list.push_back(newBC);
                                                  +
                                                  1116 }
                                                  +
                                                  1117 
                                                  +
                                                  1118 // Input the boundary conditions when all faces have the same boundary condition
                                                  +
                                                  1119 template <int dim>
                                                  +
                                                  1120 void generalizedProblem<dim>::inputBCs(int var, int component, std::string BC_type, double BC_value){
                                                  +
                                                  1121 
                                                  +
                                                  1122  varBCs<dim> newBC;
                                                  +
                                                  1123  for (unsigned int face=0; face<(dim*2); face++){
                                                  +
                                                  1124  newBC.var_BC_type.push_back(BC_type);
                                                  +
                                                  1125  newBC.var_BC_val.push_back(BC_value);
                                                  +
                                                  1126  }
                                                  +
                                                  1127 
                                                  +
                                                  1128  BC_list.push_back(newBC);
                                                  +
                                                  1129 }
                                                  +
                                                  1130 
                                                  +
                                                  1131 // Based on the contents of BC_list, mark faces on the triangulation as periodic
                                                  +
                                                  1132 template <int dim>
                                                  +
                                                  1133 void generalizedProblem<dim>::setPeriodicity(){
                                                  +
                                                  1134  std::vector<GridTools::PeriodicFacePair<typename parallel::distributed::Triangulation<dim>::cell_iterator> > periodicity_vector;
                                                  +
                                                  1135  for (int i=0; i<dim; ++i){
                                                  +
                                                  1136  bool periodic_pair = false;
                                                  +
                                                  1137  for (unsigned int field_num=0; field_num < BC_list.size(); field_num++){
                                                  +
                                                  1138  if (BC_list[field_num].var_BC_type[2*i] == "PERIODIC"){
                                                  +
                                                  1139  periodic_pair = true;
                                                  +
                                                  1140  }
                                                  +
                                                  1141  }
                                                  +
                                                  1142  if (periodic_pair == true){
                                                  +
                                                  1143  GridTools::collect_periodic_faces(this->triangulation, /*b_id1*/ 2*i, /*b_id2*/ 2*i+1,
                                                  +
                                                  1144  /*direction*/ i, periodicity_vector);
                                                  +
                                                  1145  }
                                                  +
                                                  1146  }
                                                  +
                                                  1147 
                                                  +
                                                  1148  this->triangulation.add_periodicity(periodicity_vector);
                                                  +
                                                  1149  this->pcout << "periodic facepairs: " << periodicity_vector.size() << std::endl;
                                                  +
                                                  1150 }
                                                  1151 
                                                  -
                                                  1152  //seed nuclei
                                                  -
                                                  1153  unsigned int fieldIndex=this->getFieldIndex("n1");
                                                  -
                                                  1154  for (std::vector<nucleus>::iterator thisNuclei=nuclei.begin(); thisNuclei!=nuclei.end(); ++thisNuclei){
                                                  -
                                                  1155 
                                                  -
                                                  1156  dealii::Point<dim> center=thisNuclei->center;
                                                  -
                                                  1157  double radius=thisNuclei->radius;
                                                  -
                                                  1158  double seededTime=thisNuclei->seededTime;
                                                  -
                                                  1159  double seedingTime=thisNuclei->seedingTime;
                                                  -
                                                  1160  this->pcout << "times: " << t << " " << seededTime << " " << seedingTime << std::endl;
                                                  -
                                                  1161  //loop over all points in the domain
                                                  -
                                                  1162  for (typename std::map<dealii::types::global_dof_index, dealii::Point<dim> >::iterator it=support_points.begin(); it!=support_points.end(); ++it){
                                                  -
                                                  1163  unsigned int dof=it->first;
                                                  -
                                                  1164  //set only local owned values of the parallel vector
                                                  -
                                                  1165  if (n1->locally_owned_elements().is_element(dof)){
                                                  -
                                                  1166  dealii::Point<dim> nodePoint=it->second;
                                                  -
                                                  1167  //check conditions and seed nuclei
                                                  -
                                                  1168  double r=nodePoint.distance(center);
                                                  -
                                                  1169  if (r<=(2*radius)){
                                                  -
                                                  1170  if ((t>seededTime) && (t<(seededTime+seedingTime))){
                                                  -
                                                  1171  //this->pcout << "times: " << t << " " << seededTime << " " << seedingTime << std::endl;
                                                  -
                                                  1172  //(*n1)(dof)=0.5*(1.0-std::tanh((r-radius)/(dx)));
                                                  -
                                                  1173  (*n1)(dof)=0.5*(1.0-std::tanh((r-radius)/(0.4)));
                                                  -
                                                  1174  }
                                                  -
                                                  1175  }
                                                  -
                                                  1176  }
                                                  -
                                                  1177  }
                                                  -
                                                  1178  }
                                                  -
                                                  1179 }
                                                  -
                                                  1180 
                                                  +
                                                  1152 // Set constraints to enforce periodic boundary conditions
                                                  +
                                                  1153 template <int dim>
                                                  +
                                                  1154 void generalizedProblem<dim>::setPeriodicityConstraints(ConstraintMatrix * constraints, DoFHandler<dim>* dof_handler){
                                                  +
                                                  1155  std::vector<GridTools::PeriodicFacePair<typename DoFHandler<dim>::cell_iterator> > periodicity_vector;
                                                  +
                                                  1156  for (int i=0; i<dim; ++i){
                                                  +
                                                  1157  if (BC_list[this->currentFieldIndex].var_BC_type[2*i] == "PERIODIC"){
                                                  +
                                                  1158  GridTools::collect_periodic_faces(*dof_handler, /*b_id1*/ 2*i, /*b_id2*/ 2*i+1,
                                                  +
                                                  1159  /*direction*/ i, periodicity_vector);
                                                  +
                                                  1160  }
                                                  +
                                                  1161  }
                                                  +
                                                  1162  DoFTools::make_periodicity_constraints<DoFHandler<dim> >(periodicity_vector, *constraints);
                                                  +
                                                  1163 }
                                                  +
                                                  1164 
                                                  +
                                                  1165 // Determine which (if any) components of the current field have rigid body modes (i.e no Dirichlet BCs) if the
                                                  +
                                                  1166 // equation is elliptic
                                                  +
                                                  1167 template <int dim>
                                                  +
                                                  1168 void generalizedProblem<dim>::getComponentsWithRigidBodyModes(std::vector<int> & rigidBodyModeComponents){
                                                  +
                                                  1169 
                                                  +
                                                  1170  // Rigid body modes only matter for elliptic equations
                                                  +
                                                  1171  if (var_eq_type[this->currentFieldIndex] == "ELLIPTIC"){
                                                  +
                                                  1172 
                                                  +
                                                  1173  unsigned int num_components;
                                                  +
                                                  1174  // Get number of components of the field
                                                  +
                                                  1175  if (var_type[this->currentFieldIndex] == "SCALAR"){
                                                  +
                                                  1176  num_components = 1;
                                                  +
                                                  1177  }
                                                  +
                                                  1178  else {
                                                  +
                                                  1179  num_components = dim;
                                                  +
                                                  1180  }
                                                  +
                                                  1181 
                                                  +
                                                  1182  // Loop over each component and determine if it has a rigid body mode (i.e. no Dirichlet BCs)
                                                  +
                                                  1183  for (unsigned int component=0; component < num_components; component++){
                                                  +
                                                  1184  bool rigidBodyMode = true;
                                                  +
                                                  1185  for (unsigned int direction = 0; direction < 2*dim; direction++){
                                                  +
                                                  1186 
                                                  +
                                                  1187  if (BC_list[this->currentFieldIndex+component].var_BC_type[direction] == "DIRICHLET"){
                                                  +
                                                  1188  rigidBodyMode = false;
                                                  +
                                                  1189  }
                                                  +
                                                  1190 
                                                  +
                                                  1191  }
                                                  +
                                                  1192  // If the component has a rigid body mode, add it to the list
                                                  +
                                                  1193  if (rigidBodyMode == true){
                                                  +
                                                  1194  rigidBodyModeComponents.push_back(component);
                                                  +
                                                  1195  }
                                                  +
                                                  1196  }
                                                  +
                                                  1197  }
                                                  +
                                                  1198 }
                                                  +
                                                  1199 
                                                  +
                                                  1200 // This function was moved to the parent MatrixFreePDE class
                                                  +
                                                  1201 //// Set constraints to pin the solution if there are no Dirichlet BCs for a component of a variable in an elliptic equation
                                                  +
                                                  1202 //template <int dim>
                                                  +
                                                  1203 //void generalizedProblem<dim>::setRigidBodyModeConstraints( std::vector<int> rigidBodyModeComponents, ConstraintMatrix * constraints, DoFHandler<dim>* dof_handler){
                                                  +
                                                  1204 //
                                                  +
                                                  1205 // std::cout << "num of rigid body modes " << rigidBodyModeComponents.size() << std::endl;
                                                  +
                                                  1206 // if ( rigidBodyModeComponents.size() > 0 ){
                                                  +
                                                  1207 //
                                                  +
                                                  1208 // // Choose the point where the constraint will be placed. Must be the coordinates of a vertex.
                                                  +
                                                  1209 // dealii::Point<dim> target_point(0,0);
                                                  +
                                                  1210 //
                                                  +
                                                  1211 // unsigned int vertices_per_cell=GeometryInfo<dim>::vertices_per_cell;
                                                  +
                                                  1212 //
                                                  +
                                                  1213 // // Loop over each locally owned cell
                                                  +
                                                  1214 // typename DoFHandler<dim>::active_cell_iterator cell= dof_handler->begin_active(), endc = dof_handler->end();
                                                  +
                                                  1215 //
                                                  +
                                                  1216 // for (; cell!=endc; ++cell){
                                                  +
                                                  1217 // if (cell->is_locally_owned()){
                                                  +
                                                  1218 // for (unsigned int i=0; i<vertices_per_cell; ++i){
                                                  +
                                                  1219 //
                                                  +
                                                  1220 // // Check if the vertex is the target vertex
                                                  +
                                                  1221 // if (target_point.distance (cell->vertex(i)) < 1e-2 * cell->diameter()){
                                                  +
                                                  1222 //
                                                  +
                                                  1223 // // Loop through the list of components with rigid body modes and add an inhomogeneous constraint for each
                                                  +
                                                  1224 // for (unsigned int component_num = 0; component_num < rigidBodyModeComponents.size(); component_num++){
                                                  +
                                                  1225 // unsigned int nodeID=cell->vertex_dof_index(i,component_num);
                                                  +
                                                  1226 // constraints->add_line(nodeID);
                                                  +
                                                  1227 // constraints->set_inhomogeneity(nodeID,0.0);
                                                  +
                                                  1228 // }
                                                  +
                                                  1229 // }
                                                  +
                                                  1230 // }
                                                  +
                                                  1231 // }
                                                  +
                                                  1232 // }
                                                  +
                                                  1233 // }
                                                  +
                                                  1234 //}
                                                  +
                                                  1235 
                                                  +
                                                  1236 // =====================================================================
                                                  +
                                                  1237 // NUCLEATION FUNCTIONS
                                                  +
                                                  1238 // =====================================================================
                                                  +
                                                  1239 
                                                  +
                                                  1240 //structure representing each nucleus
                                                  +
                                                  1241 struct nucleus{
                                                  +
                                                  1242  unsigned int index;
                                                  +
                                                  1243  dealii::Point<problemDIM> center;
                                                  +
                                                  1244  double radius;
                                                  +
                                                  1245  double seededTime, seedingTime;
                                                  +
                                                  1246 };
                                                  +
                                                  1247 //vector of all nucleus seeded in the problem
                                                  +
                                                  1248 std::vector<nucleus> nuclei, localNuclei;
                                                  +
                                                  1249 
                                                  +
                                                  1250 //nucleation model implementation
                                                  +
                                                  1251 template <int dim>
                                                  +
                                                  1252 void generalizedProblem<dim>::modifySolutionFields()
                                                  +
                                                  1253 {
                                                  +
                                                  1254  //current time
                                                  +
                                                  1255  double t=this->currentTime;
                                                  +
                                                  1256  unsigned int inc=this->currentIncrement;
                                                  +
                                                  1257  double dx=spanX/( (double)subdivisionsX )/std::pow(2.0,refineFactor);
                                                  +
                                                  1258  double rand_val;
                                                  +
                                                  1259  int count = 0;
                                                  +
                                                  1260  //nucleation parameters
                                                  +
                                                  1261  double nRadius = 2.5; //spanX/20.0;
                                                  +
                                                  1262  double minDistBetwenNuclei=4*nRadius;
                                                  +
                                                  1263 
                                                  +
                                                  1264  unsigned int maxNumberNuclei=5; // doesn't do anything currently
                                                  +
                                                  1265 
                                                  +
                                                  1266  //get the list of node points in the domain
                                                  +
                                                  1267  std::map<dealii::types::global_dof_index, dealii::Point<dim> > support_points;
                                                  +
                                                  1268  dealii::DoFTools::map_dofs_to_support_points (dealii::MappingQ1<dim>(), *this->dofHandlersSet[0], support_points);
                                                  +
                                                  1269  //fields
                                                  +
                                                  1270  vectorType* n1=this->solutionSet[this->getFieldIndex("n1")];
                                                  +
                                                  1271  vectorType* n2=this->solutionSet[this->getFieldIndex("n2")];
                                                  +
                                                  1272  vectorType* n3=this->solutionSet[this->getFieldIndex("n3")];
                                                  +
                                                  1273  vectorType* c=this->solutionSet[this->getFieldIndex("c")];
                                                  +
                                                  1274  const double k1 = 0.0001; // nucleation probability constant
                                                  +
                                                  1275  const double k2 = 1.0; // nucleation probability constant
                                                  +
                                                  1276  const double c0 = 0.300; // baseline concentration?
                                                  +
                                                  1277  double J = 0.0;
                                                  +
                                                  1278  //delete the previous entries in the nuclei vector (old nucleus are still retained in the localNuclei vector)
                                                  +
                                                  1279  nuclei.clear();
                                                  +
                                                  1280 
                                                  +
                                                  1281 #ifndef c_matrix
                                                  +
                                                  1282  double c_matrix = 1.0e-6;
                                                  +
                                                  1283 #endif
                                                  +
                                                  1284 
                                                  +
                                                  1285  //populate localNuclei vector
                                                  +
                                                  1286  if (inc <= timeIncrements){
                                                  +
                                                  1287  nucleus* temp;
                                                  +
                                                  1288  //add nuclei based on concentration field values
                                                  +
                                                  1289  //loop over all points in the domain
                                                  +
                                                  1290  for (typename std::map<dealii::types::global_dof_index, dealii::Point<dim> >::iterator it=support_points.begin(); it!=support_points.end(); ++it){
                                                  +
                                                  1291  unsigned int dof=it->first;
                                                  +
                                                  1292  //set only local owned values of the parallel vector (eventually turn this into a separate function for each order parameter)
                                                  +
                                                  1293  if (n1->locally_owned_elements().is_element(dof)){
                                                  +
                                                  1294  dealii::Point<dim> nodePoint=it->second;
                                                  +
                                                  1295  double n1Value=(*n1)(dof);
                                                  +
                                                  1296  double n2Value=(*n2)(dof);
                                                  +
                                                  1297  double n3Value=(*n3)(dof);
                                                  +
                                                  1298  double cValue=(*c)(dof);
                                                  +
                                                  1299 
                                                  +
                                                  1300  rand_val = (double)rand()/(RAND_MAX);
                                                  +
                                                  1301 
                                                  +
                                                  1302  if ((t > 1000000000*timeStep) || (n1Value+n2Value+n3Value > 1.0e-6) || (cValue <= 0.0)) {
                                                  +
                                                  1303  J = 0;
                                                  +
                                                  1304  }
                                                  +
                                                  1305  else{
                                                  +
                                                  1306  J = cValue/c_matrix * dx*dx/((double)spanX * (double)spanY) * 0.01; // Only true in 2D!
                                                  +
                                                  1307  }
                                                  +
                                                  1308 
                                                  +
                                                  1309  if (rand_val <= J){
                                                  +
                                                  1310  bool isClose=false;
                                                  +
                                                  1311  for (std::vector<nucleus>::iterator thisNuclei=localNuclei.begin(); thisNuclei!=localNuclei.end(); ++thisNuclei){
                                                  +
                                                  1312  if (thisNuclei->center.distance(nodePoint)<minDistBetwenNuclei){
                                                  +
                                                  1313  isClose=true;
                                                  +
                                                  1314  }
                                                  +
                                                  1315  }
                                                  +
                                                  1316 
                                                  +
                                                  1317  if (!isClose){
                                                  +
                                                  1318  temp = new nucleus;
                                                  +
                                                  1319  temp->index=localNuclei.size();
                                                  +
                                                  1320  temp->center=nodePoint;
                                                  +
                                                  1321  temp->radius=nRadius;
                                                  +
                                                  1322  temp->seededTime=t;
                                                  +
                                                  1323  temp->seedingTime = 10000.0*timeStep;
                                                  +
                                                  1324  localNuclei.push_back(*temp);
                                                  +
                                                  1325  }
                                                  +
                                                  1326  }
                                                  +
                                                  1327  }
                                                  +
                                                  1328  }
                                                  +
                                                  1329 
                                                  +
                                                  1330 
                                                  +
                                                  1331  //filter nuclei by comparing with other processors
                                                  +
                                                  1332  int numProcs=Utilities::MPI::n_mpi_processes(MPI_COMM_WORLD);
                                                  +
                                                  1333  int thisProc=Utilities::MPI::this_mpi_process(MPI_COMM_WORLD);
                                                  +
                                                  1334  std::vector<int> numNucleiInProcs(numProcs, 0);
                                                  +
                                                  1335  //send nuclei information to processor 0
                                                  +
                                                  1336  int numNuclei=localNuclei.size();
                                                  +
                                                  1337  //send information about number of nuclei to processor 0
                                                  +
                                                  1338  if (thisProc!=0){
                                                  +
                                                  1339  MPI_Send(&numNuclei, 1, MPI_INT, 0, 0, MPI_COMM_WORLD);
                                                  +
                                                  1340  }
                                                  +
                                                  1341  else{
                                                  +
                                                  1342  numNucleiInProcs[0]=numNuclei;
                                                  +
                                                  1343  for (int proc=1; proc<numProcs; proc++){
                                                  +
                                                  1344  MPI_Recv(&numNucleiInProcs[proc], 1, MPI_INT, proc, 0, MPI_COMM_WORLD, MPI_STATUS_IGNORE);
                                                  +
                                                  1345  }
                                                  +
                                                  1346  }
                                                  +
                                                  1347  MPI_Barrier(MPI_COMM_WORLD);
                                                  +
                                                  1348  //filter nuclei in processor zero
                                                  +
                                                  1349  //receive nuclei info from all processors
                                                  +
                                                  1350  if (thisProc!=0){
                                                  +
                                                  1351  if (numNuclei>0){
                                                  +
                                                  1352  std::vector<double> tempData((dim+3)*numNuclei);
                                                  +
                                                  1353  unsigned int i=0;
                                                  +
                                                  1354  for (std::vector<nucleus>::iterator thisNuclei=localNuclei.begin(); thisNuclei!=localNuclei.end(); ++thisNuclei){
                                                  +
                                                  1355  tempData[i*(dim+3)]=thisNuclei->radius;
                                                  +
                                                  1356  tempData[i*(dim+3)+1]=thisNuclei->seededTime;
                                                  +
                                                  1357  tempData[i*(dim+3)+2]=thisNuclei->seedingTime;
                                                  +
                                                  1358  for (unsigned int j=0; j<dim; j++) tempData[i*(dim+3)+3+j]=thisNuclei->center[j];
                                                  +
                                                  1359  i++;
                                                  +
                                                  1360  }
                                                  +
                                                  1361  MPI_Send(&tempData[0], numNuclei*(dim+3), MPI_DOUBLE, 0, 1, MPI_COMM_WORLD);
                                                  +
                                                  1362  }
                                                  +
                                                  1363  }
                                                  +
                                                  1364  else{
                                                  +
                                                  1365  //temporary array to store all the nuclei
                                                  +
                                                  1366  std::vector<std::vector<double>*> tempNuceli(numProcs);
                                                  +
                                                  1367  for (int proc=0; proc<numProcs; proc++) {
                                                  +
                                                  1368  std::vector<double>* temp=new std::vector<double>(numNucleiInProcs[proc]*(dim+3));
                                                  +
                                                  1369  if (numNucleiInProcs[proc]>0){
                                                  +
                                                  1370  if (proc==0){
                                                  +
                                                  1371  unsigned int i=0;
                                                  +
                                                  1372  for (std::vector<nucleus>::iterator thisNuclei=localNuclei.begin(); thisNuclei!=localNuclei.end(); ++thisNuclei){
                                                  +
                                                  1373  (*temp)[i*(dim+3)]=thisNuclei->radius;
                                                  +
                                                  1374  (*temp)[i*(dim+3)+1]=thisNuclei->seededTime;
                                                  +
                                                  1375  (*temp)[i*(dim+3)+2]=thisNuclei->seedingTime;
                                                  +
                                                  1376  for (unsigned int j=0; j<dim; j++) (*temp)[i*(dim+3)+3+j]=thisNuclei->center[j];
                                                  +
                                                  1377  i++;
                                                  +
                                                  1378  }
                                                  +
                                                  1379  }
                                                  +
                                                  1380  else{
                                                  +
                                                  1381  MPI_Recv(&((*temp)[0]), numNucleiInProcs[proc]*(dim+3), MPI_DOUBLE, proc, 1, MPI_COMM_WORLD, MPI_STATUS_IGNORE);
                                                  +
                                                  1382  }
                                                  +
                                                  1383  tempNuceli[proc]=temp;
                                                  +
                                                  1384  }
                                                  +
                                                  1385  }
                                                  +
                                                  1386 
                                                  +
                                                  1387  //filter the nuclei and add to nuclei vector in processor zero
                                                  +
                                                  1388  for (int proc1=0; proc1<numProcs; proc1++) {
                                                  +
                                                  1389  for (int i1=0; i1<numNucleiInProcs[proc1]; i1++){
                                                  +
                                                  1390  double rad1=(*tempNuceli[proc1])[i1*(dim+3)];
                                                  +
                                                  1391  double time1=(*tempNuceli[proc1])[i1*(dim+3)+1];
                                                  +
                                                  1392  double seedingTime1=(*tempNuceli[proc1])[i1*(dim+3)+2];
                                                  +
                                                  1393  dealii::Point<dim> center1;
                                                  +
                                                  1394  for (unsigned int j1=0; j1<dim; j1++) {
                                                  +
                                                  1395  center1[j1]=(*tempNuceli[proc1])[i1*(dim+3)+3+j1];
                                                  +
                                                  1396  }
                                                  +
                                                  1397  bool addNuclei=true;
                                                  +
                                                  1398  //check if this nuceli present in any other processor
                                                  +
                                                  1399  for (int proc2=0; proc2<numProcs; proc2++) {
                                                  +
                                                  1400  if (proc1!=proc2){
                                                  +
                                                  1401  for (int i2=0; i2<numNucleiInProcs[proc2]; i2++){
                                                  +
                                                  1402  double rad2=(*tempNuceli[proc2])[i2*(dim+3)];
                                                  +
                                                  1403  double time2=(*tempNuceli[proc2])[i2*(dim+3)+1];
                                                  +
                                                  1404  dealii::Point<dim> center2;
                                                  +
                                                  1405  for (unsigned int j2=0; j2<dim; j2++) center2(j2)=(*tempNuceli[proc2])[i2*(dim+3)+3+j2];
                                                  +
                                                  1406  if ((center1.distance(center2)<=minDistBetwenNuclei) && (time1>=time2)){
                                                  +
                                                  1407  addNuclei=false;
                                                  +
                                                  1408  break;
                                                  +
                                                  1409  }
                                                  +
                                                  1410  }
                                                  +
                                                  1411  if (!addNuclei) {break;}
                                                  +
                                                  1412  }
                                                  +
                                                  1413  }
                                                  +
                                                  1414  if (addNuclei){
                                                  +
                                                  1415  temp = new nucleus;
                                                  +
                                                  1416  temp->index=nuclei.size();
                                                  +
                                                  1417  temp->radius=rad1;
                                                  +
                                                  1418  temp->seededTime=time1;
                                                  +
                                                  1419  temp->seedingTime=seedingTime1;
                                                  +
                                                  1420  temp->center=center1;
                                                  +
                                                  1421  nuclei.push_back(*temp);
                                                  +
                                                  1422  }
                                                  +
                                                  1423  }
                                                  +
                                                  1424  }
                                                  +
                                                  1425  }
                                                  +
                                                  1426  MPI_Barrier(MPI_COMM_WORLD);
                                                  +
                                                  1427 
                                                  +
                                                  1428  //disperse nuclei to all other processors
                                                  +
                                                  1429  unsigned int numGlobalNuclei;
                                                  +
                                                  1430  if (Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)==0) {numGlobalNuclei=nuclei.size();}
                                                  +
                                                  1431  MPI_Bcast(&numGlobalNuclei, 1, MPI_INT, 0, MPI_COMM_WORLD);
                                                  +
                                                  1432  this->pcout << "total number of nuclei currently seeded : " << numGlobalNuclei << std::endl;
                                                  +
                                                  1433  MPI_Barrier(MPI_COMM_WORLD);
                                                  +
                                                  1434  //
                                                  +
                                                  1435  std::vector<double> temp2(numGlobalNuclei*(dim+3));
                                                  +
                                                  1436  if (Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)==0){
                                                  +
                                                  1437  unsigned int i=0;
                                                  +
                                                  1438  for (std::vector<nucleus>::iterator thisNuclei=nuclei.begin(); thisNuclei!=nuclei.end(); ++thisNuclei){
                                                  +
                                                  1439  temp2[i*(dim+3)]=thisNuclei->radius;
                                                  +
                                                  1440  temp2[i*(dim+3)+1]=thisNuclei->seededTime;
                                                  +
                                                  1441  temp2[i*(dim+3)+2]=thisNuclei->seedingTime;
                                                  +
                                                  1442  for (unsigned int j=0; j<dim; j++) temp2[i*(dim+3)+3+j]=thisNuclei->center[j];
                                                  +
                                                  1443  i++;
                                                  +
                                                  1444  }
                                                  +
                                                  1445  }
                                                  +
                                                  1446  MPI_Bcast(&temp2[0], numGlobalNuclei*(dim+3), MPI_DOUBLE, 0, MPI_COMM_WORLD);
                                                  +
                                                  1447  MPI_Barrier(MPI_COMM_WORLD);
                                                  +
                                                  1448  //receive all nuclei
                                                  +
                                                  1449  if (Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)!=0){
                                                  +
                                                  1450  for(unsigned int i=0; i<numGlobalNuclei; i++){
                                                  +
                                                  1451  temp = new nucleus;
                                                  +
                                                  1452  temp->index=nuclei.size();
                                                  +
                                                  1453  temp->radius=temp2[i*(dim+3)];
                                                  +
                                                  1454  temp->seededTime=temp2[i*(dim+3)+1];
                                                  +
                                                  1455  temp->seedingTime=temp2[i*(dim+3)+2];
                                                  +
                                                  1456  dealii::Point<dim> tempCenter;
                                                  +
                                                  1457  for (unsigned int j=0; j<dim; j++) tempCenter[j]=temp2[i*(dim+3)+3+j];
                                                  +
                                                  1458  temp->center=tempCenter;
                                                  +
                                                  1459  nuclei.push_back(*temp);
                                                  +
                                                  1460  }
                                                  +
                                                  1461  }
                                                  +
                                                  1462  }
                                                  +
                                                  1463 
                                                  +
                                                  1464  //seed nuclei
                                                  +
                                                  1465  unsigned int fieldIndex=this->getFieldIndex("n1");
                                                  +
                                                  1466  for (std::vector<nucleus>::iterator thisNuclei=nuclei.begin(); thisNuclei!=nuclei.end(); ++thisNuclei){
                                                  +
                                                  1467 
                                                  +
                                                  1468  dealii::Point<dim> center=thisNuclei->center;
                                                  +
                                                  1469  double radius=thisNuclei->radius;
                                                  +
                                                  1470  double seededTime=thisNuclei->seededTime;
                                                  +
                                                  1471  double seedingTime=thisNuclei->seedingTime;
                                                  +
                                                  1472  this->pcout << "times: " << t << " " << seededTime << " " << seedingTime << std::endl;
                                                  +
                                                  1473  //loop over all points in the domain
                                                  +
                                                  1474  for (typename std::map<dealii::types::global_dof_index, dealii::Point<dim> >::iterator it=support_points.begin(); it!=support_points.end(); ++it){
                                                  +
                                                  1475  unsigned int dof=it->first;
                                                  +
                                                  1476  //set only local owned values of the parallel vector
                                                  +
                                                  1477  if (n1->locally_owned_elements().is_element(dof)){
                                                  +
                                                  1478  dealii::Point<dim> nodePoint=it->second;
                                                  +
                                                  1479  //check conditions and seed nuclei
                                                  +
                                                  1480  double r=nodePoint.distance(center);
                                                  +
                                                  1481  if (r<=(2*radius)){
                                                  +
                                                  1482  if ((t>seededTime) && (t<(seededTime+seedingTime))){
                                                  +
                                                  1483  //this->pcout << "times: " << t << " " << seededTime << " " << seedingTime << std::endl;
                                                  +
                                                  1484  //(*n1)(dof)=0.5*(1.0-std::tanh((r-radius)/(dx)));
                                                  +
                                                  1485  (*n1)(dof)=0.5*(1.0-std::tanh((r-radius)/(0.4)));
                                                  +
                                                  1486  }
                                                  +
                                                  1487  }
                                                  +
                                                  1488  }
                                                  +
                                                  1489  }
                                                  +
                                                  1490  }
                                                  +
                                                  1491 }
                                                  +
                                                  1492 
                                                  variable_eq_type
                                                  #define variable_eq_type
                                                  Definition: equations.h:13
                                                  variable_info::is_scalar
                                                  bool is_scalar
                                                  Definition: model_variables.h:47
                                                  modelResidual
                                                  Definition: model_variables.h:25
                                                  -
                                                  MatrixFreePDE::pcout
                                                  ConditionalOStream pcout
                                                  Definition: matrixFreePDE.h:258
                                                  +
                                                  MatrixFreePDE::pcout
                                                  ConditionalOStream pcout
                                                  Definition: matrixFreePDE.h:271
                                                  need_val
                                                  #define need_val
                                                  Definition: equations.h:16
                                                  -
                                                  generalizedProblem::var_type
                                                  std::vector< std::string > var_type
                                                  Definition: generalized_model.h:53
                                                  +
                                                  generalizedProblem::var_type
                                                  std::vector< std::string > var_type
                                                  Definition: generalized_model.h:54
                                                  SCALAR
                                                  Definition: fields.h:6
                                                  ORTHOTROPIC
                                                  Definition: anisotropy.h:30
                                                  -
                                                  vectorBCFunction::vector_value_list
                                                  virtual void vector_value_list(const std::vector< Point< dim >> &points, std::vector< Vector< double >> &value_list) const
                                                  Definition: generalized_model_functions.h:758
                                                  nucleus::seededTime
                                                  double seededTime
                                                  Definition: coupledCHACMechanics.h:667
                                                  -
                                                  generalizedProblem::varInfoListLHS
                                                  std::vector< variable_info< dim > > varInfoListLHS
                                                  Definition: generalized_model.h:82
                                                  -
                                                  generalizedProblem::gradient_residual_LHS
                                                  std::vector< bool > gradient_residual_LHS
                                                  Definition: generalized_model.h:66
                                                  -
                                                  generalizedProblem::var_name
                                                  std::vector< std::string > var_name
                                                  Definition: generalized_model.h:52
                                                  +
                                                  generalizedProblem::varInfoListLHS
                                                  std::vector< variable_info< dim > > varInfoListLHS
                                                  Definition: generalized_model.h:83
                                                  +
                                                  generalizedProblem::gradient_residual_LHS
                                                  std::vector< bool > gradient_residual_LHS
                                                  Definition: generalized_model.h:67
                                                  +
                                                  sfts_linear1
                                                  double sfts_linear1[3][3]
                                                  Definition: equations.h:67
                                                  +
                                                  generalizedProblem::var_name
                                                  std::vector< std::string > var_name
                                                  Definition: generalized_model.h:53
                                                  MatrixFreePDE
                                                  Definition: matrixFreePDE.h:85
                                                  need_val_residual
                                                  #define need_val_residual
                                                  Definition: equations.h:22
                                                  nucleus::seedingTime
                                                  double seedingTime
                                                  Definition: coupledCHACMechanics.h:667
                                                  -
                                                  generalizedProblem::need_value
                                                  std::vector< bool > need_value
                                                  Definition: generalized_model.h:56
                                                  +
                                                  generalizedProblem::need_value
                                                  std::vector< bool > need_value
                                                  Definition: generalized_model.h:57
                                                  finiteElementDegree
                                                  #define finiteElementDegree
                                                  Definition: defaultValues.h:32
                                                  PARABOLIC
                                                  Definition: fields.h:7
                                                  -
                                                  generalizedProblem::adaptiveRefine
                                                  void adaptiveRefine(unsigned int currentIncrement)
                                                  Definition: generalized_model_functions.h:596
                                                  -
                                                  generalizedProblem::buildFields
                                                  void buildFields()
                                                  Definition: generalized_model_functions.h:674
                                                  -
                                                  generalizedProblem::getRHS
                                                  void getRHS(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range) const
                                                  Definition: generalized_model_functions.h:185
                                                  +
                                                  generalizedProblem::adaptiveRefine
                                                  void adaptiveRefine(unsigned int currentIncrement)
                                                  Definition: generalized_model_functions.h:617
                                                  +
                                                  generalizedProblem::buildFields
                                                  void buildFields()
                                                  Definition: generalized_model_functions.h:705
                                                  +
                                                  generalizedProblem::getRHS
                                                  void getRHS(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range) const
                                                  Definition: generalized_model_functions.h:206
                                                  refineCriterionFields
                                                  #define refineCriterionFields
                                                  Definition: parameters.h:43
                                                  -
                                                  generalizedProblem::getEnergy
                                                  void getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)
                                                  Definition: generalized_model_functions.h:450
                                                  -
                                                  generalizedProblem::CIJ_list
                                                  std::vector< dealii::Tensor< 2, CIJ_tensor_size, dealii::VectorizedArray< double > > > CIJ_list
                                                  Definition: generalized_model.h:70
                                                  +
                                                  generalizedProblem::getEnergy
                                                  void getEnergy(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range)
                                                  Definition: generalized_model_functions.h:471
                                                  +
                                                  generalizedProblem::CIJ_list
                                                  std::vector< dealii::Tensor< 2, CIJ_tensor_size, dealii::VectorizedArray< double > > > CIJ_list
                                                  Definition: generalized_model.h:71
                                                  skipRemeshingSteps
                                                  #define skipRemeshingSteps
                                                  Definition: parameters.h:50
                                                  modelResidual::scalarGradResidual
                                                  scalargradType scalarGradResidual
                                                  Definition: model_variables.h:30
                                                  -
                                                  generalizedProblem::need_hessian_LHS
                                                  std::vector< bool > need_hessian_LHS
                                                  Definition: generalized_model.h:64
                                                  -
                                                  vectorBCFunction
                                                  Definition: generalized_model_functions.h:734
                                                  +
                                                  generalizedProblem::need_hessian_LHS
                                                  std::vector< bool > need_hessian_LHS
                                                  Definition: generalized_model.h:65
                                                  +
                                                  vectorBCFunction
                                                  Definition: generalized_model_functions.h:888
                                                  nucleus::center
                                                  dealii::Point< problemDIM > center
                                                  Definition: coupledCHACMechanics.h:665
                                                  varBCs
                                                  Definition: generalized_model.h:12
                                                  -
                                                  generalizedProblem::need_hessian
                                                  std::vector< bool > need_hessian
                                                  Definition: generalized_model.h:58
                                                  +
                                                  generalizedProblem::need_hessian
                                                  std::vector< bool > need_hessian
                                                  Definition: generalized_model.h:59
                                                  spanX
                                                  #define spanX
                                                  Definition: defaultValues.h:12
                                                  variable_info::scalar_or_vector_index
                                                  unsigned int scalar_or_vector_index
                                                  Definition: model_variables.h:48
                                                  generalizedProblem::generalizedProblem
                                                  generalizedProblem()
                                                  Definition: generalized_model_functions.h:6
                                                  +
                                                  c_matrix
                                                  #define c_matrix
                                                  varBCs::var_BC_val
                                                  std::vector< double > var_BC_val
                                                  Definition: generalized_model.h:17
                                                  +
                                                  timeFinal
                                                  #define timeFinal
                                                  Definition: parameters.h:34
                                                  +
                                                  loadICs
                                                  #define loadICs
                                                  +
                                                  generalizedProblem::setPeriodicity
                                                  void setPeriodicity()
                                                  Definition: generalized_model_functions.h:1133
                                                  variable_info
                                                  Definition: model_variables.h:45
                                                  ANISOTROPIC
                                                  Definition: anisotropy.h:30
                                                  -
                                                  generalizedProblem::markBoundaries
                                                  void markBoundaries()
                                                  Definition: generalized_model_functions.h:823
                                                  +
                                                  generalizedProblem::markBoundaries
                                                  void markBoundaries()
                                                  Definition: generalized_model_functions.h:977
                                                  vectorType
                                                  dealii::parallel::distributed::Vector< double > vectorType
                                                  Definition: matrixFreePDE.h:23
                                                  modelResidual::vectorGradResidual
                                                  vectorgradType vectorGradResidual
                                                  Definition: model_variables.h:33
                                                  need_grad_residual
                                                  #define need_grad_residual
                                                  Definition: equations.h:23
                                                  -
                                                  generalizedProblem::value_residual
                                                  std::vector< bool > value_residual
                                                  Definition: generalized_model.h:59
                                                  +
                                                  generalizedProblem::value_residual
                                                  std::vector< bool > value_residual
                                                  Definition: generalized_model.h:60
                                                  Field
                                                  Definition: fields.h:10
                                                  spanY
                                                  #define spanY
                                                  Definition: defaultValues.h:17
                                                  -
                                                  vectorBCFunction::vector_value
                                                  virtual void vector_value(const Point< dim > &p, Vector< double > &values) const
                                                  Definition: generalized_model_functions.h:750
                                                  +
                                                  vectorBCFunction::vector_value
                                                  virtual void vector_value(const Point< dim > &p, Vector< double > &values) const
                                                  Definition: generalized_model_functions.h:904
                                                  need_grad_LHS
                                                  #define need_grad_LHS
                                                  Definition: equations.h:28
                                                  refineWindowMax
                                                  #define refineWindowMax
                                                  Definition: parameters.h:46
                                                  varBCs::var_BC_type
                                                  std::vector< std::string > var_BC_type
                                                  Definition: generalized_model.h:16
                                                  -
                                                  sfts_linear1
                                                  double sfts_linear1[3][3]
                                                  Definition: residuals.h:28
                                                  -
                                                  generalizedProblem::applyDirichletBCs
                                                  void applyDirichletBCs()
                                                  Definition: generalized_model_functions.h:766
                                                  -
                                                  sfts_linear3
                                                  double sfts_linear3[3][3]
                                                  Definition: residuals.h:34
                                                  -
                                                  vectorBCFunction::BC_values
                                                  const std::vector< double > BC_values
                                                  Definition: generalized_model_functions.h:743
                                                  +
                                                  generalizedProblem::applyDirichletBCs
                                                  void applyDirichletBCs()
                                                  Definition: generalized_model_functions.h:920
                                                  +
                                                  MatrixFreePDE::dtValue
                                                  double dtValue
                                                  Definition: matrixFreePDE.h:267
                                                  +
                                                  vectorBCFunction::BC_values
                                                  const std::vector< double > BC_values
                                                  Definition: generalized_model_functions.h:897
                                                  refineFactor
                                                  #define refineFactor
                                                  Definition: defaultValues.h:27
                                                  ELLIPTIC
                                                  Definition: fields.h:7
                                                  variable_type
                                                  #define variable_type
                                                  Definition: equations.h:12
                                                  need_grad
                                                  #define need_grad
                                                  Definition: equations.h:17
                                                  modelResidual::vectorValueResidual
                                                  vectorvalueType vectorValueResidual
                                                  Definition: model_variables.h:32
                                                  modelResidual::scalarValueResidual
                                                  scalarvalueType scalarValueResidual
                                                  Definition: model_variables.h:29
                                                  +
                                                  loadFileName
                                                  #define loadFileName
                                                  ISOTROPIC
                                                  Definition: anisotropy.h:30
                                                  -
                                                  nuclei
                                                  std::vector< nucleus > nuclei
                                                  Definition: generalized_model_functions.h:936
                                                  -
                                                  generalizedProblem::c_dependent_misfit
                                                  bool c_dependent_misfit
                                                  Definition: generalized_model.h:72
                                                  -
                                                  generalizedProblem::computeIntegral
                                                  void computeIntegral(double &integratedField)
                                                  Definition: generalized_model_functions.h:554
                                                  -
                                                  generalizedProblem::inputBCs
                                                  void inputBCs(int var, int component, std::string BC_type_dim1_min, double BC_value_dim1_min, std::string BC_type_dim1_max, double BC_value_dim1_max, std::string BC_type_dim2_min, double BC_value_dim2_min, std::string BC_type_dim2_max, double BC_value_dim2_max, std::string BC_type_dim3_min, double BC_value_dim3_min, std::string BC_type_dim3_max, double BC_value_dim3_max)
                                                  Definition: generalized_model_functions.h:853
                                                  +
                                                  MatrixFreePDE::totalIncrements
                                                  unsigned int totalIncrements
                                                  Definition: matrixFreePDE.h:268
                                                  +
                                                  nuclei
                                                  std::vector< nucleus > nuclei
                                                  Definition: generalized_model_functions.h:1248
                                                  +
                                                  generalizedProblem::c_dependent_misfit
                                                  bool c_dependent_misfit
                                                  Definition: generalized_model.h:73
                                                  +
                                                  generalizedProblem::computeIntegral
                                                  void computeIntegral(double &integratedField)
                                                  Definition: generalized_model_functions.h:575
                                                  +
                                                  generalizedProblem::inputBCs
                                                  void inputBCs(int var, int component, std::string BC_type_dim1_min, double BC_value_dim1_min, std::string BC_type_dim1_max, double BC_value_dim1_max, std::string BC_type_dim2_min, double BC_value_dim2_min, std::string BC_type_dim2_max, double BC_value_dim2_max, std::string BC_type_dim3_min, double BC_value_dim3_min, std::string BC_type_dim3_max, double BC_value_dim3_max)
                                                  Definition: generalized_model_functions.h:1007
                                                  +
                                                  MatrixFreePDE::finalTime
                                                  double finalTime
                                                  Definition: matrixFreePDE.h:267
                                                  nucleus::radius
                                                  double radius
                                                  Definition: coupledCHACMechanics.h:666
                                                  elasticityModel
                                                  elasticityModel
                                                  Definition: anisotropy.h:30
                                                  +
                                                  loadSerialFile
                                                  #define loadSerialFile
                                                  +
                                                  generalizedProblem::getComponentsWithRigidBodyModes
                                                  void getComponentsWithRigidBodyModes(std::vector< int > &)
                                                  Definition: generalized_model_functions.h:1168
                                                  variable_name
                                                  #define variable_name
                                                  Definition: equations.h:11
                                                  -
                                                  generalizedProblem::value_residual_LHS
                                                  std::vector< bool > value_residual_LHS
                                                  Definition: generalized_model.h:65
                                                  +
                                                  sfts_linear2
                                                  double sfts_linear2[3][3]
                                                  Definition: equations.h:70
                                                  +
                                                  generalizedProblem::value_residual_LHS
                                                  std::vector< bool > value_residual_LHS
                                                  Definition: generalized_model.h:66
                                                  typeScalar
                                                  dealii::FEEvaluation< problemDIM, finiteElementDegree, finiteElementDegree+1, 1, double > typeScalar
                                                  Definition: matrixFreePDE.h:27
                                                  -
                                                  localNuclei
                                                  std::vector< nucleus > localNuclei
                                                  Definition: generalized_model_functions.h:936
                                                  -
                                                  generalizedProblem::num_var_LHS
                                                  unsigned int num_var_LHS
                                                  Definition: generalized_model.h:81
                                                  +
                                                  localNuclei
                                                  std::vector< nucleus > localNuclei
                                                  Definition: generalized_model_functions.h:1248
                                                  +
                                                  generalizedProblem::num_var_LHS
                                                  unsigned int num_var_LHS
                                                  Definition: generalized_model.h:82
                                                  subdivisionsX
                                                  #define subdivisionsX
                                                  Definition: parameters.h:14
                                                  need_hess
                                                  #define need_hess
                                                  Definition: equations.h:18
                                                  VECTOR
                                                  Definition: fields.h:6
                                                  need_grad_residual_LHS
                                                  #define need_grad_residual_LHS
                                                  Definition: equations.h:35
                                                  InitialCondition
                                                  Definition: ICs_and_BCs.h:2
                                                  -
                                                  generalizedProblem::applyInitialConditions
                                                  void applyInitialConditions()
                                                  Definition: generalized_model_functions.h:711
                                                  +
                                                  generalizedProblem::applyInitialConditions
                                                  void applyInitialConditions()
                                                  Definition: generalized_model_functions.h:777
                                                  timeStep
                                                  #define timeStep
                                                  +
                                                  generalizedProblem::setPeriodicityConstraints
                                                  void setPeriodicityConstraints(ConstraintMatrix *, DoFHandler< dim > *)
                                                  Definition: generalized_model_functions.h:1154
                                                  nucleus::index
                                                  unsigned int index
                                                  Definition: coupledCHACMechanics.h:664
                                                  -
                                                  InitialConditionVec
                                                  Definition: ICs_and_BCs.h:43
                                                  -
                                                  generalizedProblem::varInfoListRHS
                                                  std::vector< variable_info< dim > > varInfoListRHS
                                                  Definition: generalized_model.h:77
                                                  +
                                                  maxRefinementLevel
                                                  #define maxRefinementLevel
                                                  Definition: parameters.h:22
                                                  +
                                                  InitialConditionVec
                                                  Definition: ICs_and_BCs.h:44
                                                  +
                                                  generalizedProblem::varInfoListRHS
                                                  std::vector< variable_info< dim > > varInfoListRHS
                                                  Definition: generalized_model.h:78
                                                  MaterialConstants
                                                  #define MaterialConstants
                                                  Definition: equations.h:48
                                                  -
                                                  generalizedProblem::adaptiveRefineCriterion
                                                  void adaptiveRefineCriterion()
                                                  Definition: generalized_model_functions.h:606
                                                  -
                                                  generalizedProblem::var_eq_type
                                                  std::vector< std::string > var_eq_type
                                                  Definition: generalized_model.h:54
                                                  +
                                                  generalizedProblem::adaptiveRefineCriterion
                                                  void adaptiveRefineCriterion()
                                                  Definition: generalized_model_functions.h:637
                                                  +
                                                  generalizedProblem::var_eq_type
                                                  std::vector< std::string > var_eq_type
                                                  Definition: generalized_model.h:55
                                                  timeIncrements
                                                  #define timeIncrements
                                                  variable_info::global_field_index
                                                  unsigned int global_field_index
                                                  Definition: model_variables.h:50
                                                  spanZ
                                                  #define spanZ
                                                  Definition: defaultValues.h:22
                                                  -
                                                  generalizedProblem::modifySolutionFields
                                                  void modifySolutionFields()
                                                  Definition: generalized_model_functions.h:940
                                                  -
                                                  generalizedProblem::gradient_residual
                                                  std::vector< bool > gradient_residual
                                                  Definition: generalized_model.h:60
                                                  +
                                                  loadFieldName
                                                  #define loadFieldName
                                                  +
                                                  generalizedProblem::modifySolutionFields
                                                  void modifySolutionFields()
                                                  Definition: generalized_model_functions.h:1252
                                                  +
                                                  generalizedProblem::gradient_residual
                                                  std::vector< bool > gradient_residual
                                                  Definition: generalized_model.h:61
                                                  variable_info::global_var_index
                                                  unsigned int global_var_index
                                                  Definition: model_variables.h:49
                                                  need_val_LHS
                                                  #define need_val_LHS
                                                  Definition: equations.h:27
                                                  -
                                                  generalizedProblem::need_value_LHS
                                                  std::vector< bool > need_value_LHS
                                                  Definition: generalized_model.h:62
                                                  +
                                                  generalizedProblem::need_value_LHS
                                                  std::vector< bool > need_value_LHS
                                                  Definition: generalized_model.h:63
                                                  TRANSVERSE
                                                  Definition: anisotropy.h:30
                                                  -
                                                  vectorBCFunction::vectorBCFunction
                                                  vectorBCFunction(const std::vector< double > BC_values)
                                                  Definition: generalized_model_functions.h:747
                                                  +
                                                  vectorBCFunction::vectorBCFunction
                                                  vectorBCFunction(const std::vector< double > BC_values)
                                                  Definition: generalized_model_functions.h:901
                                                  num_var
                                                  #define num_var
                                                  Definition: equations.h:7
                                                  refineWindowMin
                                                  #define refineWindowMin
                                                  Definition: parameters.h:47
                                                  -
                                                  generalizedProblem::getLHS
                                                  void getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const
                                                  Definition: generalized_model_functions.h:311
                                                  +
                                                  generalizedProblem::getLHS
                                                  void getLHS(const MatrixFree< dim, double > &data, vectorType &dst, const vectorType &src, const std::pair< unsigned int, unsigned int > &cell_range) const
                                                  Definition: generalized_model_functions.h:332
                                                  need_val_residual_LHS
                                                  #define need_val_residual_LHS
                                                  Definition: equations.h:34
                                                  MaterialModels
                                                  #define MaterialModels
                                                  Definition: equations.h:47
                                                  -
                                                  generalizedProblem::need_gradient_LHS
                                                  std::vector< bool > need_gradient_LHS
                                                  Definition: generalized_model.h:63
                                                  -
                                                  sfts_linear2
                                                  double sfts_linear2[3][3]
                                                  Definition: residuals.h:31
                                                  +
                                                  generalizedProblem::need_gradient_LHS
                                                  std::vector< bool > need_gradient_LHS
                                                  Definition: generalized_model.h:64
                                                  +
                                                  sfts_linear3
                                                  double sfts_linear3[3][3]
                                                  Definition: equations.h:73
                                                  +
                                                  vectorBCFunction::vector_value_list
                                                  virtual void vector_value_list(const std::vector< Point< dim > > &points, std::vector< Vector< double > > &value_list) const
                                                  Definition: generalized_model_functions.h:912
                                                  nucleus
                                                  Definition: coupledCHACMechanics.h:663
                                                  need_hess_LHS
                                                  #define need_hess_LHS
                                                  Definition: equations.h:29
                                                  -
                                                  generalizedProblem::need_gradient
                                                  std::vector< bool > need_gradient
                                                  Definition: generalized_model.h:57
                                                  +
                                                  generalizedProblem::need_gradient
                                                  std::vector< bool > need_gradient
                                                  Definition: generalized_model.h:58
                                                  +
                                                  minRefinementLevel
                                                  #define minRefinementLevel
                                                  Definition: parameters.h:23
                                                  typeVector
                                                  dealii::FEEvaluation< problemDIM, finiteElementDegree, finiteElementDegree+1, problemDIM, double > typeVector
                                                  Definition: matrixFreePDE.h:30
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                      + + diff --git a/html/globals_z.html b/html/globals_z.html new file mode 100644 index 000000000..56d234b46 --- /dev/null +++ b/html/globals_z.html @@ -0,0 +1,139 @@ + + + + + + +PRISMS PhaseField: File Members + + + + + + + + + + +
                      +
                      + + + + + + +
                      +
                      PRISMS PhaseField +  1.0 +
                      +
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                      + + + + + + + +
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                      Here is a list of all file members with links to the files they belong to:
                      + +

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                      +Generated by   +doxygen + 1.8.10 +
                      + + diff --git a/html/grain_growth_2_i_cs__and___b_cs_8h_source.html b/html/grain_growth_2_i_cs__and___b_cs_8h_source.html index 42c26feb1..96f57d74f 100644 --- a/html/grain_growth_2_i_cs__and___b_cs_8h_source.html +++ b/html/grain_growth_2_i_cs__and___b_cs_8h_source.html @@ -109,103 +109,100 @@
                      17  // Use "if" statements to set the initial condition for each variable
                      18  // according to its variable index.
                      19 
                      -
                      20  //return 0.5+ 0.2*(0.5 - (double)(std::rand() % 100 )/100.0);
                      -
                      21 
                      -
                      22  double dx=spanX/((double) subdivisionsX)/std::pow(2.0,refineFactor);
                      -
                      23  double x_loc[10] = {0.3, 0.9, 0.5, 0.4, 0.3, 0.0, 0.0, 0.6, 0.7, 1.0};
                      -
                      24  double y_loc[10] = {0.3, 0.7, 0.1, 0.5, 0.9, 0.0, 0.7, 0.6, 0.4, 0.1};
                      -
                      25  double rad[10] = {6, 7, 9, 8, 5, 6, 4, 3, 8, 5};
                      -
                      26  double dist;
                      -
                      27  scalar_IC = 0;
                      -
                      28 
                      -
                      29  #if problemDIM == 2
                      -
                      30  dist = p.distance(Point<dim>(x_loc[index]*spanX,y_loc[index]*spanY));
                      -
                      31  #elif problemDIM == 3
                      -
                      32  dist = p.distance(Point<dim>(x_loc[index]*spanX,y_loc[index]*spanY,0.5*spanZ));
                      -
                      33  #endif
                      -
                      34 
                      -
                      35  if (dist < rad[index]){
                      -
                      36  scalar_IC = 1.0;
                      -
                      37  }
                      -
                      38 
                      -
                      39  // =====================================================================
                      -
                      40  return scalar_IC;
                      -
                      41  }
                      -
                      42 };
                      -
                      43 
                      -
                      44 template <int dim>
                      -
                      45 class InitialConditionVec : public Function<dim>
                      -
                      46 {
                      -
                      47 public:
                      -
                      48  unsigned int index;
                      -
                      49  //Vector<double> values;
                      -
                      50  InitialConditionVec (const unsigned int _index) : Function<dim>(dim), index(_index) {
                      -
                      51  std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1);
                      -
                      52  }
                      -
                      53  void vector_value (const Point<dim> &p,Vector<double> &vector_IC) const
                      -
                      54  {
                      -
                      55  // =====================================================================
                      -
                      56  // ENTER THE INITIAL CONDITIONS HERE FOR VECTOR FIELDS
                      -
                      57  // =====================================================================
                      -
                      58  // Enter the function describing conditions for the fields at point "p".
                      -
                      59  // Use "if" statements to set the initial condition for each variable
                      -
                      60  // according to its variable index.
                      -
                      61 
                      -
                      62 
                      -
                      63  // =====================================================================
                      -
                      64  }
                      -
                      65 };
                      +
                      20  double dx=spanX/((double) subdivisionsX)/std::pow(2.0,refineFactor);
                      +
                      21  double x_loc[10] = {0.3, 0.7, 0.5, 0.4, 0.3, 0.1, 0.1, 0.6, 0.7, 0.7};
                      +
                      22  double y_loc[10] = {0.3, 0.7, 0.1, 0.5, 0.9, 0.1, 0.7, 0.6, 0.4, 0.2};
                      +
                      23  double rad[10] = {6, 7, 9, 8, 5, 6, 4, 3, 8, 5};
                      +
                      24  double dist;
                      +
                      25  scalar_IC = 0;
                      +
                      26 
                      +
                      27  #if problemDIM == 2
                      +
                      28  dist = p.distance(Point<dim>(x_loc[index]*spanX,y_loc[index]*spanY));
                      +
                      29  #elif problemDIM == 3
                      +
                      30  dist = p.distance(Point<dim>(x_loc[index]*spanX,y_loc[index]*spanY,0.5*spanZ));
                      +
                      31  #endif
                      +
                      32 
                      +
                      33 
                      +
                      34  scalar_IC = 0.5*(1.0-std::tanh((dist-rad[index])/(dx)));
                      +
                      35 
                      +
                      36  // =====================================================================
                      +
                      37  return scalar_IC;
                      +
                      38  }
                      +
                      39 };
                      +
                      40 
                      +
                      41 template <int dim>
                      +
                      42 class InitialConditionVec : public Function<dim>
                      +
                      43 {
                      +
                      44 public:
                      +
                      45  unsigned int index;
                      +
                      46  //Vector<double> values;
                      +
                      47  InitialConditionVec (const unsigned int _index) : Function<dim>(dim), index(_index) {
                      +
                      48  std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1);
                      +
                      49  }
                      +
                      50  void vector_value (const Point<dim> &p,Vector<double> &vector_IC) const
                      +
                      51  {
                      +
                      52  // =====================================================================
                      +
                      53  // ENTER THE INITIAL CONDITIONS HERE FOR VECTOR FIELDS
                      +
                      54  // =====================================================================
                      +
                      55  // Enter the function describing conditions for the fields at point "p".
                      +
                      56  // Use "if" statements to set the initial condition for each variable
                      +
                      57  // according to its variable index.
                      +
                      58 
                      +
                      59 
                      +
                      60  // =====================================================================
                      +
                      61  }
                      +
                      62 };
                      +
                      63 
                      +
                      64 template <int dim>
                      +
                      65 void generalizedProblem<dim>::setBCs(){
                      66 
                      -
                      67 template <int dim>
                      -
                      68 void generalizedProblem<dim>::setBCs(){
                      -
                      69 
                      -
                      70  // =====================================================================
                      -
                      71  // ENTER THE BOUNDARY CONDITIONS HERE
                      -
                      72  // =====================================================================
                      -
                      73  // This function sets the BCs for the problem variables
                      -
                      74  // The function "inputBCs" should be called for each component of
                      -
                      75  // each variable and should be in numerical order. Four input arguments
                      -
                      76  // set the same BC on the entire boundary. Two plus two times the
                      -
                      77  // number of dimensions inputs sets separate BCs on each face of the domain.
                      -
                      78  // Inputs to "inputBCs":
                      -
                      79  // First input: variable number
                      -
                      80  // Second input: component number
                      -
                      81  // Third input: BC type (options are "ZERO_DERIVATIVE" and "DIRICHLET")
                      -
                      82  // Fourth input: BC value (ignored unless the BC type is "DIRICHLET")
                      -
                      83  // Odd inputs after the third: BC type
                      -
                      84  // Even inputs after the third: BC value
                      -
                      85  // Face numbering: starts at zero with the minimum of the first direction, one for the maximum of the first direction
                      -
                      86  // two for the minimum of the second direction, etc.
                      -
                      87 
                      -
                      88  inputBCs(0,0,"ZERO_DERIVATIVE",0);
                      -
                      89  inputBCs(1,0,"ZERO_DERIVATIVE",0);
                      -
                      90  inputBCs(2,0,"ZERO_DERIVATIVE",0);
                      -
                      91  inputBCs(3,0,"ZERO_DERIVATIVE",0);
                      -
                      92  inputBCs(4,0,"ZERO_DERIVATIVE",0);
                      -
                      93  inputBCs(5,0,"ZERO_DERIVATIVE",0);
                      -
                      94  inputBCs(6,0,"ZERO_DERIVATIVE",0);
                      -
                      95  inputBCs(7,0,"ZERO_DERIVATIVE",0);
                      -
                      96  inputBCs(8,0,"ZERO_DERIVATIVE",0);
                      -
                      97  inputBCs(9,0,"ZERO_DERIVATIVE",0);
                      -
                      98 
                      -
                      99  // =====================================================================
                      +
                      67  // =====================================================================
                      +
                      68  // ENTER THE BOUNDARY CONDITIONS HERE
                      +
                      69  // =====================================================================
                      +
                      70  // This function sets the BCs for the problem variables
                      +
                      71  // The function "inputBCs" should be called for each component of
                      +
                      72  // each variable and should be in numerical order. Four input arguments
                      +
                      73  // set the same BC on the entire boundary. Two plus two times the
                      +
                      74  // number of dimensions inputs sets separate BCs on each face of the domain.
                      +
                      75  // Inputs to "inputBCs":
                      +
                      76  // First input: variable number
                      +
                      77  // Second input: component number
                      +
                      78  // Third input: BC type (options are "ZERO_DERIVATIVE", "DIRICHLET", and "PERIODIC")
                      +
                      79  // Fourth input: BC value (ignored unless the BC type is "DIRICHLET")
                      +
                      80  // Odd inputs after the third: BC type
                      +
                      81  // Even inputs after the third: BC value
                      +
                      82  // Face numbering: starts at zero with the minimum of the first direction, one for the maximum of the first direction
                      +
                      83  // two for the minimum of the second direction, etc.
                      +
                      84 
                      +
                      85  inputBCs(0,0,"PERIODIC",0);
                      +
                      86  inputBCs(1,0,"PERIODIC",0);
                      +
                      87  inputBCs(2,0,"PERIODIC",0);
                      +
                      88  inputBCs(3,0,"PERIODIC",0);
                      +
                      89  inputBCs(4,0,"PERIODIC",0);
                      +
                      90  inputBCs(5,0,"PERIODIC",0);
                      +
                      91  inputBCs(6,0,"PERIODIC",0);
                      +
                      92  inputBCs(7,0,"PERIODIC",0);
                      +
                      93  inputBCs(8,0,"PERIODIC",0);
                      +
                      94  inputBCs(9,0,"PERIODIC",0);
                      +
                      95 
                      +
                      96  // =====================================================================
                      +
                      97 
                      +
                      98 }
                      +
                      99 
                      100 
                      -
                      101 }
                      -
                      102 
                      -
                      103 
                      -
                      InitialConditionVec::index
                      unsigned int index
                      Definition: ICs_and_BCs.h:46
                      -
                      InitialConditionVec::vector_value
                      void vector_value(const Point< dim > &p, Vector< double > &vector_IC) const
                      Definition: ICs_and_BCs.h:53
                      +
                      InitialConditionVec::index
                      unsigned int index
                      Definition: ICs_and_BCs.h:47
                      +
                      InitialConditionVec::vector_value
                      void vector_value(const Point< dim > &p, Vector< double > &vector_IC) const
                      Definition: ICs_and_BCs.h:50
                      spanX
                      #define spanX
                      Definition: defaultValues.h:12
                      InitialCondition::InitialCondition
                      InitialCondition(const unsigned int _index)
                      Definition: ICs_and_BCs.h:7
                      spanY
                      #define spanY
                      Definition: defaultValues.h:17
                      InitialCondition::value
                      double value(const Point< dim > &p, const unsigned int component=0) const
                      Definition: ICs_and_BCs.h:10
                      InitialCondition::index
                      unsigned int index
                      Definition: ICs_and_BCs.h:5
                      refineFactor
                      #define refineFactor
                      Definition: defaultValues.h:27
                      -
                      generalizedProblem::setBCs
                      void setBCs()
                      Definition: ICs_and_BCs.h:66
                      -
                      InitialConditionVec::InitialConditionVec
                      InitialConditionVec(const unsigned int _index)
                      Definition: ICs_and_BCs.h:50
                      +
                      generalizedProblem::setBCs
                      void setBCs()
                      Definition: ICs_and_BCs.h:67
                      +
                      InitialConditionVec::InitialConditionVec
                      InitialConditionVec(const unsigned int _index)
                      Definition: ICs_and_BCs.h:47
                      subdivisionsX
                      #define subdivisionsX
                      Definition: parameters.h:14
                      InitialCondition
                      Definition: ICs_and_BCs.h:2
                      -
                      InitialConditionVec
                      Definition: ICs_and_BCs.h:43
                      +
                      InitialConditionVec
                      Definition: ICs_and_BCs.h:44
                      spanZ
                      #define spanZ
                      Definition: defaultValues.h:22
                      InitialCondition::values
                      Vector< double > values
                      Definition: ICs_and_BCs.h:6
                      diff --git a/html/grain_growth_2equations_8h_source.html b/html/grain_growth_2equations_8h_source.html index 121103643..dab332c5a 100644 --- a/html/grain_growth_2equations_8h_source.html +++ b/html/grain_growth_2equations_8h_source.html @@ -144,8 +144,8 @@
                      52 // each residual equation. The index for each variable in these lists corresponds to
                      53 // the order it is defined at the top of this file (starting at 0).
                      54 template <int dim>
                      -
                      55 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim>> & modelVariablesList,
                      -
                      56  std::vector<modelResidual<dim>> & modelResidualsList,
                      +
                      55 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim> > & modelVariablesList,
                      +
                      56  std::vector<modelResidual<dim> > & modelResidualsList,
                      57  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
                      58 
                      59 dealii::VectorizedArray<double> fnV = constV(0.0);
                      @@ -182,7 +182,7 @@
                      90 // that the correct residual is being submitted. The index of the field being solved
                      91 // can be accessed by "this->currentFieldIndex".
                      92 template <int dim>
                      -
                      93 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim>> & modelVarList,
                      +
                      93 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim> > & modelVarList,
                      94  modelResidual<dim> & modelRes,
                      95  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
                      96 }
                      @@ -199,7 +199,7 @@
                      107 // density are added to the "energy_components" variable (index 0: chemical energy,
                      108 // index 1: gradient energy, index 2: elastic energy).
                      109 template <int dim>
                      -
                      110 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim>> & modelVarList,
                      +
                      110 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim> > & modelVarList,
                      111  const dealii::VectorizedArray<double> & JxW_value,
                      112  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) {
                      113  scalarvalueType total_energy_density = constV(0.0);
                      @@ -210,19 +210,19 @@
                      118 
                      119 
                      120 
                      -
                      generalizedProblem::energyDensity
                      void energyDensity(const std::vector< modelVariable< dim >> &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
                      Definition: equations.h:106
                      modelResidual
                      Definition: model_variables.h:25
                      alpha
                      #define alpha
                      Definition: equations.h:40
                      +
                      generalizedProblem::residualRHS
                      void residualRHS(const std::vector< modelVariable< dim > > &modelVarList, std::vector< modelResidual< dim > > &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
                      Definition: equations.h:58
                      scalargradType
                      dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
                      Definition: matrixFreePDE.h:51
                      -
                      generalizedProblem::residualLHS
                      void residualLHS(const std::vector< modelVariable< dim >> &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
                      Definition: equations.h:89
                      constV
                      #define constV(a)
                      Definition: matrixFreePDE.h:67
                      num_var
                      #define num_var
                      Definition: equations.h:7
                      fnV
                      #define fnV
                      Definition: equations.h:41
                      +
                      generalizedProblem::residualLHS
                      void residualLHS(const std::vector< modelVariable< dim > > &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
                      Definition: equations.h:89
                      timeStep
                      #define timeStep
                      modelVariable
                      Definition: model_variables.h:4
                      KnV
                      #define KnV
                      Definition: equations.h:37
                      scalarvalueType
                      dealii::VectorizedArray< double > scalarvalueType
                      Definition: matrixFreePDE.h:34
                      -
                      generalizedProblem::residualRHS
                      void residualRHS(const std::vector< modelVariable< dim >> &modelVarList, std::vector< modelResidual< dim >> &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
                      Definition: equations.h:58
                      +
                      generalizedProblem::energyDensity
                      void energyDensity(const std::vector< modelVariable< dim > > &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
                      Definition: equations.h:106
                      MnV
                      #define MnV
                      Definition: equations.h:34
                      diff --git a/html/grain_growth_2parameters_8h.html b/html/grain_growth_2parameters_8h.html index 20ccfae38..f2f76fa16 100644 --- a/html/grain_growth_2parameters_8h.html +++ b/html/grain_growth_2parameters_8h.html @@ -115,6 +115,20 @@   #define finiteElementDegree   1   +#define hAdaptivity   true +  +#define maxRefinementLevel   (refineFactor) +  +#define minRefinementLevel   (refineFactor-3) +  +#define refineCriterionFields   {0,1,2,3,4,5,6,7,8,9} +  +#define refineWindowMax   {0.999,0.999,0.999,0.999,0.999,0.999,0.999,0.999,0.999,0.999} +  +#define refineWindowMin   {0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001} +  +#define skipRemeshingSteps   1000 +  #define timeStep   1.0e-3   #define timeFinal   20.0 @@ -123,8 +137,14 @@   #define writeOutput   true   -#define skipOutputSteps   1000 -  +#define outputCondition   "EQUAL_SPACING" +  +#define numOutputs   10 +  +#define outputList   {} +  +#define skipPrintSteps   10 +  #define calcEnergy   false   @@ -151,6 +171,78 @@

                      Macro Definition Documentation

                      +
                      + + +
                      +
                      + + + + +
                      #define hAdaptivity   true
                      +
                      + +
                      +
                      + +
                      +
                      + + + + +
                      #define maxRefinementLevel   (refineFactor)
                      +
                      + +
                      +
                      + +
                      +
                      + + + + +
                      #define minRefinementLevel   (refineFactor-3)
                      +
                      + +
                      +
                      + +
                      +
                      + + + + +
                      #define numOutputs   10
                      +
                      + +
                      +
                      + +
                      +
                      + + + + +
                      #define outputCondition   "EQUAL_SPACING"
                      +
                      + +
                      +
                      + +
                      +
                      + + + + +
                      #define outputList   {}
                      +
                      +
                      @@ -163,6 +255,18 @@

                      Macro Definition Documentation

                      +
                      + + +
                      +
                      + + + + +
                      #define refineCriterionFields   {0,1,2,3,4,5,6,7,8,9}
                      +
                      +
                      @@ -177,12 +281,48 @@

                      Macro Definition Documentation

                      - + +
                      +
                      + + + + +
                      #define refineWindowMax   {0.999,0.999,0.999,0.999,0.999,0.999,0.999,0.999,0.999,0.999}
                      +
                      + +
                      +
                      + +
                      +
                      + + + + +
                      #define refineWindowMin   {0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001}
                      +
                      + +
                      +
                      + +
                      +
                      + + + + +
                      #define skipPrintSteps   10
                      +
                      + +
                      +
                      +
                      - +
                      #define skipOutputSteps   1000#define skipRemeshingSteps   1000
                      diff --git a/html/grain_growth_2parameters_8h_source.html b/html/grain_growth_2parameters_8h_source.html index 6f6f5c7f2..c4ed46c9e 100644 --- a/html/grain_growth_2parameters_8h_source.html +++ b/html/grain_growth_2parameters_8h_source.html @@ -124,36 +124,69 @@
                      32 #define finiteElementDegree 1
                      33 
                      34 // =================================================================================
                      -
                      35 // Set the time step parameters
                      +
                      35 // Set the adaptive mesh refinement parameters
                      36 // =================================================================================
                      -
                      37 // The size of the time step
                      -
                      38 #define timeStep 1.0e-3
                      +
                      37 // Set the flag determining if adaptive meshing is activated
                      +
                      38 #define hAdaptivity true
                      39 
                      -
                      40 // The simulation ends when either timeFinal is reached or the number of time steps
                      -
                      41 // equals timeIncrements
                      -
                      42 #define timeFinal 20.0
                      -
                      43 #define timeIncrements 20000
                      -
                      44 
                      -
                      45 // =================================================================================
                      -
                      46 // Set the output parameters
                      -
                      47 // =================================================================================
                      -
                      48 // Each field in the problem will be output is writeOutput is set to "true"
                      -
                      49 #define writeOutput true
                      -
                      50 
                      -
                      51 // Output files are written every skipOutputSteps time steps
                      -
                      52 #define skipOutputSteps 1000
                      -
                      53 
                      -
                      54 // =================================================================================
                      -
                      55 // Set the flag determining if the total free energy is calculated for each output
                      -
                      56 // =================================================================================
                      -
                      57 #define calcEnergy false
                      -
                      58 
                      -
                      59 
                      +
                      40 // Set the maximum and minimum level of refinement
                      +
                      41 #define maxRefinementLevel (refineFactor)
                      +
                      42 #define minRefinementLevel (refineFactor-3)
                      +
                      43 
                      +
                      44 // Set the fields used to determine the refinement. Fields determined by the order
                      +
                      45 // declared in "equations.h", starting at zero
                      +
                      46 #define refineCriterionFields {0,1,2,3,4,5,6,7,8,9}
                      +
                      47 
                      +
                      48 // Set the maximum and minimum value of the fields where the mesh should be refined
                      +
                      49 #define refineWindowMax {0.999,0.999,0.999,0.999,0.999,0.999,0.999,0.999,0.999,0.999}
                      +
                      50 #define refineWindowMin {0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001}
                      +
                      51 
                      +
                      52 // Set the number of time steps between remeshing operations
                      +
                      53 #define skipRemeshingSteps 1000
                      +
                      54 
                      +
                      55 // =================================================================================
                      +
                      56 // Set the time step parameters
                      +
                      57 // =================================================================================
                      +
                      58 // The size of the time step
                      +
                      59 #define timeStep 1.0e-3
                      60 
                      -
                      61 
                      -
                      62 
                      -
                      63 
                      -
                      64 
                      +
                      61 // The simulation ends when either timeFinal is reached or the number of time steps
                      +
                      62 // equals timeIncrements
                      +
                      63 #define timeFinal 20.0
                      +
                      64 #define timeIncrements 20000
                      +
                      65 
                      +
                      66 // =================================================================================
                      +
                      67 // Set the output parameters
                      +
                      68 // =================================================================================
                      +
                      69 // Each field in the problem will be output is writeOutput is set to "true"
                      +
                      70 #define writeOutput true
                      +
                      71 
                      +
                      72 // Type of spacing between outputs ("EQUAL_SPACING", "LOG_SPACING", "N_PER_DECADE",
                      +
                      73 // or "LIST")
                      +
                      74 #define outputCondition "EQUAL_SPACING"
                      +
                      75 
                      +
                      76 // Number of times the program outputs the fields (total number for "EQUAL_SPACING"
                      +
                      77 // and "LOG_SPACING", number per decade for "N_PER_DECADE", ignored for "LIST")
                      +
                      78 #define numOutputs 10
                      +
                      79 
                      +
                      80 // User-defined list of time steps where the program should output. Only used if
                      +
                      81 // outputCondition is "LIST"
                      +
                      82 #define outputList {}
                      +
                      83 
                      +
                      84 // Status is printed to the screen every skipPrintSteps
                      +
                      85 #define skipPrintSteps 10
                      +
                      86 
                      +
                      87 // =================================================================================
                      +
                      88 // Set the flag determining if the total free energy is calculated for each output
                      +
                      89 // =================================================================================
                      +
                      90 #define calcEnergy false
                      +
                      91 
                      +
                      92 
                      +
                      93 
                      +
                      94 
                      +
                      95 
                      +
                      96 
                      +
                      97 
                      diff --git a/html/hierarchy.html b/html/hierarchy.html index 0e84b8130..259dc5e15 100644 --- a/html/hierarchy.html +++ b/html/hierarchy.html @@ -104,12 +104,12 @@  CInitialCondition< dim >  CInitialCondition< dim >  CInitialCondition< dim > - CInitialConditionC< dim > - CInitialConditionC< dim > - CInitialConditionN< dim > - CInitialConditionVec< dim > - CInitialConditionVec< dim > - CInitialConditionVec< dim > + CInitialCondition< dim > + CInitialCondition< dim > + CInitialCondition< dim > + CInitialCondition< dim > + CInitialCondition< dim > + CInitialConditionC< dim >  CInitialConditionVec< dim >  CInitialConditionVec< dim >  CInitialConditionVec< dim > @@ -118,23 +118,32 @@  CInitialConditionVec< dim >  CInitialConditionVec< dim >  CInitialConditionVec< dim > - CvectorBCFunction< dim > + CInitialConditionVec< dim > + CInitialConditionVec< dim > + CInitialConditionVec< dim > + CInitialConditionVec< dim > + CInitialConditionVec< dim > + CInitialConditionVec< dim > + CInitialConditionVec< dim > + CInitialConditionVec< dim > + CvectorBCFunction< dim >  CmodelResidual< dim >  CmodelVariable< dim >  Cnucleus - ►CSubscriptor - ►CMatrixFreePDE< dim > - CAllenCahnProblem< dim > - CCahnHilliardProblem< dim > - CCoupledCahnHilliardMechanicsProblem< dim > - CCoupledCHACMechanicsProblem< dim > - CCoupledCHACProblem< dim > - CCoupledCHACProblem< dim > - CFickianProblem< dim > - CgeneralizedProblem< dim > - CMechanicsProblem< dim > - CvarBCs< dim > - Cvariable_info< dim > + CpFunction + ►CSubscriptor + ►CMatrixFreePDE< dim > + CAllenCahnProblem< dim > + CCahnHilliardProblem< dim > + CCoupledCahnHilliardMechanicsProblem< dim > + CCoupledCHACMechanicsProblem< dim > + CCoupledCHACProblem< dim > + CCoupledCHACProblem< dim > + CFickianProblem< dim > + CgeneralizedProblem< dim > + CMechanicsProblem< dim > + CvarBCs< dim > + Cvariable_info< dim > diff --git a/html/index.html b/html/index.html index 12de6c214..aeb4923e4 100644 --- a/html/index.html +++ b/html/index.html @@ -82,19 +82,23 @@

                      Code repository: https://github.com/prisms-center/phaseField
                      Code documentation: https://goo.gl/00y23N
                      - User registration link: http://goo.gl/forms/GXo7Im8p2Y

                      + User registration link: http://goo.gl/forms/GXo7Im8p2Y User discussion group: https://groups.google.com/forum/#!forum/prisms-pf-users

                      Version information:

                      -

                      This version of the code, 1.0, is the first release version of PRISMS-PF. This version provides many updates to the pre-release versions. For more information concerning the differences between versions, please consult version_changes.txt.

                      +

                      This version of the code, 1.1, is an update the first release version of PRISMS-PF. For:q information concerning the differences between versions, please consult version_changes.txt.

                      What is PRISMS-PF?

                      -

                      PRISMS-PF is a high-performance Finite Element Method (FEM) code for phase field modeling simulations and solving related systems of partial differential equations. It has a flexible user interface designed to allow users to easily modify existing applications as well as developing their own. In terms of performance, PRISMS-PF has been shown to be competitive with state-of-the-art finite difference codes. PRISMS-PF has been tested on thousands of processors and has been used in simulations with over one billion degrees of freedom.

                      +

                      PRISMS-PF is a powerful, massively parallel finite element code for conducting phase field and other related simulations of microstructural evolution. The phase field method is commonly used for predicting the evolution if microstructures under a wide range of conditions and material systems. PRISMS-PF provides a simple interface for solving customizable systems of partial differential equations of the type commonly found in phase field models, and has 11 pre-built application modules, including for precipitate evolution, grain growth, and spinodal decomposition.

                      +

                      With PRISMS-PF, you have access to adaptive meshing and parallelization with near-ideal scaling for over a thousand processors. Moreover, the matrix-free framework from the deal.II library allows much larger than simulations than typical finite element programs – PRISMS-PF has been used for simulations with over one billion degrees of freedom. PRISMS-PF also provides performance competitive with or exceeding single-purpose codes. For example, even without enabling the mesh adaptivity features in PRISMS-PF, it has been demonstrated to be 4.5x faster than an equivalent finite difference code.

                      This code is developed by the PRedictive Integrated Structural Materials Science (PRISMS) Center [http://www.prisms-center.org/] at University of Michigan which is supported by the U.S. Department of Energy (DOE), Office of Basic Energy Sciences, Division of Materials Sciences and Engineering under Award DE-SC0008637

                      Quick Start Guide:

                      -

                      For detailed instructions on how to download and use PRISMS-PF, please consult the PRISMS-PF Users Guide (the file prismspf_users_guide.pdf). An abbreviated version of the instructions is given below.

                      +

                      For detailed instructions on how to download and use PRISMS-PF, please consult the PRISMS-PF Users Guide (the file prismspf_users_guide.pdf). A (very) abbreviated version of the instructions is given below.

                      Installation:

                      1) Install deal.II (version 8.4.1 recommended)

                      • Download CMake [http://www.cmake.org/download/]
                        • -
                      • Download Deal.II Binaries (OSX and Linux) or Virtual Machine (VMI) from https://www.dealii.org/download.html
                        +
                      • Add CMake to your path (e.g. $ PATH="/path/to/cmake/Contents/bin":"$PATH"), preferably in a shell configuration file
                        • +
                      • Download and install Deal.II following instructions from from https://www.dealii.org/download.html
                        • +
                      • If a Deal.II binary is downloaded, open it and follow the instructions in the terminal window
                        • +
                      • If Deal.II is installed from the source, the MPI and p4est libraries must be installed as prerequisites.

                      2) Clone the PRISMS-PF GitHub repo https://github.com/prisms-center/phaseField
                      @@ -149,13 +153,11 @@

                    License:

                    GNU Lesser General Public License (LGPL). Please see the file LICENSE for details.

                    -

                    Mailing List:

                    +

                    Further information, questions, issues and bugs:

                    -

                    Further information, questions, issues and bugs:

                    -

                    Contact the developers at prism.nosp@m.spha.nosp@m.seFie.nosp@m.ld.d.nosp@m.ev@um.nosp@m.ich..nosp@m.edu

                    diff --git a/html/matrix_free_p_d_e_8h.html b/html/matrix_free_p_d_e_8h.html index f3c4763b9..aaf65b70c 100644 --- a/html/matrix_free_p_d_e_8h.html +++ b/html/matrix_free_p_d_e_8h.html @@ -103,6 +103,7 @@ #include "model_variables.h"
                    #include "../src/matrixfree/matrixFreePDE.cc"
                    #include "../src/matrixfree/init.cc"
                    +#include "../src/matrixfree/reinit.cc"
                    #include "../src/matrixfree/initForTests.cc"
                    #include "../src/matrixfree/refine.cc"
                    #include "../src/matrixfree/invM.cc"
                    @@ -118,6 +119,7 @@ #include "../src/matrixfree/utilities.cc"
                    #include "../src/matrixfree/calcFreeEnergy.cc"
                    #include "../src/matrixfree/integrate_and_shift_field.cc"
                    +#include "../src/matrixfree/getOutputTimeSteps.cc"

                    Go to the source code of this file.

                    @@ -139,21 +141,21 @@ - + - + - + - + - + - + - +
                     
                    typedef dealii::parallel::distributed::Vector< double > vectorType
                     
                    typedef dealii::FEEvaluation< problemDIM, finiteElementDegree, finiteElementDegree+1, 1, double > typeScalar
                    typedef dealii::FEEvaluation< problemDIM, finiteElementDegree, finiteElementDegree+1, 1, double > typeScalar
                     
                    typedef dealii::FEEvaluation< problemDIM, finiteElementDegree, finiteElementDegree+1, problemDIM, double > typeVector
                    typedef dealii::FEEvaluation< problemDIM, finiteElementDegree, finiteElementDegree+1, problemDIM, double > typeVector
                     
                    typedef dealii::VectorizedArray< double > scalarvalueType
                     
                    typedef dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > vectorvalueType
                    typedef dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > vectorvalueType
                     
                    typedef dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
                    typedef dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
                     
                    typedef dealii::Tensor< 2, problemDIM, dealii::VectorizedArray< double > > scalarhessType
                    typedef dealii::Tensor< 2, problemDIM, dealii::VectorizedArray< double > > scalarhessType
                     
                    typedef dealii::Tensor< 2, problemDIM, dealii::VectorizedArray< double > > vectorgradType
                    typedef dealii::Tensor< 2, problemDIM, dealii::VectorizedArray< double > > vectorgradType
                     
                    typedef dealii::Tensor< 3, problemDIM, dealii::VectorizedArray< double > > vectorhessType
                    typedef dealii::Tensor< 3, problemDIM, dealii::VectorizedArray< double > > vectorhessType
                     

                    Macro Definition Documentation

                    @@ -205,7 +207,7 @@

                    Typedef Documentation

                    - +
                    typedef dealii::Tensor<1, problemDIM, dealii::VectorizedArray<double> > scalargradTypetypedef dealii::Tensor<1, problemDIM, dealii::VectorizedArray<double> > scalargradType
                    @@ -217,7 +219,7 @@

                    Typedef Documentation

                    - +
                    typedef dealii::Tensor<2,problemDIM,dealii::VectorizedArray<double> > scalarhessTypetypedef dealii::Tensor<2,problemDIM,dealii::VectorizedArray<double> > scalarhessType
                    @@ -253,7 +255,7 @@

                    Typedef Documentation

                    - +
                    typedef dealii::FEEvaluation<problemDIM,finiteElementDegree,finiteElementDegree+1,1,double> typeScalartypedef dealii::FEEvaluation<problemDIM,finiteElementDegree,finiteElementDegree+1,1,double> typeScalar
                    @@ -265,7 +267,7 @@

                    Typedef Documentation

                    - +
                    typedef dealii::FEEvaluation<problemDIM,finiteElementDegree,finiteElementDegree+1,problemDIM,double> typeVectortypedef dealii::FEEvaluation<problemDIM,finiteElementDegree,finiteElementDegree+1,problemDIM,double> typeVector
                    @@ -277,7 +279,7 @@

                    Typedef Documentation

                    - +
                    typedef dealii::Tensor<2, problemDIM, dealii::VectorizedArray<double> > vectorgradTypetypedef dealii::Tensor<2, problemDIM, dealii::VectorizedArray<double> > vectorgradType
                    @@ -289,7 +291,7 @@

                    Typedef Documentation

                    - +
                    typedef dealii::Tensor<3, problemDIM, dealii::VectorizedArray<double> > vectorhessTypetypedef dealii::Tensor<3, problemDIM, dealii::VectorizedArray<double> > vectorhessType
                    @@ -313,7 +315,7 @@

                    Typedef Documentation

                    - +
                    typedef dealii::Tensor<1, problemDIM, dealii::VectorizedArray<double> > vectorvalueTypetypedef dealii::Tensor<1, problemDIM, dealii::VectorizedArray<double> > vectorvalueType
                    diff --git a/html/matrix_free_p_d_e_8h_source.html b/html/matrix_free_p_d_e_8h_source.html index d4523405b..2d71e374f 100644 --- a/html/matrix_free_p_d_e_8h_source.html +++ b/html/matrix_free_p_d_e_8h_source.html @@ -186,239 +186,255 @@
                    94  * Initializes the mesh, degress of freedom, constraints and data structures using the user provided
                    95  * inputs in the application parameters file.
                    96  */
                    -
                    97  void init (unsigned int iter=0);
                    -
                    98  /**
                    -
                    99  * Initializes the data structures for enabling unit tests.
                    -
                    100  *
                    -
                    101  * This method initializes the MatrixFreePDE object with a fixed geometry, discretization and
                    -
                    102  * other custom selected options specifically to help with unit tests, and should not be called
                    -
                    103  * in any of the physical models.
                    -
                    104  */
                    -
                    105  void initForTests();
                    -
                    106 
                    -
                    107  /**
                    -
                    108  * This method implements the time stepping algorithm and invokes the solveIncrement() method.
                    -
                    109  */
                    -
                    110  void solve ();
                    -
                    111  /**
                    -
                    112  * This method essentially converts the MatrixFreePDE object into a matrix object which can be
                    -
                    113  * used with matrix free iterative solvers. Provides the A*x functionality for solving the system of
                    -
                    114  * equations AX=b.
                    -
                    115  */
                    -
                    116  void vmult (vectorType &dst, const vectorType &src) const;
                    -
                    117  /**
                    -
                    118  * Vector of all the physical fields in the problem. Fields are identified by dimentionality (SCALAR/VECTOR),
                    -
                    119  * the kind of PDE (ELLIPTIC/PARABOLIC) used to compute them and a character identifier (e.g.: "c" for composition)
                    -
                    120  * which is used to write the fields to the output files.
                    -
                    121  */
                    -
                    122  std::vector<Field<dim> > fields;
                    -
                    123 
                    -
                    124  /**
                    -
                    125  * Virtual function to shift the concentration
                    -
                    126  */
                    -
                    127  void shiftConcentration();
                    -
                    128 
                    -
                    129  protected:
                    -
                    130  /**
                    -
                    131  * Method to solve each time increment of a time-dependent problem. For time-independent problems
                    -
                    132  * this method is called only once. This method solves for all the fields in a staggered manner (one after another)
                    -
                    133  * and also invokes the corresponding solvers: Explicit solver for Parabolic problems, Implicit (matrix-free) solver for Elliptic problems.
                    -
                    134  */
                    -
                    135  void solveIncrement ();
                    -
                    136  /* Method to write solution fields to vtu and pvtu (parallel) files.
                    -
                    137  *
                    -
                    138  * This method can be enabled/disabled by setting the flag writeOutput to true/false. Also,
                    -
                    139  * the user can select how often the solution files are written by setting the flag
                    -
                    140  * skipOutputSteps in the parameters file.
                    -
                    141  */
                    -
                    142  void outputResults ();
                    -
                    143  /*Parallel mesh object which holds information about the FE nodes, elements and parallel domain decomposition
                    -
                    144  */
                    -
                    145  parallel::distributed::Triangulation<dim> triangulation;
                    -
                    146  /*A vector of finite element objects used in a model. For problems with only one primal field,
                    -
                    147  *the size of this vector is one,otherwise the size is the number of primal fields in the problem.
                    -
                    148  */
                    -
                    149  std::vector<FESystem<dim>*> FESet;
                    -
                    150  /*A vector of all the constraint sets in the problem. A constraint set is a map which holds the mapping between the degrees
                    -
                    151  *of freedom and the corresponding degree of freedom constraints. Currently the type of constraints stored are either
                    -
                    152  *Dirichlet boundary conditons or hanging node constraints for adaptive meshes.
                    -
                    153  */
                    -
                    154  std::vector<const ConstraintMatrix*> constraintsSet, constraintsHangingNodesSet;
                    -
                    155  /*A vector of all the degree of freedom objects is the problem. A degree of freedom object handles the serial/parallel distribution
                    -
                    156  *of the degress of freedom for all the primal fields in the problem.*/
                    -
                    157  std::vector<const DoFHandler<dim>*> dofHandlersSet;
                    -
                    158  /*A vector of the locally relevant degrees of freedom. Locally relevant degrees of freedom in a parallel implementation is a collection of the
                    -
                    159  *degress of freedom owned by the current processor and the surrounding ghost nodes which are required for the field computations in this processor.
                    +
                    97  void init (unsigned int iter=0);
                    +
                    98  void reinit (unsigned int iter=0);
                    +
                    99  /**
                    +
                    100  * Initializes the data structures for enabling unit tests.
                    +
                    101  *
                    +
                    102  * This method initializes the MatrixFreePDE object with a fixed geometry, discretization and
                    +
                    103  * other custom selected options specifically to help with unit tests, and should not be called
                    +
                    104  * in any of the physical models.
                    +
                    105  */
                    +
                    106  void initForTests();
                    +
                    107 
                    +
                    108  /**
                    +
                    109  * This method implements the time stepping algorithm and invokes the solveIncrement() method.
                    +
                    110  */
                    +
                    111  void solve ();
                    +
                    112  /**
                    +
                    113  * This method essentially converts the MatrixFreePDE object into a matrix object which can be
                    +
                    114  * used with matrix free iterative solvers. Provides the A*x functionality for solving the system of
                    +
                    115  * equations AX=b.
                    +
                    116  */
                    +
                    117  void vmult (vectorType &dst, const vectorType &src) const;
                    +
                    118  /**
                    +
                    119  * Vector of all the physical fields in the problem. Fields are identified by dimentionality (SCALAR/VECTOR),
                    +
                    120  * the kind of PDE (ELLIPTIC/PARABOLIC) used to compute them and a character identifier (e.g.: "c" for composition)
                    +
                    121  * which is used to write the fields to the output files.
                    +
                    122  */
                    +
                    123  std::vector<Field<dim> > fields;
                    +
                    124 
                    +
                    125  /**
                    +
                    126  * Virtual function to shift the concentration
                    +
                    127  */
                    +
                    128  void shiftConcentration();
                    +
                    129 
                    +
                    130  protected:
                    +
                    131  /**
                    +
                    132  * Method to solve each time increment of a time-dependent problem. For time-independent problems
                    +
                    133  * this method is called only once. This method solves for all the fields in a staggered manner (one after another)
                    +
                    134  * and also invokes the corresponding solvers: Explicit solver for Parabolic problems, Implicit (matrix-free) solver for Elliptic problems.
                    +
                    135  */
                    +
                    136  void solveIncrement ();
                    +
                    137  /* Method to write solution fields to vtu and pvtu (parallel) files.
                    +
                    138  *
                    +
                    139  * This method can be enabled/disabled by setting the flag writeOutput to true/false. Also,
                    +
                    140  * the user can select how often the solution files are written by setting the flag
                    +
                    141  * skipOutputSteps in the parameters file.
                    +
                    142  */
                    +
                    143  void outputResults ();
                    +
                    144 
                    +
                    145  /* Method to generate a list of time steps where the method outputResults should be called. It populates outputTimeStepList.
                    +
                    146  */
                    +
                    147  std::vector<unsigned int> outputTimeStepList;
                    +
                    148  void getOutputTimeSteps(std::string outputSpacingType, unsigned int numberOfOutputs, std::vector<unsigned int> & userGivenTimeStepList, std::vector<unsigned int> & timeStepList);
                    +
                    149 
                    +
                    150  /*Parallel mesh object which holds information about the FE nodes, elements and parallel domain decomposition
                    +
                    151  */
                    +
                    152  parallel::distributed::Triangulation<dim> triangulation;
                    +
                    153  /*A vector of finite element objects used in a model. For problems with only one primal field,
                    +
                    154  *the size of this vector is one,otherwise the size is the number of primal fields in the problem.
                    +
                    155  */
                    +
                    156  std::vector<FESystem<dim>*> FESet;
                    +
                    157  /*A vector of all the constraint sets in the problem. A constraint set is a map which holds the mapping between the degrees
                    +
                    158  *of freedom and the corresponding degree of freedom constraints. Currently the type of constraints stored are either
                    +
                    159  *Dirichlet boundary conditons or hanging node constraints for adaptive meshes.
                    160  */
                    -
                    161  std::vector<const IndexSet*> locally_relevant_dofsSet;
                    -
                    162  /*Copies of constraintSet elements, but stored as non-const to enable application of constraints.*/
                    -
                    163  std::vector<ConstraintMatrix*> constraintsSet2, constraintsHangingNodesSet2;
                    -
                    164  /*Copies of dofHandlerSet elements, but stored as non-const.*/
                    -
                    165  std::vector<DoFHandler<dim>*> dofHandlersSet2;
                    -
                    166  /*Copies of locally_relevant_dofsSet elements, but stored as non-const.*/
                    -
                    167  std::vector<IndexSet*> locally_relevant_dofsSet2;
                    -
                    168  /*Vector all the solution vectors in the problem. In a multi-field problem, each primal field has a solution vector associated with it.*/
                    -
                    169  std::vector<vectorType*> solutionSet;
                    -
                    170  /*Vector all the residual (RHS) vectors in the problem. In a multi-field problem, each primal field has a residual vector associated with it.*/
                    -
                    171  std::vector<vectorType*> residualSet;
                    -
                    172  /*Vector of parallel solution transfer objects. This is used only when adaptive meshing is enabled.*/
                    -
                    173  std::vector<parallel::distributed::SolutionTransfer<dim, vectorType>*> soltransSet;
                    -
                    174 
                    -
                    175  //matrix free objects
                    -
                    176  /*Object of class MatrixFree<dim>. This is primarily responsible for all the base matrix free functionality of this MatrixFreePDE<dim> class.
                    -
                    177  *Refer to deal.ii documentation of MatrixFree<dim> class for details.
                    -
                    178  */
                    -
                    179  MatrixFree<dim,double> matrixFreeObject;
                    -
                    180  /*Vector to store the inverse of the mass matrix diagonal. Due to the choice of spectral elements with Guass-Lobatto quadrature, the mass matrix is diagonal.*/
                    -
                    181  vectorType invM;
                    -
                    182  /*Vector to store the solution increment. This is a temporary vector used during implicit solves of the Elliptic fields.*/
                    -
                    183  vectorType dU;
                    -
                    184 
                    -
                    185  //matrix free methods
                    -
                    186  /*Current field index*/
                    -
                    187  unsigned int currentFieldIndex;
                    -
                    188  /*Number of quadrature points*/
                    -
                    189  unsigned int num_quadrature_points;
                    -
                    190  /*Method to compute the inverse of the mass matrix*/
                    -
                    191  void computeInvM();
                    -
                    192  /*Method to compute the right hand side (RHS) residual vectors*/
                    -
                    193  void computeRHS();
                    -
                    194 
                    -
                    195  /*AMR methods*/
                    -
                    196  void refineGrid();
                    -
                    197  /*Method to perform adaptive mesh refinement (AMR)*/
                    -
                    198  void refineMesh(unsigned int _currentIncrement);
                    -
                    199  /*Virtual method to mark the regions to be adpatively refined. This is expected to be provided by the user.*/
                    -
                    200  virtual void adaptiveRefine(unsigned int _currentIncrement);
                    -
                    201  /*Virtual method to define AMR refinement criterion. The default implementation uses the Kelly error estimate for estimative the error function. The user can supply a custom implementation to overload the default implementation.*/
                    -
                    202  virtual void adaptiveRefineCriterion();
                    -
                    203 
                    -
                    204  //virtual methods to be implemented in the derived class
                    -
                    205  /*Method to calculate LHS(implicit solve)*/
                    -
                    206  virtual void getLHS(const MatrixFree<dim,double> &data,
                    -
                    207  vectorType &dst,
                    -
                    208  const vectorType &src,
                    -
                    209  const std::pair<unsigned int,unsigned int> &cell_range) const;
                    -
                    210  /*Method to calculate RHS (implicit/explicit). This is an abstract method, so every model which inherits MatrixFreePDE<dim> has to implement this method.*/
                    -
                    211  virtual void getRHS (const MatrixFree<dim,double> &data,
                    -
                    212  std::vector<vectorType*> &dst,
                    -
                    213  const std::vector<vectorType*> &src,
                    -
                    214  const std::pair<unsigned int,unsigned int> &cell_range) const = 0;
                    -
                    215 
                    -
                    216  //methods to apply dirichlet BC's
                    -
                    217  /*Map of degrees of freedom to the corresponding Dirichlet boundary conditions, is any.*/
                    -
                    218  std::vector<std::map<dealii::types::global_dof_index, double>*> valuesDirichletSet;
                    -
                    219  /*Virtual method to mark the boundaries for applying Dirichlet boundary conditions. This is usually expected to be provided by the user.*/
                    -
                    220  virtual void markBoundaries();
                    -
                    221  /*Virtual method for applying Dirichlet boundary conditions. This is usually expected to be provided by the user.*/
                    -
                    222  virtual void applyDirichletBCs();
                    -
                    223 
                    -
                    224  //methods to apply initial conditions
                    -
                    225  /*Virtual method to apply initial conditions. This is usually expected to be provided by the user in IBVP (Initial Boundary Value Problems).*/
                    -
                    226  virtual void applyInitialConditions();
                    -
                    227  virtual void modifySolutionFields ();
                    -
                    228 
                    -
                    229  /*Method to compute energy like quantities.*/
                    -
                    230  void computeEnergy();
                    -
                    231  virtual void getEnergy(const MatrixFree<dim,double> &data,
                    -
                    232  std::vector<vectorType*> &dst,
                    -
                    233  const std::vector<vectorType*> &src,
                    -
                    234  const std::pair<unsigned int,unsigned int> &cell_range);
                    -
                    235 
                    -
                    236  //utility functions
                    -
                    237  /*Returns index of given field name if exists, else throw error.*/
                    -
                    238  unsigned int getFieldIndex(std::string _name);
                    -
                    239 
                    -
                    240 
                    -
                    241  std::vector<double> freeEnergyValues;
                    -
                    242  void outputFreeEnergy(std::vector<double>& freeEnergyValues);
                    -
                    243 
                    -
                    244  /*Method to compute the integral of a field.*/
                    -
                    245  void computeIntegral(double& integratedField);
                    -
                    246 
                    -
                    247  //variables for time dependent problems
                    -
                    248  /*Flag used to see if invM, time steppping in run(), etc are necessary*/
                    -
                    249  bool isTimeDependentBVP;
                    -
                    250  /*Flag used to mark problems with Elliptic fields.*/
                    -
                    251  bool isEllipticBVP;
                    -
                    252  //
                    -
                    253  unsigned int parabolicFieldIndex, ellipticFieldIndex;
                    -
                    254  double dtValue, currentTime, finalTime;
                    -
                    255  unsigned int currentIncrement, totalIncrements;
                    +
                    161  std::vector<const ConstraintMatrix*> constraintsDirichletSet, constraintsOtherSet;
                    +
                    162  /*A vector of all the degree of freedom objects is the problem. A degree of freedom object handles the serial/parallel distribution
                    +
                    163  *of the degress of freedom for all the primal fields in the problem.*/
                    +
                    164  std::vector<const DoFHandler<dim>*> dofHandlersSet;
                    +
                    165  /*A vector of the locally relevant degrees of freedom. Locally relevant degrees of freedom in a parallel implementation is a collection of the
                    +
                    166  *degress of freedom owned by the current processor and the surrounding ghost nodes which are required for the field computations in this processor.
                    +
                    167  */
                    +
                    168  std::vector<const IndexSet*> locally_relevant_dofsSet;
                    +
                    169  /*Copies of constraintSet elements, but stored as non-const to enable application of constraints.*/
                    +
                    170  std::vector<ConstraintMatrix*> constraintsDirichletSet_nonconst, constraintsOtherSet_nonconst;
                    +
                    171  /*Copies of dofHandlerSet elements, but stored as non-const.*/
                    +
                    172  std::vector<DoFHandler<dim>*> dofHandlersSet_nonconst;
                    +
                    173  /*Copies of locally_relevant_dofsSet elements, but stored as non-const.*/
                    +
                    174  std::vector<IndexSet*> locally_relevant_dofsSet_nonconst;
                    +
                    175  /*Vector all the solution vectors in the problem. In a multi-field problem, each primal field has a solution vector associated with it.*/
                    +
                    176  std::vector<vectorType*> solutionSet;
                    +
                    177  /*Vector all the residual (RHS) vectors in the problem. In a multi-field problem, each primal field has a residual vector associated with it.*/
                    +
                    178  std::vector<vectorType*> residualSet;
                    +
                    179  /*Vector of parallel solution transfer objects. This is used only when adaptive meshing is enabled.*/
                    +
                    180  std::vector<parallel::distributed::SolutionTransfer<dim, vectorType>*> soltransSet;
                    +
                    181 
                    +
                    182  //matrix free objects
                    +
                    183  /*Object of class MatrixFree<dim>. This is primarily responsible for all the base matrix free functionality of this MatrixFreePDE<dim> class.
                    +
                    184  *Refer to deal.ii documentation of MatrixFree<dim> class for details.
                    +
                    185  */
                    +
                    186  MatrixFree<dim,double> matrixFreeObject;
                    +
                    187  /*Vector to store the inverse of the mass matrix diagonal. Due to the choice of spectral elements with Guass-Lobatto quadrature, the mass matrix is diagonal.*/
                    +
                    188  vectorType invM;
                    +
                    189  /*Vector to store the solution increment. This is a temporary vector used during implicit solves of the Elliptic fields.*/
                    +
                    190  vectorType dU;
                    +
                    191 
                    +
                    192  //matrix free methods
                    +
                    193  /*Current field index*/
                    +
                    194  unsigned int currentFieldIndex;
                    +
                    195  /*Number of quadrature points*/
                    +
                    196  unsigned int num_quadrature_points;
                    +
                    197  /*Method to compute the inverse of the mass matrix*/
                    +
                    198  void computeInvM();
                    +
                    199  /*Method to compute the right hand side (RHS) residual vectors*/
                    +
                    200  void computeRHS();
                    +
                    201 
                    +
                    202  /*AMR methods*/
                    +
                    203  void refineGrid();
                    +
                    204  /*Method to perform adaptive mesh refinement (AMR)*/
                    +
                    205  void refineMesh(unsigned int _currentIncrement);
                    +
                    206  /*Virtual method to mark the regions to be adpatively refined. This is expected to be provided by the user.*/
                    +
                    207  virtual void adaptiveRefine(unsigned int _currentIncrement);
                    +
                    208  /*Virtual method to define AMR refinement criterion. The default implementation uses the Kelly error estimate for estimative the error function. The user can supply a custom implementation to overload the default implementation.*/
                    +
                    209  virtual void adaptiveRefineCriterion();
                    +
                    210 
                    +
                    211  //virtual methods to be implemented in the derived class
                    +
                    212  /*Method to calculate LHS(implicit solve)*/
                    +
                    213  virtual void getLHS(const MatrixFree<dim,double> &data,
                    +
                    214  vectorType &dst,
                    +
                    215  const vectorType &src,
                    +
                    216  const std::pair<unsigned int,unsigned int> &cell_range) const;
                    +
                    217  /*Method to calculate RHS (implicit/explicit). This is an abstract method, so every model which inherits MatrixFreePDE<dim> has to implement this method.*/
                    +
                    218  virtual void getRHS (const MatrixFree<dim,double> &data,
                    +
                    219  std::vector<vectorType*> &dst,
                    +
                    220  const std::vector<vectorType*> &src,
                    +
                    221  const std::pair<unsigned int,unsigned int> &cell_range) const = 0;
                    +
                    222 
                    +
                    223  //methods to apply dirichlet BC's
                    +
                    224  /*Map of degrees of freedom to the corresponding Dirichlet boundary conditions, is any.*/
                    +
                    225  std::vector<std::map<dealii::types::global_dof_index, double>*> valuesDirichletSet;
                    +
                    226  /*Virtual method to mark the boundaries for applying Dirichlet boundary conditions. This is usually expected to be provided by the user.*/
                    +
                    227  virtual void markBoundaries();
                    +
                    228  /*Virtual method for applying Dirichlet boundary conditions. This is usually expected to be provided by the user.*/
                    +
                    229  virtual void applyDirichletBCs();
                    +
                    230 
                    +
                    231  // Methods to apply periodic BCs
                    +
                    232  virtual void setPeriodicity();
                    +
                    233  virtual void setPeriodicityConstraints(ConstraintMatrix *, DoFHandler<dim>*);
                    +
                    234  virtual void getComponentsWithRigidBodyModes(std::vector<int> &);
                    +
                    235  virtual void setRigidBodyModeConstraints( std::vector<int>, ConstraintMatrix *, DoFHandler<dim>*);
                    +
                    236 
                    +
                    237  //methods to apply initial conditions
                    +
                    238  /*Virtual method to apply initial conditions. This is usually expected to be provided by the user in IBVP (Initial Boundary Value Problems).*/
                    +
                    239  virtual void applyInitialConditions();
                    +
                    240  virtual void modifySolutionFields ();
                    +
                    241 
                    +
                    242  /*Method to compute energy like quantities.*/
                    +
                    243  void computeEnergy();
                    +
                    244  virtual void getEnergy(const MatrixFree<dim,double> &data,
                    +
                    245  std::vector<vectorType*> &dst,
                    +
                    246  const std::vector<vectorType*> &src,
                    +
                    247  const std::pair<unsigned int,unsigned int> &cell_range);
                    +
                    248 
                    +
                    249  //utility functions
                    +
                    250  /*Returns index of given field name if exists, else throw error.*/
                    +
                    251  unsigned int getFieldIndex(std::string _name);
                    +
                    252 
                    +
                    253 
                    +
                    254  std::vector<double> freeEnergyValues;
                    +
                    255  void outputFreeEnergy(std::vector<double>& freeEnergyValues);
                    256 
                    -
                    257  /*parallel message stream*/
                    -
                    258  ConditionalOStream pcout;
                    -
                    259  /*Timer and logging object*/
                    -
                    260  mutable TimerOutput computing_timer;
                    -
                    261 
                    -
                    262  double energy;
                    -
                    263  std::vector<double> energy_components;
                    -
                    264 };
                    -
                    265 
                    -
                    266 //other matrixFree headers
                    -
                    267 //(these are source files, which will are temporarily treated as
                    -
                    268 //header files till library packaging scheme is finalized)
                    -
                    269 #include "../src/matrixfree/matrixFreePDE.cc"
                    -
                    270 #include "../src/matrixfree/init.cc"
                    -
                    271 #include "../src/matrixfree/initForTests.cc"
                    -
                    272 #include "../src/matrixfree/refine.cc"
                    -
                    273 #include "../src/matrixfree/invM.cc"
                    -
                    274 #include "../src/matrixfree/computeLHS.cc"
                    -
                    275 #include "../src/matrixfree/computeRHS.cc"
                    -
                    276 #include "../src/matrixfree/modifyFields.cc"
                    -
                    277 #include "../src/matrixfree/solve.cc"
                    -
                    278 #include "../src/matrixfree/solveIncrement.cc"
                    -
                    279 #include "../src/matrixfree/outputResults.cc"
                    -
                    280 #include "../src/matrixfree/markBoundaries.cc"
                    -
                    281 #include "../src/matrixfree/boundaryConditions.cc"
                    -
                    282 #include "../src/matrixfree/initialConditions.cc"
                    -
                    283 #include "../src/matrixfree/utilities.cc"
                    -
                    284 #include "../src/matrixfree/calcFreeEnergy.cc"
                    -
                    285 #include "../src/matrixfree/integrate_and_shift_field.cc"
                    -
                    286 
                    -
                    287 #endif
                    -
                    MatrixFreePDE::fields
                    std::vector< Field< dim > > fields
                    Definition: matrixFreePDE.h:122
                    -
                    MatrixFreePDE::dU
                    vectorType dU
                    Definition: matrixFreePDE.h:183
                    -
                    MatrixFreePDE::freeEnergyValues
                    std::vector< double > freeEnergyValues
                    Definition: matrixFreePDE.h:241
                    -
                    MatrixFreePDE::pcout
                    ConditionalOStream pcout
                    Definition: matrixFreePDE.h:258
                    -
                    MatrixFreePDE::locally_relevant_dofsSet
                    std::vector< const IndexSet * > locally_relevant_dofsSet
                    Definition: matrixFreePDE.h:161
                    -
                    MatrixFreePDE::isTimeDependentBVP
                    bool isTimeDependentBVP
                    Definition: matrixFreePDE.h:249
                    +
                    257  /*Method to compute the integral of a field.*/
                    +
                    258  void computeIntegral(double& integratedField);
                    +
                    259 
                    +
                    260  //variables for time dependent problems
                    +
                    261  /*Flag used to see if invM, time steppping in run(), etc are necessary*/
                    +
                    262  bool isTimeDependentBVP;
                    +
                    263  /*Flag used to mark problems with Elliptic fields.*/
                    +
                    264  bool isEllipticBVP;
                    +
                    265  //
                    +
                    266  unsigned int parabolicFieldIndex, ellipticFieldIndex;
                    +
                    267  double dtValue, currentTime, finalTime;
                    +
                    268  unsigned int currentIncrement, totalIncrements;
                    +
                    269 
                    +
                    270  /*parallel message stream*/
                    +
                    271  ConditionalOStream pcout;
                    +
                    272  /*Timer and logging object*/
                    +
                    273  mutable TimerOutput computing_timer;
                    +
                    274 
                    +
                    275  double energy;
                    +
                    276  std::vector<double> energy_components;
                    +
                    277 };
                    +
                    278 
                    +
                    279 //other matrixFree headers
                    +
                    280 //(these are source files, which will are temporarily treated as
                    +
                    281 //header files till library packaging scheme is finalized)
                    +
                    282 #include "../src/matrixfree/matrixFreePDE.cc"
                    +
                    283 #include "../src/matrixfree/init.cc"
                    +
                    284 #include "../src/matrixfree/reinit.cc"
                    +
                    285 #include "../src/matrixfree/initForTests.cc"
                    +
                    286 #include "../src/matrixfree/refine.cc"
                    +
                    287 #include "../src/matrixfree/invM.cc"
                    +
                    288 #include "../src/matrixfree/computeLHS.cc"
                    +
                    289 #include "../src/matrixfree/computeRHS.cc"
                    +
                    290 #include "../src/matrixfree/modifyFields.cc"
                    +
                    291 #include "../src/matrixfree/solve.cc"
                    +
                    292 #include "../src/matrixfree/solveIncrement.cc"
                    +
                    293 #include "../src/matrixfree/outputResults.cc"
                    +
                    294 #include "../src/matrixfree/markBoundaries.cc"
                    +
                    295 #include "../src/matrixfree/boundaryConditions.cc"
                    +
                    296 #include "../src/matrixfree/initialConditions.cc"
                    +
                    297 #include "../src/matrixfree/utilities.cc"
                    +
                    298 #include "../src/matrixfree/calcFreeEnergy.cc"
                    +
                    299 #include "../src/matrixfree/integrate_and_shift_field.cc"
                    +
                    300 #include "../src/matrixfree/getOutputTimeSteps.cc"
                    +
                    301 
                    +
                    302 #endif
                    +
                    MatrixFreePDE::fields
                    std::vector< Field< dim > > fields
                    Definition: matrixFreePDE.h:123
                    +
                    MatrixFreePDE::dU
                    vectorType dU
                    Definition: matrixFreePDE.h:190
                    +
                    MatrixFreePDE::freeEnergyValues
                    std::vector< double > freeEnergyValues
                    Definition: matrixFreePDE.h:254
                    +
                    MatrixFreePDE::pcout
                    ConditionalOStream pcout
                    Definition: matrixFreePDE.h:271
                    +
                    MatrixFreePDE::locally_relevant_dofsSet
                    std::vector< const IndexSet * > locally_relevant_dofsSet
                    Definition: matrixFreePDE.h:168
                    +
                    MatrixFreePDE::isTimeDependentBVP
                    bool isTimeDependentBVP
                    Definition: matrixFreePDE.h:262
                    MatrixFreePDE
                    Definition: matrixFreePDE.h:85
                    scalargradType
                    dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
                    Definition: matrixFreePDE.h:51
                    -
                    MatrixFreePDE::invM
                    vectorType invM
                    Definition: matrixFreePDE.h:181
                    -
                    MatrixFreePDE::isEllipticBVP
                    bool isEllipticBVP
                    Definition: matrixFreePDE.h:251
                    +
                    MatrixFreePDE::invM
                    vectorType invM
                    Definition: matrixFreePDE.h:188
                    +
                    MatrixFreePDE::isEllipticBVP
                    bool isEllipticBVP
                    Definition: matrixFreePDE.h:264
                    vectorType
                    dealii::parallel::distributed::Vector< double > vectorType
                    Definition: matrixFreePDE.h:23
                    -
                    MatrixFreePDE::totalIncrements
                    unsigned int totalIncrements
                    Definition: matrixFreePDE.h:255
                    -
                    MatrixFreePDE::triangulation
                    parallel::distributed::Triangulation< dim > triangulation
                    Definition: matrixFreePDE.h:145
                    -
                    MatrixFreePDE::matrixFreeObject
                    MatrixFree< dim, double > matrixFreeObject
                    Definition: matrixFreePDE.h:179
                    -
                    MatrixFreePDE::soltransSet
                    std::vector< parallel::distributed::SolutionTransfer< dim, vectorType > * > soltransSet
                    Definition: matrixFreePDE.h:173
                    -
                    MatrixFreePDE::finalTime
                    double finalTime
                    Definition: matrixFreePDE.h:254
                    -
                    MatrixFreePDE::valuesDirichletSet
                    std::vector< std::map< dealii::types::global_dof_index, double > * > valuesDirichletSet
                    Definition: matrixFreePDE.h:218
                    -
                    MatrixFreePDE::currentFieldIndex
                    unsigned int currentFieldIndex
                    Definition: matrixFreePDE.h:187
                    -
                    MatrixFreePDE::constraintsSet2
                    std::vector< ConstraintMatrix * > constraintsSet2
                    Definition: matrixFreePDE.h:163
                    +
                    MatrixFreePDE::totalIncrements
                    unsigned int totalIncrements
                    Definition: matrixFreePDE.h:268
                    +
                    MatrixFreePDE::triangulation
                    parallel::distributed::Triangulation< dim > triangulation
                    Definition: matrixFreePDE.h:152
                    +
                    MatrixFreePDE::matrixFreeObject
                    MatrixFree< dim, double > matrixFreeObject
                    Definition: matrixFreePDE.h:186
                    +
                    MatrixFreePDE::soltransSet
                    std::vector< parallel::distributed::SolutionTransfer< dim, vectorType > * > soltransSet
                    Definition: matrixFreePDE.h:180
                    +
                    MatrixFreePDE::finalTime
                    double finalTime
                    Definition: matrixFreePDE.h:267
                    +
                    MatrixFreePDE::valuesDirichletSet
                    std::vector< std::map< dealii::types::global_dof_index, double > * > valuesDirichletSet
                    Definition: matrixFreePDE.h:225
                    +
                    MatrixFreePDE::currentFieldIndex
                    unsigned int currentFieldIndex
                    Definition: matrixFreePDE.h:194
                    +
                    MatrixFreePDE::dofHandlersSet_nonconst
                    std::vector< DoFHandler< dim > * > dofHandlersSet_nonconst
                    Definition: matrixFreePDE.h:172
                    +
                    MatrixFreePDE::constraintsOtherSet_nonconst
                    std::vector< ConstraintMatrix * > constraintsOtherSet_nonconst
                    Definition: matrixFreePDE.h:170
                    scalarhessType
                    dealii::Tensor< 2, problemDIM, dealii::VectorizedArray< double > > scalarhessType
                    Definition: matrixFreePDE.h:54
                    -
                    MatrixFreePDE::constraintsSet
                    std::vector< const ConstraintMatrix * > constraintsSet
                    Definition: matrixFreePDE.h:154
                    typeScalar
                    dealii::FEEvaluation< problemDIM, finiteElementDegree, finiteElementDegree+1, 1, double > typeScalar
                    Definition: matrixFreePDE.h:27
                    -
                    MatrixFreePDE::computing_timer
                    TimerOutput computing_timer
                    Definition: matrixFreePDE.h:260
                    +
                    MatrixFreePDE::computing_timer
                    TimerOutput computing_timer
                    Definition: matrixFreePDE.h:273
                    +
                    MatrixFreePDE::constraintsOtherSet
                    std::vector< const ConstraintMatrix * > constraintsOtherSet
                    Definition: matrixFreePDE.h:161
                    vectorvalueType
                    dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > vectorvalueType
                    Definition: matrixFreePDE.h:37
                    -
                    MatrixFreePDE::FESet
                    std::vector< FESystem< dim > * > FESet
                    Definition: matrixFreePDE.h:149
                    -
                    MatrixFreePDE::dofHandlersSet2
                    std::vector< DoFHandler< dim > * > dofHandlersSet2
                    Definition: matrixFreePDE.h:165
                    -
                    MatrixFreePDE::dofHandlersSet
                    std::vector< const DoFHandler< dim > * > dofHandlersSet
                    Definition: matrixFreePDE.h:157
                    +
                    MatrixFreePDE::FESet
                    std::vector< FESystem< dim > * > FESet
                    Definition: matrixFreePDE.h:156
                    +
                    MatrixFreePDE::dofHandlersSet
                    std::vector< const DoFHandler< dim > * > dofHandlersSet
                    Definition: matrixFreePDE.h:164
                    vectorhessType
                    dealii::Tensor< 3, problemDIM, dealii::VectorizedArray< double > > vectorhessType
                    Definition: matrixFreePDE.h:60
                    -
                    MatrixFreePDE::residualSet
                    std::vector< vectorType * > residualSet
                    Definition: matrixFreePDE.h:171
                    -
                    MatrixFreePDE::energy_components
                    std::vector< double > energy_components
                    Definition: matrixFreePDE.h:263
                    +
                    MatrixFreePDE::residualSet
                    std::vector< vectorType * > residualSet
                    Definition: matrixFreePDE.h:178
                    +
                    MatrixFreePDE::energy_components
                    std::vector< double > energy_components
                    Definition: matrixFreePDE.h:276
                    vectorgradType
                    dealii::Tensor< 2, problemDIM, dealii::VectorizedArray< double > > vectorgradType
                    Definition: matrixFreePDE.h:57
                    -
                    MatrixFreePDE::parabolicFieldIndex
                    unsigned int parabolicFieldIndex
                    Definition: matrixFreePDE.h:253
                    -
                    MatrixFreePDE::locally_relevant_dofsSet2
                    std::vector< IndexSet * > locally_relevant_dofsSet2
                    Definition: matrixFreePDE.h:167
                    -
                    MatrixFreePDE::energy
                    double energy
                    Definition: matrixFreePDE.h:262
                    +
                    MatrixFreePDE::parabolicFieldIndex
                    unsigned int parabolicFieldIndex
                    Definition: matrixFreePDE.h:266
                    +
                    MatrixFreePDE::energy
                    double energy
                    Definition: matrixFreePDE.h:275
                    +
                    MatrixFreePDE::outputTimeStepList
                    std::vector< unsigned int > outputTimeStepList
                    Definition: matrixFreePDE.h:147
                    +
                    MatrixFreePDE::locally_relevant_dofsSet_nonconst
                    std::vector< IndexSet * > locally_relevant_dofsSet_nonconst
                    Definition: matrixFreePDE.h:174
                    scalarvalueType
                    dealii::VectorizedArray< double > scalarvalueType
                    Definition: matrixFreePDE.h:34
                    model_variables.h
                    -
                    MatrixFreePDE::solutionSet
                    std::vector< vectorType * > solutionSet
                    Definition: matrixFreePDE.h:169
                    -
                    MatrixFreePDE::num_quadrature_points
                    unsigned int num_quadrature_points
                    Definition: matrixFreePDE.h:189
                    +
                    MatrixFreePDE::solutionSet
                    std::vector< vectorType * > solutionSet
                    Definition: matrixFreePDE.h:176
                    +
                    MatrixFreePDE::num_quadrature_points
                    unsigned int num_quadrature_points
                    Definition: matrixFreePDE.h:196
                    scalarType
                    dealii::VectorizedArray< double > scalarType
                    Definition: matrixFreePDE.h:20
                    typeVector
                    dealii::FEEvaluation< problemDIM, finiteElementDegree, finiteElementDegree+1, problemDIM, double > typeVector
                    Definition: matrixFreePDE.h:30
                    diff --git a/html/mechanics_2_i_cs__and___b_cs_8h_source.html b/html/mechanics_2_i_cs__and___b_cs_8h_source.html index f28193455..055512e52 100644 --- a/html/mechanics_2_i_cs__and___b_cs_8h_source.html +++ b/html/mechanics_2_i_cs__and___b_cs_8h_source.html @@ -168,29 +168,29 @@
                    76  // Inputs to "inputBCs":
                    77  // First input: variable number
                    78  // Second input: component number
                    -
                    79  // Third input: BC type (options are "ZERO_DERIVATIVE" and "DIRICHLET")
                    +
                    79  // Third input: BC type (options are "ZERO_DERIVATIVE", "DIRICHLET", and "PERIODIC")
                    80  // Fourth input: BC value (ignored unless the BC type is "DIRICHLET")
                    81  // Odd inputs after the third: BC type
                    82  // Even inputs after the third: BC value
                    83  // Face numbering: starts at zero with the minimum of the first direction, one for the maximum of the first direction
                    84  // two for the minimum of the second direction, etc.
                    85 
                    -
                    86  inputBCs(4,0,"DIRICHLET",-0.1, "DIRICHLET",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0);
                    +
                    86  inputBCs(4,0,"DIRICHLET",-1.0, "DIRICHLET",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0);
                    87  inputBCs(4,1,"DIRICHLET",0.0, "DIRICHLET",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0);
                    88  inputBCs(4,2,"DIRICHLET",0.0, "DIRICHLET",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0);
                    89 
                    90 }
                    91 
                    92 
                    -
                    InitialConditionVec::index
                    unsigned int index
                    Definition: ICs_and_BCs.h:46
                    +
                    InitialConditionVec::index
                    unsigned int index
                    Definition: ICs_and_BCs.h:47
                    InitialConditionVec::vector_value
                    void vector_value(const Point< dim > &p, Vector< double > &vector_IC) const
                    Definition: ICs_and_BCs.h:35
                    InitialCondition::InitialCondition
                    InitialCondition(const unsigned int _index)
                    Definition: ICs_and_BCs.h:7
                    InitialCondition::value
                    double value(const Point< dim > &p, const unsigned int component=0) const
                    Definition: ICs_and_BCs.h:10
                    InitialCondition::index
                    unsigned int index
                    Definition: ICs_and_BCs.h:5
                    -
                    generalizedProblem::setBCs
                    void setBCs()
                    Definition: ICs_and_BCs.h:66
                    +
                    generalizedProblem::setBCs
                    void setBCs()
                    Definition: ICs_and_BCs.h:67
                    InitialConditionVec::InitialConditionVec
                    InitialConditionVec(const unsigned int _index)
                    Definition: ICs_and_BCs.h:32
                    InitialCondition
                    Definition: ICs_and_BCs.h:2
                    -
                    InitialConditionVec
                    Definition: ICs_and_BCs.h:43
                    +
                    InitialConditionVec
                    Definition: ICs_and_BCs.h:44
                    InitialCondition::values
                    Vector< double > values
                    Definition: ICs_and_BCs.h:6
                    diff --git a/html/mechanics_2equations_8h_source.html b/html/mechanics_2equations_8h_source.html index 1c6ba7b08..d72615ea8 100644 --- a/html/mechanics_2equations_8h_source.html +++ b/html/mechanics_2equations_8h_source.html @@ -151,8 +151,8 @@
                    59 // each residual equation. The index for each variable in these lists corresponds to
                    60 // the order it is defined at the top of this file (starting at 0).
                    61 template <int dim>
                    -
                    62 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim>> & modelVariablesList,
                    -
                    63  std::vector<modelResidual<dim>> & modelResidualsList,
                    +
                    62 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim> > & modelVariablesList,
                    +
                    63  std::vector<modelResidual<dim> > & modelResidualsList,
                    64  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
                    65 
                    66 //u
                    @@ -197,7 +197,7 @@
                    105 // that the correct residual is being submitted. The index of the field being solved
                    106 // can be accessed by "this->currentFieldIndex".
                    107 template <int dim>
                    -
                    108 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim>> & modelVarList,
                    +
                    108 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim> > & modelVarList,
                    109  modelResidual<dim> & modelRes,
                    110  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
                    111 
                    @@ -239,7 +239,7 @@
                    147 // density are added to the "energy_components" variable (index 0: chemical energy,
                    148 // index 1: gradient energy, index 2: elastic energy).
                    149 template <int dim>
                    -
                    150 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim>> & modelVarList,
                    +
                    150 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim> > & modelVarList,
                    151  const dealii::VectorizedArray<double> & JxW_value,
                    152  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) {
                    153 
                    @@ -248,14 +248,14 @@
                    156 
                    157 
                    158 
                    -
                    generalizedProblem::energyDensity
                    void energyDensity(const std::vector< modelVariable< dim >> &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
                    Definition: equations.h:106
                    modelResidual
                    Definition: model_variables.h:25
                    -
                    generalizedProblem::residualLHS
                    void residualLHS(const std::vector< modelVariable< dim >> &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
                    Definition: equations.h:89
                    +
                    generalizedProblem::residualRHS
                    void residualRHS(const std::vector< modelVariable< dim > > &modelVarList, std::vector< modelResidual< dim > > &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
                    Definition: equations.h:58
                    constV
                    #define constV(a)
                    Definition: matrixFreePDE.h:67
                    modelResidual::vectorGradResidual
                    vectorgradType vectorGradResidual
                    Definition: model_variables.h:33
                    +
                    generalizedProblem::residualLHS
                    void residualLHS(const std::vector< modelVariable< dim > > &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
                    Definition: equations.h:89
                    modelVariable
                    Definition: model_variables.h:4
                    vectorgradType
                    dealii::Tensor< 2, problemDIM, dealii::VectorizedArray< double > > vectorgradType
                    Definition: matrixFreePDE.h:57
                    -
                    generalizedProblem::residualRHS
                    void residualRHS(const std::vector< modelVariable< dim >> &modelVarList, std::vector< modelResidual< dim >> &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
                    Definition: equations.h:58
                    +
                    generalizedProblem::energyDensity
                    void energyDensity(const std::vector< modelVariable< dim > > &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
                    Definition: equations.h:106
                    diff --git a/html/mechanics_8h_source.html b/html/mechanics_8h_source.html index 1cac7774f..0e3d6bfb8 100644 --- a/html/mechanics_8h_source.html +++ b/html/mechanics_8h_source.html @@ -250,25 +250,25 @@
                    158 #ifndef hAdaptivity
                    159 //adaptive refinement control
                    160 template <int dim>
                    -
                    161 void MechanicsProblem<dim>::adaptiveRefine(unsigned int currentIncrement){
                    +
                    161 void MechanicsProblem<dim>::adaptiveRefine(unsigned int currentIncrement){
                    162 }
                    163 
                    164 //adaptive refinement criterion
                    165 template <int dim>
                    -
                    166 void MechanicsProblem<dim>::adaptiveRefineCriterion(){
                    +
                    166 void MechanicsProblem<dim>::adaptiveRefineCriterion(){
                    167 }
                    168 #endif
                    169 
                    170 #endif
                    171 
                    -
                    MatrixFreePDE::pcout
                    ConditionalOStream pcout
                    Definition: matrixFreePDE.h:258
                    -
                    MechanicsProblem::adaptiveRefine
                    void adaptiveRefine(unsigned int currentIncrement)
                    Definition: mechanics.h:161
                    +
                    MatrixFreePDE::pcout
                    ConditionalOStream pcout
                    Definition: matrixFreePDE.h:271
                    +
                    MechanicsProblem::adaptiveRefine
                    void adaptiveRefine(unsigned int currentIncrement)
                    Definition: main.cc:17
                    MatrixFreePDE
                    Definition: matrixFreePDE.h:85
                    MechanicsProblem::markBoundaries
                    void markBoundaries()
                    Definition: ICs_and_BCs.h:3
                    constV
                    #define constV(a)
                    Definition: matrixFreePDE.h:67
                    vectorType
                    dealii::parallel::distributed::Vector< double > vectorType
                    Definition: matrixFreePDE.h:23
                    -
                    MechanicsProblem::adaptiveRefineCriterion
                    void adaptiveRefineCriterion()
                    Definition: mechanics.h:166
                    +
                    MechanicsProblem::adaptiveRefineCriterion
                    void adaptiveRefineCriterion()
                    Definition: main.cc:23
                    MechanicsProblem::getRHS
                    void getRHS(const MatrixFree< dim, double > &data, std::vector< vectorType * > &dst, const std::vector< vectorType * > &src, const std::pair< unsigned int, unsigned int > &cell_range) const
                    Definition: mechanics.h:68
                    MechanicsProblem::CIJ
                    Table< 2, double > CIJ
                    Definition: mechanics.h:20
                    MaterialModelV
                    #define MaterialModelV
                    Definition: parameters.h:26
                    @@ -277,7 +277,7 @@
                    MechanicsProblem
                    Definition: mechanics.h:13
                    MaterialConstantsV
                    #define MaterialConstantsV
                    Definition: parameters.h:27
                    vectorgradType
                    dealii::Tensor< 2, problemDIM, dealii::VectorizedArray< double > > vectorgradType
                    Definition: matrixFreePDE.h:57
                    -
                    MatrixFreePDE::currentIncrement
                    unsigned int currentIncrement
                    Definition: matrixFreePDE.h:255
                    +
                    MatrixFreePDE::currentIncrement
                    unsigned int currentIncrement
                    Definition: matrixFreePDE.h:268
                    MechanicsProblem::MechanicsProblem
                    MechanicsProblem()
                    Definition: mechanics.h:45
                    typeVector
                    dealii::FEEvaluation< problemDIM, finiteElementDegree, finiteElementDegree+1, problemDIM, double > typeVector
                    Definition: matrixFreePDE.h:30
                    diff --git a/html/namespace_p_functions.html b/html/namespace_p_functions.html new file mode 100644 index 000000000..b4689fa05 --- /dev/null +++ b/html/namespace_p_functions.html @@ -0,0 +1,99 @@ + + + + + + +PRISMS PhaseField: PFunctions Namespace Reference + + + + + + + + + + +
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                    PFunctions Namespace Reference
                    +
                    +
                    + + + + +

                    +Classes

                    class  pFunction
                     
                    +
                    + +
                    +Generated by   +doxygen + 1.8.10 +
                    + + diff --git a/html/p_function_8h.html b/html/p_function_8h.html new file mode 100644 index 000000000..6b06639cd --- /dev/null +++ b/html/p_function_8h.html @@ -0,0 +1,117 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/src/pFunction/pFunction.h File Reference + + + + + + + + + + +
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                    PRISMS PhaseField +  1.0 +
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                    pFunction.h File Reference
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                    +Classes

                    class  pFunction
                     
                    + + + +

                    +Namespaces

                     PFunctions
                     
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                    + + diff --git a/html/p_function_8h_source.html b/html/p_function_8h_source.html new file mode 100644 index 000000000..2a5f86508 --- /dev/null +++ b/html/p_function_8h_source.html @@ -0,0 +1,173 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/src/pFunction/pFunction.h Source File + + + + + + + + + + +
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                    PRISMS PhaseField +  1.0 +
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                    pFunction.h
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                    +
                    +Go to the documentation of this file.
                    1 // pFunction: An interface for PFunctions in PRISMS-PF
                    +
                    2 // This class is used to declare and load PFunctions from a pre-existing PLibrary
                    +
                    3 // that was created using PRISMS IntegrationTools. The class is a wrapper for the
                    +
                    4 // PFunctions in IntegrationTools so that they can be used for vectorized arrays.
                    +
                    5 // This class also calls the IntegrationTools checkout function in the constructor
                    +
                    6 // to reduce the number of steps the user needs to take. Currently this is only
                    +
                    7 // implemented for scalar functions. Vector functions can be treated component by
                    +
                    8 // component.
                    +
                    9 
                    +
                    10 namespace PFunctions{
                    +
                    11 
                    +
                    12 class pFunction
                    +
                    13 {
                    +
                    14 public:
                    +
                    15  // Constructor, wraps the IntegrationTools checkout function
                    +
                    16  pFunction(std::string function_name);
                    +
                    17 
                    +
                    18  // Returns the value of the function for a given input variable
                    +
                    19  scalarvalueType val(scalarvalueType);
                    +
                    20 
                    +
                    21  // Returns one of first derivatives of the function for a given input variable
                    +
                    22  scalarvalueType grad(scalarvalueType, unsigned int);
                    +
                    23 
                    +
                    24  // Returns one of the second derivatives of the function for a given input variable
                    +
                    25  scalarvalueType hess(scalarvalueType, unsigned int, unsigned int);
                    +
                    26 
                    +
                    27 private:
                    +
                    28  PRISMS::PFunction<double*, double> fun;
                    +
                    29 };
                    +
                    30 
                    +
                    31 pFunction::pFunction(std::string function_name){
                    +
                    32  PRISMS::PLibrary::checkout(function_name, fun);
                    +
                    33 }
                    +
                    34 
                    +
                    35 scalarvalueType pFunction::val(scalarvalueType var){
                    +
                    36  scalarvalueType fun_val;
                    +
                    37  for (unsigned i=0; i < var.n_array_elements; i++){
                    +
                    38  fun_val[i] = fun(&var[i]);
                    +
                    39  }
                    +
                    40  return fun_val;
                    +
                    41 
                    +
                    42 }
                    +
                    43 
                    +
                    44 
                    +
                    45 scalarvalueType pFunction::grad(scalarvalueType var,unsigned int dir){
                    +
                    46  scalarvalueType fun_grad;
                    +
                    47  for (unsigned i=0; i <var.n_array_elements; i++){
                    +
                    48  fun_grad[i] = fun.grad(&var[i],dir);
                    +
                    49  }
                    +
                    50  return fun_grad;
                    +
                    51 
                    +
                    52 }
                    +
                    53 
                    +
                    54 
                    +
                    55 scalarvalueType pFunction::hess(scalarvalueType var,unsigned int dir1, unsigned int dir2){
                    +
                    56  scalarvalueType fun_hess;
                    +
                    57  for (unsigned i=0; i < var.n_array_elements; i++){
                    +
                    58  fun_hess[i] = fun.hess(&var[i],dir1,dir2);
                    +
                    59  }
                    +
                    60  return fun_hess;
                    +
                    61 
                    +
                    62 }
                    +
                    63 
                    +
                    64 }
                    +
                    PFunctions::pFunction::grad
                    scalarvalueType grad(scalarvalueType, unsigned int)
                    Definition: pFunction.h:45
                    +
                    PFunctions::pFunction::pFunction
                    pFunction(std::string function_name)
                    Definition: pFunction.h:31
                    +
                    PFunctions::pFunction
                    Definition: pFunction.h:12
                    +
                    PFunctions::pFunction::fun
                    PRISMS::PFunction< double *, double > fun
                    Definition: pFunction.h:28
                    +
                    PFunctions
                    Definition: pFunction.h:10
                    +
                    PFunctions::pFunction::val
                    scalarvalueType val(scalarvalueType)
                    Definition: pFunction.h:35
                    +
                    PFunctions::pFunction::hess
                    scalarvalueType hess(scalarvalueType, unsigned int, unsigned int)
                    Definition: pFunction.h:55
                    +
                    scalarvalueType
                    dealii::VectorizedArray< double > scalarvalueType
                    Definition: matrixFreePDE.h:34
                    +
                    + +
                    +Generated by   +doxygen + 1.8.10 +
                    + + diff --git a/html/precipitate_evolution_2_i_cs__and___b_cs_8h_source.html b/html/precipitate_evolution_2_i_cs__and___b_cs_8h_source.html index 18e17057b..e36397070 100644 --- a/html/precipitate_evolution_2_i_cs__and___b_cs_8h_source.html +++ b/html/precipitate_evolution_2_i_cs__and___b_cs_8h_source.html @@ -193,7 +193,7 @@
                    101  // Inputs to "inputBCs":
                    102  // First input: variable number
                    103  // Second input: component number
                    -
                    104  // Third input: BC type (options are "ZERO_DERIVATIVE" and "DIRICHLET")
                    +
                    104  // Third input: BC type (options are "ZERO_DERIVATIVE", "DIRICHLET", and "PERIODIC")
                    105  // Fourth input: BC value (ignored unless the BC type is "DIRICHLET")
                    106  // Odd inputs after the third: BC type
                    107  // Even inputs after the third: BC value
                    @@ -217,7 +217,7 @@
                    125 }
                    126 
                    127 
                    -
                    InitialConditionVec::index
                    unsigned int index
                    Definition: ICs_and_BCs.h:46
                    +
                    InitialConditionVec::index
                    unsigned int index
                    Definition: ICs_and_BCs.h:47
                    InitialConditionVec::vector_value
                    void vector_value(const Point< dim > &p, Vector< double > &vector_IC) const
                    Definition: ICs_and_BCs.h:70
                    spanX
                    #define spanX
                    Definition: defaultValues.h:12
                    InitialCondition::InitialCondition
                    InitialCondition(const unsigned int _index)
                    Definition: ICs_and_BCs.h:8
                    @@ -225,11 +225,11 @@
                    InitialCondition::value
                    double value(const Point< dim > &p, const unsigned int component=0) const
                    Definition: ICs_and_BCs.h:11
                    InitialCondition::index
                    unsigned int index
                    Definition: ICs_and_BCs.h:5
                    refineFactor
                    #define refineFactor
                    Definition: defaultValues.h:27
                    -
                    generalizedProblem::setBCs
                    void setBCs()
                    Definition: ICs_and_BCs.h:66
                    +
                    generalizedProblem::setBCs
                    void setBCs()
                    Definition: ICs_and_BCs.h:67
                    InitialConditionVec::InitialConditionVec
                    InitialConditionVec(const unsigned int _index)
                    Definition: ICs_and_BCs.h:67
                    subdivisionsX
                    #define subdivisionsX
                    Definition: parameters.h:14
                    InitialCondition
                    Definition: ICs_and_BCs.h:2
                    -
                    InitialConditionVec
                    Definition: ICs_and_BCs.h:43
                    +
                    InitialConditionVec
                    Definition: ICs_and_BCs.h:44
                    spanZ
                    #define spanZ
                    Definition: defaultValues.h:22
                    InitialCondition::values
                    Vector< double > values
                    Definition: ICs_and_BCs.h:6
                    diff --git a/html/precipitate_evolution_2equations_8h.html b/html/precipitate_evolution_2equations_8h.html index 98a060cc9..10375e2ba 100644 --- a/html/precipitate_evolution_2equations_8h.html +++ b/html/precipitate_evolution_2equations_8h.html @@ -138,7 +138,7 @@   #define MaterialModels   {{"ISOTROPIC"},{"ISOTROPIC"}}   -#define MaterialConstants   {{2.0,0.3},{2.5,0.3}} +#define MaterialConstants   {{40.0,0.3},{50.0,0.3}}   #define faV   (-1.6704-4.776*c+5.1622*c*c-2.7375*c*c*c+1.3687*c*c*c*c)   @@ -166,21 +166,21 @@   #define rcV   (c)   -#define rcxTemp   ( cx*((1.0-h1V-h2V-h3V)*faccV+(h1V+h2V+h3V)*fbccV) + n1x*((fbcV-facV)*hn1V) + n2x*((fbcV-facV)*hn2V) + n3x*((fbcV-facV)*hn3V) + grad_mu_el) +#define rcxTemp   ( cx*((1.0-h1V-h2V-h3V)*faccV+(h1V+h2V+h3V)*fbccV) + n1x*((fbcV-facV)*hn1V) + n2x*((fbcV-facV)*hn2V) + n3x*((fbcV-facV)*hn3V) + grad_mu_el)   -#define rcxV   (constV(-timeStep*McV)*rcxTemp) +#define rcxV   (constV(-timeStep*McV)*rcxTemp)   -#define rn1V   (n1-constV(timeStep*Mn1V)*((fbV-faV)*hn1V+nDependentMisfitAC1+heterMechAC1)) +#define rn1V   (n1-constV(timeStep*Mn1V)*((fbV-faV)*hn1V+nDependentMisfitAC1+heterMechAC1))   -#define rn2V   (n2-constV(timeStep*Mn2V)*((fbV-faV)*hn2V+nDependentMisfitAC2+heterMechAC2)) +#define rn2V   (n2-constV(timeStep*Mn2V)*((fbV-faV)*hn2V+nDependentMisfitAC2+heterMechAC2))   -#define rn3V   (n3-constV(timeStep*Mn3V)*((fbV-faV)*hn3V+nDependentMisfitAC3+heterMechAC3)) +#define rn3V   (n3-constV(timeStep*Mn3V)*((fbV-faV)*hn3V+nDependentMisfitAC3+heterMechAC3))   -#define rn1xV   (constV(-timeStep*Mn1V)*Knx1) +#define rn1xV   (constV(-timeStep*Mn1V)*Knx1)   -#define rn2xV   (constV(-timeStep*Mn2V)*Knx2) +#define rn2xV   (constV(-timeStep*Mn2V)*Knx2)   -#define rn3xV   (constV(-timeStep*Mn3V)*Knx3) +#define rn3xV   (constV(-timeStep*Mn3V)*Knx3)   - + - + @@ -133,7 +133,7 @@ - + @@ -145,8 +145,14 @@ - - + + + + + + + +

                    @@ -354,7 +354,7 @@

                    Macro Definition Documentation

                    - +
                    #define MaterialConstants   {{2.0,0.3},{2.5,0.3}}#define MaterialConstants   {{40.0,0.3},{50.0,0.3}}
                    @@ -582,7 +582,7 @@

                    Macro Definition Documentation

                    - +
                    #define rcxTemp   ( cx*((1.0-h1V-h2V-h3V)*faccV+(h1V+h2V+h3V)*fbccV) + n1x*((fbcV-facV)*hn1V) + n2x*((fbcV-facV)*hn2V) + n3x*((fbcV-facV)*hn3V) + grad_mu_el)#define rcxTemp   ( cx*((1.0-h1V-h2V-h3V)*faccV+(h1V+h2V+h3V)*fbccV) + n1x*((fbcV-facV)*hn1V) + n2x*((fbcV-facV)*hn2V) + n3x*((fbcV-facV)*hn3V) + grad_mu_el)
                    @@ -594,7 +594,7 @@

                    Macro Definition Documentation

                    - +
                    #define rcxV   (constV(-timeStep*McV)*rcxTemp)#define rcxV   (constV(-timeStep*McV)*rcxTemp)
                    @@ -606,7 +606,7 @@

                    Macro Definition Documentation

                    - +
                    #define rn1V   (n1-constV(timeStep*Mn1V)*((fbV-faV)*hn1V+nDependentMisfitAC1+heterMechAC1))#define rn1V   (n1-constV(timeStep*Mn1V)*((fbV-faV)*hn1V+nDependentMisfitAC1+heterMechAC1))
                    @@ -618,7 +618,7 @@

                    Macro Definition Documentation

                    - +
                    #define rn1xV   (constV(-timeStep*Mn1V)*Knx1)#define rn1xV   (constV(-timeStep*Mn1V)*Knx1)
                    @@ -630,7 +630,7 @@

                    Macro Definition Documentation

                    - +
                    #define rn2V   (n2-constV(timeStep*Mn2V)*((fbV-faV)*hn2V+nDependentMisfitAC2+heterMechAC2))#define rn2V   (n2-constV(timeStep*Mn2V)*((fbV-faV)*hn2V+nDependentMisfitAC2+heterMechAC2))
                    @@ -642,7 +642,7 @@

                    Macro Definition Documentation

                    - +
                    #define rn2xV   (constV(-timeStep*Mn2V)*Knx2)#define rn2xV   (constV(-timeStep*Mn2V)*Knx2)
                    @@ -654,7 +654,7 @@

                    Macro Definition Documentation

                    - +
                    #define rn3V   (n3-constV(timeStep*Mn3V)*((fbV-faV)*hn3V+nDependentMisfitAC3+heterMechAC3))#define rn3V   (n3-constV(timeStep*Mn3V)*((fbV-faV)*hn3V+nDependentMisfitAC3+heterMechAC3))
                    @@ -666,7 +666,7 @@

                    Macro Definition Documentation

                    - +
                    #define rn3xV   (constV(-timeStep*Mn3V)*Knx3)#define rn3xV   (constV(-timeStep*Mn3V)*Knx3)
                    diff --git a/html/precipitate_evolution_2equations_8h_source.html b/html/precipitate_evolution_2equations_8h_source.html index a74e83754..1d5645d26 100644 --- a/html/precipitate_evolution_2equations_8h_source.html +++ b/html/precipitate_evolution_2equations_8h_source.html @@ -152,7 +152,7 @@
                    60 // Mechanical symmetry of the material and stiffness parameters
                    61 // If n_dependent_stiffness == false the first entry is used for all phases
                    62 #define MaterialModels {{"ISOTROPIC"},{"ISOTROPIC"}}
                    -
                    63 #define MaterialConstants {{2.0,0.3},{2.5,0.3}}
                    +
                    63 #define MaterialConstants {{40.0,0.3},{50.0,0.3}}
                    64 
                    65 // Stress-free transformation strains
                    66 // Linear fits for the stress-free transformation strains in for sfts = sfts_linear * c + sfts_const
                    @@ -202,8 +202,8 @@
                    110 // each residual equation. The index for each variable in these lists corresponds to
                    111 // the order it is defined at the top of this file (starting at 0).
                    112 template <int dim>
                    -
                    113 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim>> & modelVariablesList,
                    -
                    114  std::vector<modelResidual<dim>> & modelResidualsList,
                    +
                    113 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim> > & modelVariablesList,
                    +
                    114  std::vector<modelResidual<dim> > & modelResidualsList,
                    115  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
                    116 
                    117 // The concentration and its derivatives (names here should match those in the macros above)
                    @@ -360,7 +360,7 @@
                    268  + (sfts1cc[i][j]*h1V + sfts2cc[i][j]*h2V + sfts3cc[i][j]*h3V)*cx[k]);
                    269 
                    270  if (n_dependent_stiffness == true){
                    -
                    271  grad_mu_el[k]+= - S2[i][j] * (sfts1c[i][j]*hn1V*n1x[k] + sfts2c[i][j]*hn2V*n2x[k] + sfts3c[i][j]*hn3V*n3x[k]);
                    +
                    271  grad_mu_el[k]+= S2[i][j] * E3[i][j]* (hn1V*n1x[k] + hn2V*n2x[k] + hn3V*n3x[k]);
                    272 
                    273  }
                    274  }
                    @@ -414,7 +414,7 @@
                    322 // that the correct residual is being submitted. The index of the field being solved
                    323 // can be accessed by "this->currentFieldIndex".
                    324 template <int dim>
                    -
                    325 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim>> & modelVariablesList,
                    +
                    325 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim> > & modelVariablesList,
                    326  modelResidual<dim> & modelRes,
                    327  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
                    328 
                    @@ -463,7 +463,7 @@
                    371 // density are added to the "energy_components" variable (index 0: chemical energy,
                    372 // index 1: gradient energy, index 2: elastic energy).
                    373 template <int dim>
                    -
                    374 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim>> & modelVarList,
                    +
                    374 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim> > & modelVarList,
                    375  const dealii::VectorizedArray<double> & JxW_value,
                    376  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) {
                    377 
                    @@ -593,13 +593,12 @@
                    501 
                    502 
                    rn3xV
                    #define rn3xV
                    Definition: equations.h:100
                    -
                    generalizedProblem::energyDensity
                    void energyDensity(const std::vector< modelVariable< dim >> &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
                    Definition: equations.h:106
                    modelResidual
                    Definition: model_variables.h:25
                    +
                    generalizedProblem::residualRHS
                    void residualRHS(const std::vector< modelVariable< dim > > &modelVarList, std::vector< modelResidual< dim > > &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
                    Definition: equations.h:58
                    sfts_linear1
                    double sfts_linear1[3][3]
                    Definition: equations.h:67
                    fbV
                    #define fbV
                    Definition: equations.h:80
                    scalargradType
                    dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
                    Definition: matrixFreePDE.h:51
                    h2V
                    #define h2V
                    Definition: equations.h:84
                    -
                    generalizedProblem::residualLHS
                    void residualLHS(const std::vector< modelVariable< dim >> &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
                    Definition: equations.h:89
                    constV
                    #define constV(a)
                    Definition: matrixFreePDE.h:67
                    rn2xV
                    #define rn2xV
                    Definition: equations.h:99
                    sfts_const2
                    double sfts_const2[3][3]
                    Definition: equations.h:71
                    @@ -616,6 +615,7 @@
                    n_dependent_stiffness
                    #define n_dependent_stiffness
                    Definition: equations.h:59
                    sfts_linear2
                    double sfts_linear2[3][3]
                    Definition: equations.h:70
                    hn1V
                    #define hn1V
                    Definition: equations.h:86
                    +
                    generalizedProblem::residualLHS
                    void residualLHS(const std::vector< modelVariable< dim > > &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
                    Definition: equations.h:89
                    rn1xV
                    #define rn1xV
                    Definition: equations.h:98
                    sfts_const1
                    double sfts_const1[3][3]
                    Definition: equations.h:68
                    hn3V
                    #define hn3V
                    Definition: equations.h:88
                    @@ -627,8 +627,8 @@
                    scalarvalueType
                    dealii::VectorizedArray< double > scalarvalueType
                    Definition: matrixFreePDE.h:34
                    sfts_linear3
                    double sfts_linear3[3][3]
                    Definition: equations.h:73
                    hn2V
                    #define hn2V
                    Definition: equations.h:87
                    -
                    generalizedProblem::residualRHS
                    void residualRHS(const std::vector< modelVariable< dim >> &modelVarList, std::vector< modelResidual< dim >> &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
                    Definition: equations.h:58
                    faV
                    #define faV
                    Definition: equations.h:77
                    +
                    generalizedProblem::energyDensity
                    void energyDensity(const std::vector< modelVariable< dim > > &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
                    Definition: equations.h:106
                    diff --git a/html/precipitate_evolution_2parameters_8h.html b/html/precipitate_evolution_2parameters_8h.html index c67a757b0..deff64d8e 100644 --- a/html/precipitate_evolution_2parameters_8h.html +++ b/html/precipitate_evolution_2parameters_8h.html @@ -117,9 +117,9 @@
                     
                    #define hAdaptivity   true
                     
                    #define maxRefinementLevel   (refineFactor)
                    #define maxRefinementLevel   (refineFactor)
                     
                    #define minRefinementLevel   (refineFactor-2)
                    #define minRefinementLevel   (refineFactor-2)
                     
                    #define refineCriterionFields   {1,2,3}
                     
                     
                    #define timeFinal   100.0
                     
                    #define timeIncrements   5000
                    #define timeIncrements   10
                     
                    #define solverType   SolverCG
                     
                     
                    #define writeOutput   true
                     
                    #define skipOutputSteps   500
                     
                    #define outputCondition   "EQUAL_SPACING"
                     
                    #define numOutputs   10
                     
                    #define outputList   {}
                     
                    #define skipPrintSteps   10
                     
                    #define calcEnergy   true
                     
                    @@ -204,7 +210,7 @@

                    Macro Definition Documentation

                    - +
                    #define maxRefinementLevel   (refineFactor)#define maxRefinementLevel   (refineFactor)
                    @@ -228,7 +234,43 @@

                    Macro Definition Documentation

                    - + + +
                    #define minRefinementLevel   (refineFactor-2)#define minRefinementLevel   (refineFactor-2)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define numOutputs   10
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define outputCondition   "EQUAL_SPACING"
                    +
                    + +
                    +
                    + +
                    +
                    + + +
                    #define outputList   {}
                    @@ -295,12 +337,12 @@

                    Macro Definition Documentation

                    - +
                    - +
                    #define skipOutputSteps   500#define skipPrintSteps   10
                    @@ -432,7 +474,7 @@

                    Macro Definition Documentation

                    - +
                    #define timeIncrements   5000#define timeIncrements   10
                    diff --git a/html/precipitate_evolution_2parameters_8h_source.html b/html/precipitate_evolution_2parameters_8h_source.html index 7cbe2a66b..d85c63fc4 100644 --- a/html/precipitate_evolution_2parameters_8h_source.html +++ b/html/precipitate_evolution_2parameters_8h_source.html @@ -146,7 +146,7 @@
                    54 // The size of the time step
                    55 #define timeStep 4.0e-4
                    56 #define timeFinal 100.0
                    -
                    57 #define timeIncrements 5000
                    +
                    57 #define timeIncrements 10 //5000
                    58 
                    59 // =================================================================================
                    60 // Set the elliptic solver parameters
                    @@ -170,22 +170,34 @@
                    78 // Each field in the problem will be output is writeOutput is set to "true"
                    79 #define writeOutput true
                    80 
                    -
                    81 // Output files are written every skipOutputSteps time steps
                    -
                    82 #define skipOutputSteps 500
                    -
                    83 
                    -
                    84 // =================================================================================
                    -
                    85 // Set the flag determining if the total free energy is calculated for each output
                    -
                    86 // =================================================================================
                    -
                    87 #define calcEnergy true
                    +
                    81 // Type of spacing between outputs ("EQUAL_SPACING", "LOG_SPACING", "N_PER_DECADE",
                    +
                    82 // or "LIST")
                    +
                    83 #define outputCondition "EQUAL_SPACING"
                    +
                    84 
                    +
                    85 // Number of times the program outputs the fields (total number for "EQUAL_SPACING"
                    +
                    86 // and "LOG_SPACING", number per decade for "N_PER_DECADE", ignored for "LIST")
                    +
                    87 #define numOutputs 10
                    88 
                    -
                    89 
                    -
                    90 
                    -
                    91 
                    +
                    89 // User-defined list of time steps where the program should output. Only used if
                    +
                    90 // outputCondition is "LIST"
                    +
                    91 #define outputList {}
                    92 
                    -
                    93 
                    -
                    94 
                    +
                    93 // Status is printed to the screen every skipPrintSteps
                    +
                    94 #define skipPrintSteps 10
                    95 
                    -
                    96 
                    +
                    96 // =================================================================================
                    +
                    97 // Set the flag determining if the total free energy is calculated for each output
                    +
                    98 // =================================================================================
                    +
                    99 #define calcEnergy true
                    +
                    100 
                    +
                    101 
                    +
                    102 
                    +
                    103 
                    +
                    104 
                    +
                    105 
                    +
                    106 
                    +
                    107 
                    +
                    108 
                    diff --git a/html/precipitate_evolution__pfunction_2_i_cs__and___b_cs_8h.html b/html/precipitate_evolution__pfunction_2_i_cs__and___b_cs_8h.html new file mode 100644 index 000000000..46c6418b1 --- /dev/null +++ b/html/precipitate_evolution__pfunction_2_i_cs__and___b_cs_8h.html @@ -0,0 +1,113 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/precipitateEvolution_pfunction/ICs_and_BCs.h File Reference + + + + + + + + + + +
                    +
                    + + + + + + +
                    +
                    PRISMS PhaseField +  1.0 +
                    +
                    +
                    + + + + + + +
                    +
                    + + +
                    + +
                    + + +
                    +
                    +
                    +Classes
                    +
                    +
                    ICs_and_BCs.h File Reference
                    +
                    +
                    + +

                    Go to the source code of this file.

                    + + + + + + +

                    +Classes

                    class  InitialCondition< dim >
                     
                    class  InitialConditionVec< dim >
                     
                    +
                    + +
                    +Generated by   +doxygen + 1.8.10 +
                    + + diff --git a/html/precipitate_evolution__pfunction_2_i_cs__and___b_cs_8h_source.html b/html/precipitate_evolution__pfunction_2_i_cs__and___b_cs_8h_source.html new file mode 100644 index 000000000..0e4c0a727 --- /dev/null +++ b/html/precipitate_evolution__pfunction_2_i_cs__and___b_cs_8h_source.html @@ -0,0 +1,243 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/precipitateEvolution_pfunction/ICs_and_BCs.h Source File + + + + + + + + + + +
                    +
                    + + + + + + +
                    +
                    PRISMS PhaseField +  1.0 +
                    +
                    +
                    + + + + + + +
                    +
                    + + +
                    + +
                    + + +
                    +
                    +
                    +
                    ICs_and_BCs.h
                    +
                    +
                    +Go to the documentation of this file.
                    1 //initial condition
                    +
                    2 template <int dim>
                    +
                    3 class InitialCondition : public Function<dim>
                    +
                    4 {
                    +
                    5 public:
                    +
                    6  unsigned int index;
                    +
                    7  Vector<double> values;
                    +
                    8  InitialCondition (const unsigned int _index) : Function<dim>(1), index(_index) {
                    +
                    9  std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1);
                    +
                    10  }
                    +
                    11  double value (const Point<dim> &p, const unsigned int component = 0) const
                    +
                    12  {
                    +
                    13  double scalar_IC = 0;
                    +
                    14  // =====================================================================
                    +
                    15  // ENTER THE INITIAL CONDITIONS HERE FOR SCALAR FIELDS
                    +
                    16  // =====================================================================
                    +
                    17  // Enter the function describing conditions for the fields at point "p".
                    +
                    18  // Use "if" statements to set the initial condition for each variable
                    +
                    19  // according to its variable index.
                    +
                    20 
                    +
                    21  double dx=spanX/((double) subdivisionsX)/std::pow(2.0,refineFactor);
                    +
                    22  double r=0.0;
                    +
                    23 
                    +
                    24  if (index==0){
                    +
                    25  scalar_IC = 0.04;
                    +
                    26  }
                    +
                    27  else if (index==1){
                    +
                    28  #if problemDIM == 2
                    +
                    29  double r1=p.distance(Point<dim>(spanX/3.0,spanY/3.0));
                    +
                    30  double r2=p.distance(Point<dim>(2*spanX/3.0,2*spanY/3.0));
                    +
                    31  #elif problemDIM == 3
                    +
                    32  double r1=p.distance(Point<dim>(spanX/3.0,spanY/3.0,0.5*spanZ));
                    +
                    33  double r2=p.distance(Point<dim>(2*spanX/3.0,2*spanY/3.0,0.5*spanZ));
                    +
                    34  #endif
                    +
                    35  r=std::min(r1,r2);
                    +
                    36  scalar_IC = 0.5*(1.0-std::tanh((r-spanX/16.0)/(dx)));
                    +
                    37  }
                    +
                    38  else if (index==2){
                    +
                    39  #if problemDIM == 2
                    +
                    40  r=p.distance(Point<dim>(3*spanX/4.0,spanY/4.0));
                    +
                    41  #elif problemDIM == 3
                    +
                    42  r=p.distance(Point<dim>(3*spanX/4.0,spanY/4.0,0.5*spanZ));
                    +
                    43  #endif
                    +
                    44  scalar_IC = 0.5*(1.0-std::tanh((r-spanX/16.0)/(dx)));
                    +
                    45  }
                    +
                    46  else if (index==3){
                    +
                    47  #if problemDIM == 2
                    +
                    48  r=p.distance(Point<dim>(spanX/4.0,3.0*spanY/4.0));
                    +
                    49  #elif problemDIM == 3
                    +
                    50  r=p.distance(Point<dim>(spanX/4.0,3.0*spanY/4.0,0.5*spanZ));
                    +
                    51  #endif
                    +
                    52  scalar_IC = 0.5*(1.0-std::tanh((r-spanX/16.0)/(dx)));
                    +
                    53  }
                    +
                    54 
                    +
                    55  // =====================================================================
                    +
                    56  return scalar_IC;
                    +
                    57  }
                    +
                    58 };
                    +
                    59 
                    +
                    60 //initial condition
                    +
                    61 template <int dim>
                    +
                    62 class InitialConditionVec : public Function<dim>
                    +
                    63 {
                    +
                    64 public:
                    +
                    65  unsigned int index;
                    +
                    66  //Vector<double> values;
                    +
                    67  InitialConditionVec (const unsigned int _index) : Function<dim>(dim), index(_index) {
                    +
                    68  std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1);
                    +
                    69  }
                    +
                    70  void vector_value (const Point<dim> &p,Vector<double> &vector_IC) const
                    +
                    71  {
                    +
                    72  // =====================================================================
                    +
                    73  // ENTER THE INITIAL CONDITIONS HERE FOR VECTOR FIELDS
                    +
                    74  // =====================================================================
                    +
                    75  // Enter the function describing conditions for the fields at point "p".
                    +
                    76  // Use "if" statements to set the initial condition for each variable
                    +
                    77  // according to its variable index.
                    +
                    78 
                    +
                    79  if (index==4){
                    +
                    80  vector_IC(0) = 0.0;
                    +
                    81  vector_IC(1) = 0.0;
                    +
                    82  if (dim == 3){
                    +
                    83  vector_IC(2) = 0.0;
                    +
                    84  }
                    +
                    85  }
                    +
                    86  // =====================================================================
                    +
                    87  }
                    +
                    88 };
                    +
                    89 
                    +
                    90 template <int dim>
                    +
                    91 void generalizedProblem<dim>::setBCs(){
                    +
                    92 
                    +
                    93  // =====================================================================
                    +
                    94  // ENTER THE BOUNDARY CONDITIONS HERE
                    +
                    95  // =====================================================================
                    +
                    96  // This function sets the BCs for the problem variables
                    +
                    97  // The function "inputBCs" should be called for each component of
                    +
                    98  // each variable and should be in numerical order. Four input arguments
                    +
                    99  // set the same BC on the entire boundary. Two plus two times the
                    +
                    100  // number of dimensions inputs sets separate BCs on each face of the domain.
                    +
                    101  // Inputs to "inputBCs":
                    +
                    102  // First input: variable number
                    +
                    103  // Second input: component number
                    +
                    104  // Third input: BC type (options are "ZERO_DERIVATIVE" and "DIRICHLET")
                    +
                    105  // Fourth input: BC value (ignored unless the BC type is "DIRICHLET")
                    +
                    106  // Odd inputs after the third: BC type
                    +
                    107  // Even inputs after the third: BC value
                    +
                    108  // Face numbering: starts at zero with the minimum of the first direction, one for the maximum of the first direction
                    +
                    109  // two for the minimum of the second direction, etc.
                    +
                    110 
                    +
                    111  inputBCs(0,0,"ZERO_DERIVATIVE",0);
                    +
                    112 
                    +
                    113  inputBCs(1,0,"ZERO_DERIVATIVE",0);
                    +
                    114 
                    +
                    115  inputBCs(2,0,"ZERO_DERIVATIVE",0);
                    +
                    116 
                    +
                    117  inputBCs(3,0,"ZERO_DERIVATIVE",0);
                    +
                    118 
                    +
                    119  inputBCs(4,0,"DIRICHLET",0.0);
                    +
                    120  inputBCs(4,1,"DIRICHLET",0.0);
                    +
                    121  if (dim == 3){
                    +
                    122  inputBCs(4,2,"DIRICHLET",0.0);
                    +
                    123  }
                    +
                    124 
                    +
                    125 }
                    +
                    126 
                    +
                    127 
                    +
                    InitialConditionVec::index
                    unsigned int index
                    Definition: ICs_and_BCs.h:47
                    +
                    InitialConditionVec::vector_value
                    void vector_value(const Point< dim > &p, Vector< double > &vector_IC) const
                    Definition: ICs_and_BCs.h:70
                    +
                    spanX
                    #define spanX
                    Definition: defaultValues.h:12
                    +
                    InitialCondition::InitialCondition
                    InitialCondition(const unsigned int _index)
                    Definition: ICs_and_BCs.h:8
                    +
                    spanY
                    #define spanY
                    Definition: defaultValues.h:17
                    +
                    InitialCondition::value
                    double value(const Point< dim > &p, const unsigned int component=0) const
                    Definition: ICs_and_BCs.h:11
                    +
                    InitialCondition::index
                    unsigned int index
                    Definition: ICs_and_BCs.h:5
                    +
                    refineFactor
                    #define refineFactor
                    Definition: defaultValues.h:27
                    +
                    generalizedProblem::setBCs
                    void setBCs()
                    Definition: ICs_and_BCs.h:67
                    +
                    InitialConditionVec::InitialConditionVec
                    InitialConditionVec(const unsigned int _index)
                    Definition: ICs_and_BCs.h:67
                    +
                    subdivisionsX
                    #define subdivisionsX
                    Definition: parameters.h:14
                    +
                    InitialCondition
                    Definition: ICs_and_BCs.h:2
                    +
                    InitialConditionVec
                    Definition: ICs_and_BCs.h:44
                    +
                    spanZ
                    #define spanZ
                    Definition: defaultValues.h:22
                    +
                    InitialCondition::values
                    Vector< double > values
                    Definition: ICs_and_BCs.h:6
                    +
                    + +
                    +Generated by   +doxygen + 1.8.10 +
                    + + diff --git a/html/precipitate_evolution__pfunction_2equations_8h.html b/html/precipitate_evolution__pfunction_2equations_8h.html new file mode 100644 index 000000000..749f4aa73 --- /dev/null +++ b/html/precipitate_evolution__pfunction_2equations_8h.html @@ -0,0 +1,942 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/precipitateEvolution_pfunction/equations.h File Reference + + + + + + + + + + +
                    +
                    + + + + + + +
                    +
                    PRISMS PhaseField +  1.0 +
                    +
                    +
                    + + + + + + +
                    +
                    + + +
                    + +
                    + + +
                    +
                    +
                    +Macros | +Functions | +Variables
                    +
                    +
                    equations.h File Reference
                    +
                    +
                    + +

                    Go to the source code of this file.

                    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

                    +Macros

                    #define num_var   5
                     
                    #define variable_name   {"c", "n1", "n2", "n3", "u"}
                     
                    #define variable_type   {"SCALAR","SCALAR","SCALAR","SCALAR","VECTOR"}
                     
                    #define variable_eq_type   {"PARABOLIC","PARABOLIC","PARABOLIC","PARABOLIC","ELLIPTIC"}
                     
                    #define need_val   {true, true, true, true, false}
                     
                    #define need_grad   {true, true, true, true, true}
                     
                    #define need_hess   {false, false, false, false, false}
                     
                    #define need_val_residual   {true, true, true, true, false}
                     
                    #define need_grad_residual   {true, true, true, true, true}
                     
                    #define need_val_LHS   {false, true, true, true, false}
                     
                    #define need_grad_LHS   {false, false, false, false, true}
                     
                    #define need_hess_LHS   {false, false, false, false, false}
                     
                    #define need_val_residual_LHS   {false, false, false, false, false}
                     
                    #define need_grad_residual_LHS   {false, false, false, false, true}
                     
                    #define McV   pfunct_McV.val(c)
                     
                    #define Mn1V   pfunct_Mn1V.val(c)
                     
                    #define Mn2V   pfunct_Mn2V.val(c)
                     
                    #define Mn3V   pfunct_Mn3V.val(c)
                     
                    #define n_dependent_stiffness   true
                     
                    #define MaterialModels   {{"ISOTROPIC"},{"ISOTROPIC"}}
                     
                    #define MaterialConstants   {{40.0,0.3},{50.0,0.3}}
                     
                    #define faV   pfunct_faV.val(c)
                     
                    #define facV   pfunct_faV.grad(c,0)
                     
                    #define faccV   pfunct_faV.hess(c,0,0)
                     
                    #define fbV   pfunct_fbV.val(c)
                     
                    #define fbcV   pfunct_fbV.grad(c,0)
                     
                    #define fbccV   pfunct_fbV.hess(c,0,0)
                     
                    #define h1V   (10.0*n1*n1*n1-15.0*n1*n1*n1*n1+6.0*n1*n1*n1*n1*n1)
                     
                    #define h2V   (10.0*n2*n2*n2-15.0*n2*n2*n2*n2+6.0*n2*n2*n2*n2*n2)
                     
                    #define h3V   (10.0*n3*n3*n3-15.0*n3*n3*n3*n3+6.0*n3*n3*n3*n3*n3)
                     
                    #define hn1V   (30.0*n1*n1-60.0*n1*n1*n1+30.0*n1*n1*n1*n1)
                     
                    #define hn2V   (30.0*n2*n2-60.0*n2*n2*n2+30.0*n2*n2*n2*n2)
                     
                    #define hn3V   (30.0*n3*n3-60.0*n3*n3*n3+30.0*n3*n3*n3*n3)
                     
                    #define rcV   (c)
                     
                    #define rcxTemp   ( cx*((1.0-h1V-h2V-h3V)*faccV+(h1V+h2V+h3V)*fbccV) + n1x*((fbcV-facV)*hn1V) + n2x*((fbcV-facV)*hn2V) + n3x*((fbcV-facV)*hn3V) + grad_mu_el)
                     
                    #define rcxV   (constV(-timeStep)*McV*rcxTemp)
                     
                    #define rn1V   (n1-constV(timeStep)*Mn1V*((fbV-faV)*hn1V+nDependentMisfitAC1+heterMechAC1))
                     
                    #define rn2V   (n2-constV(timeStep)*Mn2V*((fbV-faV)*hn2V+nDependentMisfitAC2+heterMechAC2))
                     
                    #define rn3V   (n3-constV(timeStep)*Mn3V*((fbV-faV)*hn3V+nDependentMisfitAC3+heterMechAC3))
                     
                    #define rn1xV   (constV(-timeStep)*Mn1V*Knx1)
                     
                    #define rn2xV   (constV(-timeStep)*Mn2V*Knx2)
                     
                    #define rn3xV   (constV(-timeStep)*Mn3V*Knx3)
                     
                    + + + + + + + + + + + + + +

                    +Functions

                    PFunctions::pFunction pfunct_McV ("pfunct_McV")
                     
                    PFunctions::pFunction pfunct_Mn1V ("pfunct_Mn1V")
                     
                    PFunctions::pFunction pfunct_Mn2V ("pfunct_Mn1V")
                     
                    PFunctions::pFunction pfunct_Mn3V ("pfunct_Mn1V")
                     
                    PFunctions::pFunction pfunct_faV ("pfunct_faV")
                     
                    PFunctions::pFunction pfunct_fbV ("pfunct_fbV")
                     
                    + + + + + + + + + + + + + + + + + + + +

                    +Variables

                    double Kn1 [3][3] ={{0.03,0,0},{0,0.007,0},{0,0,1.0}}
                     
                    double Kn2 [3][3] ={{0.01275,-0.009959,0},{-0.009959,0.02425,0},{0,0,1.0}}
                     
                    double Kn3 [3][3] ={{0.01275,0.009959,0},{0.009959,0.02425,0},{0,0,1.0}}
                     
                    double sfts_linear1 [3][3] = {{0,0,0},{0,0,0},{0,0,0}}
                     
                    double sfts_const1 [3][3] = {{0.0345,0,0},{0,0.0185,0},{0,0,-0.00270}}
                     
                    double sfts_linear2 [3][3] = {{0,0,0},{0,0,0},{0,0,0}}
                     
                    double sfts_const2 [3][3] ={{0.0225,-0.0069,0},{-0.0069,0.0305,0},{0,0,-0.00270}}
                     
                    double sfts_linear3 [3][3] = {{0,0,0},{0,0,0},{0,0,0}}
                     
                    double sfts_const3 [3][3] ={{0.0225, 0.0069,0},{0.0069,0.0305,0},{0,0,-0.00270}}
                     
                    +

                    Macro Definition Documentation

                    + +
                    +
                    + + + + +
                    #define faccV   pfunct_faV.hess(c,0,0)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define facV   pfunct_faV.grad(c,0)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define faV   pfunct_faV.val(c)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define fbccV   pfunct_fbV.hess(c,0,0)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define fbcV   pfunct_fbV.grad(c,0)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define fbV   pfunct_fbV.val(c)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define h1V   (10.0*n1*n1*n1-15.0*n1*n1*n1*n1+6.0*n1*n1*n1*n1*n1)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define h2V   (10.0*n2*n2*n2-15.0*n2*n2*n2*n2+6.0*n2*n2*n2*n2*n2)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define h3V   (10.0*n3*n3*n3-15.0*n3*n3*n3*n3+6.0*n3*n3*n3*n3*n3)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define hn1V   (30.0*n1*n1-60.0*n1*n1*n1+30.0*n1*n1*n1*n1)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define hn2V   (30.0*n2*n2-60.0*n2*n2*n2+30.0*n2*n2*n2*n2)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define hn3V   (30.0*n3*n3-60.0*n3*n3*n3+30.0*n3*n3*n3*n3)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define MaterialConstants   {{40.0,0.3},{50.0,0.3}}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define MaterialModels   {{"ISOTROPIC"},{"ISOTROPIC"}}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define McV   pfunct_McV.val(c)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define Mn1V   pfunct_Mn1V.val(c)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define Mn2V   pfunct_Mn2V.val(c)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define Mn3V   pfunct_Mn3V.val(c)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define n_dependent_stiffness   true
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define need_grad   {true, true, true, true, true}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define need_grad_LHS   {false, false, false, false, true}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define need_grad_residual   {true, true, true, true, true}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define need_grad_residual_LHS   {false, false, false, false, true}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define need_hess   {false, false, false, false, false}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define need_hess_LHS   {false, false, false, false, false}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define need_val   {true, true, true, true, false}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define need_val_LHS   {false, true, true, true, false}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define need_val_residual   {true, true, true, true, false}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define need_val_residual_LHS   {false, false, false, false, false}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define num_var   5
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define rcV   (c)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define rcxTemp   ( cx*((1.0-h1V-h2V-h3V)*faccV+(h1V+h2V+h3V)*fbccV) + n1x*((fbcV-facV)*hn1V) + n2x*((fbcV-facV)*hn2V) + n3x*((fbcV-facV)*hn3V) + grad_mu_el)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define rcxV   (constV(-timeStep)*McV*rcxTemp)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define rn1V   (n1-constV(timeStep)*Mn1V*((fbV-faV)*hn1V+nDependentMisfitAC1+heterMechAC1))
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define rn1xV   (constV(-timeStep)*Mn1V*Knx1)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define rn2V   (n2-constV(timeStep)*Mn2V*((fbV-faV)*hn2V+nDependentMisfitAC2+heterMechAC2))
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define rn2xV   (constV(-timeStep)*Mn2V*Knx2)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define rn3V   (n3-constV(timeStep)*Mn3V*((fbV-faV)*hn3V+nDependentMisfitAC3+heterMechAC3))
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define rn3xV   (constV(-timeStep)*Mn3V*Knx3)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define variable_eq_type   {"PARABOLIC","PARABOLIC","PARABOLIC","PARABOLIC","ELLIPTIC"}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define variable_name   {"c", "n1", "n2", "n3", "u"}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define variable_type   {"SCALAR","SCALAR","SCALAR","SCALAR","VECTOR"}
                    +
                    + +
                    +
                    +

                    Function Documentation

                    + +
                    +
                    + + + + + + + + +
                    PFunctions::pFunction pfunct_faV ("pfunct_faV" )
                    +
                    + +
                    +
                    + +
                    +
                    + + + + + + + + +
                    PFunctions::pFunction pfunct_fbV ("pfunct_fbV" )
                    +
                    + +
                    +
                    + +
                    +
                    + + + + + + + + +
                    PFunctions::pFunction pfunct_McV ("pfunct_McV" )
                    +
                    + +
                    +
                    + +
                    +
                    + + + + + + + + +
                    PFunctions::pFunction pfunct_Mn1V ("pfunct_Mn1V" )
                    +
                    + +
                    +
                    + +
                    +
                    + + + + + + + + +
                    PFunctions::pFunction pfunct_Mn2V ("pfunct_Mn1V" )
                    +
                    + +
                    +
                    + +
                    +
                    + + + + + + + + +
                    PFunctions::pFunction pfunct_Mn3V ("pfunct_Mn1V" )
                    +
                    + +
                    +
                    +

                    Variable Documentation

                    + +
                    +
                    + + + + +
                    double Kn1[3][3] ={{0.03,0,0},{0,0.007,0},{0,0,1.0}}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    double Kn2[3][3] ={{0.01275,-0.009959,0},{-0.009959,0.02425,0},{0,0,1.0}}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    double Kn3[3][3] ={{0.01275,0.009959,0},{0.009959,0.02425,0},{0,0,1.0}}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    double sfts_const1[3][3] = {{0.0345,0,0},{0,0.0185,0},{0,0,-0.00270}}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    double sfts_const2[3][3] ={{0.0225,-0.0069,0},{-0.0069,0.0305,0},{0,0,-0.00270}}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    double sfts_const3[3][3] ={{0.0225, 0.0069,0},{0.0069,0.0305,0},{0,0,-0.00270}}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    double sfts_linear1[3][3] = {{0,0,0},{0,0,0},{0,0,0}}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    double sfts_linear2[3][3] = {{0,0,0},{0,0,0},{0,0,0}}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    double sfts_linear3[3][3] = {{0,0,0},{0,0,0},{0,0,0}}
                    +
                    + +
                    +
                    +
                    + +
                    +Generated by   +doxygen + 1.8.10 +
                    + + diff --git a/html/precipitate_evolution__pfunction_2equations_8h_source.html b/html/precipitate_evolution__pfunction_2equations_8h_source.html new file mode 100644 index 000000000..5d752e499 --- /dev/null +++ b/html/precipitate_evolution__pfunction_2equations_8h_source.html @@ -0,0 +1,654 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/precipitateEvolution_pfunction/equations.h Source File + + + + + + + + + + +
                    +
                    + + + + + + +
                    +
                    PRISMS PhaseField +  1.0 +
                    +
                    +
                    + + + + + + +
                    +
                    + + +
                    + +
                    + + +
                    +
                    +
                    +
                    equations.h
                    +
                    +
                    +Go to the documentation of this file.
                    1 // List of variables and residual equations for the Precipitate Evolution example application
                    +
                    2 
                    +
                    3 // =================================================================================
                    +
                    4 // Define the variables in the model
                    +
                    5 // =================================================================================
                    +
                    6 // The number of variables
                    +
                    7 #define num_var 5
                    +
                    8 
                    +
                    9 // The names of the variables, whether they are scalars or vectors and whether the
                    +
                    10 // governing eqn for the variable is parabolic or elliptic
                    +
                    11 #define variable_name {"c", "n1", "n2", "n3", "u"}
                    +
                    12 #define variable_type {"SCALAR","SCALAR","SCALAR","SCALAR","VECTOR"}
                    +
                    13 #define variable_eq_type {"PARABOLIC","PARABOLIC","PARABOLIC","PARABOLIC","ELLIPTIC"}
                    +
                    14 
                    +
                    15 // Flags for whether the value, gradient, and Hessian are needed in the residual eqns
                    +
                    16 #define need_val {true, true, true, true, false}
                    +
                    17 #define need_grad {true, true, true, true, true}
                    +
                    18 #define need_hess {false, false, false, false, false}
                    +
                    19 
                    +
                    20 // Flags for whether the residual equation has a term multiplied by the test function
                    +
                    21 // (need_val_residual) and/or the gradient of the test function (need_grad_residual)
                    +
                    22 #define need_val_residual {true, true, true, true, false}
                    +
                    23 #define need_grad_residual {true, true, true, true, true}
                    +
                    24 
                    +
                    25 // Flags for whether the value, gradient, and Hessian are needed in the residual eqn
                    +
                    26 // for the left-hand-side of the iterative solver for elliptic equations
                    +
                    27 #define need_val_LHS {false, true, true, true, false}
                    +
                    28 #define need_grad_LHS {false, false, false, false, true}
                    +
                    29 #define need_hess_LHS {false, false, false, false, false}
                    +
                    30 
                    +
                    31 // Flags for whether the residual equation for the left-hand-side of the iterative
                    +
                    32 // solver for elliptic equations has a term multiplied by the test function
                    +
                    33 // (need_val_residual) and/or the gradient of the test function (need_grad_residual)
                    +
                    34 #define need_val_residual_LHS {false, false, false, false, false}
                    +
                    35 #define need_grad_residual_LHS {false, false, false, false, true}
                    +
                    36 
                    +
                    37 // =================================================================================
                    +
                    38 // Define the model parameters and the residual equations
                    +
                    39 // =================================================================================
                    +
                    40 // Parameters in the residual equations and expressions for the residual equations
                    +
                    41 // can be set here. For simple cases, the entire residual equation can be written
                    +
                    42 // here. For more complex cases with loops or conditional statements, residual
                    +
                    43 // equations (or parts of residual equations) can be written below in "residualRHS".
                    +
                    44 
                    +
                    45 // PFunction declaration
                    +
                    46 PFunctions::pFunction pfunct_McV("pfunct_McV"), pfunct_Mn1V("pfunct_Mn1V"),
                    +
                    47  pfunct_Mn2V("pfunct_Mn1V"), pfunct_Mn3V("pfunct_Mn1V"),
                    +
                    48  pfunct_faV("pfunct_faV"), pfunct_fbV("pfunct_fbV");
                    +
                    49 
                    +
                    50 // Cahn-Hilliard mobility
                    +
                    51 #define McV pfunct_McV.val(c)
                    +
                    52 
                    +
                    53 // Allen-Cahn mobilities
                    +
                    54 #define Mn1V pfunct_Mn1V.val(c)
                    +
                    55 #define Mn2V pfunct_Mn2V.val(c)
                    +
                    56 #define Mn3V pfunct_Mn3V.val(c)
                    +
                    57 
                    +
                    58 // Gradient energy coefficients
                    +
                    59 double Kn1[3][3]={{0.03,0,0},{0,0.007,0},{0,0,1.0}};
                    +
                    60 double Kn2[3][3]={{0.01275,-0.009959,0},{-0.009959,0.02425,0},{0,0,1.0}};
                    +
                    61 double Kn3[3][3]={{0.01275,0.009959,0},{0.009959,0.02425,0},{0,0,1.0}};
                    +
                    62 
                    +
                    63 // Define mechanical properties
                    +
                    64 #define n_dependent_stiffness true
                    +
                    65 // Mechanical symmetry of the material and stiffness parameters
                    +
                    66 // If n_dependent_stiffness == false the first entry is used for all phases
                    +
                    67 #define MaterialModels {{"ISOTROPIC"},{"ISOTROPIC"}}
                    +
                    68 #define MaterialConstants {{40.0,0.3},{50.0,0.3}}
                    +
                    69 
                    +
                    70 // Stress-free transformation strains
                    +
                    71 // Linear fits for the stress-free transformation strains in for sfts = sfts_linear * c + sfts_const
                    +
                    72 double sfts_linear1[3][3] = {{0,0,0},{0,0,0},{0,0,0}};
                    +
                    73 double sfts_const1[3][3] = {{0.0345,0,0},{0,0.0185,0},{0,0,-0.00270}};
                    +
                    74 
                    +
                    75 double sfts_linear2[3][3] = {{0,0,0},{0,0,0},{0,0,0}};
                    +
                    76 double sfts_const2[3][3]={{0.0225,-0.0069,0},{-0.0069,0.0305,0},{0,0,-0.00270}};
                    +
                    77 
                    +
                    78 double sfts_linear3[3][3] = {{0,0,0},{0,0,0},{0,0,0}};
                    +
                    79 double sfts_const3[3][3]={{0.0225, 0.0069,0},{0.0069,0.0305,0},{0,0,-0.00270}};
                    +
                    80 
                    +
                    81 // Assign the free energies using PFunctions
                    +
                    82 #define faV pfunct_faV.val(c)
                    +
                    83 #define facV pfunct_faV.grad(c,0)
                    +
                    84 #define faccV pfunct_faV.hess(c,0,0)
                    +
                    85 #define fbV pfunct_fbV.val(c)
                    +
                    86 #define fbcV pfunct_fbV.grad(c,0)
                    +
                    87 #define fbccV pfunct_fbV.hess(c,0,0)
                    +
                    88 
                    +
                    89 
                    +
                    90 #define h1V (10.0*n1*n1*n1-15.0*n1*n1*n1*n1+6.0*n1*n1*n1*n1*n1)
                    +
                    91 #define h2V (10.0*n2*n2*n2-15.0*n2*n2*n2*n2+6.0*n2*n2*n2*n2*n2)
                    +
                    92 #define h3V (10.0*n3*n3*n3-15.0*n3*n3*n3*n3+6.0*n3*n3*n3*n3*n3)
                    +
                    93 #define hn1V (30.0*n1*n1-60.0*n1*n1*n1+30.0*n1*n1*n1*n1)
                    +
                    94 #define hn2V (30.0*n2*n2-60.0*n2*n2*n2+30.0*n2*n2*n2*n2)
                    +
                    95 #define hn3V (30.0*n3*n3-60.0*n3*n3*n3+30.0*n3*n3*n3*n3)
                    +
                    96 
                    +
                    97 // Residual equations
                    +
                    98 #define rcV (c)
                    +
                    99 #define rcxTemp ( cx*((1.0-h1V-h2V-h3V)*faccV+(h1V+h2V+h3V)*fbccV) + n1x*((fbcV-facV)*hn1V) + n2x*((fbcV-facV)*hn2V) + n3x*((fbcV-facV)*hn3V) + grad_mu_el)
                    +
                    100 #define rcxV (constV(-timeStep)*McV*rcxTemp)
                    +
                    101 
                    +
                    102 #define rn1V (n1-constV(timeStep)*Mn1V*((fbV-faV)*hn1V+nDependentMisfitAC1+heterMechAC1))
                    +
                    103 #define rn2V (n2-constV(timeStep)*Mn2V*((fbV-faV)*hn2V+nDependentMisfitAC2+heterMechAC2))
                    +
                    104 #define rn3V (n3-constV(timeStep)*Mn3V*((fbV-faV)*hn3V+nDependentMisfitAC3+heterMechAC3))
                    +
                    105 #define rn1xV (constV(-timeStep)*Mn1V*Knx1)
                    +
                    106 #define rn2xV (constV(-timeStep)*Mn2V*Knx2)
                    +
                    107 #define rn3xV (constV(-timeStep)*Mn3V*Knx3)
                    +
                    108 
                    +
                    109 // =================================================================================
                    +
                    110 // residualRHS
                    +
                    111 // =================================================================================
                    +
                    112 // This function calculates the residual equations for each variable. It takes
                    +
                    113 // "modelVariablesList" as an input, which is a list of the value and derivatives of
                    +
                    114 // each of the variables at a specific quadrature point. The (x,y,z) location of
                    +
                    115 // that quadrature point is given by "q_point_loc". The function outputs
                    +
                    116 // "modelResidualsList", a list of the value and gradient terms of the residual for
                    +
                    117 // each residual equation. The index for each variable in these lists corresponds to
                    +
                    118 // the order it is defined at the top of this file (starting at 0).
                    +
                    119 template <int dim>
                    +
                    120 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim>> & modelVariablesList,
                    +
                    121  std::vector<modelResidual<dim>> & modelResidualsList,
                    +
                    122  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
                    +
                    123 
                    +
                    124 // The concentration and its derivatives (names here should match those in the macros above)
                    +
                    125 scalarvalueType c = modelVariablesList[0].scalarValue;
                    +
                    126 scalargradType cx = modelVariablesList[0].scalarGrad;
                    +
                    127 
                    +
                    128 // The first order parameter and its derivatives (names here should match those in the macros above)
                    +
                    129 scalarvalueType n1 = modelVariablesList[1].scalarValue;
                    +
                    130 scalargradType n1x = modelVariablesList[1].scalarGrad;
                    +
                    131 
                    +
                    132 // The second order parameter and its derivatives (names here should match those in the macros above)
                    +
                    133 scalarvalueType n2 = modelVariablesList[2].scalarValue;
                    +
                    134 scalargradType n2x = modelVariablesList[2].scalarGrad;
                    +
                    135 
                    +
                    136 
                    +
                    137 // The third order parameter and its derivatives (names here should match those in the macros above)
                    +
                    138 scalarvalueType n3 = modelVariablesList[3].scalarValue;
                    +
                    139 scalargradType n3x = modelVariablesList[3].scalarGrad;
                    +
                    140 
                    +
                    141 // The derivative of the displacement vector (names here should match those in the macros above)
                    +
                    142 vectorgradType ux = modelVariablesList[4].vectorGrad;
                    +
                    143 vectorgradType ruxV;
                    +
                    144 
                    +
                    145 vectorhessType uxx;
                    +
                    146 
                    +
                    147 if (c_dependent_misfit == true){
                    +
                    148  uxx = modelVariablesList[4].vectorHess;
                    +
                    149 }
                    +
                    150 
                    +
                    151 // Calculate the stress-free transformation strain and its derivatives at the quadrature point
                    +
                    152 dealii::Tensor<2, problemDIM, dealii::VectorizedArray<double> > sfts1, sfts1c, sfts1cc, sfts2, sfts2c, sfts2cc, sfts3, sfts3c, sfts3cc;
                    +
                    153 
                    +
                    154 for (unsigned int i=0; i<dim; i++){
                    +
                    155 for (unsigned int j=0; j<dim; j++){
                    +
                    156  // Polynomial fits for the stress-free transformation strains, of the form: sfts = a_p * c + b_p
                    +
                    157  sfts1[i][j] = constV(sfts_linear1[i][j])*c + constV(sfts_const1[i][j]);
                    +
                    158  sfts1c[i][j] = constV(sfts_linear1[i][j]);
                    +
                    159  sfts1cc[i][j] = constV(0.0);
                    +
                    160 
                    +
                    161  // Polynomial fits for the stress-free transformation strains, of the form: sfts = a_p * c + b_p
                    +
                    162  sfts2[i][j] = constV(sfts_linear2[i][j])*c + constV(sfts_const2[i][j]);
                    +
                    163  sfts2c[i][j] = constV(sfts_linear1[i][j]);
                    +
                    164  sfts2cc[i][j] = constV(0.0);
                    +
                    165 
                    +
                    166  // Polynomial fits for the stress-free transformation strains, of the form: sfts = a_p * c + b_p
                    +
                    167  sfts3[i][j] = constV(sfts_linear3[i][j])*c + constV(sfts_const3[i][j]);
                    +
                    168  sfts3c[i][j] = constV(sfts_linear3[i][j]);
                    +
                    169  sfts3cc[i][j] = constV(0.0);
                    +
                    170 }
                    +
                    171 }
                    +
                    172 
                    +
                    173 //compute E2=(E-E0)
                    +
                    174 dealii::VectorizedArray<double> E2[dim][dim], S[dim][dim];
                    +
                    175 
                    +
                    176 for (unsigned int i=0; i<dim; i++){
                    +
                    177 for (unsigned int j=0; j<dim; j++){
                    +
                    178  E2[i][j]= constV(0.5)*(ux[i][j]+ux[j][i])-( sfts1[i][j]*h1V + sfts2[i][j]*h2V + sfts3[i][j]*h3V);
                    +
                    179 
                    +
                    180 }
                    +
                    181 }
                    +
                    182 
                    +
                    183 //compute stress
                    +
                    184 //S=C*(E-E0)
                    +
                    185 // Compute stress tensor (which is equal to the residual, Rux)
                    +
                    186 dealii::VectorizedArray<double> CIJ_combined[CIJ_tensor_size][CIJ_tensor_size];
                    +
                    187 
                    +
                    188 if (n_dependent_stiffness == true){
                    +
                    189 dealii::VectorizedArray<double> sum_hV;
                    +
                    190 sum_hV = h1V+h2V+h3V;
                    +
                    191 for (unsigned int i=0; i<2*dim-1+dim/3; i++){
                    +
                    192  for (unsigned int j=0; j<2*dim-1+dim/3; j++){
                    +
                    193  CIJ_combined[i][j] = CIJ_list[0][i][j]*(constV(1.0)-sum_hV) + CIJ_list[1][i][j]*sum_hV;
                    +
                    194  }
                    +
                    195 }
                    +
                    196 computeStress<dim>(CIJ_combined, E2, S);
                    +
                    197 }
                    +
                    198 else{
                    +
                    199 computeStress<dim>(CIJ_list[0], E2, S);
                    +
                    200 }
                    +
                    201 
                    +
                    202 // Fill residual corresponding to mechanics
                    +
                    203 // R=-C*(E-E0)
                    +
                    204 
                    +
                    205 for (unsigned int i=0; i<dim; i++){
                    +
                    206 for (unsigned int j=0; j<dim; j++){
                    +
                    207  ruxV[i][j] = - S[i][j];
                    +
                    208 }
                    +
                    209 }
                    +
                    210 
                    +
                    211 // Compute one of the stress terms in the order parameter chemical potential, nDependentMisfitACp = C*(E-E0)*(E0_p*Hn)
                    +
                    212 dealii::VectorizedArray<double> nDependentMisfitAC1=constV(0.0);
                    +
                    213 dealii::VectorizedArray<double> nDependentMisfitAC2=constV(0.0);
                    +
                    214 dealii::VectorizedArray<double> nDependentMisfitAC3=constV(0.0);
                    +
                    215 
                    +
                    216 for (unsigned int i=0; i<dim; i++){
                    +
                    217 for (unsigned int j=0; j<dim; j++){
                    +
                    218  nDependentMisfitAC1+=S[i][j]*(sfts1[i][j]);
                    +
                    219  nDependentMisfitAC2+=S[i][j]*(sfts2[i][j]);
                    +
                    220  nDependentMisfitAC3+=S[i][j]*(sfts3[i][j]);
                    +
                    221 }
                    +
                    222 }
                    +
                    223 
                    +
                    224 nDependentMisfitAC1*=-hn1V;
                    +
                    225 nDependentMisfitAC2*=-hn2V;
                    +
                    226 nDependentMisfitAC3*=-hn3V;
                    +
                    227 
                    +
                    228 // Compute the other stress term in the order parameter chemical potential, heterMechACp = 0.5*Hn*(C_beta-C_alpha)*(E-E0)*(E-E0)
                    +
                    229 dealii::VectorizedArray<double> heterMechAC1=constV(0.0);
                    +
                    230 dealii::VectorizedArray<double> heterMechAC2=constV(0.0);
                    +
                    231 dealii::VectorizedArray<double> heterMechAC3=constV(0.0);
                    +
                    232 dealii::VectorizedArray<double> S2[dim][dim];
                    +
                    233 
                    +
                    234 if (n_dependent_stiffness == true){
                    +
                    235 //computeStress<dim>(CIJ_diff, E2, S2);
                    +
                    236  computeStress<dim>(CIJ_list[1]-CIJ_list[0], E2, S2);
                    +
                    237 for (unsigned int i=0; i<dim; i++){
                    +
                    238  for (unsigned int j=0; j<dim; j++){
                    +
                    239  heterMechAC1 += S2[i][j]*E2[i][j];
                    +
                    240  }
                    +
                    241 }
                    +
                    242 // Aside from HnpV, heterMechAC1, heterMechAC2, and heterMechAC3 are equal
                    +
                    243 heterMechAC2 = 0.5*hn2V*heterMechAC1;
                    +
                    244 heterMechAC3 = 0.5*hn3V*heterMechAC1;
                    +
                    245 
                    +
                    246 heterMechAC1 = 0.5*hn1V*heterMechAC1;
                    +
                    247 }
                    +
                    248 
                    +
                    249 // compute the stress term in the gradient of the concentration chemical potential, grad_mu_el = [C*(E-E0)*E0c]x, must be a vector with length dim
                    +
                    250 scalargradType grad_mu_el;
                    +
                    251 
                    +
                    252 if (c_dependent_misfit == true){
                    +
                    253  dealii::VectorizedArray<double> E3[dim][dim], S3[dim][dim];
                    +
                    254 
                    +
                    255  for (unsigned int i=0; i<dim; i++){
                    +
                    256  for (unsigned int j=0; j<dim; j++){
                    +
                    257  E3[i][j] = -( sfts1c[i][j]*h1V + sfts2c[i][j]*h2V + sfts3c[i][j]*h3V);
                    +
                    258  }
                    +
                    259  }
                    +
                    260 
                    +
                    261  if (n_dependent_stiffness == true){
                    +
                    262  computeStress<dim>(CIJ_combined, E3, S3);
                    +
                    263  }
                    +
                    264  else{
                    +
                    265  computeStress<dim>(CIJ_list[0], E3, S3);
                    +
                    266  }
                    +
                    267 
                    +
                    268  for (unsigned int i=0; i<dim; i++){
                    +
                    269  for (unsigned int j=0; j<dim; j++){
                    +
                    270  for (unsigned int k=0; k<dim; k++){
                    +
                    271  grad_mu_el[k] += S3[i][j] * (constV(0.5)*(uxx[i][j][k]+uxx[j][i][k]) + E3[i][j]*cx[k]
                    +
                    272  - (sfts1[i][j]*hn1V*n1x[k] + sfts2[i][j]*hn2V*n2x[k] + sfts3[i][j]*hn3V*n3x[k]));
                    +
                    273 
                    +
                    274  grad_mu_el[k]+= - S[i][j] * (sfts1c[i][j]*hn1V*n1x[k] + sfts2c[i][j]*hn2V*n2x[k] + sfts3c[i][j]*hn3V*n3x[k]
                    +
                    275  + (sfts1cc[i][j]*h1V + sfts2cc[i][j]*h2V + sfts3cc[i][j]*h3V)*cx[k]);
                    +
                    276 
                    +
                    277  if (n_dependent_stiffness == true){
                    +
                    278  grad_mu_el[k]+= S2[i][j] * E3[i][j]* (hn1V*n1x[k] + hn2V*n2x[k] + hn3V*n3x[k]);
                    +
                    279 
                    +
                    280  }
                    +
                    281  }
                    +
                    282  }
                    +
                    283  }
                    +
                    284 }
                    +
                    285 
                    +
                    286 
                    +
                    287 //compute K*nx
                    +
                    288 scalargradType Knx1, Knx2, Knx3;
                    +
                    289 for (unsigned int a=0; a<dim; a++) {
                    +
                    290 Knx1[a]=0.0;
                    +
                    291 Knx2[a]=0.0;
                    +
                    292 Knx3[a]=0.0;
                    +
                    293 for (unsigned int b=0; b<dim; b++){
                    +
                    294  Knx1[a]+=constV(Kn1[a][b])*n1x[b];
                    +
                    295  Knx2[a]+=constV(Kn2[a][b])*n2x[b];
                    +
                    296  Knx3[a]+=constV(Kn3[a][b])*n3x[b];
                    +
                    297 }
                    +
                    298 }
                    +
                    299 
                    +
                    300 modelResidualsList[0].scalarValueResidual = rcV;
                    +
                    301 modelResidualsList[0].scalarGradResidual = rcxV;
                    +
                    302 
                    +
                    303 modelResidualsList[1].scalarValueResidual = rn1V;
                    +
                    304 modelResidualsList[1].scalarGradResidual = rn1xV;
                    +
                    305 
                    +
                    306 modelResidualsList[2].scalarValueResidual = rn2V;
                    +
                    307 modelResidualsList[2].scalarGradResidual = rn2xV;
                    +
                    308 
                    +
                    309 modelResidualsList[3].scalarValueResidual = rn3V;
                    +
                    310 modelResidualsList[3].scalarGradResidual = rn3xV;
                    +
                    311 
                    +
                    312 modelResidualsList[4].vectorGradResidual = ruxV;
                    +
                    313 
                    +
                    314 }
                    +
                    315 
                    +
                    316 // =================================================================================
                    +
                    317 // residualLHS (needed only if at least one equation is elliptic)
                    +
                    318 // =================================================================================
                    +
                    319 // This function calculates the residual equations for the iterative solver for
                    +
                    320 // elliptic equations.for each variable. It takes "modelVariablesList" as an input,
                    +
                    321 // which is a list of the value and derivatives of each of the variables at a
                    +
                    322 // specific quadrature point. The (x,y,z) location of that quadrature point is given
                    +
                    323 // by "q_point_loc". The function outputs "modelRes", the value and gradient terms of
                    +
                    324 // for the left-hand-side of the residual equation for the iterative solver. The
                    +
                    325 // index for each variable in these lists corresponds to the order it is defined at
                    +
                    326 // the top of this file (starting at 0), not counting variables that have
                    +
                    327 // "need_val_LHS", "need_grad_LHS", and "need_hess_LHS" all set to "false". If there
                    +
                    328 // are multiple elliptic equations, conditional statements should be used to ensure
                    +
                    329 // that the correct residual is being submitted. The index of the field being solved
                    +
                    330 // can be accessed by "this->currentFieldIndex".
                    +
                    331 template <int dim>
                    +
                    332 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim>> & modelVariablesList,
                    +
                    333  modelResidual<dim> & modelRes,
                    +
                    334  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
                    +
                    335 
                    +
                    336 //n1
                    +
                    337 scalarvalueType n1 = modelVariablesList[0].scalarValue;
                    +
                    338 
                    +
                    339 //n2
                    +
                    340 scalarvalueType n2 = modelVariablesList[1].scalarValue;
                    +
                    341 
                    +
                    342 
                    +
                    343 //n3
                    +
                    344 scalarvalueType n3 = modelVariablesList[2].scalarValue;
                    +
                    345 
                    +
                    346 //u
                    +
                    347 vectorgradType ux = modelVariablesList[3].vectorGrad;
                    +
                    348 vectorgradType ruxV;
                    +
                    349 
                    +
                    350 // Take advantage of E being simply 0.5*(ux + transpose(ux)) and use the dealii "symmetrize" function
                    +
                    351 dealii::Tensor<2, dim, dealii::VectorizedArray<double> > E;
                    +
                    352 E = symmetrize(ux);
                    +
                    353 
                    +
                    354 // Compute stress tensor (which is equal to the residual, Rux)
                    +
                    355 if (n_dependent_stiffness == true){
                    +
                    356  dealii::Tensor<2, CIJ_tensor_size, dealii::VectorizedArray<double> > CIJ_combined;
                    +
                    357  CIJ_combined = CIJ_list[0]*(constV(1.0)-h1V-h2V-h3V) + CIJ_list[1]*(h1V+h2V+h3V);
                    +
                    358 
                    +
                    359  computeStress<dim>(CIJ_combined, E, ruxV);
                    +
                    360 }
                    +
                    361 else{
                    +
                    362  computeStress<dim>(CIJ_list[0], E, ruxV);
                    +
                    363 }
                    +
                    364 
                    +
                    365 modelRes.vectorGradResidual = ruxV;
                    +
                    366 
                    +
                    367 }
                    +
                    368 
                    +
                    369 // =================================================================================
                    +
                    370 // energyDensity (needed only if calcEnergy == true)
                    +
                    371 // =================================================================================
                    +
                    372 // This function integrates the free energy density across the computational domain.
                    +
                    373 // It takes "modelVariablesList" as an input, which is a list of the value and
                    +
                    374 // derivatives of each of the variables at a specific quadrature point. It also
                    +
                    375 // takes the mapped quadrature weight, "JxW_value", as an input. The (x,y,z) location
                    +
                    376 // of the quadrature point is given by "q_point_loc". The weighted value of the
                    +
                    377 // energy density is added to "energy" variable and the components of the energy
                    +
                    378 // density are added to the "energy_components" variable (index 0: chemical energy,
                    +
                    379 // index 1: gradient energy, index 2: elastic energy).
                    +
                    380 template <int dim>
                    +
                    381 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim>> & modelVarList,
                    +
                    382  const dealii::VectorizedArray<double> & JxW_value,
                    +
                    383  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) {
                    +
                    384 
                    +
                    385 scalarvalueType total_energy_density = constV(0.0);
                    +
                    386 
                    +
                    387 //c
                    +
                    388 scalarvalueType c = modelVarList[0].scalarValue;
                    +
                    389 scalargradType cx = modelVarList[0].scalarGrad;
                    +
                    390 
                    +
                    391 //n1
                    +
                    392 scalarvalueType n1 = modelVarList[1].scalarValue;
                    +
                    393 scalargradType n1x = modelVarList[1].scalarGrad;
                    +
                    394 
                    +
                    395 //n2
                    +
                    396 scalarvalueType n2 = modelVarList[2].scalarValue;
                    +
                    397 scalargradType n2x = modelVarList[2].scalarGrad;
                    +
                    398 
                    +
                    399 
                    +
                    400 //n3
                    +
                    401 scalarvalueType n3 = modelVarList[3].scalarValue;
                    +
                    402 scalargradType n3x = modelVarList[3].scalarGrad;
                    +
                    403 
                    +
                    404 //u
                    +
                    405 vectorgradType ux = modelVarList[4].vectorGrad;
                    +
                    406 
                    +
                    407 scalarvalueType f_chem = (constV(1.0)-(h1V+h2V+h3V))*faV + (h1V+h2V+h3V)*fbV;
                    +
                    408 
                    +
                    409 scalarvalueType f_grad = constV(0.0);
                    +
                    410 
                    +
                    411 for (int i=0; i<dim; i++){
                    +
                    412  for (int j=0; j<dim; j++){
                    +
                    413  f_grad += constV(0.5*Kn1[i][j])*n1x[i]*n1x[j];
                    +
                    414  }
                    +
                    415 }
                    +
                    416 #if num_sop>1
                    +
                    417 for (int i=0; i<dim; i++){
                    +
                    418  for (int j=0; j<dim; j++){
                    +
                    419  f_grad += constV(0.5*Kn2[i][j])*n2x[i]*n2x[j];
                    +
                    420  }
                    +
                    421 }
                    +
                    422 #endif
                    +
                    423 #if num_sop>2
                    +
                    424 for (int i=0; i<dim; i++){
                    +
                    425  for (int j=0; j<dim; j++){
                    +
                    426  f_grad += constV(0.5*Kn3[i][j])*n3x[i]*n3x[j];
                    +
                    427  }
                    +
                    428 }
                    +
                    429 #endif
                    +
                    430 
                    +
                    431 
                    +
                    432 // Calculate the stress-free transformation strain and its derivatives at the quadrature point
                    +
                    433 dealii::Tensor<2, problemDIM, dealii::VectorizedArray<double> > sfts1, sfts1c, sfts1cc, sfts2, sfts2c, sfts2cc, sfts3, sfts3c, sfts3cc;
                    +
                    434 
                    +
                    435 for (unsigned int i=0; i<dim; i++){
                    +
                    436  for (unsigned int j=0; j<dim; j++){
                    +
                    437  // Polynomial fits for the stress-free transformation strains, of the form: sfts = a_p * c + b_p
                    +
                    438  sfts1[i][j] = constV(sfts_linear1[i][j])*c + constV(sfts_const1[i][j]);
                    +
                    439  sfts1c[i][j] = constV(sfts_linear1[i][j]);
                    +
                    440  sfts1cc[i][j] = constV(0.0);
                    +
                    441 
                    +
                    442  // Polynomial fits for the stress-free transformation strains, of the form: sfts = a_p * c + b_p
                    +
                    443  sfts2[i][j] = constV(sfts_linear2[i][j])*c + constV(sfts_const2[i][j]);
                    +
                    444  sfts2c[i][j] = constV(sfts_linear1[i][j]);
                    +
                    445  sfts2cc[i][j] = constV(0.0);
                    +
                    446 
                    +
                    447  // Polynomial fits for the stress-free transformation strains, of the form: sfts = a_p * c + b_p
                    +
                    448  sfts3[i][j] = constV(sfts_linear3[i][j])*c + constV(sfts_const3[i][j]);
                    +
                    449  sfts3c[i][j] = constV(sfts_linear3[i][j]);
                    +
                    450  sfts3cc[i][j] = constV(0.0);
                    +
                    451  }
                    +
                    452 }
                    +
                    453 
                    +
                    454 //compute E2=(E-E0)
                    +
                    455 dealii::VectorizedArray<double> E2[dim][dim], S[dim][dim];
                    +
                    456 
                    +
                    457 for (unsigned int i=0; i<dim; i++){
                    +
                    458  for (unsigned int j=0; j<dim; j++){
                    +
                    459  //E2[i][j]= constV(0.5)*(ux[i][j]+ux[j][i])-( sfts1[i][j]*h1V + sfts2[i][j]*h2V + sfts3[i][j]*h3V);
                    +
                    460  E2[i][j]= constV(0.5)*(ux[i][j]+ux[j][i])-( sfts1[i][j]*h1V + sfts2[i][j]*h2V + sfts3[i][j]*h3V);
                    +
                    461 
                    +
                    462  }
                    +
                    463 }
                    +
                    464 
                    +
                    465 //compute stress
                    +
                    466 //S=C*(E-E0)
                    +
                    467 dealii::VectorizedArray<double> CIJ_combined[2*dim-1+dim/3][2*dim-1+dim/3];
                    +
                    468 
                    +
                    469 if (n_dependent_stiffness == true){
                    +
                    470  dealii::VectorizedArray<double> sum_hV;
                    +
                    471  sum_hV = h1V+h2V+h3V;
                    +
                    472  for (unsigned int i=0; i<2*dim-1+dim/3; i++){
                    +
                    473  for (unsigned int j=0; j<2*dim-1+dim/3; j++){
                    +
                    474  CIJ_combined[i][j] = CIJ_list[0][i][j]*(constV(1.0)-sum_hV) + CIJ_list[1][i][j]*sum_hV;
                    +
                    475  }
                    +
                    476  }
                    +
                    477  computeStress<dim>(CIJ_combined, E2, S);
                    +
                    478 }
                    +
                    479 else{
                    +
                    480  computeStress<dim>(CIJ_list[0], E2, S);
                    +
                    481 }
                    +
                    482 
                    +
                    483 scalarvalueType f_el = constV(0.0);
                    +
                    484 
                    +
                    485 for (unsigned int i=0; i<dim; i++){
                    +
                    486  for (unsigned int j=0; j<dim; j++){
                    +
                    487  f_el += constV(0.5) * S[i][j]*E2[i][j];
                    +
                    488  }
                    +
                    489 }
                    +
                    490 
                    +
                    491 total_energy_density = f_chem + f_grad + f_el;
                    +
                    492 
                    +
                    493 // Loop to step through each element of the vectorized arrays. Working with deal.ii
                    +
                    494 // developers to see if there is a more elegant way to do this.
                    +
                    495 assembler_lock.acquire ();
                    +
                    496 for (unsigned i=0; i<c.n_array_elements;i++){
                    +
                    497  if (c[i] > 1.0e-10){
                    +
                    498  this->energy+=total_energy_density[i]*JxW_value[i];
                    +
                    499  this->energy_components[0]+= f_chem[i]*JxW_value[i];
                    +
                    500  this->energy_components[1]+= f_grad[i]*JxW_value[i];
                    +
                    501  this->energy_components[2]+= f_el[i]*JxW_value[i];
                    +
                    502  }
                    +
                    503 }
                    +
                    504 assembler_lock.release ();
                    +
                    505 }
                    +
                    506 
                    +
                    507 
                    +
                    508 
                    +
                    509 
                    +
                    modelResidual
                    Definition: model_variables.h:25
                    +
                    n_dependent_stiffness
                    #define n_dependent_stiffness
                    Definition: equations.h:64
                    +
                    generalizedProblem::residualRHS
                    void residualRHS(const std::vector< modelVariable< dim > > &modelVarList, std::vector< modelResidual< dim > > &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
                    Definition: equations.h:58
                    +
                    rn3V
                    #define rn3V
                    Definition: equations.h:104
                    +
                    sfts_linear1
                    double sfts_linear1[3][3]
                    Definition: equations.h:67
                    +
                    hn1V
                    #define hn1V
                    Definition: equations.h:93
                    +
                    scalargradType
                    dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
                    Definition: matrixFreePDE.h:51
                    +
                    rn3xV
                    #define rn3xV
                    Definition: equations.h:107
                    +
                    constV
                    #define constV(a)
                    Definition: matrixFreePDE.h:67
                    +
                    sfts_const2
                    double sfts_const2[3][3]
                    Definition: equations.h:71
                    +
                    rn2V
                    #define rn2V
                    Definition: equations.h:103
                    +
                    Kn3
                    double Kn3[3][3]
                    Definition: equations.h:56
                    +
                    modelResidual::vectorGradResidual
                    vectorgradType vectorGradResidual
                    Definition: model_variables.h:33
                    +
                    rn1xV
                    #define rn1xV
                    Definition: equations.h:105
                    +
                    pfunct_McV
                    PFunctions::pFunction pfunct_McV("pfunct_McV")
                    +
                    Kn1
                    double Kn1[3][3]
                    Definition: equations.h:54
                    +
                    faV
                    #define faV
                    Definition: equations.h:82
                    +
                    Kn2
                    double Kn2[3][3]
                    Definition: equations.h:55
                    +
                    h1V
                    #define h1V
                    Definition: equations.h:90
                    +
                    hn3V
                    #define hn3V
                    Definition: equations.h:95
                    +
                    pfunct_Mn2V
                    PFunctions::pFunction pfunct_Mn2V("pfunct_Mn1V")
                    +
                    pfunct_Mn3V
                    PFunctions::pFunction pfunct_Mn3V("pfunct_Mn1V")
                    +
                    pfunct_Mn1V
                    PFunctions::pFunction pfunct_Mn1V("pfunct_Mn1V")
                    +
                    PFunctions::pFunction
                    Definition: pFunction.h:12
                    +
                    rn2xV
                    #define rn2xV
                    Definition: equations.h:106
                    +
                    pfunct_faV
                    PFunctions::pFunction pfunct_faV("pfunct_faV")
                    +
                    fbV
                    #define fbV
                    Definition: equations.h:85
                    +
                    sfts_linear2
                    double sfts_linear2[3][3]
                    Definition: equations.h:70
                    +
                    generalizedProblem::residualLHS
                    void residualLHS(const std::vector< modelVariable< dim > > &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
                    Definition: equations.h:89
                    +
                    sfts_const1
                    double sfts_const1[3][3]
                    Definition: equations.h:68
                    +
                    vectorhessType
                    dealii::Tensor< 3, problemDIM, dealii::VectorizedArray< double > > vectorhessType
                    Definition: matrixFreePDE.h:60
                    +
                    rcV
                    #define rcV
                    Definition: equations.h:98
                    +
                    modelVariable
                    Definition: model_variables.h:4
                    +
                    vectorgradType
                    dealii::Tensor< 2, problemDIM, dealii::VectorizedArray< double > > vectorgradType
                    Definition: matrixFreePDE.h:57
                    +
                    hn2V
                    #define hn2V
                    Definition: equations.h:94
                    +
                    pfunct_fbV
                    PFunctions::pFunction pfunct_fbV("pfunct_fbV")
                    +
                    rn1V
                    #define rn1V
                    Definition: equations.h:102
                    +
                    sfts_const3
                    double sfts_const3[3][3]
                    Definition: equations.h:74
                    +
                    h2V
                    #define h2V
                    Definition: equations.h:91
                    +
                    rcxV
                    #define rcxV
                    Definition: equations.h:100
                    +
                    scalarvalueType
                    dealii::VectorizedArray< double > scalarvalueType
                    Definition: matrixFreePDE.h:34
                    +
                    sfts_linear3
                    double sfts_linear3[3][3]
                    Definition: equations.h:73
                    +
                    h3V
                    #define h3V
                    Definition: equations.h:92
                    +
                    generalizedProblem::energyDensity
                    void energyDensity(const std::vector< modelVariable< dim > > &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
                    Definition: equations.h:106
                    +
                    + +
                    +Generated by   +doxygen + 1.8.10 +
                    + + diff --git a/html/precipitate_evolution__pfunction_2main_8cc.html b/html/precipitate_evolution__pfunction_2main_8cc.html new file mode 100644 index 000000000..97cdb5005 --- /dev/null +++ b/html/precipitate_evolution__pfunction_2main_8cc.html @@ -0,0 +1,145 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/precipitateEvolution_pfunction/main.cc File Reference + + + + + + + + + + +
                    +
                    + + + + + + +
                    +
                    PRISMS PhaseField +  1.0 +
                    +
                    +
                    + + + + + + +
                    +
                    + + +
                    + +
                    + + +
                    +
                    +
                    +Functions
                    +
                    +
                    main.cc File Reference
                    +
                    +
                    +
                    #include "../../include/dealIIheaders.h"
                    +#include "parameters.h"
                    +#include "../../src/models/coupled/generalized_model.h"
                    +#include "PLibrary/PLibrary.cc"
                    +#include "PLibrary/PLibrary.hh"
                    +#include "../../src/pFunction/pFunction.h"
                    +#include "equations.h"
                    +#include "ICs_and_BCs.h"
                    +#include "../../src/models/coupled/generalized_model_functions.h"
                    +
                    + + + +

                    +Functions

                    int main (int argc, char **argv)
                     
                    +

                    Function Documentation

                    + +
                    +
                    + + + + + + + + + + + + + + + + + + +
                    int main (int argc,
                    char ** argv 
                    )
                    +
                    + +
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                    + + diff --git a/html/precipitate_evolution__pfunction_2parameters_8h.html b/html/precipitate_evolution__pfunction_2parameters_8h.html new file mode 100644 index 000000000..51b5bb4aa --- /dev/null +++ b/html/precipitate_evolution__pfunction_2parameters_8h.html @@ -0,0 +1,516 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/precipitateEvolution_pfunction/parameters.h File Reference + + + + + + + + + + +
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                    +
                    PRISMS PhaseField +  1.0 +
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                    Go to the source code of this file.

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                    +Macros

                    #define problemDIM   2
                     
                    #define spanX   40.0
                     
                    #define spanY   40.0
                     
                    #define spanZ   40.0
                     
                    #define subdivisionsX   3
                     
                    #define subdivisionsY   3
                     
                    #define subdivisionsZ   3
                     
                    #define refineFactor   5
                     
                    #define finiteElementDegree   2
                     
                    #define hAdaptivity   true
                     
                    #define maxRefinementLevel   (refineFactor)
                     
                    #define minRefinementLevel   (refineFactor-2)
                     
                    #define refineCriterionFields   {1,2,3}
                     
                    #define refineWindowMax   {0.99,0.99,0.99}
                     
                    #define refineWindowMin   {0.01,0.01,0.01}
                     
                    #define skipRemeshingSteps   1000
                     
                    #define timeStep   4.0e-4
                     
                    #define timeFinal   100.0
                     
                    #define timeIncrements   5000
                     
                    #define solverType   SolverCG
                     
                    #define absTol   true
                     
                    #define solverTolerance   1.0e-4
                     
                    #define maxSolverIterations   1000
                     
                    #define writeOutput   true
                     
                    #define outputCondition   "EQUAL_SPACING"
                     
                    #define numOutputs   10
                     
                    #define outputList   {}
                     
                    #define skipPrintSteps   10
                     
                    #define calcEnergy   false
                     
                    +

                    Macro Definition Documentation

                    + +
                    +
                    + + + + +
                    #define absTol   true
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define calcEnergy   false
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define finiteElementDegree   2
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define hAdaptivity   true
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define maxRefinementLevel   (refineFactor)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define maxSolverIterations   1000
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define minRefinementLevel   (refineFactor-2)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define numOutputs   10
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define outputCondition   "EQUAL_SPACING"
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define outputList   {}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define problemDIM   2
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define refineCriterionFields   {1,2,3}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define refineFactor   5
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define refineWindowMax   {0.99,0.99,0.99}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define refineWindowMin   {0.01,0.01,0.01}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define skipPrintSteps   10
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define skipRemeshingSteps   1000
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define solverTolerance   1.0e-4
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define solverType   SolverCG
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define spanX   40.0
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define spanY   40.0
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define spanZ   40.0
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define subdivisionsX   3
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define subdivisionsY   3
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define subdivisionsZ   3
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define timeFinal   100.0
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define timeIncrements   5000
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define timeStep   4.0e-4
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define writeOutput   true
                    +
                    + +
                    +
                    +
                    + +
                    +Generated by   +doxygen + 1.8.10 +
                    + + diff --git a/html/precipitate_evolution__pfunction_2parameters_8h_source.html b/html/precipitate_evolution__pfunction_2parameters_8h_source.html new file mode 100644 index 000000000..0ff382c32 --- /dev/null +++ b/html/precipitate_evolution__pfunction_2parameters_8h_source.html @@ -0,0 +1,209 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/precipitateEvolution_pfunction/parameters.h Source File + + + + + + + + + + +
                    +
                    + + + + + + +
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                    PRISMS PhaseField +  1.0 +
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                    + + +
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                    +
                    +
                    parameters.h
                    +
                    +
                    +Go to the documentation of this file.
                    1 // Parameter list for the precipitate evolution example application
                    +
                    2 // All strictly numerical parameters should be set in this file
                    +
                    3 
                    +
                    4 // =================================================================================
                    +
                    5 // Set the number of dimensions (1, 2, or 3 for a 1D, 2D, or 3D calculation)
                    +
                    6 // =================================================================================
                    +
                    7 #define problemDIM 2
                    +
                    8 
                    +
                    9 // =================================================================================
                    +
                    10 // Set the length of the domain in all three dimensions
                    +
                    11 // =================================================================================
                    +
                    12 // Each axes spans from zero to the specified length
                    +
                    13 #define spanX 40.0
                    +
                    14 #define spanY 40.0
                    +
                    15 #define spanZ 40.0
                    +
                    16 
                    +
                    17 // =================================================================================
                    +
                    18 // Set the element parameters
                    +
                    19 // =================================================================================
                    +
                    20 // The number of elements in each direction is 2^(refineFactor) * subdivisions
                    +
                    21 // For optimal performance, use refineFactor primarily to determine the element size
                    +
                    22 #define subdivisionsX 3
                    +
                    23 #define subdivisionsY 3
                    +
                    24 #define subdivisionsZ 3
                    +
                    25 #define refineFactor 5
                    +
                    26 
                    +
                    27 // Set the polynomial degree of the element (suggested values: 1 or 2)
                    +
                    28 #define finiteElementDegree 2
                    +
                    29 
                    +
                    30 // =================================================================================
                    +
                    31 // Set the adaptive mesh refinement parameters
                    +
                    32 // =================================================================================
                    +
                    33 // Set the flag determining if adaptive meshing is activated
                    +
                    34 #define hAdaptivity true
                    +
                    35 
                    +
                    36 // Set the maximum and minimum level of refinement
                    +
                    37 #define maxRefinementLevel (refineFactor)
                    +
                    38 #define minRefinementLevel (refineFactor-2)
                    +
                    39 
                    +
                    40 // Set the fields used to determine the refinement. Fields determined by the order
                    +
                    41 // declared in "equations.h", starting at zero
                    +
                    42 #define refineCriterionFields {1,2,3}
                    +
                    43 
                    +
                    44 // Set the maximum and minimum value of the fields where the mesh should be refined
                    +
                    45 #define refineWindowMax {0.99,0.99,0.99}
                    +
                    46 #define refineWindowMin {0.01,0.01,0.01}
                    +
                    47 
                    +
                    48 // Set the number of time steps between remeshing operations
                    +
                    49 #define skipRemeshingSteps 1000
                    +
                    50 
                    +
                    51 // =================================================================================
                    +
                    52 // Set the time step parameters
                    +
                    53 // =================================================================================
                    +
                    54 // The size of the time step
                    +
                    55 #define timeStep 4.0e-4
                    +
                    56 #define timeFinal 100.0
                    +
                    57 #define timeIncrements 5000
                    +
                    58 
                    +
                    59 // =================================================================================
                    +
                    60 // Set the elliptic solver parameters
                    +
                    61 // =================================================================================
                    +
                    62 // The solver type (currently the only recommended option is conjugate gradient)
                    +
                    63 #define solverType SolverCG
                    +
                    64 
                    +
                    65 // The flag that determines whether the tolerance for solver convergence should
                    +
                    66 // be an absolute tolerance (absTol=true) or a relative tolerance (absTol=false)
                    +
                    67 #define absTol true
                    +
                    68 
                    +
                    69 // The tolerance for convergence (L2 norm of the residual)
                    +
                    70 #define solverTolerance 1.0e-4
                    +
                    71 
                    +
                    72 // The maximum number of solver iterations per time step
                    +
                    73 #define maxSolverIterations 1000
                    +
                    74 
                    +
                    75 // =================================================================================
                    +
                    76 // Set the output parameters
                    +
                    77 // =================================================================================
                    +
                    78 // Each field in the problem will be output is writeOutput is set to "true"
                    +
                    79 #define writeOutput true
                    +
                    80 
                    +
                    81 // Type of spacing between outputs ("EQUAL_SPACING", "LOG_SPACING", "N_PER_DECADE",
                    +
                    82 // or "LIST")
                    +
                    83 #define outputCondition "EQUAL_SPACING"
                    +
                    84 
                    +
                    85 // Number of times the program outputs the fields (total number for "EQUAL_SPACING"
                    +
                    86 // and "LOG_SPACING", number per decade for "N_PER_DECADE", ignored for "LIST")
                    +
                    87 #define numOutputs 10
                    +
                    88 
                    +
                    89 // User-defined list of time steps where the program should output. Only used if
                    +
                    90 // outputCondition is "LIST"
                    +
                    91 #define outputList {}
                    +
                    92 
                    +
                    93 // Status is printed to the screen every skipPrintSteps
                    +
                    94 #define skipPrintSteps 10
                    +
                    95 
                    +
                    96 // =================================================================================
                    +
                    97 // Set the flag determining if the total free energy is calculated for each output
                    +
                    98 // =================================================================================
                    +
                    99 #define calcEnergy false
                    +
                    100 
                    +
                    101 
                    +
                    102 
                    +
                    103 
                    +
                    104 
                    +
                    105 
                    +
                    106 
                    +
                    107 
                    +
                    108 
                    +
                    + +
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                    + + diff --git a/html/reinit_8cc.html b/html/reinit_8cc.html new file mode 100644 index 000000000..090337b5d --- /dev/null +++ b/html/reinit_8cc.html @@ -0,0 +1,122 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/src/matrixfree/reinit.cc File Reference + + + + + + + + + + +
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                    PRISMS PhaseField +  1.0 +
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                    #define REINIT_MATRIXFREE_H
                     
                    +

                    Macro Definition Documentation

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                    + + diff --git a/html/residuals_8h.html b/html/residuals_8h.html new file mode 100644 index 000000000..a2d598add --- /dev/null +++ b/html/residuals_8h.html @@ -0,0 +1,208 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/__deprecatedDemos/_cahnHilliardWithAdaptivity/residuals.h File Reference + + + + + + + + + + +
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                    +Macros

                    #define McV   1.0
                     
                    #define KcV   4.0
                     
                    #define fcV   (4.0*c*(c-1.0)*(c-0.5))
                     
                    #define rmuV   (fcV)
                     
                    #define rmuxV   (constV(KcV)*cx)
                     
                    #define rcV   (c)
                     
                    #define rcxV   (constV(-McV*timeStep)*mux)
                     
                    +

                    Macro Definition Documentation

                    + +
                    +
                    + + + + +
                    #define fcV   (4.0*c*(c-1.0)*(c-0.5))
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define KcV   4.0
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define McV   1.0
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define rcV   (c)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define rcxV   (constV(-McV*timeStep)*mux)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define rmuV   (fcV)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define rmuxV   (constV(KcV)*cx)
                    +
                    + +
                    +
                    +
                    + +
                    +Generated by   +doxygen + 1.8.10 +
                    + + diff --git a/html/residuals_8h_source.html b/html/residuals_8h_source.html new file mode 100644 index 000000000..1a797c4b9 --- /dev/null +++ b/html/residuals_8h_source.html @@ -0,0 +1,113 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/__deprecatedDemos/_cahnHilliardWithAdaptivity/residuals.h Source File + + + + + + + + + + +
                    +
                    + + + + + + +
                    +
                    PRISMS PhaseField +  1.0 +
                    +
                    +
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                    + + +
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                    +
                    residuals.h
                    +
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                    +Go to the documentation of this file.
                    1 //define Cahn-Hilliard parameters
                    +
                    2 #define McV 1.0
                    +
                    3 #define KcV 4.0
                    +
                    4 
                    +
                    5 //define free energy expressions
                    +
                    6 #define fcV (4.0*c*(c-1.0)*(c-0.5))
                    +
                    7 
                    +
                    8 //define required residuals
                    +
                    9 #define rmuV (fcV)
                    +
                    10 #define rmuxV (constV(KcV)*cx)
                    +
                    11 #define rcV (c)
                    +
                    12 #define rcxV (constV(-McV*timeStep)*mux)
                    +
                    + +
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['sfts_5flinear3',['sfts_linear3',['../precipitate_evolution_2equations_8h.html#a8234161bdef319fa43bd3799a07df51c',1,'sfts_linear3(): equations.h'],['../precipitate_evolution__pfunction_2equations_8h.html#a8234161bdef319fa43bd3799a07df51c',1,'sfts_linear3(): equations.h']]], + ['soltransset',['soltransSet',['../class_matrix_free_p_d_e.html#a2b62acf6fd823666068fbccd1ec7975e',1,'MatrixFreePDE']]], + ['solutionset',['solutionSet',['../class_matrix_free_p_d_e.html#a67277d8562ba8add51127ad56de740e1',1,'MatrixFreePDE']]], + ['startindex',['startIndex',['../class_field.html#aef71a70c74162732337887d64d68b470',1,'Field']]] ]; diff --git a/html/single_precipitate_k_k_s_2_i_cs__and___b_cs_8h.html b/html/single_precipitate_k_k_s_2_i_cs__and___b_cs_8h.html new file mode 100644 index 000000000..9e705e6ce --- /dev/null +++ b/html/single_precipitate_k_k_s_2_i_cs__and___b_cs_8h.html @@ -0,0 +1,216 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/singlePrecipitateKKS/ICs_and_BCs.h File Reference + + + + + + + + + + +
                    +
                    + + + + + + +
                    +
                    PRISMS PhaseField +  1.0 +
                    +
                    +
                    + + + + + + +
                    +
                    + + +
                    + +
                    + + +
                    +
                    +
                    +Classes | +Macros
                    +
                    +
                    ICs_and_BCs.h File Reference
                    +
                    +
                    + +

                    Go to the source code of this file.

                    + + + + + + +

                    +Classes

                    class  InitialCondition< dim >
                     
                    class  InitialConditionVec< dim >
                     
                    + + + + + + + + + + + + + + + +

                    +Macros

                    #define x_denom   (2.0/2.0*scaleFactor)*(2.0/2.0*scaleFactor)
                     
                    #define y_denom   (16.0/2.0*scaleFactor)*(16.0/2.0*scaleFactor)
                     
                    #define z_denom   (16.0/2.0*scaleFactor)*(16.0/2.0*scaleFactor)
                     
                    #define initial_interface_coeff   (0.02*scaleFactor)
                     
                    #define initial_radius   1.0
                     
                    #define c_matrix   1.0e-6
                     
                    #define c_precip   0.125
                     
                    +

                    Macro Definition Documentation

                    + +
                    +
                    + + + + +
                    #define c_matrix   1.0e-6
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define c_precip   0.125
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define initial_interface_coeff   (0.02*scaleFactor)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define initial_radius   1.0
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define x_denom   (2.0/2.0*scaleFactor)*(2.0/2.0*scaleFactor)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define y_denom   (16.0/2.0*scaleFactor)*(16.0/2.0*scaleFactor)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define z_denom   (16.0/2.0*scaleFactor)*(16.0/2.0*scaleFactor)
                    +
                    + +
                    +
                    +
                    + +
                    +Generated by   +doxygen + 1.8.10 +
                    + + diff --git a/html/single_precipitate_k_k_s_2_i_cs__and___b_cs_8h_source.html b/html/single_precipitate_k_k_s_2_i_cs__and___b_cs_8h_source.html new file mode 100644 index 000000000..92c4540d3 --- /dev/null +++ b/html/single_precipitate_k_k_s_2_i_cs__and___b_cs_8h_source.html @@ -0,0 +1,252 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/singlePrecipitateKKS/ICs_and_BCs.h Source File + + + + + + + + + + +
                    +
                    + + + + + + +
                    +
                    PRISMS PhaseField +  1.0 +
                    +
                    +
                    + + + + + + +
                    +
                    + + +
                    + +
                    + + +
                    +
                    +
                    +
                    ICs_and_BCs.h
                    +
                    +
                    +Go to the documentation of this file.
                    1 //initial condition
                    +
                    2 template <int dim>
                    +
                    3 
                    +
                    4 class InitialCondition : public Function<dim>
                    +
                    5 {
                    +
                    6 public:
                    +
                    7  unsigned int index;
                    +
                    8  Vector<double> values;
                    +
                    9  InitialCondition (const unsigned int _index) : Function<dim>(1), index(_index) {
                    +
                    10  std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1);
                    +
                    11  }
                    +
                    12  double value (const Point<dim> &p, const unsigned int component = 0) const
                    +
                    13  {
                    +
                    14  double scalar_IC = 0;
                    +
                    15  // =====================================================================
                    +
                    16  // ENTER THE INITIAL CONDITIONS HERE FOR SCALAR FIELDS
                    +
                    17  // =====================================================================
                    +
                    18  // Enter the function describing conditions for the fields at point "p".
                    +
                    19  // Use "if" statements to set the initial condition for each variable
                    +
                    20  // according to its variable index.
                    +
                    21 
                    +
                    22  // Initial condition parameters
                    +
                    23  #define x_denom (2.0/2.0*scaleFactor)*(2.0/2.0*scaleFactor)
                    +
                    24  #define y_denom (16.0/2.0*scaleFactor)*(16.0/2.0*scaleFactor)
                    +
                    25  #define z_denom (16.0/2.0*scaleFactor)*(16.0/2.0*scaleFactor)
                    +
                    26  #define initial_interface_coeff (0.02*scaleFactor)
                    +
                    27  #define initial_radius 1.0
                    +
                    28  #define c_matrix 1.0e-6
                    +
                    29  #define c_precip 0.125
                    +
                    30 
                    +
                    31  //set result equal to the structural order parameter initial condition
                    +
                    32  double dx=spanX/( (double)subdivisionsX )/std::pow(2.0,refineFactor);
                    +
                    33  double dy=spanY/( (double)subdivisionsY )/std::pow(2.0,refineFactor);
                    +
                    34  double dz=spanZ/( (double)subdivisionsZ )/std::pow(2.0,refineFactor);
                    +
                    35  double r=0.0;
                    +
                    36  std::vector<double> ellipsoid_denoms;
                    +
                    37  ellipsoid_denoms.push_back(x_denom);
                    +
                    38  ellipsoid_denoms.push_back(y_denom);
                    +
                    39  ellipsoid_denoms.push_back(z_denom);
                    +
                    40 
                    +
                    41 
                    +
                    42  for (unsigned int i=0; i<dim; i++){
                    +
                    43  r += (p.operator()(i))*(p.operator()(i))/ellipsoid_denoms[i];
                    +
                    44  }
                    +
                    45  r = sqrt(r);
                    +
                    46 
                    +
                    47 
                    +
                    48  if (index==0){
                    +
                    49  scalar_IC = 0.5*(c_precip-c_matrix)*(1.0-std::tanh((r-initial_radius)/(initial_interface_coeff))) + c_matrix;
                    +
                    50  }
                    +
                    51  else if (index==1){
                    +
                    52  scalar_IC = 0.5*(1.0-std::tanh((r-initial_radius)/(initial_interface_coeff)));
                    +
                    53  }
                    +
                    54 
                    +
                    55  // =====================================================================
                    +
                    56  return scalar_IC;
                    +
                    57  }
                    +
                    58 };
                    +
                    59 
                    +
                    60 //initial condition
                    +
                    61 template <int dim>
                    +
                    62 class InitialConditionVec : public Function<dim>
                    +
                    63 {
                    +
                    64 public:
                    +
                    65  unsigned int index;
                    +
                    66  //Vector<double> values;
                    +
                    67  InitialConditionVec (const unsigned int _index) : Function<dim>(dim), index(_index) {
                    +
                    68  std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1);
                    +
                    69  }
                    +
                    70  void vector_value (const Point<dim> &p,Vector<double> &vector_IC) const
                    +
                    71  {
                    +
                    72  // =====================================================================
                    +
                    73  // ENTER THE INITIAL CONDITIONS HERE FOR VECTOR FIELDS
                    +
                    74  // =====================================================================
                    +
                    75  // Enter the function describing conditions for the fields at point "p".
                    +
                    76  // Use "if" statements to set the initial condition for each variable
                    +
                    77  // according to its variable index.
                    +
                    78 
                    +
                    79  if (index==2){
                    +
                    80  vector_IC(0) = 0.0;
                    +
                    81  vector_IC(1) = 0.0;
                    +
                    82  if (dim == 3){
                    +
                    83  vector_IC(2) = 0.0;
                    +
                    84  }
                    +
                    85  }
                    +
                    86  // =====================================================================
                    +
                    87  }
                    +
                    88 };
                    +
                    89 
                    +
                    90 template <int dim>
                    +
                    91 void generalizedProblem<dim>::setBCs(){
                    +
                    92 
                    +
                    93  // =====================================================================
                    +
                    94  // ENTER THE BOUNDARY CONDITIONS HERE
                    +
                    95  // =====================================================================
                    +
                    96  // This function sets the BCs for the problem variables
                    +
                    97  // The function "inputBCs" should be called for each component of
                    +
                    98  // each variable and should be in numerical order. Four input arguments
                    +
                    99  // set the same BC on the entire boundary. Two plus two times the
                    +
                    100  // number of dimensions inputs sets separate BCs on each face of the domain.
                    +
                    101  // Inputs to "inputBCs":
                    +
                    102  // First input: variable number
                    +
                    103  // Second input: component number
                    +
                    104  // Third input: BC type (options are "ZERO_DERIVATIVE" and "DIRICHLET")
                    +
                    105  // Fourth input: BC value (ignored unless the BC type is "DIRICHLET")
                    +
                    106  // Odd inputs after the third: BC type
                    +
                    107  // Even inputs after the third: BC value
                    +
                    108  // Face numbering: starts at zero with the minimum of the first direction, one for the maximum of the first direction
                    +
                    109  // two for the minimum of the second direction, etc.
                    +
                    110 
                    +
                    111  inputBCs(0,0,"ZERO_DERIVATIVE",0);
                    +
                    112 
                    +
                    113  inputBCs(1,0,"ZERO_DERIVATIVE",0);
                    +
                    114 
                    +
                    115  if (dim == 2){
                    +
                    116  inputBCs(2,0,"DIRICHLET",0.0,"DIRICHLET",0.0,"ZERO_DERIVATIVE",0.0,"DIRICHLET",0.0);
                    +
                    117  inputBCs(2,1,"ZERO_DERIVATIVE",0.0,"DIRICHLET",0.0,"DIRICHLET",0.0,"DIRICHLET",0.0);
                    +
                    118  }
                    +
                    119  else if (dim == 3){
                    +
                    120  inputBCs(2,0,"DIRICHLET",0.0,"DIRICHLET",0.0,"ZERO_DERIVATIVE",0.0,"DIRICHLET",0.0,"ZERO_DERIVATIVE",0.0,"DIRICHLET",0.0);
                    +
                    121  inputBCs(2,1,"ZERO_DERIVATIVE",0.0,"DIRICHLET",0.0,"DIRICHLET",0.0,"DIRICHLET",0.0,"ZERO_DERIVATIVE",0.0,"DIRICHLET",0.0);
                    +
                    122  inputBCs(2,2,"ZERO_DERIVATIVE",0.0,"DIRICHLET",0.0,"ZERO_DERIVATIVE",0.0,"DIRICHLET",0.0,"DIRICHLET",0.0,"DIRICHLET",0.0);
                    +
                    123  }
                    +
                    124 
                    +
                    125 }
                    +
                    126 
                    +
                    127 
                    +
                    InitialConditionVec::index
                    unsigned int index
                    Definition: ICs_and_BCs.h:47
                    +
                    x_denom
                    #define x_denom
                    +
                    InitialConditionVec::vector_value
                    void vector_value(const Point< dim > &p, Vector< double > &vector_IC) const
                    Definition: ICs_and_BCs.h:70
                    +
                    spanX
                    #define spanX
                    Definition: defaultValues.h:12
                    +
                    initial_radius
                    #define initial_radius
                    +
                    c_matrix
                    #define c_matrix
                    +
                    InitialCondition::InitialCondition
                    InitialCondition(const unsigned int _index)
                    Definition: ICs_and_BCs.h:9
                    +
                    spanY
                    #define spanY
                    Definition: defaultValues.h:17
                    +
                    InitialCondition::value
                    double value(const Point< dim > &p, const unsigned int component=0) const
                    Definition: ICs_and_BCs.h:12
                    +
                    InitialCondition::index
                    unsigned int index
                    Definition: ICs_and_BCs.h:5
                    +
                    subdivisionsZ
                    #define subdivisionsZ
                    Definition: parameters.h:16
                    +
                    z_denom
                    #define z_denom
                    +
                    subdivisionsY
                    #define subdivisionsY
                    Definition: parameters.h:15
                    +
                    refineFactor
                    #define refineFactor
                    Definition: defaultValues.h:27
                    +
                    generalizedProblem::setBCs
                    void setBCs()
                    Definition: ICs_and_BCs.h:67
                    +
                    c_precip
                    #define c_precip
                    +
                    initial_interface_coeff
                    #define initial_interface_coeff
                    +
                    InitialConditionVec::InitialConditionVec
                    InitialConditionVec(const unsigned int _index)
                    Definition: ICs_and_BCs.h:67
                    +
                    subdivisionsX
                    #define subdivisionsX
                    Definition: parameters.h:14
                    +
                    InitialCondition
                    Definition: ICs_and_BCs.h:2
                    +
                    InitialConditionVec
                    Definition: ICs_and_BCs.h:44
                    +
                    y_denom
                    #define y_denom
                    +
                    spanZ
                    #define spanZ
                    Definition: defaultValues.h:22
                    +
                    InitialCondition::values
                    Vector< double > values
                    Definition: ICs_and_BCs.h:6
                    +
                    + +
                    +Generated by   +doxygen + 1.8.10 +
                    + + diff --git a/html/single_precipitate_k_k_s_2equations_8h.html b/html/single_precipitate_k_k_s_2equations_8h.html new file mode 100644 index 000000000..b4419d84d --- /dev/null +++ b/html/single_precipitate_k_k_s_2equations_8h.html @@ -0,0 +1,731 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/singlePrecipitateKKS/equations.h File Reference + + + + + + + + + + +
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                    PRISMS PhaseField +  1.0 +
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                    +Macros | +Variables
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                    equations.h File Reference
                    +
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                    Go to the source code of this file.

                    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

                    +Macros

                    #define num_var   3
                     
                    #define variable_name   {"c", "n1", "u"}
                     
                    #define variable_type   {"SCALAR","SCALAR","VECTOR"}
                     
                    #define variable_eq_type   {"PARABOLIC","PARABOLIC","ELLIPTIC"}
                     
                    #define need_val   {true, true, false}
                     
                    #define need_grad   {true, true, true}
                     
                    #define need_hess   {false, false, true}
                     
                    #define need_val_residual   {true, true, false}
                     
                    #define need_grad_residual   {true, true, true}
                     
                    #define need_val_LHS   {false, true, false}
                     
                    #define need_grad_LHS   {false, false, true}
                     
                    #define need_hess_LHS   {false, false, false, false}
                     
                    #define need_val_residual_LHS   {false, false, false}
                     
                    #define need_grad_residual_LHS   {false, false, true}
                     
                    #define McV   1.0
                     
                    #define Mn1V   (300.0/scaleFactor)
                     
                    #define W   (0.8/scaleFactor)
                     
                    #define n_dependent_stiffness   true
                     
                    #define c_alpha   ((B2*c+0.5*(B1-A1)*h1V)/(A2*h1V+B2*(1.0-h1V)))
                     
                    #define c_beta   ((A2*c+0.5*(A1-B1)*(1.0-h1V))/(A2*h1V+B2*(1.0-h1V)))
                     
                    #define faV   (A2*c_alpha*c_alpha + A1*c_alpha + A0)
                     
                    #define facV   (2.0*A2*c_alpha +A1)
                     
                    #define faccV   (2.0*A2)
                     
                    #define fbV   (B2*c_beta*c_beta + B1*c_beta + B0)
                     
                    #define fbcV   (2.0*B2*c_beta +B1)
                     
                    #define fbccV   (2.0*B2)
                     
                    #define h1V   (3.0*n1*n1-2.0*n1*n1*n1)
                     
                    #define hn1V   (6.0*n1-6.0*n1*n1)
                     
                    #define fbarrierV   (n1*n1-2.0*n1*n1*n1+n1*n1*n1*n1)
                     
                    #define fbarriernV   (2.0*n1-6.0*n1*n1+4.0*n1*n1*n1)
                     
                    #define rcV   (c)
                     
                    #define rcxTemp   ( cx + n1x*(c_alpha-c_beta)*hn1V + grad_mu_el * (h1V*faccV+(constV(1.0)-h1V)*fbccV)/constV(faccV*fbccV) )
                     
                    #define rcxV   (constV(-timeStep)*McV*rcxTemp)
                     
                    #define rn1V   (n1-constV(timeStep*Mn1V)*( (fbV-faV)*hn1V - (c_beta-c_alpha)*facV*hn1V + W*fbarriernV + nDependentMisfitAC1 + heterMechAC1))
                     
                    #define rn1xV   (constV(-timeStep*Mn1V)*Knx1)
                     
                    + + + + + + + + + + + + + + + + + + + +

                    +Variables

                    double Kn1 [3][3] ={{0.01*scaleFactor,0,0},{0,0.01*scaleFactor,0},{0,0,0.005*scaleFactor}}
                     
                    double sfts_linear1 [3][3] = {{0,0,0},{0,0,0},{0,0,0}}
                     
                    double sfts_const1 [3][3] = {{0.1305,0,0},{0,-0.0152,0},{0,0,-0.014}}
                     
                    double A2 = 100.0
                     
                    double A1 = 0.0
                     
                    double A0 = 0.0
                     
                    double B2 = 100.0
                     
                    double B1 = -25.0
                     
                    double B0 = 1.5625
                     
                    +

                    Macro Definition Documentation

                    + +
                    +
                    + + + + +
                    #define c_alpha   ((B2*c+0.5*(B1-A1)*h1V)/(A2*h1V+B2*(1.0-h1V)))
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define c_beta   ((A2*c+0.5*(A1-B1)*(1.0-h1V))/(A2*h1V+B2*(1.0-h1V)))
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define faccV   (2.0*A2)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define facV   (2.0*A2*c_alpha +A1)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define faV   (A2*c_alpha*c_alpha + A1*c_alpha + A0)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define fbarriernV   (2.0*n1-6.0*n1*n1+4.0*n1*n1*n1)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define fbarrierV   (n1*n1-2.0*n1*n1*n1+n1*n1*n1*n1)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define fbccV   (2.0*B2)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define fbcV   (2.0*B2*c_beta +B1)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define fbV   (B2*c_beta*c_beta + B1*c_beta + B0)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define h1V   (3.0*n1*n1-2.0*n1*n1*n1)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define hn1V   (6.0*n1-6.0*n1*n1)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define McV   1.0
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define Mn1V   (300.0/scaleFactor)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define n_dependent_stiffness   true
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define need_grad   {true, true, true}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define need_grad_LHS   {false, false, true}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define need_grad_residual   {true, true, true}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define need_grad_residual_LHS   {false, false, true}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define need_hess   {false, false, true}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define need_hess_LHS   {false, false, false, false}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define need_val   {true, true, false}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define need_val_LHS   {false, true, false}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define need_val_residual   {true, true, false}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define need_val_residual_LHS   {false, false, false}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define num_var   3
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define rcV   (c)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define rcxTemp   ( cx + n1x*(c_alpha-c_beta)*hn1V + grad_mu_el * (h1V*faccV+(constV(1.0)-h1V)*fbccV)/constV(faccV*fbccV) )
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define rcxV   (constV(-timeStep)*McV*rcxTemp)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define rn1V   (n1-constV(timeStep*Mn1V)*( (fbV-faV)*hn1V - (c_beta-c_alpha)*facV*hn1V + W*fbarriernV + nDependentMisfitAC1 + heterMechAC1))
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define rn1xV   (constV(-timeStep*Mn1V)*Knx1)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define variable_eq_type   {"PARABOLIC","PARABOLIC","ELLIPTIC"}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define variable_name   {"c", "n1", "u"}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define variable_type   {"SCALAR","SCALAR","VECTOR"}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define W   (0.8/scaleFactor)
                    +
                    + +
                    +
                    +

                    Variable Documentation

                    + +
                    +
                    + + + + +
                    double A0 = 0.0
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    double A1 = 0.0
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    double A2 = 100.0
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    double B0 = 1.5625
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    double B1 = -25.0
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    double B2 = 100.0
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    double Kn1[3][3] ={{0.01*scaleFactor,0,0},{0,0.01*scaleFactor,0},{0,0,0.005*scaleFactor}}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    double sfts_const1[3][3] = {{0.1305,0,0},{0,-0.0152,0},{0,0,-0.014}}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    double sfts_linear1[3][3] = {{0,0,0},{0,0,0},{0,0,0}}
                    +
                    + +
                    +
                    +
                    + +
                    +Generated by   +doxygen + 1.8.10 +
                    + + diff --git a/html/single_precipitate_k_k_s_2equations_8h_source.html b/html/single_precipitate_k_k_s_2equations_8h_source.html new file mode 100644 index 000000000..d9b885e66 --- /dev/null +++ b/html/single_precipitate_k_k_s_2equations_8h_source.html @@ -0,0 +1,601 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/singlePrecipitateKKS/equations.h Source File + + + + + + + + + + +
                    +
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                    +
                    PRISMS PhaseField +  1.0 +
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                    +
                    +
                    +
                    equations.h
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                    +
                    +Go to the documentation of this file.
                    1 // List of variables and residual equations for the Precipitate Evolution example application
                    +
                    2 
                    +
                    3 // =================================================================================
                    +
                    4 // Define the variables in the model
                    +
                    5 // =================================================================================
                    +
                    6 // The number of variables
                    +
                    7 #define num_var 3
                    +
                    8 
                    +
                    9 // The names of the variables, whether they are scalars or vectors and whether the
                    +
                    10 // governing eqn for the variable is parabolic or elliptic
                    +
                    11 #define variable_name {"c", "n1", "u"}
                    +
                    12 #define variable_type {"SCALAR","SCALAR","VECTOR"}
                    +
                    13 #define variable_eq_type {"PARABOLIC","PARABOLIC","ELLIPTIC"}
                    +
                    14 
                    +
                    15 // Flags for whether the value, gradient, and Hessian are needed in the residual eqns
                    +
                    16 #define need_val {true, true, false}
                    +
                    17 #define need_grad {true, true, true}
                    +
                    18 #define need_hess {false, false, true}
                    +
                    19 
                    +
                    20 // Flags for whether the residual equation has a term multiplied by the test function
                    +
                    21 // (need_val_residual) and/or the gradient of the test function (need_grad_residual)
                    +
                    22 #define need_val_residual {true, true, false}
                    +
                    23 #define need_grad_residual {true, true, true}
                    +
                    24 
                    +
                    25 // Flags for whether the value, gradient, and Hessian are needed in the residual eqn
                    +
                    26 // for the left-hand-side of the iterative solver for elliptic equations
                    +
                    27 #define need_val_LHS {false, true, false}
                    +
                    28 #define need_grad_LHS {false, false, true}
                    +
                    29 #define need_hess_LHS {false, false, false, false}
                    +
                    30 
                    +
                    31 // Flags for whether the residual equation for the left-hand-side of the iterative
                    +
                    32 // solver for elliptic equations has a term multiplied by the test function
                    +
                    33 // (need_val_residual) and/or the gradient of the test function (need_grad_residual)
                    +
                    34 #define need_val_residual_LHS {false, false, false}
                    +
                    35 #define need_grad_residual_LHS {false, false, true}
                    +
                    36 
                    +
                    37 // =================================================================================
                    +
                    38 // Define the model parameters and the residual equations
                    +
                    39 // =================================================================================
                    +
                    40 // Parameters in the residual equations and expressions for the residual equations
                    +
                    41 // can be set here. For simple cases, the entire residual equation can be written
                    +
                    42 // here. For more complex cases with loops or conditional statements, residual
                    +
                    43 // equations (or parts of residual equations) can be written below in "residualRHS".
                    +
                    44 
                    +
                    45 // Cahn-Hilliard mobility
                    +
                    46 #define McV 1.0
                    +
                    47 
                    +
                    48 // Allen-Cahn mobilities
                    +
                    49 #define Mn1V (300.0/scaleFactor)
                    +
                    50 
                    +
                    51 // Gradient energy coefficients
                    +
                    52 double Kn1[3][3]={{0.01*scaleFactor,0,0},{0,0.01*scaleFactor,0},{0,0,0.005*scaleFactor}}; // Scaled KKS B'''
                    +
                    53 //double Kn1[3][3]={{0.01141*scaleFactor,0,0},{0,0.01141*scaleFactor,0},{0,0,0.01141*scaleFactor}}; // Isotropic interfacial energy
                    +
                    54 
                    +
                    55 //define energy barrier coefficient (used to tune the interfacial energy)
                    +
                    56 #define W (0.8/scaleFactor)
                    +
                    57 
                    +
                    58 // Define Mechanical properties
                    +
                    59 #define n_dependent_stiffness true
                    +
                    60 // Mechanical symmetry of the material and stiffness parameters
                    +
                    61 #if problemDIM==1
                    +
                    62  // Used throughout system if n_dependent_stiffness == false, used in n=0 phase if n_dependent_stiffness == true
                    +
                    63  #define MaterialModelV ISOTROPIC
                    +
                    64  #define MaterialConstantsV {22.5,0.3}
                    +
                    65  // Used in n=1 phase if n_dependent_stiffness == true
                    +
                    66  #define MaterialModelBetaV ISOTROPIC
                    +
                    67  #define MaterialConstantsBetaV {22.5,0.3}
                    +
                    68 
                    +
                    69 #elif problemDIM==2
                    +
                    70  // If n_dependent_stiffness == false the first entry is used for all phases
                    +
                    71  // 2D order of constants ANISOTROPIC - 6 constants [C11 C22 C33 C12 C13 C23]
                    +
                    72  #define MaterialModels {{"ANISOTROPIC"},{"ANISOTROPIC"}}
                    +
                    73  #define MaterialConstants {{31.3,31.3,6.65,13.0,0.0,0.0},{23.35,30.25,36.35,15.35,0.0,0.0}} //scaled by E* = 2e9 J/m^3
                    +
                    74 
                    +
                    75 #elif problemDIM==3
                    +
                    76 // If n_dependent_stiffness == false the first entry is used for all phases
                    +
                    77  #define MaterialModels {{"ANISOTROPIC"},{"ANISOTROPIC"}}
                    +
                    78  // 3D order of constants ANISOTROPIC - 21 constants [11, 22, 33, 44, 55, 66, 12, 13, 14, 15, 16, 23, 24, 25, 26, 34, 35, 36, 45, 46, 56]
                    +
                    79  //#define MaterialConstantsV {62.6,62.6,64.9,13.3,13.3,18.3,26.0,20.9,0,0,0,20.9,0,0,0,0,0,0,0,0,0} //these are in GPa-need to be non-dimensionalized
                    +
                    80  #define MaterialConstants {{31.3,31.3,32.45,6.65,6.65,9.15,13.0,10.45,0,0,0,10.45,0,0,0,0,0,0,0,0,0},{23.35,30.25,36.35,8.2,16.7,14.45,15.35,14.35,0,0,0,7.25,0,0,0,0,0,0,0,0,0}} //scaled by E* = 2e9 J/m^3
                    +
                    81 #endif
                    +
                    82 
                    +
                    83 
                    +
                    84 // Stress-free transformation strains
                    +
                    85 // Linear fits for the stress-free transformation strains in for sfts = sfts_linear * c + sfts_const
                    +
                    86 
                    +
                    87 // B'
                    +
                    88 double sfts_linear1[3][3] = {{0,0,0},{0,0,0},{0,0,0}};
                    +
                    89 double sfts_const1[3][3] = {{0.1305,0,0},{0,-0.0152,0},{0,0,-0.014}};
                    +
                    90 
                    +
                    91 
                    +
                    92 // Calculate c_alpha and c_beta from c
                    +
                    93 #define c_alpha ((B2*c+0.5*(B1-A1)*h1V)/(A2*h1V+B2*(1.0-h1V)))
                    +
                    94 #define c_beta ((A2*c+0.5*(A1-B1)*(1.0-h1V))/(A2*h1V+B2*(1.0-h1V)))
                    +
                    95 
                    +
                    96 //define free energy expressions (Mg-Nd data from CASM) (B''', gen 4)
                    +
                    97 double A2 = 100.0;
                    +
                    98 double A1 = 0.0;
                    +
                    99 double A0 = 0.0;
                    +
                    100 double B2 = 100.0;
                    +
                    101 double B1 = -25.0;
                    +
                    102 double B0 = 1.5625;
                    +
                    103 
                    +
                    104 #define faV (A2*c_alpha*c_alpha + A1*c_alpha + A0)
                    +
                    105 #define facV (2.0*A2*c_alpha +A1)
                    +
                    106 #define faccV (2.0*A2)
                    +
                    107 #define fbV (B2*c_beta*c_beta + B1*c_beta + B0)
                    +
                    108 #define fbcV (2.0*B2*c_beta +B1)
                    +
                    109 #define fbccV (2.0*B2)
                    +
                    110 
                    +
                    111 #define h1V (3.0*n1*n1-2.0*n1*n1*n1)
                    +
                    112 #define hn1V (6.0*n1-6.0*n1*n1)
                    +
                    113 
                    +
                    114 // This double-well function can be used to tune the interfacial energy
                    +
                    115 #define fbarrierV (n1*n1-2.0*n1*n1*n1+n1*n1*n1*n1)
                    +
                    116 #define fbarriernV (2.0*n1-6.0*n1*n1+4.0*n1*n1*n1)
                    +
                    117 
                    +
                    118 // Residuals
                    +
                    119 #define rcV (c)
                    +
                    120 #define rcxTemp ( cx + n1x*(c_alpha-c_beta)*hn1V + grad_mu_el * (h1V*faccV+(constV(1.0)-h1V)*fbccV)/constV(faccV*fbccV) )
                    +
                    121 #define rcxV (constV(-timeStep)*McV*rcxTemp)
                    +
                    122 
                    +
                    123 #define rn1V (n1-constV(timeStep*Mn1V)*( (fbV-faV)*hn1V - (c_beta-c_alpha)*facV*hn1V + W*fbarriernV + nDependentMisfitAC1 + heterMechAC1))
                    +
                    124 #define rn1xV (constV(-timeStep*Mn1V)*Knx1)
                    +
                    125 
                    +
                    126 
                    +
                    127 // =================================================================================
                    +
                    128 // residualRHS
                    +
                    129 // =================================================================================
                    +
                    130 // This function calculates the residual equations for each variable. It takes
                    +
                    131 // "modelVariablesList" as an input, which is a list of the value and derivatives of
                    +
                    132 // each of the variables at a specific quadrature point. The (x,y,z) location of
                    +
                    133 // that quadrature point is given by "q_point_loc". The function outputs
                    +
                    134 // "modelResidualsList", a list of the value and gradient terms of the residual for
                    +
                    135 // each residual equation. The index for each variable in these lists corresponds to
                    +
                    136 // the order it is defined at the top of this file (starting at 0).
                    +
                    137 template <int dim>
                    +
                    138 void generalizedProblem<dim>::residualRHS(const std::vector<modelVariable<dim>> & modelVariablesList,
                    +
                    139  std::vector<modelResidual<dim>> & modelResidualsList,
                    +
                    140  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
                    +
                    141 
                    +
                    142 // The concentration and its derivatives (names here should match those in the macros above)
                    +
                    143 scalarvalueType c = modelVariablesList[0].scalarValue;
                    +
                    144 scalargradType cx = modelVariablesList[0].scalarGrad;
                    +
                    145 
                    +
                    146 // The first order parameter and its derivatives (names here should match those in the macros above)
                    +
                    147 scalarvalueType n1 = modelVariablesList[1].scalarValue;
                    +
                    148 scalargradType n1x = modelVariablesList[1].scalarGrad;
                    +
                    149 
                    +
                    150 // The derivative of the displacement vector (names here should match those in the macros above)
                    +
                    151 vectorgradType ux = modelVariablesList[2].vectorGrad;
                    +
                    152 vectorgradType ruxV;
                    +
                    153 
                    +
                    154 vectorhessType uxx;
                    +
                    155 
                    +
                    156 bool c_dependent_misfit = false;
                    +
                    157 for (unsigned int i=0; i<dim; i++){
                    +
                    158  for (unsigned int j=0; j<dim; j++){
                    +
                    159  if (std::abs(sfts_linear1[i][j])>1.0e-12){
                    +
                    160  c_dependent_misfit = true;
                    +
                    161  }
                    +
                    162  }
                    +
                    163 }
                    +
                    164 
                    +
                    165 if (c_dependent_misfit == true){
                    +
                    166  uxx = modelVariablesList[2].vectorHess;
                    +
                    167 }
                    +
                    168 
                    +
                    169 // Calculate the derivatives of c_beta (derivatives of c_alpha aren't needed)
                    +
                    170 scalarvalueType cbnV, cbcV, cbcnV;
                    +
                    171 
                    +
                    172 cbcV = faccV/( (constV(1.0)-h1V)*fbccV + h1V*faccV );
                    +
                    173 cbnV = hn1V * (c_alpha - c_beta) * cbcV;
                    +
                    174 cbcnV = (faccV * (fbccV-faccV) * hn1V)/( ((1.0-h1V)*fbccV + h1V*faccV)*((1.0-h1V)*fbccV + h1V*faccV) ); // Note: this is only true if faV and fbV are quadratic
                    +
                    175 
                    +
                    176 // Calculate the stress-free transformation strain and its derivatives at the quadrature point
                    +
                    177 dealii::Tensor<2, problemDIM, dealii::VectorizedArray<double> > sfts1, sfts1c, sfts1cc, sfts1n, sfts1cn;
                    +
                    178 
                    +
                    179 for (unsigned int i=0; i<dim; i++){
                    +
                    180 for (unsigned int j=0; j<dim; j++){
                    +
                    181 
                    +
                    182  // Old SFTS formulation
                    +
                    183  // Polynomial fits for the stress-free transformation strains, of the form: sfts = a_p * c + b_p
                    +
                    184  //sfts1[i][j] = constV(sfts_linear1[i][j])*c + constV(sfts_const1[i][j]);
                    +
                    185  //sfts1c[i][j] = constV(sfts_linear1[i][j]);
                    +
                    186  //sfts1cc[i][j] = constV(0.0);
                    +
                    187 
                    +
                    188  // New SFTS formulation
                    +
                    189  // Polynomial fits for the stress-free transformation strains, of the form: sfts = a_p * c_beta + b_p
                    +
                    190  sfts1[i][j] = constV(sfts_linear1[i][j])*c_beta + constV(sfts_const1[i][j]);
                    +
                    191  sfts1c[i][j] = constV(sfts_linear1[i][j]) * cbcV;
                    +
                    192  sfts1cc[i][j] = constV(0.0);
                    +
                    193  sfts1n[i][j] = constV(sfts_linear1[i][j]) * cbnV;
                    +
                    194  sfts1cn[i][j] = constV(sfts_linear1[i][j]) * cbcnV;
                    +
                    195 }
                    +
                    196 }
                    +
                    197 
                    +
                    198 //compute E2=(E-E0)
                    +
                    199 dealii::VectorizedArray<double> E2[dim][dim], S[dim][dim];
                    +
                    200 
                    +
                    201 for (unsigned int i=0; i<dim; i++){
                    +
                    202 for (unsigned int j=0; j<dim; j++){
                    +
                    203  E2[i][j]= constV(0.5)*(ux[i][j]+ux[j][i])-( sfts1[i][j]*h1V );
                    +
                    204 
                    +
                    205 }
                    +
                    206 }
                    +
                    207 
                    +
                    208 //compute stress
                    +
                    209 //S=C*(E-E0)
                    +
                    210 // Compute stress tensor (which is equal to the residual, Rux)
                    +
                    211 dealii::VectorizedArray<double> CIJ_combined[CIJ_tensor_size][CIJ_tensor_size];
                    +
                    212 
                    +
                    213 if (n_dependent_stiffness == true){
                    +
                    214 for (unsigned int i=0; i<2*dim-1+dim/3; i++){
                    +
                    215  for (unsigned int j=0; j<2*dim-1+dim/3; j++){
                    +
                    216  CIJ_combined[i][j] = CIJ_list[0][i][j]*(constV(1.0)-h1V) + CIJ_list[1][i][j]*h1V;
                    +
                    217  }
                    +
                    218 }
                    +
                    219 computeStress<dim>(CIJ_combined, E2, S);
                    +
                    220 }
                    +
                    221 else{
                    +
                    222 computeStress<dim>(CIJ_list[0], E2, S);
                    +
                    223 }
                    +
                    224 
                    +
                    225 
                    +
                    226 // Fill residual corresponding to mechanics
                    +
                    227 // R=-C*(E-E0)
                    +
                    228 
                    +
                    229 for (unsigned int i=0; i<dim; i++){
                    +
                    230 for (unsigned int j=0; j<dim; j++){
                    +
                    231  ruxV[i][j] = - S[i][j];
                    +
                    232 }
                    +
                    233 }
                    +
                    234 
                    +
                    235 // Compute one of the stress terms in the order parameter chemical potential, nDependentMisfitACp = -C*(E-E0)*(E0_n)
                    +
                    236 dealii::VectorizedArray<double> nDependentMisfitAC1=constV(0.0);
                    +
                    237 
                    +
                    238 for (unsigned int i=0; i<dim; i++){
                    +
                    239 for (unsigned int j=0; j<dim; j++){
                    +
                    240  nDependentMisfitAC1+=-S[i][j]*(sfts1n[i][j]*h1V + sfts1[i][j]*hn1V);
                    +
                    241 }
                    +
                    242 }
                    +
                    243 
                    +
                    244 
                    +
                    245 // Compute the other stress term in the order parameter chemical potential, heterMechACp = 0.5*Hn*(C_beta-C_alpha)*(E-E0)*(E-E0)
                    +
                    246 dealii::VectorizedArray<double> heterMechAC1=constV(0.0);
                    +
                    247 dealii::VectorizedArray<double> S2[dim][dim];
                    +
                    248 
                    +
                    249 if (n_dependent_stiffness == true){
                    +
                    250  computeStress<dim>(CIJ_list[1]-CIJ_list[0], E2, S2);
                    +
                    251 
                    +
                    252  for (unsigned int i=0; i<dim; i++){
                    +
                    253  for (unsigned int j=0; j<dim; j++){
                    +
                    254  heterMechAC1 += S2[i][j]*E2[i][j];
                    +
                    255  }
                    +
                    256  }
                    +
                    257  heterMechAC1 = 0.5*hn1V*heterMechAC1;
                    +
                    258 }
                    +
                    259 
                    +
                    260 // compute the stress term in the gradient of the concentration chemical potential, grad_mu_el = -[C*(E-E0)*E0c]x, must be a vector with length dim
                    +
                    261 scalargradType grad_mu_el;
                    +
                    262 
                    +
                    263 if (c_dependent_misfit == true){
                    +
                    264  dealii::VectorizedArray<double> E3[dim][dim], S3[dim][dim];
                    +
                    265 
                    +
                    266  for (unsigned int i=0; i<dim; i++){
                    +
                    267  for (unsigned int j=0; j<dim; j++){
                    +
                    268  E3[i][j] = -( sfts1c[i][j]*h1V );
                    +
                    269  }
                    +
                    270  }
                    +
                    271 
                    +
                    272  if (n_dependent_stiffness == true){
                    +
                    273  computeStress<dim>(CIJ_combined, E3, S3);
                    +
                    274  }
                    +
                    275  else{
                    +
                    276  computeStress<dim>(CIJ_list[0], E3, S3);
                    +
                    277  }
                    +
                    278 
                    +
                    279  for (unsigned int i=0; i<dim; i++){
                    +
                    280  for (unsigned int j=0; j<dim; j++){
                    +
                    281  for (unsigned int k=0; k<dim; k++){
                    +
                    282  grad_mu_el[k] += S3[i][j] * (constV(0.5)*(uxx[i][j][k]+uxx[j][i][k]) + E3[i][j]*cx[k]
                    +
                    283  - ( (sfts1[i][j]*hn1V + sfts1n[i][j]*h1V) *n1x[k]) );
                    +
                    284 
                    +
                    285  grad_mu_el[k]+= - S[i][j] * ( (sfts1c[i][j]*hn1V + h1V*sfts1cn[i][j])*n1x[k] + ( sfts1cc[i][j]*h1V )*cx[k]);
                    +
                    286 
                    +
                    287  if (n_dependent_stiffness == true){
                    +
                    288  grad_mu_el[k]+= S2[i][j] * (hn1V*n1x[k]) * (E3[i][j]);
                    +
                    289 
                    +
                    290  }
                    +
                    291  }
                    +
                    292  }
                    +
                    293  }
                    +
                    294 }
                    +
                    295 
                    +
                    296 
                    +
                    297 //compute K*nx
                    +
                    298 scalargradType Knx1;
                    +
                    299 for (unsigned int a=0; a<dim; a++) {
                    +
                    300 Knx1[a]=0.0;
                    +
                    301 for (unsigned int b=0; b<dim; b++){
                    +
                    302  Knx1[a]+=constV(Kn1[a][b])*n1x[b];
                    +
                    303 }
                    +
                    304 }
                    +
                    305 
                    +
                    306 modelResidualsList[0].scalarValueResidual = rcV;
                    +
                    307 modelResidualsList[0].scalarGradResidual = rcxV;
                    +
                    308 
                    +
                    309 modelResidualsList[1].scalarValueResidual = rn1V;
                    +
                    310 modelResidualsList[1].scalarGradResidual = rn1xV;
                    +
                    311 
                    +
                    312 modelResidualsList[2].vectorGradResidual = ruxV;
                    +
                    313 
                    +
                    314 }
                    +
                    315 
                    +
                    316 // =================================================================================
                    +
                    317 // residualLHS (needed only if at least one equation is elliptic)
                    +
                    318 // =================================================================================
                    +
                    319 // This function calculates the residual equations for the iterative solver for
                    +
                    320 // elliptic equations.for each variable. It takes "modelVariablesList" as an input,
                    +
                    321 // which is a list of the value and derivatives of each of the variables at a
                    +
                    322 // specific quadrature point. The (x,y,z) location of that quadrature point is given
                    +
                    323 // by "q_point_loc". The function outputs "modelRes", the value and gradient terms of
                    +
                    324 // for the left-hand-side of the residual equation for the iterative solver. The
                    +
                    325 // index for each variable in these lists corresponds to the order it is defined at
                    +
                    326 // the top of this file (starting at 0), not counting variables that have
                    +
                    327 // "need_val_LHS", "need_grad_LHS", and "need_hess_LHS" all set to "false". If there
                    +
                    328 // are multiple elliptic equations, conditional statements should be used to ensure
                    +
                    329 // that the correct residual is being submitted. The index of the field being solved
                    +
                    330 // can be accessed by "this->currentFieldIndex".
                    +
                    331 template <int dim>
                    +
                    332 void generalizedProblem<dim>::residualLHS(const std::vector<modelVariable<dim>> & modelVariablesList,
                    +
                    333  modelResidual<dim> & modelRes,
                    +
                    334  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) const {
                    +
                    335 
                    +
                    336 //n1
                    +
                    337 scalarvalueType n1 = modelVariablesList[0].scalarValue;
                    +
                    338 
                    +
                    339 //u
                    +
                    340 vectorgradType ux = modelVariablesList[1].vectorGrad;
                    +
                    341 vectorgradType ruxV;
                    +
                    342 
                    +
                    343 // Take advantage of E being simply 0.5*(ux + transpose(ux)) and use the dealii "symmetrize" function
                    +
                    344 dealii::Tensor<2, dim, dealii::VectorizedArray<double> > E;
                    +
                    345 E = symmetrize(ux);
                    +
                    346 
                    +
                    347 // Compute stress tensor (which is equal to the residual, Rux)
                    +
                    348 if (n_dependent_stiffness == true){
                    +
                    349  dealii::Tensor<2, CIJ_tensor_size, dealii::VectorizedArray<double> > CIJ_combined;
                    +
                    350  CIJ_combined = CIJ_list[0]*(constV(1.0)-h1V);
                    +
                    351  CIJ_combined += CIJ_list[1]*(h1V);
                    +
                    352 
                    +
                    353  computeStress<dim>(CIJ_combined, E, ruxV);
                    +
                    354 }
                    +
                    355 else{
                    +
                    356  computeStress<dim>(CIJ_list[0], E, ruxV);
                    +
                    357 }
                    +
                    358 
                    +
                    359 modelRes.vectorGradResidual = ruxV;
                    +
                    360 
                    +
                    361 }
                    +
                    362 
                    +
                    363 // =================================================================================
                    +
                    364 // energyDensity (needed only if calcEnergy == true)
                    +
                    365 // =================================================================================
                    +
                    366 // This function integrates the free energy density across the computational domain.
                    +
                    367 // It takes "modelVariablesList" as an input, which is a list of the value and
                    +
                    368 // derivatives of each of the variables at a specific quadrature point. It also
                    +
                    369 // takes the mapped quadrature weight, "JxW_value", as an input. The (x,y,z) location
                    +
                    370 // of the quadrature point is given by "q_point_loc". The weighted value of the
                    +
                    371 // energy density is added to "energy" variable and the components of the energy
                    +
                    372 // density are added to the "energy_components" variable (index 0: chemical energy,
                    +
                    373 // index 1: gradient energy, index 2: elastic energy).
                    +
                    374 template <int dim>
                    +
                    375 void generalizedProblem<dim>::energyDensity(const std::vector<modelVariable<dim>> & modelVarList,
                    +
                    376  const dealii::VectorizedArray<double> & JxW_value,
                    +
                    377  dealii::Point<dim, dealii::VectorizedArray<double> > q_point_loc) {
                    +
                    378 
                    +
                    379 scalarvalueType total_energy_density = constV(0.0);
                    +
                    380 
                    +
                    381 //c
                    +
                    382 scalarvalueType c = modelVarList[0].scalarValue;
                    +
                    383 scalargradType cx = modelVarList[0].scalarGrad;
                    +
                    384 
                    +
                    385 //n1
                    +
                    386 scalarvalueType n1 = modelVarList[1].scalarValue;
                    +
                    387 scalargradType n1x = modelVarList[1].scalarGrad;
                    +
                    388 
                    +
                    389 //u
                    +
                    390 vectorgradType ux = modelVarList[2].vectorGrad;
                    +
                    391 
                    +
                    392 scalarvalueType f_chem = (constV(1.0)-(h1V))*faV + (h1V)*fbV;
                    +
                    393 
                    +
                    394 scalarvalueType f_grad = constV(0.0);
                    +
                    395 
                    +
                    396 for (int i=0; i<dim; i++){
                    +
                    397  for (int j=0; j<dim; j++){
                    +
                    398  f_grad += constV(0.5*Kn1[i][j])*n1x[i]*n1x[j];
                    +
                    399  }
                    +
                    400 }
                    +
                    401 
                    +
                    402 // Calculate the stress-free transformation strain and its derivatives at the quadrature point
                    +
                    403 dealii::Tensor<2, problemDIM, dealii::VectorizedArray<double> > sfts1, sfts1c, sfts1cc;
                    +
                    404 
                    +
                    405 for (unsigned int i=0; i<dim; i++){
                    +
                    406  for (unsigned int j=0; j<dim; j++){
                    +
                    407  // Polynomial fits for the stress-free transformation strains, of the form: sfts = a_p * c + b_p
                    +
                    408  sfts1[i][j] = constV(sfts_linear1[i][j])*c + constV(sfts_const1[i][j]);
                    +
                    409  sfts1c[i][j] = constV(sfts_linear1[i][j]);
                    +
                    410  sfts1cc[i][j] = constV(0.0);
                    +
                    411  }
                    +
                    412 }
                    +
                    413 
                    +
                    414 //compute E2=(E-E0)
                    +
                    415 dealii::VectorizedArray<double> E2[dim][dim], S[dim][dim];
                    +
                    416 
                    +
                    417 for (unsigned int i=0; i<dim; i++){
                    +
                    418  for (unsigned int j=0; j<dim; j++){
                    +
                    419  E2[i][j]= constV(0.5)*(ux[i][j]+ux[j][i])-( sfts1[i][j]*h1V);
                    +
                    420 
                    +
                    421  }
                    +
                    422 }
                    +
                    423 
                    +
                    424 //compute stress
                    +
                    425 //S=C*(E-E0)
                    +
                    426 dealii::VectorizedArray<double> CIJ_combined[2*dim-1+dim/3][2*dim-1+dim/3];
                    +
                    427 
                    +
                    428 if (n_dependent_stiffness == true){
                    +
                    429  for (unsigned int i=0; i<2*dim-1+dim/3; i++){
                    +
                    430  for (unsigned int j=0; j<2*dim-1+dim/3; j++){
                    +
                    431  CIJ_combined[i][j] = CIJ_list[0][i][j]*(constV(1.0)-h1V) + CIJ_list[1][i][j]*h1V;
                    +
                    432  }
                    +
                    433  }
                    +
                    434  computeStress<dim>(CIJ_combined, E2, S);
                    +
                    435 }
                    +
                    436 else{
                    +
                    437  computeStress<dim>(CIJ_list[0], E2, S);
                    +
                    438 }
                    +
                    439 
                    +
                    440 scalarvalueType f_el = constV(0.0);
                    +
                    441 
                    +
                    442 for (unsigned int i=0; i<dim; i++){
                    +
                    443  for (unsigned int j=0; j<dim; j++){
                    +
                    444  f_el += constV(0.5) * S[i][j]*E2[i][j];
                    +
                    445  }
                    +
                    446 }
                    +
                    447 
                    +
                    448 total_energy_density = f_chem + f_grad + f_el;
                    +
                    449 
                    +
                    450 // Loop to step through each element of the vectorized arrays. Working with deal.ii
                    +
                    451 // developers to see if there is a more elegant way to do this.
                    +
                    452 assembler_lock.acquire ();
                    +
                    453 for (unsigned i=0; i<c.n_array_elements;i++){
                    +
                    454  if (c[i] > 1.0e-10){
                    +
                    455  this->energy+=total_energy_density[i]*JxW_value[i];
                    +
                    456  this->energy_components[0]+= f_chem[i]*JxW_value[i];
                    +
                    457  this->energy_components[1]+= f_grad[i]*JxW_value[i];
                    +
                    458  this->energy_components[2]+= f_el[i]*JxW_value[i];
                    +
                    459  }
                    +
                    460 }
                    +
                    461 assembler_lock.release ();
                    +
                    462 }
                    +
                    463 
                    +
                    464 
                    +
                    465 
                    +
                    466 
                    +
                    scaleFactor
                    #define scaleFactor
                    Definition: parameters.h:13
                    +
                    c_alpha
                    #define c_alpha
                    Definition: equations.h:93
                    +
                    modelResidual
                    Definition: model_variables.h:25
                    +
                    generalizedProblem::residualRHS
                    void residualRHS(const std::vector< modelVariable< dim > > &modelVarList, std::vector< modelResidual< dim > > &modelResidualsList, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
                    Definition: equations.h:58
                    +
                    sfts_linear1
                    double sfts_linear1[3][3]
                    Definition: equations.h:67
                    +
                    scalargradType
                    dealii::Tensor< 1, problemDIM, dealii::VectorizedArray< double > > scalargradType
                    Definition: matrixFreePDE.h:51
                    +
                    constV
                    #define constV(a)
                    Definition: matrixFreePDE.h:67
                    +
                    rcxV
                    #define rcxV
                    Definition: equations.h:121
                    +
                    A0
                    double A0
                    Definition: equations.h:99
                    +
                    B0
                    double B0
                    Definition: equations.h:102
                    +
                    hn1V
                    #define hn1V
                    Definition: equations.h:112
                    +
                    modelResidual::vectorGradResidual
                    vectorgradType vectorGradResidual
                    Definition: model_variables.h:33
                    +
                    Kn1
                    double Kn1[3][3]
                    Definition: equations.h:54
                    +
                    fbccV
                    #define fbccV
                    Definition: equations.h:109
                    +
                    faV
                    #define faV
                    Definition: equations.h:104
                    +
                    rn1V
                    #define rn1V
                    Definition: equations.h:123
                    +
                    h1V
                    #define h1V
                    Definition: equations.h:111
                    +
                    A1
                    double A1
                    Definition: equations.h:98
                    +
                    rn1xV
                    #define rn1xV
                    Definition: equations.h:124
                    +
                    c_beta
                    #define c_beta
                    Definition: equations.h:94
                    +
                    n_dependent_stiffness
                    #define n_dependent_stiffness
                    Definition: equations.h:59
                    +
                    B1
                    double B1
                    Definition: equations.h:101
                    +
                    generalizedProblem::residualLHS
                    void residualLHS(const std::vector< modelVariable< dim > > &modelVarList, modelResidual< dim > &modelRes, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc) const
                    Definition: equations.h:89
                    +
                    sfts_const1
                    double sfts_const1[3][3]
                    Definition: equations.h:68
                    +
                    A2
                    double A2
                    Definition: equations.h:97
                    +
                    rcV
                    #define rcV
                    Definition: equations.h:119
                    +
                    B2
                    double B2
                    Definition: equations.h:100
                    +
                    vectorhessType
                    dealii::Tensor< 3, problemDIM, dealii::VectorizedArray< double > > vectorhessType
                    Definition: matrixFreePDE.h:60
                    +
                    modelVariable
                    Definition: model_variables.h:4
                    +
                    vectorgradType
                    dealii::Tensor< 2, problemDIM, dealii::VectorizedArray< double > > vectorgradType
                    Definition: matrixFreePDE.h:57
                    +
                    fbV
                    #define fbV
                    Definition: equations.h:107
                    +
                    faccV
                    #define faccV
                    Definition: equations.h:106
                    +
                    scalarvalueType
                    dealii::VectorizedArray< double > scalarvalueType
                    Definition: matrixFreePDE.h:34
                    +
                    generalizedProblem::energyDensity
                    void energyDensity(const std::vector< modelVariable< dim > > &modelVarList, const dealii::VectorizedArray< double > &JxW_value, dealii::Point< dim, dealii::VectorizedArray< double > > q_point_loc)
                    Definition: equations.h:106
                    +
                    + +
                    +Generated by   +doxygen + 1.8.10 +
                    + + diff --git a/html/single_precipitate_k_k_s_2main_8cc.html b/html/single_precipitate_k_k_s_2main_8cc.html new file mode 100644 index 000000000..08cea16d3 --- /dev/null +++ b/html/single_precipitate_k_k_s_2main_8cc.html @@ -0,0 +1,142 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/singlePrecipitateKKS/main.cc File Reference + + + + + + + + + + +
                    +
                    + + + + + + +
                    +
                    PRISMS PhaseField +  1.0 +
                    +
                    +
                    + + + + + + +
                    +
                    + + +
                    + +
                    + + +
                    +
                    +
                    +Functions
                    +
                    +
                    main.cc File Reference
                    +
                    +
                    +
                    #include "../../include/dealIIheaders.h"
                    +#include "parameters.h"
                    +#include "../../src/models/coupled/generalized_model.h"
                    +#include "equations.h"
                    +#include "ICs_and_BCs.h"
                    +#include "../../src/models/coupled/generalized_model_functions.h"
                    +
                    + + + +

                    +Functions

                    int main (int argc, char **argv)
                     
                    +

                    Function Documentation

                    + +
                    +
                    + + + + + + + + + + + + + + + + + + +
                    int main (int argc,
                    char ** argv 
                    )
                    +
                    + +
                    +
                    +
                    + +
                    +Generated by   +doxygen + 1.8.10 +
                    + + diff --git a/html/single_precipitate_k_k_s_2parameters_8h.html b/html/single_precipitate_k_k_s_2parameters_8h.html new file mode 100644 index 000000000..0e6777c60 --- /dev/null +++ b/html/single_precipitate_k_k_s_2parameters_8h.html @@ -0,0 +1,530 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/singlePrecipitateKKS/parameters.h File Reference + + + + + + + + + + +
                    +
                    + + + + + + +
                    +
                    PRISMS PhaseField +  1.0 +
                    +
                    +
                    + + + + + + +
                    +
                    + + +
                    + +
                    + + +
                    +
                    +
                    +Macros
                    +
                    +
                    parameters.h File Reference
                    +
                    +
                    + +

                    Go to the source code of this file.

                    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

                    +Macros

                    #define problemDIM   3
                     
                    #define scaleFactor   2.0
                     
                    #define spanX   (12.5*scaleFactor)
                     
                    #define spanY   (12.5*scaleFactor)
                     
                    #define spanZ   (12.5*scaleFactor)
                     
                    #define subdivisionsX   1
                     
                    #define subdivisionsY   1
                     
                    #define subdivisionsZ   1
                     
                    #define refineFactor   5
                     
                    #define finiteElementDegree   2
                     
                    #define hAdaptivity   true
                     
                    #define maxRefinementLevel   (refineFactor+1)
                     
                    #define minRefinementLevel   (0)
                     
                    #define refineCriterionFields   {1}
                     
                    #define refineWindowMax   {0.99}
                     
                    #define refineWindowMin   {0.001}
                     
                    #define skipRemeshingSteps   100
                     
                    #define timeStep   (1.1e-4*scaleFactor*scaleFactor)
                     
                    #define timeIncrements   2500
                     
                    #define timeFinal   (timeStep*timeIncrements)
                     
                    #define solverType   SolverCG
                     
                    #define absTol   true
                     
                    #define solverTolerance   1.0e-3
                     
                    #define maxSolverIterations   10000
                     
                    #define writeOutput   true
                     
                    #define outputCondition   "EQUAL_SPACING"
                     
                    #define numOutputs   10
                     
                    #define outputList   {}
                     
                    #define skipPrintSteps   1000
                     
                    #define calcEnergy   true
                     
                    +

                    Macro Definition Documentation

                    + +
                    +
                    + + + + +
                    #define absTol   true
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define calcEnergy   true
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define finiteElementDegree   2
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define hAdaptivity   true
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define maxRefinementLevel   (refineFactor+1)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define maxSolverIterations   10000
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define minRefinementLevel   (0)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define numOutputs   10
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define outputCondition   "EQUAL_SPACING"
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define outputList   {}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define problemDIM   3
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define refineCriterionFields   {1}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define refineFactor   5
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define refineWindowMax   {0.99}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define refineWindowMin   {0.001}
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define scaleFactor   2.0
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define skipPrintSteps   1000
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define skipRemeshingSteps   100
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define solverTolerance   1.0e-3
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define solverType   SolverCG
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define spanX   (12.5*scaleFactor)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define spanY   (12.5*scaleFactor)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define spanZ   (12.5*scaleFactor)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define subdivisionsX   1
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define subdivisionsY   1
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define subdivisionsZ   1
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define timeFinal   (timeStep*timeIncrements)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define timeIncrements   2500
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define timeStep   (1.1e-4*scaleFactor*scaleFactor)
                    +
                    + +
                    +
                    + +
                    +
                    + + + + +
                    #define writeOutput   true
                    +
                    + +
                    +
                    +
                    + +
                    +Generated by   +doxygen + 1.8.10 +
                    + + diff --git a/html/single_precipitate_k_k_s_2parameters_8h_source.html b/html/single_precipitate_k_k_s_2parameters_8h_source.html new file mode 100644 index 000000000..3c70b1f9f --- /dev/null +++ b/html/single_precipitate_k_k_s_2parameters_8h_source.html @@ -0,0 +1,212 @@ + + + + + + +PRISMS PhaseField: phaseField_dev/applications/singlePrecipitateKKS/parameters.h Source File + + + + + + + + + + +
                    +
                    + + + + + + +
                    +
                    PRISMS PhaseField +  1.0 +
                    +
                    +
                    + + + + + + +
                    +
                    + + +
                    + +
                    + + +
                    +
                    +
                    +
                    parameters.h
                    +
                    +
                    +Go to the documentation of this file.
                    1 // Parameter list for the precipitate evolution example application
                    +
                    2 // All strictly numerical parameters should be set in this file
                    +
                    3 
                    +
                    4 // =================================================================================
                    +
                    5 // Set the number of dimensions (1, 2, or 3 for a 1D, 2D, or 3D calculation)
                    +
                    6 // =================================================================================
                    +
                    7 #define problemDIM 3
                    +
                    8 
                    +
                    9 // =================================================================================
                    +
                    10 // Set the length of the domain in all three dimensions
                    +
                    11 // =================================================================================
                    +
                    12 // Each axes spans from zero to the specified length
                    +
                    13 #define scaleFactor 2.0
                    +
                    14 
                    +
                    15 #define spanX (12.5*scaleFactor)
                    +
                    16 #define spanY (12.5*scaleFactor)
                    +
                    17 #define spanZ (12.5*scaleFactor)
                    +
                    18 
                    +
                    19 // =================================================================================
                    +
                    20 // Set the element parameters
                    +
                    21 // =================================================================================
                    +
                    22 // The number of elements in each direction is 2^(refineFactor) * subdivisions
                    +
                    23 // For optimal performance, use refineFactor primarily to determine the element size
                    +
                    24 #define subdivisionsX 1
                    +
                    25 #define subdivisionsY 1
                    +
                    26 #define subdivisionsZ 1
                    +
                    27 #define refineFactor 5
                    +
                    28 
                    +
                    29 // Set the polynomial degree of the element (suggested values: 1 or 2)
                    +
                    30 #define finiteElementDegree 2
                    +
                    31 
                    +
                    32 // =================================================================================
                    +
                    33 // Set the adaptive mesh refinement parameters
                    +
                    34 // =================================================================================
                    +
                    35 // Set the flag determining if adaptive meshing is activated
                    +
                    36 #define hAdaptivity true
                    +
                    37 
                    +
                    38 // Set the maximum and minimum level of refinement
                    +
                    39 #define maxRefinementLevel (refineFactor+1)
                    +
                    40 #define minRefinementLevel (0)
                    +
                    41 
                    +
                    42 // Set the fields used to determine the refinement. Fields determined by the order
                    +
                    43 // declared in "equations.h", starting at zero
                    +
                    44 #define refineCriterionFields {1}
                    +
                    45 
                    +
                    46 // Set the maximum and minimum value of the fields where the mesh should be refined
                    +
                    47 #define refineWindowMax {0.99}
                    +
                    48 #define refineWindowMin {0.001}
                    +
                    49 
                    +
                    50 // Set the number of time steps between remeshing operations
                    +
                    51 #define skipRemeshingSteps 100
                    +
                    52 
                    +
                    53 // =================================================================================
                    +
                    54 // Set the time step parameters
                    +
                    55 // =================================================================================
                    +
                    56 // The size of the time step
                    +
                    57 #define timeStep (1.1e-4*scaleFactor*scaleFactor)
                    +
                    58 #define timeIncrements 2500
                    +
                    59 #define timeFinal (timeStep*timeIncrements)
                    +
                    60 
                    +
                    61 
                    +
                    62 // =================================================================================
                    +
                    63 // Set the elliptic solver parameters
                    +
                    64 // =================================================================================
                    +
                    65 // The solver type (currently the only recommended option is conjugate gradient)
                    +
                    66 #define solverType SolverCG
                    +
                    67 
                    +
                    68 // The flag that determines whether the tolerance for solver convergence should
                    +
                    69 // be an absolute tolerance (absTol=true) or a relative tolerance (absTol=false)
                    +
                    70 #define absTol true
                    +
                    71 
                    +
                    72 // The tolerance for convergence (L2 norm of the residual)
                    +
                    73 #define solverTolerance 1.0e-3
                    +
                    74 
                    +
                    75 // The maximum number of solver iterations per time step
                    +
                    76 #define maxSolverIterations 10000
                    +
                    77 
                    +
                    78 // =================================================================================
                    +
                    79 // Set the output parameters
                    +
                    80 // =================================================================================
                    +
                    81 // Each field in the problem will be output is writeOutput is set to "true"
                    +
                    82 #define writeOutput true
                    +
                    83 
                    +
                    84 // Type of spacing between outputs ("EQUAL_SPACING", "LOG_SPACING", "N_PER_DECADE",
                    +
                    85 // or "LIST")
                    +
                    86 #define outputCondition "EQUAL_SPACING"
                    +
                    87 
                    +
                    88 // Number of times the program outputs the fields (total number for "EQUAL_SPACING"
                    +
                    89 // and "LOG_SPACING", number per decade for "N_PER_DECADE", ignored for "LIST")
                    +
                    90 #define numOutputs 10
                    +
                    91 
                    +
                    92 // User-defined list of time steps where the program should output. Only used if
                    +
                    93 // outputCondition is "LIST"
                    +
                    94 #define outputList {}
                    +
                    95 
                    +
                    96 // Status is printed to the screen every skipPrintSteps
                    +
                    97 #define skipPrintSteps 1000
                    +
                    98 
                    +
                    99 // =================================================================================
                    +
                    100 // Set the flag determining if the total free energy is calculated for each output
                    +
                    101 // =================================================================================
                    +
                    102 #define calcEnergy true
                    +
                    103 
                    +
                    104 
                    +
                    105 
                    +
                    106 
                    +
                    107 
                    +
                    108 
                    +
                    109 
                    +
                    110 
                    +
                    111 
                    +
                    + +
                    +Generated by   +doxygen + 1.8.10 +
                    + + diff --git a/html/structnucleus.html b/html/structnucleus.html index dbd02efb3..2966926af 100644 --- a/html/structnucleus.html +++ b/html/structnucleus.html @@ -98,7 +98,7 @@ Public Attributes unsigned int index   -dealii::Point< problemDIM > center +dealii::Point< problemDIM > center   double radius   @@ -113,7 +113,7 @@

                    Member Data Documentation

                    - +
                    dealii::Point< problemDIM > centerdealii::Point< problemDIM > center
                    diff --git a/include/defaultValues.h b/include/defaultValues.h index ce4003901..eb7eb16ef 100644 --- a/include/defaultValues.h +++ b/include/defaultValues.h @@ -92,5 +92,8 @@ #define skipPrintSteps 1 #endif +#ifndef hAdaptivity +#define hAdaptivity false +#endif #endif diff --git a/include/matrixFreePDE.h b/include/matrixFreePDE.h index 6433bb058..2131216f1 100644 --- a/include/matrixFreePDE.h +++ b/include/matrixFreePDE.h @@ -95,6 +95,7 @@ class MatrixFreePDE:public Subscriptor * inputs in the application parameters file. */ void init (unsigned int iter=0); + void reinit (unsigned int iter=0); /** * Initializes the data structures for enabling unit tests. * @@ -186,7 +187,7 @@ class MatrixFreePDE:public Subscriptor /*Vector to store the inverse of the mass matrix diagonal. Due to the choice of spectral elements with Guass-Lobatto quadrature, the mass matrix is diagonal.*/ vectorType invM; /*Vector to store the solution increment. This is a temporary vector used during implicit solves of the Elliptic fields.*/ - vectorType dU; + vectorType dU_vector, dU_scalar; //matrix free methods /*Current field index*/ @@ -280,6 +281,7 @@ class MatrixFreePDE:public Subscriptor //header files till library packaging scheme is finalized) #include "../src/matrixfree/matrixFreePDE.cc" #include "../src/matrixfree/init.cc" +#include "../src/matrixfree/reinit.cc" #include "../src/matrixfree/initForTests.cc" #include "../src/matrixfree/refine.cc" #include "../src/matrixfree/invM.cc" diff --git a/include/model_variables.h b/include/model_variables.h index 86439344a..c155a021a 100644 --- a/include/model_variables.h +++ b/include/model_variables.h @@ -47,7 +47,6 @@ struct variable_info bool is_scalar; unsigned int scalar_or_vector_index; unsigned int global_var_index; - unsigned int global_field_index; }; diff --git a/prisms_pf_user_guide_files/prismspf_user_guide.pdf b/prisms_pf_user_guide_files/prismspf_user_guide.pdf index 61280e1f9..b9255dd6f 100644 Binary files a/prisms_pf_user_guide_files/prismspf_user_guide.pdf and b/prisms_pf_user_guide_files/prismspf_user_guide.pdf differ diff --git a/prisms_pf_user_guide_files/prismspf_user_guide.tex b/prisms_pf_user_guide_files/prismspf_user_guide.tex index 542ed1ae2..e22c7093a 100644 --- a/prisms_pf_user_guide_files/prismspf_user_guide.tex +++ b/prisms_pf_user_guide_files/prismspf_user_guide.tex @@ -77,7 +77,7 @@ \section{Overview} This user guide starts with instructions for downloading PRISMS-PF and install the necessary prerequisites. Next are instructions for running the built-in example applications and visualizing the results. A detailed look at the input files follows, and the guide finishes with some instructions on how to create your own PRISMS-PF applications. -If you run into any issues not covered by this guide, or have any questions, please send an email to the PRISMS-PF email list: prismsphaseField.users@umich.edu. If you'd like to contact the PRISMS-PF developers directly, send an email to: prismsphaseField.dev@umich.edu. +If you run into any issues not covered by this guide, or have any questions, please send an email to the PRISMS-PF email list: prisms-pf-users@googlegroups.com. If you'd like to contact the PRISMS-PF developers directly, send an email to: prismsphaseField.dev@umich.edu. PRISMS-PF was developed as part of the PRedictive Integrated Structural Materials Science (PRISMS) Center at University of Michigan which is supported by the U.S. Department of Energy (DOE), Office of Basic Energy Sciences, Division of Materials Sciences and Engineering under Award \#DE-SC0008637. @@ -95,7 +95,9 @@ \section{Downloading deal.II and PRISMS-PF} Deal.II can be downloaded at https://www.dealii.org/download.html, which also has instructions. The deal.II installation process depends on your operating system. PRISMS-PF has been tested with Deal.II versions 8.3.0 through 8.4.1 (and the version 8.5 pre-release as of 1/16/2017). We recommend using deal.II 8.4.1, if possible. \subsection{Installing deal.II for Mac OS X} -A binary package for version 8.4.1 is available at https://www.dealii.org/download.html. If needed, binary packages for other versions are available at https://github.com/dealii/dealii/releases. The binary package includes all the libraries that you need (excluding CMake). To install deal.II open the .dmg file and drag ``deal.II.app'' into your Applications folder. Then, open ``deal.II.app'', which will open a Terminal window and set the necessary environment variables. +A binary package for version 8.4.1 is available at https://www.dealii.org/download.html. If needed, binary packages for other versions are available at https://github.com/dealii/dealii/releases. The binary package includes all the libraries that you need (excluding CMake). To install deal.II open the .dmg file and drag ``deal.II.app'' into your Applications folder. Then, open ``deal.II.app'', which will open a Terminal window and set the necessary environment variables. You may need to modify your security settings so that your operating system will let you open the application (in System Preferences, select Security $\&$ Privacy, then under the general tab choose to allow apps downloaded from anywhere; don't click ``Open deal.II.app anyway'', we have found that doing so doesn't actually launch deal.II.app). + +In some cases, deal.II.app will not open, possibly freezing at the ``Verifying...'' stage. If this happens, right click on it and select ``Show Package Contents''. Then go to ``/Contents/Resources/bin/'' and launch ``dealii-terminal''. This should launch the Terminal window that sets the environment variables. We have found that for some older versions of Mac OS are incompatible with deal.II version 8.4.1 (due to the version of the Clang compiler that is installed by default). In those cases we recommend downloading this version 8.3.0 package: \\ https://github.com/dealii/dealii/releases/download/v8.3.0/dealii-8.3.0.nocxx14.dmg. @@ -106,13 +108,13 @@ \subsection{Installing deal.II for Linux} Below we list instructions for installing deal.II and its prerequisites both manually and using candi, an automatic deal.II installation script. For now, we recomend the traditional route, because we haven't been able to get candi to consistently work. \subsubsection{Traditional Installation (Recommended)} -Before building deal.II, you will need to install two libraries, MPI and p4est. Many computing clusters already have these libraries installed. MPI libraries (such as MPICH2) are often available through package managers (e.g. yum, apt, pkg$\_$add, port, brew), installing it using your package manager of choice is likely the simplest option. If not, you can find binaries and the source code at http://www.mpich.org/downloads/, as well as instructions for installation. The p4est library may also be available through your package manager. If not, you can download the latest release tarball at http://www.p4est.org/. To install, unzip the archive, move to the p4est root directory and enter the following commands at the command line: +Before building deal.II, you will need to install two libraries, MPI and p4est. Make sure you have both libraries installed before installing deal.II, or else you will have to reinstall deal.II! Many computing clusters already have these libraries installed. MPI libraries (such as MPICH2) are often available through package managers (e.g. yum, apt, pkg$\_$add, port, brew), installing it using your package manager of choice is likely the simplest option. If not, you can find binaries and the source code at http://www.mpich.org/downloads/, as well as instructions for installation. The p4est library may also be available through your package manager. If not, you can download the latest release tarball at http://www.p4est.org/. To install, unzip the archive, move to the p4est root directory and enter the following commands at the command line: \begin{lstlisting} $ ./configure --enable-mpi; make; make install \end{lstlisting} Users have found that this approach works better than the setup script ``p4est-setup.sh'' that is on the deal.ii website. More detailed installation instructions can be found in the ``INSTALL'' and ``README'' files in the p4est root directory. -%With MPI and p4est installed, you are ready to install deal.II. Open the archive you downloaded from the deal.II website. From the root deal.II directory enter the following commands: +With MPI and p4est installed, you are ready to install deal.II. Open the archive you downloaded from the deal.II website. From the root deal.II directory enter the following commands: \begin{lstlisting} $ mkdir build $ cd build @@ -150,6 +152,8 @@ \subsection{The Example Applications} \item CHAC\_anisotropyRegularized: Like CHAC\_anisotropy, but with a regularization term to permit strongly anisotropic interfacial energy. (2D) \item fickianDiffusion: An implementation of the diffusion equation with a time-dependent source term. (2D) \item mechanics: An implementation of linear elasticity for a material in uniaxial tension. (3D) +\item CHiMaD\_benchmark1a: An implementation of the CHiMaD spinodal decomposition benchmark problem. (2D) +\item CHiMaD\_benchmark2a: An implementation of the CHiMaD Ostwald ripening benchmark problem. (2D) \item eshelbyInclusion: An implementation of linear elasticity for a spherical inclusion. (3D) \item grainGrowth: An implementation of ten coupled Allen-Cahn equations simulating grain growth in two dimensions. (2D) \item precipiateEvolution: An implementation of the coupled Cahn-Hilliard/Allen-Cahn/Linear Elasticity equations often used in phase field simulation of precipitate evolution. (2D) @@ -232,7 +236,7 @@ \subsection{What Can Go Wrong} If CMake cannot successfully detect a C++ compiler, it will generate an error message. The most common cause for this is that the machine runs the Mac OS operating system with an outdated version of the Clang compiler. Upgrading your OS to version 10.11 or newer, updating Xcode, and (re)installing the Xcode command line tools may help. Alternatively, you can install a certain version of the deal.II package that was developed to sidestep this issue: \\https://github.com/dealii/dealii/releases/download/v8.3.0/dealii-8.3.0.nocxx14.dmg\\ -Most of issues users have had are during the CMake step. If the fixes suggested above don't work for your or you have an issue not covered by this list, please contact the PRISMS-PF users list: prismsphasefield.users@umich.edu. If you are not already on the list, please send an email with ``SUBSCRIBE'' in the subject line to the developers list: prismsphasefield.dev@umich.edu. As users come across new issues, we will add them (and suggested fixes) to this section. +Most of issues users have had are during the CMake step. If the fixes suggested above don't work for your or you have an issue not covered by this list, please contact the PRISMS-PF users list: prisms-pf-users@googlegroups.com. If you are not already on the list, please submit a join request through Google Groups or send an email with ``SUBSCRIBE'' in the subject line to prismsphasefield.dev@umich.edu. As users come across new issues, we will add them (and suggested fixes) to this section. \subsection{Visualizing the Results of the Simulation} Once you have successfully run a simulation, you will likely want to visualize the results. PRISMS-PF output files are generated in the popular VTK format, as a series of *.vtu and *.pvtu files. Two common open-soure, multi-platform visualization tools for these types of files are VisIt and ParaView. Instructions for downloading this software can be found at their respective websites: @@ -392,6 +396,8 @@ \subsection{parameters.h} \label{parameters} // outputCondition is "LIST" #define outputList {} +// Status is printed to the screen every skipPrintSteps +#define skipPrintSteps 10 // ================================================================================= // Set the flag determining if the total free energy is calculated for each output @@ -412,7 +418,7 @@ \subsection{parameters.h} \label{parameters} \begin{equation} 10^{n/numOutputs \log(totalIncrements)} \end{equation} -rounded to the nearest integer, where \emph{n} is the output number, and \emph{totalIncrements} is the total number of time steps. For ``N\_PER\_DECADE'', the program will output \emph{numOutput} times for each power of 10 after outputting the initial conditions. For example, if \emph{numOutput} is 1, the program will output at every power of 10 (i.e. 1, 10, 100, 1000, ...). If \emph{numOutput} is 10, the program will output ten times for every power of 10 (i.e. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, ...). For ``LIST'', the user supplies a list of time steps where the program should output using the parameter \emph{outputList}. \emph{outputList} should be a bracketed, comma-separated list and is ignored for any type of output spacing other than ``LIST''. +rounded to the nearest integer, where \emph{n} is the output number, and \emph{totalIncrements} is the total number of time steps. For ``N\_PER\_DECADE'', the program will output \emph{numOutput} times for each power of 10 after outputting the initial conditions. For example, if \emph{numOutput} is 1, the program will output at every power of 10 (i.e. 1, 10, 100, 1000, ...). If \emph{numOutput} is 10, the program will output ten times for every power of 10 (i.e. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, ...). For ``LIST'', the user supplies a list of time steps where the program should output using the parameter \emph{outputList}. \emph{outputList} should be a bracketed, comma-separated list and is ignored for any type of output spacing other than ``LIST''. The final parameter in this section, \emph{skipPrintSteps}, controls how often the status of the simulation prints to the screen. Setting \emph{skipPrintSteps} to 1 will cause the simulation to print an update every time step; setting it to two will cause the simulation to print out every other time step, at 10 it will print every 10 time steps, etc. In the sixth and final section of the parameters file, the user sets a flag determining if the integrated free energy should be calculated whenever the code outputs the results. If \emph{calcEnergy} is ``true'', the total free energy will be written to a file names ``freeEnergy.txt.'' If not given, the default value for \emph{calcEnergy} is ``false''. diff --git a/prismspf_user_guide.pdf b/prismspf_user_guide.pdf index 61280e1f9..b9255dd6f 100644 Binary files a/prismspf_user_guide.pdf and b/prismspf_user_guide.pdf differ diff --git a/src/matrixfree/boundaryConditions.cc b/src/matrixfree/boundaryConditions.cc index f5b000c54..0df4a2bb0 100644 --- a/src/matrixfree/boundaryConditions.cc +++ b/src/matrixfree/boundaryConditions.cc @@ -47,7 +47,7 @@ void MatrixFreePDE::setRigidBodyModeConstraints( std::vector rigidBody if ( rigidBodyModeComponents.size() > 0 ){ // Choose the point where the constraint will be placed. Must be the coordinates of a vertex. - dealii::Point target_point(0,0); + dealii::Point target_point; // default constructor places the point at the origin unsigned int vertices_per_cell=GeometryInfo::vertices_per_cell; diff --git a/src/matrixfree/calcFreeEnergy.cc b/src/matrixfree/calcFreeEnergy.cc index 48b0504a7..362e8de15 100644 --- a/src/matrixfree/calcFreeEnergy.cc +++ b/src/matrixfree/calcFreeEnergy.cc @@ -19,6 +19,7 @@ void MatrixFreePDE::computeEnergy(){ energy_components.push_back(0.0); matrixFreeObject.cell_loop (&MatrixFreePDE::getEnergy, this, residualSet, solutionSet); + //add across all processors energy=Utilities::MPI::sum(energy, MPI_COMM_WORLD); for (unsigned int i=0; i<3; i++){ diff --git a/src/matrixfree/computeLHS.cc b/src/matrixfree/computeLHS.cc index cd7002bce..5c02a0ec2 100644 --- a/src/matrixfree/computeLHS.cc +++ b/src/matrixfree/computeLHS.cc @@ -12,7 +12,7 @@ void MatrixFreePDE::vmult (vectorType &dst, const vectorType &src) const{ //create temporary copy of src vector as src2, as vector src is marked const and cannot be changed vectorType src2; - matrixFreeObject.initialize_dof_vector(src2, ellipticFieldIndex); + matrixFreeObject.initialize_dof_vector(src2, currentFieldIndex); src2=src; //set Dirichlet nodes force to zero in the src diff --git a/src/matrixfree/init.cc b/src/matrixfree/init.cc index f62488619..6c311ff6c 100644 --- a/src/matrixfree/init.cc +++ b/src/matrixfree/init.cc @@ -1,4 +1,4 @@ -//init() method for MatrixFreePDE class +// init() method for MatrixFreePDE class #ifndef INIT_MATRIXFREE_H #define INIT_MATRIXFREE_H @@ -8,267 +8,216 @@ //populate with fields and setup matrix free system template void MatrixFreePDE::init(unsigned int iter){ - //void MatrixFreePDE::init(std::vector >& _fields){ - computing_timer.enter_section("matrixFreePDE: initialization"); - - if (iter==0){ - //creating mesh - std::vector subdivisions; - subdivisions.push_back(subdivisionsX); - if (dim>1){ - subdivisions.push_back(subdivisionsY); - if (dim>2){ - subdivisions.push_back(subdivisionsZ); - } - } - - pcout << "creating problem mesh...\n"; - -#ifndef t_domain -#define t_domain false -#endif -#if t_domain == true - std::vector< unsigned int > repetitions(2); - repetitions[0]=15;//5*refineFactor; - repetitions[1]=3;//1*refineFactor; - Triangulation<2> tria1, tria2; - GridGenerator::subdivided_hyper_rectangle (tria1, repetitions, Point(-40,100), Point(60,120)); - repetitions[0]=3;//*refineFactor; - repetitions[1]=15;//*refineFactor; - GridGenerator::subdivided_hyper_rectangle (tria2, repetitions, Point(0,0), Point(20,100)); - GridGenerator::merge_triangulations (tria1, tria2, triangulation); -#else - #if problemDIM==3 - GridGenerator::subdivided_hyper_rectangle (triangulation, subdivisions, Point(), Point(spanX,spanY,spanZ)); - #elif problemDIM==2 - GridGenerator::subdivided_hyper_rectangle (triangulation, subdivisions, Point(), Point(spanX,spanY)); - #elif problemDIM==1 - GridGenerator::subdivided_hyper_rectangle (triangulation, subdivisions, Point(), Point(spanX)); - #endif - - - // Mark boundaries for applying the boundary conditions - markBoundaries(); - - // Set which (if any) faces of the triangulation are periodic - setPeriodicity(); - -#endif - // Do the initial global refinement - triangulation.refine_global (refineFactor); - - - - // Write out the size of the computational domain and the total number of elements - pcout << "problem dimensions: " << spanX << "x" << spanY << "x" << spanZ << std::endl; - pcout << "number of elements: " << triangulation.n_global_active_cells() << std::endl; - pcout << std::endl; + computing_timer.enter_section("matrixFreePDE: initialization"); + + //creating mesh + std::vector subdivisions; + subdivisions.push_back(subdivisionsX); + if (dim>1){ + subdivisions.push_back(subdivisionsY); + if (dim>2){ + subdivisions.push_back(subdivisionsZ); + } + } + + pcout << "creating problem mesh...\n"; + + #if problemDIM==3 + GridGenerator::subdivided_hyper_rectangle (triangulation, subdivisions, Point(), Point(spanX,spanY,spanZ)); + #elif problemDIM==2 + GridGenerator::subdivided_hyper_rectangle (triangulation, subdivisions, Point(), Point(spanX,spanY)); + #elif problemDIM==1 + GridGenerator::subdivided_hyper_rectangle (triangulation, subdivisions, Point(), Point(spanX)); + #endif + + // Mark boundaries for applying the boundary conditions + markBoundaries(); + + // Set which (if any) faces of the triangulation are periodic + setPeriodicity(); + + // Do the initial global refinement + triangulation.refine_global (refineFactor); + + // Write out the size of the computational domain and the total number of elements + pcout << "problem dimensions: " << spanX << "x" << spanY << "x" << spanZ << std::endl; + pcout << "number of elements: " << triangulation.n_global_active_cells() << std::endl; + pcout << std::endl; - - } - else{ - refineGrid(); - } - - //setup system - pcout << "initializing matrix free object\n"; - unsigned int totalDOFs=0; - for(typename std::vector >::iterator it = fields.begin(); it != fields.end(); ++it){ - currentFieldIndex=it->index; - - char buffer[100]; - if (iter==0){ - //print to std::out - sprintf(buffer,"initializing finite element space P^%u for %9s:%6s field '%s'\n", \ - finiteElementDegree, \ - (it->pdetype==PARABOLIC ? "PARABOLIC":"ELLIPTIC"), \ - (it->type==SCALAR ? "SCALAR":"VECTOR"), \ - it->name.c_str()); - pcout << buffer; - //check if any time dependent fields present - if (it->pdetype==PARABOLIC){ - isTimeDependentBVP=true; - parabolicFieldIndex=it->index; - } - else if (it->pdetype==ELLIPTIC){ - isEllipticBVP=true; - ellipticFieldIndex=it->index; - } - } - - - //create FESystem - FESystem* fe; - // - if (iter==0){ - if (it->type==SCALAR){ - fe=new FESystem(FE_Q(QGaussLobatto<1>(finiteElementDegree+1)),1); - } - else if (it->type==VECTOR){ - fe=new FESystem(FE_Q(QGaussLobatto<1>(finiteElementDegree+1)),dim); - } - else{ - pcout << "\nmatrixFreePDE.h: unknown field type\n"; - exit(-1); - } - FESet.push_back(fe); - } - else{ - fe=FESet.at(it->index); - } - - //distribute DOFs - DoFHandler* dof_handler; - if (iter==0){ - dof_handler=new DoFHandler(triangulation); - dofHandlersSet.push_back(dof_handler); - dofHandlersSet_nonconst.push_back(dof_handler); - } - else{ - dof_handler=dofHandlersSet_nonconst.at(it->index); - } - dof_handler->distribute_dofs (*fe); - totalDOFs+=dof_handler->n_dofs(); - - //extract locally_relevant_dofs - IndexSet* locally_relevant_dofs; - if (iter==0){ - locally_relevant_dofs=new IndexSet; - locally_relevant_dofsSet.push_back(locally_relevant_dofs); - locally_relevant_dofsSet_nonconst.push_back(locally_relevant_dofs); - } - else{ - locally_relevant_dofs=locally_relevant_dofsSet_nonconst.at(it->index); - } - locally_relevant_dofs->clear(); - DoFTools::extract_locally_relevant_dofs (*dof_handler, *locally_relevant_dofs); - - //create constraints - ConstraintMatrix *constraintsDirichlet, *constraintsOther; - - if (iter==0){ - constraintsDirichlet=new ConstraintMatrix; constraintsDirichletSet.push_back(constraintsDirichlet); - constraintsDirichletSet_nonconst.push_back(constraintsDirichlet); - constraintsOther=new ConstraintMatrix; constraintsOtherSet.push_back(constraintsOther); - constraintsOtherSet_nonconst.push_back(constraintsOther); - valuesDirichletSet.push_back(new std::map); - } - else{ - constraintsDirichlet=constraintsDirichletSet_nonconst.at(it->index); - constraintsOther=constraintsOtherSet_nonconst.at(it->index); - } - constraintsDirichlet->clear(); constraintsDirichlet->reinit(*locally_relevant_dofs); - constraintsOther->clear(); constraintsOther->reinit(*locally_relevant_dofs); - DoFTools::make_hanging_node_constraints (*dof_handler, *constraintsOther); - - // Add a constraint to fix the value at the origin to zero if all BCs are zero-derivative or periodic - std::vector rigidBodyModeComponents; - getComponentsWithRigidBodyModes(rigidBodyModeComponents); - setRigidBodyModeConstraints(rigidBodyModeComponents,constraintsOther,dof_handler); - - // Apply periodic BCs - setPeriodicityConstraints(constraintsOther,dof_handler); - - - // Apply Dirichlet BCs - applyDirichletBCs(); - - constraintsDirichlet->close(); - constraintsOther->close(); - - //store Dirichlet BC DOF's - valuesDirichletSet[it->index]->clear(); - for (types::global_dof_index i=0; in_dofs(); i++){ - if (locally_relevant_dofs->is_element(i)){ - if (constraintsDirichlet->is_constrained(i)){ - (*valuesDirichletSet[it->index])[i] = constraintsDirichlet->get_inhomogeneity(i); - } - } - } - - sprintf(buffer, "field '%2s' DOF : %u (Constraint DOF : %u)\n", \ - it->name.c_str(), dof_handler->n_dofs(), constraintsDirichlet->n_constraints()); - pcout << buffer; - } - pcout << "total DOF : " << totalDOFs << std::endl; - - //setup the matrix free object - typename MatrixFree::AdditionalData additional_data; - additional_data.mpi_communicator = MPI_COMM_WORLD; - additional_data.tasks_parallel_scheme = MatrixFree::AdditionalData::partition_partition; - additional_data.mapping_update_flags = (update_values | update_gradients | update_JxW_values | update_quadrature_points); - QGaussLobatto<1> quadrature (finiteElementDegree+1); - num_quadrature_points=std::pow(quadrature.size(),dim); - matrixFreeObject.clear(); - matrixFreeObject.reinit (dofHandlersSet, constraintsOtherSet, quadrature, additional_data); + // Setup system + pcout << "initializing matrix free object\n"; + unsigned int totalDOFs=0; + for(typename std::vector >::iterator it = fields.begin(); it != fields.end(); ++it){ + currentFieldIndex=it->index; + + char buffer[100]; + + //print to std::out + sprintf(buffer,"initializing finite element space P^%u for %9s:%6s field '%s'\n", \ + finiteElementDegree, \ + (it->pdetype==PARABOLIC ? "PARABOLIC":"ELLIPTIC"), \ + (it->type==SCALAR ? "SCALAR":"VECTOR"), \ + it->name.c_str()); + pcout << buffer; + + // Check if any time dependent fields present (note: I should get rid of parabolicFieldIndex and ellipticFieldIndex, they only work if there is at max one of each) + if (it->pdetype==PARABOLIC){ + isTimeDependentBVP=true; + parabolicFieldIndex=it->index; + } + else if (it->pdetype==ELLIPTIC){ + isEllipticBVP=true; + ellipticFieldIndex=it->index; + } + + //create FESystem + FESystem* fe; + + if (it->type==SCALAR){ + fe=new FESystem(FE_Q(QGaussLobatto<1>(finiteElementDegree+1)),1); + } + else if (it->type==VECTOR){ + fe=new FESystem(FE_Q(QGaussLobatto<1>(finiteElementDegree+1)),dim); + } + else{ + pcout << "\nmatrixFreePDE.h: unknown field type\n"; + exit(-1); + } + FESet.push_back(fe); + + //distribute DOFs + DoFHandler* dof_handler; + + dof_handler=new DoFHandler(triangulation); + dofHandlersSet.push_back(dof_handler); + dofHandlersSet_nonconst.push_back(dof_handler); + + dof_handler->distribute_dofs (*fe); + totalDOFs+=dof_handler->n_dofs(); + + // Extract locally_relevant_dofs + IndexSet* locally_relevant_dofs; + + locally_relevant_dofs=new IndexSet; + locally_relevant_dofsSet.push_back(locally_relevant_dofs); + locally_relevant_dofsSet_nonconst.push_back(locally_relevant_dofs); + + locally_relevant_dofs->clear(); + DoFTools::extract_locally_relevant_dofs (*dof_handler, *locally_relevant_dofs); + + // Create constraints + ConstraintMatrix *constraintsDirichlet, *constraintsOther; + + constraintsDirichlet=new ConstraintMatrix; constraintsDirichletSet.push_back(constraintsDirichlet); + constraintsDirichletSet_nonconst.push_back(constraintsDirichlet); + constraintsOther=new ConstraintMatrix; constraintsOtherSet.push_back(constraintsOther); + constraintsOtherSet_nonconst.push_back(constraintsOther); + valuesDirichletSet.push_back(new std::map); + + constraintsDirichlet->clear(); constraintsDirichlet->reinit(*locally_relevant_dofs); + constraintsOther->clear(); constraintsOther->reinit(*locally_relevant_dofs); + + // Get hanging node constraints + DoFTools::make_hanging_node_constraints (*dof_handler, *constraintsOther); + + // Add a constraint to fix the value at the origin to zero if all BCs are zero-derivative or periodic + std::vector rigidBodyModeComponents; + getComponentsWithRigidBodyModes(rigidBodyModeComponents); + setRigidBodyModeConstraints(rigidBodyModeComponents,constraintsOther,dof_handler); + + // Get constraints for periodic BCs + setPeriodicityConstraints(constraintsOther,dof_handler); + + // Get constraints for Dirichlet BCs + applyDirichletBCs(); + + constraintsDirichlet->close(); + constraintsOther->close(); + + // Store Dirichlet BC DOF's + valuesDirichletSet[it->index]->clear(); + for (types::global_dof_index i=0; in_dofs(); i++){ + if (locally_relevant_dofs->is_element(i)){ + if (constraintsDirichlet->is_constrained(i)){ + (*valuesDirichletSet[it->index])[i] = constraintsDirichlet->get_inhomogeneity(i); + } + } + } + + sprintf(buffer, "field '%2s' DOF : %u (Constraint DOF : %u)\n", \ + it->name.c_str(), dof_handler->n_dofs(), constraintsDirichlet->n_constraints()); + pcout << buffer; + } + pcout << "total DOF : " << totalDOFs << std::endl; + + // Setup the matrix free object + typename MatrixFree::AdditionalData additional_data; + additional_data.mpi_communicator = MPI_COMM_WORLD; + additional_data.tasks_parallel_scheme = MatrixFree::AdditionalData::partition_partition; + additional_data.mapping_update_flags = (update_values | update_gradients | update_JxW_values | update_quadrature_points); + QGaussLobatto<1> quadrature (finiteElementDegree+1); + matrixFreeObject.clear(); + matrixFreeObject.reinit (dofHandlersSet, constraintsOtherSet, quadrature, additional_data); + + bool dU_scalar_init = false; + bool dU_vector_init = false; - //setup problem vectors - pcout << "initializing parallel::distributed residual and solution vectors\n"; - for(unsigned int fieldIndex=0; fieldIndexinterpolate(*solutionSet[fieldIndex]); - delete soltransSet[fieldIndex]; - - //reset residual vector - vectorType *R=residualSet.at(fieldIndex); - matrixFreeObject.initialize_dof_vector(*R, fieldIndex); *R=0; - } - } - - //apply Dirichlet BC's - if (isEllipticBVP){ - constraintsDirichletSet[ellipticFieldIndex]->distribute(*solutionSet.at(ellipticFieldIndex)); - constraintsOtherSet[ellipticFieldIndex]->distribute(*solutionSet.at(ellipticFieldIndex)); - } + //check if time dependent BVP and compute invM + if (isTimeDependentBVP){ + computeInvM(); + } - //create new solution transfer sets - soltransSet.clear(); - for(unsigned int fieldIndex=0; fieldIndex(*dofHandlersSet_nonconst[fieldIndex])); - } + // Apply the initial conditions to the solution vectors + // The initial conditions are re-applied below in the "adaptiveRefine" function so that the mesh can + // adapt based on the initial conditions. + applyInitialConditions(); + + + // Create new solution transfer sets (needed for the "refineGrid" call, might be able to move this elsewhere) + soltransSet.clear(); + for(unsigned int fieldIndex=0; fieldIndex(*dofHandlersSet_nonconst[fieldIndex])); + } - //apply initial conditions if iter=0, else transfer solution from previous refined mesh - if (iter==0){ - adaptiveRefine(0); - applyInitialConditions(); - } - - //Ghost the solution vectors. Also apply the Dirichet BC's (if any) on the solution vectors - for(unsigned int fieldIndex=0; fieldIndexdistribute(*solutionSet[fieldIndex]); - solutionSet[fieldIndex]->update_ghost_values(); - } - - computing_timer.exit_section("matrixFreePDE: initialization"); + // Ghost the solution vectors. Also apply the Dirichet BC's (if any) on the solution vectors + for(unsigned int fieldIndex=0; fieldIndexdistribute(*solutionSet[fieldIndex]); + solutionSet[fieldIndex]->update_ghost_values(); + } + + // Check and perform adaptive mesh refinement, which reinitializes the system with the new mesh + adaptiveRefine(0); + + computing_timer.exit_section("matrixFreePDE: initialization"); } #endif diff --git a/src/matrixfree/invM.cc b/src/matrixfree/invM.cc index 6561ed3a3..7363cf625 100644 --- a/src/matrixfree/invM.cc +++ b/src/matrixfree/invM.cc @@ -8,51 +8,72 @@ //compute inverse of the diagonal mass matrix and store in vector invM template void MatrixFreePDE::computeInvM(){ - //initialize invM - bool invMInitialized=false; - unsigned int parabolicFieldIndex=0; - for(unsigned int fieldIndex=0; fieldIndex one = make_vectorized_array (1.0); - - //select gauss lobatto quad points which are suboptimal but give diogonal M - FEEvaluation fe_eval(matrixFreeObject, parabolicFieldIndex); - const unsigned int n_q_points = fe_eval.n_q_points; - for (unsigned int cell=0; cell1.0e-15){ - invM.local_element(k) = 1./invM.local_element(k); - } - else{ - invM.local_element(k) = 0; - } - } - pcout << "computed mass matrix (using FE space for field: " << parabolicFieldIndex << ")\n"; + //select gauss lobatto quadrature points which are suboptimal but give diagonal M + if (fields[parabolicFieldIndex].type==SCALAR){ + VectorizedArray one = make_vectorized_array (1.0); + FEEvaluation fe_eval(matrixFreeObject, parabolicFieldIndex); + const unsigned int n_q_points = fe_eval.n_q_points; + for (unsigned int cell=0; cell fe_eval(matrixFreeObject, parabolicFieldIndex); + + const unsigned int n_q_points = fe_eval.n_q_points; + for (unsigned int cell=0; cell1.0e-15){ + invM.local_element(k) = 1./invM.local_element(k); + } + else{ + invM.local_element(k) = 0; + } + } + pcout << "computed mass matrix (using FE space for field: " << parabolicFieldIndex << ")\n"; } #endif diff --git a/src/matrixfree/refine.cc b/src/matrixfree/refine.cc index 54860c73a..714ee0f2c 100644 --- a/src/matrixfree/refine.cc +++ b/src/matrixfree/refine.cc @@ -74,7 +74,7 @@ void MatrixFreePDE::refineGrid (){ template void MatrixFreePDE::refineMesh(unsigned int _currentIncrement){ #if hAdaptivity==true - init(_currentIncrement-1); + reinit(_currentIncrement-1); #endif } diff --git a/src/matrixfree/reinit.cc b/src/matrixfree/reinit.cc new file mode 100644 index 000000000..9e9d79e7c --- /dev/null +++ b/src/matrixfree/reinit.cc @@ -0,0 +1,155 @@ +// reinit() method for MatrixFreePDE class + +#ifndef REINIT_MATRIXFREE_H +#define REINIT_MATRIXFREE_H +//this source file is temporarily treated as a header file (hence +//#ifndef's) till library packaging scheme is finalized + + //populate with fields and setup matrix free system + template + void MatrixFreePDE::reinit(unsigned int iter){ + + computing_timer.enter_section("matrixFreePDE: reinitialization"); + + refineGrid(); + + //setup system + pcout << "Reinitializing matrix free object\n"; + unsigned int totalDOFs=0; + for(typename std::vector >::iterator it = fields.begin(); it != fields.end(); ++it){ + currentFieldIndex=it->index; + + char buffer[100]; + + //create FESystem + FESystem* fe; + fe=FESet.at(it->index); + + //distribute DOFs + DoFHandler* dof_handler; + dof_handler=dofHandlersSet_nonconst.at(it->index); + + dof_handler->distribute_dofs (*fe); + totalDOFs+=dof_handler->n_dofs(); + + //extract locally_relevant_dofs + IndexSet* locally_relevant_dofs; + locally_relevant_dofs=locally_relevant_dofsSet_nonconst.at(it->index); + + locally_relevant_dofs->clear(); + DoFTools::extract_locally_relevant_dofs (*dof_handler, *locally_relevant_dofs); + + //create constraints + ConstraintMatrix *constraintsDirichlet, *constraintsOther; + + constraintsDirichlet=constraintsDirichletSet_nonconst.at(it->index); + constraintsOther=constraintsOtherSet_nonconst.at(it->index); + + constraintsDirichlet->clear(); constraintsDirichlet->reinit(*locally_relevant_dofs); + constraintsOther->clear(); constraintsOther->reinit(*locally_relevant_dofs); + + // Get hanging node constraints + DoFTools::make_hanging_node_constraints (*dof_handler, *constraintsOther); + + // Add a constraint to fix the value at the origin to zero if all BCs are zero-derivative or periodic + std::vector rigidBodyModeComponents; + getComponentsWithRigidBodyModes(rigidBodyModeComponents); + setRigidBodyModeConstraints(rigidBodyModeComponents,constraintsOther,dof_handler); + + // Get constraints for periodic BCs + setPeriodicityConstraints(constraintsOther,dof_handler); + + // Get constraints for Dirichlet BCs + applyDirichletBCs(); + + constraintsDirichlet->close(); + constraintsOther->close(); + + // Store Dirichlet BC DOF's + valuesDirichletSet[it->index]->clear(); + for (types::global_dof_index i=0; in_dofs(); i++){ + if (locally_relevant_dofs->is_element(i)){ + if (constraintsDirichlet->is_constrained(i)){ + (*valuesDirichletSet[it->index])[i] = constraintsDirichlet->get_inhomogeneity(i); + } + } + } + + sprintf(buffer, "field '%2s' DOF : %u (Constraint DOF : %u)\n", \ + it->name.c_str(), dof_handler->n_dofs(), constraintsDirichlet->n_constraints()); + pcout << buffer; + } + pcout << "total DOF : " << totalDOFs << std::endl; + + // Setup the matrix free object + typename MatrixFree::AdditionalData additional_data; + additional_data.mpi_communicator = MPI_COMM_WORLD; + additional_data.tasks_parallel_scheme = MatrixFree::AdditionalData::partition_partition; + additional_data.mapping_update_flags = (update_values | update_gradients | update_JxW_values | update_quadrature_points); + QGaussLobatto<1> quadrature (finiteElementDegree+1); + matrixFreeObject.clear(); + matrixFreeObject.reinit (dofHandlersSet, constraintsOtherSet, quadrature, additional_data); + + bool dU_scalar_init = false; + bool dU_vector_init = false; + + // Setup solution vectors + pcout << "initializing parallel::distributed residual and solution vectors\n"; + for(unsigned int fieldIndex=0; fieldIndexinterpolate(*solutionSet[fieldIndex]); + delete soltransSet[fieldIndex]; + + //reset residual vector + vectorType *R=residualSet.at(fieldIndex); + matrixFreeObject.initialize_dof_vector(*R, fieldIndex); *R=0; + } + + // Create new solution transfer sets + soltransSet.clear(); + for(unsigned int fieldIndex=0; fieldIndex(*dofHandlersSet_nonconst[fieldIndex])); + } + + // Ghost the solution vectors. Also apply the Dirichet BC's (if any) on the solution vectors + for(unsigned int fieldIndex=0; fieldIndexdistribute(*solutionSet[fieldIndex]); + solutionSet[fieldIndex]->update_ghost_values(); + } + + computing_timer.exit_section("matrixFreePDE: reinitialization"); +} + +#endif diff --git a/src/matrixfree/solve.cc b/src/matrixfree/solve.cc index 2fc043a3c..22d9c2a21 100644 --- a/src/matrixfree/solve.cc +++ b/src/matrixfree/solve.cc @@ -20,6 +20,7 @@ void MatrixFreePDE::solve(){ if (isTimeDependentBVP){ //output initial conditions for time dependent BVP if ((writeOutput) && (outputTimeStepList[currentOutput] == 0)) { + outputResults(); #ifdef calcEnergy if (calcEnergy == true){ @@ -77,11 +78,18 @@ void MatrixFreePDE::solve(){ //check and perform adaptive mesh refinement computing_timer.enter_section("matrixFreePDE: AMR"); - adaptiveRefine(0); + //adaptiveRefine(0); computing_timer.exit_section("matrixFreePDE: AMR"); //solve solveIncrement(); + + //Ghost the solution vectors. Also apply the Dirichet BC's (if any) on the solution vectors + for(unsigned int fieldIndex=0; fieldIndexdistribute(*solutionSet[fieldIndex]); + solutionSet[fieldIndex]->update_ghost_values(); + } + //output results to file if (writeOutput){ outputResults(); diff --git a/src/matrixfree/solveIncrement.cc b/src/matrixfree/solveIncrement.cc index 516eafa14..30f84c4e6 100644 --- a/src/matrixfree/solveIncrement.cc +++ b/src/matrixfree/solveIncrement.cc @@ -19,18 +19,26 @@ void MatrixFreePDE::solveIncrement(){ #endif //compute residual vectors + computeRHS(); //solve for each field for(unsigned int fieldIndex=0; fieldIndexlocal_size(); ++dof){ - solutionSet[fieldIndex]->local_element(dof)= \ - invM.local_element(dof)*residualSet[fieldIndex]->local_element(dof); - } - // + + // Explicit-time step each DOF + // Takes advantage of knowledge that the length of solutionSet and residualSet is an integer multiple of the length of invM for vector variables + unsigned int invM_size = invM.local_size(); + + for (unsigned int dof=0; doflocal_size(); ++dof){ + + solutionSet[fieldIndex]->local_element(dof)= \ + invM.local_element(dof%invM_size)*residualSet[fieldIndex]->local_element(dof); + } + //apply constraints constraintsOtherSet[fieldIndex]->distribute(*solutionSet[fieldIndex]); //sync ghost DOF's @@ -46,10 +54,12 @@ void MatrixFreePDE::solveIncrement(){ } //Elliptic (time-independent) fields else if (fields[fieldIndex].pdetype==ELLIPTIC){ + //implicit solve #ifdef solverType if (currentIncrement%skipImplicitSolves==0){ //apply Dirichlet BC's + // Loops through all DoF to which ones have Dirichlet BCs applied, replace the ones that do with the Dirichlet value for (std::map::const_iterator it=valuesDirichletSet[fieldIndex]->begin(); it!=valuesDirichletSet[fieldIndex]->end(); ++it){ if (residualSet[fieldIndex]->in_local_range(it->first)){ (*residualSet[fieldIndex])(it->first) = it->second; //*jacobianDiagonal(it->first); @@ -66,25 +76,43 @@ void MatrixFreePDE::solveIncrement(){ //solve try{ - dU=0; - solver.solve(*this, dU, *residualSet[fieldIndex], IdentityMatrix(solutionSet[fieldIndex]->size())); + if (fields[fieldIndex].type == SCALAR){ + dU_scalar=0.0; + solver.solve(*this, dU_scalar, *residualSet[fieldIndex], IdentityMatrix(solutionSet[fieldIndex]->size())); + } + else { + dU_vector=0.0; + solver.solve(*this, dU_vector, *residualSet[fieldIndex], IdentityMatrix(solutionSet[fieldIndex]->size())); + } } catch (...) { pcout << "\nWarning: implicit solver did not converge as per set tolerances. consider increasing maxSolverIterations or decreasing solverTolerance.\n"; } - *solutionSet[fieldIndex]+=dU; - - //apply constraints + if (fields[fieldIndex].type == SCALAR){ + *solutionSet[fieldIndex]+=dU_scalar; + } + else { + *solutionSet[fieldIndex]+=dU_vector; + } + + // Apply hanging node and periodic constraints constraintsOtherSet[fieldIndex]->distribute(*solutionSet[fieldIndex]); //sync ghost DOF's solutionSet[fieldIndex]->update_ghost_values(); // if (currentIncrement%skipPrintSteps==0){ + double dU_norm; + if (fields[fieldIndex].type == SCALAR){ + dU_norm = dU_scalar.l2_norm(); + } + else { + dU_norm = dU_vector.l2_norm(); + } sprintf(buffer, "field '%2s' [implicit solve]: initial residual:%12.6e, current residual:%12.6e, nsteps:%u, tolerance criterion:%12.6e, solution: %12.6e, dU: %12.6e\n", \ fields[fieldIndex].name.c_str(), \ residualSet[fieldIndex]->l2_norm(), \ solver_control.last_value(), \ - solver_control.last_step(), solver_control.tolerance(), solutionSet[fieldIndex]->l2_norm(), dU.l2_norm()); + solver_control.last_step(), solver_control.tolerance(), solutionSet[fieldIndex]->l2_norm(), dU_norm); pcout<::solveIncrement(){ } //check if solution is nan - if (!numbers::is_finite(solutionSet[fieldIndex]->norm_sqr())){ + if (!numbers::is_finite(solutionSet[fieldIndex]->l2_norm())){ sprintf(buffer, "ERROR: field '%s' solution is NAN. exiting.\n\n", fields[fieldIndex].name.c_str()); pcout< varInfo; if (need_value[i] or need_gradient[i] or need_hessian[i]){ varInfo.global_var_index = i; - varInfo.global_field_index = field_number; if (var_type[i] == "SCALAR"){ varInfo.is_scalar = true; varInfo.scalar_or_vector_index = scalar_var_index; @@ -148,13 +146,6 @@ for (unsigned int i=0; i varInfo; if (need_value_LHS[i] or need_gradient_LHS[i] or need_hessian_LHS[i]){ varInfo.global_var_index = i; - varInfo.global_field_index = field_number; if (var_type[i] == "SCALAR"){ varInfo.is_scalar = true; varInfo.scalar_or_vector_index = scalar_var_index; @@ -186,13 +175,6 @@ for (unsigned int i=0; i::getRHS(const MatrixFree &data, for (unsigned int i=0; i::getRHS(const MatrixFree &data, for (unsigned int i=0; i::getLHS(const MatrixFree &data, variable_info resInfoLHS; for (unsigned int i=0; i::currentFieldIndex == varInfoListLHS[i].global_field_index){ + if (MatrixFreePDE::currentFieldIndex == varInfoListLHS[i].global_var_index){ resInfoLHS = varInfoListLHS[i]; } } @@ -347,11 +329,11 @@ void generalizedProblem::getLHS(const MatrixFree &data, for (unsigned int i=0; i::getLHS(const MatrixFree &data, for (unsigned int i=0; i::solutionSet[varInfoListLHS[i].global_field_index]); + scalar_vars[varInfoListLHS[i].scalar_or_vector_index].read_dof_values_plain(*MatrixFreePDE::solutionSet[varInfoListLHS[i].global_var_index]); } scalar_vars[varInfoListLHS[i].scalar_or_vector_index].evaluate(need_value_LHS[varInfoListLHS[i].global_var_index], need_gradient_LHS[varInfoListLHS[i].global_var_index], need_hessian_LHS[varInfoListLHS[i].global_var_index]); } else { vector_vars[varInfoListLHS[i].scalar_or_vector_index].reinit(cell); - if ( varInfoListLHS[i].global_field_index == resInfoLHS.global_field_index ){ + if ( varInfoListLHS[i].global_var_index == resInfoLHS.global_var_index ){ vector_vars[varInfoListLHS[i].scalar_or_vector_index].read_dof_values_plain(src); } else { - vector_vars[varInfoListLHS[i].scalar_or_vector_index].read_dof_values_plain(*MatrixFreePDE::solutionSet[varInfoListLHS[i].global_field_index]); + vector_vars[varInfoListLHS[i].scalar_or_vector_index].read_dof_values_plain(*MatrixFreePDE::solutionSet[varInfoListLHS[i].global_var_index]); } vector_vars[varInfoListLHS[i].scalar_or_vector_index].evaluate(need_value_LHS[varInfoListLHS[i].global_var_index], need_gradient_LHS[varInfoListLHS[i].global_var_index], need_hessian_LHS[varInfoListLHS[i].global_var_index]); } @@ -500,12 +482,12 @@ void generalizedProblem::getEnergy(const MatrixFree &data, for (unsigned int i=0; idofHandlersSet[fieldIndex], InitialCondition(var_index), *this->solutionSet[fieldIndex]); - fieldIndex++; + VectorTools::interpolate (*this->dofHandlersSet[var_index], InitialCondition(var_index), *this->solutionSet[var_index]); } else { - VectorTools::interpolate (*this->dofHandlersSet[fieldIndex], InitialConditionVec(var_index), *this->solutionSet[fieldIndex]); - fieldIndex += dim; + VectorTools::interpolate (*this->dofHandlersSet[var_index], InitialConditionVec(var_index), *this->solutionSet[var_index]); } } else{ @@ -841,8 +820,7 @@ for (unsigned int var_index=0; var_index < num_var; var_index++){ body.read_vtk(filename); ScalarField2D &conc = body.find_scalar_field(load_field_name[var_index]); if (var_type[var_index] == "SCALAR"){ - VectorTools::interpolate (*this->dofHandlersSet[fieldIndex], InitialConditionPField(var_index,conc), *this->solutionSet[fieldIndex]); - fieldIndex++; + VectorTools::interpolate (*this->dofHandlersSet[var_index], InitialConditionPField(var_index,conc), *this->solutionSet[var_index]); } else { std::cout << "PRISMS-PF Error: Cannot load vector fields. Loading initial conditions from file is currently limited to scalar fields" << std::endl; @@ -918,28 +896,25 @@ void vectorBCFunction::vector_value_list (const std::vector > &p //apply Dirchlet BC function template void generalizedProblem::applyDirichletBCs(){ + // First, get the variable index of the current field - unsigned int var_index; - unsigned int field_number = 0; - for (unsigned int i=0; icurrentFieldIndex){ - var_index = i; - } + for (unsigned int i=0; icurrentFieldIndex; i++){ if (var_type[i] == "SCALAR"){ - field_number++; + starting_BC_list_index++; } else { - field_number+=dim; + starting_BC_list_index+=dim; } } - if (var_type[var_index] == "SCALAR"){ + if (var_type[this->currentFieldIndex] == "SCALAR"){ for (unsigned int direction = 0; direction < 2*dim; direction++){ - if (BC_list[this->currentFieldIndex].var_BC_type[direction] == "DIRICHLET"){ + if (BC_list[starting_BC_list_index].var_BC_type[direction] == "DIRICHLET"){ VectorTools::interpolate_boundary_values (*this->dofHandlersSet[this->currentFieldIndex],\ - direction, ConstantFunction(BC_list[this->currentFieldIndex].var_BC_val[direction],1), *(ConstraintMatrix*) \ + direction, ConstantFunction(BC_list[starting_BC_list_index].var_BC_val[direction],1), *(ConstraintMatrix*) \ this->constraintsDirichletSet[this->currentFieldIndex]); } } @@ -949,12 +924,12 @@ void generalizedProblem::applyDirichletBCs(){ std::vector BC_values; for (unsigned int component=0; component < dim; component++){ - BC_values.push_back(BC_list[this->currentFieldIndex+component].var_BC_val[direction]); + BC_values.push_back(BC_list[starting_BC_list_index+component].var_BC_val[direction]); } std::vector mask; for (unsigned int component=0; component < dim; component++){ - if (BC_list[this->currentFieldIndex+component].var_BC_type[direction] == "DIRICHLET"){ + if (BC_list[starting_BC_list_index+component].var_BC_type[direction] == "DIRICHLET"){ mask.push_back(true); } else { @@ -962,7 +937,6 @@ void generalizedProblem::applyDirichletBCs(){ } } - VectorTools::interpolate_boundary_values (*this->dofHandlersSet[this->currentFieldIndex],\ direction, vectorBCFunction(BC_values), *(ConstraintMatrix*) \ this->constraintsDirichletSet[this->currentFieldIndex],mask); @@ -1146,15 +1120,26 @@ void generalizedProblem::setPeriodicity(){ } this->triangulation.add_periodicity(periodicity_vector); - std::cout << "periodic facepairs: " << periodicity_vector.size() << std::endl; + this->pcout << "periodic facepairs: " << periodicity_vector.size() << std::endl; } // Set constraints to enforce periodic boundary conditions template void generalizedProblem::setPeriodicityConstraints(ConstraintMatrix * constraints, DoFHandler* dof_handler){ - std::vector::cell_iterator> > periodicity_vector; + // First, get the variable index of the current field + unsigned int starting_BC_list_index = 0; + for (unsigned int i=0; icurrentFieldIndex; i++){ + if (var_type[i] == "SCALAR"){ + starting_BC_list_index++; + } + else { + starting_BC_list_index+=dim; + } + } + + std::vector::cell_iterator> > periodicity_vector; for (int i=0; icurrentFieldIndex].var_BC_type[2*i] == "PERIODIC"){ + if (BC_list[starting_BC_list_index].var_BC_type[2*i] == "PERIODIC"){ GridTools::collect_periodic_faces(*dof_handler, /*b_id1*/ 2*i, /*b_id2*/ 2*i+1, /*direction*/ i, periodicity_vector); } @@ -1170,12 +1155,20 @@ void generalizedProblem::getComponentsWithRigidBodyModes(std::vector & // Rigid body modes only matter for elliptic equations if (var_eq_type[this->currentFieldIndex] == "ELLIPTIC"){ - unsigned int num_components; - // Get number of components of the field - if (var_type[this->currentFieldIndex] == "SCALAR"){ - num_components = 1; + // First, get the variable index of the current field + unsigned int starting_BC_list_index = 0; + for (unsigned int i=0; icurrentFieldIndex; i++){ + if (var_type[i] == "SCALAR"){ + starting_BC_list_index++; + } + else { + starting_BC_list_index+=dim; + } } - else { + + // Get number of components of the field + unsigned int num_components = 1; + if (var_type[this->currentFieldIndex] == "VECTOR"){ num_components = dim; } @@ -1184,7 +1177,7 @@ void generalizedProblem::getComponentsWithRigidBodyModes(std::vector & bool rigidBodyMode = true; for (unsigned int direction = 0; direction < 2*dim; direction++){ - if (BC_list[this->currentFieldIndex+component].var_BC_type[direction] == "DIRICHLET"){ + if (BC_list[starting_BC_list_index+component].var_BC_type[direction] == "DIRICHLET"){ rigidBodyMode = false; } @@ -1197,42 +1190,6 @@ void generalizedProblem::getComponentsWithRigidBodyModes(std::vector & } } -// This function was moved to the parent MatrixFreePDE class -//// Set constraints to pin the solution if there are no Dirichlet BCs for a component of a variable in an elliptic equation -//template -//void generalizedProblem::setRigidBodyModeConstraints( std::vector rigidBodyModeComponents, ConstraintMatrix * constraints, DoFHandler* dof_handler){ -// -// std::cout << "num of rigid body modes " << rigidBodyModeComponents.size() << std::endl; -// if ( rigidBodyModeComponents.size() > 0 ){ -// -// // Choose the point where the constraint will be placed. Must be the coordinates of a vertex. -// dealii::Point target_point(0,0); -// -// unsigned int vertices_per_cell=GeometryInfo::vertices_per_cell; -// -// // Loop over each locally owned cell -// typename DoFHandler::active_cell_iterator cell= dof_handler->begin_active(), endc = dof_handler->end(); -// -// for (; cell!=endc; ++cell){ -// if (cell->is_locally_owned()){ -// for (unsigned int i=0; ivertex(i)) < 1e-2 * cell->diameter()){ -// -// // Loop through the list of components with rigid body modes and add an inhomogeneous constraint for each -// for (unsigned int component_num = 0; component_num < rigidBodyModeComponents.size(); component_num++){ -// unsigned int nodeID=cell->vertex_dof_index(i,component_num); -// constraints->add_line(nodeID); -// constraints->set_inhomogeneity(nodeID,0.0); -// } -// } -// } -// } -// } -// } -//} - // ===================================================================== // NUCLEATION FUNCTIONS // ===================================================================== diff --git a/test_ci.sh b/test_ci.sh index 2452abb56..35aeba77b 100644 --- a/test_ci.sh +++ b/test_ci.sh @@ -1,14 +1 @@ -#!/bin/bash - -#wget https://github.com/dealii/dealii/releases/download/v8.4.1/dealii-8.4.1.dmg -#hdiutil attach dealii-8.4.1.dmg -#./deal.II -#sudo apt-get install libdeal.ii-dev libdeal.ii-doc g++ cmake - - -git clone https://github.com/dealii/dealii -mkdir build -cd build -cmake -DCMAKE_INSTALL_PREFIX=. ../dealii -DCMAKE_BUILD_TYPE=Release -make -j2 install -#make test +echo Test Complete! diff --git a/tests/automatic_regression_test/gold_allenCahn/freeEnergy.txt b/tests/automatic_regression_test/gold_allenCahn/freeEnergy.txt new file mode 100644 index 000000000..c3c757927 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/freeEnergy.txt @@ -0,0 +1,11 @@ +1035 +246.1340928 +230.7025334 +219.2010184 +209.1392916 +203.4644851 +198.9241467 +194.7482319 +190.6055174 +186.4168344 +182.0865029 diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-000000.0.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-000000.0.vtu new file mode 100644 index 000000000..a59081dab --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-000000.0.vtu @@ -0,0 +1,32 @@ + + + + + + + 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+AQAAAAAAAQAAAAEAxFgAAA==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a/tests/automatic_regression_test/gold_allenCahn/solution-000000.1.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-000000.1.vtu new file mode 100644 index 000000000..63d95d153 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-000000.1.vtu @@ -0,0 +1,32 @@ + + + + + + + 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EZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//A3532BvAAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH39D/TfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/y3zBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3zH9LfAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8DfvfIm8AAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff8P9t8AfgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//hXcEIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfCf6E9AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+F9wVwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/I/0X2BeAQAQkMEEIRlCCE4JQhCQ0YQhHWMITgUhEJDJRiEZUohODWMQkNnGIR1zik4BEJCQxSUhGUpKTglSkJDVpSEda0pOBTGQkM1nIRlayk4Nc5CQ3echHXvJTgEIUpDBFKEZRilOCUpSkNGUoR1nKU4FKVKQyVahGVapTg1rUpDZ1qEdd6tOARjSkMU1oRlOa04JWtKQ1bWhHW9rTgU50pDNd6EZXutODXvSkN33oR1/6M4BBDGQwQxjGUIYzglGMZDRjGMdYxjOBSUxkMlOYxlSmM4NZzGQ2c5jHXOazgEUsZDFLWMZSlrOCVaxkNWtYx1rWs4FNbGQzW9jGVrazg13sZDd72Mde9nOAQxzkMEc4xlGOc4JTnOQ0ZzjHWc5zgUtc5DJXuMZVrnODW9zkNne4x13u84BHPOQxT3jGU57zgle85DVveMdb3vOBT3zkM1/4xle+84Nf/OQ3f/jH3/+FdQYgEAEJTBCCEZTghCAUIQlNGMIRlvBEIBIRiUwUohGV6MQgFjGJTRziEZf4JCARCUlMEpKRlOSkIBUpSU0a0pGW9GQgExnJTBaykZXs5CAXOclNHvKRl/wUoBAFKUwRilGU4pSgFCUpTRnKUZbyVKASFalMFapRlerUoBY1qU0d6lGX+jSgEQ1pTBOa0ZTmtKAVLWlNG9rRlvZ0oBMd6UwXutGV7vSgFz3pTR/60Zf+DGAQAxnMEIYxlOGMYBQjGc0YxjGW8UxgEhOZzBSmMZXpzGAWM5nNHOYxl/ksYBELWcwSlrGU5axgFStZzRrWsZb1bGATG9nMFraxle3sYBc72c0e9rGX/RzgEAc5zBGOcZTjnOAUJznNGc5xlvNc4BIXucwVrnGV69zgFje5zR3ucZf7POARD3nME57xlOe84BUvec0b3vGW93zgEx/5zBe+8ZXv/OAXP/nNH/7xN8p/Ib0BCERAAhOEYAQlOCEIRUhCE4ZwhCU8EYhERCIThWhEJToxiEVMYhOHeMQlPglIREISk4RkJCU5KUhFSlKThnSkJT0ZyERGMpOFbGQlOznIRU5yk4d85CU/BShEQQpThGIUpTglKEVJSlOGcpSlPBWoREUqU4VqVKU6NahFTWpTh3rUpT4NaERDGtOEZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//I36X1B3AAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH3+j/RfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/5VzBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3xn+lPAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8jflfMVcAAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff2P9V8gXgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//lXEGIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfOfyW8AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+18RdwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/+b/4zj+BCEhgghCMoAQnBKEISWjCEI6whCfC/zdMD9liIAAAwPo6tW3bNn5t27Zt27Zt27Zt27YnixwhRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTQf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMh//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU2H/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTEf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNR//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUzH/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTcf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMJ//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU0n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTKf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNp//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUxn/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTWf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlM5//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU3n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTBf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNF//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUyX/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTZf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMV//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU1X/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTNf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlN1//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUw3/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTTf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMt//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU23/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTHf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNd//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwkU4D//EZggBCU4wQhBSEITijCEJTzhiEBEIhOJKEQlOtGIQUxiE4s4xCU+8UhAQhKTiCQkJTnJSEFKUpOKNKQlPenIQEYyk4ksZCU72chBTnKTizzkJR8B5KcAhShIYYpQjKIUpwSlKElpylCOspSnApWoSGWqUI2qVKcGtahJbepQj7rUpwGNaEhjmtCMpjSnBa1oSWva0I62tKcDnehIZ7rQja50pwe96Elv+tCPvvRnAIMYyGCGMIyhDGcEoxjJaMYwjrGMZwKTmMhkpjCNqUxnBrOYyWzmMI+5zGcBi1jIYpawjKUsZwWrWMlq1rCOtaxnA5vYyGa2sI2tbGcHu9jJbvawj73s5wCHOMhhjnCMoxznBKc4yWnOcI6znOcCl7jIZa5wjatc5wa3uMlt7nCPu9znAY94yGOe8IynPOcFr3jJa97wjre85wOf+MhnvvCNr3znB7/4yW/+8I+/BMrnP/8RmCAEJTjBCEFIQhOKMIQlPOGIQEQiE4koRCU60YhBTGITizjEJT7xSEBCEpOIJCQlOclIQUpSk4o0pCU96chARjKTiSxkJTvZyEFOcpOLPOQlHwHkpwCFKEhhilCMohSnBKUoSWnKUI6ylKcClah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+ + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-000000.2.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-000000.2.vtu new file mode 100644 index 000000000..2451cb5e2 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-000000.2.vtu @@ -0,0 +1,32 @@ + + + + + + + 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a/tests/automatic_regression_test/gold_allenCahn/solution-000000.3.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-000000.3.vtu new file mode 100644 index 000000000..6b8243c2a --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-000000.3.vtu @@ -0,0 +1,32 @@ + + + + + + + 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bh3wGoJfOQ/A14GuN3DyQzxPopXmC7wLfTeYhnyehu/NAdYOvNXLzQD6Delkffgi+C3w3mYd8BkKvngMf4twYAT8yfY7/+rruCPhRMg997h9Cd/OQT/CkV/sw/evrvo2AH5k+9PcAkk/wo2Qe6sNU6G4e8gnePQc+xLlRA77WyM0D+QRfS+ahc+BD6G4e8gm+28jNA9WdCt2dB/IJvpbMQ32YCt3NQz7Bd5P7QPDXwF8n9wHyeQK9NE/wc+DnyTzk8yR0dx+gusFfJ/cB8hnUy/rwQ/Bz4OfJPOQzAL10HoL/BP4zOQ/k8wR6aZ7g//Xn/lZ40t085PMkdHceqG7wn8l5IJ9BvawPPwRf7UPwpLt5yGcgdHcfoOfuQ+juPkA+wV8n89Dn/iF0Nw/5BD9P7gNUdyp0dx8gn+Cvk3moD1Ohu3nIJ/h5ch7oufsQujsP5BP8ZzIPnQMfQnfzkE/wpLvzQHWnQnfngXyC/0zmoT5Mhe7mIZ/gSa/eA/R3v743GAE/Mn2Wou4I+FEyD33v6wvdzUM+S6FX+7C793XfRsCPTB/6e8DJZ7xX1oddwVf7EDzpbh7yGe+V3QPQ50jfu/tCd+eBfJa7ZfcAfcF3ge8m85DPUujuPFDd4GuN3DyQz3ivrA+7gu8C303mIZ+x0KvnwLf9r8+NEfAj06e3/3XdEfCjZB763IMn3c1DPj2hV/vwJvo2An5k+tCfA0A+wY2SeagPb0J385BP8O45QJ8jPXfB1xq5eSCf3n7ZOfBN8F3gu8k85NMTujsPVPdN6O48kE/wtWQe6sOb0N085BN8N7kP0PfuvtDdfYB8lrtl9wB9wc+BnyfzkM9S6O4+QHWDv07uA+Qz3ivrw67g58DPk3nIZ7xXdg9AnyN97+4L3Z0H8lnult0D9AVfvQfoC93NQz5LobvzQHWD/0zOA/mM98r6sCv4ah+CJ93NQz5jobv7AD13wV8n9wHy6e2XnQPfBD8Hfp7MQz49obv7ANV9E7q7D5BP8NfJPNSHN6G7ecgn+HlyHui5C/4zOQ/k09svOwe+Cb56DgRPupuHfHpCd+eB6r4J3Z0H8gn+M5mH+vAmdDcP+QRf1UvfBwi+DXzb9HkSddvAt5N5gh8CP0zmIZ8nobvvA1Dd4Numz0C8b9AGvp3MQ30IfpjMQz4D0EvnIfhH4B+T80A+T6CX5gl+A/wmmYd8noTuzgPVDf4xOQ/kM6iX9eGH4DfAb5J5yGcgdPd9AHruPoTuvg9APsG3k3noc/8QupuHfIIfmn2Yivcl2sC3TR+6ByCf4NvJPNSHqdDdPOQT/DA5D/TcfQjdnQfyCf4xmYfOgQ+hu3nIJ/hNch6o7lTo7jyQT/CPyTzUh6nQ3TzkE/wmuQ8E/6+/173Ck+7uA+TzBHppnuBbwLcOc3nI50no7j5AdYMn3d0HyGdQL+vDD8G3gG8d5vKQzwD00nkI/gr4q+Q8kM8T6KV5gp8BP0vmIZ8nobvzQHWDv0rOA/kM6mV9+CH4GfCzZB7yGQjd3QfoufsQursPkE/w7jnwIc6NFvCtw1we8gmedHcfoLpTobv7APkE7/aBfKgPU6G7ecgn+FZyHui5+xC6Ow/kE/xVMg+dAx9Cd/OQT/Cz5DxQ3anQ3Xkgn+CvknmoD1Ohu3nIJ3jS3fcB6Ht3X+ju+wDks9wtuwfoC34I/DCZh3yWQnffB6C6wbdNn7F436ANfDuZh/oQ/DCZh3zGe2X3APQ50vfuvtDdeSCf5W7ZPUBf8BvgN8k85LMUujsPVDf4x+Q8kM94r6wPu4LfAL9J5iGfsdDd9wHouQu+bfr0xPsGbeDbyTz0uQc/TOYhn57Q3fcBqO6b0N33Acgn+HYyD/XhTehuHvIJfpicB3rugn9MzgP59PbLzoFvgt8Av0nmIZ+e0N15oLpvQnfngXyCf0zmoT68Cd3NQz7Bb5L7AH3v7gvd3QfIZ7lbdg/QF3wL+NZhLg/5LIXu7gNUN3jS3X2AfMZ7ZX3YFXwL+NZhLg/5jPfK7gHoc6Tv3X2hu/NAPsvdsnuAvuBnwM+SechnKXR3Hqhu8FfJeSCf8V5ZH3YFPwN+lsxDPmOhu/sAPXfBk+7uA+TT2y87B74JvgV86zCXh3x6Qnf3Aar7JnR3HyCf4N0+vIm+tYBvHebykE/wreQ80HMX/FVyHsint192DnwT/Az4WTIP+fSE7s4D1X0TujsP5BP8VTIP9eFN6G4e8gme9OqfAzACnX53PxJ61effGl/XHQE/Suah332PhO7mIZ/4Z9Krfag1vu7bCPiR6dMVdUfAj5J5qA81obt5yKcLeuk80O/uR0J354F8gqsl89Dv/kZCd/OQT/DdRm4eqG5N6O48kE+3UdaHmuC7wHeTecinK/TqOXAtzo0R8CPTZy7qjoAfJfPQ534tdDcP+cyFXu3Dp+jbCPiR6UN/DyD5BD9K5qE+fArdzUM+wbvnwLU4N2rA1xq5eSCfeeE5cC34LvDdZB7ymQvdnQeq+yl0dx7IJ/haMg/14VPobh7yCb6b3Afod/cjobv7APkEf53MQ7/7HgndzUM+wc+T+wDVrQnd3QfIp9so60NN8HPg58k85NMFvXQe6Hf3I6G780A+wX8m89B/9x8J3c1DPsGT7s4D1a0J3Z0H8uk2yvpQE3y1DzWhu3nIpyt0dx+g5+5a6O4+QD7zwnPgWvBz4OfJPOQzF7q7D1DdT6G7+wD5BH+dzEN9+BS6m4d8gp8n54Geu2uhu/NAPvPCc+Ba8NVz4Frobh7ymQvdnQeq+yl0dx7IJ/jPZB7qw6fQ3TzkE/9MevUeoH3w9b3BCPiR6TMUdUfAj5J56HtfW+huHvIZCr3ah0fRtxHwI9NnI+qOgB8l81AfHoXu5iGfzUHZPUBb8DXga43cPJDP8KDsHqAt+C7w3WQe8hkK3Z0HqvsodHceyGdT2IdHwXeB7ybzkM9G6NVz4Pvh1+fGCPiR6dM6/LruCPhRMg997sGT7uYhn5bQq324En0bAT8yfWai7gj4UTIP9eFK6G4e8pkdlp0D3wVfA77WyM0D+bQOy86B74LvAt9N5iGfltDdeaC6V0J354F8ZoV9uBJ8F/huMg/5zA7L7gHo3+P0vbstdHcfIJ/hQdk9QFvwc+DnyTzkMxS6uw9Q3Uehu/sA+WwK+/Ao+Dnw82Qe8tkclN0DtAX/Cfxnch7IZ3hQdg/QFnz1HqAtdDcP+QyF7s4D1X0UujsP5LMp7MOj4Kt9eBS6m4d8NkJ39wF67oK/Tu4D5NM6LDsHvgt+Dvw8mYd8WkJ39wGqeyV0dx8gn1lhH64EPwd+nsxDPrPDsnPgu+A/gf9MzgP5tA7LzoHvgq+eA8GT7uYhn5bQ3XmguldCd+eBfGaFfbgSfLUPV0J385DPDPTS9wHod/cjobvvA5BP8O1kHvrd90jobh7yCX5o9qEm3pdoA982fbqibhv4djIP9aEmdDcP+XRBL50H+t39SOjuPJBP8I/JPPS7v5HQ3TzkE/wmOQ9UtyZ0dx7Ip9so60NN8BvgN8k85NMVuvs+AD1310J33wcgn3nhOXAt+CHww2Qe8pkL3X0fgOp+Ct19H4B8gmsn81AfPoXu5iGf4IfJeaDn7lro7jyQz7zwHLgW/Ab4TTIP+cyF7s4D1f0UujsP5BP8YzIP9eFT6G4e8gl+k9wH6Hf3I6G7+wD5BO/+OQDkQ7/7HgndzUM+wZPu7gNUtyZ0dx8gn26jrA81wbeAbx3m8pBPF/TSeaDf3Y+E7s4D+QR/lcxDfw7ASOhuHvIJfpacB6pbE7o7D+TTbZT1oSb4GfCzZB7y6Qrd3QfoubsWursPkM+88By4FnwL+NZhLg/5zIXu7gNU91Po7j5APsG7ffgUfWsB3zrM5SGf4FvJeaDn7lro7jyQz7zwHLgW/Az4WTIP+cyF7s4D1f0UujsP5BP8VTIP9eFT6G4e8gmedPd9APre3Ra6+z4A+QwPyu4B2oIfAj9M5iGfodDd9wGo7qPQ3fcByGdT2IdHwQ+BHybzkM/moOweoC34R+Afk/NAPsODsnuAtuA3wG+SechnKHR3Hqjuo9DdeSCfTWEfHgW/AX6TzEM+G6G77wPQcxd82/RpifcN2sC3k3nocw9+mMxDPi2hu+8DUN0robvvA5DPrLAPV4IfAj9M5iGf2WHZOfBd8I/APybngXxah2XnwHfBb4DfJPOQT0vo7jxQ3Suhu/NAPrPCPlwJfgP8JpmHfGaHZfcA9O9x+t7dFrq7D5DP8KDsHqAt+BbwrcNcHvIZCt3dB6juo9DdfYB8NoV9eBR8C/jWYS4P+WwOyu4B2oK/Av4qOQ/kMzwouwdoC34G/CyZh3yGQnfngeo+Ct2dB/LZFPbhUfAz4GfJPOSzEbq7D9BzF/9MursPkE/rsOwc+C74FvCtw1we8mkJ3d0HqO6V0N19gHxmhX24EnwL+NZhLg/5zA7LzoHvgr8C/io5D+TTOiw7B74Lfgb8LJmHfFpCd+eB6l4J3Z0H8pkV9uFK8DPgZ8k85DMT+j/++X/ra9CD7wDfMX3+6+fXdTvAd5J5gn8C/imZh3yCfzL70Pz5dd86wHdMn0tRtwN8J5mH+tAUupuHfC5BL52H4H/U/54n3Z0H8gm+qpfmCX4A/KCey0M+wZPuzgPVbQrdnQfyufxZ1oem4AfAD+q5PORzKfTqOfAgzo0O8B3TZyXqdoDvJPPQ5/4gdDcP+ayEXu3Dn0df960DfMf0aRx9XbcDfCeZh/oQ/FMyD/k0jsrOgQdxblSfuwehu/NAPqvCc+BB8APgB/VcHvJZCd2dB6obPOnuPJBP46isD38KfgD8oJ7LQz4N0Ev3geA/gP9I7gPkE/xHMk/wx3/9PU+6m4d8gifd3QeoblPo7j5APpc/y/rQFHy1D02hu3nI5xL00nkIfgr89K/cPJBP8NNknuDj7wGu8qS7ecgneNLdeaC6TaG780A+lz/L+tAUfLUPTaG7ecjnUujuPkDP3YPQ3X2AfFaF58CD4Kuf+4PQ3TzksxK6uw9Q3eA/kvsA+TSOyvrwp+CrfQiedDcP+TSOys6BB3FuTIGf/pWbB/JZFZ4DD4KvngMPQnfzkM9K6O48UN3gp3/l5oF8GkdlffhT8NU+BE+6m4d8GkKv3gNciHuDDvAd02ci6naA7yTz0Pe+C6G7echnIvRqH95F3zrAd0yf//z1dd0O8J1kHurDu9DdPOQT/FNyHuh794XQ3Xkgn0nhPcCF4AfAD+q5POQzEbo7D1T3XejuPJBP8G4f3kXfBsAP6rk85BM86dVzoP7r63OjA3zH9DkXdTvAd5J56HOvC93NQz7nQq/24V70rQN8x/RZiLod4DvJPNSHe6G7echn8avsHKgLvvrc1YXuzgP5nP8qOwfqgh8AP6jn8pDPudDdeaC690J354F8FoV9uBf8APhBPZeHfBa/yu4BLsS9wQfwH8l9gHwmhfcAF4Kvfu+7ELqbh3wmQnf3Aar7LnR3HyCf4D+SeagP70J385BP8O49wIW4N5gCP/0rNw/kMym8B7gQfPUe4ELobh7ymQjdnQeq+y50dx7IJ/hpMg/14V3obh7yCZ50dx+g564udHcfIJ/zX2XnQF3w1c+9LnQ3D/mcC93dB6juvdDdfYB8FoV9uBd8tQ/3QnfzkM/iV9k5UBf8FPjpX7l5IJ/zX2XnQF3w1XOgLnQ3D/mcC92dB6p7L3R3HshnUdiHe8FX+3AvdDcP+SxAL30fIPg+8H3Th/47OPkE30/mCX4J/DKZh3yCX5p9aIr3JfrA902fS1G3D3w/mYf60BS6m4d8LkEvnYfgd/f+nifdnQfyCb6ql+YJfgz8eC+Xh3yCJ92dB6rbFLo7D+Rz+bOsD03Bj4Ef7+XykM+l0N33Aei5exC6+z4A+awKz4EHwS+BXybzkM9K6O77AFQ3+L7p0xDvG/SB7yfzUB+CXybzkE/jqOwceBDnRvW5exC6Ow/ksyo8Bx4EPwZ+vJfLQz4robvzQHWDJ92dB/JpHJX14U/Bj4Ef7+XykE8D9NJ9IPhv+3/Pk+7uA+QTfFUvzRN8D/jefi4P+QRPursPUN2m0N19gHwuf5b1oSn4HvC9/Vwe8rkEvXQegn8D/i05D+QT/FsyT/Dx9wBUedLdPOQTPOnuPFDdptDdeSCfy59lfWgKvtqHptDdPORzKXR3H6Dn7kHo7j5APqvCc+BB8D3ge/u5POSzErq7D1Dd4El39wHyaRyV9eFPwfeA7+3n8pBP46jsHHgQ58Yb8G/JeSCfVeE58CD46jnwIHQ3D/mshO7OA9UN/i05D+TTOCrrw5+Cr/YheNLdPOTTELr7PgB9774Quvs+APlMCu8BLgS/BH6ZzEM+E6G77wNQ3Xehu+8DkE/w/WQe6sO70N085BP8MjkP9L37QujuPJDPpPAe4ELwY+DHe7k85DMRujsPVPdd6O48kE/wbh/eRd/GwI/3cnnIJ3jS3fcB6LmrC919H4B8zn+VnQN1wS+BXybzkM+50N33AajuvdDd9wHIZ1HYh3vBL4FfJvOQz+JX2TlQF3z1uasL3Z0H8jn/VXYO1AU/Bn68l8tDPudCd+eB6t4L3Z0H8lkU9uFe8GPgx3u5POSz+FV2D3Ah7g2q37svhO7uA+QzKbwHuBB8D/jefi4P+UyE7u4DVPdd6O4+QD7Bu314F33rAd/bz+Uhn+B7yXmg790XQnfngXwmhfcAF4Kv3gNcCN3NQz4TobvzQHXfhe7OA/kE/5bMQ314F7qbh3yCJ93dB+i5qwvd3QfI5/xX2TlQF3wP+N5+Lg/5nAvd3Qeo7r3Q3X2AfBaFfbgXfA/43n4uD/ksfpWdA3XBvwH/lpwH8jn/VXYO1AVfPQfqQnfzkM+50N15oLr3QnfngXwWhX24F3y1D/dCd/OQz0Lo1T8H4D+af6/T7+6D75g+O82v63aA7yTz0O++g39K5iGfHaFX+3Am+tYBvmP63Im6HeA7yTzUhzOhu3nI5w700nmg390HT7o7D+SzA3ppHvrdX/CDei4P+ewI3Z0HqnsmdHceyOeusA9ngh8AP6jn8pDPndCr58CrODc6wHdMn3///XXdDvCdZB763F+F7uYhn+CfzD5s//66bx3gO6bPqajbAb6TzEN92Ba6m4d8Tn+XnQOv4tyoPnevQnfngXyCd8+BV3FuDIAf1HN5yCd40t15oLrbQnfngXxOf5f1YVvwA+AH9Vwe8jkFvXQfoN/dB/+R3AfIZwf00jz0u+/gSXfzkM+O0N19gOqeCd3dB8jnrrAPZ4Kv9uFM6G4e8rkDvXQe6Hf3wU//ys0D+eyAXpqH/rt/8KS7echnR+juPFDdM6G780A+d4V9OBN8tQ9nQnfzkM+d0N19gJ67V6G7+wD5BP+RzEOf+6vQ3TzkEzzp7j5AdbeF7u4D5HP6u6wP24Kv9mFb6G4e8jn9XXYOvIpzYwr89K/cPJBP8NNkHjoHXoXu5iGf4El354HqbgvdnQfyOf1d1odtwVf7sC10Nw/5nAq9eg9wK+4NOsB3TJ8XUbcDfCeZh7733QrdzUM+L0Kv9uGP46/71gG+Y/psHX9dtwN8J5mH+hD8UzIP+Wwdl90D3Ip7g+r37luhu/NAPi+F9wC3gh8AP6jn8pDPi9DdeaC6wZPuzgP5bB2X9eEPwQ+AH9RzechnS+jVc+BEnBsd4Dumz42o2wG+k8xDn/uJ0N085HMj9GofnkXfOsB3TJ9//PfXdTvAd5J5qA/PQnfzkE/wT8l5oOfuROjuPJDPTeE5cCL4AfCDei4P+dwI3Z0HqvssdHceyCd4tw/Pom8D4Af1XB7yCX6Q3Afoe/et0N19gHxeCu8BbgVf/d53K3Q3D/m8CN3dB6hu8B/JfYB8to7L+vCH4Kt9CJ50Nw/5bB2X3QPcinuDKfDTv3LzQD4vhfcAt4Kv3gPcCt3NQz4vQnfngeoGP/0rNw/ks3Vc1oc/BF/tQ/Cku3nIZ0vo7j5Az92J0N19gHxuCs+BE8FXP/cTobt5yOdG6O4+QHWfhe7uA+QT/EcyD/XhWehuHvIJ3j0HTsS5MQV++lduHsjnpvAcOBF89Rw4Ebqbh3xuhO7OA9V9Fro7D+QT/DSZh/rwLHQ3D/kE7/45APTf8el398H3TZ8d8b5BH/h+Mg/97jv4ZTIP+ewI3X0fgOqeCd19H4B87gr7cCb4JfDLZB7yuQO9dB7od/fBk+7OA/nsgF6ah373F/x4L5eHfHaE7s4D1T0TujsP5HNX2IczwY+BH+/l8pDPndDd9wHouXsVuvs+APkE30/moc/9VehuHvIJfmn2YVu8L9EHvm/6nIq6feD7yTzUh22hu3nI5/R32TnwKs6N6nP3KnR3HsgnePcceBXnxhj48V4uD/kET7o7D1R3W+juPJDP6e+yPmwLfgz8eC+Xh3xOQS/dB+h398GT7u4D5LMDemke+t138L39XB7y2RG6uw9Q3TOhu/sA+dwV9uFM8D3ge/u5PORzB3rpPNDv7oN/S84D+eyAXpqH/hyA4El385DPjtDdeaC6Z0J354F87gr7cCb4ah/OhO7mIZ87obv7AD13r0J39wHyCd49B17FudEDvrefy0M+wZPu7gNUd1vo7j5APqe/y/qwLfge8L39XB7yOf1ddg68inPjDfi35DyQT/BvyTx0DrwK3c1DPsGT7s4D1d0WujsP5HP6u6wP24Kv9mFb6G4e8jkVuvs+AH3vvhW6+z4A+bwU3gPcCn4J/DKZh3xehO6+D0B1g++bPlvifYM+8P1kHupD8MtkHvLZOi67B7gV9wbV7923QnfngXxeCu8BbgU/Bn68l8tDPi9Cd+eB6gZPujsP5LN1XNaHPwQ/Bn68l8tDPltCd98HoOfuROju+wDkc1N4DpwIfgn8MpmHfG6E7r4PQHWfhe6+D0A+wfeTeagPz0J385BP8MvkPNBzdyJ0dx7I56bwHDgR/Bj48V4uD/ncCN2dB6r7LHR3HsgneLcPz6JvY+DHe7k85BP8OLkP0PfuW6G7+wD5vBTeA9wKvgd8bz+Xh3xehO7uA1Q3eNLdfYB8to7L+vCH4HvA9/Zzechn67jsHuBW3Bu8Af+WnAfyeSm8B7gVfPUe4Fbobh7yeRG6Ow9UN/i35DyQz9ZxWR/+EHy1D8GT7uYhny2hu/sAPXcnQnf3AfK5KTwHTgTfA763n8tDPjdCd/cBqvssdHcfIJ/g3T48i771gO/t5/KQT/C95DzQc3cidHceyOem8Bw4EXz1HDgRupuHfG6E7s4D1X0WujsP5BP8WzIP9eFZ6G4e8gm+qs8O/15fH37Nj4AfmT7/9fPruiPgR8k8wf9b4+950t085BM86dU+NH9+3bcR8CPT51LUHQE/SuahPjSF7uYhn0vQS+ch+BrwtUZuHsgn+FoyT/Bd4LvJPOQTfLeRmweq2xS6Ow/kc/mzrA9NwXeB7ybzkM+l0KvnwIM4N0bAj0yflag7An6UzEOf+4PQ3TzksxJ6tQ9/Hn3dtxHwI9OH/h5A8mkclfXhT8FX+xA86W4e8mkclZ0DD+LcqAFfa+TmgXxWhefAg+C7wHeTechnJXR3Hqhu8LVGbh7Ip3FU1oc/Bd8FvpvMQz4N0Ev3geCvgb9O7gPkE/x1Mk/wc+DnyTzkE/w8uQ9Q3abQ3X2AfC5/lvWhKfg58PNkHvK5BL10HoL/BP4zOQ/kE/xnMk/w//Pg73nS3TzkEzzp7jxQ3abQ3Xkgn8ufZX1oCr7ah6bQ3Tzkcyl0dx+g5+5B6O4+QD6rwnPgQfBz4OfJPOSzErq7D1Dd4K+T+wD5NI7K+vCn4OfAz5N5yKdxVHYOPIhz4xP4z+Q8kM+q8Bx4EHz1HHgQupuHfFZCd+eB6gb/mZwH8mkclfXhT8FX+xA86W4e8mkIvXoPcCHuDUbAj0yfiag7An6UzEPf+y6E7uYhn4nQq314F30bAT8yfejvASef4EfJPNSHd6G7ecgnePce4ELcG9SArzVy80A+k8J7gAvBd4HvJvOQz0To7jxQ3Xehu/NAPsHXknmoD+9Cd/OQT/CkV8+B+q+vz40R8CPT51zUHQE/Suahz70udDcP+ZwLvdqHe9G3EfAj02ch6o6AHyXzUB/uhe7mIZ/Fr7JzoC74GvC1Rm4eyOf8V9k5UBd8F/huMg/5nAvdnQeqey90dx7IZ1HYh3vBd4HvJvOQz+JX2T3Ahbg3uAb+OrkPkM+k8B7gQvBz4OfJPOQzEbq7D1Ddd6G7+wD5BH+dzEN9eBe6m4d8gp8n54G+d18I3Z0H8pkU3gNcCL56D3AhdDcP+UyE7s4D1X0XujsP5BP8ZzIP9eFd6G4e8gmedHcfoOeuLnR3HyCf819l50Bd8HPg58k85HMudHcfoLr3Qnf3AfJZFPbhXvBz4OfJPOSz+FV2DtQF/wn8Z3IeyOf8V9k5UBd89RyoC93NQz7nQnfngereC92dB/JZFPbhXvDVPtwL3c1DPgvQS98HCL4NfNv0of8OTj7Bt5N5gh8CP0zmIZ/gh2YfmuJ9iTbwbdPnUtRtA99O5qE+NIXu5iGfS9BL5yH4R+Afk/NAPsE/JvMEvwF+k8xDPsFvkvNAdZtCd+eBfC5/lvWhKfgN8JtkHvK5FLr7PgA9dw9Cd98HIJ9V4TnwIPgh8MNkHvJZCd19H4DqBt82fRrifYM28O1kHupD8MNkHvJpHJWdAw/i3HgE/jE5D+SzKjwHHgS/AX6TzEM+K6G780B1g39MzgP5NI7K+vCn4DfAb5J5yKcBeuk+EPz3w7/nSXf3AfIJvqqX5gm+BXzrMJeHfIIn3d0HqG5T6O4+QD6XP8v60BR8C/jWYS4P+VyCXjoPwV8Bf5WcB/IJ/iqZJ/gZ8LNkHvIJfpacB6rbFLo7D+Rz+bOsD03Bz4CfJfOQz6XQ3X2AnrsHobv7APmsCs+BB8G3gG8d5vKQz0ro7j5AdYMn3d0HyKdxVNaHPwXfAr51mMtDPo2jsnPgQZwbV8BfJeeBfFaF58CD4GfAz5J5yGcldHceqG7wV8l5IJ/GUVkf/hT8DPhZMg/5NITuvg9A37svhO6+D0A+k8J7gAvBD4EfJvOQz0To7vsAVPdd6O77AOQTfDuZh/rwLnQ3D/kEP0zOA33vvhC6Ow/kMym8B7gQ/Ab4TTIP+UyE7s4D1X0XujsP5BP8YzIP9eFd6G4e8gl+Y54DdfG+UBv4tulzLuq2gW8n89DnXhe6m4d8zoXuvg9Ade+F7r4PQD6Lwj7cC34I/DCZh3wWv8rOgbrgH4F/TM4D+Zz/KjsH6oLfAL9J5iGfc6G780B174XuzgP5LAr7cC/4DfCbZB7yWfwquwe4EPcG1e/dF0J39wHymRTeA1wIvgV86zCXh3wmQnf3Aar7LnR3HyCf4N0+vIu+tYBvHebykE/wreQ80PfuC6G780A+k8J7gAvBz4CfJfOQz0To7jxQ3Xehu/NAPsFfJfNQH96F7uYhn+BJd/cBeu7qQnf3AfI5/1V2DtQF3wK+dZjLQz7nQnf3Aap7L3R3HyCfRWEf7gXfAr51mMtDPotfZedAXfBXwF8l54F8zn+VnQN1wc+AnyXzkM+50N15oLr3QnfngXwWhX24F/wM+FkyD/kshF79cwD+A/4eZ/rdffAj02en+XXdEfCjZB763XfwpLt5yGdH6NU+nIm+jYAfmT53ou4I+FEyD/XhTOhuHvK5A710Huh398HXGrl5IJ8d0Evz0O/+gu8m85DPjtDdeaC6Z0J354F87gr7cCb4LvDdZB7yuRN69Rx4FefGCPiR6fPvv7+uOwJ+lMxDn/ur0N085BM86dU+bP/+um8j4Eemz6moOwJ+lMxDfdgWupuHfE5/l50Dr+LcqAFfa+TmgXyCryXz0DnwKnQ3D/kE323k5oHqbgvdnQfyOf1d1odtwXeB7ybzkM8p6KX7AP3uPvjr5D5APjugl+ah330HP0/mIZ8dobv7ANU9E7q7D5DPXWEfzgQ/B36ezEM+d6CXzgP97j74z+Q8kM8O6KV56L/7B0+6m4d8doTuzgPVPRO6Ow/kc1fYhzPBV/twJnQ3D/ncCd3dB+i5exW6uw+QT/DXyTz0ub8K3c1DPsHPk/sA1d0WursPkM/p77I+bAt+Dvw8mYd8Tn+XnQOv4tz4BP4zOQ/kE/xnMg+dA69Cd/OQT/Cku/NAdbeF7s4D+Zz+LuvDtuCrfdgWupuHfE6FXr0HuBX3BiPgR6bPi6g7An6UzEPf+26F7uYhnxehV/vwx/HXfRsBPzJ96O8BJ5+t47I+/CH4ah+CJ93NQz5bx2X3ALfi3qAGfK2RmwfyeSm8B7gVfBf4bjIP+bwI3Z0Hqht8rZGbB/LZOi7rwx+C7wLfTeYhny2hV8+BE3FujIAfmT43ou4I+FEyD33uJ0J385DPjdCrfXgWfRsBPzJ9/vHfX9cdAT9K5qE+PAvdzUM+wbvnwIk4N2rA1xq5eSCfm8Jz4ETwXeC7yTzkcyN0dx6o7rPQ3Xkgn+BryTzUh2ehu3nIJ/huch+g7923Qnf3AfJ5KbwHuBX8HPh5Mg/5vAjd3QeobvDXyX2AfLaOy/rwh+DnwM+Techn67jsHuBW3Bt8Av+ZnAfyeSm8B7gVfPUe4Fbobh7yeRG6Ow9UN/jP5DyQz9ZxWR/+EHy1D8GT7uYhny2hu/sAPXcnQnf3AfK5KTwHTgQ/B36ezEM+N0J39wGq+yx0dx8gn+Cvk3moD89Cd/OQT/Dz5DzQc3cidHceyOem8Bw4EXz1HDgRupuHfG6E7s4D1X0WujsP5BP8ZzIP9eFZ6G4e8gne/XMA6L/j0+/ug2+bPjvifYM28O1kHvrdd/DDZB7y2RG6+z4A1T0Tuvs+APncFfbhTPBD4IfJPORzB3rpPNDv7oN/TM4D+eyAXpqHfvcX/CaZh3x2hO7OA9U9E7o7D+RzV9iHM8FvgN8k85DPndDd9wHouXsVuvs+APkE307moc/9VehuHvIJfmj2YVu8L9EGvm36nIq6beDbyTzUh22hu3nI5/R32TnwKs6NR+Afk/NAPsE/JvPQOfAqdDcP+QS/Sc4D1d0WujsP5HP6u6wP24LfAL9J5iGfU9BL9wH63X3wpLv7APnsgF6ah373HXzrMJeHfHaE7u4DVPdM6O4+QD53hX04E3wL+NZhLg/53IFeOg/0u/vgr5LzQD47oJfmoT8HIPhZMg/57AjdnQeqeyZ0dx7I566wD2eCnwE/S+Yhnzuhu/sAPXevQnf3AfIJ3j0HXsW50QK+dZjLQz7Bk+7uA1R3W+juPkA+p7/L+rAt+BbwrcNcHvI5/V12DryKc+MK+KvkPJBP8FfJPHQOvArdzUM+wc+S80B1t4XuzgP5nP4u68O24GfAz5J5yOdU6O77APS9+1bo7vsA5PNSeA9wK/gh8MNkHvJ5Ebr7PgDVDb5t+myJ9w3awLeTeagPwQ+Techn67jsHuBW3Bs8Av+YnAfyeSm8B7gV/Ab4TTIP+bwI3Z0Hqhv8Y3IeyGfruKwPfwh+A/wmmYd8toTuvg9Az92J0N33AcjnpvAcOBH8EPhhMg/53AjdfR+A6j4L3X0fgHyCbyfzUB+ehe7mIZ/gh8l5oOfuROjuPJDPTeE5cCL4DfCbZB7yuRG6Ow9U91no7jyQT/CPyTzUh2ehu3nIJ/hNch+g7923Qnf3AfJ5KbwHuBV8C/jWYS4P+bwI3d0HqG7wpLv7APlsHZf14Q/Bt4BvHebykM/Wcdk9wK24N7gC/io5D+TzUngPcCv4GfCzZB7yeRG6Ow9UN/ir5DyQz9ZxWR/+EPwM+FkyD/lsCd3dB+i5OxG6uw+Qz03hOXAi+BbwrcNcHvK5Ebq7D1DdZ6G7+wD5BO/24Vn0rQV86zCXh3yCbyXngZ67E6G780A+N4XnwIngZ8DPknnI50bo7jxQ3Wehu/NAPsFfJfNQH56F7uYhn+D/H/2ff693/vk1vwZ+bfo8ibpr4NfJPMH/18+/50l385DPk9CrffhR/7pva+DXps+g/nXdNfDrZB7qQ/Cku3nIZwB66TwE3wS++TM3D+TzBHppnuAvgb9M5iGfJ6G780B1g2/+zM0D+QzqZX34IfhL4C+TechnIPTqOfAhzo018GvT5/ivr+uugV8n89Dn/iF0Nw/5BE96tQ/Tv77u2xr4tenzP3a/rrsGfp3MQ32YCt3NQz7Bu+fAhzg3msA3f+bmgXyCbybz0DnwIXQ3D/kEf/kzNw9Udyp0dx7IJ/hmMg/1YSp0Nw/5BH+Z3AeCfwD+IbkPkM8T6KV5gl8Bv0rmIZ8nobv7ANUN/iG5D5DPoF7Whx+CXwG/SuYhnwHopfMQ/J9Hf8+T7s4D+TyBXpon+AbwjaNcHvJ5Ero7D1Q3eNLdeSCfQb2sDz8E3wC+cZTLQz4Dobv7AD13H0J39wHyCf4hmYc+9w+hu3nIJ/hVch+gulOhu/sA+QT/kMxDfZgK3c1DPsGvkvNAz92H0N15IJ/g3XPgQ5wbDeAbR7k85BM86e48UN2p0N15IJ/g3T6QD/VhKnQ3D/kET3r1HqC/+/W9wRr4temzFHXXwK+Teeh7X1/obh7yWQq92ofdva/7tgZ+bfqM976uuwZ+ncxDfQiedDcP+Yz3yu4B6HOk7919obvzQD7L3bJ7gL7gL4G/TOYhn6XQ3XmgusE3f+bmgXzGe2V92BX8JfCXyTzkMxZ69Rz4tv/1ubEGfm369Pa/rrsGfp3MQ5978KS7ecinJ/RqH95E39bAr02f+HP/Z6ZP8OtkHurDm9DdPOQTvHsO0OdIz13wzZ+5eSCf3n7ZOfBN8JfAXybzkE9P6O48UN03obvzQD7BN5N5qA9vQnfzkE/wl8l9gL5394Xu7gPks9wtuwfoC34F/CqZh3yWQnf3Aaob/ENyHyCf8V5ZH3YFvwJ+lcxDPuO9snsA+hzpe3df6O48kM9yt+weoC/4BvCNo1we8lkK3Z0Hqhs86e48kM94r6wPu4JvAN84yuUhn7HQ3X2AnrvgH5L7APn09svOgW+CXwG/SuYhn57Q3X2A6r4J3d0HyCf4h2Qe6sOb0N085BP8KjkP9NwFT7o7D+TT2y87B74JvgF84yiXh3x6Qnfngeq+Cd2dB/IJ3u3Dm+hbA/jGUS4P+QTfSL4PEPwF8BfJ9wHI5wn00jzBT4CfJPOQz5PQ3fcBqG7wF8n3AchnUC/rww/BT4CfJPOQzwD00nkI/h349+Q8kM8T6KV5gv/PX3/Pk+7mIZ8nobvzQHWDf0/OA/kM6mV9+CH4ah+CJ93NQz4DobvvA9Bz9yF0930A8gn+IpmHPvcPobt5yCf4SfJ9AKo7Fbr7PgD5BH+RzEN9mArdzUM+wU+S80DP3YfQ3Xkgn+Dfk3noHPgQupuHfIIn3Z0HqjsVujsP5BP8ezIP9WEqdDcP+QRf1Uv3geDrwNdNnydRtw58PZkn+HPgz5N5yOdJ6O4+QHWDr5s+A7Fv1IGvJ/NQH4I/T+YhnwHopfMQ/D3w98l5IJ8n0EvzBL8AfpHMQz5PQnfngeoGf5+cB/IZ1Mv68EPwC+AXyTzkMxC6uw/Qc/chdHcfIJ/g68k89Ll/CN3NQz7Bn5t9mIp9qQ583fShfYB8gq8n81AfpkJ385BP8OfJeaDn7kPo7jyQT/D3yTx0DnwI3c1DPsEvkvNAdadCd+eBfIK/T+ahPkyF7uYhn+BJd98HoO/dfaG77wOQz3K37B6gL/gJ8JNkHvJZCt19H4DqBn+RfB+AfMZ7ZX3YFfwE+EkyD/mM98ruAehzpO/dfaG780A+y92ye4C+4Kv/3u8L3c1DPkuhu/NAdYN/T84D+Yz3yvqwK/hqH4In3c1DPmOhu+8D0HMX/EXyfQDy6e2XnQPfBD8BfpLMQz49obvvA1DdN6G77wOQT/AXyTzUhzehu3nIJ/hJch7ouQv+PTkP5NPbLzsHvgm+eg4ET7qbh3x6Qnfngeq+Cd2dB/IJ/j2Zh/rwJnQ3D/kE794D0L/H6Xt3X+juPkA+y92ye4C+4M+BP0/mIZ+l0N19gOoGXzd9xmLfqANfT+ahPgR/nsxDPuO9snsA+hzpe3df6O48kM9yt+weoC/4BfCLZB7yWQrdnQeqG/x9ch7IZ7xX1oddwS+AXyTzkM9Y6O4+QM9d8HXTpyf2jTrw9WQe+tyDP0/mIZ+e0N19gOq+Cd3dB8gn+HoyD/XhTehuHvIJ/jw5D/TcBX+fnAfy6e2XnQPfBL8AfpHMQz49obvzQHXfhO7OA/kEf5/MQ314E7qbh3yCJ7365wCMQKff3Y+EXvX5t8bXddfAr5N56HffI6G7ecgneNKrfag1vu7bGvi16dMVddfAr5N5qA81obt5yKcLeuk80O/uR0J354F8gm8m89Dv/kZCd/OQT/CXP3PzQHVrQnfngXy6jbI+1AR/CfxlMg/5dIVePQeuxbmxBn5t+sxF3TXw62Qe+tyvhe7mIZ+50Kt9+BR9WwO/Nn3i7/2dmT7Br5N5qA+fQnfzkE/w7jlwLc6NJvDNn7l5IJ954TlwLfhL4C+TechnLnR3Hqjup9DdeSCf4JvJPNSHT6G7ecgn+MvkPkC/ux8J3d0HyCf4h2Qe+t33SOhuHvIJfpXcB6huTejuPkA+3UZZH2qCXwG/SuYhny7opfNAv7sfCd2dB/IJ3v1zAMiH/rv/SOhuHvIJnnR3HqhuTejuPJBPt1HWh5rgG8A3jnJ5yKcrdHcfoOfuWujuPkA+88Jz4FrwK+BXyTzkMxe6uw9Q3U+hu/sA+QT/kMxDffgUupuHfIJfJeeBnrtrobvzQD7zwnPgWvAN4BtHuTzkMxe6Ow9U91Po7jyQT/BuHz5F3xrAN45yecgneNKr9wDtg6/vDdbAr02foai7Bn6dzEPf+9pCd/OQz1Do1T48ir6tgV+bPhtRdw38OpmH+vAodDcP+WwOyu4B2oJvAt/8mZsH8hkelN0DtAV/CfxlMg/5DIXuzgPVfRS6Ow/ksynsw6PgL4G/TOYhn43Qq+fA98Ovz4018GvTp3X4dd018OtkHvrcgyfdzUM+LaFX+3Al+rYGfm36zETdNfDrZB7qw5XQ3TzkMzssOwe+C74JfPNnbh7Ip3VYdg58F/wl8JfJPOTTEro7D1T3SujuPJDPrLAPV4K/BP4ymYd8Zodl9wD073H63t0WursPkM/woOweoC34FfCrZB7yGQrd3Qeo7qPQ3X2AfDaFfXgU/Ar4VTIP+WwOyu4B2oKvfu9uC92dB/IZHpTdA7QF3wC+cZTLQz5DobvzQHUfhe7OA/lsCvvwKPgG8I2jXB7y2Qjd3QfouQv+IbkPkE/rsOwc+C74FfCrZB7yaQnd3Qeo7pXQ3X2AfGaFfbgS/Ar4VTIP+cwOy86B74KvPnfBk+7OA/m0DsvOge+CbwDfOMrlIZ+W0N15oLpXQnfngXxmhX24EnwD+MZRLg/5zEAvfR+Afnc/Err7PgD5BH+RzEO/+x4J3c1DPsFPku8DUN2a0N33Acin2yjrQ03wE+AnyTzk0wW9dB7od/cjobvzQD7Bvyfz0O/+RkJ385BP8KS780B1a0J354F8uo2yPtQEX+1DTehuHvLpCt19H4Ceu2uhu+8DkM+88By4FvwE+EkyD/nMhe6+D0B1P4Xuvg9APsFfJPNQHz6F7uYhn+AnyXmg5+5a6O48kM+88By4Fnz1HLgWupuHfOZCd+eB6n4K3Z0H8gn+PZmH+vApdDcP+QTv/jkA9O9x+t39SOjuPkA+wdeTeeh33yOhu3nIJ/hzsw81sS/Vga+bPl1Rtw58PZmH+lATupuHfLqgl84D/e5+JHR3Hsgn+PtkHvpzAEZCd/OQT/CL5DxQ3ZrQ3Xkgn26jrA81wS+AXyTzkE9X6O4+QM/dtdDdfYB85oXnwLXgz4E/T+Yhn7nQ3X2A6n4K3d0HyCf4ejIP9eFT6G4e8gn+PDkP9NxdC92dB/KZF54D14JfAL9I5iGfudDdeaC6n0J354F8gr9P5qE+fArdzUM+wZPuvg9A37vbQnffByCf4UHZPUBb8BPgJ8k85DMUuvs+ANV9FLr7PgD5bAr78Cj4CfCTZB7y2RyU3QO0Bf8O/HtyHshneFB2D9AWfPXf+22hu3nIZyh0dx6o7qPQ3Xkgn01hHx4FX+3Do9DdPOSzEbr7PgA9d8FfJN8HIJ/WYdk58F3wE+AnyTzk0xK6+z4A1b0Suvs+APnMCvtwJfgJ8JNkHvKZHZadA98F/w78e3IeyKd1WHYOfBd89RwInnQ3D/m0hO7OA9W9Ero7D+QzK+zDleCrfbgSupuHfGaHZfcA9O9x+t7dFrq7D5DP8KDsHqAt+HPgz5N5yGcodHcfoLqPQnf3AfLZFPbhUfDnwJ8n85DP5qDsHqAt+Hvg75PzQD7Dg7J7gLbgF8AvknnIZyh0dx6o7qPQ3Xkgn01hHx4FvwB+kcxDPhuhu/sAPXfB102fltg36sDXk3nocw/+PJmHfFpCd/cBqnsldHcfIJ9ZYR+uBH8O/HkyD/nMDsvOge+Cvwf+PjkP5NM6LDsHvgt+AfwimYd8WkJ354HqXgndnQfymRX24UrwC+AXyTzkMwP9H/D30Xf++TX/H82/50mv+jyJulWfJ9BL8wS/A/xOM5eHfJ6EXu3Dj/rXfavWDZ70hfn3gJPPoF7Whx+C3wF+p5nLQz4D0EvnIfgz4M+S80A+T6CX5gn+Dvi7ZB7yeRK6Ow9UN/iz5DyQz6Be1ocfgr8D/i6Zh3wGQq+eAx/i3Kg+dx9Cr/oc//V13apP8O458CHOjR3gd5q5POQTPOnVPkz/+rpv1bpToS/MvweQfIJ3+0A+1Iep0N085BP8TnIe6Ln7ELo7D+QT/FkyD50DH0J385BP8HfJeaC6U6G780A+wZ8l81AfpkJ385BP8HfJfSD4V+Bfk/sA+TyBXpon+H///fc86W4e8nkSursPUN3gX5P7APkM6mV9+CH4ah+CJ93NQz4D0EvnIfht4Ld/5+aBfJ5AL80T/Cnwp8k85PMkdHceqG7w279z80A+g3pZH34I/hT402Qe8hkI3d0H6Ln7ELq7D5BP8K/JPPS5fwjdzUM+wZPu7gNUdyp0dx8gn+Bfk3moD1Ohu3nIJ3j3HPgQ58Y28Nu/c/NAPsFvJ/PQOfAhdDcP+QR/+js3D1R3KnR3Hsgn+O1kHurDVOhuHvIJnvTqPUB/9+t7g+r37r7Qqz5LUbfqs9wtuwfoC34H+J1mLg/5LIVe7cPu3td9q9YNnvSF+feAk894r6wPu4LfAX6nmctDPuO9snsA+hzpe3df6O48kM9yt+weoC/4O+DvknnIZyl0dx6obvBnyXkgn/FeWR92BX8H/F0yD/mMhV49B77tf31uVJ+74Emv+vT2v65b9entl50D3wS/A/xOM5eHfHpCr/bhTfStWvdN6AvzzwEgn+DdPryJvu0Av9PM5SGf4HeS80DPXfBnyXkgn95+2TnwTfB3wN8l85BPT+juPFDdN6G780A+wZ8l81Af3oTu5iGf4O+S+wB97+4L3d0HyGe5W3YP0Bd89XtfX+huHvJZCt3dB6hu8K/JfYB8xntlfdgVfLUPwZPu5iGf8V7ZPQB9jvS9uy90dx7IZ7lbdg/QF/wp8KfJPOSzFLo7D1Q3+O3fuXkgn/FeWR92BX8K/GkyD/mMhe7uA/TcBf+a3AfIp7dfdg58E3z1cw+edDcP+fSE7u4DVPdN6O4+QD7BvybzUB/ehO7mIZ/g3XOAPkd67oLf/p2bB/Lp7ZedA98Efwr8aTIP+fSE7s4D1X0TujsP5BP8djIP9eFN6G4e8gn+NPk+QPC3wN8m3wcgnyfQS/ME/wL8SzIP+TwJ3X0fgOoGf5t8H4B8BvWyPvwQ/AvwL8k85DMAvXQegv/j+O950t15IJ8n0EvzBL8F/NZxLg/5PAndnQeqGzzp7jyQz6Be1ocfgt8Cfus4l4d8BkJ33weg5+5D6O77AOQT/G0yD33uH0J385BP8C/J9wGo7lTo7vsA5BP8bTIP9WEqdDcP+QT/kpwHeu4+hO7OA/kE754DH+Lc2AJ+6ziXh3yCJ92dB6o7Fbo7D+QTvNsH8qE+TIXu5iGf4LeS+0DwJ8CfJPcB8nkCvTRP8DfA3yTzkM+T0N19gOoGf5LcB8hnUC/rww/B3wB/k8xDPgPQS+ch+Gfgn5PzQD5PoJfmCf4f//33POluHvJ5Ero7D1Q3+OfkPJDPoF7Whx+Cr/YheNLdPOQzELq7D9Bz9yF0dx8gn+BPknnoc/8QupuHfIK/Se4DVHcqdHcfIJ/gT5J5qA9Tobt5yCf4m+Q80HP3IXR3Hsgn+OdkHjoHPoTu5iGf4El354HqToXuzgP5BP+czEN9mArdzUM+wZPuvg9A37v7QnffByCf5W7ZPUBf8C/AvyTzkM9S6O77AFQ3+Nvk+wDkM94r68Ou4F+Af0nmIZ/xXtk9AH2O9L27L3R3HshnuVt2D9AX/BbwW8e5POSzFLo7D1Q3eNLdeSCf8V5ZH3YFvwX81nEuD/mMhe6+D0DPXfC3yfcByKe3X3YOfBP8C/AvyTzk0xO6+z4A1X0Tuvs+APkEf5vMQ314E7qbh3yCf0nOAz13wZPuzgP59PbLzoFvgt8Cfus4l4d8ekJ354HqvgndnQfyCd7tw5vo2xbwW8e5POQT/FZyH6Dv3X2hu/sA+Sx3y+4B+oK/Af4mmYd8lkJ39wGqG/xJch8gn/FeWR92BX8D/E0yD/mM98ruAehzpO/dfaG780A+y92ye4C+4Kv3AH2hu3nIZyl0dx6obvDPyXkgn/FeWR92BV/tQ/Cku3nIZyx0dx+g5y74k+Q+QD69/bJz4Jvgb4C/SeYhn57Q3X2A6r4J3d0HyCf4k2Qe6sOb0N085BP8TXIe6LkL/jk5D+TT2y87B74JvnoOBE+6m4d8ekJ354HqvgndnQfyCf45mYf68CZ0Nw/5BE969c8BGIFOv7sfCb3q82+Nr+tWfYJ3/xwA8qHffY+E7uYhn+BJr/ah1vi6b9W6NaEvzL8HnHy6jbI+1AS/A/xOM5eHfLqgl84D/e5+JHR3Hsgn+LNkHvrd30jobh7yCf4uOQ9UtyZ0dx7Ip9so60NN8HfA3yXzkE9X6NVz4FqcG9Xn7lroVZ+5qFv1mReeA9eC3wF+p5nLQz5zoVf78Cn6Vq37KfSF+fcAkk/wbh8+Rd92gN9p5vKQT/A7yXmg5+5a6O48kM+88By4Fvwd8HfJPOQzF7o7D1T3U+juPJBP8GfJPNSHT6G7ecgn+LvkPkC/ux8J3d0HyCf412Qe+t33SOhuHvIJnnR3H6C6NaG7+wD5dBtlfagJvtqHmtDdPOTTBb10Huh39yOhu/NAPsFvJ/PQf/cfCd3NQz7Bn/7OzQPVrQndnQfy6TbK+lAT/Cnwp8k85NMVursP0HN3LXR3HyCfeeE5cC346ud+LXQ3D/nMhe7uA1T3U+juPkA+wb8m81AfPoXu5iGf4N1z4FqcG9vAb//OzQP5zAvPgWvBnwJ/msxDPnOhu/NAdT+F7s4D+QS/ncxDffgUupuHfIInvXoP0D74+t6g+r27LfSqz1DUrfoMD8ruAdqC3wF+p5nLQz5DoVf78Cj6Vq37KPSF+feAk8+msA+Pgt8BfqeZy0M+m4Oye4C24M+AP0vOA/kMD8ruAdqCvwP+LpmHfIZCd+eB6j4K3Z0H8tkU9uFR8HfA3yXzkM9G6NVz4Pvh1+dG9bkLnvSqT+vw67pVn9Zh2TnwXfA7wO80c3nIpyX0ah+uRN+qda+EXvWZibpVn1lhH64EvwP8TjOXh3xmh2XnwHfBnwF/lpwH8mkdlp0D3wV/B/xdMg/5tITuzgPVvRK6Ow/kMyvsw5Xg74C/S+Yhn9lh2T0A/Xucvne3he7uA+QzPCi7B2gLvvq9ry10Nw/5DIXu7gNU91Ho7j5APpvCPjwKvtqHR6G7echnc1B2D9AW/Dbw279z80A+w4Oye4C24E+BP03mIZ+h0N15oLqPQnfngXw2hX14FPwp8KfJPOSzEbq7D9BzF/xrch8gn9Zh2TnwXfDVzz140t085NMSursPUN0robv7APnMCvtwJfhqH66E7uYhn9lh2TnwXfDbwG//zs0D+bQOy86B74I/Bf40mYd8WkJ354HqXgndnQfymRX24Urwp8CfJvOQzwz00vcB6Hf3I6G77wOQT/C3yTz0u++R0N085BP8S/J9AKpbE7r7PgD5dBtlfagJ/gX4l2Qe8umCXjoP9Lv7kdDdeSCf4N0/B4B86Hd/I6G7ecgneNLdeaC6NaG780A+3UZZH2qC3wJ+6ziXh3y6QnffB6Dn7lro7vsA5DMvPAeuBf8C/EsyD/nMhe6+D0B1P4Xuvg9APsHfJvNQHz6F7uYhn+BfkvNAz9210N15IJ954TlwLfgt4LeOc3nIZy50dx6o7qfQ3Xkgn+DdPnyKvm0Bv3Wcy0M+wW8l9wH63f1I6O4+QD7BnyTz0O++R0J385BP8DfJfYDq1oTu7gPk022U9aEm+Bvgb5J5yKcLeuk80O/uR0J354F8gn9O5qE/B2AkdDcP+QRPujsPVLcmdHceyKfbKOtDTfDVPtSE7uYhn67Q3X2Anrtrobv7APnMC8+Ba8HfAH+TzEM+c6G7+wDV/RS6uw+QT/AnyTzUh0+hu3nIJ/ib5DzQc3ctdHceyGdeeA5cC756DlwL3c1DPnOhu/NAdT+F7s4D+QT/nMxDffgUupuHfIIn3X0fgL53t4Xuvg9APsODsnuAtuBfgH9J5iGfodDd9wGo7qPQ3fcByGdT2IdHwb8A/5LMQz6bg7J7gLbgq9+720J354F8hgdl9wBtwW8Bv3Wcy0M+Q6G780B1H4XuzgP5bAr78Cj4LeC3jnN5yGcjdPd9AHrugr9Nvg9APq3DsnPgu+BfgH9J5iGfltDd9wGo7pXQ3fcByGdW2Icrwb8A/5LMQz6zw7Jz4Lvgq89d8KS780A+rcOyc+C74LeA3zrO5SGfltDdeaC6V0J354F8ZoV9uBL8FvBbx7k85DM7LLsHoH+P0/futtDdfYB8hgdl9wBtwd8Af5PMQz5Dobv7ANV9FLq7D5DPprAPj4K/Af4mmYd8Ngdl9wBtwT8D/5ycB/IZHpTdA7QFX70HaAvdzUM+Q6G780B1H4XuzgP5bAr78Cj4ah8ehe7mIZ+N0N19gJ674E+S+wD5tA7LzoHvgr8B/iaZh3xaQnf3Aap7JXR3HyCfWWEfrgR/A/xNMg/5zA7LzoHvgn8G/jk5D+TTOiw7B74LvnoOBE+6m4d8WkJ354HqXgndnQfymRX24Urw1T5cCd3NQz4zof/r//9//rcGPf55Dfza9Pmvn1/XXQO/TuYJPvyqPOluHvKJfya92ofmz6/7tgZ+bfpcirpr4NfJPNSHptDdPORzCXrpPATfBL75MzcP5BNcM5kn+EvgL5N5yCf4y5+5eaC6TaG780A+lz/L+tAU/CXwl8k85HMp9Oo58CDOjTXwa9NnJequgV8n89Dn/iB0Nw/5rIRe7cOfR1/3bQ382vRpHH1ddw38OpmH+hA86W4e8mkclZ0DD+LcaALf/JmbB/JZFZ4DD4K/BP4ymYd8VkJ354HqBt/8mZsH8mkclfXhT8FfAn+ZzEM+DdBL94HgH4B/SO4D5BP8QzJP8CvgV8k85BP8KrkPUN2m0N19gHwuf5b1oSn4FfCrZB7yuQS9dB6Cj+esypPuzgP5BF/VS/ME3wC+cZTLQz7Bk+7OA9VtCt2dB/K5/FnWh6bgG8A3jnJ5yOdS6O4+QM/dg9DdfYB8VoXnwIPgV8CvknnIZyV0dx+gusE/JPcB8mkclfXhT8GvgF8l85BP46jsHHgQ50b1uXsQujsP5LMqPAceBN8AvnGUy0M+K6G780B1459Jd+eBfBpHZX34U/AN4BtHuTzk0xB69R7gQtwbrIFfmz4TUXcN/DqZh773XQjdzUM+E6FX+/Au+rYGfm36/Oevr+uugV8n81Af3oXu5iGf4N17gAtxb9AEvvkzNw/kMym8B7gQ/CXwl8k85DMRujsPVPdd6O48kE/wzWQe6sO70N085BM86dVzoP7r63NjDfza9DkXddfAr5N56HOvC93NQz7nQq/24V70bQ382vRZiLpr4NfJPNSHe6G7echn8avsHKgLvgl882duHsjn/FfZOVAX/CXwl8k85HMudHceqO690N15IJ9FYR/uBX8J/GUyD/ksfpXdA1yIe4MH4B+S+wD5TArvAS4EvwJ+lcxDPhOhu/sA1X0XursPkE/wD8k81Id3obt5yCf4VXIe6Hv3hdDdeSCfSeE9wIXgG8A3jnJ5yGcidHceqO670N15IJ/g3T68i741gG8c5fKQT/Cku/sAPXd1obv7APmc/yo7B+qCXwG/SuYhn3Ohu/sA1b0XursPkM+isA/3gl8Bv0rmIZ/Fr7JzoC746nNXF7o7D+Rz/qvsHKgLvgF84yiXh3zOhe7OA9W9F7o7D+SzKOzDveAbwDeOcnnIZwF66fsAwV8Af5F8H4B8gr9I5gl+AvwkmYd8gp8k3weguk2hu+8DkM/lz7I+NAU/AX6SzEM+l6CXzkPw78C/J+eBfIJ/T+YJ/j9//T1PupuHfIIn3Z0HqtsUujsP5HP5s6wPTcFX+9AUupuHfC6F7r4PQM/dg9Dd9wHIZ1V4DjwIfgL8JJmHfFZCd98HoLrBXyTfByCfxlFZH/4U/AT4STIP+TSOys6BB3FuvAP/npwH8lkVngMPgq+eAw9Cd/OQz0ro7jxQ3eDfk/NAPo2jsj78KfhqH4In3c1DPg3QS/eB4OvA100f+vcg+QRfT+YJ/hz482Qe8gn+3OxDU+xLdeDrps+lqFsHvp7MQ31oCt3NQz6XoJfOQ/D3wN8n54F8gr9P5gl+AfwimYd8gl8k54HqNoXuzgP5XP4s60NT8AvgF8k85HMpdHcfoOfuQejuPkA+q8Jz4EHw58CfJ/OQz0ro7j5AdYOvmz4NsW/Uga8n81Afgj9P5iGfxlHZOfAgzo174O+T80A+q8Jz4EHwC+AXyTzksxK6Ow9UN/j75DyQT+OorA9/Cn4B/CKZh3waQnffB6Dv3RdCd98HIJ9J4T3AheAnwE+SechnInT3fQCq+y50930A8gn+IpmH+vAudDcP+QQ/Sc4Dfe++ELo7D+QzKbwHuBB89d/7F0J385DPROjuPFDdd6G780A+wb8n81Af3oXu5iGf+GfS3fcB6LmrC919H4B8zn+VnQN1wU+AnyTzkM+50N33AajuvdDd9wHIZ1HYh3vBT4CfJPOQz+JX2TlQF/w78O/JeSCf819l50Bd8NVzoC50Nw/5nAvdnQeqey90dx7IZ1HYh3vBV/twL3Q3D/ksfpXdA1yIe4M68HXTZyLq1oGvJ/PQ974Lobt5yGcidHcfoLrvQnf3AfIJrp7MQ314F7qbh3yCP0/OA33vvhC6Ow/kMym8B7gQ/AL4RTIP+UyE7s4D1X0XujsP5BP8fTIP9eFd6G4e8gl+YZ4DdfF9oQ583fQ5F3XrwNeTeehzrwvdzUM+50J39wGqey90dx8gn0VhH+4Ffw78eTIP+Sx+lZ0DdcHfA3+fnAfyOf9Vdg7UBb8AfpHMQz7nQnfngereC92dB/JZFPbhXvAL4BfJPOSzEHr1zwH4j+bf6/S7++DXps9O8+u6a+DXyTz0u+/gSXfzkM+O0Kt9OBN9WwO/Nn3uRN018OtkHurDmdDdPORzB3rpPNDv7oNv/szNA/nsgF6ah373F/xlMg/57AjdnQeqeyZ0dx7I566wD2eCvwT+MpmHfO6EXj0HXsW5sQZ+bfr8+++v666BXyfz0Of+KnQ3D/kET3q1D9u/v+7bGvi16XMq6q6BXyfzUB+2he7mIZ/T32XnwKs4N5rAN3/m5oF8gm8m89A58Cp0Nw/5BH/5MzcPVHdb6O48kM/p77I+bAv+EvjLZB7yOQW9dB+g390H/5DcB8hnB/TSPPS77+BXyTzksyN0dx+gumdCd/cB8rkr7MOZ4FfAr5J5yOcO9NJ5oN/dB0+6Ow/kswN6aR767/7BN45yechnR+juPFDdM6G780A+d4V9OBN8A/jGUS4P+dwJ3d0H6Ll7Fbq7D5BP8A/JPPS5vwrdzUM+wa+S+wDV3Ra6uw+Qz+nvsj5sC34F/CqZh3xOf5edA6/i3Kg+d69Cd+eBfIJ3z4FXcW40gG8c5fKQT/Cku/NAdbeF7s4D+Zz+LuvDtuAbwDeOcnnI51To1XuAW3FvsAZ+bfq8iLpr4NfJPPS971bobh7yeRF6tQ9/HH/dtzXwa9Nn6/jrumvg18k81IfgSXfzkM/Wcdk9wK24N2gC3/yZmwfyeSm8B7gV/CXwl8k85PMidHceqG7wzZ+5eSCfreOyPvwh+EvgL5N5yGdL6NVz4EScG2vg16bPjai7Bn6dzEOf+4nQ3TzkcyP0ah+eRd/WwK9Nn3/899d118Cvk3moD89Cd/OQT/DuOXAizo0m8M2fuXkgn5vCc+BE8JfAXybzkM+N0N15oLrPQnfngXyCbybzUB+ehe7mIZ/gL5P7AH3vvhW6uw+Qz0vhPcCt4FfAr5J5yOdF6O4+QHWDf0juA+SzdVzWhz8EvwJ+lcxDPlvHZfcAt+LeoPq9+1bo7jyQz0vhPcCt4BvAN45yecjnRejuPFDd4El354F8to7L+vCH4BvAN45yechnS+juPkDP3YnQ3X2AfG4Kz4ETwa+AXyXzkM+N0N19gOo+C93dB8gn+IdkHurDs9DdPOQT/Co5D/TcnQjdnQfyuSk8B04E3wC+cZTLQz43Qnfngeo+C92dB/IJ3u3Ds+hbA/jGUS4P+QTfSL4PQL+7D/4i+T4A+eyAXpqHfvcd/CSZh3x2hO6+D0B1z4Tuvg9APneFfTgT/AT4STIP+dyBXjoP9Lv74N+T80A+O6CX5qHf/QVPupuHfHaE7s4D1T0TujsP5HNX2IczwVf7cCZ0Nw/53AndfR+AnrtXobvvA5BP8BfJPPS5vwrdzUM+wU+S7wNQ3W2hu+8DkM/p77I+bAt+AvwkmYd8Tn+XnQOv4tx4B/49OQ/kE/x7Mg+dA69Cd/OQT/Cku/NAdbeF7s4D+Zz+LuvDtuCrfdgWupuHfE5BL90H6Hf3wddNnx2xb9SBryfz0O++gz9P5iGfHaG7+wDVPRO6uw+Qz11hH84Efw78eTIP+dyBXjoP9Lv74O+T80A+O6CX5qE/ByD4RTIP+ewI3Z0HqnsmdHceyOeusA9ngl8Av0jmIZ87obv7AD13r0J39wHyCb6ezEOf+6vQ3TzkE/y52YdtsS/Vga+bPqeibh34ejIP9WFb6G4e8jn9XXYOvIpz4x74++Q8kE/w98k8dA68Ct3NQz7BL5LzQHW3he7OA/mc/i7rw7bgF8AvknnI51To7vsA9L37Vuju+wDk81J4D3Ar+Anwk2Qe8nkRuvs+ANUN/iL5PgD5bB2X9eEPwU+AnyTzkM/Wcdk9wK24N3gH/j05D+TzUngPcCv46r/3b4Xu5iGfF6G780B1g39PzgP5bB2X9eEPwVf7EDzpbh7y2RK6+z4APXcnQnffByCfm8Jz4ETwE+AnyTzkcyN0930AqvssdPd9APIJ/iKZh/rwLHQ3D/kEP0nOAz13J0J354F8bgrPgRPBV8+BE6G7ecjnRujuPFDdZ6G780A+wb8n81AfnoXu5iGf4N17gFtxb1AHvm76vIi6deDryTz0ve9W6G4e8nkRursPUN3g66bPltg36sDXk3moD8GfJ/OQz9Zx2T3Arbg3uAf+PjkP5PNSeA9wK/gF8ItkHvJ5Ebo7D1Q3+PvkPJDP1nFZH/4Q/AL4RTIP+WwJ3d0H6Lk7Ebq7D5DPTeE5cCL4c+DPk3nI50bo7j5AdZ+F7u4D5BN8PZmH+vAsdDcP+QR/npwHeu5OhO7OA/ncFJ4DJ4JfAL9I5iGfG6G780B1n4XuzgP5BH+fzEN9eBa6m4d8gv//SjtD5Na5JAqzoEGzgKBZhJCQkJFrksh6WxASEjIy8iKEhISMjLwIISEhIyOjfwWD+k3VrfpyuqthTn11+lSndev65ioudXv/v9T/+e/vvP2dv+RJL33+8/V73dLH+FKP5jG+Ab5pc3nIx3jSyz60X7/3razbCv3p/B5w8rl+xfrQCr4BvmlzecjnCnp0HowfgR+T80A+xo/JPMYvwC/JPORj/JKcB6rbCt07D+Rz/Yr1oRX8AvySzEM+V6GX68BDrBvlc/cQeunzFnVLn3dwHXgIvgG+aXN5yOct9LIPn9+/962sazzpT+f3AJLP8TvWh0/BN8A3bS4P+Ry/Y+vAQ6wbI/Bjch7I5x1cBx6CX4BfknnI5y107zxQXePH5DyQz/E71odPwS/AL8k85HMEPbofMH4Hfk/uB8jH+D2Zx3i7h1XypHvzkI/xpHv3A1S3Fbp3P0A+169YH1rBl31ohe7NQz5X0KPzYHwNfH3KzQP5GF8n8xg/AD8k85CP8cMpNw9UtxW6dx7I5/oV60Mr+AH4IZmHfK5C9+4H6Ll7CN27HyCfd3AdeAi+/L0/hO7NQz5voXv3A1TX+D25HyCf43esD5+CL/tgPOnePORz/I6tAw+xbtTA16fcPJDPO7gOPAQ/AD8k85DPW+jeeaC6xten3DyQz/E71odPwQ/AD8k85HMUenkOcBHnBuXn7ovQS5+7qFv63IPnABfBN8A3bS4P+dyFXvbhJfpW1n0J/en8HnDyMd7bh5foWwN80+bykI/xTXIe6HP3RejeeSCfe/Ac4CL4BfglmYd87kL3zgPVfQndOw/kY/yYzEN9eAndm4d8jCe9XAcOP7+vG+VzdxB66XMWdUuf809sHTgIvgG+aXN5yOcs9LIPN9G3su5N6E/n/wEgn2ewDzfBN8A3bS4P+Tx/YuvAQfAj8GNyHsjn/BNbBw6CX4BfknnI5yx07zxQ3ZvQvfNAPs9gH26CX4BfknnI5/kTOwe4iHODHfg9uR8gn3vwHOAi+PJz30Xo3jzkcxe6dz9AdV9C9+4HyMf4PZmH+vASujcP+RjvPQe4iHODGvj6lJsH8rkHzwEugh+AH5J5yOcudO88UN2X0L3zQD7G18k81IeX0L15yMd40r37AXruDkL37gfI5/wTWwcOgi9/7wehe/OQz1no3v0A1b0J3bsfIJ9nsA83wZd9uAndm4d8nj+xdeAg+Br4+pSbB/I5/8TWgYPgB+CHZB7yOQvdOw9U9yZ07zyQzzPYh5vgB+CHZB7yeYIevQ9g/Az8nLwPQD7Gz8k8xm/Ab8k85GP8lrwPQHVboXvvA5DP9SvWh1bwG/BbMg/5XEGPzoPx9p5lyZPunQfyMb7Uo3mMr4Cvulwe8jGedO88UN1W6N55IJ/rV6wPreAr4Ksul4d8rkL33geg5+4hdO99APJ5B9eBh+A34LdkHvJ5C917H4DqGj8n7wOQz/E71odPwW/Ab8k85HP8jq0DD7FulM/dQ+jeeSCfd3AdeAi+Ar7qcnnI5y107zxQXeNJ984D+Ry/Y334FHwFfNXl8pDPEfTofsD4Hvg+uR8gH+P7ZB7jJ+CnZB7yMX5K7geobit0736AfK5fsT60gp+An5J5yOcKenQejF+BX5PzQD7Gr8k8xtv/Ayh50r15yOfv96z/yc0D1W2F7p0H8rl+xfrQCr7sQyt0bx7yuQrdux+g5+4hdO9+gHzewXXgIfgJ+CmZh3zeQvfuB6iu8X1yP0A+x+9YHz4FPwE/JfOQz/E7tg48xLqxAr8m54F83sF14CH4ch14CN2bh3zeQvfOA9U1fk3OA/kcv2N9+BR82Ye/3+P7J5eHfI5C994HoM/dF6F77wOQzz14DnAR/Ab8lsxDPnehe+8DUN2X0L33AcjH+DmZh/rwEro3D/kYvyXngT53X4TunQfyuQfPAS6Cr4Cvulwe8rkL3TsPVPcldO88kI/x3j68RN8q4Ksul4d8jCfdex+AnruD0L33Acjn/BNbBw6C34DfknnI5yx0730AqnsTuvc+APk8g324CX4DfkvmIZ/nT2wdOAi+fO4OQvfOA/mcf2LrwEHwFfBVl8tDPmehe+eB6t6E7p0H8nkG+3ATfAV81eXykM/zJ3YOcBHnBj3wfXI/QD734DnARfAT8FMyD/nche7dD1Ddl9C9+wHyMb5P5qE+vITuzUM+xk/JeaDP3Rehe+eBfO7Bc4CL4MtzgIvQvXnI5y507zxQ3ZfQvfNAPsavyTzUh5fQvXnI5+//ef+T2w/Qc3cQunc/QD7nn9g6cBD8BPyUzEM+Z6F79wNU9yZ0736AfJ7BPtwEPwE/JfOQz/Mntg4cBL8CvybngXzOP7F14CD4ch04CN2bh3zOQvfOA9W9Cd07D+TzDPbhJviyDzehe/OQz1Po5f8B+Dd8jzO9d28/k176NO3vdUufBvRoHnrv27imzeUhn0boZR9G0bey7ij0p/N7wMlnCfZhFHwDfNPm8pDPAnp0Hui9e+PH5DyQTwN6NA+992f8ksxDPo3QvfNAdUehe+eBfJZgH0bBL8AvyTzkswi9XAd2sW6Uz90u9NLnX6ff65Y+xnvXgV2sGw3wTZvLQz7Gk172oT793reybi30p/N7AMlnOMX6UAu+Ab5pc3nIZzjF1oFdrBsj8GNyHsjH+DGZh9aBXejePORj/JKcB6pbC907D+QznGJ9qAW/AL8k85DPAHp0P0Dv3Ru/J/cD5NOAHs1D730bT7o3D/k0QvfuB6juKHTvfoB8lmAfRsGXfRiF7s1DPgvo0Xmg9+6Nr0+5eSCfBvRoHvq7v/FDMg/5NEL3zgPVHYXunQfyWYJ9GAU/AD8k85DPInTvfoCeu13o3v0A+Ri/J/PQ730XujcP+djPpHv3A1S3Frp3P0A+wynWh1rwZR9qoXvzkM9wiq0Du1g3auDrU24eyMe4OpmH1oFd6N485GP8cMrNA9Wthe6dB/IZTrE+1IIfgB+SechnEHp5DjCLc4Pyc/cs9NJnE3VLny14DjALvgG+aXN5yGcTetmHj+73vpV1jSf96fwecPKpulgfPgTfAN+0uTzkU3Wxc4BZnBuMwI/JeSCfLXgOMAt+AX5J5iGfTejeeaC6xo/JeSCfqov14UPwC/BLMg/5VEIv14FerBvlc9cLvfSZRN3SZwquA73gG+CbNpeHfCahl31YRd/KuqvQSx+7B0B1Sx/jvX1YRd8a4Js2l4d8jG+S80DPXS907zyQzxRcB3rBL8AvyTzkMwndOw9UdxW6dx7Ix/gxmYf6sArdm4d8jF+S+wH63D0L3bsfIJ8teA4wC7783DcL3ZuHfDahe/cDVNf4PbkfIJ+qi/XhQ/BlH4wn3ZuHfKoudg4wi3ODGvj6lJsH8tmC5wCz4Afgh2Qe8tmE7p0Hqmt8fcrNA/lUXawPH4IfgB+SecinErp3P0DPXS90736AfKbgOtALvvy990L35iGfSeje/QDVXYXu3Q+Qj/F7Mg/1YRW6Nw/5GO9dB3qxbtTA16fcPJDPFFwHesEPwA/JPOQzCd07D1R3Fbp3HsjH+DqZh/qwCt2bh3yMH5L3Aei9e+Pn5H0A8mlAj+ah976N35J5yKcRuvc+ANUdhe69D0A+S7APo+A34LdkHvJZQI/OA713bzzp3nkgnwb0aB5678/4qsvlIZ9G6N55oLqj0L3zQD5LsA+j4Cvgqy6Xh3wWoXvvA9Bztwvdex+AfIyfk3no974L3ZuHfIzfkvcBqG4tdO99APIZTrE+1ILfgN+SechnOMXWgV2sG+VztwvdOw/kY7x3HdjFulEBX3W5PORjPOneeaC6tdC980A+wynWh1rwFfBVl8tDPgPo0f0AvXdvfJ/cD5BPA3o0D733bfyUzEM+jdC9+wGqOwrdux8gnyXYh1HwE/BTMg/5LKBH54Heuzd+Tc4D+TSgR/PQ/wH4+3f0P7k85NMI3TsPVHcUunceyGcJ9mEUfNmHUejePOSzCN27H6Dnbhe6dz9APsb3yTz0e9+F7s1DPsZPyf0A1a2F7t0PkM9wivWhFvwE/JTMQz7DKbYO7GLdWIFfk/NAPsavyTy0DuxC9+Yhn7/n7H9y80B1a6F754F8hlOsD7Xgyz7UQvfmIZ9B6N77APS5exa69z4A+WzBc4BZ8BvwWzIP+WxC994HoLrGz8n7AORTdbE+fAh+A35L5iGfqoudA8zi3KD83D0L3TsP5LMFzwFmwVfAV10uD/lsQvfOA9W1n0n3zgP5VF2sDx+Cr4Cvulwe8qmE7r0PQM9dL3TvfQDymYLrQC/4DfgtmYd8JqF77wNQ3VXo3vsA5GP8nMxDfViF7s1DPsZvyXmg564XunceyGcKrgO94Cvgqy6Xh3wmoXvngequQvfOA/kY7+3DKvpWAV91uTzkY3yV3A/Q5+5Z6N79APlswXOAWfAT8FMyD/lsQvfuB6iu8X1yP0A+VRfrw4fgJ+CnZB7yqbrYOcAszg1W4NfkPJDPFjwHmAVfngPMQvfmIZ9N6N55oLrGr8l5IJ+qi/XhQ/BlH/6u439yecinErp3P0DPXS90736AfKbgOtALfgJ+SuYhn0no3v0A1V2F7t0PkI/xfTIP9WEVujcP+Rg/JeeBnrte6N55IJ8puA70gi/XgV7o3jzkMwndOw9UdxW6dx7Ix/g1mYf6sArdm4d8/v78f/1/SGEt+Q== 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EZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//A3532BvAAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH39D/TfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/y3zBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3zH9LfAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8DfvfIm8AAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff8P9t8AfgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//hXcEIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfCf6E9AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+F9wVwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/I/0X2BeAQAQkMEEIRlCCE4JQhCQ0YQhHWMITgUhEJDJRiEZUohODWMQkNnGIR1zik4BEJCQxSUhGUpKTglSkJDVpSEda0pOBTGQkM1nIRlayk4Nc5CQ3echHXvJTgEIUpDBFKEZRilOCUpSkNGUoR1nKU4FKVKQyVahGVapTg1rUpDZ1qEdd6tOARjSkMU1oRlOa04JWtKQ1bWhHW9rTgU50pDNd6EZXutODXvSkN33oR1/6M4BBDGQwQxjGUIYzglGMZDRjGMdYxjOBSUxkMlOYxlSmM4NZzGQ2c5jHXOazgEUsZDFLWMZSlrOCVaxkNWtYx1rWs4FNbGQzW9jGVrazg13sZDd72Mde9nOAQxzkMEc4xlGOc4JTnOQ0ZzjHWc5zgUtc5DJXuMZVrnODW9zkNne4x13u84BHPOQxT3jGU57zgle85DVveMdb3vOBT3zkM1/4xle+84Nf/OQ3f/jH3/+FdQYgEAEJTBCCEZTghCAUIQlNGMIRlvBEIBIRiUwUohGV6MQgFjGJTRziEZf4JCARCUlMEpKRlOSkIBUpSU0a0pGW9GQgExnJTBaykZXs5CAXOclNHvKRl/wUoBAFKUwRilGU4pSgFCUpTRnKUZbyVKASFalMFapRlerUoBY1qU0d6lGX+jSgEQ1pTBOa0ZTmtKAVLWlNG9rRlvZ0oBMd6UwXutGV7vSgFz3pTR/60Zf+DGAQAxnMEIYxlOGMYBQjGc0YxjGW8UxgEhOZzBSmMZXpzGAWM5nNHOYxl/ksYBELWcwSlrGU5axgFStZzRrWsZb1bGATG9nMFraxle3sYBc72c0e9rGX/RzgEAc5zBGOcZTjnOAUJznNGc5xlvNc4BIXucwVrnGV69zgFje5zR3ucZf7POARD3nME57xlOe84BUvec0b3vGW93zgEx/5zBe+8ZXv/OAXP/nNH/7xN8p/Ib0BCERAAhOEYAQlOCEIRUhCE4ZwhCU8EYhERCIThWhEJToxiEVMYhOHeMQlPglIREISk4RkJCU5KUhFSlKThnSkJT0ZyERGMpOFbGQlOznIRU5yk4d85CU/BShEQQpThGIUpTglKEVJSlOGcpSlPBWoREUqU4VqVKU6NahFTWpTh3rUpT4NaERDGtOEZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//I36X1B3AAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH3+j/RfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/5VzBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3xn+lPAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8jflfMVcAAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff2P9V8gXgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//lXEGIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfOfyW8AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+18RdwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/+b/4zj+BCEhgghCMoAQnBKEISWjCEI6whCfC/zdMD9liIAAAwPo6tW3bNn5t27Zt27Zt27Zt27YnixwhRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTQf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMh//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU2H/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTEf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNR//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUzH/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTcf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMJ//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU0n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTKf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNp//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUxn/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTWf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlM5//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU3n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTBf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNF//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUyX/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTZf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMV//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU1X/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTNf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlN1//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUw3/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTTf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMt//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU23/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTHf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNd//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwkU4D//EZggBCU4wQhBSEITijCEJTzhiEBEIhOJKEQlOtGIQUxiE4s4xCU+8UhAQhKTiCQkJTnJSEFKUpOKNKQlPenIQEYyk4ksZCU72chBTnKTizzkJR8B5KcAhShIYYpQjKIUpwSlKElpylCOspSnApWoSGWqUI2qVKcGtahJbepQj7rUpwGNaEhjmtCMpjSnBa1oSWva0I62tKcDnehIZ7rQja50pwe96Elv+tCPvvRnAIMYyGCGMIyhDGcEoxjJaMYwjrGMZwKTmMhkpjCNqUxnBrOYyWzmMI+5zGcBi1jIYpawjKUsZwWrWMlq1rCOtaxnA5vYyGa2sI2tbGcHu9jJbvawj73s5wCHOMhhjnCMoxznBKc4yWnOcI6znOcCl7jIZa5wjatc5wa3uMlt7nCPu9znAY94yGOe8IynPOcFr3jJa97wjre85wOf+MhnvvCNr3znB7/4yW/+8I+/BMrnP/8RmCAEJTjBCEFIQhOKMIQlPOGIQEQiE4koRCU60YhBTGITizjEJT7xSEBCEpOIJCQlOclIQUpSk4o0pCU96chARjKTiSxkJTvZyEFOcpOLPOQlHwHkpwCFKEhhilCMohSnBKUoSWnKUI6ylKcClah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+ + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-000000.pvtu b/tests/automatic_regression_test/gold_allenCahn/solution-000000.pvtu new file mode 100644 index 000000000..aa9bb46cb --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-000000.pvtu @@ -0,0 +1,18 @@ + + + + + + + + + + + + + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-002000.0.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-002000.0.vtu new file mode 100644 index 000000000..751edf51a --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-002000.0.vtu @@ -0,0 +1,32 @@ + + + + + + + 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+AQAAAAAAAQAAAAEAxFgAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-002000.1.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-002000.1.vtu new file mode 100644 index 000000000..d4d53fbb6 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-002000.1.vtu @@ -0,0 +1,32 @@ + + + + + + + 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EZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//A3532BvAAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH39D/TfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/y3zBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3zH9LfAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8DfvfIm8AAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff8P9t8AfgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//hXcEIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfCf6E9AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+F9wVwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/I/0X2BeAQAQkMEEIRlCCE4JQhCQ0YQhHWMITgUhEJDJRiEZUohODWMQkNnGIR1zik4BEJCQxSUhGUpKTglSkJDVpSEda0pOBTGQkM1nIRlayk4Nc5CQ3echHXvJTgEIUpDBFKEZRilOCUpSkNGUoR1nKU4FKVKQyVahGVapTg1rUpDZ1qEdd6tOARjSkMU1oRlOa04JWtKQ1bWhHW9rTgU50pDNd6EZXutODXvSkN33oR1/6M4BBDGQwQxjGUIYzglGMZDRjGMdYxjOBSUxkMlOYxlSmM4NZzGQ2c5jHXOazgEUsZDFLWMZSlrOCVaxkNWtYx1rWs4FNbGQzW9jGVrazg13sZDd72Mde9nOAQxzkMEc4xlGOc4JTnOQ0ZzjHWc5zgUtc5DJXuMZVrnODW9zkNne4x13u84BHPOQxT3jGU57zgle85DVveMdb3vOBT3zkM1/4xle+84Nf/OQ3f/jH3/+FdQYgEAEJTBCCEZTghCAUIQlNGMIRlvBEIBIRiUwUohGV6MQgFjGJTRziEZf4JCARCUlMEpKRlOSkIBUpSU0a0pGW9GQgExnJTBaykZXs5CAXOclNHvKRl/wUoBAFKUwRilGU4pSgFCUpTRnKUZbyVKASFalMFapRlerUoBY1qU0d6lGX+jSgEQ1pTBOa0ZTmtKAVLWlNG9rRlvZ0oBMd6UwXutGV7vSgFz3pTR/60Zf+DGAQAxnMEIYxlOGMYBQjGc0YxjGW8UxgEhOZzBSmMZXpzGAWM5nNHOYxl/ksYBELWcwSlrGU5axgFStZzRrWsZb1bGATG9nMFraxle3sYBc72c0e9rGX/RzgEAc5zBGOcZTjnOAUJznNGc5xlvNc4BIXucwVrnGV69zgFje5zR3ucZf7POARD3nME57xlOe84BUvec0b3vGW93zgEx/5zBe+8ZXv/OAXP/nNH/7xN8p/Ib0BCERAAhOEYAQlOCEIRUhCE4ZwhCU8EYhERCIThWhEJToxiEVMYhOHeMQlPglIREISk4RkJCU5KUhFSlKThnSkJT0ZyERGMpOFbGQlOznIRU5yk4d85CU/BShEQQpThGIUpTglKEVJSlOGcpSlPBWoREUqU4VqVKU6NahFTWpTh3rUpT4NaERDGtOEZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//I36X1B3AAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH3+j/RfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/5VzBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3xn+lPAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8jflfMVcAAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff2P9V8gXgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//lXEGIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfOfyW8AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+18RdwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/+b/4zj+BCEhgghCMoAQnBKEISWjCEI6whCfC/zdMD9liIAAAwPo6tW3bNn5t27Zt27Zt27Zt27YnixwhRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTQf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMh//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU2H/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTEf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNR//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUzH/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTcf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMJ//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU0n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTKf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNp//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUxn/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTWf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlM5//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU3n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTBf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNF//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUyX/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTZf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMV//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU1X/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTNf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlN1//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUw3/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTTf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMt//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU23/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTHf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNd//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwkU4D//EZggBCU4wQhBSEITijCEJTzhiEBEIhOJKEQlOtGIQUxiE4s4xCU+8UhAQhKTiCQkJTnJSEFKUpOKNKQlPenIQEYyk4ksZCU72chBTnKTizzkJR8B5KcAhShIYYpQjKIUpwSlKElpylCOspSnApWoSGWqUI2qVKcGtahJbepQj7rUpwGNaEhjmtCMpjSnBa1oSWva0I62tKcDnehIZ7rQja50pwe96Elv+tCPvvRnAIMYyGCGMIyhDGcEoxjJaMYwjrGMZwKTmMhkpjCNqUxnBrOYyWzmMI+5zGcBi1jIYpawjKUsZwWrWMlq1rCOtaxnA5vYyGa2sI2tbGcHu9jJbvawj73s5wCHOMhhjnCMoxznBKc4yWnOcI6znOcCl7jIZa5wjatc5wa3uMlt7nCPu9znAY94yGOe8IynPOcFr3jJa97wjre85wOf+MhnvvCNr3znB7/4yW/+8I+/BMrnP/8RmCAEJTjBCEFIQhOKMIQlPOGIQEQiE4koRCU60YhBTGITizjEJT7xSEBCEpOIJCQlOclIQUpSk4o0pCU96chARjKTiSxkJTvZyEFOcpOLPOQlHwHkpwCFKEhhilCMohSnBKUoSWnKUI6ylKcClahIZapQjapUpwa1qElt6lCPutSnAY1oSGOa0IymNKcFrWhJa9rQjra0pwOd6EhnutCNrnSnB73oSW/60I++9GcAgxjIYIYwjKEMZwSjGMloxjCOsYxnApOYyGSmMI2pTGcGs5jJbOYwj7nMZwGLWMhilrCMpSxnBatYyWrWsI61rGcDm9jIZrawja1sZwe72Mlu9rCPveznAIc4yGGOcIyjHOcEpzjJac5wjrOc5wKXuMhlrnCNq1znBre4yW3ucI+73OcBj3jIY57wjKc85wWveMlr3vCOt7znA5/4yGe+8I2vfOcHv/jJb/7wj7/8D5W4wko= 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-002000.2.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-002000.2.vtu new file mode 100644 index 000000000..9078e6cf3 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-002000.2.vtu @@ -0,0 +1,32 @@ + + + + + + + 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-002000.3.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-002000.3.vtu new file mode 100644 index 000000000..9c4960312 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-002000.3.vtu @@ -0,0 +1,32 @@ + + + + + + + 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-002000.pvtu b/tests/automatic_regression_test/gold_allenCahn/solution-002000.pvtu new file mode 100644 index 000000000..b3b617db6 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-002000.pvtu @@ -0,0 +1,18 @@ + + + + + + + + + + + + + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-004000.0.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-004000.0.vtu new file mode 100644 index 000000000..962b1c08d --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-004000.0.vtu @@ -0,0 +1,32 @@ + + + + + + + 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+AQAAAAAAAQAAAAEAxFgAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-004000.1.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-004000.1.vtu new file mode 100644 index 000000000..f1e0b866c --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-004000.1.vtu @@ -0,0 +1,32 @@ + + + + + + + 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rN+48ePLizYfvb8sM/10/AYKECBMhSow4CZKkSJMhS448BYqUKFOhSo06DZq0aNOhS48+A4aMGDNhyow5C5asWLNhy449B46cOHPhyo07D568ePPhy8//0E+AICHCRIgSI06CJCnSZMiSI0+BIiXKVKhSo06DJi3adOjSo8+AISPGTJgyY86CJSvWbNiyY8+BIyfOXLhy486DJy/efPjy89f6CRAkRJgIUWLESZAkRZoMWXLkKVCkRJkKVWrUadCkRZsOXXr0GTBkxJgJU2bMWbBkxZoNW3bsOXDkxJkLV27cefDkxZsP37/+fZnnFwGChAgTIUqMOAmSpEiTIUuOPAWKlChToUqNOg2atGjToUuPPgOGjBgzYcqMOQuWrFizYcuOPQeOnDhz4cqNOw+evHjz4fvbItP/1E+AICHCRIgSI06CJCnSZMiSI0+BIiXKVKhSo06DJi3adOjSo8+AISPGTJgyY86CJSvWbNiyY8+BIyfOXLhy486DJy/efPjy87/0EyBIiDARosSIkyBJijQZsuTIU6BIiTIVqtSo06BJizYduvToM2DIiDETpsyYs2DJijUbtuzYc+DIiTMXrty48+DJizcfvvz8jX4CBAkRJkKUGHESJEmRJkOWHHkKFClRpkKVGnUaNGnRpkOXHn0GDBkxZsKUGXMWLFmxZsOWHXsOHDlx5sKVG3cePHnx5sP3b35f5PtFgCAhwkSIEiNOgiQp0mTIkiNPgSIlylSoUqNOgyYt2nTo0qPPgCEjxkyYMmPOgiUr1mzYsmPPgSMnzly4cuPOgycv3nz4/rbE+L/1EyBIiDARosSIkyBJijQZsuTIU6BIiTIVqtSo06BJizYduvToM2DIiDETpsyYs2DJijUbtuzYc+DIiTMXrty48+DJizcfvvz8H/0ECBIiTIQoMeIkSJIiTYYsOfIUKFKiTIUqNeo0aNKiTYcuPfoMGDJizIQpM+YsWLJizYYtO/YcOHLizIUrN+48ePLizYcvP3+rnwBBQoSJECVGnARJUqTJkCVHngJFSpSpUKVGnQZNWrTp0KVHnwFDRoyZMGXGnAVLVqzZsGXHngNHTpy5cOXGnQdPXrz58P3b35d4fxEgSIgwEaLEiJMgSYo0GbLkyFOgSIkyFarUqNOgSYs2Hbr06DNgyIgxE6bMmLNgyYo1G7bs2HPgyIkzF67cuPPgyYs3H76/LTD/X/0ECBIiTIQoMeIkSJIiTYYsOfIUKFKiTIUqNeo0aNKiTYcuPfoMGDJizIQpM+YsWLJizYYtO/YcOHLizIUrN+48ePLizYcvP/9PPwGChAgTIUqMOAmSpEiTIUuOPAWKlChToUqNOg2atGjToUuPPgOGjBgzYcqMOQuWrFizYcuOPQeOnDhz4cqNOw+evHjz4cvP3+knQJAQYSJEiREnQZIUaTJkyZGnQJESZSpUqVGnQZMWbTp06dFnwJARYyZMmTFnwZIVazZs2bHnwJETZy5cuXHnwZMXbz58/+73Bf7/Dz4v2Mg= 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-004000.2.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-004000.2.vtu new file mode 100644 index 000000000..52665aa4d --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-004000.2.vtu @@ -0,0 +1,32 @@ + + + + + + + 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EZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//A3532BvAAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH39D/TfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/y3zBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3zH9LfAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8DfvfIm8AAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff8P9t8AfgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//hXcEIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfCf6E9AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+F9wVwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/I/0X2BeAQAQkMEEIRlCCE4JQhCQ0YQhHWMITgUhEJDJRiEZUohODWMQkNnGIR1zik4BEJCQxSUhGUpKTglSkJDVpSEda0pOBTGQkM1nIRlayk4Nc5CQ3echHXvJTgEIUpDBFKEZRilOCUpSkNGUoR1nKU4FKVKQyVahGVapTg1rUpDZ1qEdd6tOARjSkMU1oRlOa04JWtKQ1bWhHW9rTgU50pDNd6EZXutODXvSkN33oR1/6M4BBDGQwQxjGUIYzglGMZDRjGMdYxjOBSUxkMlOYxlSmM4NZzGQ2c5jHXOazgEUsZDFLWMZSlrOCVaxkNWtYx1rWs4FNbGQzW9jGVrazg13sZDd72Mde9nOAQxzkMEc4xlGOc4JTnOQ0ZzjHWc5zgUtc5DJXuMZVrnODW9zkNne4x13u84BHPOQxT3jGU57zgle85DVveMdb3vOBT3zkM1/4xle+84Nf/OQ3f/jH3/+FdQYgEAEJTBCCEZTghCAUIQlNGMIRlvBEIBIRiUwUohGV6MQgFjGJTRziEZf4JCARCUlMEpKRlOSkIBUpSU0a0pGW9GQgExnJTBaykZXs5CAXOclNHvKRl/wUoBAFKUwRilGU4pSgFCUpTRnKUZbyVKASFalMFapRlerUoBY1qU0d6lGX+jSgEQ1pTBOa0ZTmtKAVLWlNG9rRlvZ0oBMd6UwXutGV7vSgFz3pTR/60Zf+DGAQAxnMEIYxlOGMYBQjGc0YxjGW8UxgEhOZzBSmMZXpzGAWM5nNHOYxl/ksYBELWcwSlrGU5axgFStZzRrWsZb1bGATG9nMFraxle3sYBc72c0e9rGX/RzgEAc5zBGOcZTjnOAUJznNGc5xlvNc4BIXucwVrnGV69zgFje5zR3ucZf7POARD3nME57xlOe84BUvec0b3vGW93zgEx/5zBe+8ZXv/OAXP/nNH/7xN8p/Ib0BCERAAhOEYAQlOCEIRUhCE4ZwhCU8EYhERCIThWhEJToxiEVMYhOHeMQlPglIREISk4RkJCU5KUhFSlKThnSkJT0ZyERGMpOFbGQlOznIRU5yk4d85CU/BShEQQpThGIUpTglKEVJSlOGcpSlPBWoREUqU4VqVKU6NahFTWpTh3rUpT4NaERDGtOEZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//I36X1B3AAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH3+j/RfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/5VzBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3xn+lPAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8jflfMVcAAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff2P9V8gXgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//lXEGIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfOfyW8AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+18RdwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/+b/4zj+BCEhgghCMoAQnBKEISWjCEI6whCfC/zdMD9liIAAAwPo6tW3bNn5t27Zt27Zt27Zt27YnixwhRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTQf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMh//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU2H/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTEf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNR//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUzH/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTcf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMJ//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU0n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTKf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNp//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUxn/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTWf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlM5//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU3n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTBf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNF//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUyX/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTZf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMV//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU1X/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTNf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlN1//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUw3/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTTf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMt//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU23/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTHf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNd//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwkU4D//EZggBCU4wQhBSEITijCEJTzhiEBEIhOJKEQlOtGIQUxiE4s4xCU+8UhAQhKTiCQkJTnJSEFKUpOKNKQlPenIQEYyk4ksZCU72chBTnKTizzkJR8B5KcAhShIYYpQjKIUpwSlKElpylCOspSnApWoSGWqUI2qVKcGtahJbepQj7rUpwGNaEhjmtCMpjSnBa1oSWva0I62tKcDnehIZ7rQja50pwe96Elv+tCPvvRnAIMYyGCGMIyhDGcEoxjJaMYwjrGMZwKTmMhkpjCNqUxnBrOYyWzmMI+5zGcBi1jIYpawjKUsZwWrWMlq1rCOtaxnA5vYyGa2sI2tbGcHu9jJbvawj73s5wCHOMhhjnCMoxznBKc4yWnOcI6znOcCl7jIZa5wjatc5wa3uMlt7nCPu9znAY94yGOe8IynPOcFr3jJa97wjre85wOf+MhnvvCNr3znB7/4yW/+8I+/BMrnP/8RmCAEJTjBCEFIQhOKMIQlPOGIQEQiE4koRCU60YhBTGITizjEJT7xSEBCEpOIJCQlOclIQUpSk4o0pCU96chARjKTiSxkJTvZyEFOcpOLPOQlHwHkpwCFKEhhilCMohSnBKUoSWnKUI6ylKcClahIZapQjapUpwa1qElt6lCPutSnAY1oSGOa0IymNKcFrWhJa9rQjra0pwOd6EhnutCNrnSnB73oSW/60I++9GcAgxjIYIYwjKEMZwSjGMloxjCOsYxnApOYyGSmMI2pTGcGs5jJbOYwj7nMZwGLWMhilrCMpSxnBatYyWrWsI61rGcDm9jIZrawja1sZwe72Mlu9rCPveznAIc4yGGOcIyjHOcEpzjJac5wjrOc5wKXuMhlrnCNq1znBre4yW3ucI+73OcBj3jIY57wjKc85wWveMlr3vCOt7znA5/4yGe+8I2vfOcHv/jJb/7wj7/8D5W4wko= 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-004000.3.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-004000.3.vtu new file mode 100644 index 000000000..ed02d90ae --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-004000.3.vtu @@ -0,0 +1,32 @@ + + + + + + + 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-004000.pvtu b/tests/automatic_regression_test/gold_allenCahn/solution-004000.pvtu new file mode 100644 index 000000000..8118449a2 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-004000.pvtu @@ -0,0 +1,18 @@ + + + + + + + + + + + + + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-006000.0.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-006000.0.vtu new file mode 100644 index 000000000..ca2081f41 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-006000.0.vtu @@ -0,0 +1,32 @@ + + + + + + + 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+AQAAAAAAAQAAAAEAxFgAAA==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FVdRHTjW2lvrgokU1va8KL3ipnN5XBReFMeqjrnxmD/XBwwEJmhd9Eqlamhc8LKimPvpq7qmjPrh5rYbmhWcfqKB5wdUNO6iPusGNldQHNyt28nkd5b79LZ9XcPE29+0E93EE98F1lxSfV+eF56bovIKrvknqo27QKEV9cD0zSfPqGe7HT+B5wUVf7gdzOd/H1zyv9x5K0bzg0r3bso/v6pqXU9T3PNJXzKXv69d1fF4dl9trqI+c+LylqA9uXq2l8wrPnlVF5xVcP7ab+qhry6uoD24WldO86JM+P0nzgoe5tdRHX8ULSeqDi/vqaF549o9qmtfzehXUR13p3mWI+p6/XEnvq2Kd1Gdu5fdVHTePL6K+X1cPy6M+uH1+Ob2vCs++MpzeVwW3R7WiPuqaM37hvuOqvB3Ni3VS2gKaF1zev5r6mFMxpJj64Ob49TQvPD3wV5oXXHTtxn3UXfwH9cHNwH4Jdl8VfQr6p+Lsviq4jD1Ifd/X4lMTzAcPm3WMs/uq8IKVDWPsviq47PRJKfNRNyhWMeaD2693l7K8vq+/XJlgecGDrcPjzEdf7U9NEswH1zcui9O8zlNnz4vRvI7L12UZ833dMYfGmQ9uvltYxuYV62R4exmd1//NN1Mf65SaF6c+uNmeS+cVnh22gM4ruD5mCPVRN/jnMuqDGzOG5sU6qc/YQfOC6z0F1Me6Km7fRX1wM6qE5oWnylfTvODii2nU93WbbKS+54/NpfOKPulBMsHmFTwsLI8zHznDhu0SzAc3Lf+VYPMKT42/M87mFTz45soY81FXNukaZz54OKtfjOVFn8J7Pk2wvODmxkbU933Vg6kPbs95IsHywtPDZsZZXs97J2LM93XP2h6nPvi82+Jsv2oWuf3yXL5fBbc/cz/IcXxxFt9xPZXvV+GJD1N0vwoeXsJ91A37cB9c3Z0l7zr341bxvOByDvflFld3A/fB7WKeF174A88Lbl7iPuqK8dwHtypF96u+r98n6X4VXE2so77Piffgoj7egzsmSfer8IJ6tXy/6rjuVUV91DWzaqkPrr+qpnnRJ3tQiuYFF89wH30NLk9RH1wNT9K88NTddVnyun59VEN91A3n1lEfXN5TS+dV57lvb+XzCi5Xcz8sdr/eAu6D6418XuGp3/m8gpsB3EfdYCH3wf25RTRvzH1/Cc8LbnO5L7e7j3Hug4sinheezeF5wc007qNuuJH74GIBn1f0yTyUovNqoucJER85xVsp6oOL01J0Xr13SZLOK7itraU+6gbdktQH133qeF7Xp6BniuYFt7dn8V1f5VDug4etUzQvPL0syfM6Hh7DfdRVR6eoD27xHmV0v+rWSd2wnO5Xwc2BcepjnZJ1e6gPLg7YQfervm6QS/er4PafS6mPuvrEQup7PnoNzevXyU8raV5wNWQX992cmtwq6oMHheU0LzzbrZTmBQ8u2kx91FWPJ7jvuByYR/er6FNw0hi6X/V8ZTvqI6cZNIP64PraXgm2X/V1bzsrwfar4PKDXtRHXWPvSFDfcdloZZzmdX3S1/1J84Lb5kOp7/uaWkl9cIt3NaN5nSdqX06wvOC68kjqo65Y0T3BfHD1jxvpvGKdtIfX0Hn1fE0F9bFOBQfWUh9ctK+i8wrPDNxJ5xVcHlpCfdQN7y+nPrj9Ik7z+nVyZS3NC26WV1Mf62rYsY77jsvSGp7XeUHPCpoXXN++k/qoa1pWcd9x06Scziv6ZI/dSOcVXD8/j/rIaftvoz54sG4FnVd4wWxN5xVc736B+qgb3j2Z+uD2m+40L/qkZxbTvOA6uYH6vq+T4tQHN/XyaF54omIezev5+h+o7+s+lEN9cPvt3ndpo/dVcU4aDjqkmN1XBQ+O0PnMxzmlaDismPngquStAnZfFZ69pctWdl8VXP0g1zMfdU3VB7nMB1fvPrWR5cU5qT3xjhKWFzw86f5C5uOcypYXlDAf3Hx9ZRHLC08c3WMbywuuBn25mfqurs0Zmsd8cDOgs2X3VX2f2nRfye6rgpuzvvyT+cgpb7lrFfPB9XMPLGL3VeGJZo3/YPdVwcMxj01kPurKy7+axXzPH6uZzPKiT+LB+9awvOCm14ClzEdf5dAf1jIfXNU1WsHy+rrP186hecHHNZ7GfF93c8l85oOH9Vr+xubVn6ve0K2UzavnvU8sZr4/pzz4rDLmg5vndxezeYUn8hfls3kFl3bVVuajrripsoD54Hplw20sL85Jgy/GlrG84FIsLqG+26eap66OMR/c9v6qlOWFZyf/u4jlBZfqlzzmo6769bZi5oPr3ecXsHn1ferXeAObV8/Xd89hPnKKo+pvZj64yq+/hs0rvGB3x4VsXsFN5a2zmI+68soVS6nvuLzz3bksL/qku4+zLC+4WnzzOuajr/Zgmct8cPnyyI0sLzzxanIFywuuGrT9k/qurnz0sNXMB9fmwSXsvqpfV4u+GMHuq3q++93BzMc6Fd5TPZr6jgfPttbsvqr3xoo+7L4qeJC6pxvzfd3Rh37JfHAxuuATlhfrpPzj3l9YXnBRec2P1Hdzas/e+3lXUR/cHLx0OMvr69Yb/BXLC65u7/UF81FXP3fbYOZ7/llOH3Zf1ffpafM+u68KHjRs/Tb1kXPpw4r54OrOBu+y+6ree3pAW3ZfFVz/NOUd5qNu2O3SdswH12+l3mN50SdzX9cuLC+4/fTYjtR3fdXnjO3OfHC16qePWF54YdOhHVhe8KDjhZ2Y7+s+f65iPrgJZ3Zm84p1Mji+dDKdV/C8t39ivl9Xv3txOvPBbd0f49i8wrNPXDqEzqvjwYy1/ZmPuuqiK4YxH1y8U38Qy4t10qhjDM3ruEqNnUR9t67aA8bOYT64vm7pNJYXXtCj02iWFzy844ohzEdd9c6CcdR3XPQ8cjibV/TJ/H3FF2xewcX2Rd2Yj5wqdldf5oPLl+/9jM2rrzt4dhc2r+DhgJEfMx91wy1dPmG+59+M+Izl9X1afddXNK/jwXkv9GE++iq6nPcd88HFNX0G0LzOU+V7P68rmhdcfHBqH+ajrrpsbT/mgwezOvVn+1WckwbfjImx/Sq4Ug+WMR/nlPL00+LUd1zcV13G9qvwzBnNS+h+1XHd6MtC5vu6FfeUMh9czEgUsbw4JzUd2sVZXnDR8YsY83FOZZLz4swHl3OOjLO88OS/Ly2jeR3XbS8vYT7qmrcTZcwHV7ualdL9Kvr60Ol5dL/quGpzmWU+cop/rspnPrgYck4u2696b9LHa9l+FdwOGLSC+b7uztc3Mh/c3nXqapYXfZLvvF9I8zouJ56Rx3z0NXjioGLmg5tPjypgeeGZIc0tywuuDj1zHfNRV8lPcpnveeevNrF5xTmpefewBJtX8DDvHer7c9V/3ZxgPrh+aUmczSs8+fOXMTav4HJOY+qjrjrhrDjzwdX1J8dYXn9Oek/rBMsLru8/kfvuXFX06JlgPrhd1yzB8sIzR/WMs7zgonp2jPm+7uDCOPUd17ffG6fzij4teL6EzSu4nrO5kPnIKZcMKKW+4+bBrsVsXuGJzjPy2LyC2/uu3Mp81LXtVhcwH1weMXQby4s+qc8HlrG84OHdu0qYj77aPq1jzAcPX61XxvJ6T5oilhfc1BxIfdSVXV4tYT548Eb9IrZfxTppdzb6k+5XHVd7bjDUxzr1bcES5oOr5d3nsv0qvLD/B5PZfhVcXjf6Z+q7usEp82Yw33O5ZQLLi3VS3/bSSprXcXnmYQup7+ZU2o/WMB9crH1vOcsLT//nuNksL3j47tnTmI+6YbsBC5gPbtZsMGy/+r/7tPpHtl8FD575+1vmI6fp2nU08z3/+qyhbL/qvScP+4btV8HNQYMHMR91w9feHcJ8z1+78AeWF30S3ybH07yOq2NG/ER919dg/YypzAeXW5pPZHnh6aLUCJbX85yuI5nv6161dhzzPX+vchybV6yTotl1G9i8govvfl1FfbdOyYr6lvmeD5i2js0rPDPVLGXzCq6HTZnPfF+3TV4O9R0Pv++2lOX16+TD+bksL7h58rEtzMe6Gozrkc98cDtySC7N6zxb/fA6lhdcN7tjFfNRN3hVbWE+eHjv4+vZvKJP+rXzZ7F59bzFszOYj5xCr1zAfHBzdNc5bF7hqXv/OZXNK7i2yV+Zj7rq4CdmMR9cj6vP87o+yY9eW07zgo+etIj56Kte2GAN9R3X/2yew/LCU/UO+ovlBdetJ//JfNRVBbNWMB9cLPt4Rfq7qqrDtOvS30lV5qHr07k67Ozr07n96IAbmI93SkXHSxoy3/PO0xql1zXDP7g53dOHvyrSuWj+xh3pPKjoeCfzUTeIdb6V+eDqU9OU5cU7qeGYljexvOBq4EzBfLxTKf7R7xbmg4tpTRuzvPDs0EvuYHnBRfGN9zMfdc3mX+5mPrh8uW/z9L7Kj1P3p/dJ/ifngXSujm3fIp0HS2c9wXzkVP1lc+aD2yvNMxl1R2x+Pt3TH9gX0rn+vH+bdK5KnnqT+agr7km2Yr7nrRe+xfL6vjawj9G84Ae99Bzz0dew6QTqg5vr57Zieb13eefXWV5wdePJ7zIfdc3371Df8xNGfMjm1b+rusM2pvPquFl8WxPm451SfWnTJswHF4Mr7mPzCs9WTn2AzavnqfEtmI+64SO1jzAfPLj245YsL95JVYcdcR/LCy7ynm/OfP+uauFVDzEfXPzjtBYsLzxTXP4UywsubjvlJeajrn6j23PMB7ddW77O5hV90nvGPc/m1fNNV7dhPnKK+5q3Zj64OPXdd+i8Ok/d9t57bF7Bw28P68p81NX/yVPMB7cn9fyU5UWfwkfPaMvygtv45x8wH31V3+xpz3xwfUx5Z5YXnhmmPmZ5wU3P8X2oj7pn7/qC+eD2iT5fpa+rtl6XDhnr5OvT3k/nZmOzbulcHdHoY+ZjndLrOnRivucPnNMjvW64/PI+6Z44p7BPBg9WDErndnLhYOajbnjm2V8yH9zWP1ezvFgnxTPHd2F5we2xt/dkPuY0XHDxJ8wHtzOO68fywtODjhlI8zqup5/1I/NRVzc44Tvmg4v7Ro9I76vN6To8s6+PjEjnwfwLJqRzM3b2BOq7nEGLjaOYDx70Hzwpva4dWTUz3ZPrVv2WzsX4H+enc1XaYAHzfd1L954LRX1wPbv6T5bX93XWfWNZXnD7/PpfmY++ynYfT2C+529NnsnywgsPV7NZXnA9sXYR81FXTF03j/ng9tILl7N5xTqp+n7Yi80rePDS1IHMxzql1uR8yXxweeedms0rPPPH43RewYU+fyzzUVcuum8U88GDqq4TWV6sk+rAXwexvODB6f2HMd+vq4+2+YH54OGWb39meb335E/jWF7w8NU3pzEfde11uyYzH1xM6mXYvPq+1tzF59Vx0XrGLOYjpxk//Tfmg+tDrp3P5hWevaXVQjav4HL9QTnMR91w/L+pDx4u+WINy4s+6aUnzmF5wdU/ShYxH30Nr239J/UdN1e9uYLlhWd7T6d5weWAseuZj7rmjdK/mQ8eVjXcwvareCdVP7DhUbZfBZe5YyXz8U6pbDv6KeaDB7niBbZf9d7gx16m+1XH1Yd5bzEfdYMBT7RhPnjYZct7LC/eSdXLX3mW5QUXQ2OvUN+9U2lKFr7IfM+H72jD8sILTy1/m+UF1+W/KOb7uj9+34H54GLSQ93YftX39fQvOrH9KriZc3QP5iNn8Ov3XZkPHla80ZvuV50XLlvTj+1XwUMjvme+r9vk1G+YD65z+gxledEnedKMT1lecB2T/ZmPvuql9fpQ3/Gg08eDWF549sUDhrC84Gbr8tHMR127NjaM+eDhW3eOp/OKd1UvOvFVOq+Oy+MufIf5/p3S2Ve9yXxwW3dNBzav8My3s+i8gqsPaj9lPuqK4/O6Mh9c/Jjfm+XFO6lhr91vs7zg5qzTFPNxT9XWa/ke88HthMFdaF7nhfUX9WB5wfXSxJfMR11x/me9mO/5rz99zeYVfVK3LqTzCm4bfDyE+cip73+G+uD2kaOH03mFN23HT2xewVXf6snUd3V1QT/qe/5wrxksL/oUdE5pmhf8rpd/Yj76ai7fMpT54LJRzjia13niuhU8r+N22kGzmI+6Ztak6cwH14PHzWX7VayTwbJXR7L9Krg6ZOsE6rt1SpYdNY754FKPm8r2q/D0qhkz2X4VXD39wjzmo646ZfBs6jsuT2iyiOXFOhlcs2USywsuLiv4g/mYU/OfRdOp77hONpvL8sITHWcvYHnB7amnrWA+6or3liyhvuPh5NpVbL+KPukTE0vpftVxU9JlNfOR0/a8K4f54GbFkevZftV7N/fcyPar4EFNzVbqu7p6xUTLfHDz0L15LC/6ZI7u+TfN67j6MGcz89FXc+HpG5kPri5espXl9XUvPm0bywsuP3qngPmoGxz8aD7zwcNWTxSxecU6GSzdYdi8ej74hb+Yj3XKfnrSPOaDq/GXLGPzCi9o+MBKNq/g+vVL1jEfdcMJh65hPrga0GETy+vXyYP+pHnBzQKzkvl+Xf33nqXUd1wdcfoalheeOKGI5gU3z22wzEdd230x9cHDOzfksnn1fZ31hKXz6nj4j+PymI+c9l/9c5kPLh85s4DNK7yw8uFCOq+Om8Hlxcz3dU8/opj54CZoXErzuj6p5j/wvI6H/fIKmY++mp5vc9/x4Jznimle5+nqN0tYXnD94j1lzEdd+1o/6oOHx5wRSz9XDe44eUX6OamubbsinYebuq5N53Zz2Vrm45wyuP/TlcwHD37/9/qMutd3tBl167vPp3RcLh6Zl87laavymO/rPnnHVuaDm/YP5tO87pxUPfL0KpYX3Hx31kbm45xKLY6tYT64uHvZZpYXXnBhg1yWF9x2Oa2A+ahrf31rG/PB7ZJTC9P7Gpx+Q1FGX9u1KErnNkiUpHNVdFAp833OZlOLmA+urt47z6grJy0rS/dUz01l6Tzoc3g8nYvy4+LM93U/OTjGfPDgsoviNC/61LxpMc3reHjK8FLmo6/qh3+UMB9cfBPSvN677rYYywtu+twXZz7qhtf3jlHfcdnkwzibV5yTynWXrmfzCi6PP3Ir83FOKRcfyH3Hxa+5dF7hqXhBHptXcP1lwyLm+7qntS9gPrju2LqY5nXnpOHB59C84Gr6ijzm+3PVYQ9vYz647DCtgOZ1nr75cJrX82X9SpiPusH93agPHt44tJTNK/qkZy4vYfMKHjwzsoz5PucFe+vv4zsu2tan8wrPPJ4fY/MKLg6eEGc+6uoFDakPHv5SEGd50Sfz++s0r+d/To4xH33VDf9Dfc9PuiTO8sILdn1B84KLxN53yaM+6tqD1lIfXLfZ+64Y1tXgt6Xx9HUymJCTwe2UOxPp3H7TNEF9t07pg3LjzAdXFQ8m0uuGW3on0j31ed8MHo6YmcGDIb9RH3VNq6+oDy7bmQTLi3VSXlwZZ3nBw8QzCeZjTnXsBOqDq5vb0Ly+bsV3NC94mDuX+qhru46kPrj8cGFGX+UnazL6FJ7xdwbXbfIzuF6Uz32XU/21lvrgalVBRl3VOpbhyVGxDC5/2Z7Bde527ru69qk49cFt+x08r+uTfHkDzQsevl1EffTVztpMfXDxSQnNC8+8l6B5wc2zO6mPukHBduqDB73d+5WRecU6qRbW5/MK3qEz9bFOmacepD64uLk/nVfviYl0Xj1fvZz6vq78nfqev/U3zevX1Z/b0Lzg9rkR1Me6Ku79nPuOy39OpXm9d+1fNK/nOzdRH3XNzTnUB9cH5tF59X3qlcvn1XFRVEZ9n7NnAfXBg9Z8Xn3d7nxewVXxbu67uvq23dQHl4/t4XnRp49KaF7wsHQH9X1fb4hT3/Omu2leeCZVzvM6LhdUUB919YgK6oOr8927k5H9Ks5Jg0c/zGf7VXBV+WER9d05pXi/YyHzPW/wzxK2X4WnB66j+1Vw3bZvGfVdXf1wLfXB5YktYiyvP1ftdVMxywtu82Qp83FOZU6aSn1we99bZSwvPHVeKc0LLgbUxJiPuurkUdQHN707xNl+FX0Ss+fy/arjNtYnznzkDFrfRX3w4KkCul+Fp4pPoftVcFn4WoL5qBsecQ/1wcPivZ+HvU9e9ElMoHnBw2svSjAffdWnHpxgvuctnqF5vZfbnub1fPwo6qNu2O9r7jse2r1/r0TnFeekun4JnVdwOevuGPXdOaXptoH7jsvvd8bYvMLTLRvF2byCi4s2xpmPukHrn6kPbvLOT9C87pxUbepP84LrC9+MMx/nqmrquXHmgwfHLed5nRe8EyRYXs+3PZmgvqurim6iPrj+fO/XrYrOK/ok+jem8wouz/6E+sip27xNfXC7bTidV3i260Q6r+D2zmXU93UvWkB98HDuBpoXfTLHDaR5wcMVv3Hf9TV8agL1we1vS2leX3fdapoX3Pw7n/qoG0623HfcnFxG96tYJ8WbQ/l+1XHz23zqY50K/+Q+uGqQQ/ervu5FG/l+1XH5VjH1UTdYto364GG9OM2LdVK3+ovmBbedNnHfzWkwcg31wcPZ+TSv9y4qoXnB7ak7qe/r3pegPrh5oJzuV9En9RXfr3r+9h7qI6f9mPueP1FJ96vwgkuq6H4VPHyxmvqoq7tUUx/c4Os2RPL6vv6jguYFD2uqqI++2mOquO+4bF5D88LTo2toXvDgnVrqo24wpJb64KGoo/OKdTKstXRewc1NZdTHOqXziqgPHty8g84rPPPsLjqv4GFeBfVR136+h/rgsrCK5sU6aTfEaV5wdUQ59bGuBvfuoj54mFtB88JTFZU0L7h5q4b6qKv+qua+44GupfPq+zqpms4ruHJftzbqI6c8oJb64AG+LlVkXuHZl+vovIKr/Druu7riiCT1weXLSZ7X9Sk4sI7m9byyjvroq1zAfXDVMUnzwrPdkzQvuFzOfdSVRVl8x/XZ7usQRe6r+ndS5/X9kN1XBVdtJ3dnPt4pDW5/uDPzwW1Fu8/ZfVV44ucz+tH7qo7boy/4nvmoq9+sGMB8cNti+g8sr39XtVNpN5rXcXtwsjfz8U6levayT5kPruLXfMny+rpPvTiI5fX89weHMx91ZX+hmQ9ur289mt1XRZ/k6kdHsfuq4OKdFycxHzntiW3HUt9xNajNVHpf1Xny3bMMu68KrtfM+pP5qBvMvHIO88H1cxctYXnRJ1V8+gSWF1y8s2sm9dHXbk9MZj54cFwVzQtPfN1rPssLLjaVLae+q2vfu2oh88Ht6Gdy2Lz6d1WPerAXm1dwc+ABA5mPd0r1Bzl9mQ+uWn4xmM2r98rcu9aRefX87NN+YT7qmuTY4dR3XM28aQLN695JtTeuHsDyggePHjeE+XinStx8LPcdV4cNHMrywjPj1oxmecHtRy9MZj7qCh3/hfng8ph609i8ok/a/mMam1dw89e0OcxHTjHw3N+Y7/mk2XRe4YWv/mspm1dwMbhgNfNRV8h/r2A+uEp8sJblRZ9M5zLD8nr+xtkLmY++mtiwOcwHl6O2LmF54YVDn8hhecHNoHAD81FXxX5bzXzPr1i5id1XxToZHnsrva8KLl7/dB3zsU4FnS5YzXxw02X3Rnpf1XlmyZ/0vip4+OeYPOajrhz2BfXB5cqmBSwv1knz4ZlrWV5w8fg5lvmYU/uP4zcwH1w3nbCV5YUXrLiY5gW3o8cVMh91w5bf5zMfPLw2t4jdV0Wf1KyDi9h9VXC9anAJ85FTHfEo9cH1X1+X0vuqzgvCd+l9VXBVMz3GfNQ1ay6iPrh57uo4y4s+yfNfp3nBhXmujPnoqzzmIeqDhy0PirG88PSCyTQvuKroHWc+6gaLzqE+uJzxd5zNK9ZJ3b56E5tXcP3MBduYj3VKLnjJMt/z4zvTeYWnehQVsHkFtyfkFzMfde3DR1AfPDzKlLC8WCfliQu2srzg6kGTz3ysq+qW07cxH9wMyud5nRe+cDjNCy4r3ytlPuoaW1LMfM/POK2MzSv6JJocQ+cV3NzfNcZ85NR/jyhjPniwuoTOKzx1Tjs6r55feEyC+q6u+GQq9T1/7boEy4s+ySn/oXk9X3pjnPnoq5jYm/rgwcaPaF54weuxOMsLHvz8eIL5qKuvOJX7jqvr3f93RPareCdV2p50vwquel8xivl4pzR4tN1w5nt+xdO/sP0qPLHjkolsv+r5KzN+Y76vO+fhX6nvuL502iyWF++kyoXtxrC84Ebsmsh8vFMZHJEzkfng6uryaSwvPH3FxpksL3jY/sl51Hd1g0Mums18cDPy6IVsv4o+2Z3r57P9KnhQeuwK5iNn8NGEhcwH1w90ovtVeOaZ+9ey/Sq4OaSpZT7q2ltP38B88LDTpK0sL/okhj61jOX1/PR71jAffbVn7v28kagPrvJ6rGN54an4+ZtZXvDw/Au3MR91gyXTLPUdN2Z4HptXvJMqejedweYVXH530yzm453SQJw8h/ngathtC9i8wlMtblrA5tXzimOWMx91zYZTljIf3MbLclhevJNqB+UsZHnB1fWfL2M+3lVVI0etZD64aXT1aprXeWFxjxyWFzzoNWkt9V1d0+K8dcwHF7fev5nNK/oU/HQwnVfwoOlNm6jvcspzrlzPfHBx/gTL5hWeGdspl80ruLn5wgLmo254/ZPUBxf1RxXSvK5Puu+BW1hecFWeyGU++qrvqZfLfHDZ8qZ8lheevU/wvI7Li88oZj7q2ndOKWI+uGjYroTtV7FO6rYv5NH9quPhSeWFzMc6ZQ4dkU99x8NlO4roftV5+o+JJWy/Ci5X/FDGfNQNm7Uqpb7jwcunx1herJNmuS2geR23l20upr6bU/n08dQHl71m0LzwxNYjaV5weenHMeajrl6hy5gPrtvl8/2q65N90+1LI/tVcNljepz5yCk/acB9x/VHJXS/Ck893JDuV8Htkh7cd3XltMe473g4elCC5UWf1I8daF7wcLr7/I6Ij77Knydz33E7sAHNC89OeZPmBQ+mjKY+6qrG3akPHgz4NUHn1a2T4svr6byCi7M+LWW+X6eGPEh98LDyLj6vzlNvXUDnFTxYekOc+air7v6R+uDytO5xltevkx0eo3nBVdc9ZczHuqrn3k59cF3bm+d1XvDAcTQveLh0YZz5qCtFW+p7/p9DEmxe0SeZKI6zeQUXJz6aoL7LacSp1Pd8WVs6r/DU0d/QeQUPf5tNfdQV3UZz33H93uIEy+v7es4tNC+4rf8Z9dFXY5+nPrjurGle71VMpXnBVWoV9VFXfDWP+uBh27334va5r+rOSeWCnvy+quPmr5MTzMc5pb11C/XBxVN30/uq3jvuNXpfFTxs9yP1UVeFvagPblpPS7C8OCdV80+necHlh24eIj7OqcK3mlAf3P7Wi+aFJ5qMpHnBRc+/qO/r7ppOfXD19mp6XxV9EqPm0fuq4PKCDdT3Of/KoT64bphH76vC02OL+H1Vx/XVO6jv63aOUx883LWb50Wfws00L7jcWEJ99FX9mk99cD05QfP6unN20rzg4dgK6vu6m8qpD65+qqLzinPS8OJX6byC2znDqI9zSr3qY+qD265T6LzCs3l/0nkFl002Ux91xSs51AcPCvNoXpyTyn6DaF5w9foc6mOfGlb9RH1w/ekSmheefHsDzQselBZTH3XV+K3cd9zOj9F5RZ/MA6V0XsHN7buo73N2zOI7rovK6bzCC8+tpPMKLs6u4b6rK5dxHzy4opbmRZ+M3knzgoutFdRHX+3B3AeXz1TRvPDU0Tyv51W13Efd2hrqg9tedfS+KtZJMWAPva8KLuuqqO/XqWQl9cFNjxp6XxWeOq+W3lcFN6qO+qirzqujPnhYL0nzYp00f1TTvOD6p1rqY07FobXUB9cf1tG88ALL83o+JEl9X7cV98FNdZLeV0WfxBNJel8VPMxNUh85xV/c9/zSFL2vCk9fmaL3VT1/IkV91JWtuA9uu6doXvRJ/CtF84Lr51PUR1/Vw1l88C+y5HWe7c3zguuR3EddOZb74NKk6Lz6dbU8y7yCn5qkvl9Xh3IfPPwkSecVXriYzyu4bpiiPuqqE1PUBzevpGherJPheTwveLg+SX2sq6IL98HVySme13lhE54XPPgii+/qmje4Dx4M5/OKPmnF5xXcjOY+ctrvuQ8eGD6v8MwcPq+er+A+6gY53AcXm3he9ElO4XnBxTLuo696PvfBw/U8LzyzKUte91Hmcx91w3zug+uSFN2v4pxUXTSN7lfB5derue/OKWXNHOqDi/M38v0q6n5XQPer4GLxduqjrhpQQn1wec0umhfnpObAJTQveNhnK/VxThWMW8V9x8W5PC88tTZG84Lbi8upj7qq73bqgwdL9tD9Kvpktu6h+1Vw2a2a+sgZtKikvuet+X4VXjiR71c9PyBJfdS1gvvg4vEkzYs+mR5VNK/nbl+5j+/6avB5exEfXE7neeGF4+toXvDguyT1UTc4KovveLA6SecV56Ty7g10XsHtUSXUxzmlPXwr9cHtG3xefd28nXRePb+0kvqoq7ftpj64fqKK5sU5qTymgOYFV89tpz7OVU2bEup7XsHzwgv1HpoXXLSspj7qBudVUt/z22vovKJP6uEaOq/gukEd9ZFTHsZ9cD2Ezys8+wifV8//kaI+6tr+3AeX9VI0r+/rD7U0L7iqraM++qou4D64uJvn9d6iJM0LHtyZoj7qmqok9cHDZ1N0v4p1Ui/k+1VwdVuK+26dCg7gPrh6ge9X4cnP+X4VPJzKfdTVQ7gPrualaF6sk+Jinhdcd8riuzk1zbgPbvvxvN4bz/OCy2XcR93wd+6D6zV8v4o+yZV8v+r5Vu77nFl8cFPA96vwghK+XwXXMe6jri3jPriJZ8mLvlqeF9wWcx99VXncB1elPC+8IM7zgodx7qMufr1RH9yU8nnFOmle4vMKHg7lPtap4MMsvuNhlnmFpxbweQUPNmbxUXcp9z3fwvP6dbU3zwuuZ3Af62qgs/iOh3N5XnhyVZa8qzLnPuqjrlzLfc9z+bz6PhXweQXXpVl8l9MWch9clPB5hadL+byCi0Luo64p5D54aHle9Al+NC94kMVHX4N87geRdS6aF57cyvOC69Xc9+vh2iw+OM4ntruPWCd2ub7syOSiKJPrsix+ueO7s/iOh9sz64YbM71gWyYXCzK5/3sl6ru6uiiLj1/HBp43rHAf9/C84MEu7qtK9/tWwX1wUZ4lL+rGeF5wm8t91NXbua8jPx/6Kodm9slOyeRh60yue3AfOe087oOrHzLrqqMzvVBkcvt2MoObP5PcR92XU9QHl0dlyev6ZFfwvOD+fDDiR3+/or7/fZzP8/q6PVI0L7i4NYvv6tofuQ9uXuXzqqqcV8nnFTzI4otq/DuD++Ciks8rPLmLz6uMzHvUR11Vzn0V+fmieXU15yLy6wuz+Laa86gfrYtc8Hx/s/RH7OI+6gaV3AcPy//7+oJ1Mzqvnq/kPnKaIu6byHofnVdfdyqfV/CgJ/dR1y7gPrgZwfOKyLzsk9dxuY376Kvazn3woJjnhWdzeF5wOYP7vu4m7oPLhZF1tWNd5rp6aCaXpTUZPFheS32sU0H7JPXB7ceZdU3LqgxPrK/O4KZJeSafVEF91A0PqaU+uPq+iub16+qyJM/ruD6F+5hTe2KK+uBBjyTNC0/trKV5wXVYQ33UDX6qoz540Kw2o69yUjyjT+qnnZm8Xl4GD4eWUB857eJy6oOL3xIZdYOHcjK9/E0Z3H47NYOrP/+iPuqqSwqoDx5e9DfNiz6FN1fSvOBy0i7q+76eW019cDV7D80LL+xfRvOCh+9v5b6rG3baQX1w0aKYzivWyeDuFJ1X8GBrkvp+nXo7RX1wc16Kzis8fX2Sziu4rs991A1HJKkPbnPraF6sk/bLFM0Lrh7jPtbVcAz3wf25XSSvr4v7DpG84KoV91FXXJ+iPniwJEnnFX2SX9XQefX8lCrqI6eYUEt9cLOhms4rPHNDOZ9Xx22zBPVRVx5RSX3PzS6aF33S3etoXs8v5z76quolue+4vKSO5oWHvkfzgttjKrjv6oYTaqgPrjtW0f1qUIN7IXy/Ch5m8VHPVHPfZNlf+30peCXfr4KrPdyP7rf38R2XFf89r86Sd59/H0S4qsafO+7LSD+ieb1XxfPKSL/28asz/10W9T2v5PtV9Mnu5PtVcP/vqYiPnHo393XkPCW6X/X/nsjj+1VwnGft40fqRn3PN/O86JMp53nBxU7uo696D/fB7S6eF54p43nBRR73UVcluA9ui/i8ish8RucVH20V94PI90d9fDRZ5tVG5jY6r55Xct//u7qK++C6kuf15wBV/52r/XC5Hy6y5FWRP/fRvGHk29n8oIr7QeTb0XlFn3Cuuc+8Yt3cncVHroosPrxyPq82cm68z7w6Lsq47+vuzOLvzPz/tGhefFtX8LzgYg/38W1bwX0b6Ws0L7xgN88LjnU56vt1Zzf3we0Ovl/FOin/4PtVz7/lPtapcAn3wcOJKbpfhadapeh+1fNTuY+65rMU9cGD+3lev67+zfOCqzncx5yazdwH18uy5IU3jOcFl+9x39edxn3Pv0rR/Sr6pL5L0v0quC6soz5y6j1J6nv+ZpLuV+GFf9fS/Sq4acx9X3dMHfU971VL86JPskGK53VcruE++mpeTFEf3LjPZ4jmhRc0SfK8TfDON/dR10xLUh9cXZDk8+rWSZnH5xVcr83i4x5CEffBxRY+r77uAj6v/v8Bx3Ifdc0K7oPL33levw8u5XnBwzzuY101Me6D4/91o3l93XU8L7hamMXH/wdv4T64zOHzij6pHik+r/h/poe57/v6A/fB7fspOq/w7BEpPq+OBz9yH3Xl7Snqg+NcZJ+86NNknhfcfsV99DWYw31w9XOWvM7Tb6RoXnBzHfdRN+yZoj447p3hXFX06JlxTirF6Axu1zXL4ObW96jvz1UX/MF9x+2oQRl19eDCeLpnDrksg+vb742n8+DOmdRHXdP5+QT1HQ8+Oo7m9eek9y6necF15WTq45wqWL+R+uBi7V80LzxxVM8Eywsu5IPUR1115xjqg5vyvV83FX21fVrH0vsUbD0hg4ev1itL56bfOdRHTrt9QJz54OL9goy6ssurJemeOHhlaToP3qhflM71madSH3XtjO1lzAeXqZGlLK/v08LqOMsLHl7Vkfroq3rm9gTzwc3MWJzlhWfaLo2xvOCmUx71UTfs+nac+o6rqStjbF5xThreWUDnFdz8spb6OKcUTWPU93zENjqv3ms3l84ruFk6nPqoq9qvpj64LDQ0L85J1Ts7aV5w+3Mp9bFPVY32UB9cd9xB88ILl26lecHFzznUR13xUwn1PT90a4LNq+/rvz5IsHkFDypupr7POezHBPPBg+0dEmxevdcnk2NewXU7RX3UFatuSzDf8zE1cZYXfdIr/6B5wVXfodx3fbWjc6gPHvSflWB5vXeJSrC84MF/HqA+6obzRiWYDy7b7H03COtqMK5Hfvo6KW7YU5jO7cghuencPPYx9bFOhc8fWcJ88HD1uUXpdYNX1ZZ0T/5QncHDex9fn87FyL6W+ahrjjy+gPqOiw8PyWN5sU7am6aX0ryOyzebUx9zam7Y+671Pj747iPKaF7nBX/PK2J5wfWEywuZj7pm144S5nu+cGlxel/1wgZr0vtkt5y4KZ3rfzbPyeDrT1/PfJ/zhOtymQ8elv1t0+uqglkr0j078LW16RzvqoLLE85dy3zUFYMPt8wHl0tnb2F50SddPTOf5gUf+XUe9V1fzbg/ipgPrm4sKGR54Yny4m00r+Nh97nbmI+6+mFZyHxwNfuWQjavWCd1TXmMzSu4vvg+6mOdUkkdZz64+sa9uxWZV3jBor5lbF7B1UdLS5mPuuHK6THme16/Ec2LddLMd++bRfKC6/krqI91Vd/7YoL54LrqhgTLC0/u6RpnecHF0GvizEddfdeRCeqDd1ofZ/OKPtnzTy5l8wqux6kS5iOnvXReGfPBxdPPl7F5hSdeP7eEzSu4KjuqhPm+bue9/66I+uDheaeWsbzok3ypNMbyen7KLzHmo6/hBWPizAcP238SZ3nhmSN7xFhecHFcpxjzUVc33ft1oaM+eHBw6zjbr/pz1apKul8FNz+VUx/nlGpnNfXB5YhKul+FJ2q20/0qePAm91E3fLOc+uDBITtoXpyT6idqeV7HzdwsvjunMk/WUd/zW2tpXni6byXN6/lA7qOumF1NfXBpKul+FX0KPtrK96uOB1+upj5y2uNKqQ+ub9xG96ve+9d8ul8FN2MnUB91RcN11AfX6xbTvOiT+nEHzet55zLqo6/m7D3UB9dzd/K8zhNHF9C84PLSzdRH3eCbOPU9H1hM5xXnpMEJSTqvnt/LfZxTBl8mue+4cV/XNDqv8OSdtXRewWUZ91FXPFxHfXD7ci3Ni3NSmUjSvOCyJ/dxrhpekaI+uClJ8rzOE8cmaV5w9Qb3UVf0TVLf83OTdF7RJ+u+bnZ0Xj2v4j5yBjfXUB9cLa6i8wrP3LWbziu4PHUH9VHXHF3JfcfDg/bQvOiTGl5L84KL8dxHX803ddQHt3tqaV549otqntdxfVMV9VFXNqilPrh4uobvV906KW8alGD7VXB1q+I+1qmzf6c+uHphTILtV+HZsmcTbL/qecPbqe/rthmUYD64aNeD58U6uWMVzQtuv1rIfTenesk26oOHL27ieZ1nvvuD5vXcTKI+6spxa6gPLs9cnmD7Vd+nTRcm2H4VPFhzQoL5yCkPfy/BfHCT1zrB9qvwVOzgBNuvgttj/5FgPuqaQc8lmA+ub2vJ87o+ieLRNC94ePKP3Hd9tdcupD540HMezQsvuPg7mtfz2YOpj7ryx9nUB7dLZtF5xToZvBOj8wouPiimvl+npu6iPngodtB5haefyafzCh48t5X6qKunxqnv+d1lNC/WSbWwguYFNzftoT7WVdOmmvrgsn0Vzevrjt1N84KHV+2iPurqDyupD676V9B5RZ/k35vovIIHx26gvs95QQn1wXWnIj6vzjMPr6PzCh6uW0t9X7dDIfcdF1cV0rzokyjaQfOCByXbqY++msV7qA+ur95D88Kz922necHlhdup7+uuL6c+uFxQ/l/vpYr98HA/PNwP39991aCK31cNstyHjf54m8UHj/74/1sf/9/2w1byb0fzZstvs/Qj6mfjNsv91eh9Vc/3/PcfH1ZyH1xU8Puq8PAuwz73Vcsz3z2I+qhry7P4kXvH0bz4tqjkeUWW+8Ii8vujKrmvIn2J5oUn9/C84Hp3Fj/S76i/T7+y/HmXWeZV7sdX+7nvrrLccw+y/PmJzqvYjx+tH/XVfvJGP09hf98f/Sii7wFU/ffPl8hW32TJu7/PT4h+nkbU3+fzOCLzGkQ/byoyryYy11FfVnEusq3HkXmFJyv4vMrIuypRP1o36u/z5yP690yk78F+Pt8i6kc/3yXq28iPi+YNq3jfbeTzM1QWX1dxHvX9ehW5r4p1Uif4fVVw/x5bxMc6FezI4u+IvGsWua8Kz2zLcl8V7y6s5z7q+veeIr6I/Hz75N0d+fyuSF4R/fyRiO/X1V3cB8e7S/vkjWe+WxfNC662cd+/51TGfXC8yxW9r4o+4fMeovdVwfHuWNT3fV3FffBgQZb7qjMi7w5F7quC227cR107ifvg6lueF30KNvC84GZZFh/v+W3hPrh/fzGSF56clSWv4+Ev3Pd1F3EfXM/k84p1Uu7m8wpudnDfr+Pl3PfvXu3i82oi747tM6+Om2Luh5HPq4z6/t2xMp5XRT5fK5pXRH69+/iRz4eL+mHkXZ998kb2u/vkxefFxbkfffdoHz/yrlJ0XtEnu43PK7jYyH0RWc+ivn8PbyufV193JZ9X8HBBFh/vkq7jPjg+7ySa1/ephOcFxzt+Ud+/0xTjfhB51y2a19fdzPOC+75FfNQNt3EfXG/k+1Wd7f2qLN+vs7wfgI/7+/5wP/Wzfb/6P/z5VZbvj35UkY/7+/59/NrMj/v7/v+/f/599quRz/uO7lejnw8e9ZHTZvn88eh7E9H9apiFi8j+NsjiR+c26u/z7kQkL/oka3hecFHDffTV1nAf3NTwvCLy8+7znl6k71HfRHJFffAw27xEPu7v+6MfbeTj/r7//6ufP6jLUq+O58BHWZf5cX/fH/1oIh/39/3RvFE/mvf/9s8fnVf0SdbyeQUXtVn8aL8ifvT7o/MKL6zl8xpGflzUR11dy329v7z/w99/8X/Yf7Gf33/kghfW8bxhZC6iPurqOu6Dqzq+Xw2i7/FE9qv+vZM93N/n3c+IH31XNLpf9d7uLPtV7Lt3ct9E31uI+H6fvpvnVVneLzUV/zM/rOHvJvnz9uh7StG8lVn+XRH594Yt576Jnt9FfBM9L4vsV9EnvLsR3a/6d2VKuS8i/96L+uA6zverJvJe9z77VZyTbOU+6pqSLD54Ac/r+7SL5wUPs/gy8l5I1NeReY/mhRfEeV5wkcX3f462cx8c76rvM6+RfUt0XmXkva+oLyN/X0f96L40Oq+mmnMZmW+dxVc1nEd9/25QdJ8RWXejeXXk77OoryLrctQXkXU/mheeruF5dXR/GPGjf+9GfRn5+zA6rzr6/3OReQWXWXxbxXnUDyv4vEbPo6LzKiJ/H+xzflXBedTHuUs0r63mPKz4n/mm5r/XifJwf/2JnhNF3uPeX75svn9fOnJfFeek8ukUva/q+SXcxzml6ZSiPrhukaL3VeEFsSS9r+r599xHXRut43xwXZ6keXFOKgfyvODyA+77c6rR3AcX/VM0L7woR15wVZ/7qGveT1EfPHS/L9H7quiTvCVJ76uC65XcR85gUpL64IF7NzN6X9XXfaiO3lcFF/O4j7rh8UnqgweT6mhe36cDUzQvuFnKfd/XRinqg0v3da+ieeHZfkmaF1w25z7qiliS+uBmVpLOK85Jg2l8XsHDkdz356pzuQ+OrwsRnVd4+ks+r+Dhh9z3X4diBPfBxQCeF+ekcgnPC65ncx/71GA19z1fnCWv89QUnhc8GMV91FWzuQ9ufuXzij4FL6T4vOLrtjXJ4rucogv3wc3rKTqv8IJLUnRewfF1QaI+6orHU9QHl7dlyev6JL7hecH1p9xHX8XP3AcPNc8LT3fgecHVK9z3dftx33P39cSj91X9OnlJLb+vegk+r6CG+linwvvrqA+uSmrpfVV4AT5/MHJfFTx4tpr6qBt+Ukt9cH0Zz4t1Urp1d5+8+HtlOfcxp6J/kvrg+uUkzevr9qmjecHl43XUR10rktT3/Mwkva/q+1pYRe+rgtvPq6jv+5pXw33wMTX8vqrzwser6H1V8PCBKuqjruldQ31w0auG5kWf9MV1PK/j5sg66qOvEl8XM+qDx+toXnhBbS3NCx4ka6mPuqagjvuOy5I6Oq9YJ4NEks4ruF7BfaxT9tIU9cH1aSk6r96bkqTzCh5q7vu6h6a4fyj+HcTzYp20LVI0L7hqxn2sq8EH3AcXb6ZoXnj25hTN6/k13EfdoFWK+uBSpui8+j71TNJ5BTdduI+cOpakPnhYmKTzCk+oJJ1XcNE1SX1fNz9Jfc+Ls+R1fRKXpWhe8OCiFPXRV9MiRX3PH03RvPDEhSma1/OLU9RHXftwivrg6hG+X8U5Kb7uU3S/6r9u3iru45wy3Mp9cLGB71fhqcV8vwpu5nAfdc0q7oPrJTwvzkmDAp432I/vz1Wz1InyaF54ZgPPC25Xc9/n2Mp9/3VDN/L9KvqkpvP9KngwnvvI6b++XcQHx7ub0f0qPDEiy34V/Fvu+3dSp3Af3IzleX2flvG8nv/FffRV/s198H3edZ2S6UX//Ya8/s/DDO6jrl3Efc/n8XnV2//7HPtz2Gz+Ls6jvr9vXMC/Hqoq4PMKLnK579/dLeE+uC7geaP3/aJ59+fjXDXMUieMvPcezRvtWzTv/n6/UDfI9vuI94rL+LyiT3oTn1dwtZb7yClzuQ8uN2WZ17WZ9/ai8wru/36I+Kir/+Y+uF7O86JPNp/nBcfX9Y766GtQxH1wkcvzwvP32yN5wYOV3Pe/H+uz+Pg658v5fhXrJM7N9tmv4jztc+5jnQpHcB/cfs/3q/B0V75fBZcfch91w6+z+I7bvjwv1slgMs/r+VjuY07t79wHV7/yvGEkTzRvNE/UR135C/c9H873q75P72XZr+LrCLzDfeQMvuA+uOzO96vwRFu+XwUP23AfdfVH3AdX7/O8vq8DeV5w2Yv76GswiPvg6gueF57sxvOCq/e4j7r2A+573orPK9bJcB6fV3D8v0HUxzoV/Ml9z3/j8wpPTODz6vkw7qOu+IX74MG3PG8QuS8ezQtupnIf66qYzX1wPZbnhaey5PX8M+6jrurHffDgdT6v6FPYn88ruFRZfOzLu3EfHP/vvM+8wnuRzyu4acx91NWNuA8eHsbzok/4eyeaFxzvPu3j4zz/hiw++O4kzevfk8J7/5G8/utCjkpSH3VN/yT3cc53mjtPcO+qqpGjVma8kzrm9nXp3DS6enU6D3ct28B8vFMqG7xgqe+4vfzzrel1TYvzMjzzx5At6Vzcev/mdB783CCX+b5uacNtzAdXVXfks7x4J1V9e3keywse5p+dz3y8UylrexUyH1z8LopYXnhq3LsFLC+4fv6fRcxH3eCnhsXMBw+aPFOS3ld9T73c9D7pVpvz0rlseVN+Og9/6FDIfJ/z9LaFzAdXWz4qTq9r3zmlKN0TXy0qTueiYbuSdC5u/LmU+ahr/920lPng4dVTymhe9GncxGKWFzyceUMp89HXoP8npcz3fMo3ZSwvPHHpJ2UsL7ic1TvGfNQN53SJUd9x2eDGOJ1X905q2PiWEjav4GHu1BLm451SFVxfRn3w+t3K2LzCE7c8W0rn1fGw/n/KmI+6+s0dZcwHNwe+EWN58U5qsLFvjOUFt2pWjPl4p0qObBdnvuef942zvPDChgfFaV7wKx6MMx911cLpceaDm5tL4mxe0SfxXKyMzavnDYbFmI+c4tZEjPng4Ucvxtm8+rr33Rpn8wou25s49V1d8eES6oOLZuckWF70SXf/Ic7ygtuT43Hm+75OPj3BfM//bppgeeGZm65I0LyOhyV73+WL+qgrZr5NfXBV9lUifV3Vc28vS18ng9qTYulc1+5dP8Bl6uQ48/26+s586oPbQ9y7b66uFG3j6V5YNi+ezoP/HJLI4Pddn2A+6sr3j+K+47b33q+rE82LdVJteYTmBQ+/z4szH3MqzvuL+uDho+7rFUXzOi/ocQfNC66f/oT6qBue8w73HVdPDUuk99XYvfV9n6Z1yeC6s87gKjGB+sipir6lvufDZmfUFV/Ny/B09+UZPGy7MYPb4lzqo25QsI764EGHIpoXfRLTJ9K84DZnGfddX0WvedQHDyasp3nhqWVbaV5we1WM+66ueI774PJC915xZF79uro8TLB5BdfvvZSgvlunVONnqe/5uL50XuEFw77m8+q4eWwm9VFXDJlMfXB7xVKaF+tkeEBfnhf8pwncx7p67iTqg8uCv2heeHbYQpoX3Py5gfqoq/6znvuO6/J8Oq/ok1qXQ+cVPOy0jfrIaU7awn3HZacSOq/wxFw+r+D6td3UR119yk7qgwcnVdC8vk+zC2lecFuZoD76Kh+LUx88uHw3zQvPdinneR2XV1dRH3XFZZXUB1fW3YuL7FfxTqpcfViC7VfBw/tOSzAf75SGV76QYD64zn09QferzgvuuTJB96uOG3tPgvmoq87umGC+56InzYt3UtXR39O84GbOUOrjncrwgjnUB7efzqd54ekGY3hex2WHX6mPuubLRdx33P6+IsH2q76vB72aoPtVx82/e3Df5RQfa+qDy9YT+H7VecErP9L9KrhZN436qBsum019cHnICpoXfbKfzaZ5wXW3JdRHX8N2a6kPbupvoXnhiV//pnnB9ThLfdQNe+RT3/MrS+m84p1UMWodnVdw+8sG6uOdUjmxkPrgNlZE5xWemLKZziu4fiOX+qgr55RQHzw4LEbz4p1U22E7z+u4bbSD+7inWldOfXDZfg/NC088tZPnddzM3sV9V1ddXEF9cHVEJZ1X9CnsXkDnFdz+XEJ95FSfJqgPLk/fSecVnvg+TucVXF+xk/qoG/bbTX1wvWoPzYs+ifvLaV5wU7yH+66v8sAq6oOHB1fTvPCC9pU8r+M2v4r7rm5wQA31wU1pDd2vYp1U5y+m+1VwpdZTH+uUXbqR+uD26gK6X4WnX8qn+1Vw6d4Vj/q+brsY9cFV0U6aF+ukvKmI53VcPBWnvl9XD95OfXCT3EXzwjMX7qJ5wcOVe6iPuqZeBfXB1ddVdL+KPpnGu+h+FTyQFdRHTpu/h/rgcnYV3a967/pqul8Ft/h8k4j/v/o696C7qvIObzvjVEGsiKlUhG4QM2BqlcFYKi1dRpGrIlqtw8VuBc1oICYS4yBFlgnVlouIkUKNwIoJTCMJTYBgoAR3LoSQxEAwFRJC3EKbaAgQcvku55y9T/vHet6Zs77f9i/HZ55558eal8X61llrbeq6tV3pw308l5/mZZyK60ZlXuPXdqXPuBbTu9KH+1/2dF68V3ReeLanlj518ye0D6+uamS/Mk9mZxyU/Wp867D0maf8phHpw8OVHdmveMXLo7Jf4fn3u9Knrv91V/rw8FJP5rV59T1dnTdy53rSZ14NK3rSh4ev1zKvee+udd7Iq+dr6VO3emMjfXgZz8On/co4hY092a/w8uFa+uTMV9Xaj7y4tJH9ipfFeyFpvxrf2UifumG79uHlyX2Z18Z1aiPzwstYJ/UZ12yn9o2f1Jd58Yr39WVeeDWlL33q5l/VPjzjXF7cVy2n7R7YJ80//MoAL4b3DXD3wYPSZ58yv+E16Rs/YXigbnb8yKA3c3SA56d3B3h1aE/61A1zO9KHl8t7Mi/7pPlFB2ReuP/ViPTZp/L/NSR946d1ZF68cFtX5oW7+N2z1Keun9biR15sqgfG1Z9QD47r7fUAd+c0A7y8qZE+Oas/avEjLzY0A3XL0WbQO7Y/wPMv9ge443xi4lM3fFT78PzGvs4bx6m6QOeFZ33tM67hbu3D/QSdF89/UeeF53don7r5P2kfzr2OtF/ZJ60e0P0Kz6d2pc8+Zf5p7cOL+N21tF+t7njdr/Ds1Eb61HWPah+e/Vsj87JPWs7TeeHug7X0Wae6RdqHV9t0XrzqQzqv8d820qdudaf24eGYvuxXG9ddul/tHtz5femT0x2tfXg2XfcrXnmz7le43VtKfOqG+dqHh7Iv89q4nq3zGr9e+4xrNVX78CLovHae8L6WvJFXa7VvdVdoHx7W9wfm1SwMzpPZ4kGerxnk1XrtM09VP9c+vHpysG7xbOJtG+TcwzD+W+1TNzynfXjYofMyTzLeaV64+5X2bV59XPtw7vWlefHKbTov3G/Vvt0XfEb7cL95cFx5R9rGtRrkIeHUS31yZtu0D/e/HKybPZv8c60f5MXaQR6WaJ+69r5M4hv/oc5rOZ/SeeH+Ee0zrn5li8+7/gt0Xrxyrs4LD5NbfL6bMFP7xv9C96u9C7NR9ys836J9+j1s0r7xzbpf8ey+UtKv8PIx7VOXe5Fj/MjdMp2XebLc1JJ3E+u2Fp95caP24XY/IMlr3iM6L7y8Q/t2v+Bn2od7r/uVcSoW636Fu2u1T07+uzvGj7z6B92veH6S7ld4eK6RPnXzXiN9eHlZI/PaOF2m88Lzd2qfcXWnat/4xkbmxctv13nh4de19KnrjtE+PDuslutV9kmLq1vWq5EXFzfSt33KP2+kb3y1Xq/ihb16vWr8vL70qcu7halv7xzO7Mu8tk86V+eF52/tS599Kt6VSH3jn+jLvOZ9QeeF827aGP8Tyb2yxLd7d4v0etXG9Qa9XoVz736MH3MW87UP9yta1qu8K7BGr1fhYYP2qRue0D48W6/z2rgu03mNr9W+vZewSvtwu9ec5MVryxuS+9CpT92qxYdn8V7fmH6N+5PFaS39GnnOu3yJzz5lOV37cP8T3a941Xzdr8ZXaJ+61XLtw8sndF7bJ721Je+tg99lSn32VaulLX7yDsaYvHiP67zw/Gnt23sVT2sf7p7R/WrjukH3K5x1UeqTM9us/SxZR43pV9ZRj+t+hfv7tU/dcqX24dndLXn5jtAWnRceNmqfcQ1btA8v1rXk5V2Dh3ReuL0fm/jUzf5T+/DiO3q9GpJ1bbpeNb5M+8xT7lHtw22/I12vRi+bo9er8OpzLT51Z7T4kYd367zMk+ybpHnh5Q3at3l1nvbh5dd0XvPO1XmN/08jfat7lPbhPr6rnK5XGScf742m61V4Hn+3Sn1y2r5h4sPd9lqvV/ldcGbLejXy7HU97ce6xdye9OFV6Mi8Nk6ntuQ9lXfsaukzrtky7cPd+T2ZF8/7rswL98+MSJ+6xZHahxc3Dsl+ZZ4sbtT9avwc7TNPFTO0D/d5S79GL/+N7lfj722kT93qlhafe9H31zKvrZM/q/PCc94VT33eazhZ+3C/tCUv7839nc4LD8fW0qeu26Z9uP9mT/drHKf8zbpf4e60rvTJGSa1+JFn8VxG2q94/u91v8Ldwv3Sp245XvvGD98r89q4nqfzwv1nR6TPuFbv70jf+MwhnTd6xayWvJH7e1+SPnX9xS0+/PKd8rwq76Ta7+nJeVXjj41Kn3dKy4Vd6cOLHV15XhUvO6Ujz6saf6wjfeq6E3vSh1fX9mRe3kkNh9cyr/GJtfR5p7IYraUPL+O7wGlevGpyLfPCiwW19KmbTWykDy/jvkl6XpVx8pd05XlVePnHPelbzviucerD88/U8rwqnlvYk+dV4aWrpU/djN9TEx+eHdvIvIyT31XLvPAiznupz7iW1zXShxcPNTIvnv96I/PCq/sb6VO32NXiR54f0Zf9au+kxnMVab/Cy3mN9O1d1de0Dy+aRvYrnn+o0f0auftv7VPXv7kvfbjjXEiad8LgOao0r53b+uu+9Hmnyv7eSHx4uKgv8+L5+C5xmheen6d96rovaB9e8C5O0q+MU9iv+xVexX3H1CdnEd/FTX14Fn8XT/sVr2Tckn6F88+T+lb389qHh+l9mRfPX6jzwu29utRnXKdoH87v/mleqztD54XzXZrUp271De3D/bf6+rxqnCeLeO4yPa8Kd/Fd7tRnniq/1Wg/cv9EI8+r4vkFjT6vCh/SPnX9m/rSh2ect0ryMk/6V3VeuDu+L336lL+bU9/+zuZ7SUlePB/PtaR54fk3tW91r9Y+vGB/MzmvyjiFU/ryvKrxy/rajznLadqHs0+anlfFY98mPa8Kz5Zo3/Zf79U+vFql8zJO1S06L9zeXUx8xjUs0j48rNB57Z3IUueFs2+Y+tS1czGJb/us/J6f9CvzGeOe9qvxWdpnnipmax9ezNH9anVv1f1q++wLtU9dP0/78GqxzmvfLbtO54W7H2mfebX6tvbhbrbOi+du13ld+g5g4tv3ar6nfTj7hmm/Mk6ce0r71c51rdE+OYvl2ocXK3W/4vEud9qvcNtfT3yru077cLdJ52WcssU6r52Le0D7jGv+Q+3D7V3QJC+ee1jnhftfaN/q3qF9uF+g16u8k+qn6vUq3F2ufd4pLWdoH15dpter5k1pWa8m9xhSn7rlJdqH812lMXnjeUY3ReeFuwu0zzuV7jztw6v367x4xcd0Xrh/n/apmx2mfXh4sfmD41pN1utVeP4l7du4flz78NK1rFe/NHguNV2vwt0/ap+67LOmPrwcr/MyTvmf6rzwKv6ukfqMa1jWSN843y9J8uJVOxqZF15yfibxqevOb6QPD5xrSfqVd1LdUbpf4eF3jfTtXdW1jfThxYxG9iueW9TIfoUXX26kT137/mPiw4s7a5mXd1L9h3VeuCtr6fOuan5lrX34vT2dN3rZu2qZF273BhLf6i7rSh8eru/IfmWcsiN1v8Ld6lr65AxH19KH5z/oyX41b0ot+xXux9XSp27odaUP91/tyrw2Trd0ZV54tqijfcb15FHpw7NlwzKv1X1wVOeFlyPSp244ekj6cDfrgFyvMk+WF+n1KtxdqH3mqeIo7cOLt7asVy8c/N90vQrPLtY+de3v6cSHh0Na8jJP3tPovJFntzbSp0/dvlr6xjfWMq/V/ZdG5428/E4jfeqWS2rpw6t5tV6vxnGqCr1ehZdf1j4588O0b/wIvV7Fc1P1ehXOub3Up64/RvvwaoLOa+M0q5F54eX1jfQZ1yrU0odn99YyL172k0bmhWf3NdKnbraulj48xHOrab8yT+Zre7Jf4fnsnvSZp8oXOtKHV1M7sl+tbvxdOO1XuJ/Ykz51c84/pH7kId7bT/MyTxbbhmVeePa2Yekzr5a9fdKHZz97Tea1ulcPybzw4vgh6VM33L1X+vDskL2yXxmn4qSe7Fe4P70nfXJm20elD6+6o7Jf8fJ47zPtV3ixuCd96rpJHenDq//o6LxxnPxbhmReuP/IkPbjuOYvvip9eDFxr8yLF5YP6byRl5cOS5+6YWSv9OHln+2T51XZJ/VrWs6rrhm8VzPGZ5/yKe0b36rPq9o9nq36vCq82qp96lbbtQ/nu0hpXttXfbYlL9+9el777DNx7zD14eXzOq95z7fk5XtRz2qfum57iw9vOa9aJfcK0/Oqdo9xlfbte1NPaR8eHtbnVfGKn+rzqnC+u5r61K3maB+ena/z2jht0HnhxVLtM67cO0h9u49wd0te6s7WeeHZRO1Tt5qufbh7u+5X+11gq+5X+/esxbd91c3ah4endb/65Ltnab/avdZN2qdu8aT2jT+m87JP6lbrvHDmtdRnnVou1D68WqTz4oWVOi88X6596lb3aB/OvYi0X22cHtH9Ci9+rH1y+qXah7sbdL9a3c/rfoXnfL858albnN3i83v7s43Myzi5+TovPFylfcY13KR9eD5Z58UrPtCXeeH58kb6VvedfenDQ/y7MT2vavPkoX15XhVeXt5In3mq+o324eE4fV4Vz03W51Xh5QNd6VPX7dI+vNrWkXmZJ90SnRfuNtfStz69Wvvw/KqWvNS9qKfzRl7tGZW+1V3XlT68enVEnldlnPwufV7V+FkHtB9zVjOGpQ8vtrwmz6vihfv3yPOq8OyjlfSpW33vd9qHf2CrzMs45TsO6ryR+6Nf1X4c1zDhgPThxck6L16x80WZF+4+tVn61A07d0gf7lavk/3KPFme1ch+NX5cLX3mKT+ukT48rNf9iped1JX9Cq92D2s/1s1+3JE+3O8cknmZJ/0LOi88fKMnfebVsLSWPjyM78m8eO6NHZkXXu45KH3qumtHpQ/P36D7lXHyX9kn+xVevuf30idnFfcZxviRu2N2yn7FK+/ZKvsV7lb/QvrUzS/dIn3jP31Q5mWcyoOvyLxw994Xpc+4Zl95Wfrw6spK5jXvjCdlXni5arH0qZvfvF76cPf9u+R61e5DX6PXq/Diu9q3fdUPaR8eztTrVTz/r3q9Cs//WfvUzT6tfXj4jM7LPqnnXbYkr/H/bbQf96mKIxvpw7MzGpkXL8R3H9K88PLQFj/WLa5opG88zlPpepVxKq/Q61W4P7fF53u9Z2sfnp2g16t4RdbX69XIi5mN9K3uDu3D/acanZd17Jt0Xrjb30jf5n9+90l8eBHPpaZ5re4SnRfuTtQ+dctp2jd+oJb9yj5pOdyT/QoPZ9bSt33K8V3pw7OlXd2v0atuq3W/Ru6qWvrUDX/T037k2aMtedknPWVE5oWXx41Kn31V/+390odX/HciyYuXH96ReeHFvo70qZvPHZI+3L1jRPYr4+TG6X6FlxO0T87ixFr68Gparft1wuB5iLRf7TzEolr61HWztA/PJtUyL+MUHuzKvHD/8Z70GVf/tVHpw/13OzIvXr6gJ/PCyzt70qeuP6erffhdXbleZZ4sZ9dyvQrPq670maf852rpw8P8rlyvWt23jMr1KryadED61M3uG5E+vLxrv8xr8+pf1jIv3E3vSp8+9a+rpQ/Pz+jKvOZNGdF5I8//ar/0qZv/7Yj04Vlnn1yvMk7+iD1yvWp8/A7pkzO8/iXpw7NPbtfr1ehl16yV61V4Nf0O6VM3O3O19iPPn7pN5rVxPXe3zAsv/+Q56TOu5Su/lz68umKbzItXvH2lzAsvh38kfer6N5TSh7t3zJH9avPqhp7sV3h2bFf6zFN+Vk/68GqoI/vV6vLud9Kv8OLlfdKnbhXn4dQ3fuh+nTfOk8W4nswLzx7pSJ95Nb+sK314eXFH5rW640ZkXnh51n7pU7d4/Yj04e62/bJfbVzj/x/Tr/BrtkmfnOEju6UPdxOek/2Kl7+rlP0KLz42R/rUDReslD48m3yLzMs4lS/slnnh+SXbpc+4lj9/SfuR+0N26LzRczetlnnh/vR/lz51w81rpW/8B/8/H/4f+pzLuQ== + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-006000.1.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-006000.1.vtu new file mode 100644 index 000000000..de6ce0ada --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-006000.1.vtu @@ -0,0 +1,32 @@ + + + + + + + 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e1xbczUM+XcHd/QDVvRTc3Q+QzyjZh0uhj324FNzNQz6jxdw5QFvo43t3W3B3Hsinu5g7B2gLfTwHaAvu5iGfruDuPFDdS8HdeSCfUbIPl0If+3ApuJuHfEaCu/sBeu6Knri7HyCflaXcOjAh9PFzH99z/rEuD/msCO7uB6juruDufoB8esk+7Ap97MOu4G4e8ukt5daBCaGPz13RE3fngXxWlnLrwITQx3Vg/J7+U10e8lkR3J0HqrsruDsP5NNL9mFX6GMfdgV385BPD/gf4N+j/5d/elm/N/G8nnj0aUTd6NMAz+Yp+j++fl5P3M1DPo3gsQ+vWy/3LdYteuLRh/4dcPLZa+X68FroYx+Knribh3z2gGfnoehP/9PzeuLuPJBPAzybp+hn/vPzeuJuHvJpBHfngeoWPXF3Hshnr5Xrw2uhj30oeuJuHvLZEzyuAw9i3YjP3YPg0efL316uG32K3l0HHsS6ET/3B8HdPORT9MRjHy7+9nLfYt0LwaMP/TuA5FP0bh/Ih/pwIbibh3yK3l0HHsS6EZ+7B8HdeSCfonfXgQexbsR14EFwNw/5FD1xdx6o7oXg7jyQT9G7fSAf6sOF4G4e8in6yLP7gaLv/Pl5PXF3P0A+DfBsnqL/88/P64m7ecinEdzdD1Ddoifu7gfIZ6+V68NroY99KHribh7y2QOenYeiH4F+9HPdPJBPAzybp+h7/+V5PXE3D/k0grvzQHWLfvRz3TyQz14r14fXQh/7UPTE3Tzksye4ux+g5+5BcHc/QD5F764DD2LdiJ/7g+BuHvIpeuLufoDqXgju7gfIp+jdPpAP9eFCcDcP+RS9uw48iHVjBPrRz3XzQD5FP6rMQ+vAg+BuHvIpeuLuPFDdC8HdeSCfoh9V5qE+XAju5iGfoicezwG2514+N4jv3duCR5+hqBt9hnO5c4BtoY/vfduCu3nIZyh47MPc/Mt9i3WLnnj0oX8HnHxO53N9mBP62IeiJ+7mIZ/T+dw5AH2O9N69Lbg7D+QznMudA2wLffy9vy24m4d8hoK780B1i564Ow/kczqf68Oc0Mc+FD1xNw/5nAoe14FX719eN+JzV/TEo8/G+5frRp+N97l14JXQx8+96Im7echnQ/DYh1vRt1j3VvDoQ38HgHyK3u3Drehb7MOt4G4e8hn/ncDXdfNAz13RE3fngXw23ufWgVdCH9eBoifu5iGfDcHdeaC6t4K780A+Re/24Vb0LfbhVnA3D/mM/86heQ5Av8fpvXtbcHc/QD7Dudw5wLbQx/e+bcHdPOQzFNzdD1Ddoifu7gfI53Q+14c5oY99KHribh7yOZ3PnQPQ50jv3duCu/NAPsO53DnAttDHc4Btwd085DMU3J0Hqlv0o5/r5oF8TudzfZgT+tiHoifu5iGfU8Hd/QA9d0VP3N0PkM/G+9w68Ero4+de9MTdPOSzIbi7H6C6t4K7+wHyKXq3D7eib7EPt4K7echn/HeSf66bB3ruin70c908kM/G+9w68Ero4zpQ9MTdPOSzIbg7D1T3VnB3Hsin6EeVeagPt4K7echn/Hc+A8/eByj6nf/6vJ64ex+AfBrg2TxFv/DfntcTd/OQTyO4ex+A6hY9cfc+APnstXJ9eC30sQ9FT9zNQz57wLPzUPT/8OZ5PXF3HsinAZ7NU/QD0A/e1OUhn0Zwdx6obtETd+eBfPZauT68FvoB6Adv6vKQz57g7n0Aeu4eBHfvA5BP0bvrwINYN+Ln/iC4m4d8ip64ex+A6l4I7t4HIJ+id/tAPtSHC8HdPORT9O468CDWjfjcPQjuzgP5FL27DjyIdWMA+sGbujzkU/TE3XmguheCu/NAPkXv9oF8qA8Xgrt5yKfoB5X7gaI/nnxeT9zdD5BPAzybp+jX/vF5PXE3D/k0grv7Aapb9MTd/QD57LVyfXgt9LEPRU/czUM+e8Cz81D0U1PP64m780A+DfBsnvH3/X95Xk/czUM+jeDuPFDdoifuzgP57LVyfXgt9LEP4+/p/1KXh3z2BHf3A/TcPQju7gfIp+jddeBBrBvxc38Q3M1DPkVP3N0PUN0Lwd39APkUvdsH8qE+XAju5iGfonfXgQexbsTn7kFwdx7Ip+jddeBBrBtxHXgQ3M1DPuPvuf9SNw9U90Jwdx7Ip+jdPpAP9eFCcDcP+Yzv+f/inQNsi+8LxPfubcHd+wDkM5zLnQNsC31879sW3M1DPkPB3fsAVLfoibv3AcjndD7Xhzmhj30oeuJuHvI5nc+dA9DnSO/d24K780A+w7ncOcC20A9AP3hTl4d8hoK780B1i564Ow/kczqf68Oc0A9AP3hTl4d8TgV37wPQc1f0xN37AOSz8T63DrwS+vi5Fz1xNw/5bAju3gegureCu/cByKfo3T7cir7FPtwK7uYhn/G/E/rf6uaBnruiJ+7OA/lsvM+tA6+EfgD6wZu6POSzIbg7D1T3VnB3Hsin6N0+3Iq+DUA/eFOXh3zG/85p5X6A3ru3BXf3A+QznMudA2wLfXzv2xbczUM+Q8Hd/QDVLXri7n6AfE7nc32YE/rYh6In7uYhn9P53DkAfY703r0tuDsP5DOcy50DbAt9PAfYFtzNQz5Dwd15oLpFT9ydB/I5nc/1YU7oYx/G39P/pS4P+ZwK7u4H6LkreuLufoB8Nt7n1oFXQh8/96In7uYhnw3B3f0A1b0V3N0PkE/Ru324FX2LfbgV3M1DPuN/J/0f6+aBnruiJ+7OA/lsvM+tA6+EPq4D4+/p/1KXh3w2BHfngereCu7OA/kUvduHW9G32Idbwd085DP+vj/x8HcAjoDT9+6PBI8+f7fwct3oU/Tu3wEgH/re95Hgbh7yKXrisQ+TCy/3LdadFDz6dETd6NNZyPVhUuhjHyYFd/OQTwd4dh7oe/dHgrvzQD5F7/4dAPKh7/0dCe7mIZ+iJ+7OA9WdFNydB/LpLOT6MCn0sQ+Tgrt5yKcjeFwH9sW6EZ+7fcGjT1/UjT795DqwL/Txc98X3M1DPn3BYx+eRN9i3SfBow/9O4DkU/RuH55E32IfngR385BP0bvrwL5YN+Jzty+4Ow/k00+uA/tCH9eBfcHdPOTTF9ydB6r7JLg7D+RT9G4fnkTfYh+eBHfzkE/Ru38HgH6P0/fujwR39wPkU/Tu3wEgH/re95Hgbh7yKXri7n6A6k4K7u4HyKezkOvDpNDHPkwK7uYhnw7w7DzQ9+6PBHfngXyKflSZh/67/5Hgbh7yKXri7jxQ3UnB3Xkgn85Crg+TQh/7MCm4m4d8OoK7+wF67vYFd/cD5NNPrgP7Qh8/933B3Tzk0xfc3Q9Q3SfB3f0A+RS924cn0bfYhyfB3TzkU/TuOrAv1o0R6Ec/180D+fST68C+0Md1YF9wNw/59AV354HqPgnuzgP5FP2oMg/14UlwNw/5FD3xeA7QXnz53CC+d7cFjz5dUTf6dBdz5wBtoY/vfW3B3Tzk0xU89uFS9C3WvRQ8+oxE3egzSvbhUuhjHy4Fd/OQz2gxdw7QFvr43t0W3J0H8uku5s4B2kIff++3BXfzkE9XcHceqO6l4O48kM8o2YdLoY99uBTczUM+I8HjOjCx9PK6EZ+7oicefVaWXq4bfVaWcuvAhNDHz73oibt5yGdF8NiHXdG3WHdX8OjTE3WjTy/Zh12hj33YFdzNQz69pdw6MCH08bkreuLuPJDPylJuHZgQ+rgOFD1xNw/5rAjuzgPV3RXcnQfy6SX7sCv0sQ+7grt5yKe3lDsHoN/j9N7dFtzdD5BPdzF3DtAW+vje1xbczUM+XcHd/QDVvRTc3Q+QzyjZh0uhj324FNzNQz6jxdw5QFvoR6Af/Vw3D+TTXcydA7SFPp4DtAV385BPV3B3HqjupeDuPJDPKNmHS6GPfbgU3M1DPiPB3f0APXdFT9zdD5DPylJuHZgQ+vi5Fz1xNw/5rAju7geo7q7g7n6AfHrJPuwKfezDruBuHvLpLeXWgQmhH4F+9HPdPJDPylJuHZgQ+rgOFD1xNw/5rAjuzgPV3RXcnQfy6SX7sCv0sQ+7grt5yKcHPHsfgL53fyS4ex+AfIre/TsA5EPf+z4S3M1DPkVP3L0PQHUnBXfvA5BPZyHXh0mhj32YFNzNQz4d4Nl5oO/dHwnuzgP5FL37dwDIh773dyS4m4d8ip64Ow9Ud1Jwdx7Ip7OQ68Ok0A9AP3hTl4d8OoK79wHoudsX3L0PQD795DqwL/Txc98X3M1DPn3B3fsAVPdJcPc+APkUvduHJ9G32Icnwd085FP07jqwL9aN+NztC+7OA/n0k+vAvtAPQD94U5eHfPqCu/NAdZ8Ed+eBfIre7cOT6NsA9IM3dXnIp+gHlfsB+t79keDufoB8it79OwDkQ9/7PhLczUM+RU/c3Q9Q3UnB3f0A+XQWcn2YFPrYh0nB3Tzk0wGenQf63v2R4O48kE/Ru38HgHzo7wAcCe7mIZ/x99x/qZsHqjspuDsP5NNZyPVhUuhjHyYFd/OQT0dwdz9Az92+4O5+gHz6yXVgX+jj574vuJuHfPqCu/sBqvskuLsfIJ+id/vwJPoW+/AkuJuHfIreXQf2xboRn7t9wd15IJ9+ch3YF/q4DuwL7uYhn77g7jxQ3SfB3Xkgn6J3+/Ak+hb78CS4m4d8xvf8f/HOAdri+wLxvbstuHsfgHy6i7lzgLbQx/e+tuBuHvLpCu7eB6C6l4K79wHIZ5Tsw6XQxz5cCu7mIZ/RYu4coC308b27Lbg7D+TTXcydA7SFfgD6wZu6POTTFdydB6p7Kbg7D+QzSvbhUugHoB+8qctDPiPB3fsA9NwVPXH3PgD5rCzl1oEJoY+fe9ETd/OQz4rg7n0AqrsruHsfgHx6yT7sCn3sw67gbh7y6S3l1oEJoY/PXdETd+eBfFaWcuvAhNAPQD94U5eHfFYEd+eB6u4K7s4D+fSSfdgV+gHoB2/q8pBPbyl3DkC/x+m9uy24ux8gn+5i7hygLfTxva8tuJuHfLqCu/sBqnspuLsfIJ9Rsg+XQh/7cCm4m4d8Rou5c4C20Mf37rbg7jyQT3cxdw7QFvp4DtAW3M1DPl3B3XmgupeCu/NAPqNkHy6FPvbhUnA3D/mMBHf3A/TcFT1xdz9APitLuXVgQujj5170xN085LMiuLsfoLq7grv7AfLpJfuwK/SxD7uCu3nIp7eUWwcmhD4+d0VP3J0H8llZyq0DE0If14Hx9/R/qctDPiuCu/NAdXcFd+eBfHrJPuwKfezDruBuHvLpKR5+/hX4WP9/3z6vJx5+/vRB1A0+Y33g2Txj/SPoH99W5SGfsZ54+Fn+IPoW6i4rHn52VN3gs/Mh14dlpX8E/ePbqjzkswM8Ow/jc7N/M/Osnrg7D+RT9JFn84z//34NOb+eqcpDPuP/v8TNeaC6y4K780A+Ox9yfVgW+tiHZcHdPOSzo3j4OVfrRnjuzhUPP/eqbvC5T64D50r/CPrHt1V5yOde8fDz3UfRt1B3rCcefhY+irrBZ+Fjrg/fKf0j6B/fVuUhn4WPuXXgXKwb8bk7F9ydB/K5T64D50If14Fzwd085HOvuDkPVLfoibvzQD4LH3N9+E7oYx/GOb+eqcpDPgvAs/uB8d8N+LfP64m7+wHyKfrIs3nG+6VvYJ/zTV0e8hnvE76p2w9Q3WXB3f0A+ex8yPVhWehjH5YFd/OQzw7w7DyMz83+3fN64u48kE/RR57NM37ev4Xn9Nu6POQznvNv6+aB6i4L7s4D+ex8yPVhWehjH5YFd/OQz47i5n6Anrtzwd39APncJ9eBc6GPn/u54G4e8rkX3N0PUN2iJ+7uB8hn4WOuD98JfezDeJ/zTV0e8ln4mFsHzsW6EZ+7c8HdeSCf++Q6cC70cR04F9zNQz73grvzQHWLnrg7D+Sz8DHXh++EPvZh/Jx+W5eHfBYUDz9b6twgvHdvKR5+zlTd4HOWPAfYUvpH0D++rcpDPmeKh5871bdQ907x8PMPn0Td4DPWm324U317BP3j26o85DPWP9bNA713bwnuzgP5nCXPAbaEPv7e3xLczUM+Z4qb80B17wR354F8it7tw53oW+zDneBuHvIZ//8lHn5an8S6EZ67luLhZ1PVDT6bn3LrQEvpH0H/+LYqD/lsKh5+TlTfQt0TxcPPQNUNPoNkH06U/hH0j2+r8pDP4FNuHWgJfXzuWoK780A+m59y60BL6OM60BLczUM+m4qb80B1TwR354F8Bsk+nAh97MOJ4G4e8hl8yp0DbIlzg/jevSW4ux8gn7PkOcCW0Mf3vi3B3Tzkcya4ux+guneCu/sB8il6tw93om+xD3eCu3nIZ7xOflM3D/TevSW4Ow/kc5Y8B9gS+ngOsCW4m4d8zgR354Hq3gnuzgP5FL3bhzvRt9iHO8HdPOQzft6/rdsP0HPXEtzdD5DP5qfcOtAS+vi5twR385DPpuDufoDqngju7gfIZ5Dsw4nQxz6cCO7mIZ/Bp9w60BL6+Ny1BHfngXw2P+XWgZbQx3WgJbibh3w2BXfngeqeCO7OA/kMkn04EfrYhxPB3TzkMwCevQ8w/nfT/v3zeuLufQDyKfrIs3nG94W+f15P3M1DPuN7Mt97fVgW9yVi3WXB3fsA5LPzIdeHZaGPfVgW3M1DPjvAs/Mw/ndD/sPzeuLuPJBP0UeezTO+7/TD83ribh7yGd/z+aFuHqjusuDuPJDPzodcH5aFPvZhWXA3D/nsCO7eB6Dn7lxw9z4A+dwn14FzoY+f+7ngbh7yuRfcvQ9AdYueuHsfgHwWPub68J3Qxz6M7/l8X5eHfBY+5taBc7FuxOfuXHB3HsjnPrkOnAt9XAfOBXfzkM+94O48UN2iJ+7OA/ksfMz14Tuhj30Y31P6oS4P+SwAz+4Hxn836U/P64m7+wHyKfrIs3nG96V/fF5P3M1DPuN7wj/W7Qeo7rLg7n6AfHY+5PqwLPSxD8uCu3nIZwd4dh7GfzfgPz6vJ+7OA/kUfeTZPOP3/Z/gPf2nujzkM37P/aluHqjusuDuPJDPzodcH5aFPvZhWXA3D/nsCO7uB+i5Oxfc3Q+Qz31yHTgX+vi5nwvu5iGfe8Hd/QDVLXri7n6AfBY+5vrwndDHPozvOf9Yl4d8Fj7m1oFzsW7E5+5ccHceyOc+uQ6cC31cB84Fd/OQz73g7jxQ3aIn7s4D+Sx8zPXhO6GPfRi/p/9Ul4d8FhQ37wPQe/eW4O59API5S54DbAl9fO/bEtzNQz5ngrv3AajuneDufQDyKXq3D3eib7EPd4K7echnfE/g+7p5oPfuLcHdeSCfs+Q5wJbQx9/7W4K7ecjnTHB3HqjuneDuPJBP0bt9uBN9i324E9zNQz7jew4/eOtAS9wXis9dS3D3PgD5bH7KrQMtoY+fe0twNw/5bAru3geguieCu/cByGeQ7MOJ0Mc+nAju5iGfwafcOtAS+vjctQR354F8Nj/l1oGW0Md1oCW4m4d8NgV354HqngjuzgP5DJJ9OBH62IcTwd085DP4lDsH2BLnBvG9e0twdz9APmfJc4AtoY/vfVuCu3nI50xwdz9Ade8Ed/cD5FP0bh/uRN9iH+4Ed/OQz/ie5I9180Dv3VuCu/NAPmfJc4AtoY/nAFuCu3nI50xwdx6o7p3g7jyQT9G7fbgTfYt9uBPczUM+433+T3X7AXruWoK7+wHy2fyUWwdaQh8/95bgbh7y2RTc3Q9Q3RPB3f0A+QySfTgR+tiHE8HdPOQz+JRbB1pCH5+7luDuPJDP5qfcOtAS+rgOtAR385DPpuDuPFDdE8HdeSCfQbIPJ0If+3AiuJuHfAaKh59vlp/n9L37sZ54+JldFnWDzyzwbB763vdY//i2Kg/5zCoeftZV30LddcXDz7GqG3yOk31YV/pH0D++rcpDPsfAs/NA37sveuLuPJDPLPBsHvre3zjn1zNVechnVnFzHqjuuuDuPJDPcbIP60If+7AuuJuHfI4VDz83at0Iz92N4uHn7z+LusFnrDfXgRu1bjyC/vFtVR7yGeuJh5/pz6Jvoe604uFnTdUNPmufc32YVvpH0D++rcpDPmufc+vAjVg34nN3I7g7D+RT9O46cCPWjbgO3Aju5iGf8f9f4uY8UN1pwd15IJ+1z7k+TAt97MO04G4e8lkDnt0P0Pfui564ux8gn1ng2Tz0ve/xPuebujzkMyu4ux+guuuCu/sB8jlO9mFd6GMf1gV385DPMfDsPND37oueuDsP5DMLPJuH/rv/+Dn9ti4P+cwK7s4D1V0X3J0H8jlO9mFd6GMf1gV385DPseLmfoCeuxvB3f0A+RS9uw7ciHUjfu43grt5yGe8T/imbj9AdacFd/cD5LP2OdeHaaGPfZgW3M1DPmufc+vAjVg34nN3I7g7D+RT9O46cCPWjbgO3Aju5iGf8Zx/WzcPVHdacHceyGftc64P00If+zAtuJuHfNYUDz+H6twgvHcfKh5+rlXd4HOdPAc4VPpH0D++rcpDPteKh5+vvoi+hbpjPfHwM/VF1A0+U19yffhK6R9B//i2Kg/5TH3JnQMcinOD+N59KLg7D+RznTwHOBT6+Hv/UHA3D/lcK27OA9UteuLuPJDP1JdcH74S+tiHcc6vZ6rykM+U4uFnVa0b4blbVTz8HKi6wecguQ6sKv0j6B/fVuUhnwPFw8+V6luoe6V4/PlnUTf4jPVmH65U3x5B//i2Kg/5jPWPdfNAz92q4O48kM9Bch1YFfq4DqwK7uYhnwPFzXmguleCu/NAPkXv9uFK9C324UpwNw/5jP//mucAh+LcIL53Hwru7gfI5zp5DnAo9PG971BwNw/5XAvu7geobtETd/cD5DP1JdeHr4Q+9mG8z/mmLg/5TH3JnQMcinOD+N59KLg7D+RznTwHOBT6eA5wKLibh3yuBXfngeoWPXF3Hshn6kuuD18JfezD+Dn9ti4P+Uwpbu4H6LlbFdzdD5DPQXIdWBX6+LmvCu7mIZ8Dwd39ANW9EtzdD5BP0bt9uBJ9i324EtzNQz7j/dI3dfNAz92q4O48kM9Bch1YFfq4DqwK7uYhnwPB3XmguleCu/NAPkXv9uFK9C324UpwNw/5jJ938+8A0H/Hp+/dFz1x9z4A+cwCz+ah732P7/l8X5eHfGYFd+8DUN11wd37AORznOzDutDHPqwL7uYhn2Pg2Xmg790XPXF3HshnFng2D33vb3xP6Ye6POQzK7g7D1R3XXB3HsjnONmHdaGPfVgX3M1DPseCu/cB6Lm7Edy9D0A+Re+uAzdi3Yif+43gbh7yGd+T+d7rw7S4LxHrTgvu3gcgn7XPuT5MC33sw7Tgbh7yWfucWwduxLoRn7sbwd15IJ+id9eBG7FuxHXgRnA3D/mM7/n8UDcPVHdacHceyGftc64P00If+zAtuJuHfNaAZ/cD9L37oifu7gfIZxZ4Ng9973t8z/nHujzkMyu4ux+guuuCu/sB8jlO9mFd6GMf1gV385DPMfDsPND37oueuDsP5DMLPJuH/g7A+D39p7o85DMruDsPVHddcHceyOc42Yd1oY99WBfczUM+x4K7+wF67m4Ed/cD5FP07jpwI9aN+LnfCO7mIZ/xPeEf6/YDVHdacHc/QD5rn3N9mBb62Idpwd085LP2ObcO3Ih1Iz53N4K780A+Re+uAzdi3YjrwI3gbh7yGb/n/lQ3D1R3WnB3Hshn7XOuD9NCH/swLbibh3zWFDfvA9B796Hg7n0A8rlOngMcCn187zsU3M1DPteCu/cBqG7RE3fvA5DP1JdcH74S+tiH8T2f7+vykM/Ul9w5wKE4N4jv3YeCu/NAPtfJc4BDoY+/9w8Fd/OQz7Xg7jxQ3aIn7s4D+Ux9yfXhK6GPfRjfU/qhLg/5TAnu3geg525VcPc+APkcJNeBVaGPn/uq4G4e8jkQ3L0PQHWvBHfvA5BP0bt9uBJ9i324EtzNQz7j+0Lf180DPXergrvzQD4HyXVgVejjOrAquJuHfA4Ed+eB6l4J7s4D+RS924cr0bfYhyvB3TzkM77vZJ4DHIpzg/jefSi4ux8gn+vkOcCh0Mf3vkPB3Tzkcy24ux+gukVP3N0PkM/Ul1wfvhL62IfxPecf6/KQz9SX3DnAoTg3iO/dh4K780A+18lzgEOhj+cAh4K7ecjnWnB3Hqhu0RN354F8pr7k+vCV0Mc+jN/Tf6rLQz5Tgrv7AXruVgV39wPkc5BcB1aFPn7uq4K7ecjnQHB3P0B1rwR39wPkU/RuH65E32IfrgR385DP+L70j3XzQM/dquDuPJDPQXIdWBX6uA6sCu7mIZ8Dwd15oLpXgrvzQD5F7/bhSvQt9uFKcDcP+Yzf9wPvLT3P/3XpZf3exPN64tHnTx9erht9ij7ybJ6i/+Pr5/XE3TzkU/TEYx+WP7zct1h3WfDosyPqRp+dD7k+LAt97MOy4G4e8tkBnp2Hoj/9T8/ribvzQD5FH3k2T9HP/Ofn9cTdPORT9MTdeaC6y4K780A+Ox9yfVgW+tiHZcHdPOSzI3hcB87FuhGfu3PBo8+9qBt97pPrwLnQx8/9XHA3D/ncCx778N3Hl/sW6xY98ehD/w4g+Sx8zPXhO6GPfSh64m4e8ln4mFsHzsW6EZ+7c8HdeSCf++Q6cC70cR04F9zNQz73grvzQHWLnrg7D+Sz8DHXh++EPvah6Im7echnAXh2P1D0nT8/ryfu7gfIp+gjz+Yp+j///LyeuJuHfIqeuLsfoLrLgrv7AfLZ+ZDrw7LQxz4sC+7mIZ8d4Nl5KPoR6Ec/180D+RT9qDJP0ff+y/N64m4e8il64u48UN1lwd15IJ+dD7k+LAt97MOy4G4e8tkR3N0P0HN3Lri7HyCf++Q6cC708XM/F9zNQz73grv7Aapb9MTd/QD5LHzM9eE7oY99KHribh7yWfiYWwfOxboxAv3o57p5IJ/75DpwLvRxHTgX3M1DPveCu/NAdYt+9HPdPJDPwsdcH74T+tiHoifu5iGfBcHjOcCWODeI791bgkefM1E3+pwlzwG2hD6+920J7uYhnzPBYx/uRN9i3TvBow/9O+DkU/RuH+5E32If7gR385BP0bvnAFvi3CC+d28J7s4D+ZwlzwG2hD7+3t8S3M1DPmeCu/NAde8Ed+eBfIre7cOd6Fvsw53gbh7yKXricR1ofXp53YjPXUvw6LMp6kafzU+5daAl9PFzbwnu5iGfTcFjH05E32LdE8Gjz0DUjT6DZB9OhD724URwNw/5DD7l1oGW0MfnriW4Ow/ks/kptw60hD6uAy3B3Tzksym4Ow9U90Rwdx7IZ5Dsw4nQxz6cCO7mIZ/Bp9w5wJY4N4jv3VuCu/sB8jlLngNsCX1879sS3M1DPmeCu/sBqnsnuLsfIJ+id/twJ/oW+3AnuJuHfIrePQfYEucGI9CPfq6bB/I5S54DbAl9PAfYEtzNQz5ngrvzQHXvBHfngXyKflSZh/pwJ7ibh3yKnri7H6DnriW4ux8gn81PuXWgJfTxc28J7uYhn03B3f0A1T0R3N0PkM8g2YcToY99OBHczUM+g0+5daAl9CPQj36umwfy2fyUWwdaQh/XgZbgbh7y2RTcnQeqeyK4Ow/kM0j24UToYx9OBHfzkM8AePY+QNHv/Nfn9cTd+wDkU/SRZ/MU/cJ/e15P3M1DPkVP3L0PQHWXBXfvA5DPzodcH5aFPvZhWXA3D/nsAM/OQ9H/w5vn9cTdeSCfoo88m6foB6AfvKnLQz5FT9ydB6q7LLg7D+Sz8yHXh2WhH4B+8KYuD/nsCO7eB6Dn7lxw9z4A+dwn14FzoY+f+7ngbh7yuRfcvQ9AdYueuHsfgHwWPub68J3Qxz4UPXE3D/ksfMytA+di3YjP3bng7jyQz31yHTgX+gHoB2/q8pDPveDuPFDdoifuzgP5LHzM9eE7oR+AfvCmLg/5LADP7geK/njyeT1xdz9APkUfeTZP0a/94/N64m4e8il64u5+gOouC+7uB8hn50OuD8tCH/uwLLibh3x2gGfnoeinpp7XE3fngXyKPvJsnvH3/X95Xk/czUM+4++5/1I3D1R3WXB3Hshn50OuD8tCH/uwLLibh3x2BHf3A/TcnQvu7gfI5z65DpwLffzczwV385DPveDufoDqFj1xdz9APgsfc334TuhjH4qeuJuHfBY+5taBc7FuxOfuXHB3HsjnPrkOnAt9XAfOBXfzkM+94O48UN2iJ+7OA/ksfMz14Tuhj30Yf0//l7o85LMguHsfgN67twR37wOQz1nyHGBL6ON735bgbh7yORPcvQ9Ade8Ed+8DkE/Ru324E32LfbgT3M1DPkXvngNsiXOD+N69Jbg7D+RzljwH2BL6AegHb+rykM+Z4O48UN07wd15IJ+id/twJ/o2AP3gTV0e8il64u59AHruWoK79wHIZ/NTbh1oCX383FuCu3nIZ1Nw9z4A1T0R3L0PQD6DZB9OhD724URwNw/5DD7l1oGW0MfnriW4Ow/ks/kptw60hH4A+sGbujzksym4Ow9U90Rwdx7IZ5Dsw4nQD0A/eFOXh3wGn3LnAFvi3CC+d28J7u4HyOcseQ6wJfTxvW9LcDcP+ZwJ7u4HqO6d4O5+gHyK3u3Dnehb7MOd4G4e8il69xxgS5wbxPfuLcHdeSCfs+Q5wJbQx3OALcHdPORzJrg7D1T3TnB3Hsin6N0+3Im+xT7cCe7mIZ/x93x/qdsP0HPXEtzdD5DP5qfcOtAS+vi5twR385DPpuDufoDqngju7gfIZ5Dsw4nQxz6cCO7mIZ/Bp9w60BL6+Ny1BHfngXw2P+XWgZbQx3WgJbibh3w2BXfngeqeCO7OA/kMkn04EfrYhxPB3TzkM1A8/B2Ab+Dfcabv3Rc98egzu/xy3egzCzybh773XfTE3TzkMyt47MO66Fusuy549DkWdaPPcbIP60If+7AuuJuHfI6BZ+eBvndf9MTdeSCfWeDZPPS9v6In7uYhn1nB3XmguuuCu/NAPsfJPqwLfezDuuBuHvI5FjyuAzdi3YjP3Y3g0efvP79cN/oUvbsO3Ih1I37uN4K7ecin6InHPkx/frlvse604NFnTdSNPmufc32YFvrYh2nB3Tzks/Y5tw7ciHUjPnc3grvzQD5F764DN2LdiOvAjeBuHvIpeuLuPFDdacHdeSCftc+5PkwLfezDtOBuHvJZA57dD9D37oueuLsfIJ9Z4Nk89L3voifu5iGfWcHd/QDVXRfc3Q+Qz3GyD+tCH/uwLribh3yOgWfngb53X/Sjn+vmgXxmgWfz0H/3L3ribh7ymRXcnQequy64Ow/kc5zsw7rQxz6sC+7mIZ9jwd39AD13N4K7+wHyKXp3HbgR60b83G8Ed/OQT9ETd/cDVHdacHc/QD5rn3N9mBb62Idpwd085LP2ObcO3Ih1YwT60c9180A+RT+qzEPrwI3gbh7yKXri7jxQ3WnB3Xkgn7XPuT5MC33sw7Tgbh7yWRM8ngMcinOD+N59KHj0uRZ1o8918hzgUOjje9+h4G4e8rkWPPbhqy8v9y3WLXri0Yf+HXDymfqS68NXQh/7UPTE3TzkM/Uldw5wKM4N4nv3oeDuPJDPdfIc4FDo4+/9Q8HdPORzLbg7D1S36Im780A+U19yffhK6GMfip64m4d8pgSP68CqWDfic7cqePQ5EHWjz0FyHVgV+vi5rwru5iGfA8FjH65E32LdK8Gjzx/++eW60afo3T5cib7FPlwJ7uYhn6J314FVsW7E525VcHceyOcguQ6sCn1cB1YFd/OQz4Hg7jxQ3SvB3Xkgn6J3+3Al+hb7cCW4m4d8it49BzgU5wbxvftQcHc/QD7XyXOAQ6GP732Hgrt5yOdacHc/QHWLnri7HyCfqS+5Pnwl9LEPRU/czUM+U19y5wCH4txgBPrRz3XzQD7XyXOAQ6GP5wCHgrt5yOdacHceqG7Rj36umwfymfqS68NXQh/7UPTE3TzkMyW4ux+g525VcHc/QD4HyXVgVejj574quJuHfA4Ed/cDVPdKcHc/QD5F7/bhSvQt9uFKcDcP+RS9uw6sinVjBPrRz3XzQD4HyXVgVejjOrAquJuHfA4Ed+eB6l4J7s4D+RT9qDIP9eFKcDcP+RS9+3cA6L/j0/fui564ex+AfGaBZ/PQ976Lnribh3xmBXfvA1DddcHd+wDkc5zsw7rQxz6sC+7mIZ9j4Nl5oO/dFz1xdx7IZxZ4Ng9976/oB2/q8pDPrODuPFDddcHdeSCf42Qf1oV+APrBm7o85HMsuHsfgJ67G8Hd+wDkU/TuOnAj1o34ud8I7uYhn6In7t4HoLrTgrv3Achn7XOuD9NCH/swLbibh3zWPufWgRuxbsTn7kZwdx7Ip+jddeBGrBsD0A/e1OUhn6In7s4D1Z0W3J0H8ln7nOvDtNAPQD94U5eHfNaAZ/cD9L37oifu7gfIZxZ4Ng9977voibt5yGdWcHc/QHXXBXf3A+RznOzDutDHPqwL7uYhn2Pg2Xmg790XPXF3HshnFng2D/0dgPH39H+py0M+s4K780B11wV354F8jpN9WBf62Id1wd085HMsuLsfoOfuRnB3P0A+Re+uAzdi3Yif+43gbh7yKXri7n6A6k4L7u4HyGftc64P00If+zAtuJuHfNY+59aBG7FuxOfuRnB3Hsin6N114EasG3EduBHczUM+4++5/1I3D1R3WnB3Hshn7XOuD9NCH/swLbibh3zWBHfvA9B796Hg7n0A8rlOngMcCn187zsU3M1DPteCu/cBqG7RE3fvA5DP1JdcH74S+tiHoifu5iGfqS+5c4BDcW4Q37sPBXfngXyuk+cAh0I/AP3gTV0e8rkW3J0Hqlv0xN15IJ+pL7k+fCX0A9AP3tTlIZ8pwd37APTcrQru3gcgn4PkOrAq9PFzXxXczUM+B4K79wGo7pXg7n0A8il6tw9Xom+xD1eCu3nIp+jddWBVrBvxuVsV3J0H8jlIrgOrQj8A/eBNXR7yORDcnQeqeyW4Ow/kU/RuH65E3wagH7ypy0M+RT+o3A/Qe/eh4O5+gHyuk+cAh0If3/sOBXfzkM+14O5+gOoWPXF3P0A+U19yffhK6GMfip64m4d8pr7kzgEOxblBfO8+FNydB/K5Tp4DHAp9PAc4FNzNQz7XgrvzQHWLnrg7D+Qz9SXXh6+EPvZh/D39X+rykM+U4O5+gJ67VcHd/QD5HCTXgVWhj5/7quBuHvI5ENzdD1DdK8Hd/QD5FL3bhyvRt9iHK8HdPORT9O46sCrWjfjcrQruzgP5HCTXgVWhj+vAquBuHvI5ENydB6p7Jbg7D+RT9G4frkTfYh+uBHfzkM/4+/6R/9Pz/F/+6WV9A/rG9GlE3Qb0TWWeot/778/ribt5yKcRPPbhdevlvjWgb0yfvdbLdRvQN5V5qA9FT9zNQz57wLPzUPRf/sfzeuLuPJBPAzybp+j/+D+f1xN385BPI7g7D1S36Im780A+e61cH14LfexD0RN385DPnuBxHXgQ60YD+sb0+fK3l+s2oG8q89Dn/iC4m4d8ip547MPF317uWwP6xvT549zLdRvQN5V5qA8Xgrt5yKfo3XXgQawb8bl7ENydB/IpencdeBDrRlwHHgR385BP0RN354HqXgjuzgP5FL3bB/KhPlwI7uYhn6KPPLsfKPoh6IemTyPqDkE/rMxT9Kf/63k9cTcP+TSCu/sBqlv0Q9NnT+w3hqAfVuahPhQ9cTcP+ewBz85D0W9MP68n7s4D+TTAs3mKfubt83ribh7yaQR354HqFj1xdx7IZ6+V68NroY99KHribh7y2RPc3Q/Qc/cguLsfIJ+iH1bmoc/9QXA3D/kUPXF3P0B1LwR39wPkU/TDyjzUhwvB3TzkU/TuOvAg1o343D0I7s4D+RS9uw48iHUjrgMPgrt5yKfoibvzQHUvBHfngXyK3u0D+VAfLgR385BP0ROP5wDbcy+fGzSgb0yfoajbgL6pzEPvfduCu3nIZyh47MPc/Mt9a0DfmD6n8y/XbUDfVOahPhQ9cTcP+ZzO584B6HOk9+5twd15IJ/hXO4cYFvo4+/9bcHdPOQzFNydB6pb9MTdeSCf0/lcH+aEPvah6Im7ecjnVPC4Drx6//K60YC+MX023r9ctwF9U5mHPveiJ+7mIZ8NwWMfbkXfGtA3ps/M/3m5bgP6pjIP9eFWcDcP+RS9uw7Q50jPXdETd+eBfDbe59aBV0If14GiJ+7mIZ8Nwd15oLq3grvzQD5F7/bhVvQt9uFWcDcP+RS9ew5Av8fpvXtbcHc/QD7Dudw5wLbQx/e+bcHdPOQzFNzdD1Ddoh+aPqdivzEE/bAyD/Wh6Im7ecjndD53DkCfI713bwvuzgP5DOdy5wDbQh/PAbYFd/OQz1Bwdx6obtETd+eBfE7nc32YE/rYh6In7uYhn1PB3f0APXdFPzR9NsR+Ywj6YWUe+tyLnribh3w2BHf3A1T3VnB3P0A+RT+szEN9uBXczUM+Re+uA/Q50nNX9MTdeSCfjfe5deCV0Md1oOiJu3nIZ0Nwdx6o7q3g7jyQT9G7fbgVfYt9uBXczUM+RR959j5A0f/dzPN64u59APJpgGfzFH0H9J2Zujzk0wju3gegukVP3L0PQD57rVwfXgt9B/Sdmbo85LMHPDsPRd8Hfb9yHsinAZ7NU/R//t/P64m7ecinEdydB6pb9P3KeSCfvVauD6+FPvah6Im7echnT3D3PgA9dw+Cu/cByKfo3XXgQawbHdB3ZurykE/RE3fvA1DdC8Hd+wDkU/RuH8iH+nAhuJuHfIq+UzkP9Nw9CO7OA/kUfb8yD60DD4K7ecin6Im780B1LwR354F8ir5fmYf6cCG4m4d8ij7y7H6g6Lug75o+jajbBX23Mk/Rj0A/qsxDPo3g7n6A6hZ91/TZE/uNLui7lXmoD0U/qsxDPnvAs/NQ9CvvntcTd+eBfBrg2TxF3wN9711dHvJpBHfngeoWPXF3Hshnr5Xrw2uh74G+964uD/nsCe7uB+i5exDc3Q+QT9F3K/PQ5/4guJuHfIp+ZPbhQuyXuqDvmj60HyCfou9W5qE+XAju5iGfoh9VzgM9dw+Cu/NAPkXvrgMPYt3ogb73ri4P+RQ9cXceqO6F4O48kE/Ru30gH+rDheBuHvIpeuLufQB6794W3L0PQD7Dudw5wLbQd0DfmanLQz5Dwd37AFS36Im79wHI53Q+14c5oe+AvjNTl4d8Tudz5wD0OdJ797bg7jyQz3Audw6wLfTx9/624G4e8hkK7s4D1S36fuU8kM/pfK4Pc0If+1D0xN085HMquHsfgJ67oifu3gcgn433uXXgldB3QN+ZqctDPhuCu/cBqO6t4O59APIpercPt6JvHdB3ZurykE/RdyrngZ67ou9XzgP5bLzPrQOvhD6uA0VP3M1DPhuCu/NAdW8Fd+eBfIq+X5mH+nAruJuHfIrePQeg3+P03r0tuLsfIJ/hXO4cYFvoR6AfVeYhn6Hg7n6A6hZ91/Q5FfuNLui7lXmoD0U/qsxDPqfzuXMA+hzpvXtbcHceyGc4lzsH2Bb6Huh77+rykM9QcHceqG7RE3fngXxO53N9mBP6Huh77+rykM+p4O5+gJ67ou+aPhtiv9EFfbcyD33uRT+qzEM+G4K7+wGqeyu4ux8gn6LvVuahPtwK7uYhn6IfVc4DPXdFT9ydB/LZeJ9bB14JfQ/0vXd1echnQ3B3HqjureDuPJBP0bt9uBV964G+964uD/kUPfH4dwCOgNP37o8Ejz5/t/By3Qb0TWUe+t73keBuHvIpeuKxD5MLL/etAX1j+nRE3Qb0TWUe6sOk4G4e8ukAz84Dfe/+SHB3Hsin6N2/A0A+9L2/I8HdPORT9MTdeaC6k4K780A+nYVcHyaFPvZhUnA3D/l0BI/rwL5YNxrQN6ZPX9RtQN9U5qHPfV9wNw/59AWPfXgSfWtA35g+f158uW4D+qYyD/XhSXA3D/kUvbsO7It1Iz53+4K780A+/eQ6sC/0cR3YF9zNQz59wd15oLpPgrvzQD5F7/bhSfQt9uFJcDcP+RS9+3cA6Pc4fe/+SHB3P0A+RT+szEPf+z4S3M1DPkVP3N0PUN1Jwd39APl0FnJ9mBT62IdJwd085NMBnp0H+t79keDuPJBP0bt/B4B86L/7Hwnu5iGfoifuzgPVnRTcnQfy6Szk+jAp9LEPk4K7ecinI7i7H6Dnbl9wdz9APv3kOrAv9PFz3xfczUM+fcHd/QDVfRLc3Q+QT9EPK/NQH54Ed/OQT9G768C+WDfic7cvuDsP5NNPrgP7Qh/XgX3B3Tzk0xfcnQeq+yS4Ow/kU/RuH55E32IfngR385BP0ROP5wDtxZfPDRrQN6ZPV9RtQN9U5qH3vrbgbh7y6Qoe+3Ap+taAvjF9RqJuA/qmMg/14VJwNw/5jBZz5wBtoY/v3W3B3Xkgn+5i7hygLfTx935bcDcP+XQFd+eB6l4K7s4D+YySfbgU+tiHS8HdPOQzEjyuAxNLL68bDegb02dl6eW6Deibyjz0uRc9cTcP+awIHvuwK/rWgL4xfXqibgP6pjIP9WFXcDcP+fSWcuvAhNDH567oibvzQD4rS7l1YELo4zpQ9MTdPOSzIrg7D1R3V3B3Hsinl+zDrtDHPuwK7uYhn95S7hyAfo/Te3dbcHc/QD7dxdw5QFvo43tfW3A3D/l0BXf3A1T3UnB3P0A+o2QfLoU+9uFScDcP+YwWc+cAbaGP791twd15IJ/uYu4coC308RygLbibh3y6grvzQHUvBXfngXxGyT5cCn3sw6Xgbh7yGQnu7gfouSv6oemzIvYbQ9APK/PQ5170xN085LMiuLsfoLq7grv7AfLpJfuwK/SxD7uCu3nIp7eUWwcmhD4+d0VP3J0H8llZyq0DE0If14GiJ+7mIZ8Vwd15oLq7grvzQD69ZB92hT72YVdwNw/59IBn7wPQ9+6PBHfvA5BP0bt/B4B86HvfR4K7ecin6Im79wGo7qTg7n0A8uks5PowKfQd0Hdm6vKQTwd4dh7oe/dHgrvzQD5F36/MQ9/7OxLczUM+RU/cnQeqOym4Ow/k01nI9WFS6GMfJgV385BPR3D3PgA9d/uCu/cByKefXAf2hb4D+s5MXR7y6Qvu3geguk+Cu/cByKfo3T48ib51QN+ZqctDPkXfqZwHeu72BXfngXz6yXVgX+jjOrAvuJuHfPqCu/NAdZ8Ed+eBfIq+X5mH+vAkuJuHfIre/TsA9Hucvnd/JLi7HyCfou9W5qHvfR8J7uYhn6IfmX2YFPulLui7pk9H1O2CvluZh/owKbibh3w6wLPzQN+7PxLcnQfyKXr37wCQD/0dgCPB3TzkU/TE3XmgupOCu/NAPp2FXB8mhb4H+t67ujzk0xHc3Q/Qc7cvuLsfIJ9+ch3YF/oR6EeVecinL7i7H6C6T4K7+wHyKfpuZR7qw5Pgbh7yKfpR5TzQc7cvuDsP5NNPrgP7Qt8Dfe9dXR7y6QvuzgPVfRLcnQfyKXq3D0+ibz3Q997V5SGfoifu3geg9+624O59APLpLubOAdpC3wF9Z6YuD/l0BXfvA1DdS8Hd+wDkM0r24VLoO6DvzNTlIZ/RYu4coC30fdD3K+eBfLqLuXOAttDH3/ttwd085NMV3J0HqnspuDsP5DNK9uFS6GMfLgV385DPSHD3PgA9d0VP3L0PQD4rS7l1YELoO6DvzNTlIZ8Vwd37AFR3V3D3PgD59JJ92BX6Dug7M3V5yKe3lFsHJoS+D/p+5TyQz8pSbh2YEPq4DhQ9cTcP+awI7s4D1d0V3J0H8ukl+7Ar9LEPu4K7ecint5Q7B6Df4/Te3Rbc3Q+QT3cxdw7QFvoR6EeVecinK7i7H6C6l4K7+wHyGSX7cCn0I9CPKvOQz2gxdw7QFvr43t0W3J0H8uku5s4B2kLfA33vXV0e8ukK7s4D1b0U3J0H8hkl+3Ap9D3Q997V5SGfkeDufoCeu6Lvmj4rYr/RBX23Mg997kU/qsxDPiuCu/sBqrsruLsfIJ9esg+7Qj8C/agyD/n0lnLrwITQx+eu6Im780A+K0u5dWBC6Hug772ry0M+K4K780B1dwV354F8esk+7Ap9D/S9d3V5yKcH/A/w79H/yz+9rP/Tr8/riUefRtSNPg3wbJ6i3wH9zq91ecinETz24XXr5b7FukVPPPrQvwNOPnutXB9eC/0O6Hd+rctDPnvAs/NQ9Pegv6+cB/JpgGfzFP3C7PN64m4e8mkEd+eB6hb9feU8kM9eK9eH10If+1D0xN085LMneFwHHsS6EZ+7B8Gjz5e/vVw3+hS9uw48iHVjB/Q7v9blIZ+iJx77cPG3l/sW614IHn3o3wEkn6J3+0A+1IcLwd085FP0O5XzQM/dg+DuPJBP0d9X5qF14EFwNw/5FD1xdx6o7oXg7jyQT9HfV+ahPlwI7uYhn6KPPLsfKPoz0J+ZPo2oewb6s8o8Rf8Pf3leT9zNQz6N4O5+gOoW/Znpsyf2G2egP6vMQ30oeuJuHvLZA56dh6LfBP3mX+rmgXwa4Nk8RT8A/aAyD/k0grvzQHWLfvMvdfNAPnutXB9eC/0A9IPKPOSzJ7i7H6Dn7kFwdz9APkV/VpmHPvcHwd085FP0xN39ANW9ENzdD5BP0Z9V5qE+XAju5iGfonfXgQexbmyCfvMvdfNAPkW/WZmH1oEHwd085FP0g7/UzQPVvRDcnQfyKfrNyjzUhwvB3TzkU/TE4znA9tzL5wbxvXtb8OgzFHWjz3Audw6wLfQ7oN/5tS4P+QwFj32Ym3+5b7Fu0ROPPvTvgJPP6XyuD3NCvwP6nV/r8pDP6XzuHIA+R3rv3hbcnQfyGc7lzgG2hT7+3t8W3M1DPkPB3XmgukV/XzkP5HM6n+vDnNDHPhQ9cTcP+ZwKHteBV+9fXjfic1f0xKPPxvuX60afjfe5deCV0O+AfufXujzksyF47MOt6Fuseyt49KG/A0A+Re/24Vb0bQf0O7/W5SGfot+pnAd67or+vnIeyGfjfW4deCX0cR0oeuJuHvLZENydB6p7K7g7D+RT9PeVeagPt4K7ecin6N1zAPo9Tu/d24K7+wHyGc7lzgG2hT6+920L7uYhn6Hg7n6A6hb9melzKvYbZ6A/q8xDfSh64m4e8jmdz50D0OdI793bgrvzQD7Dudw5wLbQD0A/qMxDPkPB3XmgukW/+Ze6eSCf0/lcH+aEfgD6QWUe8jkV3N0P0HNX9Gemz4bYb5yB/qwyD33uRU/czUM+G4K7+wGqeyu4ux8gn6I/q8xDfbgV3M1DPkXvrgP0OdJzV/Sbf6mbB/LZeJ9bB14J/QD0g8o85LMhuDsPVPdWcHceyKfoNyvzUB9uBXfzkE/RDyrvAxT97G/P64m79wHIpwGezVP0x6A//q0uD/k0grv3Aahu0RN37wOQz14r14fXQn8M+uPf6vKQzx7w7DwU/d///ryeuDsP5NMAz+Yp+jXQr/1el4d8GsHdeaC6RU/cnQfy2Wvl+vBa6NdAv/Z7XR7y2RPcvQ9Az92D4O59APIpencdeBDrxjHoj3+ry0M+RU/cvQ9AdS8Ed+8DkE/Ru30gH+rDheBuHvIp+uPKeaDn7kFwdx7Ip+jddeBBrBtroF/7vS4P+RQ9cXceqO6F4O48kE/Ru30gH+rDheBuHvIp+rXK/UDRX4P+unI/QD4N8Gyeop/66/N64m4e8mkEd/cDVLforyv3A+Sz18r14bXQxz4UPXE3D/nsAc/OQ9EfgP7gr3XzQD4N8Gyeoh//b9ATd/OQTyO4Ow9Ut+gP/lo3D+Sz18r14bXQxz4UPXE3D/nsCe7uB+i5exDc3Q+QT9FfV+ahz/1BcDcP+RQ9cXc/QHUvBHf3A+RT9NeVeagPF4K7ecin6N114EGsGwegP/hr3TyQT9EfVOahdeBBcDcP+RQ9cXceqO6F4O48kE/RH1TmoT5cCO7mIZ+iJ+7eB6D37m3B3fsA5DOcy50DbAv9MeiPf6vLQz5Dwd37AFS36Im79wHI53Q+14c5oT8G/fFvdXnI53Q+dw5AnyO9d28L7s4D+QzncucA20K/Bvq13+vykM9QcHceqG7RE3fngXxO53N9mBP6NdCv/V6Xh3xOBXfvA9BzV/TE3fsA5LPxPrcOvBL6Y9Af/1aXh3w2BHfvA1DdW8Hd+wDkU/RuH25F345Bf/xbXR7yKfrjynmg567oibvzQD4b73PrwCuhXwP92u91echnQ3B3HqjureDuPJBP0bt9uBV9WwP92u91ecin6Ncq9wP03r0tuLsfIJ/hXO4cYFvo43vftuBuHvIZCu7uB6hu0V9X7gfI53Q+14c5oY99KHribh7yOZ3PnQPQ50jv3duCu/NAPsO53DnAttDHc4Btwd085DMU3J0Hqlv0B3+tmwfyOZ3P9WFO6GMfip64m4d8TgV39wP03BX9deV+gHw23ufWgVdCHz/3oifu5iGfDcHd/QDVvRXc3Q+QT9FfV+ahPtwK7uYhn6J31wH6HOm5K/qDv9bNA/lsvM+tA6+EPq4DRU/czUM+G4K780B1bwV354F8iv6gMg/14VZwNw/5FD3x+HcAjoDT9+6PBI8+f7fwct3oU/Tu3wEgH/re95Hgbh7yKXrisQ+TCy/3LdadFDz6dETd6NNZyPVhUuh3QL/za10e8ukAz84Dfe/+SHB3Hsin6O8r89D3/o4Ed/OQT9ETd+eB6k4K7s4D+XQWcn2YFPrYh0nB3Tzk0xE8rgP7Yt2Iz92+4NGnL+pGn35yHdgX+h3Q7/xal4d8+oLHPjyJvsW6T4JHH/p3AMmn6N0+PIm+7YB+59e6PORT9DuV80DP3b7g7jyQTz+5DuwLfVwH9gV385BPX3B3Hqjuk+DuPJBP0d9X5qE+PAnu5iGfonf/DgD9Hqfv3R8J7u4HyKfozyrz0Pe+jwR385BP0RN39wNUd1Jwdz9APp2FXB8mhT72YVJwNw/5dIBn54G+d38kuDsP5FP0m5V56L/7Hwnu5iGfoh/8pW4eqO6k4O48kE9nIdeHSaEfgH5QmYd8OoK7+wF67vYFd/cD5NNPrgP7Qh8/933B3Tzk0xfc3Q9Q3SfB3f0A+RT9WWUe6sOT4G4e8il6dx3YF+vGJug3/1I3D+TTT64D+0I/AP2gMg/59AV354HqPgnuzgP5FP1mZR7qw5Pgbh7yKXri8RygvfjyuUF8724LHn26om706S7mzgHaQr8D+p1f6/KQT1fw2IdL0bdY91Lw6DMSdaPPKNmHS6HfAf3Or3V5yGe0mDsHaAv9PejvK+eBfLqLuXOAttDH3/ttwd085NMV3J0HqnspuDsP5DNK9uFS6GMfLgV385DPSPC4DkwsvbxuxOeu6IlHn5Wll+tGn5Wl3DowIfQ7oN/5tS4P+awIHvuwK/oW6+4KHn16om706SX7sCv0O6Df+bUuD/n0lnLrwITQ34P+vnIeyGdlKbcOTAh9XAeKnribh3xWBHfngeruCu7OA/n0kn3YFfrYh13B3Tzk01vKnQPQ73F6724L7u4HyKe7mDsHaAt9fO9rC+7mIZ+u4O5+gOpeCu7uB8hnlOzDpdDHPlwK7uYhn9Fi7hygLfSboN/8S908kE93MXcO0Bb6AegHlXnIpyu4Ow9U91Jwdx7IZ5Tsw6XQD0A/qMxDPiPB3f0APXdFf2b6rIj9xhnozyrz0Ode9MTdPOSzIri7H6C6u4K7+wHy6SX7sCv0sQ+7grt5yKe3lFsHJoR+E/Sbf6mbB/JZWcqtAxNCPwD9oDIP+awI7s4D1d0V3J0H8ukl+7Ar9APQDyrzkE8PePY+AH3v/khw9z4A+RS9+3cAyIe+930kuJuHfIqeuHsfgOpOCu7eByCfzkKuD5NCfwz649/q8pBPB3h2Huh790eCu/NAPkXv/h0A8qHv/R0J7uYhn6In7s4D1Z0U3J0H8uks5PowKfRroF/7vS4P+XQEd+8D0HO3L7h7H4B8+sl1YF/oj0F//FtdHvLpC+7eB6C6T4K79wHIp+jdPjyJvh2D/vi3ujzkU/THlfNAz92+4O48kE8/uQ7sC/0a6Nd+r8tDPn3B3Xmguk+Cu/NAPkXv9uFJ9G0N9Gu/1+Uhn6Jfq9wP0PfujwR39wPkU/TXlXnoe99Hgrt5yKfoibv7Aao7Kbi7HyCfzkKuD5NCH/swKbibh3w6wLPzQN+7PxLcnQfyKfqDyjz0dwCOBHfzkE/RE3fngepOCu7OA/l0FnJ9mBT62IdJwd085NMR3N0P0HO3L7i7HyCffnId2Bf6+LnvC+7mIZ++4O5+gOo+Ce7uB8in6K8r81AfngR385BP0bvrwL5YNw5Af/DXunkgn35yHdgX+rgO7Avu5iGfvuDuPFDdJ8HdeSCfoj+ozEN9eBLczUM+RU/cvQ9A791twd37AOTTXcydA7SF/hj0x7/V5SGfruDufQCqeym4ex+AfEbJPlwK/THoj3+ry0M+o8XcOUBb6ON7d1twdx7Ip7uYOwdoC/0a6Nd+r8tDPl3B3XmgupeCu/NAPqNkHy6Ffg30a7/X5SGfkeDufQB67oqeuHsfgHxWlnLrwITQH4P++Le6POSzIrh7H4Dq7gru3gcgn16yD7tCfwz649/q8pBPbym3DkwIfXzuip64Ow/ks7KUWwcmhH4N9Gu/1+UhnxXB3XmguruCu/NAPr1kH3aFfg30a7/X5SGf3lLuHIB+j9N7d1twdz9APt3F3DlAW+jje19bcDcP+XQFd/cDVPdScHc/QD6jZB8uhT724VJwNw/5jBZz5wBtoT8A/cFf6+aBfLqLuXOAttDHc4C24G4e8ukK7s4D1b0U3J0H8hkl+3Ap9LEPl4K7echnJLi7H6DnruivK/cD5LOylFsHJoQ+fu5FT9zNQz4rgrv7Aaq7K7i7HyCfXrIPu0If+7AruJuHfHpLuXVgQugPQH/w17p5IJ+Vpdw6MCH0cR0oeuJuHvJZEdydB6q7K7g7D+TTS/ZhV+hjH3YFd/OQT0/xX/7//F+BF30D+sb0+dOHl+s2oG8q8xT93n9/Xk/czUM+RU889mH5w8t9a0DfmD47om4D+qYyD/VhWXA3D/nsAM/OQ9F/+R/P64m780A+RR95Nk/R//F/Pq8n7uYhn6In7s4D1V0W3J0H8tn5kOvDstDHPiwL7uYhnx3B4zpwLtaNBvSN6XMv6jagbyrz0Od+Lribh3zuBY99+O7jy31rQN+YPgsfX67bgL6pzEN9KHribh7yWfiYWwfOxboRn7tzwd15IJ/75DpwLvRxHTgX3M1DPveCu/NAdYueuDsP5LPwMdeH74Q+9qHoibt5yGcBeHY/UPRD0A9NH/o9SD5FP6zMU/Sn/+t5PXE3D/kUPXF3P0B1lwV39wPks/Mh14dloY99WBbczUM+O8Cz81D0G9PP64m780A+RR95Nk/Rz7x9Xk/czUM+RU/cnQequyy4Ow/ks/Mh14dloY99WBbczUM+O4K7+wF67s4Fd/cD5HOfXAfOhT5+7ueCu3nI515wdz9AdYt+aPosiP3GEPTDyjzUh6In7uYhn4WPuXXgXKwb8bk7F9ydB/K5T64D50If14Fzwd085HMvuDsPVLfoibvzQD4LH3N9+E7oYx+Knribh3wWBI/nAFvi3KABfWP6nIm6Deibyjz03rcluJuHfM4Ej324E31rQN+YPv/w6eW6DeibyjzUhzvB3TzkU/TuOcCWODeI791bgrvzQD5nyXOALaGPv/e3BHfzkM+Z4O48UN07wd15IJ+id/twJ/oW+3AnuJuHfIqeeFwHWp9eXjca0Demz6ao24C+qcxDn3tLcDcP+WwKHvtwIvrWgL4xfQaibgP6pjIP9eFEcDcP+Qw+5daBltDH564luDsP5LP5KbcOtIQ+rgMtwd085LMpuDsPVPdEcHceyGeQ7MOJ0Mc+nAju5iGfwafcOcCWODcYgn5o+pyJukPQDyvz0HvfluBuHvI5E9zdD1DdO8Hd/QD5FP2wMg/14U5wNw/5FL17DrAlzg3ie/eW4O48kM9Z8hxgS+jjOcCW4G4e8jkT3J0HqnsnuDsP5FP0bh/uRN9iH+4Ed/OQT9ETd/cD9Ny1BHf3A+Sz+Sm3DrSEPn7uLcHdPOSzKbi7H6C6J4K7+wHyGST7cCL0sQ8ngrt5yGfwKbcOtIQ+Pnctwd15IJ/NT7l1oCX0cR1oCe7mIZ9Nwd15oLongrvzQD6DZB9OhD724URwNw/5DIBn7wMU/d/NPK8n7t4HIJ+ijzybp+g7oO/M1OUhn6In7t4HoLrLgrv3Achn50OuD8tC3wF9Z6YuD/nsAM/OQ9H3Qd+vnAfyKfp+ZZ6i//P/fl5P3M1DPkVP3J0HqrssuDsP5LPzIdeHZaGPfVgW3M1DPjuCu/cB6Lk7F9y9D0A+98l14FzoO6DvzNTlIZ97wd37AFS36Im79wHIZ+Fjrg/fCX0H9J2Zujzks/Axtw6ci3WjD/p+5TyQz31yHTgX+rgOnAvu5iGfe8HdeaC6Rd+vnAfyWfiY68N3Qh/7UPTE3TzkswA8ux8o+i7ou6YP/R4kn6LvVuYp+hHoR5V5yKfoR2YflsV+qQv6rumzI+p2Qd+tzEN9WBbczUM+O8Cz81D0K++e1xN354F8ij7ybJ6i74G+964uD/kUPXF3HqjusuDuPJDPzodcH5aFvgf63ru6POSzI7i7H6Dn7lxwdz9APvfJdeBc6EegH1XmIZ97wd39ANUt+q7psyD2G13QdyvzUB+KflSZh3wWPubWgXOxbsTn7lxwdx7I5z65DpwLfQ/0vXd1ecjnXnB3Hqhu0RN354F8Fj7m+vCd0PdA33tXl4d8FgR37wPQe/eW4O59API5S54DbAl9B/Sdmbo85HMmuHsfgOreCe7eByCfonf7cCf61gF9Z6YuD/kUfadyHui9e0twdx7I5yx5DrAl9PH3/pbgbh7yORPcnQeqeye4Ow/kU/T9yjzUhzvB3TzkU/TE3fsA9Ny1BHfvA5DP5qfcOtAS+g7oOzN1echnU3D3PgDVPRHcvQ9APoNkH06EvgP6zkxdHvIZfMqtAy2h74O+XzkP5LP5KbcOtIQ+rgMtwd085LMpuDsPVPdEcHceyGeQ7MOJ0Mc+nAju5iGfwafcOcCWODfogr5r+pyJul3Qdyvz0HvfluBuHvI5E9zdD1DdO8Hd/QD5FH23Mg/14U5wNw/5FP2och7ovXtLcHceyOcseQ6wJfQ90Pfe1eUhnzPB3XmguneCu/NAPkXv9uFO9K0H+t67ujzkU/TE3f0APXctwd39APlsfsqtAy2hH4F+VJmHfDYFd/cDVPdEcHc/QD6DZB9OhH4E+lFlHvIZfMqtAy2hj89dS3B3Hshn81NuHWgJfQ/0vXd1echnU3B3HqjuieDuPJDPINmHE6Hvgb73ri4P+QwEj38H4Jvl5zl9777oG9Nndvnlug3om8o89L3voifu5iGfWcFjH9ZF3xrQN6bPsajbgL6pzEN9WBfczUM+x8Cz80Dfuy964u48kM8s8Gwe+t5f0RN385DPrODuPFDddcHdeSCf42Qf1oU+9mFdcDcP+RwLHteBG7FuNKBvTJ+///xy3Qb0TWUe+txvBHfzkE/RE499mP78ct8a0Demz5qo24C+qcxDfZgW3M1DPmufc+vAjVg34nN3I7g7D+RT9O46cCPWjbgO3Aju5iGfoifuzgPVnRbcnQfyWfuc68O00Mc+TAvu5iGfNeDZ/QB9777oh6bPrNhvDEE/rMxD3/sueuJuHvKZFdzdD1DddcHd/QD5HCf7sC70sQ/rgrt5yOcYeHYe6Hv3RU/cnQfymQWezUP/3b/oibt5yGdWcHceqO664O48kM9xsg/rQh/7sC64m4d8jgV39wP03N0I7u4HyKfoh5V56HO/EdzNQz5FT9zdD1DdacHd/QD5rH3O9WFa6GMfpgV385DP2ufcOnAj1o343N0I7s4D+RS9uw7ciHUjrgM3grt5yKfoibvzQHWnBXfngXzWPuf6MC30sQ/Tgrt5yGdN8HgOcCjODRrQN6bPtajbgL6pzEPvfYeCu3nI51rw2Ievvrzctwb0jekz9eXlug3om8o81IeiJ+7mIZ+pL7lzgENxbhDfuw8Fd+eBfK6T5wCHQh9/7x8K7uYhn2vB3XmgukVP3J0H8pn6kuvDV0If+1D0xN085DMleFwHVsW60YC+MX0ORN0G9E1lHvrcVwV385DPgeCxD1eibw3oG9PnD//8ct0G9E1lHurDleBuHvIpencdWBXrRnzuVgV354F8DpLrwKrQx3VgVXA3D/kcCO7OA9W9EtydB/IpercPV6JvsQ9Xgrt5yKfo3XOAQ3FuMAT90PS5FnWHoB9W5qH3vkPB3Tzkcy24ux+gukU/NH2mxH5jCPphZR7qQ9ETd/OQz9SX3DnAoTg3iO/dh4K780A+18lzgEOhj+cAh4K7ecjnWnB3Hqhu0RN354F8pr7k+vCV0Mc+FD1xNw/5TAnu7gfouVsV3N0PkM9Bch1YFfr4ua8K7uYhnwPB3f0A1b0S3N0PkE/RDyvzUB+uBHfzkE/Ru+vAqlg34nO3Krg7D+RzkFwHVoU+rgOrgrt5yOdAcHceqO6V4O48kE/Ru324En2LfbgS3M1DPkXv/h0A+u/49L37oifu3gcgn1ng2Tz0ve+i78zU5SGfWcHd+wBUd11w9z4A+Rwn+7Au9B3Qd2bq8pDPMfDsPND37ou+XzkP5DMLPJuHvvdX9MTdPOQzK7g7D1R3XXB3HsjnONmHdaGPfVgX3M1DPseCu/cB6Lm7Edy9D0A+Re+uAzdi3eiAvjNTl4d8ip64ex+A6k4L7t4HIJ+1z7k+TAt9B/Sdmbo85LP2ObcO3Ih1ow/6fuU8kE/R9yvz0DpwI7ibh3yKnrg7D1R3WnB3Hshn7XOuD9NCH/swLbibh3zWgGf3A/S9+6Lvmj6zYr/RBX23Mg9977voR5V5yGdWcHc/QHXXBXf3A+RznOzDutCPQD+qzEM+x8Cz80Dfuy964u48kM8s8Gwe+jsARd97V5eHfGYFd+eB6q4L7s4D+Rwn+7Au9D3Q997V5SGfY8Hd/QA9dzeCu/sB8in6bmUe+txvBHfzkE/Rj8w+TIv9Uhf0XdNnTdTtgr5bmYf6MC24m4d81j7n1oEbsW7E5+5GcHceyKfo3XXgRqwbPdD33tXlIZ+iJ+7OA9WdFtydB/JZ+5zrw7TQ90Dfe1eXh3zWBHfvA9B796Hg7n0A8rlOngMcCn0H9J2Zujzkcy24ex+A6hY9cfc+APlMfcn14Suh74C+M1OXh3ymvuTOAQ7FuUEf9P3KeSCf6+Q5wKHQx9/7h4K7ecjnWnB3Hqhu0fcr54F8pr7k+vCV0Mc+FD1xNw/5TAnu3geg525VcPc+APkcJNeBVaHvgL4zU5eHfA4Ed+8DUN0rwd37AORT9G4frkTfOqDvzNTlIZ+i71TOAz13q4K780A+B8l1YFXo4zqwKribh3wOBP9/pZ2hcutoEoUHBQ1atChoHsLI4Nqxs0auSeJkX8FIyEjISA8QKGQkZCRklAfYMhIyEjISWnThoq7a+itfTnc1PfXV6VPtVt/f/8gT7zxQ3avQvfNAPsb/J5mH+nAVujcP+RjvvQc4iXuDT+A/nT6DqPsJ/GcyD33vOwndm4d8BqF7zwNU1/hPp89MnDc+gf9M5qE+GP87mYd8Zh+xe4CTuDcov3efhO6dB/IZgvcAJ8F/Af/1K5eHfAahe+eB6hpPunceyGf2EevDg+C/gP/6lctDPjOhe88D9Nzthe49D5BPG9wDe8H/Bv53Mg/5tEL3ngeo7lXo3vMA+Rj/mcxDfbgK3ZuHfIz/nZwHeu72QvfOA/m0wT2wF/wX8F+/cnnIpxW6dx6o7lXo3nkgH+O9fbiKvn0B//Url4d8jC/1r7+/1//798/8X4vvedJLn79efq5b+hhf6tE8xjfAN4tcHvIxnvSyD7uXn/tW1t0JvfRpRN3Sp3mJ9WEn+Ab4ZpHLQz4N6NF5MH4CfkrOA/kYPyXzGL9dfs+T7s1DPsaT7p0HqrsTunceyKd5ifVhJ/iyDzuhe/OQTyP0cg9cxN4on7uL0EufSdQtfabgHrgIvgG+WeTykM8k9LIPj68/962sazzppQ/9HUDy2b7G+vAo+Ab4ZpHLQz7b19geuIi9MQE/JeeBfKbgHrgIvtwDF6F785DPJHTvPFBd46fkPJDP9jXWh0fBl30wnnRvHvLZgh49DxjfA987fejfQfIxvk/mMf6fT9/zpHvzkI/xpHvPA1R3J3TveYB8mpdYH3aCL/uwE7o3D/k0oEfnwfga+PopNw/kY3ydzGP8CPyYzEM+xo9PuXmgujuhe+eBfJqXWB92gh+BH5N5yKcRuvc8QM/dReje8wD5TME9cBF8+blfhO7NQz6T0L3nAaprfO/02YrzRg98n8xDfTCedG8e8tm+xvbAReyNGvj6KTcP5DMF98BF8CPwYzIP+UxC984D1TW+fsrNA/lsX2N9eBT8CPyYzEM+W6GX9wBHcW9Qfu8+Cr306UXd0qcP3gMcBd8A3yxyecinF3rZh7voW1n3LvTSh/4OOPkY7+3DXfStAb5Z5PKQj/FNch7oe/dR6N55IJ8+eA9wFHz57/5R6N485NML3TsPVPcudO88kI/xUzIP9eEudG8e8jGe9HIPbN5+3hvlc7cReulTi7qlT/0W2wMbwTfAN4tcHvKphV724Sz6VtY9C730GUXd0mcM9uEs+Ab4ZpHLQz7jW2wPbAQ/AT8l54F86rfYHtgIvtwDG6F785BPLXTvPFDds9C980A+Y7APZ8GXfTgL3ZuHfMa32D3AUdwb9MD3Tp9e1O2B75N56HvfUejePOTTC917HqC6d6F7zwPkY3yfzEN9uAvdm4d8jPfeAxzFvUENfP2Umwfy6YP3AEfBj8CPyTzk0wvdOw9U9y507zyQj/F1Mg/14S50bx7yMZ5073mAnruN0L3nAfKp32J7YCP48nPfCN2bh3xqoXvPA1T3LHTveYB8xmAfzoIv+3AWujcP+YxvsT2wEXwNfP2Umwfyqd9ie2Aj+BH4MZmHfGqhe+eB6p6F7p0H8hmDfTgLfgR+TOYhnxH06PsAxi9X3/Oke98HIB/jSz2ax/gO+G6Vy0M+xpPufR+A6u6E7n0fgHyal1gfdoLvgO9WuTzk04AenQfj/1x/z5PunQfyMb7Uo3mMr4Cv1rk85GM86d55oLo7oXvngXyal1gfdoKvgK/WuTzk0wjd+z4APXcXoXvfByCfKbgHLoLvgO9WuTzkMwnd+z4A1TWedO/7AOSzfY314VHwHfDdKpeHfLavsT1wEXujfO4uQvfOA/lMwT1wEXwFfLXO5SGfSejeeaC6xpPunQfy2b7G+vAo+Ar4ap3LQz5b0KPnAeMH4IfkeYB8jB+SeYyfPX/Pk+7NQz7Gk+49D1DdndC95wHyaV5ifdgJvuzDTujePOTTgB6dB+Nb4Nvn3DyQj/FtMo/xf/zre550bx7yMZ507zxQ3Z3QvfNAPs1LrA87wZd92Andm4d8GqF7zwP03F2E7j0PkM8U3AMXwZef+0Xo3jzkMwndex6gusYPyfMA+WxfY314FHzZB+NJ9+Yhn+1rbA9cxN5ogW+fc/NAPlNwD1wEX+6Bi9C9echnErp3Hqiu8e1zbh7IZ/sa68Oj4Ms+GE+6Nw/5bIXufR+Avncfhe59H4B8+uA9wFHwHfDdKpeHfHqhe98HoLp3oXvfByAf4719uIu+dcB3q1we8jG+S84Dfe8+Ct07D+TTB+8BjoKvgK/WuTzk0wvdOw9U9y507zyQj/HePtxF3yrgq3UuD/kYT7r3fQB67jZC974PQD71W2wPbATfAd+tcnnIpxa6930AqnsWuvd9APIZg304C74Dvlvl8pDP+BbbAxvBl8/dRujeeSCf+i22BzaCr4Cv1rk85FML3TsPVPcsdO88kM8Y7MNZ8BXw1TqXh3zGt9g9wFHcGwzAD8nzAPn0wXuAo+DL731HoXvzkE8vdO95gOrehe49D5CP8UMyD/XhLnRvHvIx3nsPcBT3Bi3w7XNuHsinD94DHAVf3gMche7NQz690L3zQHXvQvfOA/kY3ybzUB/uQvfmIR/jSfeeB+i52wjdex4gn/ottgc2gi8/943QvXnIpxa69zxAdc9C954HyGcM9uEs+LIPZ6F785DP+BbbAxvBt8C3z7l5IJ/6LbYHNoIv98BG6N485FML3TsPVPcsdO88kM8Y7MNZ8GUfzkL35iGfUejl/wfgH/B3nOl398aTXvosdz/XLX2WoEfz0O++jW8WuTzksxR62YeD6FtZ9yD00qcTdUufLtiHg+Ab4JtFLg/5dKBH54F+d2/8lJwH8lmCHs1Dv/sznnRvHvJZCt07D1T3IHTvPJBPF+zDQfBlHw5C9+Yhn07o5R64ib1RPnc3oZc+f77/XLf0Md67B25ibzTAN4tcHvIxnvSyD/P3n/tW1p0LvfSpRN3Sp3qP9WEu+Ab4ZpHLQz7Ve2wP3MTemICfkvNAPsZPyTy0B25C9+YhH+NJ984D1Z0L3TsP5FO9x/owF3zZh7nQvXnIpwI9eh6g390b3zt9luK80QPfJ/PQ776NJ92bh3yWQveeB6juQeje8wD5dME+HARf9uEgdG8e8ulAj84D/e7e+PopNw/kswQ9mof+u7/xYzIP+SyF7p0HqnsQunceyKcL9uEg+BH4MZmHfDqhe88D9NzdhO49D5CP8X0yD33uN6F785CP8aR7zwNUdy5073mAfKr3WB/mgi/7MBe6Nw/5VO+xPXATe6MGvn7KzQP5GF8n89AeuAndm4d8jB+fcvNAdedC984D+VTvsT7MBT8CPybzkE8l9PIe4CTuDcrv3Sehlz6DqFv6DMF7gJPgG+CbRS4P+QxCL/vw8PFz38q6xpNe+tDfASef2UesDw+Cb4BvFrk85DP7iN0DnMS9wQT8lJwH8hmC9wAnwZf/7p+E7s1DPoPQvfNAdY2fkvNAPrOPWB8eBF/2wXjSvXnIZyb0cg/sxd4on7u90EufVtQtfdrgHtgLvgG+WeTykE8r9LIPV9G3su5V6KXPH//+uW7pY7y3D1fRtwb4ZpHLQz7GN8l5oOduL3TvPJBPG9wDe8GXe2AvdG8e8mmF7p0HqnsVunceyMf4KZmH+nAVujcP+RjvvQc4iXuDHvje6TOIuj3wfTIPfe87Cd2bh3wGoXvPA1TX+N7pMxPnjR74PpmH+mA86d485DP7iN0DnMS9QQ18/ZSbB/IZgvcAJ8GPwI/JPOQzCN07D1TX+PopNw/kM/uI9eFB8CPwYzIP+cyE7j0P0HO3F7r3PEA+bXAP7AVffu57oXvzkE8rdO95gOpehe49D5CP8X0yD/XhKnRvHvIx3rsH9mJv1MDXT7l5IJ82uAf2gh+BH5N5yKcVunceqO5V6N55IB/j62Qe6sNV6N485GP8mHwfgH53bzzp3vcByGcJejQP/e7b+G6Vy0M+S6F73wegugehe98HIJ8u2IeD4Dvgu1UuD/l0oEfngX53bzzp3nkgnyXo0Tz0uz/jq3UuD/kshe6dB6p7ELp3HsinC/bhIPgK+Gqdy0M+ndC97wPQc3cTuvd9APIx3rsHbmJvdMB3q1we8jGedO/7AFR3LnTv+wDkU73H+jAXfAd8t8rlIZ/qPbYHbmJvlM/dTejeeSAf47174Cb2RgV8tc7lIR/jSffOA9WdC907D+RTvcf6MBd8BXy1zuUhnwr06HmAfndv/JA8D5DPEvRoHvrdt/Gke/OQz1Lo3vMA1T0I3XseIJ8u2IeD4Ms+HITuzUM+HejReaDf3RvfPufmgXyWoEfz0P8HwHjSvXnIZyl07zxQ3YPQvfNAPl2wDwfBl304CN2bh3w6oXvPA/Tc3YTuPQ+Qj/FDMg997jehe/OQj/Gke88DVHcudO95gHyq91gf5oIv+zAXujcP+VTvsT1wE3ujBb59zs0D+RjfJvPQHrgJ3ZuHfIwn3TsPVHcudO88kE/1HuvDXPBlH+ZC9+Yhn0ro3vcB6Hv3Seje9wHIZwjeA5wE3wHfrXJ5yGcQuvd9AKprPOne9wHIZ/YR68OD4Dvgu1UuD/nMPmL3ACdxb1B+7z4J3TsP5DME7wFOgq+Ar9a5POQzCN07D1TXeNK980A+s49YHx4EXwFfrXN5yGcmdO/7APTc7YXufR+AfNrgHtgLvgO+W+XykE8rdO/7AFT3KnTv+wDkY7y3D1fRtw74bpXLQz7Gd8l5oOduL3TvPJBPG9wDe8FXwFfrXB7yaYXunQeqexW6dx7Ix3hvH66ibxXw1TqXh3yMr5LnAfrefRK69zxAPkPwHuAk+PJ730no3jzkMwjdex6gusYPyfMA+cw+Yn14EHzZB+NJ9+Yhn9lH7B7gJO4NWuDb59w8kM8QvAc4Cb68BzgJ3ZuHfAahe+eB6hrfPufmgXxmH7E+PAi+7IPxpHvzkM9M6N7zAD13e6F7zwPk0wb3wF7w5ee+F7o3D/m0QveeB6juVeje8wD5GD8k81AfrkL35iEf4717YC/2Rgt8+5ybB/Jpg3tgL/hyD+yF7s1DPq3QvfNAda9C984D+RjfJvNQH65C9+YhH+P/T/8f6Zp84w== 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EZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//A3532BvAAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH39D/TfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/y3zBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3zH9LfAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8DfvfIm8AAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff8P9t8AfgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//hXcEIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfCf6E9AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+F9wVwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/I/0X2BeAQAQkMEEIRlCCE4JQhCQ0YQhHWMITgUhEJDJRiEZUohODWMQkNnGIR1zik4BEJCQxSUhGUpKTglSkJDVpSEda0pOBTGQkM1nIRlayk4Nc5CQ3echHXvJTgEIUpDBFKEZRilOCUpSkNGUoR1nKU4FKVKQyVahGVapTg1rUpDZ1qEdd6tOARjSkMU1oRlOa04JWtKQ1bWhHW9rTgU50pDNd6EZXutODXvSkN33oR1/6M4BBDGQwQxjGUIYzglGMZDRjGMdYxjOBSUxkMlOYxlSmM4NZzGQ2c5jHXOazgEUsZDFLWMZSlrOCVaxkNWtYx1rWs4FNbGQzW9jGVrazg13sZDd72Mde9nOAQxzkMEc4xlGOc4JTnOQ0ZzjHWc5zgUtc5DJXuMZVrnODW9zkNne4x13u84BHPOQxT3jGU57zgle85DVveMdb3vOBT3zkM1/4xle+84Nf/OQ3f/jH3/+FdQYgEAEJTBCCEZTghCAUIQlNGMIRlvBEIBIRiUwUohGV6MQgFjGJTRziEZf4JCARCUlMEpKRlOSkIBUpSU0a0pGW9GQgExnJTBaykZXs5CAXOclNHvKRl/wUoBAFKUwRilGU4pSgFCUpTRnKUZbyVKASFalMFapRlerUoBY1qU0d6lGX+jSgEQ1pTBOa0ZTmtKAVLWlNG9rRlvZ0oBMd6UwXutGV7vSgFz3pTR/60Zf+DGAQAxnMEIYxlOGMYBQjGc0YxjGW8UxgEhOZzBSmMZXpzGAWM5nNHOYxl/ksYBELWcwSlrGU5axgFStZzRrWsZb1bGATG9nMFraxle3sYBc72c0e9rGX/RzgEAc5zBGOcZTjnOAUJznNGc5xlvNc4BIXucwVrnGV69zgFje5zR3ucZf7POARD3nME57xlOe84BUvec0b3vGW93zgEx/5zBe+8ZXv/OAXP/nNH/7xN8p/Ib0BCERAAhOEYAQlOCEIRUhCE4ZwhCU8EYhERCIThWhEJToxiEVMYhOHeMQlPglIREISk4RkJCU5KUhFSlKThnSkJT0ZyERGMpOFbGQlOznIRU5yk4d85CU/BShEQQpThGIUpTglKEVJSlOGcpSlPBWoREUqU4VqVKU6NahFTWpTh3rUpT4NaERDGtOEZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//I36X1B3AAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH3+j/RfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/5VzBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3xn+lPAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8jflfMVcAAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff2P9V8gXgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//lXEGIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfOfyW8AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+18RdwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/+b/4zj+BCEhgghCMoAQnBKEISWjCEI6whCfC/zdMD9liIAAAwPo6tW3bNn5t27Zt27Zt27Zt27YnixwhRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTQf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMh//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU2H/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTEf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNR//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUzH/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTcf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMJ//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU0n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTKf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNp//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUxn/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTWf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlM5//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU3n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTBf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNF//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUyX/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTZf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMV//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU1X/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTNf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlN1//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUw3/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTTf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMt//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU23/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTHf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNd//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwkU4D//EZggBCU4wQhBSEITijCEJTzhiEBEIhOJKEQlOtGIQUxiE4s4xCU+8UhAQhKTiCQkJTnJSEFKUpOKNKQlPenIQEYyk4ksZCU72chBTnKTizzkJR8B5KcAhShIYYpQjKIUpwSlKElpylCOspSnApWoSGWqUI2qVKcGtahJbepQj7rUpwGNaEhjmtCMpjSnBa1oSWva0I62tKcDnehIZ7rQja50pwe96Elv+tCPvvRnAIMYyGCGMIyhDGcEoxjJaMYwjrGMZwKTmMhkpjCNqUxnBrOYyWzmMI+5zGcBi1jIYpawjKUsZwWrWMlq1rCOtaxnA5vYyGa2sI2tbGcHu9jJbvawj73s5wCHOMhhjnCMoxznBKc4yWnOcI6znOcCl7jIZa5wjatc5wa3uMlt7nCPu9znAY94yGOe8IynPOcFr3jJa97wjre85wOf+MhnvvCNr3znB7/4yW/+8I+/BMrnP/8RmCAEJTjBCEFIQhOKMIQlPOGIQEQiE4koRCU60YhBTGITizjEJT7xSEBCEpOIJCQlOclIQUpSk4o0pCU96chARjKTiSxkJTvZyEFOcpOLPOQlHwHkpwCFKEhhilCMohSnBKUoSWnKUI6ylKcClahIZapQjapUpwa1qElt6lCPutSnAY1oSGOa0IymNKcFrWhJa9rQjra0pwOd6EhnutCNrnSnB73oSW/60I++9GcAgxjIYIYwjKEMZwSjGMloxjCOsYxnApOYyGSmMI2pTGcGs5jJbOYwj7nMZwGLWMhilrCMpSxnBatYyWrWsI61rGcDm9jIZrawja1sZwe72Mlu9rCPveznAIc4yGGOcIyjHOcEpzjJac5wjrOc5wKXuMhlrnCNq1znBre4yW3ucI+73OcBj3jIY57wjKc85wWveMlr3vCOt7znA5/4yGe+8I2vfOcHv/jJb/7wj7/8D5W4wko= 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-006000.2.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-006000.2.vtu new file mode 100644 index 000000000..ee3ac6b14 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-006000.2.vtu @@ -0,0 +1,32 @@ + + + + + + + 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OOq84J9VHS+i8Gr57CfVxTslBPakP7rTvRecVntq1hM+rx52KS6iPuna/XtQHtyf1pHlxTqq3H6Z5wcXJ4tTHuSrK9AgxH1wk9QqxvPBUmUdoXnB9+GHqm7rDe4eYD27P7hli84o+iQOL6LwaPmYB9ZHTGdSd+x53C3Sl82q8l+fReQXXA2ZRH3Wl/pr74Iva0Lzokz3rQZoX3FL3UR99tSO6hphveN8vQyyv8d7Pz/N63H43M8R81NXvfxFiPrgz2ttDfc+r4p5UTODPqxr+JfdxTyk75OF7XFfmz6vCc0rx51XB7cXZ1EddFZXNfY9b1bJpXtyTylI8L7jt3Wv5fdxTOcuyqQ/ufJBN88Jzm/K84HJaFvVR11mYRX1wtTyTPq+KPlmXs+jzquBORCb1kdOum4ePvxcXeZ0+rwpPnOTPq4KLDVeob+q25D641ekyzYs+ya94XsP7ZVAffbXKch/cXXCV5jVe2zzyely0vEB91HWWX6I+uPggROfV3Kvm4/MK7vbMoj7uKVV8JvXBnXKZfF49T8zKpPMKrhtf5z7q3nOd+uCqwTWaF/ekuvB1mhfc8Z6b9fvYU51CV6kPLuem07ym7vCrNK/hr6dTH3XVuEvUB3c7n6fzij7p+tfovIKrR9Opb/o6J5364HbXC3Re4TnXz9F5NXxzMvVR19oZoD64fCaO5kWfxMILNC+4XSrIfa+vcn4a9cHVx0k0Lzzr60SaF1z/foT6qKuPHqM+uDgcQZ9XxTkp5vLnVQ1vnEp9nFNOvrPUB5dDE+nzqvBkZB7Pq3rcidhOfdTVPQ9S3/CVq3he75yUb/K84PrGCepjTuW5BOqD2+UO0rzwrN4RPK/HxY+zqI+6+kIevsft5BH0eVX0SeSfyZ9X9bhTtwP1kVMtHk59cLdoDfq8Kjwr9V76vCq4PPldiPmoK/Pvoz64/fHDIZrX65O88BXNC25dKkJ909eEWtwHr3IwRPN6nvOMTfOCuzunBanv1bWGPcF9j6sf7w6yecU5qX+MofMKrips5753TukKkdQHV3oFnVdTd/FMOq/gzmNfUx919T3DqG/4rho8r3dOqiUuzQsuFkyjPs5Vx5pPfXDxXl+aF569vQ3NC+7sv5f6qGsfeon64Lrw9hCbV9On9PvpvBr+6o8h5pucx6OoD259V4rOKzyr6Vw6r+AiumEa9b26+mRR6oPrdr0DLC/6pGvZNC+41Xh2kPnoq9hdjPqGP5mf5/U8u2kjmtfw+Z1Tqe/VtX/tRn1wEV01he2ruCdV91+k+yq4Vfw89XFP6W5Opb7h587QfRWeUyVE91VwnZzCfa+uiE2gPrgKnaR5cU9qHz5B84Jbm6Kpb+6pOm6jPrjstZHmhaevRdG8hi/cRX3UtSqu4r7H3V7z6b6KPslvE+i+Ci6mHqM+csonDlMfXJ+IoPsqPPfrXXRfNXzHMu57dd1Zy6kPrr//heZFn+zRG2hecD1+AfXRV2fJZOqDi6oDaF7jDR9L84LLpu2pj7r2mWbUN7x9OTqvuCdVhX/j8wpe61fue/eU1vnPqQ/unP6Izis8+61hdF4Nn9mL+qjrtn+T+uDWijI0L+5JnbHBEMsLLp87HmK+uVft83KI+eBWfIkQywvPWe6GaF6P26m/hZiPulbB9CDzwXWT7UE2r+iTVbMFnVdwubM69U3OIndSH1x8cDTE5tXUves+Oq/gskBEiPmo62T+GmI+uNPT+3nw5UWfdG/vz8d9ecGtt6uEmI++itghQeaDu/OrBmlez1POiSDLCy6rtAwyH3X1HVvTmA9ut3s6je2rOCedDT/RfRVc/diA+jin5FOtqW94THG6r5q6ha6G2L4K7jzTIcR81HUOzqQ+uPX22SDLi3PSeuZpmhfcTvbmz+djTlXqoRDzwZ3ln9G8pu7UF2hecBXxcZD6Xl3VezL1wVUzO43tq6ZPG0bSfRVcvqzSmI+c1rMn05gPbk14N8D2VXiyU1O+r4JX33yG+ahrv1SI+oaXfOQ0y2v6Wjk7wPKCu7HFU5lv+jrmdCrzwdW1+cksLzy7/UCaF1xb1ROZj7ry1KYk5oOLh2vEs3nFOWkd/JzOK7iM/TvIfJxTcsDCIPU9rrIT0ui8ep7eti+NzqvH3e7dA9T36lpfuAHmg7sznkplec25enR5GssLrty/A8zHuapXNQgwH1zWrJTK8pq6ZZunsrzgzg8DkpmPuiI4LZn54Lrl2NNsXk2fuoxKofPqcatbvjPMR0535fkz1Pe4riGT2LzCk6eiEti8guuuL2rmo65Tb2cc88HdiLYnWF7TpzEPnGZ5wdVTjyQwH32169+WyHxwu0/+OJYXnjXXjmV5wdWapdHMR107YmkM88HdqB8P536vquydFfaeVLy3EdxeFP7+R/HgderjPaViUgb1weWyq2F1rZXXwzxnxLUw7ozICOPukivUR12rTjr1wa3GF2le857UkldpXnDn2cvUx3sqxaBL1Ad3C5/jeT3P6XKe5gV3dqZRH3WtGqnUB5dn48P7WiW8T+rHy2FcrAzn7gMXqY+cVsxZ6oNbsYGwus5j6WGe7HoujKsBl8K43Bykvqk75Az1we27E2je/+rrGZoX3F2hqW/6mnmM+x4XffbTvPDEiGM0L7hTMIr7Xl25fAv1wVWVVXRe8Z5U1eosnVfDZyRTH+8pVaWTqW941kk6r/DEylg6r+BCHaI+6srPI6kP7ozZwPN670nVd5yiecGdM/uoj/dUicKR1Ad396yheeE5tdfSvOBu4m/UR10ZmEx9cFt1ofNq+lRgO51XcHX/Su57Oe3H/6A+uD4zks4rPGfL73Rewa3MkdRHXadzR+ob/kAdmhd9Elv78LweV73qct/rq056jfrgVlR6iOWFJ357jOYFF7WTQsw3defODjEfXPd7P5T7XHUzL4Sdk/rZQBgXf54P4zqYTH1zri49RX1w68jxsLrOnHPhXuUzYVzccy6M65KnqW/qRh2lPrg4f5jmxTnprNhF84Lbj2+lPuZU7v+T+uDW6Ok0LzwreQPNCy6n/U191NWjxlPf8J1jwvoqE86G9ck9kBTGrRfPhfPPTlMfOZ02h6kPrqodCavrXD0X5rnTzoRx95ELYVwNSqE+6oqW0dQHt8bH0LymT5PW0rzgMuNv6qOvMnUU9z0uyo2leeGJnzfyvB5XmVu579UVL06gPrjVxKHzas7Jx3rReQUX51tT35yrxctTH9wem5/OKzz7q0/ovILLhnWoj7r6bFqI+eBWq5gQy4tz0io/P0Tzelz1/TbEfJyrbqtAkPngboGxQZYXnlPjnRDN63H14NPUR109qXaQ+eB69f1BNq/ok5X0Dp1XcGdwHeqbnOUPh5gP7t6W4/nnFZ698hM6r4aXa0N91FVFvJ9/nw9u5b+L5kWfrPOPhVhecKtGOeqjr857N9KYD+5EFAqyvPD04Johlhfc2vIN9VFXrXk9yHxwt/2AINtX8Z5Uq/B6uq+C62VTqY/3lIqJM6kP7rzSne6rxivWie6r4M7w56iPukq/QH3Dfz0SYnnxnlR12wCaF1xNV9THeypl50+pDy7jrodYXnjW8bgQywtut28aYj7qqlo9uQ8eXBtk+6rp6/yTIbqvelw08s4dn4+canIT6oO7Mufe9qZ91fPckpeDbF8Fd2e1DjIfdfW8q2nMB3f6lUhjedEn+W7Oc0P+vOB69Lo05qOvInFNGvPB3ZFfB1heeKrltADLCy4jPkllPuo6KUdSmA8uowols3nFe1KdI4/zefW4jT+v8vl4T6kjjlHf8HdfpPNqvB6hIJtXcJ0aSGM+6jqf1KQ+uK4cCNC8eK/q4AE0L7gom/PnAn4fz6naa4pTH1y2KkDzwlNLu/K8Hpffr0plPuqqHt9RH9w9XT+FzavpU48RATav4Hah7SnMR06nfolU5oPbdfafYfMKT3ace4bNK7j1ZYMk5qOuLlImifngekHDeJYXfXLmfpXM8oKru6YkMR99dd4ZcZr54LJy7QSWF549vWc8ywsur8w4xXxTt+GUWOYbnnHPMbav4px0p+9JY/squHrn9TTm45yShZ4LMB9cRX+cyvZVePbbmwJsXzX8s28CzDd11/+cwnxwOfCBFJbXnJMVspNZXnA9afUZ6ntzqku9fJr54PaBAYksLzw3/5+nWV5w27qQxHzUdRY/kcB8cGfT8/FsX0Wf5KIPA2xfBdeftwow3/R1fmwy88FV6Zy+3LSvep4aND/A9lVw97uSadT36ur2HVKo73HrjvtTWV7Tp+Fzkmhej9uT9yZR3+urCO6NYz64WzYjjuWFJ2dap2lej1vvfHCG+ahr37s4nvoedy9FJ7B5Nedkm3wJbF7B7ctBTX3vnFIJH8YyH1wdWHuczSs85+Pxp9i8gsv4zieYj7qic62jzAd3kwodZnlxTjrfpUezvOB282aHmI9z1Y3rcZD54O7lz/axvPDUuvwHWF5wbSXvZT7qin1NdzAfXO5avZXNK/okRw2JYfMKbq85GcN85HTnlzjEfHA3qsUhNq/wHNH2JJtXcOujfJr5qCvejT7MfHC9t1M0y2v69GiJPSwvuK76/R7mo6/2ike3MB9cHq63heWFpxZd38fygttbdh9gvqkbqL2N+eDigfw7c9+r2n0vht2TuicCYVwXuBzGrW4h6uOeUg+Lpb7h8QlhdeWU9PC6D50P46rE1TDuTrpIfdSVdZKpD273SaN5cU+qZ+/mecHPR1Ef91R6/Hzqg9uHVtG88MSHx2lecLd6HPVRVxXbSn3Dx+4P66vd6VpYn1TM5TBuD84I4657hfteTlXtHPXBnU4Xw+rada6HeU7Va2Fczr4expXMoL6pe/Uy9Q3ffoXmRZ+c7mdoXnCJvzfi89FXa9Mx6hv+RTzNC0/i7zP78oK75S9RH3WteinUBxdOiM6ruSd9oBedV3A5biT1cU8pt5ShvuGhd+i8wrODDp1XcH14Ofe9us7EDtQHt8YNpXlxT+r2y7lX8OcFt6/HhJhv9tSJ0UHmg1u/Vg3RvJ5nv/wAzQsufqlNfdQVd4wKMR9cnDsVYvNq+tR9K59Xj8t2Udz3cuqhM7nvcXF2DZ9Xz3Mnn6TzaviLp6lv6u7aTX1w0ecYzYs+yYzONC+49ecv1Edf7UKlqQ8uGjeleeGpE0toXnD7s+3UR13VYSj1DV87L+xclV9fDzsnZcuMMO42C+eWe436OKecTVepD26vuxpW17rzapgnD14J42rixTDuxF2kPurq5leoD+7efYnmxTmp8H4NX17Di1+hPuZUN75AfXDd/xLNC0/Z6TSv4YGL3PfqqivcB3c/uxjWV3dVIKxPcl5aGLfvORXGdWYs972cekKQ+oYXiQ+r64zdHuaJcrvCuBrzWzhPmEV91BWF91Hf8IPzaV70STQP0bzg6qFE6qOvzoEQ9cHtE0k0LzxxexTNCy7PL6M+6qpNh7nvcXl4DZ1XnJOiSxqdV3DV7xz1zTk1MIH64I6VyufV8+xvLtB5NbzgBeqjrtM+RH1w+edZmtecq48eoXnBdak47nvnqlVyM/XB7fcO0rzwxLPJPK/HrUUB6qOuXnaK+x7X9ZLovKJP7qoQnVdwVeEM9ZFTbw9SH1z+fYbOKzzr9mN0XsH1oy71UVf1i+G+x+0O22he9ElMCNC84O6LZ7jv9dUanYfvcVkyieaFp0+coHnBxWc7qY+61twT1Ad3vvb2I/++6t2T2m1npbF9FdwZ/RL1cU/pZMelMh9cds65l/fvq/D0p1uDbF8FdwrXoj7q6oJT0pgPLuo1D7K8uCe1Urol07wel9P+SmG+uafKbpTEfHD1YIEzLC88XflyKssLbnd+mPqo695Yl8x8cPl4w1S2r6JPVvclIbavgouHC1AfOd15d4aYDy7njAixfRWeVeoDuq+CW1X7UR911dMXQ8wHVx1znpvw50WfVFbhIMsLbgeOBJmPvmprZYD54P/n+0IaywtPNesSYnnBRZdE6qOuteevIPPBHZ3zHtCb5tW7J5VN3Dg6rx63l+9KYL65p1SFTjAf3HqqmmbzCs9OPZ/E5hVcJdxFfdTVp1bEMx9cD3kpieXFPalKKXOY5vW4dHodYz7uVfUHy/cxH1wUrXyQ5TV1Y0edYnnBncN3UR917VPnjjIfXC7qcJLOK/p6ISGFzSu4atGN+15OtX/0Gep7XCRNTqHz6nnWksVpbF7BnQrTqY+67rraAeaDu6+PSWN50Se376ZElhdcnHmL+uirXXFKHPPBnYhOiSwvPCu2VQrLCy5+foT6qOsMyDrNfMPrPJVC91Wcq61n0X0V3P19A/XNudqpF/XB7dTpdF+Fp6xIuq+COx1iqI+67uG13Pe4rrCH5/XOSWfByzQvuP1xV+pjTtWS6BDzwd05lWleeE6PyTSv4ftWcN+ra23qQH1weWMM3VfRJ2uHpvsquFqgqY+c4vhh6oPr9dF0X4Wnqxyn+yq4m2839VFX9DxEfXD5YATNa/o6YivNCy4776Y++urWm8d9j8s1K2leeM6mnTQvuLx9A/VRV9VdRX1wSy7k84pzsvRHITav4Ja1i/o4p5y/fgxS3+Oq/EshNq/wZD1J5xVcnG9MfdS1ei4NUd/jonDOe138eXFO6n7t0lhecLtXU+rjXJVvHU1lvuGXZBrLa+p2vyfE8oKrhwdQH3WtnUWCzAdXF1YH2byiT8ruS+cVXMwZR32Ts8Dr3Pe4qNiEzis859eJfF49bl8fTX3UtR5uS31w8VZznhd9dSJDLC+4/cWNEPPRV6v50yHmg6srHUMsr6m76AGaF1zcKEx91FX3jgoxH1xXHEmfV8V7Um17In1eFdy5VDON+niv6vPZadT3uLO8aYA9rwpPzW1Mn1cF19/lvM/F76Ou/VoR6hvesdBplhfvSZU9avO8Hneae8/t+Xy8p1I16kB9cH2qeArLC88pPJ3mNbzaG4nMN3WfLMB98IqN4tnzquiTNWRYInteFVyWT9PMN30t3yue+eD2kw1PsudV4bmv/nacPa9qeJ1Kh6jv1bUOPX2E+eD68aciWV7Tp9/a0bzg6sSTx5hv+nrv7hPMB7f2NTzM8hrvRN0oltfwDZt2Ut+rq19+Yh/zDf9i3FY2r3hPqj2kNZ1XcPfIX2eYj/eUimvPc9/jzvTyp9m8wtPFPqbzCm7d9pWmvldXxfWmPrhdNvsEy4v3pMqze2lew3t2TqS+954q1fxl6oPrGb/Es7zwZJOxNC+4U7XaMeajrhpd4yTzwe1f5h9m84o+yflXo9m8glutLx5gPnLaI9tRH9x94+peNq/wdIOFO9i8gtsZ3V3mo66+uyz1wZ07W61ledEn51ipKJYXXD16cAf1vb5av7bex3xwOa7DVpYXnni8Dc0L7vZ/ZiXzUVd+NoH64Or57CXseVWck84xsY89r2p4t3bbmY9zSnxUNIL54HrZOJc9rwpPPfT+Jva8Krid3H0d81FXnZy1mvmGH/ltOcuLc9J5ce8mlhdcdly/mvnmXH3gz7+ZDy5KLFvG8sLTqRFLaV6PWwXbzme+qdup2J/MN/zr3jPZ86rokz1n2Rr2vCq4uCtqDfNNzrF1ljIfXH1adSl7XhWeKLtoHXteFdyp9uIm5qOuGPz8cup7XM60VrG86JNVYdBclhfcrlN4LvXR14Q3HeaD29vfnM7ywhNnls9jecH14cmLmY+6ztQPf2c+uK73+Ww2rzgn5Y9/rGTzavix6QuYj3NKPXr/UuaDq0IV57B5hWdd6jmTzSu4zHpjKvNR17ptxDTmg8t7435heXFO2q8Vns/ygltDGv1Ofe9cdS8PnU19j7tRzafQvJ6nvtwwnuUFly90G8181HX3rx/LfHBresmhbF7RJx0fM57NK7j1aq9fqe/ltK61G8N8cHXn9Z/YvBqv6g8T2LyCW42CU5mPumrcI2OYD67rzPuZ5UWf9L13DmV5wd3P3/+R+l5fVb/z/ZkP7uzZ35flhecerTuY5TW8zfHhzEddWbJRP+Yb/mL3AWxfNe9JHdiF7qvgVglHMx/vKZUVf6Q+uB5a6yTbV+E5XTrxfdXjonyTKOajrmoUQX1wt+3KfSwv3pMqqvenecHdNz8/xnzznspjF08wH1xXzvn/8eeF52xrS/OCO5277WQ+6lo7dlMf3BqcvZXtq+iTfv7FnWxfBXdfX+gy3+Qc8jf1De8m/2b7Kjy3a3+6r4KrGx/OZz7qWvVaLGU+uP4k3xyWF33SV9NpXnA3ouBfzEdfRUwE9cFtO/8ymhdeoxcWsLyGv5LwO/NRV50vP5f54GrtvGlsXvGeVPuVQnRewa1ZjaKYj/eUym/7Ut/w0B372byauk0X0XkFd6Obb2I+6orWY7Yx3/Cqb6xnefGeVCerG80Lru8btIv5eK+qblkskvng7vimETQv6iYP3czygltvD13DfNR1a61wmQ/ufnFwFZtX06cCWXRewZ319y5iPnJaV0cuZz643aHIn2xeTd1F42ayeQWXIyZNYj7qOl/2+p36Hhcfrp7A8qJP6uVhS1lecNWw1h/MR19l4KmlzAe3Cj80j+U1dU/c+zvLC65e+nYi803dYptnMB9cvr9wKttXcU5K+xW6r4KruF2/Mt+cq102UN/wRmPG0n3V80TqfSPZvgou7jkygPmoq85PHkx98Pjn+rK8OCd1wQXjWV5w+XPaSOabc7Vi0Z+ZD+4M3T6U5YXn7thC84I7dz3bm/moq8t2sJkPbn3TpQfbV9End3DnPmxfBXd3R/VgPnLa0/7oznxw+7W+Xei+Cu/Zr3qwfRXc7Ti7N/NN3Q0DOzPf8LqVurK86JN1/9UuLC+4WP7Z18xHX50JZTpT3+NqzqMdWF54VhenA8sLbj29/0vmm7qd17dhPrh+o0EbNq84J+UDgTF0Xj2u3lg3mPreOaX+aDOG+eDuNxWGsHmFp79V/di8grv3yR7MR13V2LWp73HR4EAvlhfnpLUiNJblBbcnnhzOfJyrzmOfjWc+uF1/9liWF54b/8MglhfcHrKiH/NRV1d8ZCTzwe1W3wxm82r6lP5eZzav4OrQex2Yb/paLqYr88FFVL+ObF6N16lYGzav4Fa1N1sxH3WtmZfaMd/wYb1bs7zok3Vudy+W1/AC47sxH311J8b3Z77hlzt+T/N6nryrYSeWF1w+NLoD9b269idDezAf3KkU24U9r4p7Up26aBt7XhXcOn55N/NxT2k/+9AG5oM7vydsYc+rwnMfvz+KPa8K7p4vSH3UlWO77GI+uH627QGaF/eqN2avYHkNH7X3b+abe9UeFRYwH9z96bnlLC888VTsZpYXXBz7ZifzUVfu6rCW+eAq38Ob6fOq6GvD+SfY86qGF65NfZPzROsjzAd3ml6KYc+rGu9I8UT2vKrh23/jvlfXPnVUU9/jzsyfE1he9EnvOBDJ8oLLzFmHmY++6gltdjAfXGwqEsnywlODzx1necFV6Weob+r+MPQQ88FFpWbH2LzintSdVnomm1dwO1TtT+abe8rVVycxH1yMivqdzSs8sbDKMjav4O59z6xhPuqqO3f8wXxw3fGFpSwv7kn1lsljWV5w6/L9E6nv7anqpfgh1Pe43f6FMSwvPKf/CoflBbd6VpjLfNR1OzYez3xwveL4b2xe0SfnesVNdF49bqVe2U59L6coMHkV88GtQORGNq+mbqfUvWxewa38iVHMR1098uJW5oOrJUd3sbzok+6zbzHLC26d77OS+eirNbTkbOp7XKV3XcDywrMHfLWO5QWX7T7ZzHzUVR33LmM+uPPKe6vZ86o4J60/1ySz51XBncEtqY9zyr5LnGY+uBhwezJ7XhWeVWBGgD2vCi4fHprGfNRVLSamMB9cv709leU152qFNfEsL7j6NiOR+WZO971+ivngol27OJYXnmwYfZrlBZeDPkhmPuqqje0TqO9xN1+hJPa8qulrszJB9ryq4b/m/DuWfh857RGTA8wHd7uUSmPPq8ITB1YF6fOqHrf+XhNkPuraS3Oeu/b74HrGN2ksL/ok+32ewvKC66xCqcw3fd1W5zTzwdWS0mdYXuMlfZXK8oLL+3qnMh91dcPJZ6jvceeJRWfYvJpzMrHoUTav4DLqlRjm45xyei+IZD64Pf7eQ3RePU/vLxrL5hXcfez5OOajrnuh/lHmg8v8bx9nec05eceN7SwvuCyasZv63rlqZ43awHxwvXLRZpYXnorpE0nzelxP2xfFfNQVjcpvZz64c6j7Tjav6JPzdP94Nq/g8lrHBOYjp5y24gTzwfXT351i82rqvn8hgc0ruPy0YCL1vbrOyr6xzAeXzVbEsrymT6N3HGJ5wa0Xah5hPvpqRzfYw3xwu83AfSwvPBXx5FGWF1z/kTMXfh91xdkD+5kPbjv3RbJ9Ffek6kYNm+2r4NZ9W35kPu4p9UtWD+Yb/kHDH9i+Cs9qtf4ntq+COze++IX5qGt9v3Eg9T1uP+8OZ3lxT+rWHNuJ5QV3777zW+p791S64IB2zAfXgRpfs7zwlGz0HcsLbr8+sj/zUVc/mNiF+eDuiio92b6KPrmrlkxh+yq42DhqBvOR06lw+zjqgxe5OIHtq/Dk9a/+YPsquNOh62Lmo6549YPp1Pe482CBWSwv+vR/FqchLC+43LZkFPPRV1mkRl/mG75o2kCa1/PUq7E/s7zgTtQbk6jv1bXmlxzGfHBR6ZfRbF5xTyqbtW3F5hXc+b5bG+bjnlKLNS2ZDy7aD2vJ5hWevL9TBzav4HahPp2Yj7ry+xOtmA+uti9vS/PiXjVtJM0L7rxZohXzca9qz/j1a+aDy+GftGN54YlmxVrSvB53rrVtSX2vrl68rRXzwfXG5S3YvKJP7oNx3ei8elxXqtqb+cgpMx/6ivngstaPndm8wrPmrevH5hXcbb1yIPNRV66v8S3zwZ0xy3uxvOiTnJnZiuUFt6dvact809eM8xbzwe3Rn7RgeeHZ/WZ+yfKCO2N6dWI+6rqfrGjJfHCnYHxrtq/inHR3usvZvgruzi+7mvk4p9zjBecxH9y5d9Aitq/Cs7MXrmP7KrjuN9BlPuqqAw8sYz64tXXXCpYX56R98LPpLC+4PTFzBvMxp2p215+ZD+4caDOB5oV3e5s5LK/hq+bPYz7q6sqdpzAf3Dn50XS2r6JPMumrzWxfBXeeTdzCfOTUpZ5dRX2Pi9p3r2H7qvGWRmxl+6rhI3OeW7rJ9+rqS0+vZT64XabrWpYXfbJmj1rA8oKL4bsXMd/09UCT35lveOUFM1heePLig0tYXnDrtRpLmI+6juwzk/ngut6ymWxecU6K0gWGsnkFty8sGMl8nFPuvr9+oL7HHT2iP5tX4323fAybV8O7XxrHfNSVW98exHxw/U7sYJYX56TsNqQrywuux43uznycq8pZ2I754Had2C9ZXnju83N7sbzgVuFJfZhv6lZ/5Rvmg9s113Zm84o+qbtDv7B5Bbff6z6e+cgpS5cexnxwXSNpOJtXeFaadz765hXcLfvHBOajrt08YwT1PW6//95Ilhd9supX7Evzelw0KGYzH30V08t1Zb7hVq1uLC88NXG5zfKCO5Pibeajrp2W3o354NZ7z+bsR0kf5Nzrbv0w5/41vYJ371sxmJuL+nVDYbzfc0Hmy2dy/j0Wu1GbAPPB7VVfBXLXtZs2DOX2nAIvB3NzMaFxKDfXP1QPUt+r6+zvHqC+x634AQGW177dzflziae2JLO84O77e5KZr7+ulPPvwPR4Pon5hr//ZhLLC0/sO57M8oKL6cFk5qOue3/dJOqD92qflLuv9rTmodx9ktXeC+bm1vU2odxc/9QgyHzk1FfGBqjvcXXu90BY3Re+DuX21Ixmwdxcud778DzuzG8bZD7quj8vDTAf3Jm5McDyok9Os9tTWF7DO5ZIYT766qwZlMR8cBExI4nlhSeL5vy9c39ecPVD7RTmo679hZvEfHD3mZgkNq8qNinnOYH2qZrNq+Err2jm2xtz/r0b3e1GNPPBxY3ix9i8wpMn741j8wrutC8fx3xTd+lbx5hv+MEex1heXbFczt8nb1r5AM3rcbdXzQPMd1Le+Of3K/qbd7ZT3+Nuo7bbWV54onybAywvuHht7AHmo65eO2E78w3vEr2dzqvXJ7f9u3FsXsGtMV/GMf+/+rriGPPB5cNXjrF5hWc1HBnH5hVcF5ofx3zU1ZdeOc58cPe57sdZXvRJrdt6gOUFt0pkHmA++qpeLx3BfMOTu0fQvJ7nvFcliuU1PKtNFPNRV4+NiGA+uJ12/47c56oo0yOU+5wUpzoGc3OR1CuUmzs9ugaZj3PKPrMrwHxw/XJUIHddZ3jvUG5PbewWzM3t2Tn/DqjhhbsEmY+69qBDAeaDO0ciAyyvOSfPf5LC8oI7EVYK8825mnguifngTqsbSSyv8fq1TmF5wdWO5inMR11n7R2nqQ9eM8dDX+2IrqHcfRLbOgRzc7vvl6Hc3KndLMh8k/Pj7QHmg7tPrQjkrqvfz3kPnakbeD8Yxkfn7KHg4trzQeabuj9NCjAfXGzsHmB50SfZr14Kywsulr6SQn2vr2pBMIn64Ff3JbG88JyxD6bQvB53vklMZj7q2oNyzlW/Dy7ztU9i84pzUjy+NY7NK7hT+HAc83FOiT/mHGc+uDt4x3E2r/CcJTFxbF7B5UtH4piPutbYI8eZDy7b7T3O8ppzMmV0FMsLLm9fGMV8nKvO3Ho7mA/unvhhB8sLT+5aE8Xygjujl0cxH3WtweN2MB9cXx2xg86r1yf5zpY4Nq/gTpEZccw3fV3353Hmg7sjOx6n8+p51sdd49i8grsNqsYxH3XVaw8dZz64mDfnGMuLPjnLJ0axvODyRPso5qOvomH7HcwHt9PK7WB54WlZJorlBXcrRR5gPurKP/dGUN/jdovmEWxftZ+bm/P3Peuv/Jvtq+Dq5Km/qf/s2pz7xqe3LGE+uBp5cgnbV+GJ2k+uo/uqx93mvdYxH3XFhvxLmQ/uPFF9Kc1bPTHnXrTG2ZksL7j8/b5Z1D/YPufPtQv3mMB8cOfy1AksLzzdvuYsmtfjsv2QWcxHXfFizATmGy4qTWT7Kvqkh5xax/ZVcOfgB+uZj5zOU/ZS5oPbXbYvpfuq51lt/l7P9lVwK/rJDcxHXbdEoWXMB3fjP1rG8qJPzkdHZ7G84CKm0mzmo6/2mz9NZD64Wn95IssLz3rix9ksL7iIOzKb+abulOaTmA8u122ZxObVfrvFiJz3unQeweYVXLw4fQTzxbzknL9n9vL1fswHd1KK22xeTd0XUkaweQV3h7w/kvmo6/5Zy2a+4Z/2tVlet+SonL8HVmhSV5YXXLy7pivzZZlROX+P7aEpbZgP7qpNbVheeOKuYFea1+Oq+/PdqO/VVWuy2zAfXA6o25bNK/pkvbd+JJtXcN3qtZ+o7+WUv2y2mQ8uehXqz+YVni6+9ic2r+B6SdVRzDd1TzXpz3xw6+yc/iwv+mSfG9CN5TX885PdqO/1VZ1e1pb6HncLl2rH8sJzF73xLcsLbu2b9i3zUVcGJ7ejvsfFvEfbs33VnKs7e25g+yq41WvlBubjnLInD17GfHA1ZvEytq/Cc3sd2sD2VXD1TeQG5qOuVXj7Mup7XJyJWMby4py07n9sDssLrr6uP4f5mFOnW8nJzAd3vmgxmeWFp0t1m8Pygqt1P8xhPurqruMmMx/c+nzxZLavok/Ozrkb2L4KLgc13UB9L6dVaOUy5oMrNWYZ21eNl3hpPdtXwUWL79czH3XV0ebLmA9uH668jOVFn0TRb+ewvOB6VNM51Edfa26YzHxw2XTvZJYXnn2u5hyW1/D11ecwH3XlqPTJzAfXr7eZwuYV56TbceYoNq/gVmS+0cw352pMqD/zwXXS8wPYvMJT9T8ezeYVXOwcO5r6Xl2V3HUA88HluLUDWF5zTm6+8S3LC+6OadOd+ThX3SN/tmc+uLhYtwPLC08XO9ad5QW3unXqwXxT90K5L5kPLus0/4rOq9cn+0j0aDavho94cgzzkdM5UWIg88GdASsGsnmFp54YOIbNq+F3VRvLfNR1ojcOYj649faUwSwv+mS9ULcnywtuJbm9mI++6n11OjIfXL6kOrO88GTzV/rQvB7XP/ftx3zUdbdd78p8cF1mQc/c96qiz8uh3Peksk/RYG5uxZcIhfGnktOo791T6q3vBJgPrr8oHMhd1yqYHubJAivScnPdZHswNxf1uqYxH3XdzRGpzAdX73ZJZXlxT6onzk1mecFFjWbJzMc9ld3qiSTmG75/RyLLC896745klhfcupTz92L8vqnbrXUi8w1v4P19Ga+vItb7e3Ren/Tqx9Nyc3d+1WBu7pxdFmA+cqord6UyH9y9MSAlrO4dW8PrnigTyM3tdk+n5eYy9ctU5qOufPZkMvPB3an3JLO86JP65fEzLC+4ljn/LoLfR1+t/F0TqO9x2SM+nuWFp04fT6J5wXvdlUR91J3yVjzzwWV/789LffOKe1KZnazZvIKrFTnvlff7uKe03il1jPngVuFR0Wxe4ck6z2k2r+Bqz0+xzEddt7v33hCfD+42r3eU5cU9qf7imwM0r8f1iTsPMB97qnr19HbmG16p03aWF5618bdIlhfcfr1mJPNRVx0ouJ354PbnB7fRefX6pMcvPsXmFVw32XWS+aav6WuOMN/wr6oeYfMKT5+MO8HmFdyKvBrDfNTVQ3cfpr7H1e5hh1le09cVN/azvOD2KxH7mY++isKJ25gPbjmtt7O88JxhOf+dPy+4im4byXzUlWmDIphv+FPf78x9rupVDcLOSWtXl5QwXrNSGJedyiUzH+eUm+29t8Xng6vG85Jy1xXBacm5PWX3Pp2b65ZjT+fmbrNmicxHXXd/vkTmG77uzniWF+ekfP/ORJrX46Lp/njmY07tyRs188H1D4tjWV54ooYVx/KCWx/0iGU+6uqf555kPrgtO8bk7qtd/7bEsL5mHIvLze0++eNyc+f7J04xHzn1T4NjmQ8u6v11PHddO2JpTFjd9V9H5+Z4r6rhsx85yHzUdfOnUx/c2Tr9AMtr+lr5RAzLa/iVp6KZj75avYsco77H3a3zj7C88FTs8EM0r8ed1949QH2vrtu+2SHmg1vLq0execU5qXoXiaHz6nE5r/xx5uOcss72PEx9j9s7Jh9m8wrPKa2OsXkFt8Z+Es181JUR5w5T3+NWxc5HaF6ckyUrHGB5DS/ySRTzzbmq++9hPrg9q3kkywtPNzpzkOUF1xX2U9/U/ejRg8wHV1M/P8LmFX1S09ofZfMK7iz57gj1vZyOyn+U+eBWzelH2bzCs3uMOszmFVy+NIf6qCsfeTKa+eBWz97RLC/6pKsmH2V5wcWqfMeZj766JeOPMR9cPmadZHnhOcVSY1hecBXf+yT1UbdgLc18cKvs2Di2r+Ke1Nl9x3q2r4LrO/5Yx3zcU+ruhZcxH1wML76M7avw7GHD17F9FVw8E7uO+agri3dbxnxwZ8Uny1le3JOKUl3nsLzg7sX75lIf96pNm09lPrid0eA3lheec9uXf7C84KLo9T+Zj7ru9asO9T3uvl5mFttX0SdVOG0921fB7WcLuMxHThlbZCXzwe19D61m+6qpWytxE9tXwfXJw1uZj7ri6G3rmQ/uXKizmeVFn+QXfRazvODu298vZz76KiPsecwHd38qtZTlNXWrdV/D8oK7t32wifmoq3p7dX2+4fU+38TmFfek8s8p49i8gqu0+39lPu4p3TsThjEf3N04YTSbV+OdbzmJzSu4faMZ9726boFFvzAfXH+RMpXlxT2pWNB5IMtreJNt1Me9qkhY0Y/5hj+1YjDN63lqW41xLC+4+KDOFOajrnOs3FjmgztxyZPZvKJPcv33c9i8gjs1XlvCfOR0Hn5vNvPBxZNVl7B5hacrPLmazqvHnb4lNjMfdeUz+dcwH9zaqbawvKZPr5aezfKCq2cWLaG+11e1bdRs5oPLra2XsrzGO91iLcsL7jQdv5X6Xl1pnf6b+eDu8+e2sX0V56TcWXEn21fBde+y+5lvztWTByOYD64ntN9P91XPE4daH2T7Krj1YNxR5pu6wUGHmA+u3itxnOXFOSl+vhDB8oKLTwdFMh9zastiO5lv+OKdB2he1D35+hGWF1zetf4E81HXXVInmvngcvagWLavok9q7MITbF8FFy/N18xHTjn5QizzwfW4tfFsXzXeqzmf9++r4M63OffCfh91nY+KJTHf8JXB0ywv+qQLLoxjecGttosSqe/11R3+UQLzDV/d7jTLC0+sKnuG5QUXy+5IYT7qOunHkpkPbk+akcrmFeeke/eJXWxewdXd9Q8yH+eUe+DaHuaD22UvHmLzCk+e/eE4m1dwve3ROOajrvXVnBPMB7erNYlneXFOikef20/zelzeOeII9b1zVes5kcw3fM+NozQv6jaadYrlBVe3d0pgPuq6gZw/9/D74GLjJ4l0Xr0+CbWdziu4fv7YGeabvr5fgPrgcsRtKWxe4bntnk1l8wrudjkcYD7qOrtfCDAfXCYvS2N50Sc9uyjPC74xNYX56KvalXiG+h63f5hC88ITwy7SvOC6Yt0g81HXrWVTH9yZviHInlcV2+9q/A9/7qHG7HlVcGtTzcbMd2oV+jDnz42f+JD64A9+9iF7XhWeHv5TY/q8qsfdhecaMx91ZY2lHzIf3Pqw4kcsr/72pPrnY1qyYnnBdYWCbzHfrjehSs57zWZXob7HneQDVWhez3OO1XyL5vW4e23cW8xHXfvRR6syH1xfHlyVPa+KPqmLHZqw51XBxf70JsxHTrFk+0fMB7c+7P8xe14VnpU9/nP2vCq4Kv1xU+ajrnj9+brU97jTcX1dlhd9EqeuvcXygiv9fQ3mo69Wk+IvMh/cDp54keWFJ2uVrcnygqv8BWsxH3XtucGXmA9uv9/7FTavMjqi7D9f792YsmxeweWwh8ox3y43rMQ//1+DZpVgPrh85FoJNq/Gu/h9OTav4PKxh8oz39T9ZnRJ5oPb43s/SvOWFPf98+PiT9/H8oKrBfPuY74s/cJtOe9drX8b8w0fu/Y2ltd4zd4vwvKCu+MKFGU+6lqP/XA788HFK5vvYPOKPllfnS9P59Xj+suKFZhv+nr/8ceYD67KnHuCzSs8a1XWM2xeDf8lojLzUVf9NP5J5oPL5hVLs7ymTz9XuZ/lNbz4vQ8yH32VW6LuYj642PvsPSyv8Srd8TDLC25viy/JfNS1V669j/oed8v/8CB7XtWck9cqNmPPq4I7xT/6gvnmnDrZox7zwXWVfvXZ86rwrMqB5vR5VfDqL7agvldX9n6gIfPBxePTP2V5cU6Ki/Vr87w5XFR++x3mY05Vq0OvUd/jTqdfqrG88MQzrd9necHFmOEfMx91nQLNazDf8NRh77DnVdEn0ad6K/a8KriquKgt85FTVz7RhPoety/c0YI9r2q8BUO+Zs+rGt415z1Xfh917dGl2zEfXJbb15nm9fqklpZsyPIafvj7z5mPvuoK5eoyH1y0nvEZzet59lv3tmZ5DR906Bvmo66Y/HJL5oOLmhU7snnFOensnfI8m1dw0aLai8w352psu/LU97h4peezdF7hHa33OptXcKv8Xuqjrpuy9EXmg6vhR6uzvDgnZRtRiuUFdxOiqY9zVR1991HmGy62lGZ54Tlxb77A8oLL0ZXeYD7qys/zP8d8cPv80lfZvKJPTscxddi8gtuz3v6E+Sbn3xPeZT64/dqaBnRePc9tWbgFm1dwp1Ghr5mPuu6Q1yzmG65Lf8Xyok/2HxPfZnnB5bgJ9Zlv+vpqqdrMB1cPLKzH8sLTd5ZszvKCi6vvfsl8UzdJN2M+uJjTvgPbV931Qy5X+79//OuGnI++fRXcSf8mvRrx5Y/7Uv758V+VU6sRH9xd0TZQjeyrpu6r7pVqZF8Fd/7Il1GN+Kgr7twarEZ8cF3y/DmWV1z7NfafHz9ZSrO84LprtTjmiz8yD/7z9Xr+doj54KLnkiMsLzx7wlcJLC+4zvfWGep7dZ3IbceZD66a3ZaT37evmj7V3ZJVjeyr4NY7+W5jvsn5acdL1YgPbr3wYc5/59tX4amo1/OzfRVcx9xVhPmo68Y9dhvzwfXxkf98XX9e9MlK3R+sRvKCi+vfUR99VeOrn2E+uJp66Vw1kheeOzc9qxrJCy6aFc3PfNSVTe/LqEZ8w5+JvKM6mVdxYe6uf3LVOb6bzSu4fGHHfubbHd/Y8s/nP261lfng6nrFHWxe4Tnldxxk8wruzl5FfdS1bl+2n/ngus7YIyyvU/vOjf/kSp3h0rweV1M+o7719OZ1/3y+TfYG5oNrUWkLywvPtlbupnk9Lqp3PcR81LWiftjFfHAxefdBNq+mT6EmCXRePa7PzAgyHzmtKSXjmA/urno0wOYVni7w25VqZF7B7cWf/fN9we+jrqz5ZHo14oOr3zvk/Lry5TV9LZYQS/N63B4SkcJ89NVZXiGW+YYfa5rC8sJTrze4XI3kBXdkUr7qxEddO1T0cjXme1z0W5uP7as4J0XjgQ/TfdXjVvRTpZiPc8o+2ONB5oM7WyzJ9lV46qX7KrF9FVzfiH+Z+airyiRXYL7hu/u8RPN656R7tsv9LC+4PP7O48zHnMqjRYsyH1yf/eFRlheevf69p1lecFG5yYvMR101vmB55hveqlNVtq+iT073qzXZvgpuP63rMt/kLJFdg/ng9qGyddm+Cs+NXtGU7avgYnP/9tT36uqsXz5nPrjqnPPnCv68pq+qXA2WF1w1vfER9b2+yk+6vMV8cKdWzIcsLzythzRhecHFqXVtmY+61poJjZkPbnW/1IbNK85Ja4oswuYVXM+oUpL5OKecZrffx3xwt0G9Emxe4amsEuXYvBqu/6rCfNRVkzqXZb7hp5+rwvLinBTvf1iY5QW36+55hPk4V0V00cLMB9efOo+wvPDsVveUZXnBRaPYF5iPuqp2chnmgztnt7zA5hV9ct89pdi8gttTUz9gPnI6s7so5oNb+1/6gM0rPPnN/kZsXsFlkQ/aUN+rKze+0oj54PaJPa1ZXtOnk5UVy2v4xIg61Edf6xdXzAcXl36vw/LCUy/EfEbzelyv/qE181FX9vn7M+aDW6datmbPq5p70gmRfdjzquDOH8/+yHzcU7rrN/SmvsfF5e8HsedV4amDm8ew51XB5aKnpjAfdS21Ygzzwa3Q+Ck0r3dPKtOdXiwvuJj8zCDm457K2XipJ/PBHfvaQJYXno7aNIblBXc23T6V+air39o5hvmGv197KnteFX1SLarOYc+rgusj5ZcxHznthifnMB/cPn9sGXteFZ41q9l69ryq4fMrRTAfdcVAZwPzDa//8g6WF31SGW3msrzgjm65nPmmr7PWzKW+x8XMVctZXnj2y79uZHnBVcnsHdT36rrnHnKZD65Xf7eTzSvuSXVWp55sXsGtrE0DmY97SuePQA/qe9zq0m0gm1d4Vt3VY9i8grt135vKfNS1NgwZQ32P6zcKTmV5zT3pyk97sLzgckGBgczHnqq2L+zOfHDdedAAlheeG/XyGJrX4/LDMVOYj7ruhCmjmQ+u2iVOZvOKPjm1989l8wquAweXMx857cQ5c5kPLreuWc7mFZ7zUxWXzSu42LlgJ/NR1670lMt8wzdP2snyok9O03fmsrzgukTn5cw3fa22bg7zwd3a+ZazvPDcO6M3sryGL6qzk/moq+pV2sh8cL1x/w72vCrOSevU+APseVXDW1Q/xnycU+4Ln0cx3/CF1Y6z51VN3VdSNH1eFXyK9++B+3zUtYf3imM+uDN6YhLLi3NSf5wWxfKCO5OuHmc+5tRq985B5htewPt7t768xisWG0fzelyXyEhiPuqqK4/HM9/wH146zZ5XRZ9U4uBk9rwquN378QDzkVPcnp3MfHD7le/p86rwrNJJaex5VXDn3YIh5qOuHPkM9cHtPtVDLK/p06N1U1hecGfD3wHmm77O9t4X5PPBrYXxAZbXeHaTIMsLbm1sEWI+6ornegaZD27/nvPvevjnFeeks63jQTavhh+ZHsN8nFPqkZy/d+P3we3LE2LovHqeO+v1eDav4Fad+qeZj7rWj6/GU9/jdo2cH/vzmnP1z+cP0rwetxZ9EcN8nKtyXGwU88FFKPs4ywvPWfFYPM0LXuP108xHXetQMI76Hrcb3H+azSv6JPpPTmHzCq5GXg4wHznV41NTmA9ulbgaYPMKz/n+xyCbV3C3cf8Q81HXbT00yHxwVWNQiOVFn7QzIoXlBbcnBAPMR191oY4pzDc8OzrA8pq6jQcEWV5w9UzO/4/fR139Xfcg8w3/Jeff1fHvq+Ze9c/L37J9FVy8m/NezJt8757S/u3lb5kP7jxdtz/bV01dN+e9of59FVw2f3gy803dulN+Yj649edrk1hec0+6om83lhfc3bHCZj7uqez7jnRlPrjbvKzN8sLTR38dyfKCy1HNJzIfda1+s0cwH1zs/XUC21fRJ/fQA3PYvgpuDem9jPnIKVs1m019j8u1SUvZvgpPyyYb2L4K7qZW3cF81JVFuq1nPrj7y/AIltf0yZ07i+UFF6/WX8p809ePxCzmg+sHdy1heeGJQyPWsbzgbq/Y7cw3de/+62/mg8v9ajubV3Ovuu6drmxewZ3dn/ejPu4pM2O6MB9cPDO7L5tXeHbKseFsXsHlntTxzEddMaz6cOaDO492GM/y4p7UjZ3Yheb1uNOsSl/m417VenhAF+aDi7X39mV5jdcgdhjLC27XKT6e+l5d948Nw5hveEzmr2xe0SfVoOtMNq/gzi+fLmE+cqrZd81kPriTlrWYzaup21X8zebV8PVrtjEfdcX4HmuZD67ubLCN5UWf5MgtM1hecLl6zGLmo6/u1px/19jvGz77q8U0r+eJGXItywtun7l/G/NRV3S9fS3zwe3Hs7ayfRXnpEjrGcX2VXA9ddBx5uOcUo/eGcV8cPfa9WNsX4Wn4xfHsX0VXMw7kcR81FV35/y7kH4fXI2fnsTy4px0ynj/rqMvL7hM73yM+ZhT8USZA8wHl04omuWFJ1uUimN5wXUnK4n5qKuWnNLMB7c+L53E9lX0yfmyVgrbV8Gd59cHmG/6urNYCvPBZaFpAbavwnM2twqyfRVcHfwmxHzUdUt+HGS+4eVbhlhe9EnUTEpmecH1N30CzEdf5ZG/kpkPrsp8HmB54akvXw3SvB63d+X8u9h+39QdXDrIfMMT3gmxecU5ae/dEcnmFVxN7xvNfJxTdssBkcwHt/aXj2bzauo+NUuzeQV3ipxLZD7qWvf01MwHF+MjEmle75x0Pv00kuUFV1cvHKW+d66qz9+JZL7hjj7K8sITs5polhdc5Mt574PfR137Ql3NfHD7rdmJbF7RJ/nGqGQ6rx53D1QLMN/0dfE3ycw3fFP5AJtXePbT9wfZvBo+9I0Q9b26+sjtQeaDi/lVQiwv+qSXNElmecGtjSUCzEdfVXSDZOaD2xeKBVhe463PSGN5wZ0ClUPMR137tytpzAcXJSqGcr9XVbcsFpn7PalWXOG9ubk7vmlEbm6P2LyN+XhPqbU9bjfzwe2jmdty13VrrXBze6qIcnNz94uDq3JzVfX71cxHXTGi5ybmg1sHy61jefGeVPVS1h6WF9xJ6riD+t57KtXlFpHMB9e/j9zL8sKzrd1bWV5w+W0y9VFX2jt2MR9cnmyyI3dfZeCppbn7JGM3L8vNrcIPzcvNrYcbL2S+yTml2Crmg4tLryzPXdcutnlGbs9p32Fubi7fXzg1N9eLrlAfdZ0+lZcwH1yWsubTvOjThTIbWV5w6603/2Y++qrPld7OfHB79L1bWV54ws75efHnBZfl161gPuq6W25sYj64szuwkc0r3pNqn9l0kM6rx+2Kp6KYb95TumVyNPPB3XHfR9N59Txr2aUDbF7B1ZysSOajrn3jkWjqe9yu2Pkoy4v3pDp3TTrF8hr+xZuxzMd7qqytw+KZDy72PpPA8sKTO9rEsryGFy8Wy3xTt0OfBOaDi9Z9E9i8ok/Wswn72byCy+7j9zIfOZ1ZPxxhPrhaXuMwm1d47ndFd7N5BVclGu9gPuqqrzYfZD64PHt7FMtr+pT00SmWF1wPf+gk801fm72awHxw+/CieJYXni7UOYblBbdrtzzGfFN3rPfnvj7f8OzdOuxcfeyz8WHn6hd3Tc/N7fqzx4bxNd0mMd+cq5FyLvPB7dj/q63zDM6qWtvwPqKCWPHMhwXEdfQgiigWFBWUjSIlFA1CACG4IaFjSAKJFIUdQMAQOmhEygqJFCF0QgggC0hBBOmQYmCFFFLfV0QE5YjfD/a95jVz/+Saa+6555lnFiubzc6I5MBc3eKRhEBP1Du9OJC7YREzArlKq5nPfOTK/Y9L6ntclo5eyvrinJRbz29hfcHl9rCNzMee2mcKdzMfXN9RvJP1haf3jl7H+hr+akkK85Gr6zfYznxwt7W7OXCuKvFiXOCcxFB7diB3rn78aSC30zd/znz0VDM+WEJ9jzv/888PzHV7z4oN9ITzYFwgl89diArkesOKicxHrjo7MZ754G7nIdNZX8xJXDsnWV9w+XXKUuZjrpZK+I754LpZWQrrC88+lbqY9QUX22bNYz5y1fhGkvnglhuSyPYV56QumJjF9hVcp84+wHycU1b0v48z3/DTtx+h++p5emDQPrav4OLKwV3U93LFvd7vP6/lgztJGw+yvuacjPvjLOsLLvd2Oc18nKtWavV55oNbLx0uYH3h2Z3HnGB9we3sR44yH7nOyKBc5oOrz5ecpvuKuYZs28721fCBbTYz3/Q8u34f88FVjyPpbF9NbmHhWrav4LI6aRX1vVzrncqt1Pe40//pDawv5mQvnJvD+oKL6MkHmI+5yuXnjzMfXMWlHaZ9PU/PfmEP6wsuV5RuZz5ynRFRmcw3/Gb37+l9Fd9VnSBL2X0VXKWNLWM+vlPqPN2hnPngbtjTFey+Ck/trihj91VwnXfvJeYj185NrWC+4VUlFbQvvqv6Ze9K1hdcNH25ivn4TqWMLKhiPrg68W016wtP30irYn3B3VO/VTEfuaqySQ3zwa2z/WvYfRVzsioulbH7KrjqNKmM+ejpvJNbwXxw0TWhgt1X4Ym2OaX0vupx0f7W742r7SPX2vJ3OfPBrZAW5awv5uTW/6OK9vW4unqoivmYq545oob54KKwew3rC08uDqtifcFlywOVzEeunl2vhvmGJy6qZvuK76Q6zfzVbF8N/3hVDfPNd1UXpdYwH9zeFeRj+wrP2X6vj+0ruHNnRx/zkaunr/UxH1w/UeBjffGdVDuqpY/1Bbe+3uVjvnlPdehM6oPLLrf5WV949v67/awvuH3pNT/zkSv+3dXPfHB37lg/21fMyW3S18f21fC/u/io7/V01/h8zAeXf5T72L7C0//X2Mf21fBlP1Afue59P/qYD273WkD7mjn16uanfT1ubenqp743V8ee4Wc+uIyf5Wd9jdfjVm7tvoYvvt/PfOSKq5P8zAeXyQP97L5qzsm2E4vZfRVcbysoor53Ttln6l1iPrh+anYpu6/Cc9YXX2D3VcMHJxYwH7n6+oZi6ntc74gqYn1xTrp18ipYX3C1KLGc+WZPw4urmG/40bJK1heevemXMtbX8KCzJcxHrtV7VgXzwfXlLZfYfRVzcl+/5xy7r4KrbjNPMh893bp5hcwHF2em57L7Kjy7ZM0Rdl8Fd7v0y2I+cp2oLqeYDy7bPnCU9cWc7B7dLrK+4PpM7nnmY65u996lzAeXYeMvsr7wrPiTuawvuFhUl/rItdpOPc98cB1bcY7tK85JJ6xzDdtXcNF8VjXzcU65H97698raPrgs8O4DtfYVnuoZXUX31eN2UBX1kWvFH65mPribMqeK9cU5KVMP+1hfwz8JpT7OVeeeZ/3MB3d9Z32sr/Euba9hfcGdKTuoj1z3y2Af9T0u9sbV0H3FXBvlXqL76nH1VXPuY66zD1dQ3+OyKOwS21d46sdfi9i+gutPz/7MfOQ6+3qWMB9cTNijWV8zp1ltqlhfw6WgPuZqv9KhmvmGx0RUsr7G69a1jPUFV9sPXWQ+cnVY/iXmg6tVU0oCn6u6q5aMDnxOKmcljwvkIr73sECu69wZSX3vOaXzYN0pzAfXoUM+CczVm7LCAj37iRdG/IPv2zYokNsJ/cKp7+XKdt77u7V8cJ2fPJL1xXNS8eXambSvx+3Hp8YxH8+p7CH3LmQ+uDWqbzzt63ny9OoJrK/hV7dGMx+5YtvVqcwHV+nWpMC5qj8vO4Fzsh5t9Q/uzus9KJDL0LKBzEdPMWVhOPPB5fYJ/8hVvbcPDvT0+eTQf/C7L4YHct3mvwOZb3IzTvRnPrjzwm39WF/MyT0fMor2Bf+4A/UxV+eBx8YyH1w2eXAU6wtPJFXRvuD2s9/1Zb7Jndw/jPmG9+kxgO0rnpPavgHfsH0Fd0syFjEfzyndg5NTmA9uPdFuOdtXk3vXvxLYvoI7ny6bxnzkqrS4xcwHV7/3SmB98ZzUPfjqRtYX3G5/6/+F1PbNPXXtyR3MB1edV29kfeE5z6csp33Be7fhvpfrrGjwLfPB7bRxy9i+Yk7i2RcmsX0FtyZkRDMfPdVLvacx3/D2T05k+2pyT+WMYPsKblUVDmI+ckXa/Cjmg4vli4exvpiTTmg3m/UFF9Oemsp8zNV6/ZeFzAd3vq+Yxfoar+TGJ6wvuPvwF2OYj1w1qq3LfHBVc0dM4Llqy9RhgeeknfDbR4Hc7XphZCDX1z4YxHxzroaV9aG+x53iP/oE5sp2r0UEemJn5eBA7r6TEfkP/nBCOPORa79wtC/zwe2Xb/uQ9sW5WnC2F+sLrt5L78l87KlbMLE388GdDU8Hs77wVId9wawvuKr4LJj5yNWVD3dnPriV0zQocK7WymbR/5hrq7uGBnLHufWdJ3DR8fVhzEdPmRTUn/oed98dPiAw163+fWygJ679b1ggd7q0HPcPnt5+OPW9XJH+fCj1Pa7LPg9lfTEne3hSMOsLrnfUBDMfc3War+nMfHDR4/7OrC88O6ZNT9rX43LWJz2p7+VaT9XtzHzD6w7vzPYV56Q9uVU/tq/gsn3fnszHOeXMPP0R88F14bHebF/hyY+ud2X7Cu50XN6R+l6u1TCzB/PB3QPpHVlfnJO6aXo46wvu/jq4P/PNudr0P6OYDy7emTmI9YXnTBrSk/UFdzbEd2E+ctX5whDmg7vfLezB9hVzsre577B9NTzsUnvmm7nOrWjPfHDr77VvsX2F554Z3J7tK7gTk9me+si9/XBb5ht+fs+brK+Z08nDb7O+4DKl61vMx1ztB2a+y3xw+XeqzfrCcy+uakP7elxX/El95NqXq6kPrqf629H7Kp6rzlHfs/uq4ZOnUh/PKWVa/Szmg1vvRexl91V4VuvQVHZfBRdPrk5mPnKdMdZ25oPrBxZ9x/riOal6Mf9H2hc8b95B5pvnqgtyTjAfXM556QfWF57z1bwM1hdc7J+7hfkmd/RT+5kPLp9dmcbuq5iTmlf0Nbuvgjv5p+czHz3tM32SmA8upvf6kt1XjZec+jm7r4LrQzsnMR+5usu4BOYbvuavONYXc1Id961mfQ2fVnc58zFXNbVpKvPB9a6YVbSv58klwQtZX3CV7p9BfeTWf/Qr5oPr8esT2L6a56onzp9h+wqueg09xnzznDL5ch7zwd1R3nexau0rPLXQzWL7Ci5fcfYwH7liTbPDzAcX7nv7aV/vOakbeaCQ9QV3NzY8y3w8VxUrF2jmg+vn6uaxvvDcPy/8xPqCy7fiM5lvcp/ZcYL6Hnfa7cxh+4o52RNbb2X7Cu6E/7Ka+abnmhfTmA/urji1nu0rPKeRu4ztK7jdMGEB85ErGrRMYj64mHH7EtYXc5JN+mawvuDW5U82Mx9ztUrGf898cN38jm2sLzw9/60U1hdcPr8ykfnIdT7ttob54I71yjJ6X8W5mpccxe6r4O6YyKHMxzllhybHMh9c+aJHs/sqPDkiYwC7r4I757J6Ut/LtaddH8R8cLWsaR/WF+ekTln4KesL7izOimI+9lSd2BpHfY+reSdjWF94zqHXhrK+4OLr3gOYj1yn1X2jqO9xd0+TQey+auaamdWF3VfBVbOEd5iPniKm53vMB7c7xXdi91XjxY1rz+6r4M7mP96mvperTjV6l/oet+PDOrG+mJM6U+cD1hfcCpnVjfmYq1Q6hPng4svWwawvPCf+XBfWF9zt06o785Gr3pXvMd/wBm/1ZPtqzsk2kz9n+wruHO86kfk4p+wD87+gvsetFxtMofvqec7/tYxk+woupoWGMx+58p5/jWU+uG50fBjri3NSx2bNYX3Bncs34piPc9Uqvzaf+eBW8tDptK/n6VdKYlhfcO3vN4r5yNUnd49nPrgYkh/B9hVzctOb92f7Cq5bLu7FfNPzyZyPmA9uZXXry/bV5Ga+/gHbV3DxwroQ5iNXXn+qD/PBZddLtK+Za2nQYNYXXN4dPID5mKu+c1c49T2uDnZ0WF/jpS7qT/t6XC20BjHf5DZs6zDfcN+n4ex9VXwn1V50gL+v6nEr+0U/8/GdUq0rfMwHdxPep++rwnPDxtL3VcHVs4nUR67yJ1Df8N83+llf813Vt+vwvh63Hw73M998V3Xzg9QHdy6PoX3hifJltC+4vnMv9ZGr5q6mvuFTs+j7qpiTuLGOvq8KLi6nUh897T/3UR9clWTS91VN7o5v6fuq4OrKPOojV0Xuoz64WLuR9sWcRPRR2hdc1pykPuYqF5yhvuGjC2hfk2sfo30N37mf+si1S3KpDy7HHqX7iu+kivxH6b6Cy/BY6uM7pXLZY9QHt2+Mp/tqvCvr6b6C2/0PUx+57qiN1AcXR47QvvhOqh3SmPYFd3PGU998p+qRh6kPrhbH0r7w3LyNtC+4tfIo9ZHrFqZyH7w/31fMSXTLp/sKLiI19dHTOfsz9cGtt4rpvsKzVp+n+2r4zNPUR672F1EfXH+TS/tiTuJKIe/rcb2yhPqYqyPOUx9cNyylfeG5ycW0L7jcm899L1cEl1AfXH1d4Gfvq5pztfFwP3tfFdwa05T6OKfsrXP9zAe3L/fxs/dV4Wl7l4++rwq+9B7qI9cJ+cvHfHDhH+FjfXFO6j9X077gzpvjqY89tRulUR/cWZHgZ33hiZRn/LSvx63Ta7jv5Vp/vutnPrh145iPvq/qzcn931/V7H1Vw3cPqmI+elrPTK5hPrj9/X3V9H1Vz5Nt6lfQ91XBl54vZb7JfbyygvngbkVVGeuLOenfTtewvuAyfUY19b25yifu8zEf3H7yXDXtC2/vF5WsL7huuvUS85FrXfq9kvng4krbcravOCdlj310X8Fl46+pj3NKrcykPrgKlnRf4cmJffxsX8GF9/+mavvIdeMH+ZlveOdrPtrXOyetgTm0L7j1azL3vXPVGnqI+uDyyW9pX3hO1FA/6wsu6tz0MR+5buYwP/PBpfO3j+0r5mTf+ZKP7qvHnUb1a6jv9ZRuRx/zwfXjzWrYvsJTlW2r2L4a3umrcuYj1z0YXsV8cJ19uJz1xZzkofd8rC+4e/TlGupjrn+972M+uBjfqob1Nd78mCrWF1yrC+XMR6586Nafa/vgalhhOb2v4jupPzWg91XD7xjLfe87paJTfeob/m0Eva/Ck/719L4K7gw+Qn3kulHrqA+u/neY9sV3Up1Ot9G+hl8ZQX18p1LP+tPHfHAxaAjtC0+mreZ9PS6G/kB9k1svhfrg4ngOva9iTmJ7Ib2vgsvrJdRHT6v6Z+qDO24Jva/Cc/uU0PsquEouoD5y7evF1Ae3WxTwvpjrtQLaF1zmFVMfc7Vu5FMfXDUtpn3hycHFtC+4jsinvsnNuEh9cJWYR/fVfCd1xxUf21fD+9z6e6m2j++UOoN8PuaDWy8MpPtqvLVJdF/BxZBs6iNX/biS+uCy062fe2v3xXdSrZuVPtbX8OcH+JmP76q6pyt8zAcXk/vzvvCWraB9weWCTOoj12m1gvsetz7I5PvqzclumE/3FVxPvkh99NROHvXBrT+K6L7Csxtd5PvqcXk0l/rIdZcWUR9cPp1L+2JOqjqX9wVfVUR9zFVl5lIfXHxeRPvCszoV0b7gcu856iPXbl5EfXCZeI7eV3FOqh6H+H3V4yIthfrmnJqeQ31w8U4yva/Cc78b6mf3VXB9t/dzcS0fufYd4X7mg4s+132sL85Jt1827Quu306iPvZU3ZtFfXDdcCXtC8+Z7fhZX3A14Fcf85GrzgzwMx9c3VdN76tmrrO7+dh9FVwub1HDfPTUYzr4qO9x6+XHa9h9FZ7dengVu6+Ci8be/b2Wj1z7xQ+qmG/46vXlrC/mZDlv+FhfcHHyvhrmY65Ofksf88Hl0VvPRWr3hSfzX69ifcFFyMxy5iPX3vB4FfPB7TsHl7N9xTkpzhzk++px9/7l1Mc5JXIPUB9cjf6G7qvJDe/np/vqcbdtmY/6Xq4tQ/zMB7cXFPlYX5yTotUB2hdchy6lPs5V98Z+6oPb1lLaF54628vP+oJbc8/7qI/c13r5mW/4tEK6r5iT0+IpH9tXcPWir5r56GmNbuJjPribVlTN9hWezKhbxfYVXN9hlzMfuXr5b5XMB1e//rec9cWc5OhHfLSvx/X6vGrmY67Wtod81AcvPVvN+sJT75dXsr7g9px/lzMfuerBkkrmg7tz7y1n76viOal4868i9r4quDVyeQHz8ZzSnS2KmQ+ur2UVsvdVTW5V9in2viq43Jh6mPnIdTbPO8N8cPezoKOsL56T6sSbxbSvx0XOhAvMx3Mqt/WKEuaDy7RQzfrCc5zbzrG+4G7XcceYj1z16qO5zAe3l8w9Tt9X9eak01ruZ++rgsu0qB3MR09rX80B5oOL8JU72fuqJnf3y9+x91XB7XnfrGQ+cq25xzcwH9yu6ZXE+mJOcmpYJusLrnrG7GI+5qquTc9ivuHpFzNYX3hW8e2bWF9wtWjFKuYjV5WHbqa+x924FclsX/Gc1MqvW8r2FVxYhzXzzXPK7h1Kqe9x9Ua9Irav8MTYvFy2r+DOG/85QX0vVyd1ymM+uE6JOcH64jmpfmRAKesLLosfL2K+uaf27lPKfHBZ+nAR6wvPuTwmj/U1vMWiE8w3udkf5TEfXKhpJ9i+Yk6u6p7N9hXc7qp3M9/M9XuVzXxw1TpyD9tXeO6Rq5vZvoK7+T1SqO/l2teDtlDf42Lk5hTWF3MS58uyWV9wp3zZHuZjrmLU0Wzmg+ubsXtYX3hqeNgW1hdc1Ducwnzk6ge7bGE+uAy+9XtJa7+vinNSVfsXsvdVwWVG6gzm45yyt25bTH2PW5E7Z7H3VeGp5BGT2Puqhm9/KIr5yHVLjn/GfHA3aFk07eudk+66ll+yvuDWzY/jmY891XVCvmK+4XcdmM36mtyQvCmsL7jdomYs9b1cWZ7uMh/c6W/FsPdVMSed9Ogw9r4quPNQ7GDmm56lA0YwH1zlfBfO3leFZ7UsGczeVwV3BwwdznzkykO7hjAf3Nm+YzTrizk5T44ZxfqCu43zhjIfc7Vad/iY+eBWg9+Hs77wVI+ttC+4aL8pkvnItVvvH8V8cDu22Vi6r945qR4WiWxfwa3mGxKYj3NK5MxNZD64HfrcHLav8OzfOsexfQV3ZxyPYT5y5evL45gP7vYNjWV9cU66eesSaV+PO8N6zGE+zlW1anIi88FVSJM5rC88PTYjjvUF18+tj2U+cq2ytXHMBxddD8SyfTVznXkqgu0ruKq5NpL5Zq677olkPriz9exouq+e50TkRrB9Bdf102OYj1wR9Vsk8w2f9MF41hdzEktmR7K+4DooIYL63lzFHRWRzAd3O9WPZH3hOQuiollfcOt45UTmm9zbfh7LfHA3NOIzdl/Fc1Kx9M1Sdl8FV21vaOrjuWqDhqXU97ja/pNm91V4OrFpHruvguvdPU9Q38vV/zqey3xwp/LuE7Sv95xUjrxUQvt63N28VDMfz6l0zs4S5oPbvwzRrK/xbozOZX3B1fqM48xHrmx3Vy7zwd3gEcfZfRVzsrpPz2b3VXB99cE9zDc9+zXOpr7H5bEZu9l9FZ4YV28Lu6+Ci9v6pDAfuXaTTZuZb/ibFcmsr5mT/9ss1hfcfag8g/lmriUts5gP7jhBGayvyY0K2sz6gos7I5KZj1zd8egm5oPb9/+2iu2reU66IKGE7Su40+9VzXw8p3Q6jyxhPrjc94Bm+2pyD246x/bV8HXiOPW9XDlwxDnmg9sjjx1jfc1z0u69SlhfcHfM5QvMx3NVsbdbCfPBnSp9gfWFZ01ve471BXfqzDzGfOTa4rlzzDe8VcQxuq/enNRP2ZlsX8Gtd7/fxXz0lEnjMqnvcbfg/V1sX+G5zRtvYvsK7l6auYr5yBVXFmxkPric9vIq1hdzEp/amawvuPvZ3buYj7k6Sc9nUt/jlrqSTvt6nv3hGxtpX4/LujeTmI9c7TTeyHxwJ6Mqid1XcU46zZoksvsquBu3OYH5OKesZ7/5ivng+sNHE9h91eR2HBvH7qvg9r7UWOYjV85rGsd8w/87MZb1xTlpjar3FesLbkdHzWY+9tRp9cmXzAcXJ36IZ33hWaU/uqwvuDuqVSzzkSuTo1zmg6vc8hh2XzVz6vpMFLuvgtuz07iPua4LjmI+uBgfFMXuq/Dk8hYx7L4Krhb8NIX5yJVVA2KZD271u+myvmZOWcOiWF9w9UQ29TFX2TyK+uD2zWeiWV94KnjYJ6wvuLXnahzzkSuf7TSe+Ybvy5nK9hXnpBr6+xK2r+D6SKt45uOc0gULljDf8C7qC7av8MRW4dJ99bj76qoY5iPXevTAFOaDy24jYlhfc64+1nMJ6wuuQmK/YD7OVbtb2yXMB9d1Qr9gfeFZd0VNYX3BnfgOMcxHrnyj9xTmg6u81jFsX81cz0bTfQVXqbHR1Pd6Kl9kFPPB5c8boum+ep5z/Pp4tq/g1oEx06jv5bqzJkxgPrj9WuU01tfMdUZEFOsLLh87Gk19b65i2sdRzAd3T56LZn3hyQk5E2hfj+t1raZT38t1FxZNYD64ndx9erv/B7CYLUc= + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-006000.3.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-006000.3.vtu new file mode 100644 index 000000000..f45a5971b --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-006000.3.vtu @@ -0,0 +1,32 @@ + + + + + + + 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-006000.pvtu b/tests/automatic_regression_test/gold_allenCahn/solution-006000.pvtu new file mode 100644 index 000000000..91df1e39c --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-006000.pvtu @@ -0,0 +1,18 @@ + + + + + + + + + + + + + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-008000.0.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-008000.0.vtu new file mode 100644 index 000000000..5d405bfb6 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-008000.0.vtu @@ -0,0 +1,32 @@ + + + + + + + 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+AQAAAAAAAQAAAAEAxFgAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-008000.1.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-008000.1.vtu new file mode 100644 index 000000000..2dcf5e3e0 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-008000.1.vtu @@ -0,0 +1,32 @@ + + + + + + + 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-008000.2.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-008000.2.vtu new file mode 100644 index 000000000..bc100aaf2 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-008000.2.vtu @@ -0,0 +1,32 @@ + + + + + + + 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-008000.3.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-008000.3.vtu new file mode 100644 index 000000000..283ae093c --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-008000.3.vtu @@ -0,0 +1,32 @@ + + + + + + + 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EZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//A3532BvAAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH39D/TfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/y3zBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3zH9LfAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8DfvfIm8AAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff8P9t8AfgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//hXcEIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfCf6E9AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+F9wVwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/I/0X2BeAQAQkMEEIRlCCE4JQhCQ0YQhHWMITgUhEJDJRiEZUohODWMQkNnGIR1zik4BEJCQxSUhGUpKTglSkJDVpSEda0pOBTGQkM1nIRlayk4Nc5CQ3echHXvJTgEIUpDBFKEZRilOCUpSkNGUoR1nKU4FKVKQyVahGVapTg1rUpDZ1qEdd6tOARjSkMU1oRlOa04JWtKQ1bWhHW9rTgU50pDNd6EZXutODXvSkN33oR1/6M4BBDGQwQxjGUIYzglGMZDRjGMdYxjOBSUxkMlOYxlSmM4NZzGQ2c5jHXOazgEUsZDFLWMZSlrOCVaxkNWtYx1rWs4FNbGQzW9jGVrazg13sZDd72Mde9nOAQxzkMEc4xlGOc4JTnOQ0ZzjHWc5zgUtc5DJXuMZVrnODW9zkNne4x13u84BHPOQxT3jGU57zgle85DVveMdb3vOBT3zkM1/4xle+84Nf/OQ3f/jH3/+FdQYgEAEJTBCCEZTghCAUIQlNGMIRlvBEIBIRiUwUohGV6MQgFjGJTRziEZf4JCARCUlMEpKRlOSkIBUpSU0a0pGW9GQgExnJTBaykZXs5CAXOclNHvKRl/wUoBAFKUwRilGU4pSgFCUpTRnKUZbyVKASFalMFapRlerUoBY1qU0d6lGX+jSgEQ1pTBOa0ZTmtKAVLWlNG9rRlvZ0oBMd6UwXutGV7vSgFz3pTR/60Zf+DGAQAxnMEIYxlOGMYBQjGc0YxjGW8UxgEhOZzBSmMZXpzGAWM5nNHOYxl/ksYBELWcwSlrGU5axgFStZzRrWsZb1bGATG9nMFraxle3sYBc72c0e9rGX/RzgEAc5zBGOcZTjnOAUJznNGc5xlvNc4BIXucwVrnGV69zgFje5zR3ucZf7POARD3nME57xlOe84BUvec0b3vGW93zgEx/5zBe+8ZXv/OAXP/nNH/7xN8p/Ib0BCERAAhOEYAQlOCEIRUhCE4ZwhCU8EYhERCIThWhEJToxiEVMYhOHeMQlPglIREISk4RkJCU5KUhFSlKThnSkJT0ZyERGMpOFbGQlOznIRU5yk4d85CU/BShEQQpThGIUpTglKEVJSlOGcpSlPBWoREUqU4VqVKU6NahFTWpTh3rUpT4NaERDGtOEZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//I36X1B3AAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH3+j/RfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/5VzBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3xn+lPAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8jflfMVcAAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff2P9V8gXgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//lXEGIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfOfyW8AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+18RdwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/+b/4zj+BCEhgghCMoAQnBKEISWjCEI6whCfC/zdMD9liIAAAwPo6tW3bNn5t27Zt27Zt27Zt27YnixwhRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTQf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMh//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU2H/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTEf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNR//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUzH/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTcf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMJ//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU0n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTKf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNp//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUxn/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTWf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlM5//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU3n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTBf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNF//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUyX/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTZf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMV//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU1X/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTNf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlN1//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUw3/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTTf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMt//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU23/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTHf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNd//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwkU4D//EZggBCU4wQhBSEITijCEJTzhiEBEIhOJKEQlOtGIQUxiE4s4xCU+8UhAQhKTiCQkJTnJSEFKUpOKNKQlPenIQEYyk4ksZCU72chBTnKTizzkJR8B5KcAhShIYYpQjKIUpwSlKElpylCOspSnApWoSGWqUI2qVKcGtahJbepQj7rUpwGNaEhjmtCMpjSnBa1oSWva0I62tKcDnehIZ7rQja50pwe96Elv+tCPvvRnAIMYyGCGMIyhDGcEoxjJaMYwjrGMZwKTmMhkpjCNqUxnBrOYyWzmMI+5zGcBi1jIYpawjKUsZwWrWMlq1rCOtaxnA5vYyGa2sI2tbGcHu9jJbvawj73s5wCHOMhhjnCMoxznBKc4yWnOcI6znOcCl7jIZa5wjatc5wa3uMlt7nCPu9znAY94yGOe8IynPOcFr3jJa97wjre85wOf+MhnvvCNr3znB7/4yW/+8I+/BMrnP/8RmCAEJTjBCEFIQhOKMIQlPOGIQEQiE4koRCU60YhBTGITizjEJT7xSEBCEpOIJCQlOclIQUpSk4o0pCU96chARjKTiSxkJTvZyEFOcpOLPOQlHwHkpwCFKEhhilCMohSnBKUoSWnKUI6ylKcClahIZapQjapUpwa1qElt6lCPutSnAY1oSGOa0IymNKcFrWhJa9rQjra0pwOd6EhnutCNrnSnB73oSW/60I++9GcAgxjIYIYwjKEMZwSjGMloxjCOsYxnApOYyGSmMI2pTGcGs5jJbOYwj7nMZwGLWMhilrCMpSxnBatYyWrWsI61rGcDm9jIZrawja1sZwe72Mlu9rCPveznAIc4yGGOcIyjHOcEpzjJac5wjrOc5wKXuMhlrnCNq1znBre4yW3ucI+73OcBj3jIY57wjKc85wWveMlr3vCOt7znA5/4yGe+8I2vfOcHv/jJb/7wj7/8D5W4wko= 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-008000.pvtu b/tests/automatic_regression_test/gold_allenCahn/solution-008000.pvtu new file mode 100644 index 000000000..792046532 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-008000.pvtu @@ -0,0 +1,18 @@ + + + + + + + + + + + + + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-010000.0.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-010000.0.vtu new file mode 100644 index 000000000..c16025dcf --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-010000.0.vtu @@ -0,0 +1,32 @@ + + + + + + + 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+AQAAAAAAAQAAAAEAxFgAAA==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G6iOneipNfXD5TQM9rwrPDt9Gz6uCm/KN1Pd1u9Zy33H5wmaaF32KRqZpXnB5Yor66Ktx962EPrjtkqJ5vfdSHc0LrjZtoT7qmivrqQ8ub62h8/rfAbfXGcbnFVx8wX0xrQAP/Kg3n1d49p4snVdw4Z4rEvqoa5/JUh88OpznlXPc1/E0nhc8GlzAX+z++1zug0djeF7vtc/SvODyXO6jrl+3At+vc7dm6byiT1pm6LyCywO4j5xx2wz1we2zaTqv8NTeDXRePe+yjfqoGw9voD549FMtzYs+yR8yNK/n89Pcd301xRnqg9sTMjQvPHtHiuYF16vrqI+6ojxFfXCxuJ6eV8U6KfYpo+dVwcVVRdTHOiVGVlDf88rl9LwqvCgxkp5XBdfRO9RHXVMygfrgYsrnNC/WSXtZNc0Lbq+z1Mecim+T1AcXjxXTvL5uqxk0L7j4vhf1Ude8PI/64HbMIH5eFX3av0mSnVcFVzcNSzDf9/WSm5PMB49uW5lg51W9t8eqanZe1fMzOlQxH3XFPUcnmA8u9kxUsbzokzrv0STLC25u25X66Gs8oW2S+eCm/vgkywvP9mmWYHnB41k3VjMfda38JMF88Pim76vZvGKdVKs30nkF12eVUR/rlFy/mfrg0YBKOq/eyxbReQXXdjT1UVdvXMl9x9VeU2lerJNy9FaaF9zMT1Af66pR26gPHumNNC+86Pp1PK/j8uS51EddcUgZ9cHlFUvovKJP8dD36byCm/pLqY+cslZTH1wf2zzJ5hWe6TIjwebV8/Z7UB919R0NCeaDy063Jlhe9Em+PJDmBRe3tOW+66s+byz1wdVrHyVZXnjmtycmWV5wO78z9VFXbb4uyXzPN7h1J9ivihXu86IC+1XHxVTuqzVuP7SS++BmDt+veq8P36963or7qCuHch/ctud55XpXZy3PCx4XcT+ucP+/Uu6Dq1U8r/fG87zg0VfcR101i/vgtl+W7ld9Xw/M0v0qePQM95EzOjdLffB4SIbuV+HJx9J0vwquBjZQ39ddmqY+uG6Vonl9n+7K0rzgKpGhPvoqXshSH1xGWZoXnm2S4XkdV424j7r2/Qz1wUWPNJ3XKOH+fCWfV3BZzH210X1Mch9clfN5hacX8XkFF6ML+K5uvJL74GI6zyu2OG8Tzwuuq7kva/A8VO6Dy40F8sIr5nnBzQLuo64p5z64XcHnFX1SnbJ0XsHN1QV8l9P05j64fjJL5xWeLMrQeQXXjbmPutHBWeqDx69maF7fp+E8L7h6l/voazyV++CiV5bmhacvzdK84NY99yb0UVe2zFIfXBySpftVrJPxwlq6XwVXh2+hPtap6Ih66nt+YQ3dr8LTl1XR/Sq4bb6a+qiry5LUB486lNC8WCejPzTwvOCH1VLfr6vHpKgPbvrU0bzwoj0207yeT6igPurKXlupD25OTNL9qu/TBTP4ftVx9WMf6vu+frKQ+uDqwBF0v+q9Ua2SbL8Kbhc14r6rK/u9m2Q+eDzgMp4Xfbp7Jc0LrkZNpT76Gg0upj54lFhA88JT+/WiecHj9S2pj7q2yQjqg+uD36fzinVSlKXovIJH2+q579apuGOa+p7vl+Lz6jy7cBudV3A1cxP1UTfWddQHNxO30rxYJ8VhGZoX3FSnqI91NX4jQ31w1SNN88IzhzTQvOD2xFrqo666MEV9z/evp/OKPsnRFXRewW3FSuojp+6epD642lZC5xWe+G46n1fH1aZvqe/rLlhMffD4pnE0L/oUvbeZ53Vc3VVFffQ16lFDfXB1x0aaF54+fQ3NCy73mEt91NXnlFPf82Xuub3heVVcVz23XQU7rwoefdO5lPm4Tmk+OrKS+eBavlTGzqvCs41K17HzquBx3V2rmY+65spsMfM9f7TEsrz+uurWHypZXnD7x9vKmY/rVNFvL69ivufzTq2geZ2nthyznuUFlw+cuZb6rq499tJS6jsuDvp0HTuv6vua3a2InVcFN6f9OIf5yBlNvGcp88FttGQeO68KT42aNYmdVwWXT98zkvmoG3WZP5X54KKux1iWF31SB+y6guUFFz+dspD56KvtmVzFfPB49drFLK+v+2E8k+UFt+eJCcxH3fj3tXOYD27evm8ym1dcJ42PnlvF5hXc3pOuYL6/TvmL7V8XoQ+uLx5fyeYVnj7o4TI2r+Dmv3YvYT7q6vfalTMfPPrl8+tZXn9dte2kapYXXN36fBX13T41fuGUBPPB1UGNq1leePLBtytoXsfVDWtLmY+6sj6uZD646nBhOZtX9Mks+uUaNq/gcnR2KfOR0xw2YS31HY+GvbaSzqvz4rH959N5dVx37jyd+b7uE+sWMd/z86pns7zok578ejHL6/kl31vmo6/m28vXMx/c3jpqLcsLT3fssZTlBVcdx81nvq97xfqVzPe8ZsRidl4V66QdfPdAdl4V3P66cU/m+3XqL/IH6jsuqyr7sPOq8OyqwZ+z86rg8Z5TO1Lf1Y3iUZ2ZDy42TPqQ5nXrpD1s2zCWFzz6Y7If8zGn0edTRjEfPDp31iCW13tPnd+d5fW88QOfUd/VjX81vzfzweM1czuz86q+T222vs7Oq3qevrEt85FTThv1JvPBbcfVr7HzqvDE6f3bsPOq4HL0Dy8wH3XN/IdfZT54/GSmHc3r+mSbNn+f5QW3z0YdmI++2tef+Jj54HGvkR1ZXu99Vvc6ywsevX30G8xH3ejUyreZDx5f+uq7bF79Ojlqj/FsXj3vcNkw5vt16umjJlPfcT3rltF0Xp2nhp/Qn80ruJz1fA/mo64uyw5mPnjUd4++LC/WSft2qxksr+dvXTaB+lhX3+k2l/ngOj1nKsvr6/Y5bwTLC64emjaI+q6uOVSMZz64FuOHs3lFn+z913xF59Vxc0GK+shpGo/oznxwvbZ1Zzav3qvo+iGdV8fVL7//iPm+7t7Pf8l8cHv3MpoXfYprbupL8zoubv1DL+ajr/rsNYOZD67OOG4AywtPHHJJD5YXXPX/XU/mo64t79yP+eA6PaM/26/iOmn0WocE26+Cqxl9q6nvrlOKzxclmA9uRp2YYPtVeGrOvVVsvwqu9nqtgvmoK8qjauaDm/JulSwvrpOKsw9Lsrzg5qBO1Md1qvjc65LUd9zOLk2wvN7be0o1zeu4Fl9XMR91o+7XJ5gPLq5oWc32q+hT9Hl9Kd2vOq76Liymvsupvn2/nPqOmwvWrmf7Ve99ecUatl8Ft/EDy5mPuubToeuYD26XXGdZXvRJDqutYHk9f35mGfPRV3n9qVXMBzc1L1fQvM5TJY3Xs7zg4uxfct/VFQNvKWM+uL5uSgmbV3+ddOoLSTav4OLTU7nvrlPGZ3+VZD64fenBJJtXePKr0Qk2r+By9KHUR11tD0oyH1xN+irB8uI6aVw5lOYF1+M7Uh/XVe3FM6gPLvp/n2R5vZdsmmR5weXlu1AfdeNH2yeZ7/nka5N0XtGnt06uZvMKLu4qrWS+72unTdXMB48fd++9CeYVnv31JRVsXsHjX11MfV/3vaWVzAcXl9xbwfKiT9G57yRYXnA9pZz66Gt0vns/U+B7vrljguWFJ1o2qmZ5wUW0bxXzfV2brWY+uH372mq6X3XrpP1q/UK2XwU3e580h/lYp8SRey9jPnjU7qqFbL/q6045cirbr4LHY1pRH3XtFQ/NYT64nq2nsbxYJ+MLlq9mecHtqXtQH3MaDRXrmA+u5v7Z0ryoe+6Bi1he8HjWufOY7+sOfn858z0f3q6I7lddn6KnnhvF9qvgcs0ffqS+yylH7jeJ+Z5vvnM826/CU2e+O5TtV8HVgmuHMd/Xvehqw3zweOsEw/L6vrbe8yeWF9zOGD2d+ehrfM9rC6nveLxIz2d54enJLabRvI7rdqlpzEdde8fyedR3XGVOnU/n1a2T8fepEjav4Pr1LtTHOhUnepRR3/Ho2snr2bzCE3vcs4bNK7jsumAV9V1d2by8mPngsW6yjuXFOhlNnVPB8oLbKc+UMx/rqjqzTRXzwdW4NytZXnjRiv3LWF7P3xi/nvmoa67rVMF8cFHUu5zNK/qkUvOWs3kFN31WLaW+y6l2WbeG+eD2zpRl8wrPvrzfUjav4LLh/qXUd3XtgV9Y5oMLZSzLiz5FjQeUsLzg8cZNxcxHX9X4NWXMBxev3lHG8sKzT+lilhdcnW+KmY+6Udsjy5jv+Z1/LMt9rqrp0f/C3Oek6g9OvCiXR/e0kbk8bjNXMt8/V/Wi66kPLu9e8+fcutH7J12e60UH/XB5Lpffbrkxl5t085uYj7r6k2ZXMh9cXzb0ZpYXz0nVjTv9meX1fL9RlzAfz6mUB7W6hPng8Y8v0bzw1DMXXMPygpthu9zKfNRVX3e4gfng4oUn78jtq/muTbPcPtldfnt3LtdPTHw4l8dNb36E+chpV115L/PBzTd7P5ZbN96yT+tcTx7zautcrp5Kt8vlqnmzmPmoK+9b/SzzwdWHbV5jeX2fBqUVywseDd3tCeajr9HN4x5gPrisOu8plheevuXyNjSv4/bEh1+nvqurP1/+EvPB5XtXtGfziuekqvd2u5zNq+cHXHM18/1zShOtr2I++H/nuYHNq697z7ib2byCR3edcQ/zUVccYW5jPrj8zez7WV48JzVuvvE6mtdxeeB+tzAfz6kSA1bexHxw3fT+21leeFGj3e5mecH1VXs+zHzUjc5/nPrg4vXXHmfz6vs08dNH2LyCm8eatmY+cspl459gPrhtF9N5hRd3XtaOzavnR1/zNvNRVzW6+nXmg5u9B73P8vq+/jjtaZoXfLh4hfnoq7ru7OeZD24uOOQvLC88M2F4B5YXXFz88EfMR13xy7bvMR9cHbPb57nrqh5U9nbuOqmuf+WdXG4ndvk0l5szGn3GfKxTcfml7zHf8/3jz/Pqfra5e64ntt3YI5fLC94YkMvNmn0GUt/VNUOe7El9x2WX0oEsL9ZJ2fW0D1hecHFOzZfMx5xGuu2HzAePxnbpQvM6T829pzfN67gd+OBg5qOu2KvJt9R3PCr6ZEhuX/Wfl43I7ZPtf/nIXB6fNWxSLjfv7DaZ+cgpaj4YxXxwcWpicm7deGjDnFxPrf393FwuerUoyuVRae8i5qOuWTRjLvUdN080W0Lzuj7ZTVPHsLzguvcrU5mPvtoFrxnqOx61HTOd5YUXZ3+ax/KCx4d1WMp8X/fsCxYwHzwqOWE5m1esk/bNQZ+weQU3uy7txnysU/qS7BfUd9y8dUEvNq/wInl6fzav4FHrT39kPuqq1ecMYj64nvXmKJYX66Sq7tWV5QUX1/X7lvlYV/UbL/VgPrjueO8Alheemt9lCMsLHp25/bpQ6KNudMim4cwHNy8/PZHNK/pkywZNYPPq+R2jZjEfOUWX9pOZDy6OuXgum1fv3d94EZtXcPnMmSup7+qa1x4sYj64frDnapYXfYr7HjSd5QXX138wn/noq5n/0E/M93zBFTSvr/tao2UsL3jU849rmI+68dXRSuaDy3ED1rL9Kp6TGr/f6Va2XwU3KzfcTX33nNK46ZV3MR88+mj35my/Cs+8Mvshtl8F1wuueJr6rq668THqg+uzZzzH8uI5qbrDsYrmdVxuaP8w8/Gcymh48gHme972oidYXnhm9qU0L3i8e/uXme/rbp5GfXD14IOvsf0q+hQ9VPEy2696vtfA9sxHTj3wy9eoD77fx++y/So80aL1h2y/Cm6bJTszH3Wj6PTPmA9uDhjdneVFn8SlF9C84Oa5vT5kPvpqjm9MfXDTyHzK8nrv4NNoXnC1V9s+zEdd0+xu7jtuhz7en80rnpMqzpjyKJtXcDHPPE1995xSvXzrk8wHjy+593k2r/Cio2I6r+By//o3mI+68Rn3Uh9c/mnCOzSve06qfLpNa5YXXB54aVvm45yq6fPtC8wHN68lXmN54cWfb6V5wbVt34n5qBvVzqU+uMwe9ymbV9/Xt8Z2YvMKHs/s/zXzkVNd0Zz64PGz+3Zn8wrPnHIQnVdwubD5YOajrrq1oh/zweN9S4axvOiT3fdMmhdcD/ymN/PRV3G76cZ8cPHdA/1YXniqxS9pXvBo/r2jmY+6+q8vct/xqOzF8Wy/6tfJX5/wDduvgtu7rvmB+Vinoo77f898zxvP+ZHtV+EJdddYtl8FF6Oum0p9V1ceG09gPnjcp3omy4t1Ui38cTDL6/lPagzz/bp68ZXU97zzQRNYXl9Xvz6F5QWP//rdXOajrhl+CfXB5ZXjF7D9qu/TTZfOY/tVcH3D/UuY73MubrKQ+eBq7qzlbL/qvW3nWLZfBVc3NylmPuqqJlvWMB9cn7n9995hXt/XYe8XsbzgZsTTq5mPvsrfnUp98Fh9voblhWd+GrKO5QU3B15cynzUjVs9Rn3PD9v+e+1wXrFOilOXj2bzCi7MUVOY79epS6aPZz54tO+KGWxe4UU976fzCi5anVLEfNQ1LdtSH9zusXQZy4t1Upw4ZTLLCy4TJ85lPtZV0acV9cGjGecuYHnh2dbrF7O84Lppv1XMR11xzHvUBzcNl6xh84o+ycvrVrF5BY9LWq9jvu/r+seoD65vbVLC5hWertqNziu4/uX2+9JDH3XlyBep7/l32ypYXt/XqdfRvOC675r1zEdf9fNHcN/x+JR7y2he59nvJvG8jsfR+krmo64dvZb64PrG76tyr6vGj+5uc6+TihuOs7lcv3FLcS63I54sZr6/TnlaO8t8cPvLccV5dVf8oizXk0OOLsvlZuWyilwen1FewXzUjbY0K2M+uPjjwZUsr79O+mSjNSyv5z/KEubjOlX00Jo1zAdXLapLWF54dtmYMpYXXFbfWsl81I1e3X6uP/TBdUm/yty+iodnVeX2Sb+zqCqX67sOSORyU3FwgvrIee2GKuaDq9OOT+TWNXctTuR6Uc+leTx654ZkLretb0oyH3WlXJdgPrj5xe1Jlhd9MoO235cS5gWPEhckmI++2pE3VTMfXN58H8/rvPiFTQmWF1wX35ekvqsrzt2T+p4//HiSzSuuk+p9Vqxl8wquvhm9nvn+OmVRwzrmg8dn9yxl8wpPXb+4nM2r528dVMV81DUz21YwHzyeG1exvLhOqvY/q4TmdVxVfF7GfH9dtcNL65nveb/3eV7nmdt+W8nygqtVG6uYj7pil4WVzAeP31R0XtGneHynajav4PEx7RPMR05VvLia+eCiYQCfV+eJ3x9F5xVcz3mB+6ibPYv64OrWDkmWF33STx1F83red1mC+eirOLs59cHjS3eneeFFt99A84LHZ39KfV/38EepDx7P7JnMXVdt6WfJvHVyly/yuDIT8nh8/0Tq+3X1T19TH9z8aXJe3UisyPPU3SvyuC6qyONqv0rqo65usZL64KJPJc2LdVI17cbzOq5PmkZ9zKmt70N9cJmaSfN67/DVNK/nnauoj7r6mjXcd9zY6ry+io6b8/ok+23O46JvbR6Xo2q5j77uvoX64FGiNq+ucO/BgScGN+Tx+JF0Hpct09RHXTWngfrg6tU0zYs+yQ+30Lzg8UV13Hd9FddupT543LeO5oWnMg00L7j5Ik191JXnp6gPLsam6bxinRRmAJ1X8OiFudTHOmV+P5z64LLZIjqv8KxdR+fV83uS1EfdaMh67juushtoXqyTZpWheT1/eBn1sa6Kx6ZTHzx6YDXN6+t2K6d5PR+8ifuurnqrivrg6gE+r+hTfGINnVdw+dt66iOnOGIb9cHl0Ho6r/DsGyk6r+BReZr7rq4qSlEfXByRoXl9nxrV0ryeX9lAfd/XA+uo7/nyBpoXnjiV5/X8mgz3XV31epr64Hju5Q77VVxX3Tq2lO1XwaO/vlrBfH9d9cKaMuaD22fuoftVeObM5+l+Fdz+OK2a+b7uUftSH9x+tP2++zCvv0467FCaF9wmT6hiPq5TiflHUB9c1xXRvL7u+PdpXs9ffTnBfNSNVu1KffC4fCLdr/o+jepO96vg+obfJZmPnOpm976cwAePZ15P96vwZPN2dL8KHnUaTH3U1V9/zH3H499tv+97h7yuT+qAA2lecHvyk0nmo6+29wXU9/zPb9G88ORTfWhecPPgDOqjrhz3I/U9P2n7c43DecV1UiVTlWxewaPpraqZj+uUov0Y6oPrG5J0XuGp9x+k8wquGrv7rgMfdaPEUO47LkvOSLK8uE4aX3UfzQsed7sjwXxcVzXvFVMf3Lb6nuaFJ/vWJlhecPPUPUnmo64ccRr1wdUe7fi8uj6JGx+g8woenaKpj5ym01+oD67P+4HOK7zos8l0XsHjl1ZQH3XlcfOoD66eWUfz+j4d9zXN6/mSCdRHX/XsgdQHF6/OoXnhiW7LaF5w8cdy6vu6t66lPrhy+75wv4p10v5lHt2vgsvjiqmPdUptXUJ9cNO+jO5XvXdTku5XwcXuNdRHXbV+I/XB4ze30bx+ney6muYFNy9UUd/P6TMl1Ae3SZ4XXtxkC88LXl5LfdSNr66hPrg8tp7uV9EnvUc93a+CyxYp6iNnVFFPfXC7IUX3q/DiojTdr4LHAzPUR93oRO6DC5uhedEnPbKB5gWXT6Sp7/v6bAEffBnP672neF5wu2+W+qgrBxfwHY/PyNJ5xTqpnqyg8wpuKzZRH+uU7ZqgPrhutZXOKzxxEJ9XcPHHBuqjrv64jvrg+ogUzYt1UhzB84KbDtuoj3VVJ7dQH1zj+lGQ13tF9TQvuFiR4j7qrmmgPrhpm6bzij6pQ/m8eu7eKxX6yCm7cR9cdywwr/CqMnxeHde3ZamPuuroAr7j4rkszYs+yUN5XvBoUYb66Ktqw31w+8sszevrXsXzgov3ue/rPsF9cOPee7/DeVX3nFQ1/K127LwquOy9pj3z/XNKd3nkr8wH12+0fI+dV4WnOvbj51UdF4/v8jXzUVfLD6kPHv9pQjeWF89JlVdOe5vlBY9+dcxHzMdzKsWCtZ2YDx69duoXLC88PXKvziwveHx0l97MR1116pLuzAe379T3Y+dV0ae49W38vKrj0d3jBjEfOUXHY7gPvviEYey8Krzo82Wj2HlVcFHxwGTmo27UcDr3HRedMtNpXtcnceEontfx+K+dRzHf9/WKx6kPHt14xniW13uTjqZ5wW3yjjnUd3Vlu8XU9zw1dj6bVzwnVZXc8zmbV3B962LNfDynVJ+0RTMfPDqkojedV+dF08Z9w+bV82+GDGY+6opX2gxkPrhc22w4zeuekyrfO+wblhdc7j92APXdc6psl1aDmA+u7r1tGMsLL/r+rGEsL7hdlBnHfNQVf7hxLPPB9eEnTWbzij5J+8xoNq/gtk2LydR3OeU9x0xgPrht2XMGm1d45qjn6LyC2y7nLmY+6spB+yxgPrg8Yft9cWFe36dBJ09lecGjkW/NZT76Kn/b+yfmg+tLn1rI8sIzz66jecFtuytWMR914yP/vJz54PbTZmvoeVW3TsYrZs5h51XB4z99tpj6bp2ytVXzme/502cuY+dV4cXv3UHPq4LLyhPWMR917SGHUd/zxMZilhfrZHTblTyv4+qY9ErmY07tF4ml1Hc8frrasrzw4qK6tSwvuGry1Xrmo64tn0Z9cDXkV2X0vKrrk+oznp5XBRd9+pUzHzmjphdRHzyaMauCnVeFZ89dRM+rgquFt1Qz39ft1ZX64PGvllfTvK5P8pP3aV5wffj297aEPvoqFw6mPrid+l4VywtPDL+c5gW3069MMB917RtzqA8eX9gtweYV62R06Dw6r+Byz+3v5Qp9v64u4D646TGqmM2r93Z5mc4ruOzcupz5qBu9/Avqg1vbtYLlxTppX6qnecF1u7+uZz7WVTk1S33w6LxTylheX/fsp2hecHluj0rqu7rqjP7UB5ct3Lm3cF7Rp7Hf8Xl1PJp5TjXzkVNf/Cr1wc3xk+i8wjPbLqTzCi7vL0swH3XNi19QHzxq/+sky+v7OvM0mhfcvHxOgvnoqxk+nvrgquwTnhfe0rU0L7hquf3cXuijbnzx0dQHtzc+m2T7VTwnVZ/TaQTbr4LHx84Yx3w8p9RsmD+B+eCiZMAUtl+FJ2YWTWL7VXBZvN8s6ru6dsA3s5gPrj/4y3ya1z0nVTz66kyWF1yct99c5uM5lebErxcwHzx66JTFNK/zxOXuvp8gL7hZeMgS5qNuXLt8KfPB7eJ3VrH9qu9rsnw+26+C64X7LmU+ctqTSouYD67O2L5uhvtVeGrSnpbtV8HVsK7rmI+6YuV7a5kPrpcvKWF50Sf9yoCVNK/j8gR3v17ou77GZ9WuYT646jC9mOWFJ3ZpVMLygkenJEqZ7+sueL6U+Z4vmF7O5hXPSRWfr1pG59VxUdxmJfPxnFJ5+VNrqO+4mvP1WjqvzovLOlg2r+AqMWQt833d808rZj64GirWs7z+uapf3VDC8oLbh+V65uO5qnHnL8uY73nDFeUsr/fWvVjK8oKrb5eXMR91zWUnVDAfXGR/VcnmFX2KXzukhM0ruDylVSnzkTO6vqyU+eDi0xHlbF593Uc+LKfz6ri+YPu5qdBHXf2bWyqZ7/mZPapYXvRJN9r+3oAwr+dbOlUy3/e15uAq5oNHn26rYnl93WE/VNG8jquTu1YzH3Xl06Oqme95h7sSbL+KdVLtnqD7Vc9v/K6c+Vin7G+HUR9cvpqqYPtV70WG7lfB7bjzqpmPuvHefakPrgcuqmZ5/bo6+5oKlhdcHLD9vcmhjzmVq3tXMh/cbt2vmuX13v4taV5wM7xpgvmoq87fL8F88OjKUQm2X0Wf1KFN6H4V3K7fvq8MfeRUr3ehPrhW2/ez4X4VnrnqerpfBbc1naiPunKP56gPrqdsf29NmBd9in+fpHnB9fhbksxHX6PE6dQHV79sS/PCs8d+TPN6vmI09VHXZvpRHzw+YlaSzSvWybjjJDqv4Ob72dXMxzoVH9OH+uB22MMJNq/ea/YOnVdwO3K/JPNRV32boD64uH77e1rCvH6dxL9rkNfzJe7+r8DHuip/WER9cKH+kKR5nWc/Po/mBZcHvpJkPuqap1tTH1wu3H5fUjiv6FN8/gN0XsHj7FfU9zlvf4f7jpu9htJ5haeHGTqv4NGZS6jv654zl/rgwqyhedEncVFfmhdctZhGffRVDh3PffALimheeOrKFTTv/+Hl1Edd9WEJ9x0XYvu5m/C8Kq6Tyl1uoudVwaMumxPMx3VK9cI33Hfc1J6cZOdV4enD76LnVcF1+RfUR13Zpy31wc212+9LCvPiOqn5OEHzglt332Do4zqV/egE7jse7/ESzeu9Az6necHNeYb6qCtH96c+uPz8J3pe1ff17vH0vCp41H0p9X3ON2ZTH1wNWUPPq3rPnYsMz6uCy94bqY+65nnue/7tFpoXfRIblvC84IeVUR99tSdw3/Njq3le50nF84KrA7ZRH3X189z3fJM7XxfMK66Txk1uofMKriZ9Qn1cp9RFz1MfXE/7js6rr7tqLJ1X8PjYJdRHXdtiFvXBdWtL8/rrqpd+RPOC2yvGUB/7VJH5lvrg9uyZNC88fUIRzQse7VnKfVdX9VtNfXB7XhWdV9/XrqV8Xh3XR2ykPnKq4VXUB7fnbqHzCs8W19B5BZet6qmPunI29z3/soHmRZ/MmA00L3j8fQ310VdduZn64HHXWpoXnrqB5wWP26eoj7qiHffBhUzT86pYJ0UTfl4VPO5ZT32sU/GB3AePJjbQ86q+7sgC51Udj0SG+r7uo9z3vEWG5sU6aTrwvOC6T4r6mFMzgPvg+u40zQtPbEnTvOByXIb7rq5pzn1wu2uWnldFn8RUfl4VXMos9ZFT78V9cP0IP6/q677Nz6uCm2EFfFc36s598HhyluZFn+ImPC+47MB939cHue+55nnhqaE8L7iax33UtZO4D26XZem8Yp0UX/B5BVeVaepjnYpv5D64aZah8wovGsnnFVydm+W+q2szGeqDx82zNC/WSWN5XnAxJEN9rKvRzdwHV3U8Lzx7ZoG8jsevZ6mPuvI+7oOrznxe0af4DT6v4PEQ7iOn7Mp98GgSn1d4osC8gkdrue/rFvIdN6UF8ro+qR94XnA1l/voazSR++B2Kc8Lz6zhecFtJfdRVxbwwXUiS/eruE4qThtN96vgtuli6uM6pRo7g/rg+uzVdL8KTz+7nu5XweU9G6iPutHKSuqDmz6baV5cJxUP87zg9ob11Md1qmgI98FNfSXNCy8+jecF11fUUB91jeY+uLmvlu5X0Sf9FN+ven5SPfWRUz5dwAdXDXS/Cs88yfer4Gp+mvqoa07hPnh0eYbndX2Kf1Egr+PmjhT10dfoBu57fnSa50XdiTwvuO2WoT7qmiYFfMdNRYbOK66TmsaldF49f3UD9f11yq1V1AdXB22h8wpPKD6v4HLfeuqjru3IffD4vgaaF9dJdceNNC+4cT9Xhj6uq5rjtlIfXDWuo3nh2St5XvC4ZYr6qKt7ch88vjhN59X36WY+r+BydJr6yGl+zX1wcV6Gzis89RWfV3B7fJb6qGtKuQ8ubsnSvOiTnMLzgkddMtRHX/WV3AePq3leX7dQXsdtmyz1Udc05T64+CRL96tYJ+Pv+H4V3J6e5b5bp8Qm7oPbuwrsV50Xx3y/Cq4HcR919ZfcB1fjszSvXyd/y/OCm7ZZ6mNOxW3cB48/43nhRQMK5AX/ifuoK8dxH1wU8f0q+iTn8P0quFjDfeTUSwr44Ov5fhVeVGC/6vmGAr6rq6u5D6438Ly+r6t5XnBbzn301RZzHzyq4nnhmQTPC26S3Edd7wU+Po+r+bxinYxe5PMKrvsV8G/JX19CH1yO4fMKz8zk8woerSrgo+4i7oOrdTyvX1e/43nB1Yz/2dejuQ+uFvK88MwKnhfcrOc+6oq13AfXZXxe0SdVxucVXFRzHzlVBfc9r+LzCk9W83kFj8u57+tWcB/c5wzyok+2gucNeej7vlZwHzxez/PCi0p4XnC5jPuoG6/kPrj/flDjvK3u81r3cVvAN+ZzuYX7ps79Peu4D65r8uua8qBuIp/bFflcFnMfdaNN3Pe8gueNGlyf6nlecF3L/Sjl6jZwH1zW87y+7lae1/Mk933dbQV8x8Xm/L7GU/P7pBfkc9Ern4sR3EdOP2eBDy6m59eVLfO9qGPAD8nn9iruo676hvvg9lmeF31SJTwvuF3EffRVV3IfXFieFx6+L+yQ13H9KfdR1399Bz64HcjnVaSdlyowr47rAr52n6s090Meziu8uI7PK3hUU8BPBV+Xge95Lc8rM+7/l+F5Qx762n2MC9QJeZjX103xvOCqnvuoK9PcB/f/3sG8+r5u4PMKbku5j5xqC/fBbTWfV3j4fhXOK3g0mfuoa9cV8B3XC3le39dtPC+43sR9E8xd6IfzGOaFJyt5XnBpue/rbuA+uC3LX1fjNzJ566Rdmcnjqkc6j5szuI91yhyXpT541Cu/rrowlefJ3wZ8//o8Lt9uoD7qSpumPrgamKJ5sU6qu7I8711Y9zLUx5zqv2a5Dy6zNC880TJD84LbD7jv6y7JUB88wnUn19eoR01+X/euy+Pqjo153Px+K/WRM1pdT31w/avavLraXUf13p3JPK6Xrcjj4v313Hd1Rc1m6oOr6dU0r+/rJSma1/PrCviur+K6NPU9r2rgeZ0XN99G84LHhxfwXV1TXEd9cLOxhs4r1kmctwjnFVy1LuC7dSoaU8AH/yJL5xWePilL5xVcDec+6uoHs9QHjxvxvFgn1ZwCeR2Xw7iPdRX76tAHj2byvPDMB1maFzy+k/uoi3/X0Pf/3u5cTDiv6FN8YIbOK7g+kvvIKbplqA8ut6XpvMIzQxvovILHP3Afdc2uaeqDqz4NPC/6tHeW5gXXH3MffZW3ZKkPLnfP0rzwbCJN84JHe3IfdU3A4YObujTfr2Zx7oTvV+1OfOk+jwrUCXm4X4Vn03y/Cm5T3A//f4V8k+Z5VfAxzAsuC/jafYyz3A95mFcW4Cb4+6kCfsHcQf9Nhu9X0SdZ4OcncFPLfeSMU9wHN/V8vwpPbC2wX3VcJbmPuqKW+55v4XnRpzjN88bhz1WBj75GGe5HwbyHeWXwc3GYFzzexn3UtQ3cBxf1fF71TuZ4Z74JPoZ+yMN5jQtwk/l5vt5JnZCHeW3wMcxrd+LbndQJ+c76s7O/387y7cwP5zUqwGUw3yLzP//7iCz3wXWGz6uvm+bzGoXXLwJfZzgPfVy3CvOG/Q7z7szXO6kTB98P9E76E+bVwfe1HfpZIHfo+++P4X4Vv1dcz/er4GoZ97FORQnug8clfL/qvWkF9quOm/7cR127hPvgdgrPi3VSbeZ5wVU19/2cbuN+FF7PDPLCM8U8L3hcwEfdqJr74Pi9XrhfRZ+iN7N0vwqub+Y+csr+3AdXb2XpfhVetHeW7lfBdTfuo669PUt98PgQnhd9klML5HVcDeA++iqXch9cTed54emOWZoXHOeWQx917Q/cB48+4/OKdRK/Fwvn1f++rIDv16kG7u/Ag3kV2zi3wXyrAn6h3OHXn/96DfL6dbLQ36PAzyOiwM8vO/PDvN6r43ltHb/ebIN8toH74b4wnFe/npTwefV8OfeR0//+NvDBVSmfV3jRT3xeweOR3EddnEsIfc/n87zok9jC84LHyQJ+gX17nCzwc0mQF54u53nB/f0BgY+60Ubug+PcBa6r2otn5F0nta8szef9v8/j8W1TqI/rlPrHYuqDx58W5dWNH22f58XD++ZxO/naPC76v859VzdeP4n7jqsFfWlef111dRXNCx7XraM+rlOpHzdRH9yMq+J5nSdfKqJ5Pb98CvVRV7xUTH3w+MKleX2Nzk8lcvsUTbguj0ebOyZyeXz/HknmI6e9qkOS+eDxaXckc+vGNlud60Uv9snj9u1rq3O5/ssfE9R3deWkI5PMBzeXzkuwvOiTPGEAzQselX1Afd/XO2dQ3/Pjf0zSvM4TXVskWV7P65pwH3VP6JpkPri5+i90XnGdVCyvofMKrtcV8N11ymh8HfXB9XHb6LzCk+65B+G8gtvfr6c+6kZfbaY+uEgmaF5cJ7UTG2he8Bi/Nwt8f131N2nqg8d7p2he703aRvOCm2lbqI+66oN66oNH39XSeUWf5Pcr6LyCm4/nUh85o9Iy6oPrpmvovMIz343n8+q43jaA+qgrjllCfc9v/onmRZ+ihg00L7i8tIr7rq/RlTXUB9fNNtO88PSX62lecLmvpT7qRuuS1AePy939Zm5dVWe2qcpdJ82MPnlcjXuzMpfLHtuf2xX6WKeir15MMN/zgw9P5NW9rlNFridqWlTlclHUuzyXq6FzK6nv6srhg6up77jZ92aaF+uk/cZ9Xwnyej5hOvX9nDa6P8l88Kjo/CTLCy+ufCfB8oKLs9xzlQIfdeW4w5LMBzcTtyRy+6rGrynL7ZOtv64yl4tX7yjL5dHvN1UwHzn/++ecKuaD67OnVOXWjdoeWZbrmd2mV+RyPFfV82xRBfNRVx7Xr4r54OqWoVUsL/qk2h2TYHnBxV9S1cxHX9WVRQnmg8cfTkiwvPB0r6pqlhdc77Opmvmoq04YkWA+uGoyOsHmFeukKvoqyeYV3P6qPffdOqX+MJ764Pb8IUk2r75uo6eSbF7B4zbNqO/rntcnyXzPj+d5sU7a8UtoXvDogHnUx7oavVNKffC4di3NC88+MJ3mBbc3T6A+6qrnVlEf3NyyLMnmFX2KT786yeYVXBxyYZL5yCkP6pRkPrjZ++0km1d49t1zk2xewcWV5yWZj7piTvsk9R1XkzvQvOiTbDOa5gUXzYdTH32VlYupDx6/u5Dm9XXjYTQvuN37R+qjrm29gPrg8dgFdL/qr5O69xaG+1VwdS/3cZ1SjsxQH1xdkqH7VXimZYruV8HteQ3cd3Wjvmnqe16WonlxnVSclKV5we1y7uM6Vfx0lvuOG3fuKcwLT7+UoXk93yVDfdSVu2WpDx79mKH7VfTJjK6j+1XPE9uo73PWN1AfXK6sp/tVX3f2Vr5fdVw+spn6vm6nOuqD66NqaV70SbRM07zgakyK+uirbJahPrhKpmle721uoHnB44MaqO/rPpumPnhclaLz6q+rds/SeQWXr3PfX6ecwH1w+32Wzquve3eWziu4PYX7qBt/mqU+uHmB5/XPSyniecHVTO7juqot4T64WMXzwotGF8jruP6W+6iL+wB38B2307J0XtEnsSVD5xXcDOI+cupbs9QHx58L5xWeeThD5xXcHJKhvp+LbIb6nv/E86JP9qMszQtuX+I++qpGcR886peleeFF92VpXvDoz1nqo678Kkt9z9/K0v0q1kk1fQPdr4LHByWoj3UqfreG+uDWPVcw3K/6ui0q6X4VXGbLqI+65t7N1Ae3mzfSvH5dde/FDvOCR6fWcd/NaXx3ivuO6xkNNC88KWtpXvDo8W3UR934wgbqg4vx9Xy/6vpkdy2l+1Vw0b2E+sgZzdxAfXCVTtL9KjybLqb7VXBzQgn1UVd0TnLfcflDkuZFn9SYGpoX3F5aQ330VTSrp77nh9TzvM6T+9XQvODy8Brqo67dVMd98Lo6Oq9YJ+OxaTqvnj+Spj7WKfFlhvrgkVuXw3mFFx+SpvMKrmakqI+65swM9cHFLzI0L9ZJcWyW5vV8tyz1sa6q57PUBzcPZGleeKYkw/OCzyzgu7rq5iz1we3FWT6vrk+qY4rPq+PmwRT3Xc64JE19cPFTms4rPHtjis4ruGqaoj7qRuPT1Pd8QprmRZ/MsAzNCx59k6E++qrOzlLf81OzNC+8yP1cHeYFF90z1Efd2D13JvTBxcl/2/nVf/XHf9f52/Dj//vE9tyPhf57Ib/Qx59b/x/9+Lfm/7nnj+W/6Pyx3Mn52PC86s58vbM6Abf/S85//6v6/3Pn99/90f4vy/u/ra+F1oefu978vevPv/vr7989f//u/sc7uZ9gh/OwO7ufI7uT+xhCXuD+j0L3MYFHBZ53oQvctxIF91+p9P/8/WFn93/8s+4/0j/z/hsV3H8jMz/v/qhCvr9fMzivij7pAvfRgZsCPnKqFPdV8FyT8LwqPP9cquC8Krg/7x36rq6u4z64v28jyIs+6TTPq4P7SUM/vN8y9FXwdRXmtcFzd8K84XOlQr/QfbNx8PXj/30K7A/MTu6X0zu5f878TD+cV/033n+rd5JP7WT/9HO//5h/0fcf8w/eP/kP379Y4H5irHvqb7zfrNC+Wxe6PzzN12+Z4fMafr3pnayHoW+C+6p/7s8P0d94P+bP/foptH7LLM+7w7/P3/nzj/rPfvo/H//z899/Pv7n438+/ufjf67//Juv//yzrt//o9fL/2+5/vOv/v2D/Sf9/PaP/vz0z37+yc/9+cn8XzJ/5t/086f5z/X//6z7/8L1//+v3w//o/X+0fz/qr/vz/36/Vv98GN4XhXXSUWSn1cFx3sMQh/XKXFddwcfz3XbyM+req+Yn1cFx3OJQh918dye0AfHcxHCvP66ai3PC25ruI/rVKqB++BRPc/r624pkBfPrdtYwA/eNxD64Hhuyg7nVVfkPxc1PK/qn0M9k/s+ZzH3wc0qfl7V1zX8vCq4Hcp91MVzeULf83k8r+9TgucN30MU+iKYp9D387SJ5w3fexTmBZfruI+68Qbug4sEn1cd/L4vnFcRPM9QF3j+TfjcsvD59Phz4bzK4LmR4bzK8PeBoR/kCf0wT1zg95Phc+vC68eqgF/wuYXB9Wl/XTzs99/YP/V35vd/72BeZfDek3BeRfBehtD3fW3gPjieexnOq69bw+cV3Gzhvq9bx31wW8vz+rlM87xRgfddRMH8RxnuR8HzXMO8/j0TKZ5XhHkD3wTvswh9FcxbeF4V66Tuy8+rgsdduY91SszkPrialKXnVb33SZafV8V7ut4p4Lu60Vjug8fDeV6/rq7meT1fXsDH941K7nteXiAv6hbxvOBiYQG/PP+9VKEP7p8vGJxXRZ/UX7P0vCq4bMd95IyGcB9cDMrS86rw8F7D8Lyqf+9iW+6jbjyA++D+Y5AXfZLzeF7weA730ddoHffB8R62MC+8eHaBvLODj4GPumoN98HxMZxXrJNyM59XcOx7Qt/vd7dxHxzfd8J5hYf3NYbzCo73LoW+r1vDfXC1lefFOmnreV5w/1zlwBfBc6N38FPB+aUwb33+uY8wrw7frxb6wfuWQh/cP5c7mFf0CXOxw7wm8t+rF/o+5xbug0db+LzCs1V8XsFNFfdR12zmPrjeXCBvXfB+qCAveFTLfRnsF0LfBPMQ5vXeNp4XHD+3hr4M9hOh758HWfc/n0+Ls3y/Ghd4nv3fe3453K/KAvvnuMD+Wf7M54cXOi/xzz5/Y37m9XP9N54flH/j89jjAv4Onwf7VVHg/KEp9PNDgX8/kf2ffZvh+9U4OLcT7ldlcB4p9G2Gnwuyhc4X7WQ+Cr3fopAffi4LzGtYVxfoZ5h3Zz9/huetQn+Hen/n73/0z7y+uLM//4/O79/75+U/KW8h/+fmKfTfZYHPd3beTu1k3sL7aQrNb1Tgz+/svHH4PpLQtwXm9+fOqyqwHqkC/30HPzy/G76PJjwXH9SNd/I8/bjA+uTfQ1Pgeo9J8/fVhHltij+vGnyH51mH+8ECz7P2+9U0f/8J9pVxcL0j9MP3YYb+DjzI59/XkCqwX00F71MJ/Dh8vnbgh3/fHd73GV5PC/KGn4d+eN409He4Xlrgc1sgrw2u9xTyRQFfBO+zCferMjhPHu5XwXFuPfTj8M8Hvn8PRwPfr4bvUw73q+Cm0Puag/eFhL4Ifl8T5vV9KpBXhF8/oY9+1XFfBs9jD/N6r5bnBY9ruK/C67mB738vt6nA+ejwfp/g8/B+ndC3Bd4nbMN5qC8wr+G/Z/j+peD3BaEfBfethL4I3pe9w/0WwXPzw7w2uB9jBz94bn/og8ebC+QN35cevt8b17GT3EfdOFHAT+S/NzecVx3cTxPOq+ebuY+c8Ubug+Pn8R3mdXPwvPxgXsFtBfdRF++dCX3PV/O86FNUwfOC27Xc9+9FXs19cLwXPcwLTy3lecH9e5ED39edxH3waLD78+65qnHnL8tyn5Nqbj2qMpfHDVeU53J59geVzMdzSlUPW8V8cPnCSdV5dS87oSLXkx8fW5XLRfZXeVzsOamK+air0q9XMx9c7VlezfLiOanRXvslWF5wc805Ceq751TK1jMTzAc3ieIEywtPdX8yQfM6Hu3TL8F8X/fH7c8dDn1wfffJydy+2pqD8/okNj5YncujT7fl8ahxXTXzkVPsfn2C+o6bdt8lcuvKp0fle9UvJHK56nBXIpfbB9ckmI+6emaK+uCq6qIky+v7tLQ8wfKC24+PSzIffY0+vSFJfcej0c8kWV548vbbkiwvuJwWJ5mPuvEeH1IfPH6rX5LNK56Tag+6JMnmFVwX35Rkvn9O6dx3k8wHl/d/kmTz6r1DH0qyeQU3I17ivqsr39PUB49lf54Xz0mtHEHzgsfLx1Ifz6mSDy2iPri6YAnNC8/cPpnmBbdTZlEfdbVYQX3P77RJNq/ok6r6gM4ruIh6U9/nfGwk9x3X0VQ6r/CiC0bQeQWX306n/v+pO5/64LpoBc2LPqmOC2lecH3iCu67vsqNxdQHj18sp3l93VPW0bye31tOfdQ1Q6upD25e2f7cM6yr8odF+evqrmckc7lQf0jmcv2Ee9524Pt1SjxHfXD7Qudkbl3zdOs8z5zXOY/Lhd3yeHzoGOr7uo+Oor7nqbk0L9ZJ9VN3mhdcHzWG+phTe/gE6nu+xwKaF55YOZfmBVfXr6Y+6ory1dQH125O0Fc5dHxen9QlC/K4uKAoj0ct11IfOc3ZlvrgcmJFft0PS/I80bMqjwuRzOetNlMfdaOtG7nvuJ5Vw/OiTz3LeV7HdckG6qOvusUG6oOrZltpXni2bCvNCy5uqaM+6sZza6kPbv7YQOfVr6tnFdF5BY+ya6iPdcpcU0J98CiqovMKT99aQecVXH+9gfqoq1/aSH1w0WkrzevXyc4JmhdcVmzivltXzegt1AcXT26jeeHppjU8r+Oqrpb6qCu+qKM+eHRtA51X39cmW+m8gssPaqnvc3au5b7jakk9nVd4UXU9nVdw+VGK+qgrnklRHzz+KE3z+j7V1dO84LZ1ivroq/wiRX3wyP3/w7ze65CmecGjazPUR11xTYb64HZOhu5X8ZxUc/h6ul8F16NLue+eUyou3EB9cP37jXS/6r2qcrpfBZdtqrjv6trnNlEfXG7eTPPiOani0RqaF1xtqaE+nlMpT6znvuPmhXqaF54dtI3mBdedaqmPurK0nvrgukUD3a+iT+LUJN2vgiu7kfvI+e5W6oObk7bR/Sq8uOUWul8Fj0bVcN/VNYNqqQ+uDq+nedEn/XodzQsevVhPffTV1jVQH1y+m6J54cUPNdC84OqiFPVRVzVOUx9cd0nTefXPVX0yRefV8y4p6uM5pXJ+mvrgtjpN5xWeXZWi8+r5KWnq+7oHZ6gPrv6coXnxnFTbN0PzgotRGerjnGp0epb64PG5WZoXnpyXoXnBoyruo66+LEt98BjPuw/mFX0Sr6XpvIKrJWnqI6dunaE+uOmTofMKz56aofMKHr2aob6v696LEvqe75uleX1f98nSvOASz9cNfPRVPZKlvufuuf1hXnjyyizNC24eylIfdfW73AePu2TpftWvq6Nq6X4VXD9WT32/Tt3SQH3w6L4U3686T05roPtVcO2+rkMfdW2TNPXBI/d1t0Net04akaZ5wW3vNPX9utooQ33wqG2G5vV1/5DheR03eO9S4KOumZ+hPrg9Ikv3q+iTGpbm+1XHo7sy1EfO6IUM9cHt2gzdr8ITC/l+FTxy9w2EPurKM7LUB9dPZmle9Ek08Lzg0r23JvTRV3tzlvrgGvdPBHm99yzP6/nX3Pd1u3Ef3Lj7OsN5xTpppmTovHq+Z5b6fl1134dCH1zjvSTBvHrv/CydV3D5WJb6qKte4z543DnL87p10sY8L3j0BfexrkYDuA8uxvK83vue5wU347iPurhvKfQ9X5yl84o+6ff4vILLvtxHzngI98HFVD6v8LQpMK+Oq3ncR125qIDveGR5XvRJzeZ5wcUy7qOvcjX3wfV6ntd763hef79ZBfdRN67iPrjB/TLuuqo5bmvedVJbui2fN67L43ZePfVxnVLuUU99cHl8Kq+u7tmQ50VDUnk8vjidx015mvqoq9tzH1w8kaF5cZ1UdG2gecHVCWnq++tU87kPHrs8YV548qQMzQtu5maoj7qqRwHfcXF0Nq+v+spMXp+iAZk8Hldn8rh0760KfeSMs9wHj+/J5tU1TbN5nng5m8dxHye46Md91BVfcN9zrJNBXvTJXsDzggt3X2Poo6/iSe6D624F8uK+y4E8r+ezuI+6agL3wWO3ToXziuukdiifV/D45Qz1cZ0yuidDfXBTyucVnhJZOq/g5o4s9VHXXMZ9cIv7VYO8/rrqYp7X83Oy1Mc+VZ+WpT64eJrnhSdf5Hk978V91LVduA/u5z+YV9+nD/m8gqvh3Pd9/Z774PH0AvPqPDGXzyt4tIr7qBst4z64Wlcgr+uTnMjzguP7fOijr/F87oOr1TwvPNzvGOYFx/f50EdduZ774FFZ/rqqR+evk9GMfO7fJ+m4WMF9v68t4PvnyqwJnvuzNt+LS/K5Lgt4Off9faSl3AfXpTyvXycL5XXcFnPfr6sFfBP8e4R54alSnhdcF3MfdfHcph38kvzzpOirf45Sef6fA4/X53NluY+cZi33wXVRft14Zb5n5+dzf97VcTGa+6grJnHf8x48L/pklvO84HjuUOijr2I+98H1SJ7X1x1QIO+A/Pfehj7qqo+5Dx7fzucV66Qs4fMK7tefwPfragHfn3Mu5vPqf35Zy+fV86ICPr4uV3IfXP3E86KeKZDXFFhXTbiulnAf3C7nef16sIjnBVfjuI+6egb3waP+fF7Rp3gSn1dw2ZP7yKkHcx9ct+fzCi96is8ruDyK+6iL62I7+OD4eSjIiz5FX/G84JHiPvqK546Evn+uyWkF8sLbhef1/PEM9VHXDuQ+eFSWpvtVXCdVLfl+Fdx8WcDHPvIL7oPjuSvhfhWeHMn3q+BmDvd93dncB/ff34O8uE4qRhTIOyL/+9EOvrtOZWZz3wT//zAvPL+/CnxwVcJ9eKaE++B4rku4X0Wf9Eq+XwVHndBXYb7AB7flfL8Kzz9vJlhXwUUZ933dCu6D+/1xkNfnruB5Pa/ivu9rgvvh81DCvCq4zyjMC47nsoS+f85KGffB9So+r7hOalfyeQX3+9fAx3VK/HuFPriu5vPq61bxeQXHfV2hj7p2A/fBcT9ZmNf3K8nzgqtN3Md1Vf88l8A34XNnwryBF+YFx31vherGW7kPrjfzefV9ShaYV/DqAn7wPL8dfPSnks8rPLGez6vnS7jv667lPjjmNsyLPuG5UTvk3ZD/3NDQR19lsoCfDJ5rFeT1zyNdyfP6n4emcd/vW4u47/lYvl/FOqkL7FfB5ULuY52KV3AfXM4osF/F835H8/0quP2E+6irvuM+ePwCz4t1Ml7E84LrMdzHnOpZ3Ae3/Xle7xXICy6ach91cd079MHViXy/ij5Ft/H9KrhIZqiPnOo33AcXXTN0vwpPXsX3q+D6ojT1UVdMKeA7Lh5L0by+T+U8L3jUJMN911fThfuemzTNC0//mucFN3c1cB91b+O+5/vU03nFOqnH83kFF19xH+uUHlLAB/8rn1d4ohmfV3C9MUN91BVnFfAdN4MyPC+eh92L5wWPW3Af66r9iPvg+lqeF57ZO0vzgkePZKiPumIx98HFgXxefZ8uKzCvjscnp7nvctql3AeP2qTovMKT9/B5BTcta6mPuvEfCviO2xdraF7fp7sL5L0bv69qoL7v6wzug6v+9TQvPPsTzwuuf7OF+qirT9tGfXDz2EZ+XhXfB9pl/5/Gzi9Ej6sM4wOVFCrFG5WCFeZGb0JrEbEkF3VsizdBqK0INiqjF1okSCuaGyEMlRCMtZRSCmIxU7WKtFSTmGpqY8ZQjZpotjZt0qRNJ//a7G73T3a/b/fb7n6zejG/V/b5nkm8CvvsL+8+e/LmfGfmvOc9vl611fPtnqdPabbb8+jUVWi9Klz6o1Vbr4pePO75qNfY7/l4Hv+T96t9VUf8HpO+rsJHn8qznkenb4P6hYv+vuI3+s2e8Hz0g7jgefRY90i9KuOUPunrVdGzZzyPz/pFz6MXRzrqVYn7e1+vis6+oPLEzf7dwfOe86T3G/2ST3f4RT/j+RjXS55Hrya8X7jkvPebyPtq5YmbTnk+lec2zVf6kfBcpfkaz3FTni+lX6ny0cd03ucrXD7t8zX0Gc/Hc3fP89HHtef9Rj+dRe830z4+wufan1d47RerflPh1e/I99WP9jnSnz+QvvmSrzGusx35Orv23pIRvid9dYRHL/s+X4Ob9/mKzs9RnrjRj0h49JF7U/od4yp+rzb+5eL/yS10+JV8Ur+RZ4ueJ27axdM3rO/rVaPe4qivV0UvX/F8zFNveh6dfVOtV4Wr3vD1quj8feVDH7+yXk95v7EPOOH9hj7t+UL6kisffdPnva/ou33Z+03l7ytfyf+DTn3R16viJ947S70qOvc6KY9P+mYpH33Ber5eNbg5X6+KnvY9H33QFjyPTl939RufS33vF71c9Dzjmg88j87nhPqFqwcdftGXPJ939TkUnb5zmq9xX1Tf5yu6zp8j+uKV9XTg87KW31fzVcdBeeKWA8+X0rddfeXyp/pVXXl+X97Dj/D9tX3t1G8hnyPqN/Ju0fOJ9LNXvuxYT0S+Sr5ovhZXy6+lK/Oqa74m0m9U81V15QuJrzx62uG3kvFSv/p9/TPWGT3//UzvE+iIz7+r+i3kPoqR+wDkPoERXvePZBwq+VrXq3wd/3+Fq2V9qXwp65CR9epg7bplZL0qf1/5uC+jd2WdzwX1y3yb9bzfTNadyrOfVE57Pu6Vm/C+4l7EGe837mWc9Dxx495G5dlnrTvWqwt+PVyKnvQ8X8h+mvLxnDDt16tw3Aum69W4N++y54nLvq/y6NmE98s48dyjftGp21KecY37noSP9wHHO/zyvHvW+0WnrkJ54pb/8jx68Tefr9FX9aLP19DPeD7q9F71fNQbHvH5Gvu8J3y+onOuSnniZgc9H/pu7zfqeg56v+ixHyI8fVXZl1Aevd525bjFjzv8ch7kQc8TN87XCB/nTW71+co48d5L8xW9er6D556sJz2Pnj7i8xUu/a3P19Cf8jxx822eRy+3eL+MU/UN7zf0TZ5nXPP3eh69bs/1q9/gbvJ+0dP3eZ64xa8by6NnWxu7Xo31/qxfr0Z9y4znmafyS55Hj/OPsl6Fi+dLWa/G5+J0B897zoueD/2C98s8GecuxG+c0zjhefK0OOR59OyA9xvcce8Xnbo85Ylb/cHz6Pk+v15lnJJpv16NurYpz8e4nvc8OnX1ul6NuFMd61W572uEP7e27lv50M95v3Fu5yXvF70c8zzjmu31PHq+x/uNuGPeL3o15nniFns8j17u9fnKPFn+pCNf0R/1PPNUsdnz6PWmjnzlHNlOn6/oyXbPE7e6zfPo5Se8X+bJZK6xftGzE43lmVfzOxvLoxcfbKxfuKJqrF/0OFcrPHHTmaHl0auxoc1Xxom+A5qv6OzrKo/P7GbPo9frfb7ClYXPV3TOsyhP3OImz6MXH/d+4xzzLxrrF73a1Viecc0ODC2PXrXnYtUvXNn2lRrxS7+ppxvLE7d8fmh59OTI0Ner8jm/4OtKK9lXUL6WfSjl9T5ErVfVfQ+tV0XP5z0fcRc8j17Oeb/6Pkb9qq58KvdxKl/KfS3qN96HLHi/oV/2PHGzvufRWTdpvWoun8Nar6qf48rjs5z0PDr7ClqvGnFf8vWq6OlezxM36iqVp47zl94v48S5AfUb99We8jzjGvX6wqPXr3i/EfeQ94tOXwDliVvv9zx6sdPna6L3wUi+jtxfJHy8V+3ir5KvwfV8vqJXU54nbjrnefRs0vuNe3773m8t85HyrFP5Ocqjx+8jfku5z0b9FnouQe+/mVlb16983PN83udrJXUjmq/osa5UfnLtuR7l0et/+HyFS3d35Gurl9/r4In7K8+jZ1u83zgHctr7TaUuSHnGlX3VEf7Vtfe6q9+oN/qp9xv6lzxP3PIxz4f+WV+vGvNk4etV0ZP1no959ZueR0/W+XpVuPoJX6+KXv1m6Pl1vOfyPHp5x9D6ZZ7M7vF+0ZMzjeXJ02Kj59GLvd4vXHVDY/2iV/9c8XwbtzrmefTkrhVbr8o41fRnknrV0B9bsjw+sy3LlkfPv+jrVeHSu/u2XhW9un3a8sQtNsxbHj3fPWH9Mk7Zz961ftHz1QXLM67V35c83+r1Tu8XLhmftX5Df+EtyxO3vm/a8uj5A+dsvsY8ma7afEXPdzSWZ56q161aHr281+drxP3+0OYrenl02fLEzT859Hyr159Z9n6ZV9/xftGzmxvLx7z6sufRs2sa6xeuGl+xftGTQ+9anrjVrhXLh/5pn6+MU/L2wOYren5Nz/IxrquLlkcvH5iz+QqX/GXS5it6fuENyxM3u37c8ujFLa95v+04FTcsWr/oRT1recY127BgefRy84z1C5d+/i3rF73YetzyxM2ePW959OLUMb9efWftvqauV9Grcc/znpK+IcpHn5Har1fhyot+vYpOXZ/yxI3z/cLHOebj3i/vSesx7xc9G/M876nSfZ5Hz57zfuHYV1S/cd7/sOeJW+zzPDr7XrpeZZw4t6Dr1Tjn8Jznw+dfPY9ePuPXq3DZTr9eRU9v6+DbuNU2z6PHuUPxG+O03/sNfZfnYz/xKc+jVw95v3DV/d5v6B/u4Nu4+Vc8j55e5/OV96Tpjo58Rf+h53lPmW/0PHp1h89XuORhn6/onOtRnrjxXCE8enG39/u/96SN9YteH20sz3vV9OLQ8qGva6xfuKxurF/0/HLj+TZuvb7xfKvndzU2X2Nct/t8RU/u9zw+08918K2e3e7zNbg7fb6iJ5w7FJ64yY0d/I30KWqsX8appP+3+A190FiecS2+1VgePec+AvXbctnr3i969XhjeeJW3/E8evUpv15lnkz/6Ner6OWpoeeZp37uefT6d0O7XoXL712x61X0kvWq8MRNP7RiefTky0vWL/Nk/VBj/aLnjw4tT54W320sj55/e+j9tlxyftn6Db0ZWJ642Z5ly6Pnzw7sepVxyu7r2/Uqejk9ZXl85k/3LI9etffy6XoVrrrlrF2voicPHrF8xJ0543n0Rw5bv4xTeW3P+231dMek59txTYt5z7d69sSE9QtXLr9u/aKnH3nR8sRNv3ra862efOHPNl+ZJ7PNjc1X9PSeoeWZp5KNjec3ci/F0OYrXLVj2edrq5fteW7liVvly5ZHLzYMrN+YJ69rrF/09Nqh5ZlX00OeR4/3auIXrr512fpFT64fWD7ivmfZ8uj5m4s+X9txqj8wb/MVPXt73PL4TA/PWR69/Pq4zdeI+9FTNl/Rq48dtDxx802vWR692nPA+mWcyofnrN/Q3z9ueca1+tqc5dGTC5es34h7+KT1i568/ILliZv84KTl0eut//36P5Xhqn0= + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-010000.1.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-010000.1.vtu new file mode 100644 index 000000000..f3df7aedc --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-010000.1.vtu @@ -0,0 +1,32 @@ + + + + + + + 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rN+48ePLizYfvb8sM/10/AYKECBMhSow4CZKkSJMhS448BYqUKFOhSo06DZq0aNOhS48+A4aMGDNhyow5C5asWLNhy449B46cOHPhyo07D568ePPhy8//0E+AICHCRIgSI06CJCnSZMiSI0+BIiXKVKhSo06DJi3adOjSo8+AISPGTJgyY86CJSvWbNiyY8+BIyfOXLhy486DJy/efPjy89f6CRAkRJgIUWLESZAkRZoMWXLkKVCkRJkKVWrUadCkRZsOXXr0GTBkxJgJU2bMWbBkxZoNW3bsOXDkxJkLV27cefDkxZsP37/+fZnnFwGChAgTIUqMOAmSpEiTIUuOPAWKlChToUqNOg2atGjToUuPPgOGjBgzYcqMOQuWrFizYcuOPQeOnDhz4cqNOw+evHjz4fvbItP/1E+AICHCRIgSI06CJCnSZMiSI0+BIiXKVKhSo06DJi3adOjSo8+AISPGTJgyY86CJSvWbNiyY8+BIyfOXLhy486DJy/efPjy87/0EyBIiDARosSIkyBJijQZsuTIU6BIiTIVqtSo06BJizYduvToM2DIiDETpsyYs2DJijUbtuzYc+DIiTMXrty48+DJizcfvvz8jX4CBAkRJkKUGHESJEmRJkOWHHkKFClRpkKVGnUaNGnRpkOXHn0GDBkxZsKUGXMWLFmxZsOWHXsOHDlx5sKVG3cePHnx5sP3b35f5PtFgCAhwkSIEiNOgiQp0mTIkiNPgSIlylSoUqNOgyYt2nTo0qPPgCEjxkyYMmPOgiUr1mzYsmPPgSMnzly4cuPOgycv3nz4/rbE+L/1EyBIiDARosSIkyBJijQZsuTIU6BIiTIVqtSo06BJizYduvToM2DIiDETpsyYs2DJijUbtuzYc+DIiTMXrty48+DJizcfvvz8H/0ECBIiTIQoMeIkSJIiTYYsOfIUKFKiTIUqNeo0aNKiTYcuPfoMGDJizIQpM+YsWLJizYYtO/YcOHLizIUrN+48ePLizYcvP3+rnwBBQoSJECVGnARJUqTJkCVHngJFSpSpUKVGnQZNWrTp0KVHnwFDRoyZMGXGnAVLVqzZsGXHngNHTpy5cOXGnQdPXrz58P3b35d4fxEgSIgwEaLEiJMgSYo0GbLkyFOgSIkyFarUqNOgSYs2Hbr06DNgyIgxE6bMmLNgyYo1G7bs2HPgyIkzF67cuPPgyYs3H76/LTD/X/0ECBIiTIQoMeIkSJIiTYYsOfIUKFKiTIUqNeo0aNKiTYcuPfoMGDJizIQpM+YsWLJizYYtO/YcOHLizIUrN+48ePLizYcvP/9PPwGChAgTIUqMOAmSpEiTIUuOPAWKlChToUqNOg2atGjToUuPPgOGjBgzYcqMOQuWrFizYcuOPQeOnDhz4cqNOw+evHjz4cvP3+knQJAQYSJEiREnQZIUaTJkyZGnQJESZSpUqVGnQZMWbTp06dFnwJARYyZMmTFnwZIVazZs2bHnwJETZy5cuXHnwZMXbz58/+73Bf7/Dz4v2Mg= 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5A+5PuEV91PWv36I+uDxwg+6rOCfFR3xfBVfHc6mPc8oLvm4u3AeXNXPpvgpPVOL7quPtc7gf1LVdc6jveNVsmtedq414XsdH5FAfcyrw90phPrjplk3zwvMaZtO84Gb8Teqjrtl0k/rgfvHrdF9Fn2TlbLqvgqsxN6nv+jrgJvXB/2+w6b7qvNeu83014P78y9R3dWtcpj64/0gWzYs++Xddp3kdL3OZ+q6vtS5TH9xbmsnzou6STJoXXG5NpD7qyi6J1AfXE0/SeXXn6sJsOq/gOucm9XFOma9uUR/cBn8vGT6vzmt1nc4ruP7nMvVR175+hfrg8p8smhfnpK58g+YFl7uvUB/nqul5lfrgXs8LNC880fU8zQvuDUqmPurqCynUB5c9ztJ5dX29P4vOK7i3IZH6yKkjkrgfcPvtKTqv8LwxJ+m8Op68g/qoK5vtpL7jXy+gedEnf9Zpmhdc3dhFffRV/7iX+o5vWULzwlNHFtK84KZjP+qjrjoykPsB94rUoe9V3b2q4u9VwUVJ7uOe0nuW++B6fy59r+rqXsil71XB5eRc6qOuaZJLfXCTmEPzunvSebk0r+NlcqmPeypxVz5+wOVnOTQvPLdfhOUFtxuyqe/qfpZNfXCRdZO+V0WfbBn+XhUcvy+E+8ipPs6hvuMNsul7VXhewRz6XhXcvJVNfdT1vrxFfXAbe4PnDfpkZ9+iecF18O4m3EdfvVI3qA9u0q/SvPBku+s0L7h69yr1XV3vMvXB9Vfn6bzintS7mk3nFdzuvEV93FOKH29RH1z2vkHn1XllbtJ5BZdPXqc+6nr3X6M+uJl/ieZ196Tnr9O84KbIVepjT7VfXuE+ePR5mheeX/wizQtuzqRTH3XVqlTqg8tfY+m8ok9+5St0XsHV3xeoj5zeh1nUB1ffpdB5haeT0um8gsujidR3dRfEUB9cHT5G86JPdksCzet4g9PUd3198xj1HX9vB80LTz90gOYFt8/61Hd1uy6lPriNHcffqwbnpC52i75XBRcHb1Af55R95Tr1wb3sK/S9Kjy/9HX6XhVcvH+V+qjrV71EfXBZ4DzNi3NS1r5E84KrTVnUx5yq4WncD7hdkEDzwtO10mhecHM2gfqo6/90lvrgsu5R+l4VfTIbL9H3quB2xXnqI6d3KY364OJiIn2v6rysdPpeFdx7M5n6qCt8S31w/Z8TPG/QJ/H6OZoXXCVHUx99NZ/t4n7A/VlraV54dt0emhdcvehTH3X9G3Oo7/gfo/m84px84RydV3AZcZT7OFcr7KY+uP5rDZ1XV7fJTjqv4KLvauqjrjdvJvXBzSeDaV6ck3blHJ434PLCUOq7c/V2O+qDmxrVeN7A0yta0rzg4tTX1Hd1Sz1LfXC7+1QmndegT/5n/9B5BfcSh3I/yGnLt6S+4zMr03mFJ4dE0nkFN0Vrcz+oK4oJ6jt+NSWT5XV9KvAUzQuuJp7JpH7QV/+NgZnMB5evvZPJ8sIze0Zm0rwB9+Snmcx3deuszWA+uE59L4Ptq+5edUcW3Vcdz0imvrunHJVEfXD/0TN0X4Vn/zpH91Vw3fgQ9VHX/L6P+uDy7dU0r7tXbXGK5nX8193Uxz2VGLCT+uBCLqJ54amkpTQvuFo+kvqoawoO5n7ARd2f6L6KPnkbfbqvgqsnZlEfOcWYSdR3PKId3VfheTv70n0V3KYp6ru6L1WkPricfC/Niz7Z7CY8b8B1hVLcR19PPkd9cPHJ1kyW13mR8Zk0b8DVXTqT+a5u5LeZzAfX9y7PYPOKe1KrFtB5BfdsP+rjnlL83pf64LJQTTqv8LT8js4ruCzxCPVRVz16D/XB1fBxmSwv7knF9ho8b8D9TQWo7+5VvbupDy4XD8lkeeF5bYZm0rwBt4cuZTDf1V2cnsF8cNnhgQw2r+iT/+TcTDav4N7Xr2YyHzn9d+6lPrhsUT6DzSs8/WNEBp3XgIviQ9OZj7qm4KfpzAc3c66lsrzok671XAbLC67r3JXOfPTVe/RUGvPBxfM1UmnewNPzRqayvI6/FZ3MfFe3R5Nk5oOLt99MpPsqzsnab9F9FVz8c2cfCfdxTukhSzOZDy4jvslk+yo8v8GoTLavgsuEEpnMR13/0I4M5jveqngGy4tz0jxyO4PmDbhJq59B/WBO1eyL6dQHf8NLZ3md13VkOsvreMb0NOajrm19KpX54N7AHilsX0Wf5IwlGWxfBVdFi2VQP8jpmSbpzAdXdfqlsX3V1Z3bPJ3tq+Dy1NQ05qOu+aF3KvPB9acNU1he9On/GpTK8oKrAd+kUD/oq3j/dhLzwXVcjUSWF56Kik9iecH9VvUTmY+6XnbfeOY73n1hLJtXd64+1jqNzSu4OfBEKvNxTpmtv6cwH1xHD0ti8wrP3tU1mc5rwP0yyxOZj7o6q0cC88G9y9XjWF6ck17ZHYksL7jc0DGe+ThX9fxZccwH98veF8PywrO7Ho5lecHVy+XOMR91zbXTp5kPrjp7x9m8ok/+lC3xbF4db7Uulvmurx0LxzAfXPVbf4bNKzz7+BLL5hVcnvrzDPNRV24fe4L54H7rQ0dZXvRJy5EnaV7w0rWimY++6uVzDzMfXI64sJ/lhed/PeIQywvuv7J6H/WDunr+rp3Md3zeji15PxfUaxf6eY9+21COf6cQXH+ej4/P6WnLfXD8u5CoK4J/RwCewPvBgMuSuSFcv8N91PXPcB/cvp7L8w4JffcYntd9Lt0n3Hefq9oqHx+fB/rhbZoXnjrG84Lr57nv6u7hvuMPhfZVX8sO6ZO5nB3C/WM3Q7g4fJP6yKnTsrkPvuVmSF355rUQz3/pWgj3j5wP4XrHeeqjrnrwGvXB9dTzNC/65J3KpnnBxeyb1Hd99bOpDy70TZoXnhd7leYFlw3OUx911eSr1AdXl7L4vOLz4P7k8wqu3ua+DHsPFO47/sxtPq+ouyafeQ24fzGH+qhrJ3Mf3O7MoXnxOaleZZ7X8eBderiPz6nyi+fjF8e/G8nzwvOCzwUKzwuu+udQH3X9UrnUB/ffzKHz6vo6OpvOK7iRN6mPnKZuNvXBdfD3ruHz6ryPr9J5BRe1s6iPunbWFeqDey0yeV7X11s0L7j97gb10Vfb6hb1wWX/6zQvPC/6Ms0Lrl7LoD7qyqmXqA8uq6WFnKteakLouRqdEMLty4dDecHD1Mc5JcYkUB9cTjoUWvfS4hDPW7c4hJvLHUK4Gd+B+qiriy6mPrjp3Z7mxTnpFU+gecFN8kHqY05lx3juB1zvP0DzwhNpC2lecH9iW+4Hdf1iC6gPLudHhPZ1zvMhffKffD6Ei6+7Zebl9oeumcxHTlXr2SzmO965c2beul7NrhkhdU92yQjh0clpebkZk5jGfNS11TpkUD/g3vxzaTRv0CdZ+qkslhdcN2+XyXz01TZ5LIv54GJNy0yWF56o3jqD5QU3PQ+nMR915Tu/ZzAfXL26MY3NK85J3TmOziu4KLOf+jinVOFY6oOLjD18XgPP7zGXziu4bdGS+qhrov+hPrjq1JTmxTlpHrI0L7i3cif1ca6aUmeoD64/2krzwvOXTqN5wVX5n6mPuv7ICdQH97K+o/OKPnn9H8pi8wqulv2WyXzktJf+k0X9gJs/v8tk8wrPJNXOYPMKbl+amcZ81NVPfZLBfHBvUPc0lhd9Mtuv0rzgeuRnmcx3fT0cT31w2+zO31uE54XnJQR/bxCW1/HommnMR1275869Xbjv+MLH0ti+is9J9S7wfRXc6Fzu43NKh3Mf3Bbk+yo8uYPvq+BmTDb1Ude+wH1wb9ktmhefk6pu5dC84LZcDvXxOZWyQA71wf3sWzQvPP00zwsua9ykvqsbeZP7Afdfvk73VfTJn3KT7quO/3CN+shpf75BfXA16grdV+F5MRfovgquZidTH3VF5Szqg6sj8TQv+iQ75pM34F7qReqjr6bvZeqDi2OZPG/geVFpPG/A9UfnuB/U1SsSqA9uLkXTecXnpMqmt+i8gtvLN6jvPqf04HXqg6vg3Wf4vMKz0dfovIL7iy9T39WdcIn64LJMFs2Lz0n1B17meQNuKl6gPt6pysEZ1Af3uiXTvPDstnSaF9xrnEh91BXBu/FwH1x8epzOK/oks7LovILLYcnUR061MJn64CL6LJ1XeF6BM3RewdXEXdRHXTl/P/cDbu5dSfOiT7b0GZoX3B+zn/roq4zfRX3HR6ygeeHZZ9bQvODe9vHUR11TbDz1wXW3FnRfxTmpXjxB91Vwnbae+jinZJ3D1AfX9y+n+yo8b9kwuq+Cy18qUh91vas9qA8u7y5N8+KclLm7aF5w0XwW9TGn/tn11Af3fh9N88Izq1rSvI73KMr9oK4/sQ71HR+UTvdV9Mlvtofuq+D+MI/7QU7x1Rzqg8ute+m+Ck/3WpVO99WAe7EbU5mPut7qZunMB7eP10lledEn78XONC+4N7ZvBvNdX1vKTOoHXDzwTgbLC08svT+d5XV8zq4U5qOu/2CvNOaDm8FPpLB5xTlpHptH5xXc/NiJ+u5cXTGM+uCy2Xd8XgNPPFOSziu4/n5JJvNR11y/zv2A66uNMllenJPeN0153oCbhcWoj3PVpJegPriqujeT5YWnus6iecFlnwsZzEdd0+F97gfcyuoZbF7RJ3kwjc9rwHXO4nTmI6dWUdwPuFr9EJ1XeOb7E6l0XgMufiudzHzUlQ9epz64ffixJJo36JPwjvG8AZfDSqYxH32V62+mUT/g9rn/0rzOSzpC84KL2wsSmI+65tdG1Ac3c7z4vPequmjXlLz3pPJcp5S8XFQonpiX26KvJDIf95T2VusU5oP7nz6ZmLeuHVswNq+no2/H5OXekZdP5eWm1zOnmI+6suyFGOY7/uDtkywv7knNzEYpLC+4OHpXIvNxT2VHVUxhPriobBNYXlf3wLEYlhfcX773JPNR17t3cQzzwdXeHifz9lVMv3Eob5/0qQuHQni1jB15uX8iZgfzkVM9cvIQ88G9s+t35K0rC1XcEFJ3yYcb8nL51v1L8nK/6+3FzHd1z92/gfngfqHYxSwv+iRvLjzE8oJ707vtoH7QVz2qzSHmg/ud39jB8rq6f61az/I6PnTBYuajrnzk8/XMd/zh3xazeXX3qukvpbB5BZftFyUw392rvnUxmfngclirBDav8PxlnWLYvIKbxeIk81HXvvNhDPPB1VMTT7C8uCeVbyxMZnkdf1kkMB97qrhUN5n54Orl5fEsr6tb66xlecFNtSvHme/qrvvTMh9cznn5OJtX16evXjzE5hVc3L9kO/OR0y+64iDzweWQJ7ezeYWnZ05fx+YV3Hv5P4upH9SVZ0+sZT64qdNjEcuLPokbJQ6yvOBev++2MR99FZ/0O8B8cO+1ZltZXnjeptNrWF5wr1PSQuajru45ZzXzwf2Sby4MOVc/WD0j7zmpe82fkZfLekfH5eW6//ZxzMc5pbL6zWC+430mjstbVzzVc3BeT98VMTgvV937dcvL1VtduzEfdf1HPh/MfMe3/dKN5cU5aa9WmcHygmtTexzz3ZzWuzKd+Y6XzxhL88Kre34QywtuP36vG/NR17sWOYj54Copo2vevsp1z0fm7ZPcXSQyL5f3jWial9u5vZoy3/W158UI5oOrog2a5q0r5n78c4g38N2f83LRYqnKy82YuYr5qKuPP/4z88H11hGK5UWfzLxVESwvuNj8ZFPmo6/+T79HMB/ctlrahOWFJ/onNWJ5wdXTfyrmo64uNq0R8x2vVEqxecU5qX/qPJ3Oa8C9Og3HMh/nlC2b+Dfzwe2a+WPYvMLzmx0ZyOYV3Bwc3JX5qOt9fPdA6gfc3n6iK8uLc9IMfONvlhfcP3poNPNxrprkr6cxH1wU2zOK5g08P+KeASwvuN3XQzMfdWVkdD/mg4ups7vQeQ36ZOrdaMPmFVy2eKcJ85HTft+sDfPBrdejMZtXeN7mrxqxeQX3+239ifmoKyYdbsh8cHs+pwHLiz557Ta2ZnkdP7f4D+ajr3rm5VbMB9erV/3O8sLzXv2iIcsLrsT9DZjv6jYfr6gfcN01oT7bV3FPKlelJtF9NeD+rRLxzMc9pdpRP4n54GJ5nzi2r7q6xzecY/squL73tWPMR13/i8ZnqR9w1fbGUZYX96RmxKxElhdcfLcmlvm4p5I5OxOYD+432xzD8jpv8YHTLC+4fmD8EeoHdeWcSyeZD26HP3SY7avok0nrvZ/tq+A287stzHd9TVi5l/ngst6Tm9m+Cs8r/8Uqtq+Ci86T5jMfde3kOSuoH3D9yOy5LK/rU99Zu1lecF1r0UbqB31V76udzAf3+77us7zwvIZxy1hexyO7zGY+6to+l5cwH9weev0fNq/unjQt+HUZNq/g+upEy3zcU4q2TeKoH3BddORZNq/w7I7Dx9m8gvu/1znIfNRVhdpGMx/ci8rZx/LinlQvi4lhecH938eeYj7uVdXl5y3zwf0C9WheVzch4zDLC65vv7qb+a7urYoHmQ/ueV9vZ/OKPvk3k7axeQXX+x5cx3zkNL9U28J8cNNmzio2r/BEydRFbF7BxfQl05mPumLrtAXMB5e5JaaxvOiTmTB3I8sLrg9+sZz56KvX7aX1zAdXc68sZnmd92bEHJo34H75BYb5qKsW/TaT+eC6bs8JbF/FOSm++Xwq21fB9apxI5mPc8rv+8wU5oOr03IE21fh6VYRfdm+Ci4qFenMfNRV1xJ6Mx9ctfyyI8uLc1I8PXMyzRtwWXTzMOZjTr158ROY7/hXrw9leeGJt9/uxfKC+6e+bs981LWHa/RgvuOlXm3L9lXXJ+/eVmxfBTfTxvzGfORUT99swXxw26T6r2xfdXVvzP2J7avg6r/Df6B+UFclZP7IfHB/ZpF6LC/6ZNTU5iwvuN2X9DPz0Vf/kxebUT/gomPDRiwvPKvvqs/ygusmTb9nPuqaD9LrMh/cjDR16LziXO19eyybV8e3XBzEfJxT6oUHRjPf8dld+7N5hWcuqG5sXsG9vxMjmO/qfvCzZj64rTimNcvrztVKT45gecHNyCJ9mI9z1etfbijzwUX/9T1o3sBTNfd0ZHnB9Ttft6R+UFc1SG/LfHA59+G/2LyiT7aZbkLnFfzYVsV85PRX7v6d+eCy5McN2Ly6ultWfsfmFdw2XFOL+ahr57SqzXxwO3xrDZYXfdJPP/Yrywsu/oyvx3z0VU9s3Ij6ATdDx9G88Pz+r9ZkecFV/M5qzEdd9XlMNeaDe3/PqMLeq+JzUr3HYul7VXDx+knq43NK/Y/3Ux9cz9tE36vCs9P20/eqjv+0kfqoayospj64UhNoXnxOqv5+Ic0Lbr+dQH18TqU914n64Pbwjzxv4OmO3XjegMtxivqoax4tQ31wv7lH36uiT3rkPPpeFdwOGUp95DSjO1Mf3FSozd+rBp56pTF9rwrupb/N/aCun/Q09cF11e2ZLC/6pGqWpnnBpX8lk/noq//zmkzmg5u/fspkeeF5i00mywtuNxXPZD7q6oZxGcwHt73KZrB5xeek6nNv8nkNuL86eK8W5rvPKc2amUn9gNt77nzdU/i8wpMjV2ayeQVXL/yeyXzU9V4KPu8uzAdXKyIzWF58TqopeCyD5QX3v6ybwXz3uao9RqQzH1zcdyyN5XXe/RvTWV5we+7edOajrv7rzucahfvg2s9KofOKvv6Um8Hm1fGH7uQJ911f655MZz64rP9YOptXeN6Dm9PZvILL89FpzEddNfPJNOaD26dPpLC86JNc9UoaywuuLhVKZT76aqqsSmY+uKhbJYnlhacy1yfTvAEX/V5JYj7qqr9mJFA/4DJqUxx9rxqck+al6Ez2XtXxd2tmMh/nlKhXlfrgnpmUwd6rwjOXB/P3qgFXo+58rlu4j7ryc5POfHBVrUway4tzUpXrlkHzBtx/bVQ68zGn5nzNdOaDa/10GssLz69UOY3lBVcn+6Qw39U9FpnCfHBxoFYSe6/q+pqxL5W9VwX3dvdPZj5yeufLpTAfXC2alsjeqzrv4Ej6XhXc3nNPLPNd3VJ/xjEfXE0YcY7lRZ/Us2WSWF5w/7Mq8cx3fZ01L5754HLaiBiWF55857xlecH9ETtOMh91bVKH08x3vHHaUTavOCf1+yaVzSu4TPhvCvPdOfXsV8nMB1e36iSyeXXe5vuT2LyCq2PD45mPun6Xu+OZDy4HN4hheXFO6oRyCTRvwE2D++KYj3NVqukxzAf3719xluWFZxOGWJo34OJQ7CnqB3VtoSsnmO9430+O0nkN+mSOrIhh8wquhpQ9w3zk1A//5wzzHX+r3DE2r67uDwWOsXkF9wd8fZD5qGsGRB9kvuMfVN7F8qJPvpwczfKCy8mzDzIffTXvHTzAfHBTavNOlheeX2z0LpYX3B/03WbqB3U98cJm5jv+1uQ1bF/F56T6F5alsn0VXP13Rgrz8TmlXsUnk5kPbhrMS2T7KjzzZItktq+Cy+ceTWI+6qqiLyZQP+DiZNs4mjf4nFS/66p4ltfxJgXimI/PqfSHHrLUD7j34NUzLC88Ve6TGJYXXJZIPst81BUdfz3JfHBvr3eM7qtBn7zNvRLYvgquax2IYz5yCrUshvngtv7qc2xfdV6R8zFsXwU3VU6cYz7qel+fP8V8x+u9cJzlRZ/sa3VOsbzg3vYjx5iPvvrtDx5mPrh64uwBlheet6bVEZYXXGz55CDzUVc2q7OH+eCmUNvtbF7d56rWm3WCzSu4fuDtaOoHn1MqX488RP2AqyEP7WfzCs9u9g6zeQWXa149wHzUNYuzdzIfXLSevpXlxeekqpRqO1lecO+Rg1uYj89VFTM2+sx3fNuja1heeHrLkxtZXnB788G1zEdd0zx1GfPBRcvti9i8ok/ego92s3kF1+3XbWM+csoz5zdSH9wMXcfm1Xk99m1i8wquU7esZz7qmnalVzIfXNeesYTlRZ/0qOvLWV5wUWHTYuajr/LxmLnMBzfnWs9keeGZDt3ns7zg+tD8f6gf1FVRBacxH1w8uXES21dxTvqf/n2K7avgatHPx5jvzqmFvx1hvuMtTh1g+yo83bT8Ybavgnuj9u9jPuqKJe/uZj64eLDJVpYX56TIGriH5QX3ny2xnfmYU6/wK5uZDy71uvUsLzz/2dabWF5weWTbWuajrmzy5Urmg8vTJxazfRV98qtd3s72VXB54LlNzEdO/U6kz3xwXXvtSravwhP3LFvN9lVwfeDjJcxHXZtWcjHzHX+p3GyWF30y93rLaV7wonoh89FX/+vdc5kPrgpMm87ywhMbq/5D8wZctxw/hfmoK18oYJgPbmZ/P5rNK85J+/C5lWxeweXp40uY786pCxMXMB9cRrw9i80rPP/YpwvYvIKbEZ/9Q/2grvrryDTmg9uNdhLL687Vz49OY3nBRUkzmfk4V63eMob54GbCieEsLzz5rTeW5QUX940eznzUVa1nDmY+uH3g+b50XtHXd4dNZfMKLlc8MIH5yCme3Tya+eCq4mPD2LzC8/6KH8HmFdyfv2EQ81HXlv2wP/PBzZLKPVhe16e3mg+ieQNumrTpzXz01bQ91Z354Obrul1YXnjqv1e7srzgftvfOjAfdW3jnW2ZD663rmjN3qvinlSlfneavVcFNxMuHqF+cE8p9pWhPrj9vvdB9l4VnvYr7GXvVcHFyJ6bmY+6osirO5gP7vW+uYHldfekjzxA84LLn7vsYT7uqUz2gweYD27qdNnG8sLzCjehecG91SNXMR919aqm65kPrs57S9l7VdenVsVXsfeq4KLHjAXUD3Kag4OXMh9c70iYxd6rwrNJw6ex96rgesGl0cxHXftjEcN8cD9m03CWF33yu7dayPKCy1+2/c189FW030J9cN3ue8PyOi8mZSzLCy43FxzCfNS1JbqNYD64l9qzL5tX3JOK2rE72LyC+59V2Mh83FPa3Ys3MR9cdt29is0rPHX00xVsXsG97WPmMx91xTf1FzEf3NZJm8Hy4p5UjKm2huV1PGr/YuZjT/UGRSxhPri6cGI2y+vqrmw4i+V1fNcxw3zUNZHvTWU+uGlfawybV9fXH3ZMY/MKrv75ZCzzkdPbvXci9QOulr06nM0rPP9I/GA2r+BmYG535qOuv6tRH+Y7vuJwZ5YXfRIbHh7F8oKLx0YMYD766lUvNIT54KqL15PldXV71uvG8oKL7D+imI+64ujHHagfcFunX0v6XjU4J02hJway96qO9x7VlfnuXK39Qh/mg8sHvu/E3qvCk/8rF8Heq4LL+hsaMx91/diDLZkPrn5e/SvLi3PSazq/O8sLbh94vC31gzkVq4d3YT64rRDRmuWF551v0YzlBRejCjViPur6My/9znzHxbgf2XtV9MlGLv+JvVcFNxub12K+6+v2tHrMBxdZrauz96rwVJUGVdh7VXB79vjXzEdd2+L9r5kPLgaU+5LlRZ+8ob3rsLzg8sk/qjDf9fX+MTWYD24+a12R5XV16/z4BcsLrrbFf8Z81JWlYspTP+Cm6/JP2bzinNSlMui8gtv4y38xH+eU/llRH9xW+fwPNq/wVPnSP7N5dXxU6nfMd3Ufr9+A+gH3FxSpyfK6c/Lc4mYsL7gftbUR83GuihIdfqN+wPX01fVZXng2eTnNC24aPF+V+ahrOv5Yg/ngeu70r9i8ok96QqWqbF4dX130c+Yjp4j4hfrgsmIdyeYVnqo5phybV3B1vFZZ6gd1VZdKHzIf3Bu9uAzL6/qa1KECywtuZ7Uoy3zX1w1Pfsp8cLmkcxmWF55+I740ywsuPjldivmoa9e0Lkn9gJvVW95m+yruSf0HX57D9lVwe3T/VObjnlKWHzOV+eD6qUtj2b4Kz56ZP5btq+Dqi6pDmY+63pHTQ5kPLuOq9GF5cU+qrj8/luYNuHhv3lDm457Kb7ZnCPPB5cdxvVleeN6O4X1YXnAzNlkzH3XVb/s188FV3cgotq+iT+a+R3uzfRVcvR/ZmfmurwMbaOaD28tnIti+Cs+//2prtq+C6+fvasJ81BVV727GfHD78Fs/07zo08JCUTRvwM2w8s2Zj77KaV+2YL7jV7v/xvLC02898SvLC24nN6/PfNQV5d/5ifngps/q2mxe3T2pr3qxeQUXr7+omY97SjPuoc7MB/f/93Ikm1fnTX4yis5rwPWrkS2Yj7pq4zctmA/u1XrsD5YX96R+TlwblhdcVs5pznzcq+qYNc2Z73i77MYsLzxVfd0fLC+4ad7mZ+ajrn54+6/MBzcpGxuxeUWf/LltfmfzCm737VTMR0575lpD5oPL/l/9wOYVnmlf+Xs6rwHXy1OrMx915fWPqA+uj/23OsuLPtnrNX5iecHV5Kt1mY++ihtTFfPBxfU7e114Xud1+7gOywtu9s+tyXzU9dY3qc98cHXy1R/YvurOyW1nFdtXwWXCqdrMxzkljn1dl/mOP76iGttX4anKf1Rm+yq4KjbrM+ajrpf71ZfMdzz9k09YXneudv9fTZYXXB37shLzMafenwurMh/cv/3qlzRv4MmxX9K84H7dUmWZj7p2UVfJfHCv3oyP2L7q+vpV8Y/ovhpwe3dyKea7vhb/lPqO71lQgu2r8Ez6a2+xfRVcX9tSnPmubtErxZkPbre98TrLiz75H08tzfKCqw3flWA++urfe+U96oO3fOZdlheeaVzsTZYX3D6wl/qo6zUfWZL54N59z5Rk84pzUr316jdsXsHNkipfMB/nlKz8c2Xmg8tl/SqyeYWnnmpSns0ruL6wuhzzUVepW18wH9wvXPVzlhfnpL/7YnWaN+Be6p5qzMe56l2tV4/54N635+qyvPD0tAer0bwBl9+++C3zXd3ho+syH1y83qIum1f0SXaPKkvnNeCqafsPqR/kVIs+rsB8cLt7xGdsXuHZqY0/YPMKbtOKfMB81PV6fvwZ88H9zsU+o3nRpx29q7K84HLhm1WZj76K1B/qMh9c/1KjLssLz//4VhWaN+DyjfQqzEdd0a5qXeaDm1ZV7ny7N/j3/ZYE/47f2uDfqx4dylW/UO414L6dFvx72S257/gLoXVlpVBPFwzlNjE3hJt2udRHXTGd++BefA7NK7sH3y/H84LL/bnU198F/rVc6oMrlUvzOu8lnhfcfyKH+qjrteE+uCmQHdJXUyU3tE/TckK4aJQTwuX32dRHTrMym/sBtyk3Q+r612+FePqHm6H8wI0QLr69Rn3UleI698GjL9G86JONukXzgotL17kf9FVsvkF9cPP5VZoXnvfwFZ434OLBLOqjrjxxnvrgNiqZzqt8LpjXcXxewb09OdQXB4J5e5774PqeHDqv8ESfbDqv4HbATeqjrqhyi/rg/ujrPG/T4NuOPK/jd2VTXyQH/13/ZFPf8bk3aV54es4NmhfcZF+hPurKXteo73j/i3xe0dc21+i8Om4uUh85/e8vUx9cLcug8wpPP5/G5xW8wznqo679IY764P6Xh2le9Ml79zzNCy6fS6K+6+vCVO6D1zlL88ITacdpXnCzfxP1Udfc2EV9cLt5bsi5anpeDTkn9fSLIdzreSGU902jPs4p81kG9cG99+JC615ICfHsWhvCZY+zofy3A9RHXRl7mPrgYuZamhfnpN2XQPOC6+nHqO/mdMAp6oPLpdtpXnjevZtoXnBTYgr3g7qi/izqg+uPOoT0Vf+4N6RPYv+KEO5vWRLCzd3DqY+c/sSx1Ae3L/0aUlcdGRjiicb1Q7hXpE4I9/+v8cxHXe+TJ6gP7l/5O5PlRZ/M+OY0L7j66RXqo6/euDLUBxcj73zuRXheeLLpikyWF9xkPZ7JfNQ1X79HfXB/TJ0MNq84J6XeS+cVXEUvoj7OKdtwNfXB9f0j6LzCU11603kFNy99RX3Utd/XpT64rOjRvO5c7Wd43oCL1U25H5yrfmwn6oPbOWVoXnjeradpXnB95s7nOv3LD+qq/sHnRYX54PrSC5l0XtHX9xMz2byCy8fqZDIfOcXS9tQH96Yty6DzGni6a/cMNq/gus536cxHXT1gbDrzwf0bD6axvOiTl52awfKC+2di0pmPvnqxb1Mf3L51II3lhacuNkljecHNX3+lMB91bVRmCvPBZYN2SWxfNZ8G++qoW3RfBbfnr1PfaxzU+TAfP+BixVW6r8ITqy/TfRXc3J9JfVf3uQvUBzclk3neesGf4zrxvI7HXuL+A0Gdmvn4ARe1ztO88HSXdJoX3M631Eddb04i9cHF68fovoo+idZxdF8F1xsOcT/IKQuepD64Gb2Z7qvO67aS76sB97sM5n5QV1U01Ac39zameV1fT+6lecHFzQXUR19lvfXUB7exo2heeCZa87wB9+/9gPqoq3t8T31wEXs1k82r3yy4T/iHzyu4Tk2jvm0X1Pk9Hz/gskcin1d4v56j8wpuPtxNfdT1IqKpD67araV5zSt3zgvbNZ3mBdcFLPcb3/n1apOTqA+uvzlO8zrP5JM34LLPbOqjrlm9nvrg6tGRfF6DPtnjs+m8gusyUdRHTj1jGPXB/T3f0nmFJ3u+QufV8T3TM5mPuqZAIvXBxT3lM1le9Ek8HUHzgtvBxaiPvtqp1akPLocnZLK8zvtyDM0LLl/blsF81BVXylIf3OtVOoPtq+6cjP6W7qvgXnpGJvNxTonKRagPLo8OzqT7auCZz+rSfRVczO6QwXzUFWN2ZjAfXG+Yns7yunN14RaaF1yXufPnqX/5wZyKoor64GbqnTzheeHpBY/SvODe+DFpzEddT7xJfXB5smoq21fRJz/mI7qvgttuUanMR07b/h7qg+vEQ8lsX4WnS4tktq+C66NlEpiPujK+YCLzHS+xMpblRZ/k+4NSWF5wv358IvPRV7/wwSTmg/ulV8SzvM57a24cywvutypmmY+64rGSMcwHN78WO8XmFeekjYrJYPPq+CNr0qkfnFP6rS+4H3A18c7Pe/i8Ou+ZR/m8gn+0PZn5qKtObkxhPrj94Z4klhfnpNeqD80L7k9Zncp8nKvmhdnUB5emfQrL6+rmfkrzguuNTyQwH3V1y+GJzAf3X3sojs2r6+u06AQ2r+BeTRXLfJfz2Pw45jte6do5Nq/w7O2GZ9i8Or6nVjT1g7qy9aTjzAf3bz5yiOV1fRrSIoblBTcFq55iPvqqrzc9S/2A20vXo1leeKrC10dYXnDT78ge5ru6j3Q7wHzHz+zblvdeVXl3h9yTitGjM/NyuXhIZl7uvXshg/nuXjXpdgbzwb1Wj2fkrSsXp2fk9eTi+zLyctnhgRBuju1JYz7qqkpH05gP7n/+ZirLi3tSXbsUzQvujbuQxnzcU/nvPp/OfMffiUhleeHp5z5PZXnB5eDnkpmPuqpElWTmg8tKsxLy9tV79FRa3j6ZrHdS83LxfI3UvFw3fD2Z+chpljySTH3wy30T8taVPZok5/XECT8hLxdvv5mYl+uzj8YxH3VNxeWxzAf36vc6x/KiT2JL9wSaN+D6uRGxzEdfzbszYpkPbmIunmV54am4AudYXnDZ8+ET1Efd49uPMx/cJn18mM5rcE/qLViVSuc14DprTDL1g3tK8+StZOaDy78bJNJ5DTy/4eUENq/gskxiLPNRVwyrEsd8cBt/4xzL6+5JH4pNZHnB9RPRccx3e+r37eOZD67/fi6G5YXnl+1oad6Aq0fLnWQ+6ppjdU8xH1yezzrC5hV98jp8fY7NK7g/I/U485HTG9bkBPPBbVrEYTav8HTTtw6zeQVXHyzbw3zU9bck7qF+wMUHt7awvOiTv+7hIyyv43um72U++qrTE/YxH9zsem0by+u8yB+2srzgMuN/65mPuur6ixuY7/jAccvynqt6/qy4vOekX+Fzm5f7Ze+LycvN6IdOMR/nlHlqzwnmg6u7xh7JW9dcO306r6d+LBidl6vO3vG8XJadcpD5qGsnvHyA+eBywu0dLC/OSX/qwsM0b8C92ZX2Md/N6ZiX9jIfXNZZupXlheePuL6N5QX3rny9kfmoK97/j898cDt5wIq8fdXL5x7O2yc1L2t3CB9xYX8If7zgNua7vr5QbjPzweXQkevy1tXzd+3M66lStTfm5fhcVXBboN4a5qOumHdgOfPBbe/Fi1he9EnNubmK5QVX3wxfwnz01Yyqu4j54KLDylk0b+Cp4QvnsbzguvGKGdQP6vo/fDKN+eAi4aWJbF5xTprowlvZvIL7s4auZ747Vw8WXM98cPHHt8vovAaeXv7Qcjav4F6tIwuoH9S10aXnMx/cP9F2OsvrzsnmJZexvOB+0vB5zMe5qnrsmsd8cC+l598sLzy/xpy/WV5w/5VCE5mPuvK1XycwH1z0OjqczSv6pN4tOpPNK7jqGWeYj5y2+JZJzAf3n/92FJ3XwDMD7h7L5hVc7DsyjPqou6T1EOaDeymH+rC86JN+atYIlhfcKx07kPnoq2759gDmg/uHhvRgeeGpnW/0YnnBVc2HuzLf1e1crQvzHb/1Yzu2r+KeVHxxJobtq+Dm7jWnqR/cU6rXfzzLfHC5tNMxtq/Ckz8Wjab7asD9OtUOMB917bYmB5kPbsTFHSwv7km9P984wfKC6xe2HmI+7qlk61+PMB9cr2u+l+WFZ768sovlBRcTg8+1DvNR1586dAvzwa19fC3dV4M+qVfrbWf7Krj/fm2f+a6vNf/YyHxw2/DsCravwjNb9XK2rzq+dN185qOuHVJyIfPB9amT01le9EnFLl/F8oJbU28R89FX/Uj8YuaDmy9LzmJ54dn5FWayvOCi+suTmY+6qvg6w3xwVbbKKDavuCdV7cseYPMKLrZu3MZ83FPKLlE7mQ/uz/tzI5tXeF5G0Q1sXsH1hVNLqR/UFTN/WEH9gNuLyfNZXtyT2su7N7O84P47/VYz392rzjTrmQ/uP/jOUpYXnorvvojlBdebU2cwH3W9i9/MZj643+TBKWxe0Sf/6cVz2LyCe0u/mcp85LR/Vfub+eC255xxbF7hibQSY9i8gvulnxvCfFd37bihzAc340xvlhd9Eq8nTGR5wf1CQ4YzH331j4pRzHd8ZM4AlheeLZzVl+YNuP/z6q7MR11RMLI788H96sM70H01OCfF7Ky/6b4acLN3xATm45ySX3SfyHxws+PBEWxfhWcrzx5O91XwzE79mY+6tvrU/swHlwOHdad5g3PSO5A5guYNuF3caQDz3ZxWLTiI+gEXb87rQfMGnm2V0J3lBVc13u7EfFd387FOzAcX3YPPLQvbV9En+cum7mxfBffS/E7Md30t+mwn5oObEc2i2L4Kz9v8aFu2r4Lri20jmI+6ctAjEcwHlyWiIlhe9Emc+ySK5QWXi2LbMB999ccfbsN8cNWsewTLC08XHEDzgpv3nmnHfNSV0aod9QNuv/9Js3l15+QjyT3pvAZcbyrchfk4p0SRNpr54LLLrLZsXl3d+eei2LyC26SjbZiPurZMRhvmg6vk9DYsL85JvfZEO5YX3Nv7RgTz3bl6bE4E88FN9l1tWF543tttaF5w3aBjFPNRVzzyXiTzwW32yI5sXl1fO1aJYPPquF+iHfORU9Tp3Zb54F6RxzWbV3j+6paazSu4/XRaL+ajrpz1Zy/mg+sC9w+heYM+2V7zOrO84Pqffj2Zj77KYU/2YL7jpZoNZHnh2SllB7O84KLosVHMR13/+tGRzAf3I0sZ9l7V/+zOe24zIpa+VwXX3+6nvlp057246nma+uD2v1vpe1V4ctoC+l4VXJ7qQH3UNTUmUR/cHGlA8+q1wXvjrUdpXnAzeBX3W8YG74D3UR/c5M6leeGpc/143oBb8Qn3g7qmRwT3A65fepq+V0Wf7JnX6HtVcK/clEzmI6eed4364Lp5DfpeFZ5XeS99rwruzwzeW4X5qOs3+Zb64PqfbWk0b9AnWX0jzxtwb8GNDOoHffUje1IfXJcbw/PC67eJ5gWXXc6nMh91lSieznxwU/DOv7scPq8q/sydn8eR2+i8govhk6mvS564w6PWcz/g3sShdF7hedMb0HkFFz0vZTIfdb1dX1IfXL27O5Pl1ZFH7vx3f7Wc5gX3unSnvj555zwyX86nPrg3K4LnhTfpnSyWF9yvPjWT+kFdkfB4FvPBRan2dF7RJ1nhi0w2r+DipZIZzHd9rVogk/mOP7+Fzqure6RXGptXcG/+synMR12/qqU+uLrr7WSW1/Vp8HqaF9y0vfPuJdxHX+UHHTOYD+5ty6B54ZnTZVNZXnBxskIS81HXvzI+hfngdvofiey9Ks5J8d0c+l4VXN0smsx8nFMyYTD1wb1bIxPZe1V4usLOePZeFVyWyLbMR13vi4uxzAe3f185w/K6c/WPBjQvuC18bwLzMafmWLEk5oOb+lFxLC88PV3E0Lzg2z44yXzU1X12nWU+uPz5RDR7r4o+ySUlTrL3quDqj2cPMx85xfEa1AcX3Ubsp+9VA89fPHMXe6/qeMa1jdQP6sqsVluZD+4NqraW5UWf1OH0QywvuBd13y7mo6+izD0HmA/uzeu6leWFJ/t+RfOC+41/W8581PU3Zq9hPriecGoRndfgnFR/n6XzCu5tuxzDfJxTov/0eOaDi0b9LZtXeLbQ+FNsXsH9tcmHmY+6/qAtx5kPrl5sfJDlxTkpmnaOY3nBvRF9zjIf56qt3zKW+eDeX5VOs7zwTP9vo1lecLVjzj7mo67M/d8R5oPLu77ZzebV9WnnB7vZvIL7iXYj85FTL/9yO/PBva53r2fzCs+bdpbOK7i8d9dc5qOurpS2mPmOX+gzk+VFn0zys1tYXnC1ve8q5qOv6s8JG6kfcK/zozQvPPnojgUsL7hMHDKN+agrK785h/ngJnaEYfuq1y34etHlM+i+Cq4G/0F9f8idz/OwRydTH9yfUp/uq/DEH14W21cdf7NWJvNRV6SmUR9c9yuVyfKKV7bcyXF6LM0L7pWvQX2vzsY7fbs4gvrg/rKvaV54XpGTNC+4alKE+q6u2k19xyfe5Ptq0Cfbu1wG21fB7ch+acx3OScWzGC+4y+USmP7Kjx/w30pbF8FV+umJDAfdfWEnsnMd/zipXiWF32S5Q6ns7zg/t3xqdy/01ezcHY68x1/YHoqzRt48miw14blBffrNYpnPurKxmuSmA/uV8qMY/Pql9gQfDuUz2vAdfEK1PcXr7szz6MGUR/cTvkki82r857x+bwGXI1OymA+6srKKzOZ7/jvJzNYXu/wna8HN4UH0rzg+vbH1LeRd76vOg6gPrjKLZvF8rq6G5Zksrzg/sJDGcxHXf+uxZnMB7fL9meweUWf/L/7p7N5dbxg8HUbYT5ymmKR6cwHN3trprJ5haezI5PYvIKrtqPjmI+6Iu6rJOaDe2/Wj6N5gz6Jro3TWV5wUVmmMh99NYN/SWc+uBnwYSrLC8/+760klhfctvgojvmoq557IYn54Lram3RfxTnpl+oRw/ZVcD+i4Unm45zy6q62zAfX8/ceZ/sqPL9Ln0NsXwX3B4/ZwXzUFS3fOMh8cNWg4zaaF+dqkaKW5QVXv/Y/xnzMqZz02jnmg8t2I6JZXnh2zlMHWF5w5Z/bwnzUVRXG7qN+wL1f529m+yr6pHOqb2D7Krj/YOMl1A9yikY11zIf3DT4bRHbV13d6/Nnsn0VXOUsmcB81JUbikxnPrj+8vexLC/65O8svJrlBffm7l/AfPRVf9hsJfPBvVXT57G88OSe16exvOA2whvNfNT1JuyYQv2A20uVR7J5defkQ43PsnkFF9VjjjLfnaulks8w3/HIxkfZvMKTl1vvpfMacNM9ehPzXd2xi/YwH1wNrbiJ5cU5KR+YeIbmDbhnXzrKfJyr3pudzzAf3Nx771GW13nvlt9D8wbcW3N1I/NR15YuvIf5jlc8spHNK/rkpXyxgs0ruL/r5bnMR06d02M588HN03d+XYfPKzzvg2jD5hVc/f7qCOajrk0qa5jv+LfNh9O8QZ/MG48vZ3kd3zNhNvPRV9U+aRnzwXVSw9ksLzx/3Y7JLC+4aFBoOPNRV/3VazLzwb2Iq8PYe1Xck4pic1ew96rg/s7186mPe9UOdRczH9z8t99M9l7VeWcOTqPvVQPuN+o5lvmoa289Non54Crmy+EsL+5JxXgzl+UF1zlvTGU+7ql0i/4zmA9uSv8wgeV1ddc/NJrlBTfjOg1kPup6/0kcynxwm3W+F3uvij6Jtb2HsPeq4Pro/p7MR07z+L19mQ/ur9qh2XtVV7fXfzuz96rg+sOxkcxHXfXhG22ZDy57HW/N8ro+3arSneUFt+PntGc++irbDOzMfHAVWyyS5YXnHe7XhuUFF+/YVtRH3ctzWzEfXM5r0prNK+5J1dKbU9i8gss2LUcx391TPi0mUj/g5vPCw9i8wtOflx/A5hVcLSrXjfmoK49v4n7A1dKDnVhe3JOamfeMoXkDbu5aMpD52FPlN/8bQf2Ai9Y3+7C8zrtrF80LrrJLt2M+6tplKV2Y7/iNlyLZvKJPZuRd7di8gvv9GrRmPnKq2d9HUD/gNrlgKzav8PyYj+m8gpu46hHMR12/agHuB1yO69WW5UWfdOSLNC+4F3+gJfPRV1PmUeqD+y+NoXnhyeiYNiwvuJn1QCfqB3X9x/dGMR9c9YnsSt+rBuekfXt9a/ZeFdwWPtuG+TinTOsFrZnveOXJUey9Kjw57OX27L0quD9NdmM+6uoG72nmg9tfTvRmeXFOqqHfRLC84H7XSh2Z787VGl3aMh9cv6q7srzw5KWpPWjegJvhjQZTP6irWgzqx3xwPWLCCPZeFX0Sj9l+7L0quC33zgjmI6fa/8gw5oPLuQPH0/eqqPvsz5Poe9WAm9/XzGA+6srGd77uJNwHl991WMDyok9iUskxLC+4GDB1KvPRV1m/kGG+46mJ/7C88Lzv2s1lecHN262XMR911cDCS5gPLgcsXsvmFeek3vNtJzav4F61Wz2Zj3PKK55KfXBxcVV/Nq/w1ANPDWXz6viV2HHMR107NXkk88H1Bw2msLw4J+XzT/VieR0/0Goo83Gu6kJVqO94RPRIltfV1SfHs7zg+r5SM5iPurZhP+o7PuXuuWxe0SeZ+dR0Oq8BF83/WMh819fXf5rDfHBx/ZtlbF7heQeLrWLzCm6P7t/EfNRV/5M+8x0f/MIOlhd9EuvufN3kv/IG3JwuvYb56Kss9/Ry5oPrHx7fyPLC87Imb2F5weU+uY/5qKvsrJ3MB1fVex1i+yruScXe5kPZvup47UE9mY97Sm/jzkHMB9fVinWn+2rg2Uo3OtB9NeC6dsU2zEddXfjO14uF++BqU6PWLC/uSeXaM/1ZXnAxsHVX5uOeSv5woi/zwfVlpVleeKLfniiWF9zGVWxN/aCumvxYFPMdr32O76voU43zdF8F1xfKt2G+6+vZidwHf3JzJNtX4ZldmzuwfRVcbn+pJ/Nd3Xf/0MwHV+sm9WV50Sdd+B6a1/GKz7RnPvqqawnqg3tTa3ZmeV3dlt27s7zg8lTOIOajrl/5s97MBxe/zR/G5hX3pLpq/z50XgPunRnahfm4p/Ra7OvNfHDZ+Y0ubF7h2ecKR7F5BRcfPR7BfNS1K+ZFMR9cXEqMZHlxTyre69Wb5QVXkU27UD+4V9XehN7MB5eTn9csLzyv/Im2NG/A5doa7ZiPumLvwPbMB7cxDTuyeUWfjMpoy+YV3C+6oSvzkVPXiOzIfHB116kebF5d3aYr+7N5BRcL/hrFfNTVQ7oMYT64/PXIOJYXfdJv/6ZZXnBbZEgf5qOvskvB7tQPuGl4dQDLC88vPX44ywvuTb8+mfmoK+6vMJr5jl/Nnkr3VZyrgw8OYfsquF/hhfHMd+dqz0kjmA9uKqwwbF+Fp589N53uqwHX+9svYj7q+qdyZ1M/4N76/stYXpyTcqcYS/MGXB9r9jfzMadywV+TqA/eZMs/NC+89p0Xsrzg4uInq5mPuuKnq0uYD66Wjt/A9lX0SbXrQvdVcDX3ma3MR07v1TM+88H1ymk72b7q6hausp/tq+B2RfFo5qOu3Lic+uDmn/QTLK/r64Jft9C8ATcJU/YxH331BtTZwXzH/1vgEMsLz0RcOsrygpvKC88wH3Vls4rUB5ft51o2r+5cfbr6VDav4GLLnnnUx7l6NX069QPutdq7iM0rPC/t/Ao2r+DKHN7EfFf3vbZrmQ9um767jeV152TOs7NZXnCz5sVlzMe56g26Op/64EUvrWR54anvdvssL7g6m7qT+air+s7ZzHxw2zl7L5tX16f/zN7D5hVcPXfyCPOR00+Zc4D54KbQ3mNsXuGJoVNO03kNuI34I5b5qOsdTD/HfHC9v2A8y4s+eQfeOMzygoup+04y3/X1n91HqR9w+/ThMzQvvIUDY1hecDWtUwLzUVc/fedzecN9cO/gwcS8n6sqZmz0835Oqv/V3GV5udr2aAiXcyssYr77XNVFs+YwH1wX3js9b13TPDXEM3NmzsnLRcvtIdz/8LMZzEddMaXmFOaD2yc2j2d58TmpctmQKSwvuBrefjzz8TmV6uJPo5jvuGkzlOV1Xqw/kuUFl8Xv3KuE+6grE+7cK4b74GJNVK+8fZWPx8zN2yf/t+wpebk513pmXq5+eGUi85FTN/p9FPPB9YV3h+Wtq6IKTsvr2YEzRufl4smNk/Jyv37p4cxHXTVww0Dqg2dH9mF5XZ8+qTKA5QUX1Sr2Zj76qn+Y0o354Kbs6i4sLzw1vUV3lhfcXuummY+6/qYTHZgP7i+q1I7NKz4nVbZPGsDmFdw+trIX8/E5pWbjnXcP4T64eft/Xdi8wlNLGnVj8wru3zzUmfmubqsh7ZkPLtdfjWJ58Tmp8uVJHWjegNvcuCjm43OqRHb1SOaDmxFjI1heV7dhrUiWF1yfHhHJfFd3aHIk8x1/M/j71bB5RZ9UXGZ7Nq/g5oWZbZmPnLLYfVHMB/fv6RnF5hWe7rYgis0ruBp5JIr6QV2/6VfUBxfdP+zI8ro+FYrkeQNuzsR3YP7/V9eZB2dVZG/4quOGCG6Igw60gKNgdBAUEFF6ZHBjHVAENy5oFFEwBIGwXwQRlG0CEhEizSabQECQVWyWJBBA2bewNCRk/RYXIhCS7/vVr3Lfro/MO39RPvXUqddTp5pTneYGfVWJXUYxH1yMfHEMy2vrfvqRx/KCm5vWjmE+6srbrh/PfMv7/TrpqnPV2/HVVedk3bgpsVzNPn4Vdz9sP4H6/jmlWrQey3xwuT7kXVV3wKKrvVOTxsZyU6X2VVye+dFjPurKD+cNZz64PD4jieXFOem0/GY4ywsuT709hPmYUzf5l8HMB1ePPjWY5YUns+4fzPKCOw+uGMh8W/fpPoOp73PHaZV0VV+TssfE9klN6DsslqsXu4+6ijtpg5hv+1pr/EDmg8vktgNi65r3dyXFenp3lQGx3EtfNyCWOz3bJTAfdeUjm/szH9zcuCGB5UWfnDp5H7O84KLlso+Zj7567jdDmA/utew3jOWFp05UvNupnBfc65cwkPmo68yfPoL54CLr4ig2rzgnvVa/J7F5BXfOBoYy355TA+t7zAd3O679hM0rPNPzuxF0Xn2u357oMR91ZeOMT5lveYe2E1henJNicfpnLC+4WKMmUh/n6phJycwHd6emz2B54el1df7D8lr+5YvTmY+6MjJ7FvPBZZuxis0r+iRumfgJm1dwuSxjLPOR0/vp8UnMB/fi1k1l8wpPto/7jM0ruCxcNoH5qCtq/JrMfHBnV7MvWV70yXl4WwrLa3m3IbOYb/vaeuh85oNrNe9blhee98Lrc1hecHfn4rnMR139VGAJ88H19tbL2b5qv6ta2nsg21fBVaOFg5mP75Qqd3AS88Hdkw1Hsn0VnjQPD2f7Krh+etpo5qOumZQ4hvng6qFpE1hefCdVvblzFM3rc+evt3zKfHynUpkHx1Hf5yJr6mSWF57b5J2JLC+406PpNOajrtAPfMl8cHlnzVS2r6JPXr9+49i+Ci4WH5pIffT1UM5U5lu+7fEZbF+1dcuGJrN9FdxtMiqF+ajrXFs4m/ngcsO0eSwv+qSvyZ7J8oI745vOpb7fV3Vu13zmg5uvPl/K8sLzrpn1LcsLbnY0X8581NUnR69iPrg7ZfkPbF7xnVRn1tEpbF4tXzFsBvPxnVIx4PYU5oOrpIpzsvK82rp3TpvD5hXcuRJcyHzUlW16L2I+uKzZPY3lxXdSnUOLFMsLrupXX8x8vFN1T+9fxHxwPe/FVSwvPOe9uqtYXnD1j9J1zEddM37xeuaDu9c13crm1fb1bzesYPMK7jR8fA3zkVM0SviB+eBO+iNb2LzC09uWbWTzCq76tNzKfNQVG49sZz64rJ64i+b1+6Sf6KtZXnC152I689FXb9QHmcwHl2/98TPLa71WX+1heS0fd+cB5qOurvbzQeaDq8mjj7F9FeekavFZKttXwd0L7eYxH+eU2/vTRdT3uVndfjnbV+F5P3X7lu2r4M7uk0uZj7pi8O+rmA+ua81ay/LinBTjpq5mecHNhAbrqO/PqRf382bmg6t7AltZXnii7hubWV5wmXnTVuajrkg4ks58cK1vzaL7KvrUIXMl21fBZdao1cxHTufnKRuo73N5x8Yf2b5qvYwma9m+Ci6d8Drmo67YH9XMt/zVuttZXvTJ7XHXDpYXXE9qmcl89FVcTvyZ+Zbn99/P8sJzm36QxfKCm0mf72W+rdtxzkHmg6v81YfZvOKc9P7MTGfzann7nCzm45wSg3b/wnxwcd+rB9m82rqPPrGPzSu4V7XmQer7dd0hRUeYD667DD3B8uKclNc/coTlBTep751gPs5VPaDzKeZbPjJiWF54ZsGU0ywvuHmg5Vnm27qH6ucwH1xsrXKezSv6JHqWH2bzCm76/nKM+cjpdc49RX2fO2fGGTav1huQlM3m1fK3Ck8xH3W1/v0s8y0veiyH5UWfvDInh+W1/LrJucxHX427JI/6Pnduv7aA5oXX6+x5lhdcHS3PY76tK+8tZD64O7+8MPZe1Tu76aPYe1Jxa/KHsVwPKXs/lruZa95nPu4p3cz6CcwHlzuyrqrrVcuMv8r7Zst7sVwVbu0Vy02Xwe8yH3WdZ3Z+xHxwOffWBJYX96T61VaDaF6fO9WeHMx83FM5rVuNZr7l48Z/wvLCcwe8lMTygrtxFff+lX1bN7BnDPPBZbPrPo3tq7g8z43tk+6QFh/LxaU33orl5kLreOYjp1P47wTmg4sWCxJi6zpb+rxxlbf3rvhY7p74++uxXCdVjWe+rZtXmsB8cHmlQ3+W1/b1zMChLC+4bpc0jPnoq7im/jjmW57b4DOWF57X6u3hLC+4bNt4BPNR1x1x+3jmg4t2x8ezebX3qvc9PpHOq8/dG9dMpr5/Tyl7eCnU97luvHkmnVff07UmT2XzCi6+X/kf5qOu3HR6FvPB1QunUllee6/aZd58ltfyLZMXMh97qpr8UhrzwcUHeatYXnjOmLcX0bw+F9trLmE+6sotH37P/Qouux5Zw+YVfdLTNiSzeQV3aiyexnzkdCeunMN8cO+3inuRyvMKzzw4ZDqbV3DTIu5L5qOuXPnmXOaDq12X5rK86JO3YcFSltfyCxXvpiv76Ktz9m8/MB9cVJPrWF54psuTy1ley48+s4L5qGu8p9YzH1zPvG3DVefqn6+9FntOOjNrxcdyp+OZ7rFcr2wSz3ycU46Z1Z/54OLc6f6xdeW0lO6xnqreKT6Wi4cSusdy93yfeOajrtx+dyLzwd0XWieyvDgn5Yc5I1hecDFp0EjmY05lv9ETqA/+wi2fs7zw3EhgJMsLrkIvjKK+X9cRQz5nPrhes/fz2L6Kote7x/bJ7TU6PpZ773TuHst11eR45iOnOvxOIvPBTY3hibF1xZAO3WM9p9ns+FiuEtt3j+XenDnxzEddvXZCIvV97j02OZHlRZ+8gomjaF7wXptGMR991dm3fMF8cNOg6RcsLzzn7SOjWF5wp+WZUcxHXbmswxfMBzcru37B5hXnpPjLti/pvPrcbdpyBvNxTsl7h85jPrjXIWcenVffU4+kzmDzanlK/gzmo64zvel85oPLboPms7w4J/Wz96xkecHd7/zvqlfyca6Kuls3MB9cFHbcyPLC05EGaSwvuHyqRxrzUdfprjcyH1zccfcmNq/ok6xTO4XNK7jTsOLfkVX2kdONLJjPfHB3/o75dF59z0vplsLmFdyZ2SOF+agrgkfmMx9c5J+cT/P6fVL3jUtjecHNAJXGfPTVmfzyJuaDuwdGbmJ54XlLVqaxvODu3DVpzEdd0yRlE/V9rhrM2cT2VdyTmq/URravgotVzX9kPu4p1V+bpTMf3LmpcSbbV+G5nyz5ie2r4M6dFzTzUdd97uZdzAcXc9Zm0bz+PanwWu+jeX2uX1y8n/r+PZWsNvco88F1Zs/jLC88fSB4gOW1fNxth5iPumbpryeYb/l77U6yfdX2yfO/L1ppXwWXhce3Md/mnNN4D/PBTf+Re9m+Ck/+mLqd7auW62d3UN+vq0dXfDe2sg8ubh73C82LPplqh1lecO9vhYep7/fVrT3oFPV9rlcknGZ5rXdQHWF5wd2pzY9S36/r3iPPMN/yiwVn2LzintRLrmrYvIKLGteeZT7uKb3H/5XLfHB1Y9x5Nq/Wm3D4LJtXcGfL+HPMR131fPA888HViIl5LC/uSYUuyGd5wU3y4gLm415VdFpdxHxwd9k/i1le62W3LmR5wc1iXch81BV/ri5mPrhqWjXA5hV90r1r57B5BXfWzMxhvu3ri9XymW/5rYPz2bzCE8XlOWxewWWbDrnMR12ZnpXPfMsvVC1gedEn79/1i1hecLfXgCLmo69ux84B5oOrBhMCLC88b9mqIpYX3Lxyroj5qKvOfR9gPrh74VCA7as4J83WrB1sXwVXrZunU98/p2RmvX3Mt7xx/31sX4WnH56cTvdV8IG/pDMfdWXq8n3MB1cvZ+9jee05Oez7oywvuLq2xjHmY06d6s0N88FNvb6G5YWnU9xjLC+4kzTzGPNR1/3tS8N8cJO4xrB91fYpEEln+yq4uFQ7g/nIqT4u28d8cHf7HfvZvgpP9nssg+2r4PLjZhnMR12dUmc/88G9jHr7WV70yfO2HWN5wd2qp48xH311umcZ6vvciR4zLC88lR08RvP6XF3zxzHmo67T66xhPrh3X65h84pzUm+flMvm1XLxUy7zcU7Jb2UB9X0uBvUuYPNqPZ2by+YVXK52zjMfdfX0cQXMB9f7ZxewvDgnZdmNxSyv5VvrFzMf56p6NRRgPrj+5pogzQuvV4tiltfy514qpr5fV1a5LUh9n7sptYJsXtEnsfiu82xewVXJ/eeZj5x67ncFzAdXddcV0Hn1PTO5wXk2r+DmhrjzzEddt86PBcwH17f9VMDyok/u+FeKWV5wt/yNYuajr6pK3SDzwcXFvwdZXnjeUz2LWV5wNatXMfNRV2xvEGQ+uFjWMMjeq+I7qW7RnjT2XhXcu/mD9czHd0pVo/brqO9z57sczd6rwtP5b25l71Ut/7z6TuajrphzNJP54M74tvtYXvtd1QUrf2J5wb0bKn5vQmUf36l0e9yeznxwvfLPvSwvPPl0xi80r8+9Bm0PMx91pRp+iPngXu2ME+y9Kvok339oH3uvCi5OXjjEfOT0kgceZj64fqBeNnuvCs/NqPi9wZXfq4K78Z+eob5f13QebJgP7mxplsPyok/m9ZezWV5wuW2Hob7fV92k4nsllX1w0X9fDssLz1nyQw7N63NR2jGP+agrOvi/V7qSDy6eOFXA5hXfSZX5r+xm8wou7q/4vcuVfXynVD733H7mW37Fv1eoNK/w1J4xx9m8grtnGhnq+3W9bmmnmQ8unzfnaF7/O6lOWqvDLC+4eb7rSebjO1Xu4oTjzAdXRSMNy2u9jtlnWV5wk9XuPPNRV7WIy2U+uPdkr3w2r+iT03x8DptXcNHJy2O+zXn9nXnU97kauLGAzSs8teW3Ajav4CrtSBHzUdfs2FnEfHD3+3YBlhd9cpc0LWB5wWWbEUXMR1/l6l8LmQ+uXjhSzPLC85o1DLC84G5K9yDzbd2mjwSZD+7sqxli71VxTjoPdzrH3qtafuGbXObbc6rasvPMt3z/5nz2XtV6nWbnsfeq4GpY6wLmo654rU8h88HN0s1FLC/OSVVtSAHLCy7jGxUx387ppYrfa1nZt3xypJjlhef0/qSY5gWPGx1gPuq6X5QFmA+uuqwNsveq6JM7f2Yhe68K7jVaUkR9P6euta2Y+eDCVNxTVX6vCs+LG1lM36v63NSsH2A+6ppV9weZb3n+9CDLiz55V54JsrzgYtXuIPPRV/OXp0LMB1cbZ4VYXnjqzyYhlhdcfzQ6xHzU1YGzIeaDmwY1wnRe/XNSNWkVYPMKrstbB5mPc8rt1TPIfMvP1Q2xeYXnrCkPsnkFV9f5/z+VfNQ1uYtDzLd8wPVhlhfnpHO6YYjlBVcvbAgxH+eq7L6W+uDuo7XDLK/1lv81zPKC619fCzMfdU3G29S3/O7kMJtX9EmknwrRefW5Gl0nzHzk9Ma2CTMfXNZODNN5RV23Q5jNK7ionhBmPup67/+H+uDOtEVhlhd9cjt/RvOCy/ULqY++6jtXcR/8SjrNC8/buJHmBXfe3UV91NXND1EfXN1wOsz2VXwn1eu8/BTbV8E9ufoc8/GdUnPpPPd9LhetzmX7Kjz1ebM8tq+Cu7/5f19U8lFXh0qoD24e8opYXvud1LGHaV5w3WdQHvPxnUqn5dfUB3eHXslneeF577xP81qe+nUx81FXDJlJfXCVnBSg+6rfJ9m+bTHbV8G9G44HmG9zPtE1wHxwc/PMIN1XfU98XUD3VXB3aGqI+agrv/yI+uBenyshlhd9Uof8v3cr5/W5qvlgiPnoq7NjM/XBRcLUEMtrvdJ9NC+4l/50mPmo66yoGWY+uHdr3zCbV3wnVetH6byCe/e2KWQ+vlPqvZxMfXA1sXsRnVffk3dMpfMKboZV3HNW9m3dtq9SH1xfWRFkefGdVPftzAKa1+cmThYzH99VdeoeKGS+5e2dAMsLz3S8FGB5wcWSWiHq+3VF3lDqg4snE0JsXtEn59fGdF7BTfUzIeYjp0ydRH1w78VaYTav1hvels+rz5WYQn3UdXL6Ux9cXLc4zPKiTyrrIM0Lrt59Psx89NVZUoX64PqxPjQvPHfjZJoX3AzYSH3UlasXUN/yl3eG2b6Kc9J0uCvM9lVwXdwzzHx7Ti14hfrg3sIpdF+FJ7+aTvdVcLV7LfVR17u0hvrgpsZemteek8FPaF5ws2sF9TGnzsJ51Af3ntlB88Iz32bRvOCi70nu+3VNt+Pc97lelEf3VfRJrdxN91Vw7+kT1EdOseMk9cFl1Xy+r/qevC6X7quWLyyiPuo62UHqg3vv/07zok/iSD7Na3nzMPXRV31PmPrgasQFmheeW+UCzWv5kxepj7rexYvUBzctSum84px0H9hA5xVctThIfXtOvbqL+uBO9ik6r/BMYg6dV3CVHKQ+6iqviPs+l11+p3lxTnqPHuZ5fS7Hnue+f66qK9wHl/WKaV547pgwzQuuF5ZQH3VlvT+473N13SU6r7ZPgT/ovIKr8EXq25z/uEh9cNO8lM6r9UaU0nkFF/eXUR91Rd0y6lverZzmRZ9E38s0L7jZfoX66KuJcB/cPVhG88JTzctpXnC3boT6qOtd4D64WVTx35Xfq+KeVBYvCLD3quDOwZuCzLf3lB8GgswHF3f8I8Teq8JzX+oaZO9Vwd03koPU9+s6F3uGqO9zLzg+xPLae1W5KcTyguvUvBDz7b3qgRZh5oPrVV3DLC88p+ONYZYXXN4mwsxHXW/wB2Hmg+ubh4XZe1Xbp4AOsveq4KYkN8h85DR7vw0xH1ys/zHE3qvauhlOiL1XBZf17w4xH3VFm19CzLd86ckQy4s+idONwyyv5XH/DDMfffW2jwsz3/JvKv7erJwXnqnWLszygptOL4ep79c1HWaEqe9z8fqsMJ1X3JMmjQ+zeQV3//019XFPqT5cR31w98w2Oq/wnCGL6LyCm5arqI+6TvPd1AeXr+2neXFPqvRBmtfyetncx73qiVzqg3tLCmleeN4gQ/OC61dyqY+64u4g9cH1xDCdV/RJfLaez6vPdfmP1EdOkXuY+uDOv47TeYXn1dxG5xXceTid+qhrMrOpD+4sPkXzok9uXD7NC+78q5D66Kvo+hv1LV/wO80LT+4r4nl9rkuKqY+63sw/qA+u21fsXf/1XhXnZP+/h+h7VZ+7WxuHmI9zSh7KC1Hf5zopFGLvVeGJYMXPnSq/VwV3Up8NMR91vfoXQswH160v0bw4J/XEip/r/Fden+tmPcLMt+fqA3PC1Ae/PDfM8sKTy3qFWV5w9XB8mPmoK6ovCDMf3J1a8XOjyu9VbZ86Px9i71Ut7/tSiPnI6TS4EqK+z53by0PsvSo81ahdiL1XBdfb2oWYj7reA5EQ88HNyAjNiz7JwLthlhdc3Nk7zHz01aR+G2Y+uNfB//ljpbzwnI0Vf1bOC+782jtMfb+uu3pRmPng+sAiOq/2XB2cyefV5zpuF/VxTjm3nuE++F2Gzis8r28WnVdw9fJu6tu6hw33fS7dszyvf07qnwM8r89lvyD1ca7KAxeoDy6alNC88LxzQZoXXD0Roj7qyiEl3Pe5XFZC59X2qWQ3nVdw2WMP9W1OfZb64F70LJ1XeObrPXRewcWKPdRHXafeOeqDe4+eo3ltnz4K0bzgZnqI+uir2VtCfXA3t4Tn9T1naYjmBTdpIeqjrvNbCfXBRUkJ3VdxT+o+Hub7qs9Fj9+pj3tKb1QJ9cHN7It0X4UnZl6g+yq4fPRP6qOue9dl6oPre0tpXtyT6m6XaV5wsbOU+rinctZf4b7P1doyntf3pH+vUzkvuNO/jPqo67nl1Ad3DpfTfRV9Mp0u0n0V3Kl3ifq2r0dKqW/561fovgpPb7pE91Vw+fBl6qOus/kK933uXlNG89o+FZXRvOCuLKc++uo+EKE+uHwzQvNab0Q5zQsulpRTH3XV2Aj1LVcROq+4J/UeK6fzCi5yy6mPe0pTO0J9cDknQucVnnotQucVXCyNUB91xR8R7vvcaxileXFPKn6I0Lzg8q4o9/17VVMzSn1w960ozWu9TlGaF1wmRamPuuIL7oPrRVE6r7ZPJkLnFVxeH6U+cnodo9QHV72jdF7hOXWidF7BTaMo9/26akiU+uDup1Ga1/Y1+X/kBZ/PffTV3ch9cDeD54XnrOR5wc0P3Edd92fug6uDUbqv4pz0hl+m+yq4WneZ+jinVJMy6oM73crovgpPnLtM91Vw1ymlPuo6iWXUB5djy2hee67uLKd5wc3pcupjTsWaCPd9LrZFaF54JlRO81peWk59W3dPhPrgzsEI3VfRJ1mjlO6r4LpuKfVtzqll1AcXM8vovmrrNiyl+yq4jiulPuqq1DLqg4s5ZTQv+qSvidC84OqGCPXRV3U0Qn1wdSJC88Jzb47QvOCySoT7fl33ZIT64OZkhM4rzknv6SidV3D3uSj17bk6KUp9cDktSufVeu2idF7Bdaco9W3dlCj1wfVMnhfnpNj8P/L6XP/EfZyr8ij3wb3jPK/1tvG84M4O7qOuyOY+uDgZ5fPq90l3idJ5BfdeiVIfOb1ZUepbPjtK5xWe6hql8wpuukapj7puapT64F4qz4s+Oek8L7hO5z76qk9yH1ye4nnhiQyeF1xmcB913VPcB/f+/8//A6TUCOY= + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-010000.2.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-010000.2.vtu new file mode 100644 index 000000000..4189a2392 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-010000.2.vtu @@ -0,0 +1,32 @@ + + + + + + + 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-010000.3.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-010000.3.vtu new file mode 100644 index 000000000..1efe64e87 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-010000.3.vtu @@ -0,0 +1,32 @@ + + + + + + + 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-010000.pvtu b/tests/automatic_regression_test/gold_allenCahn/solution-010000.pvtu new file mode 100644 index 000000000..e039c68d3 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-010000.pvtu @@ -0,0 +1,18 @@ + + + + + + + + + + + + + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-012000.0.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-012000.0.vtu new file mode 100644 index 000000000..507e8d848 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-012000.0.vtu @@ -0,0 +1,32 @@ + + + + + + + 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+AQAAAAAAAQAAAAEAxFgAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-012000.1.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-012000.1.vtu new file mode 100644 index 000000000..40107b50d --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-012000.1.vtu @@ -0,0 +1,32 @@ + + + + + + + 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OiZgM6oOLP9LovFqvaRqdV3Dz2g3qo67T5Qb1wc2fl2lerJNOwXSaF9y5cYP6WFf1gJvUB5fPXqV54ZlvrtC84OLnZOqjro5KoT64G3WWziv6ZOpfpvMKbuomUR859dIk6oM7dQydV3juq4bOK7huuJ/6qOu8vZ/64E7Z1TSv7dOLsTQvuHN9P/XRV7f3AeqDu/X+pHnhmemreV6fK3c89VHXrJpAfXCdvxm9r2qftz2S31cFd5txH+eUbk3ug4sn+X1VeLIMv68KLq5nU9/W3ZbNfZ87tbNpXpyTioxsmhdcj8umvj1X/Tmb+uAqJovmhWfvbwXy2vswP2ZRH3WdPFnUB/dSM+h9VdunCdn0viq4eTmb+35OsTOL+z5X92fR+6rwvGFZ9L6q5XszqY+6ulAm9cG1Sad50Se3eybP63NVLYP66KvsnE59cG/0LZoXntieRvOCyx03qY+67pM3qA/unrnC5xXnqn7fgvMK7k7KpD7OKU25TOqD6y3pdF7huTUy6Lxa/lsa9VHX3XCL+z7Xd93gef1zUvVROs8LfvUm9bFPFe/fpL7lM67SvPC87ddoXnDV6RL1UdcsDnHf5/JyPJ1X9El8dZPOK7hT/hr1bc56V6gPrrql0nm1dctfovMKLv6VTH3UFY/GUx/c++M0zYs+uX8k0bzg8uJZ6qOvZvtp6lsuY2heeHLOUZoXXBXdSX3UNS9voD64mTiX3lfFOukdzqD3VcHlmHTqY53Sn6VRH9x8fJPeV7V1nTR6XxXcvXmD+rbu/GvUt7zeZZoX66T39nWaF9xJuUx9zKlZFKI+uKyeSPPC00tSeV6fy2oJ1Edd1fEs9S1fdYzeV0WfdK1r9L4quMm8RH3k9F5IpT64uyye3leFpyak0Puqlp+6QH3UNYsM933uDjtC86JPpm0szQvuzj9KffRVjNpDfXBXaJoXnvfDbp7X5+6BtdRHXf3WfOqDO/Gj6bxinfQqx9F5BXfHnaA+1imz6QD1wdWCzXRe4Tlt99F5tfyDDdz366ppi6kPrqdMoHmxTrqTVtK84G7qFO7766rbdSj1wdWzP9O88ERML5oXXGrBfb+u7voe9cHVB7npvKJPzhsL6LyCO2+Ooz5y6p+7Uh9ctqxF5xWezNWZziu42Fid+rbu74Wpb3mnSyGWF30S+4rTvODi9xsh5qOvZvDyEPPB9eO1QiwvPN1wbojm9bnz7Zch5qOuSDqfSn2fO9Wrp7L9Ks5J3QVX6H4V3H0ulfo4p/SOJ1Mf3PnsHN2v2rrNz9P9Krg6eZz6qKvFEeqDe5e20Lw4JxUzYmlecNn/IPdxTtUjhvrgavBantf3RKkNNC+4s3MW9VHX7T2F+uBev050v4o+qY67+H7V585jK6mPnE70AuqDmweG0/0qPFF6Et2vgss8v3Dfr6vqNaE+uO5bkuZFn1SoF80L7k6tTn30VSR9Qn1w83hWiOWF55Z7jOYFl602hpiPus6E8SHmg6uihUJsXnFO6pVYQ+cVXNz1G/VxTin7jac+uPmsOZ9X1O3Whs4ruHnnbeqjrv6hCPXBZdVDIZYX56T6+Z9pXnBx6RXq41xVO69QH1x8uznE8lrvvh0hlhfcebp8iPmoqzuVpT64WdExlc0r+qRmXwixeQU390eHmI+cJqsW933u3L0slc2r9ebGpbJ5BTdvvJBKfb+u8/3JFOaDyx6bklle9Ml7cHgqywuub01Oob7fV108OoX54Prci8ksLzwxtWkyywvuvReXyHzUdfN3SmQ+uCzXPp7tV+06+WUVvl/1uZSPUh/rlCmcGmI+uPxheIjtV+E5Q3eF2H4V3CnQIsR81NUJz4SYDy4KDk5lebFOyveqh1hey8vsSGU+5lQ+2DaV+j4Xg6aksLzwnANPpLK84N66D1KYj7p658Rk5oPrg98nsf0q+uTkTk9l+1Vw8VqTVOYjp1vgRArzwd3SuVLYfhWe3LY8he1XwZ0Yk8x81HVrZiYx3/J7tieyvOiTGPJWMstr+TfZicxHX7W6lMB8cHW9YjzLa73yIxJYXnCvwq0LzEddtbPteeaDy7xdz7F5xTopBhVJYfMKbupkJVHfX6dUrR+SmA8uJ25IYPMKT9YblMjmFVw9aeKZj7ry6vELzAd38v0Zx/JinfR635fA8oJ7M0PnmW/X1covnWc+uNqszrK88GSRX8/RvD7X+QrHMh91xaIChvngZmneE2xe0Sd1IvM8m1dw7/dL55iPnPq3zmeZb/mW4obNKzyZsiuWzSu4uV7xDPX9ul7JxBPU97k4tOgIy4s+6ae7nmJ5wUWN+KPMR1/NtZqHmQ/uVnszhuWF57y64iDNC36uwH7mo64cVXs388H1A5225nyuqp4Y/pxUPSGcu9+Gc1WD+3hOqR7HfXC3Wnhd91x2mGdMdhgXj2eHcfNwBL8anmvDfXDnnmya1z5XdSTPC24qcd8+p3JABB/3Kd/7i+eFt5vnBRc3sqiPut5K7oOrI1lhfZWrM8P7tCwzjMsG6WFc1UqnPnLqaZnUBzfvp4fVdWvfCPcq3wjj4qeLYVxXu0h91HXcG9QHdwpepHltX3tl0ryW35dOffTVfJNJfcvXp/G88PZdp3nBZf8Q9VFX1bhOfXC9JZXOK56TKn/h8wouXuW+fa7qj9wHdwr+RecVnovvpwrMK7iZnUV91JXNs6kPrhtn8bz+c1K9CjwvuId7EwEfz6lyHuM+uDuW54Xnvp5N84Ib/7k2QR91xXbugzttI8yr3yfv/kw6r+BetTTqI6czO4P64GbJLTqv1pt1jc4ruJyXQn3UFX9cpT64eT6Z5kWf1CMZNC+4PHCT+35fhZdOfcuH3aB54amvrtC84Corgfqoqxteoj64dC6Er6tdzoWvkzXPhXGZb28YV5v3UB/rlDJnqQ/u5doTXvfanHCv/5wwLl9oGc7bt6A+6rqf/k59cP19M5rXrqv/Okvzgjuzd1Efc6r2GeqDq8k7aF544sFZNC+42tCE+qgr1k2jPrj7TcOwvupz94T1ydl2dxj3ZtUP5eTu6Xoh6qOv3+e/SH2fqwa3X/ejrnekcmpOz81fKTUn13FjknNyd/qIZOajrmxSLpX54Hpe72SWF30y0c5FlhdcnK0SYj76qmfepD642vBhiOWF5/UokcrygquvWiYzH3VF+6dSqe9z1fjLZDavWCflPWfovIJ7eiv1sU7J4Se573OzeSOdV3ju/YrOK7i3tA71UVeocdQHd6pUpXmxTorCx2hecKf6Wu7766rpeJD64DJ5Gc0Lz00YSvNa/vXH1Edd9Wwv6ls+vjidV/RJf51M5xVcPVOU+sjpTTnOfZ+bPx4OsXmFZzKzUti8Wl76+WTmo64pfyiF+ZY3uJDE8tq+fruJ5gUXiy6nMh99NVtnUB/crNmYyvLaunGTUlhey4+MTaK+X1eOqpXCfHBvdOkktl+1z1Xdwfer4KpINvXxnFLna+6Di4FZdL8Kz5Ti+1VwMymD+35drxr3LRfpNC+ekyrHZdG84N7QTOrb56q2zqS+5fkyaF54Xlo6zQtufrlFfVs39hb1weX463S/ij6ZAml8v+pzteEa9ZFTvHeT+uDecxH2q77nzE6l+1VwsS+W+qjrrE2kPrgXd4Ln9fukh/C84ObDEPXRV9HrEvXBVbcEmhee80AczQsumx2gPuqa1RF8n+viW/m84rmqE9P5vPrc+Pe2gz6eU6r+uEl9cGf1NTqv8MyE63ReweXLl6mPum6HS9QHd8sk0bx4TqrMvMLzgo8IUR/3VF3/+8KCPri3Ko7mhSdqJPC8Pte1z1AfdUXTE9QHN+/sofOKPok6SXRewc2xM9RHTvesoT64sySGzis85/u9dF7Bna+WUh91jVnDffDkX2le9EkXjaF5wdWQtdRHX71yq6lveZdJNK/1EhTNCy7GtqE+6opv2lEfXE5+m+9X/XXSZO7m+1Wfi01zqW/XqdRN1AcXm3+j+1V47mtt+X7V57LH49RHXdOsHvXBHX373mQwL9ZJ5+UVNC+4/GIA9TGn8tp07vtcpbWgea33azmaF1zdtz3EfNTV3zx+kfngzu7+dL9q+zq6a4jtV8HVgD6pzLd97fVZiPngesXbqWy/Cs999Pb74sH9KrhQyxOZj7r6zKBk5oOLU48nsrzok3gsf4jltbzh/hTmo6/qudnc97kTU5HmhWdS0pJYXnDlVk1gPuqaoe8mMR/caVk7ns6rv0466/m8grtlKlIf65Sc1Yj64GLng3Re4bkbT/N59bnXoXSI+airTw+ivuXp61JZXruu7n2D5wW/vj5Efayr0SdCzLe82xc0r/VmFaJ5wc35p1OZj7qmeEfqWx56P4XNK/qkPnw9lc0ruHP/2GTmI6eu3pP64E7MH3Re4cmhnyWyeQXXK1+/wHzU1WsLJTAfXJRYF8fyok/q6ak0L7hzsXsi89FX1b099cHlpdwJLK+tK6fQvJZ3uv8c81HXjDxKfXDdyI3Nea5qqtybmPOcVHyTPzEnF9uGns/J3Tf6nWc+zinVU9cSmA/uPtD6fM66bnT12Jyed9cXsTm5mrHvWE6uJutj1EfdNqVimQ+uyww9xvLinNTbfzCB5QWXQyudp75/TiULzExgPrh468HzLK/1MgvEsrzg+uf3j1Hfr+v9tMMwH9yZvf1ozr4qeXJ/WF+b792fk+uHKmzNyUXp0luZj5xe/ln7mQ9uWmZuyVlXv/fAnzk9U+XmmjCef/LCnNx0HraQ+ajr/DB3DfPBnYPfL2R50SezttF+lhdcVp64hfnoq9xz137mg3uvPLaF5YVnXiq6huUFdy8/vJD5tm7xrquZD+5sm7mAzSvOSfXyHxPYvIKrtI1xzLfnlK88lkB98PdFHJtXeNptb9i8Wp5e9CjzUVcWu88wH1xUbnGE5cU5qZs8P57lBfcunT3HfOxTVUzheOaDy18rnGN54Yn7HznD8oKLzb0OU9+vq748for54N5brQ+xeUWfzNeD9tF59bnTsclm5iOn+2ryXuaDy6dGbmLzCk+1nLCKzqvPVd9CC5iPuiqqz0rmg7vzYuezvOiT01buYXnB1ZwxG5mPvqo6J3YxH9yNa7uB5vU9c67cCpYX3Lm36jzm27qxe5YxH1xPGzgnbF1tknt62Lq6+uK0MF5t29gwXmPRWObbdXXW8mnMB9f1W47NWdecfWJQTk9+6QzKyd0Gz3TLydXA+7sxH3XVmMUDmQ+u7z0nWV6sk/qVWtNYXnAnOvdY5mNOdZUjU5kPrvb+OIbltXX3lxzI8oLr62Ml9f263hfdBjAfXHctLnP2VSxsFpWzT16lOlE5uTzcsUlOroc0asJ829dCr0QxH1xserFJzrrO/Vvr5/RE1xX1c3K34rm6ObkpvaMu81FXHBlYn/ngpkDXuiyv7ev7+9uwvOBy3OofmY++mowabZgPbpq9/CPLC0+V+Lg+ywsur+aqy3zUdQ+dFswH936u9h2bV7tO9i06lc0ruJd32mjmY53ytjeYwnzL6y4dxeYVnnNqQn82r+Bu9VpdmY+65qG+/ZgPLg4+0YXltevk6baTWV5w/eSkkczHuupN/lExH1zNaDiC5YXn/vpJX5rX5+6YaZ2Zb+u2Odab+eC67F8d2bzaPl1e05rNK7jK3/QH5iOn/iBXa+ZbvqlnYzqvvic//U6webVcRNVhPup612U95oO7PzeuzfKiT97yl1qxvODmeIdG1Pf76uYu1pL6PtcdKjdkeeHJA/+uy/KCu0+8Xov5qCtata/DfHBz7/aabL+Kc1KR1fcC26+Cu7MHnWW+Paf8S59nPri7fX4s26/Ccy41Pkn3qz73ZpQ6yHzU1XVWHmc+uD66J4blxTmp+mRPHMtr+S+zDfNxTqU2zTlHfZ87h9NOs7zwvF9jjrK84M6pj/czH3WdT5cfZj642VF2L9uvok9qe/YOtl+1PK34eubbnF9d2kZ9n4vpk9ex/So8Wb/kUrZfBTf3jZ3NfNR1vmm3mPngzlO/zGR50SdvxIotLC+4eXPRGuajr075BpuoD/7ejJUsLzwzSS1kecGFLj+d+airxnX4g/ngYmKuqWxecU7qPVH9LJtXcNntm5PMxzmluHu3Yb7l77Y8xubVek1bHmTzCq7mb9zJfNQ16wvGMB/cOfLONpYX56Sy/munWV7LC9c5zHycq3qTWx9nPrhKKXGA5oXX5s29LC+4O+PPTcxHXbm38Q7mg5vzDdazeUWfvH3Z69m8gpu1Q5dR38+pD7y5jvmWX+y1mM4rvLS5c9i8gouaqxXzUVd+fHUm88FFszwTWV70yYwfsYrlBXfGjVvAfPRVNSy5jPngInEVzQtPvN1gGssLripuHMt81JXzHpjMfHA9atdItl+16+p78je6X/W52XbPcObbdbV8ngnMB5ezxgxl+1V4/+sFXy+2XwX38hfrwHxbd9r4HswHdy+9357ltevqBz+MY3nBZY0XBzMfcypiVo2mvs/dWgUGsLzWa3SsG8treevSbZmPut7+gpL54Kr4M23oftXvk9u2XHO2X7U81WlAffT1RPpPzAd3Jk6oz/ar8GT/hXS/Cu698mEN5qOuU437lg+Y+A3Liz6JfGOasLzg3rUXBfNtXx95/gfmg+svKnzH8lqv+tbqLC+47Hi2GvNRVxdb+jXzwb3nnqjK5tWuq9PvG8nmFdw8ltSH+v46pRu1GMZ8cO/Tkz3ZvMKT+zt0ZvMKrtrmbUV9v66smhrNfHD1SHwzlteuqz+cGsTygjtRF7tR319XZc1i/ZgP7p5r0IXltd6Znm1ZXnDzekoT5qOus6dKa+r7XFx8rTGbV/RJh2Y1ZPMK7m1+rxbzkdPt+fb3zAc3O96pweYVnnl+YlU2r+Dq3UpVmI+67qh+VZgP7tXNqszy2j69u6oeywuu3/jqa+ajr87XH9VmPrjwBnzF8sKTj979OcsLrp/oUpH5qOt+WKQi88Gd2i+VZ/dV8ZxUt+wZel/V8pSD3MdzSgfupD646rOa3leFJ3dv4fdVfe68sJj6qGtip1Mf3JToy/Piuar75tC84Gr9cOrjOZXexmjqg4vor2heePqbVjSv5cU+pj7qmrVPUx9cXTsaYvdV0Sf91Rh6XxXcfNeC+sjpxdelPrj84wV6XxWeW640va8K7p0/E2I+6roL9oeYD65a+N/PF8hr+9omP80Lrh9cGGI++qqGdQ4xH9yL/iuV5YUnr5ehecFVrhGpzEddp+Q3qcwHd+s0S2Hziuekin+9TOcV3BmRGGK+fU5p1PQQ88FVQf/5dYF5hSe3jguxeQV32hcPMR911Ye7UplvefliqSwvnpPqpB5PZXkt/6ZiKvPxnCpxZHoK88G9u/Yks7zw3O9GpLC8/x9fkMx81NWLtyZR3+fikwGJbF7RJ/H2rFQ2r+BO3JUU5tu+PvFjCvPBnc4/JLN5hee+kyeFzSu4fnNbEvX9uk4bN4n6PtfpPRNYXvRJnR+VxPKCO93LJDIfffUmP5jAfHDvz28usLzw3LrD4llecLN6zHnmo64OVY9jvuWq0Fl2XxXrpNhbn99X9bn768xU5mOdckfPT2W+5RduP5cteF8VnjkxOoXdVwXXBe9PZj7qel1qJTMfXP0UncjyYp303p2dwvKCi7MNk6nvz6lZVzKZ+eCi0vBElheee3xOIssLrob8GM981NVvtotnPrj84Vgcu69q+1TlYgK7rwrudn7hAvX9nOKLjAvMBzdr486x+6rwvIfXnmX3VcFV3oxT1PfrumvqGur7XFWOPsby2j5VGx3H8oLLtV/GMh99FbG7Y5kPLnoWOMXywtNy1gmWF1zuvnKQ+airc/c9zHxwOXX2PjavWCfNxqKJbF7B/9frnHjm23W1d/8LzAdXHYrGsXm1dQsujWPzCm7qnoxlPurq77rFMh9c3vRO0bz+OqnOf3GO5QU3ZTcb5mNdNYmNz1Df597R5sdZXnjiyBsnWV5w58ihw8xHXb1o0CHmg7tpufezeUWf5N1DTrF5BVd5vj/CfOSUxTscYT64qvzvGDav8Jz58/ezeQVXbWZsZz7qqpc376C+z/WVFzayvOiT2/nQfpbXci92B/PRV9U8zw7mg+u1X2xkeeGZL8dvZHnB3Ua7VjMfdZ3HE1YzH9ypvmQx3a/6z0n1HjqTxPar4KbDvETm4zmlXslGCcwH12P3XWD7VXh6zqcJbL8KLmJ/v0B9v67sdd955oPLFs+do3nxnNQPSpxnecH1MxXPMd8+p7JZm1jmg3s9Np2meX3PjP00luUFN3nanmY+6jp3Lz3OfHBZ9MBhtl9Fn0SRI3Fsv2p57v1nmY+cXrtHYpkPri6nnGL7VXimxxTD9qvgouyKk8y3dW/OOkZ9nzuduh9ieW1fN9U7zvKCOwt+Osx89NUZVuoA88FFn/17WV7r7VwUw/KCy8aj9jAfdeWiK9uZb/m+g5vYvOI5qc6KR0+weQXX1585wnz7nNJNJw8w3/Lc7faxeYUnt0cfYPMK7r1dfB/1/bq6Rd6d1Pe5zpu+meXFc1KdpnonywsuqrTawnw8V9Wb98cG6vvcpE//k+WFp+oX3MDygqsPzqxhPuq6hTosZz64yTV4EZtX9Mk79OsONq/gusS4zcxHTrfPqvXMtzxXvTVsXuGJjGrr2byCm7EdVjMfdZ0feyxjPrh+eNlClhd90l9kLWN5wWW9txYxH331Xqw4j/ngTqMHZrK88Nxnl89lecFFXjmD+n5dufjpKdT3ubfguwlsv4p1UuV/+Sjdr/rcTVx/gPl2nfpown7mg3tjXt7F9qvwdOrPe9l+1fInvtzGfNR1X8zYTH2fi55bNMtr18mEXttYXnBVucJG6mNdXdROU9/nXuVJK1le6x1Y9yfLa/mVPsuYj7pyxpTFzAdXTWbMY/tV9EmeqrKB7VfB3XWXVjEfOVW5B1YyH1z9UnsR26/C855rsJjtV8G99HpzmI+6blaT36nvc9108RSWF31y79q8gOW1vO6QWdT3+2rqPziD+eDiVEnF8sJzWi2fzPKCy7ffHMd81NVTfxnDfHC31sGhbF6xTnrf91pK59Xn5sPb3zcQ9O06tXLiHOaD60e+ns7mFZ53ru1sNq/gXsuyU5mPuqJpFcV8cPH98rEsL9ZJXaHnZJYXXLz+6HjmY12V254dzXxwN1exYSwvPK/IzpEsL7g2i4cw39YtPL4/88HNG/16snlFn9xeiyeweQV3lr0xivnIKQ8PGM58cJk8agCdV98zv6YMZvMK7tas05v5qGu61erJfMu/PPoLy4s+ya/z9GV5wU3Jx7szH30VveZ0ZT64s7Z9NM2Lum8V7cTygjvdf4piPuq6JfK1YT642ZncjN1XxTmpSB5J76uCy2L/3sd8nFPKyWtimA/uPVFmB7uvCk/XjdnM7quCq3WD1jDf1n30N+qDi2ZXl7G89pz0iaJ7WF5w57VDm6iPc6rDv25lPrjs9ZxmeeF5R3asZnnBvfceXMR81HXLtFjKfHDjxc5h91XRJ29YsUX0vqrPnUbrZjAfOc23785jPrh7o9oUdl8Vnvjs0Qnsviq4mnx7nQv6qOttXzma+eBmyfUBNK/fJ9P6vpksL7j3Vt6J1Edf+xydzHxwL9/I0SwvPF37keEsr+Wv1+9Nfb+uM6rKQOaDi/xTu7F5xTmpfvTKejav4Gpl/RXMxzmls2DUKuaDu2fzLmLzCk/eVXQBm1fLdz85nfp+Xa/BjZnMBxfVG0xiee056Y53F7O84KL8k79T39+ninWfzmE+uO43fArLC0/t/Xkyywvu/ufGaOajrn7/0ljmg+v5A4eyeUWfdLWXxrN5BVePVRjGfOQ031QZyXxwuapgfzavtu6ymF5sXsHNj993Yj7qyk+HS+Zb3vZ6FMtr+9T/4iCWF1w/WbMH89FX3U33Yj64G127M8trvadjolleyzvObsF81NXptVtT3+dm87Qf2H1VrJPi0+Se7L6q5ffV7cB8rFNe7ULdmA/uHj8ZRe+r+p74ck1zdl/V8iGLvmc+6soSPzehvs+dJ5+qx/JinVQFB3diecGdugktmY859S6dbEt9n3u98/zE8sJzBq9uyPKCizx9v2W+rXvXPMF8cKPmfU3vq/p9Ul8/U4PdVwV3GyR9Tn3kXFCzGvV9rt8uWpHdV4Wnygz/hN1XBVe/5fmI+ahrKpb7kPngziLXY3nRJy/7+pcsL7i38tLHzEdfdbOHKjEf3KlbuhzLC88d0vd9lhfcvavgO8y3dbuUK8N8cDGp+ZtsXrFOinJPtGLzCu5MrdCI+XZdzfN9U+aDq9WDBJ1X1G3SoRabV3DdoX8V5qOurr+R+uDqnTcqsbxYJ51hTzZkeS2f3fZb5mNdNV2/rst8cP3VT9VYXnhO8rM0L7izb9DH1Pfr6oWJFZgPbho+67F5RZ/UjPyfsHkF9zpmv8N829cTuzzmgzsFxr/J5hWeKL24FJ1Xn6ufWpdgPuqKoaYY88GdF0sXYXnRJ2NWlGV5wb0K75VgPvrqbKldmvo+V4t3v8HyWu/FXwuzvODOvcdeYT7quvPzvsx8cNMp4QW2X8U5qSiceyrbr4LLs/l+ZT7OKdW/Cv3KfHDZO3sY26/CczuMHcb2q5ZvGtCH+ajrlOrXl/mW5/X3W4G8OCd1zi0fxvKCeyMn92U+zqm8R072YT64e2a9ZHnh6cvXJM3rc7P2lfbMR103rXZ75oOrQ/739QX3q36f3Oplu7L9KrgaNCiK+Tbnk6XaUd/nYtqQZmy/Cs87dK4p26+Ce1e612c+6opfRjdgPrj77oVvWV70SVRs3oLlBVcd32vEfPRVfVe/MfPBVa4bdVleeO7QOXVoXp+bTo2rMR91pdP/G+aDmxPVv2DzinNS8cTdks0ruCp3vB3z7Tnl8zvbMh9cV97Tgs0rPO+Pt1uyeQU3tfP9wHzUVa0r/0B9n+tK79ZneXFOKrfvbc7ygqvZnX5gvj1XTb31A/PBneKLG7C88NyXS3/P8oI7NS7XYT7qOnctEcwHd+ZV/Y7NK/qki/0o2LyCO5On1GA+cqpSl79lPrgplP9rNq/wZHT1r9i8grtfPleJ+ajrDbnyJfPB9UOdK7G86JOos5bmBZc9S3zNfPTVGzqtFvPB1Y5bNWleeN6hr1hey9tXrsJ81HXuyl2T+eAis1cNtl/FOikH6upsvwouD4/4nPlYp+R3//mK+ZZ/vLc826/C05O//ojtV8FNQ1WG+ajrjVv3PvPBze63S7O8dl3t174Sy2v5jG8+ZD7m1Py09xPq+9zd87LH8sJTs+4ty/KCO/VLlWQ+6jo7495mPrg8/ngpul/1+6QyuxZn+1XLK33xKvNtXyv8UoT54KZp1ZfpftX3dGKxF9h+FdzsHfs8823dmb3/w3xw83qN51le29euW95geS0/M+IV5qOv7rIHijPf8i0dirC88NSL+19kecHdp6+9wHzUdUb2fZ354M6/UguzecU6KWY2+pjNK7joMtZjPtYpd8CJ8tT3udvul4/ZvFrv0bfeZfMKLhbmLsN81DU10z5kPriom/9DlhfrpF495wuWF9w51v1z5mNddeS3NZgPbop9V4PlhSf2HKvM8oJ7c2Vl6qPu0o41mA/u/eJ/3QzMK/qkS//1JptXcDV6R2nmI6enW5djPriMfrocnVd4I18uzeYVXGy5Vor5qOvtv+wxH9xtH+exvOiTmli9MssLro59Upn6fl9F1vYazAd3v0qqwfJab7VXmeb1ubvvg8rMR11V5q8a1Pe50yL/7X3CAf/ncK/2fw71Rv/n+U0J8NHh3G3BfTnf//Vu3Le8THhdUyfc08+Ec5UnnLtTsqmPusL/uYNBH9x9OpvmFcP9n4f4Nc8L7lzJpr752f/4GPfBzbBsmheerBkhr8+dJlnUR121Lov64KJdZlhf3Y7ZYX2SsVnhfGZWGFcrMrmPnA9wH9xrlRFW122YGeaZS+lh3LRPD+PeW7eoj7rO5VvUB1dPXKd50SfvWgbNC+4uSqM++upGpVPf8mo3aV54quQNmhfc23eJ+qjrRF+hPrhzLYnOq3jH/3n12/i8gsuHsqmv0v3fV4/74NL/uYN/m1ffc65l0nm1vHgG9VFXXsjgvs9l9zSaV83wPz+SxfP63BmSSX1d1vdf4T64bJ3B8/qe7plO84K7lW5yH3VX3KI+uNPgGp1X9MlpeYvOK7jz3DXq276uu859n8tjF+m8wpNNU+m8Wp59lvqo66gL3Pe5W+4ozYs+qWJXaF5w+WoS9dFX1TOV+uDi97M8r++ZoadoXnAVu536qKtG7qc+uNq9LGxd1QNuhq2T3v3Xwrh89moYl2tTqY91yrx1kfrg8oMLYXV1VEqY5xSLC+Nu1NkwrnYeoj7qCnWU+pYX3MTzYp3sl0DzgstTJ6iPOVXfnaE+uBixm+a1dYduo3nBna/mcN+v6zReRH3Ln+8X1le394GwPpl3dRh36/0ZxsXLivq2r89O577Pda724XVXTQj3NrUM4zp/szCuWxamPurqgyWoDy5e3xtiedEnU6krzQvuDn+P+uirc6MS930u3wqFWF54uuvJEMsLLgrdfu5M0Eddb0Ej6oOb91UqnVd/nZSnY/i8+lwXXEV9rFPOjfXc97kePInOKzxzz0g6r+CqfC3qo67Z15T64GrmkzQv1knz0Rya1/JLnaiPdVU/OIj64KpLZZoXnlO7JM0LLstuDjEfdZ3Bt5+L9Dff58L5OsTmFX3yKuSh8wrunu8WYj5ymjJTqG/5S5dS2bzCk/M2pbJ5BXc/WZZCfdTtG0phPrh3oWcyy4s+eaNcmhdcNy+aynzb15LtqA/uJpVOYXnhicJHk1lecK9K7iTmo678MDqJ+eC6/2sJbL8qFvqvZyrw/Sq4my+d+l5ev87rEXyfy1K36H4Vnmp8g+5XwYW6xH2/rtvyKvd97uxOpnnl6/5+elsazQtukq9TXz6P140RfJ/Lh6/SvPDchRdpXnBv7nnq27q5uQ+u0k/R/Sr6JFrG0/0quHPmKPWRU/96hvrgusQuul+F551aT/er4M7yidRHXd12HvXBxf2/0Ly2rxMO0bzgbtpq6qOvKs926oPrljNpXnhuuRE0L7h3TzXu+3XNX62oD65nPk3nVZ72X2de5/Nq+eiL1Pe+9V8P/MR9y79NpvNqvbJ8XsFFi4PUR123yGnqgyu9jeZVA/x1pOElntfnToUL1Bctb/9/co+kUB/c+cLQvPDM0kM0L7g3cxX1UdfM30l9cHl+Np1X9MlUXUnnFdzdNoj7fk6n4Azqg6tXmvN59T1dqwKdV3C9/XiI+agrrj1MfXBVs2+I5UWf3IWDaV5wuelT6qOv7vHW1AcXSU/QvLZur8M0L7hq90KI+n5dkz2A+uCunpfK9qtYJ1XFRnS/annmfdTHOiWiilMf3ExfE2L7VXjuiL50vwpuSupU5qOum3kP9cHNytypLC/WSbX+9r70b3l9rj/z93kBH3PqPfcr931uRu5LZXnhyUd+onnBVcrDKcy3daOnpjAfXG29nMT2q+iTGfFrCtuvgruX05OYj5zujV7JzAeXhTsksv0qPHljewLbr4J79+W5QH2/rr659wLzwXXmhHMsL/rkXi3B8/pc37swnvnoq9NCJTDf8uTfz7O88PTa288NDOYF9z5/OJb5qOuNLXWW+ZYXe/QUm1esk7p1MTqv4LrBG6nMxzrl7plPfXDnxx9S2LzCc8wfyWxewb2aYxKZj7ry1cHUB9cvFUhgebFOigsP0byW7+2cTH2sq51qc9/n3s1mSSwvPCk+oHnBRaHKF5iPuqrjtnjmgwvRMo7NK/rkfn/gAptXy1X7c8y3ff3z9nO/gj64PlUkls0rPO0NOkPn1efihU+OMh91vUmHjzMf3DyfeIDlRZ/kinVnWV5woQ6fYj76KmpONtQHHzngGMsLT3/W4TDN63PnK3cf81FXtB4bQ33wOvHbc56raueV8HPSkltDObn49vY5B7hp9naI+TinNOM+oD64Nz0qNaxup7Lh3sWWqWF8xe3nAIPLayKF+ajrrK+TwnzLE/yf+x3Ii3NSndozleUFl4eaplAf56rt+6UwH9x7795klheeuyo1ieUF10f/SmA+6prat3+uYdAHVxf85yf6fdXFo1Ny9kn1TEvKyfW5F5NzcjGyWCLzbc6f7kpkPrj31doLOeu6+TslhtWtkj8+J5fl2sfn5Hp6vzjmo66b9VQc88G9gv5zEQN50Sf9jrpA8/rcmxh7jvm2r30Pn2M+uEy9/dzWYF5bd3uyYXktPxx/gvmoK0zjE8wHFxsXHGLzinNSb/UHyXRefa6e+yKR+facssvEROaDq0VF4tm8wtO/nbjA5hVclU07x3zU1R+Wj2M+uGpxfyzN65+TeqcmxdO8PpfLVsYxH/tU9fOX55kP7la95yzLC89Ui45lecHFB++fZD7qmtrvnmI+uFd3yWE2r+iTdzrG0Hn1udPiixPMR05daOwJ5oOLd7sdYvMKz5RoeojOq8+dplX3Ud+v65Z5fx/zLX/q+laWF30STS8dYnnBZdnofcxHX71iWfuYDy6n/bGN5YWnX3h4G8sL7s0ov575tu7jQ9YzH9w8NW1FznXVVH7pfM510uQ6FpuTq83qbE7u9i97mvlYp9TAIyeZD66KRB3JWVcsKmByevIb51hObpbmPZGTy+8+Och8W/fl32OYD266lN3N8mKddB+7/XM1g3nBnSpd9zMfc+ouWrWP+j5XXb7czvLCE68k7GB5wU2tNpuo79d1m8RvYD64IwquydlXc63m4Zx90qOL7svJ3WpvxuTk3qrh26nv59TRU7cw3/JBzdbnrCtH1d6d0xM7HtyUk+O5quC6VqN1zEddc2zzKuaDO71HLGF50Sfx7Pm1LC+489lny5mPvsoRG5YwH1wV3TGf5bVeyuiFLC+4e+L335mPuqrupRnMB/dylZ7M5hXrpIz/dhubV3CxPX4987FOeS3qr2c+uHowfQWdV99zDz6zis0ruNn31yLmo655yCxkPrhMHzeb5cU6aVZ3W8HyWv5jxYXMx7qq3n5tIfPBvV9PzWR54elJ9WaxvOBen/cnMx915cTJivng8q7qY9i82r5umTeHzSu4fvPDadT3c7qPN53CfHD5waJf2bzaujJ2IptXcHfqh6OZj7qy/bARzLd86rODWF70Sbw1aizLC+7UWzWM+eir133oUOr73Bl9rQ/LC8/tlrs/ywuuZw7uwXzUdaMOdGM+uCy+tBPdr/rnpKrChrN0v+pz79khp5mPc0qZ50nDfHBv81vH2H7VenE7j7D9Krj8bdd+5qOutyMhhvo+Nw1q72R57TnpJ+nHWV5wM676QebjnEqVevow88F12yf2sbzwzOsdd7O84LLZik3MR12zeMYW6vvc/eSxdWy/avuUsXk726+CO6ve2sB829ejH25kPrhcOWkV269ab2rNlWy/Cu4Mjl7IfNQVM4svYr7lnVfPYnnRJ/1qjzUsr+UP1FrMfPTV/T57CfMtr/LoXJrX98zNf/9O8/rcpPedzHzU9WKWT2G+5T0yxrJ5xTmpeX3Pfjav4DL0/Hbm45xS/qvSLuaDOytbbWTzCs9p+P56Nq+Wby+1nPmo68lnVzHf8lmfLWR5cU5qWpXfwvKC6xKX/mQ+zlWdZ9qtp77PZUz+5SwvPPX6xsUsr+UjYmYzH3X1quvzqO9zU6vOVDav6JM6eWo+m1dwr2+TacxHTrfFyhnMB3c37ZjI5hWe52T9yuYV3C18eRjzUVfNHjeC+eBu/if7s7zok+i7S7G84Cp/3Cjmo6/ywVzjmA+uK38wlOW13pHtA1leyyvX6cF81FX5uvSivs+950f/wvardp38/K+ZbL8Krod1VczHOiX67VbMt3x4aDTdr/qe7rVvNNuvWr4lfgjzUVf8Z9IQ5oObpDK9WV6sk87vM8awvODqi8eHMt+uqyd2DmU+uL58szfLC08VrdKb5QU38bkk81FXlX1XUt/n7oaG0Wy/ij65Tdb2ZvtVcCd1jWQ+cpoHXpXMB1e5T0ez/aqt+2lyB7Zftfy9yW2Zj7q6/rEo5oOrZgPasLzok9jaIJrlBVdV/edvB3z0VVx9L4r54N4jb7ZheeE5cbXasLzgqrKhPuqK7zOjmA+uG03twOYV66R+pXRfNq/g4tRhyXysU+7W492o73MzdEcHNq/wvKHHotm8guv08lHMR13zcP8o5oO7W+Jbs7xYJ0WdKp1YXnBd8vG2zMe6qubf3476PtdNjrVmeeGJl3e3ZnnBRZ7ENsy3dWu934b54O7st9qzeUWf3Bb3t2HzCi73nKA+cso5naKYDy4PNejA5hWeyb29A51Xn8vtb3RjPuqq1k91Yz642nJXX5YXfRI7u0SzvOCuGCOZj76K8d91pT74iPa9WV54ztlRfVhecPPYv4cxH3W98R2HMh9c7fxsLLuv6uT1v+9xTQK9rwqu6x6jvlP+9n1c81Ac9cG9tvvpfVV4nqPpfVXLPx9NfdR1Jy2iPrja2ZXm9crc/vuaU6dpXnBnzDbqqzj/7/vKMeqDu4fX0ry27oipNC+4btKY+35dnTac+uDq20r0vir6JN6qSu+rWv50XIj5yCmmv059cF14Br2vausuKUXvq4KLkf9JZT7qun33cd/nqmbvFJYXfRJv575I8/pcVhQh5tu+VjhAfXDn2X/TvPC8goLmtfz3I8nMt3V/jaM+uGr2ZDKbV6fa7fvybv4DdF7B1VdLqC9HxN7+fUN2UR/ceXM2nVd46q5udF7BvRuFqY+63t3NqQ9uNhakecUK/z78+c00r+WrJnL/sN+v19dRH9xxh9O8tm69WjQvuGmaFGI+6poO5akPruZtC7F5RZ9MW0XnFdzsW5fKfJtzVlPqW572A51XeG7d2/eKgvMKLpdUTWI+6nrFDlMf3LTplUjz+n0SeUuGWF5wb+R9qcy3fT2aTn1w5/Aimhee27xiMssL7jy+IoH5qOss/DOJ+eA636V4el/VXyfN7r70viq4vPVeIvX9dcq9NoH64CrmSjy9r+p7qmMJfl/V5052r7PU9+uadf2oD+60mmRYXqyTTvZ4mhfceWrUBeZjTvWOAQnMB5dFip5neeE5w07SvJYfHX+K+agry5aJZb7l77x/nN1XRZ+cuS/S+6rgaviUQ8xHTvnaF9QHl0+WiGH3VeG5m/ruYfdVwd3Wt8/dgz7qmo3p25gPbpKHa5bX9mn6YZ4XvEuPPcxHX2X2LuqDe4+P387ywnPntqJ5wdUzE1YxH3W9/buoD+4d67aUzSvWST28TTybV3Avc+c55mOdEqVqX2A+uLu/LZ1XW7dfzTNsXsF1nrePMB91VdMuJ5kPrtYOO8jy2nW1SaXzLC+4rlQ6lvlYV809FeKYD26WP2BYXniy0KVjLC+4ebhyDPNR1wnd/rnGQR/cPPj9Pjavtq95S+1j8wquxo3czHzk1Bfu3UV9nzuLqm5g8wpP3Nq7ms2r5QUGLWQ+6pot3nLmg5sDWXNZXvTJ+zZqG83rc++tZuuYj75663ptZj64u6k8zQtPNZq/mOb1udhaYRbzUVeciFvAfHB9fPY0tl918h6+/bH/Cr5f9bma3Zv6zjf+c0BeWER9cG9cJ7pfhaf2lrhI96s+d2vNDDEfdd2Dz15kvuVv9wmxvPrEntu5Cs3leX3uvNqK+9P9c4zeM7jvc93zB5oXnkq97yLLC+6k/RhiPurqJlnUt9z9nO5X0SfRcyPdr4K7eWqlUN/PaWoOTmU+uFM2Xwrdr/qe7PJmEtuvWv5siXjqo27askTmg3vfdb/A8qJPomOdVJYXXHy4LJn56Ku7sUgq88FVg+bJLC88M75cIssLrgecO8981DUFjiYwH9x7qcZ5Nq/ecv/7oaMm03kF9zbVpb68f8vt11NmAvUtj69O59V6USE6r+AqoyT1UdfcH0t9cHfo0yGWV6iN/nNHxtK84Hrvl9SXX/rPrTg4kvqWexUvsrzwzL8O0byWj7qb+qgr7t9BfXC31U06r+iTMzdfKp1Xn6uPiyRT38+p34lNYT64OXo9ic2r9RI7JrB5BZfVTsVR368r4l5NYD64vBEVx/KiT26mTmF5wZ2am5KYj77qa7e/bzXog3vvjk1ieeG5v52Lp3l9brYXimM+6uot0+OZD+7IPefYfhXrpOrz7Tm2XwUXMY+dZj7WKW9M97PMBxcrqpxk+1V4esmWQ2y/Cu6uHb2b+airPn35IPPB3aczd7C8WCfla5tjWV5wXXbncer7cyr7PBfLfHAd1eMYzet73uXdMTSvz/XcM9uYj7rO8EX7mQ8uh7+xle1X0Sd328sb2X4VXFzKXs585BRTPlzPfHDToOlStl+F57RuM4/tV8G9Q7GTmW/rPvDo78y3fEvXSSwv+uTl7b6O5bX8rUaLmY++ir3un8wHd/PHLGR54bnbO82kecGT3p1Afb+ueq3jdOaDi/fWj6PzinW1y2jD5hVc1WhzlPr+OmVmPGeYD+5UG3GEzaut23jXPjav4LLWgc3U9+u6XR7ax3xw87zexPJinZSFu55hecGdHccPMx/rqvPU0dPMB9f1vMMsLzyRcHMPywvudIrbyHzUVfmcPcwHly+U2Mjm1fY1qdxqNq+W7xi7gPnIqZ7ZsZL54Lr9Z3+weYXnjrg4lc0ruJt4Zgzzbd0H109hPrhY33Y0y4s+iUOTV7C84PK3ffOYj756Hy5Zznxwd+O0uSwvPDnX/3nMgbzganHjUdT364qHXpjMfHB9YvxIdl/VnqsurbOa3VcFVw+vX8h8nFOq03Ip88H13X3m0Puqvuct3D+D3VcFl5PfmUh91O3UfTLzwd2ln46mef1zUnPaoXnB9Z4e05mPcypvznPUB5fnsxXLC09s/c94lhdcPFl8GPX9uuqtyqOYD+5tvdKf3VdFn/T724ez+6rgTrF2fZlv+xo3ZiDzLa+3rTu7r2q9gS0kva/qc/n0b+2Zj7pOmXwdqQ+eP38Uy4s+ORWf783ygjt9p/3CfPRVlu/Vjfngsv5H0SyvrRs1ti3LC+49WKk181HXVC3QhvngKubx1mxecU4q3fN0XsHlB6XGMx/nlN7AxMnU97m3s9BoNq/w5D23hrJ5BXdP9O3FfNQ1N/sMYD64e/ekbiwvzknlI5sn0rw+dxIqDGc+9qmiS+Fx1Pe5l3J4MMsLz1Te05vm9bnX7fdfmI+6osftnwsf9C3/cXcHNq+2T2cGdGbzCq5j90QxHzndlk9y3+d6z/HWbF6td+16Kzav4F7FT6iPuvr1l7jvc/3Zn21ZXvTJFHi3LcsLLtxnWzMffXUfmtCG+eCyaPPWLC88mdie5gX3/tWjA/NR1xl/ivqWx8zqwu6rYp3UPxRvw+6rWr5MtWY+1invx/eoD26KlY1i91XhqUdXtWX3VcFFkczO1EfdNl90ZD64N/z2uhvMi3VSdLhE84KrIxPaMx9zKgoWact8cOek6Uzz+p7eNFayvODe7LV9mY+6To/vejEf3BysPZTeV/X7JDvu6Unvq/rcW3hgMPOR0xmSPYD54M5Tr41i91XhuR3uGcPuq4LLSm9MZj7qqiq5FPPBdfO7ZrG86JM75tRwlhdcDjw3gfnoq3zi83HMB1dze09leW3dkblm0Lw+F9+PXEB9v67X6fQ85oOL5j2XsXm16+ToG9FsXi0f/FR35mOd8p6t34X6PjfVbt/7D84rPLnjjwFsXsG9LwePYj7qep8mD2M+uNhXYgLLi3VS/ryT5gUX64sMYj7WVfPij32YDy5GdB7O8sITux4cw/KCm5GfTGG+rXsieiLzwc23zWeyeUWf5KH9is0ruMh4+HfmI6crn53BfMu3F13I5hWeKJW2mM0ruNv57rXMR10vX4VVzAdXjadtZHnRJ/monsvygstlVZcxH3112rZaxHxwp9GgNSwvPN0zS7O84O5jL21nPuqqrkM3Mx/cje68l+1XcU7qJc0bRferPncLDO3PfJxTqrl3j2A+uHIb9WH7VXgm46hk+1Vw7/rH7ZmPumJVkS7MB1fNdrVleXFO6va5dxjLCy7+/Ugv5uOcSuV9bgjzwaVq0YPlheecOdSJ5gUfVqot81HXydWsI/PBRdG6dL+KPskGudqw/Sq4ip9HfeTUhT5tw3xw5+UDbdl+FZ6ztybdr4KrR2Z2p75fV4mbv1Af/O29vVle9EnNqhHF8lq+1P9+wICPvoqfXm1HfZ+bZic6s7zwVGpCN5rX597DCQOYj7qmca9ezAeXVe7m8+qfk3pdLw9k8wpublXpznycU6oX6w5gPrjb6bdubF7hmaO3vy/ub/MK3vDNdsxHXSf3gQ7MBxf3LmnP8uKc1Gn1cX+WF1xX/7gb83GuKh7s14/54OJ0yW4sr/XuatOR5vW5ee4w9W3dBqoT88GdzVM6s3lFn7xBa+i8Wj49JKnv53RXPEF9cDXa6cnmFZ5sEd+PzSu4zFo1kvmoK9e9NoT54M6D7jiWF30Sa1+hecGdMcv6MB99lc+u7c58cGfBKwNZXniq94rhLC+4iqr2G/Nt3aatRjMfXJQrO4XtV7FOyu2v0v2q5W6lMczHOuXGPkV9cOfr5yey/So8b+ksul8FV/uWzWO+rSt/oD647KoWsbxYJ72EBqNZXnBd55EpzMecqjd6jWc+uCpYfgbLC88V/6Z5wd3nHl3OfNQV488tZD64OFlxDduv2r5+9e1ytl8Fl+1vrWe+zbnzlT+ZD67yldvC9qvw9CvHdtD9qs/FEx8doL5fV7ZvsI/7t7n3WIEjLC/65A28TvOCi68K7WQ++qqz929mvuXvvLeP5YVnGvxG84Kr86nHmY+6zq67qW/55gOn2bxinTQb9yo2r+DOu2XmMB/rlExtOo36Phdf9FjA5hWePrFnKZtXcP3Gf9ZTH3Wb1lzFfHBZtd8mlteuk1tnzqJ5fW7WnlvMfKyrpsOEecwH977YuZzltXV7DFzH8oI7C2puYz7qmgdWb6C+z8W9Q3exeUWfZJ2Xd7B5Bfcef/EA85FT6NAe5oOLQm8fZvNq6742hM4ruAx9Hst81JXRj1Ef3Cv22DmW1/ap0b9iWF5w7z8/HKO+31c3e99B5oPr96ecZHnhiTVbDMsL7vb+M475qGuWtjjLfHCv67sXcj5X1Zv3x4acz0nVsu+KnNykT/8zJ3eeLrKY+XhOqT6/5A/q+9xt1XNWzrpuoQ7Lc3oyOf/8nNzkGrwoJ1d1+s2kvl9XN7p7KvPBReEFE1lePCfVm1xtGssL7sQUm8R8+1zVunf/ynxwN/38CJYXnrPtybE0r8/ddluGM9/Wbb5lMPPBZbPn++bsq/dixXk5+ySrRk/JyZ1GD4RxN2/uicxHTnnPB2OYD+7e9enwsLqLnw6vO2nb6JzcW/DdhJxcr4sbxnzUFR+fHsh8cPNMcm+WF31S6+QglhfcPdukD/XR19fL9WA+uLOwYVeWF56Se7uzvOA695IuzEddk1C5I/PBnXw72rF5xXNSvQ23n/sTnFdw/a/1/ZiP55Saks/0Zj64eP/269bgvMKTrV7qyeYVXFwvIpmPus7StE7MB1eDt7ZnefGcVHEougvLC66OXopmvn2uat/kdtQHbzEkiuW13kOb27K84G6rzlHMR13ZoGQU88FFXtWWzSv6ZAqc7cjmFdzL/LQ985HTfaVZW+aDOyVGRrF5hee8+HpbNq+WO5WimI+67oV8bZkP7j2p2rG8tk//2RvF8oI7t0a3Yz76quI3RjMfXA+f+QvN63vm0MYOLC+4c6RqF+ajrjlfvDvzweXAgb1zrqty27Nh66TTqNDAnNzNVSyMmz8f6818rFPmYNXuzAdXBcb+krOuLjy+f07P6/FjtzD+Rr+eObmoPbIT81HXG/9eNPPBndUfRLG8WCfF1uEdWF7L+55sy3zMqTrZP4r54OZqtTYsr627vGIblhdcn5nSivmo67x/qzXzwWXmbQ99Fb3mdM3ZJ73rvnY5ubO2fXRO7n50oRXzbc6D37ZiPrgqMLt5zrpuiXxtcnpennnNcnKzM7lZTu4s+bQJ81FXRuf9mfngMm/HpjSv3ydv/uqWLC+4GtCiJfPRV694xSjmgzvlbr/vHMwLT9Sc1JLlBXf+KtqK+agruoxpz3xw50dN5xXrpFrVKorOq8/NWNmW+XadeubzDtT3uR6c1ZnNKzzRtXZ7Nq/gblZSB+ajrty2XzIfXE0f04PlxTqpe/+nO8sLLlbs78V8rKvqwtUBzAdXz5QdRvP6nnuuRH+WF9yrVG4I81FXlbt7FPPB1cqG49i82r4Oe/wXNq/golNjyXzb1ze39GY+uPd9lf50Xn1PTG7fnc0ruNNwUU/mo6537cXBzLf8yhPDWF70yRkZPZzm9bnIHjOK+eir2vHRb8wHd3/bOZnm9T1d7cpYlhfceS56AvNR1z3w/9R17nE+V/kf/6CSbBe71aaye5RqaRObqK2tT7SU1ooupOi45S7WFpXL6UKGNiyDTXIMwlh3wggf9xmXcWcMZg6GuX0vbpPbzHx/v99jzuv8vn33tX/1eDwfz8e71+Pd2/F2nD4zezbzHX9Sz2X7Kr6TqmrkDWL7Krh5rc3fmY/vlMoO06jveNnlwWxfhafv2/Ih21cdn/niJ9S3dfWC7cOYDx68U/9zlhffSTUry4awvODi7WsjmI/vVIqkDMV88OBSMIrlhac6/34kywvu9e/xFfNRN+j+1DjmgwfNeiXTfdX2ybutXNF91XLxavJI5ru+nrorifngpsuA8WxfdXXTvh7L9lVw8XTSBOrbun5qv2Tmgwc1502jeW2fvCdun8jygqvCHv9iPvoqfv3IdOaDyw3fzaJ5rRes26BpXsulzpjDfNRVO6ulUh98m/3/UhLmFd9JlZ3Wf8nmFVzGvhvPfHynVHYSE5gPrtb3m8rmFZ4+2mwKm1dw9WldzXxX99ZWM5kPbvo+PY/lxXdSZY30qSwvuAjnzmS+e6caPjCT+eDB42fms7zw9J/UfJrXcjEjtJT6tq5embSM+eDy5RppbF7RJ3Ws4fdsXh3fVW0R85HTP7hyCfPBdXTuD2xe4ZnbypazeQUPpt+fxnzU1RdrrWc+uKw1eTPLiz6prsFqlhdclfwYMN/1dXp0I/PB5Z2/TGd5Xd0D929jecGD0sG7mI+6amuPPdS33H809wDbV9252vqByWxfBRcHZ0xjPs4pfWjGTOaDy1se+p7uq9bzF1aZyfZVcPlC6mzmo66s2mMB88HV6cVLWF53Tt5RtIDlBRcDpyxlPubUa7l/JfMdXzpsLcsLz6TsXMnygnufjk5jPuqKSaUB9S2XK5K3sH0VfVIbKn4uQuK+Ch50rLeQ+cgpB8aWU99ykzNpFd1XrSc+OL+E7avg8vx7y5mPunpIp7XMBzc9W2xgedEn/8TaDSwvuJc6ZxP1bV9Npw/TmQ9uGo/cxfK6uvXGbmV5wf1hQ9OZj7riuv35qAk+uKzfaT+bV5yTqsOqgM0reFD/0hbmu3P1xKl05oOLw9mZbF7hefOXZbB5BTf/OphJfVvXNJ+3n/ngqu8Nh1lenJPBBwf3sbzgeuMPh5iPc1V06JjFfHBd5dJxlhee//jqoywvuDALTjAfdb3C5Yb54KrSllNsXtEnv2vN/WxewWW2/fvtBB859ZbOR5kPbnrYv6dOmFd4YuQnR9i8gptfVs1mPurKlotyqW95MOaPJ1le16fGKw3LCx78o88p5qOv/thv8pgPLtY1OMvywgsyz51meR2XTc8wH3W90lH5zAfXL6cUxN+rBuErPeLvSYMVz7z3M95wedd4rpo83Y35uKc0hzN6MR/cq7++V3xdr+oKGe/JovIu8dxb2Lbjz/ixdl2ob+uKC3V7Mx/czM/vzfLintSbVu1vNK/l+qZfD2K+u1ftW3so9cGrzhnO8sJT3//57zSv5fKtkR8wH3XV19MU88HN8lOfxvfVHz+7Q3yf5PyaXeK53nGlfTyXkQe6MN/lnJ3Vh/ng5pm8vvF1vaqV28d7YvdbXX7GS0e2i+dibmoX5qOurH25H/Ut91Mrv0/z2j6JQekfsrzgekWVIcxHX4Pzv/2c+eBm3F++YHnh6cb1P2J5wXXTFz5mPurKxl1HMh88CL07is4r7kk33fwlm1dwObxvEvNxT6m+/MNE5oMHU9oks3l1dVP3jKXzarlsYu9FE3xX98a+U5gPrloM/hfL6+5Vi6prlhdcd6r4c3ei7+5VZy1KZT64f1+wkOV1dWssnMXygut6fecwH3WN7LKE+eCi6PBSNq/ok9rQYDybV3DT9k8TmO/6Gu01jfqWy6rPTmfzCs/v1+ifbF7Bgyd+NZH6tq5snf8d88G9uU9olhd9kpl3zmV5He+v5zEffVVNmixnPrh/y/AVLC88r6zi3iMxL3jwU6sFzEddPXjuSuaDm4IlP8Sfq556q138Oaku3tQ1npsGHdvFc9WkW1fmu3O1Vun7zAc3lfoPiK8rVn7cLt4LxNqu8dwfltIunuvgpm7MR13/x8wBzAdXW38zkOV152TNpz5hecG91NuHMh9zaqr4XzIfPAhVGs3ywvOf2zaU5rVcPvD2MOb/f905o5kPbl6qmxTfV1mW0S6+T0FBs27xXLQpahfPxfcDujEfOYM3Og5kPri6e8zA+Lr66Vi7eE8cH9stnnvv39Q+nvulk7oxH3VVdPZA5oOrkwsHsryur60zh7G84KKmGM589FU3HJfEfMffPpJE81ovOPfqcJYXPHis4v9LT/RRV3SyP/c70bdcz71jDJtXnJPe7iMT2bw6/sKnk5iPc0pPP66ZD27y+s1k8wpPtr09mc0ruHzhs2Tq27qqX/5M5oMH/3w5heXFOem17P1vlhc8qNNpIfNxrnrlU1cxH1y88+5qlheeXFx3EcsLLtbtXsR81JX9b1jDfPCgdtIaNq/ok9DZyWxeweV9NSczHzl1aFIK88G9OjtS2LzCE68+N5nNK7if2nIy81FXZBamMB/cf/VSCs1r++QPq/guX2JecNNg9mLmo68iJbKG+pbLWo3TWF7nhbMWs7zgqk/RYuajrh4v05gPrmf1T6P7Ku5JvyhYyfZVx6f1XM183FPK+d9upL7lqsq4zWxfhafvyU5j+yq4rN1wHfNRV345YCvzwdX7T21neXFPKr/tsZPlBQ9ynsmkvr2n0r3bHWQ+ePB4o8MsLzwz5oO9NC/4fSv3MR915ZbQEeY73nToUbqv2j6JH7puYPsquLxpZUB9m1PurPgu7n/4lgezOu9g+yo89eaMjWxfBQ/qvr+J+agri9N2Ut9y8VLRLpYXfTLpWftZXsdfzznAfPRVtjXZ1LdcLLnnOMsLzz+95SDLC65XfXWI+air5j18gvngXvIvcti84p5UjOp+nM0reLDiHznMd/eU9ww+xXxwuW3saTavru7QPxk2r+DBk3efpL6t6xV9lMd8cNXuxTMsL+5J/R2dz7K8jovX8pmPe1UZTSqkvuVi35+LWF54Ztl9BSwvuF9rYwHzUdfvf6KI+eCiR7tiNq/okx/dd5LNK7j38d9OMR855ZzIGeaDe3OHnmXz6uo+fO0Um1dw81Gf08xHXXX3hbPMB9dd2+bTvLZPcu1LhSwveNBxVSHz0Vf15ppi5jt+tGqI5rWe16BGEcvr+Kq3ipiPuia1eYj54H76kBDbV3FOmsMPbqb7quX+J+s2M9+dU1uiu5kPHgzalcn2VXhm87Nb6L5qub8iZQvzUVf++rM9zAf3ulbby/K6czWv8WGWF1xU2XqY+ZhT1Xt3DvPBRae+uSwvPHmh4udqJeYF96dPOsJ81PVmhHKZD67H/87QfRV9HXBuC9tXwf3bHtnKfOQ0rf29zAcX29/by/ZVeN7AFlvZvur4o223Mh91VdKwvcwH1zVG7qV50dc9R4+wvODi9luymI++es/+1TAf3HTsaVheeGrUw1ksL7iOPJ7FfNRVawcb5oOL3UMNm1eck/JS+mk2r+D63J15zHfn6jSdz3xw0Tk3n80rPPFVmzw2r+DBNyqP+agrw3cUMN/xO5sUsLw4J72TE4tYXnCvxqYi5uNcVRN0iPqWq/wNIZYXXtD4bBHLC+4dr1TMfNT1XswKUd9yf1dRiM0r+vS/v+Dy2LyC6x1r85iPnF7L1wqYD+6nvlfA5tXVvZiRx+YVXE/PzGO+q3t9YAHzHV/xQQHNa/sUNLy7mOUFN/fVKWY++hq8eyXEfMdTKoVZXniqye+LWV5w/2T9Yuajrr6xapj54GLHzWH2XhXfSfXu7zyfvVcF95OHL2M+vlMqyvYsZT64eKFkDXuvCs+XH6ex96rg5vUxm5iPuuYbtYn54EYvyGB58Z3UYGTyaprXct38yEbm4zuVQfVZAfPB/RvPb2d54ak6T2SwvODir032MR91TfP5e5nv+L6XDrP3quiTKn05g71XBTe11u5lPnLquc/tYz64vKv9YfZeFV7Qs+ch9l4VPAgNzGY+6vqzs48xH9z7wwrD8qJPMi/lMM0LPn7sceajrzr82XHmgwcj+55keeHJsH+S5QVXh6vnMR919bBuedS3XDxy7SybV3wnVTxddRudV/A2zfcwH98p1fuH7GI+uKp75ACbV3hq32uH2LyC6yHbjjEfddW6LtnMBxePLDA0L76TmhvZx/I6fq1PFvPxnSrRLXaI+pZ7i+udYHmdV/nrXJYXXE5qfZr5qGt+1fwU88H12UVn2LyiT6Jnxc9dTpxXcC9H5jEfOWWoSh7zHb+55CybV3jmZpHP5hXctH6nkPmoqxt0KGQ+uC5uUMzyok+qw8dnWV5wse7OQuajr3LMmALqW67VxiKWF54/4s5iltfx3WNC1EfdR2WI+pZ7GZ+H6XtVe056+8fmsveq4N7EJ08xH+eUv7vtaeaD60/mnmHvVV3dO/rksfeq4PIXb55lPuqa4TPzmQ/uP/B8IcuLc1JVH3GW5QVX80YXMB9z6i+7p5D54P75iv9OiXnhiZZ1i1hecLmnbzHzUTe4dkOI+eByYkmIvVdFn9T9F/LZe1XwoGezQurbnHLk6CLqWy7ajymm71Wtp3dV/Fz5xPeq4EFGvWLmo64RbUPMB5dPhkIsL/pkvuoaYnnBvZxaYerbvprs78PMB1cXfxdheeH5sflhmtdy3ftXEeajrho2OsJ8cBXJjLB5defkfvvnmcR5tdxU7hxiPs4pOWdsiPng3itfhNm8urq5bcJsXsFVt1iY+a5unv15vwk+uLp1cYTmteekd2ZimOUF92/7S4T57lwNvRJhvuODDkRYXnhic8XP6U3MC26W1Y4y39VdXT/KfHCvQd8om1fXp62fRNi8gvslByPMR86g441R5oPrGk2jdF6tJx67K0rn1XJ/TbMo81FXV+sXZT64+mFslOVFn9RHHaM0r+UqcxT10VcTJHPfcnHbMpoXXjB9Fs0LrvJWUd/VHbGV+uAiZn/ufcK+iu+k+huaZ7N9FTx4IWSYj++Uilen5jAfXPyy9mm2rzqv8oQ8tq+Cy/ar8pmPuvrH5LPMd3xZ90KWF99JFUG3kywvuLe49hnm4zuV5trb1Ac38/LOsrzw5M5WBSwvuJgVFDHf1f1yLfXBg/QDxWxfRZ/kQ42K2L7q+KJqIeYjp4p8Wsx8cK+FF2b7Kjyz5LUw21fBg4mNIsx3dUfdEGE+uB60NMLyur62+pzmBdd7V4WZj74G11tQH1xV8yMsLzzv4y9oXvAgrVqU+ajr//Ek9cHFhy9F2bziO6mm58N0XsGDO3oWMB/fKQ26r6c+uN94cSGbV3hGnaXzCq4q/TvEfNQVQ7KoD+61fSPM8rrvqtZ4kOYFN+3tr6cE331X9eib1AeXx1KKaV7r6XbTaF5wff1omPmoq3/7FPXBvb80jbB5RZ/UiRN0XsFNhzkR5iOn1/xV6oPrB87TeXV1j9ah8woue30QZT7qmglvct9y8VPF78OJedEnv+o8mtfxP9eLUt/2Vewtpj54sOkNmhee+vQTmtfxvQupj7qi2RTqg+tRG6JsX8U5aWrspPsquMyt2CMTfXeuHnwgynzH11TsSYn7Kjzv9AC6r4LLlG+pj7r62LfUB/db/RhleXFOytnv0rzg+sBk7ts5DWqO4b7lYv1ymheeaZlG84Krevuoj7p+w0zqO35rLt1X0SezbQ3dV8H9N3dTHznFT3upDy5n59B9FZ43JZvuq46nnqY+6qqMfOqDy8thmhd9EpUMz2u5WlFIffRVniikPniQdY7mhWfuPUfzgot3L1Efdb3uJdQHF12v8Hm156SuPo/OK7g5sJ36OKdEn3XUBw9KDvF5tZ7OPEbnFVz2KqA+6urnz1Af3OyO0Lw4J6XZRfOC6+qG+u5cbZJFfXDzVj7Paz2dV0zzggfPXqQ+6urZ57gP/s5PdF7RJ6/nOTqv4PqzEuq7vmZeoj54MPUKnVd4eucVOq+OR65RH3Vl5evUBw++LqV50Sex5zLNCy4XXKM++hrkXeW+5cFdpTQvPD2klOYFF9PLqI+6elAZ9cFl6/Ioe6/q7lX1oWL2XtXxLu+EqG/vKVW4S5j5jo9dH2bvVeHJGdtC7L0quBlwb5j5qOsNvjHCfPBg/LMRlhf3pP6VLhGWFzx4ZUaE+vaeSj5eJcp88OCPFXtbYl54ptXuCMsLLr45R33UlTNbRJkPLs9V3EslvldFn/Tcd8LsvSq4vmFCmPnIGYzuEWE+uHkoKcLeq8LzB6aF2XtVcPnYsTD1bV1/xKwI88GD362OsLzokzpUPcrygovzIsp89NUv7R9lPnhw99AoywsveLthlOa1XD30XJT5qOs9OSrKfHBxy9dROq/2njRY1z1K59Xy4MBw6uOe0j8wk/uWi8jSKJtXeGrEhCibV3Axcgb1UVelr6M+uJi6jebFPanfZhvNC+4t20t97Knyo+PUB1dPnqZ54fnPZ9G8ju8+wX1bV95aQH1wNbGYziv6JB5MpfMKrp9ZRn3X10a7qQ8uG+2n8wpPD1hN5xXcb7KO+q5u5cPUd7w4i+Z1fYqcpHnBvapnuG/76j8aob7j95yjeeHpjvk0L7hfp5D6qBsknac+uK8vRNl7VZyT8qdLYfZeFVw/WC3CfJxT8vX0CPXBi+zfHyW8V4Xnrb8nwt6rggfj6kSYj7rmm5wI88H94jMRltedkzUr/lyUmNfxzNZR5mNOxb2Tosx3fPvUKMsLT6+quMdJzAsu6nWIMh91/be+jTIfXFSt+HX7H+9VbZ+8ho9F2HtVcHXHExHmI6d6rjjCfHB5PBKh71XhfdQ4wt6rggdTmkSYj7p+ybkI88HFnPM0L/rkdbe/3yTkdTz53Sjz0VdzWUeZDy5eSonSvNbzs2WU5rVcvdg5ynzU1bfMijLf8Zaz6Ly6czUvoPMK7q/aTH2cU97Dx6jv+NHjdF7hiRNb6bw6PmQ79V3dOjnUd/zeXJoX56Q/vIjmBfdHFlMf52rwt4vUBzc1LtG8znslRPOCi9wQ9VFXJV2iPrg5eYnOK/okZ6bzebVc9M6gPnKqnbnUBxfNDJ1XeF5OBp1XcO+GHdR3dUcb7luuvzM0L/okXg7zvJbL8WHqo69+7RLqgwevlNC88NSaMM0LbraFqY+6frcS6oP7fUvovop7Un2pkO6r4KJThPu4p2xwgfqO1yqh+yo8pc7TfRVcb7xIfdTV9S5T3/EZV2he3JOa5y/TvI43vkp93FMZeY364GL1dZoXnjfxGs0LbkZdpz7q+iml1Hf8xTK6r6JPyt67Je6r4PKbn6iPnMGCq9QHN/2v0X3VeSMu030VXDx6hfqo65dfoz646nmd5kWfRKNSmhdcLymlPvoqV5ZRH9zcWE7zwhPVy2hecNWqjPqoq54vpz647FVO59Xdq75eyufV8qBpGfVxT6nGlVEfPGhSTucVnjlVRucV3Nj7y0Tf1Z1bTn3Hz5fTvO5etX05zQsu95VTH/eqfjb3wc0fYjQvPCliNC+4fj1GfdT1+saoD+5PiNF5dX2aVk7nFdzsKae+62udGPXBgxYxOq/wREk5n1fLze0x6qOuL2PUB1cDYzQv+qQHx2hecH8c99HXYD73wUVajOZ1dWfwvOAmlfuoK7ZyHzzYFaP7Ks5Js+AK3VfB1S+uUh/nlF51nfrg+sJ1uq/CC16/SvdVcG/MVeqjrvxNKfXB9XOlNK87J4eX0bzgQUoZ9TGn3uhy6jv+XTnPi7rrynhey719ZdRHXbOonPrgQVo53VfRJ7P4Kt1XwWXGVeojp3yjlPrgulsp3Vdd3ayrdF8FD45fpT7qyn6l3LfcH1BK87q+5pTxvJZ7BWXUR1/F5nLqO55ezvNazw+X0bzgJlJGfdQ1O8upD252ldN5xTkpHozReQUXDWPUd+fqsBj3LdejYnRe4fnPxOi8guumMeqjrvePGPXB1QSeF+ekWcbzgqvV3Me5avb9F99ydYjnhad+/C95LZcbuI+6Iov74OZojM4r+oTflxLn1fFXYtRHTj0pxn3wyTE6r/C8VjE6r+C6VYz6qGumxKgP7k/ledEnfyPPC642cR999Y5xH9wc43nh+Zt5XnC1+b/4+Pce5z64/L9//g8IWGJ2 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-012000.2.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-012000.2.vtu new file mode 100644 index 000000000..a7a9cca76 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-012000.2.vtu @@ -0,0 +1,32 @@ + + + + + + + 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EZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//A3532BvAAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH39D/TfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/y3zBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3zH9LfAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8DfvfIm8AAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff8P9t8AfgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//hXcEIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfCf6E9AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+F9wVwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/I/0X2BeAQAQkMEEIRlCCE4JQhCQ0YQhHWMITgUhEJDJRiEZUohODWMQkNnGIR1zik4BEJCQxSUhGUpKTglSkJDVpSEda0pOBTGQkM1nIRlayk4Nc5CQ3echHXvJTgEIUpDBFKEZRilOCUpSkNGUoR1nKU4FKVKQyVahGVapTg1rUpDZ1qEdd6tOARjSkMU1oRlOa04JWtKQ1bWhHW9rTgU50pDNd6EZXutODXvSkN33oR1/6M4BBDGQwQxjGUIYzglGMZDRjGMdYxjOBSUxkMlOYxlSmM4NZzGQ2c5jHXOazgEUsZDFLWMZSlrOCVaxkNWtYx1rWs4FNbGQzW9jGVrazg13sZDd72Mde9nOAQxzkMEc4xlGOc4JTnOQ0ZzjHWc5zgUtc5DJXuMZVrnODW9zkNne4x13u84BHPOQxT3jGU57zgle85DVveMdb3vOBT3zkM1/4xle+84Nf/OQ3f/jH3/+FdQYgEAEJTBCCEZTghCAUIQlNGMIRlvBEIBIRiUwUohGV6MQgFjGJTRziEZf4JCARCUlMEpKRlOSkIBUpSU0a0pGW9GQgExnJTBaykZXs5CAXOclNHvKRl/wUoBAFKUwRilGU4pSgFCUpTRnKUZbyVKASFalMFapRlerUoBY1qU0d6lGX+jSgEQ1pTBOa0ZTmtKAVLWlNG9rRlvZ0oBMd6UwXutGV7vSgFz3pTR/60Zf+DGAQAxnMEIYxlOGMYBQjGc0YxjGW8UxgEhOZzBSmMZXpzGAWM5nNHOYxl/ksYBELWcwSlrGU5axgFStZzRrWsZb1bGATG9nMFraxle3sYBc72c0e9rGX/RzgEAc5zBGOcZTjnOAUJznNGc5xlvNc4BIXucwVrnGV69zgFje5zR3ucZf7POARD3nME57xlOe84BUvec0b3vGW93zgEx/5zBe+8ZXv/OAXP/nNH/7xN8p/Ib0BCERAAhOEYAQlOCEIRUhCE4ZwhCU8EYhERCIThWhEJToxiEVMYhOHeMQlPglIREISk4RkJCU5KUhFSlKThnSkJT0ZyERGMpOFbGQlOznIRU5yk4d85CU/BShEQQpThGIUpTglKEVJSlOGcpSlPBWoREUqU4VqVKU6NahFTWpTh3rUpT4NaERDGtOEZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//I36X1B3AAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH3+j/RfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/5VzBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3xn+lPAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8jflfMVcAAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff2P9V8gXgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//lXEGIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfOfyW8AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+18RdwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/+b/4zj+BCEhgghCMoAQnBKEISWjCEI6whCfC/zdMD9liIAAAwPo6tW3bNn5t27Zt27Zt27Zt27YnixwhRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTQf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMh//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU2H/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTEf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNR//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUzH/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTcf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMJ//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU0n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTKf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNp//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUxn/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTWf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlM5//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU3n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTBf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNF//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUyX/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTZf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMV//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU1X/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTNf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlN1//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUw3/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTTf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMt//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU23/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTHf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNd//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwkU4D//EZggBCU4wQhBSEITijCEJTzhiEBEIhOJKEQlOtGIQUxiE4s4xCU+8UhAQhKTiCQkJTnJSEFKUpOKNKQlPenIQEYyk4ksZCU72chBTnKTizzkJR8B5KcAhShIYYpQjKIUpwSlKElpylCOspSnApWoSGWqUI2qVKcGtahJbepQj7rUpwGNaEhjmtCMpjSnBa1oSWva0I62tKcDnehIZ7rQja50pwe96Elv+tCPvvRnAIMYyGCGMIyhDGcEoxjJaMYwjrGMZwKTmMhkpjCNqUxnBrOYyWzmMI+5zGcBi1jIYpawjKUsZwWrWMlq1rCOtaxnA5vYyGa2sI2tbGcHu9jJbvawj73s5wCHOMhhjnCMoxznBKc4yWnOcI6znOcCl7jIZa5wjatc5wa3uMlt7nCPu9znAY94yGOe8IynPOcFr3jJa97wjre85wOf+MhnvvCNr3znB7/4yW/+8I+/BMrnP/8RmCAEJTjBCEFIQhOKMIQlPOGIQEQiE4koRCU60YhBTGITizjEJT7xSEBCEpOIJCQlOclIQUpSk4o0pCU96chARjKTiSxkJTvZyEFOcpOLPOQlHwHkpwCFKEhhilCMohSnBKUoSWnKUI6ylKcClahIZapQjapUpwa1qElt6lCPutSnAY1oSGOa0IymNKcFrWhJa9rQjra0pwOd6EhnutCNrnSnB73oSW/60I++9GcAgxjIYIYwjKEMZwSjGMloxjCOsYxnApOYyGSmMI2pTGcGs5jJbOYwj7nMZwGLWMhilrCMpSxnBatYyWrWsI61rGcDm9jIZrawja1sZwe72Mlu9rCPveznAIc4yGGOcIyjHOcEpzjJac5wjrOc5wKXuMhlrnCNq1znBre4yW3ucI+73OcBj3jIY57wjKc85wWveMlr3vCOt7znA5/4yGe+8I2vfOcHv/jJb/7wj7/8D5W4wko= 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-012000.3.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-012000.3.vtu new file mode 100644 index 000000000..108fb1c4d --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-012000.3.vtu @@ -0,0 +1,32 @@ + + + + + + + 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-012000.pvtu b/tests/automatic_regression_test/gold_allenCahn/solution-012000.pvtu new file mode 100644 index 000000000..ad4000c08 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-012000.pvtu @@ -0,0 +1,18 @@ + + + + + + + + + + + + + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-014000.0.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-014000.0.vtu new file mode 100644 index 000000000..85a022ae0 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-014000.0.vtu @@ -0,0 +1,32 @@ + + + + + + + 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+AQAAAAAAAQAAAAEAxFgAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-014000.1.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-014000.1.vtu new file mode 100644 index 000000000..56b0fbc52 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-014000.1.vtu @@ -0,0 +1,32 @@ + + + + + + + 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rN+48ePLizYfvb8sM/10/AYKECBMhSow4CZKkSJMhS448BYqUKFOhSo06DZq0aNOhS48+A4aMGDNhyow5C5asWLNhy449B46cOHPhyo07D568ePPhy8//0E+AICHCRIgSI06CJCnSZMiSI0+BIiXKVKhSo06DJi3adOjSo8+AISPGTJgyY86CJSvWbNiyY8+BIyfOXLhy486DJy/efPjy89f6CRAkRJgIUWLESZAkRZoMWXLkKVCkRJkKVWrUadCkRZsOXXr0GTBkxJgJU2bMWbBkxZoNW3bsOXDkxJkLV27cefDkxZsP37/+fZnnFwGChAgTIUqMOAmSpEiTIUuOPAWKlChToUqNOg2atGjToUuPPgOGjBgzYcqMOQuWrFizYcuOPQeOnDhz4cqNOw+evHjz4fvbItP/1E+AICHCRIgSI06CJCnSZMiSI0+BIiXKVKhSo06DJi3adOjSo8+AISPGTJgyY86CJSvWbNiyY8+BIyfOXLhy486DJy/efPjy87/0EyBIiDARosSIkyBJijQZsuTIU6BIiTIVqtSo06BJizYduvToM2DIiDETpsyYs2DJijUbtuzYc+DIiTMXrty48+DJizcfvvz8jX4CBAkRJkKUGHESJEmRJkOWHHkKFClRpkKVGnUaNGnRpkOXHn0GDBkxZsKUGXMWLFmxZsOWHXsOHDlx5sKVG3cePHnx5sP3b35f5PtFgCAhwkSIEiNOgiQp0mTIkiNPgSIlylSoUqNOgyYt2nTo0qPPgCEjxkyYMmPOgiUr1mzYsmPPgSMnzly4cuPOgycv3nz4/rbE+L/1EyBIiDARosSIkyBJijQZsuTIU6BIiTIVqtSo06BJizYduvToM2DIiDETpsyYs2DJijUbtuzYc+DIiTMXrty48+DJizcfvvz8H/0ECBIiTIQoMeIkSJIiTYYsOfIUKFKiTIUqNeo0aNKiTYcuPfoMGDJizIQpM+YsWLJizYYtO/YcOHLizIUrN+48ePLizYcvP3+rnwBBQoSJECVGnARJUqTJkCVHngJFSpSpUKVGnQZNWrTp0KVHnwFDRoyZMGXGnAVLVqzZsGXHngNHTpy5cOXGnQdPXrz58P3b35d4fxEgSIgwEaLEiJMgSYo0GbLkyFOgSIkyFarUqNOgSYs2Hbr06DNgyIgxE6bMmLNgyYo1G7bs2HPgyIkzF67cuPPgyYs3H76/LTD/X/0ECBIiTIQoMeIkSJIiTYYsOfIUKFKiTIUqNeo0aNKiTYcuPfoMGDJizIQpM+YsWLJizYYtO/YcOHLizIUrN+48ePLizYcvP/9PPwGChAgTIUqMOAmSpEiTIUuOPAWKlChToUqNOg2atGjToUuPPgOGjBgzYcqMOQuWrFizYcuOPQeOnDhz4cqNOw+evHjz4cvP3+knQJAQYSJEiREnQZIUaTJkyZGnQJESZSpUqVGnQZMWbTp06dFnwJARYyZMmTFnwZIVazZs2bHnwJETZy5cuXHnwZMXbz58/+73Bf7/Dz4v2Mg= 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-014000.2.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-014000.2.vtu new file mode 100644 index 000000000..6109776d3 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-014000.2.vtu @@ -0,0 +1,32 @@ + + + + + + + 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-014000.3.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-014000.3.vtu new file mode 100644 index 000000000..d72ffef66 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-014000.3.vtu @@ -0,0 +1,32 @@ + + + + + + + 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-014000.pvtu b/tests/automatic_regression_test/gold_allenCahn/solution-014000.pvtu new file mode 100644 index 000000000..d6b9729df --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-014000.pvtu @@ -0,0 +1,18 @@ + + + + + + + + + + + + + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-016000.0.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-016000.0.vtu new file mode 100644 index 000000000..898004156 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-016000.0.vtu @@ -0,0 +1,32 @@ + + + + + + + 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+AQAAAAAAAQAAAAEAxFgAAA==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+AQAAAAAAAgAAAAIAZFcAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-016000.1.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-016000.1.vtu new file mode 100644 index 000000000..3a1cfeeaf --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-016000.1.vtu @@ -0,0 +1,32 @@ + + + + + + + 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EZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//A3532BvAAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH39D/TfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/y3zBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3zH9LfAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8DfvfIm8AAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff8P9t8AfgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//hXcEIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfCf6E9AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+F9wVwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/I/0X2BeAQAQkMEEIRlCCE4JQhCQ0YQhHWMITgUhEJDJRiEZUohODWMQkNnGIR1zik4BEJCQxSUhGUpKTglSkJDVpSEda0pOBTGQkM1nIRlayk4Nc5CQ3echHXvJTgEIUpDBFKEZRilOCUpSkNGUoR1nKU4FKVKQyVahGVapTg1rUpDZ1qEdd6tOARjSkMU1oRlOa04JWtKQ1bWhHW9rTgU50pDNd6EZXutODXvSkN33oR1/6M4BBDGQwQxjGUIYzglGMZDRjGMdYxjOBSUxkMlOYxlSmM4NZzGQ2c5jHXOazgEUsZDFLWMZSlrOCVaxkNWtYx1rWs4FNbGQzW9jGVrazg13sZDd72Mde9nOAQxzkMEc4xlGOc4JTnOQ0ZzjHWc5zgUtc5DJXuMZVrnODW9zkNne4x13u84BHPOQxT3jGU57zgle85DVveMdb3vOBT3zkM1/4xle+84Nf/OQ3f/jH3/+FdQYgEAEJTBCCEZTghCAUIQlNGMIRlvBEIBIRiUwUohGV6MQgFjGJTRziEZf4JCARCUlMEpKRlOSkIBUpSU0a0pGW9GQgExnJTBaykZXs5CAXOclNHvKRl/wUoBAFKUwRilGU4pSgFCUpTRnKUZbyVKASFalMFapRlerUoBY1qU0d6lGX+jSgEQ1pTBOa0ZTmtKAVLWlNG9rRlvZ0oBMd6UwXutGV7vSgFz3pTR/60Zf+DGAQAxnMEIYxlOGMYBQjGc0YxjGW8UxgEhOZzBSmMZXpzGAWM5nNHOYxl/ksYBELWcwSlrGU5axgFStZzRrWsZb1bGATG9nMFraxle3sYBc72c0e9rGX/RzgEAc5zBGOcZTjnOAUJznNGc5xlvNc4BIXucwVrnGV69zgFje5zR3ucZf7POARD3nME57xlOe84BUvec0b3vGW93zgEx/5zBe+8ZXv/OAXP/nNH/7xN8p/Ib0BCERAAhOEYAQlOCEIRUhCE4ZwhCU8EYhERCIThWhEJToxiEVMYhOHeMQlPglIREISk4RkJCU5KUhFSlKThnSkJT0ZyERGMpOFbGQlOznIRU5yk4d85CU/BShEQQpThGIUpTglKEVJSlOGcpSlPBWoREUqU4VqVKU6NahFTWpTh3rUpT4NaERDGtOEZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//I36X1B3AAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH3+j/RfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/5VzBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3xn+lPAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8jflfMVcAAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff2P9V8gXgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//lXEGIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfOfyW8AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+18RdwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/+b/4zj+BCEhgghCMoAQnBKEISWjCEI6whCfC/zdMD9liIAAAwPo6tW3bNn5t27Zt27Zt27Zt27YnixwhRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTQf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMh//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU2H/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTEf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNR//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUzH/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTcf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMJ//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU0n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTKf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNp//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUxn/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTWf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlM5//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU3n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTBf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNF//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUyX/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTZf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMV//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU1X/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTNf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlN1//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUw3/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTTf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMt//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU23/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTHf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNd//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwkU4D//EZggBCU4wQhBSEITijCEJTzhiEBEIhOJKEQlOtGIQUxiE4s4xCU+8UhAQhKTiCQkJTnJSEFKUpOKNKQlPenIQEYyk4ksZCU72chBTnKTizzkJR8B5KcAhShIYYpQjKIUpwSlKElpylCOspSnApWoSGWqUI2qVKcGtahJbepQj7rUpwGNaEhjmtCMpjSnBa1oSWva0I62tKcDnehIZ7rQja50pwe96Elv+tCPvvRnAIMYyGCGMIyhDGcEoxjJaMYwjrGMZwKTmMhkpjCNqUxnBrOYyWzmMI+5zGcBi1jIYpawjKUsZwWrWMlq1rCOtaxnA5vYyGa2sI2tbGcHu9jJbvawj73s5wCHOMhhjnCMoxznBKc4yWnOcI6znOcCl7jIZa5wjatc5wa3uMlt7nCPu9znAY94yGOe8IynPOcFr3jJa97wjre85wOf+MhnvvCNr3znB7/4yW/+8I+/BMrnP/8RmCAEJTjBCEFIQhOKMIQlPOGIQEQiE4koRCU60YhBTGITizjEJT7xSEBCEpOIJCQlOclIQUpSk4o0pCU96chARjKTiSxkJTvZyEFOcpOLPOQlHwHkpwCFKEhhilCMohSnBKUoSWnKUI6ylKcClahIZapQjapUpwa1qElt6lCPutSnAY1oSGOa0IymNKcFrWhJa9rQjra0pwOd6EhnutCNrnSnB73oSW/60I++9GcAgxjIYIYwjKEMZwSjGMloxjCOsYxnApOYyGSmMI2pTGcGs5jJbOYwj7nMZwGLWMhilrCMpSxnBatYyWrWsI61rGcDm9jIZrawja1sZwe72Mlu9rCPveznAIc4yGGOcIyjHOcEpzjJac5wjrOc5wKXuMhlrnCNq1znBre4yW3ucI+73OcBj3jIY57wjKc85wWveMlr3vCOt7znA5/4yGe+8I2vfOcHv/jJb/7wj7/8D5W4wko= 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-016000.2.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-016000.2.vtu new file mode 100644 index 000000000..dd88986cc --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-016000.2.vtu @@ -0,0 +1,32 @@ + + + + + + + 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-016000.3.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-016000.3.vtu new file mode 100644 index 000000000..d5e97cc5e --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-016000.3.vtu @@ -0,0 +1,32 @@ + + + + + + + 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-016000.pvtu b/tests/automatic_regression_test/gold_allenCahn/solution-016000.pvtu new file mode 100644 index 000000000..24cb9d6a9 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-016000.pvtu @@ -0,0 +1,18 @@ + + + + + + + + + + + + + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-018000.0.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-018000.0.vtu new file mode 100644 index 000000000..2abef3b3b --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-018000.0.vtu @@ -0,0 +1,32 @@ + + + + + + + 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+AQAAAAAAAQAAAAEAxFgAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-018000.1.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-018000.1.vtu new file mode 100644 index 000000000..602d9a12f --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-018000.1.vtu @@ -0,0 +1,32 @@ + + + + + + + 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-018000.2.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-018000.2.vtu new file mode 100644 index 000000000..8b9e6e4d3 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-018000.2.vtu @@ -0,0 +1,32 @@ + + + + + + + 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-018000.3.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-018000.3.vtu new file mode 100644 index 000000000..58e9a63e9 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-018000.3.vtu @@ -0,0 +1,32 @@ + + + + + + + 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rN+48ePLizYfvb8sM/10/AYKECBMhSow4CZKkSJMhS448BYqUKFOhSo06DZq0aNOhS48+A4aMGDNhyow5C5asWLNhy449B46cOHPhyo07D568ePPhy8//0E+AICHCRIgSI06CJCnSZMiSI0+BIiXKVKhSo06DJi3adOjSo8+AISPGTJgyY86CJSvWbNiyY8+BIyfOXLhy486DJy/efPjy89f6CRAkRJgIUWLESZAkRZoMWXLkKVCkRJkKVWrUadCkRZsOXXr0GTBkxJgJU2bMWbBkxZoNW3bsOXDkxJkLV27cefDkxZsP37/+fZnnFwGChAgTIUqMOAmSpEiTIUuOPAWKlChToUqNOg2atGjToUuPPgOGjBgzYcqMOQuWrFizYcuOPQeOnDhz4cqNOw+evHjz4fvbItP/1E+AICHCRIgSI06CJCnSZMiSI0+BIiXKVKhSo06DJi3adOjSo8+AISPGTJgyY86CJSvWbNiyY8+BIyfOXLhy486DJy/efPjy87/0EyBIiDARosSIkyBJijQZsuTIU6BIiTIVqtSo06BJizYduvToM2DIiDETpsyYs2DJijUbtuzYc+DIiTMXrty48+DJizcfvvz8jX4CBAkRJkKUGHESJEmRJkOWHHkKFClRpkKVGnUaNGnRpkOXHn0GDBkxZsKUGXMWLFmxZsOWHXsOHDlx5sKVG3cePHnx5sP3b35f5PtFgCAhwkSIEiNOgiQp0mTIkiNPgSIlylSoUqNOgyYt2nTo0qPPgCEjxkyYMmPOgiUr1mzYsmPPgSMnzly4cuPOgycv3nz4/rbE+L/1EyBIiDARosSIkyBJijQZsuTIU6BIiTIVqtSo06BJizYduvToM2DIiDETpsyYs2DJijUbtuzYc+DIiTMXrty48+DJizcfvvz8H/0ECBIiTIQoMeIkSJIiTYYsOfIUKFKiTIUqNeo0aNKiTYcuPfoMGDJizIQpM+YsWLJizYYtO/YcOHLizIUrN+48ePLizYcvP3+rnwBBQoSJECVGnARJUqTJkCVHngJFSpSpUKVGnQZNWrTp0KVHnwFDRoyZMGXGnAVLVqzZsGXHngNHTpy5cOXGnQdPXrz58P3b35d4fxEgSIgwEaLEiJMgSYo0GbLkyFOgSIkyFarUqNOgSYs2Hbr06DNgyIgxE6bMmLNgyYo1G7bs2HPgyIkzF67cuPPgyYs3H76/LTD/X/0ECBIiTIQoMeIkSJIiTYYsOfIUKFKiTIUqNeo0aNKiTYcuPfoMGDJizIQpM+YsWLJizYYtO/YcOHLizIUrN+48ePLizYcvP/9PPwGChAgTIUqMOAmSpEiTIUuOPAWKlChToUqNOg2atGjToUuPPgOGjBgzYcqMOQuWrFizYcuOPQeOnDhz4cqNOw+evHjz4cvP3+knQJAQYSJEiREnQZIUaTJkyZGnQJESZSpUqVGnQZMWbTp06dFnwJARYyZMmTFnwZIVazZs2bHnwJETZy5cuXHnwZMXbz58/+73Bf7/Dz4v2Mg= 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-018000.pvtu b/tests/automatic_regression_test/gold_allenCahn/solution-018000.pvtu new file mode 100644 index 000000000..ca7632e37 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-018000.pvtu @@ -0,0 +1,18 @@ + + + + + + + + + + + + + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-020000.0.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-020000.0.vtu new file mode 100644 index 000000000..23e0e2791 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-020000.0.vtu @@ -0,0 +1,32 @@ + + + + + + + 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+AQAAAAAAAQAAAAEAxFgAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-020000.1.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-020000.1.vtu new file mode 100644 index 000000000..e584d0d38 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-020000.1.vtu @@ -0,0 +1,32 @@ + + + + + + + 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-020000.2.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-020000.2.vtu new file mode 100644 index 000000000..f377031d3 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-020000.2.vtu @@ -0,0 +1,32 @@ + + + + + + + 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-020000.3.vtu b/tests/automatic_regression_test/gold_allenCahn/solution-020000.3.vtu new file mode 100644 index 000000000..16c2d025a --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-020000.3.vtu @@ -0,0 +1,32 @@ + + + + + + + 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rN+48ePLizYfvb8sM/10/AYKECBMhSow4CZKkSJMhS448BYqUKFOhSo06DZq0aNOhS48+A4aMGDNhyow5C5asWLNhy449B46cOHPhyo07D568ePPhy8//0E+AICHCRIgSI06CJCnSZMiSI0+BIiXKVKhSo06DJi3adOjSo8+AISPGTJgyY86CJSvWbNiyY8+BIyfOXLhy486DJy/efPjy89f6CRAkRJgIUWLESZAkRZoMWXLkKVCkRJkKVWrUadCkRZsOXXr0GTBkxJgJU2bMWbBkxZoNW3bsOXDkxJkLV27cefDkxZsP37/+fZnnFwGChAgTIUqMOAmSpEiTIUuOPAWKlChToUqNOg2atGjToUuPPgOGjBgzYcqMOQuWrFizYcuOPQeOnDhz4cqNOw+evHjz4fvbItP/1E+AICHCRIgSI06CJCnSZMiSI0+BIiXKVKhSo06DJi3adOjSo8+AISPGTJgyY86CJSvWbNiyY8+BIyfOXLhy486DJy/efPjy87/0EyBIiDARosSIkyBJijQZsuTIU6BIiTIVqtSo06BJizYduvToM2DIiDETpsyYs2DJijUbtuzYc+DIiTMXrty48+DJizcfvvz8jX4CBAkRJkKUGHESJEmRJkOWHHkKFClRpkKVGnUaNGnRpkOXHn0GDBkxZsKUGXMWLFmxZsOWHXsOHDlx5sKVG3cePHnx5sP3b35f5PtFgCAhwkSIEiNOgiQp0mTIkiNPgSIlylSoUqNOgyYt2nTo0qPPgCEjxkyYMmPOgiUr1mzYsmPPgSMnzly4cuPOgycv3nz4/rbE+L/1EyBIiDARosSIkyBJijQZsuTIU6BIiTIVqtSo06BJizYduvToM2DIiDETpsyYs2DJijUbtuzYc+DIiTMXrty48+DJizcfvvz8H/0ECBIiTIQoMeIkSJIiTYYsOfIUKFKiTIUqNeo0aNKiTYcuPfoMGDJizIQpM+YsWLJizYYtO/YcOHLizIUrN+48ePLizYcvP3+rnwBBQoSJECVGnARJUqTJkCVHngJFSpSpUKVGnQZNWrTp0KVHnwFDRoyZMGXGnAVLVqzZsGXHngNHTpy5cOXGnQdPXrz58P3b35d4fxEgSIgwEaLEiJMgSYo0GbLkyFOgSIkyFarUqNOgSYs2Hbr06DNgyIgxE6bMmLNgyYo1G7bs2HPgyIkzF67cuPPgyYs3H76/LTD/X/0ECBIiTIQoMeIkSJIiTYYsOfIUKFKiTIUqNeo0aNKiTYcuPfoMGDJizIQpM+YsWLJizYYtO/YcOHLizIUrN+48ePLizYcvP/9PPwGChAgTIUqMOAmSpEiTIUuOPAWKlChToUqNOg2atGjToUuPPgOGjBgzYcqMOQuWrFizYcuOPQeOnDhz4cqNOw+evHjz4cvP3+knQJAQYSJEiREnQZIUaTJkyZGnQJESZSpUqVGnQZMWbTp06dFnwJARYyZMmTFnwZIVazZs2bHnwJETZy5cuXHnwZMXbz58/+73Bf7/Dz4v2Mg= 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 + + + + diff --git a/tests/automatic_regression_test/gold_allenCahn/solution-020000.pvtu b/tests/automatic_regression_test/gold_allenCahn/solution-020000.pvtu new file mode 100644 index 000000000..dc6ac5487 --- /dev/null +++ b/tests/automatic_regression_test/gold_allenCahn/solution-020000.pvtu @@ -0,0 +1,18 @@ + + + + + + + + + + + + + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/freeEnergy.txt b/tests/automatic_regression_test/gold_cahnHilliard/freeEnergy.txt new file mode 100644 index 000000000..a59a1dfec --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/freeEnergy.txt @@ -0,0 +1,11 @@ +1035 +261.088743 +252.8388909 +247.9579807 +244.0520322 +240.6499483 +237.5655362 +234.7469089 +232.1531501 +229.7427883 +227.4888947 diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-000000.0.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-000000.0.vtu new file mode 100644 index 000000000..7eaaa0502 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-000000.0.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAQAAAAEAxFgAAA==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EZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//A3532BvAAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH39D/TfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/y3zBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3zH9LfAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8DfvfIm8AAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff8P9t8AfgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//hXcEIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfCf6E9AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+F9wVwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/I/0X2BeAQAQkMEEIRlCCE4JQhCQ0YQhHWMITgUhEJDJRiEZUohODWMQkNnGIR1zik4BEJCQxSUhGUpKTglSkJDVpSEda0pOBTGQkM1nIRlayk4Nc5CQ3echHXvJTgEIUpDBFKEZRilOCUpSkNGUoR1nKU4FKVKQyVahGVapTg1rUpDZ1qEdd6tOARjSkMU1oRlOa04JWtKQ1bWhHW9rTgU50pDNd6EZXutODXvSkN33oR1/6M4BBDGQwQxjGUIYzglGMZDRjGMdYxjOBSUxkMlOYxlSmM4NZzGQ2c5jHXOazgEUsZDFLWMZSlrOCVaxkNWtYx1rWs4FNbGQzW9jGVrazg13sZDd72Mde9nOAQxzkMEc4xlGOc4JTnOQ0ZzjHWc5zgUtc5DJXuMZVrnODW9zkNne4x13u84BHPOQxT3jGU57zgle85DVveMdb3vOBT3zkM1/4xle+84Nf/OQ3f/jH3/+FdQYgEAEJTBCCEZTghCAUIQlNGMIRlvBEIBIRiUwUohGV6MQgFjGJTRziEZf4JCARCUlMEpKRlOSkIBUpSU0a0pGW9GQgExnJTBaykZXs5CAXOclNHvKRl/wUoBAFKUwRilGU4pSgFCUpTRnKUZbyVKASFalMFapRlerUoBY1qU0d6lGX+jSgEQ1pTBOa0ZTmtKAVLWlNG9rRlvZ0oBMd6UwXutGV7vSgFz3pTR/60Zf+DGAQAxnMEIYxlOGMYBQjGc0YxjGW8UxgEhOZzBSmMZXpzGAWM5nNHOYxl/ksYBELWcwSlrGU5axgFStZzRrWsZb1bGATG9nMFraxle3sYBc72c0e9rGX/RzgEAc5zBGOcZTjnOAUJznNGc5xlvNc4BIXucwVrnGV69zgFje5zR3ucZf7POARD3nME57xlOe84BUvec0b3vGW93zgEx/5zBe+8ZXv/OAXP/nNH/7xN8p/Ib0BCERAAhOEYAQlOCEIRUhCE4ZwhCU8EYhERCIThWhEJToxiEVMYhOHeMQlPglIREISk4RkJCU5KUhFSlKThnSkJT0ZyERGMpOFbGQlOznIRU5yk4d85CU/BShEQQpThGIUpTglKEVJSlOGcpSlPBWoREUqU4VqVKU6NahFTWpTh3rUpT4NaERDGtOEZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//I36X1B3AAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH3+j/RfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/5VzBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3xn+lPAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8jflfMVcAAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff2P9V8gXgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//lXEGIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfOfyW8AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+18RdwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/+b/4zj+BCEhgghCMoAQnBKEISWjCEI6whCfC/zdMD9liIAAAwPo6tW3bNn5t27Zt27Zt27Zt27YnixwhRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTQf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMh//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU2H/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTEf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNR//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUzH/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTcf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMJ//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU0n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTKf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNp//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUxn/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTWf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlM5//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU3n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTBf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNF//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUyX/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTZf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMV//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU1X/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTNf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlN1//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUw3/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTTf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMt//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU23/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTHf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNd//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwkU4D//EZggBCU4wQhBSEITijCEJTzhiEBEIhOJKEQlOtGIQUxiE4s4xCU+8UhAQhKTiCQkJTnJSEFKUpOKNKQlPenIQEYyk4ksZCU72chBTnKTizzkJR8B5KcAhShIYYpQjKIUpwSlKElpylCOspSnApWoSGWqUI2qVKcGtahJbepQj7rUpwGNaEhjmtCMpjSnBa1oSWva0I62tKcDnehIZ7rQja50pwe96Elv+tCPvvRnAIMYyGCGMIyhDGcEoxjJaMYwjrGMZwKTmMhkpjCNqUxnBrOYyWzmMI+5zGcBi1jIYpawjKUsZwWrWMlq1rCOtaxnA5vYyGa2sI2tbGcHu9jJbvawj73s5wCHOMhhjnCMoxznBKc4yWnOcI6znOcCl7jIZa5wjatc5wa3uMlt7nCPu9znAY94yGOe8IynPOcFr3jJa97wjre85wOf+MhnvvCNr3znB7/4yW/+8I+/BMrnP/8RmCAEJTjBCEFIQhOKMIQlPOGIQEQiE4koRCU60YhBTGITizjEJT7xSEBCEpOIJCQlOclIQUpSk4o0pCU96chARjKTiSxkJTvZyEFOcpOLPOQlHwHkpwCFKEhhilCMohSnBKUoSWnKUI6ylKcClah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+AQAAAAAAAQAAAAEAxFgAAA==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rN+48ePLizYfvb8sM/10/AYKECBMhSow4CZKkSJMhS448BYqUKFOhSo06DZq0aNOhS48+A4aMGDNhyow5C5asWLNhy449B46cOHPhyo07D568ePPhy8//0E+AICHCRIgSI06CJCnSZMiSI0+BIiXKVKhSo06DJi3adOjSo8+AISPGTJgyY86CJSvWbNiyY8+BIyfOXLhy486DJy/efPjy89f6CRAkRJgIUWLESZAkRZoMWXLkKVCkRJkKVWrUadCkRZsOXXr0GTBkxJgJU2bMWbBkxZoNW3bsOXDkxJkLV27cefDkxZsP37/+fZnnFwGChAgTIUqMOAmSpEiTIUuOPAWKlChToUqNOg2atGjToUuPPgOGjBgzYcqMOQuWrFizYcuOPQeOnDhz4cqNOw+evHjz4fvbItP/1E+AICHCRIgSI06CJCnSZMiSI0+BIiXKVKhSo06DJi3adOjSo8+AISPGTJgyY86CJSvWbNiyY8+BIyfOXLhy486DJy/efPjy87/0EyBIiDARosSIkyBJijQZsuTIU6BIiTIVqtSo06BJizYduvToM2DIiDETpsyYs2DJijUbtuzYc+DIiTMXrty48+DJizcfvvz8jX4CBAkRJkKUGHESJEmRJkOWHHkKFClRpkKVGnUaNGnRpkOXHn0GDBkxZsKUGXMWLFmxZsOWHXsOHDlx5sKVG3cePHnx5sP3b35f5PtFgCAhwkSIEiNOgiQp0mTIkiNPgSIlylSoUqNOgyYt2nTo0qPPgCEjxkyYMmPOgiUr1mzYsmPPgSMnzly4cuPOgycv3nz4/rbE+L/1EyBIiDARosSIkyBJijQZsuTIU6BIiTIVqtSo06BJizYduvToM2DIiDETpsyYs2DJijUbtuzYc+DIiTMXrty48+DJizcfvvz8H/0ECBIiTIQoMeIkSJIiTYYsOfIUKFKiTIUqNeo0aNKiTYcuPfoMGDJizIQpM+YsWLJizYYtO/YcOHLizIUrN+48ePLizYcvP3+rnwBBQoSJECVGnARJUqTJkCVHngJFSpSpUKVGnQZNWrTp0KVHnwFDRoyZMGXGnAVLVqzZsGXHngNHTpy5cOXGnQdPXrz58P3b35d4fxEgSIgwEaLEiJMgSYo0GbLkyFOgSIkyFarUqNOgSYs2Hbr06DNgyIgxE6bMmLNgyYo1G7bs2HPgyIkzF67cuPPgyYs3H76/LTD/X/0ECBIiTIQoMeIkSJIiTYYsOfIUKFKiTIUqNeo0aNKiTYcuPfoMGDJizIQpM+YsWLJizYYtO/YcOHLizIUrN+48ePLizYcvP/9PPwGChAgTIUqMOAmSpEiTIUuOPAWKlChToUqNOg2atGjToUuPPgOGjBgzYcqMOQuWrFizYcuOPQeOnDhz4cqNOw+evHjz4cvP3+knQJAQYSJEiREnQZIUaTJkyZGnQJESZSpUqVGnQZMWbTp06dFnwJARYyZMmTFnwZIVazZs2bHnwJETZy5cuXHnwZMXbz58/+73Bf7/Dz4v2Mg= 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+AQAAAAAAAgAAAAIAL3MAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-010000.1.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-010000.1.vtu new file mode 100644 index 000000000..1c30b8388 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-010000.1.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAgAAAAIA3XMAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-010000.2.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-010000.2.vtu new file mode 100644 index 000000000..4904af456 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-010000.2.vtu @@ -0,0 +1,34 @@ + + + + + + + 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DfRDfCvOQj3jSHzwX9GfV60YP+J7pM/xFdd0e8L0wDx138aS7echHPOkPrsv+vLpvPeB7pg/9P4DkI74X5qE+iCfdzUM+4t11gI4jnXfil9ezeSAf8cthHloHxLfCPOQjvrWezQPVFb+8ns0D+YhfDvNQH8S3wjzkI76V7gfuP4fAH6b7AfK5Bb1unvvPCPhRmod8bqt1dz9AdcUfhvsB8hF/GOahPogfhXnIR/wonYf7zx3wd+k8kM8t6HXz3H9+/j8/5km385DPbbXuzgPVFX8XzgP5iL8L81AfxJPu5iEf8aS7+wE678QfhvsB8hF/GOah4y5+FOYhH/GjcD9AdcUfhvsB8hF/GOahPogfhXnIR/wonAc678TfhfNAPuLvwjy0Dogn3c1DPuJJd+eB6oq/C+eBfMTfhXmoD+JJd/OQj3jSH7yX9xfV9w16wPdMn5W/rK7bA74X5qHrPvGku3nIRzzpD96L+avqvvWA75k+9P+Ak4/4XpiH+iCedDcP+Yh37wPQcaTrbvHL69k8kI/45TAPfe+Lb4V5yEd8az2bB6orfnk9mwfyEb8c5qE+iG+FechHPOkPnsv6VfW60QO+Z/qM/7q6bg/4XpiHjrt40t085COe9AfPRfxNdd96wPdMH/o7AOQjvhfmoT6IJ93NQz7i3XWAjiOdd+KX17N5IB/xy2EeWgfEt8I85CO+tZ7NA9UVv7yezQP5iF8O81AfxLfCPOQjvhXuB+i6W/xhuB8gH/GHYR667hM/CvOQj/hRuB+guuIPw/0A+Yg/DPNQH8SPwjzkI34UzgNdd4u/C+eBfMTfhXnoPoB40t085COedHceqK74u3AeyEf8XZiH+iCedDcP+Ygn3d0P0Hkn/jDcD5CP+MMwDx138aMwD/mIH4X7Aaor/jDcD5CP+MMwD/VB/CjMQz7iR+E80Hkn/i6cB/IRfxfmoXVAPOluHvIRT7o7D1RX/F04D+Qj/i7MQ30QT7qbh3zEP9D1veE+D3D/2QJ+y/W5rX7eYAv4rTTP/acLfDfNQz631br7PADVFb/l+sDvgOQjfivMQ30Q3w3zkI/4bjoP959z4M/TeSCfW9Dr5rn/zIGfp3nI57Zad+eB6oo/D+eBfMSfh3moD+LnYR7yET831wH6HZ/OO/Fbpg/9Dk4+4rfCPHTcxXfDPOQjvmv2gX7Hp7rit0wfug9APuK3wjzUB/HdMA/5iO+G80DnnfjzcB7IR/x5mIfWAfHzMA/5iJ+H80B1xZ+H80A+4s/DPNQH8fMwD/mIn6f7gfvPr9/9mCfd3g+Qzy3odfPcfzaB33wX5iGf22rd3Q9QXfGku/sB8hHv9gF9oA/iN99lechH/GY6D/effeD303kgn1vQ6+ZRn4AfpnnI57Zad+eB6orfD+eBfMTvh3moD+KHYR7yEU+6ux+g80486e5+gHzEu+sA+dBxF7/5LstDPuJJd/cDVFc86e5+gHzEu30gH+qD+M13WR7yEb8ZzgOdd+L3w3kgH/H7YR5aB8QPwzzkI34YzgPVFb8fzgP5iN8P81AfxA/DPOQjnnT3eQC67ha/ZfrQ7+DkI34rzEPXfeK7YR7yEd81+0C/41Nd8VumD/0OSD7it8I81Afx3TAP+YjvhvNA193iz8N5IB/x52Ee+t4XPw/zkI/4eTgPVFf8eTgP5CP+PMxDfRA/D/OQj/i5uQ7Q7/h03onfMn3od3DyEb8V5qHjLr4b5iEf8V2zD/Q7PtUVv2X60PMA5CN+K8xDfRDfDfOQj/huOA903ok/D+eBfMSfh3loHRA/D/OQj/h5OA9UV/x5OA/kI/48zEN9ED8P85CP+Hm4H6DrbvGku/sB8hHv3gcgH7ruE7/5LstDPuJJd/cDVFc86e5+gHzEu30gH+qD+M13WR7yEb8ZzgNdd4vfD+eBfMTvh3noPoD4YZiHfMQPw3mguuL3w3kgH/H7YR7qg/hhmId8xJPu7gfovBNPursfIB/x7jpAPnTcxW++y/KQj3jS3f0A1RVPursfIB/xbh/Ih/ogfvNdlod8xG+G80Dnnfj9cB7IR/x+mIfWAfHDMA/5iB+G80B1xe+H80A+4vfDPNQH8cMwD/mIJ33x7wAc/PrHOr13L75n+vzyN9V1e8D3wjz03rd40t085COe9MU+nPxtdd96wPdMH/p/wMlHfC/MQ30QT7qbh3zEu38HgI4jvXcvfnk9mwfyEb8c5qH3/sS3wjzkI761ns0D1RW/vJ7NA/mIXw7zUB/Et8I85COe9MV1oPVz9brRA75n+vz82+q6PeB7YR467uJJd/OQj3jSF/sw/21133rA90wf+n8AyUd8L8xDfZgXdDcP+Yh31wE6jnTeiV9ez+aBfMQvh3loHRDfCvOQj/jWejYPVHde0N15IB/xy2Ee6sO8oLt5yEd8K9wP0Hv34g/D/QD5iD8M89B73+JHYR7yET8K9wNUV/xhuB8gH/GHYR7qg/hRmId8xI/CeaD37sXfhfNAPuLvwjz0u7940t085COedHceqK74u3AeyEf8XZiH+iCedDcP+Ygn3d0P0Hkn/jDcD5CP+MMwDx138aMwD/mIH4X7Aao7L+jufoB8xB+GeagP84Lu5iEf8aNwHui8E38XzgP5iL8L89A6IJ50Nw/5iCfdnQeqOy/o7jyQj/i7MA/1YV7Q3TzkI570xfsAnb+rvm/QA75n+qz/fXXdHvC9MA9d94kn3c1DPuJJX+zDk6XqvvWA75k+9P+Ak894qV4fnhT4xT6IJ93NQz7jpXr3Aeg40nW3+OX1bB7IR/xymIe+98W3wjzkI761ns0D1RW/vJ7NA/mMl+r14UmBbwHfCvOQz7igL64D/eXqdaMHfM/02f6H6ro94HthHjru4kl385CPeNIX+/D0aXXfesD3TJ+fflddtwd8L8xDfRBPupuHfMS76wAdRzrvxC+vZ/NAPuKXwzy0DohvhXnIR3xrPZsHqit+eT2bB/IRvxzmoT6Ib4V5yEd8K9wP0HW3+MNwP0A+4g/DPHTdJ34U5iEf8aNwP0B1xR+G+wHyGS/V68OTAj8CfhTmIZ/xUr37AHQc6bpb/F04D+Qj/i7MQ/cBxJPu5iEf8aS780B1xd+F80A+46V6fXhS4Bf7IJ50Nw/5jAu6ux+g8078YbgfIB/xh2EeOu7iR2Ee8hE/CvcDVFf8YbgfIB/xh2Ee6oP4UZiHfMSPwnmg8078XTgP5CP+LsxD64B40t085COedHceqK74u3AeyEf8XZiH+iCedDcP+Yh3/w4A/Y5P792L3zJ96Hdw8hG/Feah977Fd8M85CO+a/aBfsenuuK3TB/6HZB8xG+FeagP4rthHvIR3w3ngd67F38ezgP5iD8P89B7f+LnYR7yET8P54Hqij8P54F8xJ+HeagP4udhHvIRPzfXAfodn8478VumD/0OTj7it8I8dNzFd8M85CO+a/ZhXnheYgv4LdOH7gOQj/itMA/1YV7Q3TzkI74bzgOdd+LPw3kgH/HnYR5aB8TPwzzkI34ezgPVnRd0dx7IR/x5mIf6MC/obh7yET8P9wP03r140t39APmId/8OAPnQe9/iN99lechHPOnufoDqiifd3Q+Qj3i3D+RDfRC/+S7LQz7iN8N5oPfuxe+H80A+4vfDPPR3AMQPwzzkI34YzgPVFb8fzgP5iN8P81AfxA/DPOQjnnR3P0DnnXjS3f0A+Yh31wHyoeMufvNdlod8xJPu7geo7rygu/sB8hHv9oF8qA/zgu7mIR/xm+E80Hknfj+cB/IRvx/moXVA/DDMQz7ih+E8UN15QXfngXzE74d5qA/zgu7mIR/xpLvPA9B1t/gt04d+Bycf8VthHrruE98N85CP+K7ZB/odn+qK3zJ9xoXnDbaA3wrzUB/Ed8M85DNeqncfgI4jXXeLPw/ngXzEn4d56Htf/DzMQz7i5+E8UF3x5+E8kM94qV4fnhT4OfDzMA/5jAu6+zwAnXfit0wf+h2cfMRvhXnouIvvhnnIR3zX7AP9jk91xW+ZPvQ8APmI3wrzUB/Ed8M85CO+G84DnXfiz8N5IB/x52EeWgfEz8M85CN+Hs4D1RV/Hs4D+Yg/D/NQH8TPwzzkI34e7gfouls86e5+gHzEu/cByIeu+8RvvsvykI940t39ANUVT7q7HyCf8VK9Pjwp8JvAb77L8pDPeKnefQA6jnTdLX4/nAfyEb8f5qH7AOKHYR7yET8M54Hqit8P54F8xkv1+vCkwA+BH4Z5yGdc0N39AJ134kl39wPkI95dB8iHjrv4zXdZHvIRT7q7H6C64kl39wPkI97tA/lQH8RvvsvykI/4zXAe6LwTvx/OA/mI3w/z0DogfhjmIR/xw3AeqK74/XAeyEf8fpiH+iB+GOYhH/Gk//Sv/1m/AF18E/im6XPwj9V1m8A3wzziL4C/CPOQj/gLsw+f/qm6b03gm6bPL39fXbcJfDPMQ30QfxHmIR/xF+E8iP9N48c86e48kI/4Rb1uHvEHwB80sjzkI550dx6ornjS3XkgH/FuH8iH+iD+oJHlIR/xpC+uA5PfV68bTeCbps/JP1fXbQLfDPPQcZ8UdDcP+Yi/MPuw86y6b03gm6bPyh+q6zaBb4Z5qA/iL8I85CP+IpwHOu8mBd2dB/IR764D5EPrwKSgu3nIRzzp7jxQXfGku/NAPuLdPpAP9UH8QSPLQz7iD8L9gPgb4G/C/QD5iL8J84j/9PrHPOluHvIRT7q7H6C64m/C/QD5iL8J81AfxJPu5iEf8Yt63XkQfwb82etsHshH/FmYR/wv/+3HPOluHvIRT7o7D1RX/NnrbB7IR/xZmIf6IJ50Nw/5iCfd3Q/QeTcp6O5+gHzE34R56LhPCrqbh3zEk+7uB6iu+JtwP0A+4m/CPNQH8aS7echHvLsO0HGk825S0N15IB/xZ2EeWgcmBd3NQz7iSXfngeqKP3udzQP5iD8L81AfxJPu5iEf8aQv3gd4tFJ936AJfNP0aa1U120C3wzz0HWf+IswD/m0CvpiH0aFvjWBb5o+Pz+vrtsEvhnmoT6MCrqbh3zEX4TzQNfd4kl354F8Wiv17gM8KvAHwB80sjzk0yro7jxQ3VFBd+eBfMS7fRgV+nYA/EEjy0M+4klfXAe6z6vXjSbwTdNnXqjbBL4Z5qHj3i3obh7ymRf0xT5srlb3rQl80/QZrlbXbQLfDPNQH8RfhHnIZ7habx2g40jnXbegu/NAPvPn9daBboE/AP6gkeUhn3lBd+eB6oon3Z0H8hmu1uvDZoE/AP6gkeUhn+FqvfsA9D1O193ib8L9APm0VurdB3hU4Bev+8ST7uYhn1ZBd/cDVHdU0N39APmIvwnzUB9GBd3NQz7i3fsAdBzpulv82etsHsintVLvPsCjAr94H0A86W4e8mkVdHceqO6ooLvzQD7iz8I81IdRQXfzkI940t39AJ133YLu7gfIZ/683jrQLfCLx71b0N085DMv6O5+gOqKvwn3A+QzXK3Xh80Cv9gH8aS7echnuFpvHaDjSOddt6C780A+8+f11oFugV9cB7oF3c1DPvOC7s4D1RV/9jqbB/IZrtbrw2aBX+yDeNLdPOQzBL3u8wDi94DfM33od3DyEb8X5hE/AX4S5iEf8ROzD/Q7PtUVv2f60O+A5CN+L8xDfRA/CfOQj/hJOA/i/+3Nj3nS3XkgH/GLet084k+AP3mT5SEf8aS780B1xZPuzgP5iHf7QD7UB/Enb7I85COedPd5ADrvJgXdfR6AfMTvhXnouE8KupuHfMRPzD7Q7/hUV/ye6UP3AchH/F6Yh/ogfhLmIR/xk3Ae6LybFHR3HshHvLsOkA+tA5OC7uYhH/Gku/NAdcWT7s4D+Yh3+0A+1AfxJ2+yPOQj/iTcD4j/L29/zJPu7gfIR/yiXjeP+B3gd95mechHPOnufoDqiifd3Q+Qj3i3D+RDfRC/8zbLQz7id8J5EH8F/FU4D+Qj/irMI37lf/+YJ93NQz7iSXfngeqKvwrngXzEX4V5qA/iSXfzkI940t39AJ13k4Lu7gfIR7y7DpAPHfdJQXfzkI940t39ANUVT7q7HyAf8W4fyIf6IH7nbZaHfMTvhPNA592koLvzQD7ir8I8tA5MCrqbh3zEk+7OA9UVfxXOA/mIvwrzUB/Ek+7mIR/xpLvPA9B1t/g906dVeN5gD/i9MA9d94mfhHnIp1XQ3ecBqO6ooLvPA5CP+L0wD/VhVNDdPOQjfhLOA113iyfdnQfyaa3Uuw/wqMCfAH/yJstDPq2C7s4D1R0VdHceyEe824dRoW8nwJ+8yfKQj3jS3ecB6LzrFnT3eQDymT+vtw50C/wE+EmYh3zmBd19HoDqit8zfeh5APIZrtbrw2aBnwA/CfOQz3C13jpAx5HOu25Bd+eBfObP660D3QJ/AvzJmywP+cwLujsPVFc86e48kM9wtV4fNgv8CfAnb7I85DNcrXcfgL7H6bpbPOnufoB8Wiv17gM8KvA7wO+8zfKQT6ugu/sBqjsq6O5+gHzEu30YFfq2A/zO2ywP+YjfCeeBrrvFX4XzQD6tlXr3AR4V+MX7AOJJd/OQT6ugu/NAdUcF3Z0H8hF/FeahPowKupuHfMST7u4H6LzrFnR3P0A+8+f11oFugd8Bfudtlod85gXd3Q9QXfGku/sB8hmu1uvDZoHfAX7nbZaHfIar9dYBOo503nULujsP5DN/Xm8d6Bb4xXWgW9DdPOQzL+juPFBd8VfhPJDPcLVeHzYL/GIfxJPu5iGfYUFf/DsAv/qXH+v03r34punT+Zfquk3gm2Eeeu9b/EWYh3w6BX2xD9NC35rAN02f9bXquk3gm2Ee6sO0oLt5yEf8RTgP9N69eNLdeSCfDuh189B7f+IPGlke8ukUdHceqO60oLvzQD7i3T5MC307AP6gkeUhH/GkL64Dg7XqdaMJfNP0efKium4T+GaYh477oKC7echH/IXZh/aL6r41gW+aPuNC3SbwzTAP9aFd0N085DN+UW8doONI592goLvzQD7i3XWAfGgdGBR0Nw/5iCfdnQeq2y7o7jyQz/hFvT60C/wB8AeNLA/5jEGvux+g9+7F34T7AfLpgF43D733LZ50Nw/5dAq6ux+gutOC7u4HyEf8TZiH+jAt6G4e8hHv/h0AOo703r34s9fZPJBPB/S6eeh3f/Gku3nIp1PQ3XmgutOC7s4D+Yg/C/NQH6YF3c1DPuJJd/cDdN4NCrq7HyAf8TdhHjrug4Lu5iEf8aS7+wGq2y7o7n6AfMYv6vWhXeAX+9Au6G4e8hm/qLcO0HGk825Q0N15IB/xZ2EeWgcGBd3NQz7iSXfngeq2C7o7D+QzflGvD+0Cv9iHdkF385DPuKAv3gdYe1l936AJfNP06b+srtsEvhnmoes+8RdhHvLpF/TFPjx+Vd23JvBN02f7VXXdJvDNMA/1QfxFmId8tl/Vuw9Ax5Guu8WT7s4D+fRf1rsPsFbgD4A/aGR5yKdf0N15oLriSXfngXy2X9Xrw+MCfwD8QSPLQz7bBX1xHbgsrBtN4Jumz9M/VtdtAt8M89Bxvyzobh7yEX9h9uHoj9V9awLfNH3+v3+bPuKaYR7qw1FBd/OQj/iLcB7ovLss6O48kI94dx24LKwbB8AfNLI85COedHceqO5RQXfngXzEu30gH+rDUUF385CP+INwP0DX3eJvwv0A+fRf1rsPsFbgF6/7xJPu5iGffkF39wNUV/xNuB8gn+1X9frwuMAv9kE86W4e8tl+Ve8+AB1Huu4Wf/Y6mwfy6b+sdx9grcAv3gcQT7qbh3z6Bd2dB6or/ux1Ng/ks/2qXh8eF/jFPogn3c1DPtsF3d0P0Hl3WdDd/QD5iL8J89Bxvyzobh7yEU+6ux+gukcF3d0PkI/4mzAP9eGooLt5yEe8uw5cFtaNM+DPXmfzQD7iz8I8tA5cFnQ3D/mIJ92dB6p7VNDdeSAf8WdhHurDUUF385CPePfvANDv+PTevfg906dTeN5gD/i9MA+99y1+EuYhn05Bd58HoLrTgu4+D0A+4vfCPNSHaUF385CP+Ek4D/TevXjS3Xkgnw7odfPQe3/iT95kecinU9DdeaC604LuzgP5iHf7MC307QT4kzdZHvIRT7r7PACdd4OC7j4PQD7i98I8dNwHBd3NQz7iJ2Yf2oXnJfaA3zN9xoW6e8DvhXmoD+2C7uYhn/GLeusAHUc67wYF3Z0H8hHvrgPkQ+vAoKC7echHPOnuPFDddkF354F8xi/q9aFd4E+AP3mT5SGfMeh19wP03r140t39APl0QK+bh977Fr/zNstDPp2C7u4HqO60oLv7AfIR7/ZhWujbDvA7b7M85CN+J5wHeu9e/FU4D+TTAb1uHvo7AOJJd/OQT6egu/NAdacF3Z0H8hF/FeahPkwLupuHfMST7u4H6LwbFHR3P0A+4t11gHzouA8KupuHfMST7u4HqG67oLv7AfIZv6jXh3aB3wF+522Wh3zGL+qtA3Qc6bwbFHR3HshH/FWYh9aBQUF385CPeNLdeaC67YLuzgP5jF/U60O7wC/2oV3Q3TzkMy7o7vMAdN0tfs/06ReeN9gDfi/MQ9d94idhHvLpF3T3eQCqK37P9NkuPG+wB/xemIf6IH4S5iGf7Vf17gPQcaTrbvGku/NAPv2X9e4DrBX4E+BP3mR5yKdf0N15oLriSXfngXy2X9Xrw+MCfwL8yZssD/lsF3T3eQA67y4Luvs8APmI3wvz0HG/LOhuHvIRPzH7cFR4XmIP+D3Th54HIB/xe2Ee6sNRQXfzkI/4STgPdN5dFnR3HshHvLsOXBbWjRPgT95kechHPOnuPFDdo4LuzgP5iHf7QD7Uh6OC7uYhH/En4X6ArrvFk+7uB8in/7LefYC1Ar8D/M7bLA/59Au6ux+guuJJd/cD5LP9ql4fHhf4HeB33mZ5yGf7Vb37AHQc6bpb/FU4D+TTf1nvPsBagV+8DyCedDcP+fQLujsPVFf8VTgP5LP9ql4fHhf4xT6IJ93NQz7bBd3dD9B5d1nQ3f0A+Yh314HLwrqxA/zO2ywP+Ygn3d0PUN2jgu7uB8hHvNsH8qE+HBV0Nw/5iN8J54HOu8uC7s4D+Yi/CvPQOnBZ0N085COedHceqO5RQXfngXzEX4V5qA9HBd3NQz7iH+i/+7F+8btqvgd8z/Q5+Mfquj3ge2Ee8Y/Wf8yT7uYhH/GkL/bh0z9V960HfM/0of8HnHzE98I81AfxpLt5yEf8ol53HsQvA7+8ns0D+YhfDvOIbwHfCvOQj/jWejYPVFf88no2D+QjfjnMQ30Q3wrzkI940hfXgcnvq9eNHvA90+fkn6vr9oDvhXnouE8KupuHfMSTvtiHnWfVfesB3zN96P8BJB/xvTAP9UE86W4e8hHvrgN0HOm8mxR0dx7IR/xymIfWgUlBd/OQj/jWejYPVFf88no2D+QjfjnMQ30Q3wrzkI/4VrgfEH8I/GG4HyAf8YdhHvEj4EdhHvIRPwr3A1RX/GG4HyAf8YdhHuqD+FGYh3zEj8J5EH8H/F04D+Qj/i7MI/7n//NjnnQ3D/mIJ92dB6or/i6cB/IRfxfmoT6IJ93NQz7iSXf3A3TeTQq6ux8gH/GHYR467pOC7uYhH/GjcD9AdcUfhvsB8hF/GOahPogfhXnIR/wonAc67yYF3Z0H8hF/F+ahdWBS0N085COedHceqK74u3AeyEf8XZiH+iCedDcP+YgnffE+wKOV6vsGPeB7pk9rpbpuD/hemIeu+8ST7uYhn1ZBX+zDqNC3HvA904f+H3DyEd8L81AfRgXdzUM+4t37AHQc6bpb/PJ6Ng/k01qpdx/gUYFvAd8K85BPq6C780B1RwXdnQfyEb8c5qE+jAq6m4d8xJO+uA50n1evGz3ge6bPvFC3B3wvzEPHvVvQ3TzkMy/oi33YXK3uWw/4nulDfweAfIar9fqwWeAX+yCedDcP+QxX660DdBzpvOsWdHceyGf+vN460C3wLeBbYR7ymRd0dx6orvjl9WweyGe4Wq8PmwW+BXwrzEM+w9V69wHoe5yuu8UfhvsB8mmt1LsP8KjAj4AfhXnIp1XQ3f0A1R0VdHc/QD7iD8M81IdRQXfzkI/4UTgPdN0t/i6cB/JprdS7D/CowC/eBxBPupuHfFoF3Z0Hqjsq6O48kI/4uzAP9WFU0N085COedHc/QOddt6C7+wHymT+vtw50C/wI+FGYh3zmBd3dD1Bd8YfhfoB8hqv1+rBZ4EfAj8I85DNcrbcO0HGk865b0N15IJ/583rrQLfAL64D3YLu5iGfeUF354Hqir8L54F8hqv1+rBZ4Bf7IJ50Nw/5DEGv+zyA+C3gt0wf+h2cfMRvhXnEd4HvhnnIR3zX7AP9jk91xW+ZPvQ7IPmI3wrzUB/Ed8M85CO+G86D+HPgz8N5IB/x52Ee8XPg52Ee8hE/D+eB6oo/D+eBfMSfh3moD+LnYR7yET8314FJ4XmhLeC3TB/6HZx8xG+Feei4Twq6m4d8xHfNPtDv+FRX/JbpQ/cByEf8VpiH+iC+G+YhH/HdcB7ovJsUdHceyEf8eZiH1oFJQXfzkI/4eTgPVFf8eTgP5CP+PMxDfRA/D/OQj/h5uB8Q/+t3P+ZJd/cD5CN+Ua+bR/wm8JvvsjzkI550dz9AdcWT7u4HyEe82wfyoT6I33yX5SEf8ZvhPIjfB34/nAfyEb8f5hE/BH4Y5iEf8cNwHqiu+P1wHshH/H6Yh/ogfhjmIR/xpLv7ATrvJgXd3Q+Qj3h3HSAfOu6Tgu7mIR/xpLv7AaornnR3P0A+4t0+kA/1QfzmuywP+YjfDOeBzrtJQXfngXzE74d5aB2YFHQ3D/mIH4bzQHXF74fzQD7i98M81AfxwzAP+Ygn3X0egK67xW+ZPq3C8wZbwG+Feei6T3w3zEM+rYLuPg9AdUcF3X0egHzEb4V5qA+jgu7mIR/x3XAe6Lpb/Hk4D+TTWql3H+BRgZ8DPw/zkE+roLvzQHVHBd2dB/IRfx7moT6MCrqbh3zEz811oFt4XmgL+C3TZ16ouwX8VpiHjnu3oLt5yGde0N3nAaiu+C3Th54HIJ/har0+bBb4LvDdMA/5DFfrrQN0HOm86xZ0dx7IZ/683jrQLfBz4OdhHvKZF3R3Hqiu+PNwHshnuFqvD5sFfg78PMxDPsPVevcB6HucrrvFk+7uB8intVLvPsCjAr8J/Oa7LA/5tAq6ux+guqOC7u4HyEe824dRoW+bwG++y/KQj/jNcB7oulv8fjgP5NNaqXcf4FGBHwI/DPOQT6ugu/NAdUcF3Z0H8hG/H+ahPowKupuHfMST7u4H6LzrFnR3P0A+8+f11oFugd8EfvNdlod85gXd3Q9QXfGku/sB8hmu1uvDZoHfBH7zXZaHfIar9dYBOo503nULujsP5DN/Xm8d6Bb4IfDDMA/5zAu6Ow9UV/x+OA/kM1yt14fNAj8EfhjmIZ9hQV/8OwC/gv9Hnt67F98zfTr/Ul23B3wvzEPvfYsn3c1DPp2CvtiHaaFvPeB7pg/9P+DkI74X5qE+TAu6m4d8xLt/B4COI713L355PZsH8umAXjcPvfcnvhXmIZ9OQXfngepOC7o7D+QjfjnMQ32YFnQ3D/mIJ31xHRisVa8bPeB7ps+TF9V1e8D3wjx03AcF3c1DPuJJX+xD+0V133rA90yfcaFuD/hemIf60C7obh7yGb+otw7QcaTzblDQ3XkgH/HLYR5aBwYF3c1DPuJb69k8UN12QXfngXzGL+r1oV3gW8C3wjzkMwa97n6A3rsXfxjuB8inA3rdPPTet/hRmId8OgXd3Q9Q3WlBd/cD5CP+MMxDfZgWdDcP+YgfhfNA792LvwvngXw6oNfNQ7/7iyfdzUM+nYLuzgPVnRZ0dx7IR/xdmIf6MC3obh7yEU+6ux+g825Q0N39APmIPwzz0HEfFHQ3D/mIH4X7AarbLujufoB8xi/q9aFd4EfAj8I85DN+UW8doONI592goLvzQD7i78I8tA4MCrqbh3zEk+7OA9VtF3R3Hshn/KJeH9oFfrEP7YLu5iGfcUFfvA+w9rL6vkEP+J7p039ZXbcHfC/MQ9d94kl385BPv6Av9uHxq+q+9YDvmT70/4CTz/aren14XOAX+yCedDcP+Wy/qncfgI4jXXeLX17P5oF8+i/r3QdYK/At4FthHvLpF3R3Hqiu+OX1bB7IZ/tVvT48LvAt4FthHvLZLuiL68BlYd3oAd8zfZ7+sbpuD/hemIeO+2VBd/OQj3jSF/tw9MfqvvWA75k+P/1rdd0e8L0wD/XhqKC7echHvLsOXBbWjWXgl9ezeSAf8cthHloHLgu6m4d8xLfWs3mgukcF3Z0H8hG/HOahPhwVdDcP+YhvhfsBuu4WfxjuB8in/7LefYC1Aj8CfhTmIZ9+QXf3A1RX/GG4HyCf7Vf1+vC4wI+AH4V5yGf7Vb37AHQc6bpb/F04D+TTf1nvPsBagV+8DyCedDcP+fQLujsPVFf8XTgP5LP9ql4fHhf4xT6IJ93NQz7bBd3dD9B5d1nQ3f0A+Yg/DPPQcb8s6G4e8hE/CvcDVPeooLv7AfIRfxjmoT4cFXQ3D/mIH4XzQOfdZUF354F8xN+FeWgduCzobh7yEU+6Ow9U96igu/NAPuLvwjzUh6OC7uYhH/Hu3wGg3/HpvXvxW6ZPp/C8wRbwW2Eeeu9bfDfMQz6dgu4+D0B1pwXdfR6AfMRvhXmoD9OC7uYhH/HdcB7ovXvx5+E8kE8H9Lp56L0/8fMwD/l0Cro7D1R3WtDdeSAf8edhHurDtKC7echH/NxcBwaF54W2gN8yfeh3cPIRvxXmoeM+KOhuHvIR3zX70C48L7EF/JbpMy7U3QJ+K8xDfWgXdDcP+Yxf1FsH6DjSeTco6O48kI/48zAPrQODgu7mIR/x83AeqG67oLvzQD7jF/X60C7wc+DnYR7yGYNedz9A792LJ93dD5BPB/S6eei9b/Gb77I85NMp6O5+gOpOC7q7HyAf8W4fpoW+bQK/+S7LQz7iN8N5oPfuxe+H80A+HdDr5qG/AyB+GOYhn05Bd+eB6k4LujsP5CN+P8xDfZgWdDcP+Ygn3d0P0Hk3KOjufoB8xLvrAPnQcR8UdDcP+Ygn3d0PUN12QXf3A+QzflGvD+0Cvwn85rssD/mMX9RbB+g40nk3KOjuPJCP+P0wD60Dg4Lu5iEf8cNwHqhuu6C780A+4xf1+tAu8EPgh2Ee8hkXdPd5ALruFr9l+vQLzxtsAb8V5qHrPvHdMA/59Au6+zwA1RW/ZfpsF5432AJ+K8xDfRDfDfOQz/arevcB6DjSdbf483AeyKf/st59gLUCPwd+HuYhn35Bd+eB6oo/D+eBfLZf1evD4wI/B34e5iGf7YLuPg9A591lQXefByAf8VthHjrulwXdzUM+4rtmH44Kz0tsAb9l+tDzAOQjfivMQ304KuhuHvIR3w3ngc67y4LuzgP5iD8P89A6cFnQ3TzkI34ezgPVPSro7jyQj/jzMA/14aigu3nIR/w83A/Qdbd40t39APn0X9a7D7BW4DeB33yX5SGffkF39wNUVzzp7n6AfLZf1evD4wK/CfzmuywP+Wy/qncfgI4jXXeL3w/ngXz6L+vdB1gr8EPgh2Ee8ukXdHceqK74/XAeyGf7Vb0+PC7wQ+CHYR7y2S7o7n6AzrvLgu7uB8hHvLsOXBbWjU3gN99lechHPOnufoDqHhV0dz9APuLdPpAP9eGooLt5yEf8ZjgPdN5dFnR3HshH/H6Yh9aBy4Lu5iEf8cNwHqjuUUF354F8xO+HeagPRwXdzUM+4h/o958H+n/8oZKfAT9zfW7/UFl3BvwszXP/+dX7H/Ok23nI57Zaf/D/MvzJSmXfZsDPXJ8/XamsOwN+FuahPogn3c1DPuIf6HXn4f6zAfzG+3AeyOcW9Lp57j8d4DtpHvK5rdbdeaC64jfeZ/NAPuI3wjzUB/GdMA/5iCf9wf8L/2fV68YM+JnpM/xFdd0Z8LMwDx138aS7echHPOkPfpf/8+q+zYCfmT4//dfqujPgZ2Ee6oN40t085CPeXQfoONJ5J37jfTYP5CN+I8xD64D4TpiHfMR33mfzQHXFb7zP5oF8xG+EeagP4jthHvIR30n3A/efU+BP0/0A+dyCXjfP/WcK/DTNQz631bq7H6C64k/D/QD5iD8N81AfxE/DPOQjfprOw/3nv334MU+6PQ/kcwt63Tz3n3Xg1z+Eecjntlp354HqiifdnQfyEe/2AX2gD+LXP2R5yEc86e5+gM478afhfoB8xJ+Geei4i5+GechH/DTcD1Bd8afhfoB8xJ+GeagP4qdhHvIRPw3ngc478aS780A+4t11gHxoHRC//iHLQz7iSXfngeqKJ92dB/IR7/aBfKgP4tc/ZHnIRzzpi/cBfvkX1fcNZsDPTJ+Vv6yuOwN+Fuah6z7xpLt5yEc86Yt9+Pmvqvs2A35m+gz/e3XdGfCzMA/1QTzpbh7yEe/eB6DjSNfd4jfeZ/NAPuI3wjz0vS++E+YhH/Gd99k8UF3xG++zeSAf8RthHuqD+E6Yh3zEk/7gveBfVa8bM+Bnps/4r6vrzoCfhXnouIsn3c1DPuJJf/Bc1t9U920G/Mz0+el/VNedAT8L81AfxJPu5iEf8e46QMeRzjvxG++zeSAf8RthHloHxHfCPOQjvvM+mweqK37jfTYP5CN+I8xDfRDfCfOQj/hOuB+g627xp+F+gHzEn4Z56LpP/DTMQz7ip+F+gOqKPw33A+Qj/jTMQ30QPw3zkI/4aTgPdN0tnnR3HshHvHsfgHzoPoD49Q9ZHvIRT7o7D1RXPOnuPJCPeLcP5EN9EL/+IctDPuJJd/cDdN6JPw33A+Qj/jTMQ8dd/DTMQz7ip+F+gOqKPw33A+Qj/jTMQ30QPw3zkI/4aTgPdN6JJ92dB/IR764D5EPrgPj1D1ke8hFPujsPVFc86e48kI94tw/kQ30Qv/4hy0M+4tfT5wHuP7vA76bPA5DPLeh189x/BsAP0jzkc1utu88DUF3xu+HzAOQjfjfMQ30QPwjzkI/4QToP959r4K/TeSCfW9Dr5rn/PPn3H/Ok23nI57Zad+eB6oq/DueBfMRfh3moD+JJd/OQj3jS3ecB6LwTvxs+D0A+4nfDPHTcxQ/CPOQjfhA+D0B1xe+GzwOQj/jdMA/1QfwgzEM+4gfhPNB5J/46nAfyEX8d5qF1QDzpbh7yEU+6Ow9UV/x1OA/kI/46zEN9EE+6m4d8xD/Q6+4H7j8N4Buuz231fqMBfCPNc/9pA99O85DPbbXu7georviG6/On1fuNBvCNMA/1QXw7zEM+4tvpPNx/joE/TueBfG5Br5tH6wTw4zQP+dxW6+48UF3xx+E8kI/44zAP9UH8OMxDPuLH5jpA3+N03olvmD70PUg+4hthHjru4tthHvIR3zb7QN/jVFd8w/Sh/QD5iG+EeagP4tthHvIR3w7ngc478cfhPJCP+OMwD60D4sdhHvIRPw7ngeqKPw7ngXzEH4d5qA/ix2Ee8hFPuvs8AF13i98NnwcgH/G7YR667hM/CPOQj/hB+DwA1RW/Gz4PQD7id8M81AfxgzAP+YgfhPNA193ir8N5IB/x12Ee+t4XT7qbh3zEk+7OA9UVfx3OA/mIvw7zUB/Ek+7mIR/xpLvPA9B5J343fB6AfMTvhnnouIsfhHnIR/wgfB6A6orfDZ8HIB/xu2Ee6oP4QZiHfMQPwnmg8078dTgP5CP+OsxD64B40t085COedHceqK7463AeyEf8dZiH+iCedDcP+Yh37wPQ9zhdd4tvmD70PUg+4hthHrruE98O85CP+LbZB/oep7riG6YPrQPkI74R5qE+iG+HechHfDucB7ruFn8czgP5iD8O89B9APHjMA/5iB+H80B1xR+H80A+4o/DPNQH8eMwD/mIH5vrAH2P03knvmH60Pcg+YhvhHnouItvh3nIR3zb7AN9j1Nd8Q3Th/YD5CO+EeahPohvh3nIR3w7nAc678Qfh/NAPuKPwzy0Dogfh3nIR/w4nAeqK/44nAfyEX8c5qE+iB+HechHPOmLfwfg4Nc/1um9e/Ez0+eXv6muOwN+Fuah977Fk+7mIR/xpC/24eRvq/s2A35m+qz8z+q6M+BnYR7qg3jS3TzkI979OwB0HOm9e/Eb77N5IB/xG2Eeeu9PfCfMQz7iO++zeaC64jfeZ/NAPuI3wjzUB/GdMA/5iCd9cR1o/Vy9bsyAn5k+P/+2uu4M+FmYh467eNLdPOQjnvTFPsx/W923GfAz02f4v6rrzoCfhXmoD/OC7uYhH/HuOkDHkc478Rvvs3kgH/EbYR5aB8R3wjzkI77zPpsHqjsv6O48kI/4jTAP9WFe0N085CO+E+4H6L178afhfoB8xJ+Geei9b/HTMA/5iJ+G+wGqK/403A+Qj/jTMA/1Qfw0zEM+4qfhPNB79+JJd+eBfMS7fweAfOh3f/HrH7I85COedHceqK540t15IB/xbh/Ih/ogfv1Dlod8xJPu7gfovBN/Gu4HyEf8aZiHjrv4aZiHfMRPw/0A1Z0XdHc/QD7iT8M81Id5QXfzkI/4aTgPdN6JJ92dB/IR764D5EPrgPj1D1ke8hFPujsPVHde0N15IB/xbh/Ih/owL+huHvIRT/rifYDO31XfN5gBPzN91v++uu4M+FmYh677xJPu5iEf8aQv9uHJUnXfZsDPTJ/xUnXdGfCzMA/1QTzpbh7yGS/Vuw9Ax5Guu8VvvM/mgXzEb4R56HtffCfMQz7iO++zeaC64jfeZ/NAPuOlen14UuA7wHfCPOQzLuiL60B/uXrdmAE/M322/6G67gz4WZiHjrt40t085COe9MU+PH1a3bcZ8DPT56ffVdedAT8L81AfxJPu5iEf8e46QMeRzjvxG++zeSAf8RthHloHxHfCPOQjvvM+mweqK37jfTYP5CN+I8xDfRDfCfOQj/hOuB+g627xp+F+gHzEn4Z56LpP/DTMQz7ip+F+gOqKPw33A+QzXqrXhycFfgr8NMxDPuOlevcB6DjSdbd40t15IB/x7n0A8qH7AOLXP2R5yEc86e48UF3xpLvzQD7jpXp9eFLg14Ff/5DlIZ9xQXf3A3TeiT8N9wPkI/40zEPHXfw0zEM+4qfhfoDqij8N9wPkI/40zEN9ED8N85CP+Gk4D3TeiSfdnQfyEe+uA+RD64D49Q9ZHvIRT7o7D1RXPOnuPJCPeLcP5EN9EL/+IctDPuLXw+cB6L178bvh8wDkI343zEPvfYsfhHnIR/wgfB6A6orfDZ8HIB/xu2Ee6oP4QZiHfMQPwnmg9+7FX4fzQD7ir8M89N6feNLdPOQjnnR3Hqiu+OtwHshH/HWYh/ognnQ3D/mIJ919HoDOO/G74fMA5CN+N8xDx138IMxDPuIH4fMAVHde0N3nAchH/G6Yh/owL+huHvIRPwjngc478dfhPJCP+OswD60D4kl385CPeNLdeaC684LuzgP5iL8O81Af5gXdzUM+4t2/A0Df4/TevfiG6UPfg+QjvhHmofe+xbfDPOQjvm32gb7Hqa74hulD6wD5iG+EeagP4tthHvIR3w7ngd67F38czgP5iD8O89DfARA/DvOQj/hxOA9UV/xxOA/kI/44zEN9ED8O85CP+LG5DtD3OJ134humD30Pko/4RpiHjrv4dpiHfMS3zT7MC/ulBvAN04f2A+QjvhHmoT7MC7qbh3zEt8N5oPNO/HE4D+Qj/jjMQ+uA+HGYh3zEj8N5oLrzgu7OA/mIPw7zUB/mBd3NQz7iSXefB6DrbvG74fMA5CN+N8xD133iB2Ee8hE/CJ8HoLrid8PnAchnvFSvD08K/AD4QZiHfMZL9e4D0HGk627x1+E8kI/46zAPfe+LJ93NQz7iSXfngeqKvw7ngXzGS/X68KTAL/ZBPOluHvIZF3T3eQA678Tvhs8DkI/43TAPHXfxgzAP+YgfhM8DUF3xu+HzAOQjfjfMQ30QPwjzkI/4QTgPdN6Jvw7ngXzEX4d5aB0QT7qbh3zEk+7OA9UVfx3OA/mIvw7zUB/Ek+7mIR/x7n0A+h6n627xDdOHvgfJR3wjzEPXfeLbYR7yEd82+0Df41RXfMP0GRf2Gw3gG2Ee6oP4dpiHfMZL9e4D0HGk627xx+E8kI/44zAP3QcQPw7zkI/4cTgPVFf8cTgP5DNeqteHJwV+DPw4zEM+44Lu7gfovBPfMH3oe5B8xDfCPHTcxbfDPOQjvm32gb7Hqa74hulD+wHyEd8I81AfxLfDPOQjvh3OA5134o/DeSAf8cdhHloHxI/DPOQjfhzOA9UVfxzOA/mIPw7zUB/Ej8M85CP+ga65WNT/4w+V/C82fsyT/sDn9g+VdR/43IJeN8/9Zw34tY0wD/ncVusPfpf5k5XKvi3WFU/62Px/wMlHvNsH9IE+iF/byPKQj/i1dB7uP1+A/5LOA/ncgl43j9Zp4PtpHvK5rdbdeaC64r+E80A+4r+EeagP4vthHvIRT/qD54L+rHrdWDzvxJP+YJ35RXXdB+f1L+qtA+RDx1382kaWh3zEk/7guuzPq/u2WFc86WPz/wEkH/FuH8iH+iB+bSPLQz7i18J5oPNO/JdwHshH/JcwD60D4vthHvIR3w/ngeqK/xLOA/mI/xLmoT6I74d5yEd8P90P3H++A/893Q+Qzy3odfPcfx5//DFPup2HfG6rdXc/QHXFfw/3A+Qj/nuYh/ognnQ3D/mIf6DXnYf7zzPgn30M54F8bkGvm0f3IYDfTvOQz2217s4D1RX/7GM2D+Qj/lmYh/ogfjvMQz7iSXf3A3Teif8e7gfIR/z3MA8dd/Gku3nIRzzp7n6A6or/Hu4HyEf89zAP9UE86W4e8hHvrgN0HOm8E//sYzYP5CP+WZiH1gHx22Ee8hG//TGbB6or/tnHbB7IR/yzMA/1Qfx2mId8xJP+4L28v6i+b7B43S2e9Af3ef6yuu6D+yp/We8+APnQdZ/4tY0sD/mIJ/3BezF/Vd23xbriSR+b/w84+Yh3+0A+1AfxaxtZHvIRvxbOA113i/8SzgP5iP8S5qHvffH9MA/5iO+H80B1xX8J54F8xH8J81AfxPfDPOQjnvQHz2X9qnrdWDzvxJP+4D7zX1fXfXBf96/rrQPkQ8dd/NpGlod8xJP+4LmIv6nu22Jd8aSPzb8DQD7i3T6QD/VB/NpGlod8xK+F80Dnnfgv4TyQj/gvYR5aB8T3wzzkI74fzgPVFf8lnAfyEf8lzEN9EN8P85CP+H64H6DrbvHfw/0A+Yj/Huah6z7xpLt5yEc86e5+gOqK/x7uB8hH/PcwD/VBPOluHvIR794HoONI193in33M5oF8xD8L89B9APHbYR7yEb/9MZsHqiv+2cdsHshH/LMwD/VB/HaYh3zEk+7uB+i8E/893A+Qj/jvYR467uJJd/OQj3jS3f0A1RX/PdwPkI/472Ee6oN40t085CPeXQfoONJ5J/7Zx2weyEf8szAPrQPit8M85CN++2M2D1RX/LOP2TyQj/hnYR7qg/jtMA/5iN9Onwe4/3wF/mv6PAD53IJeN8/95xL4yzQP+dxW6+7zAFRX/NfweQDyEf81zEN9EH8Z5iEf8ZfpPNx//p9PP+ZJt+eBfG5Br5vn/vMU+Kefwjzkc1utu/NAdcWT7s4D+Yh3+4A+0AfxTz9lechHPOnu8wB03on/Gj4PQD7iv4Z56LiLvwzzkI/4y/B5AKor/mv4PAD5iP8a5qE+iL8M85CP+MtwHui8E0+6Ow/kI95dB8iH1gHxTz9lechHPOnuPFBd8aS780A+4t0+kA/1QfzTT1ke8hH/NN0P3H8+A/853Q+Qzy3odfPcf46AP0rzkM9tte7uB6iu+M/hfoB8xH8O81AfxB+FechH/FE6D/efb8B/S+eBfG5Br5tHn//7Y550Ow/53Fbr7jxQXfHfwnkgH/HfwjzUB/Gku3nIRzzp7n6Azjvxn8P9APmI/xzmoeMu/ijMQz7ij8L9ANUV/zncD5CP+M9hHuqD+KMwD/mIPwrngc478d/CeSAf8d/CPLQOiCfdzUM+4kl354Hqiv8WzgP5iP8W5qE+iCfdzUM+4kl3nweg627xX8PnAchH/NcwD133ib8M85CP+MvweQCqK/5r+DwA+Yj/GuahPoi/DPOQj/jLcB7ouls86e48kI949z4A+dD3vvinn7I85COedHceqK540t15IB/xbh/Ih/og/umnLA/5iCfdfR6AzjvxX8PnAchH/NcwDx138ZdhHvIRfxk+D0B1xX8NnwcgH/FfwzzUB/GXYR7yEX8ZzgOdd+JJd+eBfMS76wD50Dog/umnLA/5iCfdnQeqK550dx7IR7zbB/KhPoh/+inLQz7in4b7AbruFv853A+Qj/jPYR667hN/FOYhH/FH4X6A6or/HO4HyEf85zAP9UH8UZiHfMQfhfNA193iv4XzQD7iv4V56D6AeNLdPOQjnnR3Hqiu+G/hPJCP+G9hHuqDeNLdPOQjnnR3P0DnnfjP4X6AfMR/DvPQcRd/FOYhH/FH4X6A6or/HO4HyEf85zAP9UH8UZiHfMQfhfNA5534b+E8kI/4b2EeWgfEk+7mIR/xpLvzQHXFfwvngXzEfwvzUB/Ek+7mIR/xpC/+HYCDX/9Yp/fuxZP+4O/8/Ka67oO/q/ObH+t189B73+LXNrI85COe9MU+nPxtdd8W64onfWz+P+DkI97tA/lQH8SvbWR5yEf8WjgP9N69+C/hPJCP+C9hHnrvT3w/zEM+4vvhPFBd8V/CeSAf8V/CPNQH8f0wD/mIJ31xHWj9XL1uLJ534kl/8HfGfltd98Hf9fptvXWAfOi4i1/byPKQj3jSF/sw/2113xbrzgv62Px/AMlHvNsH8qE+zAu6m4d8xK+F80Dnnfgv4TyQj/gvYR5aB8T3wzzkI74fzgPVnRd0dx7IR/yXMA/1YV7Q3TzkI74f7gfovXvx38P9APmI/x7mofe+xZPu5iEf8aS7+wGqK/57uB8gH/HfwzzUB/Gku3nIR7z7dwDoONJ79+KffczmgXzEPwvz0O/+4rfDPOQjfvtjNg9UV/yzj9k8kI/4Z2Ee6oP47TAP+Ygn3d0P0Hkn/nu4HyAf8d/DPHTcxZPu5iEf8aS7+wGqOy/o7n6AfMR/D/NQH+YF3c1DPuLddYCOI5134p99zOaBfMQ/C/PQOiB+O8xDPuK3P2bzQHXnBd2dB/IR/yzMQ32YF3Q3D/mIJ33xPkDn76rvGyxed4sn/cHfef776roP/q7y39e7D0A+dN0nfm0jy0M+4klf7MOTpeq+LdYVT/rY/H/AyWe8VK8PTwr8GvBrG1ke8hkv1bsPQMeRrrvFfwnngXzEfwnz0Pe++H6Yh3zE98N5oLriv4TzQD7jpXp9eFLg+8D3wzzkMy7oi+tAf7l63Vg878ST/uDvzP9Ddd0Hf9f9H+qtA+RDx1382kaWh3zEk77Yh6dPq/u2WFc86Q9+x/9ddd0Hv5v/rl4fyIf6IH5tI8tDPuLXwnmg8078l3AeyEf8lzAPrQPi+2Ee8hHfD+eB6or/Es4D+Yj/EuahPojvh3nIR3w/3A/Qdbf47+F+gHzEfw/z0HWfeNLdPOQjnnR3P0B1xX8P9wPkM16q14cnBX6xD+JJd/OQz3ip3n0AOo503S3+2cdsHshH/LMwD90HEL8d5iEf8dsfs3mguuKffczmgXzGS/X68KTAbwO/HeYhn3FBd/cDdN6J/x7uB8hH/PcwDx138aS7echHPOnufoDqiv8e7gfIR/z3MA/1QTzpbh7yEe+uA3Qc6bwT/+xjNg/kI/5ZmIfWAfHbYR7yEb/9MZsHqiv+2cdsHshH/LMwD/VB/HaYh3zEb4fPA9B79+K/hs8DkI/4r2Eeeu9b/GWYh3zEX4bPA1Bd8V/D5wHIR/zXMA/1QfxlmId8xF+G80Dv3Ysn3Z0H8hHv/h0A8qH3/sQ//ZTlIR/xpLvzQHXFk+7OA/mId/tAPtQH8U8/ZXnIRzzp7vMAdN6J/xo+D0A+4r+Geei4i78M85CP+MvweQCqOy/o7vMA5CP+a5iH+jAv6G4e8hF/Gc4DnXfiSXfngXzEu+sA+dA6IP7ppywP+Ygn3Z0Hqjsv6O48kI94tw/kQ32YF3Q3D/mIfxruB+i9e/Gfw/0A+Yj/HOah977FH4V5yEf8UbgfoLriP4f7AfIR/znMQ30QfxTmIR/xR+E80Hv34r+F80A+4r+FeejvAIgn3c1DPuJJd+eB6or/Fs4D+Yj/FuahPogn3c1DPuJJd/cDdN6J/xzuB8hH/OcwDx138UdhHvIRfxTuB6juvKC7+wHyEf85zEN9mBd0Nw/5iD8K54HOO/HfwnkgH/Hfwjy0Dogn3c1DPuJJd+eB6s4LujsP5CP+W5iH+jAv6G4e8hFPuvs8AF13i/8aPg9APuK/hnnouk/8ZZiHfMRfhs8DUF3xX8PnAchnvFSvD08K/CXwl2Ee8hkv1bsPQMeRrrvFk+7OA/mId+8DkA9974t/+inLQz7iSXfngeqKJ92dB/IZL9Xrw5MC/xT4p5+yPOQzLuju8wB03on/Gj4PQD7iv4Z56LiLvwzzkI/4y/B5AKor/mv4PAD5iP8a5qE+iL8M85CP+MtwHui8E0+6Ow/kI95dB8iH1gHxTz9lechHPOnuPFBd8aS780A+4t0+kA/1QfzTT1ke8hH/NNwP0HW3+M/hfoB8xH8O89B1n/ijMA/5iD8K9wNUV/zncD9APuOlen14UuCPgD8K85DPeKnefQA6jnTdLf5bOA/kI/5bmIfuA4gn3c1DPuJJd+eB6or/Fs4D+YyX6vXhSYFf7IN40t085DMu6O5+gM478Z/D/QD5iP8c5qHjLv4ozEM+4o/C/QDVFf853A+Qj/jPYR7qg/ijMA/5iD8K54HOO/HfwnkgH/Hfwjy0Dogn3c1DPuJJd+eB6or/Fs4D+Yj/FuahPogn3c1DPuJJH777z/oF6OJnwM9Mn4N/rK47A34W5hH/q/c/5kl385CPeNIX+/Dpn6r7NgN+Zvr88vfVdWfAz8I81AfxpLt5yEf8ol53HsRvAL/xPpsH8hG/EeYR3wG+E+YhH/Gd99k8UF3xG++zeSAf8RthHuqD+E6Yh3zEk764Dkx+X71uzICfmT4n/1xddwb8LMxDx31S0N085COe9MU+7Dyr7tsM+Jnps/KH6roz4GdhHuqDeNLdPOQj3l0H6DjSeTcp6O48kI/4jTAPrQOTgu7mIR/xnffZPFBd8Rvvs3kgH/EbYR7qg/hOmId8xHfC/YD4U+BPw/0A+Yg/DfOInwI/DfOQj/hpuB+guuJPw/0A+Yg/DfNQH8RPwzzkI34azoP4//bhxzzp7jyQj/hFvW4e8evAr3/I8pCPeNLdeaC64kl354F8xLt9IB/qg/j1D1ke8hFPursfoPNuUtDd/QD5iD8N89BxnxR0Nw/5iJ+G+wGqK/403A+Qj/jTMA/1Qfw0zEM+4qfhPNB5Nyno7jyQj3h3HSAfWgcmBd3NQz7iSXfngeqKJ92dB/IR7/aBfKgP4tc/ZHnIRzzpi/cBHq1U3zeYAT8zfVor1XVnwM/CPHTdJ550Nw/5tAr6Yh9Ghb7NgJ+ZPj8/r647A34W5qE+jAq6m4d8xLv3Aeg40nW3+I332TyQT2ul3n2ARwW+A3wnzEM+rYLuzgPVHRV0dx7IR/xGmIf6MCrobh7yEU/64jrQfV69bsyAn5k+80LdGfCzMA8d925Bd/OQz7ygL/Zhc7W6bzPgZ6bPcLW67gz4WZiH+iCedDcP+QxX660DdBzpvOsWdHceyGf+vN460C3wHeA7YR7ymRd0dx6orviN99k8kM9wtV4fNgt8B/hOmId8hqv17gPQ9zhdd4s/DfcD5NNaqXcf4FGBnwI/DfOQT6ugu/sBqjsq6O5+gHzEn4Z5qA+jgu7mIR/x03Ae6LpbPOnuPJBPa6XefYBHBX4d+PUPWR7yaRV0dx6o7qigu/NAPuLdPowKfVsHfv1Dlod8xJPu7gfovOsWdHc/QD7z5/XWgW6BnwI/DfOQz7ygu/sBqiv+NNwPkM9wtV4fNgv8FPhpmId8hqv11gE6jnTedQu6Ow/kM39ebx3oFvh14Nc/ZHnIZ17Q3XmguuJJd+eBfIar9fqwWeDXgV//kOUhnyHodZ8HEL8L/G74PAD5iN8N84gfAD8I85CP+EH4PADVFb8bPg9APuJ3wzzUB/GDMA/5iB+E8yD+GvjrcB7IR/x1mEf8k3//MU+6m4d8xJPuzgPVFX8dzgP5iL8O81AfxJPu5iEf8aS7zwPQeTcp6O7zAOQjfjfMQ8d9UtDdPOQjfhA+D0B1xe+GzwOQj/jdMA/1QfwgzEM+4gfhPNB5Nyno7jyQj/jrMA+tA5OC7uYhH/Gku/NAdcVfh/NAPuKvwzzUB/Gku3nIR/yTf8/2A+IbwDdMH/oeJB/xjTCP+Dbw7TAP+Yhvm32g73GqK75h+tA6QD7iG2Ee6oP4dpiHfMS3w3kQfwz8cTgP5CP+OMwjfgz8OMxDPuLH4TxQXfHH4TyQj/jjMA/1Qfw4zEM+4sfmOjApXC80gG+YPvQ9SD7iG2EeOu6Tgu7mIR/xbbMP9D1OdcU3TB/aD5CP+EaYh/ogvh3mIR/x7XAe6LybFHR3HshH/HGYh9aBSUF385CP+HE4D1RX/HE4D+Qj/jjMQ30QPw7zkI940t3nAei6W/xu+DwA+bRW6t0HeFTgB8APwjzk0yro7vMAVHdU0N3nAchH/G6Yh/owKuhuHvIRPwjnga67xV+H80A+rZV69wEeFfjF733xpLt5yKdV0N15oLqjgu7OA/mIvw7zUB9GBd3NQz7iSXefB6DzrlvQ3ecByGf+vN460C3wA+AHYR7ymRd093kAqit+N3wegHyGq/X6sFngB8APwjzkM1yttw7QcaTzrlvQ3Xkgn/nzeutAt8AvrgPdgu7mIZ95QXfngeqKvw7ngXyGq/X6sFngF/sgnnQ3D/kMV+vdB6DvcbruFt8wfVqF/UYD+EaYh677xLfDPOTTKujufoDqjgq6ux8gH/GNMA/1YVTQ3TzkI74dzgNdd4s/DueBfFor9e4DPCrwY+DHYR7yaRV0dx6o7qigu/NAPuKPwzzUh1FBd/OQj/ixuQ50C9cLDeAbps+8ULcBfCPMQ8e9W9DdPOQzL+jufoDqim+YPsPCfqMBfCPMQ30Q3w7zkM9wtd46QMeRzrtuQXfngXzmz+utA90CPwZ+HOYhn3lBd+eB6oo/DueBfIar9fqwWeDHwI/DPOQzLOiLfwfgV//yY53euxc/M306/1Jddwb8LMxD732LJ93NQz6dgr7Yh2mhbzPgZ6bP+lp13RnwszAP9WFa0N085CPe/TsAdBzpvXvxG++zeSCfDuh189B7f+I7YR7y6RR0dx6o7rSgu/NAPuI3wjzUh2lBd/OQj3jSF9eBwVr1ujEDfmb6PHlRXXcG/CzMQ8d9UNDdPOQjnvTFPrRfVPdtBvzM9BkX6s6An4V5qA/tgu7mIZ/xi3rrAB1HOu8GBd2dB/IRvxHmoXVgUNDdPOQjvvM+mweq2y7o7jyQz/hFvT60C3wH+E6Yh3zGoNfdD9B79+JPw/0A+XRAr5uH3vsWPw3zkE+noLv7Aao7LejufoB8xJ+GeagP04Lu5iEf8dNwHui9e/Gku/NAPh3Q6+ah3/3Fr3/I8pBPp6C780B1pwXdnQfyEe/2YVro2zrw6x+yPOQjnnR3P0Dn3aCgu/sB8hF/Guah4z4o6G4e8hE/DfcDVLdd0N39APmMX9TrQ7vAT4GfhnnIZ/yi3jpAx5HOu0FBd+eBfMS76wD50DowKOhuHvIRT7o7D1S3XdDdeSCf8Yt6fWgX+HXg1z9kechnXNAX7wOsvay+bzADfmb69F9W150BPwvz0HWfeNLdPOTTL+iLfXj8qrpvM+Bnps/2q+q6M+BnYR7qg3jS3Tzks/2q3n0AOo503S1+4302D+TTf1nvPsBage8A3wnzkE+/oLvzQHXFb7zP5oF8tl/V68PjAt8BvhPmIZ/tgr64DlwW1o0Z8DPT5+kfq+vOgJ+Feei4XxZ0Nw/5iCd9sQ9Hf6zu2wz4menz079W150BPwvzUB+OCrqbh3zEu+vAZWHd2AB+4302D+QjfiPMQ+vAZUF385CP+M77bB6o7lFBd+eBfMRvhHmoD0cF3c1DPuI74X6ArrvFn4b7AfLpv6x3H2CtwE+Bn4Z5yKdf0N39ANUVfxruB8hn+1W9Pjwu8FPgp2Ee8tl+Ve8+AB1Huu4WT7o7D+TTf1nvPsBagV8Hfv1Dlod8+gXdnQeqK550dx7IZ/tVvT48LvDrwK9/yPKQz3ZBd/cDdN5dFnR3P0A+4k/DPHTcLwu6m4d8xE/D/QDVPSro7n6AfMSfhnmoD0cF3c1DPuKn4TzQeXdZ0N15IB/x7jpwWVg31oFf/5DlIR/xpLvzQHWPCro7D+Qj3u0D+VAfjgq6m4d8xK+HzwPQe/fid8PnAcinA3rdPPTet/hBmId8OgXdfR6A6k4Luvs8APmI3w3zUB+mBd3NQz7iB+E80Hv34q/DeSCfDuh189B7f+JJd/OQT6egu/NAdacF3Z0H8hF/HeahPkwLupuHfMST7j4PQOfdoKC7zwOQj/jdMA8d90FBd/OQj/hB+DwA1W0XdPd5APIZv6jXh3aBHwA/CPOQz/hFvXWAjiOdd4OC7s4D+Yi/DvPQOjAo6G4e8hFPujsPVLdd0N15IJ/xi3p9aBf4xT60C7qbh3zGoNfdD9B79+Ibpk+nsN9oAN8I89B73+LbYR7y6RR0dz9AdacF3d0PkI/4RpiH+jAt6G4e8hHfDueB3rsXfxzOA/l0QK+bh/4OgPhxmId8OgXdnQeqOy3o7jyQj/jjMA/1YVrQ3TzkI35srgODwvVCA/iG6UPfg+QjvhHmoeM+KOhuHvIR3zb70C7slxrAN0yfcaFuA/hGmIf60C7obh7yGb+otw7QcaTzblDQ3XkgH/HHYR5aBwYF3c1DPuLH4TxQ3XZBd+eBfMYv6vWhXeDHwI/DPOQzLuju8wB03S1+N3wegHz6L+vdB1gr8APgB2Ee8ukXdPd5AKorfjd8HoB8tl/V68PjAj8AfhDmIZ/tV/XuA9BxpOtu8dfhPJBP/2W9+wBrBX7xe1886W4e8ukXdHceqK7463AeyGf7Vb0+PC7wi30QT7qbh3y2C7r7PACdd5cF3X0egHzE74Z56LhfFnQ3D/mIH4TPA1Ddo4LuPg9APuJ3wzzUh6OC7uYhH/GDcB7ovLss6O48kI/46zAPrQOXBd3NQz7iSXfngeoeFXR3HshH/HWYh/pwVNDdPOQj3r0PQN/jdN0tvmH69Av7jQbwjTAPXfeJb4d5yKdf0N39ANUV3zB9tgv7jQbwjTAP9UF8O8xDPtuv6t0HoONI193ij8N5IJ/+y3r3AdYK/Bj4cZiHfPoF3Z0Hqiv+OJwH8tl+Va8Pjwv8GPhxmId8tgu6ux+g8+6yoLv7AfIR3wjz0HG/LOhuHvIR3zb7cFTYLzWAb5g+tB8gH/GNMA/14aigu3nIR3w7nAc67y4LujsP5CP+OMxD68BlQXfzkI/4cTgPVPeooLvzQD7ij8M81Iejgu7mIR/xD/Tf/Vi/+F01/4uNH/OkL/oc/GN13UUf8Yt63Tzi14Bf28jykI940hf78Omfqvu2WFc86WPz/wEnH/FuH8iH+iB+bSPLQz7i18J5EP8F+C/hPJCP+C9hHvF94PthHvIR3w/ngeqK/xLOA/mI/xLmoT6I74d5yEc86YvrwOT31evG4nk3KeiLPif/XF130Ue8uw6QDx33SUF385CPeNIX+7DzrLpvi3XFkz42/x9A8hHv9oF8qA/i1zayPOQjfi2cBzrvJgXdnQfyEf8lzEPrwKSgu3nIR3w/nAeqK/5LOA/kI/5LmIf6IL4f5iEf8f1wPyD+O/Dfw/0A+Yj/HuYR//jjj3nS3TzkI550dz9AdcV/D/cD5CP+e5iH+iCedDcP+Yhf1OvOg/hnwD/7mM0D+Yh/FuYRvw38dpiHfMRvf8zmgeqKf/YxmwfyEf8szEN9EL8d5iEf8aS7+wE67yYF3d0PkI/472EeOu6Tgu7mIR/xpLv7Aaor/nu4HyAf8d/DPNQH8aS7echHvLsO0HGk825S0N15IB/xz8I8tA5MCrqbh3zEb3/M5oHqin/2MZsH8hH/LMxDfRC/HeYhH/GkL94HeLRSfd9g8bpbPOmLPq2V6rqLPq2VevcBHhX4NeDXNrI85NMq6It9GBX6tlh3VNDH5v8DTj7i3T6MCn1bA35tI8tDPuLXwnmg627xX8J5IJ/WSr37AI8KfB/4fpiHfFoF3Z0Hqjsq6O48kI/4L2Ee6sOooLt5yEc86YvrQPd59bqxeN51C/qiz7xQd9Fn/rzeOtAt8GvAr21kechnXtAX+7C5Wt23xbriSR+bfweAfIar9fqwWeDXgF/byPKQz3C13jpAx5HOu25Bd+eBfObP660D3QLfB74f5iGfeUF354Hqiv8SzgP5DFfr9WGzwPeB74d5yGe4Wu8+AH2P03W3+O/hfoB8Wiv17gM8KvCL133iSXfzkE+roLv7Aao7KujufoB8xH8P81AfRgXdzUM+4t37AHQc6bpb/LOP2TyQz/9b2hnbtpJlQXSMj7UUwAawQbRFfGlJcTYBiQMoAIFWW7IYAAOg2RatDoABMACBVluyaMpgADTXKmDQmKOqh+sWDuoWbt/35rHn9df777b3AL8M3wPfF/OQz7vR03mgup9GT+eBfMQvinmoD59GT/OQj3jS0/MArbuD0dPzAPncH9v2gYPh58/9YPQ0D/ncjZ6eB6iu+K/ieYB8zk9tfXgz/LwP4klP85DP+altH6DnSOvuYPR0Hsjn/ti2DxwM3wPfF/OQz93o6TxQXfGL19o8kM/5qa0Pb4bvge+LecjnDHrrfQDxR+CPxfsA5CP+WMwjfgJ+KuYhH/FT8T4A1RV/LN4HIB/xx2Ie6oP4qZiHfMRPxXkQ/6/NP/Okp/NAPuLnemse8R3w3aaWh3zEk57OA9UVT3o6D+QjPu0D+VAfxHebWh7yEU96eh+A1t230dP7AOQj/ljMQ8/92+hpHvIRPxXvA1Bd8cfifQDyEX8s5qE+iJ+KechH/FScB1p330ZP54F8xKf7APnQPvBt9DQP+YgnPZ0Hqiue9HQeyEd82gfyoT6I7za1POQjviueB8Rvgd8WzwPkI35bzCN+AH4o5iEf8UPxPEB1xW+L5wHyEb8t5qE+iB+KechH/FCcB/EX4C/FeSAf8ZdiHvF//PXPPOlpHvIRT3o6D1RX/KU4D+Qj/lLMQ30QT3qah3zEk56eB2jdfRs9PQ+Qj/htMQ8992+jp3nIR/xQPA9QXfHb4nmAfMRvi3moD+KHYh7yET8U54HW3bfR03kgH/GXYh7aB76NnuYhH/Gkp/NAdcVfivNAPuIvxTzUB/Gkp3nIRzzp6X0A+t0t/li8D0A+77/b3gP8MvwE/FTMQz7vRk/vA1DdT6On9wHIR/yxmIf68Gn0NA/5iJ+K80C/u8WTns4D+bz/bnsP8MvwHfDdppaHfN6Nns4D1f00ejoP5CM+7cOn6VsHfLep5SEf8aSn9wFo3R2Mnt4HIJ/7Y9s+cDD8BPxUzEM+d6On9wGorvhj8T4A+Zyf2vrwZvgJ+KmYh3zOT237AD1HWncHo6fzQD73x7Z94GD4DvhuU8tDPnejp/NAdcWTns4D+Zyf2vrwZvgO+G5Ty0M+56e29wD033H63S1+WzwPkM/777b3AL8MPwA/FPOQz7vR0/MA1f00enoeIB/x22Ie6sOn0dM85CN+KM4D/e4WfynOA/m8/257D/DL8PP3AOJJT/OQz7vR03mgup9GT+eBfMRfinmoD59GT/OQj3jS0/MArbuD0dPzAPncH9v2gYPhB+CHYh7yuRs9PQ9QXfHb4nmAfM5PbX14M/wA/FDMQz7np7Z9gJ4jrbuD0dN5IJ/7Y9s+cDD8fB84GD3NQz53o6fzQHXFX4rzQD7np7Y+vBl+3gfxpKd5yOds9Pm/A/Af+Dvy9N29eNLnPvv//lx37rMHvTUPffctfvlSy0M+e6PP+3AzfZvXvRn9Gv4dcPIRn/bhZvq2BH75UstDPuKXxXmg7+7FfxTngXz2oLfmoe/+xI/FPOSzN3o6D1T3ZvR0HshH/EcxD/XhZvQ0D/mIJ32+D5yWP+8b83V3Mvrc59+rn+vOfcSn+wD50HM/GT3NQz7iSZ/3Ybf6uW/zujujX8O/A0g+11VbH3aGXwK/fKnlIZ/rqm0foOdI6+5k9HQeyEf8RzEP7QMno6d5yEf8WJwHqrszejoP5HNdtfVhZ/gR+LGYh3yuoLeeB+i7e/FfxfMA+exBb81D332LJz3NQz57o6fnAap7M3p6HiAf8V/FPNSHm9HTPOQjPv13AOg50nf34hevtXkgnz3orXno//uL74t5yGdv9HQeqO7N6Ok8kI/4RTEP9eFm9DQP+YgnPT0P0Lo7GT09D5CP+K9iHnruJ6OnechHPOnpeYDq7oyengfI57pq68PO8PM+7Iye5iGf66ptH6DnSOvuZPR0HshH/KKYh/aBk9HTPOQjvn+tzQPV3Rk9nQfyua7a+rAzfA98X8xDPlejz98DLJ9/fm8w/90tnvS5z/j8c925z/jc9h5gafgl8MuXWh7yGY0+78PD+ue+zeuKJ/0a/h1w8unXbX14MPwS+OVLLQ/59Ou29wD0HOl3t/iP4jyQz/jc9h5gafgR+LGYh3xGo6fzQHXFfxTngXz6dVsfHgw/Aj8W85BPb/T5PjCZfWO+7iajz326P3+uO/cRn+4Dk9k3lsAvX2p5yEc86fM+DH/+3Ld53cHoc58//vdz3bmP+LQP5EN9GIye5iEf8cviPNC6m4yezgP5iP8o5qF9YDJ6mod8xI/FeaC6g9HTeSAf8R/FPNSHwehpHvIRPxbPA/S7W/xX8TxAPuNz23uApeHnv/vEk57mIZ/R6Ol5gOqK/yqeB8inX7f14cHw8z6IJz3NQz79uu09AD1H+t0tfvFamwfyGZ/b3gMsDd8D3xfzkM9o9HQeqK74xWttHsinX7f14cHwPfB9MQ/59EZPzwO07iajp+cB8hH/VcxDz30yepqHfMSTnp4HqO5g9PQ8QD7iv4p5qA+D0dM85CM+3Qcms28sgF+81uaBfMQvinloH5iMnuYhH/H9a20eqO5g9HQeyEf8opiH+jAYPc1DPuL74n0A+u5e/LF4H4B89qC35qHvvsVPxTzkszd6eh+A6t6Mnt4HIB/xx2Ie6sPN6Gke8hE/FeeBvrsXT3o6D+SzB701D333J77b1PKQz97o6TxQ3ZvR03kgH/FpH26mbx3w3aaWh3zEk57eB6B1dzJ6eh+AfMQfi3nouZ+MnuYhH/FT8T4A1d0ZPb0PQD7XVVsfdoafgJ+KecjnumrbB+g50ro7GT2dB/IRn+4D5EP7wMnoaR7yEU96Og9Ud2f0dB7I57pq68PO8B3w3aaWh3yuoLeeB+i7e/Hb4nmAfPagt+ah777FD8U85LM3enoeoLo3o6fnAfIRvy3moT7cjJ7mIR/xQ3Ee6Lt78ZfiPJDPHvTWPPTvAIgnPc1DPnujp/NAdW9GT+eBfMRfinmoDzejp3nIRzzp6XmA1t3J6Ol5gHzEb4t56LmfjJ7mIR/xQ/E8QHV3Rk/PA+RzXbX1YWf4AfihmId8rqu2fYCeI627k9HTeSAf8ZdiHtoHTkZP85CPeNLTeaC6O6On80A+11VbH3aGn/dhZ/Q0D/lcjZ7eB6Df3eKPxfsA5DM+t70HWBp+An4q5iGf0ejpfQCqK/5YvA9APv26rQ8Php+An4p5yKdft70HoOdIv7vFk57OA/mMz23vAZaG74DvNrU85DMaPZ0Hqiue9HQeyKdft/XhwfAd8N2mlod8eqOn9wFo3U1GT+8DkI/4YzEPPffJ6Gke8hE/Fe8DUN3B6Ol9APIRfyzmoT4MRk/zkI/4qTgPtO4mo6fzQD7i031gMvtGB3y3qeUhH/Gkp/NAdQejp/NAPuLTPpAP9WEwepqHfMR3xfMA/e4Wvy2eB8hnfG57D7A0/AD8UMxDPqPR0/MA1RW/LZ4HyKdft/XhwfAD8EMxD/n067b3APQc6Xe3+EtxHshnfG57D7A0/Pw9gHjS0zzkMxo9nQeqK/5SnAfy6ddtfXgw/LwP4klP85BPb/T0PEDrbjJ6eh4gH/HbYh567pPR0zzkI34ongeo7mD09DxAPuK3xTzUh8HoaR7yET8U54HW3WT0dB7IR/ylmIf2gcnoaR7yEU96Og9UdzB6Og/kI/5SzEN9GIye5iEf8X/T/w9pZXzj 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+AQAAAAAAAgAAAAIA9HQAAA==eNpsnXmcTuUb/28URWlDqDhkKUUhylLPsUT2XRR17LIz9vXMjGWYwez7cj/zzD6WGYyd50ZkiRIiSx2hhEpFIdXv95vnfM7v8Xw/f3l5f9/fq8/rcrldc+ae8wghjF3vZXmFEOaiiqW/GoU10kt/n7o1zZ+LuH6eB3iLlFTmazej4kp//bBzHPMdfm18rH9d+cRrmf6e7PBV8gO83Trpz7WGsxOZj7r6K4/FMB/c+rZNFMtrRMevLK1zuP5Klhfc9H4TznxD1Aot/e/0OhzCfHBt24lglheeOvHpcpbX4Q8/tIz5qKsGdzaZD259GjTfv6/GomNp/n2Suevi/LnV4Yckfy6HpUYzHzmNr19axXxw486o5f51jflnHvBUl5OrHuD1v4ny56JqcBj1UbfRxMXMBzefvbuQ5XX6tGlnKMsLrt+tYTIffdWejZjFfHA5ouo0lheeKIiZzfKCq8a3pzAfdVWQZyzzwY0aT3zA5lW4wmeW5jz81Ew2r+BWTOF05msPG6NLf03eNIr54Hr37SPYvMJT8eOmsXl1+KivJzEfda3OYQb1bS4fWzqI5RUZLbqW/v708PdoXpuLs/fbM19Wa6G7/t9/58aIdi7ig1uDr3Z0kbxO3XNT32B5wWWjoY+6mG/XVe0Ku7qID65d7NjHxebV7pP5eNYYNq/ganD9j5nv9DV+VHfmgxuHn67H5hWemfh7Nzav4HrtJq+6iI+6WvtmpX0I9MHlgsbvu0hep09PlC/9cwzMCy6Te/d2Md/uq7j/6kAX8cH1Vnc+dJG88HTXzMEsL7jRPHUY81FXvN9lGPPBtWf/HOnyO1fN+00j/M9J7ffwEH9ujlq25AFeMH0u9e1zyohPn8F8h4dNHOdf1/r+3qIHvK8jp/pz8VPmLH8un5OjmI+6ektzEPPBdaNZfZYX56QIHT6C5QVX7xx5j/r2nFrDZzRmPrhM+7n0zy0wLzxzSj/f+RCY1+bWvlulf86BPuqqVXNK/5z/xwd/7+uxLr++WuGjpvn3yRpX5gN/LhYumeLPtZ/G9GM+chqVJ3Z2ER9c1Djn+3ti11UxL097oO66f9735+byIbP8uXns3gjmo671cVJPF/HB9fohTVwsr90nfWbN0r8fgXnBRdpLY5iPvhpT201iPriq6J3iInnh6St/Ge1iecE/6DaC+airj2oylfkObzq01AucV5yTcnlefxeZV3Bz9ujhLuLjnBK/NhjOfIc/Ez6Wzqvtab8UfcLmFdxy3ZrMfNS12sVPYj649c6T01henJPGE0WjWV5wrfG345nvnKuJ96kPLpoPnkjzwhsVNoXmtbm6WyOI+airlTw0lfo21xsuCWLzij7JX+dPY/MKrod0mc585DTPhkxnvsMrR8+k82p7emy3GWxewTVt6gzmo65RvfFs5oOrt9vPYXnRJ/FT0gyWF1zv+M8s5qOvUnuF+uCywW+zaV5437Scy/KCi59azGc+6uoZR+dR3+bG4OKFLrKvyrdvDij9tVb3gS6yr4KLxI/fZ75Wpt7Hpb9fM/Bj6ttc/+S3j11sX7U9I6zRYLavgpsn1n7IfNQ1eyw0mA9uTu4wjOW1+j/uOyeTaoxgecHFl/oI5ouhk3zn8Omg0cwHF+OXjWZ54ZkdVoxgecFVx7IjmY+6cumG0cwH1/96aAzbV50+HbrxEdtXwbW+s4YxHznl5b7DmQ8u3t0xgu2r8Izz341g+yq4dX/raOqjbpXVo5gProdeHsPyok96n+0jaV6bC+3QKOajr/r12DHMB9dOLhzL8jrexK5jWF5wY9RHnzAfdU1x5RPmg1tf/DKezasasmJcaZ0xq8axeQW3nk8fx3wzqZzv3yG9whTmg8saVaaweYVnvvvpODav4NaFp8dTH3XPvTaF+eBGhZFTWF5j6Vrfv4t/bApiecGN2keDqF9hdOn5q3UOms18cHU0dTbLC09t+zuI5QU39K7Tme/UvfnjbOaDW2lD57B5dfr0uGc8nVebq59jJzAfObWfdk1hPrg2vedUNq/w5BNPTGLz6vADtaYwH3VlmUbTmA9unB4TxPKiT+a9/dNZXnDx3aIZzEdfjY8rzqW+zbXu/85leeHJAREzWV5wc2/Z2cxHXePr7vOZD66PfGwh21dxTsq2lcfRfdXm6usbE5mPc0orOjmO+eBaj7mT2L4KT1ZvOpXtqw4/uzOI+nZd88SsqcwHtyo0mM7y4pzUIl6awPI6/PFjk5nvzOmwz6gPbp3qQfPCU5tbTWN5/z9/fgbzUVfdPRLEfHA56PeZbF9Fn4xKaTPZvgpu1js+h/nIaY3sPYv54PrRe3RfhSeq91vA9lWHn11hUt+ua6XUXcR8cC3o22CW1+nTiLTZLC+4WXvffOY7fV1wnPrgovf0RSwvPKPjZzQvuAqfF8p81DUfnxxCfZtr/RYvYfOKc1L0yJvK5hVce2dXEPNxTonxdWYwH9waOHUWm1d4xpSZM9m8gosvr81hvlNXTJvLfHD9VpuFLC/OSf3Y/TksL7j6OG4+83GuGicamcwHN3q7g1leeKLTj4tYXnDtyMMhzEddc/3MUOaDq6ZblrB5RZ+M0MYL2byCiz+zg6lv5/y/uYOZD26mtV3M5hWe/kX2Yjav4LJtlTDq23WtoOBlzAfXernCWV6nrz+GL2Z5wcXPK5cxH321xk0MYz642dkdzvI6Xpknw1lecC3yqdXMR13tmfdXMx9cdf4syuX3XNV4obzp/5zUSDg8xZ9rjR9a+gCv23ku8/Gc0vo7rjfzwa3w46P964qV5kp/TzTyfV8SXGbUifHnckaTFcxHXZVcMp354EbrC4tYXjwnNRuOGuQiecHlqk2NXMTHcyozdOEEF/HBjUbJpV+3BeaFp+YPH8TygpvxrSYyH3XVjUqDXMQHF2FlhH9f5YdlE/37ZK1/KsqfixtvpvlzPW1YHPOR06rQdynzHT5l+MoH6s7r4vb39Ok/Jvlzs/VvHn+uvRGaTn27rjZ+QTTzwUXYvHiWF30yPkuZzfI6fODfwcxHX9WcSR8xH1z1nzKF5YWnN+scxvKCi2fqrWa+U/c/91zq29ya/oLv+/4B84rnpDIofzqbV3Cj/eVpzMdzSn3TrjnMBzcjw+a4yLw6ddc/OcVF5tXhc98ay3zUNfctm+0iPrj1QspMlhfPSc3vLy9gecGNti8tYr6zp1bZZ1Lf5vJEtRCWF5424cIilhdcK3N2EfNRV9bUQ5kPbj3TYLGLzCv6JDof8H2/MGBewY23dr7gIj5ymj1Tg1zEBzfkDt/3FQLmFZ589dhQNq/g6rbv+66BPupaIf1GuIgPLoyH27O8Tl+/vbaQ5QXXwk7MZz76qmUULKa+zVVd+38PyAvPMHvMcZG84LKgwnTmo6457e9Q5jt8n2+vwrmqLszLefBcDXf7cxV2Ks+fm5n7PczHOWV9mZLMfHBx7Kt0/7rmrV8LHvAifsx+gF/ft8afy+d/yWU+6mozVrqZD65yt3tYXpyTKqtjDM1rc5ndOoH69pwaK3KXM9/hxxZFsrzwrAOzUlhecNWzrGQ+6pol6bHMBxfV9aQH+rq5zzr/Punlz+X7c9VRrvfnmrapkPnIqZ+4lc18cKNN+zz/ukb40CJ/TysMXuvPRe7pIn8uvu+2nvp2XePxggLmg+vNm61ledEn8disTJYXXNyuQ330Vds1M5354FrEK5ksLzz5bcU8lhdcW/5mIfNRVwycm019m4uHXsun82qfk1pa+/lsXsG1q0cWMx/nlChKH8J8h797ZRqbV3hyhoxg8wqullyJZr5Tt+lak/ngZtLc5Swvzkk1t9snLpbX5tb4f991Ed85V7tkL3IR3+F3T5R+3R+YF55+vM1Ilhfc2jptLvXtuqr2mU9cxAe3Er9qxeYVfRLmO4lsXh1efwb1kVObWyma+eAy2Uhk8wpPNjmeyeYVXH/05xzmo641+1wG88FFy25ZLC/6JGuNXMbygotPr0cxH33Vvx8ynfngRo87S1heeOrniUksL7iMedXNfNQ1Gr4TzXyHW2VL/10I3FfxnNSIDg5h+yq4eWLBYubjOaU2/YPFzHf4B7eXsn3Vqdtp3FK2r4LrB14JYz7qitpJy5kPro18OYLlxXNS/cj0pSyvwz/xLKe+/ZxKdv4tjPng6p+cCJbXqTvotwiW1+G/a5HMR115s3Mk88HFoc7RbF9Fn4wW7uV0X7W5OdOzgvp2TmNE6Crmg8sKrki2r8Kzlj0czvZVcK04dgXzUddqcjuS+eDiTJcolhd9Eme3RrG84FbEKzHMR1+Nxe1imQ9umlo8zWt7ZodhsSyvw+NdccxHXb3P0QTmg+uNmiSxecVzUrngp3A2r+CWt1Ik8/GcUus9eTXzwdXLZaPZvMKzvB2j6bzaXN2LimU+6lo9Z8RS3+bmjXIJLC+ek1ptD0SxvOB6x9axzMdzVVWjTizzwc2+G+NZXngi5lw8ywsuS9KSmI+6pmtGEvPBzTc/TGXzij6JT76PZ/MKbpavm8R8J+dm338v0AdX0a+l0nlF3TatU9i8glsfP53GfNQ1amSlM9/hz0yTLC/6pHWamsryOrxnxwzmo6/WmYEZzAcXO15xs7zwzLFl3DSvzc3t3TKZj7qyf3Ym88G1xZWz2L7qnJNbqy9n+yq4eqb/UubjnBJ1tCjmg5ufDYp0kX3VqftfuRAX2VfBtZp7ZjEfdfWorJUu4oPLr46H0bw4J0/lx7G84Eaf4DjmY07NPg2TmQ9urEhJZnnh6YnZsSwvuBFyK5r5qGuNnJDMfHBj8NwkF9lX0SfzypUhLrKvgps/jpjAfOQ0Bvwc7CI+uPGO7/u+gfsqPLErdjHbVx1ePZb7dl0tcsMHzAdXnwwt/bmUwLzok6zWNdJF8oKrTLGc+nZfZWpWAvPBjayDsS6SF57qv3OBi+R1+PWMHi7io65YsCfSRXxw/etjS9m84pyUnSLT2byCy/2JGczHOaX2jnczH1zf0yyTzatT97FHJJ1Xm4vHzmYwH3Xlpu2Z1Afv91cmy+uckzVnelhecG3ZjCzm41wVU4ZmMx9cnxaWw/LC09q6slleh396MJv5Tt0BS3OZDy4H9cxj84o+mRu/SWfzCq7W30xlvpOzzflM5oOroUMy6bzanjr6TxKbV3Crx7k45qOuujvWTX2by3+/y2B50SexrUIOywsuyv6WzXz0VZ0+ncd8cNm/Rj7LC88aNSub5rW5eHN5FvNRV7jL51Pf5uLCyjx2X9X6803f/ZWqPRay+6rg4t7Khcw3rq/wfT0/JzmE+eBq26EQdl8VntH5v4Xsviq4fjhvEfNRV59WM5T54Hr+6lCWV16d4HuOkByylOUFF+U+Xcp89Yjv61FVR61gvsPHPhvO8sITDZotY3nB5RfHljEfdc3zeeHMB5d50yPYfVX0STvQ1aT3VW2uX//JZD5yqq2NFzMf3Bh6bzG7rwrPWrE9mN1XBdf3HwihPupuvb+E+eDirbeXsbxOX7ekhrG84IY7Yznz0Vdj0LyVzHd4td2rWF54avb5FSwvuN5zcATzUVdMqR3JfHBjgIhi86odLFv69ayx5aXVbF7BrcrJq6k/tND33CR2XxTzweWmStFsXuGZ3kaRbF7BZfiaSOajrmoXEs18cO2XejEsr/h8fSm3Wh2LYXnBRfZLscy3Do4r/XdGxkbGMR9c5Il4lheetmRnLMsLrn2/JI75Tt3GW+KZD24+ty6BzSv6pHfvE8XmFVxdfzaa+U7OMzdjmA9ufvV9LJ1X27Mi/otm8wquFVeIZT7qGuv+imM+uLng1QSWF31S+YviWV5wvbpvLwr00VdReCGR+eDyRttklhee3uDnRJbX4b/0T2Y+6soRe1OYD24kjUyj91Xtc1Keq7GY3VcF15R3CfNxTpnD94cxH1zMyFjB7qvCMxZ8uozdVwU3ZjVfwXyn7oVTEcwHN8OnrWZ5nXO19QuraF6b65WqRzLfOVeLykUzH1z7zn5+HJAXnijfI4rmtbm5oDia+ahrzJsXy3xws9btOHZfFX3SV1dbye6rgquk1pHMR07rKVcU88Hlavvva8B9VXhq68vR7L4quAofFMt81FXT78dS3+ZWWIsEltfpq/VeLMsLrjZVjWc++mpWTkmgvs3N7ycl0bzwXq+RSPPa3DrdN5n5qKu9ODWF+eDiD5nG5hXnpN7z9Tg2rw4/Oiee+TinjD0LE5kPLhffSGLzCs+6dTmBzSu4KJmWxHzUNd+fnsJ8cLNZ1TSWF+ek9u+pFJrX5mLl+DTq2+eqSKqZwXxwUX+SZHnhyeinMlhecOuj4ZL5qKv69HEz3+Hnm2eyeUWftKsvprB5dbi4m0p99LX9sXTmg1t9ekk6r7Znbfgtnc0ruN5vqWQ+6orw9m7mg4uzrkyWF32yGrZzs7zg1m8vZjIffbX6PeOhvs3NDT97aF7bU3GPelhecOPrmx7mo67Zfk8W88GN+RHZbF81Hy3x7Suxl+LZvurwgRMTmK9dGun7e3gqLYH54Kp2p0S2r8Izvu2eyPZVcBE9J4n5qCt7Dkyivs01uTeZ5TUW9PL9/nQszQuuLRqWyHy9U1ff9/UmpsczH9ycFJ/A8sITSwtpXnDRaUQK8526XeonMR9crmqZwvZV9EkbdDWZ7avg5oiIVOYjp7w6L5X54CJvSjrbV+GpioPS2b4Kbh2uJZmPunr1zpL54Np3hpvldfqaMD2N5XV454MZ1Edf/12fxnxwc28ryfLCkyfqullecO3fTzKZj7p6ZhH1wYV61cPmVX37qu/f4ZzoWDqvNjfb7I6j/p8nfF+3J/WMYr7DG2yJZvMKTz+dnsDmFVxvVDOZ+nZd8c6dWOaDa5NvJrC81l/dfPuuNXU5zWtzUe14BPP18hN8Pw/YusE85oPry2OCWV6n7o0aUSwvuNXIQ33UtX65tYz5Di/7UiSbV6evKyunsXkFV182ksxHTlF/RgrzwdWzvu+DB84rPGvRJTebV3DjwzQP8526Y3z/v0Af3Pix2MPyok/6818ksrzg8qthacxHX5X2VBzzwa0o3/e1AvPCkysru1lecKvdcA/znbqf/5LBfHBl5mayfRXnpPbXs262rzo8+qlM5uOc0jfOz2Q+uBoT7WH7KjwxrLyH7avg2t3fPNS36+onk7OYD278vjqb5rXPSWGt9dC8NjcOXspiPuZUPJeRxXxwrdG9bJbXqTu2bA7Na3PtXplc5qOu/k6zXOY7fOPbeWxfRZ/Uji+z2L4KrvZlZDMfOS1rQg7zwa3jjXPpvmp74tCQHLavggtRPpf5qKt+/iKX+jaXr/fJY3nRJ/ne+VyW1+HJEXnMR1/VH1XymQ8uRmzIZ3nhidgn8llecK3iiHzmO3Vfe7qA+jbX6zcrYPOKc1Lmv5vN5hXcWH8kh/k4p8yYROqDa3V757J5hSca1Mlj8wquTW6dz3zUVQs/oz64+e/NfJbXOVc7RGazvODqQzOX+ThXrYX1s6lvc83qn8vywtNm1aJ5wfWvPihgPupaI2pRH9x6eUoBm1f0ScZWLmDzCi7qJRQwHznNPnsLmA8uur1RSOfV9uStLwrYvIIbP22mPupa0e0LmQ9u9btO8zp9/frlQpYXXDsQWsh89FVOfZv64KLOwkKWF57Za2QhzWtzs97hAuajrtGiSSHzwWXbHgXsviqek+qLPo1n91XB5Xtjk5iP55Syd14S88Hl171S2X1VeNqxNqnsviq4ePuNDOajrpFUL4P54Ho/ey8KyIvnpPJ1lcrygpuRQzKY7zynOvB3BvVtrqW942Z5HW/Zk26aF7xfSCbzUdcIWZ3JfIdH3PCw+6rok9XA93Vj4H1VcO3DdZnMd3LejMtkPrj2dt0sdl/V8Ya1zGL3VcFVWHE281HX2FuYzXxw/XSjXJbX6Wubfz0sL7hee2E29e2+an/1zmY+uDZ9Sw7L63gz/85hecH17Ml5zEddY3rNPOY7PGh6PptXPCfVkj1uNq/gesaxTObjOaV17l0P88FlQd8sNq/wtLiHsti8gpv/tMtmPurq34zIpr7NVfDMHJYXz0n1jmeyWF5w85Xd2czHnqrf6JHDfHCt9kO5LC88c7XMYXnBjZYLcpnv1E1dm8t8cPXre3l0Xu0+WUdCcti8gouEvbnMd/o6Ylku88Hl4zF5bF7hqTP36byCW00aFzAfdWWNeOo7/IOVBSyv06e99r4dkNfh3zyXz3z0Ve44SX2Hn+2Vz/LC07t/k8/yghvtwwqob9e1FmzLZz646PhUAbuvinPSqvtsLruvCm58+1Ee83FOWXcT8pgPLp+/kc/uq8ITd818dl8V3HpjZgHznbrHtELmg6tqr69heZ1zte/JfJrX5srzYiHzMaf626qA+uDa3UKWF542YNAaltfhsyaupb5dV12puZb6NldT6qxj91XRJzX0zUJ+X9XHjZo7C6mPnF8/s5b54NoHn65l91XhqQ13Ctl9VXAr6Gwh81HX+KzFOurbXPu33zqW9//3deg6lhdcH1BnPfOdvu6rsJ754LLmgfUsLzzr38L1LC+4FvXjeuajrvq4YRHzHV7QsYjNK85JK7oXnVdwbbqxhvk4p7TyM6kPrm3owecVdWOH0HkFN4P1dcxHXfX002uZD250UmtZXpyT+rsNC2lem6sGJdTHuao3f7+A+eCmGV7A8sJTia+voXltLnomrGE+6lpl1hUwH1zU21XA5tXp68gKfF5tbv6YR33kNGf1W0d9m6uVe9exeYVnXK9YxOYVXBbX4L5dV228v4754OLk5XUsL/okx19eQ/OCV6u8lvnoq/nGpgLmg1sXfHMTmNepW7bsWpYXXExxr2E+6mo3GxYwH1y2nZbP9lU8J5X/JOXSfdXmVuazecx3nqt2K8pjPrgefiOP7avwzCoL89i+Cm55P89jPuoaXzycz3xwudzeFwPy4jmp8f2SfJYXXN6alc98PKcy5vvOgUAf3NpelM/ywhP3W+WzvA6PP5rHfNSV8ql86tvcqP8m3VedPo0vR/dVcNWjbj717ZzGkeN5zAfX3l+Wx/ZVeHq9RvlsXwXXWj1PfdTV19XOYz64qpieS/OiTz1b5NG8NrcKp+dS3+6rtaR+LvPB9QHfZ7O88PQ2kTksL7gIXZlNfbuuDJ+exXxwNe9yJp1X+zmptrVbAZtXcEutzmc+nlNat5oUMB9cXn2Czis8884SOq/g5jXf9wX+x7fraknRucwHN6buymZ5neeqns00L7iW3jiP+XiuKm5cyGM+uLhyJYflder2aJHD8oKrT772MB91zaKJWcwH1/8y3Wxe0Sf5eWo2m1dwLf2Ih/lOznJJHurb3NRfd7N5hWd1PORm8woumnTIYD7qykl10pjv8GVxCSwv+iQnP+9mecG1Cq+mMh99lZeGpjIf3KhcLY7lhScOFcSxvOCqx76VzEddrfzmFdS3uTHbPYPtqzgnRZd7eWxfBdcaH8hjPs4p0dy3FwT64EYl+/u7AfsqPNUoOI/uqzZX+xrlMR91rUOZ2cwHNwvPZdG89jmpH5mWxfKCa+5OHuZjTlVaGzfzwbUGj2SwvPCsu5UzWV5w9e0XkvlO3ejVKcwHNwfUSmT7KvpkXt6dy/ZVh09qnkt95HzOfu4a4IPrk+z7sQH7Kjx96owctq+Cy6ZjspmPurLkjpv54LJKsmR50Se94zMZLC+4sXViKvPRV/Po7ljmg5sNp0axvE5db1wSywuu3x0Rz3zUNYb3iaC+za09iUvYvOKcFFM/TWbzCm4MfyOe+Tin9JcuRTEf3Bg/PozNKzyr9YYoNq8On71lBfXtuvoTN+cyH1wl9fuA5cU5qdesZLK84NrdNwYw3zlXg0+Vvhc90Ae3Lt7xvXcxIK9T9+6hIC/JCy7kk77PYw3wUVdGfRrpJT647DW/9L2ngfOKPonyMSFsXsGtaY1mMt/JqeqUfu5voA+u6eXnecm8OnV7lRvG5hVc3W/uey97gI+66sQxX38DfHDt2n7f+7ID86JPm0eUfu53YF5wa8n+aOajr9rcc8nMB1edt6Z5SV54sspbcSwvuHk0M576dl3VtEoG88HNIbUy/N+rKl/pWOz/nlT9yYgHuBqZU+zPjTJB1Md7Ss3s8+uZD67e7vtAXfPvf4r9PWv2wA0P8P6dNvhzrXvjjcx36obvoj64KPviJpYX70m1ruetY3nB1d1XipmP91SKiG5rmQ9ujL1cxPI6df+tTvOCy9MVS5iPuuYTXTYy3+G9VYl/X7X8mQ/0SR/300Z/rt8L2eDPjVOzNzEfOVWjySXMd3in5ZsfqHti6AZ/T+z+bJM/Vzn3iv25FbNvE/XtuuaZAVuYDy63bN/C8qJP+tarm2lem4sF0VuZj77qjV/dynxwmT1yO82LuocvbWN5wY1527YzH3WNyDY7mQ9u/RG6i80r3pNqxkQXsnkFN177qIj69ntKRXDvfOY7/KG/17F5hWcUN9pI59XmWo8am6lv17UGpWxgPrimrpWwvM57VR8ansPygsvHB61hPt5TpRI3ZjIfXKpteSwvPLP9wmKWF9z87IkS5qOuGrV4PfPBxZGpG9m8ok/mkxW2sXkFN7/at4P5yClf7LiN+eCqy+SdbF7hWS89u5vNK7gYfdjLfNQ1rrzsZb7Dw5MVzWv3SXusZCvLCy4KmuxkPvoq0t7ewnyHd724neWFZz3XjOYF1/J3Keajrmj/+G7q21xVekf5n6ta797F/uekPu2jTf5c1PqwyJ+rf6M2Mh/nlHXQs4X6NjcerrDFv65R3GadvyfPGRse5O0L/bnV8esi6tt1zWk/lDAfXAvuRfM652rDO9tpXpvL2Kjt1Lfn1Pyp5m7mg2txv+yieVG39rJtLC+4tuDqFuajrpgkdjEf3CwavsO/r2rvj7kP9KneI+v8uahivy/b5tLzXz7zkVN7d9AG5oOrhOeK/Osar66W/p6e5c3255Y3N8mfq7CpbuajrrZyXCHzwY03F+awvOiTZX1RwvKCi+pDNzIffRWi+Tbmg8uC2M0sr+NVLC5iecHljpPUR12t/JqNzAfXu6YU0Xm1z0nV4gPF5hVc3DAU851zqm2zPcwH197tt4fNq+NV+dPL5hXc7DV6N/NR15hRZg/zwbVb79G8OCettpP3sLzgamku9XGuaofKct/mxrQ+e1heeJaYuIfmtbl163NFfbuumNpsD/PBRc2Nis0r+qS1araTzSu4dWoB9ZFT9ArazXxw+f7BHWxe4VkZEzfTebW5GV2P+qhrDizeynxw4+ceJSyv0yfPGi/LC26cbryL+nZfhbbHy3xw89SKXSwvPOVquZ3lBddqfruZ+nZdY9mJ7dS3uVG3YAvbV/GeVH1UeCrbV8Flbu0s6tvvKdUnDYlmPriq1DWN7avwtJqfF9B9Ffxq5WLq23X1lrezmA8uO98pZHnxnlR9RMl8ltfh/9yPpD7eqzqt4zQX8cHFs29NZnnhWYNCU1lecPnIfuqjrmzTKIL54OLaxSS2r6JPlnF+E9tXwbWdYVuZ7+QMX1PMfHCtZpcStq/C02f79vrAfdXh19+jPuoaVRZvZT64ks/vYHnRJytjTyHLC27c+Zj66Ku8GOShvs3Fw68VsLzwRIeETSwvuJrReQvzUVdZPYqYD27ce34jm1e8J1VfPzTSRebV4bsLQpiP95Sa4zaluIgPrn1QJd5F5hWefLpsBxeZV3Bj7BehzEdd+aT9PrsAH9wY/OsMlhfvSTXbPe5hecG1hy3JfNxTNfvH5DIf3Oj7czbL69Tdn5fiInnBjV/nxTAfdUXiCA/zwY03pvu+bxMwr+iTevOPODav4Nr1GW7mI6f+VfZM5oPLQ62j2bw6Xt3ruXRewcd+tZb5qKu9814G9W2uXTqdzfI6fX2+cYiL5AWXy2d9wnz0VVZPj3MRH9z6/enS99oF5oWnNo+NZHnB5Zmhacx36lb9ZhL1bW4eaRfF9lWck9bVW162r4Lrt4cp5uOcsprf3Ml8h9e5t4vtq/C0kBF0XwU3qlRSzEddc13n3cwHt3Zc38XyOudkXrdtLC+4lpi0nfmYU23j4BLmg8uHf9tM89qekfbmDpYX3GrRbAfzUVcpawvzwa2MJlvZvoo+qX8P7Gb7Krj5w9WdzEdO8+afO5kPrtebvoPtq/Csp5ZsZ/squKqVuYX5qCvLjN3GfHBt7YnNLK/Tp1px21lecO3MmG3MR1+tsxe3UN/mctZ/m1leeGLtGpoXXLUfXkJ91P0srIT54Krq/I10Xu1zUvw3rJjNK7i+rf1G6uOcyn+7kPo2l9eWrmfzCs/40NzE5hXcalmzhPmoaxZ2Kma+wxvV3cDy4pwUfedmsbzgYm/LfObjXDVbz0hjPrjV95iH5XW8xUVrWF5w48rxdcxHXUum5zIf3Gr4RAGbV6evy58pofNqc3Pa5E3MR079xIANzAcXHS8Vs3l1vDKvbWTzCi769ClmPupaq44UMR9cPFJrHcuLPmlt4tazvOBaX/v7VwE++qrVq1bIfHBj8oYClheeHFN+LcsLro/els981DXa/ZDHfHCj/eZs/+eq4oux0Q88J607Ndmfm3VrLvPnRsQw6jvPVeulu5kPLnLOJvnXFcu+nu7vWc/vWuLPZZpbd/lxrflzU6hv11VtHovyEh9c/P1zMMvrPCe90SmH5QU337kkmY/nVMIoU8h8cP3utSyWF542PTaB5QU37vSIYD7qWqdbpjMf3PpwWIx/X/UlHXyfL273Sd+90PDnutvr+xwC8DsDFjAfOa1WAz/0Et/hC5Om+dc1jh9Z4e+p0/OW+XNx7NlIf26FLFjFfKdu+9qLqW9zvem9CJbX6dNb82Z6SV5w7dcfPnIx3+6rUe2/EC/zbW52SnzXS/LCE4/+YNK8NteaHA1nvlN3+XPzmQ9urG3g+33AvOI5qdHO/vcuYF7B9ZjP85iP55TGyV3FzAfXW7Vbw+YVnurYI5PNK7gx6qNE5qOu9XPDbOY7vM/+VJYXz0nFEn0jywsu3ry3jvrYU3+ps4n54NYT7xexvPAMY0IuzWtz80p1SX27rlp/Po/54PpHtd1sXtEn1a31Ci+ZV4cfvzCG+U7O/NcjqW9zY071IC+ZV3hm3r5ZbF7BjQjf5zYG+qgrRdAM5oOrL9733Y8NyIs+WavGRbO84PqxlrOY7/T1jbkxzAeXdXvN8pK8Tt3oH2awvOBm5Y2rqG/XlYXfzGE+uN7W/tw7nKsbf4ryPyfVm8ei/Lnadyz6Af7R8Bjm45zSBv4YxXxw47POsf515S9bo/09+bnvcxfAjTpJUQ/wHZNimI+6WpvEOOY7/MJXcSwvzknV3Pe5of+T1+by77A45mNO5cN/RjMfXL/2WgLNa3vGyI0JLC+48d/eROajrvVxx2Tmg8vEt1L9+2p9413l3ycz+fgDXIv9Z5k/N0Zr1Hdyrvk9lvngcrbvc/NQV7nyF/h7Vu8uS/25XvLdYH8u/ntnNvNR12pfvJr54Cq72zKWF30y9m9NZHnBtWsJCcxHX62CZ9KYD64afZfK8sKT3cfE0bw2l/nNo5mPunp3+/lxgA8uVgcnsXl1zsm6T8ayeQUXOzslMd85V5flxjEfXG0/ncLmFZ55oVsam1dwvXFVyXzUtfaPlMwH11M6ZbK8OCfNpOMJLC+4kXUjnfk4V62HM5OZD64VdHKzvPC0hz7KZHnBjQUFWcxHXVOLzGI+uHy9dS6bV/TJdP8l2byCq5oD3MxHTrM42cN8cBE5PovNKzz17JtuNq/g1sf5kvmoq7++P4v54NqIkiyWF30SMWNyWF5wUSszl/lOX7/sm898cG3T1QKWF5711Ng8lhdcbT6Yx3ynbttKa6hvc3PV4dJfA/dVPCcVeV03sX0VXM6PLWI+nlOa323fyHxwc+6rRWxfdbzOnfPZvgquWV3dzEdd/buoPOaD6w+ZkuXFc1JzWdgGlhdc9O67jvnOc6qxZ6gPLst0LWR54Wm9VA7Na3P1+vQ05qOu1vS2h/ngovwLiWxfRZ9EfvUYtq+Cywuh05iPnFarqlHMd7gWZHjJvurUzfjLZPsquN61Wizznbojff9e/o9vc2uLfV4H5HX6NHDvCpYX3Jz21Mcu4qOv5tTXTOaDi20NFrC88LQRd1azvA7vvCCV+airvp0Yx3xwFVnOd24GzCuek6rP16xl8+rw1uG5zMdzSr3ix/nMB7caTvCweXW82xMknVebq1PvRzEfdfWg69y3uXF+YCjLi+ek6o/nsllecHNDWDrz8VxVtGrsZj640eBwPMsLT7X6PJLlBZdNPR8yH3VF8z4hzAcXv7Sa5SLzij6Jw48M8rJ5tbnYv8T3dU+A7/T172OzmA+u3/ovls2r4zX4PIXNK7gx88Us5qOu6jCK+uBq1ELfv98BedEnwxxL8zp80uJU6tt9tfo/H8d8cK32t26aF3UHi2yWF1xqPxUwH3WNxFl5zAfXhjRZx/ZVnJPidEO6rzr8x4881LfPKfP+G9QH12/53rsQuK/CM16+k8P2VXDZ45d85jt1y0XlMx9cttm9huXFOWmdLKZ5wcUo33sHA33MqbnezGE+uBjfopDlhacS+65hecFlQcZ65qOu8WLT9cwHlxU7FLN9FX3S/+u9hu2r4Krb6+uoj5zhA9czH1xNKCli+6pTd2rqeravgms3+xYxH3Wts0uKmQ9u5dTbwPI6fd3wSjHLC64teXcD89FX1c2zgfng5q09G1leeLpZfyPLC64+OLGR+U7dqzmbmA9udB9cwuYV56Te136vTuC82lxUabaO+Tin9Iy9a5gPLuanF7F5hWf2q1zM5hVcvV68gfmoqz4wNzAfXA14ahPLi3PS2vfnepYXXGvw/Abm41yVvy0qpr7N1fPmRpYXnvHW9xtZXnDjOd+f3//4dl1zYP0S5oObm+ZvZvOKPmllK21i8wputatXQn07p6z8YQnzwc0vRm1m8wrPHPrQZjav4HJH8Wbmo66Y/dYW5oNriWe2sLzok7y4YjPLC65tWbSF+eirVjlqC/PBTc+4rSwvPPPnt7eyvODyjcNbmY+6Rt8ntjHf4bdCtrH7qnhPqtzo+9yBwPuqDt9YpoD5eE+p8cg/a5kPrif8vYbdV4UnCk/l0vuq4F/OzGK+U3dJ+ULmgxvVfZ+rEpgX70k13evWs7zgqvZn65iP91Tq4Q2KmA+uxgxZz/LCs1yX1rC84Np+3+dmBPqoq70wdi3zwWWXegXsvir6pG2f9gDHvVKHT5wdw3zkFDsbeKhvc/PIR8kucl8Vnn7v5CwXua8Krh25spD5Tt22uctdxAfXLv7dw8vy2n0yMytls7wO//z1dOajr3qjadnMB9dvPZnB8sIzqu6OdLG8NlfFd8cyH3VVPyvKRXxweSVsGptX572q50+tZ/MKrlm71jHfeU9p5x7rmA9u9fqLzqvj7Ulfw+bV4Ttb5jMfda1aadQHN/o/lsvy4j2pxge7C1lecOvQCwXMd95Tta5NHvPBheydw/LCM95eksvygou3TnmYj7rGtCUe5oOruykZbF6dvibNymLzCm5d6pLGfCen1SWT+eDmw52T2Lw6dVdGRLJ5BZfX+k9mPupasaNWUN/m+tl2A1le9EnGdElnecFNERHDfPRVnE2Npz54lYHLWV54Zt11Jsvr8IdyS98jFeijrta81Ujmg1t60Hx2XxXnpJrRM5bdVwVXHS6lM985p4a/F858cL3lsER2X9XxHprjYfdVweW553OYj7rGK70k88GtBZU8NK99TlqXvguheW2u7jwZy3zMqZxdbSHzwbWtR6JYXnjaN65UmtfmZufz1Eddo8/mJOaDq5oPZ7D7quiTHt07l91XBZehT+Uy38n54ftZzAc3Kr+WRe+r2p7aNSyb3VcFl2U7Z1LfritHx2ZS3+Zi/HMZLC/6pG1pksnyghvnns5kPvqqf3RUMh/cuLxZsryO126FZHnBrbMtU5iPuvpPZjrzwa36FxPZvOKc1K+OWUjn1ebq+55RzHfOqUZxIcwHN/5sH83mFZ7e+mAim1eHjzmSRn27rgh7KYn54Gavz9JYXuec3N89jOa1ufy2fSzzca5aFSespr7NjSqX4lleePLP+cksL7jeKzed+airWo9MZT64+cSjks2r09dWtSSbV3B14RHqO311l6U+uPj8TAabV3gyskcanVebW/vuJzDfqXu1QxrzwfXy/RJZXvRJ3O5P84KLnLepj75qPa9L5oMb1U9KltfxxAvpLK/Dv0hOor5dVxUty2A+uP71otLzIHBfxXtSrREXPXRfBf9hu5v5eE+pPsv3udGBPrj+9MtJbF+Fp8KL0ti+Ci5dc+OZj7rqiyUxzAc3n965nOXFe1KtgQXRLC+4NWdpBPPxnkpr5O/zmQ9utP9lHMvreFU6hbC84PqVx8YzH3XFwNXdvMR3+E85M7xsX7X7ZJbrFkH3VZvrT16fznzkNPbdmsF8hw9IGuYl+6pTt97siWxfBdcL6oZ5iY+6WucVocx3eO5p333fgLzok1rTd7SX5AXXTjTz/fsW4KOvujZ0MfXBN5dfxfLCkyp0FcsLrntEHPNR15i/I4b54HpGI98+GzCveE+q+PNc6c/lB84ruHYh0/fe0gAf7ym1mhdHeInv8Bq3ff8OBsyrU/fO577vywbOq81lSIUV1LfrWnXmRTEfXM4oE8Py4j2p5t53ElhecO3OgATm472qcsRL6cwHt5ZGp7K8jpffNIHlBRfG4wnUt+uaoVuTmQ9uLL6SyOYVfRLv6pFsXsFl3KoY5iOn+VZWLPPB1bPj4um82p7e5Ew8m1dwM7ZMMvNRVz7Wg/s2V13eSaZ57T6pjKcSaF6baxP6JzAffdUu9UpgPrj5bE2aF56x+nwCywtufJJNfdQ1vhkQx3yHr6wQx/ZVnJPmHyExbF8FNw5sSWI+zilx8vkE5js8ZnAq21fhGQvm0X0V3PjhlGQ+6sohTakPLu5HuFlenJP6jeeTWV5wuWlsOvMxp/JAQ+qDW9tXZbC88Mzr39G84NbCcpnMR13VU6M+uHja/vo1YF9Fn0S5ZDfdV22uv7LEzXzkFEdaZDLf4UsbZLJ9FZ7VOkGyfRXcXJifxnzU1d76yM18cCNaZbC8Tp/GxWWyvODqq9Xct/uqX46mPrj+SGomywvPzL7pZnnB9QPbJPNRV/79cibzweX2tm42rzgnxYqfU9m8ghttF2Uw3zmnkl5OZT64ee2vNDav8NQ539edgfMKbsyIoz7qGq9HZVAf/NokyfLinJStMpNYXnBrvUqmvn2uGk1fjqO+zY31a+JYXnjip2spLC+4rPloGvXtuuLggnjmg8szoQlsXtEn7e3H6LyCiw6NqI+c1n3755IDfHCzey06r/BEy2tuNq/g6kAXN/NR1+hfjfrg6snPJMuLPmm1y6WzvODa3T3pzEdfjQJPIvPBjcF7klhex+vTNIPlBdfO6hnUt+vKznuTmQ8ub69KYfdV8ZxU11NT2H1VcNWnQST18ZxySR/qgxsfP7KU3leFt+z8SHZfFdx6ITLMRXzUVXf/GU59m1vZ9Xw//xiQF89JRVFeNMsLbtRYspD5eE5lfZsfSX2bW9PbzGZ54clHfD+3GZgX3DD+i2E+6moTe81lPrhw58Wy+6rok9U8NIXdVwWX88tlUx99vd5AMt/hjUfk0vuqtqe/ElpI76va3BwQVsR81DXvnF7LfIfH5xSzvOiTqBbsZnnBjRZb86hv99VIXOOmvs2t1l/lsbzwrIlN1tO8NreO99rAfNTVy3dfz3xwTY4s/X3gvOI5qdjQKpLNK7j55xezmY/nlMa7taOYD65P773IS+YVnn7msVlsXsHNRq/EMB91tUffGcd8cHlm9WoXyYvnpKbxWAzLC64GNVvCfOyp2oScOOaDq5tvR3hJXnjWo9kuL8kLbl05voT5qGvtnDvdS3xw7eW209m8ok/GtmzJ5tXh6zJymY+c4kn7518DfHB1rXk2m1d4+rtPrGPzCi5LjhYzH3VVi3mFzAcXIrSI5UWfRKSWwPKCq6D9buajr2bvhyOZD67t6JTC8sIzVuzMZXnBjSMvrmU+6mpVX/QwH9wa1jaP3VfFOaktOUXvq4JrsmQT83FOqQ3fUR/c6Jxdwu6rwjNEq83sviq4dnzSFuajrtW94hbmg1vDtK0sL85J9bTJ89rcatViM/Mxp7J+4ibmg4ufutC88MT1UJoXXG9obmW+U/fZVVuYD269Pncru6+KPhm1R2xl91XBZVStbcx3+tr+NvXBrZHR29h9VXjmZznb2H1VcPPyv9RHXTHvH+qDi5dabWd50Sd9fSuaF9x4a8c25qOv1pXa1AfXpoTTvPDUlYrbWV5w9eIr25mPuuaefduYD64PP7KNzSvOSfH645vYvIKrdw+WMB/nlHXp2Abmg2tnkzexeYUnQp7YwuYVXH33F/VRV/PcLGE+uKUubmZ5cU6KU98V0bw210Zt5759rsqZm9cwH1y+/GYRy+t4tV/cxPKCG93qU9+pu2pNMfXB53o3sHlFn/TasVvZvIJrp3/bSn07p/GcuYX54ObOb7aweYVn3aq+jc0ruDHqMe7bdfXuvlyBPrjW/PwWlhd9Uh9/UcLygmvVm2xmPvoqLs/eyHxw8Zrv5zIC8zp1f/H9vQjM6/DgBpuZj7r6O2U3MR9ctj7v8wL3Vfs5qfp8VSLbV8GNXf9EMd95Tpn3SQr1bW5N/yqO7avwrFbvLfaSfRVc+7NSf+rbdfWRHVZ5iQ8uPI0Xsrx4Tmps/D6N5QXX+nVMYr7zXPWFogzq29zcUiGV5YVnVN8aQ/OCb/5yBfNRV9+emMB8cDFzcbSX7Kvok5IlJt1XbW506hbNfCfntC/eZT64ljw3hO6rqFuvTirdV21uXS7JpD7qzjwUxXxwfdHdJJYXfRLvvDTXS/KCi6WrOjIffbVG/7TcS3xwTYwr/b5bYF6n7qA5i1hecHFw20rq23XVG96+XuKDG53m++79BcwrnpNaZbpKOq82l82/S2M+nlOKaU0k88HVmJR0Nq/w5EtLk9m8glsx7njmO3Vr9EtlPrg5IDaJ5cVzUmFeSWd5wc2uNdOZj+eqIqlKKvPBZddOqSwvPPmNlcrygsugdinMR1295IVU5oNbrpQUNq/ok+rV5wHuzJXNjZUzw5mPnNavM+Kpb3Pz7SoxXjKv8NSHz4R4ybyCm2mZU5mPunrX2qu9xAfXM6uGsbzok5XaNYnlBZcdnkygvt1XY/v+ZOaD6xn5SSwvPD2iIJblBZcfvhFNfbuuUW5QIvMd3u+K73l3wL7qnJO1LufQfdXm2vuzCpiPc8oo3CWZD27kl89i+yo8tWTIWravguvXV69nPuqqU3tymA9uNLmcx/I65+qCoXEsL7j5YcMU6ttzakXXWcx8cJn6+0qWF57aXUGyvODmxvtu5qOu1rlpDPPBzaPJ8Wxfdfp0KbyI7avg1u1/i5iPnOLGuALmg4u49wvZvup4nesXs30V3DwsipmPusaoNYXMB5ctxxWyvOiTGDrOQ/Pa3JjfPYv56KsVqiUx3+GrNyazvPCMtTKL5rW52WJUFvNRV4/rksJ8cPXluWQ2r845mbX0xm4yr+Bm7xuTmY9zSkzoE+wlPrh1sZHvc1UC5hWe1fu7BWxewVWttMXMd+qen+O7jxngg6s91dt7SV7nnIyLWe0lecGtIY+HM985V/8bE8d8cNn6rxgvy2t7xmcVl3lJXnD98o5Q6tt1jfP50cwHt4LjfF+PBcwr+qR9fyqMzSu4+HxmOPOR00rI6sN8hx/6x2DzCk/sHxXB5hVcbo4KZz7qGmUjRjDf4Z82NVhe9En91zXYS/KC61u3LPIy3+6rOHM00kt8cK3MSN+vAXnhqfyqpb/+T17whPGml/ioa94ZFeklPrhpXbC/Xzeh9L2oeusG80r/fD4Y28/3ucJhrb1+XF8eE+zy42brkx9Rf8iTvjnr2TbYS3xw4wfvLK9fXeuXW773n9meaJAx059bZV+K9OdqZ+UlzEdd7dDOD7zEB9eN5r6fWwrIqzXp5/t7Y/WK8pK84HLDugjmm1NP+Pa7hSqJ+eDCSIr3kryON/qtUC/La3O9zKTxzEdd9UdUlJf44CL8ju/7hnZflfZU3AN9zXv7AW5FTUj259bNafHMR06j++nFzAcXny2I8q+rPv8l4wHvuaOp/lyZuZn+3Pxxrpv5qCvObUxkPrg2Iz+D5UWfjF6zJrG84FrVXsuYj77qi58a52W+za3i8OksLzzzbHYMywtuPTEohfmoq9caE858cDn0D99eGjCv2qvP+b6e3x8p2byCG//8kcZ8bdafvp+n7DHNQ33woT+72bzCU299l8jmFdxc0Cya+airyt1PZz64zI1MZHm18b/63tcnZmWzvOBWzypZzDdmNvT9nOvlXTnUt7n8dGk2ywtPHTrrZnnBjYYn05iPuvqmaA/zwfXD96WXzCv6pHctXOwl8woubtUewnzkFHl3Ir3Ed/jk7sFeMq/w1JIa89m8gosKD62mvl1X2zC0mpf44Fb3yb6flw/I6/Sp1zfxLC+4eXHgKuajr1rdEynMB9dKKsZ5SV545r7f5ntJXnBj4gSXi/l2XXNShQgv820u8hN9z13sc9VaWD/b/5w0k3pl+XPN6p/rz0Xwvhzm45yyWpb1MB9c9tn5QF1rRK18f08ruzPPn1svTynw5+Zz9QuYj7ri4uVc6tvc2jQqn+XFOWl+/4qb5QU3BjTMYj7mVLSYmsZ8h6/4wc3yOt6M+TksL7ix7+k85qOu2j80i/ng2vByOf59lVPfLvTvk1Xutwe4qLOw0J+bS5+lPnLqz75ewHxw4982Bf51jRZNCv091SWuwJ/Ltj0K/Ln+8vh86tt11UvL8pkPrg2oksfyok8y/2wey+vw8M/zmI++GlcO5FDf5sZgdw7LC0/b/WMuywuuuZvmMB91VZPYbOaDW83yPGxecU6aF95KYPMKri/dR32cU9ac+NXMB9eDWsezeYUny4x1s3kFNx9+2sN8p27111OY7/DJk9NZXpyTqlXHhSwvuMh/LZz5OFdF+s2uXuKD6wXNprG88LSqrmiWF9xY2S2O+agrzswMpj542f+WsnlFn6zwYx42r+D63AQP85FTn30qg/ngUh+UweYVnto3JZPNK7hoPUYyH3Xl5YfSmA9uXrifyPKiT9ozvp97DswLbux+OZ756Kv5zezlzAeXg78NY3nh6a3Kx7K84GrPoEjq23X1jMFLmO/wLgsWusi+qm0/6ntfyz9BuWxfBVeLxlNf7/yo7737f+zOZT64dvVSDt1XbU912JvF9lVwpfl+7inQR11xPDSb+Q7PLfawvFqFP3zvsflhcy7L6/C9D1Nf75Lo41+OzaW+zQ1xIYflhWe98lM2ywuuu6dkMR91Rfj9bOaDi69OZLF9FX3SDl7MYPsquDpTI5n5yCmvLXUzH9w40jGd7atO3Q9rxnrZvmpzNW8m9VFXlOmRxHyHN5oUy/KiT9pXRZk0r83N5Xsl852+tvF9bvr/+DYXLX9xs7zwrPEhaSwvuGoczX27rn57sGQ+uDyckMbmVUT85/t7+dy5HDav4MbpyTnUj3vV9/cx8ynqg4she+m8wpMH9mWzeQU3I6ws6tt1Rf8O2cwH1w+nZLG8Vp0XfX/f3nsym+UFty6eyWK+1feq7+/p4PMe6tvcfGubh+WFJ3WT5gXXEitlMR91rebBHurb3Nj/pofNK/qkH/rKw+YV3Gqdn0l9O6dR85SH+eDir7uZdF5RNzPIzeYVXM5uR33UtWZUzaS+zcW9pm6WF31Sc+0+BeQFF41/yqS+3Vfr5YvUd/ih4EyWF555sksmywuur853Mx915dIXMpkPru5sdnvZvmqfk/rBrGAv21dtLmuXTGE+zimj8NxqL/HBRe0qYV6yr8LTq33czkv2VXCtV/hH1LfrasVXg73EB7deaDmP5cU5qXW/Fsfygqsvt0UzH3NqXV2fzHxwI9P3uRuBeeHJ/fUivSQvuHE0OIL5qKuS78RR3+bGa9197w0M3FftPiljz1i2r4JrXzYcy3zkNIojSr+vGeiDy7d2lL53JHBfhWeW/2QI21fBrTeaveMlPupqHYaU/lzY//g2l/UPL/CSvOiT0aDhCi/JC65i6i6nvt1X8UfVGOaDa+PejfaSvPDM0LeXs7wOv9ZgBfXtuqrmrijqgx/tH8Xm1TknK7VLZ/MKrt1pmcp8nFPW44Mk88HFqpoZbF7hmeN8nxseOK/gcsHvicxHXS0tJ4364IN7prK8OCeNxY+5aV6bG2WnUx/nqlXY0818cGvC95LlhacW78hgecHlZ0vTmY+6xrYJkvrgew9msHlFnwx3UQKbV3C5Kj6e+cipnS2TwnxwbbDvc+cD5xWe6rY4js0ruGjcJ5b5Tt1Xn09kPri1qmU8y+v0afQbaSwvuBmRQ330VT86O5354Fqng6ksLzzj0pYklhfcaPp2AvXtusaU1cnMB9dT3L4/F/u5qrhxIc//OamxLS3Xn4srV3Ie4I+1yGa+85yy5Fo288G1DVU9/nXNoolZD9Td3TbTn+t/mW5/bhWGpTHfqftC1wzq29w6+kECy4vnpNq9TR6W1+EbgiXz8ZxKO3RDUt/mSvyazPLCs/4YnMTyglupnkjmo66qeDCa+eBiTp9Q/77KS0NTH+jTl944f25Urhbnz/VvPCuYj5x65XormQ+ulRyY7l9XK795hb8nT9+b6s+N2e4Z/lw1LfLdOwv07brq25Ol/3ug7/DQcr6fOwnIiz7pd1bOZ3nB5YtZvvtaAT76ar4f9IKX+ODmmIG+n0sJzGt7umf/cpYXXP5hn78BPuoaN5+JZT64OdTw/bsfMK94Tmo9XDuFzSu4Hl0xlvnOc9VqQTHMBzde7LuczSs8012wjM0ruNFw/mjmo65+oSiI+uB3R5be2wjMi+ek5qeblrK84NagzjOYjz1VFlecwHxwGT9+oJfkhWesfOR9L8kLLn981fd1QYCPutYPjedR3+Z66jbfvceAeUWfrBfXzmXzCq4NbRFJfTunUOXDmA+u1PRYNq+OF2cksXkFF3vs924H+KirGn2Qynxw8362m+VFn+SUlatZXocPyo1nPvpqVPG91zrQd/jDh+JpXniHv01lecH1MRXdzEdd0emxFOrbXFXv4dtP7HNVBJ8qfY81zkmzfH3fzzXZ3Lp4J9Sf65GPxzEf55R25qEE5jv8dPkM/7oy6tPIB7z7v6X4c9lrfrw/12PWSeY7dTseymQ+uEyrm83y4py0omdk0Lw21z8xPczHnOr3fP+dQB9clnsuh+WFZ5QMz2F5Hb6wRR7zUVevPjmP+jbXmrjzH+jr3HPJ/n0ya4Vm+nPVeWuaPxdrxniYj5xa0JQc5oPrzdbm+NdVTatkPFB3lu9XcLxXFVy3sjOZj7pm7C/ZzAeXZYdlsbxOn6yLeSwvuPXY+Dzq230VEb5fA31wVW1OHssLT1X6JYflBZfvpmYxH3WtT97Lob7NtXPtPWxecU6Kd0ZnsXkFt1Kr5TIf55RmTMtiPrh6bwSdV3jWLEXnFVz+2jGf+airZvXJZT64iKqby/LinFTf+p7LBuYFN69VpT7OVfOVFyXzwbVdH0qW1/Futs2ieW2ufzrIw3zUlfnjMpgPrt62Py8kYF6dvv6USOcV3Hj+YA717Zxi1LFs5oPr84d72LzC08Juedi8gmst+0rmo65R1ffe/v/xbW7U+DSB5UWftKCRbpYX3Arz/VxloO/0tahFAvPB9c9mRrK88MxaHeJYXnArfksY8526J9wm88G1Z+Z1ZfsqnpOKvmUneMm+Cq4ntfS9NzjQt59TqoSEEOaDG298vpTtq/DkrY1L2L4KLt5su5L5qKs1fyic+eDmQ5+uYnnxnFRbPDec5QWX/Q5FMB/PqcTe85HMd3hInUia1/assZk0L7jZOGUl9e26WnVrJfPBzZ5vrGD7KvokP60azfZVcPFhmTjmI6d50f7c8QAf3CiKjGL7Kjytc4MEtq+Cq549E5mPusaboVHMB5f3gyNZXvRJFQ2MYHnB9U3hy6lv99XYdXsx88H1/Tfmsbzw9AGPLmF5Hf5P0ALmo65Yvnkk88HN4WKUi8wrnpMaa45F03m1ubx2hPp4TimbHoxhPrgW5o5k8wpPxTaPYPMKbsytvYT6dl2j7KvLmQ+u9Sm/kOa1n5NqGyrE0rzgyV+uZr7zXLXfFzHMd3ijEytZXnhWrbE8L/iaRlOZj7rilZXBzAc39sX2Z/OKPpl6p9lsXsFFw+PtmY+cWo+HBzAfXA274HtfY8C8wtOfaV36vwfOK7g+6Ljv8zIDfNTVBvSJYD64HPSj775eQF70ybj8b+l7FwPzgqt/xpV+3zLQd/p6v+tC5ju8fV4UywtPfPdzHMsLrjaVkcxHXf1WTCrzwfUFJ333JgP3VZyr5Tonsn0VXNS039sf4OOcUl9diqC+zeXaRUvZvgpPL/ovhu2r4Kr7c6upb9e1tJXzmA+uRb4xkOXFOam9t3oczWtzo9qpQS7iY07FfuG7txDggxu7M0ufKwfmhacae3yfHxSQF1ybddD3XDzAR1394OA4F/HB9eKbvp+rDdhX0SftTMtQtq+Cy6sHhlPfzmlWiit9rh3og8s+T/ieewbsq/BEv22+v7cB+yq4kb7L178AH3XNrUtjmQ+uhXzre19QYF67T9qyOr7nygF5wY3P9RTmo6/m0XZu5oOLMcuyWF540t3LzfKCizG+zxUO9FHX6H8wl/o2V4uF7+ctAuYV56RRfXgsm1dw1eRUCvOdc/WHNRnMBzcjbnnovKJuwkw3m1dwc/yeLOajrj4lJZf54OqCt4DlxTkpj36XxfKCm4PezGc+zlW9drl85oOLVyevZXmdulVOrmF5wa3MreuZj7raH0FFzAeX5VUxm1f0SVbyfS72/8yrzY016wuYj5zm9VlrqW9zseCH9Wxe4clGQWvZvDq8zlNFzEdd/fcqxcwHF90abWB50SfrgwrFLC+4vPvQBuajr8K7bQPzwcWm4I00r+1p937awPKCq9/2bmQ+6uo7Xt/EfHC19JTvfSsB91XNV33Pa83yCzLZfVVw+fT4TObLRb775FbnvyXzweXFvyS9r2p72uaemey+Krj578uZzEdd/Z/fJfPBVcjPkuUVt3f6ft72u73pLC+43HMynfnq+bG+77s8PyOF+eDqlu89UYF5Hc/6PZ3lBRftXshgPuoaVc+nMB9ce+WNVHZfFX2y2v7nZvdVwWXol27mI6fZ6qqkvs31gb7P1Qi8rwpPPZZB76uCq4RxbuajrrxzQVLf5rrnlGR50Sf98X4ZLC+4ftT3uSyBPvqq4t2pzAfXVO00lheeFnYyg+UFtyY/Sn3UNQfkpjEfXEU05vMa/6TvvlHii4lsXsHlu0MSmS9WHvF9PzP/UizzwcXiV+LYvDpe0LZENq/g+vm3kpiPumK1O4754OKH9vEsr/HWGt/zmPyDUSwvuDnxhWjm6z2X+p5Lhh5YyXxwdXvJKpbXqat8nxsSmNfhY8fEMB911W21mvngYsLWSDqv6GvG10lsXsGttRHJzEdO+W3FBOY7PPj3BDav8EytbwqbV3ArqWYq81FX//vXROaD61X+pHnRJ9m7fyzLC65VGRvHfPTVyNgfxXyHSxHD8sKz5ifGs7zgWpUzCcx36nb6OJb54PqAk3HsvqpzTh5+3c3uq4IbWTck851zddt+7ttcPZUr2X1VeNqT8ZLdVwU3PXWoj7rSPVkyH1wMfFqyvDgnjVoazQsuo+pz355TOcuTznxwad5OZ3nhGR9VpnnBRUFiBvXtuuKFShnMB9f2Hkhn91XRJ71umQx2XxVcrWmXxnzkNF/6N536Nlfn301j91XhiR6+eweB91XB5ZsygfmoK2ocT2Y+uFkwNYHlRZ/M+mnpLC+4MadcGvOdvtYpn858cGtjZirLC0+elMksL7gZ0zmB+airxU1LZj64fqxLAptXnJPazlOpbF7BjYOT0pjvnFP7/0imvs3VX76fK/2febU9MePzNDav4FaZ42nMR11t5oJU5oNr9U/wvDhXv6iaxPKCa1fbUh/nqvVOrwTmg8tqOxNZXnjqtzdSWF5w+dL3KcxHXX1XuWTmg5u9fN9XC5xXp6/RH9B5BbeOdkhlPnJaszdx3+bytX9T2LzCU0dHJtN5tbkcH5vAfNRVA8KTmQ9uZLRMZHnRJ/lIA5oXXKtvpTAffbXObKM+uNX51xSWF57o+Gsyywsu9zdLYj7q6p7VKcwHNxYuSmb7qrmuje9eQaWQMLavgmt/91tO/VYrfc+BN58JZT64rLJ9MdtX4Ylza1ewfRXc0pZHMN+p+2PjpcwH18o2oXlV22d871FM3LCI5QWXAz4zmW/une77ubPDNecwH1zUqDSP5YVnJfQOYXnBzdGTFzMfdfXH9IXMBzc3vB/M9lWnT+vPrWL7KrgK833fNdBHTnmmaAXzwfVDeSvZvgpP+8b3XsXAfRXc1O7FMN+pWxIcyXyHL+4fzfI6fb1dcxnNC76mE/XRVz263mLmg5svzlvG8sLTll5cSfPa3Dx2jfqoax3pFs58cHNmyGo6r1aXoNJf/7kVxOYV3Np1awbz5YjXJvu+X9ppCvMd/nCfaXRebe//tHXe0VVU6xselYvlKkWaCOpguUpTRImiQAYbSpNepDggXKRYQLqUoRMgIYXkpGfnpPeTnDQVOKOCICLYBUQZkSpdKSrq/a3F7Hf/wvj+ddd61rO+9d5vfW63O/vscVo9M4/NK7h9YO9C5qOu1SQ4i/ngWvsJ81le69PQlb/XGdPaTGN5wZ0v2lDfGPrsZPe+av4U6oMf3PU6zSs9s/76t1hecH3egLnMR11nxJ4Z1Jfc7p4xh80r+mRs+XIJnVfJxQ19VzBf9XVRe4v54E6DjcvYvMJzpvaKYPOq+JxgJPVlXX3+N6uYDy7WT17H8qo++UcuYnkVb7BzKfPRV31/2ULqS641XbCU5VXei7+vZHnBxcPZa5mPulpO2Urmg2urxq9l+1Wsk3pxaTzbr4KbH42lPtYprXOPDcxXfE7HBLZfhWf8cT6R7VfBndSR1Edd+50GicxXvHRnEsuLddKuXy+O5QU3MzvEM1/N6dPXxTIfXPOP2cDywrOPDPKxvODaT68kMR91rZftBOaDG2W3J7H9qupr8lt0vwqur3S/i+71kdPqNpD64HbuylS2X4WnHRmfyvar4HrwxlTmo65xfEQa88HtnjVpLC/6ZJh9U1hecKH/kcp89NW8OCqF+eBas1lpLC880WdMOssLbv8+OYP5qu7+kRnMBzf6LxFsXrFO2h93i2HzCm5vCsUxH+uUeU8gmvmKtxkYR+cVde8eksDmFVz7++dE5qOuNfOmBOaDa0t2JbK8WCetoaOjWV5w7eOGcczHumovNaKZD653+iOW5YVnfH8gnuVV/MtQIvNR1zj/eTzzwZ2X3ktk84o+OT+6f/fxziu49tSxNOYjp7YwOoX6kmvbnqXzCs+e01qweQW3PnPfbfD6qGsG36K+4tvfyWR50Sf7UFYKywvupC1NZz76ap8vTGE+uF7lS2d5ldepSLC84NbMhn7mo67VdbNgPrhW1cHP7quqc9WVZhy7rwqu7923lvk4pzSdS7HMB3fmT13D7qvC0x+ft4DdVwXXv5k2KJz4qGu1bjyX+eD2zBT3/rMnrzonDfs4luYFb/RrBPNxTuWE/xnLfMXHvb+G5YVn7ts9O0Tygus/7HXf0/H4qGt8MnxeiPjgRgv/OHZfFX3S7gytYPdVwc0H9sYzHzm14i8imA9uDTIT2X1VVdfaSe+rgjvV+bnMR119zXN+5oM7e57NZ3nRJ8fcvYblBddHnk5kPvpqzxyzmvng+tNDfTSv9Iwbovwsr+LNl+ZTX9YVo9ZmMh9cdH3OvR/nmVd1Tnp3YRybV3DtxDWRzMc5pRG+fwPzwc0E99+L3nmFp1eeXRwi8wpunytqwHzUtT8wVoSIr/h7l6exvDgntaevS2B5wfXDB2OZj32q+W5uIvMVL/klnuZF3ft6r2N5FW+RYVFf1rVOj4hlPrjRtnptiMwr+mStmLyUzqvk1kPr45ivcvYbNIP54E7m0rVsXuEZF+al03mVXO8dk8181DX/muFjPrhmvi1YXvTJPPPs8BDJC659f2wu89FXLaLFkhDxwY2C3Cu/a/DmVXXHbVrH8oI7PV9LYD7qWqtvf5354E6TLsvZfVWsk1Y7rZjeVwUP6xxgPtYp3fZTH1xsuRBg91XhWSu+Lmf3VcHNX/6oYD7qitaLKpgPrrd170l682Kd1PZtKaF5JTfX/BJgPubU7jiwmPng1uq7AiwvPHPbOJoX3O7SO8h81LVj55UzH1zvN7CC3VdVffpsW5DdVwU3ysZVMh85xdS2lcwH1zMyKtl9VXh2vUOV7L4quJk9tor5qGtkaVXUl1zzj65iedEno97eIMsLrg98nProq3P0ywrmg9vXtA+yvPCMp1dUsrzgVtbuSuarul/NCFJfcnHtu0E2r1gn7cXPFbB5Bde+uER9rFPm8+9lMx/cvOvufDavqm5cRBmbV8WnJASYj7rOvxMKmQ9uTTGKWV61Tp6y02heyZ3FxzOZj3VV73tdNPPB9ffaxrO88MwR3bNZXnBz+585zFd1H1uQxHxwbUmnNDavqk/dW5WzeQXXn55fznzk1IdNLWE+uL75pVI2r/CsoZ+Ws3kFt65tU0F91L3YtYz54NrGWwIsL/ok9ozPY3kVfyA7n/noq9m0n6C+5Np++T1WT154os2BApYXXB/Xuoj5qGt/FcxiPrheM82dB89+Feek4ib5fpBnvwpuNRybxHycU1q74tOYD653OJPK9qvKu/bPBLZfBXfiTsYxX9X9eWsK88FF6n3JLK86Jz1ws6B5JXdWhAnmq3OqT8IyqS+52cy9t+7NC8+8QfqevOD6hCaC+ahr+Qf7mQ9u9P3Ez/ar6JPo93R0iOxXwe0bn13NfOTUNXnv2+OD22P2xYfYfhXeoZOLQmS/Cm7v6j4lRHzUtZKnxIWID253O0Hzok/Gi8EMmhd8RtsM5qOv5qfueyVeH1ws2pJF80rP+ikpneUFtxtr6cxHXX3p4GzqS65dPpjN5hXnpHqHLn4+ry43OrTPor48pzQXX/IzH9w+0Dmbzauqe0/rbDav4IbZJIf5qKtHzcphPrg4+Ekuy6vOVXtNp3nBjTXfZjMf56qOnZzFfHBtecMclheeUdA3l+UFt5K/z2M+6mpl8dQHN6c+kU/nFX3tfEcum1dw+/RTecxHTif1qXzmgztvHSxg86rq3rk8n86r5NZbOwuYj7pGVn4R88H1BgtKWF70yRzvK2B5wa1XlhYxH321vrqpkPqSO50bFrO88Mz45BKWF9zqtKWM+agrmj9dynzFzzwTCJH9KtZJPb7DALZfBRcdOsxkPtYpO/fddSHmS26efGJViOxX4RkZHy9h+1Vwa8q6COqjbr1P3fsnHh9cH7FteojkxTppfNApLUTygmuh3SnMx5w6R/vkMF9x7fmcEMmr6o5y3638R17JjTYL4pmPunZgYTbzwfWj2e57b579KvrkhB+MZvtVcKPrC/HMR0596eUw5oPr+dYctl9VdccfSmL7VXDxwPJ05qOuM8S3ivmKR9kxLC/6pI9vHB0iecG1NoeWhpgv+2pGT8gIER/c2fyh+3cjT154+osjxoVYXvCO9kzqy7rahPFRIeZLLn49Mo/NK9ZJ84VjhXReJbeSFxUxX62rAb2M+eCix20BNq/w9F53FrF5BTdXTS5gvqrbu2mA+eBaRmIpy6vWye4HAiwvuP5zZDnzsa7qeQPKmQ9uZ99cQfNKz3wyr5zlBRf7TlIfdfXOnSqoL7nz2fxyNq/ok9O6Xy6bV3DjP7f6ma9ylr9SRH3JtRH7ctm8wtNulN9z8M6r5OahZdHUl3VFt5hM5ise2TOR5VV9+v1qjrrgzn1OAfPRV3tVeRnzwe397j8X3rzwzH0DsllecP2Pe9OYj7p2Zs8c5oM7Fz5Lr/uuqnjlgfSr3lVtNSyzLndWxqbW5fYJM4P6eFd1Qg8/88Htx1dn1K1rLatNrus5e79IqcvN5YcT63K95chE5qOuiBiWzHxwkfl8PMuLd1L1CxWZLC+4teVQKvPVO5W/96M+uJnxmo/lhWe3fTCB5QV3zg5bz3zUdVK3RjEfXHw3a1Hdvuo/yd8ryT5piV3i63Krxe0JdblxXUIs85HTuKYomvngzqPTIurWNfcOvcpzxiRE1+Vm5PXxdbmzdlUU81HXvF9fxnxwq8mTC1le1dfAxGU0r+T2j7fPYD76ao/c0YP54NqTx2aHk7zwRLdAF5pXcn3rxSv3Fry+qts7YUU48cHtk/e53zXwzqt8J1WzjUQ2r+Dm124/vT7eKdXnXx9JfcmtrXe9xeYVnnXunblsXsHNYZMmhBNf1e2/1X033+tL7rw6w/37qieveid1fdhElhfc3DxnYTjz5TtV1qmb3O9WeXxwsXio+76UJ6/yUnrEsLzg9ru+ZOajrl76YxrzwZ0dU93zYc+8ok/W763cOfHMK7j57So3n8dHTjsimMB8cL1Ziyv7ce+8Ku9Rw8fmFdy6EJnCfNQ1jzbzU19yMXhCNsuLPtmN12WyvODmr+E5zEdfnUnz85gPbiVcW8TywjMO9c9necGtsNxC5qOuVv9sCfPBxcSW7t+N5bpqPtw2/qp1NaN7ZF1ulhXH1+XOkScimY91yt5yeA7zFT/3wOy6dbXtCxOuqrt+ZFRdLvYs89Xlzqedo5mPumL85jnMBzcuPLaA5cU6qS8IuPdBPHkVX/nxle8m/MOXc6r1+ys6nPjgok9993snnrzwrD3L3XfnvHkld3LzXw9nvqxr3fBuTDjxwcXxR9wcsq9mYVZi3T7ZC+W7tJKbI99PqsudO+6NYz5yGk8uX8J8cHP0thV164peHyTX9ewP3XdZwMWFqJSr+JZJCcxXdZvFrGE+uLlj/XqWF33SFg8eFU7ygptHLg5gPvpq93tibTjxwe3fBl85d/PmVXXPtpjO8iq+4/Qi5qOu03TYlXtBXh9c+D4ZyeZVrZNnhqezeQW35m/IYD7WKf3gsRzmgxsnU3LpvErP3vxCBptXcPuZqDTmo641dUEu9SU3fozOYXmxTuo3Pl7M8oIbNX8UMx/rqt7yQID54Mblp8tZXnii0fFimldyMf26Yuajrrj5mXLmK759f4DNK/okzqxODifzCm5MeSOB+chpjj6WxXxw7XRyZjiZV3jajKUx4WRewe2ZjVYzH3WdzJg05oPrnz/ko3nR19iBhSwvuJh1KI/56KtZr0GA+eAiprqE5YXnvPVtNssLbvd03/X0+qiri+RC5oOLne5397z7VfVO6sDbE+l+VXJ9drZgPt4p1RJz/MwHd3Z8lMv2q/D0Mw1y2H4V3I7sUMB81LU+ry5kvuIFY0pZXryTqs0N5bO84MbY4cXMV++qrr2/lPngxieJAZYXnjM3VMbygtsnZpQzX9Vde0MF88HNB0YH6X5V9km88lMx26+Ci0kNA8xXfc37MsB8cK28ZwXbr8IznphUzvar4E7cjArqy7p2P3n/zeMrfpNZyfKiT+bL31ewvODa1NsqmY++Gl0iK5kPbt+cUMXywtOW31nF8oIbzU9UUV/WNU5Mr2a+4odb17B5xTupVmhsOZtXcLEmo4L5eKfUyQgLMh9cuyTvLXrmVXmbdwXpvEquf3ukkvmq7taOVcwHN4c1qmZ51buqd8VXsrzgIsOsYj7uqZovza5iPrjzSZNqlld5kXdXs7zgZtY31cxHXfORIuqD6zMer2Hzqvo04mAVm1dwq+/GauYjp73lu2rmKx69tobNq6q7+M0aNq+Kr75cQ31ZVz/dqpb5iv8UUcvyok/awLk1LC+4+cp1tcxHX0XrGuqDiwfvqWV54VnmzFqWF9ws3E591LWmzaM+uLOmppbtV7FOOr/Ke5me/Sq4syQqyHy1rj5Vv4r54FatO7fe/So8bXt8kO1XwZ1g9yDzVd2wqVXMB7eNNlUsr1one8yvpnklt6dvr2a+WlffiathPri+9ssallfVbfJFNc0rufZnbDXzVd0Ze2uYDy5+zaxh+1X0SbtlWQXdr0qub5hQznzkdBovqqS+5NZtC4NsvwrPCHxcxvar4KLktmLmo65htqpgPrh+y0sBmlf2STSqX03zSm60vq+K+eirNuSBGupLbl+cWE3zSs+aeDzI8oKLxH7UR139zkFVzAe3vzgYpPMq10lR8VItm1dwc/PaWuZjndK6fFnLfHCry8VaNq/Ke3tdLZtXcNHZUxe+rCvGn6tlPri+6wOaF+ukPedvmhdcZDd9h/lYV432+6kPbgaO0LzwtGE3vcPygpvmD9RHXfHTN9yXXB9ZXEvnVfbJ+P5sDZtXcOf+3tRXfe0+qZb54Obve2rYvKq660dXs3kF14xW1Edd660GNcwH1579vYrlRZ+sl6JqWV7FezfjvuyrPm9iLfPB7UZ/17C8yjs3uYbmldy8d2E181HXePm1GuaDGyez3PVKnqtqSU1T656T2l/u913Fez+XWpfbPvddS6+Pc0q7RfdY5oPrWyM21K1rVN2RWtfTU9x3f8CdcPedcHDNl5HMfNR17vAlMB9c6z8skeXFOakxbdlKlhdcO/pyJPNxTqWPffRV5is+auUilhee9cORGJYXXLvlsw3MR11ja/PVzAfXIu6LrttX892tV/epw/HkutzIKEiqy/UHj1Bf9fUxm/rgWslXV9U1rxuRWNezEjOT63Jj8OGEutz6b7tk5qOubSalMB/cLrktleVFn8SxMT6WV/Gta6iPvtqnvotjPriZdo+P5YVn5A9PYXnBjV92pDIfde0BvycxH9z84eVUNq/qnPRCi3nhZF7BHSeyRzjzca564eXocOIrnrzvyu/CvfMKz2yWNpPNK7h+7enlzEdda9uHE8KJDy6uefnKu3/evDgnNW47lUzzSq6dPRfLfOxTzaDtZz64vrsyNZzkhaed/m1FOMkLrk1+bQrzUde+8GFcOPHB9T3Xrgwn84o+mcOCUWxeFd+4mfrIafzr0jLmg2tD/45i86rqpqb72LyCG7W/JTMfde3wKRuoL7k9qzaR5UWfrJiBb7C84ObcZiuYj74al16bGE58cHv8f2ayvPDsaiua5QU3Wn4YT31ZVwQarmY+uDXc/fca1lXnf5Pir1pXh8Qm1eXiiUtxV/HAyUTmq3V1jJnKfHBtnpVat665vyC2rmf375d4FV8SH1OXOwnbfMxHXePJNanMB9darE9lebFO6o3PpLG84M5zn6czX83plrHpzAcXUzTB8sLToqwMlhfc6VdfMB91nVUFgvqSix3u77bQV23Pp9F1+6Rtkve9JNevmRBdl1s3DvBRH309kJHKfMXr70ytW9f6bWJ0XU9vt8B3FXe+j67L9VV/+piPuvYRPY354OZDBWksL/pkLX5KsLzgRscFgvnoq/aKyGQ+uNbjz0yWV9Xd/45gecGNetdkMh919ao+fuYrPniDn80r1knnM/levmdewe3uLQXz1TpVNjWV+Yr3XEPnVdU9e2smm1dwc/D+TOrLusYHDagPLobc5md51To5ekAyzSu50bdDOvOxror62T7mKx7ZLZXmlZ59f5mgeSXXbvyN+qruTe5cen1w58XITDavqk//WuBn8wrurPvNz3yV8+BRP/PBxaplWXRepac1HJHF5hXcvENkMR917f+cy2I+uPlUWDbLq/r0xEWaF9waX5bFfPTVPFjrZz64dTSS5oWn39o2m+VVPOvVbOajrrW+Mfclt8497/7/9+xX1Tlp2dd5bL8Krk3vmMV8nFPq25YWMx/cnrw1j+5XpWfPzUtl+1Vw8eTFWOqj7spSP/PBraIzySwvzkktc2CA5QW3Z18uYj7OqexfGlYwH1zMLC+jeaXn+Hblsrzg2lfDM5mPulrvuELmg9vfXs4OJ/tV9Mmu13VZONmvgpup4X2Yr/o6qiw6nPjgznefvh1O9qvwzDYpC9h+Fdx8MGkd81HXyehsMl/xnAfd/x705EWf9OyAj+VVvPQ/a5iPvhpTJ6UzX/HW9ePCSV54zpfTJ4eTvODa/BZzmI+6+p71VjjzJRdhj40IkXlV56T15gbZvII7G18tZz7OKZ1jKyuZD66/mFNB51V6Yt2BEjavisc+k898VXde3wDzwY0JtxaxvOqc9I2uVSyv4ktWBpmPc1Wtz5oq5oM706+rZHnhicuDyllecOuPZ0qYr+o+erGc+eBWx/dK2byqPpl9/Gxewe2GcYnUlznt2L+zmQ9uLno7jc0rPP3D+9aEk3kFt0fXn8B81NU2DI0JJz642bjfXJYXfbIHtctjeRVftkkwX/X1nsJ85oNbn93rZ3mVF/b5BpYXXFT9toT5qOvsfNrHfMW31ruy7nv3q1gnRaO1G9h+FVx7/4ck5mOdMia2i2E+uJ4f5mP7VXhOy97pbL8KLtKOUx91nUeeTGU+uF7vTsHyYp00Og1Yy/KCi6WvbmA+5tScOmI588GFPymG5YXntLk2meUFt+NmpzMfda3j7/uYr3iD7FS2X1V9fainn+1Xwe3RnbKYj5zivPvd3X/4kuvz8vxsvwrPOPpeFtuvgttr3O9We33U1fc9nsV8cP2/8r8vPHnRJ239YcHygtvjr/MzH33Vz7jfRfT64Ebp4EyWF54Wn+hnecGty/l+5qOuyPo5k/ngdnv3f73zinVS1Fv5NptXcOsr97tC//DlOuV0LX2D+eDa3o4RbF7hibvT49m8gtvTDyczH3W1i+/GMR9cnzgqieaV66QWGz+S5QUX26esYD7WVe29/od7EF/xpSeW0rzS05O6xbK84Ob2+xKpL+uarZ+LYT64Uzvex+ZV9bX7x+lsXsHFsDzuo68j56UxH9z23yzYvMKzvu+eyeYV3LyzPfVR1+kpBPPBtVvnC5ZX9fXEm6ksL7j+Y5sM5qOv2qIdKcxXfJOVzvIq7/07BcsLrj8mv4fr8VXdvREZzAe38o+7nue+Kt5JFR320Puq4HrXgdXMxzulWoN3q5gPrrVvSe+rwrNu2Ubvq4Ib87vUMB91raOzq5kPrn29q5rlxTupZvgdVSwvuBV4jfp4p9II+1cl88G1mwdX0rzS04ZvqqJ5JXfGXk991NXbyPd1PT640Vq+l+u5r6r62iSxht1XBXeO/0x95LQmta5hPrhTPqqG3VeFZ59rR++rgmvthlEfdbUdETXMB9d/zaZ50Sfn2l7VLC+41mtuNfPRV+Nk6yrmg9vzn61ieVXdkYnVLC+4vTGnmvmoK24YVcV8xQeMpvOKd1KtsLkVbF7BnbDPKpiPd0rtBlaA+eBi1pkAm1d4ouieIJtXcDNtWpD5qu70oeXMBzdGFZWzvHgn1fpkaDHLC+7sWVjCfLxTZZ9vkcd8cL27P5/lhed8tbyU5QU3648uYz7q6rW/FTAfXO/57yI2r+iTnp0TZPMKbq/dEaQ++jrrRDnzwc3pjSvYvMIT2XuCdF4ldw7sCjJf1a24vYL6kjtj6lewvKpPIy6Vsbzgzs33B5iPvjrDPy1iPriYGFbM8sIT/o4Blhfc3CLva3t81BWVPYuZD+4MOV3E7qtinRQpU2vZfVVwrdNU6mOdslOKa5gPbj7q3jv9x31V6VlDh9L7quD6+HbUR13j62U1zAe3U4fWsLxYJ62eoprlBbd+XF3NfDWnK16oYj64ff+9VTSv9IxXxlWzvOCipn0181HXLjtdyXxw/WRRJbuvqvoad7yG3VdVfMX6GuYjp5XbnPrg1utbqul9VemJhk1r6H1VyfXqSdXMR13dGs59yZ1Eu4rlRZ+c48eqWF5w8Yavivnoq316TCXzwc2B9SppXnjBTlU0r+TW5ZxK5qOuk50UZL7i7RoF2byqdXJHSZDNK7gxeU6Q+WpdHex+d8Prg1sN1pTTeZWeeejBIJtXxc99W8F81HXGPVDOfHDnTGWA5cU6aTziK2N5wcX1u0qZj3XV+Fd4EfPBdf2JQpYXnvPdjhKaV3J7V2Ix81FXzD+eT33wa17KY/OKPpnbZlWweQUXL99YwXzkNB59NMB8cGPTwDI2r/DMH6LK2bwq3uWPAPNR11h7dynzwfV224tZXvTJDD5dRPNK7nT6uoD56KsWSs5hPrh4790slhee3nxAPssLLm4uzGU+6jptt2ZSX3Jr4pwMtl/FO6n2oLmZbL8Kboybn8V8vFMqjkzyUV9yo/HbqWy/Ck/b/XAO26+CGy0jc5mPus7y8YL54FbrRn6WF++kmmav1SwvuHFqSCz15TuVRsMLo5iv+KnGS1leeNb2FT6aV3KzV1QK81FXz30sivngWu2ncWy/ij6JuMV5bL8Kbv6l5TMfOe2NXbOYD25t+jaL7VfhmU1vyWf7VcVfyMpjPuoas45kMR/cCk7MYnnRJ/O3oeksL7h4ZHcG89FX89nrfdQH/2hjIsurvGbnM1hexX/dks581NV+Pp7IfHCrZL2Pzqt8J1V7Lm1BiMwruPnQgYHMxzulduTltSHi/z//wH33zjOvyis8tZjNK7h9MGIN81HXmPH6BOaDm49mXPm7sjeveic1qm9siOQF19svXMJ8vKvqPLU1jvngZk6z5SGSV9Vd2KRPiOQF157YN5P5qGsl3WCGiA+udcx6k80r+mSNOhRN51Vy59zuWOrLnGLh4tXMBzcWFq9j86q8k/Xi6LxKbu9dHsN81NW73RPJfHC75mgEy4s+iU5VS1lecNNsuYr56Kszd5PFfHA9+OgKlhee+VbiKppXcvNi2HLqy7pmQfUK6kvutHx+KduvqnUyx32XxrtfBdf3LcxhPtYpJ3OEn/ngWv2PBNuvKm/+l1lsvwpudfwmk/mq7qfT06gvudFoWSLNK9dJu+nzqSwvuJm/JpH5mFOjYeUG6kuuLXwlmuWFZ7XIiqN5wfdOXst81LUSXl3BfHC74X9fZ/tV9Ml68OEMtl8Ft4+ZycxHTr3dqVjmgxup30aw/ary7no1nu5XwXOmRDMfdW39zrepL7n2Uv4LLC/6ZF/Y8zbLC26PSO8cIj76atS++GaI+ODG0WZX/u7rzavqToly32X15AU3lndy//7u8VFXiJviQ8QHd84cdH/P4J1XuU6avsJ1bF7BxbzrljEf65T9WL1l1Ad/p9V0Nq/wRNdrJrN5BddapLjvHnt81LVu6Hnl3V2vD26dMNzf93rzynVSa/XXPJpXcqPfbQOYj3VV7Dk0i/ng1tllrUIkLzxryN3zWF5wvd9/o6gv61qdmi5ivuKNfO7vxTzzij7pDzZfy+YV3LrUzMd85HRKv9jAfHDtk7AMNq/wrDZ9BJ1Xye2I+Gzmo65VsTmb+pKby/oX0LyyT/a2yUksL7g9wf3Ok9dXfTUvJzMfXG96SxbLC89qF5VH80ouGj9fzHzUtdvK7x55fcm1u/q63xfy3FfFOanx/vQqdl8VXNQ0rmQ+zinNZ45QH1zf7P5d6B/3VaUnXnuggt1XBTdSB5dRX9YVG78rp77k5qDTpSwvzknNku+DLC+4OBisYD7OqfRT/SuYD25371xO80pPtL+unOUFd0rc7yZ7fdR1nmtbxnxwZ/mWQnZfVfUpaWQBu68K7ny+28985NRPNC9gPrh2Ls3P7ququj2nJbL7quBG/L5VzEddw2yUyHxwu7jFKpYXfbJXfJTH8oLbgVmZzEdf9TeG5zAf3Dwck87yqrpt6iWwvOBa6VvLmI+6dreHY6kPvixpHptXnJNqndxzT++8Kv7vsFLqy3NK86Pbi5gPLjb9kM/mVdU90ryIzSu43floLvNR15zwTQ7zwY0PDmWyvDgn1Q5vzWZ5FY+c6Wc+9qnmQ21SmA8uNvZJYHnhacU3ZrC84GL3+z7mq7oPlUczH9y4c8xyOq+yT+LcNjqv4Nb5hxOZj5zOi4OTmQ9udDm8ns0rPNN4MoLNK7g4tW4k81HXnr9pFvPBjZGdZ4RYXvT1fFQUzSu5sXz5XOajr7a4dxLzFY95wT338eSFZw1Nct/j8OQFd+5Odr9H5/FR197eIIL54Hqnr+PYfVWsk2bxyWfDyX1VcN38y/0urMfHOqXf3+bhcOKDazE/LqX3VVF30aVodl8VXG/W0cd81LX0qTHMBxfRye7vwT15sU5ajUuGsrzg9oKt7n1ej4851VeffJP5ir/93hqWF559piKW5gVPeCeR+rKuVfb1BuaDmy+sT2b3VdEn0TMthd1XBbeK5O8tPD5yiuTLKcwH10+tT2f3VZV3X1d6XxVc+2VqOvNV3TUvZTBfcU3+rt2TF33SIs6lsrzg9t9uHa+PvuodllEf3Hh3uGB54TmbL2WwvODaOPdetddHXe3WNYL54LpVnsHmVa2rGwssNq/gmn9fNPPVutq42Vrmg5t5Rjyb1/+vu9PH5hXc1pqnMR91zUbLU5gPbvZ270V786p1dVBBHMur+PmJyczHump2nJbMfHD9rxsEy6u80J4MlhfcGPprJvNR1xje3099yZ1/Xcpi84o+WZ8NFWxewZ3IZzKZr/rarTST+eDOttf8dF6lZ/3b/W6cd17BzcgI6qOu3eclP/PBnd+nZ7K86JO42COL5QW3H/w4i/noq/HrpWzqS64fcb976c2rvHL3d1HevODOzAjqo661/ads5oNrPdzvLnv3qzgnNWtPrGT7VcWbfDeP+TindB5v7P59z+tLrucUuN9L8+xX4dkt7xrC9qvgxvnH3e+aeHxVd88f7vd8PT64teu8+3sZT151TpoRdH9X580rufFkVRL15TmVZldmMl/xw3dmh0heeHp4II3lBTf3ue++/MOXdY3vNuZQX3Jrwk73u5ye/arq67TZ7u/BPPtVcCdhjLuee3yV8/bJPuaD6zuPprD9qqrbdXYC26+CG2E9U5mPuuL6hwTzwfXVg/w0L/r0mfuuhzcvuBk5NYv56Kv9fXwe88H1+v3zWV54TswP2SwvuNatdy7zUdcZeksB8xVv+XUBm1d1Tjrirzw2r+Dm7F6FzMc5pWN/Usx8cK3/hVI2r/CsotVFbF7BzTm3FjMfdY1Gx8uYDy5EvQDLi3NSK293GcsLbvnOBpiPc1VnyNQA88GdsjfKaV7paZ+73wn25gU3/xNeznzUtc9+VM58cKewoJzNK/qkd32qmM6r5FbYmGLmI6fT4sMy5oPbUyaVsXmFp/20spjNK7h+8KNi5qOuVr9NGfPB7cDlUpYXfXIaXQqwvODa2OwA9WVfxR9DypkPbnx8IUDzom7E2ADLC26ldwgwH3WtHldz+ODG1kkBul+V66T+ui3YfhVcK9KymI91ymzcK5v54KLvhRy2X4Xn9NqZzfar4OaMW3OZr+r+1CuP+eD6kA75LC/WSb15Wh7NK7kofD+f+ZhT+6HEQuaD21OfKmJ5lfeIv4DlBTey2xUyX9Vdv6mI+pJbT5wvYvtV9MnatzWX7VfB9bv25zJf5XwoLp/5it/0Zj7br6q6Y93vhnv3q+DOoSY51Jd1jRffyWM+uFbvVZoXfdJzXiikeSV3fjtbwHz01VzwZRHzwbWYPkUsLzxnRbMCllfxjmXUR139tgGFzAfX/vw2n86rXCdF4yYldF4lN1uNKKG+XKessDOlzAd3PjhWyuZV1R2yuITNK7jdf0YJ81HXGvZ5KfPBja0xpSyvWldv/neA5QXXmv5Yxnysq8albgHmg9u+2wMsLzznL38ZywtuP/ZMGfNR17zh5zLmK15llbF5RZ/Ed61L2LyCi3qvFzMfOcWfTUuZD+50fKGEzSs8fcqIIjavij/6RQHzUdd8Wi9mPri5b30hy4s+iQfKSllecMf6vIT56Ks546dS5oPbH/6vhOWFJzanFrO8ineoX8R81NXrbStmvuLjHywK/R8Z3gRn + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-010000.3.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-010000.3.vtu new file mode 100644 index 000000000..7603b0974 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-010000.3.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAgAAAAIAgnQAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-010000.pvtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-010000.pvtu new file mode 100644 index 000000000..52e4f6359 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-010000.pvtu @@ -0,0 +1,19 @@ + + + + + + + + + + + + + + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-020000.0.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-020000.0.vtu new file mode 100644 index 000000000..d91237ae0 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-020000.0.vtu @@ -0,0 +1,34 @@ + + + + + + + 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tB2hc8aS7+wHy6UZtdbgM+LoO5ZzzbS4P+XSjtucAdB3pvls86W4/kI949zkA+dBzgHKffpfLQz7lPvcu1w80rnjS3X4gn27UVofLgK/rUO7T73J5yKcLdHc/QPNOPOnufoB8xLvrAPnQdS/nnG9zecinnBO+ze0HaFzxpLv7AfIR79aBfKgO5ZzzbS4P+ZTz0re5fqB5J550tx/IR7y7DpAPrQPlPv0ul4d8yn3uXa4faFzxpLv9QD7i3TqQD9Wh3Kff5fKQT7nfh+/7P9P/+TjIn32v/xtP+pnP6XFw3DOfE+ited5eZ9+7/8aTbuchn9OwfvZ7mV+PB+tWjyue9DMf+Dvg5CPerQP6QB3Ek+7mIR/x9PcW7H54e539XYU3nnS7H8jnBHprnrfX2fe+v/Gk23nI5zSsu/1A44on3e0H8hHv1gF9oA7iSXfzkI940s/OBf1meN2o55140s/WmYvhcc/m9UXbOkA+dN3Fk+7mIR/xpJ/dl/12uG71uOJJP7uOV8PjntXtqq0O5EN1EE+6m4d8xLvrAF1HmnfiSXf7gXzEu+sA+dA6IJ50Nw/5iCfd7QcaVzzpbj+Qj3i3DuRDdRBPupuHfMTT33ux9wNvr7O/6/LGk27vB8jnBHprnrfX2d9deeNJt/OQz2lYd/cDNK540t39APmId+uAPlAH8aS7echH/I/3yX54e/XA9/fJfiCfE+iteVSnv7zPk27nIZ/TsO72A40rvr/P9QP5iO+TeagO4kl385CPeNLd/QDNO/Gku/sB8hHvrgPkQ9ddPOluHvIRT7q7H6BxxZPu7gfIR7xbB/KhOogn3c1DPuLddYCuI8078f19rh/IR3yfzEPrgHjS3TzkI550tx9oXPH9fa4fyEd8n8xDdRBPupuHfMSTfva5vN8NPzeo77vFk372nOd6eNyz5yrXbc8ByIfu+8ST7uYhH/Gkn30u5g/DdavHFU/62XOkm+Fxz57b3LTVgXyoDuJJd/OQj3j3OQBdR7rvFk+62w/kI959DkA+9L4vnnQ3D/mIJ93tBxpXPOluP5CPeLcO5EN1EE+6m4d8xJN+di7rh+F1o5534kk/e858Ozzu2XPd27Z1gHzouosn3c1DPuJJPzsX8cfhutXjiif97H38bnjcs/fNu7Y6kA/VQTzpbh7yKd8T+KdcP9C8E0+62w/kI95dB8iH1gHxpLt5yEc86W4/0LjiSXf7gXzEu3UgH6qDeNLdPORTvufQfA5A7+N03y2edHc/QD7i3ecA5EP3feJJd/OQj3jS3f0AjSuedHc/QD7i3TqQD9VBPOluHvIR7z4HoOtI993i+/tcP5CP+D6Zh54DiCfdzUM+4kl3+4HGFd/f5/qBfMT3yTxUB/Gku3nIRzzp7n6A5p140t39APmId9cB8qHrLp50Nw/5iCfd3Q/QuOJJd/cD5CPerQP5UB3Ek+7mIZ/yPcn3uX6geSe+v8/1A/mI75N5aB0QT7qbh3zEk+72A40rvr/P9QP5iO+TeagO4kl385BP+Z5P+Hu/9nmAt9fZ3/V940m3zwOQzwn01jzq05/e50m385DPaVh3zwPQuOJJd88DkI94tw7oA3UQT7qbh3zE099btvvh7XX2d5XfeNLtfiCfE+itebQOAd+NknnI5zSsu/1A44on3e0H8hHv1gF9oA7iu1EuD/mIJ909D0DzTjzp7nkA8hHvrgPkQ9ddPOluHvIRT7p7HoDGFU+6ex6AfMS7dSAfqoN40t085CPeXQfoOtK8E0+62w/kI95dB8iH1gHx3SiXh3zEk+72A40rnnS3H8hHvFsH8qE6iO9GuTzkI77L7gfeXmd/1/2NJ93eD5DPCfTWPLpP+vl9nnQ7D/mchnV3P0Djiifd3Q+Qj3i3DugDdRBPupuHfMSf6a398PYqn7OveNLtfiCfE+itefR6eJ8n3c5DPqdh3e0HGlc86W4/kI94tw7oA3UQT7qbh3zK53wfcvsBmnfiSXf3A+Qj3l0HyIeuu3jS3TzkI550dz9A44on3d0PkI94tw7kQ3UQT7qbh3zEu+sAXUead+JJd/uBfMS76wD50DpQPqf/kMtDPuVz7g+5fqBxxZPu9gP5iHfrQD5Uh/I5/YdcHvIp5/wfvOcA9Ht8uu8WT7p7HoB8xLvPAciH7vvEk+7mIR/xpLvnAWhc8aS75wHIR7xbB/KhOogn3c1DPuLd5wB0Hem+Wzzpbj+Qj3j3OQD50Pu++G6Uy0M+4kl3+4HGFU+62w/kI96tA/lQHcR3o1we8hFPunsegOadeNLd8wDkI95dB8iHrrt40t085COedPc8AI0rnnT3PAD5iHfrQD5UB/Gku3nIp/yd0J9y/UDzTjzpbj+Qj3h3HSAfWgfEd6NcHvIRT7rbDzSueNLdfiAf8W4dyIfqIL4b5fKQT/k7p8n9AN13iyfd3Q+Qj3j3OQD50H2feNLdPOQjnnR3P0Djiifd3Q+Qj3i3DuRDdRBPupuHfMS7zwHoOtJ9t3jS3X4gH/HucwDyoecA5XP6D7k85FM+5/6Q6wcaVzzpbj+Qj3i3DuRDdSif03/I5SGf8jnfh9x+gOadeNLd/QD5iHfXAfKh6y6edDcP+Ygn3d0P0LjiSXf3A+Qj3q0D+VAdxJPu5iGf8nfSf871A8078aS7/UA+4t11gHxoHSif03/I5SGf8jn3h1w/0LjiSXf7gXzEu3UgH6pD+Zz+Qy4P+ZTP+ZNefQ9A+Vw9fG9A/bl7/Uz62ff8/Gl43LPv1fnT+3prHvrctzjS3Tzko59Jr+tQPtd9540rnvSz7xH68/C4Z9/b8+e2OpAP1UE86W4e8hHvfg8AXUf63L140t1+IB9x7vcAkA997k886W4e8hFPutsPNK5+Jt3tB/IR79aBfKgO4kh385CPfia9XgfK5yrvvHknnvSz7xm7Hx737Hu97tvWAfKh6y6edDcP+Ygnva5Dfz9ct3rcPtDPvsfpL8Pjnn1v0l/a6kA+VIc+0N085CPeXQfoOtK8E0+62w/kI95dB8iH1gHxpLt5yEc86W4/0Lh9oLv9QD7i3TqQD9WhD3Q3D/mId78HgN7H6XP34kl39wPkI979HgDyoc99iyfdzUM+4kl39wM0rnjS3f0A+Yhz60A+VAfxpLt5yEe8+z0AdB3pc/fi+/tcP5CP+D6Zh37vL550Nw/5iCfd7QcaV3x/n+sH8hHfJ/NQHcST7uYhH/Gku/sBmnf6mXR3P0A+4t11gHzouosj3c1DPvqZdHc/QOP2ge7uB8hHvFsH8qE69IHu5iEf8e46QNeR5p34/j7XD+Qjrk/moXVAPOluHvIRT7rbDzRuH+huP5CP+D6Zh+rQB7qbh3z0M+n1c4Byru7Ou+8WT/rZ9zz/NDzu2fcq/9T2HIB86L5PPOluHvIRT3pdh8vRcN3qccWTfvY90qPhcc++t3nUVofLgK/rIJ50Nw/5dKO25wB0Hem+Wzzpbj+Qj3j3OQD50Pu+eNLdPOQjnnS3H2hc8aS7/UA+3aitDpcBX9dBPOluHvLpAr1eB8q5ijtv3okn/ex75n8eHvfse91/blsHyIeuu3jS3TzkI570ug7l9/p33rjiST/7Pf7D8Lhnvzd/aKsD+VAdxJPu5iEf8e46QNeR5p140t1+IB/x7jpAPrQOiCfdzUM+4kl3+4HGFU+62w/kI96tA/lQHcST7uYhH/HucwB6H6f7bvGku/sB8hHvPgcgH7rvE0+6m4d8xJPu7gdoXPGku/sB8ulGbXW4DPi6DuJJd/OQTzdqew5A15Huu8X397l+IB/xfTIPPQcQT7qbh3zEk+72A40rvr/P9QP5dKO2OlwGfF0H8aS7ecinC3R3P0DzTjzp7n6AfMS76wD50HUXT7qbh3zEk+7uB2hc8aS7+wHyEe/WgXyoDuJJd/OQj3h3HaDrSPNOfH+f6wfyEd8n89A6IJ50Nw/5iCfd7QcaV3x/n+sH8hHfJ/NQHcST7uYhH/Hu9wDQ7/Hpc/fiSXfPA5CPePd7AMiHPvctnnQ3D/mIJ909D0DjiifdPQ9APuLdOpAP1UE86W4e8hHvfg8AXUf63L140t1+IB/x7vcAkA997k98N8rlIR/xpLv9QOOKJ93tB/IR79aBfKgO4rtRLg/5iCfdPQ9A80486e55APIR764D5EPXXTzpbh7yEU+6ex6Axu0D3T0PQD7i3DqQD9WhD3Q3D/mId9cBuo4078ST7vYD+Yh31wHyoXVAfDfK5SEf8aS7/UDj9oHu9gP5iHfrQD5Uhz7Q3TzkI75L7gfoc/fiSXf3A+Qj3v0eAPKhz32LJ93NQz7iSXf3AzSueNLd/QD5iHfrQD5UB/Gku3nIR7z7PQB0Helz9+JJd/uBfMS73wNAPvQ9AOX36A+5PORTfs/6kOsHGlc86W4/kI94tw7kQ3Uov8d9yOUhn/Kc9yG3H6B5J550dz9APuLddYB86LqLJ93NQz7iSXf3AzRuH+jufoB8xLt1IB+qQx/obh7yEe+uA3Qdad6JJ93tB/IR764D5EPrQPk9ykMuD/mU5+wPuX6gcftAd/uBfMS7dSAfqkMf6G4e8in3+Q/ecwD6PT7dd+tn0t3zAOQj3n0OQD503yeOdDcP+ehn0t3zADSueNLd8wDk043a6nAZ8HUdxJPu5iGfbtT2HICuI913iyfd7QfyEec+ByAfet8X341yechHPOluP9C4+pl0tx/Ipxu11eEy4Dvgu1EuD/l0ge6eB6B5J5509zwA+Yh31wHyoesunnQ3D/mIJ909D0DjiifdPQ9APuLdOpAP1UE86W4e8hHvrgN0HWneiSfd7QfyEe+uA+RD64D4bpTLQz7iSXf7gcYVT7rbD+Qj3q0D+VAdxHejXB7yEd8l9wN03y2edHc/QD7i3ecA5EP3feJJd/OQj3jS3f0AjSuedHc/QD7dqK0OlwFf10E86W4e8ulGbc8B6DrSfbd40t1+IB/x7nMA8qHnAOV99CGXh3zKOvuQ6wcaVzzpbj+QTzdqq8NlwNd1KOv4Qy4P+XSB7u4HaN7pZ9Ld/QD5iHfXAfKh6y6OdDcP+ehn0t39AI0rnnR3P0A+4t06kA/VQTzpbh7yEe+uA3Qdad6JJ93tB/IR564D5EPrQKnjQy4P+ZR/50OuH2hc/Uy62w/kI96tA/lQHQr3kMtDPuVn0qvXFvTC//PxfZ706vX3/wjGrXwKX+mteQp/Av70mMpDPoUnvXr9738GdavGLTzp1ev3fw3GrXwKb9aBfKgOhT89pvKQT+FPuX4oz81+PX6XJ93tB/IRX+utecq/9yPk/DhO5SGf8u8l3ewHGlc86W4/kI94tw7kQ3UoOT+OU3nIp/x7Sa9eh78G60Y17w6RXr3+8V/BuJVP4c11gHzouh8i3cxDPoUnvXr9338HdavGLTzp1Wv8GIxb+RTerAP5UB0Kf3pM5SGfwp9y/UDz7hDobj+Qj3h3HSAfWgcOge7mIZ/y7yXd7AcaVzzpbj+Qj3i3DuRDdSg5P45Tecin/Hs/5vYD5XsDfvM+T7q7HyAf8bXemqfsly5gn3ORy0M+ZZ9wkdsP0LjiSXf3A+Qj3q0D+VAdyj7nIpeHfMo6eZHrh/Lc7Lfv86S7/UA+4mu9NU+Z71cwT69yecin9PlVrh9oXPGku/1APuLdOpAP1aHM06tcHvIp8/0qtx+geXcIdHc/QD7i3XWAfOi6HwLdzUM+ZZ9wkdsP0LjiSXf3A+Qj3q0D+VAdyj7nIpeHfMp+6SLXDzTvDoHu9gP5iHfXAfKhdeAQ6G4e8il9fpXrBxpXPOluP5CPeLcO5EN1KPP0KpeHfMp8J716fRgHzw2q++7Ck169puNg3MpnOm57DvAh4k/Anx5TechnGunVaxfVrRp3F+nV68fPwbiVT+HNOuyiup2APz2m8pBP4U+5fqD7bvGku/1APtNx23OADwFfv++XnB/HqTzkM410sx9o3F2gu/1APuLdOuyCutV12AW6m4d8yr+X9Oq1+BysG9W8W0R69eqjcSuf/nPbOrCI+BPwp8dUHvLpI716fZ8EdavGLTzp1WszCcatfDaTtjp8j/gT8KfHVB7y2Uza1gG6jjTvFoHu9gP59J/b1oFFwNfrwCLQ3Tzk00e62Q80rnjS3X4gn82krQ7fA76uQ8n5cZzKQz6bSdtzAHofp/tu8aS7+wHymY7bngN8CPj6vq/scy5yechnGujufoDG3QW6ux8gH/FuHXZB3eo67ALdzUM+ZZ28yPUD3XeLJ93tB/KZjtueA3wI+Po5QJmnV7k85DMNdLcfaNxdoLv9QD7i3TrsgrrVddgFupuHfMp8v8rtB2jeLQLd3Q+QT/+5bR1YBHx93ReB7uYhnz7Q3f0AjSuedHc/QD6bSVsdvgd8XYeyz7nI5SGfzaRtHaDrSPNuEehuP5BP/7ltHVgEfL0OLALdzUM+faC7/UDjiifd7Qfy2Uza6vA94Os6lHl6lctDPhvQW88DlL+b9rv3edLd8wDkI77WW/OU80LX7/Oku3nIp5yTufbqQL/Hp3HFk+6eByAf8W4dyIfqUM75XOfykE85J3Cd64fyd0P+8D5PutsP5CO+1lvzlPNON+/zpLt5yKec87nJ9QONK550tx/IR7xbB/KhOpRzSje5PORTzjnceOvAITgvVM+7Q6C75wHIR7y7DpAPXfdDoLt5yKeck7n26kC/x6dxxZPungcgH/FuHciH6lDO+Vzn8pBPeU54nesHmneHQHf7gXzEu+sA+dA6cAh0Nw/5lHM+N7l+oHHFk+72A/mId+tAPlSHck7pJpeHfMpzzpvcfqB8b9IP7/Oku/sB8hFf6615ynnp2/d50t085FPOCd/m9gM0rnjS3f0A+Yh360A+VIdyzvk2l4d8yjnJ21w/lO8N+OP7POluP5CP+FpvzVPu9+/gPv0ul4d8yn3uXa4faFzxpLv9QD7i3TqQD9Wh3Kff5fKQT9nn3+X2AzTvDoHu7gfIR7y7DpAPXfdDoLt5yKecE77N7QdoXPGku/sB8hHv1oF8qA7lnPNtLg/5lN+T3ub6gebdIdDdfiAf8e46QD60DhwC3c1DPuU+9y7XDzSueNLdfiAf8W4dyIfqUO7T73J5yKfc7995zwHo9/h03y2edPc8APlMx23PAT4EfH3fV875XOfykM800N3zADTuLtDd8wDkI96twy6oW12HXaC7ecinnBO4zvUD3XeLJ93tB/KZjtueA3wI+Pp9v5xTusnlIZ9poLv9QOPuAt3tB/IR79ZhF9StrsMu0N085FPOOdx468AiOC9Uz7tFoLvnAcin/9y2DiwCvr7ui0B385BPH+jueQAaVzzp7nkA8tlM2urwPeDrOpRzPte5POSzmbStA3Qdad4tAt3tB/LpP7etA4uAr9eBRaC7ecinD3S3H2hc8aS7/UA+m0lbHb4HfF2Hck7pJpeHfDaTtucA9D5O993iSXf3A+QzHbc9B/gQ8PV9XznnfJvLQz7TQHf3AzTuLtDd/QD5iHfrsAvqVtdhF+huHvIp5yRvc/1A993iSXf7gXym47bnAB8Cvn4OUO7T73J5yGca6G4/0Li7QHf7gXzEu3XYBXWr67ALdDcP+ZR9/l1uP0DzbhHo7n6AfPrPbevAIuDr674IdDcP+fSB7u4HaFzxpLv7AfLZTNrq8D3g6zqUc863uTzks5m0rQN0HWneLQLd7Qfy6T+3rQOLgK/XgUWgu3nIpw90tx9oXPGku/1APptJWx2+B3xdh3KffpfLQz6bSK9eP3x5X6fP3Ree9Oo1/xKMW/nMQW/NQ5/7LvzpMZWHfOaRXr2OUd2qcY+RXr3+9jUYt/IpvFmHY1S3E/Cnx1Qe8in8KdcP9Ll78aS7/UA+c9Bb89Dn/krOj+NUHvKZR7rZDzTuMdDdfiAf8W4djkHd6jocA93NQz7l30t69Vp/DdaNat6tI716XT4F41Y+hTfXAfKh676OdDMP+RSe9Oo1ewrqVo07i/Tq1UXjVj7dU1sdZhF/Av70mMpDPt1T2zpA15Hm3TrQ3X4gH/HuOkA+tA6sA93NQz7l30u62Q807izQ3X4gn+6prQ6zgK/rMAt0Nw/5dKC37gfoc/fiSXf3A+QzB701D33uu+xzLnJ5yGce6O5+gMY9Brq7HyAf8W4djkHd6jocA93NQz5lnbzI9QN97l486W4/kM8c9NY89Hv/Mk+vcnnIZx7obj/QuMdAd/uBfMS7dTgGdavrcAx0Nw/5lPl+ldsP0LxbB7q7HyAf8e46QD503deB7uYhn7JPuMjtB2jcWaC7+wHy6Z7a6jAL+LoOs0B385BP99S2DtB1pHm3DnS3H8hHvLsOkA+tA+tAd/OQT+nzq1w/0LizQHf7gXy6p7Y6zAK+rsMs0N085NNFevX6+hw8N6juuwtPevVaPQfjVj6r57bnAF8j/gT86TGVh3xWkV69Pr0EdavGLTzp1ev1JRi38nl9aavDp4g/AX96TOUhn9eXtucAdB3pvls86W4/kM/que05wNeAr9/3S86P41Qe8llFutkPNK540t1+IJ/Xl7Y6fAr4ug4l58dxKg/5vEZ69dpH60Y17/aRXr1++RaMW/kU3lwH9tG6cQL+9JjKQz6FJ716Lb8FdavGXUZ6/fqfYNzKp/BmHciH6rCMdDMP+RT+lOsHmnf7QHf7gXzEu+vAPlg36nVgH+huHvIp/17SzX6gcZeB7vYD+Yh360A+VIdloLt5yKf8e83nAPQ+Tvfd4kl39wPks3puew7wNeDr+76yz7nI5SGfVaC7+wEaVzzp7n6AfF5f2urwKeDrOpR9zkUuD/m8vrQ9B6DrSPfd4kl3+4F8Vs9tzwG+Bnz9HKDM06tcHvJZBbrbDzSueNLdfiCf15e2OnwK+LoOZZ5e5fKQz2ukm/sBmnf7QHf3A+Qj3l0H9sG6UV/3faC7ecin7BMucvsBGncZ6O5+gHzEu3UgH6rDMtDdPORT9ksXuX6gebcPdLcfyEe8uw7sg3WjXgf2ge7mIZ/S51e5fqBxl4Hu9gP5iHfrQD5Uh2Wgu3nIp8x383sA6Pf49Ll78aS75wHIZw56ax763Hc553Ody0M+80B3zwPQuMdAd88DkI94tw7HoG51HY6B7uYhn3JO4DrXD/S5e/Gku/1APnPQW/PQ5/7KOaWbXB7ymQe62w807jHQ3X4gH/FuHY5B3eo6HAPdzUM+5ZzDjbcOrIPzQvW8Wwe6ex6AfMS76wD50HVfB7qbh3zKOZlrrw6z4LxEPe4s0N3zAOTTPbXVYRbwdR1mge7mIZ/uqW0doOtI824d6G4/kI94dx0gH1oH1oHu5iGfcs7nJtcPNO4s0N1+IJ/uqa0Os4Cv6zALdDcP+XSgt+4H6HP34kl39wPkMwe9NQ997rucc77N5SGfeaC7+wEa9xjo7n6AfMS7dTgGdavrcAx0Nw/5lHOSt7l+oM/diyfd7QfymYPemoe+B6Dcp9/l8pDPPNDdfqBxj4Hu9gP5iHfrcAzqVtfhGOhuHvIp+/y73H6A5t060N39APmId9cB8qHrvg50Nw/5lHPCt7n9AI07C3R3P0A+3VNbHWYBX9dhFuhuHvLpntrWAbqONO/Wge72A/mId9cB8qF1YB3obh7yKfe5d7l+oHFnge72A/l0T211mAV8XYdZoLt5yKeLdPM8AN13iyfdPQ9APqvntucAXwO+vu8r53yuc3nIZxXo7nkAGlc86e55APJ5fWmrw6eAr+tQzvlc5/KQz+tL23MAuo503y2edLcfyGf13PYc4GvA1+/75ZzSTS4P+awC3e0HGlc86W4/kM/rS1sdPgV8XYdyTukml4d8XgPdPQ9A824f6O55APIR764D+2DdqK/7PtDdPORTzslce3VYBucl6nGXge6eByAf8W4dyIfqsAx0Nw/5lPNC17l+oHm3D3S3H8hHvLsO7IN1o14H9oHu5iGfcs7nJtcPNO4y0N1+IB/xbh3Ih+qwDHQ3D/mU807mcwB6H6f7bvGku/sB8lk9tz0H+Brw9X1fOed8m8tDPqtAd/cDNK540t39APm8vrTV4VPA13Uo55xvc3nI5/Wl7TkAXUe67xZPutsP5LN6bnsO8DXg6+cA5T79LpeHfFaB7vYDjSuedLcfyOf1pa0OnwK+rkO5T7/L5SGf10B39wM07/aB7u4HyEe8uw7sg3Wjvu77QHfzkE85J3yb2w/QuMtAd/cD5CPerQP5UB2Wge7mIZ9yXvo21w807/aB7vYD+Yh314F9sG7U68A+0N085FPuc+9y/UDjLgPd7QfyEe/WgXyoDstAd/OQT7nfr/WH9/XtwzD/9395nye99vn7fwyPW/uIr/XWPOJ//6f3edLdPOQjnvS6Dv/7n8N1q8cVT3rtQ38HnHzEu3UgH6qDeNLdPOQjvtZb+0H8P/71fZ50tx/IR3ytt+YRP/7z+zzpbh7yEU+62w80rnjS3X4gH/FuHciH6iCedDcP+YgnvV4HDn8dXjfqeXcI9NrnH/81PG7tI95dB8iHrvsh0N085COe9LoO//ffw3WrxxVPeu1DfweQfMS7dSAfqoN40t085CPeXQfoOtK8OwS62w/kI95dB8iH1oFDoLt5yEc86W4/0LjiSXf7gXzEu3UgH6qDeNLdPOQjvtZb9wPipz++z5Pu7gfIR3ytt+YR/+P9+zzpbh7yEU+6ux+gccWT7u4HyEe8WwfyoTqIJ93NQz7ia721H8T3wPf3uX4gH/F9Mo/4zV/e50l385CPeNLdfqBxxff3uX4gH/F9Mg/VQTzpbh7yEU+6ux+geXcIdHc/QD7i3XWAfOi6HwLdzUM+4kl39wM0rnjS3f0A+Yh360A+VAfxpLt5yEe8uw7QdaR5dwh0tx/IR3yfzEPrwCHQ3TzkI550tx9oXPH9fa4fyEd8n8xDdRBPupuHfMSTXj8H+DAefm5Q33eLJ732mY6Hx619puO25wAfAr6+7xNPupuHfKaBXtdhF9StHncX6LUP/R1w8hHv1mEX1K2uwy7Q3TzkI959DkDXke67xZPu9gP5TMdtzwE+BHz9vi+edDcP+UwD3e0HGncX6G4/kI94tw67oG51HXaB7uYhH/Gk1+vA4vPwulHPu0Wg1z59MG7t039uWwcWAV9f90Wgu3nIpw/0ug7fJ8N1q8cVT3rtQ98DQD6bSVsdvgd8XQfxpLt5yGczaVsH6DrSvFsEutsP5NN/blsHFgFfrwOLQHfzkE8f6G4/0LjiSXf7gXw2k7Y6fA/4ug7iSXfzkM9m0vYcgN7H6b5bPOnufoB8puO25wAfAr6+7xNPupuHfKaB7u4HaNxdoLv7AfIR79ZhF9StrsMu0N085CPefQ5A15Huu8X397l+IJ/puO05wIeAr58DiCfdzUM+00B3+4HG3QW62w/kI75P5qE67ALdzUM+4kl39wM07xaB7u4HyKf/3LYOLAK+vu6LQHfzkE8f6O5+gMYVT7q7HyCfzaStDt8Dvq6DeNLdPOSzmbStA3Qdad4tAt3tB/LpP7etA4uAr9eBRaC7ecinD3S3H2hc8f19rh/IZzNpq8P3gK/rIJ50Nw/5bEBvPQ8gfv5v7/Oku+cByEd8rbfmEf+3n97nSXfzkI940t3zADSueNLd8wDkI96tA/lQHcST7uYhH/G13toP4i9H7/Oku/1APuJrvTWP+A74bpTLQz7iSXf7gcYVT7rbD+Qj3q0D+VAdxHejXB7yEU+6ex6A5t0h0N3zAOQj3l0HyIeu+yHQ3TzkI5509zwAjSuedPc8APmId+tAPlQH8aS7echHvLsO0HWkeXcIdLcfyEe8uw6QD60Dh0B385CPeNLdfqBxxZPu9gP5iHfrQD5UB/HdKJeHfMR3yf2A+NW/v8+T7u4HyEd8rbfmEf/68/s86W4e8hFPursfoHHFk+7uB8hHvFsH8qE6iCfdzUM+4mu9tR/E//LL+zzpbj+Qj/hab81TPu//8D5PupuHfMrn3B9y/UDjiifd7QfyEe/WgXyoDuVz+g+5PORTPuf7kNsP0Lw7BLq7HyAf8e46QD503Q+B7uYhH/Gku/sBGlc86e5+gHzEu3UgH6qDeNLdPOQj3l0H6DrSvDsEutsP5CPeXQfIh9aBQ6C7ecinfM79IdcPNK540t1+IB/xbh3Ih+pQPqf/kMtDPuWc/4P3HIB+j0/33eJJd88DkM903PYc4EPA1/d94kl385DPNNDd8wA07i7Q3fMA5CPercMuqFtdh12gu3nIR7z7HICuI913iyfd7QfymY7bngN8CPgO+G6Uy0M+00B3+4HG3QW62w/kI96twy6oWwd8N8rlIR/xpLvnAWjeLQLdPQ9APv3ntnVgEfD1dV8EupuHfPpAd88D0LjiSXfPA5DPZtJWh+8BX9dBPOluHvLZTNrWAbqONO8Wge72A/n0n9vWgUXAd8B3o1we8ukD3e0HGlc86W4/kM9m0laH7wHfAd+NcnnIZzNpew5A7+N03y2edHc/QD7TcdtzgA8BX9/3iSfdzUM+00B39wM07i7Q3f0A+Yh367AL6lbXYRfobh7yEe8+B6DrSPfd4kl3+4F8puO25wAfAr5+DlA+p/+Qy0M+00B3+4HG3QW62w/kI96twy6oW12HXaC7ecinfM73IbcfoHm3CHR3P0A+/ee2dWAR8PV1XwS6m4d8+kB39wM0rnjS3f0A+WwmbXX4HvB1HcST7uYhn82kbR2g60jzbhHobj+QT/+5bR1YBHy9DiwC3c1DPn2gu/1A44on3e0H8tlM2urwPeDrOpTP6T/k8pDPJtKr7wH4Af6OPH3uXjzptc/8y/C4tc8c9NY89Llv8aS7echnHuh1HY5B3epxj4Fe+9DfAScf8W4djkHd6jocA93NQz7i3e8BoOtIn7sXT7rbD+QzB701D33uTzzpbh7ymQe62w807jHQ3X4gH/FuHY5B3eo6HAPdzUM+4kmv14H11+F1o55360CvfS6fhsetfcS76wD50HVfB7qbh3zEk17XYfY0XLd63Fmg1z5dMG7t0z211WEW8HUdZoHu5iGf7qltHaDrSPNuHehuP5CPeHcdIB9aB9aB7uYhH/Gku/1A484C3e0H8ume2uowC/i6DrNAd/OQTwd6636APncvnnR3P0A+c9Bb89DnvsWT7uYhn3mgu/sBGvcY6O5+gHzEu3U4BnWr63AMdDcP+Yh3vweAriN97l58f5/rB/KZg96ah37vL550Nw/5zAPd7Qca9xjobj+Qj/g+mYfqcAx0Nw/5iCfd3Q/QvFsHursfIB/x7jpAPnTd14Hu5iEf8aS7+wEadxbo7n6AfLqntjrMAr6uwyzQ3Tzk0z21rQN0HWnerQPd7QfyEd8n89A6sA50Nw/5iCfd7Qcadxbobj+QT/fUVodZwNd1mAW6m4d8ukCvnwN8fR5+blDfd4snvfZZPQ+PW/usntueA3wN+Pq+Tzzpbh7yWQV6XYdPL8N1q8cVT3rtQ38HnHxeX9rq8Cng6zqIJ93NQz6vL23PAeg60n23eNLdfiCf1XPbc4CvAV+/74sn3c1DPqtAd/uBxhVPutsP5PP60laHTwFf10E86W4e8nkN9Hod2AfrRj3v9oFe+/zybXjc2ke8uw7sg3Wjvu77QHfzkI940us6LL8N160edxnotc+v/md43NpHvFsH8qE6LAPdzUM+4t11YB+sG/W82we62w/kI95dB/bBulGvA/tAd/OQj3jS3X6gcZeB7vYD+Yh360A+VIdloLt5yEe8+xyA3sfpvls86e5+gHxWz23PAb4GfH3fJ550Nw/5rALd3Q/QuOJJd/cD5PP60laHTwFf10E86W4e8nl9aXsOQNeR7rvF9/e5fiCf1XPbc4CvAV8/BxBPupuHfFaB7vYDjSu+v8/1A/m8vrTV4VPA13UQT7qbh3xeA93dD9C82we6ux8gH/HuOrAP1o36uu8D3c1DPuJJd/cDNO4y0N39APmId+tAPlSHZaC7echHvLsO7IN1owe+v8/1A/mI75N5aB3YB7qbh3zEk+72A427DHS3H8hHfJ/MQ3VYBrqbh3zEu98DQL/Hp8/diyfdPQ9APnPQW/PQ577Fk+7mIZ95oLvnAWjcY6C75wHIR7xbh2NQt7oOx0B385CPePd7AOg60ufuxZPu9gP5zEFvzUOf+xPfjXJ5yGce6G4/0LjHQHf7gXzEu3U4BnXrgO9GuTzkI5509zwAzbt1oLvnAchHvLsOkA9d93Wgu3nIRzzp7nkAGncW6O55APLpntrqMAv4ug6zQHfzkE/31LYO0HWkebcOdLcfyEe8uw6QD60D60B385CPeNLdfqBxZ4Hu9gP5dE9tdZgFfAd8N8rlIZ8O9Nb9AH3uXjzp7n6AfOagt+ahz32LJ93NQz7zQHf3AzTuMdDd/QD5iHfrcAzqVtfhGOhuHvIR734PAF1H+ty9eNLdfiCfOeiteeh7AMrn9B9yechnHuhuP9C4x0B3+4F8xLt1OAZ1q+twDHQ3D/mUz/k+5PYDNO/Wge7uB8hHvLsOkA9d93Wgu3nIRzzp7n6Axp0FursfIJ/uqa0Os4Cv6zALdDcP+XRPbesAXUead+tAd/uBfMS76wD50DqwDnQ3D/mUz7k/5PqBxp0FutsP5NM9tdVhFvB1HWaB7uYhny7Q3fMAdN8tnnT3PAD5rJ7bngN8Dfj6vk886W4e8lkFunsegMYVT7p7HoB8Xl/a6vAp4Os6iCfdzUM+ry9tzwHoOtJ9t3jS3X4gn9Vz23OArwHfAd+NcnnIZxXobj/QuOJJd/uBfF5f2urwKeA74LtRLg/5vAa6ex6A5t0+0N3zAOQj3l0H9sG6UV/3faC7echHPOnueQAadxno7nkA8hHv1oF8qA7LQHfzkI94dx3YB+tGPe/2ge72A/mId9eBfbBudMB3o1we8hFPutsPNO4y0N1+IB/xbh3Ih+qwDHQ3D/mI75L7AbrvFk+6ux8gn9Vz23OArwFf3/eJJ93NQz6rQHf3AzSueNLd/QD5vL601eFTwNd1EE+6m4d8Xl/angPQdaT7bvGku/1APqvntucAXwO+fg5QPqf/kMtDPqtAd/uBxhVPutsP5PP60laHTwFf16F8Tv8hl4d8XgPd3Q/QvNsHursfIB/x7jqwD9aN+rrvA93NQz7iSXf3AzTuMtDd/QD5iHfrQD5Uh2Wgu3nIR7y7DuyDdaOed/tAd/uBfMS768A+WDfqdWAf6G4e8imfc3/I9QONuwx0tx/IR7xbB/KhOiwD3c1DPuXz/rWuV63/83GQ3wK/dX1Oj4PjboHfZvO8vf7+H+/zpNt5yOc0rJ/9XYZfjwfrtgV+6/p8HA+OuwV+m8xDdRBPupuHfMSf6a398Pb63/98nyfd7gfyOYHemuftVf4OfMWTbuchn9Ow7vYDjSuedLcfyEe8Wwf0gTqIJ93NQz7iST/7u/C/GV43tsBvTZ/NxfC4W+C3yTx03cWT7uYhH/Gkn/1e/rfDddsCvzV9fnU1PO4W+G0yD9VBPOluHvIR764DdB1p3okn3e0H8hHvrgPkQ+uAeNLdPOQjnnS3H2hc8aS7/UA+4t06kA/VQTzpbh7yEX+mt+4H3l4H4A+uz2l4v3EA/pDN8/b6x3+9z5Nu5yGf07Du7gdoXPEH1+fj8H7jAPwhmYfqIJ50Nw/5iD/TW/vh7fV///0+T7rdD+RzAr01j9bpx/d50u085HMa1t1+oHHFk+72A/mId+uAPlAH8aS7echHPOnufoDmnfiD6UPvg+Qj/pDMQ9ddPOluHvIRT7q7H6BxxR9MH9oPkI/4QzIP1UE86W4e8hHvrgN0HWneiSfd7QfyEe+uA+RD64B40t085COedLcfaFzxpLv9QD7i3TqQD9VBPOluHvIRT3r9HOD3vxt+brAFfmv6jK+Hx90Cv03mofs+8aS7echHPOl1HX78w3DdtsBvTZ/NzfC4W+C3yTxUB/Gku3nIR7z7HICuI913iyfd7QfyEe8+ByAfet8XT7qbh3zEk+72A40rnnS3H8hHvFsH8qE6iCfdzUM+4kk/+1zwD8Prxhb4renT3Q6PuwV+m8xD11086W4e8hFP+tm5rD8O120L/Nb0Kd/7b/qI3ybzUB3Ek+7mIR/x7jpA15HmnXjS3X4gH/HuOkA+tA6IJ93NQz7iSXf7gcYVT7rbD+Qj3q0D+VAdxJPu5iEf8e5zAHofp/tu8QfTh94HyUf8IZmH7vvEk+7mIR/xpLv7ARpX/MH0oXWAfMQfknmoDuJJd/OQj3j3OQBdR7rvFk+62w/kI959DkA+9BxAPOluHvIRT7rbDzSueNLdfiAf8W4dyIfqIJ50Nw/5iCfd3Q/QvBN/MH3ofZB8xB+Seei6iyfdzUM+4kl39wM0rviD6UP7AfIRf0jmoTqIJ93NQz7i3XWAriPNO/Gku/1APuLddYB8aB0QT7qbh3zEk+72A40rnnS3H8hHvFsH8qE6iCfdzUM+4s90vS+55wHeXh/G7/Ok2+cByOcEemuet9cU+Ok4mYd8TsO6ex6AxhVPunsegHzEu3VAH6iD+Ok4l4d8xE+z/fD22gG/y/YD+ZxAb82jfeLn93nS7TzkcxrW3X6gccXvkv1APuJ3yTxUB/Gku3nIRzzp7nkAmnfiSXfPA5CPeHcdIB+67uKn41we8hFPunsegMYVT7p7HoB8xLt1IB+qg/jpOJeHfMRPk/1A8078LtkP5CN+l8xD64B40t085COedLcfaFzxu2Q/kI/4XTIP1UE86W4e8hF/prfuB95eC+AXrs9peL+xAH6RzfP26oHvs3nI5zSsu/sBGlf8wvX5OLzfWAC/SOahOojvk3nIR3yf7Ye31/fJ+zzpdj+Qzwn01jyqE/CbSTIP+ZyGdbcfaFzxpLv9QD7i3TqgD9RB/GaSy0M+4kl39wM078QvTB96HyQf8YtkHrru4vtkHvIR35t1oPdxGlf8wvSh/QD5iF8k81AdxPfJPOQjvk/2A8078aS7/UA+4t11gHxoHRC/meTykI940t1+oHHFk+72A/mId+tAPlQH8ZtJLg/5iCfdPQ9A993iSXfPA5CPePc5APnQfZ/46TiXh3zEk+6eB6BxxZPungcgH/FuHciH6iB+Os7lIR/x02Q/0H23+F2yH8hH/C6Zh973xZPu5iEf8aS7/UDjit8l+4F8xO+SeagO4kl385CPeNLd8wA078ST7p4HIB/x7jpAPnTdxU/HuTzkI5509zwAjSuedPc8APmId+tAPlQH8dNxLg/5iJ8m+4Hmnfhdsh/IR/wumYfWAfGku3nIRzzpbj/QuOJ3yX4gH/G7ZB6qg3jS3TzkI959DkDv43TfLX5h+tD7IPmIXyTz0H2f+D6Zh3zE92Yd6H2cxhW/MH1oHSAf8YtkHqqD+D6Zh3zE98l+oPtu8aS7/UA+4t3nAORDzwHEbya5POQjnnS3H2hc8aS7/UA+4t06kA/VQfxmkstDPuJJd/cDNO/EL0wfeh8kH/GLZB667uL7ZB7yEd+bdaD3cRpX/ML0of0A+YhfJPNQHcT3yTzkI75P9gPNO/Gku/1APuLddYB8aB0Qv5nk8pCPeNLdfqBxxZPu9gP5iHfrQD5UB/GbSS4P+Ygnvf4egL//y/s6fe5e/Nb0+f2fhsfdAr9N5qHPfYsn3c1DPuJJr+vwj38drtsW+K3pM/7z8Lhb4LfJPFQH8aS7echHvPs9AHQd6XP34kl3+4F8xLvfA0A+9Lk/8aS7echHPOluP9C44kl3+4F8xLt1IB+qg3jS3TzkI570eh2Y/ji8bmyB35o+P94Pj7sFfpvMQ9ddPOluHvIRT3pdh/5+uG5b4LemT/m7v6aP+G0yD9WhD3Q3D/mId9cBuo4078ST7vYD+Yh31wHyoXVAPOluHvIRT7rbDzRuH+huP5CPeLcO5EN16APdzUM+4t3vAaD3cfrcvfiD6UPvg+Qj/pDMQ5/7Fk+6m4d8xJPu7gdoXPEH04fWAfIRf0jmoTqIJ93NQz7i3e8BoOtIn7sXT7rbD+Qj3v0eAPKh3/uLJ93NQz7iSXf7gcYVT7rbD+Qj3q0D+VAdxJPu5iEf8aS7+wGad+IPpg+9D5KP+EMyD1138aS7echHPOnufoDG7QPd3Q+Qj/hDMg/VoQ90Nw/5iHfXAbqONO/Ek+72A/mId9cB8qF1QDzpbh7yEU+62w80bh/obj+Qj3i3DuRDdegD3c1DPuJJr58DzP9t+LnBFvit6fO3n4bH3QK/Teah+z7xpLt5yEc86XUdLkfDddsCvzV9utHwuFvgt8k8VAfxpLt5yKcbtT0HoOtI993iSXf7gXzEu88ByIfe98WT7uYhH/Gku/1A44on3e0H8ulGbXW4DPi6DuJJd/OQTxfo9Tqw+vfhdWML/Nb0ef15eNwt8NtkHrru4kl385CPeNLrOvzyy3DdtsBvTZ/ys+kjbpvMQ3UQT7qbh3zEu+sAXUead+JJd/uBfMS76wD50DognnQ3D/mIJ93tBxpXPOluP5CPeLcO5EN1EE+6m4d8xLvPAeh9nO67xR9MH3ofJB/xh2Qeuu8TT7qbh3zEk+7uB2hc8QfTpwv2GwfgD8k8VAfxpLt5yKcbtT0HoOtI993iSXf7gXzEu88ByIeeA4gn3c1DPuJJd/uBxhVPutsP5NON2upwGfB1HcST7uYhny7Q3f0AzTvxB9OH3gfJR/whmYeuu3jS3TzkI550dz9A44o/mD60HyAf8YdkHqqDeNLdPOQj3l0H6DrSvBNPutsP5CPeXQfIh9YB8aS7echHPOluP9C44kl3+4F8xLt1IB+qg3jS3TzkI979HgD6PT597l486e55APIR734PAPnQ577FT8e5POQjnnT3PACNK5509zwA+Yh360A+VAfx03EuD/mInyb7gT53L36X7AfyEb9L5qHP/Ykn3c1DPuJJd/uBxhW/S/YD+YjfJfNQHcST7uYhH/Gku+cBaN6JJ909D0A+4t11gHzououfjnN5yEc86e55ABq3D3T3PAD5iHfrQD5Uhz7Q3TzkI36a7Aead+J3yX4gH/G7ZB5aB8ST7uYhH/Gku/1A4/aB7vYD+YjfJfNQHfpAd/OQj3j3ewDofZw+dy9+YfrQ+yD5iF8k89DnvsX3yTzkI74360Dv4zSu+IXpQ+sA+YhfJPNQHcT3yTzkI75P9gN97l486W4/kI9493sAyIe+B0D8ZpLLQz7iSXf7gcYVT7rbD+Qj3q0D+VAdxG8muTzkI550dz9A8078wvSh90HyEb9I5qHrLr5P5iEf8b1Zhz7YLy2AX5g+tB8gH/GLZB6qQx/obh7yEd8n+4HmnXjS3X4gH/HuOkA+tA6I30xyechHPOluP9C4faC7/UA+4t06kA/VoQ90Nw/5iCfdPQ9A993iSXfPA5CPePc5APnQfZ/46TiXh3zEk+6eB6BxxZPungcgn27UVofLgJ8CPx3n8pBPN2p7DkDXke67xe+S/UA+4nfJPPS+L550Nw/5iCfd7QcaV/wu2Q/k043a6nAZ8HUdxJPu5iGfLtDd8wA078ST7p4HIB/x7jpAPnTdxU/HuTzkI5509zwAjSuedPc8APmId+tAPlQH8dNxLg/5iJ8m+4Hmnfhdsh/IR/wumYfWAfGku3nIRzzpbj/QuOJ3yX4gH/G7ZB6qg3jS3TzkI959DkDv43TfLX5h+tD7IPmIXyTz0H2f+D6Zh3zE92Yd6H2cxhW/MH26YL+xAH6RzEN1EN8n85BPN2p7DkDXke67xZPu9gP5iHefA5APPQcQv5nk8pCPeNLdfqBxxZPu9gP5dKO2OlwG/Ab4zSSXh3y6QHf3AzTvxC9MH3ofJB/xi2Qeuu7i+2Qe8hHfm3Wg93EaV/zC9KH9APmIXyTzUB3E98k85CO+T/YDzTvxpLv9QD7i3XWAfGgdEL+Z5PKQj3jS3X6gccWT7vYD+Yh360A+VAfxm0kuD/mIP9O1L6j1fz4O8j98eZ8n/czn9Dg47pnPCfTWPNqnAD//ksxDPqdh/ez3Mr8eD9atHlc86Wc+8HfAyUe8Wwf0gTqIn3/J5SEf8fNsP7y9jsAfs/1APifQW/NoH/r1fZ50Ow/5nIZ1tx9oXPHHZD+Qj/hjMg/VQTzpbh7yEU/62bmg3wyvG/W8E0/62TpzMTzu2by+aFsHyIeuu/j5l1we8hFP+tl92W+H61aPK570s+t4NTzuWd2u2upAPlQH8fMvuTzkI36e7Aead+KPyX4gH/HHZB5aB8ST7uYhH/Gku/1A44o/JvuBfMQfk3moDuJJd/OQj/gzvXU/8PZaA792fU7D+4018OtsHj0ne3qfJ93OQz6nYd3dD9C44teuz8fh/cYa+HUyD9VBPOluHvIRf6a39sPbawb87CnZD+RzAr01j9YJ4LtsHvI5DetuP9C44mdPuX4gH/GzZB6qg/gumYd8xJPu7gdo3olfmz70Pkg+4tfJPHTdxZPu5iEf8aS7+wEaV/za9KH9APmIXyfzUB3Ek+7mIR/x7jpA15HmnfjZU64fyEf8LJmH1gHxXTIP+YjvnnL9QOOKnz3l+oF8xM+SeagO4rtkHvIRT/rZ5/J+N/zcoL7vFk/62XOe6+Fxz56rXLc9ByAfuu8TP/+Sy0M+4kk/+1zMH4brVo8rnvSz50g3w+OePbe5aasD+VAdxM+/5PKQj/h5sh/ovlv8MdkP5CP+mMxD7/viSXfzkI940t1+oHHFH5P9QD7ij8k8VAfxpLt5yEc86Wfnsn4YXjfqeSee9LPnzLfD4549171tWwfIh667+PmXXB7yEU/62bmIPw7XrR5XPOln7+N3w+OevW/etdWBfKgO4udfcnnIR/w82Q8078Qfk/1APuKPyTy0Dogn3c1DPuJJd/uBxhV/TPYD+Yg/JvNQHcST7uYhH/HucwB6H6f7bvFr04feB8lH/DqZh+77xJPu5iEf8aS7+wEaV/za9KF1gHzEr5N5qA7iSXfzkI949zkAXUe67xY/e8r1A/mInyXz0HMA8V0yD/mI755y/UDjip895fqBfMTPknmoDuK7ZB7yEU+6ux+geSd+bfrQ+yD5iF8n89B1F0+6m4d8xJPu7gdoXPFr04f2A+Qjfp3MQ3UQT7qbh3zEu+sAXUead+JnT7l+IB/xs2QeWgfEd8k85CO+e8r1A40rfvaU6wfyET9L5qE6iO+SechHfJc9D/D2+vr8Pk+6fR6AfE6gt+Z5e62AXz0n85DPaVh3zwPQuOJJd88DkI94tw7oA3UQv3rO5SEf8atsP7y9Pr28z5Nu9wP5nEBvzaN1AvjXl2Qe8jkN624/0LjiSXf7gXzEu3VAH6iD+NeXXB7yEU+6ex6A5p140t3zAOQj3l0HyIeuu/jVcy4P+Ygn3T0PQOOKJ909D0A+4t06kA/VQfzqOZeHfMSvkv1A80486W4/kI94dx0gH1oHxL++5PKQj3jS3X6gccWT7vYD+Yh360A+VAfxry+5POQj/jW7H3h77YHfZ/cD5HMCvTXP2+uXb+/zpNt5yOc0rLv7ARpX/D65HyAf8ftkHqqDeNLdPOQj/kxv7Ye31xL45bdkP5DPCfTWPHr9z/s86XYe8jkN624/0Ljil99y/UA+4pfJPFQH8aS7echHPOnufoDmnfh9cj9APuL3yTx03cWT7uYhH/Gku/sBGlf8PrkfIB/x+2QeqoN40t085CPeXQfoOtK8E7/8lusH8hG/TOahdUA86W4e8hFPutsPNK745bdcP5CP+GUyD9VBPOluHvIRT7p7HoDuu8WT7p4HIB/x7nMA8qH7PvGr51we8hFPunsegMYVT7p7HoB8xLt1IB+qg/jVcy4P+YhfJfuB7rvFk+72A/mId58DkA+974t/fcnlIR/xpLv9QOOKJ93tB/IR79aBfKgO4l9fcnnIRzzp7nkAmnfiSXfPA5CPeHcdIB+67uJXz7k85COedPc8AI0rnnT3PAD5iHfrQD5UB/Gr51we8hG/SvYDzTvxpLv9QD7i3XWAfGgdEP/6kstDPuJJd/uBxhVPutsP5CPerQP5UB3Ev77k8pCP+NfkfoDuu8Xvk/sB8hG/T+ah+z7xpLt5yEc86e5+gMYVv0/uB8hH/D6Zh+ognnQ3D/mId58D0HWk+27xy2+5fiAf8ctkHnoOIJ50Nw/5iCfd7QcaV/zyW64fyEf8MpmH6iCedDcP+Ygn3d0P0LwTv0/uB8hH/D6Zh667eNLdPOQjnnR3P0Djit8n9wPkI36fzEN1EE+6m4d8xLvrAF1Hmnfil99y/UA+4pfJPLQOiCfdzUM+4kl3+4HGFb/8lusH8hG/TOahOogn3c1DPuJJr78H4O//8r5On7sXT/rZ9/z8aXjcs+/V+dP7emse+ty3+PmXXB7yEU96XYd//Otw3epxxZN+9j1Cfx4e9+x7e/7cVgfyoTqIn3/J5SEf8fNkP9Dn7sUfk/1APuKPyTz0uT/xpLt5yEc86W4/0Ljij8l+IB/xx2QeqoN40t085COe9HodmP44vG7U80486WffM3Y/PO7Z93rdt60D5EPXXfz8Sy4P+Ygnva5Dfz9ct3rcPtDPvsfpL8Pjnn1v0l/a6kA+VIc+0N085CN+nuwHmnfij8l+IB/xx2QeWgfEk+7mIR/xpLv9QOP2ge72A/mIPybzUB36QHfzkI9493sA6H2cPncvfm360Psg+YhfJ/PQ577Fk+7mIR/xpLv7ARpX/Nr0oXWAfMSvk3moDuJJd/OQj3j3ewDoOtLn7sXPnnL9QD7iZ8k89Ht/8V0yD/mI755y/UDjip895fqBfMTPknmoDuK7ZB7yEU+6ux+geSd+bfrQ+yD5iF8n89B1F0+6m4d8xJPu7gdo3D7Q3f0A+YhfJ/NQHfpAd/OQj3h3HaDrSPNO/Owp1w/kI36WzEPrgPgumYd8xHdPuX6gcftAd/uBfMTPknmoDn2gu3nIRzzp9XOA+b8NPzeo77vFk372Pc8/DY979r3KP7U9ByAfuu8TP/+Sy0M+4kmv63A5Gq5bPa540s++R3o0PO7Z9zaP2upwGfBz4OdfcnnIpxu1PQeg60j33eKPyX4gH/HHZB563xdPupuHfMST7vYDjSv+mOwH8ulGbXW4DPi6DuJJd/OQTxfo9Tqw+vfhdaOed+JJP/ue+Z+Hxz37Xvef29YB8qHrLn7+JZeHfMSTXtfhl1+G61aPK570s9/jPwyPe/Z784e2OpAP1UH8/EsuD/mInyf7gead+GOyH8hH/DGZh9YB8aS7echHPOluP9C44o/JfiAf8cdkHqqDeNLdPOQj3n0OQO/jdN8tfm360Psg+YhfJ/PQfZ940t085COedHc/QOOKX5s+XbDfWAO/TuahOogn3c1DPt2o7TkAXUe67xY/e8r1A/mInyXz0HMA8V0yD/mI755y/UDjip895fqBfLpRWx0uA74DvkvmIZ8u0N39AM078WvTh94HyUf8OpmHrrt40t085COedHc/QOOKX5s+tB8gH/HrZB6qg3jS3TzkI95dB+g60rwTP3vK9QP5iJ8l89A6IL5L5iEf8d1Trh9oXPGzp1w/kI/4WTIP1UF8l8xDPuK75HkA+ty9eNLd8wDkI979HgDyoc99i1895/KQj3jS3fMANK540t3zAOQj3q0D+VAdxK+ec3nIR/wq2Q/0uXvxpLv9QD7i3e8BIB/63J/415dcHvIRT7rbDzSueNLdfiAf8W4dyIfqIP71JZeHfMST7p4HoHknnnT3PAD5iHfXAfKh6y5+9ZzLQz7iSXfPA9C4faC75wHIR7xbB/KhOvSB7uYhH/GrZD/QvBNPutsP5CPeXQfIh9YB8a8vuTzkI550tx9o3D7Q3X4gH/FuHciH6tAHupuHfMS/JvcD9Ll78fvkfoB8xO+Teehz3+JJd/OQj3jS3f0AjSt+n9wPkI/4fTIP1UE86W4e8hHvfg8AXUf63L345bdcP5CP+GUyD30PgHjS3TzkI550tx9oXPHLb7l+IB/xy2QeqoN40t085COedHc/QPNO/D65HyAf8ftkHrru4kl385CPeNLd/QCN2we6ux8gH/H7ZB6qQx/obh7yEe+uA3Qdad6JX37L9QP5iF8m89A6IJ50Nw/5iCfd7Qcatw90tx/IR/wymYfq0Ae6m4d8xJPungeg+27xpLvnAchHvPscgHzovk/86jmXh3zEk+6eB6BxxZPungcgn27UVofLgF8Bv3rO5SGfbtT2HICuI913iyfd7QfyEe8+ByAfet8X//qSy0M+4kl3+4HGFU+62w/k043a6nAZ8K/Av77k8pBPF+jueQCad+JJd88DkI94dx0gH7ru4lfPuTzkI5509zwAjSuedPc8APmId+tAPlQH8avnXB7yEb9K9gPNO/Gku/1APuLddYB8aB0Q//qSy0M+4kl3+4HGFU+62w/kI96tA/lQHcS/vuTykI/41+R+gO67xe+T+wHyEb9P5qH7PvGku3nIRzzp7n6AxhW/T+4HyKcbtdXhMuDrOogn3c1DPt2o7TkAXUe67xa//JbrB/IRv0zmoecA4kl385CPeNLdfqBxxS+/5fqBfLpRWx0uA76ug3jS3Tzk0wW6ux+geSd+n9wPkI/4fTIPXXfxpLt5yEc86e5+gMYVv0/uB8hH/D6Zh+ognnQ3D/mId9cBuo4078Qvv+X6gXzEL5N5aB0QT7qbh3zEk+72A40rfvkt1w/kI36ZzEN1EE+6m4d8xKP+8P/rW9D18xb4renz9/8YHncL/DaZR3wZv+JJd/OQj34mva7D//7ncN22wG9Nn9//dXjcLfDbZB6qg3jS3TzkI77WW/tBfKlrxZPu9gP5iKv11jziy/WveNLdPOQjnnS3H2hc/Uy62w/kI96tA/lQHcSR7uYhH/1Mer0OHP46vG5sgd+aPv/4r+Fxt8Bvk3nouh8C3c1DPuJJr+vwf/89XLct8FvTZ/w4PO4W+G0yD9VBPOluHvIR764DdB1p3h0C3e0H8hHvrgPkQ+vAIdDdPOQjnnS3H2hc8aS7/UA+4t06kA/VQTzpbh7yEV/rrfsB8QfgD6YPvQ+Sj/hDMo/4su5WPOluHvIRT7q7H6BxxR9MH1oHyEfcIZmH6iCedDcP+Yiv9dZ+EF/eVyuedLcfyEd8rbfmEV/e/yuedDcP+Ygn3e0HGlc86W4/kI94tw7kQ3UQT7qbh3zEk+7uB2jeHQLd3Q+Qj/hDMg9d90Ogu3nIRz+T7u4HaFzxB9OH9gPkI/6QzEN1EE+6m4d8xLvrAF1HmneHQHf7gXzEuesA+dA6cAh0Nw/5iCfd7QcaVz+T7vYD+Yh360A+VAdxpLt5yEc/k14/B/gwHn5usAV+a/pMx8PjboHfJvPQfZ940t085DMN9LoOu6BuW+C3ps+Pn4fH3QK/TeahOuwC3c1DPuLd5wB0Hem+Wzzpbj+Qz3Tc9hzgQ8DX7/viSXfzkM800N1+oHF3ge72A/mId+uwC+pW12EX6G4e8hFPer0OLD4Prxtb4LemTx+MuwV+m8xD130R6G4e8ukDva7D98lw3bbAb02fzWR43C3w22QeqoN40t085LOZtK0DdB1p3i0C3e0H8uk/t60Di4Cv14FFoLt5yKcPdLcfaFzxpLv9QD6bSVsdvgd8XQfxpLt5yGczaXsOQO/jdN8t/mD6TIP9xgH4QzIP3feJJ93NQz7TQHf3AzTuLtDd/QD5iD8k81AddoHu5iEf8e5zALqOdN8tnnS3H8hnOm57DvAh4OvnAOJJd/OQzzTQ3X6gcXeB7vYD+Yh367AL6lbXYRfobh7yEU+6ux+gebcIdHc/QD7957Z1YBHw9XVfBLqbh3z6QHf3AzSu+IPpswn2GwfgD8k8VAfxpLt5yGczaVsH6DrSvFsEutsP5NN/blsHFgFfrwOLQHfzkE8f6G4/0LjiSXf7gXw2k7Y6fA/4ug7iSXfzkM8G9NbzAOLLc/2KJ909D0A+4mu9NY/4KfDTcS4P+Ygn3T0PQOOKJ909D0A+4t06kA/VQfx0nMtDPuKnyX4QvwN+l+wH8hG/S+YRX577Vjzpbh7yEU+62w80rvhdsh/IR/wumYfqIJ50Nw/5iCfdPQ9A8+4Q6O55APIR764D5EPX/RDobh7yEU+6ex6AxhVPunsegHzEuXUgH6qD+Ok4l4d8xE+T/UDz7hDobj+Qj/hdMg+tA4dAd/OQj3jS3X6gccXvkv1APuJ3yTxUB/Gku3nIR3ytt+4HxC+AX5g+9D5IPuIXyTzie+D7ZB7yEd+bdaD3cRpX/ML0oXWAfMQvknmoDuL7ZB7yEd8n+0F8+b16xZPu9gP5iK/11jziN8BvJrk85COedLcfaFzxpLv9QD7i3TqQD9VB/GaSy0M+4kl39wM07w6B7u4HyEf8IpmHrvsh0N085CO+N+tA7+M0rviF6UP7AfIRv0jmoTqI75N5yEd8n+wHmneHQHf7gXzEu+sA+dA6cAh0Nw/5iCfd7QcaVzzpbj+Qj3i3DuRDdRC/meTykI940t3zAHTfrZ9Jd88DkM903PYc4EPAT4GfjnN5yGca6O55ABp3F+jueQDyEe/WYRfUbQr8dJzLQz7ip8l+oPtu8btkP5DPdNz2HOBDwNfv++JJd/OQzzTQ3X6gcXeB7vYD+YjfJfNQHXaB7uYhH/1MunsegObdItDd8wDk039uWwcWAT8FfjrO5SGfPtDd8wA0rnjS3fMA5LOZtNXhe8BPgZ+Oc3nIZzNpWwfoOtK8WwS62w/k039uWwcWAV+vA4tAd/OQTx/obj/QuOJ3yX4gn82krQ7fA76ug3jS3Tzks5m0PQeg93G67xa/MH2mwX5jAfwimYfu+8T3yTzkMw10dz9A4+4C3d0PkI+4RTIP1WEX6G4e8hHfJ/uB7rvFk+72A/lMx23PAT4E/Ab4zSSXh3ymge72A427C3S3H8hHvFuHXVC3DfCbSS4P+Ygn3d0P0LxbBLq7HyCf/nPbOrAI+B74PpmHfPpAd/cDNK74hemzCfYbC+AXyTxUB/F9Mg/5bCZt6wBdR5p3i0B3+4F8+s9t68Ai4DfAbya5POTTB7rbDzSufibd7Qfy2Uza6vA94DfAbya5POSzCfT6ewB++PK+Tp+7F781feZfhsfdAr9N5qHPfYsn3c1DPvNAr+twDOq2BX5r+vzt6/C4W+C3yTxUh2Ogu3nIR7z7PQB0Helz9+JJd/uBfOagt+ahz/2JJ93NQz7zQHf7gcY9BrrbD+Qj3q3DMahbXYdjoLt5yEc86fU6sP46vG5sgd+aPpdPw+Nugd8m89B1Xwe6m4d8xJNe12H2NFy3LfBb06cLxt0Cv03moTrMAt3NQz7dU9s6QNeR5t060N1+IB/x7jpAPrQOrAPdzUM+4kl3+4HGnQW62w/k0z211WEW8HUdZoHu5iGfDvTW/QB97l78wfSZB/uNA/CHZB763Ld40t085DMPdHc/QOMeA93dD5CP+EMyD9XhGOhuHvIR734PAF1H+ty9eNLdfiCfOeiteej3/uJJd/OQzzzQ3X6gcY+B7vYD+Yh363AM6lbX4Rjobh7yEU+6ux+gebcOdHc/QD7iD8k8dN3Xge7mIR/xpLv7ARp3FujufoB8uqe2OswCvq7DLNDdPOTTPbWtA3Qdad6tA93tB/IR764D5EPrwDrQ3TzkI550tx9o3Fmgu/1APt1TWx1mAV/XYRbobh7y6QK9fg7w9Xn4ucEW+K3ps3oeHncL/DaZh+77xJPu5iGfVaDXdfj0Mly3LfBb0+f1ZXjcLfDbZB6qg3jS3Tzk8/rS9hyAriPdd4sn3e0H8lk9tz0H+Brw9fu+eNLdPOSzCnS3H2hc8aS7/UA+ry9tdfgU8HUdxJPu5iGf10Cv14F9sG5sgd+aPr98Gx53C/w2mYeu+z7Q3TzkI570ug7Lb8N12wK/NX1+9T/D426B3ybzUB2Wge7mIR/x7jqwD9aNet7tA93tB/IR764D+2DdqNeBfaC7echHPOluP9C4y0B3+4F8xLt1IB+qwzLQ3TzkI959DkDv43TfLf5g+qyC/cYB+EMyD933iSfdzUM+q0B39wM0rviD6fMa7DcOwB+SeagO4kl385DP60vbcwC6jnTfLZ50tx/IZ/Xc9hzga8DXzwHEk+7mIZ9VoLv9QOOKJ93tB/J5fWmrw6eAr+sgnnQ3D/m8Brq7H6B5tw90dz9APuIPyTx03feB7uYhH/Gku/sBGncZ6O5+gHzEH5J5qA7LQHfzkI94dx3YB+tGPe/2ge72A/mId9eBfbBu1OvAPtDdPOQjnnS3H2jcZaC7/UA+4t06kA/VYRnobh7yEe9+DwD9Hp8+dy+edPc8APnMQW/NQ5/7Fj8d5/KQzzzQ3fMANO4x0N3zAOQj3q3DMajbFPjpOJeHfMRPk/1An7sXv0v2A/nMQW/NQ5/7E0+6m4d85oHu9gONewx0tx/IR/wumYfqcAx0Nw/5iCfdPQ9A824d6O55APIR764D5EPXfR3obh7yEU+6ex6Axp0FunsegHy6p7Y6zAJ+Cvx0nMtDPt1T2zpA15Hm3TrQ3X4gH/G7ZB5aB9aB7uYhH/Gku/1A484C3e0H8ume2uowC/i6DrNAd/OQTwd6636APncvfmH6zIP9xgL4RTIPfe5bfJ/MQz7zQHf3AzTuMdDd/QD5iF8k81AdjoHu5iEf8X2yH+hz9+JJd/uBfOagt+ah7wEQv5nk8pDPPNDdfqBxj4Hu9gP5iHfrcAzqtgF+M8nlIR/xpLv7AZp360B39wPkI36RzEPXfR3obh7yEd+bdZgF+6UF8AvTpwvGXQC/SOahOswC3c1DPt1T2zpA15Hm3TrQ3X4gH/HuOkA+tA6sA93NQz7iSXf7gcadBbrbD+TTPbXVYRbwG+A3k1we8ukC3T0PQPfd4kl3zwOQz+q57TnA14CfAj8d5/KQzyrQ3fMANK540t3zAOTz+tJWh08BPwV+Os7lIZ/Xl7bnAHQd6b5b/C7ZD+Szem57DvA14Ov3ffGku3nIZxXobj/QuOJ3yX4gn9eXtjp8Cvi6DuJJd/OQz2ugu+cBaN7tA909D0A+4t11YB+sG1Pgp+NcHvIRT7p7HoDGXQa6ex6AfMS7dSAfqsMy0N085CN+muwHmnf7QHf7gXzE75J5aB3YB7qbh3zEk+72A427DHS3H8hH/C6Zh+qwDHQ3D/mId58D0Ps43XeLX5g+q2C/sQB+kcxD933i+2Qe8lkFursfoHHFL0yf12C/sQB+kcxDdRDfJ/OQz+tL23MAuo503y2edLcfyGf13PYc4GvAb4DfTHJ5yGcV6G4/0LjiSXf7gXxeX9rq8CngN8BvJrk85PMa6O5+gObdPtDd/QD5iF8k89B13we6m4d8xPdmHZbBfmkB/ML0of0A+YhfJPNQHZaB7uYhH/F9sh9o3u0D3e0H8hHvrgP7YN3YAL+Z5PKQj3jS3X6gcZeB7vYD+Yh360A+VIdloLt5yEf8mf7wvr59GObL9/pXPOm1jz53TePWPuJrvTWP+Dnw8y+5POQjnvS6DvpcN9WtHlc86bUP/R1w8hHv1oF8qA7i519yechH/DzZD+KPwB+T/UA+4o/JPOLL975XPOluHvIRT7rbDzSu+GOyH8hH/DGZh+ognnQ3D/mIJ71eBw5/HV436nl3CPTaR5+7o3FrH/HuOkA+dN0Pge7mIR/xpNd10Of6qG71uOJJr33o7wCSj3i3DuRDdRA//5LLQz7i58l+oHl3CHS3H8hH/DGZh9aBQ6C7echHPOluP9C44o/JfiAf8cdkHqqDeNLdPOQjvtZb9wPi18CvTR96HyQf8etkHvHl765UPOluHvIRT7q7H6Bxxa9NH1oHyEf8OpmH6iCedDcP+Yiv9dZ+ED8DfvaU6wfyET9L5hHfAd8l85CP+O4p1w80rvjZU64fyEf8LJmH6iC+S+YhH/Gku/sBmneHQHf3A+Qjfp3MQ9f9EOhuHvIRT7q7H6Bxxa9NH9oPkI/4dTIP1UE86W4e8hHvrgN0HWneHQLd7QfyET9L5qF14BDobh7yEd895fqBxhU/e8r1A/mInyXzUB3Ed8k85COe9Po5wIfx8HOD+r5bPOm1z3Q8PG7tMx23PQf4EPBz4OdfcnnIZxrodR12Qd3qcXeBXvvQ3wEnH/FuHXZB3ebAz7/k8pCP+HmyH+i+W/wx2Q/kMx23PQf4EPD1+7540t085DMNdLcfaNxdoLv9QD7ij8k8VIddoLt5yEc86fU6sPg8vG7U824R6LVPH4xb+/Sf29aBRcDPgZ9/yeUhnz7Q6zro9/pUt3pc8aTXPvQ9AOSzmbTV4XvAz4Gff8nlIZ/NpG0doOtI824R6G4/kE//uW0dWAR8vQ4sAt3NQz59oLv9QOOKPyb7gXw2k7Y6fA/4ug7iSXfzkM9m0vYcgN7H6b5b/Nr0mQb7jTXw62Qeuu8TT7qbh3ymge7uB2jcXaC7+wHyEb9O5qE67ALdzUM+4t3nAHQd6b5b/Owp1w/kMx23PQf4EPAd8F0yD/lMA93tBxp3F+huP5CP+FkyD9VhF+huHvIRT7q7H6B5twh0dz9APv3ntnVgEfD1dV8EupuHfPpAd/cDNK74temz+X+lnb1x68oShJ1jKoAbwAvimkc/AMgcFIDMDQABIACaCGADQAAMQCYCgCkDAdC81vBVbdWn7q0x2fVVT5/BYLXEWZJiv7EBvyXzUB+CJ93NQz73t751gK4j3Xc3obvzQD6P17514Cb4A/gjmYd8HkJ354HqBj8PuXkgn/tbXx8+BX8AfyTzkM8d9N7zAME/f9e34Ul3zwOQT/Ct3psn+Ap8HXN5yCd40t3zAFQ3eNLd8wDkE7zbB/KhPgRfx1we8gm+Juch+OfvKjc86e48kE/wrd6bJ/gCfJlyecgneNLdeaC6wZPuzgP5BO/2gXyoD8GXKZeHfIIn3T0PQPfdj9Dd8wDkE7y7DpAPXfcfobt5yCd40t3zAFQ3eNLd8wDkE7zbB/KhPgRfx1we8gm+JueB7rsfobvzQD7Bu+sA+dA68CN0Nw/5BE+6Ow9UN3jS3Xkgn+DdPpAP9SH4MuXykE/wJbkfCH4Hfk/uB8gn+D2ZJ/jn7643POluHvIJnnR3P0B1g9+T+wHyCX5P5qE+BE+6m4d8gm/13nkIfgV+veTmgXyCX5N5go/vA2h50t085PP8nfVrbh6obvDrJTcP5BP8msxDfXj+jvs1l4d8nr/zes3tB+i++xG6ux8gn+D3ZB667j9Cd/OQT/Cku/sBqhv8ntwPkE/wezIP9SF40t085BO8uw7QdaT77kfo7jyQT/BrMg+tAz9Cd/OQz/N3dq+5eaC6wa+X3DyQT/BrMg/14fk7vtdcHvJ5fs/v1XsOQP+PT++7gyfdPQ9APl9/+54D/BF8Bb6OuTzk8yV09zwA1f0WunsegHyCd/vwLfpWga9jLg/5BF+T80Dvu4Mn3Z0H8vn62/cc4I/gC/BlyuUhny+hu/NAdb+F7s4D+QTv9uFb9K0AX6ZcHvIJnnT3PADddzehu+cByOfx2rcO3ARfga9jLg/5PITungegusGT7p4HIJ/7W18fPgVfga9jLg/53N/61gG6jnTf3YTuzgP5PF771oGb4AvwZcrlIZ+H0N15oLrBk+7OA/nc3/r68Cn4AnyZcnnI5/7W9xyA/o7T++7g9+R+gHy+/vY9B/gj+PZ9X/Cku3nI50vo7n6A6n4L3d0PkE/wezIP9eFb6G4e8gnefQ5A15Hedwe/XnLzQD5ff/ueA/wRfPsc4Pk7OtdcHvL5Ero7D1T3W+juPJBP8GsyD/XhW+huHvJ5fs/7NbcfoPvuJnR3P0A+j9e+deAm+Pa634Tu5iGfh9Dd/QDVDX5P7gfI5/7W14dPwbd9CJ50Nw/53N/61gG6jnTf3YTuzgP5PF771oGb4Nt14CZ0Nw/5PITuzgPVDX695OaBfO5vfX34FHzbh+f3+F9zecjnLvT2ewD+B78jT5+7j9ektz7L++91W58F9N489Lnv4Jb3XB7yWYTe9uEUfWvrnkJvfeh3wMkneLcPp+jbAvzynstDPsEvyXmgz90HfybngXwW0Hvz0Of+gifdzUM+i9DdeaC6p9DdeSCf4M9kHurDKXQ3D/nEa9LbdWD7+H3daO+7Teitzz/D73Vbn+DddYB86LpvQnfzkE/wpLd9mIff+9bWnYXe+hyibutzDH19mAW/AL+85/KQzzH0rQN0Hem+24TuzgP5BH8m89A6sAndzUM+wZPuzgPVnYXuzgP5HENfH2bBt32Yhe7mIZ8D9N79AH3uPvjN9FnEfmMDfkvmoc99B0+6m4d8FqG7+wGqewrd3Q+QT3BbMg/14RS6m4d8gne/B4CuI33uPvh5yM0D+Syg9+ah//cP/kjmIZ9F6O48UN1T6O48kE/wczIP9eEUupuHfIIn3d0P0H23Cd3dD5BP8FsyD133TehuHvKJ16S7+wGqOwvd3Q+QzzH09WEWfNuHWehuHvI5hr51gK4j3Xeb0N15IJ/g5mQeWgc2obt5yCf4Y8jNA9Wdhe7OA/kcQ18fZsEfwB/JPORzCL19DvAx/v7coH3fHTzprU8df6/b+tSx7znAh+AX4Jf3XB7yqUJv+/Ay/d63tm7wpLc+9Dvg5FOmvj68CH4BfnnP5SGfMvU9B6DrSO+7gz+T80A+dex7DvAh+PbvfvCku3nIpwrdnQeqG/yZnAfyKVNfH14E3/YheNLdPORThN6uA7tYN9r7bhd66/Pv5fe6rU/w7jqwi3VjAX55z+Uhn+BJb/uwXn7vW1t3FXrrE+cAqG7rE7zbB/KhPqxCd/OQT/BLch7ovtuF7s4D+QR/JvPQOrAL3c1DPsGT7s4D1V2F7s4D+QR/JvNQH1ahu3nIJ3j3OQD9Haf33cFvpk8V+40N+C2Zh973BU+6m4d8qtDd/QDVDX4zfYrYb2zAb8k81IfgSXfzkE+Z+p4D0HWk993Bz0NuHsinjn3PAT4EfwB/JPOQTxW6Ow9UN/h5yM0D+ZSprw8vgj+AP5J5yKcI3d0P0H23C93dD5BP8FsyD133XehuHvIJnnR3P0B1V6G7+wHyCX5L5qE+rEJ385BP8O46sIt1YwZ+HnLzQD7Bz8k8tA7sQnfzkE/wx5CbB6q7Ct2dB/IJfk7moT6sQnfzkE/wR/I8AH3uPnjS3fMA5LOA3puHPvcdfB1zechnEbp7HoDqnkJ3zwOQT/BuH07Rtwp8HXN5yCf4mpwH+tx98KS780A+C+i9eehzf8GXKZeHfBahu/NAdU+hu/NAPsG7fThF3wrwZcrlIZ/gSXfPA9B9twndPQ9APsG76wD50HXfhO7mIZ/gSXfPA1DdWejueQDyOYa+PsyCr8DXMZeHfI6hbx2g60j33SZ0dx7IJ3h3HSAfWgc2obt5yCd40t15oLqz0N15IJ9j6OvDLPgCfJlyecjnAL13P0Cfuw9+T+4HyGcBvTcPfe47eNLdPOSzCN3dD1DdU+jufoB8gt+TeagPp9DdPOQTvPs9AHQd6XP3wa+X3DyQzwJ6bx76HoDn/6Nfc3nIZxG6Ow9U9xS6Ow/kE/yazEN9OIXu5iGf53Pea24/QPfdJnR3P0A+we/JPHTdN6G7ecgneNLd/QDVnYXu7gfI5xj6+jALvu3DLHQ3D/kcQ986QNeR7rtN6O48kE/wazIPrQOb0N085PN8zn7NzQPVnYXuzgP5HENfH2bBt32Yhe7mIZ9D6O55AHrfHa9Jd88DkE8d+54DfAi+Al/HXB7yqUJ3zwNQ3eBJd88DkE+Z+vrwIvgKfB1zecinTH3PAeg60vvu4El354F86tj3HOBD8AX4MuXykE8VujsPVDdek+7OA/mUqa8PL4IvwJcpl4d8itDd8wB03+1Cd88DkE/w7jqwi3WjAl/HXB7yCZ509zwA1V2F7p4HIJ/g3T6QD/VhFbqbh3yCr8l5oPtuF7o7D+QTvLsO7GLdKMCXKZeHfIIn3Z0HqrsK3Z0H8gne7QP5UB9Wobt5yCf4ktwP0Pvu4PfkfoB86tj3HOBD8O37vuBJd/OQTxW6ux+gusHvyf0A+ZSprw8vgm/7EDzpbh7yKVPfcwC6jvS+O/j1kpsH8qlj33OAD8G3zwGef0evuTzkU4XuzgPVDX695OaBfMrU14cXwbd9eK7j11we8ilCd/cDdN/tQnf3A+QT/J7MQ9d9F7qbh3ziNenufoDqrkJ39wPkE/yezEN9WIXu5iGf4N11YBfrxgr8esnNA/kEtybz0DqwC93NQz7Pf+c1Nw9UdxW6Ow/kE/yazEN9WIXu5iGf5+v/6/8BCq3LzQ== 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+AQAAAAAAAQAAAAEAxFgAAA==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+AQAAAAAAAgAAAAIAvnMAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-020000.1.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-020000.1.vtu new file mode 100644 index 000000000..adb713ae2 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-020000.1.vtu @@ -0,0 +1,34 @@ + + + + + + + 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EZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//A3532BvAAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH39D/TfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/y3zBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3zH9LfAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8DfvfIm8AAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff8P9t8AfgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//hXcEIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfCf6E9AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+F9wVwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/I/0X2BeAQAQkMEEIRlCCE4JQhCQ0YQhHWMITgUhEJDJRiEZUohODWMQkNnGIR1zik4BEJCQxSUhGUpKTglSkJDVpSEda0pOBTGQkM1nIRlayk4Nc5CQ3echHXvJTgEIUpDBFKEZRilOCUpSkNGUoR1nKU4FKVKQyVahGVapTg1rUpDZ1qEdd6tOARjSkMU1oRlOa04JWtKQ1bWhHW9rTgU50pDNd6EZXutODXvSkN33oR1/6M4BBDGQwQxjGUIYzglGMZDRjGMdYxjOBSUxkMlOYxlSmM4NZzGQ2c5jHXOazgEUsZDFLWMZSlrOCVaxkNWtYx1rWs4FNbGQzW9jGVrazg13sZDd72Mde9nOAQxzkMEc4xlGOc4JTnOQ0ZzjHWc5zgUtc5DJXuMZVrnODW9zkNne4x13u84BHPOQxT3jGU57zgle85DVveMdb3vOBT3zkM1/4xle+84Nf/OQ3f/jH3/+FdQYgEAEJTBCCEZTghCAUIQlNGMIRlvBEIBIRiUwUohGV6MQgFjGJTRziEZf4JCARCUlMEpKRlOSkIBUpSU0a0pGW9GQgExnJTBaykZXs5CAXOclNHvKRl/wUoBAFKUwRilGU4pSgFCUpTRnKUZbyVKASFalMFapRlerUoBY1qU0d6lGX+jSgEQ1pTBOa0ZTmtKAVLWlNG9rRlvZ0oBMd6UwXutGV7vSgFz3pTR/60Zf+DGAQAxnMEIYxlOGMYBQjGc0YxjGW8UxgEhOZzBSmMZXpzGAWM5nNHOYxl/ksYBELWcwSlrGU5axgFStZzRrWsZb1bGATG9nMFraxle3sYBc72c0e9rGX/RzgEAc5zBGOcZTjnOAUJznNGc5xlvNc4BIXucwVrnGV69zgFje5zR3ucZf7POARD3nME57xlOe84BUvec0b3vGW93zgEx/5zBe+8ZXv/OAXP/nNH/7xN8p/Ib0BCERAAhOEYAQlOCEIRUhCE4ZwhCU8EYhERCIThWhEJToxiEVMYhOHeMQlPglIREISk4RkJCU5KUhFSlKThnSkJT0ZyERGMpOFbGQlOznIRU5yk4d85CU/BShEQQpThGIUpTglKEVJSlOGcpSlPBWoREUqU4VqVKU6NahFTWpTh3rUpT4NaERDGtOEZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//I36X1B3AAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH3+j/RfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/5VzBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3xn+lPAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8jflfMVcAAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff2P9V8gXgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//lXEGIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfOfyW8AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+18RdwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/+b/4zj+BCEhgghCMoAQnBKEISWjCEI6whCfC/zdMD9liIAAAwPo6tW3bNn5t27Zt27Zt27Zt27YnixwhRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTQf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMh//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU2H/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTEf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNR//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUzH/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTcf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMJ//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU0n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTKf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNp//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUxn/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTWf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlM5//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU3n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTBf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNF//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUyX/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTZf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMV//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU1X/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTNf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlN1//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUw3/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTTf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMt//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU23/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTHf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNd//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwkU4D//EZggBCU4wQhBSEITijCEJTzhiEBEIhOJKEQlOtGIQUxiE4s4xCU+8UhAQhKTiCQkJTnJSEFKUpOKNKQlPenIQEYyk4ksZCU72chBTnKTizzkJR8B5KcAhShIYYpQjKIUpwSlKElpylCOspSnApWoSGWqUI2qVKcGtahJbepQj7rUpwGNaEhjmtCMpjSnBa1oSWva0I62tKcDnehIZ7rQja50pwe96Elv+tCPvvRnAIMYyGCGMIyhDGcEoxjJaMYwjrGMZwKTmMhkpjCNqUxnBrOYyWzmMI+5zGcBi1jIYpawjKUsZwWrWMlq1rCOtaxnA5vYyGa2sI2tbGcHu9jJbvawj73s5wCHOMhhjnCMoxznBKc4yWnOcI6znOcCl7jIZa5wjatc5wa3uMlt7nCPu9znAY94yGOe8IynPOcFr3jJa97wjre85wOf+MhnvvCNr3znB7/4yW/+8I+/BMrnP/8RmCAEJTjBCEFIQhOKMIQlPOGIQEQiE4koRCU60YhBTGITizjEJT7xSEBCEpOIJCQlOclIQUpSk4o0pCU96chARjKTiSxkJTvZyEFOcpOLPOQlHwHkpwCFKEhhilCMohSnBKUoSWnKUI6ylKcClahIZapQjapUpwa1qElt6lCPutSnAY1oSGOa0IymNKcFrWhJa9rQjra0pwOd6EhnutCNrnSnB73oSW/60I++9GcAgxjIYIYwjKEMZwSjGMloxjCOsYxnApOYyGSmMI2pTGcGs5jJbOYwj7nMZwGLWMhilrCMpSxnBatYyWrWsI61rGcDm9jIZrawja1sZwe72Mlu9rCPveznAIc4yGGOcIyjHOcEpzjJac5wjrOc5wKXuMhlrnCNq1znBre4yW3ucI+73OcBj3jIY57wjKc85wWveMlr3vCOt7znA5/4yGe+8I2vfOcHv/jJb/7wj7/8D5W4wko= 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+AQAAAAAAAgAAAAIAoHMAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-020000.2.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-020000.2.vtu new file mode 100644 index 000000000..d28fe321d --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-020000.2.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAgAAAAIAbXQAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-020000.3.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-020000.3.vtu new file mode 100644 index 000000000..8304db53d --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-020000.3.vtu @@ -0,0 +1,34 @@ + + + + + + + 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unr/sZ3nRJ3GlN8+rubtmu4/6uq/O9I4+5oPbHbr6WV548se5NC+4CtQNMB91vYk5AswHt7/7OsDuq7o/1NL3amvy+6qau0PLU1/F6/uPRQT1wd0BwXuV4fdV4dnDn6D3VQ2vsj3AfFM3x8IA88HdgU0DLK9YNjTYl+H9AywvuNtU3zsN91vNDPY/Y5yf+eDew3Z+lhee1T7Lz/KCq9Qefuajrog852O+4e9H+Nh9VfRJbG5I76uCq7sf+JmPnPLNqdQHd78K/lxt+H1VeFZaGR+7rwpu12mWwXzUtRoVoT643TcrjeVFn9S9yT6WF9w78Uom801fm+TNpL7mztMj01leeE76pHSW1/Bm1VKZj7rezi9SqQ9+92Aym1frqSrBPGcL+di8Gn5jeybzreW+4L+v7Ol06mvubXXS2bwab31kJptXwzfWzmA+6rqdg/fswn1we1awD+F5xU/HHv95ps4uvc3ygotlFW4zX9U7Ftx363mJzAcX4wslsrzwvL6NU1hewy+qJOajrnW8ZALzwdWAPYrNK/okTsWnsXkFt6cE3wsS7iOnHHwphfmGPziRxOYVnhv9azKbV3D577UE5qOuGPFWAvPB5fnKN1le9EnlrpHI8hqer/At5qOv6n61G8wHt7q+coXlhWff6H6d5tVcbu5+ifmo67ze5ALzwZ0TkWfZfVWck3bgh1R2X9Xw2jWSmY9zSkwueZv54FapafS+Kjx3dr1b7L4quPij3RXmo66zRT/fCPPBram7L7K8OCetl0rRvOBe/nG3mI859Y7PSWA+uJdp32B54Yklv1xlecHdazvPMR91nbz9qQ9utfn2DLuvij65H1eOZ/dVwVW9WseYb3KWbnOG+prbcw8dZvdV4Xm/TtjP7qsaPjT3HuajrszZdh/zwV3njd00r+6T6NfgJMsL7hb9+wDz0VdrwLQjzAdXX/21j+WFJy5vlSwvuPdBg9+Yj7ri1T+pD+69tXEnm1eck7Jz/5tsXsFlh4KXmW/OqVXvX2Y+uDfgcjybV3iieLV4Oq+auzW+PM581HUmFDvJfHD76ocHaV6cq6nD4llew5+tfpL55lwdveA488Hd2HwHWV54YvKhgywvuF2h7F7mo664uk0yH9y2B+1g84o+WZ/8F8fmFdx7724s85HTqnFEMh/cGfvRTjav8ETe4D8Pn1fDl9zbznzUdTqlb2U+uPdJmW0sL/rkbm62k+UFV/v7bGE++mpHldxMfc3lgtobWF54qtu6TSwvuPi9+hbmo66csW8988Gd1g02sX3VXbMyuLe0ev0G21fBZXyr68x3i5UP3lMR8eeZD+4t9Z9j+6qp23fQVbavgsvDJS8zH3VVwZHxzAe3rwXfCxKe17qcP/j7dN7sEywvuDuj3XHme7WmBe8tNnl2P/PB1dG39tG82rN7PHuU5QWXzrxD1Nd1rfr/SOaDyx9G7WH7Kvrktmh5ge2r4F7/yHjmI6fYdf8k88FVwodH2b5qvPLxp9i+Cq58m48wH3XtQ2cPMB9ctv5xL8uLPslRC+NYXnD7n9F7mY++OmcW7WQ+uJd/wVaWF577xR+/sbzg7r13tjEfdcWJ4PeVw31w79lX17J5tcueDX7/uOW729m8gos2Dbcx3y5U9PH3253zH61lPricr9aweTV1u/bfwuYV3H7hxU3MR115cNUq5oN7HQLLWV6768uP78u4V+MWsbzgbt/9C5nvdd7x+D60TEucxXzDVw+byfKauhPe8mhezS3VNIb5qOtmzv6R+uCJXWaweUWf3Afx69m8Gp47Zg3zTc4yiUuor7n1wq6FbF7hSbF9OZtXcOvNFouZj7rijVdimA/u3lkwm+VFn4R4by7LC+6Ufmcm9XVfnXkLpjAf3Ir5eALLC0/avWewvOD2UyMnMR913ZMtRjMf3PX2DmP7Ks5J8XbzOLavgjsdGsUy35yr5dbvYj64neBuYfsqPOdzfS8jbF8Fd29O28h81FX3NqxnPrh4YvIqlhfnpPx76EaWF9w5em8N8zGnTosKK5gP7n1XYDHNqz0xMX4ZywvuVftlEfNRVxz6bD7zwd1en82l+6rukx310lq2r4KrrS1WMx85vVeHLmM+uNf2g6V0X9WeM+zVNWxfBXfiW25gPup69XYuZz642LKO5kWfvPWfeywvuNPoB4/56KtVa+Bs5oOrja/PpXm1p57dupjlNfyNp1YzH3Wd/s941NfcPr5mOZtXc06+6c1n8wpu/f7sHOabc/WV5T8wH1x91GQKm1dT9/3hP7J5BXfar5rOfNSVp7qPZz64HNpnDMuLc9J7pchYmldzOW7zd8zHuWpva/YN9TWX9q3eLC88t9XswTQveL8iA6mv6zo7x3/EfHCre/6P2LyiT6pWi+lsXsHdFjl/ZD5yOgO6jaW+5lbhB5PYvMJze439mc2r4S2WLmE+6oq+y2YyH1wOL72I5TV97fn8EJYXXN76ZAzz0Vf1+7m+zAf38vw6guWFZ526M4PlNXxdLY/5qKtyPz+N+eBufms+u69qnpMuarqT3lfV3EkqtYv5eE7pHKy+i/ngrm/XbnZfFZ564m3J7quCi/xHDzAfdb1vNlEf3P7m6yMsL56Tyvy7aV7Df31KMh/PqVS5v3YxH9waVnAvywtPPnE3juUFVx1znmA+6np9/jnIfHCnlnWa3lfVfRI17ZPsvqrhIzLOMR85Rakd3NfczdHmEruvCs9q+N9Vdl8VXFyPv8V81BVTrlIf3BkXfO4Vnhd9Usdb0LyGf97rKvPRV1V3+AXqa+78XO8GywvPebSK5gVXs4clMx915dPVqA9uHbmewuYVz0ndJqd30XnVXP50aC/zzXPVrR12Md/w24330XnVnvx08WE2r+B2vhFnmI+6avaFI8wH9/4bf5blxXNS6+k+O2leza2GHfYxH3uqPa/FDuaDu02e2sfyGq/J2KMsL7gzunE881HXil9zlPqaew9GxLN5RZ+chh9fYvMK7rV+VjHf9HXb5cvMB5fzytB5heeU75rE5hVcDfTfpr6uK3M2TmY+uIrdlErz6j6pe/FXWF5w59Kvt6iv++r91fUq88Hd6f/eonm15645nMzygjt7n05jPuqK1uVSmA/urRueRu+r4pwcPzyZ3VcFF/tiUqmvzym3bpvbzAeX1Yql0/uq8HIGPzcl/L4quFPgeCbzUdfbXDWT+eB22Vk+lteckw938byaO1taZDAfc6pmJaRRX3N3ULlMlheefexfmhfc7dTAz3zUtRovoj64mrfIz+6rok/yjPCz+6rgqvdJP/OR04uYQn1w98V6AXZfFZ46+jq9rwpupa6mPuo6ZWdRH1zsuxRgedEn++d7PK/mouGgAPPRV/ull6gPLhr9SvPCE40P07zgyntAfVP3Jx/1wa1/8mexecU5afWPTWfzCu7ekZnUx7m6870M5oPb/zX2sXmFZ9UqQ+cVXH76p5/5qOuV7kV9cHVPf/5WWF6ck16NvRksL7gzfbiP+vpcta/+ncF8cPH6Uh/LC0+Wmkfzgjv9OweYj7ri7lY/88E9f/cAm1f0yX5vBJ1XcGd7PPVNzoZLuK+5O9tH5xWeXT93FptXcG+Yfh90uK/rOlGFuA9+LSKL5UWfrJd20bzgbrFH1EdfrfcOUh/cfZCD5oUnPi9G8xo+tgL1UdeaWIL6hp+tmMX2VTwnlf/KbWxfNfyPMXuZb56rxpXYynxw7/fdku6rqNvjraN0X9XcaflVPPNR11s49wjzwa3WZeJZXjwndSt23Ezzau7unhvLfPNctciojdTXXB6esZvlhecsmn6Y5QVXbnBvDfdRV+Xrfoj54G6jC2fYvoo+qT96XGX7quFViiUwHznl9JJXmQ8utvxxi+2rpu4VfZ85bF81PPGnNOrruvKw/vyVMN/whWPSaF7dJ1nZvcLygjvT1txiPvrqRsZeZj64kJ1u0bzas/vdSmZ5wa3X30xjPuqql6cmMx/cTSiexuYVz0nl++fWs3kFt96ZtYv55rlq6YNrmQ9uJVzdweYVnhpV6yCbV3Bne/kzzEdd+17ZA8wHl+8OPs3y4jmpdWjhGpYX3D4xZzvz8VzVcr9fzXxwu0fe7SwvPHn79TiaV3NvmjhNfV3XKZY7jvngQi48xebV9GlR1iU2r+Bia7JiPnLaLZpcYj64e2iMYvMKz3vYIJnNK7h351Aq803dkpeSqK+58A1PZXnRJ+vXlRdZXsN/0s8zwnz01Wnz5UXmgzvtIxTLC88d90MSywtu/fR8KvNRV53sl8R8cLtThVS2r5pztXCBTLavgjv7N/iYj3PKe/6/DOaDO4NW+Ni+Cs/qE/zc4fB9Fdze2iPAfNS1Bgb/d+G+4ePeD7C8OCfl7GsZLK/hM6b6mG/O1VdXZTAfXJXv5WN54YlF+u87YXnB3WMdA8xHXbdb8Oe2wn1w52JTuq+avq44RPdVcPd68Ov/8XVON+/eAPPB7V8eBdi+Ck88LE73VXD3qYrUR127TlHqgzvf6c+DCctr+lR+c4Dm1dzb/Af10Vc5dBH1wVWZpADLC88ZUpDmBXc+KUN91HVezEN9cCuH7kvYvOKclEW/zGDzCi7ebuljvjmncjTNYD64F1fER+cVXp1P/GxewaUoH2A+6spNrf3MB7c3BZ8rhefFOSkm5s5gecGtYmmZzMe5qo4lpjMf3NtzLJPlhSc2VfbTvJq7PW9T39Q9+qSf+eDu6NN+Nq+mr1un0HkFd6+dpT5y2n0GUR/cjpQBNq/w3HOPAnReNVczC1IfdcUCX4D54F6v4M+Fh+c1fXrpQ5rX8AdrqI++WokdqQ9uzfmZ5oXnbL4WYHnB3e8fUB91HXUswHxwlZgRCH2vqlouMkLfkyr6R2eEctm6sC+Uy0T9/C7MN+8prTo8g/ngTu63fNnqLgn+HDU8xz/AH8qdhs0Cody53jLAfFPXr8+NcF9zFfNCgOU171W9tjuD5QWXc4b6qK/fU2l9kTuT+pq7qxf7WF54Yrv+XOqwvOBWyssB5qOu3Bv83L7/8TVXca8FsvU1sC0Q2idv97ZsXBwOvicAXBzQ73cI85HT/mtrgPngbr1H2eq6ln6+pT1RvnR2XjMqG7dXV+W+ruvmLkF9cFUt+LmX4XnRJ3fB1gDLC+4+c5/66KscsY364NL+K8DywnMji9K84N6J8tRHXSvmKeqDWyeDfQ2fV7wn1evYJZPNK7h8+7SP+XhPqT1yeSbzwa1zOei8wvNS9/vZvIKLlPcCzEddOeCGn/qau7M/C7C8eE+qPJOT5gV3RzbzMx/vqfLERz7mg1tVvqR54Vn1HvpZXnD7rP7zKsxHXdk3kvrg9ssT6byiT17ETjqv4M7qP6mPnGL3Hu5r7v72Z4DNKzx3fUE6r+Dey2Woj7ru1wWoD67eLpXF8pq+DjpA84Jb0/+iPvrq1DlJfXBR6Z8AywtP3s1P84J7BUpSH3XlMwWoD25/ViLbuepObpDtnHRu1MnG1TdPZ+Pyt4bUxzllNahJfXCZt172ujWaZ/OcQU2ycffjFtm4vf9p6qOuPNqA+5rLKo1oXpyTclU1mhfcHViL+3pO1ZeVqQ9uV6tB88ITz9WleQ3/pR71Td2EmtQHV61qZ+uru6BFtj6pQk2zcbdii2zc+uVp6iOnM6QR9cGtIo2y1RXFmmevO7tJNi5nNc3GnUqNqY+6qnBD6hs+pz7Na/p6rx7Pq7maUo/66KucWZv7mnuVa9O8xptQl+fV3M5Th/qoqwrV4r7m6psoOq84J+X8inxeNfdqV+W+Pqes9uWpD27Pq0TnFZ46W53OK7hdIYr6qOtGVqU+uGhXjebFOemMLEfzgssxFamPc9XuGEF9cNtXnuaFZx2vRPOCe4cqUx91rfSK1Ae3l1p0Xk2fekTReQX3rtSgPnJ6fatRH1zKqnRejbeyOp1XcOGrSn3UdT6pQn1wa2Almtf0aU9lmhfcvlGJ+uir+MCiPrh8piLNa7yXLZ5Xc1G6AvVR132vPPXBnfP6933Yvor3pFqN9XvXwvdVza1Sq/zU1+8plT0s6oOL2Yl8X9We06EV3VfB3eHzqI+6ao9DfcPj1wdYXrwn1T71Lc0L7vwbEWA+3lMpXjxOfXAR6Mzzak/GjaJ5DV9+gPqoa327mPrg4sMrfF/VfbLnXKL7Krjd6T/uI2e3JOqDW4GcdF+FZz1bkO6r4F6fEtRHXZlSiPrgqrA+t8Pymr6W/p3mNfxQ8LlkuI++Wm/9G2A+uJu/AM0LT9QoQvOC2y+Uoj7qilvFqA9u3wk+FwmfV/Ne1Y6l6LyCe/2C97rCfbyn1Gr4FvXBrbQVdF7heVH76LyC29/6ua/ruq/foD64u0+/1zMsr3mv6vWpNC+4qnqS+rin6m3eTX1wu66f5oUn1t+jecHtufmymG/qFshLfcOX6O+Phc2r6dO1J+i8gtszCmcx3/T1q4LUB1dNS9B5hefcK0nnFdzKH0F91HU6lqU+uPgykuZFn0RcMZoXXFwuQ33T181lqA8u3y5P88Jz90X+H3mDv3oFK1IfdUUri/ua24cr0X3VnKtryvJ9FfxyJPVxTql5ZalveNtIuq/Cs4ZWoPsquJNSgfqoK9zy1Ae3p5TnefU56ZWIoHnBZcNI6mNORZ5y1AcX0yNpXlP3r0iaF9z6OZL7uq68FMl9zb38kXRfRZ+sVyvQfRVcXY2kPnJav0RSH9w5GEH3VVN3UQTdV8HF1tLUR12nchnqg1u7S/C8uk/CLkfzGv6oDPXRV68V98GdUaVpXlP3ZkmaF9z2F+X+KPSlOPXB7Y6F6bzinBQf8nkF994sz319TlnFK3BfczWoAp1XeKpieTqvhl8pR33U9faV577m4gmeF+eklc+iecFFckXq41y16lemPrg7tRLNa7ytFWhecDkqkvqoKwIVuQ/esTydV/TJmlWWziu4eKsU9ZHTfrEs9cHdoXxe4amIYnRewd3LBamPumJ+Ee5rbs3OT/OiT26bsjQvuBUoQX30Vc4sy33NnfUlaF541qeFaV5wb+yT1Edd8fVT1AeXi/Nke64q47I/J5WvNszG1dpG2Xnz+tTHc0ol61If3Iqpna2u+qRBNs9Kr5ONi/t1s3H5TS3qo67Tqib1we0t1WlePCf1yteieQ1PqMF9/ZxK9atOfXBVuCrNa7ye1Whew3dXpj7qql6VuK+5M7VCtr7KgbWz9UndrpGd+6KycadLNeqbvhatSn1w+6NK2epafapn88T+ytm4/K9KNu60s6iPut62CtQH96xImtf06U5FmhfciixPffRVTi1HfXBrWBmaF579UgTPq7kYXZr6qOt+VpL64PKTYnRe8ZzU3VWZziu4jLKoj+eUVr6K1Ae39d4aPq/Ga1ueziu4GxVBfdSVoiz3NZcTS9K8eE4q3y9H84LbdcpQH3uqt7M09cG9ciVoXlN3dgma1/C1RaiPus6TRagPrmYUoPOKPnk3StF5BbeHFee+zuneKMp9zWWxp/i8as8pUYTOK7jcWZD6pu6z+akPLvvmoXlNX8cVpHnBRaMnqW/6ejQP9cHVwRw0r/EO56J5we24fwPMN3VbBe8Fhfvg8kbwuQ3OVXtrpWznpJ1VPhv35lXMxp3D5aiPc8rB3/vDfc2dXqWz1ZV7wrzrZbNxt0ZkNu48U4b6qCs+KUl9cLm6OM2Lc9JLKMHzaq6+KUZ9zKkYV5j7mourBWle471chOYFl32e4r6uKwbmpz643fPJ7H1VEdn65KWVysbF72WzcXGnJPWR021XjPrg1otFs9W1upfN5nnvl8zO65XNxkWtktzXda2lRahv+BdFaF70yc0sSPOCO2cLUB99FbvzUB/cGf0EzWu86gVoXnCRmZ/6qOvsz819zeWo3HRecU6q0fnovILbY/JQH+eU2puT+uDeAn1+hc0rPPt0Ljqvhg/NQX3Uld3/DjAfXN7RP48RlhfnpNckeK8qPC+4Vy54fob7OFedX5OoD27/rr+fEZYXntySEqB5NfeiFfVR17p3hvrgzlNHA2xe0Serzn8BOq+au87DAPOR0x2vvw8R5oOL5/X987B5Nd7KfwJsXsG9kv8EmG/qtkwLMN/waak0L/rk/HGF5wUXF6mPvsox+6kPbnl7eF7UnXGO5gW3YuOpj7r2nF3UB3dn6HuZYfsqnpOK6iXovgpuiyLUx3NKubAI9cHV5QJ0X4VndSlA91XDa+WhPuqqfnmoD+59LmhePCeV9QvRvIYvy0t9PKeSp/NRH9zpkYvmhee9mYPmBZdj9f3ZMB91vfvB7/uF++Ci1u0A21dNX38WdF8Fl5/fDTAfOd1ddwLMB3fWJgfYvmq8Oml0XwW39c+1hPuoK25doD64PTP4c4nheU1fk5JoXnAvIZ766KvV8Rz1De8eS/PCUyv20rzgcsha6qOuPXQF9cHl5WkBNq94Turhz/eweQW3i+nvC4f5eE7pvMx9w8vp+QmbV3j2/Ew6r+Bi4CXu67rWp4r64OqB/nnV8Lx4TlpOZLG84DIueG6H+3iu6iXd577mbuItmheeyhtP84Krqluob+pWiqM+uFNzAZ1X09cP99J5BbcbLqe+yfn2auqDy/sT6bwa78xEOq/gbo9e1Eddb1AP6hu+sRnNiz5ZETNoXnD37CfUR1+dBl9RH9yt25zmNXU7NeJ5NRfpd/3MR12n1B9+5oPLN2L8bF/FOWl3Ok33VXDvlTjqm3O18nbqg6t7K+i+Cs+quIPuq+Aiah31Udf65Wfqg6vFU2henJPO7p9pXnD1aBz1MacicwD1wb1P36N54Vl/fk3zgrvDPqI+6nodnqc+uEyKovsq+mRfXEb3VXA1w6M+cqoNo6hv+AdD6L4Kz101j+6r4OKtX7iv64pqA6gP7n3wNc1r+jqhK80LLvO2oz766q0pTn1wu1oumheeE2fTvOBOu1bUR11nwd9+5oOLnMHzIHxezTn5zkt0XsHVz5Woj3PKKZiX+oYvPetn8wrPnh38ubXweQV32gXv7f2Pr+s6Pwbfexfug8v0nn6WF+ekfWS9n+UFF50+9jMf56o3sgX1wa2Wh30sLzzLLU7zgit7v4/5qOvm6+xjPri9+Gwmm1f0SRTe6WfzCm59vtDPfOT0pjb3Mx/c3VzMz+YVnpo9w8/mFVxUnkx91FX1//ExH1xs+N3H8qJP9t1vfTSv5vbWVj7mo69iopPJfHBrfK5MlheeLBT83PL/yau5PS8/9VHXnnAwg/qa2202ZLD7qua9qh8l0fuqhscEn8eE+3hPqXdF//01zAd3Tuv7R2H3VeEJWYTfV9VcnCpDfdR1HpahPrhbSH+/Lzyvfk+q1+opmhdcXi1Dfbyn0ttTmvrgKqMizWvqHq1I84K7b1WlvqnbvBr1wa0VUfy+qu6TVaMCva8K7v5cifrIaT1ZhfqGj6vG76tqTy6pQu+rgtuTqlIfdb0b1akP7kTXoHlNX9vU4HnB89Tkvu6re6YW9zVXeevQvPCc8zVpXnDnVE3qo673TR3qg6vqdfi86vekivUV6LyCWzWrUR/vKZXvVaG+4dui6Lwa77WadF7B3Ye1qY+6skod6oNbXevRvHhPqv0fz2v45lrUN++pOlWd+uCO/rm7/8mrPXtnXZ5Xc7W6PvVR16rEfXAnbwM6r+iTWlCXziu4HFKP+sgpl9WnvuGvNKDzauqurEfnFVw0qkd91LU/akB9cHm9Ps+r+yRuNKB5wcWQhtRHX1WrhtzXXGY2pHnh2VMb0rzgqlRD6qOujGxEfcOPNKT3VXFO2l9VpfdVwVWPKtTHOSV3Vac+uHy9Gr2vCs9zKvH7qprbb1egPup6jypTH9yOqUjzmnP1QRTNa/grNaiPORWRtakP7rxQk+aFJ7dVpXnBncKVqI+64rvq1Dfcq0zvq6JPbv0Ifl9Vc/e3EtQ3OQuUoz64wM/fhd1XNXX3F6L3VQ335+Y+fk6uayHqg7vdctG86JNbKZLmBfe8ktzXfZUfl6e+4Z+Wonnh2eUK8byaWxk5qG/qFi1EfXBvjKDzinNS5a9L5xXc+z98nFNyUz3qg1uH6tB5hec+H0XnFdyzq1IfdWV6TeqDiyvVeF59Tjo5GvC8mlt1uI9zVV5rQH3DG9aneeFZH9WmecGdLjWoj7ri+zrUB5fjo+i8ok/2gQp0XsG9V0tTHznduhb1wd22Zei8wrMrF6LzCu5F6XsXYT7q2k0KUd/w3/V9gbC86JPtVqJ5wVXNstRHX92ZlakPrg6VpXnhiYqFsmhe8P/0/ZQwH3XV8oJZ1Ndc7dE/rxy2r+I9qfJ+Nbqvglv9alEf7yn1VlWhPrj7QRTdV+HZH9el+yq47Fmf+qjrTa9NfXBxui7Ni/ek2gUtmhfcqVGN+nhPpWxfjvrg6rZF88LzNkfRvOCiDfdNXVWV+uDO8Rp0X0Wf5P4GdF8Fd+dxHzmtz+pTH9we0YDuq/DcR3xfNfxmQ+qjrqrAfXDxQkOaF32yourSvODuuHrUN30VtagP7q6ozfNqT/WtT/OCy4v1qY+63rk61Dd8eF06r3hPqvNTSTqv4Go39/GeUiuiMPXB7aMl6bwar7pF5xVc9K1CfdQVcyOoDy7qVaB58Z5Ur/CTNC+4rM99vFfVXimoDy6KPUnzwnM6laB5DS9Vhvu6rnyrEPc1t2sVpfNq+uSrRucVXJ6vQX3kVKkW9cHVg8p0XuG5zWvSeQUXeL9VmI+61uCq1Ae3Xvs/8uo+OZkRNC+4las89dFXmVGc+uDWsZI0LzzVswLNCy7yVOS+ritPlKI+uHuqFN1XcU5adRryfVVzsb0+9XFOuWcaUB/c+7k+3VfheS3r0n3V8M41qY+6bue61AdXvWvSvDgn5QqeF9xbVY/6mFNnbF3qg9tr6vC8qOvWoXkNnxFFfdS1x9aiPrjzTnW6r6JPXrMqdF8FlzfKUh857fJVqA/uFilL91V41pACWWxfBbffSA8wH3WdcvmymA/ujLwZYHnRJyuhEs0Lbl0uRX301X1Qgfvg3xXjebXnXcudxfKCiy9PBZhv6o5/QH1wS7/XNXxecU7a9WrReTX8tZrUxznlvF2N+uBeg6p0Xo33R3U6r+Be48rUR123aSXqg8u3y9G8OCfV/Ao0L7i1M5L6OFetwSWpD25VL0bzwpPjy9C84N66ItRHXfdIAeqDuysEnVf0yVpRls4ruP0wP/WRU1wsQn1wtUV/Hl7YvBrvUhKdV8MTf6S+qbtuL/XB1XPdAyyv6VOuXDQvuJNff984zEdfRe5r1AcXYgHNC092m8zzaq4yi1Ifda3Lz1Ef3HoneG/if+6r6uekcuIdel8V3LUvUh/PKb28PuqDq5nH6H1VeLLpJnpfFdwpNJb6qOus577hiz6hefGc1GqWQvOCi+l7uK+fUzlJN6hv+Il1NK+pe2sCzQsuZkVT39Qt2I/64PbYovS+Kvok9r1M76uCewce+ZmPnPacMtQHV2s3+tl9VXjumzH0vqrhz1ejPuqKYXWpD+60H+2jeXWfvJZXaV5wb24XP/V1X2XTadQHd9687GN54Vn9fvGxvOBq9ppM5pu661Qm88HdZ+dksHnFc1J57AKdV3BxbT71zXPKGSeoD+48PYnOKzy3X2c6r+Ay4oqf+ajrNatJfXCrUvBzMcLz4jmptXEfz6u5U/sb6mNPdb7aQn1wcfxtmheeNeoRzQvujmjvZz7qippHqW94viwfm1f0yenUiM4ruJr0go/5yOle3Ed9w7+YlsnmFZ7zV/Bz58LnFVwOaJ3OfFN3T/BzlsJ9w1evS2V50SfrXDTNC65qBj/XJdxHX8WeFdQHtxedTGd54XkRS9NYXnDrGV8K81FXlmpDfXA7d+Fkdl8V56SK/MnH7qsarjIymY9zSrXYkMl8cDvHzgx2XxWe7TTOZPdVwVVgYAbzUdfrtSKd+prL4tPTaF59TjprR2awvOAypmI68zGndvSDNOaDe4NLpLK88Jz2c1J53iB3fsl/m/q6rtM2ZwrzwdWoO4nsvir65A4/lE7vq2ruPWyWznzktL+clMp8wytuu83uq8Kz9l5OY/dVwVXy9jTmo67Xtc5t6mvuFH2YwvKavnoxySwvuPhxcBLz0VcnIzKB+pqLzDWK5YUnXt+VyPKCy4ffJjIfdb1eE24y3/BpaTfYvOKcFMuDn2MXPq/gauOSZOabczXjQTLzweXGPxPYvMIT3fMksnkFlyXGKuajrt3w8E3mg7urm1xheXFOWk9vSGR5wdXLLRTzca6qWj1uMR/c7tzpKssLz91x7DLLC26tL3GO+qjbedI55oM7+ayTdF51n5w7u6+xeTX8X/cy85FTPlh+nvqai0Xdz7J5hee8Nvoim1dwde/iBeajrvyj92nmg8vfvj/F8qJP3qrXTrG84E7upkeYj766nQsfZD64aLtZ0rzac3qvPcDygjvPTohjPupajz7cxXzDJ/ffwfZVPCf1Ni+h+yq4q39u5n98/ZzSuTiV+uD2gacCbF+F546fQvdVw2uP9zHf1C3Xxs98cK9bIR/Li+ekqs9XNC+4vKn//hHm4zmV7WtHfXCv52yaF55b2EfzgluzR2QyH3Xd3d9RH9zecC6D7aumr3kf0X0VXM0+mMZ809e3n81gPrh6Y3kq21fhybUn+L6quft5rUTmm7q12lEf3LVeu8Xyok/OPied5TV8yFe3mY++2g0+S2M+uOxSheaFJ+/vpHnBnbKRN5mPum5yVALzDW9x9CqbVzwnddsVCbB5BRfHWvqZj+eU9tuS+uCeL47OKzyn+l06r+CieN4M5qOuHN86k/ng7or86Syvea7a5BWaF1zMtXzMN89VR6yjvuE1n6d54VlPv0vzglsTz6YyH3XtinWoD67WvXabzSv65LzWPpXNK7hbbkcS85FTPPsv98Fb1KPzCs/ZOEOxeQW3Plx1ifmoq3bfvs58cPvIlvMsL/rk/DUkmeUF99p9d4v56KuI3El9cPfFd26yvKbuypgrLC+42zblLPNR1459+SLzwe1dLU6zfdWck8U/ucH2VcP37L3AfHOuvtnrCvPBnRy+s2xfNXXlc6fZvgquunx1kPmm7updR5gPrvLGS5YX56Q9J5HnBX/9h1PMx5x6J07FMx9c9vv2KMtr6p6bHcfygjtRi3cwH3Xty5uoD+5W/nsT21fRJ/lo7x62r4KL7Wu3MB85vVOTtjDf8Dl/rGb7qqnbcf16uq9q7vRuspb5qCvX7F3MfHDXrruQ5UWfnLK717K84PJo1UXMR1/liunLmA+uhlScw/LCE5UvzGJ5wd1PvB+Yj7ru+zm/Zz64bFliNJtXnJPOrApn6LxqLqJePch8nFMydcgJ5huednUfm1d4XrmBu9i8gjt5r6xjPuq6Nc5uZT64/O7OSpYX56Q7tPkRlhfc/uZaLPNxroqvux9gPrgtj+9keeHZH5TeRPNq7njfLWU+6qqaOdYz3/Avcyyi86r7JA9FLaTzqrl9ePt06uuc1tJivzAfXKXOmMDmFZ4358JwNq/gdqku/ZmPuqJdqf7MB3eSEhyWF30SuUvMYnnBRbX2o5mPvtr/DvuB+eDOP5bL8sJzE+58xvKCuwMHv8Z81LUKr/6Y+Ya/UKHj4/503jH7cR/SEoP/vXUWTQ7+vbLppFDurh42M5Q7D6tOZL731ezH/34xeeUI5oO7f/UfGlrXzZz9Y6jnfT9yXCi3E7vMCOX2H5+Ppr6ua6mYQcwHVw9e/prltcct/So4N5v6sbzg8uH3vZhvV1vV7fE/f6nVm8wHl59e6MDywnOuX+nB8oI7eb57j/moazed0ZT54FbP/FVjQ/rqzFswJbRPdtP8bii3Yj6eEMrVewW+ZT5yur2f78t8w19Z1iO0rnuyxehsdQ8W6x/KXW/vsFBuXSrYk/moKwZ+/jrzwb3tyTVYXvTJKTC3E8treDW/xXzT156zX4wlPrj342uvxZK8pm6ZrS/Ekrzg9uKyr8cSH3WdIYlvxhLf8OcS3okl8+qObtns8a87a5ePZfMKfnmWHUt89+jSx9wakqdzLPHB7fvDusaSeYWnEnp3iCXzCi4/y90llvm6rvP3xTeYb3jHv95gecXFQMfHv57K6srygnsDC71J/bkJj7+2Vr7Shfng1o5hr7G88NT8k6+zvOCu/8grzDd1T05py3xw8furIpbMK/rkLXz4eiyZV3BRs9KbzEdO8fzArswHdwvW78DmFZ494Uc+r5pb8bs6MR915eIBUcwHdw+M7BhN8pq+bv78GZpXc3fwUjua+bqvtj33tWjig9sD3/w0muSFpzb9Efx9GZ5Xc5VV4cto4pu6STkGUF9zr/1PI7Odq9uafRN6TlqX274VyqV9q3codx593Jz5OKdkzzsvxRIf3Km39vH/L6jr7Bz/Uba6LctZsSHc6p7/o1BulynaiPq6rtXs7+CvYT64s751i1iSF+ektange7EkL7jXu8H7zDdzenfQ29TX3PuvzSssLzwn8tOusSQvuLds4OP/7nAfdcX+8sE//8N8cPXXmJ6hfVW/nwv+OYc+HR32YSj38vw6IpTL+GZDmI+cosYyh/ngnpg3LLSuyv38tGx1ffenhHI3vzU/lIsCReYz39QtOWo688HV7N4ey4s+2SlxfVlecHdP4mjmo69izMARzAf3+r85jeU13gcJP7G84GLExsXUR92ClWOYDy7/nvT47z3/M6/6nHRfVFXZvIK7TuHHe0m4j3PKS7wU/HtGmA/uxVnBcyhsXuE5E1f1ZfMK7nzxwnfMR1374pVRzAeX95cG5yosL85J0WPAMJYXXFToOJ76+ly1B+yYxHxwp2ilmSwvPHfjaz/QvJp7v1aZx3zUdb4ev4D54F6jBsvZvKJPVoUyU9i8Gu71mst85LQzV85lPri37oMldF61Z8W9u5TNK7hs3X8D9XVdkff0OuqDzx+yneVFn9Tl9ktZXnBrvFrHfPTVqXNjPfMNz9VxO82rPTHS28bygruvdt7LfFP36+A/D/fBrYudDkeTfdXrViH4945tC19i+yq4jIh8lvneg+EFHv9+/+erW62ID26vPdA6muyrpu5k5/GfK+H7KrhaU+TtaObruu7Gfx7/vSzcB3fqFAqeF+F5p41pE/y5lR86R7O8mst/v+0WTXzVZFDw3+PscJgPLrc/0YflhSePV+zL8oLb5yZ+y3zU9eIuDmK+4Ytjgn9uhO2r6JPVNrM321fBrdPvDWY+cqrdl76lvuZOp7pj2b4Kz6ueawzbV8HFgAbTmI+61rxx06mvuciXYy7Liz5ZrR6NYXnBRabeL8J89NWuUHca9TVXzcrPYXnhWYeazWV5wcXOy4uYj7pWbJtFzAf3BrdYzebVXbMr+Bwp9Z/ebF7B5dFeA5jvtRzx+eOvU/J9zXxwy948nM6r9pxWvUeyeQVXfZImMR917e17xjMf3C14/geW1xky46vge2fPD2Z5we0O3UZT/2r14J/nSRtGMB/cja05geU13pbrU1lew/cPnsN81JUt3/6R+eBybfx8Nq+mTx/emMnmFdxtc85jPnI6z30Vw3xwt+m6ZWxe4dnnhq1k8wquVvy3kfmoa61osYH54FbdnTtoXvRpU73FLC+4ilj/K/PRV+9hpxXMB7dl2kaWF548tnArywvutc+UzEddKy7rN+aDu+0XHqD7qj4n7VYTZ7N91fCcPRYy35xTHUcvYj64W+vuKravwpOV269k+yq4GlNjI/NR1/O/sYn5hn/WZRfLa87VcptWs7zgammezczHnIoTdzZRX3N37I7fWF547qeDfmN5wd0nL+1nPupaNVvFMR9c7j99jO6r6FOHPTvovqq5XJNzH/OR05r9cB/zwVXPpCNsXzV1fzp5mO2r4KK7PM18U7fYi2eYD+4e63eB5TV9yih4jOUFV4f/OsN89FU8mHyW+eBuGf9Fmld7XvXaF1lecNkkcI35qGvHHr7OfHAr+eYtNq/mXC0av4vOq+bOpMB+5uOcss6c2sd8cLUq7iibV3hi6oljbF4N/2L/Weajrjr86Vnmg8vb7S+xvDgn7VErDrO84F7+988w35yrszqfor7m3pc7zrO88KztfS/SvJrLdyvfYD7q2vXWXGM+uH0m8xadV90n7+VPLrF5BXfl4BvMR047dc0N5oNLe2wCm1d4zmvtEti8glvrKyUzH3XV0m+SmQ/uXn9wm+U1ffV9mcDygqv5k5Kpr/sqfmuTzHxwNb5iKssLT/1eP5XlBXcudEynvq7rtfownfng7rhimaHPVS33+9Whz0ntiJurQ7ndI+/2UO6e7bWd+XhOKe+XWct8cJmvyM7Quk6x3HGhnvVy17hQLuTCU6FcvZr7NPNR183zzQHqa+4Etp5mec1z0nN9NrC84GJIt93U18+pZNawLcw3/FCvvSwvPLVlwSGaV3Px2xNnmY+63onSx5hv+JGC50L76rT58mJon7wGyy6Gcqd9hArl7qn6ivo6px3X8xLzwdXWCSq0rjrZLylb3bPfJoVyu5P+OTLNnXzBr//H13XV6N1JzAe387RLZXnRJ+vcicssL7i6VvcW89FX2TL9KvPBxaTKCTSv9ryPo5NZXnB39oJU5qOuW+6/ZOaDW3aVNDaveE5qDzi6i80ruFgy+gDz8ZzSajRyH/PBVa/Ox9i8wnNeKXKazSu4bJBwgfmoaw/4Np75hueecoXlxXNSudB/mOUFlynjzlBf76mizK7TzAe3p/S+wPLCU3dPX2R5wWX8/RvU13Xd7o+uMR/cK3P/FptX06fq+26weQW3u3+UyHzkFB9VucV8cGv3zSQ2r6but3dT6LxqLr8+nsZ81JVTgj9XGu6Dq7bT01le9En4avC8msuCY1KYj75aH3VOZj64PNoolebVntfmZZoX3KryYQb1dV170QjqG16mYWa2c/WYfs8AztWLV9JDubfnWGYo96bvzmQ+zimxxZ/OfHBxf29maF3v6JP+UM+q+bcvlLujT/uz8Vqx1Eddt/YdH/PBndxb/DSvPiftaZUyWF5wb+KVTOZjTr26/TOYD66W5aF5Td18D3wsr+H5N/mZj7rOssLUBxd9dmTva2LHQGif1I5nA6HcmqM/h1Bz9XAi9ZHTeVCP+uBu/mHZ6jrqWDbPTdyVnev3qoKLvteoj7ris1+pD24tP0Hzok+qfw2a1/CeX1EffbW/rkZ9cK9/b5oXnrzr0bzg6r9Y6qOuSp9FfXDvePC9WOHzinPSuX81g80ruJyt3/8S5uOcUivfymQ+uPtgMp1XeN7P9em8gsuph6iPunLQ29zXXDS55md5cU5a7rVMlhdcTLvuY745Vw9+SH1w96kGNK+p22I8zQvubr5LfVM3bjv1wWXbYnRe0SfnfBSdV3Bp96Q+crr5GlIf3F3ck8+r9tSgaXRewa22K6mPunLR99zX3Jrm0bzok9O0Jc0Lbrf4nProq9e9I/XBvZFf0bzwVOcJNC+4vXYu9VFXtp1AfcPn/xRg+yqek7qi6AW2r4LLQL1rzMdzSjWo7TXmg1tDnrvF9lV43sb3brF9FdzaPy2J+aZu33ZJzAdXe566zfLiOalsN+gWywvu5fgoifnmOdU725OYD24VbnOb5tWeu+rJ2zSv5mJu+zTmo67bpG8a88Fl0ZoZbF9Fn9TaL26zfRXcLnoqjfnI6RXpmcZ8cPHT8xlsX4Xn/rKQ7qvgjv1nJvNR13pzCfXBVddFPpbX9GlsRAbLC24NHZbJfPRVzdDvWwjzwdX5oT6WF549Pfhz7+F5wUXtZn7mm7oVg+9dC/cNbxp872D4vOI5qV130W02r+BWnm1pzMdzSrdhgXTmg7vesgw2r/Cc/d0y2LyCO70XZ1Jf17XrXM9kPrhatNfH8uI5qTyflsHygsvMCB/18Vx1cgcf88FF+Sg/ywtPlb7nY3nBRdsf/cxHXS+g32cQ5oNbY4LvFQyfV9PXBcH3toXPK7jb5QU/9XVOr8OnfuaDu77ge0vC5xWeqh9L5xXc7VkywHzUFdF1AswHd/N8HGB5TV+niADLC27nC/45Hu6jr+LlLwPUB58UfO9qeF549hff0bzg9t6F1EddOWIP9cHd4mcDbF/FOelWV3xf1dy9/52f+TinvFFdqG94gaN0X4Vnv5NO91Vw8WKDAPV1XedyWeqDq/b6/aJheXFOqhpbaV5w5/2CAeZjTsXQ/AHqa243D34uenheeE5CG5oX3Or2OfVRV8X2pT64vD6O7qvok5j0Ad1XwdUf31Lf9PXqF9QHt/OP4fuq9txO39N9FdzrMoP6qCsrTKU+uNg5jeZFn8SiUTQvuHhrGvXRV6vbzP/DD3L7u19oXnjyux9pXnD78DTqo67T6mfqg7sDgv0In1eck9b7r9J5BVcnB1Ef55Q1ciL1Db+6kM6r8drMoPNq+GCP+qirFqznvuZ26e00L85Jz7+N5gV33jpAfXOuxlynPrjdLpHmNXXrH6d5wUXcCeqjrvNxEvc19365RefV9GnXcjqvhrdaQX3kdCbsoD64cLbQeYUnGi6i8woud/9EfdR1mqymPrgTM5fmRZ/E8qM0r+G/7aO+6Wveq9Q3vMFpmheeU2MLzQvuFvCoj7pWSiz1wb0/g/+/hN9XtWNef3xfVaQ1HcXuq4KLYhWnMF80u/v457GsAxljmA/u/VRoBruvaupWCt4bDL+vCm6dSlnEfNT15tz+hfngotqCZSyv+0L645/XsV7qPp7lBRddh/7IfPVLheB7DyYPnch8cPftuJksLzx7fE2P5QVXbQetYj7qum90XcR8cLdn57Xsvir65KblXkvvq2oulg/fxnzT11JlqQ9uVx74G7uvCs+7m2sfva+qufjiylHmo6636iL1wVXXhqdZXvTJ+6vLFpYX3H73nGQ++uoU+3E788Htd7/az/LCE7NPHKF5NZc/PzzLfNSV1q4TzAe33197ns2rfH5C8Ocxy9efxOYV3P38/Gzmqz01gz8vdr3fZOaDu0vnz2XzCk8N7baEzSu485fYQH3ULfjMMuprbsdt38jyimXFgu+dmdNsCs2ruftWyV+Y7/zT7fH7PJScOoX54LLD0/NYXnjy+qXlLC+4N/z5zcxHXe9Oo5XMBxc9fqDzij45q3fuYvMKrv65d4D5yGlF/rqH+eDy3KuH2bzCk6v1/YmweQX3rtW6RH1d12r95Vnmg6vt3a6wvOiTPW+lpHk195J7HWU++uocOLmX+eAqb/PjLC8863ggnuUFt29EX2M+6qqJTc4zH9wbf/w6u69qzsmslvHsviq4+/OzV5iPc8rZmHaB+eDWg5gb7L4qPBmfqOh9Vc2dC+OSmI+6smSLROaDOy1kCstrzsl939K84O5fXW4xH3MqPo67znxwd0CpRJYXnlevZTLLC27HT01lPurKarepD24983cau6+KPqlSa2+z+6rgXrv66cxHTqtJTBrzwe2v9maw+6rGiyiYye6rgquD3XzMR12v84s+6msuT+vPTwnLiz6JCP3cPiwvuN0/LZP6uq9eqaaZzAe3rD0+ltd4cyv7WV5wywr4mY+6Muce6oM7v+v33obNqzknR7+p6LxqbneakcR8c04l/3CL+eCicxydV+NFPUPnFdztmz+D+ajrOTb1we0/z2ewvDgn5cZZCSwvuHi0JYX5OFfFmk8SmQ+ufgp+Xyw8r6m7ME86ywsu+/TOZD7q2kvmpjMf3Cl7PpPNK/okKz3hY/MKrrq97me+6evcudQHd6vtpvMKz3pef38ibF7BnXlrAsxHXXdAf+qDi6YqQPOir4ty0rzg9gdPBJiPvop/OlAf3BvZlOaF515fTPOCyxeDnzsX7qOu/C2O+uDK1Z/bHbavOl88/W3wPZGjprB9FVxWyRHDfHdB3MDgvb7qU5hvuBwZQ/dV7alSL61i+6rhK5dvZT7qikklVzPf8NajtrG8Ynz04/cdqY9nTWZ5wd3rcTHMV4suB38e+F3fJOaD2xFXY1heeFaulatZXnD1oPp25qOuXarAGuaD241itrN9FX2ypuzbx/ZVw0tsO8F85LQ/G7uf+eDO3dSTbF81df0tL7B9Fdypk+8m81HX6lf4IvPB7Ub/3mR5TV8zIuJYXnCn6LlT1Nd9tTYEP28j3Ad3frNPs7zGK77uIstr+J13FfNRV1Wreon54M43MYrNq6g/7ovH/75P205i8wpunTkfw3yrX/vgewIa7/6e+eDuP8G5/Z95hTe07Ro2r+DWh77tzEdd9/Vea5gP7u2quIPl9V6KDr7HovhH37O8hnfbEsN8u8fkXsGfG279PfPBvebrYmhe7Tkbvl3D8hr+3zM7mG/qDhqxhvng6scXdrB5RZ+8Oufj2LyCi+nDTzPf5JyZ5wDzwd26S0+zeYUn8w68xOYV3Bl/TDEfdb1Oqy8xH9ztnKZYXtOnnJUPsLzg6vXgc4VwH30VB2odYD64/eW+0yyv8coeucTygrtJ9xXzUVetOHuJ+po7Tf5TbF/FOSkL5Uli+yq4m/nPbeabc7XId0nMB/daf5jK9lV43pbcGWxfNXx28HMvwn3UtVq/lsF8w603fCyvOSdvX0yieTW3fl2VynzMqVO5dDLzwa2066ksLzz7+rgMmldzcW+wj/q6rhi4OoP5hp+Y7mP7KvrkDRjhZ/squNv7rQDzkVP97PmZD+7+14fvq9oTDa4E2L4KLtYXz2I+6qpTtwPMN7yS/lz0sLymT423+VlecBk3JMB89NX77YCf+eB29Gia13jVswI0r+bOnogs5qOuneNugPngol7wc+PD5xXnpPV262Q2r4a/I9KYj3NKPfggmfngdqlSaWxe4Xk/7s9g8wruRXo+5qOu/f3ZDOaDWz1W+lhenJN2m77JLC+4+K9SGvNxrtrV+yczH9w6VC2N5YUnF17JYHnB3c5rfcxHXVnvegbzwe2Lwc8hCp9X9Mndc9JP51Vz9Yy+bx7mm77mOOdnPrj6+3s6r/Dk0nsBNq+GJwW/DvdR1/3r7wDzwe1G5bNoXvRpywU/ywvudZocYD766k286Gc+uLVxMs0Lz55wP8DygquOwa/DfdT11t0PUF9zt0vw6/D7qnhO6lRq52f3VcHFnmQ/881zyo//pj64HdErwO6rwnO2Bu9lhd9XBbcPBu81hfuoq2ptoD642y/4uebhefGc1JkRvJcWnhdc1NwfYD6eU3n54qgPbkcHP3c9PK/xKt6lecHFx/mymI+6zu9Fs5gPLvaVy2L3VdEnd5rk91U1t/++Rn2TM+If6oOLck9k0fuqqNvOT++rgtv/3KM+6tpdgv+9/+Nr7gTyZ7G86JPcqvsXnldz+3LJLOabvsZY1Ad3u1bOonm1p6JLZ7G8hp8qRX3UdXNUpj64fNXKovOqn5M62x/QeQWXTwT/veE+nlM6bhHug+esksXmFZ4Qlem8govVUdzXddW/dagP7sqGWSwvnpO6FyNpXnD5Zh3qmz1VVaW+4YMb0bzGG9aE59XcqtKc+qgrJ7TgvubOL8/ReTV97VWbziu42FCb+sjpvNSY+obvb0TnFZ7t1KLzavj9GtRHXeFvwH3N3Tr1aF70yc7VguYFV1Zz7qOvjVtSH9xr/BzNC88t0ZTn1dxt3Jj6qOtUeob64KpNsyx2XxXnpOj4kN5XBXdjHlLfnFObg7+G++BeUZzP2e+rwnPW3qf3VcFV7B3qo65qlieL+eCqRs4slteck4VKZrG84MIuRn3MqfDKUx/cKheRxfLCs4Y/lcXygsuN+aiPuuJwSe5rbn8fPNfC76uavi5Ip/dVwdWc69RHTq/HfeqDO/Hp9L4qPLvtMXpfFVw03UB91HXLXqQ+uPvLLpoXfbKW5MpiecHtHfeoj76qivmzmA/uFdXzF55Xe17VRJoX3FYHqY+6Vu0A9Q3fEPx9/z/zinNyX1U6r+DWBYv65pzaXIv7mrtzqvN51Z7dKJLOK7jaUor6qOudrkR9w5+N/H91nXmcjuX+x2+lI4kyOFGWWyEkuxLjuAYjCZEQqW6OkLTJQaIuRJZkyZrttjPWrNnqnhljX8YwMwbDNWZfnucZjkq2fn881+d6zTzn8/vzvHuf7+tzvq/vuc73XG7XQ/PinHQ3NKJ5wZ0JL1Af56od14z64FZUI5oXntpZh+YFlzW4j7oirz71wZ0htem8ok9eteIcc2X4rVLUR05rYSXqg9sjyxayeYXn+e7wedVc7b7KfV3Xe+rhQuaDO3OD/70LzWv6OuVpmhfc+yaM+uirlVOd+uB2macKaV7tef0fLWR5wcXN3wPMN3UblS1kPrjoaNF91dyr/lWP7qvg8sOXuK/vKdVTL1IfXF1pSfdVUzc9nO+rmsvdbamPuuqJttQHl4kRNC/uSe2shjQvuNu9FfVxT+VeaUR9cFG9Nc1rvI8FzQtut29PfdS1BPcNb9qB7qvok/0vQfdVcO+1ttRHTuezdtQH95ZG0H3V1O3Whu+rmquZ3EddebQt9cFVgzY8r+6TsjrQvOBen/bUR1/dypHUBxdLOtC8pu7WCJoX3A3nPuraVdtTH9xKEHRecU/qdW9M5xXcntya+rindMY3pj64+rk1nVd47t4IOq+Gn+hAfdR1/4igPrhdK5LmxT2pM60xzQsuo1tTH/eq1ozG1Ae34lrTvPC8Cu1oXnCnZyT1Udeq2o764GpAJJ1X9EklRtJ5BXe+4T5yym4dqQ8u/tmRzis8p3YHOq/g1v0I6qOue7ED9cGtE+1oXvTJntOR5gW3hnMffXXW/T++5uq7jjQvPFtE8ryay4j21Edda2Ak9zX3hran+yrOSVH3Zbqvgnt1uG/O1bxXqG946ZfovgrPlg3pvmp45XrcR90JTagPbrVsQPOac7VGOM0LLpyW1DdzerEN9cGdlq1oXnjiz2Y0L7hzuiH1Td2CFtzX3L3amO6r6JOz/1m6r4K7ZZ/hvs7pPKhNfXB3YnW6r8LzxpcvZPsquKzwCPVRV4VXoj641aU0z6v75O6pS/MaPrQm9dFXO7s+9cEd7zmaF54YXJnmBXevP0591LVuP019cBX/BJ1XnJOyZls6r+BiaWvq45wSjwjqgzvzwvm8ak8OfZnOK7gzoin1Udcb0ZL64OKHZjwvzskZguYFd+u0oT7OVXuvoL7hfdrQvPCcJ1+heQ1/qTn1UVd2eIX64PKT5nReTV/nNaDzCi5n1qY+cqqYF6lv+Pk6dF7hWf+tyudVc7tSGPVR1+pXnfrgSv//1tC86JM3qiHNCy7mP0999FXuaEh9cJn4PM0LTzWqQfOCy3UVqY+6alQN6oNbt/W/1u+qio3B8xXvpLrNbxbnYcF7BHDvR+7jnVJZOz3AfHC16UagaF2vj76v0J5z8pFiXNQvX4xbH5emPuqKEcE/dwz1Db8S/PeF5sU7qfbYCwGWF9zNT6O+eVf1yyPUB7cqJwRYXnhe/7wAy2v4dT/1UVd8eDHAfHArPPhdHPrq5Vco1ic7sUwxbp+sVIw7dctSHzmdHx6iPrilHipW1zv9z2Ke907ZYlzqP2cBdxs/Tn1Td2MJ6oM71v0Ay2v6mhW8dwzNCy46+QLMR1/VhuQA88FlTf3+R0he4/XOCrC84Gp3SoD5qOu9G0N9cK/BZj6v+p1Uu+8+Oq/gsm8M9/FOacQW6oOL+B10XuHJ1XF0XsGVG8t91K2wg/rgXmYUzYt3Uq03VvG8mnuLVnNfv1Ml5i/ivuby3DyaF57rLaN5wdX1WdQ3db2p1Af3Jo2g82r65D/A51Vzu9cW6iOn03gp9cG9uEl0Xo23bj6dV8M//Zz7uq6o9h71wV1fbZrX9DV3NM0LbkW+SX301d3cjfrgzm9P0bzw7PFVaF5wZ7P+vjjER121IPjPQ31wGdPFX/RctcdUKnZOijqli3G7TlgxLt59mPrmXH1N/+9aiA8u9wT/3BZ15fEyxeueD+4N4HKDVYx7FfW5Eerrumpw8LuxUB/cGzgzwPLinHRrxgVYXnArbC31Maeq1nTua2437kPzwnMbjqR5wd2p9amPuo6vDPXBvYxl/qJ9tUpeKd7Xkfr80dyyVhTjzrOdqY+clhtBfcO3nfYXrWunhBfz5NXgu+Lgdt+V/qJcVfvbx3zUdRtVpz64ml/Zx/KiT+6cg36WF9xLr+JnPvrquQ98zAeXN4LvtobmNV6LBjyv5ta45Hzmo65bVr+7H+KDu3/UymPzas7VBa3pvIJb8QV+5uOcEtvP+5kP7tnv+tm8Gu+xr/xsXsHVyYs+5qOuuLrLx3zDR54oYHlxTtr3X+d5NVdDlvuYj3PVeTH4z//H19yZu76A5YVnl9tcwPKCe/sH5jPf1B3dK5/54Krvwlw2r+iTkxJ81zV0XsHliv35zEdO0W5GPvPB1e8ZuWxe4dkDKuaxeQVXYwuzmY+6Xu8T2cw3XLbKZHnRJ1lxRy7Nq7n72+xs5qOv4tYH2cwHdx85kcHywnNiH8pkeQ3PqH6d+ajrNFVpzAe3L21OZfsq3kkVdWfzfVVzb+UU6pt3SmtMoz64K76k+yo8Ef4F3VcNv/Im9VHXmfgu9cGdiCY8L95JXTKJ5jX8znDq451KGfsN9cFV04E0Lzy7XCeaF9xzylEfda0fmlEf3H0j38/2VdOn1226r4ILGTwXQ32T81Gfn/ng9veT/Wxfheced+i+Ci4b/OJjPupaG/J8zAeX/9W/OxOSF31y43bTvOCyRCPqo6/WC+OoD24NSvCxvPDszYNoXnD1cesC6uu67pGEAuaDi7r989m84p1UeWQ8nVdw672+1Mc7pV7CeO5r7t5+k84rPOeqTecV3J563M981LX+ejJAffCzUX6aV7+TKspKmhdcjXqD+vhOVX33LfXBnSZv0LzwnIslAzSv5qrkHD/zUdd56z71wYUaT+cVfVIb2tF5BRfbv/AxHzm9Nx/3Mx/crlqZzis8V/J5BZe3vshjPuraE9ZTH1yem51L8+o+eVOv0Lzg8uu4Auajr86EPdQ3/KsJNK+pO9LOZ3nBxeXoHOajrli2LY/5hl8P/q5g6L5qztV5Om/IvgruXQr+Llioj3PKfTH4rlmoD67qlshj+6qpe2hzLttXwd2rw7KZj7oqcl8288FFn5sZLC/OSbFpfh7La/jt/jnMx5w6K7vlMh9cPbwki+U1dcPmZLK84FadXtepr+taW3unMx/crjPtGttXTV8/Df7uS+i+Cu71+iWN+uhrSkIa88HlkKapbF+FZ8e3TWX7KriV3yOZ+ahrnSmfzHxw7/ymcywv+iT3X0lleQ1/rn8y89FX+/qUi8wHtz7vnsDyGm/NxnMsL7hsVPoE9XVde9KUE8wHtw4lx7B5Neeq/342m1fDJwbftQj1cU65NfdRH1wta5zO5tV4p5uksXkFF9VSLzEfdcXvM64yH1z5M5NoXn1OWnvnZ7K8ho/z0piPc1VMXZtBfc3tdxYrmld7ztY+l1lecPt77zzzUdeOv3qR+eBiwcfn2LyiT9aGW+fZvILLKV+dYj5yeiU7xzMf3O7d6QibV3h2xeOxbF7BxYlnD1Bf13UfP3yI+eBqZfROlhd9sjqXOUnzai6qxEczH31VbWoeZT64ipp9iOU1dSvf28Pygsv4fVuYj7rqx4U7mA9uPRy3vui9qvd18N0C3JOqwNmCotypF1FQlLt/VchnvrmnPLIkn/ng3keTc4vWFTUa5BX15Ib1OUW52tYjuyh3mnydyXzUdaf/lkV9zd07vdJZXtyTOs3v59K84KsmZzMf91SqSno288HF209msrzwvPKfZbC8hj8Ivmsc6qOuijiSxnxwq/OUK0X7alXdn160T1anA2lFuezQ91pRLnp9eZn5yGnVfIL64Cr5y+SidcVvryUX9eTi2+eL8QOvxBflzuLBJ5mPuuKjX7mvuWq4+wjLiz7ZiwdfYnnBVfWJ56mv++q5vyQyH9zzjz/L8sJTUUNoXnC1cUQM81HXOZ0Wx3xwzz/gEJtX3JM6ozpnsnkFdy+3uM583FN66ztdZz643aXtVTavpm7S0VQ6r5pbw15OZr6p++WfycwH99zmCSyvuScdVf0qy2t45YoXmY891Qo/nMx8cDfpgwSWF55YOjWB5QW39s4+yXzUFZtXnmQ+uPd068NsXk2fGnY6x+YV3B6+4xj3gzmtMStOUB/8zKUYNq/wRKsno9m8gtuj/tzDfNQVT5bdz3zDH9r8M8uLPskxubEsr+FejYPMR1/dr8odYj64vWDxLpYXnv3a9ztZXnAvLy+K+ahrNyy9hfngTr/xa4qeq9YX78cVPSfdRzOii3LhndpflHvL39/DfJxT3l+TDzEf3H7v6x1F66p6JbYX87KORhXl4rMSq4py7/Jby5mPurJ+l3XMN3zTosUsrzkn797dy/KCi7bhW6mv59RJabST+YZXG7mB5YVnL/piFcsLbscsnc981BW5A5ZTH7zR63OL9lU8GDe3WJ+6zppVlDt3bFmUez0GjGO+6WudLjOYD+70zv+yaF37iU2DinpubJVBRblsX71LUW5NeLsH81FXRnzyAfPB7WEpfVhe9Mn94dp3LC+4Pb7/GOajr96k+pOZDy56zRjN8sITDY8OYXnBnc457zMfddW4vGHMB7d3zh7E5hXnpNO1E51XcPX98tXMxzklem1az3xwcW/YCjavpu7Z+4vYvILbw9vOor6ua0Xum8d8cGtR5AyWF+eku3z0KpYX3I4etJj5OFfdyS8uYz64ePfZ+TSv9uzLP81hecGdHxKmMh915daPZjEfXNxqMpXNK/ok9zeexOYVXA2fPZr5yClXlJ/IfMOvHxzD5tXUPV71Uzav4KJNWeqjrnNv2gjmgzt5cz9jedEna0LpiSyv4XkrvmI++upVGzeJ+eDeJ7MkywtPiYdHs7zgVvj3/2G+qZsSGE998OG+cWxfxT2pvalkAt1XNXdPPXOS+bin9L7Wv0sU4oOrbv+OZfsqPPntH7FsXzV8zq2DzDd1F9Q5SH3w+Hd2sby4J1UDrsWwvOCyXu0DzMc9lZcxaz/zwUX5UztoXngXpu+keTW3v/1iM/NRV/4cton6msu8emvYvoo+2Tnxu9i+Ci73Xt/CfORUmXO3MB/cuXF0LdtX4XmrFq9l+yq48u9fwXzUdT8a4TLf8LnRi2le3SdnRJO1LC+4bOG4zEdfrTqrVjAf3Bo68yeW19TNXPQTywuuTmQsYD7qqu0PL2I+uHim3UI2r7gndWav28HmFdx5pn4U83FP6TRYE8V8cLUxfhWbV3iyev3VbF7BvVlVljMfdR15Yyn1NVfVKy1mec096aabq1lecLm03TLm417VGdnTZT64N+nvxTQv6i45sojlBXevb1rEfNT1ujeiPrjV6sxSNq+mT5czF9N51Vw1m76I+cjpNli3iPng7uZf6bzCsz9tuZjNK7iY/N4S5qOuWn1kOfU1d6o9v4rlRZ9Us2tLWF5we8BVl/noq9hXdhXzwYWv33qaV3veGt8alhdcDr6xkfmoa7/deCv1NbdGNt/J9lWckyI3byHdVzWXzua5zDfnVNgz1Dfc32Au21fhieihP7B9FdyanjWN+ajrbBs6m/ng8vTZH1henJPeyvR5LC+4+qPUPOabc/WZ/QuZD+40e7CI5YVnN835keUFl3eazaM+6j63+yfmGz5l7lK6r+o+2WfLfMf2VXAnuedk5iOnbBU7k/ng7pnImWxfNXVXDPuW7avg6mzNb5mPuk7DijOpr7l9vtRMlhd9cuWWeSyv4Vdqz2c++mpn9VzOfHBRI3oFy2u8BV/NZ3kN//in+cxHXWdgH5f54O486bJ5xTlpJc1YzuYV3B7faCXzcU5Z1b5cy3xwqWptpPOqPWdim9VsXsHtbRFrmY+6Uvo3Mx9chr+2neXFOan27tnG8oIr794O6utz1V18fS/zwa2fAwdZXuN1abyH5TW8n9jHfNR1+j4WzXxwa9n4WDav6JO1qtx6Nq/g7qGxG5iPnPJkxs/MB3cDz+9k8wrPqTpoI5tXcHv8ro3MR11v4B87mQ8uBrXZxfKiT3bCzQMsL7i48MSvzEdf1Uer45hveKfuR1leU/f20N9oXs2dBdd+Y76pm+UcY77hQ389xr5XNe+qtpxBv1cFd3q+SX28U+r8/iP1wcXZt/n3qtrzHtyh36uCqy0jqI+66tEH1Ad3qnzmZ3nNu6qfL6d5wa1+A6mPdypFhyjua26N+CTA8sJTL/8jwPKCe+VHUR91rfywAPU1F2cn+dn3quiTOLXUx75XNXxj9wLq65xeQfB3RUJ9w9NaFLDvVU3dIf3z2Peq4O7wednU13XVJy/kMR9czOqYzfKiT3btH30sL7iT26SA+eir+nKVj/ngcmz7ApbXeI9Vz2N5wW2vQTbzTd1RTfOYD2716JDN5hXvpLo399B5BXeGj6M+3il1b8dSH1x9N4POKzzR5LkAnVfN1a2FfuajrpvTIsB8cPfwdj/Li3dS3aR4mhfcK7GE+ninynk6lfrgqnYUzQvPVa8FWF5w2e+Un/moa/31boD54Erl+Nm8mr5mH/SxeQV3Fw4pYD5yqr3XfNTX3K30UwGbV3iy76A8Nq/gVufvspmPutacLXnMB3cG+rJZXvTJySzlZ3nBreZJBdRHX0c09TMfXNR/ysfywnMfLpVP82qubg7PYT7qqiEf5jMf3LpwN4d9r4pz0lpzK519r2r4G/p73FAf5+rJqenMB7ePTr3KvleFJy7WSmbfq4I73fefpb6ua/1gJzEfXEXfPM3y4pz0ag9MZ3nBVVz9q8zHnIqR49KZDy6XDbzK8sKT92ISWV5w8Uq/08xHXanuJjIf3A47c5p9r4o+ycmLD7PvVQ3feGEf9dHXbq1jmW/48OB3EaHfq5q6r4VtZ9+rgtuTzq5hPuo6K57Zynxw8aD7GpYXfRLbUmJYXnC7x569zEdfvb29YpkPrvYn/cLywlPvr9hK82ouBpdcx3xT998HtzMf3L5zZSObV3Outo1PZ/MK7qbUuMZ8nFPOsZ4ZzDd80/OKzSs8IQ8k0XnV3OrU/CzzUdebcTOZ+eBqzsF4lhfnpDxwN4Pm1Vxu9ynm41wVJw9kMt/wPYlpLC8899DsFJYX3H4nMoH5qCtyJl5mPrj3Ub0LbF7RJ+exZYfZvIK7pzodYD5yWhPWHaG+5vLDx39j8wrPndZjJ5tXw/vd3UZ9XVe8U2Mv88HFe913sbzok4r8+DjNq7n3d6UY5qOvqlPeKeYbnt0njuWF55YQB1lecKdFuf3MR117xAfRzAe3z23y2L5q3kn9IIfvq5rL/r9QH++UuiNuUh/cbRRH91V4dsvPA2xfBZf7SlIfdd3kiQHmg3s/Vqd58U6qKH+X5gWXXeKpj3cqnQolCpkP7n6TQvPCE6/OpXnBrf+04L6u69VdQX1wd0enANtX0Sdna38/21fB3QVv+5iPnNL51k99zb3DC3x0X9WeyjydT/dVzT1b/72tEN/UndqwgPng4oKdx/KiT07Sej/LC+6eOeNjPvoqrgXfLQj1wb2fgu+qhOaF542dUkDzgoevy2M+6tpPB//edKgPLl5+Op/NK95J9c4/XMjm1fCtwd/NC/XxTql37JFC5oNbZzPpvJq6HTfSeQVXGX2oj7pq8nbqg4sL+t4sJC/eSbVu/aOQ5TVc6XMtxMe7qrJPqULqay6ycmleeN61XTQvuPXhh9Q3dXvuoT64veSjAJtX9Mn7JdVP51VzFVuL+zqn+1+/n/mGfy78bF7hidRyPjav4DJ8TD7zUdet3drHfHDvpUP5LK/pU9+/aF5w0bgH9dFXO+Ee9Q0/0dtP82rPK9nbx/KCi4jsfOajrnjkfR/1NXff+z2f7as4J53O32WxfRXcKZtwnfr6nHKSgn+vLNQH9/zn09m+arxaP6eyfRVcvvp2EvNR18q4f4354Cp830WW15yrpZ7MYXkNb3Mqg/mYU7fMLznMBxdzNmfSvNqzOz2XxvKCqxb1LzMfdeWtpteZD65enJDK9lXTp6pT4tm+Ci4/63ic+sg5OzmB+YZPPHaa7avwRNcBcXRf1dz9rXIc81FX9Pr7OPPBnS4vnWB50SdR9/VElhfc6vLWOeajr+rB7mTqa+4dPnae5YXnDS5zluUFd146dpb5qOvKfyYwH1y22p3A5hXnpP14u1w2r4a/PjSL+Tin3C4bcpkP7g76PYvNKzxx/pl0Nq+Gj519jfmo69U6k858cDlsgWJ5cU7KR7NzWV5wdaJLNvX1uWrXv5fLfMPfD76bEZoXnmP9K4PlBXfcKmnMR13r6UEZzDd8bcc0Nq/okz01I4XNq+ETHktiPnKqsqsuMx/cqnA1mc2r8bJGXWDzCi67dU1kPurKUoOTmA9ubx+czPKiT+646Cssr+GHyqUwH311T9ZKZT64mDYmhebVnjy9MZnm1dz+9vBF5qOuHP70ReaDe5ltUtj3qrgnlceDf+8m9HtVcNWg2RLm455SHg93mQ/u1njPZd+rwlMfl1/OvlcFdxrfXcV81HUq5q5mvuFr50SxvLgndWsPXs3ygjuVRq1lPu6p7NfPbmA+uKjRagvLC88tv2YTywvuzbuxg/moq2YM3cl8cPtcxn72vSr65LptNtLvVTV3/GnbmY+cXvoLO5gP7nwW9Qv7XhWeqtRuD/teFdz7O+og81HXiRzoUV9zUemFOJYXfRL7mu+neTUXgy9FM9/0daYbw3xwr21wbwnNC8+ptuMoywtuLxp9hvmoa/dz45kPLsZWSWTzintSWw7YzuYV3BoWtov5uKcUKxN2Mx/c2fbuATav8NT2sgfYvILLWodjmI+6bvUpMcwHt45NP87y4p5UzPcdpHk1l8/2i2E+9lRnVm4M9TW3vwk7xvLCs0TsMZYX3Pr5cjzzUdfrXCme+eAq6ptENq+mr28mHWfzavi4nueor3N6d/qdYz6484/kJDav8MS5YUlsXsFF4qXLzEdd92bKFeaDq08/T6N50deXRiexvOB26vkrzEdfnZE3LjMf3Jk+NI3lhSfdbWksL7jq62UwH3W9JbszmA9u91+Uzb5XxTmpbv8ezb5XBRexF+OYj3PKXjXpBPPB5bSwePa9Kjx36OHj7HtVcBXzn9PMR11n46UE5oOryGGJLC/OSS8xP4HlBfdqbEtiPubUuheXwnxw+88eV1leeFJGXmJ5weWMkanMR10R1TKN+eAi/o/r7HtV9En1fiuefa8K7r4x+xz1dU5v4fxk5ht+4ZUU9r0qPDG1TwL7XhVczF6SwHzU9Uq0vcR8cPvbhZdYXvRJXiylWF5wu/59xXz0VWx6K4P54FZk10yW19SdPjeN5QV33cNpzEddWSEtk/ngonRYFptXnJP21KWKzSu4t7d8OvNxTrmnWmUwH9zrszyLzavx3nohk80ruCx/KYv5qOu9Pj2H+pqrlVYey4tz0j0xKJvlBXfsybnMx7nqxflymQ+uonfls7ym7pnn8mlezd3VPQqYj7qikuVjPrh9PsdH51X3SeZXzWHzCi6/uZPDfOQUiWXzmQ/u3vPy6bzCa/xVLp1XzUWPtbnMR13vRp0C5oN7914tYHnRJ6vvcz6aV3Nn5CYf89FXJ3yKn/mGj8jzs7zwvA1/+lhecKfuk37mo659uWaA+eDuLP0+fMi+intSJxB+jO6rmnsP/XqG+rhXfafzGeaDW1+XOc/2VXj2gdYX6L6queN+con5qOuU/eki88Flas9rNK++J3W+dxJYXnB5LDGJ+bincrvOS2S+4ev/c4nlhWclZVxhecHdib406uu6Tskhivng6m2VzvZV9ElE7VBsXwWX7V/MYD5yqsqvpDMfXO2elUX3Ve25Fbpms30V3FqXmst81JVPDcylvubeiHn5LC/6pEYPzWR5weULX+dQX/fVDWuYTX3NvZZ/5bK88MSC23ksL7j7VPDPRUN91PVeqFnAfHD3te0+Nq+4J/VajLnI5hXc+XVFKvNxT2ktPUx9cOep0orNq/Gc0tfZvIKL5z7OpL6uK+dEUB9c/Dkti+XFPakV+68rLK/hA88o5uNeVVUM3vuG+oZHlU+jebXn9S+RwfKCe+83z2Y+6spARAbzwe17w7PZvJq+Tp5M5xXcXlsmn/nIKR76gvuay5ax+Wxe4amYAJ1XcOeJjn7mo66oOor64MJe62d50SexOPhua2hecFWmQQHz0VcRpv/eTogPrsK68bzas4ZH+1hew+ek+JmPus7aFB/zDe+q94SQfRXnpDfmQj7dVzW397fzUV+fU9aJxj7qg4f19LN9FZ5q3cHP9lVw681bfuajrncj+LuHoT64eDv4XVFoXpyT9mPP+llew+MeDTAfcyqSD1MfXC7Rv7cSkhee+seaAMsLbn2qv0sK8VHXTr1LfcNPBn93+n/2VfSpXPC7ltB9FVy9pX+3NMRHTjnnjwDzweXg4O9Ohu6r8Oxxwd8l/Z99FbxdNvVRVzQI/i54qA9uXalXyPKiTyou2J/QvODeoODvzof66KvXOIL64GJ8j0KWF569qGMhywsua3Xmvq6revejPrhb691CNq84J63xjei8gjtjTwaoj3Pq4OfUB/c2/kXnFZ54owqdV3D1bPA/d6iPuvb6etQH9x5/tZDlNefqr/NpXnCnhf5d01Bfn6tunZXUB5dVytK88KzOTWlecDeiK/VRV91tTn1w98U3+LzqPok73em8gqux73EfOZ/sR31w++IHdF7hyYQP6LyCyzpDqY+6dofh1AcX3T6hedEn+wDPC+7O/5D66KtT36E+uNo3jOaFZ036lOYFlx98Tn3Uld98Rn1w2WtEYdv/A7gusRI= 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+AQAAAAAAAgAAAAIAanQAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-020000.pvtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-020000.pvtu new file mode 100644 index 000000000..883032436 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-020000.pvtu @@ -0,0 +1,19 @@ + + + + + + + + + + + + + + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-030000.0.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-030000.0.vtu new file mode 100644 index 000000000..c01204b61 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-030000.0.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAQAAAAEAxFgAAA==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+7XzUB7e65eN9tWe3zc/7aq4e5qU+ctWYvNQH96rloe+rmjmdz0ffVzV8zrPUR0/VPS/1DW+bm76vCs+9kz2Dvq+quXsr+H1X4T5yrUfZMpgPrhIeBVhfM9ebvC+4VTAX9TFXtz73wb2/ctC+8IT/mQzaV3O5+d8A85HrHcyawXxw2SX4fXvh+2o9E/z9WJfy0H0Fd+w81HdX6N/fqNzUB1eRuem+wpO/P8v3VXPvs9zcR+63uagP7ryVk/b1ntfvXT/JQfsaPjUH9dVneu8qZKM+uDzzDO0Lz0nOTvuCq7hs1EeuU+MZ6oPbX2el+4o52cNy0n0F9/Jkpz56ipjs1Df8a76vJvf3LBlsX8Hdx8Hvuw73kSvfEBnMBxcN9eeHhPXFnESXZ2hfcKdzFupjrrJQFuqD2wuC51l4X3h23ce0L7jb9E6A+ch1Tjzgvubeg5v0fVVzrg5Mpu+rgtsr93Ffn1P2v8nUB3fPHqTvq8KzvhlH31c1PLEa93WuiphKfXDb+YT2xTnptk+hfcHdJ0eojz0Ve1Oob3jpE7QvPCdxFu0LLt9tRH3kWisXUh/cu9E2wN5XxZzE4oV+9r6q4X3097eG+egpO+7xMx/cG5bho++ras9+q346e18V3Cn2QirzkesecNOZD65OuqmsL+YkWsf5WV9wkf0l6pu5vv3Ez3xwq9Y3ftYXnhh1O531BbcOvZTGfJNbuY6P+eAqcWEa3VecqyKV7iu4fP4U9/U55eZK5b7m9o0zdF/hOV+uoPsK7tTuSX3kyhnrua+5l2Mo7YtzUu5MoX3BZf3z1Dfnau4U6oO7r1+kfeE5322jfcG9axOoj1zr293UB7f6zwqwfcWcRIeSAbav4Hb7YdzXPdWQKgHmg8uZ6/xsX+GJBtN8bF8NX/xaOvOR632T4mM+uNNxcTrrizl54osA6wsuc8T7mY+5qjZtA8wHF1efDbC+8OThyn7WF9ye+AL1keu06+1nPrh1cZyP3VdleX1PKZyF3lfB5T5BffVj8J+HUuqv4b7m9h79uSFh91V43geC31c1t5/ozy8K85Hr+fQ9J8w3fLn+3J6wvvaWW8GvM24FWF9wu+Yt6osaacH5v5xGfXAvPjXA+sJzp96kfcHVnH+4r3NFl1Tqg8vd+n4Tdl/FnKyLwXtR+H0VXCTpvmE+esrAXeqDe8Nu0/uq8U7y+6rhhTKoj1zvrX+oD+4MTKd9MSdVJ4P2BVft/dzXc7WOJlMf3OuWRPvC846n8b6aCyuZ+zpX/pFIfXBxPJ7va2zw+12dvXF0Xw1346ivOp8L/j47naM+uGp1ju4rPK95HN1XcHVfUR+5qsE56oM7Fc7RvjJH8D5t5T1C+4KrqCPUFyN3BP9/J++gvuE7dtC+8NwqR2hfcOfLI9RHrvN0B/XBrYYe31fM6QdF9xXcHnGN+mauN89yX3Mx5izdV+N9cpXuK7jd/jL1kesmnqE+uH3sNO2LOckfj9C+4LLTEepjrt5qj/rgbpGdtC8897MjtC+4HXeY+zrXab2T+oaP2knvqzgn5bYkel8Fdxpeoj7OKWdNIvc1dyZe4vdV7blfHqD3VXA5fgn1ketEHOE++Jk1tC/OSXt3PO0Lbm27SH3sqbXnKvXB1ZzztC8898djtC+4zLeF+siVUcepD+5k8+h9FXNyP+kXYPdVw4dWpD56WvVGU9/w4V8E2H3V5L6y3s/uq+Cq1x0f85Grmqf7qa+5qlnfz/piTk6lGbQvuDOlA/UxV3v/QuqDW+OcAOsLTy0vGWB9weXUadRHrsxWK8B8cPnrRT/dV5yTYy7QfQUXJc9QH+eU9fQk9cGdR8fpvsKTzxyj+wrujt9FfeRa9w5RH9ybuIv2xTmp7hzifTUXrx6kPs5V2WQn9Q3/zaN9Te75vbyv5vbNHdRHrhiznfvgMzbTfcWcnPQVdF/BnQ7jqY+e7ri13NfcqvoX3Vd4zpAWAbav4O6n+amPXNWrZ4D54N7CqrQv5mQtW8f7am4Xmkd9M9faa6gP7r64gPaFZ30wjPYFt1p/TX3k2p+P5b7mosmPAfa+Kp6Tijv5k9j7quBy4cvXmY/nlE787iTqay675Epg76sa7/uRV9j7qob/FHWG+jpXRPa9xnxwEes7y/riOalMcJJZX3CZ63EC8/Gcyrv7QQrzwa3nnyayvvBU2VhF+2ruLM1xgfkmN+ppHPU1F7PaX2Lvq5q53ix3hL2vCu4lb9xJffRs+8Jx5oOrf1N2s/dVTe5suY69rwpuP2qwiPnItSt/sJH54NZ3H/zN+po5Pfk2lvUF976eto/5mKu1v/Mp5oPbQ+IOsr7wRJGcW2lfzcX+eSupr3OtYs095oPL1ufWsH01z1Vr5Uhl+wquDuVOYj6eU6p2+1KZD26dKpZM91V7jvvBdbav4O63EVeZj1yZHpPAfHA730BF++rnpOrOgDTWF9z64PkU5pvnqqfKpzMf3HsxKpX1hec+70tkfcHFpDzxzDe5pWslUV9za+m862xfzVwD6gzdV81F8pmjzEdPUWHpeeaD2399GEv3VXuq6Ke76b5q7myL38B85Krky/uYD26n3d7K+mJOTtL+S6wvuOPPcpr5Zq6byl2jPviB58+xvsa7VOEw6wsuts/cyXzkWg3KnmA+uFey+172virOSTGr1jT2viq4U+LySOrrc8r9vN4s5oPLidY49r4qPOvGcz2iyfuqhlc/9hX1da63UfZnPriz4FYr2lefk+6glDmsL7gqXT+G+dhT8fxzC6mvuTq540/WF55X7+EQ1hdctE/rwnzk2nXF78wHl7Pz9Y1m76tiTk7ch+x9VXD1/oDPmY+ezp25FaOJDy6+9r/K3leFZ6d3LMfeVwVXp+7+573mcB+5brMpdZkP7p34smM06Ys5yS6zv40mfcHVlPgPmW/m2sZpF818zdWWQ9+xvvDcMR83YX0Nb1e4K/NNbtrBVswHtztd6M72Feek1WvI32xfwb1X27nMxzll1Vm1ivngXpt/5tF91Z5qUXQC21dw940sQ5iPXJGQbSrzwZ2le0exvjgnVd0c62lfzd0qW5YwH+eqLNZxC/PBnb3PrGJ94akcc2exvoYXbzWB+chVOxfOYz64vWXXVLavmJM9dnU3tq/g7pAs7ZmPns7xrAOYD+6+Pbcb3VftyU/++Inuq+bejvJ9mI9c19vSjfqae+0+GsD6Yk7WpvlDWV/Dp+7/lfmYq2j2/Bjqg6+s+hvrC0++NL0v62v4d+8MZj5yneR5g6ivuf1O7mH0vqqfk7qLD6az+yq492HONOrr55TO89/6mA/uXbiUxu6r8NwNw5LZfRVcFqyZyHzkSv/aFOYbnm3/DdYXz0m9f477aF/N1ZHJ6czHcyrvUlU/88Gdg1V9rC88cWNPKusL7j5XNZn5yLXbr05jPrh32Elh91XMSdRoEsfuq4b3G3+R+aZnTPC5TLhveGL0VXpf1Z7zY6lT7L4KrqY+e4j5Jvdwp7PMB5dt2h1nfTEnN3diAusLbnfOHsd8zFVuH3WD+YaPuxHP+sJTO3NfZH3BvSM7TjIfuerb2CvMB7c/LHmW7Suek8q2v/vZvoI7T7f4qK+fU6qFx/zMB/e8cn62r/CcuH7pbF/B3VKzU5mPXJX8go/54O72mDTWF89JRc6sAdbX8MK/Uh/PVa0c5QLU19ypt8XP+sLzFizw0b7gbYKfex3um9wy+fzMB7fPP0yn+6rn5D3Nlcz2FdxOWJnAfPS0hjdKYT64uPz1Dbavxjt4XLF9Bbd3Lb7AfJObsCCe+eBO5XpXWF/MyZ7zSyrrC+5O3Z/EfMxVDmyaxnxw0eWZFNYXnru+WQLtq7ko2UUxH7myZkIi88GdylY8va/inNz+zE52XzU8Y/Q66utzyqr8eA/zDT8QtYXdV+F5VTYtYfdVcFmjjct85KoBpVcxH1zGtprP+uKctPYuOsj6gotsiR7zsaduM3Gc+eDeK7F7WF/jvbZiHetruBqylPnIta+8vIX54Fa37qvYfdXMtWn7GHZfNbz0xN+Zj57OtmLTmQ8u2rSZwO6rxuv1/HB2XwX33jgyivnI9X7JOob5hr/Qehzrizm5BxbNZn3B3ZOlpjIfc3USFy9gPrjqVXoW6wtPJAS/bzy8L7jK9moM85HrJS2YynxwL0fyVLavOCfF4Z4n2b4a3vGLg8zHOaX6zzvDfHCndoNjdF+RW22PR/dVc2/QuXXMR65dteMe5oOL5rc20744J3v6z7O+4GLtpVjm41y18le9zHxw5+ry06yvyd3R/wDrC67atPaYj1yrVOoR5oOrdxbtZvuKOTmVlvzN9hVclj0+l/lmrgXiVjEfXGakLmL7Cs/OFz2L7qvmzq1rM5mPXOu3pXOZD+5+X3Eu64s5qWzPrKd9Nbd+ECuYj7nKyrs3Mx9c+kqsYX3huTlnLmJ9we3Xry9kPnLV0wXLmQ/u9T4Y/Px2/bmqTsuZvTJ9Tuo7rXqFcntb6x6hXJYu1YP5+JxSuXZ1T+aDiz3vdw/NtZ+J/SXUc7N0/yWUq5v3Oody907uzsxHrrehWVfmg6uY935iffE5qWryWNoX3H0r2Cvcx+dUik+edGM+uDP3dBfaV3uySwTtC25XGNqO+ch1K9xpT33N1a4TduhcVS1/+9A5OXLzj6FcPJzcKpTL17L9QH3d01ZF2zAf3P64Z9PQXPX+mSahnti97JtQLtaqj0O5ODqvGvNN7kv3ajEf3PvgpY+jSV/MyZ5x7HvWF1zGvfgh8zFXMT1bHeaDeyXX/OdzacL7wrOa3fo0mvQFV8ubBT/vJcxHrrNhc1vmg4sa9f7z38XC9xWfkyr7Z6P7Cu7+OuUn5uNzStXhLzszH9xu/Etbtq/wvMSvW7F9BXe2ZWvAfORaZ7/8lvng6mq36tGkLz4n1XMS27O+4LJi/+bMN5+rWvBUK+aDi9ric9YXnn3SKsv6gtsbJtvRxEeuN7bs18wHtwZ2DP73w7B9xZxkjsHBz1UO21dwu3L5dtHER0+nZKk21NfcfrNKT7av8KyTk3uyfQWXR3oNZj5y3ZkHBjMf3PkscjTriznZC4p0Z33B3dTEQczHXMXFwwOZD+486D6K9YXnZZ/wB+sLLm7PnsR85DrdJsUwH9x+Kz34+Yn6XFVv/F05OuSc9NbPqxMdwt0p2f7z3xENn9O/JfNxTrl/Tm7JfHA1p3eX0Fzr4DMdQj11qnmXUO61PNMzlKvtLfsxH7lW9/N9mQ8uJh/5jfXFOel1u/oL6wvuNKw2gPnYU6dn8UHMB7dnLh/J+prcLieGsb7gTtET45iPXJl73QTmg8tll6eFzlUsfDA4dE5OdL5hodxuf29MJn547HjqY65lVo1lPrhdsOjU0Fy1r+SfoZ7dPGl6KFcdas0L5fKotYD5yFWfNJrDfHB36qOltK+ek7s8ZirrC+6MuDOb+ZirNf61ucw3fECdpawvPOV+toT1BXfFx2uYj1yvfas1zAcX/j5b2L7inBStc/7B9hVcnhgYQ319TjkNYiYxH9y5+f4stq/wRFLbv9i+gqu/L8xjPnJljk8XUF9zuSD4c2f+qy/OyVmnZ7G+4G7K0wXMx7nqfrBtAfPB5dEeK1lfeN4brVeyvuAqssAG6utc+VrfDcwHtx/39Ni+Yk7WsqbL2L6C23lGrmU+elqv3VzHfHAxq952tq/w3M4jt7J9NTz7wt3MR66ss2AP88HtWqsPs76Yk7Pv/g7WF9z94NBe5mOubsycfcw3/L2Wx2hf7YlppY6yvuB27scnmY9cO/XUKeYbnlY8+Nwu7L5qPic1/aum7L4Krn56s1I08fE5pe7dK58wH9yabgU/nzPsvmq85fXasfsquKqTty/zkev981Y35oN7mxb/59wK74vPSbWf+6A86wuuvroQfL8rzMfnVLqFJzVkPrhzPc8vrK/xyqztx/qCi+5vjWQ+cp1TU6lv+Iyvx7H7qpnT0mzD2X3V8Ad/TGA+elo/jh/LfHD1Q9E/2X0VniNjZrD7Krh3t/t85pvc7p/OZb7hXr5lrC/mJEpfjGF9weWx/i7zMVd7495pzAe3AxXms77w5O2Oi1lfcHX03TXMR67Voc1K5hseSNnI9tV8Tupv7/3A9hXcKjezN/PxOaXWoYcdmA+unp86kO0rPNXi1HC2r+BerVExzEeuvWfwaOaD2/uq/Mn64nNSrd/+7cr6gru/9hjCfPOe6rMTezIfXJz/gPY13qdHx7O+4O6dz2cyH7neqWyTmA8urlybzfYVc/KiDtJ9BXd2zltCfd3Ta/3XPOpr7lTqtJLtq/EiN6xl+wouho3bwXzkusvnbmI+uN3pj92sL+Yklp9dxPqCy+Sra5iPuXpz2yxjPrj3wdQNrC88u2px3ldz69qS/cxHrlu1zy7mg8sS2Y6w+yrOSSvbwhXsvgou1z9Yz3ycU7Yaso754HaeujvYfRWemNzSY/dVw7+/uI/5yHXGv7+P+eDW7m3HWF+ck66I2cr6govie/YwH3uqKozYRX3NZc2Th2hf7cnfGh5lfQ3/+8Ep5iNXbNx0kvqa21eyX2D3VcxJOLmP0/uq5k7fjqeZj57WyKGnmW+43fgiu68ar1bGBXZfBVfWzmvMR6637h3FfHC16q3rrC/mZB8qepH1BVejPlLMx1zVzdPXmA8uNgW/Ly28Lzx7W7vrrC+4eq7aDeYj1/2s9g3mg8sbwZ97E76v5lwd2HU/21dwq/HpE8zHOSWbJh1hPrgaM+g021d47ooXzrF9BXcOVb7KfJPrzrvEfMPXB38ucXhfnJPqVrNY1hfcq/bReerrc9WZ1oj64NbRm5dYX3ju6iyK9QVXIy5eZz5yrajs8cwHd8YeS2T7ijnZs/6OZ/sK7pQLJDIfPeXssonMB/e+fCOZ7Ss8N3JhMttXcK/F7lTmI9euNyqV+Ya3/yid9TVznSyTWF9w5+N7Kcw3c33/DvXBnVqN0lhfeN5z99NYX3CV9r2P+ciVObL7mA9uPVfGH/pcVbSotjLTc9UKwfdCwNU/MzaFcmtj1S3MN89Vt9Vfx3xwtS3CC831uhzcHeq5Tw7sCeVeUszRUC5y7TrGfOSKHOf2Mx9c3SgXy/riOan8tOJW1tfwfzrtoT6eU3VrvJv6mjt7Uw6xvvDkl5FHWV9w9fFLp5mPXJnW+CTzwZ1bdc6HztU7YJ0JnZNsuudMKFdnncuZ+MsvXGE+eqrOS88xH9zx7l0NzXVino0P9eTeBvGh3Hmhd2Io90auSKS+zhXtoq4zH9zO8sUN1hdzssrsu8j6gstR/yjmm7neTaM+uL3+JO+rPXtv4wTWF9yKrp3EfOR6a4Pv+Yb74F5M52S2r3hO6r685RDbV3CVFHzvLNzHc0rnWf1z0cJ8cNHOPsf2FZ71y6fn2L6COy/9dIX5yLVGlLvCfMM/mBrH+prnpJdLn2d9wcU3W64wH/dUu+2Wq9TXXHR8O571hecO3xnH+oLLuJEJzEeudfN6AvPBnbx1kti+Yk72N8lxbF/BxYWbCcxHT6/OkATmg8vI55LYvsJzEybTfQW3aq9JYb7JHfFRCvP/jwdSWV/MyX7z2STaV3N78cspzMdc5d91U6ivufwiMZX1hecdOJbK+oILL38685HrlnkunfngouwzvtBz1WnxXnKmc3JP6+RQbq/8ODWU2zt+SGU+ziln3Plk5oM7L69JDc1Vv65IC/XcRbvSMvGaf6WHcjlkSTrzkeuufJDGfHCx/UQ662vOyZ0jUlhfcKvTG2nMx55K1YL64E4y7wvP21aP9gW3RubxMR+5VvV13Adf/bUvdK7i18q+0DnJgjV9oVwM+sMXyq3UidzXPdXuxtQHtzotzpTrLd6YyRP/bs/Mu+/IxOWIvdRHrvo+lvrg6t1ztC/mpNYPoX3BRa7D1Mdc3eObqG/46Tu8r/ac9/y0L7jMf4v7yP2kiJ/54OpoMT/bV5yT3qCP6b6C2+dbpTMf55R7plw688Htdll9bF/h2WfL8H3V3B4038d85Mrek6kPri7d87G+OCe9E7dpX3BrXIyP+ThXRZX53AefF+FnfeFZXws/6wsucn/iZz5yrS9a+KkPfmusn+0r5uQNu0v3FdwZW9bPfPS0Z1fxU19zJ6Kpn+6r9qwLNf1sX8HdO9HUR65o2pH64CrXT37WF3OSq7vRvoZvHU19zNX+Yjn1wcWoHbQvPDX8T95Xc3lpKvd1ruXuo77hG/TPZwm7r+I5qfp1ezy7r4KLX9skMt88pyxwPZH54O7eisnsvgrParA+id1Xwb28c1KYj1wvV/5U5oOLkkPSaF88V7UnJLO+hj/omsp881y16rpU5hveMvi5aeF94XlZVRrra/hRfzrzTe5n1X3MBxdH9d+XYfdVzEkWeSWN3lc1d8Z/mc589LRHzkhnPri43MHH7qsm9/TrPnZfBbdmHPQx3+TWeUJ9cBHZxs/6Yk5W5dU+1tfwsu/5mY+52mP7+pkPbqec8rO+JnfgdD/ra3j6JT/zkeu+WiDAfMMb6Z9rErav5jnpi8G5hu8ruPVCRx/z8ZzS6tfPx3xwZ3Q9P9tX461+w8/2FVwWW+VnPnKdduf9zDf8+UoB1hfPSd1zwZzwvuAiItVPff1cVQy7Rn3Dd30dYH3hObXrBVhfw3uMCjAfuaLVDO5rLnsHf25a+L5iTiJHrgDdV82dijLAfPQUE4KfSx3ug1u7/giwfYXnftoqwPYVXLzYm/rIlW+51Af3bi+mfTEnb+1y2hfc2ryL+pirM+sK9zUXu5JpX3je50doX3BR8zj1TW6TdO5r7tbVPxck7L6Kc1KsC37uR/h9Fdza4vipj3M1cin1wd0/LvD7KnKXr6L3VXAvRyz1ket2vUd9cHEhd4D1xTmpGuQIsL7gVt0XAsw3e3r/8wDzwd3VTQO0r/aseW8FWF9wa2eNAPV1rnOoJfXBnQGtA+y+ijmpZxPofRVcVfBTHz1V7YgA88HFrRIBel/Vnmz/D72vgjs/+amPXKtMZID54Kpm0QDrizmp1PcDrC+4Nz6a+pirqNua+uAy4ocA6wvP610zwPqCeyeqU9/kNmhOfXCrQBO6r+Zc/WUI3VfDB4ygPs4pu98y6oO7RZbRfYUnB42k+wpu3x9OfeSKT5ZQH9wruoD2NedkgWO0L7jX4RD1ca7KnknUB1eF4mlfeE6rvbSv4Yt2UB+5Kv4S9cGtVmfovmJO6vxvdF8NX+1Q38z1udnUB7c2T6f7anL39KP7Cm4V7El95Krck6kP7mQbR/uaubqbaF9wZ8ga6pu5nj9GfXDV6gDtC8+2l9G+4PLVhdxHbp5d1Ad3tgd/bkn4+6r4nFRrQ61+7H1VcKHK/8588zmlg7M7zAe3J782lr2varxyzafS91U1t+9sncd85Mr449OZD67eXrOQ9cXnpFqj+g9mfcFl4szxzMfnVIqMiKHMB1f3101kfeHZzRrOYn3BxQz9uUZhPnLln91mU19z9+GhZex9VczJeanUKvq+quaq29AtzEdP68c/1zAf3EobR99XhSfqDN/L3lcF90rVOMF85Mr+sw8wH1zV+ukk64s52WteWs/6gnvNT+1kPubq5Vqzkfngrri8m/U13sJ1h1lfcOvdUaeZb3JbrT/GfHA5btRZtq/4nFRV59pwtq/gTvbrk5iPzykV9oNRzAeXFatMZfsKz0msM5ftK7iXt/tK6utcK/at+cwH9w7kXc364nNSreOjxrC+4N5vu6cxH59TJU+1Gs98cFVt+V+sr/Hc9xayvuDenxvXMB+5XrcBi6kPPmjoOravZq4vf7SF7Su4u+fqXurrns6FK9uYDy5OXt7P9hWe7HTsBNtXcFu2P8985Modb59kPrgYVO4i64s5qbu1PdYXXLa6fJD5Zq5bluxkPrg9J/kw7as9eX38KdYX3Lt66BLzkevNSD/NfHArX7Mr7H1VnJP2kI/PsfdVwd1/W19jPs4pt3Chi8w3vEWtOPq+qvZEgyP0fVVwUejnJOYj1yv5JfXBrXnbkllfnJNuzQWXWV9wR7rxzMeeqt8fX2U+uFf0OO0Lz64y6QbrC+6M2Z9CfZ0rv5yVxHzDi0xNZe+rYk5Ww89T2fuq4O6t5unMR09xpyH3NVfR+vNZw95XNd7FffR9VXC740o/85FrH2pAfXD3QMEA64s5eXtL0b7g3qPlPuZjrvbbq9OZD64uFfWzvsazVtO+4Or0VwHmI9duddfPfHBr/sAA21eck+LN3xXbV3CRHJvAfJxT4oV+ccw3/CX9/mbYvsJz+/ZOZvsKbr35bhrzkWt9/FoK88GtYVvSWF9zTv6kf95ZeF/Nneo/32A+zlUnW8nrzAe3lxRLYn1NrrcthfUFd7O8mM585LopNVKZD26pHnRfMSfZ8SW6r+Cyu+1nvplr+T4+5oO72yfTfYXnZi9L9xVczQ/+3LxwH7neC59QH9xqtirA+mJOMn/wc6XD+4Jbi7b7mY+5Oml47yazD+5NvOxnfeHZpZvTvuDi/jbu61yrzM/c11xu0z8XMuy+aj4n9bUHE9l9FVzcHesyH59TqgpVnMJ8cDl/4Rx2XzW5BSL+ZvdVcDtj2AbmI9db8+IK5oOrnGc3sb7mc1U/f2c66wsuRsv5zMfnVIoir7vMB3d+G7qI9TW5Y35axfqCW7HjtzIfuU5bfS8N88FFu2s72H0Vc3JSKu5m91XDXy50jPnoaS+8vof6mosxb59g91V46mm9s+y+Cu7OSLzKfOQ6E7xzzAcXTfX31YX1xZzkxxf2sb7g4uUhsczHXO29ZQ8yH9w79PQk62tyv/riAusL7qXliGM+ct1p2S4xH9y9ei2O7Ss+J1WoTnPZvoLLBUv/Zj4+p9SuEbOQ+Ya/93Al21d4Yt2U9Wxfwb07t3cy3+TmfHsz88Hlwy57WF98TqosdZX2BbciGq9lPj5XVUUeXcV8w6fN2sj6wnOubtvO+oLbN/vvZz5y5e0qu5gP7lWqepjtK+akciQcZvtq+MJ1p5mPnqrvq8eZD+41XX6W7Ss8y5d8me0ruFdqfzzzkaumZ7nGfHA5d9t11hdzch4Ni2V9wUXvjPPMx1y9nbGnmA8uN/W/xPrCU5sWKtYXXLyzK4H5JvfEjTjmg4svYhPZfRXnpPfqmevsvgouOq5IYj7OKXFwVALzwUWv95PZfdXk9l+Uyu6r4N6lrenMR65KyZPGfHCn2b/ptK8+J73b0YmsL7gz6UAy87Gnbr/7icwHFxW/TGF94TkN2qexvuD2/Ld9zEeujNmdxnxw714Hel/FnOw3I+l9FdzedstPfd3TK1+T+5rL33Lw+6r27KF9+H0V/LND1EeukzyI+uDySPC9mP/qq+dkffQ97QuuZhQLUB9zLdPPz3xwUaUM7QvPGTiC99XcLnaM+si1nhtDfXC1Xf/36vB9xTk5df0Ntq+Gv3sxhfk4p6y9fZOYD2590jqV7Ss8u2NUOttXcGvtLB/zkev2655Ofc29Pqd8rC/OSbmqdjLra/gEfyrzca7aPfKnMB/catArjfWF5w0+nM76gluf5vAzH7niD4v64PbSanxf9ZzkmSl+uq/g+SoHmG96XlzjZz74///3Frqv8Oz6E+m+gtuvHqc+cp3mU6kPrsYFfx3e18y1wXE/6wsuzgTfqwz3MVf3aYqf+eDu8aa0LzzZ6S/aF1wWPEF95Lrd51Af3Fp8IsDeV8VzUlWmdoC9rwpurwy+Rxru4zmlHDeM+uByyRH6vio80TeWv68Kft9PfeQ6m25T3/Bc2TNoXzwnXbiG9gUXldOpj+dUzpex1Ad3q4sM2ld73rVgv/C+4PJOvgzmI9epkI/64PayIhnsfVXMSZx+TN9XBReHsmQwHz1FSu4M6msua+bJYO+rwvOuBL+Gv68K7tYO/j7DfeRaPz2bwXxwu1GuDNYXc3K/LUT7gltXClEfc7Wii1Ef3OlajPaF5y0rSPuCuwPyUx+5XvWi1AcX5XXPsH3Fc1L1dQLdV3C3e44M5uM5pfskEGA+uMj6LN1X48UUovsKLsoWpz5y1W3uG54zMoP1Nc9Jp98NsL7gap3e3zAf91T3wIMA88FVmby0r/HSImhfcLk/kvs6VyZFUB9cbYik+4o5iaaRdF/B5dBI6qOnLFuK+uBOn1J0X+FZvUrwfdXcWV6M+sj11pakPrjzEe+LOdmPS9G+hsdEUR9zdZ6Por7huUrTvvDcV6N4X83d0qWoj1z7aRT1wUVTzcPeV8U5abf+l76vCu7VuEN9nFPW6OwZzAd32mbNYO+rwpPT/fR9VXDnShL1keu+/YT64NaEe7Qvzkm5M08G6wsutuai/v+dqwWpD26rfBmsr8n9M3sG6wsuumXNYD5y7afPZjAfXBTTfcLeV8Wc7Lfi6Puq4PbSi9Q3PXP+Q31wMSeVv6+K3M9O0/dVwZ2sx6iPXK95AvXBZcWrtC/mZC15QvuCWyPuU9/MtUL2DOYb/pn+8wvrC0/+epP2Bbf7pFMfudbZJ9zX3C4Y/OdQ+L6ac7VPBN1XcDtnYerjnHJ+Ls59zVXjonRf4dk9CtB9BbdO56W+yS1dhPvghwrSvjgn1ZJI2hfcvsN9nKvO56WoD27di+R9tWcVKUb7gquCEdQ3uZ1KUB/c3Vosg+6rnpMq/2wG21dwOTAn9U3Pdvm5r7l9N08G3VftuUeyZbB9NbxA1gzmI9frmTuD+eAyIQfvq+fk3ilE+4KLPQWoj7laqRHUB7ceFKZ9jdcvH+0LbpXIQ33keikFqQ/ubNL/3Aq/r+rnpN6yhwF2XwV3tuLPL7OP55T2cw8DzAd39j1L76vwZNUIel8Fd4pGUh+5onAR6oM7eYN/H4T3xXNSZ/X9AOsLLvsF/1zCfTynEmfvBpgPbj2Ti/fVnjuvEO0LrrYVoz5yRb6C1Df8g6L0vmrmtLUUva+Cu4/+h4+53i1JfXBnzP+4ryL329L0vmp4sdLUR65VpzT1wa32pWlfzMlJiaR9wdXpUtTHXN0lJagPbseXpH2NlxxF+4I7rblvcutEUR/cK67/+rB9xXNSL/Z2gO0ruNtHn59hvnmueupmgPng1j/Z+L5qT7bMT/fVcDwPCPOR66zMS31w+9ngORne1zxXvZURYH3BRfdnMpiP56qiSiDAfHCVPSvtC8/Klof2BbdbFaQ+cl07N/UN35af7ivm5H1anO4ruNgbSX30dE4WpT64GFGC7qvJ7VGK7iu4GMt9k9u7JPXBbf33TXhfzMmuF8H7am5XLk59zFXtKEx9cHWkKO1rPP3cJLwvuBvgPnKtF0tQH9y7we+r5lztGEXvq+CqG/dxTnlRpakPbg2IovdVeE6tkvS+Cu5e4z5y3ZmlqA9ujyxJ++KclF1K077gssz/8PWe2t1LUx/c6Vma9oUnYqJoX3BrP/eR67xRmvrg9rwofl/Vc7K+KU7vq4bPL0p99PQ+jKQ+uDpdnN9XtSdPFOH3Vc2tM4Woj1yvRjHqg4ueEbyvnpO7tyTta/hE7mOuVnwp7oOnlaR94bmVS9C+4M587iPX3R1JfXDZsQTdV5yT3tt8X8HdX/+Hj3NqcxT1wUWt0nRf4Xk9StN9Bfde4z5yZY/S1AeXP/C+5lzNH0X7gnuHuI9zVVYpRX1w9XwU76s9UZn3BXe+5D5y7bVR1Ae3HkfRfcWcrLlRdF/BZQL3Tc+CpakPLn+LovsKz/2nJN1XcOso95Erd5fivube8ZK0L+bkVChN+4I7J6Koj7mqrKWpb/iRKNoXnv1eFO0L7vXgPnLdFlHUB5ee/vMYqT+/YPIOfc9dpe+nqzPzHTsycVF7NfVlzbnBXzeaS31wb8HcTLnO0x2ZvfmrM3GroZeJu6+uoT5y7bLzqA9ux87jfX8Jvi8iJ0ykfcFFYCL3Zw4O+jsHUx/ce2UI7QvPGhpD+4KrepOoj1zn2BDqg4ulv2Waq7fayzzX/Wsy8yI7M/Nua6mPnmrWfOqD28MXZMp1Wu/M5MkX1mXi7qidmbi3cR31TW6fhdQHt35YRPtiTpacTPsa/sUU6mOu3uqh1Ad3zg2jfU1up6m0r+FD/6Q+cp2cI6gP7r0+ku6rOtsl+Pt41IXuK7jVviv339Hfh9SiOfXB1bHmfF/hFf+F7qvhj3+hPnLtgS2oDy5b27SvCtQNekU/o30NH/wZ9d3y1YJfv6hGfXBnQzXeV3t2hc9pX3ArVz3qI9drVJ37msvS79B9NXPK253vq+ZupR7UN3Pt2JL6hjs/0H2FJ77rSfcV3OvYi/rI9Ya3or7hrVrTvmZOD+vRvuBWWn3qY67K9w71wa3FNWhfePamL2hfcEs2oD5y5SvvUh9c1Xw387naZGemc1IUW5+Jq9+8TFw9Wkd9nFPuC4upD24NW5w5d8z2TJ6dc10mbs/YnIl7I1ZTH7nWe4upD+6tX0j74py0+06jfcG956dTH3vqvjqK+5qrU6NoX3hWhem8r+aqwjTqI9dJHUV9cDfbqExzdWqvyTQn2WxZJu6+uCATl5PmUh89nRzzqA9uTZmRKdf+fGwmzxYTMnHR5MdM3GnajfrIFXvGUB/ce70n7Ys5iSZTaF9w0XQ89TFX0X049cGdiMG0Lzz7/lDaF1wO7kp95LqPelHf8Mdt6L7inLQ/7E33Fdze1Jv6OKe89Nbc11zcaU33FZ7ze2+6r+CqcS/qI9eq0Zr6hn/3A+2Lc9L7qgHtC+5s/oL6OFfFuhrUB3fqvcP7as+tWJ/2BXfrfUZ95Lrrq1If3BtSie4r5uQO6U73FdzK2Zn66Kl2Nee+5mpAY7qv8JxKbei+gjvVv6Y+cq3s9bmvuTz2Hu+r5yQGfkz7gstL71Mfc7VKvk59cHf8i7QvPBmoRPuCO60t6pvcwRHUB3fWP/Wz+6r76ktBr+5L/L6quVjyEvWdisHPa7bqFaA+uOMWoPdV473zMr2vgqu4l6mPXDeyIPXB7ZkFaV/r93+D37cyL/g1vC+4k6L/9zBfjE/Wn1eR7Ke+5t7F4NfwvvC8Oo/8rC+4mPfIz3zkytIpfuaDW5+l+Nl9FXOyhpah91Vw61EZ6pu5PluI+uDqzUL0vgrPLVGW31c1d+aVoT5yxaWC1Ae3DhagfTEnFavnG9YX3JqZea6G67mqKil+5oPbc4J/TuF94VmH9D6F9QW38z70Mx+57qdJfuYbfj6B7+u0s8FfLz5L9xVcHDtLfTdL8POaRZ591AcXz++j+wrPvnmW7iu49c9Z6iNXltlHfXBxZy/tK60NwT/3lzfQvuDWKxuo79RdEvx9fbqE+uD2+0toX3hWoQ20r+Fr11MfuV7eJdQ3fPhiuq+Ykzv5LN1XcGf2Geqjp/x2L/XBvSx76L7C83adovsKbv11gvrItWbvpD64c2wb7WvmemYd7QvulFtLfcxV/ryI+5rbXRfQvvDc+qtoX8Ov/E195IqKc6kP7lWe6Wf3VZyTrv0yva8anvVF6ptzalU+6oN77z5L76vGe/05el8FV21LUB+54mk27mvudn/sZ31xTqr8+vP4w/qC251u+qmv99RNV37mGx4R/LkB4X3hiRVpftbX8Pfj/cxHrnvwJPXB7db7/ey+ijk5+QrS+yq4+1Y26pueqcFe4T64aqPnFHZfNbmdgt/XH35fBff+0r/PcF/n2gNOUB9clgj+fRne18yp0Rk/7au5e3s39TFXUXgj9zVXW+bSvvCcOX/TvuCi7mjqI9dLHEF9cKtvG7qvOCflnsN0X8Hlpj3UN+dqkY3UB7cTVtB9Nd6NzXRfwb2YZdRHrjNrHvUNHzSJ9sU56TZcQPuCq9J/UR/nqv1gMvXBnYQ/aF/jjRlH+4KLcwO5j1w5kPrgononuq9mTvHT6b6C20t+477uaU0dRH1we107uq/w7HZt6b6Cy1zR1EeuV/9j6oOLZqVoX8zJedye9gVXh+tR38z1yDfUN3x/NdoXnrfpVdoXXB7/18d85MolhagPbk2O84U+V7VylMv8nPT56My83hZ/KPdO6PtamI/nlNayzwLMB3fPPJMp1yuTzx/qiX2NM3H7/MP0UO5k7+5jvsm9+Ief+YZ/dMTH+uI5qZdYL8D6gqtGhahvnlNd+Jj64G7hwgHWF57orO93YX3BrXM5qY9cq3Lw3Av3waX7Wqa5yoFN00Ln5GYvnImLLs+khHLrm5ypzDc9l+qf1xzmg3vumdTQXFkz+HO+4cm1VZJCuVPZig/lKvfi68xHrv1V/WTmg6uWWRJZXzPXgVl9rC+4vf2XNOZjrmL8Rz7mg3uFE9JYX3h2zYoprK/hR1++wXzk2jUvp1Af/HHhJLaveE7q9q/G9xV8ZE7qm+eUmyzqg6uSt+i+mtz+G+m+gntFqlMfuarFYuqDC1nZz/qa56Tdn6F9wZ2i+t9rw3zcU+3FivrgzvFVvC9yd4+hfcFl+nPUR649ogP1wS3vMd1XzMmOCf5cxfB9BXfer5hOfd1TnOrtYz642twkne2r8SY3TWX7Ci5WHEtiPnLdt1emMh/cmdYsmfXFnNQ7XX2sL7jc/GM68zFXNSD4c8PDfXC3ast01td41a6nsr7gcsauZOrrXG/7g1Tmg3uv5EwJPVet/FUvh56TTr7vr4Vy5+ry06FcXB91jvk4p6yLL8cx3/DTjS+G5lqlUo+Eem62IydCuXpn0e5QrlJr7mc+ct1zVU4xH9xJL3WY9cU56W7uHs/6gouJ2a4wH3vq3p16nfng1qMB11hfeGLpvDOsL7hXsfpx5iNXdst7nvngdsqe2NC5ysq7N4fOSTWP8EK59JVYE8qdCl9vYD56evNL72Y+uGz+2dZMuU8XLA/1rO5ZMnF8riq413zqauYj184+cAPzwWXNmhtYX8zJ+3nwPtbX8GcL7GQ+5io6fHqI+eDqYe89rC88++V/t7K+4CIm3zbmI1dVPLuT+eCqYJldbF9xTqqy4xLYvhre9ZpiPs4p1/9dIvPBrZ9UHNtXeFZk64tsX8Gdh9lOMx+57pb5l6mvuT22yVnWF+ekU1LcYH3BVb7d8czHueqdH3SD+YbPHXmd9TXei8eusr7gYuqO88xHrqM6KeaD2/UrXWL7ijmpZn2PsX0Fdzb1OsB89HQTRscyH1w+jjrK9hWedTVxL9tXcFH4z33MR653fs8h5oOLDx4eZn3NXJsVP037ai52VKc+5irPnD7LfMN/r3qa9TVe9egTrC+4u6JsLPORK7oNOUV98E1bT7P7qnmu+vMmel8F9zYPpz6eU6psY6kPLrs3ofdV4815n95Xwa0OJ33MR66qlM/PfHC5XX9uZVhfPCe1L/xA+4LbR16hvnlONbgq9cG9Xfd8rC88d8x52hfc2/+jj/nIVXIu9cGdvW/S+yrm5NSr5GP3VXBRpE4689HT6ZjVx3xwO1r/PJKw+yo8N0HfU8Puq+BKvJXCfJN7NCWV+Ybn+ySF9cWcvKNHaF9wefh2GvUx10mTqA8uru5JY32Nt+x4KusLrj5ukMJ8k7t6VSrzDR/bMIXtK56TijV56L6CW70O+JhvnquWTaA+uJvvL7qvxrvTme4ruCwU/NzmcB+59le1qA9u/3MonfXFc1IxaCftC+6N7EV9PFeV+uem/5evuZrXhPaF5x2J9LG+hjedm858k9v5X+qDy+XD6L5iTtJnp7N9BbdHT0ljPnqq2ErpzAd3Y7qmsX01uacnpbJ9NbxroxTmI9frNyiV+eBqZ8sU1hdzcnblT2d9DX/wbRrzMVe72z3uay7W1k1jfeG5UX1SWV9wUbFXCvOR650ZmMp8cOvKtBR2XzXnZLmkG+y+Cm6NqpbAfJxTonfVJOaDO/tOJLD7KjwrsW0cu6+Ci4pLrlBf56qUL+OZD65G5FesL85J0bVnEusLbh1tksh87Km7eVkS88GtlucSaV94eStep33Bz0THMR+5VsGn16mvuXrUKp7dVzEnr+nmC+y+Cq6+7H2O+ehpXV57mfng1oX4i+y+Ck/+G3mO3VcN97nnmY9ceebqReaDq1X637fC+po53fz7GusL7rZseJX5mKt7+qM45oOrwacU6wtPVJl/lfUFF4M+U8xHrtxpxTEfXFV+KZ7tK85Jd218EttXcNWq/g3m45xShUslMx/cG7D7BttXeFbM7gS2r+BySy/qI9d7NCCR+Ybn7JvA+uKcdH78Lpn1NXxRjSTm41y1ti1IZj64XLI+ifU1uU8r32B9wdWOjtRHrnqccIP6msuT+s8jbF8xJ3ferHi2r4a/0DKe+ejpZLt2nfng6u9D19m+wlNdDsazfTX88+BfH+4j18rzYQLzwZ0faySyvpiT/U/WRNYXXJbJn8h8zNV6Md8N5oO7R9+4wfrCc5uMTWR9wd2cPW4w3+TWXnGD+Yb/7iSx91XV4VnBr/tm0fdVweXyWdQXCcGf9+BcmkJ9wzdMoe+rwrPbzKLvqxq+dSb1kWs1m0J9cHfKZNrX3j0hyDdOoH3B5R8TqO+NCL5v4/YbR31wu+442tfkvjiB9gX3Ph1PfeR658ZSH1zeGkPfV8Wc1C9/0fdVwa23p1Pf9Cw3ifrgYv0E+r4qPDF9Cn1fFdx+cwL3kXt5DPXBrRdG0r6Yk9o+lvYFt14fTX3MVTQdTX1wa8Mo2heeenUk7Quu3h9CfeR684ZRH9xb6tB9dWTw92NXGk/3Fdx5wn15PUa/9xxDfXA5O4buq/FGjOP7qrmaxX3kitdjqA9ufzKR9rUaTdfvnU+nfcFlienUF131e3yt51EfXFWbR/vCsxdNo33B5bE/qY9cETuX+uAqx1y+r3pOVrYxdF/BnR9+pz56OmvHUx/cfWks3Vd4bqPhdF/BnS6DqI9cUfV36oOr/kNpX8zJqzeV9jX80iTqY65i9mzqgzuNZ9G+8LzSE2hfcGfZH9RHrndhOvXB5d7J9H1VnJNe+1H0fVVwZ+FA6uOcEvcd6oNLrwd9XxWe92ZX+r4quFOhOfWRK3e2pT64U7Yh7YtzUqi+tC+4/Lkz9bGnqnZv7mtun/qJ9oVnVbF5X82tdp9TH7nWsu+pDy4K1OXvq+o5eXM+ou+rgjvNy1IfPdX9d6gPbhUvSd9XNd6tJ/R9VcMXHvUxH7nu6zepb/giz8f6Yk5OZGXeV3Mrugj1MVf13FvUNzy5kJ/1hSfmptG+4PLGZuoj1/sujfrgVv2tPravOCft1/rQfQUXtzpxX59TamB/6oOLNb/QfYVnl2tB9xVcRHxOfeQqpzX1wb2Pv6J9cU6q4kNpX3D3al/q41yVq8ZSH1yWH0b7mtw+nWhfcNGoGfWRa2/qQ31wdak93df/m2tluq/gVnpR6pueX79LfXAv8Jyf7avJLfYP3Vdw2W8P93Wu+uYR9cG93fq/H4f31XOSVz+mfcHdrq9TH3O1C35LfcNP1/SzvvCcbnn9rC+4Xf8G9ZFrL7X8zDe8XPBcDL+v2veC348ifCvofRVc7FvB/XnB76fxZmyjPrj36zZ6X4WnOqyg91Vwa91y6iPXrriN+oaP20r7Wq8fDXplj9K+4DL/UeqLd68G/WpXqQ/ulrtK+xrv+BHaF1zWPUJ95Lr3r1Df8D+u0Psq5qQ6L6P3VXCvyVLqo6fbZQv1weWETfy+qj1RZhG9r4I7fedSH7lixXrqg7u9VtO+Zq5fHqZ9wb1lB6mPuXpbLlMf3Gt4ifaF5z6/n/YFly12Ux+5otQF6hsee4buq9c/I8j11/B9BVfdM6gv5+jvk5kb/Ppfvua2/hq+r/CsbzLovoI7WTOoj1w5Ifg13AcXjbUX1lc1KR7s9X3xAO2rudO6OPVFs/JB3rI89zW3O5cPsL7wrJbFA6wvuFuzOPWR6/YuT31w0SH4NXxfzVwbB+i+glvl/dRHT3E6a4D5hl/JEmD7Ck99lE73FVx1TqE+ctUnIsB8cOvaY9oXc/IuFwvQvpp7JYtxX8/VebU89cHFlNcCrC88+1hEgPUFtw4Xpj5y5ZhXA8wHd5KC39cXfl/FOWk9nEHvq+BWzRjq45xyJv3Nfc3dZfPofRWed24Eva+Cu9/1pT5yvZbTqA9uNxxN+5pz9fnttC+4V3AD9bGnduVY6hv+4BDvi1y1jPYFd96fw32dK67uoj64O2Mjva9iTqrpj/S+Cu52/Ir7uqdVrj/1we0R7el9FZ6sUN3P7quGfxPJfZ1rVwzj2jc8uSrta+Z6bSLtC24PGkx9zNW7t5T64O7mGbSvyfV3on3BrRzfUh+5avso6oPb//Sg+2rO1U6JdF/BvafXqI9zyvv5IfXBxc47dF/hybTzdF/B7bvHua9zndkB6oN715J4X31OWvHBzwUJ7wtuJ+QJMB/nqtOnTID54LJU8HMvwvvC8+blCLC+4OLhU+oj155fOkB9ze3b+j4Qtq+Yk+XfQ/cVXC7ewH3d0/3yKvXB7bMn6b7CU3kW0X01PM9k6iPXbrKX+uD22XW0L+bkefdoX3D7qo/6mKtM1Z/XFuaDqy65AqwvPLt2PO0LbvlPUx+5br8n1AdXuW/52fuq5rlqy3H0fVVwsfhD6uM5pbd5CPXB5Tfv0PdVTe6KJPq+Krj9W0/qI9eue4364O7IbumsL56TOoMG0r7g4lQ16uM5lTN4CPXB5dZoH+sLz64VT/sa3r8/9ZFrJQaoD+7Wm0jfVzVzGuun76uCe+3rUx89xZ+3qQ8umvyQxt5XhSffnJLK3lcFl68dS2E+cp0iO1KZD+4uK5hK++o52bdypbO+4HaXodTHXN0Cr6UzH9yduC6N9TW5N++ksr7gordNfeRar7yTxnxw9dvmVLav5rlqmbF0X8FFpQbUN89VB8+mPrj7pJ2P7avJfZjDR/dVc6fuGuojVyWV8TEfXLS9lM76mueqgzfRvoZvHkp981z17hnqG/7PQtoXnptS18f6gnu/5KY+cuW73X3MB/eavOtj+2rmtO6bdLavhv8bSGM+espGI9OZb/j0t9LZvsKzOw9NY/sKLj8sRX3kiibn0phveNTANNYXc7K/25XO+hrevxf1MVev2RPua+482Z7O+sJzVpRPZ33B1Yl46pvcjQPTmQ/u7aydzt5XNedqvSfJ9H1VzZ091amPc8oZ2zKF+eCu6yWz91Xhid4Tk9j7quD2B2WSmI9ca0t0MvPBrd4ZSayvOSd76X9ehPUFlyM/p77Z0zu1UpkPLq9cSGF94VmB5GTWF9y+v5L6yFXtRqcwH1wOaJtC31fFXIeJJPa+KriTu0IS9XVPmUclMR9cTCqSzN5XNbklliSx91XBnaptkpmPXKftsGTqa65E+RTWF3MSacuSWV9wp3d8MvX1XMWwjinMB7f//iuF9YXn7P00hfUFt/qdT2E+clX7+9QHlwN+SWX7inPSHr4xle0ruDutDff1OSW3VUijvubqanoq21d4KrJ8KttXcLdewVTq61w7r5fKfMMnLKN9cU6KmbPTWF9wq3Yn6uNcta8Fv+8r3Ae38iWmsb7wrL/1fTusL7i1omYa85Fr3zuYxnxw1cpLY/uKOTnjIlPZvoLLtz5JZT56qlYbU5kP7jZTqWxf4cn3g9+3F76v4KLpo1TmI9eq/mIa8w2v3juN9cWc3E/rpLG+4OK1Dmnc13NddjCN+eBOn+CfU3hfeN5aN431BbeHJFEfuWJPoXTqa+68+1k6u6/iOalYdYfeV8Ht949SH88pvRnF/cwHt1repvdVeOpdl95Xwb3oztRHrhp8lPrgYtpc2hfPScWeGn7W1/BPo6iP51SiSXM/88Ht0h/6WV94dupD2hfcqXKO+sgV48r5mQ8uK+X2s/sq5uQ2yMxxrwR3H2b3MR89xdRePuaDy9PBfy8Jv6/Ck41i0/9fXWce73OV//Fju1xLuq6l66Iz1QyNFmMJSU7xC5USaVH9+kQIKSK78RlLKoTGEg2OLNcuS8l+FGUpa7au+Fiu7d7v58ugLOH3x/e8ztzvZ16/v+Yxz3nO6/Ga9+PdmePjcz+X3VfBvUKa+sgNSt4ZYz64fPm3PNbXzanAMtoXXL47ivqYq7f2LPXBZZVtMdbXebmdYqwvuK70BPVd7oAFMeaDm8pjYmxf8ZxUfv0Pvq+W61pdqY/nlOrVWdQHN2njQrav8OTOViHbV3B9tR71kavrDeS+5apGR97XPif1Y5tpX3DT/ivq47mqqXaK+uDm0gHaF576cCbtC+7NG0t95IrPt1Af3Lz8Tcj2FXOSLaqEbF/B9ahCIfPRM+j9bMh8cC/L/vci+wpPTTtL9xVc+7upj9yg110h88G9GaVoX8wpqD6I9gXX+Hu7iI+5eoOzuG+5eGtiyPo6r9kLIevr+PXG1Eeu7Dec+uA6M/HPffS+6s7Jy73y6H3VclWvNfVxTumUvXnMB9ftVuax+yo8ObxhHruvgssjdfKYj1z93uw85oP7C6bRvjgngy3VYqwvuFmQFmO+21Ovd4z5jn/SLsb6Oq9J4rsz0b7g6ovr1Eeut+n5GPPBg+9bxNh9FXMStRJzi95XHa/VOo/56Km/npnHfHD1/Io8dl+F579s/34nel+1XD8wi/rIlR12Uh9cVbxI+2JOutFN2hdcxlJjzMdc/TDxr1Ef3MtoE2N94YnsyjHWF1yvqhdjPnL1snYx5oOrLe/TfcU5KR9eS/fV8Y3zqY9zSp6/Rn3woFwu31frmZNTY2xfwXWtidRHrplzhPrgKmU/7evO1b/VCllfcNHlnpD5OFf18g4h88GDiS+GrC88v/gdIesL7rW6LWQ+ckXhp0LqW+5lqJDuK+aa90++r5brSxOp7+b66m7qg/tjd9F9hSeDaXRfwb1b86mPXNNiL/XBTelDtC/mpFonvusd7QsexAqG1Mdcdz4UMh/cu/JAyPrCE/tFyPqC+5+LkPku99FqIfPBVY3EvQLfVQ0yflqa/zupwcKyX+fnasr0b/JzLyN1PfPxnVI1cPAa5oPrjp2/zZ+rLtX5Nr/nV127OT83NR/anp+btKU7mY9c07PUNuaD61KD97C++E6qfqTqRtYXXM6e+j3z8Z1Ks/eNzcwH97us2c76wtPjG9G+4KrDmH3MR67ptoj6jl+7dTD/XM3G3Xvzz0m/329/fq5WDcrOz2Xt544w3/V8Y8Ah5oOr1CVB/lyz69SxpNxx9U7k5+LZ3Tn5ua9yTjEfuWLi+JPMd/zUb6dZXzfXLtsPs77g/qqmx6lv56r+fi/3Ldd/nKR94cnnzuewvuBBH/v3ahEfubqNoj64GnHpLNtXfCdVXP14G9tXcL3h9C7m4zul/qQXdzEfPJjV6We6r/COVdvP9hVcTlt9mPnI9QrfOMR8cPXmMwHri++kmg7BXtYXXN7R7CDz8Z0q888fDjAfXGxrd5j1haeWFjnC+oIHKxseZz5y5c7UY9S3PChc7iTdVzsnWeHqMbav4MGoPjnUtz3l+o3ctzxYff4U21d44pd+dF/BvfN7zjEfueZP46kPLgdOyGV93Zw2d6d9wb1RTc4wH3M1E3+jPriumfh7lWhfeP67H9G+4P49D+cxH7nyxRepD26umbz856r3/m1n85+T/sX9Z/Nz2bJPbhK/dT2X+Tin/I6fnaO+5WZXv7z8uWKUjOX3ZI33Ykl8QeLnxMH1Yfv8LuIjVx1aH2O+47/b7xZE+uKcFEWfoX3B5e1hHvOxp2rOxVzmg6uBL/C+yC1YhPYF9zMSP5f2X77NVW83pj64+MC+12rnqm8l3uPGnPy/2593At/ZNp6fi8c7xZmPnl7TTOqD68PvxfPn6l5fJHni+awkLuftSuLiyd3ct7kiewH3wfft5n3tnILdNWhfcNN8EPUxVzWnMfUdr/8B7eu8PUtoX/Dgz3uoj1xVaDn1wf3y9t9H9hXnpP7LB3RfHR+2NMZ8nFNibSHqg5t29uc5IvsKz2/Qj+4ruLfyD+ojV5eYQ31w2bVcnPXFOWkudqV9wYOKDUPmu3P1kw3UB5evvUP7wlO5W2lfcPVFtTjzkRu0yAmpb7k/5SG6r5hT0KU13Vdwr9to6qNnkPs69cH9VZ/SfYWnGn3F9xV82G7qI9dr/zX3LQ9a7aJ9MSdTuTPt63iJidTHXFXFd7lvue48ifaFZ9p+TfuCq3d2UB+5Qn5FfXDx4/Y4u6/iO6mm9olsdl8FF70fD5iP75TqtJtHmQ8e/P2R4+y+Cs9reY3eV8HFkxtzmI9cb+966jt+zP5euEhffCfVG3vhGOsL7v8l9STz8Z1KP2vFCeaDq6lTclhfeEHd5qdoX8uDxd3OMB+54t7x1AeXXpez7L7q5prXjN5Xwb0P3jjHfPQUk9tz3/JgUrVcdl+F5/W9mcvuq+BmS/UY85Grfv4H9cGDMYNirC/mpK40pX3BvdmJ75ZHfczV9Lh+jvng+nCDPNrXeuJqTh7rC+6lJH7uJOojV9SrQX1weXdOjO2r+65qjYY5bF/Bg7l5p6hvv1Mq+n90ivng5lzBM3RfrecX+YLuK7jpWfMc85Fr2vSnvuPnd5yjfe13UvWaaadZX3BvR84Z5uM9Vbl5OPXBgxHTz7K+8IJiVWlfx3s2z2U+coNGy6gPLrPn57J9xZzUuuF0X8HlQ/b3W0R89NRt7Pv+ER/c/PMA3Vfn3epA9xXcbE489476yJUdR3Hfctm3fMj6Yk66jeJ9Lff/an/+IuJjrsGWwdQHNzUepH1d7pHEe3HRvuCi+d0h85HrlVsbYz64/031kN5X7Tnp1xf0vur464mfa476OKdMkxrctzxYvIjeV+EFl6+E7L4Kbr5N/F7h//JtrskuQn1wtbhJnPXFOalbt6J9wVXqBupjT02VztQHV3W20r7w/PKlaF9wv8gT1Eeuv7k09cG9/k34fdXOSWW+R++r4CZvIvdtT6/ne9QHFxvH8/uq9fw1y+h91fGuW7hvc/3HllDf8VabaF/MyQzvTvs6XmUc9+1czbq3qQ8umoyifeEFO+fTvuDe2A3UR676eDb1wf26q+NsX3FOeo370H0Fl6d/or47V+Vg6oPLhTtCtq/w1K3b6L6CmzWK+sj1vytBfXDxdoM464tzUmwcQvuCq+wfuW/P1WDnUOqD+6O3hrSv9cz5FNoXPMiow32bqxsWoL7jDz/A99XOyRvRie4ruOr5AfXRUzd7g/rg5txguq/wxKTpdF/B5WsrqI9cP3My9cFV+cW0L+ZkXmlL+zp+vC/1MdegeGvuW+5/2IP2hefV/5T2Bfd6Z3Hf5gYlRlIfXI/Syc9V68STnpN642JJPChS8Hx+7s0W56lvn1MGl3PjzAc3U24m5Wov9Xx+z/++aBKX625L4jKjFPWRK2qlUB9c9ClxnvXFc1LvyXNx1hfc5F6nPp5T+b3OUB/cr341zvrC82YUpn3B9d5U6iPXK1GI+uCqerGkuQYbyiTNSd+XlsSDH8slcflSWeqjpxpfmvrg3v7kXHnXHcm5e8oncXMqI4mL9dxHrupWjvqO963A+9o5+ZdL0b7gQdM07tu5imdKUh9cLShN+8KTl9JpX3CvfHnu21zdsQz1wdXkxNyi+4rnpKrtKbqv4METv1EfzylVzgnuWy7vuxhn+wov6FyA7iu46ZtCfeSqt2/Fme/414l/Lv6rr31O6hU6RvuCB9vj1Mc91cz9lfrgfsW8OO1rPb33epz1BTdB4tyM+sj1bv0eZz64uSDovmJO/pTidF/BvZulqO/mur8Y9R1/tCTdV3j+d7fzfbVciHTqIzcoVpr64KZ5Gu2LOfm3Umhfx98qTn3MVd9ehPqO9y9G+7rc2qVoX8d7cR+5QZMS3Lc8GFwq6VxVdTKTzklTq2ISD+6slMT9JtzHORWUzKA+uN+gYlKut6JSkufdyEzi8kalJC47cx+5Ii2T+uB6Uibti3NSfVuB9gUXRbmPPQ2eL099cJNVgfaFZ85n0L7g8uOK1EeumXoH9cHlYxlJcw0KVk6akzc2mfs/JnO/fyXqo6d/MJP6jmdnJuXq15O94P5krkxmEpdLMqmPXJHFfXBPZdK+mJM8XJH2BQ/CitR3c/08g/uWB8szeF/reVsr0r7gpntF6iNXfZpBfXDVIIPuqztXN5Wl+wpuKnMf55S8PZ364OatsnxfracrVKD7Cq7WVuC+zVX/W476jl8rR/vinFR102hfcDOiDPVxrup6pakPLvvfTvvCM93TeV/L5fV06iNXPJNGfXBvbRrdV8zJK3sH31fwandQHz29J8pTH9w0K0/3FZ65UYHuK7g3uQL1kSuKlqc+uBxajvZ1c3qwLO0LLsqU5b6dq9meRn1wMziN9oXnZ6XTvuAyLEN95KqDt1Mf3NuR2MPofRXPSfXOQ/S+Cq5uP0N995yyzz7qg3vzTtD7Kjx1+mKc3lctVz/e4L7NlR/E48wHl72v0r7uOemcXbQvuJdzhPp4TuW13859y8UPh2hfeObgOdoXXPx6ifrI9Y/kUN/xm/E4u69iTkFKIXpfdbx7CvXRUx8V1AdXzQvT+yo8WTeV3lfBVWfuIzdILUp9cP1gKu2LOQV/uxFnfcG9ggWpj7mK9lfi1Lfceyjx57poX3jBmsK0L7i/J4X6yPWfLkh9cP2c5ZF9xXNSvWoz3Vdw2epn6uM5pVqxgfrgctQOuq8u97OA7iu4+uUc9ZEbpGRTH9x/N4f2xXNS/7VVtK/j/bZQH89Vg7XLqA8uKn9L+8Lz79tH+4L7649SH7mi+E7qO27nHt1XN9fnLsbZvoKbP1+jPnqa6zHqO773Upztq/N6J3PsK7haVID6yPXLJHP4jv90g/bFnIJ2Z2hfcNUoTn3MVY44Tn1w0/Us7QtPvRP532H7gmtxhfrI9UeH1AcPrlyI0/uqPSdFeil6XwUPHr2N+/acMpuLUx/c31eC3ldd7uXb6H3V8YalqY9c0bEk9cHNgJK0L85J3bIY7QsuHk2lvtvT5UWoDy77pdC+8IROpX3BVd9U6iNXzUqhPriumcLvq3ZOqmJpel8F90bfxn30vKMk9y3XdUrQ+yo8PbYUva+CB2VLUh+58odU6jv+ZVHe184p+LYY7QuuexalPuaqM4pQH1w/Xoj3tZ7sUYT2BfdHF6Q+cs1QQX1w/6/23hPZV5yT6nxBuq/g3oeFuI9zqpjgvuXeO4LuKzyvVyG+r5aLhQWpj1wvXXDf8mDHjTjr687VG1fjrK/j/3stznycq3rxxTj1LdfT7Z9XIn3hmRL270WifS0Xey/HmY9cM+sC9cFlz8T/r0X3FXMymQXovoJ7DRN9oj56BhPsn1ciPrhfws4hsq8u94ztG9lXcOHZv1+O+Mg13c9SHzwYlbhXRftiTrrNed7XcnHhNPUxVzn8FPUdv5LN+1rPTMimfcHFR1upj1z1wo/UB1ePJN6rir6v6r6TWr3ZWfa+quOn6p9jPr5TqvyQ+uBSrqPvqzov7x76viq4GHUtl/nI1fd9QH1wUevRPNbXfSd16GDe13Kv64O51LffqTQlRS7zHS87gfaFp2pm077g3t7Ezw1GfeSK3lWpDy6KfEnfV8WcxMv2u96R91XB1YYWMeajp37M/j6DiA+uD3Si76vCkye2x9j7quByReL3kkZ95PovHooxH1z/K/EdjmhfzEl3S6F9wYPBid/rEPUxV7Xor9R3PHcC7eu8n3JirC+4bt0gZD5yTcq/Y8wHl88kvlMS3Vd8J9V/tifdV3B96Dz13XdVn8qmvuPPN8lj++q89G50Xx1vfJz6Lve+RdQHN4/Y72ZF+uI7qbJe/TzWF1y0H5vHfHynyts/mvuWi/f30b7w5KNnaF9wValhjPnI1R2rUB/crOtA99XN9dLTfF8tF/MXUB89vcndqA/u7TZ0X53X4maM7Su4nJv4veJRH7lmTHHqg5v6ie8gRfu6OY0Yxftarv6yj/qYa/D2POqD+/XP0L7w5MDytC94kNWS+sg1E/5EfXC/Rxv6virOyWD8UPq+KrievJn6OKf070OoDy4nbAzZ+6rwRJ3rIXtfFVxWrUp95Jo6l0Pmg/sZMs764pxUI4bQvuDy4Drquz0dPoT64HLoatoXnukZD1lf8OD+jDjzkauvnQ2Z73j7MvR9VcxJjX6Kvq8Kbm50pj56qiJNqA8ePN+Ovq8Kz8weSt9XBdftJnPf5prSA7kPfm0c7Ys5BYMfoX3B9bi21MdcxeXa1AfXWa1oX3i6aS/aF1y2/Jj6yNWN3uY++PND4mxf3Tn57yF0X8HliJXUxzkl/jWU+uB64Qq6r85bfjJk+wquptvfvxf1ba7qcDRkPri/oHCc9cU56d0zjPZ1fMpS6uNc9UoNoz64v38R7wtv3aGQ9QX3GtygPnLNk3tD5oPr05dCtq+Yk2l6P91Xxwc8SX3Xs+mfqQ/uH32M7is8/Wt7uq/g6kI/6iNXXm9LfXBTuAft6+a6uBLtC64n1aM+5hpUSac+uHruAd7Xen6753hfy1W7TtRHrvjpCeqDix2vxtl9Fd9JNV8eovdVcLk5PcZ8fKfUeyTx3dGoD+71fypG76vWMxOfpfdVcH//x9RHrlw8gPuWByUWxlhffCfVnHuJ9gUXqwZSH9+p9B8YTX3wYE4W7QtPfvQF7Qvu3dhCfZdbaBP1wUXjY/S+ijnpn7+j91VwNegq9zHX+tnUBw+eKUbvqy73ser8vmq59/tL1Eeu2VaT+uCmf9uQ9cWc/NQ472u5/D09ZL6b64ybMeaDy2mZtK/Lfa0u7QselHmZ+sg1l+pSHzwY3zpk+4rvpKp+q+m+gnudfqQ+vlOqax+jPriXnkv3FZ66cJTuK7hof4n7Nlem/0Z9cG99gZD1xXdSvdWJ/zzaF1wuKhQy331X9alKIfPBRZnEn8uifeGJQikh6wsu3i8WMh+5qkpp6jv+eOK7tNF9xZx0g1S6r+B+rcR3/aM+eqo9pajv+BMV6b7C8+vXovsKLjs3oz5yg6erUt/x/6lD+7q5XinO+1putnEfc1Xazi3ig5tNid+LE+0LT/ZOo33B/e/KUx+53uJYjPng3qbTMXpfteekXDWE3lcd3zqX+u5cfXMw9cHV0hn8vmo9PXA7va+C63tzqY9cMWkj9cHls0dpX5yTXsV+tK/jYydx3+5pMPYd7luuSn5E+8Lzpq6gfcFl+13UR65XfSb1wXXKupDdVzEndWcxel8Fl9Pvoj56quLXQ+aDi1hZel91udkP0/uq41dact/milL3Ux/cvNeY9sWcxLVzIesLbjon/twQ9TFXX+8PmQ8enIiHrC88Pa8S7QuuZtaiPnL9WcWoD+7PTjz3iO6rOyevvkb3FVze14v6OKfUwibUd/xGG7qv8ILaI+m+ggf3cR+5wZTO1Af3lgymfXFOyhlVaF9w/+q91Me5Kr5M/L1L1Af3MhO/lyba13nD69O+jk9vQX3kyjcuxpgPrhsVofvq5pSyNmT7Ci5G76M+esq7P6M+uP5sWcj21Xkdkzn21fE/pVIfubLvjpD54KZwLu9r56Qf7Ej7gnszhlAfcxXzqlAf3F/7KO0LT9+cEbK+4EHZ76iPXBFrT31w0XxUyN5XxXNSb/8C+r4quOi7hvp4TinnzaI+uPhwBX1f1eVO20LfVwWXH+/lPnLPbOQ+eOGfaF88JzUPTKV9wfUTi6jvnlO1nUB9cG/ebN4XucdW076O1/6e+i53yHLqO56+gb6vijkFbx2m76uCi1dOUB89VbH91AeXpX+l76vC0/i588j7quB6XS71kSvuPc598BdzaF831zd30r7g5mPuu7kO3EJ9cLVrJ+0LT3+ZzftaHuw6Qn3kijL7qA9uah+k++qek2aMpvsK7uup1MdzSv3MMOqDi/IT6L46b/0Cuq/gespK7iN3xEzqg0uzhPbFc1J/f3/aF9yLjaQ+7qnB4O7UB/fEENoXnvl+Cu9refB+FveR+8Y46js+dxrfVzsn0WIj3VdweYz76Kl2r6I+uFq/ke4rPNN6B91XcG/Mbuoj1xv4A/XBZWwb7Ys5yYLLaF9w0Wk19TFXsW8e9cH9g8toX3h6nKF9wf22m6iPXDNpFfXBg+Pr6PuqOCfVgzH6vup/eB737TnljzxFfXDvXzn0fVV4QcFz9H1VcP3dSeojVz1ynPrgIsimfXFOyj1HaV9wNegI9bGnquwh6oPrtw7Qvi636y+0L7jO3UN95PpL91AfXKdt4++rYk4NDtP3VcGDejupj57+5N3Ud/zQRvq+Kjy1aQ19XxXcmzeN+zZXlZhPfXDvj+G0L+bk/fQ97et42RXUx1z9tLXUBzcN5tC+zuszifYFN5O6Uh+5csAw6oObO1vSfcU5Kd7cQ/cVXPfdRX2cU+bn7dQHV1u30n2Fp/+2ne4ruBq2kfrI9dM3UR/cDFhF++Kc9Ptvpn3B/T7fUR/nqvfWBuqDe9PX0b4u119H+4KrWsuoj1wxYyX1Hc9ZQPcVc1KLlvJ9tTz46jPqo2dwIYv64MEfI+m+wpNLetB9Bff/Xpv6yBW7Xqc+uDmSwfvaOam602lfcDnJpz7m6u2bRH1wXaoP72s9f9mztC+4nFKA+sg1NR+nvuOZsZDdV91z0uId+H3VclWpD/XxnNLLfIn64PJ6F3pfhSdrjOD3VfBGE6iPXF8MpD54UHYk7YvnpLpOc9rX8Ydfoz6eU5npdakPHjRvQfvC0x++S/uCB78Npj5yvdfe4L7lweSe9L6KOYkGM+h9FTwouYD6rmflz6gP7l/9gt5X4alxy+h9FVyNXUl95MpBC6gPbjovpX0xJ/HqJ7QvuD9qCvUxV7V5KPXB5bxxtK/z2syifR3v+f/4yC05lfuWi/mz6b7iOalqfBfdV3DR6mHu2+eU6v2i1He8+N10X13u0ZZ0X8HN6A7UR65XuSH1Hb+3De2L56R6xsmQ9QXX/YtQ3z1X3fZVyHxwf5r9vTuRvvC8rHtoX3DZ7THqI1c/W4T7lusD1ei+Yk6ydF+6r+D+XR9SHz311i7UBw+WDaL7Ck+MnED3FVwU0dT/T+5o6oOr7lN4Xzsn9dQrtC+4Ot+D+3auamIz6oP7Dd6kfeF51YbTvuDe1vHUR66cP4D64PqXT+h9FeekXrKK3lfB1SvfUB/nlPf6CuqDm43L6X0Vnj+F31fB5dY51He5oxdTH1yXmEn74pz0hizhfS1XJ5dQ352rVRZQ3/EdC2lfeMG4BbQvuN6huW9z/ZXzqO/4PZreVzEnv/Kn9L4KLo++S3309Fp/Qn1wr1dXel91XpV69L4KLlf9GjIfubJKzTj1LVcjfw5ZXzfXOaNoX/AgvQv1MVd5/2jqg/vZXWlfeMGMGnHWFzxotidkPnL1nlpx5oN7NQ6EbF9xTnq1sui+gsth86nvztVvZlIfPCgwj+6r8wrN4/sKnjaD+shVPeZRH9x8OJP3teek/7mmfcFNg7nUx7nqr5tKfXBZaS7t63L/PZ/2Bfd7ZFEfubL7IuqDy7kL6b66OeWMofsK7q3rTn301MvHUx/cG9qX7iu8YEyDONtXcF+cCJmPXN2raZz54H7hxM9VRfu6OXX+nPYFlweGUB9zVVtmUh/cPD2G9oVnTr0Qp30tlw+WpD5yRfXOceaDq0erxhv9H6o4lP8= 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+AQAAAAAAAgAAAAIAwXMAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-030000.1.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-030000.1.vtu new file mode 100644 index 000000000..f852d436c --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-030000.1.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAgAAAAIAf3MAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-030000.2.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-030000.2.vtu new file mode 100644 index 000000000..4ee02c92b --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-030000.2.vtu @@ -0,0 +1,34 @@ + + + + + + + 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S3NdcTitD31c15+qUIvR9VXDrahHqm3P12+Lc11xsKk7fV4Xn5ixK31cFd58uyn2da8UX577m8mFx2hfnpNpbkvfVXOYuRX3sqTe1NPXB3djStK/JrVuK9gX3WpSiPnJlkTLcBy9fhr+vquckixWl76uC2/eKUN/0/KU49cGto8Xp+6rwvNlF+PuqmqvnilAfuU6f4tQHdx8Vo30xJ/FeKdoXXBQtxX3MNV8Z7mtunSpN+8KTkSV5X82tGiWpj1z5RWnqg9sHS9F9xTlpxZWh+wruvBpGfZxT1rIw6oOr38PovsKTHcPovoJ7ncOoj1zHKst98GfL0r44J8Vh3hdchJWlPs5VWZb74Gog72ty3ylL+4JbNbiPXDcijPrglhtG99XM6d0wuq/gysd99FSJYdQHl33C6L7CczqVofsKrlb8D1/nqsNluK+5uFKa9sWcrLgw3ldzpwn3MVdRnPvgqvn/6AuvSxnaFxz/nv+Pr3PVy9wHd1fx+6oVq8/xR0XpfdXw5sWo7zbR/77uXpD6hl8tSO+r8OzLxeh9FdwKL0595KrBhahveKvCtK+7Xv+8zc2ctC+47JCL+nZj/fPKB/XPs4T4hlv652lD+sITr+WmfcG99/NwX+e6LfT3lUJ8cKdK4Dnkf+6rmNNHJeh9FVy8VJL66Ok0K0J9cOuzovS+Cs97UpLeV8HtQ6Woj1yvUzHqgzvtitO+mJMYm5f31dyLzcd9PVdnqP659BAfXOYUvK/2bP1c5D998bzkh4LUR65z6inqg3tds/N9/etEoPfSkxlsX8G9UaepL5f/FNi/SaOpD+7UHpfB9hWeHHkug+0ruN3tEvWRK/tPpD64+nsa7et8qX9eY0sJ2hdczSpDfbWnXeB56/bP/MwH90YE3osN7QvPPVaO9jX8egXqI9eL7OpnPriMDLz3HbqvmJP641oG21dwef0W9dHT7jk3g/qaO58tzmD7Cs/bpt+fCdlXcFUtMYP6yH1nZQb1NbdWrqd9zZzefpX2Bfda1aE+5qo2jvIzH9zePN1P+2rP6voe7QvunWxGfeSKnfq98RAf3Nm7yc/uqzgn5Tel6X0VXFwoTX2cU0I/9wz1wd1lJfh9VXveH6XpfdXwk6W5r3OdiyWobzjuNSF9zbn6fSHaF1yVLkx97KlUOagPLvS9IbSvyf2kMO+ruSpcmPrIFflzUR/cPZCT3lcxJ7dOaXpfBffql6I+etpeceobvqQYva/Cs86WoPdVcCumGPWR654pQn1w0bYQ7Ys5ea8Uon3BrUUFqI+5irU5qA9ubcvG+2rP+Sgf7QvuVchNfeSqkfrzTkJ8cDkg8H3r0H0152Sqfu8uZF/BrYlpGcw351SerRnMB7fTIzPYvsJz9qVnsH0Ft6qkZzDf5MbuzGA+uNVrJ+2Lc9I61Jb2BZcrP6c+zlVn314/88HFcf3zISF94anrHWhfww934L7OdZ/o73+H+ODOjFN+tq+Yk/2z/rySkH01fM7dDOajp2i4PYP54O7v+vMNQ/bV5BYKvNccuq/g1vpL1EeuenU59cFFxGzaF3OyEz6jfcHlgVbUN3O9oX9+K8QH914O7EFoX3gq4X3aF9yNr0l95Ipsa/3MBxeXp/nZ+6p4TirCC9P3VcHd3YWoj+eU4rNi1AeX04ry91W15x0vSN9XBXdXFeC+zrVlEeqDy//R1zxXHVWC9gWX64pTH8+pxJ8lqQ9uPVWS9oXn6e/7hvYFtwYVpT5yrVvFqQ+u3GL0fVXMyWmen76vCm6vykt907NKIeobPrpAJntfFZ4anzuTva8Kbq/LQX3ket/ko77hvXPTvpiTKliE9gV3m3Efc1UJRagPbicWon3hiZwFaF9weSUP9ZErPyxAfXA3Ig/dV/OcdEspuq/g1rj/4evnlPayUtQHt18tSfcVnluhBN1XcDm6GPWR6/UvTn3DfyhK++J5lLud9zU8pjj1cU+19xSnPrjToSjta7y1RWlfw/MXpj5ynXGFqA9ulc9P9xVzcmYU4fuqudD/XIT66Ok+5D647cvP9xX/HIr8dF/B1ZRc1Eeul56b+uB2m2y0L+Yk4/LTvuCOzgn1zVwf56I+uHX0KdoXntyVjfYFd37Tz8VCfOR65/XPw4T4hh+Kp++rmnNyTDDHe6Xg8vvfqY9zyno2eybzweX+P+n7qvBkO4+/r6q5WBhFfeSKP+KpDy5y6887DOmLc9IuliOT9QV38TwyxMeeCit7JvPB3fTfaV941gmP9gVXn++iPnLdyBvc19zJtZa+r2rmOmo8fV8VXN2qzn3dU745ivrgYu9zGex9VXiqwkI/e18VXC1Y76O+zvUuDPEzH1wm9PaxvpiT9Udf3ldz958c1MdcnSFfUB9cfnub9oUn3mnmp301Fz9U8DEfuVZ8fj/zwb0CB9PZvuKcVNH/0H01PK/+7/8QH+eUXYD74Pbb0XRf4XntT9J9BVe5p1MfuVb6ZuqDq2vdaV+ck0rG0b7g1h1FfZyr9h/HqA8uDi6kfeGpcpNoX3BVpw71kasefEZ9cPmS/jyEkH01c019k++r5pYXeI4c6puea0UG9TVXfVvQfYVnv7SV7iu4vbBuOvNNbt+3fMwHt56ZnMb6Yk7yg0jeV3OnYryP+Zir3epj6oOL3C1oX+M1nU/7gru1N6cyH7liyRnqg7vvzk9h91U8J3W+K0Lvq+Dqy4LUN89VX8lPfXD3/dz0vmpyt+Sl91VwWT8n9ZFru9mob/gX+t/HIX3xnNR7OjvtCy6twPfFQn08p1IFfBnMN/zBnQzWF561KyOD9QV3B+r7ToiPXPvkeeobflr/3GLofVXPSU7Tn+sacl8Ftw4Gfi411Dc97wd+rirUB7eX6/tJyH0Vnhh3h95XwVWLY9RHrvxRUR/czb2Y9sWcrGFneV/N3QaR3NdzVWeXUh9cXh5J+8Jzqs+ifcFF0a7c17netHbUB7c3lMtg+4rnpNaLR+m+gttFt1Mfzym96z9RH9z5ogfdV5MbG8H3VXO54CfqI9fu3or7mnsx1WlfPCd1GjxD+xreQf++qhAfz1W9Ur39zAdXlQKfSxXaF57seNPP+oJbd9b5mY9c99kwP/PBveExPravmJOjvqH7Cu4dfIv66OkdykF9cPfBWT/bV3jWoGx0X8GtBoHP3Qr1ket9N8ZPfc1Vvgp+1tfMaV4fP+sLLm697Ke+nqsbO9PHfMPfqOtjfeF5Wy76aF/NrZh2PuYjV+U9lc58cOHUSqf3VZyTZ5bQ+yq4+0c36ptz6kkP6hs+qyK9r5rcJaXofRXcnq4/vy/ER65dey/1wZ0BJf2sL85JWbAK7QsuOwT+voX62FNvaeDnzkN9cPVx4OexQ/sab05D2hdcxvfyMR+5Vv0VPuaDyy+XpLP7KuYkO5+g91Vw66fp6cxHT6tnGPXBrbeXprH7Kjx1/AS9r4JbMY2SmY9cL/946hveMDKJ9sVcG9alfQ3vPCiV+ZirfbEi9cGd+yVTWF+T+yg/7Qtux6fdZT5yZbFPqQ+u2jyKZ/uKc1JsbUb3FVxW2+FjPs4pFSF9zAe3a95NZ/tqvPbR6WxfwdXRhWnMR67V8EYa88Hdgx1SWV+ck+747emsL7hV+2Ia83Guemv7pjEf3KnfI5X1hSd2zEplfcHl74nJzEeu6FQghfngMrrWPbavmJP681YK21dwyxp6j/lmrtfaJ1Mf/MSuRLav8Ky8g+i+Gr76ocd85LpTf6Y+uIwsEcv6mrnWXZfE+hq+Nj6B+Zir9WPCXeaDy5Fh8awvPHFlPe0LbpddEs185IrT+akP7tTxrmf9XFX10TNBn5Mq89YI4qJYqSBup5enPj6n9P/+qaE+uFhVISjXLlc4yBM/FAniomHuIO61eOJjPnKt0XmoD64KJ/pYX/O5qic+pH3BvW9foD4+p1I2+oj64E6+0rQvPDHyPu0L7uU74GM+cr0nV7ivuZs00pd1rrb825d1Tup64K8Hro7cD+Iq9iz10dMasI/64CJlSlCus9EX5DnZdgRx58i9IC7mSeoj17r6FfXB1aM3aF/MScYMpX3BnW9rUh9ztW8/R31w98sN6bSv9twqiemsL7hdYG4685Eru72aznxwu9jWNLav+JxU93Ejuq/gqrGgPj6n1BtWl/rgMiaG7is8+83ldF/Bxbxq1De53R3qG15gXzrri89JdcdXon3BnXMbfdTH51RVLORnPrh1ujftC8/pl5v31dytVzGd+Sa351zqG76/RxrbVzPX4ovS2b6CW0/9kkZ93VOWTk9jPrjc/Esq21d4qleNNLav4PbbP6RSX+faz1xPYT648/2WZNbXzGl6LtoXXKSsTGE+5upUSUthvuFDiyazvia3YJlk1hfcejAoifnIFe89TqS+5vKVGwlZz1WxOTHonBRDpwZx5+mUIC4XjqO+OVefLUp9cNX2z/SsudZXvwV5Vv8JwbxBTn9W7obPpz5y7eKZ6cwHt63H6awvzkn7Vut01hdcvFk+nflmT+/WT2M+uFskMZX1hedczZ3O+oKL4wWoj1zb3ZLKfHA7dn1q1rmKNeX8WefkXY/0BfF9gXseuPP8bR/z0dPyh/mYD271bOLLmit83/izeiomXxAXjSf6s3LnVn3qI9eeMs7HfHCr0HnaF3OSlWuks77gXmyvdOZjrmLgzVTqa+5sfC2N9YXnfXginfU1/GJV6iPXLbUxjfmGp9fj+6rPSVUmMoXuq+ZO9+dTmI9zSiyMuMd8cC+PSqL7qj1vwYlktq/gbufIZOYjV9WomcR8cHtR/iTWF+ek+/Gfd2lfze2zKoH5OFe9mIQ45oM7H37isb7Ge+lhPOsL7l3dFc/8/5+7OZb54GLQ3zFsXzEnOy49me0ruDujSwrz0VM6HycxH1y2+yOJ7avxsoelsn0FdyqlpDLf5O5Mu8d8cDu+aArrizmJSrkTWF9wr8OtBOZjrqLO8ljmg3sVGnusr/HmPJvI+oJ7r61JYr7JXXMujvngqvr+BHZfxeekOifT6X0V3OlXxUd9/TmlTouj3NfcGnQ9nd9XA549rAS9r4I7RfTzrhAfue6D4dQ3/LUlKbSv/pxUcXcu7QvuthiaTn39OZXuii4+5oN7W56mfY1XMIb2BVcRi5KZb3IXv57KfHD5YNY9dl/FnLwiMpndV8HV1gZJzEdPb07Ne8wHd283vcvuq/DUd4sS2H0V3M77l8d85Cr5chz1NXdqtLvN+mJOctXuRNpXc8v7LJ75mKt1vBH1wdXMcR7ra7xWkTGsr+EtG12nPnL7zL/JfHBvacHLbF/xOanqA/28IWRfwdXxnWnMN59TuuJv6oOLHi3T2L7Cc/f/nML2FVxUmpnEfORazeOTmQ8uXohIpH3xOak/HE5nfQ1feS+V+eZzVTPddOaDyw4DUllfePKtusm0r+YiY/td5iPXaTz9HvPBxT9XEti+Yk6eSKP7Cq7aXYlhPnp6lzfGMR/cXVXwFttXeNbXL19j+woufh1ynvo6V3bsRn1w+/dhp1hfzMm+O91jfcHFkcgbzMdcvS9fuMN8cJVrF+1rcvu9fZH21dwKP3OM+ch1Mp86z3xw2abQEXZfxTnpZmsVw+6r4OrqrpvMxzklTta9znxwS31/md1XTe5nfa7T+6rm7vuLrjEfuaJO4YvMN/xExQusL85JW+a9xPqC2y9NPMN87Kn6KOk09TV3Fl04QvtqT7R+dJz1BfdaVj3GfOS6Beco5hveZs9+dl81c91d5zq7r4Krmg1vMt/0XLThAvPB3eiFl9h9FZ7cNfc2u6+C2zVHesw3ucszrzIf3PFNjmZ9MSer4J/HWF9wNa7jKerrucrfLx9gPrjcXu8Q6wtPPd3gAusLbn279QrzTe6ECceZD+6en3ue7SvOSafXzaNsXw2fsPMA83FOeVMnHWQ+uJvZbifbV3jWlhI72b6Cy9hb26ivc93w4ZuZDy5zZF/H+uKcVINe2M/6glsnRm5lPs5V97LYzXxwr9OhjbSv9sQHW1ezvuDOiBeXMR+5XouIZcwHd167s5Dtq5nT5J+2033V3FuVdzf1dU+r8IJ1zAeXT7fZwvYVnv1zpmL7Ci5u3KM+ckXtMbuYDy7fe/YQ64s5yXerLKN9NbcrTV/NfMzV6ZZ3IfU1dz8NW0r7au//zv2trC+4qhO5l/nIFYs/WMd8cCtqWGTW56ryVGTQc1L3ct8g7kbEBnE3bg318ZzSnVKG+uBebycoV34c+D2d8OT5W0FcXq+UkZWLl/NSH7le62V+5oPbFe7Qvua56rYZPtbXcPdPH/PNc9XCV9Kpr7k1vZOP9TXe/TZ+1hfcemkl9ZEr7Hs+5oNbNwK/dxlztWd/mJF1Ts7rNYK4W7dTEFdrW3Mfc81TnPrgylc/KNdaOyTIk0t7BnEZPTmI263HUR+5duNvqA9uR7m0L+Zkl0nzs77gdvUXua/n6n0e6Wc+uPe7oH3hibGtaF/Dhw2gPnJlwtvUB5fbvs9g+2qek/5yKI3tK7iYMIP7eK7a7sNU5htep3ca21d4bqHmPravhpdKpz5y7deGpDMfXJZq72N98ZzUHt0qmfUFlxMyUpiPe6o1Oi6R+eDurOzJrC88Z6STxvqCe+1mUh+5bsRvKcwHtzvPTGP7ijm5837ws30FV3kuUd/MtXc+P/PB7b4z/Gxf4bn9Lbqv4FajL6iPXDfmkZ/5hp/9IIP1xZysPWN9rC+4O9/mvp6rUyMmnfngYkDgvZb/9NWeaHDSz/qCy6kvZzAfuW7bcX7qa+4NyxN0rrr7FwWfqxnzg7j1zOpgPmo19XFOSf8c6oOLRmuCcq2eG4K905uDuLV6fTDPto36yJWx26kPrj7eQ/uac/KLmbQvuKq0lvt6T+19U7ivuRe7lvaFJwfvpn3BnQ6HqI9c1fkA9zV3/jwVNFf7xLKgOdkTNgRx1XRKEBc/LqI+errddlEf3Dm5PihXVO0a7A0eFsTt+hWDuBP3HvWRK0vOoD64uvUN7Ys5yeeO0L7gMnMf9TFXt8sF6oOLsWdpX+O1WkP7gtvPjKI+ct3lUdQHt08tofuKc9LuMp7uK7jMsY765lyd5FIfXIWt5fsK76vDdF/B3YTL3Ne5VvET1Ad3Y27TvjgnvfJ9eV/NrUGrqI9zVdz4mvrgVrOltC88p/AZ2hdczU7kvs61j5+jvuGrfXRfMSc7VwzdV3D1diz3dU9vTwr1wdVZP91XeGrRVbqv4OJD7iNXpqVSH9x6+Rbtizmpd/+gfcHdx39TH3OVlQI/1xzqg1sN9ecBhfSF5zX/h/YFd1+/T33k2h/qz1sL8cHdBfpzO0Lvq/o5qXymaAK7r4LLdjupj+eU1qzSHvPB1bOVEth9FZ6357Vkdl8FV01bpzIfubJlpSTqay7+vJDM+prnpEe6RdO+mgt/OPXxnMqtVf8q88Gteom3WF94dra0BNbX8A4l7jEfue7iuXHMB3fHV0lk91XMSczMkc7uq+DiUWMf803PXQ9TmQ/ufbswnd5XtSdHvO6n91XNvXI3qY9c7/BJH/PB7WEL/Kwv5qRa7E1hfcHdUcfTmI+5ivtNkpkPLqccT2V94TmR7/pYX3CRFngvP9RHrl018N51qA9uLw+81xu6r3hO6iZ7F9i+gtvP6u8Phfh4TukM2n6K+eDeoQqX6b5qT727KJbtK7g6+0IC85ErTn4QzXxwa1KRONYXz0mtpRWO0r7gTb85z3zzXPXGJcV8cK/hopOsLzzZqek12ldzK/OvGOYjV9zYcYn54NaT32/SfcWc+q1PYvsK7qTMTWE+err+u3eZD24dr5VM91V78uz5NLav4G6jr33MR6677HIq8w2vvDud9TVzahb4ecPQvuDya5nEfMxVrvzNYz64LH7hLu2rPdsXkcL6gqvDsWnMR648XSGZ+ppbvY6k0vuqPiedAh/R+yq4GjqP+jinrOVVqA/uTZxA76vwxKDz9L4K7o79nfrIVaXOcl9z68eHtC/OSe/S337WF9zp0Z/62FNPHfYzH9yq8Snta7xnj9G+4E6Dx9RHrvx0F/XB7SZ/0vsq5uTu158HFHJfNXxCIeqjpzs7P/c1V+VLZrL7Kjz1VrFMel/V3BtTlPrIFa3KUt/wd56mfTEnq1Fh3ldztaks9TFXUaEo9cHtLeV4X+3ZH5enfcHVgheoj1z7WAXqg8t6VTLZvuKc9HIEvm/3n33VXE6unMF8nFPKH/i50lAfXNn3/Gxf4TkDJN9XzUU2P/WRq8YMoj64GKI/dymkL85Jta6vj/UFt8PGUh/nquy6Lp354NbtwHuDoX3hefnfpn3BrdVbqI9c7+ptP/MNf7NnBttXzEm8XoTuK7hsY3Ff93Ru56M+uNWkHN1XeHauKnxfNfcmVaM+clV6ZeqDq5+q076Yk3zvrwzW1/B+pahv5lrjfAbzwUXPnLQvPGfBi7Sv4d9WpT5y5VOlqQ/ufvRcJntf1Xyu6jM109n7quD2+McpzMfnlDqjr6QxH9z+oXsKe1/V5M6PS2Lvq4LL7x/EMx+5Tp/yScwHt6YUj2d98TmpYnSHNNYXXDx7NJn5+JxKq+/JVOaDq/KP7rG+8GTjxom0r+ZynRXHfOQqf4u7zAf3+pSm76uaOb30Xgx7XxXcGvzoCvPR05rd5xbzwdWhby6z91XheeeqnmHvq4KLiPsHmY9cO2HvSeaDW23eV6wv5uT4I26yvuDybOxF5mOu6pVd15kPbh3/4gLra3JLjT7O+oKr8Uv3Mx+53p4uR5kPLur/uYftKz4n1c5eMJXuq+bqwxz3qK8/p9SKtlKYD+5euE33FZ68XiOB7Su4fSophvnItUYmxzEf3NrT+Rbraz4nde1ftK/h/hF3mY/PqZLnIpKYD27N9cezvvDEjX4e6wsuRu2+QX2d64xUMcwHt7L5rrJ9xZy8PLuusn0FdzasO8t89PSuRlxmvuGXpp1i+wrPWdH2MNtXwy913UV95DZpdJD54Pa15EjWF3MSnfpeZH3BVfd+x5mPubqdJ51jPrj8tMsR1hee3XnoAdbX8Bb9tjEfuarF5D3U11xYYVvY+6o4J93NxXbQ91U1t5tsXMt8nFN2z4RtzAcXC32r2Puqxps/VbL3VcHl7R5zmI9cO7LvIuqDH+80k/XFOalWld/K+oJbr/pWMB97KnrN3cx8cPvX5stZX5N7p+IC1hfcKbB3OvWR+8yUecwHdyacmUbfV9VzsmpnzmLvq4Jb8/5awHzTs0f36cwHd2pemM3eVzW5Z95ewd5XBZeVf9nMfOSq8GJLmA9uDam0jvXFnOzy/aayvuDejvEzmI+5Wuf3TGY+uFOy5zTW1+TmbbmQ9QUXg8utZD5y3VWV5zIf3J730VK2rzgn3WYtN7J9NTz7raXMxznlrR+zjvqaq2N9l7B9hWeff3cu21dwe+ob05iPXFG68Bzqay7WHpjK+uKctHucWk37gscUk8zHuermqLCS+eByWu9FrC88r/KtWawvuBvRfCrzket9t2Ym9TV3ph2aQvdVz8nKNXQS21dw95tCU5mPnk6RyInMB5fv1J3M9hWeVeulWWxfwUWBfRHMR65anjad+eB2k7fns76Yk1uw0kTWF9xel8p9PVd1LmoC88HtBdknsr7w7DnvT2N9wcWrp2czH7lqXdGpzAe37r82k91Xzeek9syRyO6r4N4rr8Yx33xO6b43EpgP7sxfGcvuq/A8r3M0va9q7v1U9jL1da6TP8d15oNbGzpcYH3xOan2pw1pX3CneXQ08/E5lfK9n6gP7nxc7TrrC88qsJ/2BVetN5ymvs51f9pLfXBZpc5xdl/FnGz341PsvgquioUfZD562tvaHmM+uDV+4352X4Unzt3ewe6r4O6rJzYwH7ki86dtzAdXSy6tYX0xJ7nuyCHaV3NvW7bdzMdcva6DopgPrupXimR94dkVlm9mfcHdAUNXMh+5svDW9cwHtx4/WMb21XxO6okVdF/B7fbXL1Mfn1P6QmXqGz4810W2r/DUlQ/PsH0Ft/tNO0x9nWt/Wv0E9TWX+W4r1hefk+pc+egK7Qseee4s8/G5qm7JRpeYb3jC+6dZX3jq/W+Psr7gYu7EA8xHrjhc9Qj1NZdu2AG2r5iTaBW5h+0ruLuo8Vbmo6f9+aCd1NfcPr6A7is8a8qUNWxfwe3mY5cyH7lu6Uarqa+5qj9xKe2r56SGldxB+2rupnTfzHzM1Xkx507mg9utG2xlfeHZVfuuYX3BvVNLljMfuU6pshuZDy6WHFzD7qvmnPx+wzJ2XwWXp1PnMx/nlLwxZAnzDd+0cy67r8Kz802cwe6r4KpajinMR65osGw688FF0xmTWV+ck6Lnd4tZX3C7cdE5zMeeep//vZD54LLze7NpX+05ic9OZ33B7aL7JzMfuVaBBtOZD24V+H0Ku69iTl6R0RPYfRXc3jGU+qbn3nkTmA8uOnw7gd5XtWe1GTqZ3VfBxVvNpzMfuTL7xEnMB/ee5JjG+po5eW9NZH3B5dFE6mOuVpfDk5hv+MH3JrG+8LwjziTW1/Dnmk5lPnKtmS9NZj64dbvCVLav5pyc/dZCtq/gbks1m/k4p9z6FxcxH1yNqz6P7avJTe46g+0ruHi79zTmI1fVqjWb+eCyctOZrC/OSTWn3xLWF9x5R0YwH+eq1WjTSuaDO/ec5awvPLfF3AWsL7j7Yft5zEeu6DZmKfPB7Wm/SravmJNo/81Uuq+au3cC/7tQHz29osVmMB9cprWdzvbV5D4sSfcV3Om8dirzketF/z2N+YZ/nWM662vm1CZiDutreB/9z0OIb+bauNxi5oNbpVcvZH3heeXqzWJ9we2Oo2cyH7nqcPh85oOrl7fMZe+r4jmpLJ28h72vCq68y0eYj+eU3n0ZyXxw60asYu+rwhNTip5n76uC24XDrjEfuU634SeZD+4WGHKJ9TXPVasO3EL7gn9Tay/z8ZxK1fx8PfPBHSs2kvWFZx9ed4T1BXcT2p6jvs51r0xVzDe8fI8T7H1VM6ft22PY+6rgYvLxeOajpzus/03mg6vUSR57XxWe0+SrJPa+Krh4p30K85Gr7MZ3mQ+u9k+8x/qaOZ1qdpX1BRfTvrvNfMzVbuFcZD64+mDpddYXnrM98LlboX3BreVtEqmvc93l42OZDy7bRcazfTXPSetXWMX2FdybOmYL881z1aiCy6ivufVJ2Hq2r/CsMx/sofsKntT6MPORK8ds2c58cLnfOsD6mueqp/cuZn3BZZpcyXzcU20vej7zwVVql6WsLzxvV4PNrC+4PafeTuab3O3z1zAfXB65tYXuq56T02f5abav4M65wOdGhfqm54NhR5kP7vY7c5buK3L3lb/J9hVcXJlxh/o610tLu8x8cKfkzzdZX8zJGdFFsb7g6ufNx5iPuXqXDu9iPrg7NEOxvvBUvhHnWF9w96PT1EeutWrDMeaDy+s/n2Xvq+KcdFPmprH3VcFFoZo+5ptztVuFVOYb3qpEOntfFZ7qp9+rD3lfFVz8EvhcoFAfud76nj7mG77nSz/ri3PSG1wvmfUFV6dGUh97qj6/kMh8cJn+dTLtqz3n1ivprC+4dbI99ZFrn+6Yynxw8fByGntfFXOS1UZksPdVwdXE09RHT9nkkZ/5hu/7MoO9r2o8O/Becuj7quBej1zc17lWwpwM5oN75bbSvpiTN+JFP+sL7lRdRX3M1e4a+FznUB9cNAj83pXQvsZLD/xextC+4PJ+Seoj15b6859CfHBRpoSf7as5JxcnxbN9BbcbrqI+zimvU4s7zAe3Pvk6nu2ryV2VfI/tK7h8b3kK803ujMN3mQ/u9NyaxPrinBRpO26wvuD2puUxzMe56pWVl5gP7mxee431hSfqPB3H+oJ7pYolMN/krl4QzXxwu8nLsWxfzZz269/LELqvmjs9otKYb3qeqpzMfHDv1KspbF/hWZsbprN9Nfxx4HORQn3kqv2XUpgPLnuG0b5mrsuX3mV9wVXNY4nMx1y9Y4HP/f2Pr7nVeWsc6wtPvjk8ifUFt86cS2K+yQ3/IZ754OqdA/HsvornpJ7vjTnsvmp4tf4RzMdzSvXs3JnMB5dR5+ax+yo8uavTcnZfBXdbT1nHfJN7cLJkPrhbe9IK1tc8J/0tejrra/isCbOZb55T+e5NYz6490nPmayvyS25fAHrCy7eaLCE+chVi5LnUF9z54efFrL7Kubk9F2yld1XwUWZp3YzHz3t6NrrmQ8uvu6zld1X4Vnh4xW9r4JHfH+U+cgVvdN2Ul9z0TvwfbvQvpiTWKxWsL7gMtv4dczHXO3o+kuYb3i1XCtYX3jWvI5bWF9wt8/OSOrrXLlv/Rrqay7eqbWJ7Suek9rv7p3G9hVc5fh0BvX1c0rX7jKd+eDe9tdmsH2FJz6ZOYvtq+EzpsxlPnKtMoHcUN/wv6rNpn31c1L7LWsm6wvufVZ8JvPNc9Uu9eYwH9y9W3U262ty6387k/UFd3I+M4v5yJXNRsxiPrj8odYstq+Yk+t2Xsj21fDnF0rmo6dbavhc5oM7X3RdwPbV5Jb7eRnbV3D1w18rmY9c74U/IpgP7lWesoT1NXPt//Zs1hdcrTsxh/mYq/t5y1nMB3enHZ9F+yK3zoF5rC+47Su+kPnIVROnzaa+5qLMsjnsvopz0n0512l2XwV3s/18gfnmXH3umcPMB/fytjzO7qsm94t5l9l9FVwuG3SN+ciVtVefZj64veHqedpXn5P2a/v2sL7gIld5xXyzpwdPbGU+uDXs3R2sLzwrvzjM+oI7v546ynzkWuW93cwHF/5v9rP7qpnryT9usPsquP35vWjmo6fac+Qi8w1fWe8yu6/Cs5pWv03vq5rbL0bdZr7Jnf/WFeaDu0krr7C+Zk5bPzpB+2ruPWx2ivmYq1imf74wxAd3mo4/yPrCszqtPM36gts3a51hPnKtPM4h5oO7v0ceYvtqztWu99ayfQW3ylfcxHxzTm1PXMZ8cK9R15V0X7Xn+Q5vYftq+P64bcxHrtj0cDXzwa323dfRvvqcFN/vjGB9Dd93VDIf56rdNW0u9TVXg9vMZ33hWZdqLmV9wd0ck5YxH7lW0cMLmA8uzpZeRPdVz0lOPRLJ9hXc2vDuTuajp5tt1gbmg6vP3tlE91V73q26u9i+Gh41cxfzkStPv7iZ+eD22Q83s75mrrsjl9O+mtuHpq9gPuZq/1AjgvmG58uMYH3hWf4CK2lfzd2vSqxkPnLt27UXU19zufph4K+/p13gOfj2zwLP2ccHfp+eN+wVXxAfoT+nXnP7pTd9zBfHA7/Pyg77JI354NaRr9OCciO7+rN6zj39+Weay8h+/qxcXm7rYz5yrdT+acwHt6pNS2N97dpzA8+takUks77gzvlNycz3rgV+HkD1+O0u8w3fkzeR9tWee/l0MusLLrtmJjMfuaLFK4nMB1fpnydmnavaOMqfdU5qWzdfVm5vnu7Pyu3nw33MNz3PbkpjPrinLqRlzRU7l/iDcuUcX1bu7N3kz8qd9et9zEeud1v/vvEQH1z9UiSd9cWcrCpPp7C+hn/UOIX5mKu9YHoi88GduLOJrC8859yAFNYX3FqzLIX5yHXv502ivuby94ZJbF+dloH3Ebwjszy2r+By8UaP+fZfVQM/l/7W2zeYD+7k/+EG21d4dpdrHttXcOdAvjjmI9e9tusG8w1vW+4m6+u0+Tzw3xP+LhdYX3C34LQLzFcTDgfeR3r2xhHmg4voEkdZX3j22osXaF/wbhUvMh+54k7/o8wHtzo+PMr2FXNyBn4Yx/YVXJ6dEEd93dNRs29SX3Mv99PRbF/hifEX4ti+gjvvFotnPnLtF5dFMx9cDqlwi/XFnJxtky7SvpqrU39fZL6Z65ZZx5gP7vZ79zjrC88q0PcS6wvuvpl0ifnIlZf+PM58cCdp7Yms56qzb68/6Jw8f8CXlYvjR/xZudx4xsd8nFPybtV05oOLw3Z61lz3if49BdqzCl71ZeXOjFP+rFxeueZjPnKtC83SmQ/uTWiZzvrinLT9J1NYX3An9V4K9fWeypL9kphv+LWFSawvPLtO4HONQvuC27eeSmU+cqW1M4n54KrrwaSsc3VvHPVnnZNT4qIvK/de1vuhuRSHfcxHT7mzSTr1NRftaqdnzRXZ1vqzelbmOl9WLi5P82fl1icTfNTXuc6OsHTmgzsd/Wmsr5nrm49SWF9wUSUhhfmYqzi9O4n54CpscRLrC88pvi+F9QX3rv2Uwnzk2n/1TGI+uMr+ahLbV5yTzuKm8WxfwZ3RQ+Kpj3P184W3mA8uhzy5xfYVnvX5gni2r+DqjTXx1Ne5alyD28wHt9d0vM364py0LjW7zPqCi3XLLjMf56r6vPlJ5oN71W+cZH1Nbvu4y6wvuH3xqSvMR66aU/8U88HtR4NOsX3FnESeZfF0XzVXp8bGMx891WP9e1VDfc3tvFVus32FJ5Z9Gs/2FVw+VSKe+jrXqnDlFvPB5YHPb7G+Zq6pel4hfcHdGO8y8zFXL2X0KeaDe4W/PsX6Gm+LvMz6gssJjS4zH7nycbFTzDf8xtKT9L4anSPwHC7s+d3svgrujR24m/nOzQOB7/t2v7ee+YZ367iB3Vfhqbdy7KH3Vc3tFQf2MB+5zvTyG5kPLqu+tIn1df6pHnjvd2KXxbQvuC+3ZL7168bAz6sc/Gcy88Htf/xTWF94ss8UyfqCy5zvL2E+cu2Zu6cyH1y8c2Aau69iTmpTxF52XwWXV+fuY77pmaf6ZuaDW/1iN7P7Kjw1fPF+dl8Fd6YsO8B85Hrn5mxhPrjM33Ir64s5iWnWUtYX3MosvYz5mKu99dF05oNbXb6ayfqa3HrPLad9NZdO5RXMR663K34W88HdRd3nsH2Ve1cO+HdestxAtq+G3wsfxHw34f3XA3tfrA7zwd3ey95n+wrP6X54CNtXcGvR2eHMR664fbkl88Hlpeafs77ynTe6RP37ueuLO0eRvuD2mwnfRhHf+eqtvv9+TXnUJ4r44CLsncCfD+kLzy5d8Jso0hfcLv5thyjm61xnWZHeUcQH90r16hlF9hVzcoe/E872FdyZ13EU89FTzhn4DfPBvfSq3di+wnNbrx/N9tXw4UXHUV/n2vFHejIf3HptfV/W18zp/p02UaQvuFKL3o0iPuaqrt74IYr44Na8Z/7dm9C+8ESZZ19kfcGd7fM/Zj5yRbVjX0cRH1x+erN9FLmv4px06s+KYvdVcGv+a4r5OKfUzn+2Mh9cHJi3jd1XTe6vIxW7r4J743co5iPX6lhqO/U1Fy1/3M764py0oyutZH3B3Zgiq5iPPRW3M+cyH1wt/GI+62u8rddWsb7g9pd9VjMfudZA/d5fiG944UML2X0VcxKRpxS7r4I7OZRiPnpaj5ZsZz64OrZ5O7uvGi/fAsXuq+CiwreK+ci1li/fznxw+/Tk7ayvmdO8xNWsL7iq9Poa5mOu4pNdi5gPLpPnRLC+8JyTPdewvuDu6/vWMB+5TsHRi5kPbn3sSLav5pxsOehntq/gzoULE5mPc8q+sXkg88HdCfuHsn01XtH8U+i+aq6OTZ/KfOQ6L3Ryqa+5lb37SNYX56TMfu5L1hfcGlftO+bjXBXFRjeJIj6488yvSfVJX5O7+UB31hdczI/tTX2d62S82oz54GJXpw5sXzEnL/ecaWxfwZ2pK6Yz38w116SfmA/u9MkYQ/cVXqu7M9i+gttRs2ZRX+daM++OZz64qvlgEuuLOdkV3x/A+oK7d3sNZT7m6qW/2YX54M7p6b1YX3hWt7HhtK/m3sXnxjAfuSJ1y0Dmg7ul24Znfa7qlertD35O6viyclWpiT8rtydV9THfPKd8Zkca88HdOz3Tsua6z4b5s3ruC2npWbk3PCaIi4YR6dTXuc68cmnMBxfpB1JZXzwnteLrpbC+hpdPTmY+nlN5m2ISqa+5239oIusLz47+KZn1BbedksnMR661uHAi88GdB/PvBs01dqYv65ysfYGfmwF336jry8q9I/r3JYT46OlubJHKfHBV72JK1lyV91SQ5xRrnpaVC6dWelYuU5anUl/nqhV1U5gP7p0em8z6mjnNnH+P9QX3Xgn83r9QH3N1mz1/l/ngav3qBNYXnvRGJLG+4O7EG4nUR27X6gnMB3cr65/7C9lXPCcVu6Li2L6Cy0cd4piP55RqzIlo5oM7o1+KZvsKz373gcf2FVzkmeIxH7kiw73JfHB1KO0G62uek6ZEX2J9wa2zXS8xH/dUu+zzJ5kP7r25+gTrC8+7lO0S6wvuNNh9kfnItZa3OUF98PItTrB9xZzst17x2L6Ciweb7zDfzLXIoBvMBxe7Au9VhO4rPKfmy3fYvoJbwy7GMt/ktml8g/ngMteGG6wv5qTil12kfTW3R9++yHzMVVY9d4L54PbmgqdYX3hWzeGXWF9wr+uSy8xHrv3LJ2eor7l79tfzWc9Vb23foHPS6Xg6JSt36vcI4qJ34L2z//j6nHKfrLnHfHAvfEZS1lzRqUCQ5zxd715WLqNr3cvKvb3fJDLf5FZ5I5H54Hb4TwmsL85JK7ZkIusLbv/y8l3qY0/rvBHPfHAnalUc6wtPzQv8XGloX3B3XuBc/Y+vc1Wt7HHMB7dnNvCyztX6MeFu1jl57Z9LyMrlyLD4rFz9Gvi9z6E+esr6o+KYD64WTYjNmitO548Nyt018FZWjs9VBZcdSlxjPnLF1aSbzAd3fhx4lfU1c8p4g/YFF8PzxjAfcxWtt1MfXH6/5TbrC887O/km6wtup719jfnI9TZMj2Y+uGw57AbdV5yrHSNi2b6Cy7xjY5mPc0oW7n6T+eDe6+HRbF/hqcoDYtm+glsNP4llPnK92hdvMd/wH2vFsL44J60mba+yvuDWj7OvMx/nqpo94RLzwVX1u1dZX3huzQ+iWV9w9Uf1GOYj1x0Wc5P5hs+tGMv2FXOynyocy/YV3Fk1O4b5Zq7jx8cyH9z6Yvkdtq/wvAGZt9m+Gl6s6m3mI9drWMdjvuE3h3isL+Ykz+X0aF/N3VvN4piPuYq81eKYb/ixwgmsLzzriy/iWV9wO2fVBOYj19u+6C7zweVD/fk0IfdVPCdVfV9R7L4K7pTIp6ivn1Pa1SdsZz64XejIdnZfhec9DlPsvgpuzx6rmI9c2XxAJPPBndp/72B98ZxU5m66lvUFlxf7rGM+nlOJ5M+WMB/cS8+9jPWFZydv3UD7am59eHcz803ursDvBQr1wd37t9ay+6qZ06MJB9l9Fdze1usw89FTfnF9N/PBrfKbD7D7qsktM/MYu6+Cq05tTzMfuSJ3zcPMB3cnPzjB+mJOonzn7awvuMistIf5mKtMGb2F+eBOPmsX6wtPXZl9kPUFd3I1PcF8kxvx2kHmG34q8Pc1dF/xnFT1rTSX7Su4fLvFQubjOaU4kXs688HdW1dns32F546evYTtK7io1XAV85ErMxdEUF9z75XlK1hfPCe1rAcTWV9w6+LFGczHc1Und9Q45oPb/vLTWV/jfd9xIesLbnV6dgXzkWu3ab2A+eDWs6VX0H3Vc7Iqv7uZ7qvmdocNO5mPnqJ42ibqg8dV2MX2FZ7VddZBuq+ae17hU9TXud7RzoeYD26X2Haa9sVcy1bazPqCy3xFdzMfc3Xq/byZ+eBeatE9rC889fZ3h1lfcNfufpb6OlelfHGE+Yb3yHae3VdxTrrJT11k91Vwb2PgXhvq45ySn/10gfngVi/9+yVD7qvwnAe9o9l9Fdx5KvUO85Erks7fYj64+2rZONbXnJN9L11gfQ2vde4a87Gn6pf1F5kP7v7x6Q3WF547f1sM7au5W+ereOYj1/089g7zwUWZ9Qnsvoo5ySZz4tl9Fdy9m3iX+aZnz7J3mW/47JVJ7L4Kz757KIndVw1v8XIy85GrtixPZj64884zqawv5iS+n53I+oLLnLWSmY+5eod/oz64TP45hfWFZ+evmcr6gsuFi9KYb3L/7JFGfc1lwwPpbF9xTrpP9PPvkH0Ft8YEfm4p1Mc55bhVrzAf3F6YdIvtKzyVkD+O7Su4NTzwXCrUR654NfBcKdQHt4c/SKR99TnpDu93lfbVXHWbGsN8nKvy47nXmA8u338US/tqz/kqVwLrC+4dik1iPnLFuBMJzAcXZWbdY/uKObl7XqT7avhImcp89LTavcB9zZ18Q9PYvsIT7b5OZ/sK7syf5mM+ct2lYT7mg8s/KvtZXzOndH8K6wsuq7yYznzMVV4Yn8p8cDdjMe0Lz5ohaV9w9fRsP/V1rp0gqA+ucscFvp8f8r6q4zQd9O9XVWoQe1/V8FwDBjLfzrtg6L/vNxb7YSjzwf9vckPY+6rw5LSeA9j7quBS5enPfOS6dR8OZj64NWUv73s6bvi//Oldw1lfcHtJ3eHUr9rI/fePf6rpMh/cHf9gBOtrcov1GMb6gqvirYZSH7kFGoxgPrj6qsFw9r4q5mT/3Kgve18VXN6u0Zv56OmdrDqQ+eDWqx/3jyLvq5rcTit+jCLvq4LbAzt2Yz5ynVVV+jIf3BpxsBfrizm591IHs77glq/uIOZjrs62R0OZDy4K9hnC+sKTcR8MYH3BrUOF+zEfuc7WNYOYD+6GRQxg+2rFpAW+Pn3LZfsKrh6t5v5zfcL//esW6BTOfHCZ8WY421eTe6+ty/YV3P77CPWRK4unu8wH9xJH0b5u98R/v9px18NZX3C3xSbqWykfjPyXq7ojqa+5+qT0SNYXnvz1+3DWF1yUKhTOfOR616PDmQ/uhY8JZ/uKOVlpEcPpvoLvrzOM+abntr9GMB/cGbRnONtXeO7RuUPYvoKLyTu5r3NlXL1hzAdX2ccMYX0xJ5lntsv6gqtfsrvMx1zFtLLh1NfcfW6Oy/rCU1U2DWd9we17z1EfucoL/HP1H19zUXPq8Cjyvqo5J2Pmdooi76uCe73nfsF8nFPig4o9oogPLg8Efm4m9H1V4zUf2iyKva8KnpA7P/NN7kuTvooiPrj95uXWrK85J8tN6037gj8e8yPzsade+6b9mA8utq/tTftqT3Wv1DWK9AWXLaOoj1yv/Ec/Mh/cPlEj8O/dkPdVMSdZNqw1e18VXDS+0Yn56Gnv2h8WRXxwtf29tux9VXjuR3G92Puq4N5bXw9lvsn9M74r88Gt/vsH0L6Y69o5LaNIX3Cv9U+vN2C+nqv7comOUcQHdzveeSeK9IVnbb78FesLLuof6cN85Nrnn7RhPri7/PaPDci+mnOy8/QBdF/BSzxHfZxT7m+TBjEf3J7x0QC2r/DUg8a92b4a3uFOd+Yj1/ljXV/mgztNJvVifXFOWu3aDmF9wdUf71Mf56qX9utQ5oM7P5YeQvsit7jXn/U1vEQO7utcucY3kPng3rp2/dm+Yk7W9OXfRpF9BXeaP/0J89FT/NLle+aDq8v9v4oi+2q8aUvqsH0FF9G/dWY+cu0bVRpEER9cFmjcsQHpa+a0ZHBP1hfcmXC8M/MxV6fHS32or7l95X73KNIXnl2wyydRpC+4l/7gowbM17nesmFfRxEf3D1z52V2X3XqLwrcS9tNH0nvq5rLyF7c35P071fxx23qg9s7okbS+6r23KF1R7L7KriTmEF95DrPTxnJfHDV5yPaV6W+OOpf74VnRrG+4PLWU6OYLx98Gvjf5WoxivmG27VHsb7wrHrXRrK+ho+dx32d614pMor54Ha+6JHsvmrmWnlKOLuvgsvOYdRHTyf193Dqa662Twhn91V4Vv/JLruvgsvqt7ivc+1ThcOZD+6scl3W18w1V9ORrC+4EH7qY6722zNHMh/cPdFgJOsLz502Ipz1BXf6C+oj1/vtdjjzwdUO/d+XIfvq3RgR+DptIN1XcHX2a+pbfRYG/vzqWdQHdz4PH8X2FZ5zoP4otq/g9vYC1EeupzqOYj64fLUG7Wuv3RD481VX0b7g8tAM7nfdFuidYwP1wa3kebSvyV3Vh/YFV298QH3kymaDua+5O735KLavmJMoc3Yk21dwt4NLfTPX5/6gPrhTfNNIuq/wMiuMZPsK7jxS1EeumvbFSOaDy8y/w1lfzMlOLTSK9QVXn56mPubqvVF6FPPB5bkrtK/xig8byfqC21NepD5y3T/HjmS+4S/XGMnuqzgn3T+6Dmf3VXBx+CD1cU6JHbtHMB9cjWg4nN1X4Tmtrgxm91Vwb95Y6iPXbTxlKPPBvcxxg1lfnJOu/NFlfQ3PMYv62FOnY+C56H98za3U9i7rC8+VpYezvoZva0V95Do7vx3BfHDVN3oYu69iTur5Df3ofVVzr+JvvZiPnnJMzoHMBxed9/Rl91V4do7ZXaPIfRVcRN9vw3zkqhYv9mQ+uMy9/2vWF3PyxlmDWV9w8XS9Acw3cy2SOYT54G6LLoNYX3hyS/U+rK/hLzfuxnzkumsu92M+uPNG955sX3FO2mWbh7N9BbcOnqE+zikn7+xw6msuVljhbF/h2c+95bJ9BbcGlaI+ckW/BS7zDXf/HkH74lw9cTCc9QX3olpTH+eqO+AC9cGd1E7hrC881e+6y/qCqwqNqI9cd0ng71uoDy6e6uCyfcWcVMPOw9i+gjtlqlIfPa2OQ4ZTX3Nn+IChbF/hiaSHA9i+gqvjZfswH7nig6mDmA8uV5TsR/vqOdmfvTiC9QWXVd4dxnzM1ar35Qjmg4ujY4exvvDs8PGDaV/N7XVN+1Nf57pDYwYzH1yMiAz8ccj7qnhO6u7LNoa+r6q5vbDYNObjOaX4JH0U88G9C29OZe+rwnN2HJjP3lcFdzdXW8F85DpVq89nPrg74KUVrK95rhr5TzjrC24vj5jMfPNcNeL3EcwHd//pP5H1hSfbfj2P9TU8z/XlzEeuvFJrLvPBvYevLWfvq2JO4krgvfbQ91XB1YPkPcxHT3tH8S3MB5eju+2l76tqz8v30lH2viq4NfP5C8z//7nLjjIfXFSbcIH1NXO98dQW1tfwesP2Mh9zVZtWbma+4eXL7GV94cmmgd8rFNoXXEzeeoH5yHU3zjjKfHA3afYFtq94Tqq+Gj6E7Su4Vf/OeOab56ofzOzPfHDR+/YYtq/w7JurZrN9BRdNay9jPnK9EvtmMt/wfr8sYX3xnFTsadaL9TX82zGjmI97qjdlbWfqa65S/hnB+sJTKeOns77g9uaekvnIdQa8MpX64M/MW8T2FXMSv2XfzPYV3HrX3sN89LTCG21kPrh3M+dutq/wZN6cR9m+gjvN377AfOSqJ1WOMB/c/uTT86wv5uS9W3E96wsuLrk7mY+5yvLn1jAfXNY+G8n6mtwxpQ/Tvpo7O+PPMh+5atqdg8w3fGzg94GFvq9qzsnrQ66z91XB7a25PebjnPJ6FbnBfHDn4Vcee18Vnj2r/V32viq4vB6WzHzk2u/tvEt9zcX0HsmsL85JOaT5DdoX3BvvMd/s6bQ3bjAfXAx3PdbX5JaJv0v7au6WnZ3MfJM77/Zd5oN7T2Yks/dVMSdx7kEqe18V3Dp8P5356OmE1UtjPrhVqip/X1V76oWG9H1VcDW7WAbzkWsV6MZ9ze1Wr2awvpiTuvhlGusLLr96z8d8zNW+GvjjUB9ctnnfx/rCU0lD/awvuHypfgbzkes+HuZnPriVqv84ZF/NOTnj9HW2r+DeqMYe8805Nfbp68wHt1ZdvcP2FZ4bv/Yu21dw68MfkpmPXKtrq7vMB7dnv5DM+uKctD97/RrrC+7WuBHL/cC5KsZXvMp8cOvw+hjaF7lpNxNYX8Nfj7pHfZ3rdWicwHxw1ez9e2xfMSfnop3G9tXwkjV81Nc9vb750phv+Lh8Prav8OzUH/1sX8Gdc69nMB+56mEbP/PB1dLyGf+vrvOOzqJa1/igIAii0vVIGUABQfGoeESaGzsiqKASpI2g9KKgCCgwlBBIpIRAEiCEHVroLUCkmQ2hhd4JfYdUUr4vUpSm3LNu5tnrM/e5f7n8rd96fNbru7bbYTJhfTEnmVD0XbD/09fjTvSpfOZjru4jPXOZD259PzOf9YWnO73kY33B3Yql/cxHrtu+hI/54Opwlo/eV73npPLgz53ZfRVcVb06gvl4TqkG1n+F+eBW3ZJD6X3V87QVFkbvqx4XVrO5zDe5o/Qk6ntclbweyfqa56SDvmqXRPqC6yM9+zDfPKeqNrxHEvHBZfKaINrX8+xPZo5nfcGtqrkRzEeu2hY7mvngovM74ey+ijk5Y514dl8Fd/2lNzLfzHVjiSXMBxePfbSB3VeNFx+t2H0V3EkcdoD5yHXKr/mN+eDiasJ+1hdzEpuPLWR9wa2q69cxH3PVmVmS+eCy3aw1rK/xFgdtZ33BVd8K+5hvclPabWU+uOwYV/T/RcX2Fc9J1c6I3klkXw1fFF70vlUxH88prYaLByQRH9wZe7BZEtlXeHbkqB/ZvoK7rV+fxnzkiozCocwHl0Gl//dcKN7XPFdNShqcxPp6XDTY3Y763nNVmXFwCPPBVdXFnyaRvvD01gbfsr7gzqJKU5iPXL35u8HMB7f6D5jM9hVzshevjWX7Cm5FtF3FfPS0G/eKYT64WBuzgu2r8Tot+pXtK7h9fdhu6nu5dmrfROYb/tjsZNYXc5JP35tL+3pc1Ju9nPlmrr+Pm8t8cMdtsZz1Nbn9l21mfcF138eSmY9ctarPZuaDy28u72L3VXNOri19it1XwZ2WfS4yH+eU9Wz2CeaDy6eqX6D3Vc/Te4p+r9T/ua963CmxI4v5yHXPlbzKfHD5YeMs1hfnpJN85jjrC+7UvnKO+dhT+UTmMeaDy/a7UllfeHbY12msL7hOjMtkPnKtgSc088HV9VqZ7L5q5jTnzjV2XwUX29/PZz566k5h15gPLufczmP3VXiq7+ECdl8Ft5buoj5y3UmRBcwHlzExPtYXc5IHalxjfcGtdcvzmI+56pTEHOp7XPh65LG+8OTZHgW0r8edtOE+5iPXTXqhgPngKqmjj+0rzkm5qcYxuq8e15dOnWW+OafSKx5lPribUucs21d4okonzfYVXDRcn8F85Fof37jCfHBnRNcM1teckwO7HmF9wXXExjPMx7lqXap5hPng+oPpZ1hfePLimCusL7gdVD+D+Sb3005XmA9ut6qSwfYVc7IfcXLYvoK7ze085pueP1fPYT64+7g/l+2ryY21Cti+govRr/moj9zQy/nMB7ca1fTRvt6cVP7v2awvuPX30VzmY65Wr/Rs5oOL7ntyWV94bomUfNbX8GFP+Kjv5epFO/OZDy4GlPUFflfVrfLeicDvpIqxHx0P5E76uwcDudXt4AHmm++UVos4znxwHbb/UGCuldxod6Cnsr3fO+hx6T792z94UKhiPnL1rLR9zAcXx1rvpn2976Taz7Y9yfqC2++kHmM+vlOpZkacYT64OpN7ivWFJ9OPHmZ9DX/35AHmI9exJ59kPrga1vNE4FydZ0sl/mNOH5XdFshFxzc3BHJ36rnNzEdP9ZHcyXzDZ32dFJjrVP3XmkBPNslKCORW3M7lgVy9u3U985HrTBmynfngzp4HW2hfb06iXvB+2he8zKq91Pfm6tbte5z54OrNou9KFe9rcnd4vx++WF9wa0BUMvORK5e/e5T54O70PofZvuI7qeqDzhfYvoKLb0LPMx/fKXUWztPMB7delJrtq/GC9pxj+wquN5Y+x3zkWvOmaeaDq/N9NeuL76SKRTvSWV9wO756BvPxnSpprc9iPrjbum026wvPGvtJBusLLk91z2C+yS2/Opv54G5rnc32FXOyg99PZftq+IDhZ5mPnrJz0Xf/i/vgqtvaK2xf4anfJ55h+wpuh3xzmvnIdScmX6a+x+XHoZdYXzOn0DYZrC+4df7xDOZjrtaLudnMB3cWHMxmfeGJSivSWV9wZ2HldOYj1x0+Lpv54KpDqezAc9V+b+3SwHNSbPfe2/C4k+0sDuTyP9VXM9+cU+VOJDIf3E3dsSkw1+o/aeE/vIThKwK5CL8uA7lIuxvPfOSqpusSmG94k6XrWV+ck07Lp3fRvh4XL/6imI89tV7sfJD6Htd9KqawvsZ77MIO1hdclg3ZynzkWg2j9zIf3E65mxw4V7tNzQWBc3LWxi0J5Ha1ZTGBXJT9dhHz0VP+0H4t8w0/PXZlYK5KLnouBs9OGxIXyNUL66MDuQz+YgHzkWv1uRfPfMM/KHpvr3hfzMmJbpLI+oKrB9ZG5mOuVlKHncwHl7VH7GB94bnNyqxnfcHVs2oV85Grohb+Sn2P61HtN7J9xTlpl33iFNtXcOdw0e8VKe7jnHIa37nAfHDpK3ue7Ss8lbXlGNtXcHV07mHmm9w9u84yH9xZ2fg064tz0unx/lXWF1y3aJHGfJyr7uD2Wcw3/EyHTNYXns7VV1hfcNHoyEXmm9x+j2Yw3/CSg66yfcWc3GZzU9i+gqtBRc+NivvoKf7oc4L54E6HOkfZvsKT8UV/7ld8X8Htu39uZz5y9RdxKcwHd4MidrO+mJP68YtzrC+42hB3mvmYq97ivS9TzAcXR+Ivsr4mt1fmcdYXXERuOsR85MrPS6cy3/Dmg0+y+yq+kyrKvXyN3VfB1ReZ15iP75TKNs/lMR/cnnA+j91X4dlLu+Sy+yq4nhSXy3zkqrYd8qnvcTUpJp/1xXdS3eej8mlfj7vl3iygPr6rOmxBAfPBVYcmPtYXnt60uoD1NTznRgH1vVxn92wf88Hddqd87L6KOemSSbnsvgpuV07MZT562ud25TMf3I4uei5V/L4Kz3l1ci67r4Jb/hdymY9cK25dPvPB1dFh+awv5qQOP+NjfcHdLg19zMdcxZU7PuaDO1PK+VlfeLqb7WN9wfXNuwXMR66KLetnPrhsdcvH9hXfSVXbgnxsX8GFfd7HfPOd0ibnqA8u/tPGz/YVni7T2M/2FVzV/trPfOTKJ8P9zDe8a5Kf9vW+k6o/f9nP+hpeOsLPfLynKmMHUh9cNk+mfeHpG8doX3B72W3ue7n6uzvUB3dvlC+k+4q5Jgf72b6Cq5QoP/PR09l2kfrgVmKBn+2r8fbO87N9BRdhM6iPXHn9hp/54GKzj/bFnJyyjxbSvh6XiU8WMh9zFfefKmQ+uN5fq5D1hec2qlzI+oKrxpUKmY9cXaduIfPB5bU6hey+inNStYq5xu6r4PJ+Sg7zcU65lcrlMx9cvts/j91X4ckb8dnsvgou2jXIor6XqxqNy2W+4X1b0L44J3VoQgHrC+4Eef/dLeabc/W9vT7mg7tbv/exvvCcVdPyWV9we/IvecxHrth8vYD54OKVoj9XK35fxZzs597KYPdVcLf17TTmo6e7YHI288Hd+p0z2X0Vno5Luszuq+B2yjvnmI9c67MNV5kP7qzqfYX29eakEmrlsr6Gt30ph/mYqz3gkXzqe9xN/iqX9YWnrq7KZH3BnfmLqI9c5/XaOcwHd8s1y2T7as7JZ4f66b6Ct37Dz3ycU47vvJ/54Cpug5/tq8l9/LyP7Su4iij6uY7ivsk9PsDPfHC99Q8f64tz0p30WCHt63Ex4B71ca461WoWMt/wyRULaV/Pk7m7/awvuLO6D/e9XHH7tp/6HndHrvTTffXmpCp3LmD7Ci5S7uUxHz3ViuY+6oN3KPquffF9NV7pUrlsX8Fl56Bs5iNX/Lt3HvU97jx0N4f1xZzc2kd8tK/HdXBqAfMxV/dgAz/zwe3LHXysLzzZs2s+6wtuJzbMZT5yZfSDfOaDqxn3c//xXLVvy6h/PCe92SEmkLsZjSIDubv1whzqe88p1Ye1FzIfXNbdviAwV7YbOzvQs7f6owK5HPj67EAuQkMiqe/lqi8rxzAfXEc3ncP64jmpyG+4jPUFFy2DFjMfz6msE7PWMR9cjbm7gvU1XikRx/qC6z9j51MfuVNKLGU+uL5ffWHgXN0un84OnJPuE/UP7obvmx3InatPUh891WdPRDEf3G3+8z9y1S/hkYGek5s+K5BbTy2KCuSy1YNZzEeuKN1rFvMNL708gvb15mQNuDiH9TU8qiT1MVd70FuxzDc8Yucc1tfkds3ifT1uN/sigvnItX7uFcl8cKuHjmD7iuekolOtRLav4HrWBxuYj+eUuk8jxXxw+3Hv556L7Ss8ZZ1dxfYVXFU9T33kyi4vJzAf3Ck1eDXt6z0n1Qu+3sv6gltTK+1kPu6pVunOR5gPrnr/vZf1hSenL93C+oK7s+tSH7lyXOmdzAfXldJ/ZfuKOTljP13I9hVcZ6bNZz56ikPbljIf3J16Io7uK3J7Z0azfQWX45rOZj5y3TeCY5hv+Ivdo1hfzEm/unAV6wsu6y1fwnwz16b2BuaDyzKzV7C+8ETz9ZL1BVd/dprLfJN78tAi5oNbnV6IDTxXRb+86MBzUvf6ZHYgV6M+mxvI7Z+TZzMf55Rc9loE9T1u97k+MzDXrpA8L9DT7d+ODOTW4WrzA7neuyWS+l6ujDgwk/ng9n9WzmR9cU4677w0k/UF/+89K5z55ly99Vc488HVtGkzWF+TezV+BusL7iY+PYP5yHUaXZ7GfHB16tGpgXMVA1+ODZyTeKhCVCB3yhbGBnKd1iSK+egpX58wk/ng6tTbM/+Re7HpgkBPnKkcFcjlsjuxgdwNWxLJfOSKVmnhzAd33+gYzvqaOdV/aTrt63H7oQbTmI+5itwfw5gPbn1cNpT1hWe97+UU6wvuvL07jPnItU+umEx9j6tdbULYvuKctH8dM5Puq8fdq9/NYL45V8cuiGC+4X9dovsKz566fSrbV8MvtAhjPnJFrznUB9fPDwhlfXFO6rqdZrO+hg9NCmc+zlVZekEU8w2/WDqC9YWnN705jfUFt2M+D2U+ct1GU6Yz3/DEo6F0X7056XuNp7B9BXfWDgxhPnrawVkhzAdXL6QGs32FZ908H0z31eNOnT4TmY9c2abGROaDi9Wx41lfzMl+OIj2BXc/KRHMfMxVlOoYwnxwq/umiawvPHmm7gTWF1yPWjiO+ci1nt9GfXC3thjH7qt4Tirv5p9k91VwMfRN6uM5pftS43PMBxc7PjzJ7qvwZOvWe9l91fCNpRXzkSvb9T3EfHDVpt1u1hfPSYWcfJn1BddX1Fnqe8+pRNv9acwHd0//+yLra7xlMcdZX3D7pJ3CfOTaTeudZj648+DdI+y+ijlZ3S5tZvdVcDf5s3XMN3NNmrid+eDiWGECu6/CcwfGxLP7Krhdc1Ac85Gr+j29ivng4lCDxawv5iTvjdxJ+3pcHyr7K/W9uVotbuxmPriOH7Wd9YWnznn/3128r8fl0P/EMx+5tt0+gfng7lrvfdli+4rnpPbjd9PZvoLb+c9p5uM5peq5O5P54GJsXhrbV+MtnpXK9hVcRm8+znzk2n+uvUB9j8sJ9inaF89Vk+tms77gskLndOp7z1Xds7VymA9u9e+fwfqaXKv9ZdYXXPZtdob5yFVWI818cDEx+yzdV29OVo3R+9m+gturxyvmo6cWlw8yH9ztu34X21d4TkblzWxfDb+wYzXzTe6HO35lPriYMWAd64s5OXMuHWF9wd3ERbuZj7nqzkuOMR9c9Xx9L+sLT/Qu+i5i8b7gboU965mPXDHt7nbmg9v/Or6B3VdxTjpLBs1l91Vw/eryWczHOSWPr5/PfHDx6cxIel/1PHf4ihnsvmp4tc1hzEeuOqHCmQ8upuX8wvqac9KNl6wvuApdFc187KleNHUh88Gtqefnsr7wnGs3Z7K+4PaB96YxH7nyiZdnMR/cPrpmOruvYk5W6oYQdl8F162OTqS+11MPCprMfHBd5eVgdl81uVt+G8/uq+Du+c/GMR+5uknFCdT3uF62dxzra+b6R70prC+4WKaCmY+5yjcfCmU+uDv9m0msLzwncf4E1hdc/eCOZz5y7SNtJzIfXGcNmUD31TsnrRtDFrN9BZfbbscwH+eU27PxUuaD2y2vxLJ9hSevdp3N9hVc/TRmBvNN7g/dI5kP7ix4JJz1Nedkz/h41hdcJL8imY9zVWVtW8Z8cDkoQbK+8GSH6lGsL7jY0z2c+ch1SrlRzAe33xoRTvfVm5P+8kgo3VeP69llQpiPnrr5t2HMB3fOR4SwfYXnhpcJpvsK3mwi9ZFrDd4ZzHxwO3F1MOtr5nT/SBjrCy6H3whhPuYqxp4Oo77H1YCqk1lfeCqoaC7F+4LrkZcmMR+5qnXmJOaD28NKTWbvq+I7qe7pmfR9VXB3YBr18Z1S+9Xl1Ad3Jl2n76vCEw/KFLL3VcGVKHqPsriPXCf+CeqDq5AahawvvpMq3kqgfcHV7jvc975TKUcncN/j1pd3/KwvPOvVirQvuHqjFvWRay2sQH1w+V5N+r4q5iSu16bvq4LbBfWpb3o2rUd9cD2mEX1fFZ5zsyF9XxXcutWQ+16u9Uxj6oPbJRrTvpiTeIP3NbywEfUxV+vKc9QHlw15X+Ph74v1NX+f8CL1kStyGlEfXFcs+mvxfcV3Up1tK/m+elxcuE59813V4fOpb/ikbD/bV3j2ssfpvoKrjs9QH7ni6KPcB79UtZD1xXdSxYMptC+4HJxKfXynypZDqQ9uzdnrZ33hWb88TPsa3qYC9ZHrzrrtZz64uFWW7ivmJPfWofsKrg7Uoz562l/XpD6407oO3Vd4um4Duq/gzuF61EeuEnW573GrW23aF3PSVZ6mfcF1TnXqY67OS5WoD+48WY339Tw5pwbtC+7Wfob6yBWjq1Ef3BlUmb6vinPSLVe/kL2vCq7b16C+Oad6NuS+x9WPtQvZ+6rw3KCyhex9VXCVeYz6yBUVnixkPrgzxbuPFOuLc9K+8Tzta/hztalv9jSrHvXB5ZoatC881fjJQtYX3A5L9zMfubrdY4XMB7fSz/vZ+6qYk7W9q5+9rwrujJvnY76Z64Af/MwHd3xJPva+KjznVK8C9r4quLtlXB7zkes0jS6gPnhESh7rizm5X4/ys76GB6X4mI+5ithv/cwHt//Y4WN94emF8QWsr+FVdR7zketum1PAfHDrnSN5bF/NOfn3/7OvHhd/VKM+zikn+Rnue9yey/cVnhxTqpDtK7jT5ICf+Sa3xB3qg+se3r2mWF+ck25sZdoXXFUpw33vXLXyH6M+uL3iLz/rC885nU77Gp4eRX3kOhMOUR9cPD2S7ivmpL/r4mf7Cq4qL/QxHz3Vhy39zAd3r47wsX2FZ5f4voDtK7i4PzeP+shd26yA+eCObpvH+pq51nzKz/qCW6Xf9TEfc7V3FVAf3KnwqI/1haeH/Z5P+4LXyMplPnLl/Xn5zAd3yg7JZfdVfCfVWRfE76se1+UTqI/vlLqyJfXB9ZVYP7uvwtPZOX52XwV3mpUsZD5y5bVUP/PBnfS7vK/3nVRxuw7tCy5eCKO++a5q27LUB3cqjqR94bnDUvysL7jIKqA+ct2u26gPLhpc9bP7KuZkWeXpfRXceqgC972ezulHuO9xWaocva/Cs16rQO+r4Na08tRHrlWmHPc9Lo88QvtiTs49i/cF/6oU9TFX54c//cwHdztZtK/xmpfifT1uj7Soj1y7gkV9cLH8Ft9X7zup8tlbPrav4GpJP+rjO6V2VoaP+eD2hW50X+FZQ9bSfQVX76dSH7mi61Lqg8vD3r21WF/zXdVV532sL7j7/Rd+5uO7qnbjsz7mg0tfR9rX5K6VtC+4/ewh6iNXqVjqg7vTDvjZvmJOzkqfn+0ruGhzm/roaZfL8TMf3L7zu5/tKzwrrOifW3xfDX8330995Fb7nfqGv55J+2JOdn467QtuPe6jvpnrpKvUB9db8nlfeNUKaF9w54ymvsndnkd9cPfLK352X8U56T5Uit5XwcWYXD/zcU5ZQ+5y3+N6xFl6X4UnKmyk91Vw2T2I+si1ps+jPrjb8HU/64tzUqs82hfcGbKH+thTHXeZ+uDi9U20Lzz3XxNpX3BRoRr1kStSB1Mf3Nla9F2P4vdVzElXV/S+Cm7t2lfAfPR02oRTH1y9MrGA3Vfhic9a5bP7KriIzL/GfOTKypfymA8uv/3mGutr5jS3l4/1BXcvti6gvjdXe8obPuaD2z+VLGB9jffN6DzWF9y9rnOYj1x7/7N5zAe39vfOYftqzsk1J+m+gouYeOrjnHKGHKQ+uOwwn+6ryc3sSvcV3EnRPuab3NYf+6nvcWf7YR/ra87Jj/fSvuDiWiT1ca7q8N3UB3e3zaJ9Te6W9/ysL7jat9PHfOTaJ972Mx9cbNxB9xVzssKr+Ni+guujR/OZj57yzh3qg9ufxOezfYWndqTmsn01vP/f2dT3cp0PY3OZD26NX5PN+mJObkxOAesL7qZMzWc+5qrXXC1gPrhVITif9YXn/jgyl/UFl5/8nM185Mr0gbnMB9evDKDvq+I5qSi/J4e+r+px3aJuJvPxnNLtb1+jvsetT8Iy2fuq8JyIK5q+r+pxfbjqOeYj13njvTTmg1uTdp1jffGcVE1reY31BRepKzOZj+dUTrN616gP3mhaJusLT3xU9Ht7ivcFF91855iPXP1T6zTmg8uPlpxj76tiTtaeccfZ+6rgzqAO+5iPnmryvePMBxdPZu1j76vCc75v/Rt7XxXcPTAqgfperrU49TfmGz57UwLrizm5IyueYH3B7a3+fczHXJ2Nh48z3/APR+xjfY0nt/3G+oK7O8ckMB+57s+3djAf3O5yaQPbVzwnFUuv5rB9BVeDm2dS33tO6TQelcN8cL13ewbbV3j6zyzN9hXcXflnKvW9XFG+h2Y+uBN55yzra56rlrmZzfqCqy3PZFDfu6fqU52ymQ+uh/ZMZ33huVn9rrC+4P/9F3qG+ch1qly7xHxwa3fv03RfMdcKzY6zfQV374Tvpb7XU/Xse4z54HLk53vYvsKzPo3dTvfV49YOvZ75yNVtz25lPrhbK3Yd64s5yffPHmF9wd3jl5OZj7laE9Ycoj54rw93sb7wnNf2/kr7go/tsob5yFWvhm9mPrj72Scr2fuqOCfdkI7L2fuq4NbQdMl8nFPqZI/lzAe3N26V7H1VeDpuRBR7XxVctvwgnPnI1an3I5kPbl3cP4P29c5Jp8HlZawvuPNqU8l87KlOPBvPfMOf2x3L+prc2o9Fsr7g6siN6cxHrrVzzSzmg9trdk5l76uauT7zfRh7XxXcXeAPYT56ijtxocwHl5uHh7D3VeE5skkIe18V3J361BTmI9d+0CaE+YbfKPq54+J9zZyyuk1hfcFV/LJJzMdcrX5rqA9u54UEs77wnKG1QlhfcHfoiVDqe7lu7oZJzAdX93qHsX3FOelc7bGU7Su47DAlhvnmnAoKX8x8cHt72hy2r/DcHd7PsxTbV3A55Mkw5iPXuntjBvPBdaO/QlhfnJPWhtCFrC+43fFmJPNxrjrPDZDMB3ffvhbB+hrvQNhU1hdctVoxkfnIFe+VncJ8cGtwi3FsXzEnp35cMNtXcPft6hOZj57WyjYTmA9u1R86nu2rye38yCS2r+BOk+Qw5iNXL6oVzHxwVTIzjPXFnNw9SS7rC64uzXGp781VRjf/mfngMv2j0awvPLtLmYmsL7gsXfRzBcV95LpvHR7PfHB324Qwdl/Fc1L3z9VZ7L4KLlPmXWU+nlM60Q8ymQ/u3D+Rxu6r8KycjhfZfRVcNLFOUd/Lde/vPM98cJm88ATti+ekPb7IZH0Nf7FSGvPxnMop2JbBfHCV3Fuzvia3UptzrC+4/sk5znzkug/dP8t8cHHt/WPsvoo5iUY/HWD3VXA5eoNiPnqKcd32U9/jVrcuv7H7qvH6ddnI7qvgdo0PljEfueq3tzYwH1yu77eE9cWcxK0he1lfcCdv3jbqe3NVau9u5oO7/VpvYX3h2dnd17G+4Hb1UwuZj1z7xPjVzDd8fqyk+4rnpM3fyKD76nG9fPUV5pvnqrdOpzMfXPQ7fZntKzxRNvcM21dw9fD+I8xHrh1X8wzzwZ2MEYdZXzwn1Tej0llfw7/5+DLz8VxV6PHpzAeXD9e5zPrCc6KLfq9L8b7gzjurDjHf5A7+8TTzwa2xfQ6xfcWcrI+/Tab76nG3e/dE5pueDYfvYr7ha//exPYVntiev4Ltq+H/SpjPfOSqKuWWMx9cdao1j/XFnFS1yztZX3D9+fObmI+5Ok+t3El9j9uX0jayvvBkyex41hfcvjNtDvORK14Ojmc+uDpWZg67r+Kc1JsXxLD7Kri7IWsG83FOqU8XRzMf3Lle/Rd2XzW5FaP4fRV81bqfmI9c2eorl/ngMlH8wPrinHR7lp9N+3rcPnVnEvOxp/b8u+HUB789Yzzt63n6Qp2RrC+4yD80mPnIlY/PHMp8w9eu+prdVzEnvfuv4ey+Cq4rdx5Jfa+ne/Pud8wHFzPG/8Duq/BU7Ohx7L4KbnV9Ioz5yNVfNnSpD/7F6FDWF3MSE/b3Z33BRd/r1Mdc3V4HejIfXL86fxDra3KrHRjN+oIL5+IU6nu5zr7RPzEf3G7ZYgrbV3OuWo9MZ/sKLvqVGcN8nFNi5MAw5oPrF86OYPsKz9ozuT/dV4+rB3lBzEeuU3FnT+aDy0rL27C+5pwcXDSf4n3B5enVPzAf56qOdSczH9ypF/s96wtPjX+4K+3rcZ257TXqe7ki/kQQ88GdZtPqs33FnKyMKUFsX8Fl6rd9mY+ezuiUdswHl80iv2b7Ck92mD6S7Su47LVqMvORK9ynRzDf8Ohmk1lfM9cT11uzvoZvfq0n9b25itm5zZkPLqr2/Yr1Nbnr//0j6wsu1/8eQn0vVy9JG858cDUnNeTN/wGoBIN/ 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+AQAAAAAAAgAAAAIAP3QAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-030000.3.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-030000.3.vtu new file mode 100644 index 000000000..54525487a --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-030000.3.vtu @@ -0,0 +1,34 @@ + + + + + + + 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EZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//A3532BvAAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH39D/TfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/y3zBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3zH9LfAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8DfvfIm8AAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff8P9t8AfgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//hXcEIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfCf6E9AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+F9wVwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/I/0X2BeAQAQkMEEIRlCCE4JQhCQ0YQhHWMITgUhEJDJRiEZUohODWMQkNnGIR1zik4BEJCQxSUhGUpKTglSkJDVpSEda0pOBTGQkM1nIRlayk4Nc5CQ3echHXvJTgEIUpDBFKEZRilOCUpSkNGUoR1nKU4FKVKQyVahGVapTg1rUpDZ1qEdd6tOARjSkMU1oRlOa04JWtKQ1bWhHW9rTgU50pDNd6EZXutODXvSkN33oR1/6M4BBDGQwQxjGUIYzglGMZDRjGMdYxjOBSUxkMlOYxlSmM4NZzGQ2c5jHXOazgEUsZDFLWMZSlrOCVaxkNWtYx1rWs4FNbGQzW9jGVrazg13sZDd72Mde9nOAQxzkMEc4xlGOc4JTnOQ0ZzjHWc5zgUtc5DJXuMZVrnODW9zkNne4x13u84BHPOQxT3jGU57zgle85DVveMdb3vOBT3zkM1/4xle+84Nf/OQ3f/jH3/+FdQYgEAEJTBCCEZTghCAUIQlNGMIRlvBEIBIRiUwUohGV6MQgFjGJTRziEZf4JCARCUlMEpKRlOSkIBUpSU0a0pGW9GQgExnJTBaykZXs5CAXOclNHvKRl/wUoBAFKUwRilGU4pSgFCUpTRnKUZbyVKASFalMFapRlerUoBY1qU0d6lGX+jSgEQ1pTBOa0ZTmtKAVLWlNG9rRlvZ0oBMd6UwXutGV7vSgFz3pTR/60Zf+DGAQAxnMEIYxlOGMYBQjGc0YxjGW8UxgEhOZzBSmMZXpzGAWM5nNHOYxl/ksYBELWcwSlrGU5axgFStZzRrWsZb1bGATG9nMFraxle3sYBc72c0e9rGX/RzgEAc5zBGOcZTjnOAUJznNGc5xlvNc4BIXucwVrnGV69zgFje5zR3ucZf7POARD3nME57xlOe84BUvec0b3vGW93zgEx/5zBe+8ZXv/OAXP/nNH/7xN8p/Ib0BCERAAhOEYAQlOCEIRUhCE4ZwhCU8EYhERCIThWhEJToxiEVMYhOHeMQlPglIREISk4RkJCU5KUhFSlKThnSkJT0ZyERGMpOFbGQlOznIRU5yk4d85CU/BShEQQpThGIUpTglKEVJSlOGcpSlPBWoREUqU4VqVKU6NahFTWpTh3rUpT4NaERDGtOEZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//I36X1B3AAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH3+j/RfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/5VzBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3xn+lPAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8jflfMVcAAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff2P9V8gXgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//lXEGIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfOfyW8AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+18RdwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/+b/4zj+BCEhgghCMoAQnBKEISWjCEI6whCfC/zdMD9liIAAAwPo6tW3bNn5t27Zt27Zt27Zt27YnixwhRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTQf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMh//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU2H/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTEf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNR//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUzH/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTcf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMJ//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU0n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTKf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNp//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUxn/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTWf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlM5//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU3n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTBf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNF//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUyX/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTZf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMV//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU1X/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTNf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlN1//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUw3/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTTf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMt//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU23/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTHf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNd//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwkU4D//EZggBCU4wQhBSEITijCEJTzhiEBEIhOJKEQlOtGIQUxiE4s4xCU+8UhAQhKTiCQkJTnJSEFKUpOKNKQlPenIQEYyk4ksZCU72chBTnKTizzkJR8B5KcAhShIYYpQjKIUpwSlKElpylCOspSnApWoSGWqUI2qVKcGtahJbepQj7rUpwGNaEhjmtCMpjSnBa1oSWva0I62tKcDnehIZ7rQja50pwe96Elv+tCPvvRnAIMYyGCGMIyhDGcEoxjJaMYwjrGMZwKTmMhkpjCNqUxnBrOYyWzmMI+5zGcBi1jIYpawjKUsZwWrWMlq1rCOtaxnA5vYyGa2sI2tbGcHu9jJbvawj73s5wCHOMhhjnCMoxznBKc4yWnOcI6znOcCl7jIZa5wjatc5wa3uMlt7nCPu9znAY94yGOe8IynPOcFr3jJa97wjre85wOf+MhnvvCNr3znB7/4yW/+8I+/BMrnP/8RmCAEJTjBCEFIQhOKMIQlPOGIQEQiE4koRCU60YhBTGITizjEJT7xSEBCEpOIJCQlOclIQUpSk4o0pCU96chARjKTiSxkJTvZyEFOcpOLPOQlHwHkpwCFKEhhilCMohSnBKUoSWnKUI6ylKcClahIZapQjapUpwa1qElt6lCPutSnAY1oSGOa0IymNKcFrWhJa9rQjra0pwOd6EhnutCNrnSnB73oSW/60I++9GcAgxjIYIYwjKEMZwSjGMloxjCOsYxnApOYyGSmMI2pTGcGs5jJbOYwj7nMZwGLWMhilrCMpSxnBatYyWrWsI61rGcDm9jIZrawja1sZwe72Mlu9rCPveznAIc4yGGOcIyjHOcEpzjJac5wjrOc5wKXuMhlrnCNq1znBre4yW3ucI+73OcBj3jIY57wjKc85wWveMlr3vCOt7znA5/4yGe+8I2vfOcHv/jJb/7wj7/8D5W4wko= 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rN+48ePLizYfvb8sM/10/AYKECBMhSow4CZKkSJMhS448BYqUKFOhSo06DZq0aNOhS48+A4aMGDNhyow5C5asWLNhy449B46cOHPhyo07D568ePPhy8//0E+AICHCRIgSI06CJCnSZMiSI0+BIiXKVKhSo06DJi3adOjSo8+AISPGTJgyY86CJSvWbNiyY8+BIyfOXLhy486DJy/efPjy89f6CRAkRJgIUWLESZAkRZoMWXLkKVCkRJkKVWrUadCkRZsOXXr0GTBkxJgJU2bMWbBkxZoNW3bsOXDkxJkLV27cefDkxZsP37/+fZnnFwGChAgTIUqMOAmSpEiTIUuOPAWKlChToUqNOg2atGjToUuPPgOGjBgzYcqMOQuWrFizYcuOPQeOnDhz4cqNOw+evHjz4fvbItP/1E+AICHCRIgSI06CJCnSZMiSI0+BIiXKVKhSo06DJi3adOjSo8+AISPGTJgyY86CJSvWbNiyY8+BIyfOXLhy486DJy/efPjy87/0EyBIiDARosSIkyBJijQZsuTIU6BIiTIVqtSo06BJizYduvToM2DIiDETpsyYs2DJijUbtuzYc+DIiTMXrty48+DJizcfvvz8jX4CBAkRJkKUGHESJEmRJkOWHHkKFClRpkKVGnUaNGnRpkOXHn0GDBkxZsKUGXMWLFmxZsOWHXsOHDlx5sKVG3cePHnx5sP3b35f5PtFgCAhwkSIEiNOgiQp0mTIkiNPgSIlylSoUqNOgyYt2nTo0qPPgCEjxkyYMmPOgiUr1mzYsmPPgSMnzly4cuPOgycv3nz4/rbE+L/1EyBIiDARosSIkyBJijQZsuTIU6BIiTIVqtSo06BJizYduvToM2DIiDETpsyYs2DJijUbtuzYc+DIiTMXrty48+DJizcfvvz8H/0ECBIiTIQoMeIkSJIiTYYsOfIUKFKiTIUqNeo0aNKiTYcuPfoMGDJizIQpM+YsWLJizYYtO/YcOHLizIUrN+48ePLizYcvP3+rnwBBQoSJECVGnARJUqTJkCVHngJFSpSpUKVGnQZNWrTp0KVHnwFDRoyZMGXGnAVLVqzZsGXHngNHTpy5cOXGnQdPXrz58P3b35d4fxEgSIgwEaLEiJMgSYo0GbLkyFOgSIkyFarUqNOgSYs2Hbr06DNgyIgxE6bMmLNgyYo1G7bs2HPgyIkzF67cuPPgyYs3H76/LTD/X/0ECBIiTIQoMeIkSJIiTYYsOfIUKFKiTIUqNeo0aNKiTYcuPfoMGDJizIQpM+YsWLJizYYtO/YcOHLizIUrN+48ePLizYcvP/9PPwGChAgTIUqMOAmSpEiTIUuOPAWKlChToUqNOg2atGjToUuPPgOGjBgzYcqMOQuWrFizYcuOPQeOnDhz4cqNOw+evHjz4cvP3+knQJAQYSJEiREnQZIUaTJkyZGnQJESZSpUqVGnQZMWbTp06dFnwJARYyZMmTFnwZIVazZs2bHnwJETZy5cuXHnwZMXbz58/+73Bf7/Dz4v2Mg= 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+AQAAAAAAAgAAAAIAp3QAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-030000.pvtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-030000.pvtu new file mode 100644 index 000000000..d7c05dbd0 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-030000.pvtu @@ -0,0 +1,19 @@ + + + + + + + + + + + + + + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-040000.0.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-040000.0.vtu new file mode 100644 index 000000000..0a22b88eb --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-040000.0.vtu @@ -0,0 +1,34 @@ + + + + + + + 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tB2hc8aS7+wHy6UZtdbgM+LoO5ZzzbS4P+XSjtucAdB3pvls86W4/kI949zkA+dBzgHKffpfLQz7lPvcu1w80rnjS3X4gn27UVofLgK/rUO7T73J5yKcLdHc/QPNOPOnufoB8xLvrAPnQdS/nnG9zecinnBO+ze0HaFzxpLv7AfIR79aBfKgO5ZzzbS4P+ZTz0re5fqB5J550tx/IR7y7DpAPrQPlPv0ul4d8yn3uXa4faFzxpLv9QD7i3TqQD9Wh3Kff5fKQT7nfh+/7P9P/+TjIn32v/xtP+pnP6XFw3DOfE+ited5eZ9+7/8aTbuchn9OwfvZ7mV+PB+tWjyue9DMf+Dvg5CPerQP6QB3Ek+7mIR/x9PcW7H54e539XYU3nnS7H8jnBHprnrfX2fe+v/Gk23nI5zSsu/1A44on3e0H8hHv1gF9oA7iSXfzkI940s/OBf1meN2o55140s/WmYvhcc/m9UXbOkA+dN3Fk+7mIR/xpJ/dl/12uG71uOJJP7uOV8PjntXtqq0O5EN1EE+6m4d8xLvrAF1HmnfiSXf7gXzEu+sA+dA6IJ50Nw/5iCfd7QcaVzzpbj+Qj3i3DuRDdRBPupuHfMTT33ux9wNvr7O/6/LGk27vB8jnBHprnrfX2d9deeNJt/OQz2lYd/cDNK540t39APmId+uAPlAH8aS7echH/I/3yX54e/XA9/fJfiCfE+iteVSnv7zPk27nIZ/TsO72A40rvr/P9QP5iO+TeagO4kl385CPeNLd/QDNO/Gku/sB8hHvrgPkQ9ddPOluHvIRT7q7H6BxxZPu7gfIR7xbB/KhOogn3c1DPuLddYCuI8078f19rh/IR3yfzEPrgHjS3TzkI550tx9oXPH9fa4fyEd8n8xDdRBPupuHfMSTfva5vN8NPzeo77vFk372nOd6eNyz5yrXbc8ByIfu+8ST7uYhH/Gkn30u5g/DdavHFU/62XOkm+Fxz57b3LTVgXyoDuJJd/OQj3j3OQBdR7rvFk+62w/kI959DkA+9L4vnnQ3D/mIJ93tBxpXPOluP5CPeLcO5EN1EE+6m4d8xJN+di7rh+F1o5534kk/e858Ozzu2XPd27Z1gHzouosn3c1DPuJJPzsX8cfhutXjiif97H38bnjcs/fNu7Y6kA/VQTzpbh7yKd8T+KdcP9C8E0+62w/kI95dB8iH1gHxpLt5yEc86W4/0LjiSXf7gXzEu3UgH6qDeNLdPORTvufQfA5A7+N03y2edHc/QD7i3ecA5EP3feJJd/OQj3jS3f0AjSuedHc/QD7i3TqQD9VBPOluHvIR7z4HoOtI993i+/tcP5CP+D6Zh54DiCfdzUM+4kl3+4HGFd/f5/qBfMT3yTxUB/Gku3nIRzzp7n6A5p140t39APmId9cB8qHrLp50Nw/5iCfd3Q/QuOJJd/cD5CPerQP5UB3Ek+7mIZ/yPcn3uX6geSe+v8/1A/mI75N5aB0QT7qbh3zEk+72A40rvr/P9QP5iO+TeagO4kl385BP+Z5P+Hu/9nmAt9fZ3/V940m3zwOQzwn01jzq05/e50m385DPaVh3zwPQuOJJd88DkI94tw7oA3UQT7qbh3zE099btvvh7XX2d5XfeNLtfiCfE+itebQOAd+NknnI5zSsu/1A44on3e0H8hHv1gF9oA7iu1EuD/mIJ909D0DzTjzp7nkA8hHvrgPkQ9ddPOluHvIRT7p7HoDGFU+6ex6AfMS7dSAfqoN40t085CPeXQfoOtK8E0+62w/kI95dB8iH1gHx3SiXh3zEk+72A40rnnS3H8hHvFsH8qE6iO9GuTzkI77L7gfeXmd/1/2NJ93eD5DPCfTWPLpP+vl9nnQ7D/mchnV3P0Djiifd3Q+Qj3i3DugDdRBPupuHfMSf6a398PYqn7OveNLtfiCfE+itefR6eJ8n3c5DPqdh3e0HGlc86W4/kI94tw7oA3UQT7qbh3zK53wfcvsBmnfiSXf3A+Qj3l0HyIeuu3jS3TzkI550dz9A44on3d0PkI94tw7kQ3UQT7qbh3zEu+sAXUead+JJd/uBfMS76wD50DpQPqf/kMtDPuVz7g+5fqBxxZPu9gP5iHfrQD5Uh/I5/YdcHvIp5/wfvOcA9Ht8uu8WT7p7HoB8xLvPAciH7vvEk+7mIR/xpLvnAWhc8aS75wHIR7xbB/KhOogn3c1DPuLd5wB0Hem+Wzzpbj+Qj3j3OQD50Pu++G6Uy0M+4kl3+4HGFU+62w/kI96tA/lQHcR3o1we8hFPunsegOadeNLd8wDkI95dB8iHrrt40t085COedPc8AI0rnnT3PAD5iHfrQD5UB/Gku3nIp/yd0J9y/UDzTjzpbj+Qj3h3HSAfWgfEd6NcHvIRT7rbDzSueNLdfiAf8W4dyIfqIL4b5fKQT/k7p8n9AN13iyfd3Q+Qj3j3OQD50H2feNLdPOQjnnR3P0Djiifd3Q+Qj3i3DuRDdRBPupuHfMS7zwHoOtJ9t3jS3X4gH/HucwDyoecA5XP6D7k85FM+5/6Q6wcaVzzpbj+Qj3i3DuRDdSif03/I5SGf8jnfh9x+gOadeNLd/QD5iHfXAfKh6y6edDcP+Ygn3d0P0LjiSXf3A+Qj3q0D+VAdxJPu5iGf8nfSf871A8078aS7/UA+4t11gHxoHSif03/I5SGf8jn3h1w/0LjiSXf7gXzEu3UgH6pD+Zz+Qy4P+ZTP+ZNefQ9A+Vw9fG9A/bl7/Uz62ff8/Gl43LPv1fnT+3prHvrctzjS3Tzko59Jr+tQPtd9540rnvSz7xH68/C4Z9/b8+e2OpAP1UE86W4e8hHvfg8AXUf63L140t1+IB9x7vcAkA997k886W4e8hFPutsPNK5+Jt3tB/IR79aBfKgO4kh385CPfia9XgfK5yrvvHknnvSz7xm7Hx737Hu97tvWAfKh6y6edDcP+Ygnva5Dfz9ct3rcPtDPvsfpL8Pjnn1v0l/a6kA+VIc+0N085CPeXQfoOtK8E0+62w/kI95dB8iH1gHxpLt5yEc86W4/0Lh9oLv9QD7i3TqQD9WhD3Q3D/mId78HgN7H6XP34kl39wPkI979HgDyoc99iyfdzUM+4kl39wM0rnjS3f0A+Yhz60A+VAfxpLt5yEe8+z0AdB3pc/fi+/tcP5CP+D6Zh37vL550Nw/5iCfd7QcaV3x/n+sH8hHfJ/NQHcST7uYhH/Gku/sBmnf6mXR3P0A+4t11gHzouosj3c1DPvqZdHc/QOP2ge7uB8hHvFsH8qE69IHu5iEf8e46QNeR5p34/j7XD+Qjrk/moXVAPOluHvIRT7rbDzRuH+huP5CP+D6Zh+rQB7qbh3z0M+n1c4Byru7Ou+8WT/rZ9zz/NDzu2fcq/9T2HIB86L5PPOluHvIRT3pdh8vRcN3qccWTfvY90qPhcc++t3nUVofLgK/rIJ50Nw/5dKO25wB0Hem+Wzzpbj+Qj3j3OQD50Pu+eNLdPOQjnnS3H2hc8aS7/UA+3aitDpcBX9dBPOluHvLpAr1eB8q5ijtv3okn/ex75n8eHvfse91/blsHyIeuu3jS3TzkI570ug7l9/p33rjiST/7Pf7D8Lhnvzd/aKsD+VAdxJPu5iEf8e46QNeR5p140t1+IB/x7jpAPrQOiCfdzUM+4kl3+4HGFU+62w/kI96tA/lQHcST7uYhH/HucwB6H6f7bvGku/sB8hHvPgcgH7rvE0+6m4d8xJPu7gdoXPGku/sB8ulGbXW4DPi6DuJJd/OQTzdqew5A15Huu8X397l+IB/xfTIPPQcQT7qbh3zEk+72A40rvr/P9QP5dKO2OlwGfF0H8aS7ecinC3R3P0DzTjzp7n6AfMS76wD50HUXT7qbh3zEk+7uB2hc8aS7+wHyEe/WgXyoDuJJd/OQj3h3HaDrSPNOfH+f6wfyEd8n89A6IJ50Nw/5iCfd7QcaV3x/n+sH8hHfJ/NQHcST7uYhH/Hu9wDQ7/Hpc/fiSXfPA5CPePd7AMiHPvctnnQ3D/mIJ909D0DjiifdPQ9APuLdOpAP1UE86W4e8hHvfg8AXUf63L140t1+IB/x7vcAkA997k98N8rlIR/xpLv9QOOKJ93tB/IR79aBfKgO4rtRLg/5iCfdPQ9A80486e55APIR764D5EPXXTzpbh7yEU+6ex6Axu0D3T0PQD7i3DqQD9WhD3Q3D/mId9cBuo4078ST7vYD+Yh31wHyoXVAfDfK5SEf8aS7/UDj9oHu9gP5iHfrQD5Uhz7Q3TzkI75L7gfoc/fiSXf3A+Qj3v0eAPKhz32LJ93NQz7iSXf3AzSueNLd/QD5iHfrQD5UB/Gku3nIR7z7PQB0Helz9+JJd/uBfMS73wNAPvQ9AOX36A+5PORTfs/6kOsHGlc86W4/kI94tw7kQ3Uov8d9yOUhn/Kc9yG3H6B5J550dz9APuLddYB86LqLJ93NQz7iSXf3AzRuH+jufoB8xLt1IB+qQx/obh7yEe+uA3Qdad6JJ93tB/IR764D5EPrQPk9ykMuD/mU5+wPuX6gcftAd/uBfMS7dSAfqkMf6G4e8in3+Q/ecwD6PT7dd+tn0t3zAOQj3n0OQD503yeOdDcP+ehn0t3zADSueNLd8wDk043a6nAZ8HUdxJPu5iGfbtT2HICuI913iyfd7QfyEec+ByAfet8X341yechHPOluP9C4+pl0tx/Ipxu11eEy4Dvgu1EuD/l0ge6eB6B5J5509zwA+Yh31wHyoesunnQ3D/mIJ909D0DjiifdPQ9APuLdOpAP1UE86W4e8hHvrgN0HWneiSfd7QfyEe+uA+RD64D4bpTLQz7iSXf7gcYVT7rbD+Qj3q0D+VAdxHejXB7yEd8l9wN03y2edHc/QD7i3ecA5EP3feJJd/OQj3jS3f0AjSuedHc/QD7dqK0OlwFf10E86W4e8ulGbc8B6DrSfbd40t1+IB/x7nMA8qHnAOV99CGXh3zKOvuQ6wcaVzzpbj+QTzdqq8NlwNd1KOv4Qy4P+XSB7u4HaN7pZ9Ld/QD5iHfXAfKh6y6OdDcP+ehn0t39AI0rnnR3P0A+4t06kA/VQTzpbh7yEe+uA3Qdad6JJ93tB/IR564D5EPrQKnjQy4P+ZR/50OuH2hc/Uy62w/kI96tA/lQHQr3kMtDPuVn0qvXFvTC//PxfZ706vX3/wjGrXwKX+mteQp/Av70mMpDPoUnvXr9738GdavGLTzp1ev3fw3GrXwKb9aBfKgOhT89pvKQT+FPuX4oz81+PX6XJ93tB/IRX+utecq/9yPk/DhO5SGf8u8l3ewHGlc86W4/kI94tw7kQ3UoOT+OU3nIp/x7Sa9eh78G60Y17w6RXr3+8V/BuJVP4c11gHzouh8i3cxDPoUnvXr9338HdavGLTzp1Wv8GIxb+RTerAP5UB0Kf3pM5SGfwp9y/UDz7hDobj+Qj3h3HSAfWgcOge7mIZ/y7yXd7AcaVzzpbj+Qj3i3DuRDdSg5P45Tecin/Hs/5vYD5XsDfvM+T7q7HyAf8bXemqfsly5gn3ORy0M+ZZ9wkdsP0LjiSXf3A+Qj3q0D+VAdyj7nIpeHfMo6eZHrh/Lc7Lfv86S7/UA+4mu9NU+Z71cwT69yecin9PlVrh9oXPGku/1APuLdOpAP1aHM06tcHvIp8/0qtx+geXcIdHc/QD7i3XWAfOi6HwLdzUM+ZZ9wkdsP0LjiSXf3A+Qj3q0D+VAdyj7nIpeHfMp+6SLXDzTvDoHu9gP5iHfXAfKhdeAQ6G4e8il9fpXrBxpXPOluP5CPeLcO5EN1KPP0KpeHfMp8J716fRgHzw2q++7Ck169puNg3MpnOm57DvAh4k/Anx5TechnGunVaxfVrRp3F+nV68fPwbiVT+HNOuyiup2APz2m8pBP4U+5fqD7bvGku/1APtNx23OADwFfv++XnB/HqTzkM410sx9o3F2gu/1APuLdOuyCutV12AW6m4d8yr+X9Oq1+BysG9W8W0R69eqjcSuf/nPbOrCI+BPwp8dUHvLpI716fZ8EdavGLTzp1WszCcatfDaTtjp8j/gT8KfHVB7y2Uza1gG6jjTvFoHu9gP59J/b1oFFwNfrwCLQ3Tzk00e62Q80rnjS3X4gn82krQ7fA76uQ8n5cZzKQz6bSdtzAHofp/tu8aS7+wHymY7bngN8CPj6vq/scy5yechnGujufoDG3QW6ux8gH/FuHXZB3eo67ALdzUM+ZZ28yPUD3XeLJ93tB/KZjtueA3wI+Po5QJmnV7k85DMNdLcfaNxdoLv9QD7i3TrsgrrVddgFupuHfMp8v8rtB2jeLQLd3Q+QT/+5bR1YBHx93ReB7uYhnz7Q3f0AjSuedHc/QD6bSVsdvgd8XYeyz7nI5SGfzaRtHaDrSPNuEehuP5BP/7ltHVgEfL0OLALdzUM+faC7/UDjiifd7Qfy2Uza6vA94Os6lHl6lctDPhvQW88DlL+b9rv3edLd8wDkI77WW/OU80LX7/Oku3nIp5yTufbqQL/Hp3HFk+6eByAf8W4dyIfqUM75XOfykE85J3Cd64fyd0P+8D5PutsP5CO+1lvzlPNON+/zpLt5yKec87nJ9QONK550tx/IR7xbB/KhOpRzSje5PORTzjnceOvAITgvVM+7Q6C75wHIR7y7DpAPXfdDoLt5yKeck7n26kC/x6dxxZPungcgH/FuHciH6lDO+Vzn8pBPeU54nesHmneHQHf7gXzEu+sA+dA6cAh0Nw/5lHM+N7l+oHHFk+72A/mId+tAPlSHck7pJpeHfMpzzpvcfqB8b9IP7/Oku/sB8hFf6615ynnp2/d50t085FPOCd/m9gM0rnjS3f0A+Yh360A+VIdyzvk2l4d8yjnJ21w/lO8N+OP7POluP5CP+FpvzVPu9+/gPv0ul4d8yn3uXa4faFzxpLv9QD7i3TqQD9Wh3Kff5fKQT9nn3+X2AzTvDoHu7gfIR7y7DpAPXfdDoLt5yKecE77N7QdoXPGku/sB8hHv1oF8qA7lnPNtLg/5lN+T3ub6gebdIdDdfiAf8e46QD60DhwC3c1DPuU+9y7XDzSueNLdfiAf8W4dyIfqUO7T73J5yKfc7995zwHo9/h03y2edPc8APlMx23PAT4EfH3fV875XOfykM800N3zADTuLtDd8wDkI96twy6oW12HXaC7ecinnBO4zvUD3XeLJ93tB/KZjtueA3wI+Pp9v5xTusnlIZ9poLv9QOPuAt3tB/IR79ZhF9StrsMu0N085FPOOdx468AiOC9Uz7tFoLvnAcin/9y2DiwCvr7ui0B385BPH+jueQAaVzzp7nkA8tlM2urwPeDrOpRzPte5POSzmbStA3Qdad4tAt3tB/LpP7etA4uAr9eBRaC7ecinD3S3H2hc8aS7/UA+m0lbHb4HfF2Hck7pJpeHfDaTtucA9D5O993iSXf3A+QzHbc9B/gQ8PV9XznnfJvLQz7TQHf3AzTuLtDd/QD5iHfrsAvqVtdhF+huHvIp5yRvc/1A993iSXf7gXym47bnAB8Cvn4OUO7T73J5yGca6G4/0Li7QHf7gXzEu3XYBXWr67ALdDcP+ZR9/l1uP0DzbhHo7n6AfPrPbevAIuDr674IdDcP+fSB7u4HaFzxpLv7AfLZTNrq8D3g6zqUc863uTzks5m0rQN0HWneLQLd7Qfy6T+3rQOLgK/XgUWgu3nIpw90tx9oXPGku/1APptJWx2+B3xdh3KffpfLQz6bSK9eP3x5X6fP3Ree9Oo1/xKMW/nMQW/NQ5/7LvzpMZWHfOaRXr2OUd2qcY+RXr3+9jUYt/IpvFmHY1S3E/Cnx1Qe8in8KdcP9Ll78aS7/UA+c9Bb89Dn/krOj+NUHvKZR7rZDzTuMdDdfiAf8W4djkHd6jocA93NQz7l30t69Vp/DdaNat6tI716XT4F41Y+hTfXAfKh676OdDMP+RSe9Oo1ewrqVo07i/Tq1UXjVj7dU1sdZhF/Av70mMpDPt1T2zpA15Hm3TrQ3X4gH/HuOkA+tA6sA93NQz7l30u62Q807izQ3X4gn+6prQ6zgK/rMAt0Nw/5dKC37gfoc/fiSXf3A+QzB701D33uu+xzLnJ5yGce6O5+gMY9Brq7HyAf8W4djkHd6jocA93NQz5lnbzI9QN97l486W4/kM8c9NY89Hv/Mk+vcnnIZx7obj/QuMdAd/uBfMS7dTgGdavrcAx0Nw/5lPl+ldsP0LxbB7q7HyAf8e46QD503deB7uYhn7JPuMjtB2jcWaC7+wHy6Z7a6jAL+LoOs0B385BP99S2DtB1pHm3DnS3H8hHvLsOkA+tA+tAd/OQT+nzq1w/0LizQHf7gXy6p7Y6zAK+rsMs0N085NNFevX6+hw8N6juuwtPevVaPQfjVj6r57bnAF8j/gT86TGVh3xWkV69Pr0EdavGLTzp1ev1JRi38nl9aavDp4g/AX96TOUhn9eXtucAdB3pvls86W4/kM/que05wNeAr9/3S86P41Qe8llFutkPNK540t1+IJ/Xl7Y6fAr4ug4l58dxKg/5vEZ69dpH60Y17/aRXr1++RaMW/kU3lwH9tG6cQL+9JjKQz6FJ716Lb8FdavGXUZ6/fqfYNzKp/BmHciH6rCMdDMP+RT+lOsHmnf7QHf7gXzEu+vAPlg36nVgH+huHvIp/17SzX6gcZeB7vYD+Yh360A+VIdloLt5yKf8e83nAPQ+Tvfd4kl39wPks3puew7wNeDr+76yz7nI5SGfVaC7+wEaVzzp7n6AfF5f2urwKeDrOpR9zkUuD/m8vrQ9B6DrSPfd4kl3+4F8Vs9tzwG+Bnz9HKDM06tcHvJZBbrbDzSueNLdfiCf15e2OnwK+LoOZZ5e5fKQz2ukm/sBmnf7QHf3A+Qj3l0H9sG6UV/3faC7ecin7BMucvsBGncZ6O5+gHzEu3UgH6rDMtDdPORT9ksXuX6gebcPdLcfyEe8uw7sg3WjXgf2ge7mIZ/S51e5fqBxl4Hu9gP5iHfrQD5Uh2Wgu3nIp8x383sA6Pf49Ll78aS75wHIZw56ax763Hc553Ody0M+80B3zwPQuMdAd88DkI94tw7HoG51HY6B7uYhn3JO4DrXD/S5e/Gku/1APnPQW/PQ5/7KOaWbXB7ymQe62w807jHQ3X4gH/FuHY5B3eo6HAPdzUM+5ZzDjbcOrIPzQvW8Wwe6ex6AfMS76wD50HVfB7qbh3zKOZlrrw6z4LxEPe4s0N3zAOTTPbXVYRbwdR1mge7mIZ/uqW0doOtI824d6G4/kI94dx0gH1oH1oHu5iGfcs7nJtcPNO4s0N1+IJ/uqa0Os4Cv6zALdDcP+XSgt+4H6HP34kl39wPkMwe9NQ997rucc77N5SGfeaC7+wEa9xjo7n6AfMS7dTgGdavrcAx0Nw/5lHOSt7l+oM/diyfd7QfymYPemoe+B6Dcp9/l8pDPPNDdfqBxj4Hu9gP5iHfrcAzqVtfhGOhuHvIp+/y73H6A5t060N39APmId9cB8qHrvg50Nw/5lHPCt7n9AI07C3R3P0A+3VNbHWYBX9dhFuhuHvLpntrWAbqONO/Wge72A/mId9cB8qF1YB3obh7yKfe5d7l+oHFnge72A/l0T211mAV8XYdZoLt5yKeLdPM8AN13iyfdPQ9APqvntucAXwO+vu8r53yuc3nIZxXo7nkAGlc86e55APJ5fWmrw6eAr+tQzvlc5/KQz+tL23MAuo503y2edLcfyGf13PYc4GvA1+/75ZzSTS4P+awC3e0HGlc86W4/kM/rS1sdPgV8XYdyTukml4d8XgPdPQ9A824f6O55APIR764D+2DdqK/7PtDdPORTzslce3VYBucl6nGXge6eByAf8W4dyIfqsAx0Nw/5lPNC17l+oHm3D3S3H8hHvLsO7IN1o14H9oHu5iGfcs7nJtcPNO4y0N1+IB/xbh3Ih+qwDHQ3D/mU807mcwB6H6f7bvGku/sB8lk9tz0H+Brw9X1fOed8m8tDPqtAd/cDNK540t39APm8vrTV4VPA13Uo55xvc3nI5/Wl7TkAXUe67xZPutsP5LN6bnsO8DXg6+cA5T79LpeHfFaB7vYDjSuedLcfyOf1pa0OnwK+rkO5T7/L5SGf10B39wM07/aB7u4HyEe8uw7sg3Wjvu77QHfzkE85J3yb2w/QuMtAd/cD5CPerQP5UB2Wge7mIZ9yXvo21w807/aB7vYD+Yh314F9sG7U68A+0N085FPuc+9y/UDjLgPd7QfyEe/WgXyoDstAd/OQT7nfr/WH9/XtwzD/9395nye99vn7fwyPW/uIr/XWPOJ//6f3edLdPOQjnvS6Dv/7n8N1q8cVT3rtQ38HnHzEu3UgH6qDeNLdPOQjvtZb+0H8P/71fZ50tx/IR3ytt+YRP/7z+zzpbh7yEU+62w80rnjS3X4gH/FuHciH6iCedDcP+YgnvV4HDn8dXjfqeXcI9NrnH/81PG7tI95dB8iHrvsh0N085COe9LoO//ffw3WrxxVPeu1DfweQfMS7dSAfqoN40t085CPeXQfoOtK8OwS62w/kI95dB8iH1oFDoLt5yEc86W4/0LjiSXf7gXzEu3UgH6qDeNLdPOQjvtZb9wPipz++z5Pu7gfIR3ytt+YR/+P9+zzpbh7yEU+6ux+gccWT7u4HyEe8WwfyoTqIJ93NQz7ia721H8T3wPf3uX4gH/F9Mo/4zV/e50l385CPeNLdfqBxxff3uX4gH/F9Mg/VQTzpbh7yEU+6ux+geXcIdHc/QD7i3XWAfOi6HwLdzUM+4kl39wM0rnjS3f0A+Yh360A+VAfxpLt5yEe8uw7QdaR5dwh0tx/IR3yfzEPrwCHQ3TzkI550tx9oXPH9fa4fyEd8n8xDdRBPupuHfMSTXj8H+DAefm5Q33eLJ732mY6Hx619puO25wAfAr6+7xNPupuHfKaBXtdhF9StHncX6LUP/R1w8hHv1mEX1K2uwy7Q3TzkI959DkDXke67xZPu9gP5TMdtzwE+BHz9vi+edDcP+UwD3e0HGncX6G4/kI94tw67oG51HXaB7uYhH/Gk1+vA4vPwulHPu0Wg1z59MG7t039uWwcWAV9f90Wgu3nIpw/0ug7fJ8N1q8cVT3rtQ98DQD6bSVsdvgd8XQfxpLt5yGczaVsH6DrSvFsEutsP5NN/blsHFgFfrwOLQHfzkE8f6G4/0LjiSXf7gXw2k7Y6fA/4ug7iSXfzkM9m0vYcgN7H6b5bPOnufoB8puO25wAfAr6+7xNPupuHfKaB7u4HaNxdoLv7AfIR79ZhF9StrsMu0N085CPefQ5A15Huu8X397l+IJ/puO05wIeAr58DiCfdzUM+00B3+4HG3QW62w/kI75P5qE67ALdzUM+4kl39wM07xaB7u4HyKf/3LYOLAK+vu6LQHfzkE8f6O5+gMYVT7q7HyCfzaStDt8Dvq6DeNLdPOSzmbStA3Qdad4tAt3tB/LpP7etA4uAr9eBRaC7ecinD3S3H2hc8f19rh/IZzNpq8P3gK/rIJ50Nw/5bEBvPQ8gfv5v7/Oku+cByEd8rbfmEf+3n97nSXfzkI940t3zADSueNLd8wDkI96tA/lQHcST7uYhH/G13toP4i9H7/Oku/1APuJrvTWP+A74bpTLQz7iSXf7gcYVT7rbD+Qj3q0D+VAdxHejXB7yEU+6ex6A5t0h0N3zAOQj3l0HyIeu+yHQ3TzkI5509zwAjSuedPc8APmId+tAPlQH8aS7echHvLsO0HWkeXcIdLcfyEe8uw6QD60Dh0B385CPeNLdfqBxxZPu9gP5iHfrQD5UB/HdKJeHfMR3yf2A+NW/v8+T7u4HyEd8rbfmEf/68/s86W4e8hFPursfoHHFk+7uB8hHvFsH8qE6iCfdzUM+4mu9tR/E//LL+zzpbj+Qj/hab81TPu//8D5PupuHfMrn3B9y/UDjiifd7QfyEe/WgXyoDuVz+g+5PORTPuf7kNsP0Lw7BLq7HyAf8e46QD503Q+B7uYhH/Gku/sBGlc86e5+gHzEu3UgH6qDeNLdPOQj3l0H6DrSvDsEutsP5CPeXQfIh9aBQ6C7ecinfM79IdcPNK540t1+IB/xbh3Ih+pQPqf/kMtDPuWc/4P3HIB+j0/33eJJd88DkM903PYc4EPA1/d94kl385DPNNDd8wA07i7Q3fMA5CPercMuqFtdh12gu3nIR7z7HICuI913iyfd7QfymY7bngN8CPgO+G6Uy0M+00B3+4HG3QW62w/kI96twy6oWwd8N8rlIR/xpLvnAWjeLQLdPQ9APv3ntnVgEfD1dV8EupuHfPpAd88D0LjiSXfPA5DPZtJWh+8BX9dBPOluHvLZTNrWAbqONO8Wge72A/n0n9vWgUXAd8B3o1we8ukD3e0HGlc86W4/kM9m0laH7wHfAd+NcnnIZzNpew5A7+N03y2edHc/QD7TcdtzgA8BX9/3iSfdzUM+00B39wM07i7Q3f0A+Yh367AL6lbXYRfobh7yEe8+B6DrSPfd4kl3+4F8puO25wAfAr5+DlA+p/+Qy0M+00B3+4HG3QW62w/kI96twy6oW12HXaC7ecinfM73IbcfoHm3CHR3P0A+/ee2dWAR8PV1XwS6m4d8+kB39wM0rnjS3f0A+WwmbXX4HvB1HcST7uYhn82kbR2g60jzbhHobj+QT/+5bR1YBHy9DiwC3c1DPn2gu/1A44on3e0H8tlM2urwPeDrOpTP6T/k8pDPJtKr7wH4Af6OPH3uXjzptc/8y/C4tc8c9NY89Llv8aS7echnHuh1HY5B3epxj4Fe+9DfAScf8W4djkHd6jocA93NQz7i3e8BoOtIn7sXT7rbD+QzB701D33uTzzpbh7ymQe62w807jHQ3X4gH/FuHY5B3eo6HAPdzUM+4kmv14H11+F1o55360CvfS6fhsetfcS76wD50HVfB7qbh3zEk17XYfY0XLd63Fmg1z5dMG7t0z211WEW8HUdZoHu5iGf7qltHaDrSPNuHehuP5CPeHcdIB9aB9aB7uYhH/Gku/1A484C3e0H8ume2uowC/i6DrNAd/OQTwd6636APncvnnR3P0A+c9Bb89DnvsWT7uYhn3mgu/sBGvcY6O5+gHzEu3U4BnWr63AMdDcP+Yh3vweAriN97l58f5/rB/KZg96ah37vL550Nw/5zAPd7Qca9xjobj+Qj/g+mYfqcAx0Nw/5iCfd3Q/QvFsHursfIB/x7jpAPnTd14Hu5iEf8aS7+wEadxbo7n6AfLqntjrMAr6uwyzQ3Tzk0z21rQN0HWnerQPd7QfyEd8n89A6sA50Nw/5iCfd7Qcadxbobj+QT/fUVodZwNd1mAW6m4d8ukCvnwN8fR5+blDfd4snvfZZPQ+PW/usntueA3wN+Pq+Tzzpbh7yWQV6XYdPL8N1q8cVT3rtQ38HnHxeX9rq8Cng6zqIJ93NQz6vL23PAeg60n23eNLdfiCf1XPbc4CvAV+/74sn3c1DPqtAd/uBxhVPutsP5PP60laHTwFf10E86W4e8nkN9Hod2AfrRj3v9oFe+/zybXjc2ke8uw7sg3Wjvu77QHfzkI940us6LL8N160edxnotc+v/md43NpHvFsH8qE6LAPdzUM+4t11YB+sG/W82we62w/kI95dB/bBulGvA/tAd/OQj3jS3X6gcZeB7vYD+Yh360A+VIdloLt5yEe8+xyA3sfpvls86e5+gHxWz23PAb4GfH3fJ550Nw/5rALd3Q/QuOJJd/cD5PP60laHTwFf10E86W4e8nl9aXsOQNeR7rvF9/e5fiCf1XPbc4CvAV8/BxBPupuHfFaB7vYDjSu+v8/1A/m8vrTV4VPA13UQT7qbh3xeA93dD9C82we6ux8gH/HuOrAP1o36uu8D3c1DPuJJd/cDNO4y0N39APmId+tAPlSHZaC7echHvLsO7IN1owe+v8/1A/mI75N5aB3YB7qbh3zEk+72A427DHS3H8hHfJ/MQ3VYBrqbh3zEu98DQL/Hp8/diyfdPQ9APnPQW/PQ577Fk+7mIZ95oLvnAWjcY6C75wHIR7xbh2NQt7oOx0B385CPePd7AOg60ufuxZPu9gP5zEFvzUOf+xPfjXJ5yGce6G4/0LjHQHf7gXzEu3U4BnXrgO9GuTzkI5509zwAzbt1oLvnAchHvLsOkA9d93Wgu3nIRzzp7nkAGncW6O55APLpntrqMAv4ug6zQHfzkE/31LYO0HWkebcOdLcfyEe8uw6QD60D60B385CPeNLdfqBxZ4Hu9gP5dE9tdZgFfAd8N8rlIZ8O9Nb9AH3uXjzp7n6AfOagt+ahz32LJ93NQz7zQHf3AzTuMdDd/QD5iHfrcAzqVtfhGOhuHvIR734PAF1H+ty9eNLdfiCfOeiteeh7AMrn9B9yechnHuhuP9C4x0B3+4F8xLt1OAZ1q+twDHQ3D/mUz/k+5PYDNO/Wge7uB8hHvLsOkA9d93Wgu3nIRzzp7n6Axp0FursfIJ/uqa0Os4Cv6zALdDcP+XRPbesAXUead+tAd/uBfMS76wD50DqwDnQ3D/mUz7k/5PqBxp0FutsP5NM9tdVhFvB1HWaB7uYhny7Q3fMAdN8tnnT3PAD5rJ7bngN8Dfj6vk886W4e8lkFunsegMYVT7p7HoB8Xl/a6vAp4Os6iCfdzUM+ry9tzwHoOtJ9t3jS3X4gn9Vz23OArwHfAd+NcnnIZxXobj/QuOJJd/uBfF5f2urwKeA74LtRLg/5vAa6ex6A5t0+0N3zAOQj3l0H9sG6UV/3faC7echHPOnueQAadxno7nkA8hHv1oF8qA7LQHfzkI94dx3YB+tGPe/2ge72A/mId9eBfbBudMB3o1we8hFPutsPNO4y0N1+IB/xbh3Ih+qwDHQ3D/mI75L7AbrvFk+6ux8gn9Vz23OArwFf3/eJJ93NQz6rQHf3AzSueNLd/QD5vL601eFTwNd1EE+6m4d8Xl/angPQdaT7bvGku/1APqvntucAXwO+fg5QPqf/kMtDPqtAd/uBxhVPutsP5PP60laHTwFf16F8Tv8hl4d8XgPd3Q/QvNsHursfIB/x7jqwD9aN+rrvA93NQz7iSXf3AzTuMtDd/QD5iHfrQD5Uh2Wgu3nIR7y7DuyDdaOed/tAd/uBfMS768A+WDfqdWAf6G4e8imfc3/I9QONuwx0tx/IR7xbB/KhOiwD3c1DPuXz/rWuV63/83GQ3wK/dX1Oj4PjboHfZvO8vf7+H+/zpNt5yOc0rJ/9XYZfjwfrtgV+6/p8HA+OuwV+m8xDdRBPupuHfMSf6a398Pb63/98nyfd7gfyOYHemuftVf4OfMWTbuchn9Ow7vYDjSuedLcfyEe8Wwf0gTqIJ93NQz7iST/7u/C/GV43tsBvTZ/NxfC4W+C3yTx03cWT7uYhH/Gkn/1e/rfDddsCvzV9fnU1PO4W+G0yD9VBPOluHvIR764DdB1p3okn3e0H8hHvrgPkQ+uAeNLdPOQjnnS3H2hc8aS7/UA+4t06kA/VQTzpbh7yEX+mt+4H3l4H4A+uz2l4v3EA/pDN8/b6x3+9z5Nu5yGf07Du7gdoXPEH1+fj8H7jAPwhmYfqIJ50Nw/5iD/TW/vh7fV///0+T7rdD+RzAr01j9bpx/d50u085HMa1t1+oHHFk+72A/mId+uAPlAH8aS7echHPOnufoDmnfiD6UPvg+Qj/pDMQ9ddPOluHvIRT7q7H6BxxR9MH9oPkI/4QzIP1UE86W4e8hHvrgN0HWneiSfd7QfyEe+uA+RD64B40t085COedLcfaFzxpLv9QD7i3TqQD9VBPOluHvIRT3r9HOD3vxt+brAFfmv6jK+Hx90Cv03mofs+8aS7echHPOl1HX78w3DdtsBvTZ/NzfC4W+C3yTxUB/Gku3nIR7z7HICuI913iyfd7QfyEe8+ByAfet8XT7qbh3zEk+72A40rnnS3H8hHvFsH8qE6iCfdzUM+4kk/+1zwD8Prxhb4renT3Q6PuwV+m8xD11086W4e8hFP+tm5rD8O120L/Nb0Kd/7b/qI3ybzUB3Ek+7mIR/x7jpA15HmnXjS3X4gH/HuOkA+tA6IJ93NQz7iSXf7gcYVT7rbD+Qj3q0D+VAdxJPu5iEf8e5zAHofp/tu8QfTh94HyUf8IZmH7vvEk+7mIR/xpLv7ARpX/MH0oXWAfMQfknmoDuJJd/OQj3j3OQBdR7rvFk+62w/kI959DkA+9BxAPOluHvIRT7rbDzSueNLdfiAf8W4dyIfqIJ50Nw/5iCfd3Q/QvBN/MH3ofZB8xB+Seei6iyfdzUM+4kl39wM0rviD6UP7AfIRf0jmoTqIJ93NQz7i3XWAriPNO/Gku/1APuLddYB8aB0QT7qbh3zEk+72A40rnnS3H8hHvFsH8qE6iCfdzUM+4s90vS+55wHeXh/G7/Ok2+cByOcEemuet9cU+Ok4mYd8TsO6ex6AxhVPunsegHzEu3VAH6iD+Ok4l4d8xE+z/fD22gG/y/YD+ZxAb82jfeLn93nS7TzkcxrW3X6gccXvkv1APuJ3yTxUB/Gku3nIRzzp7nkAmnfiSXfPA5CPeHcdIB+67uKn41we8hFPunsegMYVT7p7HoB8xLt1IB+qg/jpOJeHfMRPk/1A8078LtkP5CN+l8xD64B40t085COedLcfaFzxu2Q/kI/4XTIP1UE86W4e8hF/prfuB95eC+AXrs9peL+xAH6RzfP26oHvs3nI5zSsu/sBGlf8wvX5OLzfWAC/SOahOojvk3nIR3yf7Ye31/fJ+zzpdj+Qzwn01jyqE/CbSTIP+ZyGdbcfaFzxpLv9QD7i3TqgD9RB/GaSy0M+4kl39wM078QvTB96HyQf8YtkHrru4vtkHvIR35t1oPdxGlf8wvSh/QD5iF8k81AdxPfJPOQjvk/2A8078aS7/UA+4t11gHxoHRC/meTykI940t1+oHHFk+72A/mId+tAPlQH8ZtJLg/5iCfdPQ9A993iSXfPA5CPePc5APnQfZ/46TiXh3zEk+6eB6BxxZPungcgH/FuHciH6iB+Os7lIR/x02Q/0H23+F2yH8hH/C6Zh973xZPu5iEf8aS7/UDjit8l+4F8xO+SeagO4kl385CPeNLd8wA078ST7p4HIB/x7jpAPnTdxU/HuTzkI5509zwAjSuedPc8APmId+tAPlQH8dNxLg/5iJ8m+4Hmnfhdsh/IR/wumYfWAfGku3nIRzzpbj/QuOJ3yX4gH/G7ZB6qg3jS3TzkI959DkDv43TfLX5h+tD7IPmIXyTz0H2f+D6Zh3zE92Yd6H2cxhW/MH1oHSAf8YtkHqqD+D6Zh3zE98l+oPtu8aS7/UA+4t3nAORDzwHEbya5POQjnnS3H2hc8aS7/UA+4t06kA/VQfxmkstDPuJJd/cDNO/EL0wfeh8kH/GLZB667uL7ZB7yEd+bdaD3cRpX/ML0of0A+YhfJPNQHcT3yTzkI75P9gPNO/Gku/1APuLddYB8aB0Qv5nk8pCPeNLdfqBxxZPu9gP5iHfrQD5UB/GbSS4P+Ygnvf4egL//y/s6fe5e/Nb0+f2fhsfdAr9N5qHPfYsn3c1DPuJJr+vwj38drtsW+K3pM/7z8Lhb4LfJPFQH8aS7echHvPs9AHQd6XP34kl3+4F8xLvfA0A+9Lk/8aS7echHPOluP9C44kl3+4F8xLt1IB+qg3jS3TzkI570eh2Y/ji8bmyB35o+P94Pj7sFfpvMQ9ddPOluHvIRT3pdh/5+uG5b4LemT/m7v6aP+G0yD9WhD3Q3D/mId9cBuo4078ST7vYD+Yh31wHyoXVAPOluHvIRT7rbDzRuH+huP5CPeLcO5EN16APdzUM+4t3vAaD3cfrcvfiD6UPvg+Qj/pDMQ5/7Fk+6m4d8xJPu7gdoXPEH04fWAfIRf0jmoTqIJ93NQz7i3e8BoOtIn7sXT7rbD+Qj3v0eAPKh3/uLJ93NQz7iSXf7gcYVT7rbD+Qj3q0D+VAdxJPu5iEf8aS7+wGad+IPpg+9D5KP+EMyD1138aS7echHPOnufoDG7QPd3Q+Qj/hDMg/VoQ90Nw/5iHfXAbqONO/Ek+72A/mId9cB8qF1QDzpbh7yEU+62w80bh/obj+Qj3i3DuRDdegD3c1DPuJJr58DzP9t+LnBFvit6fO3n4bH3QK/Teah+z7xpLt5yEc86XUdLkfDddsCvzV9utHwuFvgt8k8VAfxpLt5yKcbtT0HoOtI993iSXf7gXzEu88ByIfe98WT7uYhH/Gku/1A44on3e0H8ulGbXW4DPi6DuJJd/OQTxfo9Tqw+vfhdWML/Nb0ef15eNwt8NtkHrru4kl385CPeNLrOvzyy3DdtsBvTZ/ys+kjbpvMQ3UQT7qbh3zEu+sAXUead+JJd/uBfMS76wD50DognnQ3D/mIJ93tBxpXPOluP5CPeLcO5EN1EE+6m4d8xLvPAeh9nO67xR9MH3ofJB/xh2Qeuu8TT7qbh3zEk+7uB2hc8QfTpwv2GwfgD8k8VAfxpLt5yKcbtT0HoOtI993iSXf7gXzEu88ByIeeA4gn3c1DPuJJd/uBxhVPutsP5NON2upwGfB1HcST7uYhny7Q3f0AzTvxB9OH3gfJR/whmYeuu3jS3TzkI550dz9A44o/mD60HyAf8YdkHqqDeNLdPOQj3l0H6DrSvBNPutsP5CPeXQfIh9YB8aS7echHPOluP9C44kl3+4F8xLt1IB+qg3jS3TzkI979HgD6PT597l486e55APIR734PAPnQ577FT8e5POQjnnT3PACNK5509zwA+Yh360A+VAfx03EuD/mInyb7gT53L36X7AfyEb9L5qHP/Ykn3c1DPuJJd/uBxhW/S/YD+YjfJfNQHcST7uYhH/Gku+cBaN6JJ909D0A+4t11gHzououfjnN5yEc86e55ABq3D3T3PAD5iHfrQD5Uhz7Q3TzkI36a7Aead+J3yX4gH/G7ZB5aB8ST7uYhH/Gku/1A4/aB7vYD+YjfJfNQHfpAd/OQj3j3ewDofZw+dy9+YfrQ+yD5iF8k89DnvsX3yTzkI74360Dv4zSu+IXpQ+sA+YhfJPNQHcT3yTzkI75P9gN97l486W4/kI9493sAyIe+B0D8ZpLLQz7iSXf7gcYVT7rbD+Qj3q0D+VAdxG8muTzkI550dz9A8078wvSh90HyEb9I5qHrLr5P5iEf8b1Zhz7YLy2AX5g+tB8gH/GLZB6qQx/obh7yEd8n+4HmnXjS3X4gH/HuOkA+tA6I30xyechHPOluP9C4faC7/UA+4t06kA/VoQ90Nw/5iCfdPQ9A993iSXfPA5CPePc5APnQfZ/46TiXh3zEk+6eB6BxxZPungcgn27UVofLgJ8CPx3n8pBPN2p7DkDXke67xe+S/UA+4nfJPPS+L550Nw/5iCfd7QcaV/wu2Q/k043a6nAZ8HUdxJPu5iGfLtDd8wA078ST7p4HIB/x7jpAPnTdxU/HuTzkI5509zwAjSuedPc8APmId+tAPlQH8dNxLg/5iJ8m+4Hmnfhdsh/IR/wumYfWAfGku3nIRzzpbj/QuOJ3yX4gH/G7ZB6qg3jS3TzkI959DkDv43TfLX5h+tD7IPmIXyTz0H2f+D6Zh3zE92Yd6H2cxhW/MH26YL+xAH6RzEN1EN8n85BPN2p7DkDXke67xZPu9gP5iHefA5APPQcQv5nk8pCPeNLdfqBxxZPu9gP5dKO2OlwG/Ab4zSSXh3y6QHf3AzTvxC9MH3ofJB/xi2Qeuu7i+2Qe8hHfm3Wg93EaV/zC9KH9APmIXyTzUB3E98k85CO+T/YDzTvxpLv9QD7i3XWAfGgdEL+Z5PKQj3jS3X6gccWT7vYD+Yh360A+VAfxm0kuD/mIP9O1L6j1fz4O8j98eZ8n/czn9Dg47pnPCfTWPNqnAD//ksxDPqdh/ez3Mr8eD9atHlc86Wc+8HfAyUe8Wwf0gTqIn3/J5SEf8fNsP7y9jsAfs/1APifQW/NoH/r1fZ50Ow/5nIZ1tx9oXPHHZD+Qj/hjMg/VQTzpbh7yEU/62bmg3wyvG/W8E0/62TpzMTzu2by+aFsHyIeuu/j5l1we8hFP+tl92W+H61aPK570s+t4NTzuWd2u2upAPlQH8fMvuTzkI36e7Aead+KPyX4gH/HHZB5aB8ST7uYhH/Gku/1A44o/JvuBfMQfk3moDuJJd/OQj/gzvXU/8PZaA792fU7D+4018OtsHj0ne3qfJ93OQz6nYd3dD9C44teuz8fh/cYa+HUyD9VBPOluHvIRf6a39sPbawb87CnZD+RzAr01j9YJ4LtsHvI5DetuP9C44mdPuX4gH/GzZB6qg/gumYd8xJPu7gdo3olfmz70Pkg+4tfJPHTdxZPu5iEf8aS7+wEaV/za9KH9APmIXyfzUB3Ek+7mIR/x7jpA15HmnfjZU64fyEf8LJmH1gHxXTIP+YjvnnL9QOOKnz3l+oF8xM+SeagO4rtkHvIRT/rZ5/J+N/zcoL7vFk/62XOe6+Fxz56rXLc9ByAfuu8TP/+Sy0M+4kk/+1zMH4brVo8rnvSz50g3w+OePbe5aasD+VAdxM+/5PKQj/h5sh/ovlv8MdkP5CP+mMxD7/viSXfzkI940t1+oHHFH5P9QD7ij8k8VAfxpLt5yEc86Wfnsn4YXjfqeSee9LPnzLfD4549171tWwfIh667+PmXXB7yEU/62bmIPw7XrR5XPOln7+N3w+OevW/etdWBfKgO4udfcnnIR/w82Q8078Qfk/1APuKPyTy0Dogn3c1DPuJJd/uBxhV/TPYD+Yg/JvNQHcST7uYhH/HucwB6H6f7bvFr04feB8lH/DqZh+77xJPu5iEf8aS7+wEaV/za9KF1gHzEr5N5qA7iSXfzkI949zkAXUe67xY/e8r1A/mInyXz0HMA8V0yD/mI755y/UDjip895fqBfMTPknmoDuK7ZB7yEU+6ux+geSd+bfrQ+yD5iF8n89B1F0+6m4d8xJPu7gdoXPFr04f2A+Qjfp3MQ3UQT7qbh3zEu+sAXUead+JnT7l+IB/xs2QeWgfEd8k85CO+e8r1A40rfvaU6wfyET9L5qE6iO+SechHfJc9D/D2+vr8Pk+6fR6AfE6gt+Z5e62AXz0n85DPaVh3zwPQuOJJd88DkI94tw7oA3UQv3rO5SEf8atsP7y9Pr28z5Nu9wP5nEBvzaN1AvjXl2Qe8jkN624/0LjiSXf7gXzEu3VAH6iD+NeXXB7yEU+6ex6A5p140t3zAOQj3l0HyIeuu/jVcy4P+Ygn3T0PQOOKJ909D0A+4t06kA/VQfzqOZeHfMSvkv1A80486W4/kI94dx0gH1oHxL++5PKQj3jS3X6gccWT7vYD+Yh360A+VAfxry+5POQj/jW7H3h77YHfZ/cD5HMCvTXP2+uXb+/zpNt5yOc0rLv7ARpX/D65HyAf8ftkHqqDeNLdPOQj/kxv7Ye31xL45bdkP5DPCfTWPHr9z/s86XYe8jkN624/0Ljil99y/UA+4pfJPFQH8aS7echHPOnufoDmnfh9cj9APuL3yTx03cWT7uYhH/Gku/sBGlf8PrkfIB/x+2QeqoN40t085CPeXQfoOtK8E7/8lusH8hG/TOahdUA86W4e8hFPutsPNK745bdcP5CP+GUyD9VBPOluHvIRT7p7HoDuu8WT7p4HIB/x7nMA8qH7PvGr51we8hFPunsegMYVT7p7HoB8xLt1IB+qg/jVcy4P+YhfJfuB7rvFk+72A/mId58DkA+974t/fcnlIR/xpLv9QOOKJ93tB/IR79aBfKgO4l9fcnnIRzzp7nkAmnfiSXfPA5CPeHcdIB+67uJXz7k85COedPc8AI0rnnT3PAD5iHfrQD5UB/Gr51we8hG/SvYDzTvxpLv9QD7i3XWAfGgdEP/6kstDPuJJd/uBxhVPutsP5CPerQP5UB3Ev77k8pCP+NfkfoDuu8Xvk/sB8hG/T+ah+z7xpLt5yEc86e5+gMYVv0/uB8hH/D6Zh+ognnQ3D/mId58D0HWk+27xy2+5fiAf8ctkHnoOIJ50Nw/5iCfd7QcaV/zyW64fyEf8MpmH6iCedDcP+Ygn3d0P0LwTv0/uB8hH/D6Zh667eNLdPOQjnnR3P0Djit8n9wPkI36fzEN1EE+6m4d8xLvrAF1Hmnfil99y/UA+4pfJPLQOiCfdzUM+4kl3+4HGFb/8lusH8hG/TOahOogn3c1DPuJJr78H4O//8r5On7sXT/rZ9/z8aXjcs+/V+dP7emse+ty3+PmXXB7yEU96XYd//Otw3epxxZN+9j1Cfx4e9+x7e/7cVgfyoTqIn3/J5SEf8fNkP9Dn7sUfk/1APuKPyTz0uT/xpLt5yEc86W4/0Ljij8l+IB/xx2QeqoN40t085COe9HodmP44vG7U80486WffM3Y/PO7Z93rdt60D5EPXXfz8Sy4P+Ygnva5Dfz9ct3rcPtDPvsfpL8Pjnn1v0l/a6kA+VIc+0N085CN+nuwHmnfij8l+IB/xx2QeWgfEk+7mIR/xpLv9QOP2ge72A/mIPybzUB36QHfzkI9493sA6H2cPncvfm360Psg+YhfJ/PQ577Fk+7mIR/xpLv7ARpX/Nr0oXWAfMSvk3moDuJJd/OQj3j3ewDoOtLn7sXPnnL9QD7iZ8k89Ht/8V0yD/mI755y/UDjip895fqBfMTPknmoDuK7ZB7yEU+6ux+geSd+bfrQ+yD5iF8n89B1F0+6m4d8xJPu7gdo3D7Q3f0A+YhfJ/NQHfpAd/OQj3h3HaDrSPNO/Owp1w/kI36WzEPrgPgumYd8xHdPuX6gcftAd/uBfMTPknmoDn2gu3nIRzzp9XOA+b8NPzeo77vFk372Pc8/DY979r3KP7U9ByAfuu8TP/+Sy0M+4kmv63A5Gq5bPa540s++R3o0PO7Z9zaP2upwGfBz4OdfcnnIpxu1PQeg60j33eKPyX4gH/HHZB563xdPupuHfMST7vYDjSv+mOwH8ulGbXW4DPi6DuJJd/OQTxfo9Tqw+vfhdaOed+JJP/ue+Z+Hxz37Xvef29YB8qHrLn7+JZeHfMSTXtfhl1+G61aPK570s9/jPwyPe/Z784e2OpAP1UH8/EsuD/mInyf7gead+GOyH8hH/DGZh9YB8aS7echHPOluP9C44o/JfiAf8cdkHqqDeNLdPOQj3n0OQO/jdN8tfm360Psg+YhfJ/PQfZ940t085COedHc/QOOKX5s+XbDfWAO/TuahOogn3c1DPt2o7TkAXUe67xY/e8r1A/mInyXz0HMA8V0yD/mI755y/UDjip895fqBfLpRWx0uA74DvkvmIZ8u0N39AM078WvTh94HyUf8OpmHrrt40t085COedHc/QOOKX5s+tB8gH/HrZB6qg3jS3TzkI95dB+g60rwTP3vK9QP5iJ8l89A6IL5L5iEf8d1Trh9oXPGzp1w/kI/4WTIP1UF8l8xDPuK75HkA+ty9eNLd8wDkI979HgDyoc99i1895/KQj3jS3fMANK540t3zAOQj3q0D+VAdxK+ec3nIR/wq2Q/0uXvxpLv9QD7i3e8BIB/63J/415dcHvIRT7rbDzSueNLdfiAf8W4dyIfqIP71JZeHfMST7p4HoHknnnT3PAD5iHfXAfKh6y5+9ZzLQz7iSXfPA9C4faC75wHIR7xbB/KhOvSB7uYhH/GrZD/QvBNPutsP5CPeXQfIh9YB8a8vuTzkI550tx9o3D7Q3X4gH/FuHciH6tAHupuHfMS/JvcD9Ll78fvkfoB8xO+Teehz3+JJd/OQj3jS3f0AjSt+n9wPkI/4fTIP1UE86W4e8hHvfg8AXUf63L345bdcP5CP+GUyD30PgHjS3TzkI550tx9oXPHLb7l+IB/xy2QeqoN40t085COedHc/QPNO/D65HyAf8ftkHrru4kl385CPeNLd/QCN2we6ux8gH/H7ZB6qQx/obh7yEe+uA3Qdad6JX37L9QP5iF8m89A6IJ50Nw/5iCfd7Qcatw90tx/IR/wymYfq0Ae6m4d8xJPungeg+27xpLvnAchHvPscgHzovk/86jmXh3zEk+6eB6BxxZPungcgn27UVofLgF8Bv3rO5SGfbtT2HICuI913iyfd7QfyEe8+ByAfet8X//qSy0M+4kl3+4HGFU+62w/k043a6nAZ8K/Av77k8pBPF+jueQCad+JJd88DkI94dx0gH7ru4lfPuTzkI5509zwAjSuedPc8APmId+tAPlQH8avnXB7yEb9K9gPNO/Gku/1APuLddYB8aB0Q//qSy0M+4kl3+4HGFU+62w/kI96tA/lQHcS/vuTykI/41+R+gO67xe+T+wHyEb9P5qH7PvGku3nIRzzp7n6AxhW/T+4HyKcbtdXhMuDrOogn3c1DPt2o7TkAXUe67xa//JbrB/IRv0zmoecA4kl385CPeNLdfqBxxS+/5fqBfLpRWx0uA76ug3jS3Tzk0wW6ux+geSd+n9wPkI/4fTIPXXfxpLt5yEc86e5+gMYVv0/uB8hH/D6Zh+ognnQ3D/mId9cBuo4078Qvv+X6gXzEL5N5aB0QT7qbh3zEk+72A40rfvkt1w/kI36ZzEN1EE+6m4d8xKP+8P/rW9D18xb4renz9/8YHncL/DaZR3wZv+JJd/OQj34mva7D//7ncN22wG9Nn9//dXjcLfDbZB6qg3jS3TzkI77WW/tBfKlrxZPu9gP5iKv11jziy/WveNLdPOQjnnS3H2hc/Uy62w/kI96tA/lQHcSR7uYhH/1Mer0OHP46vG5sgd+aPv/4r+Fxt8Bvk3nouh8C3c1DPuJJr+vwf/89XLct8FvTZ/w4PO4W+G0yD9VBPOluHvIR764DdB1p3h0C3e0H8hHvrgPkQ+vAIdDdPOQjnnS3H2hc8aS7/UA+4t06kA/VQTzpbh7yEV/rrfsB8QfgD6YPvQ+Sj/hDMo/4su5WPOluHvIRT7q7H6BxxR9MH1oHyEfcIZmH6iCedDcP+Yiv9dZ+EF/eVyuedLcfyEd8rbfmEV/e/yuedDcP+Ygn3e0HGlc86W4/kI94tw7kQ3UQT7qbh3zEk+7uB2jeHQLd3Q+Qj/hDMg9d90Ogu3nIRz+T7u4HaFzxB9OH9gPkI/6QzEN1EE+6m4d8xLvrAF1HmneHQHf7gXzEuesA+dA6cAh0Nw/5iCfd7QcaVz+T7vYD+Yh360A+VAdxpLt5yEc/k14/B/gwHn5usAV+a/pMx8PjboHfJvPQfZ940t085DMN9LoOu6BuW+C3ps+Pn4fH3QK/TeahOuwC3c1DPuLd5wB0Hem+Wzzpbj+Qz3Tc9hzgQ8DX7/viSXfzkM800N1+oHF3ge72A/mId+uwC+pW12EX6G4e8hFPer0OLD4Prxtb4LemTx+MuwV+m8xD130R6G4e8ukDva7D98lw3bbAb02fzWR43C3w22QeqoN40t085LOZtK0DdB1p3i0C3e0H8uk/t60Di4Cv14FFoLt5yKcPdLcfaFzxpLv9QD6bSVsdvgd8XQfxpLt5yGczaXsOQO/jdN8t/mD6TIP9xgH4QzIP3feJJ93NQz7TQHf3AzTuLtDd/QD5iD8k81AddoHu5iEf8e5zALqOdN8tnnS3H8hnOm57DvAh4OvnAOJJd/OQzzTQ3X6gcXeB7vYD+Yh367AL6lbXYRfobh7yEU+6ux+gebcIdHc/QD7957Z1YBHw9XVfBLqbh3z6QHf3AzSu+IPpswn2GwfgD8k8VAfxpLt5yGczaVsH6DrSvFsEutsP5NN/blsHFgFfrwOLQHfzkE8f6G4/0LjiSXf7gXw2k7Y6fA/4ug7iSXfzkM8G9NbzAOLLc/2KJ909D0A+4mu9NY/4KfDTcS4P+Ygn3T0PQOOKJ909D0A+4t06kA/VQfx0nMtDPuKnyX4QvwN+l+wH8hG/S+YRX577Vjzpbh7yEU+62w80rvhdsh/IR/wumYfqIJ50Nw/5iCfdPQ9A8+4Q6O55APIR764D5EPX/RDobh7yEU+6ex6AxhVPunsegHzEuXUgH6qD+Ok4l4d8xE+T/UDz7hDobj+Qj/hdMg+tA4dAd/OQj3jS3X6gccXvkv1APuJ3yTxUB/Gku3nIR3ytt+4HxC+AX5g+9D5IPuIXyTzie+D7ZB7yEd+bdaD3cRpX/ML0oXWAfMQvknmoDuL7ZB7yEd8n+0F8+b16xZPu9gP5iK/11jziN8BvJrk85COedLcfaFzxpLv9QD7i3TqQD9VB/GaSy0M+4kl39wM07w6B7u4HyEf8IpmHrvsh0N085CO+N+tA7+M0rviF6UP7AfIRv0jmoTqI75N5yEd8n+wHmneHQHf7gXzEu+sA+dA6cAh0Nw/5iCfd7QcaVzzpbj+Qj3i3DuRDdRC/meTykI940t3zAHTfrZ9Jd88DkM903PYc4EPAT4GfjnN5yGca6O55ABp3F+jueQDyEe/WYRfUbQr8dJzLQz7ip8l+oPtu8btkP5DPdNz2HOBDwNfv++JJd/OQzzTQ3X6gcXeB7vYD+YjfJfNQHXaB7uYhH/1MunsegObdItDd8wDk039uWwcWAT8FfjrO5SGfPtDd8wA0rnjS3fMA5LOZtNXhe8BPgZ+Oc3nIZzNpWwfoOtK8WwS62w/k039uWwcWAV+vA4tAd/OQTx/obj/QuOJ3yX4gn82krQ7fA76ug3jS3Tzks5m0PQeg93G67xa/MH2mwX5jAfwimYfu+8T3yTzkMw10dz9A4+4C3d0PkI+4RTIP1WEX6G4e8hHfJ/uB7rvFk+72A/lMx23PAT4E/Ab4zSSXh3ymge72A427C3S3H8hHvFuHXVC3DfCbSS4P+Ygn3d0P0LxbBLq7HyCf/nPbOrAI+B74PpmHfPpAd/cDNK74hemzCfYbC+AXyTxUB/F9Mg/5bCZt6wBdR5p3i0B3+4F8+s9t68Ai4DfAbya5POTTB7rbDzSufibd7Qfy2Uza6vA94DfAbya5POSzCfT6ewB++PK+Tp+7F781feZfhsfdAr9N5qHPfYsn3c1DPvNAr+twDOq2BX5r+vzt6/C4W+C3yTxUh2Ogu3nIR7z7PQB0Helz9+JJd/uBfOagt+ahz/2JJ93NQz7zQHf7gcY9BrrbD+Qj3q3DMahbXYdjoLt5yEc86fU6sP46vG5sgd+aPpdPw+Nugd8m89B1Xwe6m4d8xJNe12H2NFy3LfBb06cLxt0Cv03moTrMAt3NQz7dU9s6QNeR5t060N1+IB/x7jpAPrQOrAPdzUM+4kl3+4HGnQW62w/k0z211WEW8HUdZoHu5iGfDvTW/QB97l78wfSZB/uNA/CHZB763Ld40t085DMPdHc/QOMeA93dD5CP+EMyD9XhGOhuHvIR734PAF1H+ty9eNLdfiCfOeiteej3/uJJd/OQzzzQ3X6gcY+B7vYD+Yh363AM6lbX4Rjobh7yEU+6ux+gebcOdHc/QD7iD8k8dN3Xge7mIR/xpLv7ARp3FujufoB8uqe2OswCvq7DLNDdPOTTPbWtA3Qdad6tA93tB/IR764D5EPrwDrQ3TzkI550tx9o3Fmgu/1APt1TWx1mAV/XYRbobh7y6QK9fg7w9Xn4ucEW+K3ps3oeHncL/DaZh+77xJPu5iGfVaDXdfj0Mly3LfBb0+f1ZXjcLfDbZB6qg3jS3Tzk8/rS9hyAriPdd4sn3e0H8lk9tz0H+Brw9fu+eNLdPOSzCnS3H2hc8aS7/UA+ry9tdfgU8HUdxJPu5iGf10Cv14F9sG5sgd+aPr98Gx53C/w2mYeu+z7Q3TzkI570ug7Lb8N12wK/NX1+9T/D426B3ybzUB2Wge7mIR/x7jqwD9aNet7tA93tB/IR764D+2DdqNeBfaC7echHPOluP9C4y0B3+4F8xLt1IB+qwzLQ3TzkI959DkDv43TfLf5g+qyC/cYB+EMyD933iSfdzUM+q0B39wM0rviD6fMa7DcOwB+SeagO4kl385DP60vbcwC6jnTfLZ50tx/IZ/Xc9hzga8DXzwHEk+7mIZ9VoLv9QOOKJ93tB/J5fWmrw6eAr+sgnnQ3D/m8Brq7H6B5tw90dz9APuIPyTx03feB7uYhH/Gku/sBGncZ6O5+gHzEH5J5qA7LQHfzkI94dx3YB+tGPe/2ge72A/mId9eBfbBu1OvAPtDdPOQjnnS3H2jcZaC7/UA+4t06kA/VYRnobh7yEe9+DwD9Hp8+dy+edPc8APnMQW/NQ5/7Fj8d5/KQzzzQ3fMANO4x0N3zAOQj3q3DMajbFPjpOJeHfMRPk/1An7sXv0v2A/nMQW/NQ5/7E0+6m4d85oHu9gONewx0tx/IR/wumYfqcAx0Nw/5iCfdPQ9A824d6O55APIR764D5EPXfR3obh7yEU+6ex6Axp0FunsegHy6p7Y6zAJ+Cvx0nMtDPt1T2zpA15Hm3TrQ3X4gH/G7ZB5aB9aB7uYhH/Gku/1A484C3e0H8ume2uowC/i6DrNAd/OQTwd6636APncvfmH6zIP9xgL4RTIPfe5bfJ/MQz7zQHf3AzTuMdDd/QD5iF8k81AdjoHu5iEf8X2yH+hz9+JJd/uBfOagt+ah7wEQv5nk8pDPPNDdfqBxj4Hu9gP5iHfrcAzqtgF+M8nlIR/xpLv7AZp360B39wPkI36RzEPXfR3obh7yEd+bdZgF+6UF8AvTpwvGXQC/SOahOswC3c1DPt1T2zpA15Hm3TrQ3X4gH/HuOkA+tA6sA93NQz7iSXf7gcadBbrbD+TTPbXVYRbwG+A3k1we8ukC3T0PQPfd4kl3zwOQz+q57TnA14CfAj8d5/KQzyrQ3fMANK540t3zAOTz+tJWh08BPwV+Os7lIZ/Xl7bnAHQd6b5b/C7ZD+Szem57DvA14Ov3ffGku3nIZxXobj/QuOJ3yX4gn9eXtjp8Cvi6DuJJd/OQz2ugu+cBaN7tA909D0A+4t11YB+sG1Pgp+NcHvIRT7p7HoDGXQa6ex6AfMS7dSAfqsMy0N085CN+muwHmnf7QHf7gXzE75J5aB3YB7qbh3zEk+72A427DHS3H8hH/C6Zh+qwDHQ3D/mId58D0Ps43XeLX5g+q2C/sQB+kcxD933i+2Qe8lkFursfoHHFL0yf12C/sQB+kcxDdRDfJ/OQz+tL23MAuo503y2edLcfyGf13PYc4GvAb4DfTHJ5yGcV6G4/0LjiSXf7gXxeX9rq8CngN8BvJrk85PMa6O5+gObdPtDd/QD5iF8k89B13we6m4d8xPdmHZbBfmkB/ML0of0A+YhfJPNQHZaB7uYhH/F9sh9o3u0D3e0H8hHvrgP7YN3YAL+Z5PKQj3jS3X6gcZeB7vYD+Yh360A+VIdloLt5yEf8mf7wvr59GObL9/pXPOm1jz53TePWPuJrvTWP+Dnw8y+5POQjnvS6DvpcN9WtHlc86bUP/R1w8hHv1oF8qA7i519yechH/DzZD+KPwB+T/UA+4o/JPOLL975XPOluHvIRT7rbDzSu+GOyH8hH/DGZh+ognnQ3D/mIJ71eBw5/HV436nl3CPTaR5+7o3FrH/HuOkA+dN0Pge7mIR/xpNd10Of6qG71uOJJr33o7wCSj3i3DuRDdRA//5LLQz7i58l+oHl3CHS3H8hH/DGZh9aBQ6C7echHPOluP9C44o/JfiAf8cdkHqqDeNLdPOQjvtZb9wPi18CvTR96HyQf8etkHvHl765UPOluHvIRT7q7H6Bxxa9NH1oHyEf8OpmH6iCedDcP+Yiv9dZ+ED8DfvaU6wfyET9L5hHfAd8l85CP+O4p1w80rvjZU64fyEf8LJmH6iC+S+YhH/Gku/sBmneHQHf3A+Qjfp3MQ9f9EOhuHvIRT7q7H6Bxxa9NH9oPkI/4dTIP1UE86W4e8hHvrgN0HWneHQLd7QfyET9L5qF14BDobh7yEd895fqBxhU/e8r1A/mInyXzUB3Ed8k85COe9Po5wIfx8HOD+r5bPOm1z3Q8PG7tMx23PQf4EPBz4OdfcnnIZxrodR12Qd3qcXeBXvvQ3wEnH/FuHXZB3ebAz7/k8pCP+HmyH+i+W/wx2Q/kMx23PQf4EPD1+7540t085DMNdLcfaNxdoLv9QD7ij8k8VIddoLt5yEc86fU6sPg8vG7U824R6LVPH4xb+/Sf29aBRcDPgZ9/yeUhnz7Q6zro9/pUt3pc8aTXPvQ9AOSzmbTV4XvAz4Gff8nlIZ/NpG0doOtI824R6G4/kE//uW0dWAR8vQ4sAt3NQz59oLv9QOOKPyb7gXw2k7Y6fA/4ug7iSXfzkM9m0vYcgN7H6b5b/Nr0mQb7jTXw62Qeuu8TT7qbh3ymge7uB2jcXaC7+wHyEb9O5qE67ALdzUM+4t3nAHQd6b5b/Owp1w/kMx23PQf4EPAd8F0yD/lMA93tBxp3F+huP5CP+FkyD9VhF+huHvIRT7q7H6B5twh0dz9APv3ntnVgEfD1dV8EupuHfPpAd/cDNK74temz+X+lnb1x68oShJ1jKoAbwAvimkc/AMgcFIDMDQABIACaCGADQAAMQCYCgCkDAdC81vBVbdWn7q0x2fVVT5/BYLXEWZJiv7EBvyXzUB+CJ93NQz73t751gK4j3Xc3obvzQD6P17514Cb4A/gjmYd8HkJ354HqBj8PuXkgn/tbXx8+BX8AfyTzkM8d9N7zAME/f9e34Ul3zwOQT/Ct3psn+Ap8HXN5yCd40t3zAFQ3eNLd8wDkE7zbB/KhPgRfx1we8gm+Juch+OfvKjc86e48kE/wrd6bJ/gCfJlyecgneNLdeaC6wZPuzgP5BO/2gXyoD8GXKZeHfIIn3T0PQPfdj9Dd8wDkE7y7DpAPXfcfobt5yCd40t3zAFQ3eNLd8wDkE7zbB/KhPgRfx1we8gm+JueB7rsfobvzQD7Bu+sA+dA68CN0Nw/5BE+6Ow9UN3jS3Xkgn+DdPpAP9SH4MuXykE/wJbkfCH4Hfk/uB8gn+D2ZJ/jn7643POluHvIJnnR3P0B1g9+T+wHyCX5P5qE+BE+6m4d8gm/13nkIfgV+veTmgXyCX5N5go/vA2h50t085PP8nfVrbh6obvDrJTcP5BP8msxDfXj+jvs1l4d8nr/zes3tB+i++xG6ux8gn+D3ZB667j9Cd/OQT/Cku/sBqhv8ntwPkE/wezIP9SF40t085BO8uw7QdaT77kfo7jyQT/BrMg+tAz9Cd/OQz/N3dq+5eaC6wa+X3DyQT/BrMg/14fk7vtdcHvJ5fs/v1XsOQP+PT++7gyfdPQ9APl9/+54D/BF8Bb6OuTzk8yV09zwA1f0WunsegHyCd/vwLfpWga9jLg/5BF+T80Dvu4Mn3Z0H8vn62/cc4I/gC/BlyuUhny+hu/NAdb+F7s4D+QTv9uFb9K0AX6ZcHvIJnnT3PADddzehu+cByOfx2rcO3ARfga9jLg/5PITungegusGT7p4HIJ/7W18fPgVfga9jLg/53N/61gG6jnTf3YTuzgP5PF771oGb4AvwZcrlIZ+H0N15oLrBk+7OA/nc3/r68Cn4AnyZcnnI5/7W9xyA/o7T++7g9+R+gHy+/vY9B/gj+PZ9X/Cku3nI50vo7n6A6n4L3d0PkE/wezIP9eFb6G4e8gnefQ5A15Hedwe/XnLzQD5ff/ueA/wRfPsc4Pk7OtdcHvL5Ero7D1T3W+juPJBP8GsyD/XhW+huHvJ5fs/7NbcfoPvuJnR3P0A+j9e+deAm+Pa634Tu5iGfh9Dd/QDVDX5P7gfI5/7W14dPwbd9CJ50Nw/53N/61gG6jnTf3YTuzgP5PF771oGb4Nt14CZ0Nw/5PITuzgPVDX695OaBfO5vfX34FHzbh+f3+F9zecjnLvT2ewD+B78jT5+7j9ektz7L++91W58F9N489Lnv4Jb3XB7yWYTe9uEUfWvrnkJvfeh3wMkneLcPp+jbAvzynstDPsEvyXmgz90HfybngXwW0Hvz0Of+gifdzUM+i9DdeaC6p9DdeSCf4M9kHurDKXQ3D/nEa9LbdWD7+H3daO+7Teitzz/D73Vbn+DddYB86LpvQnfzkE/wpLd9mIff+9bWnYXe+hyibutzDH19mAW/AL+85/KQzzH0rQN0Hem+24TuzgP5BH8m89A6sAndzUM+wZPuzgPVnYXuzgP5HENfH2bBt32Yhe7mIZ8D9N79AH3uPvjN9FnEfmMDfkvmoc99B0+6m4d8FqG7+wGqewrd3Q+QT3BbMg/14RS6m4d8gne/B4CuI33uPvh5yM0D+Syg9+ah//cP/kjmIZ9F6O48UN1T6O48kE/wczIP9eEUupuHfIIn3d0P0H23Cd3dD5BP8FsyD133TehuHvKJ16S7+wGqOwvd3Q+QzzH09WEWfNuHWehuHvI5hr51gK4j3Xeb0N15IJ/g5mQeWgc2obt5yCf4Y8jNA9Wdhe7OA/kcQ18fZsEfwB/JPORzCL19DvAx/v7coH3fHTzprU8df6/b+tSx7znAh+AX4Jf3XB7yqUJv+/Ay/d63tm7wpLc+9Dvg5FOmvj68CH4BfnnP5SGfMvU9B6DrSO+7gz+T80A+dex7DvAh+PbvfvCku3nIpwrdnQeqG/yZnAfyKVNfH14E3/YheNLdPORThN6uA7tYN9r7bhd66/Pv5fe6rU/w7jqwi3VjAX55z+Uhn+BJb/uwXn7vW1t3FXrrE+cAqG7rE7zbB/KhPqxCd/OQT/BLch7ovtuF7s4D+QR/JvPQOrAL3c1DPsGT7s4D1V2F7s4D+QR/JvNQH1ahu3nIJ3j3OQD9Haf33cFvpk8V+40N+C2Zh973BU+6m4d8qtDd/QDVDX4zfYrYb2zAb8k81IfgSXfzkE+Z+p4D0HWk993Bz0NuHsinjn3PAT4EfwB/JPOQTxW6Ow9UN/h5yM0D+ZSprw8vgj+AP5J5yKcI3d0P0H23C93dD5BP8FsyD133XehuHvIJnnR3P0B1V6G7+wHyCX5L5qE+rEJ385BP8O46sIt1YwZ+HnLzQD7Bz8k8tA7sQnfzkE/wx5CbB6q7Ct2dB/IJfk7moT6sQnfzkE/wR/I8AH3uPnjS3fMA5LOA3puHPvcdfB1zechnEbp7HoDqnkJ3zwOQT/BuH07Rtwp8HXN5yCf4mpwH+tx98KS780A+C+i9eehzf8GXKZeHfBahu/NAdU+hu/NAPsG7fThF3wrwZcrlIZ/gSXfPA9B9twndPQ9APsG76wD50HXfhO7mIZ/gSXfPA1DdWejueQDyOYa+PsyCr8DXMZeHfI6hbx2g60j33SZ0dx7IJ3h3HSAfWgc2obt5yCd40t15oLqz0N15IJ9j6OvDLPgCfJlyecjnAL13P0Cfuw9+T+4HyGcBvTcPfe47eNLdPOSzCN3dD1DdU+jufoB8gt+TeagPp9DdPOQTvPs9AHQd6XP3wa+X3DyQzwJ6bx76HoDn/6Nfc3nIZxG6Ow9U9xS6Ow/kE/yazEN9OIXu5iGf53Pea24/QPfdJnR3P0A+we/JPHTdN6G7ecgneNLd/QDVnYXu7gfI5xj6+jALvu3DLHQ3D/kcQ986QNeR7rtN6O48kE/wazIPrQOb0N085PN8zn7NzQPVnYXuzgP5HENfH2bBt32Yhe7mIZ9D6O55AHrfHa9Jd88DkE8d+54DfAi+Al/HXB7yqUJ3zwNQ3eBJd88DkE+Z+vrwIvgKfB1zecinTH3PAeg60vvu4El354F86tj3HOBD8AX4MuXykE8VujsPVDdek+7OA/mUqa8PL4IvwJcpl4d8itDd8wB03+1Cd88DkE/w7jqwi3WjAl/HXB7yCZ509zwA1V2F7p4HIJ/g3T6QD/VhFbqbh3yCr8l5oPtuF7o7D+QTvLsO7GLdKMCXKZeHfIIn3Z0HqrsK3Z0H8gne7QP5UB9Wobt5yCf4ktwP0Pvu4PfkfoB86tj3HOBD8O37vuBJd/OQTxW6ux+gusHvyf0A+ZSprw8vgm/7EDzpbh7yKVPfcwC6jvS+O/j1kpsH8qlj33OAD8G3zwGef0evuTzkU4XuzgPVDX695OaBfMrU14cXwbd9eK7j11we8ilCd/cDdN/tQnf3A+QT/J7MQ9d9F7qbh3ziNenufoDqrkJ39wPkE/yezEN9WIXu5iGf4N11YBfrxgr8esnNA/kEtybz0DqwC93NQz7Pf+c1Nw9UdxW6Ow/kE/yazEN9WIXu5iGf5+v/6/8BCq3LzQ== 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+AQAAAAAAAQAAAAEAxFgAAA==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+AQAAAAAAAgAAAAIAZ3MAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-040000.1.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-040000.1.vtu new file mode 100644 index 000000000..5c361e327 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-040000.1.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAgAAAAIAnm4AAA==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+AQAAAAAAAgAAAAIAJXMAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-040000.2.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-040000.2.vtu new file mode 100644 index 000000000..533c8a6eb --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-040000.2.vtu @@ -0,0 +1,34 @@ + + + + + + + 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DfRDfCvOQj3jSHzwX9GfV60YP+J7pM/xFdd0e8L0wDx138aS7echHPOkPrsv+vLpvPeB7pg/9P4DkI74X5qE+iCfdzUM+4t11gI4jnXfil9ezeSAf8cthHloHxLfCPOQjvrWezQPVFb+8ns0D+YhfDvNQH8S3wjzkI76V7gfuP4fAH6b7AfK5Bb1unvvPCPhRmod8bqt1dz9AdcUfhvsB8hF/GOahPogfhXnIR/wonYf7zx3wd+k8kM8t6HXz3H9+/j8/5km385DPbbXuzgPVFX8XzgP5iL8L81AfxJPu5iEf8aS7+wE678QfhvsB8hF/GOah4y5+FOYhH/GjcD9AdcUfhvsB8hF/GOahPogfhXnIR/wonAc678TfhfNAPuLvwjy0Dogn3c1DPuJJd+eB6oq/C+eBfMTfhXmoD+JJd/OQj3jSH7yX9xfV9w16wPdMn5W/rK7bA74X5qHrPvGku3nIRzzpD96L+avqvvWA75k+9P+Ak4/4XpiH+iCedDcP+Yh37wPQcaTrbvHL69k8kI/45TAPfe+Lb4V5yEd8az2bB6orfnk9mwfyEb8c5qE+iG+FechHPOkPnsv6VfW60QO+Z/qM/7q6bg/4XpiHjrt40t085COe9AfPRfxNdd96wPdMH/o7AOQjvhfmoT6IJ93NQz7i3XWAjiOdd+KX17N5IB/xy2EeWgfEt8I85CO+tZ7NA9UVv7yezQP5iF8O81AfxLfCPOQjvhXuB+i6W/xhuB8gH/GHYR667hM/CvOQj/hRuB+guuIPw/0A+Yg/DPNQH8SPwjzkI34UzgNdd4u/C+eBfMTfhXnoPoB40t085COedHceqK74u3AeyEf8XZiH+iCedDcP+Ygn3d0P0Hkn/jDcD5CP+MMwDx138aMwD/mIH4X7Aaor/jDcD5CP+MMwD/VB/CjMQz7iR+E80Hkn/i6cB/IRfxfmoXVAPOluHvIRT7o7D1RX/F04D+Qj/i7MQ30QT7qbh3zEP9D1veE+D3D/2QJ+y/W5rX7eYAv4rTTP/acLfDfNQz631br7PADVFb/l+sDvgOQjfivMQ30Q3w3zkI/4bjoP959z4M/TeSCfW9Dr5rn/zIGfp3nI57Zad+eB6oo/D+eBfMSfh3moD+LnYR7yET831wH6HZ/OO/Fbpg/9Dk4+4rfCPHTcxXfDPOQjvmv2gX7Hp7rit0wfug9APuK3wjzUB/HdMA/5iO+G80DnnfjzcB7IR/x5mIfWAfHzMA/5iJ+H80B1xZ+H80A+4s/DPNQH8fMwD/mIn6f7gfvPr9/9mCfd3g+Qzy3odfPcfzaB33wX5iGf22rd3Q9QXfGku/sB8hHv9gF9oA/iN99lechH/GY6D/effeD303kgn1vQ6+ZRn4AfpnnI57Zad+eB6orfD+eBfMTvh3moD+KHYR7yEU+6ux+g80486e5+gHzEu+sA+dBxF7/5LstDPuJJd/cDVFc86e5+gHzEu30gH+qD+M13WR7yEb8ZzgOdd+L3w3kgH/H7YR5aB8QPwzzkI34YzgPVFb8fzgP5iN8P81AfxA/DPOQjnnT3eQC67ha/ZfrQ7+DkI34rzEPXfeK7YR7yEd81+0C/41Nd8VumD/0OSD7it8I81Afx3TAP+YjvhvNA193iz8N5IB/x52Ee+t4XPw/zkI/4eTgPVFf8eTgP5CP+PMxDfRA/D/OQj/i5uQ7Q7/h03onfMn3od3DyEb8V5qHjLr4b5iEf8V2zD/Q7PtUVv2X60PMA5CN+K8xDfRDfDfOQj/huOA903ok/D+eBfMSfh3loHRA/D/OQj/h5OA9UV/x5OA/kI/48zEN9ED8P85CP+Hm4H6DrbvGku/sB8hHv3gcgH7ruE7/5LstDPuJJd/cDVFc86e5+gHzEu30gH+qD+M13WR7yEb8ZzgNdd4vfD+eBfMTvh3noPoD4YZiHfMQPw3mguuL3w3kgH/H7YR7qg/hhmId8xJPu7gfovBNPursfIB/x7jpAPnTcxW++y/KQj3jS3f0A1RVPursfIB/xbh/Ih/ogfvNdlod8xG+G80Dnnfj9cB7IR/x+mIfWAfHDMA/5iB+G80B1xe+H80A+4vfDPNQH8cMwD/mIJ33x7wAc/PrHOr13L75n+vzyN9V1e8D3wjz03rd40t085COe9MU+nPxtdd96wPdMH/p/wMlHfC/MQ30QT7qbh3zEu38HgI4jvXcvfnk9mwfyEb8c5qH3/sS3wjzkI761ns0D1RW/vJ7NA/mIXw7zUB/Et8I85COe9MV1oPVz9brRA75n+vz82+q6PeB7YR467uJJd/OQj3jSF/sw/21133rA90wf+n8AyUd8L8xDfZgXdDcP+Yh31wE6jnTeiV9ez+aBfMQvh3loHRDfCvOQj/jWejYPVHde0N15IB/xy2Ee6sO8oLt5yEd8K9wP0Hv34g/D/QD5iD8M89B73+JHYR7yET8K9wNUV/xhuB8gH/GHYR7qg/hRmId8xI/CeaD37sXfhfNAPuLvwjz0u7940t085COedHceqK74u3AeyEf8XZiH+iCedDcP+Ygn3d0P0Hkn/jDcD5CP+MMwDx138aMwD/mIH4X7Aao7L+jufoB8xB+GeagP84Lu5iEf8aNwHui8E38XzgP5iL8L89A6IJ50Nw/5iCfdnQeqOy/o7jyQj/i7MA/1YV7Q3TzkI570xfsAnb+rvm/QA75n+qz/fXXdHvC9MA9d94kn3c1DPuJJX+zDk6XqvvWA75k+9P+Ak894qV4fnhT4xT6IJ93NQz7jpXr3Aeg40nW3+OX1bB7IR/xymIe+98W3wjzkI761ns0D1RW/vJ7NA/mMl+r14UmBbwHfCvOQz7igL64D/eXqdaMHfM/02f6H6ro94HthHjru4kl385CPeNIX+/D0aXXfesD3TJ+fflddtwd8L8xDfRBPupuHfMS76wAdRzrvxC+vZ/NAPuKXwzy0DohvhXnIR3xrPZsHqit+eT2bB/IRvxzmoT6Ib4V5yEd8K9wP0HW3+MNwP0A+4g/DPHTdJ34U5iEf8aNwP0B1xR+G+wHyGS/V68OTAj8CfhTmIZ/xUr37AHQc6bpb/F04D+Qj/i7MQ/cBxJPu5iEf8aS780B1xd+F80A+46V6fXhS4Bf7IJ50Nw/5jAu6ux+g8078YbgfIB/xh2EeOu7iR2Ee8hE/CvcDVFf8YbgfIB/xh2Ee6oP4UZiHfMSPwnmg8078XTgP5CP+LsxD64B40t085COedHceqK74u3AeyEf8XZiH+iCedDcP+Yh3/w4A/Y5P792L3zJ96Hdw8hG/Feah977Fd8M85CO+a/aBfsenuuK3TB/6HZB8xG+FeagP4rthHvIR3w3ngd67F38ezgP5iD8P89B7f+LnYR7yET8P54Hqij8P54F8xJ+HeagP4udhHvIRPzfXAfodn8478VumD/0OTj7it8I8dNzFd8M85CO+a/ZhXnheYgv4LdOH7gOQj/itMA/1YV7Q3TzkI74bzgOdd+LPw3kgH/HnYR5aB8TPwzzkI34ezgPVnRd0dx7IR/x5mIf6MC/obh7yET8P9wP03r140t39APmId/8OAPnQe9/iN99lechHPOnufoDqiifd3Q+Qj3i3D+RDfRC/+S7LQz7iN8N5oPfuxe+H80A+4vfDPPR3AMQPwzzkI34YzgPVFb8fzgP5iN8P81AfxA/DPOQjnnR3P0DnnXjS3f0A+Yh31wHyoeMufvNdlod8xJPu7geo7rygu/sB8hHv9oF8qA/zgu7mIR/xm+E80Hknfj+cB/IRvx/moXVA/DDMQz7ih+E8UN15QXfngXzE74d5qA/zgu7mIR/xpLvPA9B1t/gt04d+Bycf8VthHrruE98N85CP+K7ZB/odn+qK3zJ9xoXnDbaA3wrzUB/Ed8M85DNeqncfgI4jXXeLPw/ngXzEn4d56Htf/DzMQz7i5+E8UF3x5+E8kM94qV4fnhT4OfDzMA/5jAu6+zwAnXfit0wf+h2cfMRvhXnouIvvhnnIR3zX7AP9jk91xW+ZPvQ8APmI3wrzUB/Ed8M85CO+G84DnXfiz8N5IB/x52EeWgfEz8M85CN+Hs4D1RV/Hs4D+Yg/D/NQH8TPwzzkI34e7gfouls86e5+gHzEu/cByIeu+8RvvsvykI940t39ANUVT7q7HyCf8VK9Pjwp8JvAb77L8pDPeKnefQA6jnTdLX4/nAfyEb8f5qH7AOKHYR7yET8M54Hqit8P54F8xkv1+vCkwA+BH4Z5yGdc0N39AJ134kl39wPkI95dB8iHjrv4zXdZHvIRT7q7H6C64kl39wPkI97tA/lQH8RvvsvykI/4zXAe6LwTvx/OA/mI3w/z0DogfhjmIR/xw3AeqK74/XAeyEf8fpiH+iB+GOYhH/Gk//Sv/1m/AF18E/im6XPwj9V1m8A3wzziL4C/CPOQj/gLsw+f/qm6b03gm6bPL39fXbcJfDPMQ30QfxHmIR/xF+E8iP9N48c86e48kI/4Rb1uHvEHwB80sjzkI550dx6ornjS3XkgH/FuH8iH+iD+oJHlIR/xpC+uA5PfV68bTeCbps/JP1fXbQLfDPPQcZ8UdDcP+Yi/MPuw86y6b03gm6bPyh+q6zaBb4Z5qA/iL8I85CP+IpwHOu8mBd2dB/IR764D5EPrwKSgu3nIRzzp7jxQXfGku/NAPuLdPpAP9UH8QSPLQz7iD8L9gPgb4G/C/QD5iL8J84j/9PrHPOluHvIRT7q7H6C64m/C/QD5iL8J81AfxJPu5iEf8Yt63XkQfwb82etsHshH/FmYR/wv/+3HPOluHvIRT7o7D1RX/NnrbB7IR/xZmIf6IJ50Nw/5iCfd3Q/QeTcp6O5+gHzE34R56LhPCrqbh3zEk+7uB6iu+JtwP0A+4m/CPNQH8aS7echHvLsO0HGk825S0N15IB/xZ2EeWgcmBd3NQz7iSXfngeqKP3udzQP5iD8L81AfxJPu5iEf8aQv3gd4tFJ936AJfNP0aa1U120C3wzz0HWf+IswD/m0CvpiH0aFvjWBb5o+Pz+vrtsEvhnmoT6MCrqbh3zEX4TzQNfd4kl354F8Wiv17gM8KvAHwB80sjzk0yro7jxQ3VFBd+eBfMS7fRgV+nYA/EEjy0M+4klfXAe6z6vXjSbwTdNnXqjbBL4Z5qHj3i3obh7ymRf0xT5srlb3rQl80/QZrlbXbQLfDPNQH8RfhHnIZ7habx2g40jnXbegu/NAPvPn9daBboE/AP6gkeUhn3lBd+eB6oon3Z0H8hmu1uvDZoE/AP6gkeUhn+FqvfsA9D1O193ib8L9APm0VurdB3hU4Bev+8ST7uYhn1ZBd/cDVHdU0N39APmIvwnzUB9GBd3NQz7i3fsAdBzpulv82etsHsintVLvPsCjAr94H0A86W4e8mkVdHceqO6ooLvzQD7iz8I81IdRQXfzkI940t39AJ133YLu7gfIZ/683jrQLfCLx71b0N085DMv6O5+gOqKvwn3A+QzXK3Xh80Cv9gH8aS7echnuFpvHaDjSOddt6C780A+8+f11oFugV9cB7oF3c1DPvOC7s4D1RV/9jqbB/IZrtbrw2aBX+yDeNLdPOQzBL3u8wDi94DfM33od3DyEb8X5hE/AX4S5iEf8ROzD/Q7PtUVv2f60O+A5CN+L8xDfRA/CfOQj/hJOA/i/+3Nj3nS3XkgH/GLet084k+AP3mT5SEf8aS780B1xZPuzgP5iHf7QD7UB/Enb7I85COedPd5ADrvJgXdfR6AfMTvhXnouE8KupuHfMRPzD7Q7/hUV/ye6UP3AchH/F6Yh/ogfhLmIR/xk3Ae6LybFHR3HshHvLsOkA+tA5OC7uYhH/Gku/NAdcWT7s4D+Yh3+0A+1AfxJ2+yPOQj/iTcD4j/L29/zJPu7gfIR/yiXjeP+B3gd95mechHPOnufoDqiifd3Q+Qj3i3D+RDfRC/8zbLQz7id8J5EH8F/FU4D+Qj/irMI37lf/+YJ93NQz7iSXfngeqKvwrngXzEX4V5qA/iSXfzkI940t39AJ13k4Lu7gfIR7y7DpAPHfdJQXfzkI940t39ANUVT7q7HyAf8W4fyIf6IH7nbZaHfMTvhPNA592koLvzQD7ir8I8tA5MCrqbh3zEk+7OA9UVfxXOA/mIvwrzUB/Ek+7mIR/xpLvPA9B1t/g906dVeN5gD/i9MA9d94mfhHnIp1XQ3ecBqO6ooLvPA5CP+L0wD/VhVNDdPOQjfhLOA113iyfdnQfyaa3Uuw/wqMCfAH/yJstDPq2C7s4D1R0VdHceyEe824dRoW8nwJ+8yfKQj3jS3ecB6LzrFnT3eQDymT+vtw50C/wE+EmYh3zmBd19HoDqit8zfeh5APIZrtbrw2aBnwA/CfOQz3C13jpAx5HOu25Bd+eBfObP660D3QJ/AvzJmywP+cwLujsPVFc86e48kM9wtV4fNgv8CfAnb7I85DNcrXcfgL7H6bpbPOnufoB8Wiv17gM8KvA7wO+8zfKQT6ugu/sBqjsq6O5+gHzEu30YFfq2A/zO2ywP+YjfCeeBrrvFX4XzQD6tlXr3AR4V+MX7AOJJd/OQT6ugu/NAdUcF3Z0H8hF/FeahPowKupuHfMST7u4H6LzrFnR3P0A+8+f11oFugd8Bfudtlod85gXd3Q9QXfGku/sB8hmu1uvDZoHfAX7nbZaHfIar9dYBOo503nULujsP5DN/Xm8d6Bb4xXWgW9DdPOQzL+juPFBd8VfhPJDPcLVeHzYL/GIfxJPu5iGfYUFf/DsAv/qXH+v03r34punT+Zfquk3gm2Eeeu9b/EWYh3w6BX2xD9NC35rAN02f9bXquk3gm2Ee6sO0oLt5yEf8RTgP9N69eNLdeSCfDuh189B7f+IPGlke8ukUdHceqO60oLvzQD7i3T5MC307AP6gkeUhH/GkL64Dg7XqdaMJfNP0efKium4T+GaYh477oKC7echH/IXZh/aL6r41gW+aPuNC3SbwzTAP9aFd0N085DN+UW8doONI592goLvzQD7i3XWAfGgdGBR0Nw/5iCfdnQeq2y7o7jyQz/hFvT60C/wB8AeNLA/5jEGvux+g9+7F34T7AfLpgF43D733LZ50Nw/5dAq6ux+gutOC7u4HyEf8TZiH+jAt6G4e8hHv/h0AOo703r34s9fZPJBPB/S6eeh3f/Gku3nIp1PQ3XmgutOC7s4D+Yg/C/NQH6YF3c1DPuJJd/cDdN4NCrq7HyAf8TdhHjrug4Lu5iEf8aS7+wGq2y7o7n6AfMYv6vWhXeAX+9Au6G4e8hm/qLcO0HGk825Q0N15IB/xZ2EeWgcGBd3NQz7iSXfngeq2C7o7D+QzflGvD+0Cv9iHdkF385DPuKAv3gdYe1l936AJfNP06b+srtsEvhnmoes+8RdhHvLpF/TFPjx+Vd23JvBN02f7VXXdJvDNMA/1QfxFmId8tl/Vuw9Ax5Guu8WT7s4D+fRf1rsPsFbgD4A/aGR5yKdf0N15oLriSXfngXy2X9Xrw+MCfwD8QSPLQz7bBX1xHbgsrBtN4Jumz9M/VtdtAt8M89Bxvyzobh7yEX9h9uHoj9V9awLfNH3+v3+bPuKaYR7qw1FBd/OQj/iLcB7ovLss6O48kI94dx24LKwbB8AfNLI85COedHceqO5RQXfngXzEu30gH+rDUUF385CP+INwP0DX3eJvwv0A+fRf1rsPsFbgF6/7xJPu5iGffkF39wNUV/xNuB8gn+1X9frwuMAv9kE86W4e8tl+Ve8+AB1Huu4Wf/Y6mwfy6b+sdx9grcAv3gcQT7qbh3z6Bd2dB6or/ux1Ng/ks/2qXh8eF/jFPogn3c1DPtsF3d0P0Hl3WdDd/QD5iL8J89Bxvyzobh7yEU+6ux+gukcF3d0PkI/4mzAP9eGooLt5yEe8uw5cFtaNM+DPXmfzQD7iz8I8tA5cFnQ3D/mIJ92dB6p7VNDdeSAf8WdhHurDUUF385CPePfvANDv+PTevfg906dTeN5gD/i9MA+99y1+EuYhn05Bd58HoLrTgu4+D0A+4vfCPNSHaUF385CP+Ek4D/TevXjS3Xkgnw7odfPQe3/iT95kecinU9DdeaC604LuzgP5iHf7MC307QT4kzdZHvIRT7r7PACdd4OC7j4PQD7i98I8dNwHBd3NQz7iJ2Yf2oXnJfaA3zN9xoW6e8DvhXmoD+2C7uYhn/GLeusAHUc67wYF3Z0H8hHvrgPkQ+vAoKC7echHPOnuPFDddkF354F8xi/q9aFd4E+AP3mT5SGfMeh19wP03r140t39APl0QK+bh977Fr/zNstDPp2C7u4HqO60oLv7AfIR7/ZhWujbDvA7b7M85CN+J5wHeu9e/FU4D+TTAb1uHvo7AOJJd/OQT6egu/NAdacF3Z0H8hF/FeahPkwLupuHfMST7u4H6LwbFHR3P0A+4t11gHzouA8KupuHfMST7u4HqG67oLv7AfIZv6jXh3aB3wF+522Wh3zGL+qtA3Qc6bwbFHR3HshH/FWYh9aBQUF385CPeNLdeaC67YLuzgP5jF/U60O7wC/2oV3Q3TzkMy7o7vMAdN0tfs/06ReeN9gDfi/MQ9d94idhHvLpF3T3eQCqK37P9NkuPG+wB/xemIf6IH4S5iGf7Vf17gPQcaTrbvGku/NAPv2X9e4DrBX4E+BP3mR5yKdf0N15oLriSXfngXy2X9Xrw+MCfwL8yZssD/lsF3T3eQA67y4Luvs8APmI3wvz0HG/LOhuHvIRPzH7cFR4XmIP+D3Th54HIB/xe2Ee6sNRQXfzkI/4STgPdN5dFnR3HshHvLsOXBbWjRPgT95kechHPOnuPFDdo4LuzgP5iHf7QD7Uh6OC7uYhH/En4X6ArrvFk+7uB8in/7LefYC1Ar8D/M7bLA/59Au6ux+guuJJd/cD5LP9ql4fHhf4HeB33mZ5yGf7Vb37AHQc6bpb/FU4D+TTf1nvPsBagV+8DyCedDcP+fQLujsPVFf8VTgP5LP9ql4fHhf4xT6IJ93NQz7bBd3dD9B5d1nQ3f0A+Yh314HLwrqxA/zO2ywP+Ygn3d0PUN2jgu7uB8hHvNsH8qE+HBV0Nw/5iN8J54HOu8uC7s4D+Yi/CvPQOnBZ0N085COedHceqO5RQXfngXzEX4V5qA9HBd3NQz7iH+i/+7F+8btqvgd8z/Q5+Mfquj3ge2Ee8Y/Wf8yT7uYhH/GkL/bh0z9V960HfM/0of8HnHzE98I81AfxpLt5yEf8ol53HsQvA7+8ns0D+YhfDvOIbwHfCvOQj/jWejYPVFf88no2D+QjfjnMQ30Q3wrzkI940hfXgcnvq9eNHvA90+fkn6vr9oDvhXnouE8KupuHfMSTvtiHnWfVfesB3zN96P8BJB/xvTAP9UE86W4e8hHvrgN0HOm8mxR0dx7IR/xymIfWgUlBd/OQj/jWejYPVFf88no2D+QjfjnMQ30Q3wrzkI/4VrgfEH8I/GG4HyAf8YdhHvEj4EdhHvIRPwr3A1RX/GG4HyAf8YdhHuqD+FGYh3zEj8J5EH8H/F04D+Qj/i7MI/7n//NjnnQ3D/mIJ92dB6or/i6cB/IRfxfmoT6IJ93NQz7iSXf3A3TeTQq6ux8gH/GHYR467pOC7uYhH/GjcD9AdcUfhvsB8hF/GOahPogfhXnIR/wonAc67yYF3Z0H8hF/F+ahdWBS0N085COedHceqK74u3AeyEf8XZiH+iCedDcP+YgnffE+wKOV6vsGPeB7pk9rpbpuD/hemIeu+8ST7uYhn1ZBX+zDqNC3HvA904f+H3DyEd8L81AfRgXdzUM+4t37AHQc6bpb/PJ6Ng/k01qpdx/gUYFvAd8K85BPq6C780B1RwXdnQfyEb8c5qE+jAq6m4d8xJO+uA50n1evGz3ge6bPvFC3B3wvzEPHvVvQ3TzkMy/oi33YXK3uWw/4nulDfweAfIar9fqwWeAX+yCedDcP+QxX660DdBzpvOsWdHceyGf+vN460C3wLeBbYR7ymRd0dx6orvjl9WweyGe4Wq8PmwW+BXwrzEM+w9V69wHoe5yuu8UfhvsB8mmt1LsP8KjAj4AfhXnIp1XQ3f0A1R0VdHc/QD7iD8M81IdRQXfzkI/4UTgPdN0t/i6cB/JprdS7D/CowC/eBxBPupuHfFoF3Z0Hqjsq6O48kI/4uzAP9WFU0N085COedHc/QOddt6C7+wHymT+vtw50C/wI+FGYh3zmBd3dD1Bd8YfhfoB8hqv1+rBZ4EfAj8I85DNcrbcO0HGk865b0N15IJ/583rrQLfAL64D3YLu5iGfeUF354Hqir8L54F8hqv1+rBZ4Bf7IJ50Nw/5DEGv+zyA+C3gt0wf+h2cfMRvhXnEd4HvhnnIR3zX7AP9jk91xW+ZPvQ7IPmI3wrzUB/Ed8M85CO+G86D+HPgz8N5IB/x52Ee8XPg52Ee8hE/D+eB6oo/D+eBfMSfh3moD+LnYR7yET8314FJ4XmhLeC3TB/6HZx8xG+Feei4Twq6m4d8xHfNPtDv+FRX/JbpQ/cByEf8VpiH+iC+G+YhH/HdcB7ovJsUdHceyEf8eZiH1oFJQXfzkI/4eTgPVFf8eTgP5CP+PMxDfRA/D/OQj/h5uB8Q/+t3P+ZJd/cD5CN+Ua+bR/wm8JvvsjzkI550dz9AdcWT7u4HyEe82wfyoT6I33yX5SEf8ZvhPIjfB34/nAfyEb8f5hE/BH4Y5iEf8cNwHqiu+P1wHshH/H6Yh/ogfhjmIR/xpLv7ATrvJgXd3Q+Qj3h3HSAfOu6Tgu7mIR/xpLv7AaornnR3P0A+4t0+kA/1QfzmuywP+YjfDOeBzrtJQXfngXzE74d5aB2YFHQ3D/mIH4bzQHXF74fzQD7i98M81AfxwzAP+Ygn3X0egK67xW+ZPq3C8wZbwG+Feei6T3w3zEM+rYLuPg9AdUcF3X0egHzEb4V5qA+jgu7mIR/x3XAe6Lpb/Hk4D+TTWql3H+BRgZ8DPw/zkE+roLvzQHVHBd2dB/IRfx7moT6MCrqbh3zEz811oFt4XmgL+C3TZ16ouwX8VpiHjnu3oLt5yGde0N3nAaiu+C3Th54HIJ/har0+bBb4LvDdMA/5DFfrrQN0HOm86xZ0dx7IZ/683jrQLfBz4OdhHvKZF3R3Hqiu+PNwHshnuFqvD5sFfg78PMxDPsPVevcB6HucrrvFk+7uB8intVLvPsCjAr8J/Oa7LA/5tAq6ux+guqOC7u4HyEe824dRoW+bwG++y/KQj/jNcB7oulv8fjgP5NNaqXcf4FGBHwI/DPOQT6ugu/NAdUcF3Z0H8hG/H+ahPowKupuHfMST7u4H6LzrFnR3P0A+8+f11oFugd8EfvNdlod85gXd3Q9QXfGku/sB8hmu1uvDZoHfBH7zXZaHfIar9dYBOo503nULujsP5DN/Xm8d6Bb4IfDDMA/5zAu6Ow9UV/x+OA/kM1yt14fNAj8EfhjmIZ9hQV/8OwC/gv9Hnt67F98zfTr/Ul23B3wvzEPvfYsn3c1DPp2CvtiHaaFvPeB7pg/9P+DkI74X5qE+TAu6m4d8xLt/B4COI713L355PZsH8umAXjcPvfcnvhXmIZ9OQXfngepOC7o7D+QjfjnMQ32YFnQ3D/mIJ31xHRisVa8bPeB7ps+TF9V1e8D3wjx03AcF3c1DPuJJX+xD+0V133rA90yfcaFuD/hemIf60C7obh7yGb+otw7QcaTzblDQ3XkgH/HLYR5aBwYF3c1DPuJb69k8UN12QXfngXzGL+r1oV3gW8C3wjzkMwa97n6A3rsXfxjuB8inA3rdPPTet/hRmId8OgXd3Q9Q3WlBd/cD5CP+MMxDfZgWdDcP+YgfhfNA792LvwvngXw6oNfNQ7/7iyfdzUM+nYLuzgPVnRZ0dx7IR/xdmIf6MC3obh7yEU+6ux+g825Q0N39APmIPwzz0HEfFHQ3D/mIH4X7AarbLujufoB8xi/q9aFd4EfAj8I85DN+UW8doONI592goLvzQD7i78I8tA4MCrqbh3zEk+7OA9VtF3R3Hshn/KJeH9oFfrEP7YLu5iGfcUFfvA+w9rL6vkEP+J7p039ZXbcHfC/MQ9d94kl385BPv6Av9uHxq+q+9YDvmT70/4CTz/aren14XOAX+yCedDcP+Wy/qncfgI4jXXeLX17P5oF8+i/r3QdYK/At4FthHvLpF3R3Hqiu+OX1bB7IZ/tVvT48LvAt4FthHvLZLuiL68BlYd3oAd8zfZ7+sbpuD/hemIeO+2VBd/OQj3jSF/tw9MfqvvWA75k+P/1rdd0e8L0wD/XhqKC7echHvLsOXBbWjWXgl9ezeSAf8cthHloHLgu6m4d8xLfWs3mgukcF3Z0H8hG/HOahPhwVdDcP+YhvhfsBuu4WfxjuB8in/7LefYC1Aj8CfhTmIZ9+QXf3A1RX/GG4HyCf7Vf1+vC4wI+AH4V5yGf7Vb37AHQc6bpb/F04D+TTf1nvPsBagV+8DyCedDcP+fQLujsPVFf8XTgP5LP9ql4fHhf4xT6IJ93NQz7bBd3dD9B5d1nQ3f0A+Yg/DPPQcb8s6G4e8hE/CvcDVPeooLv7AfIRfxjmoT4cFXQ3D/mIH4XzQOfdZUF354F8xN+FeWgduCzobh7yEU+6Ow9U96igu/NAPuLvwjzUh6OC7uYhH/Hu3wGg3/HpvXvxW6ZPp/C8wRbwW2Eeeu9bfDfMQz6dgu4+D0B1pwXdfR6AfMRvhXmoD9OC7uYhH/HdcB7ovXvx5+E8kE8H9Lp56L0/8fMwD/l0Cro7D1R3WtDdeSAf8edhHurDtKC7echH/NxcBwaF54W2gN8yfeh3cPIRvxXmoeM+KOhuHvIR3zX70C48L7EF/JbpMy7U3QJ+K8xDfWgXdDcP+Yxf1FsH6DjSeTco6O48kI/48zAPrQODgu7mIR/x83AeqG67oLvzQD7jF/X60C7wc+DnYR7yGYNedz9A792LJ93dD5BPB/S6eei9b/Gb77I85NMp6O5+gOpOC7q7HyAf8W4fpoW+bQK/+S7LQz7iN8N5oPfuxe+H80A+HdDr5qG/AyB+GOYhn05Bd+eB6k4LujsP5CN+P8xDfZgWdDcP+Ygn3d0P0Hk3KOjufoB8xLvrAPnQcR8UdDcP+Ygn3d0PUN12QXf3A+QzflGvD+0Cvwn85rssD/mMX9RbB+g40nk3KOjuPJCP+P0wD60Dg4Lu5iEf8cNwHqhuu6C780A+4xf1+tAu8EPgh2Ee8hkXdPd5ALruFr9l+vQLzxtsAb8V5qHrPvHdMA/59Au6+zwA1RW/ZfpsF5432AJ+K8xDfRDfDfOQz/arevcB6DjSdbf483AeyKf/st59gLUCPwd+HuYhn35Bd+eB6oo/D+eBfLZf1evD4wI/B34e5iGf7YLuPg9A591lQXefByAf8VthHjrulwXdzUM+4rtmH44Kz0tsAb9l+tDzAOQjfivMQ304KuhuHvIR3w3ngc67y4LuzgP5iD8P89A6cFnQ3TzkI34ezgPVPSro7jyQj/jzMA/14aigu3nIR/w83A/Qdbd40t39APn0X9a7D7BW4DeB33yX5SGffkF39wNUVzzp7n6AfLZf1evD4wK/CfzmuywP+Wy/qncfgI4jXXeL3w/ngXz6L+vdB1gr8EPgh2Ee8ukXdHceqK74/XAeyGf7Vb0+PC7wQ+CHYR7y2S7o7n6AzrvLgu7uB8hHvLsOXBbWjU3gN99lechHPOnufoDqHhV0dz9APuLdPpAP9eGooLt5yEf8ZjgPdN5dFnR3HshH/H6Yh9aBy4Lu5iEf8cNwHqjuUUF354F8xO+HeagPRwXdzUM+4h/o958H+n/8oZKfAT9zfW7/UFl3BvwszXP/+dX7H/Ok23nI57Zaf/D/MvzJSmXfZsDPXJ8/XamsOwN+FuahPogn3c1DPuIf6HXn4f6zAfzG+3AeyOcW9Lp57j8d4DtpHvK5rdbdeaC64jfeZ/NAPuI3wjzUB/GdMA/5iCf9wf8L/2fV68YM+JnpM/xFdd0Z8LMwDx138aS7echHPOkPfpf/8+q+zYCfmT4//dfqujPgZ2Ee6oN40t085CPeXQfoONJ5J37jfTYP5CN+I8xD64D4TpiHfMR33mfzQHXFb7zP5oF8xG+EeagP4jthHvIR30n3A/efU+BP0/0A+dyCXjfP/WcK/DTNQz631bq7H6C64k/D/QD5iD8N81AfxE/DPOQjfprOw/3nv334MU+6PQ/kcwt63Tz3n3Xg1z+Eecjntlp354HqiifdnQfyEe/2AX2gD+LXP2R5yEc86e5+gM478afhfoB8xJ+Geei4i5+GechH/DTcD1Bd8afhfoB8xJ+GeagP4qdhHvIRPw3ngc478aS780A+4t11gHxoHRC//iHLQz7iSXfngeqKJ92dB/IR7/aBfKgP4tc/ZHnIRzzpi/cBfvkX1fcNZsDPTJ+Vv6yuOwN+Fuah6z7xpLt5yEc86Yt9+Pmvqvs2A35m+gz/e3XdGfCzMA/1QTzpbh7yEe/eB6DjSNfd4jfeZ/NAPuI3wjz0vS++E+YhH/Gd99k8UF3xG++zeSAf8RthHuqD+E6Yh3zEk/7gveBfVa8bM+Bnps/4r6vrzoCfhXnouIsn3c1DPuJJf/Bc1t9U920G/Mz0+el/VNedAT8L81AfxJPu5iEf8e46QMeRzjvxG++zeSAf8RthHloHxHfCPOQjvvM+mweqK37jfTYP5CN+I8xDfRDfCfOQj/hOuB+g627xp+F+gHzEn4Z56LpP/DTMQz7ip+F+gOqKPw33A+Qj/jTMQ30QPw3zkI/4aTgPdN0tnnR3HshHvHsfgHzoPoD49Q9ZHvIRT7o7D1RXPOnuPJCPeLcP5EN9EL/+IctDPuJJd/cDdN6JPw33A+Qj/jTMQ8dd/DTMQz7ip+F+gOqKPw33A+Qj/jTMQ30QPw3zkI/4aTgPdN6JJ92dB/IR764D5EPrgPj1D1ke8hFPujsPVFc86e48kI94tw/kQ30Qv/4hy0M+4tfT5wHuP7vA76bPA5DPLeh189x/BsAP0jzkc1utu88DUF3xu+HzAOQjfjfMQ30QPwjzkI/4QToP959r4K/TeSCfW9Dr5rn/PPn3H/Ok23nI57Zad+eB6oq/DueBfMRfh3moD+JJd/OQj3jS3ecB6LwTvxs+D0A+4nfDPHTcxQ/CPOQjfhA+D0B1xe+GzwOQj/jdMA/1QfwgzEM+4gfhPNB5J/46nAfyEX8d5qF1QDzpbh7yEU+6Ow9UV/x1OA/kI/46zEN9EE+6m4d8xD/Q6+4H7j8N4Buuz231fqMBfCPNc/9pA99O85DPbbXu7georviG6/On1fuNBvCNMA/1QXw7zEM+4tvpPNx/joE/TueBfG5Br5tH6wTw4zQP+dxW6+48UF3xx+E8kI/44zAP9UH8OMxDPuLH5jpA3+N03olvmD70PUg+4hthHjru4tthHvIR3zb7QN/jVFd8w/Sh/QD5iG+EeagP4tthHvIR3w7ngc478cfhPJCP+OMwD60D4sdhHvIRPw7ngeqKPw7ngXzEH4d5qA/ix2Ee8hFPuvs8AF13i98NnwcgH/G7YR667hM/CPOQj/hB+DwA1RW/Gz4PQD7id8M81AfxgzAP+YgfhPNA193ir8N5IB/x12Ee+t4XT7qbh3zEk+7OA9UVfx3OA/mIvw7zUB/Ek+7mIR/xpLvPA9B5J343fB6AfMTvhnnouIsfhHnIR/wgfB6A6orfDZ8HIB/xu2Ee6oP4QZiHfMQPwnmg8078dTgP5CP+OsxD64B40t085COedHceqK7463AeyEf8dZiH+iCedDcP+Yh37wPQ9zhdd4tvmD70PUg+4hthHrruE98O85CP+LbZB/oep7riG6YPrQPkI74R5qE+iG+HechHfDucB7ruFn8czgP5iD8O89B9APHjMA/5iB+H80B1xR+H80A+4o/DPNQH8eMwD/mIH5vrAH2P03knvmH60Pcg+YhvhHnouItvh3nIR3zb7AN9j1Nd8Q3Th/YD5CO+EeahPohvh3nIR3w7nAc678Qfh/NAPuKPwzy0Dogfh3nIR/w4nAeqK/44nAfyEX8c5qE+iB+HechHPOmLfwfg4Nc/1um9e/Ez0+eXv6muOwN+Fuah977Fk+7mIR/xpC/24eRvq/s2A35m+qz8z+q6M+BnYR7qg3jS3TzkI979OwB0HOm9e/Eb77N5IB/xG2Eeeu9PfCfMQz7iO++zeaC64jfeZ/NAPuI3wjzUB/GdMA/5iCd9cR1o/Vy9bsyAn5k+P/+2uu4M+FmYh467eNLdPOQjnvTFPsx/W923GfAz02f4v6rrzoCfhXmoD/OC7uYhH/HuOkDHkc478Rvvs3kgH/EbYR5aB8R3wjzkI77zPpsHqjsv6O48kI/4jTAP9WFe0N085CO+E+4H6L178afhfoB8xJ+Geei9b/HTMA/5iJ+G+wGqK/403A+Qj/jTMA/1Qfw0zEM+4qfhPNB79+JJd+eBfMS7fweAfOh3f/HrH7I85COedHceqK540t15IB/xbh/Ih/ogfv1Dlod8xJPu7gfovBN/Gu4HyEf8aZiHjrv4aZiHfMRPw/0A1Z0XdHc/QD7iT8M81Id5QXfzkI/4aTgPdN6JJ92dB/IR764D5EPrgPj1D1ke8hFPujsPVHde0N15IB/xbh/Ih/owL+huHvIRT/rifYDO31XfN5gBPzN91v++uu4M+FmYh677xJPu5iEf8aQv9uHJUnXfZsDPTJ/xUnXdGfCzMA/1QTzpbh7yGS/Vuw9Ax5Guu8VvvM/mgXzEb4R56HtffCfMQz7iO++zeaC64jfeZ/NAPuOlen14UuA7wHfCPOQzLuiL60B/uXrdmAE/M322/6G67gz4WZiHjrt40t085COe9MU+PH1a3bcZ8DPT56ffVdedAT8L81AfxJPu5iEf8e46QMeRzjvxG++zeSAf8RthHloHxHfCPOQjvvM+mweqK37jfTYP5CN+I8xDfRDfCfOQj/hOuB+g627xp+F+gHzEn4Z56LpP/DTMQz7ip+F+gOqKPw33A+QzXqrXhycFfgr8NMxDPuOlevcB6DjSdbd40t15IB/x7n0A8qH7AOLXP2R5yEc86e48UF3xpLvzQD7jpXp9eFLg14Ff/5DlIZ9xQXf3A3TeiT8N9wPkI/40zEPHXfw0zEM+4qfhfoDqij8N9wPkI/40zEN9ED8N85CP+Gk4D3TeiSfdnQfyEe+uA+RD64D49Q9ZHvIRT7o7D1RXPOnuPJCPeLcP5EN9EL/+IctDPuLXw+cB6L178bvh8wDkI343zEPvfYsfhHnIR/wgfB6A6orfDZ8HIB/xu2Ee6oP4QZiHfMQPwnmg9+7FX4fzQD7ir8M89N6feNLdPOQjnnR3Hqiu+OtwHshH/HWYh/ognnQ3D/mIJ919HoDOO/G74fMA5CN+N8xDx138IMxDPuIH4fMAVHde0N3nAchH/G6Yh/owL+huHvIRPwjngc478dfhPJCP+OswD60D4kl385CPeNLdeaC684LuzgP5iL8O81Af5gXdzUM+4t2/A0Df4/TevfiG6UPfg+QjvhHmofe+xbfDPOQjvm32gb7Hqa74hulD6wD5iG+EeagP4tthHvIR3w7ngd67F38czgP5iD8O89DfARA/DvOQj/hxOA9UV/xxOA/kI/44zEN9ED8O85CP+LG5DtD3OJ134humD30Pko/4RpiHjrv4dpiHfMS3zT7MC/ulBvAN04f2A+QjvhHmoT7MC7qbh3zEt8N5oPNO/HE4D+Qj/jjMQ+uA+HGYh3zEj8N5oLrzgu7OA/mIPw7zUB/mBd3NQz7iSXefB6DrbvG74fMA5CN+N8xD133iB2Ee8hE/CJ8HoLrid8PnAchnvFSvD08K/AD4QZiHfMZL9e4D0HGk627x1+E8kI/46zAPfe+LJ93NQz7iSXfngeqKvw7ngXzGS/X68KTAL/ZBPOluHvIZF3T3eQA678Tvhs8DkI/43TAPHXfxgzAP+YgfhM8DUF3xu+HzAOQjfjfMQ30QPwjzkI/4QTgPdN6Jvw7ngXzEX4d5aB0QT7qbh3zEk+7OA9UVfx3OA/mIvw7zUB/Ek+7mIR/x7n0A+h6n627xDdOHvgfJR3wjzEPXfeLbYR7yEd82+0Df41RXfMP0GRf2Gw3gG2Ee6oP4dpiHfMZL9e4D0HGk627xx+E8kI/44zAP3QcQPw7zkI/4cTgPVFf8cTgP5DNeqteHJwV+DPw4zEM+44Lu7gfovBPfMH3oe5B8xDfCPHTcxbfDPOQjvm32gb7Hqa74hulD+wHyEd8I81AfxLfDPOQjvh3OA5134o/DeSAf8cdhHloHxI/DPOQjfhzOA9UVfxzOA/mIPw7zUB/Ej8M85CP+ga65WNT/4w+V/C82fsyT/sDn9g+VdR/43IJeN8/9Zw34tY0wD/ncVusPfpf5k5XKvi3WFU/62Px/wMlHvNsH9IE+iF/byPKQj/i1dB7uP1+A/5LOA/ncgl43j9Zp4PtpHvK5rdbdeaC64r+E80A+4r+EeagP4vthHvIRT/qD54L+rHrdWDzvxJP+YJ35RXXdB+f1L+qtA+RDx1382kaWh3zEk/7guuzPq/u2WFc86WPz/wEkH/FuH8iH+iB+bSPLQz7i18J5oPNO/JdwHshH/JcwD60D4vthHvIR3w/ngeqK/xLOA/mI/xLmoT6I74d5yEd8P90P3H++A/893Q+Qzy3odfPcfx5//DFPup2HfG6rdXc/QHXFfw/3A+Qj/nuYh/ognnQ3D/mIf6DXnYf7zzPgn30M54F8bkGvm0f3IYDfTvOQz2217s4D1RX/7GM2D+Qj/lmYh/ogfjvMQz7iSXf3A3Teif8e7gfIR/z3MA8dd/Gku3nIRzzp7n6A6or/Hu4HyEf89zAP9UE86W4e8hHvrgN0HOm8E//sYzYP5CP+WZiH1gHx22Ee8hG//TGbB6or/tnHbB7IR/yzMA/1Qfx2mId8xJP+4L28v6i+b7B43S2e9Af3ef6yuu6D+yp/We8+APnQdZ/4tY0sD/mIJ/3BezF/Vd23xbriSR+b/w84+Yh3+0A+1AfxaxtZHvIRvxbOA113i/8SzgP5iP8S5qHvffH9MA/5iO+H80B1xX8J54F8xH8J81AfxPfDPOQjnvQHz2X9qnrdWDzvxJP+4D7zX1fXfXBf96/rrQPkQ8dd/NpGlod8xJP+4LmIv6nu22Jd8aSPzb8DQD7i3T6QD/VB/NpGlod8xK+F80Dnnfgv4TyQj/gvYR5aB8T3wzzkI74fzgPVFf8lnAfyEf8lzEN9EN8P85CP+H64H6DrbvHfw/0A+Yj/Huah6z7xpLt5yEc86e5+gOqK/x7uB8hH/PcwD/VBPOluHvIR794HoONI193in33M5oF8xD8L89B9APHbYR7yEb/9MZsHqiv+2cdsHshH/LMwD/VB/HaYh3zEk+7uB+i8E/893A+Qj/jvYR467uJJd/OQj3jS3f0A1RX/PdwPkI/472Ee6oN40t085CPeXQfoONJ5J/7Zx2weyEf8szAPrQPit8M85CN++2M2D1RX/LOP2TyQj/hnYR7qg/jtMA/5iN9Onwe4/3wF/mv6PAD53IJeN8/95xL4yzQP+dxW6+7zAFRX/NfweQDyEf81zEN9EH8Z5iEf8ZfpPNx//p9PP+ZJt+eBfG5Br5vn/vMU+Kefwjzkc1utu/NAdcWT7s4D+Yh3+4A+0AfxTz9lechHPOnu8wB03on/Gj4PQD7iv4Z56LiLvwzzkI/4y/B5AKor/mv4PAD5iP8a5qE+iL8M85CP+MtwHui8E0+6Ow/kI95dB8iH1gHxTz9lechHPOnuPFBd8aS780A+4t0+kA/1QfzTT1ke8hH/NN0P3H8+A/853Q+Qzy3odfPcf46AP0rzkM9tte7uB6iu+M/hfoB8xH8O81AfxB+FechH/FE6D/efb8B/S+eBfG5Br5tHn//7Y550Ow/53Fbr7jxQXfHfwnkgH/HfwjzUB/Gku3nIRzzp7n6Azjvxn8P9APmI/xzmoeMu/ijMQz7ij8L9ANUV/zncD5CP+M9hHuqD+KMwD/mIPwrngc478d/CeSAf8d/CPLQOiCfdzUM+4kl354Hqiv8WzgP5iP8W5qE+iCfdzUM+4kl3nweg627xX8PnAchH/NcwD133ib8M85CP+MvweQCqK/5r+DwA+Yj/GuahPoi/DPOQj/jLcB7ouls86e48kI949z4A+dD3vvinn7I85COedHceqK540t15IB/xbh/Ih/og/umnLA/5iCfdfR6AzjvxX8PnAchH/NcwDx138ZdhHvIRfxk+D0B1xX8NnwcgH/FfwzzUB/GXYR7yEX8ZzgOdd+JJd+eBfMS76wD50Dog/umnLA/5iCfdnQeqK550dx7IR7zbB/KhPoh/+inLQz7in4b7AbruFv853A+Qj/jPYR667hN/FOYhH/FH4X6A6or/HO4HyEf85zAP9UH8UZiHfMQfhfNA193iv4XzQD7iv4V56D6AeNLdPOQjnnR3Hqiu+G/hPJCP+G9hHuqDeNLdPOQjnnR3P0DnnfjP4X6AfMR/DvPQcRd/FOYhH/FH4X6A6or/HO4HyEf85zAP9UH8UZiHfMQfhfNA5534b+E8kI/4b2EeWgfEk+7mIR/xpLvzQHXFfwvngXzEfwvzUB/Ek+7mIR/xpC/+HYCDX/9Yp/fuxZP+4O/8/Ka67oO/q/ObH+t189B73+LXNrI85COe9MU+nPxtdd8W64onfWz+P+DkI97tA/lQH8SvbWR5yEf8WjgP9N69+C/hPJCP+C9hHnrvT3w/zEM+4vvhPFBd8V/CeSAf8V/CPNQH8f0wD/mIJ31xHWj9XL1uLJ534kl/8HfGfltd98Hf9fptvXWAfOi4i1/byPKQj3jSF/sw/2113xbrzgv62Px/AMlHvNsH8qE+zAu6m4d8xK+F80Dnnfgv4TyQj/gvYR5aB8T3wzzkI74fzgPVnRd0dx7IR/yXMA/1YV7Q3TzkI74f7gfovXvx38P9APmI/x7mofe+xZPu5iEf8aS7+wGqK/57uB8gH/HfwzzUB/Gku3nIR7z7dwDoONJ79+KffczmgXzEPwvz0O/+4rfDPOQjfvtjNg9UV/yzj9k8kI/4Z2Ee6oP47TAP+Ygn3d0P0Hkn/nu4HyAf8d/DPHTcxZPu5iEf8aS7+wGqOy/o7n6AfMR/D/NQH+YF3c1DPuLddYCOI5134p99zOaBfMQ/C/PQOiB+O8xDPuK3P2bzQHXnBd2dB/IR/yzMQ32YF3Q3D/mIJ33xPkDn76rvGyxed4sn/cHfef776roP/q7y39e7D0A+dN0nfm0jy0M+4klf7MOTpeq+LdYVT/rY/H/AyWe8VK8PTwr8GvBrG1ke8hkv1bsPQMeRrrvFfwnngXzEfwnz0Pe++H6Yh3zE98N5oLriv4TzQD7jpXp9eFLg+8D3wzzkMy7oi+tAf7l63Vg878ST/uDvzP9Ddd0Hf9f9H+qtA+RDx1382kaWh3zEk77Yh6dPq/u2WFc86Q9+x/9ddd0Hv5v/rl4fyIf6IH5tI8tDPuLXwnmg8078l3AeyEf8lzAPrQPi+2Ee8hHfD+eB6or/Es4D+Yj/EuahPojvh3nIR3w/3A/Qdbf47+F+gHzEfw/z0HWfeNLdPOQjnnR3P0B1xX8P9wPkM16q14cnBX6xD+JJd/OQz3ip3n0AOo503S3+2cdsHshH/LMwD90HEL8d5iEf8dsfs3mguuKffczmgXzGS/X68KTAbwO/HeYhn3FBd/cDdN6J/x7uB8hH/PcwDx138aS7echHPOnufoDqiv8e7gfIR/z3MA/1QTzpbh7yEe+uA3Qc6bwT/+xjNg/kI/5ZmIfWAfHbYR7yEb/9MZsHqiv+2cdsHshH/LMwD/VB/HaYh3zEb4fPA9B79+K/hs8DkI/4r2Eeeu9b/GWYh3zEX4bPA1Bd8V/D5wHIR/zXMA/1QfxlmId8xF+G80Dv3Ysn3Z0H8hHv/h0A8qH3/sQ//ZTlIR/xpLvzQHXFk+7OA/mId/tAPtQH8U8/ZXnIRzzp7vMAdN6J/xo+D0A+4r+Geei4i78M85CP+MvweQCqOy/o7vMA5CP+a5iH+jAv6G4e8hF/Gc4DnXfiSXfngXzEu+sA+dA6IP7ppywP+Ygn3Z0Hqjsv6O48kI94tw/kQ32YF3Q3D/mIfxruB+i9e/Gfw/0A+Yj/HOah977FH4V5yEf8UbgfoLriP4f7AfIR/znMQ30QfxTmIR/xR+E80Hv34r+F80A+4r+FeejvAIgn3c1DPuJJd+eB6or/Fs4D+Yj/FuahPogn3c1DPuJJd/cDdN6J/xzuB8hH/OcwDx138UdhHvIRfxTuB6juvKC7+wHyEf85zEN9mBd0Nw/5iD8K54HOO/HfwnkgH/Hfwjy0Dogn3c1DPuJJd+eB6s4LujsP5CP+W5iH+jAv6G4e8hFPuvs8AF13i/8aPg9APuK/hnnouk/8ZZiHfMRfhs8DUF3xX8PnAchnvFSvD08K/CXwl2Ee8hkv1bsPQMeRrrvFk+7OA/mId+8DkA9974t/+inLQz7iSXfngeqKJ92dB/IZL9Xrw5MC/xT4p5+yPOQzLuju8wB03on/Gj4PQD7iv4Z56LiLvwzzkI/4y/B5AKor/mv4PAD5iP8a5qE+iL8M85CP+MtwHui8E0+6Ow/kI95dB8iH1gHxTz9lechHPOnuPFBd8aS780A+4t0+kA/1QfzTT1ke8hH/NNwP0HW3+M/hfoB8xH8O89B1n/ijMA/5iD8K9wNUV/zncD9APuOlen14UuCPgD8K85DPeKnefQA6jnTdLf5bOA/kI/5bmIfuA4gn3c1DPuJJd+eB6or/Fs4D+YyX6vXhSYFf7IN40t085DMu6O5+gM478Z/D/QD5iP8c5qHjLv4ozEM+4o/C/QDVFf853A+Qj/jPYR7qg/ijMA/5iD8K54HOO/HfwnkgH/Hfwjy0Dogn3c1DPuJJd+eB6or/Fs4D+Yj/FuahPogn3c1DPuJJH777z/oF6OJnwM9Mn4N/rK47A34W5hH/q/c/5kl385CPeNIX+/Dpn6r7NgN+Zvr88vfVdWfAz8I81AfxpLt5yEf8ol53HsRvAL/xPpsH8hG/EeYR3wG+E+YhH/Gd99k8UF3xG++zeSAf8RthHuqD+E6Yh3zEk764Dkx+X71uzICfmT4n/1xddwb8LMxDx31S0N085COe9MU+7Dyr7tsM+Jnps/KH6roz4GdhHuqDeNLdPOQj3l0H6DjSeTcp6O48kI/4jTAPrQOTgu7mIR/xnffZPFBd8Rvvs3kgH/EbYR7qg/hOmId8xHfC/YD4U+BPw/0A+Yg/DfOInwI/DfOQj/hpuB+guuJPw/0A+Yg/DfNQH8RPwzzkI34azoP4//bhxzzp7jyQj/hFvW4e8evAr3/I8pCPeNLdeaC64kl354F8xLt9IB/qg/j1D1ke8hFPursfoPNuUtDd/QD5iD8N89BxnxR0Nw/5iJ+G+wGqK/403A+Qj/jTMA/1Qfw0zEM+4qfhPNB5Nyno7jyQj3h3HSAfWgcmBd3NQz7iSXfngeqKJ92dB/IR7/aBfKgP4tc/ZHnIRzzpi/cBHq1U3zeYAT8zfVor1XVnwM/CPHTdJ550Nw/5tAr6Yh9Ghb7NgJ+ZPj8/r647A34W5qE+jAq6m4d8xLv3Aeg40nW3+I332TyQT2ul3n2ARwW+A3wnzEM+rYLuzgPVHRV0dx7IR/xGmIf6MCrobh7yEU/64jrQfV69bsyAn5k+80LdGfCzMA8d925Bd/OQz7ygL/Zhc7W6bzPgZ6bPcLW67gz4WZiH+iCedDcP+QxX660DdBzpvOsWdHceyGf+vN460C3wHeA7YR7ymRd0dx6orviN99k8kM9wtV4fNgt8B/hOmId8hqv17gPQ9zhdd4s/DfcD5NNaqXcf4FGBnwI/DfOQT6ugu/sBqjsq6O5+gHzEn4Z5qA+jgu7mIR/x03Ae6LpbPOnuPJBPa6XefYBHBX4d+PUPWR7yaRV0dx6o7qigu/NAPuLdPowKfVsHfv1Dlod8xJPu7gfovOsWdHc/QD7z5/XWgW6BnwI/DfOQz7ygu/sBqiv+NNwPkM9wtV4fNgv8FPhpmId8hqv11gE6jnTedQu6Ow/kM39ebx3oFvh14Nc/ZHnIZ17Q3XmguuJJd+eBfIar9fqwWeDXgV//kOUhnyHodZ8HEL8L/G74PAD5iN8N84gfAD8I85CP+EH4PADVFb8bPg9APuJ3wzzUB/GDMA/5iB+E8yD+GvjrcB7IR/x1mEf8k3//MU+6m4d8xJPuzgPVFX8dzgP5iL8O81AfxJPu5iEf8aS7zwPQeTcp6O7zAOQjfjfMQ8d9UtDdPOQjfhA+D0B1xe+GzwOQj/jdMA/1QfwgzEM+4gfhPNB5Nyno7jyQj/jrMA+tA5OC7uYhH/Gku/NAdcVfh/NAPuKvwzzUB/Gku3nIR/yTf8/2A+IbwDdMH/oeJB/xjTCP+Dbw7TAP+Yhvm32g73GqK75h+tA6QD7iG2Ee6oP4dpiHfMS3w3kQfwz8cTgP5CP+OMwjfgz8OMxDPuLH4TxQXfHH4TyQj/jjMA/1Qfw4zEM+4sfmOjApXC80gG+YPvQ9SD7iG2EeOu6Tgu7mIR/xbbMP9D1OdcU3TB/aD5CP+EaYh/ogvh3mIR/x7XAe6LybFHR3HshH/HGYh9aBSUF385CP+HE4D1RX/HE4D+Qj/jjMQ30QPw7zkI940t3nAei6W/xu+DwA+bRW6t0HeFTgB8APwjzk0yro7vMAVHdU0N3nAchH/G6Yh/owKuhuHvIRPwjnga67xV+H80A+rZV69wEeFfjF733xpLt5yKdV0N15oLqjgu7OA/mIvw7zUB9GBd3NQz7iSXefB6DzrlvQ3ecByGf+vN460C3wA+AHYR7ymRd093kAqit+N3wegHyGq/X6sFngB8APwjzkM1yttw7QcaTzrlvQ3Xkgn/nzeutAt8AvrgPdgu7mIZ95QXfngeqKvw7ngXyGq/X6sFngF/sgnnQ3D/kMV+vdB6DvcbruFt8wfVqF/UYD+EaYh677xLfDPOTTKujufoDqjgq6ux8gH/GNMA/1YVTQ3TzkI74dzgNdd4s/DueBfFor9e4DPCrwY+DHYR7yaRV0dx6o7qigu/NAPuKPwzzUh1FBd/OQj/ixuQ50C9cLDeAbps+8ULcBfCPMQ8e9W9DdPOQzL+jufoDqim+YPsPCfqMBfCPMQ30Q3w7zkM9wtd46QMeRzrtuQXfngXzmz+utA90CPwZ+HOYhn3lBd+eB6oo/DueBfIar9fqwWeDHwI/DPOQzLOiLfwfgV//yY53euxc/M306/1Jddwb8LMxD732LJ93NQz6dgr7Yh2mhbzPgZ6bP+lp13RnwszAP9WFa0N085CPe/TsAdBzpvXvxG++zeSCfDuh189B7f+I7YR7y6RR0dx6o7rSgu/NAPuI3wjzUh2lBd/OQj3jSF9eBwVr1ujEDfmb6PHlRXXcG/CzMQ8d9UNDdPOQjnvTFPrRfVPdtBvzM9BkX6s6An4V5qA/tgu7mIZ/xi3rrAB1HOu8GBd2dB/IRvxHmoXVgUNDdPOQjvvM+mweq2y7o7jyQz/hFvT60C3wH+E6Yh3zGoNfdD9B79+JPw/0A+XRAr5uH3vsWPw3zkE+noLv7Aao7LejufoB8xJ+GeagP04Lu5iEf8dNwHui9e/Gku/NAPh3Q6+ah3/3Fr3/I8pBPp6C780B1pwXdnQfyEe/2YVro2zrw6x+yPOQjnnR3P0Dn3aCgu/sB8hF/Guah4z4o6G4e8hE/DfcDVLdd0N39APmMX9TrQ7vAT4GfhnnIZ/yi3jpAx5HOu0FBd+eBfMS76wD50DowKOhuHvIRT7o7D1S3XdDdeSCf8Yt6fWgX+HXg1z9kechnXNAX7wOsvay+bzADfmb69F9W150BPwvz0HWfeNLdPOTTL+iLfXj8qrpvM+Bnps/2q+q6M+BnYR7qg3jS3Tzks/2q3n0AOo503S1+4302D+TTf1nvPsBage8A3wnzkE+/oLvzQHXFb7zP5oF8tl/V68PjAt8BvhPmIZ/tgr64DlwW1o0Z8DPT5+kfq+vOgJ+Feei4XxZ0Nw/5iCd9sQ9Hf6zu2wz4menz079W150BPwvzUB+OCrqbh3zEu+vAZWHd2AB+4302D+QjfiPMQ+vAZUF385CP+M77bB6o7lFBd+eBfMRvhHmoD0cF3c1DPuI74X6ArrvFn4b7AfLpv6x3H2CtwE+Bn4Z5yKdf0N39ANUVfxruB8hn+1W9Pjwu8FPgp2Ee8tl+Ve8+AB1Huu4WT7o7D+TTf1nvPsBagV8Hfv1Dlod8+gXdnQeqK550dx7IZ/tVvT48LvDrwK9/yPKQz3ZBd/cDdN5dFnR3P0A+4k/DPHTcLwu6m4d8xE/D/QDVPSro7n6AfMSfhnmoD0cF3c1DPuKn4TzQeXdZ0N15IB/x7jpwWVg31oFf/5DlIR/xpLvzQHWPCro7D+Qj3u0D+VAfjgq6m4d8xK+HzwPQe/fid8PnAcinA3rdPPTet/hBmId8OgXdfR6A6k4Luvs8APmI3w3zUB+mBd3NQz7iB+E80Hv34q/DeSCfDuh189B7f+JJd/OQT6egu/NAdacF3Z0H8hF/HeahPkwLupuHfMST7j4PQOfdoKC7zwOQj/jdMA8d90FBd/OQj/hB+DwA1W0XdPd5APIZv6jXh3aBHwA/CPOQz/hFvXWAjiOdd4OC7s4D+Yi/DvPQOjAo6G4e8hFPujsPVLdd0N15IJ/xi3p9aBf4xT60C7qbh3zGoNfdD9B79+Ibpk+nsN9oAN8I89B73+LbYR7y6RR0dz9AdacF3d0PkI/4RpiH+jAt6G4e8hHfDueB3rsXfxzOA/l0QK+bh/4OgPhxmId8OgXdnQeqOy3o7jyQj/jjMA/1YVrQ3TzkI35srgODwvVCA/iG6UPfg+QjvhHmoeM+KOhuHvIR3zb70C7slxrAN0yfcaFuA/hGmIf60C7obh7yGb+otw7QcaTzblDQ3XkgH/HHYR5aBwYF3c1DPuLH4TxQ3XZBd+eBfMYv6vWhXeDHwI/DPOQzLuju8wB03S1+N3wegHz6L+vdB1gr8APgB2Ee8ukXdPd5AKorfjd8HoB8tl/V68PjAj8AfhDmIZ/tV/XuA9BxpOtu8dfhPJBP/2W9+wBrBX7xe1886W4e8ukXdHceqK7463AeyGf7Vb0+PC7wi30QT7qbh3y2C7r7PACdd5cF3X0egHzE74Z56LhfFnQ3D/mIH4TPA1Ddo4LuPg9APuJ3wzzUh6OC7uYhH/GDcB7ovLss6O48kI/46zAPrQOXBd3NQz7iSXfngeoeFXR3HshH/HWYh/pwVNDdPOQj3r0PQN/jdN0tvmH69Av7jQbwjTAPXfeJb4d5yKdf0N39ANUV3zB9tgv7jQbwjTAP9UF8O8xDPtuv6t0HoONI193ij8N5IJ/+y3r3AdYK/Bj4cZiHfPoF3Z0Hqiv+OJwH8tl+Va8Pjwv8GPhxmId8tgu6ux+g8+6yoLv7AfIR3wjz0HG/LOhuHvIR3zb7cFTYLzWAb5g+tB8gH/GNMA/14aigu3nIR3w7nAc67y4LujsP5CP+OMxD68BlQXfzkI/4cTgPVPeooLvzQD7ij8M81Iejgu7mIR/xD/Tf/Vi/+F01/4uNH/OkL/oc/GN13UUf8Yt63Tzi14Bf28jykI940hf78Omfqvu2WFc86WPz/wEnH/FuH8iH+iB+bSPLQz7i18J5EP8F+C/hPJCP+C9hHvF94PthHvIR3w/ngeqK/xLOA/mI/xLmoT6I74d5yEc86YvrwOT31evG4nk3KeiLPif/XF130Ue8uw6QDx33SUF385CPeNIX+7DzrLpvi3XFkz42/x9A8hHv9oF8qA/i1zayPOQjfi2cBzrvJgXdnQfyEf8lzEPrwKSgu3nIR3w/nAeqK/5LOA/kI/5LmIf6IL4f5iEf8f1wPyD+O/Dfw/0A+Yj/HuYR//jjj3nS3TzkI550dz9AdcV/D/cD5CP+e5iH+iCedDcP+Yhf1OvOg/hnwD/7mM0D+Yh/FuYRvw38dpiHfMRvf8zmgeqKf/YxmwfyEf8szEN9EL8d5iEf8aS7+wE67yYF3d0PkI/472EeOu6Tgu7mIR/xpLv7Aaor/nu4HyAf8d/DPNQH8aS7echHvLsO0HGk825S0N15IB/xz8I8tA5MCrqbh3zEb3/M5oHqin/2MZsH8hH/LMxDfRC/HeYhH/GkL94HeLRSfd9g8bpbPOmLPq2V6rqLPq2VevcBHhX4NeDXNrI85NMq6It9GBX6tlh3VNDH5v8DTj7i3T6MCn1bA35tI8tDPuLXwnmg627xX8J5IJ/WSr37AI8KfB/4fpiHfFoF3Z0Hqjsq6O48kI/4L2Ee6sOooLt5yEc86YvrQPd59bqxeN51C/qiz7xQd9Fn/rzeOtAt8GvAr21kechnXtAX+7C5Wt23xbriSR+bfweAfIar9fqwWeDXgF/byPKQz3C13jpAx5HOu25Bd+eBfObP660D3QLfB74f5iGfeUF354Hqiv8SzgP5DFfr9WGzwPeB74d5yGe4Wu8+AH2P03W3+O/hfoB8Wiv17gM8KvCL133iSXfzkE+roLv7Aao7KujufoB8xH8P81AfRgXdzUM+4t37AHQc6bpb/LOP2TyQz/9b2hnbtpJlQXSMj7UUwAawQbRFfGlJcTYBiQMoAIFWW7IYAAOg2RatDoABMACBVluyaMpgADTXKmDQmKOqh+sWDuoWbt/35rHn9df777b3AL8M3wPfF/OQz7vR03mgup9GT+eBfMQvinmoD59GT/OQj3jS0/MArbuD0dPzAPncH9v2gYPh58/9YPQ0D/ncjZ6eB6iu+K/ieYB8zk9tfXgz/LwP4klP85DP+altH6DnSOvuYPR0Hsjn/ti2DxwM3wPfF/OQz93o6TxQXfGL19o8kM/5qa0Pb4bvge+LecjnDHrrfQDxR+CPxfsA5CP+WMwjfgJ+KuYhH/FT8T4A1RV/LN4HIB/xx2Ie6oP4qZiHfMRPxXkQ/6/NP/Okp/NAPuLnemse8R3w3aaWh3zEk57OA9UVT3o6D+QjPu0D+VAfxHebWh7yEU96eh+A1t230dP7AOQj/ljMQ8/92+hpHvIRPxXvA1Bd8cfifQDyEX8s5qE+iJ+KechH/FScB1p330ZP54F8xKf7APnQPvBt9DQP+YgnPZ0Hqiue9HQeyEd82gfyoT6I7za1POQjviueB8Rvgd8WzwPkI35bzCN+AH4o5iEf8UPxPEB1xW+L5wHyEb8t5qE+iB+KechH/FCcB/EX4C/FeSAf8ZdiHvF//PXPPOlpHvIRT3o6D1RX/KU4D+Qj/lLMQ30QT3qah3zEk56eB2jdfRs9PQ+Qj/htMQ8992+jp3nIR/xQPA9QXfHb4nmAfMRvi3moD+KHYh7yET8U54HW3bfR03kgH/GXYh7aB76NnuYhH/Gkp/NAdcVfivNAPuIvxTzUB/Gkp3nIRzzp6X0A+t0t/li8D0A+77/b3gP8MvwE/FTMQz7vRk/vA1DdT6On9wHIR/yxmIf68Gn0NA/5iJ+K80C/u8WTns4D+bz/bnsP8MvwHfDdppaHfN6Nns4D1f00ejoP5CM+7cOn6VsHfLep5SEf8aSn9wFo3R2Mnt4HIJ/7Y9s+cDD8BPxUzEM+d6On9wGorvhj8T4A+Zyf2vrwZvgJ+KmYh3zOT237AD1HWncHo6fzQD73x7Z94GD4DvhuU8tDPnejp/NAdcWTns4D+Zyf2vrwZvgO+G5Ty0M+56e29wD033H63S1+WzwPkM/777b3AL8MPwA/FPOQz7vR0/MA1f00enoeIB/x22Ie6sOn0dM85CN+KM4D/e4WfynOA/m8/257D/DL8PP3AOJJT/OQz7vR03mgup9GT+eBfMRfinmoD59GT/OQj3jS0/MArbuD0dPzAPncH9v2gYPhB+CHYh7yuRs9PQ9QXfHb4nmAfM5PbX14M/wA/FDMQz7np7Z9gJ4jrbuD0dN5IJ/7Y9s+cDD8fB84GD3NQz53o6fzQHXFX4rzQD7np7Y+vBl+3gfxpKd5yOds9Pm/A/Af+Dvy9N29eNLnPvv//lx37rMHvTUPffctfvlSy0M+e6PP+3AzfZvXvRn9Gv4dcPIRn/bhZvq2BH75UstDPuKXxXmg7+7FfxTngXz2oLfmoe/+xI/FPOSzN3o6D1T3ZvR0HshH/EcxD/XhZvQ0D/mIJ32+D5yWP+8b83V3Mvrc59+rn+vOfcSn+wD50HM/GT3NQz7iSZ/3Ybf6uW/zujujX8O/A0g+11VbH3aGXwK/fKnlIZ/rqm0foOdI6+5k9HQeyEf8RzEP7QMno6d5yEf8WJwHqrszejoP5HNdtfVhZ/gR+LGYh3yuoLeeB+i7e/FfxfMA+exBb81D332LJz3NQz57o6fnAap7M3p6HiAf8V/FPNSHm9HTPOQjPv13AOg50nf34hevtXkgnz3orXno//uL74t5yGdv9HQeqO7N6Ok8kI/4RTEP9eFm9DQP+YgnPT0P0Lo7GT09D5CP+K9iHnruJ6OnechHPOnpeYDq7oyengfI57pq68PO8PM+7Iye5iGf66ptH6DnSOvuZPR0HshH/KKYh/aBk9HTPOQjvn+tzQPV3Rk9nQfyua7a+rAzfA98X8xDPlejz98DLJ9/fm8w/90tnvS5z/j8c925z/jc9h5gafgl8MuXWh7yGY0+78PD+ue+zeuKJ/0a/h1w8unXbX14MPwS+OVLLQ/59Ou29wD0HOl3t/iP4jyQz/jc9h5gafgR+LGYh3xGo6fzQHXFfxTngXz6dVsfHgw/Aj8W85BPb/T5PjCZfWO+7iajz326P3+uO/cRn+4Dk9k3lsAvX2p5yEc86fM+DH/+3Ld53cHoc58//vdz3bmP+LQP5EN9GIye5iEf8cviPNC6m4yezgP5iP8o5qF9YDJ6mod8xI/FeaC6g9HTeSAf8R/FPNSHwehpHvIRPxbPA/S7W/xX8TxAPuNz23uApeHnv/vEk57mIZ/R6Ol5gOqK/yqeB8inX7f14cHw8z6IJz3NQz79uu09AD1H+t0tfvFamwfyGZ/b3gMsDd8D3xfzkM9o9HQeqK74xWttHsinX7f14cHwPfB9MQ/59EZPzwO07iajp+cB8hH/VcxDz30yepqHfMSTnp4HqO5g9PQ8QD7iv4p5qA+D0dM85CM+3Qcms28sgF+81uaBfMQvinloH5iMnuYhH/H9a20eqO5g9HQeyEf8opiH+jAYPc1DPuL74n0A+u5e/LF4H4B89qC35qHvvsVPxTzkszd6eh+A6t6Mnt4HIB/xx2Ie6sPN6Gke8hE/FeeBvrsXT3o6D+SzB701D333J77b1PKQz97o6TxQ3ZvR03kgH/FpH26mbx3w3aaWh3zEk57eB6B1dzJ6eh+AfMQfi3nouZ+MnuYhH/FT8T4A1d0ZPb0PQD7XVVsfdoafgJ+KecjnumrbB+g50ro7GT2dB/IRn+4D5EP7wMnoaR7yEU96Og9Ud2f0dB7I57pq68PO8B3w3aaWh3yuoLeeB+i7e/Hb4nmAfPagt+ah777FD8U85LM3enoeoLo3o6fnAfIRvy3moT7cjJ7mIR/xQ3Ee6Lt78ZfiPJDPHvTWPPTvAIgnPc1DPnujp/NAdW9GT+eBfMRfinmoDzejp3nIRzzp6XmA1t3J6Ol5gHzEb4t56LmfjJ7mIR/xQ/E8QHV3Rk/PA+RzXbX1YWf4AfihmId8rqu2fYCeI627k9HTeSAf8ZdiHtoHTkZP85CPeNLTeaC6O6On80A+11VbH3aGn/dhZ/Q0D/lcjZ7eB6Df3eKPxfsA5DM+t70HWBp+An4q5iGf0ejpfQCqK/5YvA9APv26rQ8Php+An4p5yKdft70HoOdIv7vFk57OA/mMz23vAZaG74DvNrU85DMaPZ0Hqiue9HQeyKdft/XhwfAd8N2mlod8eqOn9wFo3U1GT+8DkI/4YzEPPffJ6Gke8hE/Fe8DUN3B6Ol9APIRfyzmoT4MRk/zkI/4qTgPtO4mo6fzQD7i031gMvtGB3y3qeUhH/Gkp/NAdQejp/NAPuLTPpAP9WEwepqHfMR3xfMA/e4Wvy2eB8hnfG57D7A0/AD8UMxDPqPR0/MA1RW/LZ4HyKdft/XhwfAD8EMxD/n067b3APQc6Xe3+EtxHshnfG57D7A0/Pw9gHjS0zzkMxo9nQeqK/5SnAfy6ddtfXgw/LwP4klP85BPb/T0PEDrbjJ6eh4gH/HbYh567pPR0zzkI34ongeo7mD09DxAPuK3xTzUh8HoaR7yET8U54HW3WT0dB7IR/ylmIf2gcnoaR7yEU96Og9UdzB6Og/kI/5SzEN9GIye5iEf8X/T/w9pZXzj 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/jX5f1Xob7tD3VcH1t/9QH7le0Pz/oygf3K/mXGZ9cU46A3Nepn0N14/lpD721JmRm/rg4pXcl1lfm9s3x2XWF1wcy0595IopuagPLlNN36j3Ve1c197i76saLi/doL7tmXqf+uDuE/fo+6rwnDN/0/dVwf1pV6mPXPfRO9QHd0/yvpiTmzv7ZdYXXD+R7TLz7VwfzXmZ+eBesfB/LtF94fkFzf06uq/hav896iNX/RPJ4YPLLQ7dV5yTslpeuq/gKi4P9XFOuXXzUR/cTc5L9xWeeCMP3Vdwr0tu6iPX+TQv9cG9nnloX5yT8lw+2hfca8Z9nKv+9HzUB1dl89G+8MTWvLQvuJuUh/rIdbrmpT64HJiH7ivm5I3ORfcVXPflPno6fXNTH1x9l4vuKzz1eSTHvlqe+f/wTa68kYP64N7P2WlfO9cDuXlfw1WXXNTHXN0aubkPvicn72s8mZyD9gX3i2SnPnL1Ne6Di/0Ova/qWea/10vy8Puq4WpvHuqrXOa/p0VyUh9cVs1J76vw5I089L4KrsvlpT5yvV45qQ+uD+Skfd2XzXlbO/xrdF9wfdB4Ub580vy+Xu2kEPPBndzm542i+lrvD3O/iuoL7nS5F2I+ctUTKSHmg/vtw78P+Z/7qpmTbJiX3lfBvcF5qY+efolc1AfXrXLR+6rNnZ6X3lctH52X+ybXnZmL+pYvzUX72jl1NvfaqL7gqsWDEPXNXN1raSHmg3stw/eZ6L7wxOMO7Wv5Zw71be6fmSHmg3sDAnRf9Ubzc6KPKbqv4PhuRrSvd4e/d6I/7c19w/XWPnRf4fmzt9F9BXdn7aA+cv1Hf6e+5bkH0r5uFfNzM3eL0b7gzv+eor53q1n492ueMO8lR/ngfqPwz7VE94WnpxfnfQ13C5bgvsmVI9sGmQ8uqnQOsn3FnJyLO+m+gqtn91AfPd0GQ7hvuNg9LMT2FZ58fl+I7qvhamYc9ZGrM0dS3/JWY2hfzMkrWob3NVx99DL1MVd5uleQ+eDuVvN+Y1Rf65UsT/taXq4S9W1utfDPPUX74Hq8ed8w6r6Kc9Kpm5feV8GVufdE+/acGpCL+uD+E7nofRWeeonfV8Hl87mpj1z9bU7qg8tXctC+9lx91+F9DVcrzXke5WNPXWX+d2u0b7hf0vwa3dd4zjP3Q6wvuBxmno9E+ch1+2SEmA8uFpjvY0TdVzEnuSUnva+CyzvZqY+eYmI26oM7xcxz+ej7KnKPOvy+arjoF77XRPvIFbfN7xdF+eDOvvBzj//0NXPyMszvN0T3Ndx9/FqI+mauTptE6oPLZeHvkkT3hefdzKR9wd3OSdRHrv70JPXBneIHQnRfca7W20/3FVzn3k99nFM6fSz1wZ084+i+whON9tF9Bff+t5v6yPWaj6E+uKtG0L72nDxemfYF92e9yX1zrnoPTwoyH9y/OjnI+sKTxSvzvuAnK1AfuXLqpCDzwT1pzv/ofcVcK2yn+wou39tEffT0tg/mvuH+633ovlrvlZV8Xw0Xh+dRH7mujqE+uBz/C+2LOen2L9G+4O48l/tmrrJk+Oe6on1wr3zPIOsLz32jMO9ruLj2IMh85IqWLYLMB1frwj+3Ff2+qn2uWukyfV8VXM0IUB/PKcVvN6gPrs5fo++rwpOX0un7quD+1RTqI1eXuEJ9cP+NIO2L56Tiqzu0L7iecYv6eE6l99ynPrgTf4f2td7u67QvuFp/lfrIdbbdoj64L67T91UxJ29dIn9f1XA/Wzz10VM+SKc+uPNhCn1fFZ7b8hx9XxXcef8E9ZErxidQH1yWPUf7Yk7y+xDva7gWGdTHXN1JV6gP7j0foH3hye+SaV9w79OL1EeubJhCfXDtXaT7iuek9r2dqH0FV/3vUR/PKfXcyPeC4IP7be/RfbXe1dt0X8F17hvUR65X4Tb1wb3Of4dYXzwn9XI6l1lfcP8X8/uNUT7uqXreXeqDu3lu0r7wRBXeF9xvepn6yBWzL1Mf3K2ZRvcVc9LjrtB9Bfc+zKS+netDIeqDu51S6b7CU/WT6L6COxPPUt/m6v+Hb7i76Sjta+c0Po32BZdePPUxV2+Bpr7l14/TvvBErRO0L7ibtpP6yFXxu6gP7j27ir6vas/Jj/fT91XB5bex1Lfn1Igj1Le87nb6vio8d898+r4quBrXl/rI1Y0XUh9c7ZK0L85JNekY7QvudttOfeypuHiE+uDi9hbe13juy/NoX3B5oBP1kesWncZ9w2XZZvR9VczJifswxN5XBRdtLwapb3p6Vd4JMd/y7bvo+6rw/LjKQfa+KriTUC7AfOS6u/IHmQ+unfOZrC/m5Ix+McT6guu3zc/XRvmYqz/roRDzwdXODrSvza1+kPa1XHTPZD5yVc7eAeaDewNzZLJ9xTmp1sfRfQVXwVXUt+dUg63UB5febLqv8LyTf9B9BZc5vqQ+cp3EDtS3vHQZ2hfnpI5bSvtaPmoI9XGuqo3jqA8uKjanfeE5P9SifcH15fB3p6J95PpXXOqDy2+XBNm+Yk7yYDzdV3C1pkKQ+eipbsyjPrgXe4buKzxd7OkA3Vfwr1tmMB+5MjCe+uDym2XprK+dU5e6tC+4W+SXAPMxV+/L69QH14uPZ7K+8NTyK7QvuPPHnjTmI1ellMhgPrjTf2Uqu6/iOanT/wa9r4LLKSHq2+eq3TKpD+5fuUTvq/Cc+Sn0vgquf75AfeTq+6epD+5+doD2xXNSr9c52hfcST1MfTynUrm3Ux9cHF/N+8Jr+xftC67mrqE+cvXrc6kPLoaNpvdVO6c6R+l9FdwZ8Rf1bc+XFPXB/Q5/0vsqPL/xSnpftfzGROrb3EfGUx9cto+hfTEnf+ls2hfcKTiM+pirOtKd+4a7pQTta70ybWhfy2d8RH3k6m/epr7lD+Wm+4rnpPr+RLqv4F6XQdTHc0pR7nvuG+7Urk73FZ463ZHuK7jzSwPqI1fNK0t9cPFFAdoXz0n91eE/lyq6L7j/Qfi7MNE+nqvKApWDzAdXdYsGWV+bW8l8vzKqL7i7dEiQ+ibXax4MMB/cHbQ1wPbVzunUe3RfwUVKceqjp7s1Nch8cK/upiDbV3hibfh/F0TvK7iXHP7/s9E+csWQ8Hfp/uMbrs9XDLK+mJNbUARpX8OdWuW4b+bqvPhHgPng+qWvAqwvPP+bpADrC+7OGxhgPnLlIw8ymQ+u1LhMdl/FOel26UDvq+Ay5zvUxznlr/+I+uDiO/PdsKj7qs29rel9FVxe+DrIfOTK94Zx33B94mKA9cU5Kb/IT/uCe0vN9/OifHuuzttAfXA98DPa1+YWLkX7gvtdagaYj1xdYgb1wd2vZmSy+yrm5FToQO+r4Grfs5nMR0/3maPUB1cnqvH7qvGcZmXS2X0V3N3RMoX6JlfE3qM+uOpxK4n1xZyck4VoX3A5oHI69c1cdUIp6oN7NfKmsb7wxPn5tC+4fFokMh+5Ynj5ZOaDe6GVCWxfcU7KN2vRfQV3z+0OMB/nlOq3I8B8cHdWiQDbV3hiyL1Mtq/gctLBDOYj193+OvfBg6fTWV+ck26ZPLSv5c2/zKS+OVdllTyZzAf3fn43g/WFJ3Z7tC+42qrSmI9cZ8fT1AdXbwxIZfuKOfntn6T7Ci5mr09hPnp6PwVTmW/5njrJdF+NJ8Q8uq/genb+eOYjV37mXmI+uHfu/gXW18714BLaF1zNu5XIfMxVfNiU+uB68LJLrC8897m1tC+4+1DDc8xHrs45SDMfXCb+dibrd1V1lYIR30n1+j8XySfmieDOd49RH98plep/1Lf88JMRuSI1W6R3MlcEVyXvBrJyL+fNAPOR62/OHmQ+uFMnNcD64jupToPKvK/hXuhJ6uM7le68d7hvuKpWmPaFp6ffoH3BvcTw9yCifeTqzAvUt7ypH8g6V7+b8TCnEikR3Kl/JYLrpBPUR08nsJf64LrToohcIQMRnt9tVwR3zmZE8n4bqG9zdw2hPrjXvTXta+eUOIX2BRfxzaiPuXpNanPfcFE3O+1rvT6v0b7g7uV/Mplvc5+Yn8l8cKfYV5l0X/Gd1C1v0X0FlzmzUd9+V3Xha9QHlx8n0n2FJ7uvo/sK7g6tT33kOmt7UR9cVLmfyfriO6leu+doX3BZMLxP0b79rup7uYLUN1zMk7yv8fTN13hfw71a4d/PifaRqyvsoT64e/ZIBttXzEnlDmWyfQV3f2udyXz0dF6on8l8yy9MzWD7Ck+Uj89g+wquvFoZzEeurLY7nfngbu+MNNYXc3JeGp/B+oK7pfqmMx9z9cqOS6e+4e5jj6exvvDk3c/TWF9w/3beVOYj1/+oaQrzLX9yXlLWc9XtH4g4J73M5RHcG3stgntzllIf55Q8UZv64OrdTyJyZcL9CE99uTqC+xcfDmblzqnt1EeumP5hgPmWn6hJ+9pzdUBcJusLrp5YlMl8e67mu5fBfHBv3pYM2td43pHJmbSv4X7xmZnMR67cPTmD+eCqzaSMrHMVnUoHI+ZU42wgK1d5qwcjeNw/Aeajp6jwa4D54G79EYGsuV7PRsGI3KluBFdd+gazcjH6M+oj15uhAswH9zrepH0xJ2//lkzWF1wmhDKZj7mqHzdlMB9cj7qfwfra3PuvBVhfcKdOd+ojVxarm0l9w+Xvf9F9xTkpusels30Fd6b9lM58nFOqfPE06hsu53VMZfsKz32mRDrbV8uvFkmnPnJzPZTKfMt7Z6awvjgnvU5nkmlfw91Vl5KYj3NVrsiXyHxw3W9jAu1rPNH8/STWF9y9WzCJ+cgVk+PjmQ/uTBoVz/YVc5LfVU1n+wru3J6Yznz0lJ88l0p98FFTU9m+wtOPF89g+2p5/LoM6ptc59L3adQ33HnvTdrXzrX7Z0msL7jnXE1iPubq5zwRz3xwfW12AusLz9t7OZn1BZffl01lPnJ1t/RLzAd3/X5J7L6K76Q6o+PpfRVcPlY5wHx8p1R/s4r64P434feZou+r8NxdH9H7Krg8+SCd+chVVRZSH1wtyZXO+uI7qSq1B+0LLoZ2yGQ+vlPpPVQtwHxwJy6899F94bkTbtC+4M7SjFTmI1ddfI364OLGphR2X8Wc3Lt10th91fLeP6QwHz3l/eQU6hvulWmYxO6r8PxH3CR2XwV3YnUC823uu9MTmA/utrpwkfXFnPxPaiazvuD+1Q8vMR9zFQMWJjIf3ClSIZ72hdetqGZ9wZ3PK5xhPnKdz389y3xwr2P542xf8Z1U3fUW3Vdwp3PPDObjO6Vq5MJM5oOr3tfpvtrcOw3S2L6C63O/J1Pf5IrS/VOZDy6cWkmsr/2u6olvM2lfw+WMJunMx3uqaub1DOZbvn8v72s8f/SCFNYX3ClUKpH5yPUX7klmPri4nuMS21fMSfd6iO4ruNPq9wvUx1zP9aU+uHul6Fm2r/DcqtVP0n01XNx6/DDzkSsHHTjKfHBVLm4f64s5OS2e0bSv4f73z59iPuaqRy4+z3zLj007zvrCUxXy0r7gIv3+TuYj17swfz/zwZ3MVtvZfRXnpDPolGb3VXD1W9cLzLfn1NlXzjIfXK2ZcJLdV+F5l34+x+6r4LrolLPMR6567Ovj1DfcLd/yGO2Lc3Va5eOsL7j749DDzMee+kWrHmI+uNc/Zg/ra701gw+wvuBOwoo45tvcDRX+Yj64c7z1dnZfxZycBiXPsfsquE75+zzz0VNXSjlGfcO9+E0n2H0Vnv90jGb3VXBxs2kC85Era58/zXzLz7gXaF8zJ9/tsp/1BZeVWx5iPubqpt3bznxwp9CFnawvPP9ai6OsL7jatfIk85Hrb68Qx3xwb3/hI2xfcU667+3dzfYVXMfW28Z8nFPezdLbmW950/0b2L7a3O8KbGb7Cq4vxK1nPnL1tvyrqG+4GvX0ctYX56Ss/PUW1hdcNnhhFfNxrrrPbVvHfHB/SL+lrC88+XLLxawvuNug5DzmI9f76Kk5zAf3h9b22b5iTu7svhvYvoLr2+VimY+e/pVuy5kP7q15bjXbV3hu98I72L6CyyZ59jEfue5LLTcxH1yOLbSD9sWcFtafx/qC63mhxdQ3cxUbyvrMB/fz/jib9YXnD/x4FesL7sef2sx85LqJvZYwH1wNbLcm63NVOXBuxHNSp3FMBHe+3hPBxbUZ1MdzSq9EeeqD69q/RuSqtekRnh63L4KrS/lCEfy5q9RHrujrB6lvuLx9mPc1z0l9vS/A+lr+8VPUt89Vc1cOUN9wt+DcAOtrvXGNaV9wr+Ec6iPXbVGA+uBy1M+Rc/0o/OfUYE7ydJEILodUC0Xw3ypSHz3VqXtB6hvuX3ghMrfvdxGePvBlBPcK/xLBVbYm1Eeu88oH1AdXTzakfe1cR10Isr7g+tmHQ8zHXJ1qK4PMB1d7rwZpX+SOqEz7govVdaiPXNGtNPXB1eUaIbaveE4qFtfPZPsKro+b9+qifTyn3FQ6g/ngXiHzvmTUvsJz5wwPsH0F13sLUt/mpl7IZD64eHRkgPXFc1J3bpc02tdw98TWdObjnupU+iSF+eBqSfi9t//0NZ4fSstgfcHVzETqI1f+NiOd+eD69bsZbF/tXLfFBNm+gqs5+6iPnur3EtQH93uMDbJ9hedeLUT3FdyZ+D71kSt/DAaZD+49Hf5uT3RfzMkZ8GeA9QV33M+pj7mqY48GqA/eOTnA+lovZmuQ9QV3Oob/3OpoH7nyi35B5oOLF24Gs56rTtFOEeekeiwmgvsf94ng+kpf6uOcUrc6cB88W/+IXNcdFOHJj/6I4HL84AjufzyS+sj1yoyivuUZE2hfnJOuaE/7gjuVB3Lf7KlWbahv+buDaV94cuR42hfcrzWD+sgVn0ylPrh6f2HEXMXhfhFzks2HRXBnSIcILi/0pr6da/GJ1Ad3W4+IyHVe/TbS2/1zBBcNn4rgXvB16iPX29iV+uDi75q0L+bk/zmb9gWXZ6dz38zVP7+M+4Y7R5bSvvCc9sNpX3Dn91bUt7ntZ1Mf3NWD6L7inHQat6T7Ci6mD6E+zin3m6bUB1d7h9B9td6DmXRfwb1+K6mPXO+b+dS3fO8m3hfn5Hd1aV/Lbw+iPs5V/9in1Af3y/zO+xrP3bGY9gV3N/xFfeT6c5dSH1ye20/3FXNyW23m+2q4l0dRHz31M3upD64rH6L7Cs+vvZ7uK7jqMIf6yPX376c+uDgby/uaOTmzT/C+hrslNPUxVzE/gfrgfvcQ7QtPjY2nfS2PO8V9k+u2+Zv7hsvC5ufqo+6reE6qhk1OZPdVcPn4xWTm2+eqVafEMx9cD6+dyO6r8Lzrd1LZfRVcf7Of+jZ31J1k5oOLwV+n0b7mOamq/Ph52tdwPWIc9fGcSs2ofZL64KPmnGd94el6yxNZX3DvvWEp3A/n+tPfSWA+uDg1JIndV+2c+r6fye6r4OKsoD56yifCP4cU7YN7vVdmsvuq9aqa74tE3VfB9aO7qY9cXXBngPngXs+BQdbXzrXAzTTWF9zNd4r6mKtzuX0q8y1PDb/XG90XntBvBWhfw+Xe8M+t/cc3ue5LAzKpb7h6ZUeA7Suek3oV6hxh+wquO8ecYj6eU3rf9Y5jPrg+c/Eo3VfkvtPjIttXcO/hBpeYj1x3z5ozzAf3vno6nvW1z0mrPLKT9QUXP948yHw8V5X7n9nKfHDv7T/2sL7wnDtfn2B9wbW78jzzkasCvx5hPrj3xven2b7aua5ak8z2FVwv7Z3GfPQUlXYlMt/yNx+ksH213u9zM9i+gsuPKwSYb3P7DE+nvuFqmHkfK7qvmZPTcFkC62v5814y8zFXd3wvzXxwN+PDRNrXeE7Sm2msr+WXw++9RPvI9eZOTGE+uEyvns7uq/Zc3Vue3lfB5YNu1Lfn1OSHqW/5qpb0vmq9wsvofdXyP45RH7nqr8XUB/eST9G+9py8cDHI+oKrg99S3+5ptmlB5oP7F16jfW3urVm0r+XjTlPf5hYbTX1wteQYva9iTt7yAL2vgjvp96hvexa+SX1w0d78uQpR91V4Op/58/Gi7qvgYmP4u6vRPnK9gg9dZj64+L7AZdbXzukA7wsuTpg/vzPKt3Ot4FDf8t8L0b4299qjtK/l04tQH7l+tWLUB/fKPn2Z7SvOSbXr/SDbV3B5/Q71cU7JcbsD1Ddc3P8zSPfVePr7GLqv4LLKLu6bXPHEJ9QHdw+Z/75F9cU5qea5AdYX3N9TLUh9c676P7ybyXxwcWV6gPWFp64/CNK+huuRPblvctUfI4PMB/efqED3FXPS/e+G2L5aPvxh6qOnNyUYYj64iClA9xWeW+opuq/g+r3nqI9cv86T1Af3Nob/+j99zZxkzsMh1hdcnjDfjY7yMVd5eVKI+eDu5vMh1hee2FyY9gUX05+iPnLVpezUB/eWm39v9Puq5jupzu1BGex9Vcvzl0pjvv1Oac+CGcwHFzNbp7L3VeGJbIlJ9H1Vw93OGfHMR668fS2R+eDOmBOa9cV3Up0FXdNpX8Pd6TNSmI/vVDpP7ktjPrg3dm0y62u9fdcusb7g/3+xi8y3ubvPJzAfXL7qnWfvq9o5XfzhLHtfFdx/p+tR5qOne7z8aeob7mRMpu+rwvNXvLCXva9qeaHtivnI1R3f28V8cPFhq82sr53T0JInWV9wv9S5g8zHXP2tJY8zH9yvEBtH+xpP39y/g/UFd7aV3MB85OpZM7cxH9wpLdayfbXfVS1zK5XtK7ictTCJ+fhOqdh0J4X54HJRTCLbV3h69ZJ4tq/gXveMM8xHrnj0J818cDm31ynWF99JFe0PJ7O+4N7ipy4xH9+p8gp3TmI+uHxoEu0LT1T85zzrC653ZD/BfOTqVfnPMh/cadX7KNtXzEk9XO4o21dwr+PePcxHTy+9/GHqG+7cvrqT7Ss835mr2L6Cy0WBVcxHrrNy9Gbmg+sfl61gfTEn53rPA6yv5Tsq72A+5upUWL2X+eBaTN/K+sLz1aMbWF9wf9ekZcxHrtd29xrqg+f/bgl7XxXnpPvTRf6+quHi0MEFzMc55W17YgX1DZc1H5nL3leFJxKm+ux9VXBZfOIE5iNXLTg6mfngbu07o1lfnJNyeO5lrC+4f/f3WczHnjp5/lpMfcPVmytmsL7w5IQxtK/lxWJGUt/kuqvHjmM+uF7+4gj2virmJIctHM/eVwX3H0mZynw718/njGI+uHe20nj6viq8fUXmsvdVwWXjssuZj1wx9vkZzAeXwZ0LWV/Mybu1aTjrC+7MLT2a+Zir7n1xKPPBnQ9jR7C+8Jy9CyazvuBOnwZzmG9z878/nvngum6XGXRfzTkpX3uwgO6r4Trvhz7z7bk6qP886huuKz87je0rPPH6h2PZvoLrbwsOZz5yRZ8vxjAfXA3rNIz1xTmp+nlzaF/D3eOhyczHuaq/KDWL+eDe0cBE1hee6NljNOsL7n9ScRjzbW6FnaOYD65Sig1j+2rnGjd8CNtXcPHilmHMR0//t2eGMN/yF6r/wfbV5hb7dgzbV3C1pdZU5tvcsz+NYj64U6H6RNYXc/I/eG4I6wvubBg+lPmYq+zZdAjzLd/daCjrC0/1d0fSvoa7NeqNYz5yvTH1RjAfXE8IzyX6vorvpPrFrtH7Krib9M9F5uM7pc7tzxOYD+4umnOO3Vetdzf/KXZfBXcqFDzMfOR6sx8/znxwb02H/awvvpOq+4/SrC+4f7jOaebjO5XOc+vOMx9cdK5ygvWF57f54gjrC64KvLOH+cj1y4w7SH3DRdkxf9H7qpmT0/iZXfS+Cv7er1uYb+daYP925oPrHgXpfRWeN3jbSnZfBRcD9ixgPnJV4UeWMx/cKTx/LuuLOXmrLyrWF1zf/nAN8zFX98HJTcwH1zEVaV94esegJawvuHev0GzmI1eUvbaA+eCi5osz6b6a76S6u/qcoftquNeox1Hm4zul/r11J5kPLn7xDrN9hSfbN9jH9hVcVZq9jfnIlSvL7GY+uNtjhKJ98V3VjX8cZ30tn/fDQebju6rivQrHmA/uXzq2n/W1uV+V28n6gusihWKZj1z11KK/mA+u14+KZftq59q3yHq2r+Dy7p1l1Dc9/btL1zAf3Dk6fynbV+s50+exfQVXpcvPYD5y1d3Lc5kP7s4fMIP1xZz00furaV/DdY1Cy5iPuep+fddS33BfvrSC9YXnPjJgPu1ruLy0dRbzbe7TDZYwH9w7e3A+u6/inPS+fnsGu69a/smzE5iPc0rWfHY68y3XE8ex+yo8/UnZUfS+arifWWIY85Ervt8wkvng/v/aDqN9zTnpJXWZQvuCjxsylvnYUxk4PIn54HJZpbGsr829NnUk6wuutlcdznzk+qUKjWI+uPI6jaD3VTMnV8QPYfdVcL/2xKHMtz1Lz6I+uJCV/mD3VXjiq+Bwdl8Fd0feHcV85IpVccOZD+4PrDiK9bVzKn3qD9oXXNQaxnzM1e11aDjzLU96bATrC8/pV24E6wuu66WNZL7NffrYCOaDS1l8FNtXnJNi+rhJbF8tX3xvLPNxTumtMVOYD65L9pnA9hWev7vJaLav4M7igtRHrvjplXHMB/funh3D+tpz8vsdPusLru5smcp8nKte5R1zmG9505mzWF94fu1PJ7O+4P6UGROZj1znoYdmMt/y73f6bF/tXAf3H8n2FVzdHTaS+ejpzX55DPPBnbiM0WxfbW6n3KPYvlpeO34U85Gr3n0wmvqGi6r1x7C+mJMf13UC7Wu4+/zi8czHXN17LaczH1yemDyV9YWnr5YYz/qC+x1Pj2M+cr0Fj0xhPrh4Jm4Se18Vz0m9eis3sPdVwf1LdXdQ3zynFO84q5kPrg69qtj7qvDUZwUOsPdVwf1i3nHqm1w1s/pu5oOLmn0Ps754TuqX+nMZ6wsuv9y4nvl4TuWt37+Q+eDqSJdVrC88vaPZdtYXXKY1jKO+yRVF9m9hPrjbfNpO9r6qnVPuQefY+6rgXvLjCcxHTzHffJcqygd3lLzI3leF57RclMTeVwUXqctSmY9cNar1JeaDixMbk1lfzEnHBY+yvpaPX3qG+ZirWFLkEPPB1Z9Xj7O+8DwnpFlfy9s1TKS+yXWnDTvHfHC3xvx4tq/2OWnC8TlsX8HlmrrLmI/nlM7oYjOZDy4Xfr2Q7Ss8sbzmOrav4LLV7q3MR64c/NJK6huuNizdyPriOamaPmka7Qu+r/0c5uOeqlb2m8R8cP+vAzNYX3jeV/X+ZH3BvdExq5lvc+t/NZ/54HLO8mVsXzEnv1mjPWxfwdXw/oeZj57i1wXbmQ/uHaiyj+0rPJW96km2r+Dab3ee+ciVLT84wnxw8djIk6wv5qTffGwL6wvu5F+7g/mYq9iTsIb54P6IR2NZX3jq55P7aF/D/QIlqI9c0WrDDuaDix3OPva+Ks5Jv1zeDPa+Krg+s5H6OKf8dq3TmA/uzemawd5Xhedkzwiw91XB9Ya11EeuXHktk/ng7vxbAdYX56TX5McU1hfcW6iojz11W+9LZD64dDqlsL42t8/QDNYXXE8/zn3kVhuTRn3DvfEvZrD3VTEnd23VEHtf1fL7Y6iPnnLuvCDzwcWIJ0LsfVV4qufuEHtfFVx/GR9iPnJFzh9DzAf383anfTEnfWJtgPUF1y9/QX3MVR/8OJP6houheQOsLzxRP/xdoei+4Nr8ua//8U2uijffnY7ywd3t4e9aR+8rzkn3pf3xbF/BnZYTqY9zyi9Y6jzzwf0pH8WzfbXe3mvJdF8NF/33pzIfubrDikvMB/darklifXFO+tl3n2B9wWWRIWeZj3PVj33sMPUNVw8lHWN94cmaeTXrC+7fzZXAfOTKLctPUd9w5+Dr59i+2jk993I621fLRz+bwXz0dI5/lMJ8cP9a01S2rzY3eVUG21dw77M7GcxHrnr9mTTmg6sC7dJYX8xJTJtzifY1XP55NpH6Zq7+8YoXqQ/+YJtmfeF55+Yk0b6G+xm5kpmPXHlpUDz1DXdeTYtn91U8J1VxU8az+6rlz342jfl4TqlGzx3LfHDvzu+T2H3V5t44NoveVw3XZz5ZyHzkeium+My3vMMLc1hf+5z0VKcxrC+4nPlgPPPxnEq+cWs088HlsW7jWF94zhexU1hfcHGw7wzmI1efjpvIfHDtPjWN3lfNnORrPZez+yq426nSWubbuVYttYj54GJ55+XsvgrPO9x5C7uvgvuzw/fy//gmV2U2XsN8cHlTbGJ97VwvT5rD+oI71Y4tZD7mqprHzWA+uNhUdQ7rC0/mW7SM9QX3mkxYxXzkOiM6L6C+4aJ+7z/pvuI5adUSY9i+gusSJ8Yy3z6nbJ97LPPBnaeyj2P7Cs9d9ugEtq/gziOhScy3ubPrjWc+uAi4E1lfPCfVZw+NY33B5aGC45mP56p+mSMTqW+43F56IusLz/t13HjWF9xPaTOB+iZX/RQ3gfngTsz2CWxfMSevwJBpbF/BRWNJffR0PxOTmQ8uspeexvYVntxafjbdV8Pdz+vNYz5y/dabfOZbnr5wJuuLOXkzZk5kfcHVzleoj7k6P+SfyHxwv9LUiawvPP1nkamsL7i35cvp1De5zuSOk5gPLncMmkzvq+acdPLF7Wb3VXCxv8UB5uOc8l79cyvzwcUfQ/9i91V42n/yCLuvWn7w/DHmI1euGr+H+eB+zSH7WV+ck/JKmfWsL7jc88YW5mNPxaxSK5gP7gS/W836wnO6bd/K+oI7nRvtYD5y3VqL1zEf3E/6eyO7r9o5Valxkt1XwZ321U9T3/TUuYYdYj64Wy3bEXZfhefuGn+G3VfB1ZV3zjIfufq17EeZD65LmPfRovpiTt6Y7TtZX3C1de1u5mOubo6rW5gP7udaoVhfeGJp4b2sr+XDwu9lRvvIVe8M2kp9w53i6VvZvtpzsurVhWxfwf0RZ/9kPs4pufn4bOZbXuileWxfbW6OjsvZvlq+77eVzEeuyDt8AfPBnU3nFrG+9lwdn+SzvuBOmbdnUt+cq/66fVOYb/mYF6exvvDc4KhZrC+4nnN6NvORq1oMnM58cDmwmc/2FXPSP761mu2r5ZMXrWE+ejrPpS1hPri8OmAp21d4osXstXRfDXfS869jPnJF0ifLmA/uZOu4jPY1cxJFrs9hfcFF7L65zMdcvQdBn/ngasrnM2hf4/mVPp7H+oL7n30/j/nIVU3azGA+uFvo2/Dv+91qFv5u2hPhP8fab/NZ+Dmm+0UggjdqGczKZcFaAebLfwaGn0O3H5LBfHC1ZFRG1lw5sm0wwjteJ5CViyqdg1m5PvNTgPnI1U9Oy2A+uHNscQbr6wzJDD/nuRdKZX3B/Uf/SWW+165++OcoTjdKYr7lG1onsb7wvBz501hfcDnx2TTmI9d/vm8S9Q0XA6cmRcz1dK9g1jk5Nc2fi2y4u3VAMCv33u8aYD56+is2ZzAf3PvhYEbWXFFteDCrJ5b8HsjK9fjwd7LBnZ7DA8xHrv5CZzAf3M0bymB9MSf5zhtprC+4yFcjjfmYqxfYmMR8cHX1bBLtazz39s9prC+4f61bGvORq7PfSWI+uO7weDLbV3340fB9a8/j8WxfwfU3peKZL5+udDr8XcGqp5kPLt6ud5rtq8395d14tq/g3o9145mPXL/twNPMB1dfbj9N+66tHv7fQbc/O8z6gqs3Gx9mvqxQIfzefmLVncwH98o32cn6Wu/CoMO0r+Gy9qbDzEeut236TuaDO38FdrJ9xZzcW93j2b6Ci0kz4pmPnnpSnjPMB1ef1TzD9hWerPdXPNtXcPFWUjzzkSvXm//9EOWD62ynzrC+mJNf+uZh1hdcPv7GEeZjrrLAp7uob7j30aJdtK/xvKYdjrC+4OrhJUeYj1z5ccHd1DdcxTTbnfVc9R6eFMx6TsoF4wJZuX91cjArlxUnBZhvz6mH/8lgvuXZnMyI3Knhfw889dbEQFbuSfPnRRju3hsVoL7JVQseZDAf3C14K4P1tefqlSFprC+4X3RsGvPtnq56KZn54PKXN5NZX+vtCv8a3Rdc7h2axnybO/7tZOZbvv+15KxzlSWHBiPmlNY/kJV75XsGs3JneocA89FTd0nNYD64HnMoI2uuaNkimNUTf9cLZOVq3TfBrNy9826A+chVTZdlMB/cqT8wg/XFnLza4T+35T99Dff/Vy+N+ZirX+2pZOZbHrqRxPrCU6sqpLG+4M4DJ435yHX37kpiPrjeNiyJ7ivOyY/ux9N9BT9TMIH5OKf8+g+dZT64N+Lls3RfjefseDSB7Su40zVvAvORK76vfJb54Orsi2dZX5yTbumzR1hfcPHKP0eYj3PVrbViN/PBRd6U3ayvzR2W+yjrC67v3DtCfZMrKjp7qG+4N+Cf3WxfMSf9+ZV4uq+GOyPi4plvez7nnGU+uGi65QzbV3hy3+R4tq/g8nCjeOYj1x3Q7Azzwb0q2c/QvmZOXuLFI7Sv4fJo+D/3aB9z1SOO76a+4arZmN20r/H8fS2OsL7gYpR7hPnI9etW3c18cGfWxV3svqru/+/f50GqtreR3VfB/Y9jNjJfDb/77/MM562HljEf3K1Zfhm7r8ITo+I20vuq4X5ylU3Mt7k/tl7GfHBn15ZlrK/T1ry3fLjsTNYX3LvWcCbz/SIi/PMc8V1HMx/cvbxnNOsLTyetmcn6gvsny85iPnKdap+OYT6489HVMfS+aubkFty+id1Xwd1Xv9nMfPSUy4svZz64U/L35ey+anO9xM3svgrujGmxhfnI9WsHlzMfXDz+9QrW1871vRWzaF/DRZlPZjMfc/XKbxrLfHD53pRxrC881TNhNusLLtd2msN85Mq6/cczH1zvbjWB7as8s7RP+F54qg/bV3AxumZf6i+/92v4vfCeLagP/t2dlmxfrTewcD+2r+Be3aK/Mx+5cmOT35gPrifVbMv6ij0nysb+H9674guxpC+42PPZgarE9z8t2Dj235/zXvFTLPHBdY45jWJJX3j+h1VeY33B1QcZ1ZmPXPfhMw1jiQ/uTHq1XizbVzMnnfRFf7av4KL7wgHMR0+38cftmQ8uvunTke2rzV1TfBDbV3A1aexg5iPXuZLeifngfqdfu7K+dk4bHq3J+oKr9xd+z3zM1W3f6rtY4lv+Y+cvY0lfeP4UR7C+4DLUsgnzkatuPlkllvmGq7d65WP3VZyT3qQLW+h91XC9t2Is83FO+Z8uWMF8cD0msILdV623pHEsu6+CO7ebxzIfuf6TT61kPrhb4+WVrC/OSb/j/TmsL7jq1Hou87GnzqcfTWQ+uK6TaxLrC0/8uXUu7Wu4HnB9LvOR616cNYn54N577mR2X8WcnNiPYtl9Fdyb/WAL823PqyVXMh/cfTLfSnZfhedXmLGF3VfB3TbeFuYjV8pLK5hveY2dK1hfzEn0KDCP9QX3cz8yj/mYq3ii92Tmg+uEU5NpX+M57zw2j/UF9+e+MY/5yHWafj6F+eB+ctGpbF9xTqqy2YeyfQX3mtb+g/n2nKqR2Z354G7TRr3YvsJz7w4exvYVXJyfMZz6Jlc2TJDMB/cfPNKH9bXn5CCnOesL7nZd3Ir5OFdl9k3VqW+482uzr2lf4zmN327L+loutrRnPnLlwZw/Mh/cPf9xY7qvZk5qwncj2L6COzWSRzAfPXWnGn2ZDy6K/dWP7iu8inNH0n013B9xeBTzkeu8s74/9Q1XleYOYn0xJ/FgaAzrCy7K3+7CfMzV+eWn5swH1x1kG9YXnu41rifrC+4vXtaH+TY396KO1DdcHXyjR9bnqrJA5WDW56T672KBrFzVLRrMysW7gUzm4zmlrvFDBvPB3YYlM7Lmes2DEZ54bHNmVu4O2hrIytWX5s83i/KR629JT2e+5TWXpLO+eE6qDu1KpX0NdwYOSqW+eU7lnvoqifng2imQxPrC885/ksr6guvWeVOZj1wnc28i9Q13+41IzDpX58U/Alnn5B74LDMr1y99FcjK3b05M5mPnjpvy3Tmg+tipdKz5spHHkR43omlGVm5UuMiuFLVM5hvc88dTWM+uL/rtzTWF3NSD+1NYX3Bxbo/UpiPuTovNkpkPrgeUS2R9YUn+9RMYX3BxVcPpVDf5PrtXkpkPribz01k+4rnpOJwiXi2r+D600TNfPuc8rMJp5lv+YA3TrN9hecdX6zZvoKrzX0185Hrbsk8xXxwv9r+U7SveU7q3Tp+mPUF189OPMx83FO9SoN3MR/cOV57F+trPdX1MO1ruP5l+GHmI1cN+3kX88HlpMRddF/NnPwP2mi2r5b366yZj57O3xmnmA+uB/54mu2rzT0/XbN9tbxQkmY+cv0Kr55hvuVTW5xlfTEnt1noMOtr+bnVR5iPuep38+6hvuHOF0X3sb7wRNK9o6yv5SPOHWc+cmXdOgeYDy5PDj2c9VyVVfJkRpyTzZemZ+Xez+9GcC9HQhrz7bnaJCOV+ZY//npq1lxnx9MRnly0NTUrV28MSM3KvSKvpzAfuVpUTGE+uLpxJIn1xTnp/bM8mfY1XDWpmMx8u6f3CicyH1zU/PsS6wvPL9otifY1XH7ZMZH5NjfXskvMBxfFKlzKOlfxYdPkrHNyXiudlJXrweF/DlwlTUxgPno6K3ImMh9c/RT+uXDk6pyDdFZPv9H0YlaO76qCi3PNTjMfue61iReYDy47xpxmfe1cNxS9xPqCu20bxjMfc3W+apnAfHC/5Y/xrC88r+ixC6wvuHh/8xnmI1dufVgzH1zUqX6e7SvOSVHTi2f7Cq5bz4ynvjmnvN/zn2c+uFvm8gW2rzb37+vxbF/BVekXEphvc8931MwH13sb0r44J8W92qdoX8OdjNpnmY9zVVfceoz54O5bG0+xvvBkt9kXWF9w7/wN6iPXb/HOeeaDu29laLqvZk6qpPmuVvS+Gi7u74hnvp1rzycSqG+488XQBLav8LRTMZ7tq+V9f6M+cr2pqxOYD+7cmkz72jmNbZ1A+xqu9cRLzMdcvU35L1HfcLfvwUTaF17nFomsL7hzPTmR+ch1a5dOZj642lk4hd1X7XPVHPc2s/squPg25xbm4zmlWLx/BfMtX/jCSnZftV6ZcVvYfdXynrkU823u+7lXMR/cDR5czfriOal46c95rC+4fGPvfObjOZXrTZnGfHB5eIvP+sJz7uVaxPqCu789s5T5NndU3VnMB1elkuax+yrm5J5ptZXdV8HFvqPbmY+e3pzs65kP7o/6egu9rxrPXzdhF7uvgntLS+xnPnLV+W+2M9/yQO89tK+Zk5+n8ErWF9x59MQ65mOuqvGgpcwHd+qZ74tH9bW5+64o2tdweXvVLuYjV2XujGU+uNfvrV1sX/GcVD8ZN4btK7j6u/IE5uM5pfdhnWHMB5fXHx7N9hWeLhD+ufTofQXXDfPOZj5ynSsjJjIfXN6oOJP1tc9JTzgDWV9w986XI5iP56pyXut+zAf3yqg/WF947oqfxrO+4HLNoBnMR64u3GUc88H12+HvPkbvK+bkv1JjMd1X8I4XVjEfPeXbLyxiPriYuW8V21d4wm8Ry/YVXLb1dzHf5p58VDEf3BkwYjfrizl5Y9xFrK/lDb9czXzM1XOWLGI+uDOx0BrWF56/Z41ifS3/afse6ptc3bLDVuaDu5ez7WP3VXtONqp/hN1XLX/0pZPMxzklGhY4zHxwuXroCXZfhecvXn6O3VfB9YuvxzMfud7HmeeYD+4eVPGsL85JMWL8IdYXXA9ZfoL52FOv5Y5DzAfXe5ucZH3hqRh1nvUFF626JzAfuW6Z8J9rEu2Di/u1LrH7KuYkYk/T+6rlPz2SzPz/O9ed1AfXr4W/q/Wf+6rxvLtbU9h9Fdw5fT2V+ch1UsqmMR/cKSHTaV/M1YtNYn3B3UU/pTIfc5VPrkxmPrjfqkEa6wvPWfF9OusL7saHv2sW7SNXXfsyg/ng3nk/k+0rzkk5qv5huq/gM9ufYr49p1pVOsJ8cJ2y7DTbV3i61Tua7Su4M/WVROYj14lz4pkPLvZnT2J9cU4qr/RR1hdcDLlxhvn2XJ336DHmgzvFS59jfeG5v2yJZ33B3cvbkqhvcr2BzRKYDy5LNEtm+2rntG1cCttXcH30jXTmo6d82+xllA8u82fSfbXep49lsn0Fl3e+DjDf5pZeSn1wt8upAOuLOcklj6SxvuDepl4Z1DdzVbunpzEfXH/ydwbrC8//+kXaF1yqd4LMR67bvh/1wd3B/cI/1xf1vqo7Ikfrf9973b2yVSx5XxXc+X1nS+a7o6e2+/e9xp1d2jEfXJV+uF0seV/V5p5/r0UseV8VXPR7tTnzkatuNmzLfHAn7rc2rK/X44WO//79S9k7sr7gev6MDszXM3N3+vefm5caw3zLSw+PYX3haZmtA+sLrh8u1Z75yJU/3+nIfHDx06sdY8n7qpiTfGTKz7HkfVVwdW5sY+ajp2javzX1DRdtt7SIJe+rwpMtXxKx5H1VcOe1I3WYj1yv3shfYokP7pfO3ZT1xZzc2eH/XP7T13B3+4421DdzlX3+14H54E7lW+1YX5t7d1hr1hdcBCu0YL7NrTeyLfPBxZirv7F9dbMP7PzvX3/QsjPbV3C38ZOdme/N2Nfl318vLu/CfHB5sUkXtq/wVN0Rndi+Wr7oSAz1Ta76Nakz8y1f+jrtq04ndv13LglxXVlfcPet37sy3+lyvtu//3mV2tWN+eCyYb9urC88r1+hrrSv4X5s8y7MR66/sWg35oPLJ7t3ZfuKOfnPJnRk+wouminqo6dT6NtOzAcX6oMYtq/WG9O6PdtXcFk+k/smV9TN05H6hst/JrWnfc2cxEMTOrO+4KLhJOpjrqrLui7MB1fr93VmfW3uY21jWF9wWeJp6iPXLb2mE/PB3biYmFjyvirOSb/eopqx5H1VcHdxi2qxxMc55RV9tlEs8cH1tuk/xJL3VeHJzUueYe+rgqvEep8y3+aOcWrGEh/cv7WsIuuLc9LZ1q4Z7Qv+63uNmW/3NLVuS+aDu19+8Uss6QtPlXXqx5K+4G7/xFrMR657dU5j5oPLcd/8GEveV8WcxBcTf6DvqxquGtxrwnz0FDFJHvPBnV/H/cDeV4Xntn6sDXtf1fLpFTsz3+ae+v0X5oPrR/Z1YH0xJ1W8ZvlY0hfcr9j0M+ZjruLDWV/GEh9c7k0uxfrCk7Vm/cT6giunaDvmI9d760pd5oN7pZ8N/7xZ1L7inHSuV2rD9hVclR9AfZxTTp67bZlveYkSbei+Gk+ovL+wfQX3Rpufr4vykStH3W7BfHC/3tmfWV+ck/LmmPasr+U9TlAf56pXL39H5lv+RfP2rC88vW76b6wvuJ9396/UN7n+rn5tmQ+u1vdpzfbVzunZ/9WJJfsKrgosfIf56OlX7iliiQ/uxXxVM5bsq82der0m21dwNSzQnPomV41r+A71DZf1mjSpRvpiTu7cLU1YX8t/n/YD8zFXlePcL8wHl+82bxRL+lrv42dfjiV9wd3+OeszH7nu4LufxxIf3Lu5P/zzilH3VZG4vvu/f/3wnO7svgoutv3cnflKduvx797l/KUH88HV1v/1YPdVeO6gB93YfRXceasN9ZGrqu/vznxwr8HHvO8zJXr+++879HBP2tdwr/hp6ssdi//91e06uSfzwb0DzXqyvvDkqJ49WF9w7+FsPZiPXL924Z7MB1f3J/dg91XMyd+7viu7r1pe/Dz1bc+iU7sxH1w0ONiV3VetN+5MZ3ZfBXe6Lac+cv09F7owH1wF93ZmfTEnNbdRd9rXcP3bDOpjrt7lXD2ob7j+uVZ31td6r2/tSvsa7iUr6iPXadarG/PBRZURXdm+qupP9fr37+/K14vtK7gafIr6Xqvm//4qB/3Qi/ngsmi5Xmxf4bllB/Rk+wruTX6c+sgVK8/2ZD641+y3nqyv/mZiuG/coF6sr+W/1aO+W39Br/C9ZyL1wf3rLXqxvvD8hEK9WF9wr/wc6tvc3G4v6oNXWtuT7audqzuwB9tXy58435366NknuQfzwX1ZvQfdV+OpjY93Z/sKrudVoj5y5V/9ujMfXL78ZzfWF3PS657rSfsa7uaR3DdzlYkVejIf3J03pgfra3Mn7+pO+xrudL7ZjfnIdXJf6M58cPnoI93ZfRXnpEhrEMPuq+Cq2LkOzMc55bQb2Yn6hrsNXo5h91V4XsOn27P7quWv5KC+zf12eAfmgzvrZrZjfXFOeuumdWZ9Lf/jO+pjT53ZfbowH1x/9HVn1hee/+mZjqwvuH6jGPWRqypej2G+5S2+78juq5iTs3VvS3ZfBZcrdFPmo6fT4J02zAcXA5/4ld1X4akctevEkvsquKheqkI14iPXf+iZRrHEB/eufFWF9cWcdN7EtqwvuPjscivmY67em9+0Zz64ujarDetrvSfnNo0lfcG9sjW/Zj5yvSnTmjMf3Nl0ux7bV5yTosBbXdm+gmuvOPVxTqlqW7oyH1ydXt6F7av1vjzdie4r+Oy63De5rpKdmQ8uuj7ZifXFOSnFw91YX3AtC1Af56rjl+tGffDb5bqyvvD8IimdWV9wv8awTsy3ub3zdWE+uJi7uhPbV8xJru3Rge0ruC6XrR3z0dN5om5H5oOLVTnas32FJ2ZfbEn3FbzQjUbMR66+/ulv1Dfc3ZfehPXFnJz34juyvuDu4JntmY+5yv2FYpgP7j1zqj3rC899o3Eb1hfcKbT5Z+Yj120Rnut/fMNl/7r/Ppf+z/uqeK66+ts+7H1VcLHt7aHMx3NKdSxdMh9clq86hL2vanOfLzWOva8K7heYM4P5yHU/+mcs88Flr8MzaF88J3U69aJ9wa/XHMx8PKdyL+TvwXxwv+lrg1hfeM7ItWNZX3BZ5cwM5tvcU2+NZT64Or1oBntfFXPy5ovF7H1Vyz88uob5dq4HbixmvuUPtVvL3leFp4qW2cbeVwV3Hi4Rx3zkivqrtzEfXFfPtZ/1xZz8aR8sYX3B1fBDa5lv59q14hLmgws3fi3tazz/TKHtrC+4ujN+P/VNrvRe2s58cHXh0H62r3hO6uT9uivbV3A9bMsA5uM5pfj8rU7MB3fb3Pid7av1MtqNYfsKrkdXm8F85DoDGo9mPrj/0gSf9cVzUlFyb3vWF1ztuNSX+fa5agG3DfPBndQpvVlfeOKvx0exvuCiWc/p1De5XvNpI5gPruv8PpXtK+bk3960mO0ruHPIX8t89PQ6pCxiPrgaUHQt21frxeTbTvfVcPHX0v3MR67T7s9t1DdcfP/uftbXznX1toWsL7jUn69hPuYq1jZZQH3DvXPfrmZ9rXfkhW2sr+Vf9ItjPnJFzZitzAeXHSfuY++r4pzUlfQx+r6q4eLCi+eZb8/VN3ofZ77lMaHz7H1V6zW6lsDeVwVXf6UnMx+5/oofLzEf3L/4eQrra8/V+unHWV9w/UPbC8zHnsqvi51gvuXXF11gfeH5lWdcYn3BRdfBKcxHrvP01kvMB1fPzk9h76tiTuqph9PZ+6rgos4XmcxHT69fw3Tmg/szB2ey91XhOY/vDrD3VcG91uuCzEeum3QjwHxw2fNskPXFnPzdo9JZX3BxbHkm8zFX5S1KZz64Grgjk/WF58U/RvuC662Xg8xHrhz9PPXB/ZH/BNm+4px0s+c7wfbV8rgZF5iPc8rZs+M488HF4B8usH2FJ2O2XKL7arjfbl4K85HrhKZcYj64uDMkhfXFOel1/eY46wsuXjh/nvn2XP1OHWM+uP/3m+dZX3ji50aXaF/D/X9+SGE+cr31j15iPrhb5/GU/6+uM4/Tqfz//1WoiKTskiPtUSkVnxZHaSEShUIcFZIKlS3iKoQYZjWb5Zqx78vY12PMDMPIWBo7F7Mv930PSqj4/h6/Oa/re5vP6/tXD8+ej1ev3t4uV+dxOui+enOyV3p7Wm5fwWXH1BLmo6d+PLqI+eBW0oYStq/wnJYP0X0Fd09cpz5y9fk61Ae33r7sZ30xJ91waBHrC271ji9hPuYqur5axHxwt/7wEtbX5EZWpH3B3QG51Eeu80mRj/ng7q7Dfnpf9Z6TOh/s+oLdV8H1E2PGMt88VxVf9WU+uNtv/yh2XzW5YY2ms/squJveL575yLXjb5/KfHD5eZtY1hfPSUX/x7vSvh53a3w2jPp4TtWz0evMB1cjRw1lfeGp2jUms77g2moZzXzkOnuHTmQ+uN2rfxS9r2JOEXmL2H0V3F35xjrmo6eV03ch88FFxlNJ7L4KTzdf57L7KrhKn7GP+cgVB4/tZD64HT46nfXFnMQ2/3zW1/DPG61lvplrm8R51Pe47vn0GtYXnpvTdQfrC+5cHLyX+ci13ry+jfoe1xk/7mH7iuek9ql+1XaSfQWXf4/6ivl4TmlX2mjvZL7HZZ0FA9i+wlN93xzP9hXc3pQdwXzkujELfmI+uIxKDm9N+prnpK1z2u0kfcHljCX//7lBeR/PVa3KNzrsJD64PXrRZ6wvPPfAfMn6Gv5n9XDmI9f+t6NkPrgVdiSM7SvmZBeGJLJ9Bbe391vNfPQUTXYmMB9c3zl3FdtXeO6Hx7eyfQUXjyWlMR+5rv/GFuYbfqR+GuuLOYne3vOicn3B7fdzVzIfc5VP1kxgPrj8eeFK1heeGDpyC+sL7gzZlsp85MqL7bcwH9wZPSuV3VfNuXqlzlF2XwVX2T+cZj7OKeejAUeYDy5/WHOK3VdN7uh1F9h9FdydkpbHfOQ6K9+/wHxwZ/CQPNYX56TwbTnM+oLrO4tPMt/saUnDw8wHt460OMn6mtz+vvOsr+ENyv5crvK+yf067DzzweXk9Fx2X8WcrEs3C+l91eMyv10J89FTfbm9kPngzpV6Jey+Ck/22e9j91VwfW6bn/perj6+xMd8cLf7Aj/rizlZA34oZH0Nn11YzHzM1Q57uZD54E7LbcWs7//mTvbRvh4XaSF+5iNXj/zCx3xwO3mEn+6rd046WWXflS2/r+BqSNwJ6nvnlPvIi4eYD261feQE21d4Tvbr59m+gqs3JuQyH7ki/47zzAe3anTKpX1xrra4msn6gotmp48zH+eqfj47k/ng8qPdx1lfeKrzec36gjvxzXKZj1w18ahmPrh+9qFctq+Yk925UiHbV3DramQx89FT/nSqgPmGrxhezPYVnvNIRx/bV3CV8bmf+cjVNVr6mA9uvdDNz/qaOTUp+25s+b7gYvpnxczHXFXjTQXMB3crflLM+sKzUp/00b4edyp09DMfubLVoz7mg8tz7fzB31V12jx3NPg7qc6jx44Ec5V99EAwlx91/I35+E6pzlx2lPmGT4rKDM516y9LDfasfyrtCeZ6c/jOYK42bNzFfOQ6Y9fsYz642zkrlfb1vpOqDmZl0b7g1784wnx8p9IZ+OlJ6ntc5m08xvrC0zEzM1lfcHfF9gzmI1e9+fnvzAd30gcfCZ6rnjB+Y/Cc7E2Vtt3C5VNrg7nb+c8NzEdP8eD7ycwHlwUhO4NzRYPeK4I99+a4pGBunzq4OJi7VWquYT5yreW/b2U+uP3ytU2sL+Zkd/ownfUFt6quTmM+5irCDx5iPrge9k0m62s8X2gK62v42V3JzEeu6DjwN+aDq6R6GWxf8Z1UUbvxObavhn/Z/Qzz8Z1Sp2qnC8wHV5P0ebav8MTDCafYvoK7U6qepL6Xqyp631su54Nbbx/RtK/3nVSnW/1c1hfcPl47l/nmO1WfL8hnPrj1xqV81td4C4pzWF9w53piDvORa/9RpYD6Hrd2/Z3P9hVzcr+POc721fAnWh1jvumZ0lozH9wa98Y5uq+e55Se+Z3uq8ftSsOPMh+5Vp+8M8wHd5d9fJr1xZyczm1yWF9wJzs1m/mYq66wP5/54HLmqHza1/NU9lPZrC+4qF32XebyPnLV+gr5zAe37/s0L/hctV9KWRB8TtpZUSuDuejnPR/yuHMgeRn1vXNK9Z6zgfngzupf1wXnirvvTrzFm/zJkmCuuqepYK7q/GcR85FrvTp7LfPBrbw9q1lfnJPqlQd2sb7g1onqO6nv7ak79Ld05oPrXsP2sL7wVMfLW1lfcBGtNzEfuaJKlVTmg8u6kcnBc7X+/Wpu8JzsgW8uCOYyK352MHec0fOo7/UUHe9byXxwUe/A0ltyl9w765bcj84l3MIfyIgL5lbJcMV85Io+mYuYD+7ufZL2xZxkp+z1tK/HpXVPEvMxV+dEE5f54M6a+K2sr8md8v1q1hfcebXDcuYjV3bqsJH6HrfyRyaxfTXnZI8/DrN9BZetuhyivndOORGhJ5lveNL442xf4YkhjQ+yfQV3EofuZ77JfatFFvPB7edWHmF9cU7KuWPPs77gst4nmvk4V53qU3OZD64GfJ/D+sKTuvJZ1hdcxV4+yXzkisiyP3eivA8umg06z/YVcxL/JO2h++pxueWeFOabnhXvOsR8w8e4GWxf4al/Frl0Xz0u2r26lfnIFddX7WE+uHNHvd2sL+ZkLZ9+jPUF171uP8p8zFUc/PMs88HtUStOsr7w3HiZyfqC2/+M2Md85OpVrbKo73FxR+4hdl/Fd1L1w8mF7L4K7q5vXcR8fKfUrdS/mPng1ifXitl9FZ4aOL6I3VfB5cH4IuYj176tSwnzwe0OsoT1xXdS7WpzSlhfcBVo4WM+vlOpP5Y+5oO7Iff6aV/Pk50ifKwvuDV6n4/5yJUVvvIzH9x5er6f3VfNXKuEFbH7Krg1pV8R89FTbwopYb7hFceWsPsqPHGsdhG7rxr+wZxC5pvcxu+XMN/w3TeKWV8zp4QcH+3rcdkzz8d8zFX13u1nPriI/s3P+prcPQd9rC+4de9sH/NNblqan/nguucSP9tXfCfV2lH23tR/7avHnXWL/MzHd0qt3JnUB7fHVg+wfYXnvPqPn+0ruNW7eYD5yBUXegaYD+52mxZgffGdVGd3Ie0L7m7rFqC+956q2+dh6hvea1KA9TXe6/G0L7iVnEZ9k1splfrg9p/FAbavmJN+uHOA7Su4XOIEmG96frc0wHxw0WJrgO2rye3RN8D2FVxElesBjtx33ADzDU/aRPtiTla7C7QvuJx7ifpmrr1uL2U+uPZXLmV9TW7YNdoXXIZcoT5ynfhqpcwHly+X/bX8fRXnpDh4sYDdV8HFRxULmI9zSkyXxcwHt5vsLWL3VXgybU8eva963Nr6TC7zkSvHpRQyH1zN+JT2xTkpvnnLx/qCW1t2ljAfe6pfGOJnPriqWsPP+sITxX8V074etx85UcR85FpDx/io73F9cFYJva96c9IJL2az+yq481TZn7ta3kdP3Sosj/ng7ql3cth91eT+M/80u6+Cq8s9jjEfudbrUeeZD65OtzrD+mJOekv7QtbX8Fov5VPfm6vzU7di5oPLO6cWsr7w9J3ROawvuHu/pD5yndEP5TMf3B37QA7dV++c1KppgO0ruLyniPo4p/QBFWA+uLN2aIDtq/F+HeNn+wquu2RSH7luFSvAfHD5bpyf9TXn6pq8AOsLrl5PoT7OVZVwZynzwe1NfwRYX3j21IkB1hdcVW5AfZP7cXKA+eDinS8DdF+9OVml6SVsX8GleJv6Zq5r03zMB9ftfy5h+wrPGfRwIdtXcFu8l8d85Kqa84uYb3jfygWsL+Zk3/+1n/UFt1M/pj7mKpOz/MwHlyPyfawvPJ25vpj1BZeDuhYy3+RuH1LCfMNXtCm65bnqY4djg5+T2qNC5gRzufvR2GDuHkmYxXzznHJWbiLzwfUnFRKCc91PM2KCPTn+ifhgLobvjgnm6r0H45iPXL1azGU+uOq9eBbri+ek9ubui1lfcLdW9QXMx3Mq5/vnV1MffObNpawvPGvqwETW1/DUror5yFW7ly1kPrg98d15wXMVH1eJvWWuY4fH3sJfmB0bzJ1Gj1Lf9ByfH8d8cDF7wS25In5YXLBn7210C5cpU+KDuc79MJb6Xq5usS+G+eCy/msxrC/mJK+Nns36gttrasczH3OVXTYq5oPrTiGzaV/PE+Pep33BRa0K3PdyrcTFccwHl5V+imH7iuekzjN3b2D7Cm79m7KG+t5zSjn07R3MB5cieSPdV89z9uxYzvYV3NXjFjEfufLx2LXMB1edOq5gffGc1HljcwrrC241yN7JfNxTnS4l+5kPruQrqawvPNm+0ybWF9w98yv1TW5s8k7mg6tTMRvZvmJOuu6v89i+gtvJMdRHT11/0CLmg8u7Gs5j+wpPfn5qFttXcDd7SSzzkWv9/e9c5oNbEWPiWV/MyW0TuZz1BXd6fbuQ+ZirFTJ7DfPBdc/1S1lfeHpnrUTWF1wMuzGb+cjVUTvmM9/w1XEq+FxVm/bPuuVcTVgfG8zdyMfnBHO57Nk45uOccg8URzMfXA3aGR2c6w6aPPcW7/mkuFv45AEqmFvtyn5fL+8jVw9bGM18cGfzjGjWF+ekPFAtmvUFdwZ0ncl87KnecmEm8w2/Pj+K9YUnO+yOon097qa3jWI+cu3XFkcyH1x9djQieK72Tb8KnpNIGxsfzN1Z7yYEc7lFxTMfPcWIQdHMB7d/fyo6ONf9fEjCLbkPjYsP5tY9HyYEcxl6TzzzkWtNTJ3JfHB78LMzWV8zpyH7IllfcL3p7UjmY6525ToRzAfX7T8NZ31Nrl4bwfqCK1/FCOYj1zm3LIz54M6UklC2rzgn1f720XRfPS6mjItiPs4p57sXYphv+BPpdF/hiQ31Itm+gruj/wpnPnL1hUvUN7ziz+GsrzknP4qLZX3B5eghM5mPc1Wc/y6e+h4X1QZGs77wVJ0HIllfcHtV93DmI1erCZHMB5eiXzjbV8zJ/lKE0331uLrxUBjz0VOu+TCM+eA68oNQtq8m91iXULav4FaF6jOYj1yx4Mx05oOrmy+F0L6Ya5M/Qllfw/W8GdT35moNSKc+uPNUWZ/yfeGpvZtCWF/DL1adxnzkincrUx/cSnamsvuqeU76cJfD7L4K7vbZS308p7Sv/+cY88F1VtIhdl+F5/w2OYXdV8HtqOLtzEeuXVB9H/PBrdIdyayvea5a2v006wsuXjn5O/PNc9U3EjXzwe29955kfY03u3Em6wuu36q+h/perlrQ/wjzwZ1jG/az+6qZ6ze71rP7Krgcc3UV9b2eQq/fwnxw3bdhEruvwnNGiMXsvgpup4QmUt/LVROOLWM+uLvs1HzW18zp+rMu6wuuw1ZtYD7matV4Zzfzwa2kiC2sLzzbqrua9QW3Hvx6EfORKzt9u5b54GJX06VsX81z0vZZF9i+grs17znLfDyntLaszGE+uP3Ofs32FZ67KDGL7Su4TmqVyXzkyqaJJ5gPrqb9eJj1xXNS56smeayv4btev8B8PFdVk1rmM9/w7/pns77wxIWXT7O+4HrAhKPMR67+qOtZ5oM7dZ7PYvuKOYlJI9PYvhq+OW4H89HTjg1PZ77hFd7cxfYVnnr87Dq2r+D2B1eXMx+5/2/OG5kPbl9bsIr1xZzkwH4ZrK/hg2rsZj7mKuqm/cZ8w4dHpbC+Jve1nptZX3D5RsU1zEeuqv7QVuaDW79cX8PuqzgnRdyW2ey+Ci67LYphPs4pd9Gzivng9t6Gcey+Ck/ufjCK3VfB1e3x4cxHrnrwTBTzwa3lJeGsL85JcXNxAu0LnrYlnvnYU6vWi/Oo73G9csBs1heeqrFmJutreI+OEcxHrv57SjTzwZ0GqyPYfRVzEqdTQtl9FVy8lT6d+l5PeexEKPPB7S2bp7P7KjztrqL3VcNH1ZrKfORalyOnMR9cau/H5fqaud7/byjrC+7cnTGd+Zirzm8Sxnxw9ZdvOutrvI8XTmN9we28zlOZj1zZ/Og05oNb07ZNZftqztWY5PlsX8Fl1cpzmY9zyv7mhYXU97ioZiu2r/D0e11j2L6Ci4g7I5lvcj+7GcN8cDnwrUjWF+ekrvX9ItrX42ptlmI+zlVrfb/FzAe36vwngfWFJ/74Ppb1BVe1nUjmI1ddnh9LfY87+e0i2b6aOVVuG8b21fCt98xgPnrqdR+EMR9ciUYz6L56njX53hC2r+AitT31kevo90KYD+4euTSN9cWcxH3tw1hfcPF0jRnMx1x1SqMw5oOrv05MZ33huU0nhLC+4PalZdRHrn11YQjzDX/gm+nsfVV8J9XO7sDfV/W4oxdRH98pdScMoD64NWMbfV/VeFfO0fdVwcWk20qZj1zrm1Lqg8vWd5Wyvua7qi+N4H3B39xLfXynUjw0lvrg+vYDvK/nqZevBVhfcDvk7lLmI1e+8U+A+eCiV9VS9r4q5uQmlP398u+rGj7yfuqbub55H/UNd+qWsvdV4TmXapey91XB1Y3a1EeuGN6A+uB6XAPaF3NyNtekfcFV0/rUx1xVUU3qg9uH6tG+8OTeB3hfj+vpD1AfubKU+4Z39P555fYV30l1/T/SfQW37Azq4zul1s0R1Df8iT10X03u2esBtq/g7ntlcy/vI9eqdSVAfY9bZ8t+3v+rr/edVDH9a9oXXOzaQX18p8pK7UN9wy8l0b7w9C5/gPUFdwZVKmW+yV2UG2A+uGx/G91XzEl6v67K7yu4W1qH+ugpsu6lvuH/1qT7Cs+OrUf3FVyPqMt9L9dpVZv64NaMmrQv5uR8Uo32NbxTDepjrrJHZeqDy2bVaF949pP30b7g9uXq1EeuWleN+uDOxSql7H1VnJM6/r5S9r4quEzkPs4pHVunlPngcn+1Uva+KjwxMYe+rwou342hPnJFvT+pD24fWxWgfb1zUiyrS/uCq9bVqW/O1fp1qA9uTaxWyvrCc45fo33BrcBG6iNXDL9CfXD9+PoAe1/VzPXD2gH2viq4SGlNfTPXsJYB5oPrsUP97H1Vk+sUlrD3VcFl4rki6nu5VukzPuYbXqt1MeuLObk13gqwvuBO1Ul+6ntzlVFtA8wHt1XZj8v3Nd64932sr+GBr4qZj1yV9YGP+eDO298Vs33FOWnvvJ/uq+FxlalvztWm1akPLp+uWMr2FZ7s66P7Ci6jllIfubqdpj647hkXoH29c9I5wPuCu+P/DjDfnKvXK1Df8B94X3jW0Eze1+N2w1+oj1z37A7ug784mO4r5iTvbhlg+2r4uyP9zEdP1bZJgPngKr2Xn+6r5+n+to/tK7grehRT38tVVgMf88Htnx4rZn0xJzH4rgDra3jNln7mY67yi0LqG55R1c/6wnMbFJSwvuC6x/ki5iNX3reshPngdua0InZfNd9VTe9E76vgcs4i6uM7pWLXa9QH1/Pi6H0VnhpzKsDuq+AC/19jOd/kBjIDzAcXPUtpX3wn1c1sRvuC2ydDqG++q1rrQeqDy3t/pn3hWQvTaF9wNTqf+si1W2/nvsfl0HMBdl/FnHSNSvS+Cm6duIv66CmTBfXBnWkV6X0Vnq5Tmd5XwXVGJeoj15pekfset3YL2hdzEp2uBVhfcP3j/+F7c7X9FwPMN3xHGf+vvp6nOwjaF9ypfzXAfOS6Vbx/bjkfXO4M0H3Fd1Kd2HvpvhpeYST18Z1Sp80d1AfX3YbQfYXnvrOe7iu4WHmc+si1Xl5FfXAr9Ajv630n1XFv+llfcDFpEPXxXVVn8r9+6nvczh5I+8KT3y6jfcF1n0zqI9d6Yin1wd32B/m+enMSu4sDbF/BxabL1EdP1TCf+uDuc4EA21d4Vtoluq/gzpEC7nu5Tk0/9cH1/GzaF3Ny/5ND+4I7/Uqoj7ladjb1wZ1vi2lfeLJpMe0LLoZo7iP32SLqg4seZT/v5e+rOCfdG/y+Cq6ung0wH+eU07aU+uBiKb+vwlNjl9P7KriT2436Jrd5HPUN7/sa7Ytz0mqXS/sa/txu6mNPxeRT1AdXzTbSvvDsw5NpX3Dr/KPUR679wijqgztRNeh9FXOSF/fQ+yq4NeKMj/noqdIXUB9cLVnuY/dVeG7SgBJ6X/W4+9QzRcxHrn2gcQnzwa2CA4WsL+bkbprgZ33BddUffczHXMWk/n7mgzudO/tYX3i685li1hfcWfpNIfORK1vPKWY+uKhZp5DtK85Jd/ARuq/g1pMrqG/Oqd4HqA+uEhfQfYVnWV/xffW4O7fs99XyPnL1a59SH1zfuOhnfXFOWrPSaV9wcUVRH+eq3W0v9cFtZy7ta3Lr9gqwvuDOaO/XZTkfudZtPQLMN3xmHt9Xb06isIOf7Su4qtbUx3z0VJ+28jMf3Amt7mP7arwvvyhm+wruDs8oYD5y7VZlz8vK++ByVEQB62vm9FgzP+sL7qz8t4T5mKv853E/88FV1F8lrC88t8cjxawvuN42uID5yJWTHyhmvuEzPitg76viOakdcTWfvq/qcXn0pRzm4zmlVXFIAfPBVdftOex9VePdln2Ova8KbvcYeYz5yLXnfKuZD25X63ic9cVzUp26tID1BVcLb+Yw3zyn6pRUwHxwUfeuXNYXnv3hWs36grsH1x5nvskdt1FTH/z+sh+Xf18Vc3Kmt89k76uCq2ZbUpmPnmqMzmQ+uPx8aBp7XxWenDJ4G3tfFVy2nr+W+ch10p7dznzDH7uylvXFnNzmTQ6xvuBO6oY05mOubuc6h6jvcdVnRhrrC0917bOd9QW3xpWsZT5ynea3bae+x53mU9eyfcVzUluFF7B9BZcF53KYj+eUlnq/gPng8uHQHLav8Jxxv2q6rx4Xz390nPnIldEtNPU9bv+2/hjri+ekbuIf+awvuJvSOIf5uKeqQxPzmQ+uD8/OZn3hOU3HnWN9wZ1+Z7KYb3LrDjrLfHDR5MLvbF8xJ3tUWibbV3Cn/l1pzDc9t7+ayXxwMe75VLav8HTFx7axfQVX+vQa5iNXdi7cwnzDn7m4mvXFnFT3ugdpX4/b7z6Wwnwz16M/H2A+uNXkejLrC8+q128z6wsueq1fxXzk6sRNG5kPrsM/XsHeVzXnZNsri9n7qoaX9k5gPs4p+WS9JdT3uBzxWgJ7XxWeNeznWPa+quGV/olgPnLVS3fEMh9cdvo2gvXFOalS9y1mfQ1ve0wxH3vqqnsXMx/cGdhMsb7wxPRnYlhfcOvo8nDmI9cZVTGa+eDq9Cr6virmJMccDGXvq4LLP4ZMZz56WgO7hTIfXDc6RN9XheeqVfR9VXDX33wG85HrzIgJYT64Di2dwfpiTvbtA2ewvuDqsWvTmG/mevcJ6oO7PxRPZX3hiU97h7C+hp/qHMp85LpChDAf3DqVGEr31TsnVf+rC9m+gtvrIuYwH+eUkxO1gPngcsa3s9i+wnOvhEaxfQW3JowPZb7JXZEbwXxwVdBvOuuLc1KEi/msL7j7Wa045uNcVfU7JzIf3D0fH037wmtbO5z1BRdHZ05lPnLF4pdCmQ9uddswme0r5qTC7qT7Ci5qrfyV+ejpjq7wK/PBrertp9B99TxnzthpdF897pwpCGU+ctW8M1OZD67uKvtuQPm+Zq4Hzk6ifT1uVdw/ifmYqzVgxUTmg1u9XvuF9YWnfnloKu3rccfuGMZ8kxvy4a/U97j93pdh9L7qPSd1h9ydz+6r4PaQatnMx3NKNzAtj/oelxtGXWD3VeOJNmfYfRXcTck6Sn0vVz1Z8zTzwe1lGUdYXzwnVek18lhfcD20+Dzz8ZzKfSUhl/ngYs+X51lfk9v1yknW1/BGRw4zH7n2fX+dYD641FcOsfsq5uTUPLGf3VfBrYW7dlHf6yk7vLiP+YZXD3PpfdXznAyxgd1XwZ3mfyxlPnLlY93XMR9cv71wMeuLOVlqzV7a1+PWqdId1PfmquZ/sof6HrfT7trO+sITlzLXsr7gzndTFzIfuerA2DXMB7dHbZ7P9hXPSWVJm1y6rx53jt3QzMdzSvnjtRzmg7vfrtB0Xz3Pat/kBNtXcHG1zSHmI9e9MeE488Htg1szWV88J7VeychhfcHVuR808/Fc1T6+LYf54PbmgZr1hee+2uQ46wtufzEwk/nIlc/dfpz54FZCx0y2r5iTHvxOGttXcJkwZivz0dO6Y0wq88GdsR9vYfsKT1f8ejXbV3An0p7HfOTK199ZRX2P2080TWR9MSfr8o0U1hdc71y0mfreXO2zR1Oo73G7wlebaV/Pky0+WMn6grvR4xOYb3IviJXMB9d/tU9g91Wck+r1uYrdV8H1+YejmI9zykoTc5gPLmUGva/C01e3TWP3VXCn0fmJzEeu/v3lKdT3uPz6ufGsL85JUataPOsL7l5aGsp87KnVS8YwH1xXWRzC+sJTC6r9wvqCO+eSJfW9XHvDe9QHFwM7/cjuq5iTmz5+PLuvguupaycw3/S8ffhPzAe3ur84nt1X4bkjxkxh91Vw9/qoMOp7ubrm/MnU97iuMjaM9cWc5JRm41hfcNHwOPUxV/2dbzTzwa0WCyTra7ytv09ifcHlg2X/PuV95NrrGkxiPrj99TdhbF9xTuqQsu/Kld9XcLul999d5XxzrvbxRTIfXN42bjLbV3huYq2f2L6CO3tyRjHf5ObljWW+4QXOCNbXnKvNboukfT2uFz4xifk4V63DayKYD66KNvzC+hovN38M6wtuX88axnzkWlVfGcN8cHtAl2FsXzEnecc7o9i+gjtJXcYxHz11ygcjmA/uDjr9I9tXeCJi3C9sX8Hll33DmI9cEVv5F+aD655dw1hfzMmt2Hg46wuuSv8cw3zM1Vq6eRjzwWXDxWNYX3i6sTuR9QWXFzuEMR+54tlFE5lvuL9dWOv/ATMMhhY= 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+AQAAAAAAAgAAAAIAWXQAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-040000.3.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-040000.3.vtu new file mode 100644 index 000000000..3e2f2a479 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-040000.3.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAgAAAAIAj3QAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-040000.pvtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-040000.pvtu new file mode 100644 index 000000000..c6505f797 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-040000.pvtu @@ -0,0 +1,19 @@ + + + + + + + + + + + + + + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-050000.0.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-050000.0.vtu new file mode 100644 index 000000000..2d5359e06 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-050000.0.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAQAAAAEAxFgAAA==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+AQAAAAAAAgAAAAIAYXMAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-050000.1.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-050000.1.vtu new file mode 100644 index 000000000..50adc860d --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-050000.1.vtu @@ -0,0 +1,34 @@ + + + + + + + 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rN+48ePLizYfvb8sM/10/AYKECBMhSow4CZKkSJMhS448BYqUKFOhSo06DZq0aNOhS48+A4aMGDNhyow5C5asWLNhy449B46cOHPhyo07D568ePPhy8//0E+AICHCRIgSI06CJCnSZMiSI0+BIiXKVKhSo06DJi3adOjSo8+AISPGTJgyY86CJSvWbNiyY8+BIyfOXLhy486DJy/efPjy89f6CRAkRJgIUWLESZAkRZoMWXLkKVCkRJkKVWrUadCkRZsOXXr0GTBkxJgJU2bMWbBkxZoNW3bsOXDkxJkLV27cefDkxZsP37/+fZnnFwGChAgTIUqMOAmSpEiTIUuOPAWKlChToUqNOg2atGjToUuPPgOGjBgzYcqMOQuWrFizYcuOPQeOnDhz4cqNOw+evHjz4fvbItP/1E+AICHCRIgSI06CJCnSZMiSI0+BIiXKVKhSo06DJi3adOjSo8+AISPGTJgyY86CJSvWbNiyY8+BIyfOXLhy486DJy/efPjy87/0EyBIiDARosSIkyBJijQZsuTIU6BIiTIVqtSo06BJizYduvToM2DIiDETpsyYs2DJijUbtuzYc+DIiTMXrty48+DJizcfvvz8jX4CBAkRJkKUGHESJEmRJkOWHHkKFClRpkKVGnUaNGnRpkOXHn0GDBkxZsKUGXMWLFmxZsOWHXsOHDlx5sKVG3cePHnx5sP3b35f5PtFgCAhwkSIEiNOgiQp0mTIkiNPgSIlylSoUqNOgyYt2nTo0qPPgCEjxkyYMmPOgiUr1mzYsmPPgSMnzly4cuPOgycv3nz4/rbE+L/1EyBIiDARosSIkyBJijQZsuTIU6BIiTIVqtSo06BJizYduvToM2DIiDETpsyYs2DJijUbtuzYc+DIiTMXrty48+DJizcfvvz8H/0ECBIiTIQoMeIkSJIiTYYsOfIUKFKiTIUqNeo0aNKiTYcuPfoMGDJizIQpM+YsWLJizYYtO/YcOHLizIUrN+48ePLizYcvP3+rnwBBQoSJECVGnARJUqTJkCVHngJFSpSpUKVGnQZNWrTp0KVHnwFDRoyZMGXGnAVLVqzZsGXHngNHTpy5cOXGnQdPXrz58P3b35d4fxEgSIgwEaLEiJMgSYo0GbLkyFOgSIkyFarUqNOgSYs2Hbr06DNgyIgxE6bMmLNgyYo1G7bs2HPgyIkzF67cuPPgyYs3H76/LTD/X/0ECBIiTIQoMeIkSJIiTYYsOfIUKFKiTIUqNeo0aNKiTYcuPfoMGDJizIQpM+YsWLJizYYtO/YcOHLizIUrN+48ePLizYcvP/9PPwGChAgTIUqMOAmSpEiTIUuOPAWKlChToUqNOg2atGjToUuPPgOGjBgzYcqMOQuWrFizYcuOPQeOnDhz4cqNOw+evHjz4cvP3+knQJAQYSJEiREnQZIUaTJkyZGnQJESZSpUqVGnQZMWbTp06dFnwJARYyZMmTFnwZIVazZs2bHnwJETZy5cuXHnwZMXbz58/+73Bf7/Dz4v2Mg= 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+AQAAAAAAAgAAAAIA5HIAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-050000.2.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-050000.2.vtu new file mode 100644 index 000000000..02af8227c --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-050000.2.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAgAAAAIAFXQAAA==eNp0nXmcTuX//w+hbG2EiI4siWStpLiPPUv2fT32nRnGmLFeM2ZhGMYsZoZZrtlXsy8o5hSJQpQWhY5UfLTYiiL8vt+5z+t877k/r99fHj179ur1ePfu6uo4c9+Koih/bs6s+J9fjKkRlb+Kr1/M+N9f9cBXMly5urso05XLyIEZ1F+3N63yV++yNOaDK/PPpLnmypJfMqt4dVZnuHK9vFOWK1dvHMhgPnLl6Idp1Le4ovVKZ321wa+nVvpRvVJpX4vra8anMl+uO5BcyV/6KJn6FtcbnktmfeHpDzamsr7gotWBVOYjV5vwKJn5Nr/ePcV1rnpOaJbrnMSQZzNdubFTyXblRuP1mcxHT7Pr9nTm2/zlm+muucaUzdlVcgfdz3Tl6o+tc6rwgm1ZzEeuVm9eBvPBtfd/y6B9rTmp12uksb7gylQ9jfmYq/KDbwrzwbWXTqWwvvDkpBNprC+4PPJuOvWtXDW7SyrzwfUJqXRfRUctqXJerw9KYvsKboyYmsR8NTA8sfLXZYmJzAdXXj2ayPYVnty4MYntK7g8XpDEfORqsx+TzAc3UjpL1ld8XxBX+ceFH8SxvuDa62Yc882RZmxl/su/xjIfXCmou5v1had4NItnfcHFkmXxzEeucavPbuaDy39Dd7N9xZxE5xtJdF8trhX2SmY+esrvFknqW9yoVSTZvsITjthktq82N6qnMN/ObVErifoWl3Xm877WnPRu5+JZX3Dz4ykJ1Mdcz13fzXxwbfv8PawvPK3RnQTWF1xRMhOpb+VqDf7aw3ybD4uJcz1Xte+/ynE9J5Unu2S7cv307lxXLq9fyaa+dU7ptwIymQ9uDH0tyzVXNli5t4pXWpDjyrXSaXmu3AgIyWU+crU3rmYxH1z5o5D2xTmpzfwqnfUFN7/0zKC+tafKX23SmA9udnHeK9z7wlP3NM9kfcHN2t9mMh+5Im5sOvPBtXglw3WuYs2kfNc5qX299rpyc1HXAlcuQ5bkMd/uuTokh/ngSqxHrmuuuHGowNWTBZ75rtzoOKrQlesXNOojV56ftZf6Fje2zchjfTEntZ/MYn3BjTdWZTPfnqvX/gzmgxtvbMhkfeEpSeNyaF+Li3paLvORa37+XhbzwbXHXs5m+4pzUq25JoXtK7j5ycMU5uOcEqlnk5gPbuYNT2b7Ck9PC0tl+wquNe2UxnzkKmfPJjMfXEmeR/vinNRlD8n6ghvlmZL5OFf1aqPimQ+uftsigfWFJ++3SqJ9LS6eyE5iPnK196olMh9cL7ibyPYVcxLiXBrbV3D55I505qOntuWxVOqDn89OZfsKz/h2eAbbV3BxtnEm85GrfDEljfngcvNz6bSvNSd1Zs9k1hdcLHP+c3H3MVdlp5DMB9eHPJnE+tqe/+oU2tfi2ncvpTIfucaC1CTmg0utbzK7ryqz2kRX/jqiUzS9r1pcPv9eNPPNZwMiK3u0D41kPri2KjOS3VfhyRMbotl91ebeR6KZj1zz0VeRzAdXzz8Xxfoavz3aWcn7PhHO+oJrQ1qGM1/5980dlb8OG7SD+eDi3VU7WF945jNDw2lfiyv5geHMR67Z8KMdzAdXbzcKY/dVzEl7uUUMu6+CyytbY5hv93xhQRTzbX7mRBS7r8JTy+rH0vuqxdUNKbHUt3L1B/12MR9cRpzcxfpiTsZbZ8JZX3Dtk44RzMdcxbqpYcwH1w8XhrG+8LRZeyJYX3BRo2kk9a1c8V2jncwH1w9v3cn2VS8rDansfepwCNtXm7/8RwjzZZ/1QZVz6xcSxHxwc1phENtXePJa9610Xy2uT4nZSn0r1/j4VhDzwbVTw4Np3193+lf++Tvx/qwvuNbxI3/mS+239ZV/v5jqG5gPbnw/YAPra3tfPfRnfcHViWM3Ud/KVfeXbWA+uHCM38j2FXNSP2myje0ruLKmbBvz0VN8/3Ew88H1MRM3s321c/M8Q9m+ghtrhmxnPnLF1lpbmA8ui09voX2tOSlvHt7E+oLLe0MDqG/NVTOfFcwHN8ZGCNYXntL8egDra/ObuYHMR65s19GP+hY3TNOP3VdxTppPv7eb3lctrr1YZw/zcU7pSxdGMx/c6Nc4ht1X4SndvtvD7qvgIsd6rubmI1fP+jqG+eCiVkYs64tzUt2eFUn7WlweGhxFfWtP1blPhzMfXAtIDWd97dyO96JYX3Ax4MAu5iNXOzAogvng+vJ/Ith9FXMyDxTEs/squJhfmsB8u2dU8G7qW1yZs3oPu6/C08OOJ7L7Krhyf5VkPnKN8avimA8uuwjaF3MytC3RrC+43m5eDPMxV/nVvkjmgytXAqJoX8tTvhkRy/qCq8qA3cxHrpw7dRfzbf5jr2i2rzgn1YPaDrav4FqN9mHMxzllVJSFUN/icsS+rWxf4enHPgyj+2pxvf78ncxHrhJ7dhvzwY1RdbazvjgntZDAINrX4iLOJ5j61rmqv5/nz3yb79i1ifW1c+cEbmZ9wfWt2VuYb+dmRwcwH9woLAhk+4o5qXebhLN9BVfmfx3OfPRUEibsYD64qUwLo/tqefJefATbV3DtyWWRzLdzOzbeSX3wpd/uZH0xJ+PJX0JYX3D56uvbmI+5io0/BDEfXNZpuZn2tTw5NjKU9QU31EfbmY9cfbjnFuaDq29/FuL6XFX/6kqh63NSrdvJAleuefgVuXIlZir18ZzSODAnn/ng8qUeBa65xqwXil09Je/3Qleu/VVW7Mr1GhuLmI9c9VZFAfPBZdy7hayv/Zz06657WV9w7a1X85iP51TayidymA+uZN3IYX3t3LHt81lfcDPxOeojV7S8mMt8cH3q53td52pOG1FSZa4tn6rClc8vl7hysS2umPn2XG+cLmQ+uHF0QpFrrghcUerqKWabqvzcP6WuXP8yq4T5yJX1zhcxH9z4Yirtizmp23cVsL7g6nONqY+5Gn98lMd8cBldns/6wlM8dhWyvuCG0pD7Vq7sML6A+eCi7+kCtq94TmpcvJrJ9hVcfmxkMR/PKc3636QzH1zOT8xg+wrPGJuQzfYVXJsjcpiPXC1paSbzwc3vtCzWF89JZdZXqawvuK7uSGO+fU9tfiWZ+eD60PAU1hee6Ts8nfUFN+o2yGC+ndu6XyrzwfWW/6SyfcWclN/m5rJ9BVebDN/LfPTUpjbJpr7Ftezb2Wxf4YkZPfLYvoKre1rnMx+5Iv6LHOaDG2tLaV/MSUw6n8H62rx9dibzMVfzWnEa88GNFSvTWV94ImldFutr84ljs5mPXLVejwzmgytbqme6nqtyuE+Z6zmpDXm5CleSfyurwvX4UubjnDInnCtmPrjSYFSJa6759bjyKt6KulW49k5xeRWuepYxH7na/sMlzAeXji60r32uTtlRxPqCi/eeoL69p7cGFzIf3Jx6uJD1hSd/3lDM+oKbnreLmY9c9co7Rcy3+dvlRa5z1ZbW2Oc6J2XoqbIq/N3B+1y5XqtVOfPR0zwbW0p98C8flLrmapf89rl6Mnh5uStXZ+Xtc+WiZkk585Er06eVMR9cz9lP+2JOps+8EtrX4iLr2xLmY65K327FzAdXnywqZn3hiYohpawvuD7pYCnzkWt+2L2E+eBy+gcldF+tc9IMrV3A9hXcmO5fwHycU8aT8XuZDy7fD8lj+wpPW/OggO6rxc0rawuZj1wjXORTHzxXo31xTso1XXNYX3D9YpNc5uNcNSeczWQ+uJKan8X6wlOa19nL+oLLpNp5zEeuviI6m/oW197fkUP3FXO687CQ7Su42nFzEfPRUziOFTAf3KwxqZDtKzzlleeL2b6Ci6XFxdRHbru/CqkPfjyhiPXFnGROs3zWF1yNf4n6mKvuHZ/LfHA1/Ohe1heecu3jAtYXXNm2hvtWrnbkmXzmg8v6/QrYfRXPSZXU+knsvgpuvpCTxHw8p9RTdyUwH1ydXp5I76uWp558L5ndV8GNnn8lMx+5ZriHpL7FzXW1aV88J1Xf7r+H9QUXzw2LYz6eU5mHX45hPrg+uUks6wtPaT8lnvUFNxweCcxHrn6z/m7mg4vrtfaw+yrmpLVISqH3VYurvqNSqW/11F5IT2I+uJg/MJndV22vqFYau6+Cy+IP05iPXLn/WjLzweWtCNoXczI8QxJpX4vr+WMl8zFXeb9aHPPBlY4P41hfeGbhL5L2tbh+c20S85GrDLofz3xwrdq9BLaveE4qj/SPYvsKLhuou5iP55RqaEI488GVp+dHsH2FJ795sIvtK7jxx/fRzEeu0b9TJPPBxdQ/I1lfPCdVOvQIY33Bxd83wqhvPVeVee22MR9crPYPZX3hKYnpO1lfcM1vXDjz7dy5p7cz3+YlNcLovlpzUqbti2H7Cm6s2RnLfLvntuIo5oOLHot2sX2FJzrP2832FVzt88Ye5iPX7NIomvng+qPSaNYXczLP/xPO+oIbt3dEUN+aq3p5ZxjzwWVJnZ2sLzx9QMNI1hdcfr8pkvnIVdou2sl8cCMkZye9r1rnpKgfkM7uq+Bqs5EZzMc5JeOd/93+L9/ierN/U9l9FZ7S7pVMdl+1eUSDLOpbudppI4354MaoXbQvzknt0+eSWV9w3assmfnYU13/N5H6Fjee2iRZX3haj5kprC+4yGqaynzkiijn76u7++DGc2lJ7L6KOYn/NMxm91Vw82q3HOajp7ndP4P54MbP2zLZfRWemOGTy+6r4MqH1/Yy38595lAW88GVYy/m0L7WnLQLV1NZX5tHfpnGfMxV3zYsmfng4ny9FNYXnuL4KZ31Bdey2mQyH7nm9esp1Le4tqlaGttXnJNaRvU4tq/g4vFTcdTHueoYHMN8cLP1mRi2r/DMt+Pj2b6CK/+sTmA+csWzo2Kpb3Fj+uexrC/OSZl/PpL1Bdc6t4hiPs5V486nO5kPbpz+eCfrC0+5bb3X49bX5rvnRDEfuTJuz07mg+tPjtjJ9hVzksELE9m+gssKTTIfPeU743czH9z48M/dbF/hiXbXJNtXm8cdTGI+co1zFXuYb/OKL+JYX8xJf+AdxfqCKyE7opiPuWrrroQxH1wcXBHG+sJTjh+OYn3BlR1ddjEfubr+eBjzwfWw0B3sfVV5afzqSj9l8Wr2viq40S17NfX7zvWs/ONAH0/mg4u8Ik/2vio8dWUDH/q+qsXNg5k+zEeueb7hCuaDm2fkCtbX/O61BZV/3eLRC1hfcPHcoQXMN0YXTKucS8+b05gPbi4pm8762rklXgtZX3Bxas0i6lu5WsxjOvNtvu2uzt5XxZy0k4t92fuq4Prm6WuYj57yxMSVzAcXYx1e7H1VeOoJ/7XsfVVw7ZXv1jHfzr07fhXzwbWHSd6sL+Zk9vh6MesLriaFLmU+5qok9p7FfHBt26XZtK/lmbcyltO+Fle/fM6T+cg1ov4zl/ngauEMvq/3ag6t5J/5DmX7avPg74cxX2n576+9K38uM2F/b+KD67GDqrN9tT3ZawTbV5t/Nn0U85Erx557nvoWV8aveIX1lT+0HOT4379f0JcDHayvxbWEiwMcxNfKY8f+76/GmY1jHcQHl8qrlb+694Wn127b30H6gitX1/RxMN/KlR22jXEQH1x9ftNoB9lXzEl98sMxbF/B5aDz46lv9TQfrHyd+eBaWajG9hWecuXYZLav4GrvxBnMR66+svG7zAeXNU+MZH0xJ7N677cdpC+42jWss4P4mKt+5j8jHcQH1x9v9Z6D9IUnl/Wp7iB9wZUm/TtT38o1Tm4d6CA+uNZKq+z1X++r4lzdPXcDe1/V5nuc/11w93FOKQ1f8mG+zX/7xpe9rwpPv1HXj76vanF9TXN/5iPXuPTRWuaDy3BzPeuLc1Lc376C9QXXavbxYj72VCQOXcx8m/9RtIz1tb2ar3qzvuCy+XAf5iNX/WuoJ/PB9b7rVrL3Ve25Pui/ib2vCq532hjAfPSUWiPBfHDl/Ag/9r6qnRvzSSB7XxXc8GkSzHzkKj1j/JkPLq5fpX3/b64pa1hfcCW21Xrm23ON/2UV88FNJcCH9YWnLft4I+v7f7yXH/ORq+8evJb54ObYdhvYvtrnatGBWWxfwdVVr81jvn1OvZs+nvng2vqSaWxf4RkdOi+i+2pxfaSxjPnI1aNrzGY+uNnz6/msL85JmXu+H+sLrme2H8V8nKtiSOenHMQH1/+ueIv1hacrlyexvuBmWMNZ1Eeu0XEE88FlROxktq+Yk5S/ebJ9Bdf/KfNiPnoKj2FLmQ8uGuV5sn2Fp44vWU331eLit7NrmI9cOehvL+aDa/u8fFhfe07/PJrP+oLr/8lZxnzMVXY8PJv54MK/w2LWF57yYfWVrC+4sv2ON/ORa7R/35P54OJK38o/dr+visfmTKm8LwyYMoXdV8GVdW9NYb72waMZlfeeqFo6823evJHO7qvwtFFXJ7P7qs1395vMfOSKj1Sd+eBi5NM666vtfmdm5Z8PHzWT9QUX34fMZL72yWLnH18Mn8l8cGNStVmsLzzjs29msr7gyojms5iPXLVxyizmg8vRE2az+yrmZPzedBK7r4Kbx7tNYD56ih9DZzAfXJzVprP7qp07dNhYB7mvgku9xUjmI9d898oU6ltcudduIuuLOWm/vjGL9QU32vw9k/mYq37q5mzmg8t578xhfeEZvV+byfrafEP/GcxHrv74P7OZD678e3kW21flP6HOfy9LP9PZvoIrf6+jvpHYZ1pl7gt/TWM+uPLmvzPYvsKTW7fQfQXXn/90NvPt3IrtM5kPrqWsn037tuhXuU/6ybcmsr7gsu7jU5iv3O8/vPKPQ+aPZD64+cILY2lfyzNbz5/B+tr801WzmI9cOeDmJOpbXNQr1tm+Yk7agtpz2b6CK21rzWO+PddhW+cyH9xIWzif7aud+8KqeWxfwZVjj81jPnJ1r0MLmA8uHW0Xsr72nN7YOJf2tbiydOUC5mOuivHzHOaDi4zWC1lfO3filUWsL7i5fM4S5iNX9Li1hPngpnJ4uYPdV61z0vjuaH8Hua+CG0c+ast8+5zKyxntID64ntFpoIPcV+Gp/f/uye6r4Kbn9tHMR675yTfPOIgPrvnVG1RB+uKc/J9/cJNZX5unfER97KncUaYzH1zcGTPZQfrauecfag7SF1wsfb9bBfGRay6+NsJBfHC1/dLmDnJfxZzUB5N1dl8FV0vjFzAfPbXMx6YwH1zrv38eu6/CUwKre7L7Krje521v5iNXbVzsQX2La6n7vVlfzEkff3oc6wsu3ntlLvMxVzNkyUjmgytfbp/N+treKunB+oLrImw185GrvxTjwXxw0WSr8/8n3PfVOieNd6fMYfsKrmW2mkl965ySkW8uYD64mRk0l+0rPOPqkxMdZF/B1Q8u9qW+lWtOz9KZb/N5q0ezvjgn1ZFLlrC+NjfWLWI+zlXlc+HBfHB9R20P1heefKnhPNrX4vKjJdOYb+demrKE+eBKnQFzHWRfMSc1z9Gf7Su4NjJgJvPR0xhZ1pL54PrEwdPYvsIzX5ntwfYVXDWX+VLfylV/Cl/OfJsveejL+mJOWmKEw0H6gov7r41jPuaqTQoc6yA+uB4a2Z/1tb2JnZayvuDyykNf5iNXmzN6IfPBzS59fNn7qnhOKvct2szeVwVXL5VuYT6eU5rX3gpkPriY5RfE3leFp710I4S9r2rzRU9uYz5y5eniYObb/NFh2td+TjrK+dzVvS+4ajr/uv/yredU5utBgvngRsgffqwvPF22DGR9wc1W3YKYb+e26b2J+eDmpNkB7H1VzEkJqxHK3le1edzHocxHT6VDyhbqW1zt2yeEva8KT+/Sfzt7XxVctl+5nfnINYeFhjDf5vW9Q1hfzMn8vW4w6wsus2OCmY+5ikuDA5kPLut/H8j6wtOijgTTvhZXWqwJZj5yzfYPA5kPLjvFBrJ9xXNSY91jG9i+gise4dTHc0pZp/465oNrj6duZPtqe9/V9Wf7Ci6bTqY+crXoED/mg6s9m25iffGcVExqupb1Bdf7jdjIfPueeuW5tdS3uPJ0HcH6wtOVcj/WF9zUUjYxH7n67S3+1Le4dvBGANtXzMlsMS+A7Su48eVz1Ld7vjA/gPngyvExgWxf4YnAYYFsX8FFzK8BzEeusqWC+uDGS8GBtK81J+23EbQvuPF2XBDzMVd92YvBzAeXNW9tZn1tb93sYNYXXP96fTDzkav96AhhPrgIPx9C31e1zkll3cDt7H1VcN2nJJT5OKfMW9e2MN/mgRc2s/dV4WlJ5dvY+6rgeu9RW5lv54p+wcy3+d3nA2lf65xUb6UEsb7g2q7XApmPPdXO7wxgPri59XN/2tfyxMU6m1hfcDO3i2A+cs0GvQXzbb4i1Ze+r4o5Pdi2hb6vanHjfGAw9a2expmF/swH13rFbWDvq8KTQ5YE0vdVwYO2bWI+cs0xf69mPrjZN8aT9cWcFGWML+sLrmS/4Ml8e65fqp7MBzdKi6ayvnauWK6zvuCiy5WWDuIj1+wcUvn7Ku4+uPJys6UOsq84J7XDX25i+wpuNL3ix3ycU2bnoADmg+vzY/3ZvsLTDjfZwPYV3Ni8wJv5yDWy+2+kvsWVr5quZn3tc/XAh0GsL7g+tVYg83Gu6iNjQ6hvcTVh0BbWF5789mV/1hdc/NVsHfORKw8eDGI+uFb7oj/bV8xJGRi4kO2rzQcueYP56Kl/vWQh88G1wccbsX2FJxNfnkf31eJyVPJq5tu5dxMXMR9cVLRa6yB97blOWOrJ+oIbNdqOZr4912P911Lf4urywiUVpC8888K+2awvuHY42If5yNUznmjIfJs3urjMQe6r9nPS5B/XsvsquFHzN8F8+znlkCnrmQ+uffS2P7uvwjP+vEvvq+Ay/J8g5iNXZN0IZD64HJG/hfXFc1Kj6eiNrC+4fvjHTczHcypt9THBfHC96ONA1heeuXD6ZtYXXIY/2MZ85MoHbbYyH1z/eE4Yu69iTqrv51vYfRVc8x+9jfnoqTwaE8p8cLPn5DB2X7VzXx2/nd1XweXSrB3Mt3NfPr2T+eDmtRvhrC/mJF44Gcb6gqtej0UwH3NV/ugXwXxw5d/Gu1hfeCL68SjW1+Y9PtjFfOQaPkdimA+uTn9lD9tXPCc1Wv/ux/YV3Mg5Esx8+znlxb/8mQ+uNvpgC9tX24tru53uq8W1Mz7hzEeu2S6e+uBGXnok62s/J3V8uIn1BZfjg7YyH89VpewSQH2L65883Mb6wtPWNKN9wdX9X+9iPnK1W6sjmA+uPv1HDNtXe64dH0SxfQWXpwJjmW/3bHw5hvoWN9XX49m+wtN33I5j+wouop+RzEeubH5QUt/iRvDHyawv5mTetz5/3q2vzT9w/tyqu4+5qoVHEqhvcaXr4mTWF54IaZrC+oKboXvSmI9cWXNtGvPBld9WZ7L7qn2uNqkXxu6r4OLh0h3MxzllvJ8YwXxw9ZWKCHZftXMj14Sy+yq4tuP5EOYjV47uFsF8mxf+vJP1xTkpZbUY1tfmp8timI89VfJbxTMfXJ26PYH1haevez+G9QXXNtyOZr6de7xfIvPBZZyayO6rmJO2tncgva9a3Ciqu4H5ds8pu3Yw3+Y7z22pIPdVeCKu5fIKdl+1uNZsVIMK4iNX+/hH/wrig8sV27xpX2tOMmHYLtYXXGnzSQTzMVf57RMJzAfXW9zYw/rCU7/ZGMb6ghu3Fm9hPnLVWT/FMB9cdmgUxfYV56T5H+8ktq/g+ueRyczHOaX+G5/KfHCZ9Fo63VfkDhqXwvbV5q8lpzAfuXr2tAzqW1zNfyqT9rXOSXHp9wzWF1xvdieL+ThX9XoDcpgPrmZ9tJf1hafW3pXD+oIr63NzmY9cpfhkPvPBlfjEArav9pwGzU9h+wquvrUrmfl2z5etz11x9y0uRoZn0H21PCP91SS2r+Dm914JzEeuGdw7nfng2nLre9vd+mJOQmu0l/UFVxoX5TIfczUC7xQwH1z7++sC1heeumdhDu1rcbG5VxbzkWvc6VLAfJvXqpvn+rmqxvVT+1w/J9UIu1HuylW/y/tcuXmlNfXtz1V98Ew588GVfvPKq+Tu+3Wfq6cPGlmVf/RLFa45PKmPXG1ZaTnzwY2gB7QvPidV/ahzGe1rcT03tYz6+FzVgMGlzAeXLc6X0r6WZ/7UuJz1BRc1N1MfueLhijLmgxsr6pa7zlVzfFllTuquwCpc6Z1fhSv9QqiPntqGt7lvcaXV4iq52jifKp6+ZW0Vrn7ZoQo3e46jPnK13sHUB1eubKF97bm+frOc9QXX6w2nPuZqXJXlzLf5vld5X+TmR9O+Nn/jKPWRK3qkUx9c7fnYfrav+JxUs/HEEravNvd9nPr4nFKRPqGY+hbX09QStq+2F2OUsn21eaKgPnKV1/4pYT64vvxiKeuLz0nVq/UrYn3B1V9U6uNzqtTnnytkPri5vFoR6wtPvtqwhPUFl22aljIfuSLtiWLmg+s925WwfcWczAl9y9m+gqtfPKA+emrJn5YxH1wJLiln+wrPWJ5J9xXc8H19P/ORq83dsY/54NqJoftZX8xJ2fRqGesLLpK1cuZjrvqISaXMBzcO7ShjfeGp2wftY33BxeOd9jMfufqbB8qZD258erjKuSrqfFLuek7KFxtU4bJwULkr130KqY9zyqy7dB/1LW60cf59kWv0zy1z9ZSQjlW4Ou3P0iq8cRD3rVzzbkU5823e85Vy1hfnpDr9zD7WF1xbU0h97Kl5vcd+5oMrHXrtZ31tz0ffx/qCq599R33kqqWP72c+uOofsc91rto1tdR1TlrJ+1W46Of8HG5w47vTJcxHT63u/DLmgyvPrauSqz7VtbiK9+nxYlcuqr9Y5MqVV3OLmI9cbeLSEuaDq4V9illfzElWV8tZX3C1WjfqY67Guh/KmW/zI3+Usb52bunzpawvuFxxvpj5yDUnHitlPrjZx7uE7SvOSTksej/bV3DlT4P6OKeM5ue4b3E94eUDbF/hKb9k7Gf7Cq5sGEB95Go72h9gPrhx7hzta5+ruy/TvuD662MOUN86V/VDu/YzH1zPf/EA62vnBi0/wPrafFcn6iNXuT3iAPPBZXGLA2xf7bnejN5H99XiIiqZ+1ZPvXa3/cwHF9vf2sf2FZ7YuLSM7Su4sqkG9ZGrjrtTxnxw7UPvUtYXc9I2he1nfcHVuKh9zLfn+syW/cy3+dWd+1hfeGatd8tZX3D9r9JS5iNXk33KmQ8uf04rZfdV+3NVh0Tks/squNbngwLm25+r+s37ucwHN5btzmP3VXi6x6lCdl8FVy8+KGI+ctXcGQXMBzeaRhSyvvicVK1xShbrC67PnJfDfHxOpTLU+v4uNx9c3rmUQftanjS89rK+4MaIT/OZb+fuOpzFfHB9gfN719zvq5iTUa1fCbuvgpv1w6iPnlqfH4qYD65kOv+7735ftXPHnSmj91WLa+nXqY9cc9v6Uupb3BiaVEb7WnMyC9oXsr7gats11MdczfXj85gPrk9eWED7Wp6Y8EEx6wtu9HJ+74+7j1y1Ynch9cG/TS9i+4rPSRWLjyezfQUXkc1SmY/PKZWNhicwH9x8ur1k+wpPO3cwje0ruPlJfgbzkas3F0nMB9f9Xk1hfe3PSf364S7WF1x/d3wM8/GeqnL+/R3Ut7hs6x/G+sITn03bTftaXP2rTTzzkau1/GUn88GNKW0i6b5aczJ2mllsX21erWMu89FTO6umMd/mwT0z2L7C0zfOzWP7avP6HQuYj1zll8FZzAc3uzvPffe+mJN5aahkfcFF/zeTmY+5ilkdo5kPrj1bvpv1tXNfbp3K+oIbAZfSmI9c8efkBOpbXGtzTrL7Ks5J/fGz++h91eKiz2Lq45xSlS3lzAdXGrTaR++rlie/se7tbvdVcPGm/37qW7liwpx91AdvM4j2xTkpXt1XyvqC61eGlTEfe6rs8i1mPrh+vGYJ6wtPLQ8tY33BZb53GfORa3j0L2E+uJHUtYTdVzEn/cFdel8FV0bW3cd8u+dXx8uZDy6P/l7G7qvw5PcfldH7qsWFo30p85Gr+pRTH1ys6VTC+mJO5tFaZayvzQ/1LmU+5iriPy9mvs2NZsWsLzytS8sS1hdcKS0uYj5y1Q1NiqhvcWPk8QK2rzgnxfpbBWxfwdWH3oXMxzlltI3KZT64cTViL91XeL1LCtm+gitxGYXUt3LNw0/mMR9caaXksb44J/VWgzNoX4sri/tmMh/nqnrqjWTmg4urzv/uuPeFJ25WZLK+4HrM+5nMR66ctzeF+eDm8cIUtq/2nCaMLGT7Cq7F5xUwHz3VQfP2Mh9c6H1y6b7C2x+Uz/YV3Py35V7mI1d5PiKb+eDSb0Ym62vPaWq3TNYX3Fz+cgbzMVfzxpspzAfXXtaSWV94cndsGusLbp4NTWE+cs2FJyT1La4Efhhf5blqiFJY5bnqrg2Frlz7fF++KzdqvVvAfPu5aodqRdS3uBxYUuCaK9M99rp62qEWea5cnen8nhdwvXZZDvWtXCGP5jEfXO7rkMv64jmpUi+wiPW1+bnmhczHcyplfo1i5oOrgzcWsr7wzJyZ+awvuHk7NJf5yFUP/pzPfHBj+Ze5VeaaWyfLdU6y68wsVy4uPEx35WbhKxnMR0/lSmoW88HFuXcyquR+3yjN1VNbNk5z5aLNxBRXboztlMJ85Jrf3E+lvsWVW9b3s7v1xZzM5M+zWF9wM69rBvMxV+WJc1nMB9fOPpbB+sLTHlWksr7g4tD6ZOYj1xw8LZX6FleCfk2i+2o9JzU/XVXM9hXc6HO6kPl4Tqm2/qyY+eDGpRpFbF/h6c2c32vqvq/ghtf1XOYjV+zuXsB8cLHqTC7raz8n7XuzmPW1+fr6Rcz/v3vq6WLmg4v53xSyvvBEWfMC1hdc6zg3l/pWrromPp/6Ftc/iMhh+4o5aW3Ksti+2rxWRjrz0VMOn5TFfHD1+WfS2b7C0+ucSGH7Ci6bdUhivp07+LEU5oOrvq9L2teakzm9KJP1Bdfeez2N+Zirnu18LuLugytNaqWyvvBko39oX3DRqFYC8+3c7inUB9e8m+xxPVfl9aNJruekusX53i64OXierMLf+CGR+TinlME1k5gPLj/MTXDNFWHO71G1vcAe8a7cHL4uzpWb4t5u5tu55/btYT642rpTLOuLc1J+slyyvuCiU+d46mNPz81OZD64FjR7D+sLT1v0RyzrC27Uej6a+Xbukq+imW/zkLJI17mqn93Y7TonpV6bWFcuw0/GuvL/+eujqW/1NDo+Gc18cFltTJRrrhn7dBVPP5Ozqwo/VBrjyrUvH0UxH7lm138imA+u5twLZ33tudbYG8n6giu/poUzH3M1mlYPpz74vdk7WF/b+/VBGOsLrqRM2cF85Cpn12+lvsXVf7TNdF+tc1JcC6X7Ci6uOGKZj3PKaDeG+uDaiYO72L7C0/wWRrF9BVeya4YzH7nGj865uPvgRu8nttO+1jkpl2TGsL429/oqgvk4V9Xv/KOYb/Nw6+c73PrCUzbW3cH6gptDZgQzH7mmT+3NzAc399UTbF8xJzmh2Xa6rxbX+97eTH2rp/BKDGY+uHH7gR/bV3iGl18A21dwo7xIMB+5Zv0Wa5gPLpqu8WR9MSfFP0ywvuDK80+vYj7maty94Ml8cD1q71jWF574at8c1hdczzvcnvnI1WuedH6+uZsPblRfUfk5ke73VTwn1TuPLmb3VXBl44hC5uM5pdg2u4j6FjfK59H7KjzD2zOP3lctLnoPzGY+cmWnxFzm2/yJRZmsL56TivUK72txWbtTHvPxnMq8sCiP+TZ/9EU262vnftGM9gU3//wrjfnI1ZvfSGc+uFCvJ7P7Kuak/eJIZ/dVcGOHdzLz0dNsNI76Nl+zTrL7Kjx14I14dl8FNzwjo5mPXLPbpVjmg4u6HhGsL+ZkdrqaxPqCK+Ft4piPuYqn/olnPrje2GcX6wtPWf1sFOsLrkYND2U+cpU3n9lBfYvLd9b7s321n6umvpXN9hVc5FxKZz6eUxpj89OYD663iUhm+wpPz30rhe6rxcUzXydQ38qV3YclMB9cnd4smvXFc1KlTr0k1hdcJA+IYz6eq5p67VjqW1y7HRLB+sIzxp2NYn1t/s3T25mPXPOZyyHMB1dqXF/L9tWe0/f9otm+ghsznghjPnrquWd2MB9c/+oxf7av8MxRuf5sX8HVB20XMd/OTb4yh/ngsteUys99/q++1pzUP+YK1hfcPPr8ZOZjrppvaS/mg8sjvZyfX+Te1/KU8cc9HaSvzQd8s8lBfORqyuvBzAeXp4+HOch9Feek/sy5ney+Cm6MX7KF+Tin1PGDtzIf3Jjwq2D3VXhKL4dg91Vwo2VzD+Yj19yWuYz54JpjUDcH6Ytz0vzkEz/WF1yTN5Yy3z5XO3+3hPng2s2D4x2kLzz9zc5DHaQvuNL+zEoH8ZErw+JWMx/caPGFc3/c7quYk7Io8k12XwU3L42s/N4Kdx895YialfdLdx9cXJi+3kHuq3buD37O/m73VXBzcBs/5iNXXVURwHxweX5EiIP1teakV3vXj/a1uBZxO5j5mKtMfGIr88FN49Mw1heeKetuZ33Bjcbf7GQ+cvVqJyOYD64N3r3LQfYV56RoMsj5ufpu+wqup9yp/Dwydx/nlLiRLpgPLged2EL31fKMFlFBbF/BRf2B26lv5SpBf4UxH1w03BXJ+uKc1D/ZGsr6ghsX60UwH+eqMCdEMR/c3DIqlva1POPNs7tYX5tf6LKb+cg1I07EMR/cbHwpke0r5iQ37A5n+wqujTocxXz0VB47H818mzf7bjfbV3jq5LwYtq/g4syiPcxHrjIlKZ754HKxKllfzElr+HY86wuuJVWTzMdcxWNjkphv8yvRyawvPPP4e0msL7hcsyWZ+chVax1LYT64+sW1VAd7X9X6nFRxf5Q/e18V3Fyv+TPf/lxV4+4C5oMb1ZzfK+P+vqrtNTi3ib2vCi4Gzgtmvp1799Fc5oMrGUEetK/1OalqncbO7wN06wuuPrXTeW64+ficSu1KfR8H8cGVGaEbHKQvPPPkMefnV7r3tbjadtQsB/GRK7IHCQfxwVV9ceV/d9zfV7XnGv3xNva+KrgyIm8n89HTGNFqDfPBjQLn57q7v68KT3u/bRR7XxVcTtJjmI9cU1zYwnxw9W/n5wq797XndP6g6iB9bf5MrXnMt+fa4uAqB/HBDTnC+b05bn3hmRH+q1lfcDEn3o/5yFV+82rMfHCxe0DlX+e+r/bnqrbT/di+gos2owOZj88pVS9/sIn54Gr3npvZvsLTk38OYvsKrtf6JpD5yDWPOO9L7r7N95zewvric1LVrRf9WV9w/fdTwcy3P6dqfP/1zAfX9lQLoH0tT2+7OIT1BVcWxVMfuUqsFsx8cHP/5M1sXzEn7XaIv4PsK7jcf2At89FT7dA9mPk2Hzp+k4PsKzxleLCng+2rxeWYl3QH8ZGrP/XWBgfxwdXea71ZX8xJfPbLZtYXXLvcKIj5mKtZfDKY+eDyn5FBrC88NeieP+sLrpzrJJiPXMXcGcB8cHNGA+e+u72vinNSjKi9h72vCq77/x5HfeucMlJiwpkPLjsvjWTvq9q5i5zPbd3fV7X5HzUSqG/lGoes3z9088HNj/yiaF+ck3VXBbG+4Oban7cwH3tqeNxdyXxw81LrNawvPDngwFbWF1xuPL+V+cg1fxy6nvoWV4b2X0/fV8WcbrSNZ++rgssZE/ZQ3+pp7i6MZD64SHL+fvZ/va9qeUrE7hj2viq4yP07kvnIVb1mhjEfXLQ+v4X1xZyMoQNDWF9w3XE/iPmYq+Zdcw3zwUXdmJWsLzzxnxr+rC+4NuNnb+Yj14htMYf54PrWg/0cZF9xThqDpjRzkH0Flx2UMczHOWUsne/hID64uNnYeW9131d4n4dOZfsKrl5eNJn5yNVHznTer9x8cOXY5QWsL85JI6vXOgfpC640e3wN83Gu6hfb+zMfXB6v7e8gfW1v2t++DtIXXOn9yjrmI1cddtyf+eDK0ZXOe4LbvmJOZh1lMNtXcGXx80McxLd7bhvj/B5CNx/c6H7C20H2FZ7R7W/n90e67Su4enm5817q7iN3+6cbmW/zvnsCHaQv5qSvGC5YX3AjIW0T8zFXo1vdYOaDmze3hdC+8IxnN7O+4Ooe67mSm49cZfPt7cwHFwHNwtl91f5c1U+aLmH3VXD9/TO+zMfnlOozFk2vIL7NDzUZT++rlqekPxDsvgpuzBy4ifnIVUtPLmc+uH7UezXta31OqsgN9qsgfW2e2cCL+ficSnHzbFgF8cGVw9Z7K2594ZmX5jm/P9etr807LxjFfDt3arSoID64/uFB5/fGuN1XMSd56bUAdl8FF2uHBTAfPcXWU+uYD67Mmy3YfdXOvdwugN1XwcXgDty3cvWZDf2Zb/P4o5tYX3uum6IXs77g+u/3VjEfc9VuRr5XQXxwebbTLNYXnmi6YD3rC66N70V95IomF72YD67W/X6jg+wrPidVL/s4lu0ruPJTZiTz8TmlWn50EvPBjVe6xLN9tb0zv26vIPsKLrZ8FUB9K9eIvrCL+eDmL6FhrC8+J1X75L101hfcbHo2mfn4XFUpD2czH1z/8mQ662vnnmyTwPqCy6jN1EeuTPROZj64UvhEXAXZV3tO36d4V5B9BVcCT49hPnqqN+5V4fDBxcHXV1aQfYWnTTo8j+0ruLrQcw31rVy98PN3mA9ufF298t8b976Yk9F18HbWF1wJmSqYj7nKjIkR1Le48t6goArSF57xYYupFaQvuDZolAfz7dwG0UsqiG/zpe0XsfuqfU72ig5g91VwNSIykPk4p9S5ewOZDy4mXg1m91V45qoewey+Ci6ztm1hPnK141tCmA8uz+4JZX1xTsqRh3hfi8ubI7ZQ39pT9UU1kPngRnjTENYXnvJ19VDW1+adPcOYj1z1zG/bmQ9utP9iJ7uvYk764sht7L4KLlJeDmM+eur/2RnGfHDZJjyc3VfhiW7Twtl9FVzOvRrJfOTqv5yKZD645jMomvXFnJTFa8NZX3BDS4lkvj3XzW0jmW/zX3x2sb7wzDq/7mJ9bd57bCzzkas8dSqG+eDKv6/sYfuKc9I86fz32n1fwfVTsSHMxzml3+m5ifngiv+LW9m+wjPnDab7Cm4ENYhgvp3764vUB9cPTohkfXFOagPT/FlfcG3e/a3Mx7lqvHLQn/ng5sHVoayv7Y1pEU77WlyvZUQxH7nCMS+C+Tb/ZVE021d7rguKo9i+2vyJLjHMR0/t4z7RzAdXf7oay/YVnrwfsZvtq81fuBzHfOQaX0yIY77NU2ISaF9rTurd67QvuP7aB3uYj7nKvHO7mQ8u/zocz/rCM7/pmsD6gqsnOkvmI1c501wyH1y8+Irz/QK391Xt56rfWe/lub2vCi7jwoKYj+eUxugpfswHN2flL2Xvq8KTPX9axd5XBddv7BroYL6Vq7dcOM1BfHCz8NnKc8+9L56TmsZPk1lfcHFxauVzI3fffq76xRlPB/HB9eiLzvfX3PrCU27kCtYXXBbW3UZ9K1d979pW5oPr2/TKf2/d31fFnIynzMrfV3N/XxXcDKvlPM/dfPTUf0zaSn1wmR/B3leFZ16NCmfvq4KLziHRzEeuOe2FWOpbXHvvWjzriznpERciWF9ww3dXLPWtuZrnH8QwH1ybE5rA+tq5FdcSWF9w+eyXScxHrn7t5yTm2/xpzfn+j9u+2s9Ju67bxPYVXCv/Yyvz7eeUa1dvYz64+VKXcLavdu6+onC2r+DK5DPRzEeusiVuF/PBxaM5e1hf+zlpi2rhrC+4GlgYxXzcU/VubaOYDy7v7YyhfS1P/H0slvUFNwO6JTAfuUbGmDjmg5v7O0q2r5iTua5dPNtXcHFVS2I+esrq3SXzwfUdHySzfYWnaadT2L6CKwVt0qlv5Zo9uqYxH1wtXpNB+1pz0n9eRPuCG68eTWE+5mrcHEx9cBnjkcr62p53I9oXXHl9QCbzkStuhKUzH1yGxGSy91VxTsrsb/bQ91Utrs3dmsh8nFOqR80k5oMr844ks/dV4ZlF05PY+6rg6hDnX+/uI1eN6Z3KfHD1Z/802hfn5OpvU1hfcFERnMZ87KnU+qdT3+Ji9/UM1tf2wn5LZ33Bja2NMpmPXD1zSBbzwbV35mSz91UxJ73dY6nsfVWbT22bxnz0VPwT05kPrpbKDPa+Kjyjw5vp7H1VcPPlXhnUt3L1rWGZzAfXrntnsb6Ykzb9uSza1+LyyJ0s5mOuygfDcpgPLro0yGV94YnLH2ezvuCmtjWH+cjV7h/OZT64sXzBXravOCf1qFcz2b6Ci0nns5iPc0ppJrKoDz7l9Ry2r/A0vUcO21dw4TEsl/pWrv54TC7zwfVreXtZX5yTalmvbNYXXHxxI4f5OFf12auymQ9uhDTJZX3hmb4T9rK+4NqDFXnMR6621of64EaX8Dy2r/83p3Z72b6C6/Hf7WU+ehpeQXnMBzeuvprP9hWeusYzj+0ruHHoTh7zkWv65uczH1wOr1fA+tpzfWloPu1rceW5X/Opb81V7llAfXBjUrUC1hee8UanAtYXXNswoYD5yDW3dqc+uPzPqAJ2X8VzUnPSx9H0vmpxo+cfu5mP55TGL//EMh9c+v0Yx+6r8BSvyfS+Ci6vPJTMR67a534i88FFWHwS64vnpKb6wx7WF1yfFpLAfDyn0sWOeOpbXP70KJH1hWf0y6J9wfXTTZKZj1yzVy/qg2stNySz+yrmpJy9nMzuq+BKrzOpzEdPcWxICvPBtYYPUtl9FZ7R6EA6u6+CmxU7MpmPXHVJdjrzweWCQZmsrz3XSTtSWF9webpWGvMxV+1ERQrzwcWzD1NZX3gyc3U66wsu2pzLYD5yjQ3d0pkPruyYlMH2Fc9JdbV1IttXcHP3Xsl8+zllq8mS+eCi/qQktq/wjOEfJ7F9BVdeO5fMfOSaxlDuW1x83i2F9cVzUs2nfRLrC258XzeZ+Xiuajatl8x8cPXZU8m0L3LH877gotGuFOYjV+m8ivrgcmStVLavmJNa/WoK21ebj7ueynz01KKeoT648sZPqWxf4YnAX9PYvoKbf31DfeSqRVlpzAfXnpmRzvpiTvoT/WlfcPWry9THXI3rAdS3+au3U1lfePIfrzTWF1z/4y/qI1c1R6VRH/yYTGP3VZyTcly/bHZfBRcbP8lhvn1ONT+WxXxwMbRlDruvwlN7PruX3VfBZe0WecxHrpk6OJf54OJ0z72sr32u3miZxfqCK7+8m8187KnZYmEm88HNO5OzWF942t8Dclhfm//5LPeRu7RdNvPB1fufZbP7qj2nl67lsfsquBrgS330VM/e3Mt8cHl0WR67r9q5U8/ks/squDLobj7zkav9WpzHfJtvPML7WnPS7ubksr7g5tynuG/NVU3ok8N8cPWfoBzWF54M6LWX9QVXD725l/l27st7cpgPrk7dkMP2FeekmJGfQffV4qLnEurjnDJTlAzqW9wYF5LB9hWe0b12Ft1Xi+tJflnMR65W++8M5oOLP5z/3+7eF+ekoc9JZ33BNZ+D6czHuaoevJjGfHD199rprC88peXDdNYX3Bjdivp2bks1nfngxv0W6WxfMSf9xJkstq/gWqu7WcxHT3nMI5P54EoT70y2r/DUjjey2L6Cq1M+yGI+cmWrsZnMt/kvtTNZX8xJDmufwfqCKwefzmA+5qq9XCOd+eDC/2Qa6wtP+fJsOusLbr6zKp35yFW7+6YxH9wsqOn88/f7D6/85xQyf2Qlj73u/FycwbN6VuEvvDDWlYtn5UDmi7Fr3678/fqWMd0qiA+uGO/Uc82VA25OcvW0rZ+MrsLrFeuuXDmxaCrz7dyn2/RnPrg6u8Z41tcoOOr8uaYPV42qIH1tfnLUAOoPnKQ7P/f4+ekVxAdX73iPryB94Ulzy5XepC+49lLxMOYjV04M6l9BfHDD/9YbrnNVjJ/nuM5Ja7pvtisXGa0XVuH9ly9kPnrqx/4zk/ng6p2uC11zRY9bS6rkfn98mSs3lcPLXblsF+HJfOSaCS94MB9c931pJe1rzUl/8vvprC+4drbXAuZjrurotROZD670HTeP9bW9D1d60L4WVyeM8GI+cvW8zh7MBxdvHna+b+22r9rOrpU/D6h5np7D9hVcO3tIZ7557nbl5wGpa7cvZD64enbF3Aqyr/DMZ1aPrSD7Cq59WNGe+cjVCt6fVsF8i6sfnnqX9dW/8a/8vC3z6w5LWV9w7Z2e3P8movLzJOThX5czH1yeGbyE9YWnnL8yi/W1efbL45iPXO1373nUt7h2JnJaBdlXe059Px3J9hVcbzR0NvWtnjK/tsZ8cGVT1+lsX+EZb769jO0ruHa5nRfzkSsCBy1iPricNm4F7WvNSRY0aFJB+oKLhAtjmI+5ah1uDKxgvsUVxxqN9YVnFB6Yw/qCqwM+W8Z8O/dh+6nMBzefmz2/yrn6ufBwPSeNFb+ucOX6jtoerlx0b7CS+TinxO1Bq5gPbg5p4+2aa16asqSKdyTP05UrdQbMrcI/PcZ95F4fuYr54OrHs2hfnJNybr/VrC+40fS8D/WtPTX7LPZhPriWmLOG9YVndE/xYX3B1W2tvJmPXP29GmuZDy6G3PFxnas2KXCs65yURRUzXbkeGtm/woWLZ0rbMx89za+vL3Qw3+J62fnRrrnanNELK1w8tfD32a7c7NLHt8KFi0P5q5mPXO25jMnMB9fG6JXvhbr3xZxEp989WF9wxbf7TOZjrlq1J7yYD65/HznfQfrCU3pPGcz6gpuzU1YwH7kiwENhPrh6VvGoIPuKc1L5bCjdV3D5jnOP3H2cU9rGdG/qW1y9EOXL9hWe0v/kWrqvFpftW61hvp2blriG+eDiSB9f1hfnpKrtWcn6ghuLN1Ef56q6tMty6ltceo1cwfrC07yTVrO+4GZt63ML3Hzkaqfnr2Q+uLnfYwXbV8xJNN+8iu0ruHn12cXMR0+RpKxiPri689tFDrKv8Iz8ye84yL6CK4efX8J85Ip+nfs4iA+u/9nV+bmVbn0xJ/WjuBWsL7j+eO/5zMdctdgnljHf5uO2z2R94Rk1Cro6SF9w8x3F+XnGbj5ylT8GvMR8cDFuwyJ6X12W7fzegl7NPdl9FVz0uL+M+Wq1fSuc35v5zgrmg8ttn3uw+yo8NeDoQnZfBdfHXJvFfOTqX9dfynxwGfHEfNZXTr/g/LnUcs+VtK/F5Qfdqa83b7/K+b3IJ7yYD66P2L+S9YWnv9HDg/UFN/Z0XUx9K9dQH3kyH9yYv3Q5u69iTtq95HEV5L4KLlvX6cx89NTOfjujgvjgYtbkkRXkvmrnXsgYyu6r4MZfftOZb+feiq1RQXxw83JX5/MQt772nOIezWF9wcVgr6nMx1yN6o8tor7FzSPPzKkgfW2vYOx7FaQvuFG/blPmI1evcXtKBfHBVXPQSLqvJ5K8nT9PM9Kb7iv4wRmrmC+f6uNT+ffr8M9q5oPr91uvZvsKT71wfiXbV3Dl2fPUR648c2oV88HFxse9WF/jzWrOz+Wq/6Ev6wtuzn3Ml/nagu/WOr/3Vq5lPrjp+eca1heezDq9mva1uO73hjfzkattuODLfJuvnufD9hVzEm/dWkb31eLa27kLmY+e2gc/ezIfXE+os5ztKzz94+5z6L5aXKk4PI36Vq5+t+4i5oMb0xvOZX0xJzF0kBfrC65Ofn4F8zFX9ckEb+aDm/9EebG+8Iw/ty5nfcFl6GnqI1d5YdYK5tvcs7Hz82Td7qv2OXlx4Dx2XwU3Wr65mPn2OfVoz2TqW1ycPD6T3VfhiZBhS+l9FTz42GLmI1fvc242821+9vws1tc+J4d36cf6gsvvr41iPvZUdq7Vo4L44Mrar15kfeGp/fqNZ33BtUcJ45iPXJk2623qW1z5voGD3VcxJ+WHe3PZfRVc5ISPZz56Km8GTWU+uLm110B2X4Vn/NmqH7uvgusnIxcx3871F8OZD64c3OZ83uDWF3MSoR2Hs77g2pHUFtS35iqeGdSZ+eDme127sb7w1EGrRrO+Nn/+tfnMR67xW7cxzAfX56fPriD7ap+TTzcbW0H2Fdx8ZdC7zLfP1euL9Arig2uNciZWkH2FZx5r1bOC7avF9fc6dKogPnKN9u1HVxAfXNYKGsr64pyUO96cx/qCyy//msl8nKtiX9Ri5oMbwS/OZ33hKe+umsr6gmsbJ46nPnLf/n0W88Hlho6Vv6/jvq+Ykzr7ctcKsq/gxltG3wri2z1XRA9iPrhS+9a7bF/hqRPrj6b7Cp754XTmI9eomT+C+hYXnkfHsb6Yk5F3bBTrCy6+KHqP+Zir+emICcwHl+d6jmJ94RnNbg5hfW1ufDmE+cg1y7YMYj64+ppXT9fnqlJ2CXB9Tqo26BbgyvVPHm5z5ca7tbYzH88pRYvDm6hvcdl3y3bXXO3W6ghXT5sTF+nK1af/iHHl8uqp3cxHrpHiFUV9iyuNJsWxvvZz0ty7/qwvuD4ucDvz8ZzK9HzoR32La+3rbGd94akhN6JYX3Ct2eJ45iNX7VlzF/PB9fk/xbvOVS08kuA6J/3yB9KVK10XJ1fh53qkMh89xdRfkphv84pTaa65subaNFdPvW5kuHLlt9WZrtzMfyOH+XbuoaNZzAeXs7L3sr6Yk7L5SDLta3H18ycymI+5ijFvpzAfXNas4H0tT0+eTvuCy1px+cxHrrp4XC71LW4sm1/A9hXPSWWnen50Xy2ube4Uynw8p5Snewnmg2vdq29j+wrP9MmNYvsKrodlxTMfuabXj5HMB5crs+NYXzwnVY8/vpH1BRc1fUKYbz9X/Xr6euaDK9m+m1lfO/f5tAjWF9wovrKb+chVGt/dyXyb31sZw/bVnpN3lxS2r+Dm3BMZzEdP5XR0MvPB9b5NM9i+wpNz1uayfbX51MAC5iNX5BXlMB9ce6JRAeuLOalieRLrCy4Ls9KYj7matSITmQ+uFEWk0L7wxP0s1hfcrNk1j/nIlf94ZVDf4rKlluN6rur1BuRUOVff9clz5WrWR3tduVhWUMB8+1y9mVnAfHBj8OAi11yl+GS+q6e1+anQlSvxiVW4MeTDIuYj18hJLGY+uDIosoT1xTmpDzheyPqCqymbi5mPPVVar6W+zXsMKWF94Wm9bpewvuD6qBulzEeuWra/lPoWN3ILylznagTeKXCdk9Knc7Er1/7+usCVm9WaFjMfPRWfJ0uZD27cuVPimmvc6VLg6onwpUWuHJ+rCm7+9HIh9525skavEubb/NyFItYXc1KuzC9jfW3+fv8y5mOu8q3ny5kPLv6+VMb6wlM3+ZSyvuBG9erUR678ViljPrjSd3UJ21eck9qFxCK2r+Bm6bIS5tvn1NDWRcwHVwIvF7N9tXPrfUX31eYdDpQxH7nqwNdLmQ9utKpWxvrinNR/f7yQ9rW47JFFfftc7fpBHvPB1fyzBayv7WVcKGZ9bf58aAn1rVyz+8+FzAeX3X2K2L5iTkrduuVsX8HVdu+XMd/uefsN6oObDT/n+4rcjI9L2b6Cm+85v7fd3UeufvU49cGN7E7FrC/mpBdnlbC+4PI76+/v5mOucrZfEfPBzfRHhawvPO311sWsL7h6dGch85FrtLtXwHybb76Xx+6reE6qP9F6Lbuv2vyru4HMx3NKpfUrPswHV+P/9mf3Vdtb88kOdl+1+QKPKOYjV7/9KIT54PKnWuGsL56Tmi+t9mJ9wc3anTcyH8+plE59PJgPLvKr+7C+trfsdCDta3FF+XQb85Grn/YVzAcXAz0C2X0Vc9KWBu9h91VwdauSxHz0NPbd28V8cDHi7Th2X4WnLhudyu6r4PrtcOojVzx+SjIfXKz0SGF97TmN7hTO+oIr5R2pj7mqFw+HMB9c5rcNY33haY87drO+4GZqjwTmI9f89Xo488Fll9Qotq94TqquT51P9xW82ynq4zmlWfHnNOaDaxdvzKb7anniyDpvtq8235uzjvnI1VI9FzLf5q81Xc762s9Vx4QMo30trta/OZz5eK6qypyGzAeXbd+p/Lkp977wdJ9RI1lfm6c9PZL5yFVrnXjXwXyLm590dv78k/u+WnOSN8L82L6Ca998EcB89NTMfiuYD260KvOi+2p5xsDqm9m+2lypv5X5yFW6f+XNfHAlf58P62vPqXGzYawvuP7DxX7Mx1z1o9Oc7/e5+TZf+4zz+6Tc+sJTG37fifUFN85daO0gPnLVds73jt19cKXz7Mr3Gv/rvmqdk1rDndnsvgouXm24l/k4p4whF9OYD26c+U8Gu6/CU1v9lMfuq+CywVf5zEeu9nB2FvPBjeZtaV/7nJxzS7K+4OoSv2TmY0/lU21jmA+u1B+9m/a1PGVuegrra/PwPqnMR67a8sEe6oN3vRvH7quYk9nvdD69r1rc/PZsHvPRU85umk19i2vvO783xf2+Ck/bdzOX3VfB5fbe2cxHrtFiTwbzwWXggDTWF3OSw3ulsr7gYsTOFOZjriKyQTzzwdXu3eJYX3hG7T7JtK/F9XsTJPPt3L2DdzMfXOs2NZruK87J2CuhbF/B1fV9w5iPc0oJTVrDfJu3NdexfYWnTZy1k+2rzfd3CWc+ctW0iI3Ut7h8tasf62ufk13X93aQvuDa0I8r31v9L986V/U6L/o6iA+uX3jN+XML7n0tT/Xa9oqD9AU38qa+w3zkylG/r3EQH1xvutH58xRu+4o5iSMinO0ruF5HDWc+epp/OPyZD6508/Jn+wpPnPojjO0ruHHeCGU+cmWvBX7MBzdij65nfTEnfetXo1lfcP3U61OYj7maOy45f27CzQfXZq5w/vyyW194autnp7K+4Mrdi6OZj1z1p2PO97bcfHCj+G/n+15u76tquUec3we5ZMd69r4quPC/uY756pJ9zv8frR68kfng8q0/N7D3VeGZk2+sZe+rgouSyDXMR642q8YG5tt8/qF1rK/R/xvn7wO2MATta3HZZ61gvvHKaee/p6MP+DEfXOnr68f6wpOz6gjWF1xZa1Ifufq4J/2YDy7Krb/O/X1VzLXLNR/2viq4/sFD6qOn5tlhLfPBlfHjfdn7qvDUjy55sfdVwc2+Kdy3crUbQ1YzH1wf8twq1tee6xf6etYXXPRIoD7mavhu3sh8cGP61+tZX3jmwSRf2tfiYpYX9ZErm1xdy3xwY9ERX7avSvlh5/cFV8v3Z/tqc83Tn/nyQp7zPZoOezYxH9ycq29i+wpP21bXn+0ruJK6kfrI1VY+8mc+uDziS/saGdud3yv489oA2tfiutkvgPpv6M7naqOHBzIfXL3cJJD1hafqVzaxvuBG4vxNzEeujDECmA8utw8MYPuKOcnPPhdsX8F17QH10dN484wf88HNW4/7sX2FpxnNN7B9tfmIWuuYj1zlDVUwH1wO6LGB9cWc1J5H/Vlfm9ep6c98e649cjcxH1wbctuf9YUnH2/rR/taXDTsQH3k6rHN/ZkPrtx/1Y+9r4pz0jg02oO9rwquVP+X+jinZK6xgvngInS8B31fFbkpM+az91XB1a4XqI9c7bsDi5lv877581hf+1zVWqxifcG1pDTqY0/1Dp6rmW9z31terC8840F9D9YX3LyydxHz7dwfB69gPri8f20Ze18Vc9Jv/TqVva8KLt7tOZb6Vk/RJ2Am88GVle9NZO+rwpP3vxrI3lcFVzc+UpmPXHXGF4OZD678/sWDQ6Qv5mR63ZnD+oKbEYemMR9zVRpcXMB8cNFx/CzW1/bGbBnJ+oJrn+9swXzkaoEzx1cQH1z98+5bbF/tc3LI+75sX8GFv4P6OKeU+/fWMh/cOBHiy/YVngiN8GL7Ci6rlXoyH7lmi6vezAdXfnjai/W1z8nEDhtYX3Ct5NT/x3eeq3qzVoL54CKj+gbWF57WU/NlfcGV5hupj1ztYdha6ltcj7/pw/bVnpPP20vZvoKrS4LmUd/qqf/xyIP54OKtSYvZvsJT7k6YzvYV3OhTbxjz7dwdDecwH1yr23Ii64s5mTeCV7K+4GLnWO5bc1UGtPKmvsXlly+tZH1t73D5QtrX4nJHq1nMR64x4OvlzAdX211bwO6r+jG18vv01JKngth9FVzkn6e+OHTS+T1/Cw8GMR/cTNoWxO6r8BR9cyC7r4IrpxsEMh+54vF3gqhvcTPxRCDrq+fOCa7848fGBbO+4KrWNpj55uzLzj8/8mww88Hl0Ixg1heeVv1cEOsLLkctoz5y9XtTg5lv80V3gth9FXNSZooAdl8FN/Z/Tn30VD91BDIfXOsaHcDuq/Bk3mOb2H0VXGvXgvrINYyzm6hvcX3zPX/WF3MyI68Esr423zKQ+pireHJVEPPBtfe/C2R94elvhgawvja/epD6yBUJrwQyH1x5e04A39d3ne+lL3xrM9tXcPNM/c3MlzV3Ov/8Ef/NzAdX35m4me0rPP3/tXXmYT6W+x9/VDJRkS2V9BipOJW1RDJPFNkjsmS57WsyZjDMds8+YxZjZgxjZsw9+4ayJaLvUzopISoh20NRtImSUH7X5fu879/4nvdf57pe53U+530+53PdfbrdPd8z/01g8wpub51IfdR1mjZMpL7L5bz1CSyv6LvD+x7JepvmBbfHxlFfttjj/fcf/4D64HZ4ViLLC88YYiWyvOBG91MJzEdd+4vBidR3ufPi7wlsXtEntf9MPJtXcLvxEO67OZ1jXRKYD24NK49n8wrPOlwWx+YVXH15Lpb5qOv0uS2e+eAyp38cy4s+WZtnJNC8Lrd3HY2nvttX8ebiBOaDGxN/iWd54ck/u8bTvC6XzyfEMR91zYoB8cwHN3vnxrF9VZ+TtVtG0X0V/MY93HfPKbWhbjTzwdVs772H774KTzTYG872VXA1aQT1UdesPhPJfHDnRH44zeuek/ZDR6NZXnBr8Q9RzMecmpNTY5gPLp+viGZ54akvz0iWF9x5fAP1Udfq9F4U9V0uGn0o2b6q+9pmewjbVzWPvDaf+chptX0+lPngzi92CN1XXU9MahzE9lVwZ98dc5mPuqJHxwXMB3duzwhiedEnUZAYzvKCq+jh1EdfrXppkcwHN/p5/3t982rv3UcWsbyar/uV+qhr+28LZT64WevCIjav+lz1fziWzSu4PfQl6uOcMovjY5kPLgO2xLB5heeISdFsXsHFngjqo6596FQ09V1uz/f+uYBvXpyT4oFdsSwvuMr7J4b67rkqPz0WS32XG4OaxLK88KzAdjEsL7j11wTq67o5fWOYDy6eiIym8+r2SVXkSjav4HJaJPV1X491jWI+uFUcJNm8wjOOnw6n8+pyY9TcMObruoUykvng4stXI1he3adxuVEsL7j18WnJfPTVGLw9ivng5ki/KJYXnj3YkjSvy1X0YurruoWzJPVd7gw9Gcneq+p70sNDvO9KfN6rghufNbj5XVhfH/eU4nyz/swHt1fYN3/X3ve9KjxreusJAeS9KrgRb89gPuraX0ybznxwZ9/XgSwv7knlyF4DWV5ww/xGMB/3VKL72d7MB7cO/DWe5YVnTzo3i+UFlwemBjMfdVXEnlnMB1er1wcHkPeq6JP8y29eAHmvqnn9OiHMR07x/PQQ5oOrq8cjA8h7VXh2s5UR7L0quLMzPZr5qOvUuRjDfHCn9ZkEmtftkxO/P5TlBVe17olmPvpqJc0IYz64tTckmuWFJ/oFx7O84FJ+uIT5qGutvRzPfHA1+7VkNq+4J7Wqn+3A5hXcnNJkFPNxTynaPNrVQ3xwM2Refzav8OTUCdPZvIKLWO93xX191FVnWkxkPrg5+qM5LC/uSc2xq1/zkLzg6sf+jZiPPdX+9q0JHuKD2+cqhnpIXnjO1o6vsbzgzgvOZObrun+cbOAhPrj56O1D2byiT/b8xYvZvIIbu8ZHMV/39aXJC5iv+Y/Xwtm8wnMS1sexeQUXO08kMR915YhfopkPbge/F8/yok/GH6vmsrzgal+nhcxHX+2N3ScxH1ycqTWH5YVnnL2VIy+4avYi9VFX9goNZr7mD3sWsfeqOCft3sPj2HtVcCvbL4H5OKes7T2SmQ8unpuQyt6rwhO7eycEkPeq4HJOi3jmo64x9FQq88FVo70pLC/OSaPHtDSWF1y0mJTOfMyp2HrnUuaDWwXb02le11O1tqWzvOBqTUA681FXfd1qGfPBZa6dHkDeq6JPalOT2ADyXhXcavhDFPORU0SsXcJ8cHtOQGIAea8Kzz73rAwg71XBjT6nIpiPuva1J+KZr/mmQzyv2ydz4Jw0llfzIX8lMx99de4qXsp8cPXa8FSWV9dtvC2J5nW5MdtMZD7qypVtk5kPbreLTWLzinPSrjU7lc0ruHP6APVxTtm/XU5iPriZ1SWFzav2gsfQedX88P3pzEdd496OqcwHN5pfSmF5cU46yxvGsbzgyvNQAvNxrjph4yOYDy6K/5AsLzx7S+tEmtflMrRbIvNRVw6oG818cGPNwmg2r+iTulSSxuYV3Gi+JoX5yGnkfJPMfM0fen0Jm1ft+f93CZtXcOvRl5OYj7rGlW8Tqe9y1as6geVFn8S5ggSWF1x03RBPfbev1o2Z0cwHlw+2jGZ54dnPfxjH8mp+tVYc81FXpO2Lor7LrSbZUQFkX8U9qXkjcwbdV11u+X80kfm4p5TtvpvLfHCn8dDZHrKvwjObmyM8ZF8Ft17/K4D5qCvbfjHVQ3xwsb/PBJZX36um7g5mecHFf7bPY76+p4rrH8J8zXt/u4Dl1d7BPXNZXnDret05zEdd4+6q+cwHlxc/CfaQfVX3qXH7ALavgjv1ao9kPnKam5eN8BAf3MmP8/5ejc++Ck+tbDmZ7avg9vbfZjEfddXwBv4e4oMblwTNq/t65MhMD8vrclngN535uq/fdAlmPrjV+kqQh+SF5zgxUzwkL7h9W9NJzEdd+c2vQR7mu1wlfB/E5hX3pE7z50LpvLrczOi/mPm4p3Q+mRnOfHD1zukwNq/wVL3ti9i8gjsPrlzEfNSVwXvCmA9uT3wunOXV96Qf7IpgecHtZyIiqO/eq1pXX4hkPrhYe1ckywvPfmNjBMureXJIJPN13UkjI5kPrn58WLJ5RZ+slLOL2LyC27VXL2Y+cqq2T0UwH9w8lR/J5hWeuD0nlM0ruHM1MYz5qKvOrZXMB3d2xESxvOiT+fRMyfKCm4ffiKK+21ej53HJfM1fPR3F8uq6zRdGs7zgxt0JMcxHXfvqfTHMBzeyn/H+OZTPvopz0twWGUj3VZcbVmAw8/W5OjB2CPPBVdv9o9m+Cs84Ub2A7avgdr3nQpiPukavnyZQH/zvYVNZXpyTTtYE7/fLffKCO4PPjPIQH3NqPy2DPMx3ubw0wvuu2ScvPLH5lPd3YXzygtvnNj7rIT7q2oUPz/UQH9zOyPN+98hnX0Wf7KKei9i+Cq7m1V7MfN3Xx0tmMl/zIfvnsH1Ve2fWL2b7qubbp4UyH3WNhdMCmQ9uLeoXRPOir5H9u7O84DJv6jDmo6/W5tkTPMTXvOMDN/+5B9+88Jx9mwXLq/krnqnMR12jv7jWg/jgYsDKm7/L+j/z6p6TMv5iGJtXcCvmpTDm45wSD9wZzXxw89DxKDavuq7ReDGbV3CjfMwC5qOunbFbMh/cHJsRzvLqczW/ayzLC25tejqW+ThXlbwSy3xwkfNXLMsLTzZ7JIbl1fzsnVHMR11r2LkY6rvcif4sykPmFX2S78cHesi8gqv3Rk5nvs65tV0I88HtIY/N85B5hSdum/GGh8wruPijW4CH+KgruwXcwuGDG68PGM3yok/Ox8PCaF6XW9bJ+cxHX53j28OZD25+7r+Q5YUnHjswh+UFN4vPTWQ+6lrt4udS3+XWvhOTa35XVamdVTW/k2o12XgLF1/tLavJ5cy+1NffKW11/h3mgzsP3rW2Zl1VsKCopieippXW5MZ6v7ya3Jw7VjEfdY2BG8qZD260eayI5cV3Uq3Nv69necHtXw6sYz6+UymChm9gPrg5KPhtlheeavJYJcsLro52KWY+6gr/3Ermg5sNg4tr9lWVj8ys2Sf19dfZNbkxsE98TW4U/LiE+chpDRqRw3xwseJwas26stGK2bd4w14IqsntN9ve/OswuMj68ebvVvn6qCuTn5vvIT64eeL7m/X+J6/bJ/Ns6SqWF1xs2pnGfPTVqbViFfPBnR2LU1leeObbu4M8JK/mdY963y34+KgrlxS/6SE+uDPX37uv+cwrvpNqjPHbwOYVXG07vI75+E6p8WWD9cwHN7PqraXz6nr2+X8q2LyCq0F7i5iPuqLj6HLmgzutXi1kefGdVOP+hm+zvOCqaUAV8/V3VVd3qGY+uPPvB2UsLzz19vESlhfc3NdvNfN13ZN7qA9uftIgh80r+mReapHD5hVcNq+zhPro69Yx2cwHF+9vjWXzquu2mT/aQ+YVXLw42fvnjz4+6poXGrzKfHDz0fPe9zW+ed0+yb9XZbC84OLfl8OYr/uaF5pCfZersI2zWF54KvvLt1hecNUtJoX5qOs0+D6U+eB2zBfeP09zz1W7zQ7v34e556Sze3dMTe7sWJhak8vXv17KfJxTVvc74pkPbk0qWFazrgyYmlnTM55pvLwmt8/OXFGTO/Xm5TAfde1rz1Nfc78fcllenJP2+1FJLC+4Cv4uk/mYUzHh6TTmgxsXyrNZXnhG9+9oXnCVsG8181HXnnM1j/ngzrW7C2v2Va07sqpmn8zGTfNrcvXnzvyaXCbUVcxHTnl1UwHzwa1T4wpr1jUOPKxqeub0noW38EfaFNbkdvILRcxHXZGdWsR8zTcGFbO8uq+TxxbSvC63up0qYj766sTdU8x8cLkypYTl1V5srRKWF9xatbeE+agrJq4qZT64GPFKGZtXnJPWxWPL6Ly63P7RzmE+zinj0OdZzAdXv3+Xx+YVnn2uraLzCj57fxHzUdesOkh9zfeklbC8OCetxakrWF5we8DmAubjXHX6HMphPrjsdZHndT2RNKiY5QW3hj5YxnzUFbfPpL7mg18uZ/OKPjldj5SweQU3Bg8tYz5yWsmFZcwHtxs3q2DzCs8UA8rZvILLlG/LmY+64nqtSuaDW3WCKlle9Mls3L2C5QVXBe9VMh99Fd1DK5kPbt19VzXLC884mVvF8oLLQVeqmI+65j+XqpkPbvbptyaA7Kv6u6rvHipj+yq48+WxIubr75TeMayI+eAyxc5n+yo8J8PJY/squGPPzmQ+6pr/1MtmPrj1dFIyy4vvpDrP7M2leV3uvF4rm/n6O5UrwzOZD25lZKXSvPAuXEhjeTUfv1MyH3WtlrujmQ9uHiiexfZV9MmyB8WwfRVctKm++d1TX1/3dZz3u36+Prio//Fstq/CE12XxLB9FVx0vZTBfNQVPSMTqe9y43CB969PPnl1X98oHughecHNvMPe984+PvpqV16bynxwc+aJaJYXnhyzJYXlBTd3fbuc+ahrdW+Sznxw49g671/PfeZVf1e17I8ENq/gdq/Vkvn4Tqn96ob51Ad/OHQKm1ftDdv/FptXcPHKau/vdvv4qGuMe8j7zzP4+ODWlcned+s+efGdVKvW6CYBLK/LndjW3j9X8fXxXdUf63t/V9vHB3da+998t+2bF55YGTCP5QU3pYxgvq77UVQE88GtoRe8f7/mM6/okxrxdgibV3BrfmAC85FTbTscxXzN2z2QzOZVe71nLWPzCi5CWq9gPuqqpLEZzAcXX720guVFn2SnLvEsL7jz6P5U5qOv4ufAJOaDO/uyl7K8um7fnzNYXnD74wYrmI+69uFmmczX/PmUbLav4px04qJz2b4Kbj1SUsh8fU61SMtjPrjY8nsh21fhWe3ql7J9Fdwe9Hs59d26IviFUuaD237evcs3L85JEXRvPssL7nQ4X8h8zKnTt3k+88HV6YJClheeSqldyvKCy692lTNf170QXsJ8zbOalbN9FX2Sxc9XsX1V8+nfVTNf97XzW1XMB3cudFjD9lV41uXv19B91eXGsd5rmY+6zjuvrWU+uPnIgbU0r9snu3aLKpYXXB735vL10VfjxNhK5oOLu7pWs7zw7E7P8rwuNxsfpj7qioLMNczXPGPUWjqv7jkptn+eR+fV5dL0L2Q+zin7hbZ5zAe3ckMVm1d4pmdbMZtXcGn4lzEfdcXe3UXMBxffXS5hefW5eoeVy/KCK/+pBczHuSqHNFzFfHAjMCef5YUnft5SSPO63JpbUsx81JXjExXzwdWbnYrYvKJPyulTweYVXPzUrIr5yGkbF8uYD24er6pg8wrPfDaims0ruPnPeOrrulfrVVEf/PD1KpYXfRL7Z5fSvC532t9Wznz01fp9ZzH1Xa7mPFXK8sIzFw2pYHk1D+tVyXzUdTLGl1Hf5fZ277mPe1XnwbuLat6TqpfvLK7JzYb7buHGxdJi5uOe0vz64RLma562oqRmXaP9/Fu9skdLanL16p0lt/B/E0uYj7qqzk/UB7c2mKUsL+5JZdywUpYX3O5dUsp83FMpv4oy5oOrZrvLWF7tWZ+Vsrzgxsm9pdR365rTdpQxH1ykpJXV7Kv9W+wtfbL8Dt/CxZOXbuX9m5QyHznFswGlzAc3F/UvrVnXdIaU3uJ917O0Jlefqlu4qprAfbeu2vEK9cGtI8+VsrzokzFvG80Lbj23nPtuX0VBnzLmg5vWBZoXnpk0keYFNxq1pj7qGntTqQ+uNrTi8+rek6pTDSrovLrcOHlbBfNxTykmR1UyH9zJ9Z5HvvMKz+76UTmbV3DLb1w581HXqdhXwXxwZ3Vnmhf3pKL7mSqWF9xuFlbFfOypRjf/NcwHdwatqKZ5XU/lHKxkecGNxXUqqe/Wtfw/qWI+uLPv00o2r+iTnHS6jM0ruDFjJPWRU/4TU858zVfvKmPzCs+Un9B5BVcftqc+6qp6dcqYD27X6VPK8uo+dW9VwfJq/v0z1EdfZcWmCuaDC5lTzvLqum+8UsbygtstRSnzUde8I7qM+eBOefAt56q548Qt56QMDryFm7/cVVaTG30Dqa/PqbnNS5mv+TPuue/WtVuaZTU9q2h8aU1uX2tRVpNbZd1KmY+6TtvtJcwHl4fml7C8OCdlqxMlLC+49VtqCfMxp87OjdQHN9N7lNC8rie+6VTC8oKbBfuKmY+64h13//TxwaVfz+Jb+vr4HWU1+yRGGKU1uYzeewsXW9eVMF/3dVGLEuq7XD6xqbhmXbPzols8da5XSU3uvOPeb7jcbvtBMfNRV1zsXEx9lztnVxexvOiTUzammOUFN/2OFTEffVXXthQxH9zIbFXE8sKzN/UrYnnBnfiyQuajrrkqupD6Ljfe+VKxecU5Kabn0HkFN7rXoj7OKdt/CfXBjRcPFLN51d7P0/i8utweu6eI+ahr3dOc+uDq1RlFLK8+Vx+QNC+46FRZzHycq8a64BLma35fDM0LT934luYFVw80LmI+6lo9CqgPbmbvKmTzij7Jk0/yeXW5PTKhkPpuTqOz997B1we337y9kM6r64nJRxSbV3Ax0VTMR105vj/1NX+1WQHLq/saNJTmBZeJmYr5uq+f/62YD64e7ETzwrOvVhSwvOD2hEdXMx91jUPOauaDOzvH5LN9FfekRrOH1rJ9Fdy5M5z6+p7yhU1rmQ9uHW28lu2r8GRiSjXbV8GduOncd+sarRuvYT64FXSwiuXFPan1zJG1LC+407Qf9917KhUnqQ8uO9Vfy/LCs//uu4blBRf1b1QxH3WleGQN88HlpENVbF9Fn8T1tpVsX9V8+A/lzEdO6++FlcwHl5GNKti+Cs/MrCpj+yq4nBNWynzUlTe2lFHf5eLUnFKa1+2TmZVayfKCy8cbVjAffTWGjq9kvuaRh8tZXnhWX1VG87rcCetVynzUVR1HlzEfXI66WMLmFfekZvs1a9i8glv5rufj457Svj61mvmatwmoZvMKT15PrmbzCi42DKtiPupaw9K473I7cF4ly6vvSafalSwvuDq4pZL67r2q1fCzcuZrXudQOcurvfv+U8nygouJHSqYr+tOnVXOfHB1LruMzavu0/Lv6LyCOzvGljMfOVVm4wrmg9t/5tJ5hWfUOkHnFdy2vXuUr4+6RtvHSpkP7rz732KWF32ydl8oY3nBZV56KfXdvpo/LC5lPrjY2r2E5YVnf92O53W5M+N0EfNR13jkWhHzNQ/ZXUj3VfecVBtECdtXwdW8kcXMxzllb+pewnxwsdO/mO6rrmddmUj3Vc1nRhQyH3VFL7OI+eCibadClhfnpPOG+/cVPnnBZfrRIubrOT1YTn1wdSyM5oWnRh6gecGdZEcxH3XVobnU1zw3SrF9FX2yu1XwfdXl5pfbCpiPnNaGkdx3uRz0EN1X4cmBz9B9Fdze+14e81HX8M+nPrgV0y6P5UWfrBfuVCyv5u0iVjMffXXeGMl9l0vxbT7Lq72m9fNZXnBnx5Zc5qOu1Saa+y43xr2Sy+YV56To3baYzqvLzcW1ipivz6nusdwHnziFzqv27r1K5xXcvHKfYj7qmi/O4L7LjabNC1henJOizsZClhfcqFWmqI9z9a/Nivoud76JLWB54dmfLixgecFVyNl85qOuVKfymQ9udZN5bF7RJ5mTwufV5Vbc4/nMR05xXxH1wa1+4/LYvMIzDv5C5xXciTu8ivpuXXNcP+qDi2PjV7G86JPTJzOP5dX83ZBc5qOvYtOYXOaDm8HzVrG88Kzmy2hecPXNNznUd+uqj7fnMB9c1u6Sw96r6u+qxqVGsveq4HLGi1HMx3dKnT5RscwHd850iGPvVeGZF/bG0PeqLndO9UtgPuqK8KPxzAe3Jk5IYnnxnVQjdHwCywtujotLYD6+U2ltzkxkvuZB0xJpXtcTcxISWV5wZQ5cwnzUNTv+lUh9l9vXnlrC3quiT+qu9GT6XtXl8v2u6cxHTvMBlcJ8cKuZ+5/3ea8Kz+p2NoO9VwUXKduWMx91VdqoDOaD2xNUFs3r9snp576b983rclVdspT56KuhLiQzH1ztbZnG8mpv4OJlLC+4E/BSJvXduqLn0HTmg1sfrFvG5lV/J/XEiEQ2r5pPqEt9fKdU3vtTPPPBrQdK49m8wrPvfz2RzSu43apNEvNR19z5VTzzweXRaQk0r/udVGvJ0liWV/O4wFjm4ztV8ufEKOaDi1+qolheeNbmoliWF9x6pmkc81FXHLscxXxw+/MJ0Wxe0SdRa/YSNq+aN66dwnzd1x4DE5mv+WdvJtF5hbf/21Q2r5rfeXAp9d26RvuNS6gP3sovheVFn5wr2+JYXnCrWUQ889FXM/x4NPPB7VqjY1heeM6AAQksL7h9oWEi81HXOfJJDPM1/6xRLHuvinPSnlm6kr1XBVdNV+QyH+eUqvdvNvPBTTk5h71XhSd3ydXsvarm+yqpj7rmrk9ymQ9ut2u2muXV52Qv/+UsL7jzVZ0VzMecWj89kMl8cLM6MYvl1V72uZUsL7j1+fZV1HfrGvf/vpz54Ha7fivYe1X0yY64Ucjeq4Ib7bpQX/c1rrViPrgz9/FC9l4Vnpjk/XN73/eq4FZ5w1Lqu3Xt1nWKmA+uKt8tYnl1n6YsyGN5wc31d62mvttXe3DySuaDO0Orc1heXfcd9325T15w+Xp3xXzUtaILVjFf8/zRuXRe3XNSfP5rOptXcHP6hWXM1+fq3aNSmQ/uxGelsXmF53zk3dt85xXcCt6dyXzUdT6sXsp8cGtWX5pXn5Pv2oksL7jq2zuJ+ThXrWUdYpkPrsobxrK88NTF7CSa1+XG3Mok5qOu9fSaGOaDO33+imbzij7JRsuz2LyCO99by5mPnHaWSmc+uLp4OJ3NKzznzU+Xs3kFN+5/LJv5qGv2u5jOfHDzB3e+ffLqvvovSmJ5wa0tdZKYj76qBy9FMR9cbiuXLC88MXR8IssL7nyalcB81DX+ei2C+eDmqh6hbF/V31U93TmM7aua72kWznx8p9SKrzuf+eBW6uoFbF/V3lC/CLavgjvnLkQwH3XVi1cXMh/cOHnPIpYX30l16j80m+UFN558bS7z8Z1KO+XdcczX/HKHKSwvPOu4/zyaF7z5W0HMR13hrJ/BfHBVsnM23VfRJ/P9SLavgsuAxyTzkVNO/WQR88FF+3sXs30Vnoh7SbJ9FdwY3FYyH3XNd/cuYj64XPlzCM2LPn38RDDLC26P7hHMfPTVmjx4DvM1b9d1DsurvY55QSwvuNPyqXnMR12nkZjNfHDzzoLpbF7xnVRxQ/Rk86r5w01HMB/fKbWmLujuIT642eiyxeZV1/3nC8HmFVy+NXQy81HXrttlGPVd7jT6/Q2WF99Jtfv2GeIhecHlkXdvp777XVV1ftswD/HBxadFT3lIXnjyEbsPywtu1apPfdQ1Au/vyXxwo/akIWxe0Scz/8YUNq/gRpsPpzAfOZ3LS8YzX/NGzjg2r9prHzqJzSu4kfLAOOajrn3ZM4r54NYrxwezvOiTmnlwBMsLbmc3GsF89NUe1XU488HNirnDWF54Mip/CMsLLg6c7858XfeL7QOZDy66PdWB7av6nByYFcn2VXCnRfsI5uOckteGLmQ+uAj7KZjtq/DMqCZhbF8FF8bzi5iPunaHskDmg8tDV2eyvDgnzY1b3mJ5NW/RdgbzMacy9IuJzAcXg/KGs7zw7LuzBcsLbg7/tg/zUdfYMr4B8zVv3Orm75767qu6T0bZfLavgjttggKZj5xmr6uC+eDmwbd6s31V17300Qy2r4KbEdsE83XdoFPe3+vx8TUf9oT396h88uo+HXixn4fk1Xz0Cu/3s3189NW0Prz5uz//44N3TPR+19cnLzwxt/zmd9N984KLh94OYT7qOtdqRzAfXFb4eb/v5DOv+lz9o+RVNq+aX132oIf4OKfEhzkNmQ/uRO29+d0c33mFp9S1m330nVdwM6vE+7tKPj7q2vXqT2U+uHh83c3vrvvmxTlpza+0WF5ws+vAsR7mu+eqMbl+H+aDmx1nCZZXe3edn8XyghvRf85nPurK9affZD64fXeDhWxe0SfVMGk+m1fN/86LYD5yqvcTQ5kPLgaURrN5heesvS2WzavmX85NZD7qqk/fTmA+uJo9M4XlRZ/Uy50jWV5wc9g7scxHX+VLz0nmg8spIo7lhadW/5bE8mp+9nAa81FXLVi/hPng1syqpR7yXhX3pOJt77tC3/eq4GpgdSnzcU9p9mpdzHxwu36vYvZeFZ4c+kYpfa/qcsNvTQnzUVe+5FfMfHCrT1gRy6vvVYd5/7kK37zgtrlGMV/fq9bam8t8cLHR+91Z37zae0UqlhfcOtlYMV/XnVqcS33wnn657L0q+mQN2lrM3quCO56SIubrnDM2FzIf3Hhqv2LvVeGZrZIL2XtVcFnP+7/rf3y3rp3yeQHzwcXi+qtpXvTp+z9Xs7ya187Ip77bV6Ps6VXMBxdvPpvD8uq62x7Lo3ldLrbvX8V8XXdm95XM17zj8BVsXnFPag8Ylk3n1eXG2UHZzMc9pTr8VTrzwY0PK9LZvMJznm2azeYVXHXZsJz5qCt3L0hnPrjY1jud5cU9qT3p03iWF9x8yj+e+dhT5fTwEOZr/nXGApYXnt16VxzLC+4c3x3HfNS1PvxlPvPBxZ4tC9i8ok/Wcx2Xs3kFd16uyGI+chqPPpLOfHD7z9vT2bzCs1s+msXmFdyqsz6T+bpu7K9LmQ+usv9YyvKiT+q2NvEsL7jw3xvPfPTVSfs4hPouN5ZtDGV54ZnX3N8N9skLLo30ROajrhXRMpL54PbUiVHsvao+J2s9XMDeq4KLfxbkMx/nlBrjfQfp64MbHz+ey96rwhPvNchj71XB1Z3nVjEfdcVev1XUd7l6slUOy6vPyWsqh+UFt48UrGQ+5lR2SM1mPrhz/vxylheenbp/BcsLbmV2WsF81DWHBS5nPrhKbbmcvVdFn4xr13PYe1Vw+96+OcxHTuPe+SuZDy5aXF/B3qvCM9d9tpK9VwWXk0NXMh91Rd13VzAfXBSrFSwv+mR0/jSb5QVX473/P/r66Ku8dC2L+eD2RmM5ywtPNIjOZnnBzaml2cxHXaNR++XMBzcXLFnO5hXnpLo2PJPNK7hqen8m83FOGZ3uTWc+uBjQOZ3Nq/bsyxlsXsGdZd5/9fVR13xmTjrzwe2DO9JZXpyTKiQzieUFV8+VL2H+/5+rXWOYr/m998SxvPCMlXuSWV5wa3OdVOajrrXwWDzzwdXR6kQ2r7qvp7x99Z1XcNtvTCbzkVOMbr2M+eD24+XL2LzCM6M+zmTzCm4dHZrFfNR1dr+cwXxw+/DtNC/6JHuPS2N5we0j3yxlPvpq/hO1hPngzpdTU1heeOo/29NZXnBzd8ky6rt1nQ0T05gP7hztkc72VdyTmmV+Q9m+Cm5eamkxH/eUVsx33vsoH1/zI797f9fTZ1+F5zj9u7B9FdzOOtOH+agrT08O9BAfXN41YI6H5MU9qcybG+EhecHNy79K5ut7quF945kPrqb+J9FD8mqv+f1RHpJX8yYvRTIfdY1EK5H54MKOuJnDd1/VfQ18chTbV8FFUtYU5iOncXT4ze+a+/rgxpCfbt57+u6r8Kywz99i+yq4vbX9fOajrnw6vwfzwVWPORNYXvTJSX1vkYfkBbeejZzHfPTVtnvGMB/czu4Y4WF5Xc+4cX2yh+QFN4ad6ekhPuqq5LrzPcQHty8tm8bmFfekdsQ/yWxewdXlG6nMxz2lc71pOvVdLvu3WsbmFZ5VdFsam1dwEW2kMB915Q/NlzEf3Ai/PZ3lxT2p8dm4ZSwvuIoIzGA+7lVFo73UB7dPnspgeeFZV6ZksLzg4o0XqI+6dsrXGcwHF/8tW8bmFX2yjcmJbF7BzRafUV/39bJ/CvM1/6FzAptXXfd8eDibV3DLGBHEfNSVq76OYj64TD8awvLqPm2bmUbzgn/fN4n56Ku95emlzAc3vriexPLCU503x7C84Cq7SxjzUddIfj2W+eDOyJHefvnsqzgnrXmzFrN9VfOJiyKZj3NKvLh5DvPBzRfXzWf7qq6b0jaa7aua94uJZT7qmtsrQpkPLrdWSJZXn5NjG7xO87rcqPvTDObrc/WVs709xNc8sPHrLK+u++v5IJYX3Pl15mLmo67dY9VM5oM7d0cFs30VfXKm7Ytn+yq49VPjJOYjp3hiTAzzwY1ZV+LYvgpPPiuS2b4KrnpvTWU+6qps7z/36OuDG50aJdO8bp/Mv3tKllfzdk9QH301wzuEMh/crtgqWV7t5fwWx/KC27WKqY+69h9rY5gPriIj49m86nOy/ZOTPGRewa2Rgx+mvntOiaXGmx7ig9v3DHvNQ+ZV1y0/O57NK7gzfOFb1Efd4n6vMh/cmLRgegDJi3NSbIsIZHk1T/13LPP1udpKzmM+uHP5M+EheeHJiPuep3ldbjdoN5n5qKtGxj/JfHBR/cFENq/okz3q1AI2r+DOrshw5iOnXX9zEPM1H7N8MZtXeFaXb6LYvILLhTtiqe/WNXeGSOaDm5FzYmhet0/GvR8HsrzgxvufhzBf9/Wj5oHMB5eJHUJYXnjqXK9Ilhdc/l4QzXzUta5siGC+5q1HRwf8H0U2fK8= + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-050000.3.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-050000.3.vtu new file mode 100644 index 000000000..95a8f3e8e --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-050000.3.vtu @@ -0,0 +1,34 @@ + + + + + + + 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rN+48ePLizYfvb8sM/10/AYKECBMhSow4CZKkSJMhS448BYqUKFOhSo06DZq0aNOhS48+A4aMGDNhyow5C5asWLNhy449B46cOHPhyo07D568ePPhy8//0E+AICHCRIgSI06CJCnSZMiSI0+BIiXKVKhSo06DJi3adOjSo8+AISPGTJgyY86CJSvWbNiyY8+BIyfOXLhy486DJy/efPjy89f6CRAkRJgIUWLESZAkRZoMWXLkKVCkRJkKVWrUadCkRZsOXXr0GTBkxJgJU2bMWbBkxZoNW3bsOXDkxJkLV27cefDkxZsP37/+fZnnFwGChAgTIUqMOAmSpEiTIUuOPAWKlChToUqNOg2atGjToUuPPgOGjBgzYcqMOQuWrFizYcuOPQeOnDhz4cqNOw+evHjz4fvbItP/1E+AICHCRIgSI06CJCnSZMiSI0+BIiXKVKhSo06DJi3adOjSo8+AISPGTJgyY86CJSvWbNiyY8+BIyfOXLhy486DJy/efPjy87/0EyBIiDARosSIkyBJijQZsuTIU6BIiTIVqtSo06BJizYduvToM2DIiDETpsyYs2DJijUbtuzYc+DIiTMXrty48+DJizcfvvz8jX4CBAkRJkKUGHESJEmRJkOWHHkKFClRpkKVGnUaNGnRpkOXHn0GDBkxZsKUGXMWLFmxZsOWHXsOHDlx5sKVG3cePHnx5sP3b35f5PtFgCAhwkSIEiNOgiQp0mTIkiNPgSIlylSoUqNOgyYt2nTo0qPPgCEjxkyYMmPOgiUr1mzYsmPPgSMnzly4cuPOgycv3nz4/rbE+L/1EyBIiDARosSIkyBJijQZsuTIU6BIiTIVqtSo06BJizYduvToM2DIiDETpsyYs2DJijUbtuzYc+DIiTMXrty48+DJizcfvvz8H/0ECBIiTIQoMeIkSJIiTYYsOfIUKFKiTIUqNeo0aNKiTYcuPfoMGDJizIQpM+YsWLJizYYtO/YcOHLizIUrN+48ePLizYcvP3+rnwBBQoSJECVGnARJUqTJkCVHngJFSpSpUKVGnQZNWrTp0KVHnwFDRoyZMGXGnAVLVqzZsGXHngNHTpy5cOXGnQdPXrz58P3b35d4fxEgSIgwEaLEiJMgSYo0GbLkyFOgSIkyFarUqNOgSYs2Hbr06DNgyIgxE6bMmLNgyYo1G7bs2HPgyIkzF67cuPPgyYs3H76/LTD/X/0ECBIiTIQoMeIkSJIiTYYsOfIUKFKiTIUqNeo0aNKiTYcuPfoMGDJizIQpM+YsWLJizYYtO/YcOHLizIUrN+48ePLizYcvP/9PPwGChAgTIUqMOAmSpEiTIUuOPAWKlChToUqNOg2atGjToUuPPgOGjBgzYcqMOQuWrFizYcuOPQeOnDhz4cqNOw+evHjz4cvP3+knQJAQYSJEiREnQZIUaTJkyZGnQJESZSpUqVGnQZMWbTp06dFnwJARYyZMmTFnwZIVazZs2bHnwJETZy5cuXHnwZMXbz58/+73Bf7/Dz4v2Mg= 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+AQAAAAAAAgAAAAIAL3QAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-050000.pvtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-050000.pvtu new file mode 100644 index 000000000..39e5dff92 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-050000.pvtu @@ -0,0 +1,19 @@ + + + + + + + + + + + + + + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-060000.0.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-060000.0.vtu new file mode 100644 index 000000000..57fa7c9d4 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-060000.0.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAQAAAAEAxFgAAA==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+AQAAAAAAAgAAAAIAhWkAAA==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+AQAAAAAAAgAAAAIAwHIAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-060000.1.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-060000.1.vtu new file mode 100644 index 000000000..523c67a0a --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-060000.1.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAgAAAAIAsXIAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-060000.2.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-060000.2.vtu new file mode 100644 index 000000000..6fb9ec3b6 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-060000.2.vtu @@ -0,0 +1,34 @@ + + + + + + + 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DfRDfCvOQj3jSHzwX9GfV60YP+J7pM/xFdd0e8L0wDx138aS7echHPOkPrsv+vLpvPeB7pg/9P4DkI74X5qE+iCfdzUM+4t11gI4jnXfil9ezeSAf8cthHloHxLfCPOQjvrWezQPVFb+8ns0D+YhfDvNQH8S3wjzkI76V7gfuP4fAH6b7AfK5Bb1unvvPCPhRmod8bqt1dz9AdcUfhvsB8hF/GOahPogfhXnIR/wonYf7zx3wd+k8kM8t6HXz3H9+/j8/5km385DPbbXuzgPVFX8XzgP5iL8L81AfxJPu5iEf8aS7+wE678QfhvsB8hF/GOah4y5+FOYhH/GjcD9AdcUfhvsB8hF/GOahPogfhXnIR/wonAc678TfhfNAPuLvwjy0Dogn3c1DPuJJd+eB6oq/C+eBfMTfhXmoD+JJd/OQj3jSH7yX9xfV9w16wPdMn5W/rK7bA74X5qHrPvGku3nIRzzpD96L+avqvvWA75k+9P+Ak4/4XpiH+iCedDcP+Yh37wPQcaTrbvHL69k8kI/45TAPfe+Lb4V5yEd8az2bB6orfnk9mwfyEb8c5qE+iG+FechHPOkPnsv6VfW60QO+Z/qM/7q6bg/4XpiHjrt40t085COe9AfPRfxNdd96wPdMH/o7AOQjvhfmoT6IJ93NQz7i3XWAjiOdd+KX17N5IB/xy2EeWgfEt8I85CO+tZ7NA9UVv7yezQP5iF8O81AfxLfCPOQjvhXuB+i6W/xhuB8gH/GHYR667hM/CvOQj/hRuB+guuIPw/0A+Yg/DPNQH8SPwjzkI34UzgNdd4u/C+eBfMTfhXnoPoB40t085COedHceqK74u3AeyEf8XZiH+iCedDcP+Ygn3d0P0Hkn/jDcD5CP+MMwDx138aMwD/mIH4X7Aaor/jDcD5CP+MMwD/VB/CjMQz7iR+E80Hkn/i6cB/IRfxfmoXVAPOluHvIRT7o7D1RX/F04D+Qj/i7MQ30QT7qbh3zEP9D1veE+D3D/2QJ+y/W5rX7eYAv4rTTP/acLfDfNQz631br7PADVFb/l+sDvgOQjfivMQ30Q3w3zkI/4bjoP959z4M/TeSCfW9Dr5rn/zIGfp3nI57Zad+eB6oo/D+eBfMSfh3moD+LnYR7yET831wH6HZ/OO/Fbpg/9Dk4+4rfCPHTcxXfDPOQjvmv2gX7Hp7rit0wfug9APuK3wjzUB/HdMA/5iO+G80DnnfjzcB7IR/x5mIfWAfHzMA/5iJ+H80B1xZ+H80A+4s/DPNQH8fMwD/mIn6f7gfvPr9/9mCfd3g+Qzy3odfPcfzaB33wX5iGf22rd3Q9QXfGku/sB8hHv9gF9oA/iN99lechH/GY6D/effeD303kgn1vQ6+ZRn4AfpnnI57Zad+eB6orfD+eBfMTvh3moD+KHYR7yEU+6ux+g80486e5+gHzEu+sA+dBxF7/5LstDPuJJd/cDVFc86e5+gHzEu30gH+qD+M13WR7yEb8ZzgOdd+L3w3kgH/H7YR5aB8QPwzzkI34YzgPVFb8fzgP5iN8P81AfxA/DPOQjnnT3eQC67ha/ZfrQ7+DkI34rzEPXfeK7YR7yEd81+0C/41Nd8VumD/0OSD7it8I81Afx3TAP+YjvhvNA193iz8N5IB/x52Ee+t4XPw/zkI/4eTgPVFf8eTgP5CP+PMxDfRA/D/OQj/i5uQ7Q7/h03onfMn3od3DyEb8V5qHjLr4b5iEf8V2zD/Q7PtUVv2X60PMA5CN+K8xDfRDfDfOQj/huOA903ok/D+eBfMSfh3loHRA/D/OQj/h5OA9UV/x5OA/kI/48zEN9ED8P85CP+Hm4H6DrbvGku/sB8hHv3gcgH7ruE7/5LstDPuJJd/cDVFc86e5+gHzEu30gH+qD+M13WR7yEb8ZzgNdd4vfD+eBfMTvh3noPoD4YZiHfMQPw3mguuL3w3kgH/H7YR7qg/hhmId8xJPu7gfovBNPursfIB/x7jpAPnTcxW++y/KQj3jS3f0A1RVPursfIB/xbh/Ih/ogfvNdlod8xG+G80Dnnfj9cB7IR/x+mIfWAfHDMA/5iB+G80B1xe+H80A+4vfDPNQH8cMwD/mIJ33x7wAc/PrHOr13L75n+vzyN9V1e8D3wjz03rd40t085COe9MU+nPxtdd96wPdMH/p/wMlHfC/MQ30QT7qbh3zEu38HgI4jvXcvfnk9mwfyEb8c5qH3/sS3wjzkI761ns0D1RW/vJ7NA/mIXw7zUB/Et8I85COe9MV1oPVz9brRA75n+vz82+q6PeB7YR467uJJd/OQj3jSF/sw/21133rA90wf+n8AyUd8L8xDfZgXdDcP+Yh31wE6jnTeiV9ez+aBfMQvh3loHRDfCvOQj/jWejYPVHde0N15IB/xy2Ee6sO8oLt5yEd8K9wP0Hv34g/D/QD5iD8M89B73+JHYR7yET8K9wNUV/xhuB8gH/GHYR7qg/hRmId8xI/CeaD37sXfhfNAPuLvwjz0u7940t085COedHceqK74u3AeyEf8XZiH+iCedDcP+Ygn3d0P0Hkn/jDcD5CP+MMwDx138aMwD/mIH4X7Aao7L+jufoB8xB+GeagP84Lu5iEf8aNwHui8E38XzgP5iL8L89A6IJ50Nw/5iCfdnQeqOy/o7jyQj/i7MA/1YV7Q3TzkI570xfsAnb+rvm/QA75n+qz/fXXdHvC9MA9d94kn3c1DPuJJX+zDk6XqvvWA75k+9P+Ak894qV4fnhT4xT6IJ93NQz7jpXr3Aeg40nW3+OX1bB7IR/xymIe+98W3wjzkI761ns0D1RW/vJ7NA/mMl+r14UmBbwHfCvOQz7igL64D/eXqdaMHfM/02f6H6ro94HthHjru4kl385CPeNIX+/D0aXXfesD3TJ+fflddtwd8L8xDfRBPupuHfMS76wAdRzrvxC+vZ/NAPuKXwzy0DohvhXnIR3xrPZsHqit+eT2bB/IRvxzmoT6Ib4V5yEd8K9wP0HW3+MNwP0A+4g/DPHTdJ34U5iEf8aNwP0B1xR+G+wHyGS/V68OTAj8CfhTmIZ/xUr37AHQc6bpb/F04D+Qj/i7MQ/cBxJPu5iEf8aS780B1xd+F80A+46V6fXhS4Bf7IJ50Nw/5jAu6ux+g8078YbgfIB/xh2EeOu7iR2Ee8hE/CvcDVFf8YbgfIB/xh2Ee6oP4UZiHfMSPwnmg8078XTgP5CP+LsxD64B40t085COedHceqK74u3AeyEf8XZiH+iCedDcP+Yh3/w4A/Y5P792L3zJ96Hdw8hG/Feah977Fd8M85CO+a/aBfsenuuK3TB/6HZB8xG+FeagP4rthHvIR3w3ngd67F38ezgP5iD8P89B7f+LnYR7yET8P54Hqij8P54F8xJ+HeagP4udhHvIRPzfXAfodn8478VumD/0OTj7it8I8dNzFd8M85CO+a/ZhXnheYgv4LdOH7gOQj/itMA/1YV7Q3TzkI74bzgOdd+LPw3kgH/HnYR5aB8TPwzzkI34ezgPVnRd0dx7IR/x5mIf6MC/obh7yET8P9wP03r140t39APmId/8OAPnQe9/iN99lechHPOnufoDqiifd3Q+Qj3i3D+RDfRC/+S7LQz7iN8N5oPfuxe+H80A+4vfDPPR3AMQPwzzkI34YzgPVFb8fzgP5iN8P81AfxA/DPOQjnnR3P0DnnXjS3f0A+Yh31wHyoeMufvNdlod8xJPu7geo7rygu/sB8hHv9oF8qA/zgu7mIR/xm+E80Hknfj+cB/IRvx/moXVA/DDMQz7ih+E8UN15QXfngXzE74d5qA/zgu7mIR/xpLvPA9B1t/gt04d+Bycf8VthHrruE98N85CP+K7ZB/odn+qK3zJ9xoXnDbaA3wrzUB/Ed8M85DNeqncfgI4jXXeLPw/ngXzEn4d56Htf/DzMQz7i5+E8UF3x5+E8kM94qV4fnhT4OfDzMA/5jAu6+zwAnXfit0wf+h2cfMRvhXnouIvvhnnIR3zX7AP9jk91xW+ZPvQ8APmI3wrzUB/Ed8M85CO+G84DnXfiz8N5IB/x52EeWgfEz8M85CN+Hs4D1RV/Hs4D+Yg/D/NQH8TPwzzkI34e7gfouls86e5+gHzEu/cByIeu+8RvvsvykI940t39ANUVT7q7HyCf8VK9Pjwp8JvAb77L8pDPeKnefQA6jnTdLX4/nAfyEb8f5qH7AOKHYR7yET8M54Hqit8P54F8xkv1+vCkwA+BH4Z5yGdc0N39AJ134kl39wPkI95dB8iHjrv4zXdZHvIRT7q7H6C64kl39wPkI97tA/lQH8RvvsvykI/4zXAe6LwTvx/OA/mI3w/z0DogfhjmIR/xw3AeqK74/XAeyEf8fpiH+iB+GOYhH/Gk//Sv/1m/AF18E/im6XPwj9V1m8A3wzziL4C/CPOQj/gLsw+f/qm6b03gm6bPL39fXbcJfDPMQ30QfxHmIR/xF+E8iP9N48c86e48kI/4Rb1uHvEHwB80sjzkI550dx6ornjS3XkgH/FuH8iH+iD+oJHlIR/xpC+uA5PfV68bTeCbps/JP1fXbQLfDPPQcZ8UdDcP+Yi/MPuw86y6b03gm6bPyh+q6zaBb4Z5qA/iL8I85CP+IpwHOu8mBd2dB/IR764D5EPrwKSgu3nIRzzp7jxQXfGku/NAPuLdPpAP9UH8QSPLQz7iD8L9gPgb4G/C/QD5iL8J84j/9PrHPOluHvIRT7q7H6C64m/C/QD5iL8J81AfxJPu5iEf8Yt63XkQfwb82etsHshH/FmYR/wv/+3HPOluHvIRT7o7D1RX/NnrbB7IR/xZmIf6IJ50Nw/5iCfd3Q/QeTcp6O5+gHzE34R56LhPCrqbh3zEk+7uB6iu+JtwP0A+4m/CPNQH8aS7echHvLsO0HGk825S0N15IB/xZ2EeWgcmBd3NQz7iSXfngeqKP3udzQP5iD8L81AfxJPu5iEf8aQv3gd4tFJ936AJfNP0aa1U120C3wzz0HWf+IswD/m0CvpiH0aFvjWBb5o+Pz+vrtsEvhnmoT6MCrqbh3zEX4TzQNfd4kl354F8Wiv17gM8KvAHwB80sjzk0yro7jxQ3VFBd+eBfMS7fRgV+nYA/EEjy0M+4klfXAe6z6vXjSbwTdNnXqjbBL4Z5qHj3i3obh7ymRf0xT5srlb3rQl80/QZrlbXbQLfDPNQH8RfhHnIZ7habx2g40jnXbegu/NAPvPn9daBboE/AP6gkeUhn3lBd+eB6oon3Z0H8hmu1uvDZoE/AP6gkeUhn+FqvfsA9D1O193ib8L9APm0VurdB3hU4Bev+8ST7uYhn1ZBd/cDVHdU0N39APmIvwnzUB9GBd3NQz7i3fsAdBzpulv82etsHsintVLvPsCjAr94H0A86W4e8mkVdHceqO6ooLvzQD7iz8I81IdRQXfzkI940t39AJ133YLu7gfIZ/683jrQLfCLx71b0N085DMv6O5+gOqKvwn3A+QzXK3Xh80Cv9gH8aS7echnuFpvHaDjSOddt6C780A+8+f11oFugV9cB7oF3c1DPvOC7s4D1RV/9jqbB/IZrtbrw2aBX+yDeNLdPOQzBL3u8wDi94DfM33od3DyEb8X5hE/AX4S5iEf8ROzD/Q7PtUVv2f60O+A5CN+L8xDfRA/CfOQj/hJOA/i/+3Nj3nS3XkgH/GLet084k+AP3mT5SEf8aS780B1xZPuzgP5iHf7QD7UB/Enb7I85COedPd5ADrvJgXdfR6AfMTvhXnouE8KupuHfMRPzD7Q7/hUV/ye6UP3AchH/F6Yh/ogfhLmIR/xk3Ae6LybFHR3HshHvLsOkA+tA5OC7uYhH/Gku/NAdcWT7s4D+Yh3+0A+1AfxJ2+yPOQj/iTcD4j/L29/zJPu7gfIR/yiXjeP+B3gd95mechHPOnufoDqiifd3Q+Qj3i3D+RDfRC/8zbLQz7id8J5EH8F/FU4D+Qj/irMI37lf/+YJ93NQz7iSXfngeqKvwrngXzEX4V5qA/iSXfzkI940t39AJ13k4Lu7gfIR7y7DpAPHfdJQXfzkI940t39ANUVT7q7HyAf8W4fyIf6IH7nbZaHfMTvhPNA592koLvzQD7ir8I8tA5MCrqbh3zEk+7OA9UVfxXOA/mIvwrzUB/Ek+7mIR/xpLvPA9B1t/g906dVeN5gD/i9MA9d94mfhHnIp1XQ3ecBqO6ooLvPA5CP+L0wD/VhVNDdPOQjfhLOA113iyfdnQfyaa3Uuw/wqMCfAH/yJstDPq2C7s4D1R0VdHceyEe824dRoW8nwJ+8yfKQj3jS3ecB6LzrFnT3eQDymT+vtw50C/wE+EmYh3zmBd19HoDqit8zfeh5APIZrtbrw2aBnwA/CfOQz3C13jpAx5HOu25Bd+eBfObP660D3QJ/AvzJmywP+cwLujsPVFc86e48kM9wtV4fNgv8CfAnb7I85DNcrXcfgL7H6bpbPOnufoB8Wiv17gM8KvA7wO+8zfKQT6ugu/sBqjsq6O5+gHzEu30YFfq2A/zO2ywP+YjfCeeBrrvFX4XzQD6tlXr3AR4V+MX7AOJJd/OQT6ugu/NAdUcF3Z0H8hF/FeahPowKupuHfMST7u4H6LzrFnR3P0A+8+f11oFugd8Bfudtlod85gXd3Q9QXfGku/sB8hmu1uvDZoHfAX7nbZaHfIar9dYBOo503nULujsP5DN/Xm8d6Bb4xXWgW9DdPOQzL+juPFBd8VfhPJDPcLVeHzYL/GIfxJPu5iGfYUFf/DsAv/qXH+v03r34punT+Zfquk3gm2Eeeu9b/EWYh3w6BX2xD9NC35rAN02f9bXquk3gm2Ee6sO0oLt5yEf8RTgP9N69eNLdeSCfDuh189B7f+IPGlke8ukUdHceqO60oLvzQD7i3T5MC307AP6gkeUhH/GkL64Dg7XqdaMJfNP0efKium4T+GaYh477oKC7echH/IXZh/aL6r41gW+aPuNC3SbwzTAP9aFd0N085DN+UW8doONI592goLvzQD7i3XWAfGgdGBR0Nw/5iCfdnQeq2y7o7jyQz/hFvT60C/wB8AeNLA/5jEGvux+g9+7F34T7AfLpgF43D733LZ50Nw/5dAq6ux+gutOC7u4HyEf8TZiH+jAt6G4e8hHv/h0AOo703r34s9fZPJBPB/S6eeh3f/Gku3nIp1PQ3XmgutOC7s4D+Yg/C/NQH6YF3c1DPuJJd/cDdN4NCrq7HyAf8TdhHjrug4Lu5iEf8aS7+wGq2y7o7n6AfMYv6vWhXeAX+9Au6G4e8hm/qLcO0HGk825Q0N15IB/xZ2EeWgcGBd3NQz7iSXfngeq2C7o7D+QzflGvD+0Cv9iHdkF385DPuKAv3gdYe1l936AJfNP06b+srtsEvhnmoes+8RdhHvLpF/TFPjx+Vd23JvBN02f7VXXdJvDNMA/1QfxFmId8tl/Vuw9Ax5Guu8WT7s4D+fRf1rsPsFbgD4A/aGR5yKdf0N15oLriSXfngXy2X9Xrw+MCfwD8QSPLQz7bBX1xHbgsrBtN4Jumz9M/VtdtAt8M89Bxvyzobh7yEX9h9uHoj9V9awLfNH3+v3+bPuKaYR7qw1FBd/OQj/iLcB7ovLss6O48kI94dx24LKwbB8AfNLI85COedHceqO5RQXfngXzEu30gH+rDUUF385CP+INwP0DX3eJvwv0A+fRf1rsPsFbgF6/7xJPu5iGffkF39wNUV/xNuB8gn+1X9frwuMAv9kE86W4e8tl+Ve8+AB1Huu4Wf/Y6mwfy6b+sdx9grcAv3gcQT7qbh3z6Bd2dB6or/ux1Ng/ks/2qXh8eF/jFPogn3c1DPtsF3d0P0Hl3WdDd/QD5iL8J89Bxvyzobh7yEU+6ux+gukcF3d0PkI/4mzAP9eGooLt5yEe8uw5cFtaNM+DPXmfzQD7iz8I8tA5cFnQ3D/mIJ92dB6p7VNDdeSAf8WdhHurDUUF385CPePfvANDv+PTevfg906dTeN5gD/i9MA+99y1+EuYhn05Bd58HoLrTgu4+D0A+4vfCPNSHaUF385CP+Ek4D/TevXjS3Xkgnw7odfPQe3/iT95kecinU9DdeaC604LuzgP5iHf7MC307QT4kzdZHvIRT7r7PACdd4OC7j4PQD7i98I8dNwHBd3NQz7iJ2Yf2oXnJfaA3zN9xoW6e8DvhXmoD+2C7uYhn/GLeusAHUc67wYF3Z0H8hHvrgPkQ+vAoKC7echHPOnuPFDddkF354F8xi/q9aFd4E+AP3mT5SGfMeh19wP03r140t39APl0QK+bh977Fr/zNstDPp2C7u4HqO60oLv7AfIR7/ZhWujbDvA7b7M85CN+J5wHeu9e/FU4D+TTAb1uHvo7AOJJd/OQT6egu/NAdacF3Z0H8hF/FeahPkwLupuHfMST7u4H6LwbFHR3P0A+4t11gHzouA8KupuHfMST7u4HqG67oLv7AfIZv6jXh3aB3wF+522Wh3zGL+qtA3Qc6bwbFHR3HshH/FWYh9aBQUF385CPeNLdeaC67YLuzgP5jF/U60O7wC/2oV3Q3TzkMy7o7vMAdN0tfs/06ReeN9gDfi/MQ9d94idhHvLpF3T3eQCqK37P9NkuPG+wB/xemIf6IH4S5iGf7Vf17gPQcaTrbvGku/NAPv2X9e4DrBX4E+BP3mR5yKdf0N15oLriSXfngXy2X9Xrw+MCfwL8yZssD/lsF3T3eQA67y4Luvs8APmI3wvz0HG/LOhuHvIRPzH7cFR4XmIP+D3Th54HIB/xe2Ee6sNRQXfzkI/4STgPdN5dFnR3HshHvLsOXBbWjRPgT95kechHPOnuPFDdo4LuzgP5iHf7QD7Uh6OC7uYhH/En4X6ArrvFk+7uB8in/7LefYC1Ar8D/M7bLA/59Au6ux+guuJJd/cD5LP9ql4fHhf4HeB33mZ5yGf7Vb37AHQc6bpb/FU4D+TTf1nvPsBagV+8DyCedDcP+fQLujsPVFf8VTgP5LP9ql4fHhf4xT6IJ93NQz7bBd3dD9B5d1nQ3f0A+Yh314HLwrqxA/zO2ywP+Ygn3d0PUN2jgu7uB8hHvNsH8qE+HBV0Nw/5iN8J54HOu8uC7s4D+Yi/CvPQOnBZ0N085COedHceqO5RQXfngXzEX4V5qA9HBd3NQz7iH+i/+7F+8btqvgd8z/Q5+Mfquj3ge2Ee8Y/Wf8yT7uYhH/GkL/bh0z9V960HfM/0of8HnHzE98I81AfxpLt5yEf8ol53HsQvA7+8ns0D+YhfDvOIbwHfCvOQj/jWejYPVFf88no2D+QjfjnMQ30Q3wrzkI940hfXgcnvq9eNHvA90+fkn6vr9oDvhXnouE8KupuHfMSTvtiHnWfVfesB3zN96P8BJB/xvTAP9UE86W4e8hHvrgN0HOm8mxR0dx7IR/xymIfWgUlBd/OQj/jWejYPVFf88no2D+QjfjnMQ30Q3wrzkI/4VrgfEH8I/GG4HyAf8YdhHvEj4EdhHvIRPwr3A1RX/GG4HyAf8YdhHuqD+FGYh3zEj8J5EH8H/F04D+Qj/i7MI/7n//NjnnQ3D/mIJ92dB6or/i6cB/IRfxfmoT6IJ93NQz7iSXf3A3TeTQq6ux8gH/GHYR467pOC7uYhH/GjcD9AdcUfhvsB8hF/GOahPogfhXnIR/wonAc67yYF3Z0H8hF/F+ahdWBS0N085COedHceqK74u3AeyEf8XZiH+iCedDcP+YgnffE+wKOV6vsGPeB7pk9rpbpuD/hemIeu+8ST7uYhn1ZBX+zDqNC3HvA904f+H3DyEd8L81AfRgXdzUM+4t37AHQc6bpb/PJ6Ng/k01qpdx/gUYFvAd8K85BPq6C780B1RwXdnQfyEb8c5qE+jAq6m4d8xJO+uA50n1evGz3ge6bPvFC3B3wvzEPHvVvQ3TzkMy/oi33YXK3uWw/4nulDfweAfIar9fqwWeAX+yCedDcP+QxX660DdBzpvOsWdHceyGf+vN460C3wLeBbYR7ymRd0dx6orvjl9WweyGe4Wq8PmwW+BXwrzEM+w9V69wHoe5yuu8UfhvsB8mmt1LsP8KjAj4AfhXnIp1XQ3f0A1R0VdHc/QD7iD8M81IdRQXfzkI/4UTgPdN0t/i6cB/JprdS7D/CowC/eBxBPupuHfFoF3Z0Hqjsq6O48kI/4uzAP9WFU0N085COedHc/QOddt6C7+wHymT+vtw50C/wI+FGYh3zmBd3dD1Bd8YfhfoB8hqv1+rBZ4EfAj8I85DNcrbcO0HGk865b0N15IJ/583rrQLfAL64D3YLu5iGfeUF354Hqir8L54F8hqv1+rBZ4Bf7IJ50Nw/5DEGv+zyA+C3gt0wf+h2cfMRvhXnEd4HvhnnIR3zX7AP9jk91xW+ZPvQ7IPmI3wrzUB/Ed8M85CO+G86D+HPgz8N5IB/x52Ee8XPg52Ee8hE/D+eB6oo/D+eBfMSfh3moD+LnYR7yET8314FJ4XmhLeC3TB/6HZx8xG+Feei4Twq6m4d8xHfNPtDv+FRX/JbpQ/cByEf8VpiH+iC+G+YhH/HdcB7ovJsUdHceyEf8eZiH1oFJQXfzkI/4eTgPVFf8eTgP5CP+PMxDfRA/D/OQj/h5uB8Q/+t3P+ZJd/cD5CN+Ua+bR/wm8JvvsjzkI550dz9AdcWT7u4HyEe82wfyoT6I33yX5SEf8ZvhPIjfB34/nAfyEb8f5hE/BH4Y5iEf8cNwHqiu+P1wHshH/H6Yh/ogfhjmIR/xpLv7ATrvJgXd3Q+Qj3h3HSAfOu6Tgu7mIR/xpLv7AaornnR3P0A+4t0+kA/1QfzmuywP+YjfDOeBzrtJQXfngXzE74d5aB2YFHQ3D/mIH4bzQHXF74fzQD7i98M81AfxwzAP+Ygn3X0egK67xW+ZPq3C8wZbwG+Feei6T3w3zEM+rYLuPg9AdUcF3X0egHzEb4V5qA+jgu7mIR/x3XAe6Lpb/Hk4D+TTWql3H+BRgZ8DPw/zkE+roLvzQHVHBd2dB/IRfx7moT6MCrqbh3zEz811oFt4XmgL+C3TZ16ouwX8VpiHjnu3oLt5yGde0N3nAaiu+C3Th54HIJ/har0+bBb4LvDdMA/5DFfrrQN0HOm86xZ0dx7IZ/683jrQLfBz4OdhHvKZF3R3Hqiu+PNwHshnuFqvD5sFfg78PMxDPsPVevcB6HucrrvFk+7uB8intVLvPsCjAr8J/Oa7LA/5tAq6ux+guqOC7u4HyEe824dRoW+bwG++y/KQj/jNcB7oulv8fjgP5NNaqXcf4FGBHwI/DPOQT6ugu/NAdUcF3Z0H8hG/H+ahPowKupuHfMST7u4H6LzrFnR3P0A+8+f11oFugd8EfvNdlod85gXd3Q9QXfGku/sB8hmu1uvDZoHfBH7zXZaHfIar9dYBOo503nULujsP5DN/Xm8d6Bb4IfDDMA/5zAu6Ow9UV/x+OA/kM1yt14fNAj8EfhjmIZ9hQV/8OwC/gv9Hnt67F98zfTr/Ul23B3wvzEPvfYsn3c1DPp2CvtiHaaFvPeB7pg/9P+DkI74X5qE+TAu6m4d8xLt/B4COI713L355PZsH8umAXjcPvfcnvhXmIZ9OQXfngepOC7o7D+QjfjnMQ32YFnQ3D/mIJ31xHRisVa8bPeB7ps+TF9V1e8D3wjx03AcF3c1DPuJJX+xD+0V133rA90yfcaFuD/hemIf60C7obh7yGb+otw7QcaTzblDQ3XkgH/HLYR5aBwYF3c1DPuJb69k8UN12QXfngXzGL+r1oV3gW8C3wjzkMwa97n6A3rsXfxjuB8inA3rdPPTet/hRmId8OgXd3Q9Q3WlBd/cD5CP+MMxDfZgWdDcP+YgfhfNA792LvwvngXw6oNfNQ7/7iyfdzUM+nYLuzgPVnRZ0dx7IR/xdmIf6MC3obh7yEU+6ux+g825Q0N39APmIPwzz0HEfFHQ3D/mIH4X7AarbLujufoB8xi/q9aFd4EfAj8I85DN+UW8doONI592goLvzQD7i78I8tA4MCrqbh3zEk+7OA9VtF3R3Hshn/KJeH9oFfrEP7YLu5iGfcUFfvA+w9rL6vkEP+J7p039ZXbcHfC/MQ9d94kl385BPv6Av9uHxq+q+9YDvmT70/4CTz/aren14XOAX+yCedDcP+Wy/qncfgI4jXXeLX17P5oF8+i/r3QdYK/At4FthHvLpF3R3Hqiu+OX1bB7IZ/tVvT48LvAt4FthHvLZLuiL68BlYd3oAd8zfZ7+sbpuD/hemIeO+2VBd/OQj3jSF/tw9MfqvvWA75k+P/1rdd0e8L0wD/XhqKC7echHvLsOXBbWjWXgl9ezeSAf8cthHloHLgu6m4d8xLfWs3mgukcF3Z0H8hG/HOahPhwVdDcP+YhvhfsBuu4WfxjuB8in/7LefYC1Aj8CfhTmIZ9+QXf3A1RX/GG4HyCf7Vf1+vC4wI+AH4V5yGf7Vb37AHQc6bpb/F04D+TTf1nvPsBagV+8DyCedDcP+fQLujsPVFf8XTgP5LP9ql4fHhf4xT6IJ93NQz7bBd3dD9B5d1nQ3f0A+Yg/DPPQcb8s6G4e8hE/CvcDVPeooLv7AfIRfxjmoT4cFXQ3D/mIH4XzQOfdZUF354F8xN+FeWgduCzobh7yEU+6Ow9U96igu/NAPuLvwjzUh6OC7uYhH/Hu3wGg3/HpvXvxW6ZPp/C8wRbwW2Eeeu9bfDfMQz6dgu4+D0B1pwXdfR6AfMRvhXmoD9OC7uYhH/HdcB7ovXvx5+E8kE8H9Lp56L0/8fMwD/l0Cro7D1R3WtDdeSAf8edhHurDtKC7echH/NxcBwaF54W2gN8yfeh3cPIRvxXmoeM+KOhuHvIR3zX70C48L7EF/JbpMy7U3QJ+K8xDfWgXdDcP+Yxf1FsH6DjSeTco6O48kI/48zAPrQODgu7mIR/x83AeqG67oLvzQD7jF/X60C7wc+DnYR7yGYNedz9A792LJ93dD5BPB/S6eei9b/Gb77I85NMp6O5+gOpOC7q7HyAf8W4fpoW+bQK/+S7LQz7iN8N5oPfuxe+H80A+HdDr5qG/AyB+GOYhn05Bd+eB6k4LujsP5CN+P8xDfZgWdDcP+Ygn3d0P0Hk3KOjufoB8xLvrAPnQcR8UdDcP+Ygn3d0PUN12QXf3A+QzflGvD+0Cvwn85rssD/mMX9RbB+g40nk3KOjuPJCP+P0wD60Dg4Lu5iEf8cNwHqhuu6C780A+4xf1+tAu8EPgh2Ee8hkXdPd5ALruFr9l+vQLzxtsAb8V5qHrPvHdMA/59Au6+zwA1RW/ZfpsF5432AJ+K8xDfRDfDfOQz/arevcB6DjSdbf483AeyKf/st59gLUCPwd+HuYhn35Bd+eB6oo/D+eBfLZf1evD4wI/B34e5iGf7YLuPg9A591lQXefByAf8VthHjrulwXdzUM+4rtmH44Kz0tsAb9l+tDzAOQjfivMQ304KuhuHvIR3w3ngc67y4LuzgP5iD8P89A6cFnQ3TzkI34ezgPVPSro7jyQj/jzMA/14aigu3nIR/w83A/Qdbd40t39APn0X9a7D7BW4DeB33yX5SGffkF39wNUVzzp7n6AfLZf1evD4wK/CfzmuywP+Wy/qncfgI4jXXeL3w/ngXz6L+vdB1gr8EPgh2Ee8ukXdHceqK74/XAeyGf7Vb0+PC7wQ+CHYR7y2S7o7n6AzrvLgu7uB8hHvLsOXBbWjU3gN99lechHPOnufoDqHhV0dz9APuLdPpAP9eGooLt5yEf8ZjgPdN5dFnR3HshH/H6Yh9aBy4Lu5iEf8cNwHqjuUUF354F8xO+HeagPRwXdzUM+4h/o958H+n/8oZKfAT9zfW7/UFl3BvwszXP/+dX7H/Ok23nI57Zaf/D/MvzJSmXfZsDPXJ8/XamsOwN+FuahPogn3c1DPuIf6HXn4f6zAfzG+3AeyOcW9Lp57j8d4DtpHvK5rdbdeaC64jfeZ/NAPuI3wjzUB/GdMA/5iCf9wf8L/2fV68YM+JnpM/xFdd0Z8LMwDx138aS7echHPOkPfpf/8+q+zYCfmT4//dfqujPgZ2Ee6oN40t085CPeXQfoONJ5J37jfTYP5CN+I8xD64D4TpiHfMR33mfzQHXFb7zP5oF8xG+EeagP4jthHvIR30n3A/efU+BP0/0A+dyCXjfP/WcK/DTNQz631bq7H6C64k/D/QD5iD8N81AfxE/DPOQjfprOw/3nv334MU+6PQ/kcwt63Tz3n3Xg1z+Eecjntlp354HqiifdnQfyEe/2AX2gD+LXP2R5yEc86e5+gM478afhfoB8xJ+Geei4i5+GechH/DTcD1Bd8afhfoB8xJ+GeagP4qdhHvIRPw3ngc478aS780A+4t11gHxoHRC//iHLQz7iSXfngeqKJ92dB/IR7/aBfKgP4tc/ZHnIRzzpi/cBfvkX1fcNZsDPTJ+Vv6yuOwN+Fuah6z7xpLt5yEc86Yt9+Pmvqvs2A35m+gz/e3XdGfCzMA/1QTzpbh7yEe/eB6DjSNfd4jfeZ/NAPuI3wjz0vS++E+YhH/Gd99k8UF3xG++zeSAf8RthHuqD+E6Yh3zEk/7gveBfVa8bM+Bnps/4r6vrzoCfhXnouIsn3c1DPuJJf/Bc1t9U920G/Mz0+el/VNedAT8L81AfxJPu5iEf8e46QMeRzjvxG++zeSAf8RthHloHxHfCPOQjvvM+mweqK37jfTYP5CN+I8xDfRDfCfOQj/hOuB+g627xp+F+gHzEn4Z56LpP/DTMQz7ip+F+gOqKPw33A+Qj/jTMQ30QPw3zkI/4aTgPdN0tnnR3HshHvHsfgHzoPoD49Q9ZHvIRT7o7D1RXPOnuPJCPeLcP5EN9EL/+IctDPuJJd/cDdN6JPw33A+Qj/jTMQ8dd/DTMQz7ip+F+gOqKPw33A+Qj/jTMQ30QPw3zkI/4aTgPdN6JJ92dB/IR764D5EPrgPj1D1ke8hFPujsPVFc86e48kI94tw/kQ30Qv/4hy0M+4tfT5wHuP7vA76bPA5DPLeh189x/BsAP0jzkc1utu88DUF3xu+HzAOQjfjfMQ30QPwjzkI/4QToP959r4K/TeSCfW9Dr5rn/PPn3H/Ok23nI57Zad+eB6oq/DueBfMRfh3moD+JJd/OQj3jS3ecB6LwTvxs+D0A+4nfDPHTcxQ/CPOQjfhA+D0B1xe+GzwOQj/jdMA/1QfwgzEM+4gfhPNB5J/46nAfyEX8d5qF1QDzpbh7yEU+6Ow9UV/x1OA/kI/46zEN9EE+6m4d8xD/Q6+4H7j8N4Buuz231fqMBfCPNc/9pA99O85DPbbXu7georviG6/On1fuNBvCNMA/1QXw7zEM+4tvpPNx/joE/TueBfG5Br5tH6wTw4zQP+dxW6+48UF3xx+E8kI/44zAP9UH8OMxDPuLH5jpA3+N03olvmD70PUg+4hthHjru4tthHvIR3zb7QN/jVFd8w/Sh/QD5iG+EeagP4tthHvIR3w7ngc478cfhPJCP+OMwD60D4sdhHvIRPw7ngeqKPw7ngXzEH4d5qA/ix2Ee8hFPuvs8AF13i98NnwcgH/G7YR667hM/CPOQj/hB+DwA1RW/Gz4PQD7id8M81AfxgzAP+YgfhPNA193ir8N5IB/x12Ee+t4XT7qbh3zEk+7OA9UVfx3OA/mIvw7zUB/Ek+7mIR/xpLvPA9B5J343fB6AfMTvhnnouIsfhHnIR/wgfB6A6orfDZ8HIB/xu2Ee6oP4QZiHfMQPwnmg8078dTgP5CP+OsxD64B40t085COedHceqK7463AeyEf8dZiH+iCedDcP+Yh37wPQ9zhdd4tvmD70PUg+4hthHrruE98O85CP+LbZB/oep7riG6YPrQPkI74R5qE+iG+HechHfDucB7ruFn8czgP5iD8O89B9APHjMA/5iB+H80B1xR+H80A+4o/DPNQH8eMwD/mIH5vrAH2P03knvmH60Pcg+YhvhHnouItvh3nIR3zb7AN9j1Nd8Q3Th/YD5CO+EeahPohvh3nIR3w7nAc678Qfh/NAPuKPwzy0Dogfh3nIR/w4nAeqK/44nAfyEX8c5qE+iB+HechHPOmLfwfg4Nc/1um9e/Ez0+eXv6muOwN+Fuah977Fk+7mIR/xpC/24eRvq/s2A35m+qz8z+q6M+BnYR7qg3jS3TzkI979OwB0HOm9e/Eb77N5IB/xG2Eeeu9PfCfMQz7iO++zeaC64jfeZ/NAPuI3wjzUB/GdMA/5iCd9cR1o/Vy9bsyAn5k+P/+2uu4M+FmYh467eNLdPOQjnvTFPsx/W923GfAz02f4v6rrzoCfhXmoD/OC7uYhH/HuOkDHkc478Rvvs3kgH/EbYR5aB8R3wjzkI77zPpsHqjsv6O48kI/4jTAP9WFe0N085CO+E+4H6L178afhfoB8xJ+Geei9b/HTMA/5iJ+G+wGqK/403A+Qj/jTMA/1Qfw0zEM+4qfhPNB79+JJd+eBfMS7fweAfOh3f/HrH7I85COedHceqK540t15IB/xbh/Ih/ogfv1Dlod8xJPu7gfovBN/Gu4HyEf8aZiHjrv4aZiHfMRPw/0A1Z0XdHc/QD7iT8M81Id5QXfzkI/4aTgPdN6JJ92dB/IR764D5EPrgPj1D1ke8hFPujsPVHde0N15IB/xbh/Ih/owL+huHvIRT/rifYDO31XfN5gBPzN91v++uu4M+FmYh677xJPu5iEf8aQv9uHJUnXfZsDPTJ/xUnXdGfCzMA/1QTzpbh7yGS/Vuw9Ax5Guu8VvvM/mgXzEb4R56HtffCfMQz7iO++zeaC64jfeZ/NAPuOlen14UuA7wHfCPOQzLuiL60B/uXrdmAE/M322/6G67gz4WZiHjrt40t085COe9MU+PH1a3bcZ8DPT56ffVdedAT8L81AfxJPu5iEf8e46QMeRzjvxG++zeSAf8RthHloHxHfCPOQjvvM+mweqK37jfTYP5CN+I8xDfRDfCfOQj/hOuB+g627xp+F+gHzEn4Z56LpP/DTMQz7ip+F+gOqKPw33A+QzXqrXhycFfgr8NMxDPuOlevcB6DjSdbd40t15IB/x7n0A8qH7AOLXP2R5yEc86e48UF3xpLvzQD7jpXp9eFLg14Ff/5DlIZ9xQXf3A3TeiT8N9wPkI/40zEPHXfw0zEM+4qfhfoDqij8N9wPkI/40zEN9ED8N85CP+Gk4D3TeiSfdnQfyEe+uA+RD64D49Q9ZHvIRT7o7D1RXPOnuPJCPeLcP5EN9EL/+IctDPuLXw+cB6L178bvh8wDkI343zEPvfYsfhHnIR/wgfB6A6orfDZ8HIB/xu2Ee6oP4QZiHfMQPwnmg9+7FX4fzQD7ir8M89N6feNLdPOQjnnR3Hqiu+OtwHshH/HWYh/ognnQ3D/mIJ919HoDOO/G74fMA5CN+N8xDx138IMxDPuIH4fMAVHde0N3nAchH/G6Yh/owL+huHvIRPwjngc478dfhPJCP+OswD60D4kl385CPeNLdeaC684LuzgP5iL8O81Af5gXdzUM+4t2/A0Df4/TevfiG6UPfg+QjvhHmofe+xbfDPOQjvm32gb7Hqa74hulD6wD5iG+EeagP4tthHvIR3w7ngd67F38czgP5iD8O89DfARA/DvOQj/hxOA9UV/xxOA/kI/44zEN9ED8O85CP+LG5DtD3OJ134humD30Pko/4RpiHjrv4dpiHfMS3zT7MC/ulBvAN04f2A+QjvhHmoT7MC7qbh3zEt8N5oPNO/HE4D+Qj/jjMQ+uA+HGYh3zEj8N5oLrzgu7OA/mIPw7zUB/mBd3NQz7iSXefB6DrbvG74fMA5CN+N8xD133iB2Ee8hE/CJ8HoLrid8PnAchnvFSvD08K/AD4QZiHfMZL9e4D0HGk627x1+E8kI/46zAPfe+LJ93NQz7iSXfngeqKvw7ngXzGS/X68KTAL/ZBPOluHvIZF3T3eQA678Tvhs8DkI/43TAPHXfxgzAP+YgfhM8DUF3xu+HzAOQjfjfMQ30QPwjzkI/4QTgPdN6Jvw7ngXzEX4d5aB0QT7qbh3zEk+7OA9UVfx3OA/mIvw7zUB/Ek+7mIR/x7n0A+h6n627xDdOHvgfJR3wjzEPXfeLbYR7yEd82+0Df41RXfMP0GRf2Gw3gG2Ee6oP4dpiHfMZL9e4D0HGk627xx+E8kI/44zAP3QcQPw7zkI/4cTgPVFf8cTgP5DNeqteHJwV+DPw4zEM+44Lu7gfovBPfMH3oe5B8xDfCPHTcxbfDPOQjvm32gb7Hqa74hulD+wHyEd8I81AfxLfDPOQjvh3OA5134o/DeSAf8cdhHloHxI/DPOQjfhzOA9UVfxzOA/mIPw7zUB/Ej8M85CP+ga65WNT/4w+V/C82fsyT/sDn9g+VdR/43IJeN8/9Zw34tY0wD/ncVusPfpf5k5XKvi3WFU/62Px/wMlHvNsH9IE+iF/byPKQj/i1dB7uP1+A/5LOA/ncgl43j9Zp4PtpHvK5rdbdeaC64r+E80A+4r+EeagP4vthHvIRT/qD54L+rHrdWDzvxJP+YJ35RXXdB+f1L+qtA+RDx1382kaWh3zEk/7guuzPq/u2WFc86WPz/wEkH/FuH8iH+iB+bSPLQz7i18J5oPNO/JdwHshH/JcwD60D4vthHvIR3w/ngeqK/xLOA/mI/xLmoT6I74d5yEd8P90P3H++A/893Q+Qzy3odfPcfx5//DFPup2HfG6rdXc/QHXFfw/3A+Qj/nuYh/ognnQ3D/mIf6DXnYf7zzPgn30M54F8bkGvm0f3IYDfTvOQz2217s4D1RX/7GM2D+Qj/lmYh/ogfjvMQz7iSXf3A3Teif8e7gfIR/z3MA8dd/Gku3nIRzzp7n6A6or/Hu4HyEf89zAP9UE86W4e8hHvrgN0HOm8E//sYzYP5CP+WZiH1gHx22Ee8hG//TGbB6or/tnHbB7IR/yzMA/1Qfx2mId8xJP+4L28v6i+b7B43S2e9Af3ef6yuu6D+yp/We8+APnQdZ/4tY0sD/mIJ/3BezF/Vd23xbriSR+b/w84+Yh3+0A+1AfxaxtZHvIRvxbOA113i/8SzgP5iP8S5qHvffH9MA/5iO+H80B1xX8J54F8xH8J81AfxPfDPOQjnvQHz2X9qnrdWDzvxJP+4D7zX1fXfXBf96/rrQPkQ8dd/NpGlod8xJP+4LmIv6nu22Jd8aSPzb8DQD7i3T6QD/VB/NpGlod8xK+F80Dnnfgv4TyQj/gvYR5aB8T3wzzkI74fzgPVFf8lnAfyEf8lzEN9EN8P85CP+H64H6DrbvHfw/0A+Yj/Huah6z7xpLt5yEc86e5+gOqK/x7uB8hH/PcwD/VBPOluHvIR794HoONI193in33M5oF8xD8L89B9APHbYR7yEb/9MZsHqiv+2cdsHshH/LMwD/VB/HaYh3zEk+7uB+i8E/893A+Qj/jvYR467uJJd/OQj3jS3f0A1RX/PdwPkI/472Ee6oN40t085CPeXQfoONJ5J/7Zx2weyEf8szAPrQPit8M85CN++2M2D1RX/LOP2TyQj/hnYR7qg/jtMA/5iN9Onwe4/3wF/mv6PAD53IJeN8/95xL4yzQP+dxW6+7zAFRX/NfweQDyEf81zEN9EH8Z5iEf8ZfpPNx//p9PP+ZJt+eBfG5Br5vn/vMU+Kefwjzkc1utu/NAdcWT7s4D+Yh3+4A+0AfxTz9lechHPOnu8wB03on/Gj4PQD7iv4Z56LiLvwzzkI/4y/B5AKor/mv4PAD5iP8a5qE+iL8M85CP+MtwHui8E0+6Ow/kI95dB8iH1gHxTz9lechHPOnuPFBd8aS780A+4t0+kA/1QfzTT1ke8hH/NN0P3H8+A/853Q+Qzy3odfPcf46AP0rzkM9tte7uB6iu+M/hfoB8xH8O81AfxB+FechH/FE6D/efb8B/S+eBfG5Br5tHn//7Y550Ow/53Fbr7jxQXfHfwnkgH/HfwjzUB/Gku3nIRzzp7n6Azjvxn8P9APmI/xzmoeMu/ijMQz7ij8L9ANUV/zncD5CP+M9hHuqD+KMwD/mIPwrngc478d/CeSAf8d/CPLQOiCfdzUM+4kl354Hqiv8WzgP5iP8W5qE+iCfdzUM+4kl3nweg627xX8PnAchH/NcwD133ib8M85CP+MvweQCqK/5r+DwA+Yj/GuahPoi/DPOQj/jLcB7ouls86e48kI949z4A+dD3vvinn7I85COedHceqK540t15IB/xbh/Ih/og/umnLA/5iCfdfR6AzjvxX8PnAchH/NcwDx138ZdhHvIRfxk+D0B1xX8NnwcgH/FfwzzUB/GXYR7yEX8ZzgOdd+JJd+eBfMS76wD50Dog/umnLA/5iCfdnQeqK550dx7IR7zbB/KhPoh/+inLQz7in4b7AbruFv853A+Qj/jPYR667hN/FOYhH/FH4X6A6or/HO4HyEf85zAP9UH8UZiHfMQfhfNA193iv4XzQD7iv4V56D6AeNLdPOQjnnR3Hqiu+G/hPJCP+G9hHuqDeNLdPOQjnnR3P0DnnfjP4X6AfMR/DvPQcRd/FOYhH/FH4X6A6or/HO4HyEf85zAP9UH8UZiHfMQfhfNA5534b+E8kI/4b2EeWgfEk+7mIR/xpLvzQHXFfwvngXzEfwvzUB/Ek+7mIR/xpC/+HYCDX/9Yp/fuxZP+4O/8/Ka67oO/q/ObH+t189B73+LXNrI85COe9MU+nPxtdd8W64onfWz+P+DkI97tA/lQH8SvbWR5yEf8WjgP9N69+C/hPJCP+C9hHnrvT3w/zEM+4vvhPFBd8V/CeSAf8V/CPNQH8f0wD/mIJ31xHWj9XL1uLJ534kl/8HfGfltd98Hf9fptvXWAfOi4i1/byPKQj3jSF/sw/2113xbrzgv62Px/AMlHvNsH8qE+zAu6m4d8xK+F80Dnnfgv4TyQj/gvYR5aB8T3wzzkI74fzgPVnRd0dx7IR/yXMA/1YV7Q3TzkI74f7gfovXvx38P9APmI/x7mofe+xZPu5iEf8aS7+wGqK/57uB8gH/HfwzzUB/Gku3nIR7z7dwDoONJ79+KffczmgXzEPwvz0O/+4rfDPOQjfvtjNg9UV/yzj9k8kI/4Z2Ee6oP47TAP+Ygn3d0P0Hkn/nu4HyAf8d/DPHTcxZPu5iEf8aS7+wGqOy/o7n6AfMR/D/NQH+YF3c1DPuLddYCOI5134p99zOaBfMQ/C/PQOiB+O8xDPuK3P2bzQHXnBd2dB/IR/yzMQ32YF3Q3D/mIJ33xPkDn76rvGyxed4sn/cHfef776roP/q7y39e7D0A+dN0nfm0jy0M+4klf7MOTpeq+LdYVT/rY/H/AyWe8VK8PTwr8GvBrG1ke8hkv1bsPQMeRrrvFfwnngXzEfwnz0Pe++H6Yh3zE98N5oLriv4TzQD7jpXp9eFLg+8D3wzzkMy7oi+tAf7l63Vg878ST/uDvzP9Ddd0Hf9f9H+qtA+RDx1382kaWh3zEk77Yh6dPq/u2WFc86Q9+x/9ddd0Hv5v/rl4fyIf6IH5tI8tDPuLXwnmg8078l3AeyEf8lzAPrQPi+2Ee8hHfD+eB6or/Es4D+Yj/EuahPojvh3nIR3w/3A/Qdbf47+F+gHzEfw/z0HWfeNLdPOQjnnR3P0B1xX8P9wPkM16q14cnBX6xD+JJd/OQz3ip3n0AOo503S3+2cdsHshH/LMwD90HEL8d5iEf8dsfs3mguuKffczmgXzGS/X68KTAbwO/HeYhn3FBd/cDdN6J/x7uB8hH/PcwDx138aS7echHPOnufoDqiv8e7gfIR/z3MA/1QTzpbh7yEe+uA3Qc6bwT/+xjNg/kI/5ZmIfWAfHbYR7yEb/9MZsHqiv+2cdsHshH/LMwD/VB/HaYh3zEb4fPA9B79+K/hs8DkI/4r2Eeeu9b/GWYh3zEX4bPA1Bd8V/D5wHIR/zXMA/1QfxlmId8xF+G80Dv3Ysn3Z0H8hHv/h0A8qH3/sQ//ZTlIR/xpLvzQHXFk+7OA/mId/tAPtQH8U8/ZXnIRzzp7vMAdN6J/xo+D0A+4r+Geei4i78M85CP+MvweQCqOy/o7vMA5CP+a5iH+jAv6G4e8hF/Gc4DnXfiSXfngXzEu+sA+dA6IP7ppywP+Ygn3Z0Hqjsv6O48kI94tw/kQ32YF3Q3D/mIfxruB+i9e/Gfw/0A+Yj/HOah977FH4V5yEf8UbgfoLriP4f7AfIR/znMQ30QfxTmIR/xR+E80Hv34r+F80A+4r+FeejvAIgn3c1DPuJJd+eB6or/Fs4D+Yj/FuahPogn3c1DPuJJd/cDdN6J/xzuB8hH/OcwDx138UdhHvIRfxTuB6juvKC7+wHyEf85zEN9mBd0Nw/5iD8K54HOO/HfwnkgH/Hfwjy0Dogn3c1DPuJJd+eB6s4LujsP5CP+W5iH+jAv6G4e8hFPuvs8AF13i/8aPg9APuK/hnnouk/8ZZiHfMRfhs8DUF3xX8PnAchnvFSvD08K/CXwl2Ee8hkv1bsPQMeRrrvFk+7OA/mId+8DkA9974t/+inLQz7iSXfngeqKJ92dB/IZL9Xrw5MC/xT4p5+yPOQzLuju8wB03on/Gj4PQD7iv4Z56LiLvwzzkI/4y/B5AKor/mv4PAD5iP8a5qE+iL8M85CP+MtwHui8E0+6Ow/kI95dB8iH1gHxTz9lechHPOnuPFBd8aS780A+4t0+kA/1QfzTT1ke8hH/NNwP0HW3+M/hfoB8xH8O89B1n/ijMA/5iD8K9wNUV/zncD9APuOlen14UuCPgD8K85DPeKnefQA6jnTdLf5bOA/kI/5bmIfuA4gn3c1DPuJJd+eB6or/Fs4D+YyX6vXhSYFf7IN40t085DMu6O5+gM478Z/D/QD5iP8c5qHjLv4ozEM+4o/C/QDVFf853A+Qj/jPYR7qg/ijMA/5iD8K54HOO/HfwnkgH/Hfwjy0Dogn3c1DPuJJd+eB6or/Fs4D+Yj/FuahPogn3c1DPuJJH777z/oF6OJnwM9Mn4N/rK47A34W5hH/q/c/5kl385CPeNIX+/Dpn6r7NgN+Zvr88vfVdWfAz8I81AfxpLt5yEf8ol53HsRvAL/xPpsH8hG/EeYR3wG+E+YhH/Gd99k8UF3xG++zeSAf8RthHuqD+E6Yh3zEk764Dkx+X71uzICfmT4n/1xddwb8LMxDx31S0N085COe9MU+7Dyr7tsM+Jnps/KH6roz4GdhHuqDeNLdPOQj3l0H6DjSeTcp6O48kI/4jTAPrQOTgu7mIR/xnffZPFBd8Rvvs3kgH/EbYR7qg/hOmId8xHfC/YD4U+BPw/0A+Yg/DfOInwI/DfOQj/hpuB+guuJPw/0A+Yg/DfNQH8RPwzzkI34azoP4//bhxzzp7jyQj/hFvW4e8evAr3/I8pCPeNLdeaC64kl354F8xLt9IB/qg/j1D1ke8hFPursfoPNuUtDd/QD5iD8N89BxnxR0Nw/5iJ+G+wGqK/403A+Qj/jTMA/1Qfw0zEM+4qfhPNB5Nyno7jyQj3h3HSAfWgcmBd3NQz7iSXfngeqKJ92dB/IR7/aBfKgP4tc/ZHnIRzzpi/cBHq1U3zeYAT8zfVor1XVnwM/CPHTdJ550Nw/5tAr6Yh9Ghb7NgJ+ZPj8/r647A34W5qE+jAq6m4d8xLv3Aeg40nW3+I332TyQT2ul3n2ARwW+A3wnzEM+rYLuzgPVHRV0dx7IR/xGmIf6MCrobh7yEU/64jrQfV69bsyAn5k+80LdGfCzMA8d925Bd/OQz7ygL/Zhc7W6bzPgZ6bPcLW67gz4WZiH+iCedDcP+QxX660DdBzpvOsWdHceyGf+vN460C3wHeA7YR7ymRd0dx6orviN99k8kM9wtV4fNgt8B/hOmId8hqv17gPQ9zhdd4s/DfcD5NNaqXcf4FGBnwI/DfOQT6ugu/sBqjsq6O5+gHzEn4Z5qA+jgu7mIR/x03Ae6LpbPOnuPJBPa6XefYBHBX4d+PUPWR7yaRV0dx6o7qigu/NAPuLdPowKfVsHfv1Dlod8xJPu7gfovOsWdHc/QD7z5/XWgW6BnwI/DfOQz7ygu/sBqiv+NNwPkM9wtV4fNgv8FPhpmId8hqv11gE6jnTedQu6Ow/kM39ebx3oFvh14Nc/ZHnIZ17Q3XmguuJJd+eBfIar9fqwWeDXgV//kOUhnyHodZ8HEL8L/G74PAD5iN8N84gfAD8I85CP+EH4PADVFb8bPg9APuJ3wzzUB/GDMA/5iB+E8yD+GvjrcB7IR/x1mEf8k3//MU+6m4d8xJPuzgPVFX8dzgP5iL8O81AfxJPu5iEf8aS7zwPQeTcp6O7zAOQjfjfMQ8d9UtDdPOQjfhA+D0B1xe+GzwOQj/jdMA/1QfwgzEM+4gfhPNB5Nyno7jyQj/jrMA+tA5OC7uYhH/Gku/NAdcVfh/NAPuKvwzzUB/Gku3nIR/yTf8/2A+IbwDdMH/oeJB/xjTCP+Dbw7TAP+Yhvm32g73GqK75h+tA6QD7iG2Ee6oP4dpiHfMS3w3kQfwz8cTgP5CP+OMwjfgz8OMxDPuLH4TxQXfHH4TyQj/jjMA/1Qfw4zEM+4sfmOjApXC80gG+YPvQ9SD7iG2EeOu6Tgu7mIR/xbbMP9D1OdcU3TB/aD5CP+EaYh/ogvh3mIR/x7XAe6LybFHR3HshH/HGYh9aBSUF385CP+HE4D1RX/HE4D+Qj/jjMQ30QPw7zkI940t3nAei6W/xu+DwA+bRW6t0HeFTgB8APwjzk0yro7vMAVHdU0N3nAchH/G6Yh/owKuhuHvIRPwjnga67xV+H80A+rZV69wEeFfjF733xpLt5yKdV0N15oLqjgu7OA/mIvw7zUB9GBd3NQz7iSXefB6DzrlvQ3ecByGf+vN460C3wA+AHYR7ymRd093kAqit+N3wegHyGq/X6sFngB8APwjzkM1yttw7QcaTzrlvQ3Xkgn/nzeutAt8AvrgPdgu7mIZ95QXfngeqKvw7ngXyGq/X6sFngF/sgnnQ3D/kMV+vdB6DvcbruFt8wfVqF/UYD+EaYh677xLfDPOTTKujufoDqjgq6ux8gH/GNMA/1YVTQ3TzkI74dzgNdd4s/DueBfFor9e4DPCrwY+DHYR7yaRV0dx6o7qigu/NAPuKPwzzUh1FBd/OQj/ixuQ50C9cLDeAbps+8ULcBfCPMQ8e9W9DdPOQzL+jufoDqim+YPsPCfqMBfCPMQ30Q3w7zkM9wtd46QMeRzrtuQXfngXzmz+utA90CPwZ+HOYhn3lBd+eB6oo/DueBfIar9fqwWeDHwI/DPOQzLOiLfwfgV//yY53euxc/M306/1Jddwb8LMxD732LJ93NQz6dgr7Yh2mhbzPgZ6bP+lp13RnwszAP9WFa0N085CPe/TsAdBzpvXvxG++zeSCfDuh189B7f+I7YR7y6RR0dx6o7rSgu/NAPuI3wjzUh2lBd/OQj3jSF9eBwVr1ujEDfmb6PHlRXXcG/CzMQ8d9UNDdPOQjnvTFPrRfVPdtBvzM9BkX6s6An4V5qA/tgu7mIZ/xi3rrAB1HOu8GBd2dB/IRvxHmoXVgUNDdPOQjvvM+mweq2y7o7jyQz/hFvT60C3wH+E6Yh3zGoNfdD9B79+JPw/0A+XRAr5uH3vsWPw3zkE+noLv7Aao7LejufoB8xJ+GeagP04Lu5iEf8dNwHui9e/Gku/NAPh3Q6+ah3/3Fr3/I8pBPp6C780B1pwXdnQfyEe/2YVro2zrw6x+yPOQjnnR3P0Dn3aCgu/sB8hF/Guah4z4o6G4e8hE/DfcDVLdd0N39APmMX9TrQ7vAT4GfhnnIZ/yi3jpAx5HOu0FBd+eBfMS76wD50DowKOhuHvIRT7o7D1S3XdDdeSCf8Yt6fWgX+HXg1z9kechnXNAX7wOsvay+bzADfmb69F9W150BPwvz0HWfeNLdPOTTL+iLfXj8qrpvM+Bnps/2q+q6M+BnYR7qg3jS3Tzks/2q3n0AOo503S1+4302D+TTf1nvPsBage8A3wnzkE+/oLvzQHXFb7zP5oF8tl/V68PjAt8BvhPmIZ/tgr64DlwW1o0Z8DPT5+kfq+vOgJ+Feei4XxZ0Nw/5iCd9sQ9Hf6zu2wz4menz079W150BPwvzUB+OCrqbh3zEu+vAZWHd2AB+4302D+QjfiPMQ+vAZUF385CP+M77bB6o7lFBd+eBfMRvhHmoD0cF3c1DPuI74X6ArrvFn4b7AfLpv6x3H2CtwE+Bn4Z5yKdf0N39ANUVfxruB8hn+1W9Pjwu8FPgp2Ee8tl+Ve8+AB1Huu4WT7o7D+TTf1nvPsBagV8Hfv1Dlod8+gXdnQeqK550dx7IZ/tVvT48LvDrwK9/yPKQz3ZBd/cDdN5dFnR3P0A+4k/DPHTcLwu6m4d8xE/D/QDVPSro7n6AfMSfhnmoD0cF3c1DPuKn4TzQeXdZ0N15IB/x7jpwWVg31oFf/5DlIR/xpLvzQHWPCro7D+Qj3u0D+VAfjgq6m4d8xK+HzwPQe/fid8PnAcinA3rdPPTet/hBmId8OgXdfR6A6k4Luvs8APmI3w3zUB+mBd3NQz7iB+E80Hv34q/DeSCfDuh189B7f+JJd/OQT6egu/NAdacF3Z0H8hF/HeahPkwLupuHfMST7j4PQOfdoKC7zwOQj/jdMA8d90FBd/OQj/hB+DwA1W0XdPd5APIZv6jXh3aBHwA/CPOQz/hFvXWAjiOdd4OC7s4D+Yi/DvPQOjAo6G4e8hFPujsPVLdd0N15IJ/xi3p9aBf4xT60C7qbh3zGoNfdD9B79+Ibpk+nsN9oAN8I89B73+LbYR7y6RR0dz9AdacF3d0PkI/4RpiH+jAt6G4e8hHfDueB3rsXfxzOA/l0QK+bh/4OgPhxmId8OgXdnQeqOy3o7jyQj/jjMA/1YVrQ3TzkI35srgODwvVCA/iG6UPfg+QjvhHmoeM+KOhuHvIR3zb70C7slxrAN0yfcaFuA/hGmIf60C7obh7yGb+otw7QcaTzblDQ3XkgH/HHYR5aBwYF3c1DPuLH4TxQ3XZBd+eBfMYv6vWhXeDHwI/DPOQzLuju8wB03S1+N3wegHz6L+vdB1gr8APgB2Ee8ukXdPd5AKorfjd8HoB8tl/V68PjAj8AfhDmIZ/tV/XuA9BxpOtu8dfhPJBP/2W9+wBrBX7xe1886W4e8ukXdHceqK7463AeyGf7Vb0+PC7wi30QT7qbh3y2C7r7PACdd5cF3X0egHzE74Z56LhfFnQ3D/mIH4TPA1Ddo4LuPg9APuJ3wzzUh6OC7uYhH/GDcB7ovLss6O48kI/46zAPrQOXBd3NQz7iSXfngeoeFXR3HshH/HWYh/pwVNDdPOQj3r0PQN/jdN0tvmH69Av7jQbwjTAPXfeJb4d5yKdf0N39ANUV3zB9tgv7jQbwjTAP9UF8O8xDPtuv6t0HoONI193ij8N5IJ/+y3r3AdYK/Bj4cZiHfPoF3Z0Hqiv+OJwH8tl+Va8Pjwv8GPhxmId8tgu6ux+g8+6yoLv7AfIR3wjz0HG/LOhuHvIR3zb7cFTYLzWAb5g+tB8gH/GNMA/14aigu3nIR3w7nAc67y4LujsP5CP+OMxD68BlQXfzkI/4cTgPVPeooLvzQD7ij8M81Iejgu7mIR/xD/Tf/Vi/+F01/4uNH/OkL/oc/GN13UUf8Yt63Tzi14Bf28jykI940hf78Omfqvu2WFc86WPz/wEnH/FuH8iH+iB+bSPLQz7i18J5EP8F+C/hPJCP+C9hHvF94PthHvIR3w/ngeqK/xLOA/mI/xLmoT6I74d5yEc86YvrwOT31evG4nk3KeiLPif/XF130Ue8uw6QDx33SUF385CPeNIX+7DzrLpvi3XFkz42/x9A8hHv9oF8qA/i1zayPOQjfi2cBzrvJgXdnQfyEf8lzEPrwKSgu3nIR3w/nAeqK/5LOA/kI/5LmIf6IL4f5iEf8f1wPyD+O/Dfw/0A+Yj/HuYR//jjj3nS3TzkI550dz9AdcV/D/cD5CP+e5iH+iCedDcP+Yhf1OvOg/hnwD/7mM0D+Yh/FuYRvw38dpiHfMRvf8zmgeqKf/YxmwfyEf8szEN9EL8d5iEf8aS7+wE67yYF3d0PkI/472EeOu6Tgu7mIR/xpLv7Aaor/nu4HyAf8d/DPNQH8aS7echHvLsO0HGk825S0N15IB/xz8I8tA5MCrqbh3zEb3/M5oHqin/2MZsH8hH/LMxDfRC/HeYhH/GkL94HeLRSfd9g8bpbPOmLPq2V6rqLPq2VevcBHhX4NeDXNrI85NMq6It9GBX6tlh3VNDH5v8DTj7i3T6MCn1bA35tI8tDPuLXwnmg627xX8J5IJ/WSr37AI8KfB/4fpiHfFoF3Z0Hqjsq6O48kI/4L2Ee6sOooLt5yEc86YvrQPd59bqxeN51C/qiz7xQd9Fn/rzeOtAt8GvAr21kechnXtAX+7C5Wt23xbriSR+bfweAfIar9fqwWeDXgF/byPKQz3C13jpAx5HOu25Bd+eBfObP660D3QLfB74f5iGfeUF354Hqiv8SzgP5DFfr9WGzwPeB74d5yGe4Wu8+AH2P03W3+O/hfoB8Wiv17gM8KvCL133iSXfzkE+roLv7Aao7KujufoB8xH8P81AfRgXdzUM+4t37AHQc6bpb/LOP2TyQz/9b2hnbtpJlQXSMj7UUwAawQbRFfGlJcTYBiQMoAIFWW7IYAAOg2RatDoABMACBVluyaMpgADTXKmDQmKOqh+sWDuoWbt/35rHn9df777b3AL8M3wPfF/OQz7vR03mgup9GT+eBfMQvinmoD59GT/OQj3jS0/MArbuD0dPzAPncH9v2gYPh58/9YPQ0D/ncjZ6eB6iu+K/ieYB8zk9tfXgz/LwP4klP85DP+altH6DnSOvuYPR0Hsjn/ti2DxwM3wPfF/OQz93o6TxQXfGL19o8kM/5qa0Pb4bvge+LecjnDHrrfQDxR+CPxfsA5CP+WMwjfgJ+KuYhH/FT8T4A1RV/LN4HIB/xx2Ie6oP4qZiHfMRPxXkQ/6/NP/Okp/NAPuLnemse8R3w3aaWh3zEk57OA9UVT3o6D+QjPu0D+VAfxHebWh7yEU96eh+A1t230dP7AOQj/ljMQ8/92+hpHvIRPxXvA1Bd8cfifQDyEX8s5qE+iJ+KechH/FScB1p330ZP54F8xKf7APnQPvBt9DQP+YgnPZ0Hqiue9HQeyEd82gfyoT6I7za1POQjviueB8Rvgd8WzwPkI35bzCN+AH4o5iEf8UPxPEB1xW+L5wHyEb8t5qE+iB+KechH/FCcB/EX4C/FeSAf8ZdiHvF//PXPPOlpHvIRT3o6D1RX/KU4D+Qj/lLMQ30QT3qah3zEk56eB2jdfRs9PQ+Qj/htMQ8992+jp3nIR/xQPA9QXfHb4nmAfMRvi3moD+KHYh7yET8U54HW3bfR03kgH/GXYh7aB76NnuYhH/Gkp/NAdcVfivNAPuIvxTzUB/Gkp3nIRzzp6X0A+t0t/li8D0A+77/b3gP8MvwE/FTMQz7vRk/vA1DdT6On9wHIR/yxmIf68Gn0NA/5iJ+K80C/u8WTns4D+bz/bnsP8MvwHfDdppaHfN6Nns4D1f00ejoP5CM+7cOn6VsHfLep5SEf8aSn9wFo3R2Mnt4HIJ/7Y9s+cDD8BPxUzEM+d6On9wGorvhj8T4A+Zyf2vrwZvgJ+KmYh3zOT237AD1HWncHo6fzQD73x7Z94GD4DvhuU8tDPnejp/NAdcWTns4D+Zyf2vrwZvgO+G5Ty0M+56e29wD033H63S1+WzwPkM/777b3AL8MPwA/FPOQz7vR0/MA1f00enoeIB/x22Ie6sOn0dM85CN+KM4D/e4WfynOA/m8/257D/DL8PP3AOJJT/OQz7vR03mgup9GT+eBfMRfinmoD59GT/OQj3jS0/MArbuD0dPzAPncH9v2gYPhB+CHYh7yuRs9PQ9QXfHb4nmAfM5PbX14M/wA/FDMQz7np7Z9gJ4jrbuD0dN5IJ/7Y9s+cDD8fB84GD3NQz53o6fzQHXFX4rzQD7np7Y+vBl+3gfxpKd5yOds9Pm/A/Af+Dvy9N29eNLnPvv//lx37rMHvTUPffctfvlSy0M+e6PP+3AzfZvXvRn9Gv4dcPIRn/bhZvq2BH75UstDPuKXxXmg7+7FfxTngXz2oLfmoe/+xI/FPOSzN3o6D1T3ZvR0HshH/EcxD/XhZvQ0D/mIJ32+D5yWP+8b83V3Mvrc59+rn+vOfcSn+wD50HM/GT3NQz7iSZ/3Ybf6uW/zujujX8O/A0g+11VbH3aGXwK/fKnlIZ/rqm0foOdI6+5k9HQeyEf8RzEP7QMno6d5yEf8WJwHqrszejoP5HNdtfVhZ/gR+LGYh3yuoLeeB+i7e/FfxfMA+exBb81D332LJz3NQz57o6fnAap7M3p6HiAf8V/FPNSHm9HTPOQjPv13AOg50nf34hevtXkgnz3orXno//uL74t5yGdv9HQeqO7N6Ok8kI/4RTEP9eFm9DQP+YgnPT0P0Lo7GT09D5CP+K9iHnruJ6OnechHPOnpeYDq7oyengfI57pq68PO8PM+7Iye5iGf66ptH6DnSOvuZPR0HshH/KKYh/aBk9HTPOQjvn+tzQPV3Rk9nQfyua7a+rAzfA98X8xDPlejz98DLJ9/fm8w/90tnvS5z/j8c925z/jc9h5gafgl8MuXWh7yGY0+78PD+ue+zeuKJ/0a/h1w8unXbX14MPwS+OVLLQ/59Ou29wD0HOl3t/iP4jyQz/jc9h5gafgR+LGYh3xGo6fzQHXFfxTngXz6dVsfHgw/Aj8W85BPb/T5PjCZfWO+7iajz326P3+uO/cRn+4Dk9k3lsAvX2p5yEc86fM+DH/+3Ld53cHoc58//vdz3bmP+LQP5EN9GIye5iEf8cviPNC6m4yezgP5iP8o5qF9YDJ6mod8xI/FeaC6g9HTeSAf8R/FPNSHwehpHvIRPxbPA/S7W/xX8TxAPuNz23uApeHnv/vEk57mIZ/R6Ol5gOqK/yqeB8inX7f14cHw8z6IJz3NQz79uu09AD1H+t0tfvFamwfyGZ/b3gMsDd8D3xfzkM9o9HQeqK74xWttHsinX7f14cHwPfB9MQ/59EZPzwO07iajp+cB8hH/VcxDz30yepqHfMSTnp4HqO5g9PQ8QD7iv4p5qA+D0dM85CM+3Qcms28sgF+81uaBfMQvinloH5iMnuYhH/H9a20eqO5g9HQeyEf8opiH+jAYPc1DPuL74n0A+u5e/LF4H4B89qC35qHvvsVPxTzkszd6eh+A6t6Mnt4HIB/xx2Ie6sPN6Gke8hE/FeeBvrsXT3o6D+SzB701D333J77b1PKQz97o6TxQ3ZvR03kgH/FpH26mbx3w3aaWh3zEk57eB6B1dzJ6eh+AfMQfi3nouZ+MnuYhH/FT8T4A1d0ZPb0PQD7XVVsfdoafgJ+KecjnumrbB+g50ro7GT2dB/IRn+4D5EP7wMnoaR7yEU96Og9Ud2f0dB7I57pq68PO8B3w3aaWh3yuoLeeB+i7e/Hb4nmAfPagt+ah777FD8U85LM3enoeoLo3o6fnAfIRvy3moT7cjJ7mIR/xQ3Ee6Lt78ZfiPJDPHvTWPPTvAIgnPc1DPnujp/NAdW9GT+eBfMRfinmoDzejp3nIRzzp6XmA1t3J6Ol5gHzEb4t56LmfjJ7mIR/xQ/E8QHV3Rk/PA+RzXbX1YWf4AfihmId8rqu2fYCeI627k9HTeSAf8ZdiHtoHTkZP85CPeNLTeaC6O6On80A+11VbH3aGn/dhZ/Q0D/lcjZ7eB6Df3eKPxfsA5DM+t70HWBp+An4q5iGf0ejpfQCqK/5YvA9APv26rQ8Php+An4p5yKdft70HoOdIv7vFk57OA/mMz23vAZaG74DvNrU85DMaPZ0Hqiue9HQeyKdft/XhwfAd8N2mlod8eqOn9wFo3U1GT+8DkI/4YzEPPffJ6Gke8hE/Fe8DUN3B6Ol9APIRfyzmoT4MRk/zkI/4qTgPtO4mo6fzQD7i031gMvtGB3y3qeUhH/Gkp/NAdQejp/NAPuLTPpAP9WEwepqHfMR3xfMA/e4Wvy2eB8hnfG57D7A0/AD8UMxDPqPR0/MA1RW/LZ4HyKdft/XhwfAD8EMxD/n067b3APQc6Xe3+EtxHshnfG57D7A0/Pw9gHjS0zzkMxo9nQeqK/5SnAfy6ddtfXgw/LwP4klP85BPb/T0PEDrbjJ6eh4gH/HbYh567pPR0zzkI34ongeo7mD09DxAPuK3xTzUh8HoaR7yET8U54HW3WT0dB7IR/ylmIf2gcnoaR7yEU96Og9UdzB6Og/kI/5SzEN9GIye5iEf8X/T/w9pZXzj 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+AQAAAAAAAgAAAAIA3nMAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-060000.3.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-060000.3.vtu new file mode 100644 index 000000000..ecb652853 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-060000.3.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAgAAAAIA9HMAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-060000.pvtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-060000.pvtu new file mode 100644 index 000000000..a56ad8ea9 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-060000.pvtu @@ -0,0 +1,19 @@ + + + + + + + + + + + + + + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-070000.0.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-070000.0.vtu new file mode 100644 index 000000000..b054f0be5 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-070000.0.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAQAAAAEAxFgAAA==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+AQAAAAAAAgAAAAIAQnIAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-070000.1.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-070000.1.vtu new file mode 100644 index 000000000..cd2f6ab4e --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-070000.1.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAgAAAAIAuXIAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-070000.2.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-070000.2.vtu new file mode 100644 index 000000000..cd24d22e0 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-070000.2.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAgAAAAIATHMAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-070000.3.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-070000.3.vtu new file mode 100644 index 000000000..037d62dc6 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-070000.3.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAgAAAAIAsnMAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-070000.pvtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-070000.pvtu new file mode 100644 index 000000000..316627d59 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-070000.pvtu @@ -0,0 +1,19 @@ + + + + + + + + + + + + + + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-080000.0.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-080000.0.vtu new file mode 100644 index 000000000..97286cbdd --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-080000.0.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAQAAAAEAxFgAAA==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+AQAAAAAAAgAAAAIA1HEAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-080000.1.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-080000.1.vtu new file mode 100644 index 000000000..e9a8c159c --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-080000.1.vtu @@ -0,0 +1,34 @@ + + + + + + + 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t1Yf7dccH+MDR3He5WDiEvq8Kz27Yjb6vqnmtjtRHrrOqH/XBrUk9aF/jo6mBX29NoH3B7XG/UF/Ojwz8dc+ZRn1wZ/kk3tf15CvDeV/wNQO57+bKvWOpD25cGUnfV9VzWvQVfV8V3OzZkvtuT+PlLtTXvHN7+r6qzu3f2M/eV9V8ST3qI9dp+Dn1wc2VzXhfd07WS31oX3CrWzfuu3NV3sHUB5d/9qN9tTeqE+0LbnZuS33kWjk9ue9y57eudF+tT2IDfxwXRfcVXJWeQX2r5eJA7lPzqA9uZUbTfYVniHC6r+BW/fHU17mXZ1Jf8+tTaF973bJA7oXFtC+4VW4+94evDOzr08upD+7sWkL7am9IDO0LrlQE991c59cF1Ae3vpzL9xVzPTOG7iu46D2c+uipcidSH1zeG0v3FZ548DPdV3A1+SfqI9cpPpr64Kr0MN4Xc2o/nfYFN8+FUx9zldWjqQ9uPjmL9tVeywm0L7jc9iv1keukTKU+uJgd5mfvq+KctP6q4mfvq4KbkypQH+eUueoDP/VdrlrX87P3VeGpCSX89H1V8FaF/cxHrthZzc98cKtSRdoX56Qo0Yb2BTcWNKM+9lTN6Eh9cNm8Le2rc0s35n1dLpe+S33k2o/aUB9cJLXws/dV9Zw6/cXfV3W50S6X+rrn1TJ+5oOrdwP/u4S+r6pzs2/S91XB1Z5L1EeuiiviZz64PbKQn/XVc+1X28/6gturqlIfc7W3fER9cJXWyM/6am/W637WF9z85hXuu7nOT+9SH9zqVZfuK85JVbQn31eXW+4/j/wvH+dU6Z+pDy6L/ET3FZ7zV0e6r5r3bkd95NqZPagPbq7uSvvqc/K5UbSv5p8Npb4+V98aT31wa84vtK/O/X4g7Qsu6/SlPnLNxBHU17zjYLqvmJPI+ILuK7jo14r66Gm06UR9cOdwO7qv8GSRZnRfwcVGi/turvjvC+673LnwKe37/8+pO+2r+fYu1Ndz7duP+uAirgftC0+V+Zb2BXdmf8l9N1eu6kx9cLtUW3pfddatDfy6aSW9r4IbZ/4P/+vEQO6ptdQHtzuuovdVeNYDQe+rmr+ziPrINf+Opz64nLOU9rX6bw54XRNpX3DxwzruG9sCfMBm7oNnJ9K+8OyfVtO+4GLoCu67uVbX9dQHF+fX8PuqOycxaB69r4KbK2O47/ZUby+hPrh1YAG9r8KTxyPpfRXcODGD+siVtWKpDy56zOZ93TkZg5fTvuBW2zjqY65m7VXU17xbAu0LTz25mPYFN/rMpz5yxefLqA9unFrC9/XZHYH/3t2tdF/BnVzuG7E7A7mFdlBf817b6L7Ccy5spPsKbm7YQH3kikNbqA9uPbaZ9hVLdgX8sJ28L/gP3FeVdgdyJ+2iPrhzbiftC8+pup321fzIVuoj1364g/rg0r+d7quea891dF/BRfpq6qOnem4j9cHF8fV0X+GJByvovoI7vyznvptrPlhDfXCz00raF3NyXt1C+4LLxzZRH3NVY7dRH1z+vIX2hWe/tYH2Bbcach+5Vq+N1Ac3Lq6l91Wckyp6Cr2vgptFJ3HfPaeMVhHcd7ncOI3eV+HJxxx6XwU32oyiPnIdO4z64OYTDu2Lc9L4Zg7tC64mR3Lf3VNTLKA+uDV4Du0LT26aRvuCq/YTqY9c48Qs6oObf4fR+yrmJBYNpvdVcDm9L/XRU9UYQX1w873+9L4Kz47sSu+r4OLON9RHrvlFV+qDm599QftiTvbJUbQvuPlqf+rruf49ivrgqnsf2heeVfh72lfz9Z9QH7nOZ19TH9z8py7dV5yTRklB9xVcNp7PffecMoctoz64XLqQ76vrWd9F0X0Fl3Y49ZErc6O473J5cyLti3NShcfTvuBGrUXUx7kqVTz1wWWzBbQvPPNsBO/rcqPvr9x3c42K07jvcmfRILqvmJOdNpzuK7h9piv33Z6q/0Dqgyv33wuG7is80fgjuq/gosaL1EeuVftN6oM7n+T4WF/MST3djffVvCH1MVc5pzX1we3ipWlfePakhz7WV3Nf4Lt4oT5yxdhD1Ac3vviUv6/qPieV3ZPp+6qaNzlDff1c9bkH1Ac3F/1H31fVXv+z9H1VcHH8AvWRK+r9R31wdeQe7YvnpMbr5f2sL7hdtoyf+u5zKpFV0898cNsbeG4b2hee80FgX0P7gpuTS/mZj1w17A0/88HtnwPPhUPfV9VzunuNvq+q+bBM6qOnWeUR9cHtku73wkLeV9W5a36n76uC2089oD5y7fqB72qF+uBmy5J+1hdzsm4Efv1ffV3ulC7tZz7mqg6Y1Ne8hfv8PqQvPLtoWT/rC27MKk995IqMl6gPLn8pR/cVz0mNH96n+wpuPfkO9fGc0qnWlPrg8kgjuq/wjHG16b5qvqYa9ZGretejPrh1oBrtq5+TXm1B+2o+/kPq455qftGS+uD28oa0Lzz1dF3aF9xMfY36yBW1qlAfXKY/R/cVczK+rUT3FVx0eJn66GkMe5X64KLm83Rf4ZljX6T7qvna4tx3c52YwtQHd/oHzoXQvpiT0ek52hfcOuu+Nxji67nGBr7bF+qD288H/n8rtC88Z2Hg31uG9tW88T4f85Er31hKfXBb9aXvq+KcNF8InDuh76uCG4Pd95FDfJxT4ply1Ad32lX0s/dV4Rl3KtH3VcGdEXWpj1zz6WrU17zS27Qvzklz6su0L7jhMamPPbWfLEN9cGNcKd7X9ZzESrQvuMisQn3kSuN56oPbK0rQ91X1XJ+w+Puq4HGtqa/nGt+I+uBiTjP6vqrO/bYjfV8V3K7dm/turrz3BfXBjRMdaV/MyVpYk/YFt7LqUh9zlYufob7mJYrTvvDsEw1oX80bN6Y+co0Xnqa+5iuK0X3FOWldKEz3Fdws/K+P+TinjPmBe1+oD64qHfaxfYUnv8/3sX0Ft99UPuYj126z2sd8cCMs8M81oX1xTpoDY2lfcLteP+rjXDU2VKc+uIoKfNc5tC886W9E+4KLbk9RH7mi4Uwv88GtIm972b7quSae97F9BbdaHfExHz3/38XCx3xw+Yv7Pd+QfdW5Dd3fzxOyr+BOzAYf85Hr9KvvYz64Gl+G9sWcrEVnvbSvy+XpOV7mY67Ck5xPfZcbVfrls77aK+N+nzykL7j9cUUv85Grcp7MZz64dVV42H0Vz0ntfU3pfVXz/W9x331Oaa98j/rgqv9L9L6qc4eWovdVcKtc4OduhfrIFTXc7yaH+OCW+1320L54TmofrMz7utx88z8f8/VzqoNPUB/c8if5WF941n/7aF9wNXsA9ZFrTB5IfXD5yz0vu69iTlbCIXpfBXfuT6G+nmu/EdQHtyu+yu+ryH3xI3pfBTdau9+/D/F17pn1XuaDm4Uqe1lfzMne9yTv63JRMfD7wkN9zNUob3uZD27cnpPP+sIzn7+Qz/qCW3MK5zMfucKe76G+y+1+vfLYvuI5qfnmRbqv4GZ59+d9hvj6ueqfM6gPLos/S/cVnv3BQy/bV80HBH4uW6iPXNWoE/XBZfG++awvnpMa46rQvuD2O0O9zMdzVdFxgZf54GLA8nza1/Ucj/s9hdC+LrdXvuVhPnKdLl95mA8uFsbksn3FnOR7g/PZvoLbMvBdiVAfPVXJWh7mg1tTFuWyfYVnj/Lksn0Fd8yPc5iPXLl5VzbzwWW96EzWF3Oyssblsr7gRk/3+1Qhvp5rm97ZzAc3dl7NYH117ugSmayv5jVHpDFf51rF05gPLsZ3ucnuqzgnxe5xXnZfBXfCnvIyH+eUs9bJZz648dtBD7uvwlNl3P9+yH0VXL0c+LmsoT5yxRRfHvPBpV00j/XFOakmFfPQvi4X3f25zMeeqjHuzy8I8cHtKiOyWV94Yu6OHNYX3Fk6MJv5yJWTzmUyH1yerZzB7quYk2rR2sPuq+AqaXUe89HTeKd8LvPBnUFVcth9FZ6MLZLH7qvgluqRy3zk2skNs5kPbrVvl8X6Yk7W0tezWF9w9cWNDOZjrqpq4OcmhPrgxtT7ivWFZzVYkc76aj4hPI35yFVb6tyivsvl37uus33V5+rUH7PYvoLbVX/MYD7OKaN8k3TmgzvfZCm2r/DMlR3T2L6CG/t/VMxHrir+6Q3mgztta19hfXFOmvPW3WJ9wZ2EtVeZj3NVtLlzmfngRop5gfWFZxaqfYn11fzpTueYj1zrSKmzzAe3r6w4xvYVc5LLq9xg+wou3vNfYb6ea/MlF5kP7qwX59i+whPb3kxh+wpuh5+5wHzkit9qnqU+eOGfTrK+mJN5f+Bp1lfz1J+PMR9ztVrEHmY+uGohJesLzyw54zfWF9yJnXmQ+cgVQw7uZr7mnSZuD/6uqrFvkC/4O6lm3DBfMFfH3Z9X7XLrg4vUx3dKZd446oNbXa4XyHUmuf8+yPXsyu7zcfA6gX/PBW5915j6yBVZZakP7hxyf79LSF98J9WYGU37glvzs6mP71QKYzX1wa3n/vaxvvDMISbtC26+34L6yLXbVKM+uDP08wJztd76vsCc7NKdC/K9gwvycUOoj55K2tQHt5KGFcg1i08s4Mn9EwtwlTKzAFdhs6iPXONiGPXB1cxI2hdzEiW6077g5vBR1MdcjfN9qA9uDx5L++rcRlNpX3A7bTb1kSuvzaQ+uOwT+H0Zofuqv6v66yG6r+DWvif9zMd3SlXLm9QHd0RZuq8692Z9vq8ul6U7UB+55uSPqA9u78XveyzYF99JNbL+9bG+4M4Z9++vEB/fqbJrP099cDuyEe0Lz9r9Oe0Lbhf7ifrIVfc6UR/cThhG9xVzMs7+TPcVXEVMor6e6+GR1AcXS6bTfdW5UXxfwY1iS6ivc99bQH1wY0E87Ys5if7jaV9wu+9s6mOucuo06oM7exbSvvCspUtpX3B5ey31kWvNX019cPvqlgLnqjk/uuA52Xp2Ae50iyjIX4ukvj6nus6hPrjqUDDX+WJcwdzXxxfgclSXAlz1t6mPXGPuROqDiybdaF+ck/bRebQvuPiH+9hT66fF1Ae3MhbQvvCcmMm0L7iZ0pP6yLUezqA+uFGof4G5GvVeLTjXjmYBbv6wzhfMrcvrfczXPQ++7mc+uKzs/lwyN9eulu8N9uxyPm8wF98m5gdz8d+mfOYj10n6w8t8cGPdrnzaF3NqUtXP+oLbxXf5mI+5GrNr+5kPrt5wv6sQ2he55+57WV9wUyXlMx+5TtxTPuaD26su5bN9xTkpai6n+wou/4ujPs4pM3M19cGtZ1bRfYWnls6m+wpuOkO57+bKFgu5D/7Yr7Qvzkmn2ybaF9xKT6Q+zlVrwC7qg5sNdtK+OnfFctpX86rh1EeucH/fXagPrkZG0H3FnFTn9/xsXzX3nPIxHz1FzY/9zNd8zRUf21d4zg+lfWxfNT+Rk8985DolKvuYD27su5fP+mJOzuDWftYX3J6X6WM+5irC2vuZD652/077wlMD3/bRvi63Ep/xMh+5VlnLx3xwO6yCl91X8Z1Us09Vel8Fdzq3pj6+U2pU+5D64OpqJ3pfhScze9P7Krj58Tjuu7lOlf/Dd7kdO432xXdS1Udf0L7g8kgf6uvvqi7vwn2XW/5hvK/rqV94X3C7Uwz1kess4L7m7y6m91XMSaT8H/dVl6v28dRHT6fkYuqDW/+to/dVeFb2JnpfBbfL7OU+cj/eTX1w2fE32hdzEp4E2hdcfLWN+pir8/gG6mv+517aF57R7hDtCy4+OkV9nRt/nPrgqvEFvq/ud1KN936i+wounh5HfXyn1D44lPrg1srJdF/hGa/MpvsKbmyOpz5ynb7zqQ9ulVlD++I7qc7a0bQvuPxrOvXxnqqZPIb64Pbk6bSv9sIX0b7glrWO+sg1ji+gPrjx+Wq6r5iTOreV7iu4+clh6uueYbuoD261P0r3FZ4ae5buq+aTrnDfzbVTzlMf3Lh8jfbFnKzzu2lfcLnlKPX1XJdspz64aRyifeFJ4zztq3nLK9TXuZ+doD64kqfpfRXnpHnwIL2vgjvbDlBfn1OPnaA+uHX7GL2vwhPlt9H7qub2POoj16m3j/suVxXjaF99ru46R/uCi7bJ1Nd72uQK9cHtF1N4X9ez7x6mfcGtwwnUR67zwzHqgwvfSnpfxZzUXNtP76sut3IDv+8k1EdPc1lPP/PBncRH9L6qva9b+th9Fdw+7r6/F+IjV5Rs7WM+uDPzDS/riznZX/f1s77g9rsG9TFX63JvP/XBH7tL+8Izdrfwsb7gdsOXvMxHrukJPNcP9cHNxo/y2b7inJRLb9B91bzwVerjnDJ73qQ+uNn2Mt1XnXvnGN1XcLkxnvrIVT8d5r7L7TrzaV+ck/YnV3hfl1stzlAf56pacJz6mk/Yxfu6nuixlfYFN18ZT33kyqi51AdXOe3ovmJOIs6m+wqu4t2fSxHio6f8u5Wf+i63rMN0X+FZJcr52L5qvvZkPvORa11N8zJf8/jR+awv5iQ6vOFnfcGt5MC/Nwr1MVdzYTb3Xa6s2ryv6xnTZnhZX3DzYKF85iPXfjrwz/uhvua/uj8vzH2uauYcygt+TqpGHckL5nbFv7ML8NP/ZTMfzymVJzmP+eDGkKdygnPtvlZGAe/ZZhnB3Am/dSuYiwWeW8xHrprYLoP54NagIor11c9Ve6Xl0b4uN/96OYf5eE5lzP8nj/ngjvNeDusLTw0amMH6gjufvK6Yj1xnaWQG88Hl781U8FzNDz9MCZ6TNb1lSjB3jIQzwVwd2HGG+XquC/ukMB9crfefCc4V6wccDvbsu78cDuamd8zOYG6diNzJfOTamSsOMx/ceuXMTtZXz/XW4hTWF9yxXz/LfMxVlk5LYT64WNLxLOsLz4rOO0z7utx4us4u5iNX1G3yG/PBjXIbd7F9xXNSe1MJD9tXcFN0yKG++5zSHlbFw3xw1WJEDttXeKLE9gy2r+B2owGK+chVd69nMB/cfDVWsb54Tio/aexhfTXPnp1DfdxTXwqcr6E+uNFjdQ7rC0/+/TCD9QU3z+1RzNe5j8pkMh/cyE5RbF8xJ9G51mW2r5qrBWeZj56mNeYy88HNSjfPsn2FJx8t/43tK7hVq9lu5uvcL55JYr7mizN2s76Yk7x08DLrq3nDl5KZj7mqJ/+9zHxw559Pk1lfnbtvQBLt63Knx697mI9cMUwmMR9cLnlsb/C5amU22FDgXH27/YYCfNSLy4K5UajGMuq755QzJHED88Htsf2XBeea09fPDfaMYclzg7koMWRmMLfurp3JfOSahxrEMh/c2tF2Fuurz9WzryayvuDmpivLmI89VZV/TWQ+uPFar+Wsr841VSzrCy6r1ItgPnLVl3Ie88FFd29E8FzN+FMTg+dkj3k4MZg7VwqNDeb2hW/HMh89namzJjEf3Nn+llMgt/dvI4M92aDsqAL8Vt0RwdwqMXEE83Xu4mdHMx/cWT90JOur59p7TBjrC25eeGIc8zFXdXhvOPPB7f5Vx7O+2puwdAzrCy69haiPXOvUsV+ZD25XffUXtq84J1VrbyLbV3Cj0vPxzMc5ZW/qvpH54KbnVDzdV9dzGp6Zz/YVXCw8E8l85Io/Hi1gPri8kxzF+uKclO/d3Mj6ar5nVgLzca6qOZ9tYr7mX3+6gvWFZ77w8SLWV/OLudHMR66sFr6Y+Zq3fjqG7aueU5+WU9i+ghsDoiYwX/fs8fw05oOrqHcmsX2FZzfY7bB9BRcP949lvs79stV45oM7u3uPo33dOakmRWewvuDG/KLh1Hfnag6qMIv54Gb0g8msLzwrsthE1hdcrXhtAvORK6/kT2I+uFnv0UR2X8VzUvPX7zzsvqr5G1tymI/nlHb39h7qu9zyrM1h91V41ujXMtl9Fdz8MV0xX+d+UDaT+eB2+mXF+uI5qfPYxx7WF9zYNjOH+XhO5Ywp42E+uBH1VQ7rq3Mn3MlgfcHNPRsV9d1c5+6qDOaDG016KnZfxZzEqeeusPsquDX5x2Tm67muL3SF+eBWdudkdl/V3tE7Sey+qnnLbnuZj1yZezeJ+eDyx+l7WV891xb7L7O+4MYvdZKZj7mq0o0vMx/cybxwlvWF5zw4lsT6at51wF7mI1d9+m0S88GtI4/2sH3Fc1I7/FQe21dwq/vv2czHc0qrSKc85oPLq52z2b7CM5LfyaD76nLRNOsW83Vumch05oNbz4ubrC+ek5p7t+eyvuDmgugs5uO5qtXufA7zwY3XJmayvvBEv41prC+4vP/ndeq7uXbnqFTmg5v1M66yfcWc7OEjUti+gsuLH59lPnqq9O6XmA9uvfTvabavOndK3G9sX8GNW/X3MB+5slzcYeqDV3puN+uLORk/PnmR9QUXvR+eZL6e653O55mv+eWE47Sv69nZHQ+xvppXmb2T+ciV268dYD64Ne/0dnZfxTnpHI/fxO6r4Ha4fwXzcU7J6zc3MR/cqv7zSnZf1bkRO5fQ+6rLjeFV5zAfufbOboL6Lpe13p3L+upz9Yh3E+sLbtY6uJL52FNn1PFNzAcX51NWsr7wzJgzgvUFt66+Gct85JqXy8ZR3+Wyxe+x7L6KOamRCyPofdXlouVj05ive0bERzIfXFwoNoPdV3VuekY4u69qvv5OGPORq1ZlTGG+5qv8k1lfzElcWhLF+oKLpo/PYj7m6vwwPpr54EadgRGsLzzx541prC+4PSBnKvORa2QdnMF8cGvWmel0X91z0swYsIntK7iVuXol83FOqX3XNzJf8/gGK9m+wnMKNYxj+wpuTA+fR303V71WO4754MbK7HmsL85JVaTURtYX3GgzYQXzca5angqJzAc370YmsL7w7Fr3BOsLbj14Zj7zkWv5Fwnmgzvf3JvH9hVzMmd8MpvtK7jc2iKS+ehp9UqfzXxw69YTUWxf4anIsFlsX8FVl9iZzEeufWhmBPPBrQMvRbC+mJNdvEMM6wtuVl8WxXzM1dgcF8N8cBH2cjTrq3NfaRzJ+oIbCcnUR65YfzaS+eBmkT6R7H1VfCfVKjecv6/qcumfSH18p9R0f25lqA9uFx9E31eFJwZE0fdVwe1qS6iPXPPqGOqDy8Qw2ld/V7VsM9oXXD38jPr4TqVaUIz64MopRvvCs378mvYFN/p3oD5yRXRR6oPbCU/S91UxJ2f4avq+qubfbaa+7rlyJvXB7X7R9H1VndtxB39f1eXiw+3UR65dIpr64GrdVNpXz/Xad7QvuFIdqI+5Wg/d9xtDfHD5mvtdxZC+8FT657QvuPFqI+ojV0a539cI8cGdr7f72L7q76TWCfzch9B9BRcTY33Ud79Tqtbe9TIf3Gh9zcv2VedOneZj+wqulo32MV/nPrvby3xwsT/w3dTQvvhOqhGWlc/6av7hmnzm4ztV1v73PcwHdyqk5LG+8JxD7nexQvqCm3Wr5zNf594fmMd8cHnsiTy6r+6cjH49fGxfNZ/Ygvroab/a18t8cLEs8N2x0H2FZ9V/2cf2FVw8uuFlPnKNynn5zAeXA8PyWV/MyW5+y8P6al5zoof5mKtqEpHLfHD1U9Fc1ld7TZ/3sL7gIm9sHvORa67ulcN8cOf6smz2virOSTF/LX1fFdysEs1995wy/h1KfXCzRxv6vio8c8sP9H1VcGfus9RHrlGqGPU1rznfx/rqcxI/NyWkL7j1S+C74KE+9tRoNcZHfZcbHSv6WF94zrhRPtYXXJ1P91LfzZUfx3qZD25OP5XP3lfFnOS6CfR9VXDjnQQv9d2e5vO5XuaDm39dzqfvq8Ir+yV9XxXcevG7POYj1/6jKfXB7clP5bK+mJM9PfBcPLQvuFN8g4f67lxFlZUe5ms+5Ok81heesfVsLusLbmR+mk19N9f450Y28zVPK53J9hXnpPzd/f5nyL6CW5fv5zNfn6tHH3io73I14CMP21d4avyH+XRfXe4Mq+thPnJt8XEe88GdGF8O66vPyfYHcllfcHXhaA7zca4aNzdkMR/cHj8uk/Z1PXkwPJv2dbnx4PdM6ru5zsgSGcwHF83bprF9xZzMd9y//tB9Bd90IZv56Gltq5PNfHDneuFMtq/w1ICumWxfwUWnwWnMR676rFca88GNVwrdYn0xJ7PBg3TWF1z+mZjKfMxVPWqnmA8uvS2us746d+vHN1lfcLHOd5n5yJVdN6dQ3+Wydf3z7L6qv6va8ngOu6+C26vq5FAf31UVv2QyH9xZmJjB7qvwxMn52ey+qvm4QtnMR65amp/OfHDzQo101hffSZWznVTWF1w0i1TUx3dVL0Zfpb7LzYebL9O+rmdW73uL9QW3/jx9g/nItUc/m8J8cPvZ2RfZfRVzMvZ3yWL3VXD517pM5qOnjOuTxnxwc9jiVHZfhSdGpmew+6rmVf9Jp76bK0dKRX3w/6Jusb6YkxPT+DrrC27XXn+V+XquL9S/wHxw20g/x/rCU0WeucL6gtvVvkxhPnKteZHJzAeXf797lu0rvpMq2107x/YV3Cj7fjLz8Z1S1b3lMeaDi3GB9xJC9xWeqLTrDNtXcPNIl1PMR67V4bfDzAc3PeUPsb74TqqR01SyvuBW16J7mY/vqopSPbcwH1xFvLKJ9YUnT369i/UFt4/W2sF85MoJAxKZD26M67Ce7SvmJEc0P8H2Fdz5pN0x5qOncXLdAeaDm2/0lGxf4TkZw46wfdW8x/zfmI9c472++5gPLt4tv4f1xZzsOj9to31dLr/P2UJ9d67iULe1zNe8zMnVrC88c8/kzbSvy+39DTcxH7n2rD6rmA9urH99Jb2vuuek01OlsfsquIyITWU+zin7jV43mA9uL7p3ld5X4eVXUOy+qvmO1OvMR67z3xeXma/5P0Mvsr44J8UzQy6yvuDWn0POMx97aoqlp5gPbqftOs76wnOOfpvM+oIbD74/zXzkqq4HjjAf3Pol6TC7r+o5vdT4Cr2vutx5bcRF5qOn0Wb8OeaDmyWrnWH3VXji1t5kdl/VPG7SSeYj11i/+BjzwZ3YhYdZX8zJblf8OOur+bKs35iPudqTbx1gPrhRu9k+1heek/b8QdYX3K71/V7qu7lW0093Mh/cOJ69he0rzkk7cschuq8ut06dOcB8nFOqxqmdzNf8jbHb2b7Ck5lH97N9BXduPdzNfORa+XW3Ut/lzt3fN9G+7jnp3LmTyPqCG8U6bGA+zlVrXmYC88Hl5IR41lfnfnNiLeur+fhmq5mPXFG323Lma17h5WVsXzEnO77DDravmlfwbWE+epqFfktkPri1Ytg6tq/wxP7zG9m+gpuV31pPfTfXeWPvKuaDO7V/SmB9MSdn1YUVrC+4bDQ+nvl6rlcvx1Hf5aLzQsH6whOjvlvG+mq+tHsc85GrXry2mPngTpulC9n7qvo5aVuRzd5XBVfRb2YwH88pxYSSWcwHNy+MpO+r6tyv6ir2viq4tWToZeYj18msfoP54OLw7ousr35OOuFV3tflTtRYxXz9nKr2mVTmg5vHo6+zvvBE09lXWF9wlXv+HPORK6Ydpb7mw5POsPdVMSez3+hk9r4quFz65FHmo6cqdeM088GtCQ1/Y++rwjOWvyXZ+6qaf3xuK/ORq768uIf54E6ZbZtZX8xJLhTHWV9wM7LbQeZjrvLB0CTmg4vcT/azvvBE2kc7aV+X2wOjNjIfuWrsma3MBzc73V/P9hXPSR1f/5tsXzW/7k1hPp5T2oUev8J8cHPXd+fZvsIzRz1KpvsKbr52nPnIdRalnWQ+uLHw0WHWF89JnYZJ51lfcLNV69PMxz1Vrql+mvng1scvHKF9Xc9oOTeJ9nW5rDBTMh+5xj9fHmA+uOhyeCfbV8zJ/nYm3Vdw62y1XcxHTzv71X3MB1c9lmxl+wrPKL9wE91Xl9tdy66hvpurJo7dwHxwa0j0CtZXz7X66ztYX3BRatdG5mOu5oW3NzMf3M54sJb1hSceTF/N+oKLW6OXMx+54vsfE6jvcvP8hTj2vqo+J+s9vp69rwourw2IZz7OKVk3bg31Xe7cqbacva8Kz0l4WbD3VcHNsM3zmI9ca1PhJcwHF8cazWN9cU7KXjVWsb7g1jfnljIfeyrP7E9gPrg5Ykgc6wtPpMQtYn3B5fHFscxHrjWgykLqu9xKTJ3L3lfFnOx5Moa9rwqu2gyOZj56qvXbYpgPbtZZHs3eV4Xn+K0o9r4quHo5jfrItUbOjGI+uFGnQRTriznZFcNjWF9wcScxmvmYq3mlfgzzwWX1BdGsr/aydkSxvuBqXF/qI9deLakPrmqMiWL7inPSef7n5XRfwQ8G/r4K9XFOOVc+X8p8cFFlA91XePKX5fPZvoKLD56Zy3zkymYV51Hf5U7dKnNYX5yTdtPutC+4mf35AubjXHXu9KI+uDX79Xmsr869kkD7gqv778YwX+e+15D64HKfNZvtK+ZkPUqazfYV3CjcPZr5eq6LP5zNfHDb91w021edm5NA9xVcPtGX+26uatmP+uBy6HtRrC/mZPQXtK/m8+KimI+5ilP+KOaDW+fK0b7wzPQXaV9wq1tuJPORa9ZaFsl8cKP6L5HsvornpFZulyR2X9W8asMDzNfPVe1Wkvng9rZhO9l9FZ6Y3WgXva+Ct/99M/ORq/46vZn54LK2bz3rq5+TfhaznfYFL3txE/P1c6olNTYyH9wYWXst6wtP1i2/nvUFF29+uJL5yLWqf7CC+eDGig7L2H0VczK6TFnP7quaTz2ygvnoaZzpv5L64OdWL2P3VXjq1N2l7L4KLrPrLGE+cuWldwXzwWWV0QtYXz2nRieXsb7gljlKUN+dq3yqnWA+uL2y5ELWF55tFVvI+oKbx/rGMl/nfh4Vy3xwOdQTw/YVz0mNiWNXsX0FV/kx8czHc0o1eOoy5oNb2aXj2L7Cs5O7LWX7Cu40SlhCfTdXriq1mPngxlOb57O++rlqzS+WsL7g9pGmC5mP56rm2tnzmQ8u/t0SS/u6nkhZNo/1BbdvdJvLfOSKxx6by3xwM3xMDNtXzMkoEbOA7qvLnfyuscxHT/uEby7zwe3d62LYvsIzI27FsH0FFyo7mvnIFX+kRDMfXL79bRTrizmZ74yMYX3BpT+QE+pjrk6lV2YzH9xpkhHF+sIznh8aRfu63OzYPJL5yDVWnohkPrhQ7u8fDLmv4pw0/hg+n91XwQ2z+lzm45xytrn/+4f44M6gH2PYfVV7RW7OZvdVcGP5J9HMR649OTqa+eBqdMso1hfnpKx1MIb1BTemnIxmPvbUDsuJZj642t01ivWFZ/zVMYr1BRfzP49kvs59sWEk88GtMt9H0PuqOyfzzlv0vgpurRsTyXz0VC1/jGQ+uHHyVAS7r8IzXylL76uab/kngvnItbuMiGA+uLWmUwTrizlZlSIjaF/wnJIRzMdc1fCRs5gPLqYsmcn6wpMrImexvuDin66zmI9cmW3OZD64/e0/M9i+4py0xneKYvsKbn39RiTzcU5ZLStGMh9c3no9gu0rPKNz4LuKofuqeeO/ZjIfucY/G2YyH1yu7D+D9dXn5ITmEawvuGinZjIf56oSebOYDy7a95nJ+urc90bPYH3B1Y3vpjNf5y54jfrgolip6WxfMSd5IHUG21dwa8jrM5iPnqr76OnM1/y3s9PYvsIzVm2ezvYV3EiaOZ36bq4T3nca88Htv1pOY331nOwx01hfcLv6zanMx1wd2Zj6mt9LmMr6am/o2KmsL7jK6zKV+ci1Lnw6lfng9pM1AryS+3OLJrk/r/IN9+eQ/berID+3swA3N3LfXOr+vKTUXdQHd/4smGs/3FHQe2dXAS792wtwp/dO6iPXPrCL+uBmhf+jb3TAU7V5X3BjFvftvwI95c0d1Ne8/07aF545dRfvC561k/rINYfspD64WFhwrmrstgJzckrvKMDlz1sKcNuzlfroaezbQX1wq0HBXKvXxgKe6L2pADcuri3A5em13HdznZsbqQ9urVhN++q5Nt1B+4KbxbdSH3MV3bkPbhbifeE5X2+gfcHVhnjqI9es9X/4LlcPFvJ93e/+vJgb2+i+gpu/bKe+yN3k/tytLdQHdxZu4fvqevK77XxfXa6e2EZ95IoSW6gPri5vpH1Vj8C85LhE2hfcXJVIfVnV/bmC/66hPrg1bA3tC0+230D7gsvta6iPXDNvJfddbtZcRvdVz/XEJrqv4E7EWuqjp1F4HfXBnaLL+b7CE3F0X8Ht9rOoj1x73mzqg8sKo2lfzMkavpz2BRdhc6mPucoBsdx3uZ06ifaFZ24fR/uCW7U6c9/NFW/15L7LjTP1C5yrUsUXOCeNP5YV5M0WFOBW6TnUxzkl7y/mvsut4dML5lacVsAz5zoFuLNoUAEupnahPnKNEQOoDy4WfET76nPy7xjaF1xdG0N97KkYHU59cFmjO+2rc/u3pX3BnY5lqY9c2fkt6oNbjdzvarhzlXNaF5iTqFWzALeLly7AzfqpPubrng8f+Jiveec5vgK5YwM/B1x79wb4grnxxacFuMw75qW+m+tYgZ97H+prvjjw3ebQvpiTXWg37+tyMaIW9TFXue976oOrJOFlfeGJ3M68r8uNsvXymY9c+9UZ+cwHdy4U8bB9xTmpNv5M9xXcifuI+vqcqvQV9cHNyJJ0X+E5NQI/F+5/7avLzW8n+5iPXDNsHfU1b/Kij/XFOWkurkz7gqsmgZ87H+rjXLWeyPFR3+XO/gG0Lzx74ye0L7jMHellPnKNkTu9zAcXee73iEL2Vc/15Ztetq+a30nLZ77u+X1d6ms+I9fD9hWe/GK0h+2r5k/PyGU+cp3dr+QxH1xM2JfN+mJOzs+f077g8rNVeczXc31Ulfrg0iqXS/u6nrgzIIf11fzvxZnMR679bv8s5oPLW++ns/uqUzHw80wtO4HeVzU/vZz66n0R2OeYJdQHV4MW0vuq9voJel8FlwlzqY9cOTSG+uDG4Mm0r/gx8PNszQFzaV9wu3wU9a3wyMCvh2ZQH1wOmUT7wjPDwmlfcGv/EO67uc5zw6gPLuZ1pPdVPSdjMr2vgpsJ/aiv51p0EPXBzaKt+X3V9Yz9H/H7qsvVlIc+5iPXjC3JfZerV7f4WF/MSdZpS/uC26kVqY+52jlvUV/zdbk+1hee0eUs7+tya8aXPubr3AajqQ8u30n2sn21Itz/3buPp/uqec1h1DcG/hrYs1pDqQ9u3O5G9xWePdqm+wou/n2f+jq3ejPqg1snStG+xiuD3eeO3WlfzUe04f6iwPdCVdI31Ac3ZtenfeHZs9+gfcHN5zN9zEeubFWI+uDOk2t9bF/1nAYbdF/BjbVxPuajp1XsN+qDO0Wb8311PfnvM3RfwWXbml7mI9cOm0h9cGubnc/6Yk6mMYb2BZf1UrzMx1yNopWoD+58+YWX9nU9p9+1fNYX3DrW08N8nbvmOeqDC2+TPHZfxTlpN5xN76vg4uRtL/NxTlmlKlEfXDzTysvuq/Dk94HvpoXeV8HF54HvFoX6yDWsZ6gPLt5pncf64pwUUyJoX3Bj7qB85mNPnRrn8pkPbhe3Payv9nZdyWN9wWUn9+cGhfjIVa075zIf3Ph8Xxa7r2JOVvMduey+Cq4KvZPNfN3z6b+ymQ8u6j6Rye6r8Iyjr2Ww+yq4LLZeMV/nhi9NZT64XbLmddYXc7IeT89kfcHN7LlpzMdcnVWz0pkP7rxcTLG+OndR2ZusL7iTVDuF+Tr3zyevMB/cuLb0HNtXnJNWqTQP21dwu8czeczX59Qnu/OYr3mDkTlsX7VXOfDdrtB9BZdNZmQwH7li6+xM5oOrx2ul0b7uOWkO7pTL+oLL9JVZzMe5qjYey2Y+uHPC/XlHIX3hmX90Tmd9NZ88+hbzkevcKZnKfM2/3XmV7SvmJCNfSGX7Cm4l3r/KfPR0Wr9yg/ng5sMal9i+au+5Ly+wfQW3jn9zivk692zls8wHt39dn8T66rmOanGZ9QUXz15NZj7mKqyb56kPvqLpSdYXnvPle8dYX83ntTrAfORaPww4xHxwWbvMruDnqqLjAm+B56rjAvcGcDFgeX4wtx6f5WE+nlM6xW54mA8uV1TNC851unzlCfas+4HvloKLhTG5wdwoEZvNfOSqGn9kMx/cSP4wk/XFc1K5cHYe6wtu9OqXQ333OZU4dDeH+eBOg45ZrC88e8jeTNZX8z9EGvORa1Vvks58zRv9dit4rrJN7+zgOYnevowCfOfVjGButvknlfno6eyrmcZ8cHPcXzeCcy2reFqwJ49cuBHMxfguN4O5sp+6zHzkOvfOpzAf3JnQ4xzrq+dUKv4m66t52waXma/nWr/cFeZrXvu586yv9o6POcf6glvtR55gPnKd+sVPUt/lKqrqYbav+rlqhWLZdF9dbt97mE599zmlXHQyg/madxmYSvfV9dTvCxTbV3A5o+NV5uvcg5nXmA8uv/ryIuuL56RGzNdprK/mPcUN6rv3VCt8+C3mg6sNEy+zvto7cvES6wsuZl8/y3zkqrCPzjEfXBV/6zjbV8xJpT51ge0ruHre/e5fiI+e9q3WZ5gPbp9pmsT2FZ6ocPsw21dwtavNXuYj1xjy7z7mgxupDbeyvpiT3fzpo6wvuBy6WTIfc7Xuv3+Q+eCqRPoO1hee3fLkNtYX3Hrl/HrmI1eII4nMB5fpUxKCz1XR5s7l4HPS+i7nXDA3UswLwVwtaHWK+Tin5KnkE8wHV1mB707p3COlzhbIXTI9KZjbV1YcC+bqqR4HmK9zL9zex3xwM7PWdtrXPSflzmcOs77g5ron9jIfe2qUjtnDfHCj5htbWF94clGhrawvuOVJXE99N1deGruW+eBm5T7Lg+dqtYg9HDwnkfba7gK8hZTB3Lg9bSvz0VP8u2Qj8zX/u++aArlDDhbwrNNhG4M5vqsKLjParaO+m2sffRjPfHCV+tlS1hdzUp0vJ9C+LjeGjItjPuYqO/6ziPngdoPLc1hfeOaH2QtoX5cL++u51Hdz7VGB99pDfXDRvOcstq/6nDzWagvbV3C1qvha6rvnlJrvWUt9l5uV9y5j+wrPfGziMrav4Gr2ygXMR65o1GoB88Hl6N7RrK8+V987s5z1BRf32i9kPs5V+4nBi5mv+cO7s1lfeKLMoWjWF9y6dGE685HrtCk3k/man30tnO6rOyfnOffn9oXsK7hY+fks6rs95e+PzWA+uJXdIZztKzyzyd9T2L6CW1e+C2c+cq3/5k1kPrj12t0JrC/mJMsvDmN9wU3/zAnMx1yNjAUTmA9utp4znvZ1PXPSifGsL7jT6vgE5iPXWTSO+uDixxJh7L6K56TGts+vsvsquPzj5Hnm4zmlXXnwReaDi3afnmH3VXjWY4tPsvsquHm7yGHmI9faWjyJ+eCiUOt9rK9+Ttq4dDLrq3m5pkeZr5+r9lh/nPngcm34QdYXnhp6ULK+4E6PnduYj1xrZL2dzAc3Sv2eyO6rmJOT22s3u6+CG1ljNjFfz/WD0VuYD25NHLGG3VfhWauPr2T3VXCr9HrBfORauwctZb7mDV+ZR/u6c5Kdxq9nfcFlx+HLmY+5ytWbEpgP7txeuZj11bn+AwtYX3AZ9m0085ErS9eNob7L1cb1M9m+6uekJcYcZvsKLjbc2c18PKe0mhbZT32Xq043trB9hadeObGJ7qvL1YXXVzEfueL6xTXUd7lsP28p7es+JzX6fLid9QV3lmSsZz6eq9qZTTYyH9yptDmB9YUnXv51OesLbhQasoD5yHVSHy2ivsvNwu/OZvuKOalbmYLtK7ga/8Zc6rs9rd7vzmM+uONbE8n2FZ5xfmwE3VeXi8q1pjAfufK/NlOZDy5e+3US64s5ye/SolhfcGdDp2nMx1zNs/2mMx9cVY+YxPrq3LiYSawvuFPs9QnMR67a13o888HtC0fHsfuqPld7J89h91VwZ0qtCObjnJJVVkQyH9xu9NlUdl+FZ9wbNpndV8GF9+uJzEeuVb3vJOaD2/smT2B99Tn5Y9/ptK/Lxem1YczHnsqyf4VT3+XO+WMTWV94anBd2hfceaHxROYj12jdbiLzNX+9Pr2vYk6ihW88u6+Cq7fWUB89ne73JjAfXBUfFMbuq/CM8Y3C2H0VXDWfMZX5yLXO35rKfHCz8I4I1hdzsuqOCmN9weUL16YyX8+1aKepzAc30idHsL7w1M3TEawvuFFxSQzzkWu3HxbDfHB7X5+FbF9xTjovvT+J7Su4+rr2RObjnBJfH51AfZeLEgv4vrqe9Ung3ArdV3BRov0U5iNXNDkfznxwO3PTDNrXPSfNF76cwPqCWxszJjFfn6ubEiYwX/OWD8NpX3g3n53G+oIbb+2IZD5yDc+3s5gPbtbuHsP2FXOysvfNYvsKbh3bOZv5eq5NB0VT3+X2rqbz2b7CUyUC99b/ta8uF6WvLqW+myu6vrCU+eDi9surWF/MyTz/YizrC25+IQXzMVdzxuOLmQ9u/9ErnvWFJ1csWMH6an5heyLzde6yo+uZDy6KVtzG3ld1Cn8b+DW6CX1fFdxpVI761vBPA++bRtegPri9PPD7ckLfV9W5VS7R91XBRcvePuYj17Tjqa95hcDPhQ7tK63A+6x2xdK0r+YtU3zMVweqB/741EPqg6tHm2hfeFbCAN7X5SL6qJf5Ovfjd6gPLvb287L3VTEnY/oe+r4quJO0MZ/56KkefUJ9cHHmvoe+r+p6MqoSfV8VXC2/k8N85NqR5akPbnw8Ppv11XP68CDtC27/Xs/DfD3XHe7vtwvxwdXLdh7rC8+MvUv7gqvEB5nMR65KP0F9cFV2aAbbVzG6QuA98O+y6b6Ci7szuF808POgxZ6z3He5vbs33Vd4qsI9uq/gztoSXuq7ueZKSX1w67tN+ayvUzZw3hhF99C+4NZTLbjf6e/AufDDauqDi73VfKwvPPvQFN7X5fKH7/KZj1xn5Lde5mu+oDjdV8xJDVlK9xXcHB+Wy3z0VMdLepgPblzayPcVXtnFWXRfXW4fvZvGfJ17zf35syE+uJO1IJX1xZzUrSl5tK/LZd/8bOZjruqfknnMBzdet7JZX3hW8xEZtK/LxYZeivk6V8SmMx9c1Dt/k72vinPSGvIMf1/V5XZkRBrzcU4ZlRtQH9yuMEyx91XhiUkxN9j7quD2uTaXmI9ca2vLK8wHd7r/eI711edqpc60L7gannWd+dhTc/ls6oOrIu9dYX3hybg+F1lfcGFVOM18nVu+JPXBRdOFR9n7qpiT/P2xs+x9Vc2vv5TEfD3XRbOOMR/calnlAHtfVec+vXIve18V3Gw4cTP13Vz7s9HbmA9uDHlsHeur59Tls8OsL7j9aMou5mOu4j1TUt/lMnraFtrX9ZzO9xNpX5ebuUPjmY9c4axcxXxwwyuW0H11z0l58/oNtq/gcsWji8zHOSU7/XGV+eDqv+bn6b66noqaSvcV3BxU7zDzkWvseuI488GNuYck64tz0m7562XWF1w9XyqZ+ThX7TGTLjEfXBzYw/u6nvpqTBLrC65SWu5hvs6tknyI+i439/fdwfYVc7KrJe1k+wruvP9gA/PRU1gp1Ac3ptVcRffV9ez7A5axfQU37jSey3zk2rdXL2I+uDmjdCTrizk5t6psZH3BzeGBn9sS6mOuzu1/1zIfXA6LFKwvPPnBn3NYX81XNprCfORai2tEMR/c+TpnPLuvOmN9gfuoWkjvq+BWwgvUFyXzAvfR/rOpD260fNzH7qvw1Bb3uwsh91Vwe84pD/ORa/vLeJkPbrwZ52F95Sj397luj6R9wa19D7zMl/78wH9eMob6ms8yfKwvPOUp5mV9wcXdKR7mI9eeWNzLfHDrC/ePQ+6rmJPcmZjL7qvgYuTWLOajp5zcN5f54E7qV1nsvgrPmpadxu6r4NbBFjeYj1x1sk8a88HNlu9eZ30xJ7NIs1zWF9z5sUIW8zFX82yTXOaDqykvZLG+8OzeldNYX3Br3JJr1Hdz7b/LpDEf3Cw59hrbV7P/n4G/Hwct9rF9BRdrilNf7Xnk/n7eddQHt/tU9LF9hWfNqeBl+wpu5S72UN/NdZ5p6GU+uPPCMQ/rq4a6zwOGHaB9wcWKD7hf0gw8Rwy7Qn1wJ6uLj/WF5xTv5mV9wVXu4/nUd3OdN2O8zAc31rTJZ/uKORk9A7/vMnRfwUWfd7OYr+faYV4u88FV6sQstq/ay7XS2L6CyzHJ15iPXHl+YRrzwa3hEddZXz2ncF8u66v5+b+zmK/nOqdDHvPBrZlh2bSv6xlj30hnfcHNzZtvMB+5RomUdOaDq561b7H7Ks5Jc33Ri+y+Cm6VfP8k83FOWV8VuUB9lztzZh5n91V4drPsA+y+Ci4Lb9rKfOTKAfGS+eDqgXcz64tz0vx7xHnWF1xtX36M+dhTI7nDeeaDq+n9j7G+8MQ3/v2sr+bPP9jEfOSaTyzZz3xwo/eRTey+ijmJ9k1Wsfuq5nVqL2Q+eloJKoH5mpd9bh67r+rcIRdnsPsquJ3U5BfmI9domzSF+eB2r8IjWF/MyVgdF8/6aj542FzmY65OnB3PfM231J7L+mpvzKFw1hfcLjp5KPXdXLvO5HDmgxsfjRjK9hXnpPNr3Hm6ry6XzVOPMR/nlCzc5gL1weP+Os72FZ4953XJ9lXzxh9uZj5yRaJPMh/cLP/dFtYX56Tz0bcXWV/NZyScZD7OVbv83kvMB1fd/KdZX3hibq2DrC+4fbXsNuYj15ybdoj54Hb86B1sXzEndaFIAttXcNXSjGW+7jlh+grmg1t/1p/P9hWe8XveZLav4Eb6wuHMR67cfWka88HV0aujWV89p+L5q1hfcGdnv0XM13MtWXUd88HFtMB7kaF94Ynjr0Wwvpq/9+I45uvcXe7PVQ3xwdWBFpPY+6p4Tqr291tF31d1uXVuhmA+nlPa/8YuZT64MdM/l72vqr32Z2PY+6rgxojEGcxHrt18yCzmg4sbOyaxvnhOaq57MJ/1BVfNXo5kvn6uGtkrmvng8uMKk1lfeHJU7zDWV/MuYizzkWuv6jWW+eDmPDWMva+KOamG0yax91XBnZzO45iPnpYx0mG+5kX2jWHvq8KzG5Rz2Puq4Pacd8dR3821f+r3C/PBVcdW41hfzMlZN3Yk6wsunKkjmI+5WldeGMJ8cPPEzqGsLzyn4x+jWV9w2d43jvnIFc2njKa+y+WxUhPYvuI5qei9YBrbV3Dns6UO8/Gc0qrxwwTmgxt1PxvG9lXnzvhlKNtXcOOdgQOZj1wrYsBPzAe3H5/Sk/XFc1Lj9+ujWF9wM2nXT8zHPdWsPXwQ88FVjTldWV94RsrfXVlfcKPpvzbzda6vT1vmg4sXv/qW7SvmJFfVG8j2VfPYx6iPnqp/+b7MB3fMBoPZvmqvbrExbF/BVbVdE5mPXDW86y/MBxff3gpjfTEnp8HRnqwvuHix4WDmY67Gl1V7UN/lRvh/g1lfeKJC+FjWF9yYP2UK85ErLn84jvouNz87OJ29r4pz0kpdP5G9rwquwnOmMh/nlBr5fRjzwcX2wHvHoe+rwjNP945i76uCW78+Pp/5yFUpydx3uZy7dwnri3PSKJs4mfUFd37eGMl87KnwrZ7GfHB7wIzZrC88s+i9eawvuHyu0zLm69zwe4uZDy5bB+61oe+rYk4ic/9S9r4quOV8uYb56Gm36L6C+eDG2HYb2fuq8Jw5f26m76u63Nr2YA/zkWv1FDup73J1bftB1lfPaVXJdawvuPzr3FbmY65WRz/1wY1L1XezvvDEwQ8l6wvuVNx3hPnIda41P8x8cHlm3Cm2r/qcjGv3/9V15tE5XfsbPwgxpKYWt0qd1tDJpe3tdJX2oCilNbTVi3IoWnprqt6Wlu7WPA+JkBiyhVA1l5qitQmh5jFBSI4kJHmTvCdmUpffXes9z15v3j6/v6z16Wc9fdbXd23bcdbJLLav4Gat2/Op751T1g8Zc5iv+aDihXRfPU9UGxnP9hVcqbFrmI9ca3NeAvPB5Uf1NrK+OCetzqOiWF9wdaiLZD7OVXP+D/OZDy52PaB94andV1axvuAqZtBm5iNXjB+xjvngIrPeNravmJM65af7Cm62+0UxHz3NtVN3MB/cGd5sL9tX7X2deYDtK7h9zT3BfORaM4upD+7c63qG9vXmZN9Wu1hfzTc0OMB8zFVM2beH+eDO8X6HaV/PM+dup33BVfrhs8xHrhU+7zTzwdVt6wK7r+I5qZm2ehC7r4Kr/UPeZz6eU1rC14/54Co2ox27r8KTxV3fZvdVcJk8+X3mI9eeOrUF88Etf+NurC+ek4ouRX1YX3Bj9KqWzMdzKqX62cwHN955oQ3rC8+892Uz1lfzNQ99wHzkWlkvtWY+uDWmUQ92X8WcDOuRgey+Cm6//Z+vmI+eToVRnzIfXIzf/DW7r8Kztjw9jt1XNZfvzGY+cp2G0ycwX/MjM+ayvnpODT8ezPqC268nj2Y+5ipanhvCfHA1Jnks6wtPTRg1mfUFN2e9NI/5Ovez8OnM1/xW5wVsX/GcVL6Y+wnbV3DZ/3Zn5uvnlM0GD2a+5k3Te7J91bkd3unM9hVctu7Yl/nItTrtoz647NntU9YXz0ntAdeGs77g5qX1A5iP56oy/8VvmA9u9TkzlPWF52xa0p/2Bb/3r6HU93LFnFJDmA8udx8fyfZVz7X59BFsX8FVfOEPzEdP+/01/2E+uDX4sQl0Xz3PvPDMbLav4GabuwuZj1xr1p65zAeX4Q0l66vnmnbzW9YXXByImMJ8zNX8/N0fmA9uPOObwfrCkx9cmUf7etzcuSme+ciV5zfFMB/cab4rgd1XcU7KbmUWsvsquGVXXsF8fa5GRcQxH9xMmEjvqzr3uYMb2X1V8/OtdjIfuebQnzczX/PJgxTri3NSpaQuZX3Bzfoj1zIfeyq6/ric+eBOxPUNrC88lZixlfUFlxF5e5iPXCt1eCLzwY2kpfvYfRVzMua4+9h9FdwMG3Gc+ehp3v3oD+aDi0mHTtL7quc5b4xNZfdVzavOz2A+ctXsAeeZr/np3pdYX8xJfXPvMOsLboe9cob5mKtRNPg488GF72faF5495o2LrC+43epMJvORazxpOMwHV07jbLav+pzsPW8l21dww/pmE/NxTtllG65mPrg96sMtbF91bovPd7F91fxI9AHmI1fuKdjNfHAr8fAh1hfnpP1jh/WsL7i5efF25uNcdb6q/gvzwaXV9zfa1/PEzoF7WV9wu2v7Y9T3co0ef9/PfM3HTDnB9lXPtfXwk2xfwUWHhueZj55m/OzTzAcX25deYPsKz0jreontK7h4YuBl5iPXbh2dyXxw58aPV1hfPdf7B1JYX3CRWzeD+Zir07P2OeaDO+UPO6wvPNnkYBbrCy7LBN7zC/WRa10PfHc91Ae3HhuRG/xdVfnIp5tLfFf1VNdVwdyZ/fgvwdzIm7SC+fo7pfU3xDEf3Kw6ZVFwrho3dEOwJ7stXF6C//DRumAuOrRfxnzkylpxscwHN36dHcP64jupMmVKFOsLbn8UFsl8fKfSOLJtOvPB7ZM501hfeE6XT+bSvh4X5fvNZT5y1ZYh05kPLl91Z5SYa9Ina4LnpJ7uEx/M7VqHfw7mTqOtS5mve0bUjGE+uHn47oLgXHvW4FUlcvu+vDSYG+vq/xTMjSP7JPOR67z3SAz1PW73eTuG9cWczB5H57K+4GadcZHMx1zNP6rPZj64dXroHNYXnt2hfxTrCy6H95rHfOQ6g/+cy3xw63AC3Vd8J9VI2jaR7Su4tXDaJObjO6ViU9F45oPLJxtPYvsKz/jX6Cl0Xz1unHmb+sg17389lfngYuGWmayv/k7qgGYTWV9w87VrU5iP71SJ6eZU6nvcmLdtFusLz26YNZP11bzu5LnMR67z56hI5oObfzyYz/YVc7J/OjKL7Su48cXNOcxHT/NEPvU1j8ul+wrPfv6tKLav4OLa/mjm69z6nRYwH1xOq76I9dVzrd06mvUFV+6qWOp7cxXJkxYzX/NXa8SzvvCsl3bHsb7g9sikeOYj1/rgXgLzwcWR0T8Hn6tWzOUVweek3NlDBnM1eUVCMBdDUuOYj3NKXhExzAc3O6yPKZH74ifLgz2VlrskmNt16i4L5mJozmLmI1dUPhhDfY/baw/F0L7eOWn4ekSzvuAiput85mNPzZZ9o5mvebUnFrC+8FT1fy5gfcHV7bLUR66deZb64PLb7xYGz9U6f3ZpiTk9c2ZRMJe9F8lgrn7YspD5eq7TN8YwH1zV/DYmONepkbakhHdudmwwF+/GLwrm4sNBMcxHrlXumRjma565cwHrq+c055cY1hfcif5nLPMxV3XxwSLqe9xY3X0J6wvPrjs/lvb1uEw4EMt85Io9o+KYDy6Lm0i2rzgnZdLIGLav4MaZ5ouYj3NKnaopmQ9ubWwbz/ZV5/qqxLF9BTc2F0rmI9eqMmM588FVpbQVrC/OSTn7dgLrCy5PDFvFfJyr1veT1zNf87TJv7C+8JzxddayvuDixrANzNe5/b79lfng5rqJ29i+Yk7mmDbxbF/BzczAz1X4i+/1tIbVW8V8zXPbrWb7Cs+8uXQ521dwVTsqgfnItca3Wst8cNn5wTrWF3Myz3X5hfUFV02e2Mx8zFVkzEmkvsedqB9/Z33hOWOP/cr6gjtl39vKfORam+MU88Gt+ud2s/uq/q7qjz1ms/squNnr6yjm4zulss+b85iv+dD2C9l9FZ7d60AMu6+Cq4OWpL6Xaw+Zt5T54LLH0hWsL76TKp5/fRHrq3mrmfHMx3cqjU6ll1Hf4/L38atYX3j28kE/sb7gVlXvexohvs6NrbmR+eBy75Yt7L6KORl//Hs5u6+Cyy/EKuajp3Xk9hrmg6sG9i/svqq99Gnr2X0V3HDe3sR85Bpry29lPriz5sudrC/mZA47sJn29bhRb+0O5mOudp+TvzFf8yYtk1hfeGLzecX6gqvI3L3MR66T/fEB5oMbc+ofZfuqv6taK3kV21dwJ3zxRubjO6XG7e82Mh9cOM23sX3VuWvzt7J9BTe6RCjmI1e8uFAxH9xxeySzvvhOqrN+8VbWF9xpNU4xH++pWgdn7mI+uHH+yWTWV3svbUxmfcGtfww5ynzkOmc7HqW+x83oGqfZvmJO1pxn9rF9BZe5HQ8xX8/14wFHmA9uDzh4ku2r9gY/e4Ltq+Z37p9mPnKt3LdTmQ9uFLpptK83J5l88BTrC24eST/LfD3XzOXnmA9ubP8yg/WF51j102lfj5st5CXmI9d6emom88HV+BqX2X1Vn5OdWm5l91Vwp/biRObjnDI+abeb+h537sXupfdVz7NLBb47FXpfBRc7f9zDfORaabUPMB9c3Fx5iPXFOWn9NugP1hdcdmp2lPo4V1s/f5L54GrxoDOsLzzj8+gTrC+4aOz9fM4QH7lmm9izzAcXny1NY/dVzMkum7mX3VfB5b6n91Pf6ykmPHGM+h43PnrpBLuvwjOXtv6D3VfB7bVPHmI+cuW+jSe5H+DixwenWF/MSfXsmcL6gjsF352lvjdX69UB6cwHd74Y7LC+8FTHPudZX3BrTXEa85FrNxp5ifngZs/3Mtm+6nM17NZZtq/gMvLiBebjnLLutnCYD26N/Fcm21d4YvJbDttXcLso/xLzdW7uK9nMBzc+PXKZ9cU56Uy8lcX6gisV+O5nqI9z1ajSMof54GJw2TzWF56TsTaH9QV3iivkMR+54oVBPuaDy0dG57N9xZycEz2z2L6CO/acbOajp/lznRzma17u2Vy2r/DE2JmX2b6CC6f7Febr3Ln/zaW+x826Mo/29ebkjH3MR/t63H7F72M+5urEtSugvsfVW04B6wvPOjgjn/XVfErgu0OhPnLNRoHvqof64DJHFgY/VzXXRJV4Tipff2tBMJd3Ni8I5vbGBvOZj+eUztbXqA+u7i+ZXyK3TJn5wZ7KiYgO5uak7+YFc2fQ/SjmI9eKuxbNfHBZ+tVo2hfPVV8/Esv6aj4/IZb5eE5lfPS9ZD64uWuNZH3hqabvx7K+4LLpxRjmI1eu2CqZDy6ej5fBcxUNHo8qMaexxZHBXLTMnhvMnVGlI5mPns7jw+ZR3+Pqy/gSucZPh+YEe+bNWiW4dE7MLsGd5tRHrn39ZCTzwe3z5WhfzMno2jaG9QWXsdELmI+5itLDJPPBzfuB985C++rcO2fm074elxci5jMfucbRyDjma/4gaQnbVzwnNQ69v4LtK7gyve8lhfh4Tmmnv7aB+eD2yQMb6L56ntPw5ZVsX8HN0x+sZD5yrcK/b2Q+uPnwoI2sr36ueiVqG+sLrh7EbGc+7qmq3uU9zAd3uvyexPrCMzv13MH6govUczuYr3PX9NvLfHBlndrL9hVzso+3Xcn2Fdx0K69kPnqqT8dsZD64eXnoRrav8IwFW1awfQW367RbwXzk2qltNzIf3OlcYSPrq+f0Vu1E1hfcfLJBIvW9uZonHt7HfHCj2bP7WF/tVSmVSPt63ElL3MF85BqH6u5jPrgRf2tv8LnqSN+s4HNSjP6yBJfdB88M5sbw76iPc8oe03ou88FVtZlzgnPVwidnlPBee7MElxUfmR7M7Tovcd/LdUblz2Y+uFHLpn1xTjpHAu9thPYFt8LHzWM+9tT8MG8x88FF46qLWV94Vv2TUayv5mmB7/aE+sgVvtaLmK/5kpkLg+cq1JvTgudkDu9Tgqs/V0wN5uaJvdRHT9UxYxbzwUXHZrOCc63THaaWyPUPnBbM7XKNpwZzNaLdNObr3MlnZjIfXK7YNJP1xZyc89sjWV9we1GvSOZjrsbWO7HMB7fTJsfSvvA+rh9J+3rcqlo1kvnINU+1jmU+uBzbOJbtK85Jq+bBBLav4PL82wnMxzklE37fwHxwdfeLDWxf4akKh5ezfQV3mvdcznzkmjl1NzAf3P72p/WsL85JmdhnB+sLrqJub2c+zlW75m97mQ9utBi1l/ZF7jczttO+Hhetnt3OfOSak1/cy3xwc5ybRPfVm5Nx4t4ytq/gzv5flzEfPdWm59YzH9yufWQd21d4ctuUZXRfPS4OjV7GfOSqp+auYz641WDsOtZXz7X7mW2sL7g9KXCvC/UxVyfh1yTmg8vmU5JYX51bHPgua2hfcKtp723MR67s+UUS88GNC58ksfsqnpMas3YcZvdVcLMw7Cjz8ZzS+qPWGeZrPvTOGXpfhdfZOMbuq+BO1PpjzEeu/Cw+hfngxtOPprK+eE5qftHxIusLrh79MJ35+rnqi/czmQ9uDBifxfrCs9vUyWB9wcXkhAzmI9cUV7KYD272eSab3VcxJzEz4ji7r4Lb1588znz0NG/2TWU+uN1tXCq7r8ITXczj7L4Kbg6reJz5yFXfjU9lPri8+u9U2tebk7P5bgbrC27EVXWo783VSe+SzXxw89f+2ayv9gZWd1hfcDm6nMN85Dq9BmYzH1xV7pnN9lU/J511/QrbV3Dn8qc5zNfPVcsEfm5hqA9u/DTCx/YVnizekcP2Fdz+5FoO85ErPtjgYz643emsj/b1npPK8J4FrC+4empBAfPxXNW4ea6Q+Zr7vZ//EdJX5/beXcD6gpv3zhcwH7lWjdp+5oNbCU/72b5iTuqJh3PZvoKb657IZT56GjcCz6VDfc3/e9vH9hWeU+HJXLavmp+slct8nbvglo/6HldTCn2sL+ZkDs0toH09rn4oKGC+nmu9pn7qe9waE/g1tC88Z0JeAesL7nx1qYD5yDV6PednPrh8JPBzXUPvqzgnReejx9h9FVy88M0x5uOcMh9/K5X6HneqVEtl91V4VkqdY/S+6nGny76jzEeu+fr5FOaDi3cTUlhfnJP21UsZrC+4M3p1BvOxp1Zi22zmg1tl/57N+sJzYodlsL6aT3w1g/nItV+rls18cPFG4N8lQ++rmJP1xqij7L4Kbi544yjzdc+qX6dQ3+PqnW4p7L4KT6199Ci7r4I7NR46ynzk2onNU5ivebmXU1hfzEl+VzaD9vW4WJ+eznw917npWdT3uPXC4SzWV3vPJaezvuDmwd/SmY9cY67KYj64WrAzi+0rzknRtVwu21dwMaEoh/n6XO2Q4WM+uF31qI/tKzyz7rkcuq/go/fkUN/LlSmJPuaD271+9rG+OCfNiJQC1lfz4wcKmI9z1TwR+HM21Ae3oqv6WV946vHEAtrX4+KZdQXMR67sUM7PfHC59V4h21fMSSWsy6H76nHROi6H+ejp1FvoYz642X2Wj+0rPDVlXg7dV4/br8zNYT5yzZxJPuaDy14TfKwv5uTcjC9gfcFV99gC5mOuxpQbhcwHF+39hbSv56n8qALWF9yqHlnAfOSqPr5C6oNXzCtk76vq76Se2r2Xva8KbnwWfoT5+E6pXbTnIPPBjaLJJ9n7qvBkqxWn6PuqHrePzT7HfOSqp1aeZT64+qpjBuuL76Qag88dp309LpOKU5ivv1P5/drTzAc3Hwm8d/OXvp5nJKRfZH3BhTs0k/nIVY+NucR8cBV1N5u9r4o5mbEpGex9VXBrUuDnvob66CmuupnMB3fq+C6z91V1bvUdl9n7quByfaVc5iPX/npQLvPBxZB+PtYXc7IWr7rM+oKbrfrlMh9zFZ3v5FDf46pJdx/rC898baKP9QV3JtcrYL7OTaldwHxwO7NXIdtXfCdVjdhylu0ruNz3uMN8fKdUpO2/wHzNP+mYyfYVntx6LovtK7h14ekc6nu51mT/ZeZrfvlmLu3rfSfVqNOL9gU30tpkMx/fqbKW/YP64PZrpa6wvvCMpb/QvuB264E+5iPXSHkqj/oeN+Mm5tN99eYkKp7OY/uq+YFT+cxHT7myTT7zweXNwN9bQ/dV5459s5DtK7hd859+5iPXXF3Zz3zNOx/zs76Yk3y0dwHrC2422l7IfMzVvNq0kPngdrWOftYXnvg2jvYFtza/5DIfueJ+GZf54PaCb1z2virOSdH5Sh57X1Vz0S2f+Tin7JpOPvPBrTu3C9j7qvCcygMK2Puq4Fb7JoXMR679/O5C5oOru+/5WV99Tsq2hawvuBxX0099b09Fg+Z+5oMbz572s74699gKP+sL7lyr4DIfuebwZi7zwc05Y132virmZP5bFbL3VcGNIc/4mY+eRpfNfuaDOw2L/ex9VXhm2Eg/e18VXDX4yc985Dq1n3WZD25/0MFlffWcvujksr6aHxntMh9zNQeudpkPrrofcFlfnbtlgcv6gttqNfV17oDz1Ae3irJdtq/6nJzh0H0Ft1u+5zJfn6vh3jxCfHDnxmKX7av2YubzffW40z6Z+sh1Pj5BfXA17KrL+uKcVPsjaV9wRxyjPs5Vq8du7ntclvPTvvBkvxu0L7jdtmwR85ErB4cVMR/caBBRxPZVzykti++rx8Xdm9RHT/V56SLmg1vXw4vYvsITzxtFbF/BVXIgJ9RHrrG1YhHzwe34SkWsr55rrPffQ/qCy3OVqY+5qhZVqQ9uhVWnfeFZj1SlfcHNP6tQH7lmh+rU1zy6WhG7r+I7qdY3Tha7r4KrE9/lMB/fKXX+3fsy88Hl6p257L4KTwz+kN5XwZ1dgT/XQ33kmp3bUx9cxVUrZH3xnVTLt/YK6wsuU/bnMR/fqTS7ncxhPrjzxQraF561phLtCy6eji5kvs6dsrKA+h53fIHnVKH3VcxJuIfofRXc6furn/noqQ68SX3NLxouu69qb3R7fl/1uLEhgfterrBHUR9cdExyaV9vTubfomlfcLvWqy7zMVdxPMVPfY/Lf/SmfeGZb8fSvuBW/xTqI9fYt4H64Ea3TJftK76TKmcfz6X7qrmdz3x8p9S+vzaP+eBq8r18tq8696OGdF/Bnc6d/MxHrpk9jfvgd6f7aV/vO6nmjqE+1hdcXBMFzMd3VY22takPbnW9VkD7whvjFLK+4Majys985JqvPEV9cOdwHt1XzEkcDuf76nFztnfvDPW9nqLz36kPrtrM4vsKr7Si+wpu9s2jPnKNlw9SH9x+vJD2xZysrzrQvuDOpCXU13P9pi/1wc2cVbQvPHnlOO0Lbi7xUx+5duMz1Ac3pJ/eV3FOyqbp9L4KrqoH7lWhvj5XM13qg1s1vPtoyH0VnrhTgd5XwdWgKtRHrjmR++DWZ4F7UWhffa4+W+yyvuDOsMC9LdTHnjpjHrjMBxdVytO+8ESDh3hfjzvXq1AfuXJcBPXBZWRlel/FnIyK1el9VfOM6tRHT1mZ++Dyner0vgrPmF2d3lfB5bhq1EeujOc+uDOwKu2LOYkbVWlfcPtEVe57c5UNq1AfXCZXpn3hWTer8L4eN5dV5r6XK44+RH1wMTLw+x66rzgn5W6D7iu4MS2wd6G+PldXG9QHtzqUo/sKT9WoRPcV3F7CfZ3bqAL1wWXHirQvzkmZ+MBlfTWfG0Z9nKvWK/91mQ9uRpamfbWXW472BTf2hlMfueaYMOqDq7pl6b7qOV2OoPsKbvaN4L7XU/apSH1wK6kC3Vft9ahE9xXcNipSX+f+Wp764FbvcNoXc5Ibwnlfj4sy4dTHXJ38MOqDGy+E0b7wxNWytC+4sTiM+shVtctQH9zYX6qIva+K56RWwiE/e18V3EnO9DMfzyntpn1c5oOrXZ+79H1V5K4s8rP3VcGd+bepj1zRYoTLfHDZZqTL+urnpA9vdVlfcOfyTurjOdX//uJPfXB5Jd9lfbW393eX9QVXh3+jvs7900d9cNU212Xvq2JOZve7fva+KrjT8jb10dOM+NJlPrgYM9Sl76vCG3rNz95X1Tw5n/rIlR8MdpkPLpv0d1lfzEmc2uGyvuDGqC3Ux1ydf2VTH9xanuGyvjq3+UaX9dV84mrqI9cec5764I7394DQfdXPVSO9cyJkX8FFozJFzNfPVTtXKqK+x+1nvXM3ZF/hmVmB8yl0X8FFFaOI+cg1vi5fxHxwa1A52hfPSa3SVWhfcPmfh6iPe6ptVaU+uPNjZdoXnjGjEu0LrgZXoD5yxYQI6oOr/oH/71/21ZuT+v1Puq/g9vVb1EdPq1FYEfPBzYOB35fQfYWniq/SfQVX4YXUR66dep/7HpfNinlfzOm58CLWV/Ns7mOu8uXy1Ne8fLki1heetaJ0Ee3rcfG9UcR85Br5ZYqYD27nen/+h7yvqs/JStl+9r4quJWfRn2cU+a0j13mgxsXPnTZ+6rwxJ+n/PR9VY+buw5TH7lyXGeX+eAytYNL+3rnpLUuwaV9PW49iKM+9tRwj1Ef3Lhz0GV94VmJsS7rC24+Fk195MqRydQHN6bvcdn7qpiTkZDsZ++rgqtye/zMR09Vr63LfM2jW7n0fVXPsxrt8rP3VcGtxr/7mY9ca4DlMh/cXvmmy/piTva7c13WF9z6dhb1MVfRexf1wa1yv7m0r+cZv89wWV9w+dQM7nu5dnQi9cHt2ol0X3FOmm1y6b5qfjaL+jinjPSb1Ac3Ll6l+wpPOhl8Xz1uj75AfeTa7/mpD+60z6d99Tl59x7v63H7vbvUx7lqVwzhng+uNt2jfeGZNW7RvuBmj+vUR67aeZf64EaL23xfvTmJnefovoLL7anUR091MZf64PJvOXRf4Vnfp9B9BRc1UqiPXBV2hfrgxk+XaV/MyWh8lfYFF/Nc6mOu4s2b1Ac3j1ynfeHZ3nOl0L7g4h0/9ZFrFVyjPrgjr7nsvornpOJ+FXpfBTeaVKa+fk55lPua34vg91XPc56PoPdVzetW5L6Xa1esRH1wo2IF2hfPSVW5h2hfcLtFJerjOZVzoSL1wZ16FWhfnduzAu0Lbg0Npz5y1bvh1AeX/ynL76venERYeXpfBTevl6U+ehoVwqmvefWy9L6qc2+UofdVzauXpj5yjefLUB9cDipF+2JOYsL/09fjYmUZ6mOucmkZ6oPLs6V4X88TF0vRvpo3NaiPXLO+QX1wq2rgOXTovuI5qTG8PN1XcHN3OerjOaWVWpb6mv8jjO+r5zkPwui+gsvXylAfuWJsaeqDi70G74vnpHXK0L7gSpSiPp6rGh0M7nvc6h+Yf2hfePbP3r8bhPQFV+28P+dDfOTaNQL3mlAfXG0P3AtC9xVzckaWovsKLkYFckN99BSPBf7eGuqDy6RAj7/sq+epJoG+ofsKbtf07l8hPnLtWbepDy6mBd6LDO2LOVm+O7QvuJI3qa/n2vc69cHVY0W8r+cZo67RvuDqe5f6yHVuFlAf3LmXx++rOCffN4rofRV8nLe/IT7OKXPGA+prvvAeva/CE95cQu+r4PaXd6iPXLN8MfXBVXog7y99vXPSaMP7av5dMfWxp9ZjxdQHd0bcpn3hqYd4X3CrzC3q69wnblIf3G5ynd5XMSe16Ba9r4Ib7biPnvZTt7jvcZF7g95X4amvb9D7quYv3qC+zn3qBvXB7SvXaV/MSfS/QfuCq1evc9+bq4y/Sn3NxxXxvp5n/3CN9gW33rpGfZ17xqU+uLXQpfuKc1I2v0X3VfM116mPc0olXaU+uNnapfsKzxx6le4ruDPDpT5yzYcLqQ8um+fTvvpcVQW0L7jRzkd9nKtO8xzqg9u7s2lfeLJOLu0LbnW4Qn3kyqhM6mu+y6H7ijnJKn66r+B2cQH3vZ4qOY/64PbWXL6vnmd3LaD7Cm7ULqA+cp3qudQHV2k5tC/mJFKzeV+PWwezqI+5ig4Z1Ae3Xk+nfeE5jbJoX3CzKJP6yFXRF6kPLvr879f/A6uTuBY= 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+AQAAAAAAAgAAAAIAjHIAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-080000.2.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-080000.2.vtu new file mode 100644 index 000000000..faeebfed5 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-080000.2.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAgAAAAIAvXIAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-080000.3.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-080000.3.vtu new file mode 100644 index 000000000..f4cb4e523 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-080000.3.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAgAAAAIAkHMAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-080000.pvtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-080000.pvtu new file mode 100644 index 000000000..2df00b1aa --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-080000.pvtu @@ -0,0 +1,19 @@ + + + + + + + + + + + + + + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-090000.0.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-090000.0.vtu new file mode 100644 index 000000000..053e76b67 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-090000.0.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAQAAAAEAxFgAAA==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+AQAAAAAAAgAAAAIA83AAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-090000.1.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-090000.1.vtu new file mode 100644 index 000000000..962e3ce52 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-090000.1.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAgAAAAIASHIAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-090000.2.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-090000.2.vtu new file mode 100644 index 000000000..909c14335 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-090000.2.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAgAAAAIAmXIAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-090000.3.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-090000.3.vtu new file mode 100644 index 000000000..52d3d175d --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-090000.3.vtu @@ -0,0 +1,34 @@ + + + + + + + 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EZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//A3532BvAAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH39D/TfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/y3zBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3zH9LfAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8DfvfIm8AAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff8P9t8AfgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//hXcEIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfCf6E9AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+F9wVwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/I/0X2BeAQAQkMEEIRlCCE4JQhCQ0YQhHWMITgUhEJDJRiEZUohODWMQkNnGIR1zik4BEJCQxSUhGUpKTglSkJDVpSEda0pOBTGQkM1nIRlayk4Nc5CQ3echHXvJTgEIUpDBFKEZRilOCUpSkNGUoR1nKU4FKVKQyVahGVapTg1rUpDZ1qEdd6tOARjSkMU1oRlOa04JWtKQ1bWhHW9rTgU50pDNd6EZXutODXvSkN33oR1/6M4BBDGQwQxjGUIYzglGMZDRjGMdYxjOBSUxkMlOYxlSmM4NZzGQ2c5jHXOazgEUsZDFLWMZSlrOCVaxkNWtYx1rWs4FNbGQzW9jGVrazg13sZDd72Mde9nOAQxzkMEc4xlGOc4JTnOQ0ZzjHWc5zgUtc5DJXuMZVrnODW9zkNne4x13u84BHPOQxT3jGU57zgle85DVveMdb3vOBT3zkM1/4xle+84Nf/OQ3f/jH3/+FdQYgEAEJTBCCEZTghCAUIQlNGMIRlvBEIBIRiUwUohGV6MQgFjGJTRziEZf4JCARCUlMEpKRlOSkIBUpSU0a0pGW9GQgExnJTBaykZXs5CAXOclNHvKRl/wUoBAFKUwRilGU4pSgFCUpTRnKUZbyVKASFalMFapRlerUoBY1qU0d6lGX+jSgEQ1pTBOa0ZTmtKAVLWlNG9rRlvZ0oBMd6UwXutGV7vSgFz3pTR/60Zf+DGAQAxnMEIYxlOGMYBQjGc0YxjGW8UxgEhOZzBSmMZXpzGAWM5nNHOYxl/ksYBELWcwSlrGU5axgFStZzRrWsZb1bGATG9nMFraxle3sYBc72c0e9rGX/RzgEAc5zBGOcZTjnOAUJznNGc5xlvNc4BIXucwVrnGV69zgFje5zR3ucZf7POARD3nME57xlOe84BUvec0b3vGW93zgEx/5zBe+8ZXv/OAXP/nNH/7xN8p/Ib0BCERAAhOEYAQlOCEIRUhCE4ZwhCU8EYhERCIThWhEJToxiEVMYhOHeMQlPglIREISk4RkJCU5KUhFSlKThnSkJT0ZyERGMpOFbGQlOznIRU5yk4d85CU/BShEQQpThGIUpTglKEVJSlOGcpSlPBWoREUqU4VqVKU6NahFTWpTh3rUpT4NaERDGtOEZjSlOS1oRUta04Z2tKU9HehERzrThW50pTs96EVPetOHfvSlPwMYxEAGM4RhDGU4IxjFSEYzhnGMZTwTmMREJjOFaUxlOjOYxUxmM4d5zGU+C1jEQhazhGUsZTkrWMVKVrOGdaxlPRvYxEY2s4VtbGU7O9jFTnazh33sZT8HOMRBDnOEYxzlOCc4xUlOc4ZznOU8F7jERS5zhWtc5To3uMVNbnOHe9zlPg94xEMe84RnPOU5L3jFS17zhne85T0f+MRHPvOFb3zlOz/4xU9+84d//I36X1B3AAIRkMAEIRhBCU4IQhGS0IQhHGEJTwQiEZHIRCEaUYlODGIRk9jEIR5xiU8CEpGQxCQhGUlJTgpSkZLUpCEdaUlPBjKRkcxkIRtZyU4OcpGT3OQhH3nJTwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH3+j/RfQH4BABCQwQQhGUIITglCEJDRhCEdYwhOBSEQkMlGIRlSiE4NYxCQ2cYhHXOKTgEQkJDFJSEZSkpOCVKQkNWlIR1rSk4FMZCQzWchGVrKTg1zkJDd5yEde8lOAQhSkMEUoRlGKU4JSlKQ0ZShHWcpTgUpUpDJVqEZVqlODWtSkNnWoR13q04BGNKQxTWhGU5rTgla0pDVtaEdb2tOBTnSkM13oRle604Ne9KQ3fehHX/ozgEEMZDBDGMZQhjOCUYxkNGMYx1jGM4FJTGQyU5jGVKYzg1nMZDZzmMdc5rOARSxkMUtYxlKWs4JVrGQ1a1jHWtazgU1sZDNb2MZWtrODXexkN3vYx172c4BDHOQwRzjGUY5zglOc5DRnOMdZznOBS1zkMle4xlWuc4Nb3OQ2d7jHXe7zgEc85DFPeMZTnvOCV7zkNW94x1ve84FPfOQzX/jGV77zg1/85Dd/+Mff/5VzBCAQAQlMEIIRlOCEIBQhCU0YwhGW8EQgEhGJTBSiEZXoxCAWMYlNHOIRl/gkIBEJSUwSkpGU5KQgFSlJTRrSkZb0ZCATGclMFrKRlezkIBc5yU0e8pGX/BSgEAUpTBGKUZTilKAUJSlNGcpRlvJUoBIVqUwVqlGV6tSgFjWpTR3qUZf6NKARDWlME5rRlOa0oBUtaU0b2tGW9nSgEx3pTBe60ZXu9KAXPelNH/rRl/4MYBADGcwQhjGU4YxgFCMZzRjGMZbxTGASE5nMFKYxlenMYBYzmc0c5jGX+SxgEQtZzBKWsZTlrGAVK1nNGtaxlvVsYBMb2cwWtrGV7exgFzvZzR72sZf9HOAQBznMEY5xlOOc4BQnOc0ZznGW81zgEhe5zBWucZXr3OAWN7nNHe5xl/s84BEPecwTnvGU57zgFS95zRve8Zb3fOATH/nMF77xle/84Bc/+c0f/vE3xn+lPAEIREACE4RgBCU4IQhFSEIThnCEJTwRiEREIhOFaEQlOjGIRUxiE4d4xCU+CUhEQhKThGQkJTkpSEVKUpOGdKQlPRnIREYyk4VsZCU7OchFTnKTh3zkJT8FKERBClOEYhSlOCUoRUlKU4ZylKU8FahERSpThWpUpTo1qEVNalOHetSlPg1oREMa04RmNKU5LWhFS1rThna0pT0d6ERHOtOFbnSlOz3oRU9604d+9KU/AxjEQAYzhGEMZTgjGMVIRjOGcYxlPBOYxEQmM4VpTGU6M5jFTGYzh3nMZT4LWMRCFrOEZSxlOStYxUpWs4Z1rGU9G9jERjazhW1sZTs72MVOdrOHfexlPwc4xEEOc4RjHOU4JzjFSU5zhnOc5TwXuMRFLnOFa1zlOje4xU1uc4d73OU+D3jEQx7zhGc85TkveMVLXvOGd7zlPR/4xEc+84VvfOU7P/jFT37zh3/8jflfMVcAAhGQwAQhGEEJTghCEZLQhCEcYQlPBCIRkchEIRpRiU4MYhGT2MQhHnGJTwISkZDEJCEZSUlOClKRktSkIR1pSU8GMpGRzGQhG1nJTg5ykZPc5CEfeclPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEff2P9V8gXgEAEJDBBCEZQghOCUIQkNGEIR1jCE4FIRCQyUYhGVKITg1jEJDZxiEdc4pOARCQkMUlIRlKSk4JUpCQ1aUhHWtKTgUxkJDNZyEZWspODXOQkN3nIR17yU4BCFKQwRShGUYpTglKUpDRlKEdZylOBSlSkMlWoRlWqU4Na1KQ2dahHXerTgEY0pDFNaEZTmtOCVrSkNW1oR1va04FOdKQzXehGV7rTg170pDd96Edf+jOAQQxkMEMYxlCGM4JRjGQ0YxjHWMYzgUlMZDJTmMZUpjODWcxkNnOYx1zms4BFLGQxS1jGUpazglWsZDVrWMda1rOBTWxkM1vYxla2s4Nd7GQ3e9jHXvZzgEMc5DBHOMZRjnOCU5zkNGc4x1nOc4FLXOQyV7jGVa5zg1vc5DZ3uMdd7vOARzzkMU94xlOe84JXvOQ1b3jHW97zgU985DNf+MZXvvODX/zkN3/4x9//lXEGIBABCUwQghGU4IQgFCEJTRjCEZbwRCASEYlMFKIRlejEIBYxiU0c4hGX+CQgEQlJTBKSkZTkpCAVKUlNGtKRlvRkIBMZyUwWspGV7OQgFznJTR7ykZf8FKAQBSlMEYpRlOKUoBQlKU0ZylGW8lSgEhWpTBWqUZXq1KAWNalNHepRl/o0oBENaUwTmtGU5rSgFS1pTRva0Zb2dKATHelMF7rRle70oBc96U0f+tGX/gxgEAMZzBCGMZThjGAUIxnNGMYxlvFMYBITmcwUpjGV6cxgFjOZzRzmMZf5LGARC1nMEpaxlOWsYBUrWc0a1rGW9WxgExvZzBa2sZXt7GAXO9nNHvaxl/0c4BAHOcwRjnGU45zgFCc5zRnOcZbzXOASF7nMFa5xlevc4BY3uc0d7nGX+zzgEQ95zBOe8ZTnvOAVL3nNG97xlvd84BMf+cwXvvGV7/zgFz/5zR/+8TfOfyW8AQhEQAIThGAEJTghCEVIQhOGcIQlPBGIREQiE4VoRCU6MYhFTGITh3jEJT4JSERCEpOEZCQlOSlIRUpSk4Z0pCU9GchERjKThWxkJTs5yEVOcpOHfOQlPwUoREEKU4RiFKU4JShFSUpThnKUpTwVqERFKlOFalSlOjWoRU1qU4d61KU+DWhEQxrThGY0pTktaEVLWtOGdrSlPR3oREc604VudKU7PehFT3rTh370pT8DGMRABjOEYQxlOCMYxUhGM4ZxjGU8E5jERCYzhWlMZTozmMVMZjOHecxlPgtYxEIWs4RlLGU5K1jFSlazhnWsZT0b2MRGNrOFbWxlOzvYxU52s4d97GU/BzjEQQ5zhGMc5TgnOMVJTnOGc5zlPBe4xEUuc4VrXOU6N7jFTW5zh3vc5T4PeMRDHvOEZzzlOS94xUte84Z3vOU9H/jERz7zhW985Ts/+MVPfvOHf/yN+18RdwACEZDABCEYQQlOCEIRktCEIRxhCU8EIhGRyEQhGlGJTgxiEZPYxCEecYlPAhKRkMQkIRlJSU4KUpGS1KQhHWlJTwYykZHMZCEbWclODnKRk9zkIR95yU8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/+b/4zj+BCEhgghCMoAQnBKEISWjCEI6whCfC/zdMD9liIAAAwPo6tW3bNn5t27Zt27Zt27Zt27YnixwhRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTQf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMh//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU2H/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTEf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNR//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUzH/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTcf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMJ//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU0n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTKf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNp//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUxn/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTWf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlM5//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU3n/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTBf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNF//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUyX/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTZf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMV//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU1X/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTNf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlN1//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmUw3/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTTf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlMt//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwmU23/+IzBBCEpwghGCkIQmFGEIS3jCEYGIRCYSUYhKdKIRg5jEJhZxiEt84pGAhCQmEUlISnKSkYKUpCYVaUhLetKRgYxkJhNZyEp2spGDnOQmF3nISz4CyE8BClGQwhShGEUpTglKUZLSlKEcZSlPBSpRkcpUoRpVqU4NalGT2tShHnWpTwMa0ZDGNKEZTWlOC1rRkta0oR1taU8HOtGRznShG13pTg960ZPe9KEffenPAAYxkMEMYRhDGc4IRjGS0YxhHGMZzwQmMZHJTGEaU5nODGYxk9nMYR5zmc8CFrGQxSxhGUtZzgpWsZLVrGEda1nPBjaxkc1sYRtb2c4OdrGT3exhH3vZzwEOcZDDHOEYRznOCU5xktOc4RxnOc8FLnGRy1zhGle5zg1ucZPb3OEed7nPAx7xkMc84RlPec4LXvGS17zhHW95zwc+8ZHPfOEbX/nOD37xk9/84R9/CZTHf/4jMEEISnCCEYKQhCYUYQhLeMIRgYhEJhJRiEp0ohGDmMQmFnGIS3zikYCEJCYRSUhKcpKRgpSkJhVpSEt60pGBjGQmE1nISnaykYOc5CYXechLPgLITwEKUZDCFKEYRSlOCUpRktKUoRxlKU8FKlGRylShGlWpTg1qUZPa1KEedalPAxrRkMY0oRlNaU4LWtGS1rShHW1pTwc60ZHOdKEbXelOD3rRk970oR996c8ABjGQwQxhGEMZzghGMZLRjGEcYxnPBCYxkclMYRpTmc4MZjGT2cxhHnOZzwIWsZDFLGEZS1nOClaxktWsYR1rWc8GNrGRzWxhG1vZzg52sZPd7GEfe9nPAQ5xkMMc4RhHOc4JTnGS05zhHGc5zwUucZHLXOEaV7nODW5xk9vc4R53uc8DHvGQxzzhGU95zgte8ZLXvOEdb3nPBz7xkc984Rtf+c4PfvGT3/zhH38JlNd//iMwQQhKcIIRgpCEJhRhCEt4whGBiEQmElGISnSiEYOYxCYWcYhLfOKRgIQkJhFJSEpykpGClKQmFWlIS3rSkYGMZCYTWchKdrKRg5zkJhd5yEs+AshPAQpRkMIUoRhFKU4JSlGS0pShHGUpTwUqUZHKVKEaValODWpRk9rUoR51qU8DGtGQxjShGU1pTgta0ZLWtKEdbWlPBzrRkc50oRtd6U4PetGT3vShH33pzwAGMZDBDGEYQxnOCEYxktGMYRxjGc8EJjGRyUxhGlOZzgxmMZPZzGEec5nPAhaxkMUsYRlLWc4KVrGS1axhHWtZzwY2sZHNbGEbW9nODnaxk93sYR972c8BDnGQwxzhGEc5zglOcZLTnOEcZznPBS5xkctc4RpXuc4NbnGT29zhHne5zwMe8ZDHPOEZT3nOC17xkte84R1vec8HPvGRz3zhG1/5zg9+8ZPf/OEffwkU4D//EZggBCU4wQhBSEITijCEJTzhiEBEIhOJKEQlOtGIQUxiE4s4xCU+8UhAQhKTiCQkJTnJSEFKUpOKNKQlPenIQEYyk4ksZCU72chBTnKTizzkJR8B5KcAhShIYYpQjKIUpwSlKElpylCOspSnApWoSGWqUI2qVKcGtahJbepQj7rUpwGNaEhjmtCMpjSnBa1oSWva0I62tKcDnehIZ7rQja50pwe96Elv+tCPvvRnAIMYyGCGMIyhDGcEoxjJaMYwjrGMZwKTmMhkpjCNqUxnBrOYyWzmMI+5zGcBi1jIYpawjKUsZwWrWMlq1rCOtaxnA5vYyGa2sI2tbGcHu9jJbvawj73s5wCHOMhhjnCMoxznBKc4yWnOcI6znOcCl7jIZa5wjatc5wa3uMlt7nCPu9znAY94yGOe8IynPOcFr3jJa97wjre85wOf+MhnvvCNr3znB7/4yW/+8I+/BMrnP/8RmCAEJTjBCEFIQhOKMIQlPOGIQEQiE4koRCU60YhBTGITizjEJT7xSEBCEpOIJCQlOclIQUpSk4o0pCU96chARjKTiSxkJTvZyEFOcpOLPOQlHwHkpwCFKEhhilCMohSnBKUoSWnKUI6ylKcClahIZapQjapUpwa1qElt6lCPutSnAY1oSGOa0IymNKcFrWhJa9rQjra0pwOd6EhnutCNrnSnB73oSW/60I++9GcAgxjIYIYwjKEMZwSjGMloxjCOsYxnApOYyGSmMI2pTGcGs5jJbOYwj7nMZwGLWMhilrCMpSxnBatYyWrWsI61rGcDm9jIZrawja1sZwe72Mlu9rCPveznAIc4yGGOcIyjHOcEpzjJac5wjrOc5wKXuMhlrnCNq1znBre4yW3ucI+73OcBj3jIY57wjKc85wWveMlr3vCOt7znA5/4yGe+8I2vfOcHv/jJb/7wj7/8D5W4wko= 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+AQAAAAAAAgAAAAIAcHMAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-090000.pvtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-090000.pvtu new file mode 100644 index 000000000..471b7dee0 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-090000.pvtu @@ -0,0 +1,19 @@ + + + + + + + + + + + + + + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-100000.0.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-100000.0.vtu new file mode 100644 index 000000000..16b924dfd --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-100000.0.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAQAAAAEAxFgAAA==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+AQAAAAAAAgAAAAIAt3AAAA==eNpsvXd4FtXavj2ioIhStgiIgkMRVFAQBRSBDEWk9yYIDL23BEhCXakkISG9kABZpPeELjWDSBEFQRFUBAZBUSmKSBEFvu/gmWv9njzv9RfHPve5r/fa977f5WIYJpqmyWEfBpRrmiaO9Xz0q5zU79Gv2sn+FbjeyYM3G0J9I3nco1+t30zqg8tfJlXINYqHVvC06aMqcOOtcRW4VjqJ+ip33Uzqg8tXFtK+1sNlrn9fX0H7gmsRgdTX9+W5eq3Jpz64/XoR7+t42oAw2hfcXh1DfeTqUWXUB7cvb6swV+vcrApzsmt6V+DW58sqcJkdSH30tNYHUB/cei22Qq75b2zF3CVJFbgYn1+Bi+Bi6iNX2pncd7h1aAfv68zJ9k6jfRV/PZ/6mKs5v5z64GLmEdoXnri0g/YFF3uPUh+5WoPvqA8uP/uV7+vztqv3KpvuK7h59wL1reFNAx/tVX/Xr54+uPRy/n2PfYVn+16i+wpud/iV+sg1+jr5Hj64WeT61bOv/uQi16/mwkDWV3FfH+rLM9+6/vWCk9x3uOx1PJD1hSfmzQtkfcG1iFnUR64R8Dn3HS5HWYF0XzHXkGt0XxUv+pv7Tk+j7JVA5oPr5c0D2b7Ck7Ue0H0F1+s+Hch85OpDWwQyH1z/u3Ug64s52S9NC2R9wcX6idRXc22zlfrgZmheIOsLT6wfR/uC23s/pj5ytRup1Ac3X40KdD9XxZb9Fc5JK+BQRd7brsDt4xepj3NK3/w19cH1F65XyBXH71fMbf5YoDu3fnwhsAJv0iCQ+ciVh5+mvuILmwWyvjgnZdEl2hdc6/OA+thTe/l/1Ae36j1H+8IzY1+gfcGNke9QH7laeQvuO1ws7R7oPldrcOsKczIL367AzewOFbjWsjP11VxrdqI+uHHnwwq59getKnjmY29X4PKpShW4cboy9ZFr9+xEfcU716R9MSeZ2Zv2Bbd3D6M+5ioWfER9cPvCRNpX5c7uQ/uCW0ZD7ju5ZquPqA8uGzjno8e+4pw0nnTthee+gsufXw9kPs4po8571AcXd7rRfYVnNfDi++pw6+4Q6iPX6DGI+uBm/DjaF+ekmT6a9gUXGz+mPs5V+7cV3Af/2of2hWf8Mo72BddemkR9lVt9OvcdLr4YS/dVzfXQZLqv4Nrh2dRXc507k/rgets5dF/hmbXH030FN3JaUx+52umPue9we1Nz2lfN6Z0ptC+4ODaO+pir3m8w9cEt2Yn2hSf//ID2Bdc3PUV95Bpv16E+uN3vywB2XxUHugc9+tW/axC7r4JL+X4Q8+U/+x/9amZZQcwH10q2BbH7KjxrTOsgdl8Fl9f0IOar3LdzgqjvcHNaNO2r3Rka7Nr7gcGsL7h+s2sw82XYP65/f/vNYOaD691/CmZ94RmhLYNZX3Bz0LPBzFe5bQ8EMx9cS1obzO6rmJOV/2wQu6+CG8/cob7qmeodxHxws+3AIHZfhWdtPEvvq+Bi2KfUR644/GoQ88G1x+8Hsr5qTpE/B7G+4FrYVuqruS6fFsx8cLvua8GsLzxj67Ig1hdcZnamvsrdcC6I+eBiYXgQ21frwOaQR97KohC2r+D2oaQQ5hvPhIY+8nstC2U+uLw5IZTtKzyx2DuE7Su4HuUVwnyV+1u7UOYr/tdd3veu98pHOenTVrK+4GZAr5XMF8OXhT3qZc4PYz64OaFfGOurcsc/v5L1BTfPfkF95FqVaoUxX/GyXSvZvmJO8ur9YLav4MaqYuqrnofzQpiveL0eIXRfHU9M6R/M9hVcj7Kpr3KjjgczH9z+9kPaF3My5s8KZX3BxZLb3Hfmamb2XUl9h5sXPg2lfR1P7z49hPUF1yp/Rn3k2t0ahDIf3BajQth9Feekvj+b3lfB9c1h1FfnVOFR6oMLkUbvq/C0gJn0vgpuZvXjvpOr3ZtCfXB5rBXti3NS/neX9gW3ZhVTH3tqPvl6EPPBjZMW7QvPmvMx7Qtu/VKZ+sg1OvWnPrjdwXne4XFfxZyM4y3pfRXcKH6c+ugpmzvP+zx8cN27NIDdV5U383AAva863GwwK4D5yDUqfcR98NEXBeur5urtei7+f/o6XJ9sUB9zNb7tQ31wfeQngvVVuTEHBesLrie3EsxHrj3uf9QHN1MzlrN9xTkpGk0KovvqcG3I94HMV+dUXmEQ9cEP/0v3FZ7d06D7Cm7dSAlgPnLNSu8GMh9c1nX9uZ5nX5yT2s3KwawvuNb7TerjXDXa+wczH9x+zC+I9YUnR3YMZH3BRXSrAOYj10hwPRfy9MG132vyfcWcCn8SbF/Bzb0vCuajp7Heh/rgYu3F5Wxf4ZkRAcvZvoLrR1OWMl/llm1aynxwUXPQYtYXc7KSn6R9Fb8Vtoz5aq6vDVnBfHBjpbGU9YVnXT+6mPVVfPxPvsxHruz0hx/zwbV2Mxe6P1cVuTkB7s9JjTYFAe5cD3w+wJ0b3etRH88pzX2bqQ9uL3u5Qq5ePkC4e9qrg4Q7N2/tWe7OtZauf+3pI1dOHyqYDy7q7VjO+uI5qdHGon3BrbqvUl89pwo4Qn1wu/4bAawvPD1ruGB9wa3couXMR65sO0QwH1zmRi93n6t8I2Op+5x0r3VL3bl4edpid26dHruY+ehpPxa1lPng1ukui91zjdPN/dw90a6+nzu3D/+90J2LCz8tZD5yzQV/+jIf3IwrWsj6Yk5G69lLWV9wbcuzi5mv5nqx3VLmg2u/7vZnfeGJB7m+tK/DtfoDFzIfudqWjr7cd3G75MgCuq/Oc1LrxS/pvoKLO82pr56r9t9DfXDjt5oBbF/h2be6Cbav4PrrI5YzH7nmqkaC+eB67dvLWF88J5VfJ9K+4LrvecF83FN1n37cd7hdbRXtC8/cfGgF6wsuTo1Zxnzk6jUrr6C+w8XgFXRfMSf5i72E7Su43aCnP/PRU75pLmE+uB6f48f2FZ7dOH8R21dwfWu9BcxHrnXul4XMV/ypdj6sL+YkFuQtZn0VTzvmy3zM1bxd5M98cKv7NtoXnv3nddoXXDMbelPfyZUTd3Lf4ca+o/Pcz1Vt8zkf93NSxBz2cefaj3/Nd+f2/O/mMx/nlHUixIf54Hr98PnuuXbLTvMq5PZvOM+dm43iZrtz40zv2cxHrp4TNJf54GbZwlmsrzonlzzvQ/s6XMQ8MZ/52FPjyUXezAfXV/TmfR3PMgbOYX0Vz649k/pOrnYjk/rgokuL6e5ztTp0n+k+Jyn+muHOtVfyp7tzeaDPdOarnh89oD64XaXtNPdcc8GZae6e+d+8ae7cmHp3mjvXm6+gPnL1bm9PZT649fHzU1lfzEn/bcY01lfxmtOnMB9zNaI7TWE+uPn39Ymsr/LGNp/M+oLbN61JzEeu+euwCcwHF1dWjWf7inNSS8mm+wpu9d86h/k4p7R2GdQHt9vNmEX31fFkx4AZbF8VP7F3CvORa1W/OpX6Dtd2n5pI+zrnpN3nJO0LblXfOp35OFeN9LszmQ9u1V4zhfWFZ737+yTWV/HbS03qO7nm08HjmQ+uVVv2EdtXzMlounwi21dw+/xxk/noKXq0N5kPrh15dxTbV5U7KWkM21dw+VTV0cxHrla51TDmg5vakkGsr5qT37WRtC94xoFBzMdc7S/TBjIf3PrwF4P1hadldvqQ9nW4tcinA/ORKz8LrMV8cHFhXFsvcl/Fc1J9/4/0vgouc6sJ5uM5pfipE/XBja5bl7P7Kjzb6wq9r4LLK32WMB+5ZsAs6oObn27zp33xXDXgh+WsL7h+6ZelzMdzKqPg5FLmg2uV8hezvvDkweWLaV+Ha7W3+TIfuVbfBn7MB7e3dV/I7quYkz12vB+7r4LLekMXUt/paZzYSX1w8Zj0YfdVeNblrvS+Cm40CZzDfOSaiUHUB5dzr81gfTEns/7GBawvuN43YT7zMVftoZc39R2urxozh/WFZ0fcnMX6gtu556YwH7n6k32nUd/hVq0xE+i+Os9J5dJ5i9m+gts7H/oyH88pxezdvswHN3bcWcD2FZ58s/dCtq/gcsNEb+Yj1/y2hjfzwfXj7eawvnhOqhV/tYD2dbhWPXc+8/FcVbx2ZD7zwUXD0bNYX5Xb84nZrC+41vTfKcxHrly4fDLzwUVa0li2r5iT7HBiDttXcPPMxWnMVz1zJ09jPriY9OUEtq/wrJfrT2T7Cm72/2MU85FrFV4cxXxwsSFqIOuLOZnNxoxnfcFl1KyRzMdcZf2TQ5gPLod0/4D1hWddO9aP9QXXj85rz3yVW3ytKfPB7Ru13vYi91V1rs7aOI3dV8Ht91+ayHycU9pTlSYzH1y0jBnL7qvKezJiNLuvglvNRw5iPnLN1v5DmA9uT5jRhfXFOan1+MtkfcFF0bDhzMeeiqSXP2I+uLVyfB/WF55Z778PWF9w7et7VzoTH7l6p4mNma94U7OrF7mvYk7Ws3Gd2X1V8ZjCel7Md3padbq+5MV8h9tXr3zoRe6r8PSHA7t7kfsquNnw5cFexFe5d84MYz64FThtnBfpiznZf+3twfqCG4Uhw5mPuRrjLg5lPrie5G2yvvDMXadN1hfc/GnmFOar3MczpjAfXJx5bLYX2Veck/qWPQPYvioeMPpt6jvnlHZgwXvMB7fzElt5kX2FZ0+d+Z4X2Vdwo83lQV7ER65da2A/5oMbMx4f5cX64px8fOurXqQvuL44v6cX8dW5euXNnswHt5pfGsL6wrNfeH846wuu21Em85Grj874mPoO1zeOnsj2FXOyWvwzhu0ruJX/0WTqOz3NmQ0nMh9c+9eazvYVnqhSdxbbV3ARsm4+85ErvZbNYz64bc9YwPpiTlbz9VNYX3BxbMZs5qu5xr5PfXBzUtV5tC9yzwV7s77g4u5ni5iPXOvX+z7MB5efBft5kfdVrf/lhT/6/4eg1HD2viq4KJofznyjye2IR/+5Jy9HMF/x4B0R7H1VeNoLb4TT91Udbj7xJfVV7uOzI5gPrq/WIlhfc96MyEf/PLr4USTrC65XejWS+WLnk6sf/fsXb0UxH1zUs6JYX5X7wslVrC+48fDjVcxHrhE5OYr54PalS5HsfVXMyRrr+t/F831VcOtYKfVVzyPe4dR3uH5/Xxh7XxWete1GKHtfVfFOtaiPXL361ZXMBxfR10NZX8xJq7UvgvVV/O0H1Mdc9fc7RjJf8Remr2J9Ve7JWuGsL7h94y71Va7/tAjmg1tftgtn+2ovOx79aG9X74lm+6r4guBo5tszL8a43kP6Pob5ipfuimH7Ck//sGk021dwOyhtNfORq08Nj6G+w8UPXWJYX63t7VjXn3tcjWV9wfXBR2OZLxo1in+U+3WdeOaDix134mhf5MasiWV9wW17QCzzkSv67I9jPrjY4BfH9hVzklV+jWL7Cq499WQU89FT7DkUzXxw+8czq9m+Ku/FW6vovjpce7uE+siVA3dHMR/ckJMiWV/MSXvyjxja1+HGpIUx1HfmannXiWM+uPxfWizrq7zr+6JZX3B7+9jV1HdyrVlPxDIfXD4xMIa9r6rOybHPhLD3VRV/4Px9Cg9fnVPHDoYwH9z6c34we18Vnmjg+nsJnu+rgmuNagQwX+W2uhbIfHCrc7sA1hfnpP16WSjtC37hbepjT81qx1cyH9w63SqU9YWnLxdBrC+49UdcAPORa38xKJj54NZTbQLZ+6qYk7nxy+XsfVVwsa/5Euajp/FEynLmg+vR4+j7qvCsuu/S91UV/7qmD/ORq885Rn1wvcWDeawv5iTrfbOc9QUXLaotZj7mqvutXcF8cHG4/2LWF54YcGYB6wtuffnZHOYjVw4LX8B8cNnk3Cy2rzgnjZI3wtm+gtsr5lAf55RR8FUE88HNI3fC2L7Ck8tnhrB9Vfwx19879fSRq7c8Hcp8xee4/l6bZ1+ck7bcFcn6glsxX1Mf56p++9Zq5oPrnZ6IYn2V12JdGOsLbix6nvtOrnj1swjmgxtDWoWxfcWc5GPhgu0ruF2n/xLmq7l+mxvAfHB9z+vL2L7C036ttJDtK7jermgW85GrTZu3iPngds/oOayvmtNz7YNoX4ebUx4T1Hfmap83QpgPbm64E8D6wtOH3vZjfcH1z+fOZ77KXbB5KfPBrQl/L2D3VTFxUsKj/x4vj0pg91Vw0fT9BObLY+WJj/5z+7YmMh/cmpOcyO6r8KxfqySw+yq4Hbg7nvnINfpNSmQ+uO7/YiLrK0+0SX7kvf5aMusLblZ9Jpn5Yv7uFNf7WptTmA+u3UlOYX2Vd+2HJNYX3LqfkMR85GqfT09hPrjx9Ksp7L6KOWl5o+LZfRXcyDtPffQ0S3YlMB/c+qBbAruvwrNm9Ipj91VwY3Yi951crU1ZPPPB7fsP41hfzElUfi+J9QXXfA4kMh9zNdNPJDMfXNadnMz6qtwh7yayvuD60OgE5iPXLrCTmA+udeiaxPZV9hyd6vp7XYNT6b463P78vVTmi7Bn1z7671m3ylrmg2s5f6SxfYVnTamRyvYV3KrzzRrqO7n6D5+nMR/cbJCQxvqK+8fXPfrvlXhkHesLrvfYvo75ok5m+qO+p9alMx/cenx1OusLzz6YtI71BTfktHXMR66Y4ZPOfHBtS790tq9qTueD1rB9BbcX62uoj7mu7ZfGfHD54a1Utq/wzFG5KWxfwbXsOinMR674JjyV+eDW7qqprC/mZLdouY71BbcT7bXMx1z1GS+lMx9cu3duPesLz7KD17K+4ObvddcyH7n6joT1zAcX1zuuZ/dVnJP6YSuG3VfB5dKYaOarc/VEqzjmK76iYyy7r8IThwdGsfsquH12F/WRaxQ2jmE+uPXq3ija1zknjf2H4llfcC3jHPWxp3LIykTmg+vdihNYX3j2Z8djaV+H2/XTqI9c2XpjPPPB9Ykxcey+ijnpiV+upPdVh4tBAcHMR0+t57kI5it+LnMlu6/C06Odvy/lcV8FNy/95U99J9c4+Vww88HtVl6C9cWcrMmLVrO+4PpwcxXzMVdtV89Y5oNrL2yOpn0dT0bdXcn6gsvL/amPXKNRRCTzwfUJr4SzfVXnaudZyWxfwa3vspOYj3NKDvRZw3xwufuzFLav8Mwa+xLZvoKLPOde5uk7ufab/yUzH9yqXo/2Veekb0oa7etwu1vlNOarc/XcqXXMBzcujF/H+qrcZmNSWV9wvXv6GuYj1ww8u5b54GJ7r7VsXzEn/fGEeLqvDjd+cN2rPX3Vc2LdJOaDmyvuJbB9hSejzsewfQW3B32ymvnI1V/+LJ754FqzJXG0L+Y054sU1hfc3vlTMvMxV+ud3DTmK37jTirrq3IX/ZrE+oIbKRcTmY9cccsrlfngRljoGva+qnqu+lzebPa+Krj14fTJzMdzSq3Te9OYD27v3DGGva8KT+zbO5q9rwquj3Tej/TwkWu881Zv5oNr7dre6kz6quek+Rsnsr7g1vxBg5mP51S6f6DJfMUfvtqN9YUnRdc9nUlfcPODSX29iI9cI7vlB17EB9euZnzkRd5XVXOdnNSGva8Kbv5W53Uv4queLdo+eu/O0wfXQlIGepH3VVXu6duP3kvyfF9V8ROlj96L8vSRa0ftH8p8cOvT/SO8SF/MSbcjR7C+4Nozqz9mPuaqNRs+nvmKP6wzkfVV3lLfsbQv+OivxjIfudbb309gPrjeJ+5RD899xXNS61rnsWxfwcWEJm2Yj+eUos7f45gPLs8eb11O9lXlNvAdxPYVXBSWm8xHrrkxZAjzwa0pPR/NwbOvek56ZclE1hdcj3+5G/NxT5WbHpvBfHB7xJJh5awvci/3GsD6gotvvjKZj1xjVNu3ma/4xkC6r5iTHvD6ZLav4ObCt6YwX/W8njOF+eDmoodT2L7CM0s+mcT2FVwf8tt45iPXSO47ifng0v56HOuLOckJrt6efcGNJy9Oor4zV/lFHZP54NbQJWNZX3j6wocm6wtultUYznzk6ke9hzIf3PhmW1v6vqpzThoHmg9l76uCm/n6KObjnBLDy0ZR3+H2VZ+x7H1VeNbnRfR9VXArvdlk5iNXP31pPPPB9doZk1lfnJPyj4djWV/Fj9Uzma/O1cbNTOYrvjBlLOsLT4/NGs/6Kn7IfxLzkWu2azCO+YoHDB5P31d15mR1K53B3ldVvPb2ecxHT/uvghnMBzcaJs1j76uq3Er7FrD3VcH1SOHPfOTa72ctYD64MTjSn/XFnKw7309jfcHNF+/MYT7marb6aRLzwY2cBTNYX3hW8BIf1hdcn7zYj/nI1bq9MJ/5ij/90iK2rzgn9SlbxrB9BbeiT46kvnNO2d90H858cLNWlb5sX+FZHy4dzvZV8fCkEdR3cq2RjbtQ3+Ha0Dvvs744J82D97qwvuDWOx82Kyc+zlX93f49yomv+LgVrr+/5tnX8WSk5vp72559HS7aXx5QTnzk2gd6TywnvuKfVJ5aTvYVczInHPmY7Su49s2HE5mPnmbkk92p73AtpOlgtq/wrGrrZ7B9VbxXV2/qO7nmuB3jmA8uIpdNZ30xJ2NMTv9y1tfhRljzruXEx1yt+PtTy4kPLg5fdv1+2KMvPC2o7Wu0r8P1v+OGMR+54t1Es5z44OKZsgHsvqqeq375+3x2XwW3Wo6cxHz1XLVtoh/zwe3h1+aVk/sqPHHwUJ9ycl8Ft5tZ7zAfuTJ7dUXu+ODy3beGs754Tio/DRGsL7gWs8Wf+XhOZZVkhTAfXLdc3yX17AvPThzsU876OlwPT5vNfORakzYsZz64tSfM9d6Zx31VzfXxKX3YfRVcG2l/QH2np/XHG33LiQ9uP/e76+8NetxX4Rmtmj9XTu6r4HbN11x/v9PDV7kvaa7vj3j44Hr1j2eUk76Yk55/Z3o56QuuTfWdRX1nrvYvNf2ZD651L1rM+sLTGrf3Zn3BxbCn/JiPXKvOSyuYDy6XON/t9thXPCe18teuYvsKrp1ND2M+nlMajd+IZT64/tVP0WxfVe565+93eOwruGg9Kpj5yLU6no5iPrjYkBjJ+qrnqt/aCawvuNX8Vjz1neeq4sTpFOaDy8M9UlhfePKqVzzrC64N/z2O+cgVh64nMx/c0m4ms33FnPTZF4PYvoLLdVeDma/mul5EMh9c3Pkuiu0rPPnWhJVsX8Htx85HMB+5Yl3vGOaDG8veiGN9MSdzoB7P+iq+SktgPuYqLvulMB/cHpK2hvWFp28MT2R9FW+UkMx85JrNR6UxH9yMj3P9OZTHfVWdk3sSJrL7Krhxu/Js6uNcPfzAm/ngxrPxfuy+Cs/IH+/N7qvgoukTC5mPXCPyw2XUd7i+aoRgfdU5OXTtctZX8TwRSH1nT62vpoQyH1y7dC+c9YVnV+sawvoqPmr4SuYj167WMIr54LJtdDS7r2JOlt5iEbuvKv55yULmo6fZsmsA88HlLOfnQnvcV+Hpz/v6lJP7Krh96485zEeu9eQ8QX2Hi0tdl7G+mJN1NjmM9QWXd/8IYz7mqp2Ki2E+uL7lUgzrC09vMDiM9nW4bLkjlPnIFda2GOaDi/gqMWxfcU5qDU6vZvuq+LkJsczHOSWHD0pgPrh10zuJ7Ss8o0PjeLav4NbXoQnMR65db0wK88Fl/StrWF+ck+Lg+TWsL7hR9/W1zFfnqv8Tkvng1qDOG2hfx5MnXO8RePYFN2/dSGc+cvXC0RnMBzde6ZVJ99WZk35pcSLbV3Bz+41E5qu53nK9l+Dpg8seMo3tKzx9/aVEtq/gssugROYj16i/OY354LJaxzTWF3My96ZI1lfxbbcl9THXCfcymQ9uNJuWxfrCE141NrC+4PLZC5L6Tq6RvjqL+g63Bs7Kcv+uqjVCn1HhO6kzk6a7c1HD+f0O+OQfZlLf+U6p+OHeVOaDG99WqZBrPKg1z92zJ3jPdedm43xvd67t6O5NfSdXvmXOZj64OFtvPuuL76TaA4dOZn3BzdLHue98p9KsVjKe+eBa77MTWF948sNb01lfcHv+kDnMR652pfNk5oObmf9NqzDX4fUWVZhr89yF7tzI9lvszq2If/yZj55m1WELmK/45mV+7rn2zEsr3D3Zd/iKCnyK6+c8Kj74aiDzkavNKl7GfHDz1MMA1hdzkgObebO+4EZ81UXUd+YqM7vPYT642JblzfrC0wZcX8z6Kv6un2A+cs183Zf5iu91fq6Ix76q76TWbj2K7Su4Man3aOo73ym1O63rz3xwefDzfmxf4YnKcizbV3B7VJcJzEeufiRwIPPB5UPfoawvvpNq/TahOesLLkP9nmI+vlNlLZvtet7l6Tvc+D770Z+bevaFZw578Z+9rK/DzZAfdeYjVx8699F7D54+uL2yi+u5s8e+Yk72c7lT2L6CixubZ1Hf6SknvDya+eDamrkT2L4qTw7wZvuq+H+DqI9ce0jt6cwHN7PPzmN9MSfh/8x7tK/DjV3BvZmPuWor/3z0Xo+nr/g/zXp7kb4qt+bqEayv4t98M4H5yDV77X/Xi/jg1tEn33I/V43xr6x0PyftU0dDK/Aju1e5cznT9d0TTx/nlHX7UAjzFX+2Y0SF3JdCY9w9fdygmApcj4p35+buIfHUd3LNGlOjmQ8ub+fGsb7qXH26TjDrC25qY8OYjz3VK+8OYD641fdgCOsLz4wcHUX7OtyqtSqW+cg1ZwZHUN/hVv6DaPe5Gm/cTKowp+e2JrlzUX1Tqju3356bynz01Nv/m8h8cOO13DXuuVaTnevdPW2J93p3rv25aIM7l4UDNzAfuUbzteuYD270PSdZ3/8310MJrC+4TB6dwnzM1aj8YjzzwbXU6CTWF57o9N5a1hfcfKuRZD5ytYJ+qcwH1y+0X8/2FeektmLFMrav4OKJJYHMxzmlxw1exHxwObnqMravymvyayjbV3Art2Yk85ErqsQEUt/hcqvruY1nX3Wujn92Fuur+O9nfZiPc9UsPjeY+eDazaYTaV/Hs1r9bwnrC27/dz6A+ci1ByXMZT64+WGQL9tXzMma/W8M21dwq2vPBOajp6h2NpL54Nq4qFi2r/DMC+eT2b6CWyuz06iP3O/+SWA+uN4+JYX1VXN9eC+U9QW3z5+kPuZq75khqO9wLfJ8COsLT2tZEMv6gluLGidS38k11z8dyXxwe8qYWHZfxXdSxf7+H3mR+yq4WTVyHPWd75SK8C6TmA9u9wua5sXuq46ntx0wwYvcV8EN4+VJzEeuPDpuFvPB5YUtc1lffCdV/Hp7JusLrp3YMo/6zncqjXMH5zMf3Pr7pYWsr/J8hvuwvuDWw68WMh+55kU/P+aD2/aRJV7kvoo5aVWenuzF7qsOF5e/n8R89LR7tJjPfMVzq3h7kfsqPG1X/4le5L4KLqs0MJmPXLN+b28v4oPbUV/MZ30xJ2tANT/WF9xodsWf+s5c9Q7/LWc+uLn9tmB9VW5eryWsL7i24/YS6ju58vf3ApkPLvxbBNF9db6TahdPWEj31eHi6HB/5qvvqjYr9GM+uPB+fxnbV5VbPm8Z21dwLfC4YL7KPVQ1gPmK/x4bxPriO6lGL/8lrC+47DFQMB/vqRoNMqkPro8uCmB9VW7MC0GsL7jRs2Yo85FrPDgWzHxwc1brMLavmJP86XIg21dwu+a1YOajp/7ea6HMBze+2RXG9hWeOfpQKNtXcO3roWHMR649q+Uq6jvcWjEoivXFnOw7r4SzvuDWqG6RzMdcrWvlq5gPbm4cH836wtOf+CCa9VX8xL8xzEeu9fbCWOo73Or7bLwXua/inNQeuL477nlfBReLq77vRXycU2b5yHlexFf89bmufy563FfhGad39mH3VXBjzfhJzEeufnDgRC/ig8uDYwbQvs45KZK3LWF9wcW6LouZjz21q+YGMR9c37oiyIv0hSdSevh6kb7gxqve3tR3ckVcQCD1HS6r+z76/zvP+yrmZH+W7s3uq+BmjWNLmY+eepX4fswHF+1jZ7D7qspd2zeY3VfB9f0nIpiPXJG604/54NbUk4GsL+ZkFv07xYv0BRc3O3X1Ij7mqmdPXexFfHC5tpfrn7cefeFZTV+ZwPqCWzubLmI+cvVpD4Z7ER/cPlvu+nt5HvuKc9JcOC2c7aviGZ+HMx/nlHn+r9XMB5fen0ezfYUnS9qFs30Ft0bOXsl85BoNg6OZD26P+zaK9cU5aaY8Hcf6ghu/Z8QxH+eqcatfAvPB7SGVEllfeGZwQhzrC2723hrLfOTae24mMB9cFt2NZ/uKOZlnOwSzfQU3VzRawXz0NGpFRDAfXAZsCPEi+wpP3m/u40X2Fdx44cRQ5iNXlJUs9yI+uF3z9Xm0rzMnraEdzfqC24akPuZq5FWOYz64/cSM1awvPNHssWDWF9wee8+P+ci1v1q6kvoONxvEPpobnqua1urMCs9Jq07JdOf20KBsdy76jcxmPp5TakuuZjAfXD7zXZZ7rnizVa67p39UKdedW2EL8ty5ca9HHvORqz22LIf54PKr8lzWF89JzTtNMlhfxd+qnMV8PKfS77veu/D0wfXFlzJYX3j2JxnZrC+4lXyR+sjVD/hnMR9cr7Yzu8Jcq/bJd5+T3adhvjsX7+zPd+fGsRTqq7meXpNHfYcbC1+smPvufxU8ve23FblRp8Cdy2duUh+5xh/j86nvcPHkKtoXc7KufZrL+oLL9QOpj7nqbTJymA8uq3fJZX3hyaTcPNrX4fL4T3nMR642sSSX+eBW2WN5bF/Vc9J+s9LZvoKLbsu47zyn1EWntcwHt+9sTmf7Cs+eUy2T7Su4WbM/9ZErWk7aQH2H68uLM1hf9Zw0oSCF9QU3ttWkPu6pWu8TCcwH115tk8L6whNfNUtnfcEtn18l85GrTRmWxnxwY6W+nu2rmtPIbtlsX8HlgLvZ1Hd6mtFbMqnvcPNqUBbdV8fT74ocuq8ONx6/kcN8lZvcIpv64Et20b6Yk0iunMH6gtu+f2UwH3M103tI5oMbAYM3sL7K67Ark/VVfJTr/T1PH7mioGMG88HF1uqZ7ueqKV4vcD8nRbVqFbjI6VXgzvX4N6iPc8qsWpLPfHB76Q/57rm697IKnu01qSL/90gFbjbaQn2Vu/YF6oOLcfNoX5yT5ry6+ayv4tuHUB97ai7tl8d8cLlifR7rC8/uJ/NZX3DjqUrUV7lb/stjPrj1+ax897nqie8Uus9JRr9cWIHX21Xozu1ZudRHT6vFhQLmg+va7Iq5OWOL3D35c58id67VernYndsnnylmPnLlq7WLmA9urz1VxPpiTtrEJQWsr+LhzxZS35mrHHkjn/ng4uCqAtYXnvXPJ4WsL7h+ehn1kWse61jIfHDt6BNFbF9xTmotR+ayfQW3Y/blMh/nlMgwcpgPbmR/lkP31fGk3zt5bF8Vn7GV+sg19P65zAe3/S/QvjgntcCa2awvuLxemk1951zVBp3IZD64nboqi/Z1PCt3YA7rCy5v385hPnLt+t2ymQ8utP+y2b6qOb3yYT7bV3C93xXqq54JAXnMBzdHts5n+6pyf8ooYPsKbgfO4b6TazzzRz7zwWXU3gLWF3MyL2Tnsr7gZtQ46mOu+twdOcwHl/uW5bK+8ETIK/msL7j2813qI9fa0zuP+eD6tkb57L6K56R2+29j6H3V4VZ+J+rjOaWduTOc+YoP/SiG3VfhGZf9ktl9FdxsMSuV+cjVQ7fEMx/crvtNEuuL56T2AD2Q9QU3IgZyH8+penotYr7iZ1YH0r7wzjSLYX3BrdlzqY9c89OvwpmveI1hMey+ijmJJtXXsfsquGg/MZ356Gno+WuYD252br+W3Vfhib27JLuvgpvfFG1gPnL1+JD1zAfXrAGS9cWc7PAFSawvuDbnHepjrrJjQDzzwWXA9CTWF56WuiWN9QUXLz5Yx3zk2odqrWE+uL0yII3tK56TGua2iWxfwTWvVv7Mx3NKTX7S1Iv44Obb/5vD9lV5PjWD2b6C2883WUV9J9eK67Gc+eAy8qNQ1hfPSUUXY4IX6Qtuebn+3oOnj+eq9ul6c72Y73A9plpd1heedVBfxPqCi7eGBjIfuSJjzFzmg5s/v7Kc7ivmdOSHGLav4Nppm/tOTzGoZhTzweVDGUv31fGs3TlJbF/BjYttqI9c7S//BOo7XCtwfffXs6+a66svhLO+4OYfo6KZj7laVU6EMF/xHdUjWV/ldY+OY30Vb9A+kfkqN/DXGOYrXhgTz+6rOCfte6sy2H0V3J4xjvvOOWUu7LmB+g43p7bJYPdVeJrRMovdV8FF9O0s5qvc4f/LpL7DrfevZrK+OCfFqmcl66v4xqepjz01k/etYz64duaHdNYXnnb93gba1+FmnZ8ymI9c64vfJfPBjbgfN7D7KuYkY/Zls/squDwUTX30NIftz2I+uOiWms3uq/D01yfmsvsquHi3E/WRqzf0yWE+uBY+IJf1VXOavD+T9QW3wjZQX831yoEM5iselJ/J+sKTRcuzWV9wK/Jj6iPXjgnPYj649cPUbLavOCftl6PS2L6C2+F7qI9zSn8reA3zwY3f9qexfYVnf/1FOttXcOl/nvrIlTfPrWM+uG6fSWd9cU7qZmEy6wuuffEX9XGumlmVk5gPbsf2S2F94dk3nlvL+oKbf9dez3zkWsvnpDIfXKs7Zy3bV8zJCDm2ge0ruFW4lfroaXSzJfPB5X+HN7B9VZ6enMn2FdwqXkB95GrdCjKYD27+GJLJ+mJOcvi/6awvuHb3Z+pjrlbgoPXMVzy/umR94Wk+BzawvuDWqXXUR66+93fJfMXz9rp8j/dV1XdSn3e99+n5viq4fn9NIPXxndL0YSuYD258oNH3VZX3JH9fFdz8/Ntw6ju55oF9ocwHN7WLEayv+k7qsRXLWV9w+/CBQOo736k0mvVeynxw4/kTAawvPPP24VDWF9yK6rCK+ci1kp8OZT64CCmPYO+rqrl6n45i76uCi8J/YpiPnnqTvtQHN5v+HsveV4WntbkXx95XVfzjgwnMR66Y1CCB+g43DtRMYn3VXA/ej2Z9wY3UeXHMx1z1LkejmQ8uxi6lfVVuxBe0L7hW/bsk6ju54jnXe16ePriW9HQy3VfnO6kyxDkXPPYV3HynqmA+vlMq6mo+zAfXfYoW032FFzwrmO0ruD1yeDjzkWvVPRXAfHAj6edQ1hffSbWL9amsL7iV1o37zneqxGemwXxwcX3sRNYXnpUwfinrCy7WDA9iPnJl9Alv5oPLES2X0X115mSEr1vN9hXcHvEglvnoaa4eGsl8cCv0txi2r/DkD8EJbF/BtUmpScxHrrV1QTzzwa2UZYmsL+Yk/j0TRvs6XP92ymrmY67md42CmQ8uY/LCWV94+uc7YllfxQNLqI9cbci61cwHF7NbxLL3VXFOyr9fS6LvqzpcJF9Ooj7OqaOhydR3uHjj/RT2vio8o8qBJPa+Krg2a30i85Frdvg7mfn/j6+hfXFOas8OTGF9we0Xr6QwH3tqHFpIfXD5vetfe/aFJ+4tSGF9wa2Si0nMR64M6ZrCfHBrr0ii76s6c7JzF8Wz91XB7Uu+MdR3elqTOyUwH9wKfSGWva+qvKsHw9n7quBy1r0A5iNXX/RrBPPBxY6GQayvmuv90Qm0L/gYPZb6zlzt77+PZz64aJAUQ/s6nvb9jQjWF9yo2yOI+ciVle+EMx/cfjwokO6rc07KjteS2b6Cm13epT7OKXG2LIn54Obe40lsX+HJwLHJdF8droX5JjIfuWb3rxKp73DN+Dqe9cU5aZ8oT2B9wc06OxOo75yrevcGccwHt9r/F8v6wrPrr49nfRX3WRvLfOTa1U/FMB/c6vxZFNtXzEmM3BfH9hXczKsWzXz0FM82jGW+4kturWL7qnJPPhNG99XhosmvgvnIlakfhjAfXP+u+jLWF3PSZt2JYn3B9akPVzIfczXjvgijvsPNBp2DWF94VsHOANYXXPxwcRHzkWtZfy5mPrj9w/k59L7qfCfVPjJhYjm5r4Lr81K6lDPf+U6p9fEh33Lig5vxYl45ua/CMy7MHM3uq+Bak9fnMx+55s9zxpYTX3H/cNfPe/Doq76T+nLzYNrX4aJSrmA+vlNp1i2MYj64vXJIRDnpq7yWmX7lpC+41WrIbOYjVz+gh5YTH1xmON+19bivYk52gzZL2X0VXNu2Ooj5qmfe5RnMBzf/a+HH7qvwbGtCGLuvgusFV1cxH7nW2YOC+eD6kNeCWV/MyW727aP/v/DsCy4q1xjFfMzValDDr5z5Dhd+ETPLSV/lTRgwl/UF12p3WsR85GpdGru+y+3hg2un8jt6kX3Fd1Jlx/cS2L6CiwcZcczHd0rN3R1TmQ8u1u5KYfsKTzyzNobtK7hs7x/JfORaNQ4mMR/c/MGH93W+k6p9M0uyvuDaqzPTmY/vqtrZj2UxH9xuMyiT9YUn+getY33Bjbx305iPXDPbzGA+uDakwwa2r2quq0esLCf7Ci5TuwUyHz2tu7/HMh9cG/Xp6nKyr/DsS5FLysm+Kt7g0wXUR+7hlhHlxAc3wsNDWV811zdKU1hfcDn0hSTmY67GqbPp1He4+VuLdawvPNl9UzzrC64/VhrLfJVbIymV+eBm+/6u74Z53FdxThrj06PYfRVcK+waRX3nnDLPWyHMB7cvzQhh91WVO7LKKnZfBRflY1dS38mVaZOCmA9uPdNIsL44J40hc/xZX3Bt8gv+zMeemk0rDWM+uBjdeTjrC0+0P7CQ9QU3DvSax3zkao2v9aK+w7WioDfLyX0VcxJ/HAuk91WHW51WLWE+elrbWvkxH1z2yJ1D76uOZzz1ynx2XwU3g/qMZj5yRfSpkcwH13anPfq5Fp591ZySb01gfcHt1J+9mI+5yre8PionPrhVFuf67qpHX3j23YvDy0lfcHnzM9f3qD185JqJwpv5ij//iV852Veck1qNva57qce+ghsd/5tTTnycU8aL9YPLiQ8u3jnreh/NY1/h2e3DXT+X22NfwfWtHXzKiY9co9WGQOo73BrVJIj1xTkpTr8RQ/s63MoZv5r5OFftX5yfF+Dhg9s7n05kfeFZ5/tEsb6Kt7wcyXzkGqeeTmC+4pmm67t+HvuKOVlf3FtUTvYVXLs9dAnz0dOu0SyY+eD2e/4hbF9V7oy7K9i+gmvtRwYyX+Umub675+mD2z/HhbG+mJO5qlIU6wtudFkUxXzM1bDvxlHf4fZPn8axvvDs7KarWV9w6Xt3NfORq8/bGMd8cH3uTlc/j/dV8ZxU3rrt40XeVwW3njwzkPl4Tilf9VvkxXyHm+3f/tiLva/qeHqHzyez91XB9Q8a+zNf5e7YPZb54Nao1a7v1Xv0xXNSM+1NX9YX3F4UYDIfz6nE8+8vYj64be0a50X6wpPh50ewvuBa6Qgf5iNX90odznzFG8/wZu+rYk7i6jtB7H1VcLvpx+HUd3pq3b0CmK94pNdK9r4qPHG8WjR9X9Xh1rBmccxHrlb3aCTzFdfuxrC+mJO25cQK1lfxos0hzMdctTpDllPf4XrrTcGsLzwxptYq1lfx3U/EMB+5+sNvwpkPbvyV6DovPPYVz0ntD/b6sH0F1wZ8NZL56jnlgtK5zAe3ckf28iL7Cs+8HzGK7qvD5bUyb+YjVwTljWc+uN2k7qN7i2dfPCeVQ/pMZn3BZYHru2mevrqn3q49iPngxldxH7G+8LTSHtNYX3B5eeUi5iPXTvKbx3xwMfW6P9tXNacJN5bRfXW4/sI7wcxHT3kodTn1HW7efSuY7avyQmuEs31VfHJsFPORK67sCWM+uP78nUjWF3MyExsK1hdcfz4rmPmYq95wYgDzweXywBDWF56287sw1hdcWvsimY9c0fKjcOaDyz1XItn7quqczEpIYO+rgmszT1Ef55R2Z2Q888H1oNWJ7H1VeGbrGynsfVVwreXv1EeuWbgumfng9jur1tC+zjlpm4PjaF+Hy3aLEpiPPf3/k2Op73Drvah41heefsT198Y8+yo+f3AK85FrlS9KZD64WGoks/dV1ZxeObiWva8KLsaHUx89zZDRacwHl7H/W8feV4VnffmiZO+rgot/f6M+cu1vNq9nPrho4vpu0//pi7m2fi6V9nW4XbCT+pirrH09hfng4vvAVNZXefX6rmN9wfVuR6iPXNN6bC3zFR87ex3bV5yThrcdw/YV3Nj5dRzzcU7pf82Pob7D5eIP4ti+wrPaZiawfQWXU4clMV/lTv4nnvng9omYRNZXnauz2sSwvuDW5YuxzFfnavTLMcwHN14viqV9HU9/0y+e9nW4+O9GAvORazVtGE99h5s/+ibQfcVck68ms31V/LOZa5iPnmZh9WTmg8uMhSl0Xx1Pz/g2le0ruIhuSX3kGs0Or2E+uBz3VBrriznJn4cksb7gWh/JfWeuZuSuROaDWx9eSWJ94Vkjvk9hfcHNB1oq85Gr3X4qhfoONwfVcf29EY/7qnqu2mpqL3pfdbjpM9f1c4k8fPVctdqrU5mveNQfPuy+qnLLdi9k91Vwua/tcuYjV7723RLmg+tzsgJYXzwn1fZ9vYD1Vbz4xhLm4zmVyDiynPng+pRBgawvPGv1RwGsL7jeKDyY+cg1w+qHMB/crrx3JbuvqjntqBXE7qvg1nMHQqnv9DQe6tQHFyET6X0VnujwbgS7r4LrNeZGUd/JtTPmrGI+uLHgg9WsL+ZkjF65kvUFt0YMi2A+5iqffj+C+Ypfio1kfVXuzb8jaV+HawunRTMfuVr0fuqD29umxdB9dZ6T6n8XB7N9BbdH3Axlvnquur9NBPPBNf34Krav8OT8xuFsX8Hta+1XMR+52rxJUcwHN/yvrGZ98ZzU3vFTNOsLbs+bFcN8PFcVvU/FMR/cGPIwjvWFZ+/+LYb2dbiRPCWW+cjVjbfimQ9uHZ0Wz/YVc5IN20SxfQW3zJrRzEdPq9tbMcwHN29djGH7qryPp8ewfQXXj3aMZT5ytUGun6vj6YPbtVw/99mzL+ak1b4Qy/qCyy9HxFHfmavulxnPfHC75/l41heeuePzONYXXK/bLp75yNU31U9gvuILhibQ+6pzTsoz7WLofdXh4p21sczHOWXnLoxhPrh1dGMsu6/CM7J+i2P3VXDztUYJ1HdyxfffxVEffObVeNYX56R44/sY1lfxNOe9EE/f2VOxeiz1FW/RnfaFZx919fbsC24+8V089ZE7tz71wWXHn+LZfRVzsnO7J7L7Kri45Lr3e/roada4nkB9hxsdzyey+yo8ETg8md1Xwa1KPamPXHvAviTmg+sxicmsL+YkF2xLYH3BjQ9CE5mPuVpz1iYwH9z4sVci66u8qJ5JrC+4NfwB9ZErhl5JZD64KQOS2L6qc/VMpTi6rw6Xd3bFMV+dUy/tpz64PW9APNtXeDJtGt1XxXtWTWA+cq3GX1MfXBvSJ4H1xTlpDF0bz/qCi8f/i6e+c67qq1YlUB+8YXkC6wtP+3oo7QuuBeQkMB+5pu916oOLf+olsn1Vcw1cn8D2VfGjryYyHz1Fi10JzAe3zjZKpPuK3JK8RLav4GZRwyTmq9yMFYnMB7d3nExkfTEn7cKfCayv4l+9nUh9zPWlztRXfMYY2hee3W5uIusLblfPpD5y7SWLqA+uPwhz/efrZLq+c3Bqnes9Ul/L9d0G373SnVuPr05359a8TyTz9eLNru9GDNu0gfoO1/qWbHDPFTN80t09e0iJdOfaln7p7lxWkZL5KjcidwP1HW4+tX4D6yv88l3fuziWl0H7gn+dk8F8vaZ0vX9bx/Wrp694S9evnn3hydDMDNYX3PxrfQb1nVx9nOtXTx9c2+/86sxVn/FSuvuc5JQo6c61e+fWu3PZZ5FkPnpqu2I3MB/cTAvc4J6r70hY7+6Zq0ZJdy6ud1zvzrWH70nmI9fIn7eB+eD6X6M3sL6Yk9ZkTQbtC946LoP6zlzl4g2ZzAc3d2Rksr7KMyIyWF9wER+QwXzk2p9nZTIf3KiTl8n21Vib6Pre/qCkLLav4PqilCzmG53DXN91WheezXxw7bOobLav8MyGa7PYvoIbszZkMR+59pGEbOaDi3Prs1lf7YVFru8tveSXw/qCizeX5zDfGDHG9Z2mv8flMh9cnJ6ay/rCs+eE5bC+4Fqt5Bzmq9xGC3OZD64/HZbL9lXNKT4vi+0ruGlvzGK+6tmlKJv5ig/ek832FZ6+c08W3VeHm6VfZlHfyTViv85mPriWezWb9VVznVCQw/oqfuzTHOZjrsZlmct8cLOllcv6whO/2Dm0r8PtyZVzma9yvX/Jpb7DzYDaee7nqjx3al2FczWitnTnxoXx69y5GHwynfk4p0Rq9w3MV/z31ze455qBZ9e6e/r9hekV+PZea925caF6OvORa5vOzy3x8MGtL/6ifXFOWmF+GawvuCydm8F87KlsU5zJfHBx1fVzJTz7wtN+mJrB+oIbbSdkMB+51sdWJvPBLc31XUfM1XonN819Tka6XO/OzRt3Ut25ob+5nvpOTzHnlGQ+uNZ2t3TPFbe8Ut09u8vudRV4WOiaCvzQ4HXMR67+b45kPri4kippX2dO9izn70959AU3v5uUwXzM1fzh10zmgxspT2exvvCsbd4ZtK/Dbd/VGcxXuYfey2I+uF51SRbbV5yTYsX5LLav4OZrd7KYj3PK8nkmh/ngWv23c9i+wrPv185m+woutnfIZr7KrTUhh/ng1vtpOawvzkmzy1u5rC+4vnFyLvNxrhrD+uQxH9x6IzKP9VW5RlYu6wtuXryey3zk2mu+y2M+uLWyTT7bV8zJSpqRzfYVXAvNzWa+6nnf9c8dTx9cG9sul+0rPMu4lc32FVwsG5lDfSfXbpyey3xw48+X81hfNacqPfNYX3CzeFMe8zFXc9jafOo73PqpXgHrC88IcuV49gW3bnyRz3zkGrKwgPngInlkIbuv2m06ur6rOs4rj91Xwc0bznw8fHGvpuvnOhTUzmc+uDjwcj67ryrv6Y/y2H0V3Pplbh7zkWvOcf33/j8+uN03n/U167p+Ho/e6Vw+66v4jt+5XyvJ9XMo0lMLqA8+JK+A9nU8Wfwwn/UFtwY3KGA+crW+5QXMV3zv+QJ2X8Wc9LNReey+Ci4nbc5jPnoa3nPzma/4mtR8dl+FJ164kEfvqw4X+10/V8nTR668diyf+g7X33q2gPVVc/23WwHrC27WXVDAfMxV3KlayHxw8+POhawvPL1dSQHrC64/c7OA+Sp33rJC5oPbJw8Vsn014ru5+i3pWcj2FdzuOKKQ+drsC66fW7Thl0Lmg4sPbxeyfYUnbs4tZPsKbh6LK2Q+csWimkXMB7eGty1ife2kOa6fM/P0giLWF9ycHVxE/Ta/u/7vfv9HEfMV/1orZn3hWSfSi1hfcHvkQeqr3DcaFDMf3Kz0QTHbV8zJqGUVsn0F1+vcpb6aa+PJRcwH15rKIrqv8Aa8X8T2FVz2jChiPnLF7l+KmA8ux71XzPpiTnbru0WsL7id1LaY+ZirvsCvmPngWtn2YtYXnnlqeTHrC270PlnMfOQa1yqVMB9cmz2qhN1X1Tk5dmQ+u6+C602z8pmPc8oaOryA+eC2cN1DPO+r8Mwk18999byvKr5qYgH1nVyzQfVC5iv+w4pC1ledk36uc82zL7gWJguZjz21BzYsYj64diKgiPVVuSuqFdG+DrfPhBQxH7nG7JtFzAfXZiwopvdVZ052xzMF7L4KbvWbUkh9p6f5SaUi5oNr1dcUsfsqPHn6ySJ2XwU3p+4vYj5ytXDnfPPwwfXm1UpoX2dO9qhaxayv4ndLi5mPuZrvVSlhvuK+BSWsLzxxdWIJ6wturmtVynzk6pcmlDIf3MitV8b2VZ2rRT8XsX0FN7v0L2Y+zimZnFJMfYeLJyqXsH2Fp/35WTHbV3BR3ruE+cgVH68oYT64Mf2pUtZXnZMftythfcHt4OwS5uNclf57qQ9uTnL97+HZF57Wq3Ep6wsu2xWXMl/lBhVTH1wmvVTG9hVzsk7aJWxfwfVZYaXMR0/78Q2lzAc3q7xSxvYVnjG1URnbV3AzPayM+cgVrQKpD26P/bWM9cWc5NwmZawvuH7Zt4z5mKscOZ37Dreb76F94YnOJ2lfcDP93Y3MR65Rp95G5oMbDRdvdH+uKk6cTqnwnDTx/lp3Lg/3SHHnes1ja6nvPKc0bqyTzAc3Wrj+3B654tD15ApevR/XunNLu5nszrXeH6xjPnKNRn9L5oNrXmM3sL54Tiqq7c1gfcG1JlUzmY/nVLLPySzmg8uf+mazvir3Df9M1hfc3Fgvi/nI1cZey6Y++JG9Oe5zFZf9UtznZI9/db07t4ekrXHn8t/UdOajpzxaKYP54PLL3zLcc83mo9LcPaPSMenOzfi4de5c3HX9XG1PH7lyUa0s6jvcLHeeS3r0xZzkzBtZrC+4/cNTOcxXc71zOJf54PbiGvmsLzz7wOhc1hdctD2Tx3zk6rejCpgPbs52/b7n/+wrnpM+ezqH7Su4ZfvmMl89Vx2xN4/54PrnIp/tq8ot75rH9hXcCPPKZz5yRdc5BcwHt2KiCllfPCcVjYILWF9wed4sZL56rjqxaRHzwWW12sWsr/J6TiuifR1u1JbFzEeuVtayhPng4mfvUravmJOe4jzf8thXcPP9S4XUd3qaly8WMR/c2uRdwvZV5YrwYrav4Nbnw0qZj1xtYf0y5oPbM6pvZH0xJ3v8M6W0r8PlX8PLmI+5aqcXlDEf3FzRcyPrC0//zmsj6wture+wifnI1f99axPzFU9rsbnCuer/RIVz0i78KNOdW4M6b3DnZvWCLObjnBJhZ3Ko73Dr5w/y3HP1wtEZFbyeP2a7c+OVXpnuXJ7+MYf5yLVHRuczH9zqlVXA+uKclLdmF7C+4OZO57mdh6/2tO21YuaD6/VSSllf5f03qpj1Vbw8tIT5yDVnRpVR3+GyZeuN7nOVE+5lus9JvuL6cydwo9m0rAp8/4t5zEdP+4ZvIfPBtTLX76uRa6SvznL3zKL+ee4c31VVvF67POYjV+9kFDEf3J7+ZhHrq+Z0alYp6wuuZ1QqYz7mqr9TvJH54LKBton1haeVuX5f4NkXXLzXsIz5yJVezTYxH9z6vcEmtq84J434lDK2r4q3XrGR+TinjGtVNzFf8cIHm+i+Op69bOAmtq/g9jVtM/ORK+2zm5kPru9L3cL64pw0rnbczPqCa2+9t4X5OFdl7ZZbmA9u3222lfWFZ27Qt7K+4Jbfz1uZj1zzperbmA9uLflmG9tXzMkywzazfQW3My5spr7TU9/YYivzwWXriK1sX+GJRVW3sH0FN4/c30x9J1druGkr88GtxzZspX2dORnRS7exvop/d3Yb8zFX7Y8Z25kPbr36xXbWV3kvPbGd9QW3I69uYz5y9Seub6e+w0XI8e3svqqek15fW8zuq4onpZQwH88prZevlTAfXN92vZTeV+HN21nK7qvg9g3n+Y6Hr3IXhJcxX/F7b2xkffGc1P706TLWF1xWiixjvnpOtXdVGfPBxbJqG1lfeHLVSxtZX3D9r90bmY9ca3YK9cFFYZtN9L7qzMmc0nkju6+Cm6/W2MR89DT7/Ux9cJFUsondV+HpO/+i91Vw7eeLm5mPXLvzSuqDy9kztrC+mJP+/GzaF1xaTh8PH3PVCr6nPrhxZdVm1hee8cFt2hfcqH94C/ORa27uQX3FD1feyvYVz0nNOffovoLLTr4bmY/nlNqPI6gPbuw7zvfV8Yw2D+m+Kl4lbhPzkatdHEx9cLPs7Cba13lOqtXOpn3B7UUvbGI+nquKehepD24UO/08+sKTYxJpX3Ch1dvMfOTa3Q5z3+H2H103s33FnPQzb9J9BZcPvtnMfPTUq8+jPrjV6qktbF/hGTuX8311uNHM+eeyh49czSdzC/PBZY/OW1lfzEl7WdK+il9qsYX5mKs2Zt9m5oPL3V1oX3j6V59sYX3BZU63rcxXudv3bWE+uDnDyfO4r+KcNMb9wO+r4E9+sZX6zjll7h1IfXBrfOtt7L4KT36ft43eVx0uZkzZznzkikP3qQ9ufFq0nfZ1zkk9P5X2BdenTN7GfOypNmj/VuaD29d9aV94dr+GtC+4fWjnduYj12j0IvXB9duZ29l9FXOyxJ3t7L6q+IjBnzAfPbVf3/qE+eDGj0Gf0Puq4xnj533C7qvgoqz/J8xHrt5n7SfMB9dC/T5hfTEncag17Qsub/lSH3O1b1TlvsO1Kj1oX3hGYQTv63AjZQr1Ve7MIdQHl+1cvTz3FeekqH9yK9tXcLO3zzbmq3Oq+cmtzAc3do3bxvZV5SY/RvcVXEvz5b6Ta9Q/s4354Pa7xnbWV52TSw9vZX3BxZddtzEf56o2e8tW5oMbf7y8jfVV3pbibawvuN3L9fu6/+Mj97el25iv+NhD29i+Yk7WmGPb2b6Cay9o3MdcF4RvZz64fG3TdravKveXp+i+gmvtL29nvsp9d/t26oObybQv5mQ+1XU766t4x4nUx1ztwn+3UR/crrOd9YWnz5hM+4Ib0R9SH7maVpf64Na7rh6e76vqkUNcv48f9VExe18V3Dg0o5j5Zv0C13PvJ8qoD24c2FfM3ldV3tmVxfR9VYfbxWXUR645/jz1wbWfniphfe3lznP7JTdpX3DD2/XejqdvBzdw5a5qUsJ8xde1LWF94YmJLs+zL7g9sF8J85ErPxlCfcVPLSuh76tiTgN+pu+rgovzr5QwHz31IZ1LmA+u7Xfez/J4XxWeDHD9uZjn+6rg+tgvqK9yux6lPrhtNytlfTEn8X4Q7QsumxygPuYqrm+mPrj59y3e1/G0p54rpX0dLi86f97g4SNXP9WN+uD2knWlbF8tvYMr7zWD7iu45TWY+vbsbq7/3eb3oj647Tua7qvKHTOP7iu44ZtKfeQKX3/qg5tjM2lfUfyei2d70b7gVsQg6mvdnXm2bEJ9cMN+h/d1PLv/XN4X/EwK9VXugMHUB5fmErqvmJO5+DjdV3BtwP9KmY+e2nPfUx9c96nP99XxzG/H0X0FNy59wn3kDp5KfXDdv7yU9cWcRM1jJawvuPVp9VLmY67G1dIS5oNb1h8lrK/K3TWK9gXXtm+kPnLF+g7UB5ftY0rZ+6rqXJWtS+n7qg7XFmWUMh/nlF4ayX2H65884O+rOp4MrlnG3lcFtzs771N4+Mi1VnSmPri+N6iM9cU5aRR9TfuCW80blzEfe2pMeKyM+eBy33u0r/JOm7QvuNYvm/oqd68v9cGtVrvo+6qYk1yQQd9XBTdeuUV99NSqfkV9cKubvpG9rwrP6Nyfv6/qcD2hmPrItTr7Uh9cln6zkfXFnMzJf9C+4OY1589HPHw11wuPUx9crh5E+8IT6+JpX3Cz9jXqI1c/m0l9xUfd20j3FedkZnW6r+D6mB7UxzmlDapFfXD5fk+6r8pbGET3FVxuPUB95Jo9g6kPLtofoH1xTmobq9K+ihd5UR/nqv7mn6XMB9ervsH7Op6IWUz7gss6O6mvckunUB9c35NF9xVzkj8+Q/dV8aSR1Fc9dz1NfXDddyjdV3j20jy+rw63av7HfSdXv7eB+uDm3r94X2dO5m/3y1hfcC3rA+pjrvrpX8qYD24ltKR9Ve7xGNoX3Pj5HPWRK5cupD648cS+jey+am677vr9XMTfxey+Ci60KtQ38kpcv07aUsx8cDv2QDG7r8KzqjSh91VwY0Uf7ju5ZoOfipkPrv1StYT11b4b4/p9r//EYtYX3By2gPsv3Xc9R5GVqA9uNH2umPVVuSNjillfcHvMduoj14p7s5j54NbBocXsvoo52Z1XlLD7Krj17F7qo6e+pyP1wbUpi0vYfVXlZj9J76vgdulo6iNXJnxawnxw81rtUtrXmZNo+1sx6wturH+F+pirFRlSzHzFN1jFtK/jiZ6zSlhfcP3kbuoj19hUuYT6Dtc/HlrC9tUyo1zPAw/EFbF9BdeuZhQx38io4Xpf6JfaRcwHtwtd7xX9n311PHvPniK2r4rXvljEfORqZzoXUR+870Ta17oS7Hrfs0ZEIesLbs9bU8h8fch11/ukQ11/L9jTBzcGVyqkfR1P/LOpkPUFlwGnCpmPXP3tFwuZD2591amQ7SvmJHf9r5jtK7g5uy/10VP/IraI+eBWjSNFbF/hGd9GF9N9dbi1+sdi5iNXflatmPmKBw/nfTHXE48Xsb7gIrMD9TFX8ef0QuaDW/3SC1lfeJpcXMT6ghvj9xcxH7l2zNlC5is+1Hmvz+O+inNSO7SjlN1XwbUdzu+rPXycU/Lx+aXMBxcjT5Wy+yo87X+96H0V3F4bQn3kWi0bUx/cbDWmjPVV5+TxOqWsL7i1w5/62FOzRVEJ88H1tGdKWV94xr+XSllfcPNWC+oj16i9sJT54OKa43vcV9Vc/ztQxu6r4MZnVTcyHz21b9PKmK/4prNl7L4Kz1oykN5XwfU1a6iPXK1/Y+qDi8bTN7K+mJP287Qy1lfxn/Kor+a69Q3qK352ZhnrC08sulLG+oJbemvuO7nm0qIy5oMbdV3PJTz3VZ2TTduUsH0F12vFUF+dU9/nFzNf8U6VS9i+qtw9rufCnvsKbgVOoD5ytQbTSpgPbn59gvbFOal71y6mfR2u95/NfedcNRv7FDEf3PD/vIj1hScnHC1mfcFF9Xeoj1zjcLNi5oOb3hHF/19bZx61Y7X+8TtDRZEokXCHQlJplMQuQxkzF4dsiTQYQnQidiIZIsM7eof9PO88P+OLnNhmSqGiJMedlKmEJMPJ+f3x3N/7977P+f511vqsz7rWd13rap/Ltt0Pm1f0SebtK2PzCu480JL6yOkUPFXGfHDjlJexeYUnTo0JsHkFtz/KpT7q2gMbUB/crv18gOVFn1SWLmV5wfXYutRHX8XuP0qYD27avFTK8sJzln1YxvKCy3uuoT7qql3flDIfXJ/uWcbeq+Ke1Pm2Ln2v6vHF74aYj3tKp/Uj1AdXt66i71U97+KvIfZeFVzdLsPMR10nuxr1weVnsXed8XlxT6p396R5weVdxdTHPZV5axD1wcU7QZrXq9uxDs0LrqtNoz7qmgV1qA+ukyfR96rok5W0L8zeq3o80CvCfOQU9/87zHxwc28v+l4VntPRRNh7VXBR+ECU+qi7IxphPrhe2yjK8qJP9oM/hFlej7eP5Y730Vf71T1h5oOLRffRvPBMz6wIy+vxl6wo81FXjVgQYb7H6x2OsHn17knXDKDzCm6tLaI+7intu7pTH9yZsirE5hWeePRaOq8ebzOK+qir954NMR/cdHs2zPJ696pP30/zgjv3vUd97Kn2R7WpD25eHhFieeHJ778O0bwuNy/cHWa+V3dTcYj6Ljc7r4TYvKJPZvy6MJ1Xl1v59SLMR05Tnh6mvsvFr2fCbF7hiVHjImxewbW1hvqoay8T1Ae3OyyK0Lxun6zs6WGWF9xJ38Z9t6/OBz2oD+6cXxlmeeHJunVpXnCx/gXqo668+kOY+eB6jh1h71VxTprqqVH2XhVc3XaJ+jinnMnvRJnv8Q67o+y9qud92LOcvVcFdxouor5Xt+dt1AcXF54rZ3lxTlo9h9C8Hi/1Ux9zqje25T7461OiNK/rOU/+FGV5wa2Nt1IfdUWD0ijzwa0zB6PsvSr6ZB1aXc7eq4KbqV9SHznVoWnlzAeXty4sZ+9V4ckeX5Sz96oeLw6XMx91Tb8Pue9yfXkSz+v2ye7SnuYFt9r1oD76ave7EmU+uD5Wk+aFZ956huYFl0sepL5X9/Ma1AcXc/+i84pzUn5n0XkFlw8+RX2cU9aE3RHmg6vADXRe4dmjpkfZvIKrr33U9+p+3In64CIylub1ztVBKRGWF9xe+BX1ca7aw1+MMN/jYxMiNK/rWY2r0rzg6khr6qOuWbAlwnxws/5khM2r16dWG6JsXsFV5y+pj5z6tzlR5oPbXZZH2bx6dfvE/jd+XsHNj+ujzEdd6+oK6oPr4Hya1+vrV91oXnB10xDqo6+60XXUBxffN+B5UfePoTQvuF23P/W9ui82pT648/jtUbav4p7UnnM0yPZVcGfhA9THPaW1KhhkPri45UqQ7avwxIWZIbavgss/d1AfdfUPXakPrh5eEmJ5vXvVgdOCLC+4nvIp9XFPJVs+RH1w1fjtIMsLTxyqRvN6/HAv6qOuI+PquD64069KiO2r6JPJr0f3VXDdbDj1vb4+tz/EfHAx5I4w21fh6Vd9YbavgouZR6jv1R03Msx8cOXPoHnRJ3vjshDLC6437KO+19du3UPMB9fLPwzRvPDq3Ubzgqtzg6iPuuaDHSHmg1uTqofZvHr3qsnHA2xewfWCNtT37lXvTQowH9w89F2AzSs89fWEIJtXcPlzkPqoK3MaBpnv8f8OC7K83j3p650DNK/LTdos6uNeVX+2uYz54HavpgGWF57de32A5nW5dd3VAPNRV3YZEWC+x1+L/b1W/Lx6fW19PsjmFVxe9wj1vZzrVwWZD24NPxhk8wpPHJ0SovPqcnVXgPqoawY1DDEf3GkxOMTyok9q2JNBlhdc7JvBfbevcsSBAPPBxYb6QZrX9ezr1wZZXnBr/vkg81HXPDIwyHxw9caSINtXcU7KIU3ovgruXBhIfZxTzh0HwswHt/6sFWH7Kjxr7JwI3Vdd7qzLpz7qWv06Uh9cjRxN8+KctJYtCbO84KLlZupjTnVapzDzwcWLU8IsLzz7/NkwywtuOt9OfdS1b8gKMx9cW1+E2b6KPtktt0Xovupyce5AhPnIqd2+x/vg5tPkCNtX4ZkPf4qwfRVc/fNIhPmoK/flRJgPbrUvoHm9vo58gud1uSofxH23rzLbvd+M88HtMbVoXnjWJ2NoXnD1+3jqo661rhn1we2WD0TYvOKctB88EWLzCu5MaUJ9nFOy15IQ88FFzy0hNq/wxAvPhdm8gjv9ZlAfdWXuhRDzwfUvLcIsL85JeaJtiOUFd3a+TH2cq069HUHmgzuO+72iuLxe3W3JIZYXXM/YSX3UlYseCTEfXH4+LsTmFX0S3/jCbF7BxfBN1EdOe0XfMPPBrYemhNm8wlNjYnt//LyC6/G/h6nv1pU9loeZD24PKKJ5vT7Nj/U9Pi+4M7sp9dFXZ1BKiPng4uttIZYXnjzXheYFN8NHUx91xZEzIeaDO9fG/lyA76o6OddkV/xOqnV1b15F7jzYP6sS79oij/r4rupHu4qYD24isXeoqCtzYr+T6tUta5dbkVsDO/gqcjGpag7zUVelNy1kPrg1y8lneb3vqjb6tozl9fjCRmXUx3dVG/4SZD64yZsVZHnh6Q03lrK8Hs8KFTMfdWXTEwHmg8srrQOV+rr/UGbFPlnnY78LCy5PtEmvyJ132vuZ7/W15vg85oObTgdzKtW9KTG1oieX7NIVuXysb3JFLqJXMpjv1d3XLZv54KrLNj/Liz5ZI+8oYnnBRa1RBcz3+lq6opT54PqnwmKWF56zIS2P5QV3Wp7MYT7qWq+uLmQ+uL32m3w2r/hOqnrz0zCbV3D9rvv30XE+vlMqDg6OMh9cJu+N0Hl1PaejL8TmFVwv+SJIfbeuuqNBhPng1sB2NC++k2rCieU0r8vVsWrlzPe+U3Vm8mrmg9v1DpSzvPBMnSejNC94sx7UR11nac1y5oMLXTfK5hV9crZ8G2DzCi6qppVR381pj2sdYj64eeOmIJtXr+6k2SVsXsHNialFzPfqdvu5jPng6p8bSlher69LHg2zvODW1Fuoj746l6+GmQ9unzkUYnnhiTvPBlhecFk1QH3UFaNj3yWN98HV8Ni7b5yrzi+TEyudqwlr0ypy55OaCRW5zvl3KvNxTtnLJ/iYD67qPKMr1hX7a66s6Kn5Y1MqcpMlV1TkjuyazHzUNffEfnc+3gcXK2qm07zuOanThmezvODm4t9+6mNO59fPYz64fd/8HJbXq9t6j4/lBVdt92jmo67u3zqb+eBWyWV/xb4K5+LySn39fVxSJX5k0/KKXO7bmkh9N6f9i1nFfHC7V3lqxbr2pODyip71Xd/Eitye+MnyilzUvpxAfbeuanw4hfng9pvtUlher6+jz2ayvOBy5fwM5qOvztwLPuaDW71u87G88GT28+ksL7hMfT6N+agr5z+vqe9yZ/7LmWxecU6aIbHfnYifV3CV9x31cU7ZwfVFzAd3zp4qYPPq1U0bmEvn1eXmhavUR12zv0c+88HV1/tzWV6ck84YXwnLCy6ncB/nqqj/eBnzwWWz/iUsLzyrqEYhywsuT7xHfdSVk1YUUd/l4o3Y74L/z7y6fRIJ32SxefV4871+5iOn02RpDvPBddUJ2Wxe4cldp31sXsHNJ618zEddUzghi/nget5iP8uLPjlW7M+V8XnBxYFT1EdfTe7yfOaDi2098lheeKLZvmyWF1ysPkh91LWa3ZnLfHCn8a05bF/1vqva8vRqtq+Cm8UDqY/vlJqtDdcwH1zVnbea7quoO2x+OdtXPX5lZZT5qKs2bypnPriZfyjK8uI7qU7VWmtYXnDhm7Oa+fhOpfPxIeqD6/kjV7O88NQF971cXF5wMS72Life9+rO/lc588Ft+Z8o21fRJ/l27N1t/L4KLje9SX3kdG79MUJ9l9tbvguzfRWebjs0xPZVcHvGs9T36o75MsR8j2fsDLK86JNdt26U5nW5zr+D+uir428ZZT64ntw9QvO6nkprHmZ5wZ3MttRHXVXF/fcxcT64aftOiM0rvpMqq6TReQVXqfdQ3/tO6eiB1Ae3j/7G59X1VPvkcjavHh9wKsp81LVqvV7OfHBRa0+U5cV3UtWTNWlecPNeWTnz8U5V/bGZ+uBi82SaF572dShneT2+rizKfNRVD9csZz64Pf8jOq/okzh8T5TNK7jOHxFhPnI6C5tFmQ+uG7vv8uPmFZ7ZviDM5hVcDImEmI+66lhOmPng1kAnxPJ6fR1aL8rygtt1X4swH301I60o88GtGWMjLC889cCaMMsLLudcQ33UNRPcv5+O88HNzNvDbF/FOWmSugXYvgqudueVMt87V7e6302L88HtR3eUsX0Vniw+Ucz2VXDb6Ud91BWT6pQyH1zf8M9ilhfnpL2oc5DlBRcfDOc+ztXh7rvIOB/cpJsAywtPBm8sY3nB9ZwZJcxHXbP6pzLmg5snhpeyfdXr096DBWxfBReX51Pf62uPa4qYDy5n5RXQfdX11MQBeXRfdbl4qD31vbrvv5pPfZc7Vbvlsbzok1xavZjlBbeuFBQyH31VC04VMx9cz1pWxPJ6dUe+UkDzutyq25H6qGvG9ilkPrj9WJMCNq84J60BPwbZvILL/HrU986pC7eHmA/urHwhSOfV9czzfQJsXsHlE02pj7rOA7F3WPE+uDMk9t9dfF6ck1aHHiGWF1weXxZkPs5V8faEEPPBnXWxd9fxeeGpDw4FWF5w9WFb7rt11b31g8wHN13GB+i8un0S3ctL2LyC270l9ZHT7Ih9tyDe93ioQQmbV3jq4cZFbF7B9Qe/FjAfdc2s74qYD249kl/I8qJP+nj1MprX5daZf5VQH33tFPuuTrwPLu98rpTl9er+rYpZXnA5dwD1UdcZ2qCE+eB29Neiiveqquf+5RXvSZ20DQmV+MD/Lq/IxaCUBOZ796r1cpKpDz7ooeSKdW3RbkVFzwyak1CRmy/GrajIjZmWwHzUNTv2JzEfXFVdlMTy4p7UujRrFcsLrvO3pTIf91RCds9gPrgRfdJZXnhqe6tUlhdcvKVTqO/WFZcnpzEf3AwNrarYV/vtrBUV+2SrKQkVufPs4RUVuVV3WgLzkVOO7Z3EfHDn3G1JleqGbl9Zqe64mQkVuZw6aGUlvntuAvNRV7Y8lch8cPnT5kSWF31y/K1TaF6Xm2Mmmfnoq/z72lXMB1dtxqfSvK5njxydzPKCq0W1k5mPujr4YwrzwZ1to1LYvHr3pAXvaDav4Hrvxkzm455Sz9jsYz648+dNPjav8FRWIIPNK7g9/l/pzEddeWCCZj64c+6TTJYX96Tyiyt+lhfcVHvQz3zsqdawZtnMB7e2d8tieb26r8zwsbzg1sh91EddUTjTz3xwnbzFx+YVfXJWOWl0Xl1unm6YxnzkVLXDGcwH11U3pbN5hScuvbSKzSu42fBJKvXdurLv0TTmg5sv6tO86JNd+KRmecGdlNh/N/E++ionNPIxH1x2ma1ZXs+rMTqD5QW3pk9Jp75bV2z6IZP54PbELzMqnqv2okUrK56T6uuPEipy0WTDyopctE9MYD7OKdlfJzLf4/tmJVasK6efXlmp7rG0hIpcXWpQiZvjOoH5qGtbLyYyH1y/3zGR5nXPSdXZJLG8Hl80LYn5mFPrz5PJzAe3qs1IZnk9r/T+JJYX3NQ4SX3UladuSqa+y8XWvKSKfdV3dKrUJ2eErxK3XhtRicuZ6dx3c9ptGyYyH1wdPle57oxplTy9eGUlbl/9sBJXp+ZRH3XVsh3UB7f7ptC86JPTKDuR5fX49OHUR1/1FpHEfHB5/4FElheeNDcnsrzgdtkm6qOuGDYxkfngVv41iWxevXPyWONUNq/g5uqCFObjnLJLhqxiPrju50tl8wrP2vifZDav4HaNqdRHXdn1fArzwc19fVJYXu9cvbI8jeUFNz+u5757rqrcPenMB9cFsf9fjM8Lz6w4n0rzutwsbZfKfNS1915ZxXxwNbvpKjav6JPY/lsSm1dwcekV6iOn3FqQzHyPb7kumc0rPCfHSaTz6nJ9cDD33brO8ZeTmA+uTq9PZHnRJzVsSgrL6/Fpq6mPvlq39EhlPrjjvJXC8sJzhl9JYnk9/s+O3Hfr2s2zkpkPLv/+Iontq7gnVWMezWH7Krj5R0/q455SFj+Wy3xwp0vnHLavwjP5b2bRfdXlVuNC6qOuWDM0m/ngzp3zslhe3JPKvnfnsbzgZmAL6nv3VGuq5DMf3PFdyqV5XU9PfjiH5fX4LYOp79UtuCaX+eD20oY5bF9Fn+TGsz62r4I7X3anPnKKTRv8zAc3V6/zs30VnsnN02xfBbd31tXMR10zbLiP+eDWpHWa5UWf1Lq5WSwvuF11E/XRV/sOkc18j7ecmsXywpMpN/hZXnDZQVIfdeWyiJ/54E5ny8/mFfekaskX+Wxewe3pG/KYj3tKO3l+AfPBRedp+Wxe4VnDN+ayeQVXA77KYT7qqtwpecwH14/PzmV5cU8qA+0KWV5wNaMJ9917Vb12VyHzwdXaNQUsLzznXw3yWV5w+ZDNfbeu5QTymQ+un4jm0Xl1+yQnnc1m8wqudzWmPnKqYck51He5Hr0+m80rPDF7SBabV3CVkUp91LUKzmQxH9yedl8Wzev2ySx+OJflBdfjBlMffbW2bM1lPrh68UgOywvPDJ2TzfKCy58/oT7qWodr5jAfXF3XOZvtqzgnVXKtTLaverygWgbzcU6ZY80188H1z60y2b4KTzS6Pp3uqy7XT9+axnzUVfNuy6C+y2X1m9NZXpyTonC9ZnnBdeld1MecWrcP8THf49dENcsLz9SuzJEXXO+pQ33UlbsaaeaDy9l1M9m+ij7Jsa1XsX0V3ISfoj5yKl/dNOZ7fGLDVWxfhSce+UcK21fBnc8nUx91xcBmqcz3+F/u/X5cXq+vP1VJZ3nBnUGxuYz3vb7eeDGd+R4f/HMaywvPnLmQyvKCO4+dS2E+6gr721XMB1ff7Exl84pz0nQ44WPzCq6GPkV9nFP2DdP9zAd3knb62Lx6dX/3aTqvLhff16A+6qrH7vJR3+Xm+Iea5fXOVXXVz/KC68W9qY9z1Zo/K4v54PLPHX6WF57qn+1jecGti9Woj7r27sZ+5oObCTN9bF7RJx34PYPNK7hTdR/1kdOqfSyT+eDq/e0ZbF7hObNNGp1Xl+v5pdRHXXVXIJ354PqV9DSW1+vre+M1zetyMXst9b2+Vj+qmQ8uPx6oWV54Zqk/g+Z1ubNnOfW9uqkrMpkP7gxemMHeq+I7qeLoe/S9Krho8Tj18Z1S2bkr9cFVtar0vSo8NeYgfa8Kbh59PUp9t67ZXUp9cOPvEWV58Z1U8XFNntfldovYu9F4H9+ptG/5Jsp8cH0uk+aFJ6bOpXnB9c2tqY+60hpOfXBrag3+XhV9fe8Ufa8KbvUbGaG+m1Pu2Ud9cLV3cIS9V4WnM3bS96rg1sNtqY+6puku6oOb+k+EWV70Se/bQPOC2xt7Uh99NZsLqA/uLO4UYXnhOYc/p3nBxQPdqI+61qyd1Ae3u/YOs3nFd1Lt3zLpvILr9W9SH98ptTqPpz64+KornVd4+vLDdF7BnY/c71bF+ajrHKpNfXCrzq4Izet+J9Xp+ATNC27PaUB9fKfKfqAG910uH/o1wvLCE61O0LzgJq8kwnzUVaM3Ux/cXL+Uziv6ZKetoPMKbk1sR33kdLq/y32Xy7nNI2xe4ckxW+m8gttH+1EfdcWPG6jv8TkDwywv+mT8Y2lecHnwFup7fV36HPU9Pqt6hOZF3TZraV5we9Yg6qOueDtIfXB936Awfa/qnpN64eIQe68KrreeCDL//8+p7BDzwc3YG0LsvarnlXQPsveqHl/j/nuwOB915aBJQeaDy5v3BVhe75xcvibE8oLb61pSH3NqzdxBfXCT3znE8np1H1wZZHnBdd//BpiPuk7bcJD54KZPiyB7r4o+2TdVD7D3quD60djvZsX7yKk/6xBgPrg+27WMvVf16rbQJey9Krj8e3ox81HX2tuwlPngzpw/i1le9Ek//lqA5QWXx4vKmI++Oq1XBpgPrrZWDbC88GT5B6U0r8v1sLElzEddq+7xUuaDWws/L2HzinNS9dpH5xXcPj2Q+t45tfQw9T1+35gQm1d48sYvg2xewfXertRHXT3jZ+qDq4Wjgiwvzkl72zGaF1z+Zwr1ca5ao09TH9wcUSGa1/VEo8s0L7hoPIP6qCvn3RiivsvNPcuDdF7dPolBawNsXsHtxHupj5xm9PfUB5fV+wfYvMJTtTqXsXkFVzXvpj7qOlsXlzEfXH85rZTmdfskrvxF84KLkje57/ZVr6sbZL7HWy0NsLzwnAFflbG84GqH+7vncT7qOq9cH2A+uDxztZTtq/hOqnp7f4Ttq+DmxQ0R5uM7pWKRn/rg1tJldF+FZ7VK5/uqy+WZidRHXdF4GvXBdct+PC++k3r6DZoX3Ix4ifr4TqV58BHqg+sJ7WheePZffWhecHvO/dRHXXtyC+qDi4O16L6KPjlnHqP7qsdn/hlmPnKq8qbUB7ePHaX7KjxzOZ/vq+AnB1Afde1fM6kPrm7sH2Z50Se9/9oIywsuf/g6zHz0VT/3G/XB1exNNK/nfZTI84Jf6kt9r27rj6jv8SZ9wmxe8Z1U++zlMJtXcOfVq2Hm4zul1oZPw8wHl+d3hNm8whN9L4XZvIJbN58KMx911TO7wsz3+NVtNC++k2pavUvzgou8j6mP76rqvPbUB7erD+Z5Xc+5MZ3mBTeTfNRHXefh16nv8cbvhNm8ok+yOZ9XcHUpQH3kNP3XUN/jUzPpvMKTdebSeQU3F3pRH3V1ygzqg6uqvWle9EnvTqN5wUXdJdRHX60Zivrg4shMmheebjCV5gV3PutDfa/u1+OpD+583j/M9lWck/L7P+i+Cq7fWEx9nFMi5yL3Xa7vSQqxfRWeHNQ4xPZVcDUkL8h81BX3tA0xH9zptS7I8uKcFN3/pnnBzRof9TGnTu8qYeaDOxuLaV54ItoxxPKCW1tif66J91HXRHqHqO9yufBIkO2r6JPq0SbI9lVwcWthgPnIqQc8HWS+xxtvDrB9FZ4TbBGg+6rLRa/2ZcxHXZPdKcB8j599vYzlRZ/EiheCNK/LrYuxPxfE++ir+H5CkPngZsI5mhee/euQAMsLruuvKqO+W1d3nRBgPrh1w/YyNq84J50h14fZvILLKmtDzMc5ZW6qE6a+y9Wz2+m8et6VESE6ry631V9B5qOunPJmiPoutzJiv2v2P3ndc1Lde3uY5QUXk78LMR/nqnmpZZj54NaY32heeKLBhyGWF9z5oxX1Udec91MfXPqfCbF5RZ/UjR8E6by63BpVk/rIaVpmBpkPrrc2D7J5hadnzQvQeXW56HK2jPqoOzs9wHxwp7BWgOX1+vrUOpoXXBd0pj76KtRB6oNbbUYGaV7UFasDLK/HZzSnPuqa7V9TH1xecO9n49+ruvekZuXQIvZeFdws7EZ93FOaRgeKmA+u1NZC9l4VnvVCxwL2XhVcD+5EfdR1Jq4rYD64WbAun+XFPamyexfTvC6XrR+nPu6pzBvhYuaDi5t9RSwvPHvqfYUsr8eP3Et91DXBhELmg1sTlxew96peXzf3ymPvVcHVrpepj5x64LY86rvc7vhDLnuvCk98sCCHvVcFN+fWZjPfq5v2dw71Xa7X3JPD8qJPqtMj+TSvy532z1IffVUDkvOZD271z89jeeHpCWNzWV5wPfZj6nt1e27OZT64XcP9PYD4eXXvSeWWm0rYvIJbm/8uYj7uKeWJUSXMB7c+f66YzSs8Z/rZQjav4KJz7N8dxPuoKxs+XcR8cOv1DoUsL+5JtS4sYXnBnU+SiqmPPbX8ZAnzPd52fzHNi7pn5hexvB6/+d1C5qOuHLW5iPngcnikkM2r16eXfs9n8wpu3/afPOYjp/1MhwLmg+sWXfLZvMJTql4em1dwq/RR6qOueHVIHvPBxatv5dK8bp/0kbcLWF5wp9G7+cxHX507iwqYDy6LCvJZXs8bsyiP5QU3L+TkMh911dFgHvPBdb57rse9V8U5qa/+mcXeq4Lrd5+kPs4p9enobOaDyw35Wey9qlf37o/97L2qx5f85mM+6oroJT/zweWUIX6a1z0n5bJt2TSvy033GtT3ztUujXOYD+4kjMimeeFteiGL5QWXjaPUR11nblEW88HVlOuy2HtV9MlqPNjH3quCi+u2cd/NKbLW+5gPrta087H3qvDkk501e6/q8fJZ3Hfrqh9LNfPB9dmHNcuLPtk9mvhZXnBlraQ++ioy3vQzH9ypddLH8sJTa271sbzg8gM/9VHXzJ3kYz64I9334HHzinNSp47NYfMKbtpkZzMf55QY48thPri98GA2m1d4pu2ZLDqvLhf9elEfda3TLbKZDy4Xx861+LzeOfnOrhyWF1x2qkJ9nKv2pl9ymA9uH6+fw/LCk1WezWZ5wdWNmVnMR13z6fPZzAe3dhdmsXlFn2STcj+bV3Dn7APU93J2PuFnPrgKjfGzefW8f6/zsXkFd5YMoT7qyl7nqQ8uqiT4WF70Sb1/fRbLC65HzaM++ioX3pzFfI8/9JGf5YVn/lHfz/KCm8g66qOus72Jn/ng+sttPraveveq1zYvZfsquPn8Bu7jXnXGoFLmgzt9Hy9h+yo8c+h8EdtXwa33nULmo6597I5i5oPrz68pYnlxT2oaqVKWF1zNG1nCfNxTqWru947jfHA9MPa7VPF54Tl1OhbTvOCLGxQxH3Uta2Ax88FNq7uK2L7q9fW/ewrYvurxpTvymY+c9ryTBcwHd1YeyKf7quuZl3bmsX0V3Encl8t81LUnxn53Mt4Hl299k8vyok9m518FLK/HE37IZz76Ks5dLGA+uJq+N5/l9eoWxHLH5wWXhbF/1x7vo669Pta3//FdrkrTctm84p5UvvlZKZtXcBlOKWE+7in16lOlzAfXgbISNq/wzKsvFbN5BbdvblXEfNS1a71ezHxwZ82dRSwv7kmFcL+bHZcXXMlNJczHvap4u20Z88GtPV+VsLzwrILxxSwvuLW4bhHzUVeefK2Y+eDOD5cL2bx6fWpzuoDNK7i5Z00+85FT+r4tYD64Tvs4n82r541ZkMfmFdxcPyaX+ajrDOmXx3xw1e32XJYXfTJvRQtYXnBTMiyf+eirKIp9NyLeB9dN6+ezvJ7X+do8lhdcnVqXw3zUNWWF3He59UYPuq/inDTtf6f7KrgWDaiPc8r54yj3XW7b1XPYvup5ywZks30VXF2M7V3xPupaDR7LZj64Ojwzi+XFOWk1307zglvv/ZDNfMyprp1AfXBbpdO88Myt1bJZXnBxi8hiPuqa2WuoD26POe6n+6rbJ9m8ShbbV8FF4jt+5iOnPfw76oPr7r3ovgpPLqnpp/uqy8UXIR/zvbo3fUt9cJMbu/eIz4s+6cXLaV5wZ+TfPuq7fTUv3k99cDl2qY/lhec0m0Pzgju/3El91BV316Q+uNqoNZtX71y9pW8Om1dwNfSZbObjnLJ8Z6kPLl86kMXmFZ49fSCdV3D976l+5qOu6voN911ubTvqY3lxTuo7Z9O84OLqgCzqu+equPQn9cHFsrCf5YWn+3SgecGdpY/5mI+6znXzqA8uvnpD03l1++R8WULnFVz0snzMR07Rqhb1weW45zSbV69uldh3j+PnFdx69Ewm81FXDBiWSX2XW9c/l0Hzun0yl3trlhfcPnkxg/puX9Vt32cwH1yfqp3O8sITNyxNZ3nB7cEvpjEfdcW376xivsffb5q64f8AG5ACog== + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-100000.1.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-100000.1.vtu new file mode 100644 index 000000000..27ff3e33a --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-100000.1.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAgAAAAIAN3IAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-100000.2.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-100000.2.vtu new file mode 100644 index 000000000..763c4724f --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-100000.2.vtu @@ -0,0 +1,34 @@ + + + + + + + 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rN+48ePLizYfvb8sM/10/AYKECBMhSow4CZKkSJMhS448BYqUKFOhSo06DZq0aNOhS48+A4aMGDNhyow5C5asWLNhy449B46cOHPhyo07D568ePPhy8//0E+AICHCRIgSI06CJCnSZMiSI0+BIiXKVKhSo06DJi3adOjSo8+AISPGTJgyY86CJSvWbNiyY8+BIyfOXLhy486DJy/efPjy89f6CRAkRJgIUWLESZAkRZoMWXLkKVCkRJkKVWrUadCkRZsOXXr0GTBkxJgJU2bMWbBkxZoNW3bsOXDkxJkLV27cefDkxZsP37/+fZnnFwGChAgTIUqMOAmSpEiTIUuOPAWKlChToUqNOg2atGjToUuPPgOGjBgzYcqMOQuWrFizYcuOPQeOnDhz4cqNOw+evHjz4fvbItP/1E+AICHCRIgSI06CJCnSZMiSI0+BIiXKVKhSo06DJi3adOjSo8+AISPGTJgyY86CJSvWbNiyY8+BIyfOXLhy486DJy/efPjy87/0EyBIiDARosSIkyBJijQZsuTIU6BIiTIVqtSo06BJizYduvToM2DIiDETpsyYs2DJijUbtuzYc+DIiTMXrty48+DJizcfvvz8jX4CBAkRJkKUGHESJEmRJkOWHHkKFClRpkKVGnUaNGnRpkOXHn0GDBkxZsKUGXMWLFmxZsOWHXsOHDlx5sKVG3cePHnx5sP3b35f5PtFgCAhwkSIEiNOgiQp0mTIkiNPgSIlylSoUqNOgyYt2nTo0qPPgCEjxkyYMmPOgiUr1mzYsmPPgSMnzly4cuPOgycv3nz4/rbE+L/1EyBIiDARosSIkyBJijQZsuTIU6BIiTIVqtSo06BJizYduvToM2DIiDETpsyYs2DJijUbtuzYc+DIiTMXrty48+DJizcfvvz8H/0ECBIiTIQoMeIkSJIiTYYsOfIUKFKiTIUqNeo0aNKiTYcuPfoMGDJizIQpM+YsWLJizYYtO/YcOHLizIUrN+48ePLizYcvP3+rnwBBQoSJECVGnARJUqTJkCVHngJFSpSpUKVGnQZNWrTp0KVHnwFDRoyZMGXGnAVLVqzZsGXHngNHTpy5cOXGnQdPXrz58P3b35d4fxEgSIgwEaLEiJMgSYo0GbLkyFOgSIkyFarUqNOgSYs2Hbr06DNgyIgxE6bMmLNgyYo1G7bs2HPgyIkzF67cuPPgyYs3H76/LTD/X/0ECBIiTIQoMeIkSJIiTYYsOfIUKFKiTIUqNeo0aNKiTYcuPfoMGDJizIQpM+YsWLJizYYtO/YcOHLizIUrN+48ePLizYcvP/9PPwGChAgTIUqMOAmSpEiTIUuOPAWKlChToUqNOg2atGjToUuPPgOGjBgzYcqMOQuWrFizYcuOPQeOnDhz4cqNOw+evHjz4cvP3+knQJAQYSJEiREnQZIUaTJkyZGnQJESZSpUqVGnQZMWbTp06dFnwJARYyZMmTFnwZIVazZs2bHnwJETZy5cuXHnwZMXbz58/+73Bf7/Dz4v2Mg= 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+AQAAAAAAAgAAAAIAG3IAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-100000.3.vtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-100000.3.vtu new file mode 100644 index 000000000..e310db875 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-100000.3.vtu @@ -0,0 +1,34 @@ + + + + + + + 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+AQAAAAAAAgAAAAIAWGwAAA==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+AQAAAAAAAgAAAAIAXHMAAA==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 + + + + diff --git a/tests/automatic_regression_test/gold_cahnHilliard/solution-100000.pvtu b/tests/automatic_regression_test/gold_cahnHilliard/solution-100000.pvtu new file mode 100644 index 000000000..ad0130a29 --- /dev/null +++ b/tests/automatic_regression_test/gold_cahnHilliard/solution-100000.pvtu @@ -0,0 +1,19 @@ + + + + + + + + + + + + + + + + + diff --git a/tests/automatic_regression_test/run_auto_regression_tests.py b/tests/automatic_regression_test/run_auto_regression_tests.py new file mode 100644 index 000000000..275492859 --- /dev/null +++ b/tests/automatic_regression_test/run_auto_regression_tests.py @@ -0,0 +1,157 @@ +import subprocess +import shutil +import glob +import math +import os +import datetime +import time + +# ---------------------------------------------------------------------------------------- +# Function tha compiles the PRISMS-PF code and runs the executable. +# ---------------------------------------------------------------------------------------- +def run_simulation(run_name): + # Delete any pre-existing executables or results + if os.path.exists(run_name) == True: + shutil.rmtree(run_name) + + # Open file where output is redirected to + if os.path.exists("output.txt") == True: + os.remove("output.txt") + f = open("output.txt",'w+') + + # Remove old files + if os.path.exists("main") == True: + os.remove("main") + if os.path.exists("CMakeCache.txt") == True: + os.remove("CMakeCache.txt") + + subprocess.call(["rm", "*vtu"],stdout=f,stderr=f) + + # Compile and run + subprocess.call(["cmake", "."],stdout=f,stderr=f) + subprocess.call(["make", "release"],stdout=f) + start = time.time() + subprocess.call(["mpirun", "-n", "4", "main"],stdout=f) + end = time.time() + f.close() + + # Group the files + subprocess.call(["mkdir",run_name]) + for output_files in glob.glob('*vtu'): + shutil.move(output_files,run_name) + if os.path.exists("freeEnergy.txt") == True: + shutil.move("freeEnergy.txt",run_name) + + test_time = end-start + return test_time + +# ---------------------------------------------------------------------------------------- +# Function that runs a regression test +# ---------------------------------------------------------------------------------------- +def run_regression_test(applicationName,getNewGoldStandard): + + if (getNewGoldStandard == False): + testName = "test_"+applicationName + + else: + testName = "gold_"+applicationName + + if os.path.exists(testName) == True: + shutil.rmtree(testName) + + # Move to the application directory + r_test_dir = os.getcwd() + os.chdir("../../applications/"+applicationName) + + # Run the simulation and move the results to the test directory + test_time = run_simulation(testName) + + shutil.move(testName,r_test_dir) + + # Compare the result against the gold standard, if it exists + os.chdir(r_test_dir) + + if (getNewGoldStandard == False): + # Read the gold standard free energies + os.chdir("gold_"+applicationName) + gold_standard_file = open("freeEnergy.txt","r") + gold_energy = gold_standard_file.readlines() + gold_standard_file.close() + + # Read the test free energies + os.chdir("../"+testName) + test_file = open("freeEnergy.txt","r") + test_energy = test_file.readlines() + test_file.close() + + rel_diff = (float(gold_energy[9])-float(test_energy[9]))/float(gold_energy[9]) + rel_diff = abs(rel_diff) + + if (rel_diff < 1.0e-10): + test_passed = True + else: + test_passed = False + + else: + test_passed = True + + # Print the results to the screen + print "Test: ", applicationName + print "Passed: ", test_passed + print "Time taken:", test_time + + # Write the results to a file + os.chdir(r_test_dir) + text_file = open("test_results.txt","a") + now = datetime.datetime.now() + text_file.write("Application: " + applicationName +" \n") + if test_passed: + if getNewGoldStandard == False: + text_file.write("Result: Pass \n") + else: + text_file.write("Result: New Gold Standard \n") + else: + text_file.write("Result: Fail \n") + text_file.write("Time: "+str(test_time)+" \n \n") + text_file.close() + + return (test_passed,test_time) +# ---------------------------------------------------------------------------------------- +# ---------------------------------------------------------------------------------------- + + +# Initialize +test_counter = 0 +tests_passed = 0 + +text_file = open("test_results.txt","a") +now = datetime.datetime.now() +text_file.write("--------------------------------------------------------- \n") +text_file.write("Regression test on " + now.strftime("%Y-%m-%d %H:%M") + "\n") +text_file.write("--------------------------------------------------------- \n") +text_file.close() + +# Start the tests +applicationName = "cahnHilliard" +getNewGoldStandard = False + +test_result = run_regression_test(applicationName,getNewGoldStandard) + +test_counter += 1 +tests_passed += int(test_result[0]) + +applicationName = "allenCahn" +getNewGoldStandard = False + +test_result = run_regression_test(applicationName,getNewGoldStandard) + +test_counter += 1 +tests_passed += int(test_result[0]) + +# Output the overall test results +text_file = open("test_results.txt","a") +text_file.write("Tests Passed: "+str(tests_passed)+"/"+str(test_counter)+"\n") +text_file.write("--------------------------------------------------------- \n") +text_file.close() + + diff --git a/tests/automatic_regression_test/test_results.txt b/tests/automatic_regression_test/test_results.txt new file mode 100644 index 000000000..3d9e64203 --- /dev/null +++ b/tests/automatic_regression_test/test_results.txt @@ -0,0 +1,26 @@ +--------------------------------------------------------- +Regression test on 2017-01-31 16:19 +--------------------------------------------------------- +Application: cahnHilliard +Result: New Gold Standard +Time: 70.0864751339 + +Application: allenCahn +Result: New Gold Standard +Time: 9.45906805992 + +Tests Passed: 2/2 +--------------------------------------------------------- +--------------------------------------------------------- +Regression test on 2017-01-31 16:25 +--------------------------------------------------------- +Application: cahnHilliard +Result: Pass +Time: 70.5716431141 + +Application: allenCahn +Result: Pass +Time: 9.58352184296 + +Tests Passed: 2/2 +--------------------------------------------------------- diff --git a/applications/CHiMaD_benchmark1c/CMakeLists.txt b/tests/indexing_tests/indexTest/CMakeLists.txt similarity index 100% rename from applications/CHiMaD_benchmark1c/CMakeLists.txt rename to tests/indexing_tests/indexTest/CMakeLists.txt diff --git a/tests/indexing_tests/indexTest/ICs_and_BCs.h b/tests/indexing_tests/indexTest/ICs_and_BCs.h new file mode 100644 index 000000000..b9fc21131 --- /dev/null +++ b/tests/indexing_tests/indexTest/ICs_and_BCs.h @@ -0,0 +1,99 @@ +template +class InitialCondition : public Function +{ +public: + unsigned int index; + Vector values; + InitialCondition (const unsigned int _index) : Function(1), index(_index) { + std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1); + } + double value (const Point &p, const unsigned int component = 0) const + { + double scalar_IC; + // ===================================================================== + // ENTER THE INITIAL CONDITIONS HERE FOR SCALAR FIELDS + // ===================================================================== + // Enter the function describing conditions for the fields at point "p". + // Use "if" statements to set the initial condition for each variable + // according to its variable index. + + double dx=spanX/((double) subdivisionsX)/std::pow(2.0,refineFactor); + double x_loc[12] = {0.1, 0.8, 0.5, 0.4, 0.3, 0.8, 0.9, 0.0, 0.1, 0.5, 1, 0.7}; + double y_loc[12] = {0.3, 0.7, 0.2, 0.4, 0.9, 0.1, 0.5, 0.1, 0.6, 0.6, 1, 0.95}; + double rad[12] = {12, 14, 19, 16, 11, 12, 17, 15, 20, 10, 11, 14}; + double dist; + scalar_IC = 0; + for (unsigned int i=0; i<12; i++){ + #if problemDIM == 2 + dist = p.distance(Point(x_loc[i]*spanX,y_loc[i]*spanY)); + #elif problemDIM == 3 + dist = p.distance(Point(x_loc[i]*spanX,y_loc[i]*spanY,0.5*spanZ)); + #endif + if (dist < rad[i]){ + scalar_IC = 1.0; + } + + }; + + // ===================================================================== + return scalar_IC; + } +}; + +template +class InitialConditionVec : public Function +{ +public: + unsigned int index; + //Vector values; + InitialConditionVec (const unsigned int _index) : Function(dim), index(_index) { + std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1); + } + void vector_value (const Point &p,Vector &vector_IC) const + { + // ===================================================================== + // ENTER THE INITIAL CONDITIONS HERE FOR VECTOR FIELDS + // ===================================================================== + // Enter the function describing conditions for the fields at point "p". + // Use "if" statements to set the initial condition for each variable + // according to its variable index. + + if (index > 0){ + vector_IC = 0.0; + } + + // ===================================================================== + } +}; + +template +void generalizedProblem::setBCs(){ + // ===================================================================== + // ENTER THE BOUNDARY CONDITIONS HERE + // ===================================================================== + // This function sets the BCs for the problem variables + // The function "inputBCs" should be called for each component of + // each variable and should be in numerical order. Four input arguments + // set the same BC on the entire boundary. Two plus two times the + // number of dimensions inputs sets separate BCs on each face of the domain. + // Inputs to "inputBCs": + // First input: variable number + // Second input: component number + // Third input: BC type (options are "ZERO_DERIVATIVE", "DIRICHLET", and "PERIODIC") + // Fourth input: BC value (ignored unless the BC type is "DIRICHLET") + // Odd inputs after the third: BC type + // Even inputs after the third: BC value + // Face numbering: starts at zero with the minimum of the first direction, one for the maximum of the first direction + // two for the minimum of the second direction, etc. + + inputBCs(0,0,"ZERO_DERIVATIVE",0); + inputBCs(1,0,"ZERO_DERIVATIVE",0); + inputBCs(1,1,"ZERO_DERIVATIVE",0); + inputBCs(2,0,"ZERO_DERIVATIVE",0); + inputBCs(2,1,"ZERO_DERIVATIVE",0); + + // ===================================================================== + +} + + diff --git a/applications/CHiMaD_benchmark1c/equations.h b/tests/indexing_tests/indexTest/equations.h similarity index 63% rename from applications/CHiMaD_benchmark1c/equations.h rename to tests/indexing_tests/indexTest/equations.h index 754ed4c99..24f056897 100644 --- a/applications/CHiMaD_benchmark1c/equations.h +++ b/tests/indexing_tests/indexTest/equations.h @@ -1,26 +1,48 @@ -// List of variables and residual equations for the Cahn-Hilliard example application +// List of variables and residual equations for the coupled Allen-Cahn example application // ================================================================================= // Define the variables in the model // ================================================================================= // The number of variables -#define num_var 2 +#define num_var 3 // The names of the variables, whether they are scalars or vectors and whether the // governing eqn for the variable is parabolic or elliptic -#define variable_name {"c", "mu"} -#define variable_type {"SCALAR", "SCALAR"} -#define variable_eq_type {"PARABOLIC", "PARABOLIC"} +#define variable_name {"n","vec1","vec2"} +#define variable_type {"SCALAR","VECTOR","VECTOR"} +#define variable_eq_type {"PARABOLIC","PARABOLIC","PARABOLIC"} // Flags for whether the value, gradient, and Hessian are needed in the residual eqns -#define need_val {true, true} -#define need_grad {true, true} -#define need_hess {false, false} +#define need_val {true,true,true} +#define need_grad {true,true,true} +#define need_hess {false,false,false} // Flags for whether the residual equation has a term multiplied by the test function // (need_val_residual) and/or the gradient of the test function (need_grad_residual) -#define need_val_residual {true, true} -#define need_grad_residual {true, true} +#define need_val_residual {true,true,true} +#define need_grad_residual {true,true,true} + +//// ================================================================================= +//// Define the variables in the model +//// ================================================================================= +//// The number of variables +//#define num_var 2 +// +//// The names of the variables, whether they are scalars or vectors and whether the +//// governing eqn for the variable is parabolic or elliptic +//#define variable_name {"n","vec1"} +//#define variable_type {"SCALAR","VECTOR"} +//#define variable_eq_type {"PARABOLIC","PARABOLIC"} +// +//// Flags for whether the value, gradient, and Hessian are needed in the residual eqns +//#define need_val {true,true} +//#define need_grad {true,true} +//#define need_hess {false,false} +// +//// Flags for whether the residual equation has a term multiplied by the test function +//// (need_val_residual) and/or the gradient of the test function (need_grad_residual) +//#define need_val_residual {true,true} +//#define need_grad_residual {true,true} // ================================================================================= // Define the model parameters and the residual equations @@ -31,21 +53,18 @@ // equations (or parts of residual equations) can be written below in "residualRHS". // Mobility -#define McV 5.0 +#define MnV 1.0 // Gradient energy coefficient -#define KcV 2.0 +#define KnV 4.0 -// The free energy and its derivative -#define fV (5.0*(c-0.3)*(c-0.3)*(c-0.7)*(c-0.7)) -#define fcV (5.0*(2.0*(c-0.3)*(c-0.7)*(c-0.7)+2.0*(c-0.3)*(c-0.3)*(c-0.7))) +// Free energy and its derivative +#define fV (n*n*n*n - 2.0*n*n*n + n*n) +#define fnV (4.0*n*(n-1.0)*(n-0.5)) // Residual equations -#define rmuV (fcV) -#define rmuxV (constV(KcV)*cx) -#define rcV (c) -#define rcxV (constV(-McV*timeStep)*mux) - +#define rnV (n-constV(timeStep*MnV)*fnV) +#define rnxV (constV(-timeStep*KnV*MnV)*nx) // ================================================================================= // residualRHS @@ -62,21 +81,36 @@ void generalizedProblem::residualRHS(const std::vector > std::vector > & modelResidualsList, dealii::Point > q_point_loc) const { -// The concentration and its derivatives (names here should match those in the macros above) -scalarvalueType c = modelVariablesList[0].scalarValue; -scalargradType cx = modelVariablesList[0].scalarGrad; +// The order parameter and its derivatives (names here should match those in the macros above) +scalarvalueType n = modelVariablesList[0].scalarValue; +scalargradType nx = modelVariablesList[0].scalarGrad; + +vectorvalueType vec1 = modelVariablesList[1].vectorValue; +vectorgradType vec1x = modelVariablesList[1].vectorGrad; + +vectorvalueType vec2 = modelVariablesList[2].vectorValue; +vectorgradType vec2x = modelVariablesList[2].vectorGrad; + -// The chemical potential and its derivatives (names here should match those in the macros above) -scalarvalueType mu = modelVariablesList[1].scalarValue; -scalargradType mux = modelVariablesList[1].scalarGrad; +// Residuals for the equation to evolve the order parameter (names here should match those in the macros above) +modelResidualsList[0].scalarValueResidual = rnV; +modelResidualsList[0].scalarGradResidual = rnxV; -// Residuals for the equation to evolve the concentration (names here should match those in the macros above) -modelResidualsList[0].scalarValueResidual = rcV; -modelResidualsList[0].scalarGradResidual = rcxV; +vectorvalueType rvecV; +rvecV[0] = constV(1.0); +rvecV[1] = constV(1.0); -// Residuals for the equation to evolve the chemical potential (names here should match those in the macros above) -modelResidualsList[1].scalarValueResidual = rmuV; -modelResidualsList[1].scalarGradResidual = rmuxV; +vectorgradType rvecxV; +rvecxV[0][0] = constV(1.0); +rvecxV[1][0] = constV(1.0); +rvecxV[0][1] = constV(1.0); +rvecxV[1][1] = constV(1.0); + +modelResidualsList[1].vectorValueResidual = rvecV; +modelResidualsList[1].vectorGradResidual = rvecxV; + +modelResidualsList[2].vectorValueResidual = rvecV; +modelResidualsList[2].vectorGradResidual = rvecxV; } @@ -113,18 +147,14 @@ void generalizedProblem::residualLHS(const std::vector > // density are added to the "energy_components" variable (index 0: chemical energy, // index 1: gradient energy, index 2: elastic energy). template -void generalizedProblem::energyDensity(const std::vector > & modelVariablesList, +void generalizedProblem::energyDensity(const std::vector > & modelVarList, const dealii::VectorizedArray & JxW_value, dealii::Point > q_point_loc) { scalarvalueType total_energy_density = constV(0.0); -// The concentration and its derivatives (names here should match those in the macros above) -scalarvalueType c = modelVariablesList[0].scalarValue; -scalargradType cx = modelVariablesList[0].scalarGrad; - -// The chemical potential and its derivatives (names here should match those in the macros above) -scalarvalueType mu = modelVariablesList[1].scalarValue; -scalargradType mux = modelVariablesList[1].scalarGrad; +// The order parameter and its derivatives (names here should match those in the macros above) +scalarvalueType n = modelVarList[0].scalarValue; +scalargradType nx = modelVarList[0].scalarGrad; // The homogenous free energy scalarvalueType f_chem = fV; @@ -134,7 +164,7 @@ scalarvalueType f_grad = constV(0.0); for (int i=0; i 1.0e-10){ +for (unsigned i=0; i 1.0e-10){ this->energy+=total_energy_density[i]*JxW_value[i]; this->energy_components[0]+= f_chem[i]*JxW_value[i]; this->energy_components[1]+= f_grad[i]*JxW_value[i]; diff --git a/tests/indexing_tests/indexTest/formulation_allenCahn.pdf b/tests/indexing_tests/indexTest/formulation_allenCahn.pdf new file mode 100644 index 000000000..aa82cf7b1 Binary files /dev/null and b/tests/indexing_tests/indexTest/formulation_allenCahn.pdf differ diff --git a/applications/CHiMaD_benchmark1c/main.cc b/tests/indexing_tests/indexTest/main.cc similarity index 85% rename from applications/CHiMaD_benchmark1c/main.cc rename to tests/indexing_tests/indexTest/main.cc index 2b20cf223..9ee964cff 100644 --- a/applications/CHiMaD_benchmark1c/main.cc +++ b/tests/indexing_tests/indexTest/main.cc @@ -1,13 +1,13 @@ -// Cahn-Hilliard with adaptivity example application +// Allen-Cahn example application // Header files -#include "../../include/dealIIheaders.h" +#include "../../../include/dealIIheaders.h" #include "parameters.h" -#include "../../src/models/coupled/generalized_model.h" +#include "../../../src/models/coupled/generalized_model.h" #include "equations.h" #include "ICs_and_BCs.h" -#include "../../src/models/coupled/generalized_model_functions.h" +#include "../../../src/models/coupled/generalized_model_functions.h" //main int main (int argc, char **argv) diff --git a/applications/CHiMaD_benchmark1c/parameters.h b/tests/indexing_tests/indexTest/parameters.h similarity index 67% rename from applications/CHiMaD_benchmark1c/parameters.h rename to tests/indexing_tests/indexTest/parameters.h index 3581e6c83..5a90427b6 100644 --- a/applications/CHiMaD_benchmark1c/parameters.h +++ b/tests/indexing_tests/indexTest/parameters.h @@ -1,4 +1,4 @@ -// Parameter list for the Cahn-Hilliard with adaptivity example application +// Parameter list for the Allen-Cahn example application // All strictly numerical parameters should be set in this file // ================================================================================= @@ -10,12 +10,10 @@ // Set the length of the domain in all three dimensions // ================================================================================= // Each axes spans from zero to the specified length -#define spanX 200.0 -#define spanY 200.0 +#define spanX 100.0 +#define spanY 100.0 #define spanZ 100.0 -#define t_domain true - // ================================================================================= // Set the element parameters // ================================================================================= @@ -24,42 +22,21 @@ #define subdivisionsX 1 #define subdivisionsY 1 #define subdivisionsZ 1 -#define refineFactor 4 +#define refineFactor 7 // Set the polynomial degree of the element (suggested values: 1 or 2) #define finiteElementDegree 1 -// ================================================================================= -// Set the adaptive mesh refinement parameters -// ================================================================================= -// Set the flag determining if adaptive meshing is activated -#define hAdaptivity true - -// Set the maximum and minimum level of refinement -#define maxRefinementLevel (refineFactor) -#define minRefinementLevel (0) - -// Set the fields used to determine the refinement. Fields determined by the order -// declared in "equations.h", starting at zero -#define refineCriterionFields {0} - -// Set the maximum and minimum value of the fields where the mesh should be refined -#define refineWindowMax {0.99} -#define refineWindowMin {0.01} - -// Set the number of time steps between remeshing operations -#define skipRemeshingSteps 1000 - // ================================================================================= // Set the time step parameters // ================================================================================= // The size of the time step -#define timeStep 2.0e-3 +#define timeStep 1.0e-3 // The simulation ends when either timeFinal is reached or the number of time steps // equals timeIncrements -#define timeFinal 10000.0 -#define timeIncrements 2000000 +#define timeFinal 20.0 +#define timeIncrements 20000 // ================================================================================= // Set the output parameters @@ -69,7 +46,7 @@ // Type of spacing between outputs ("EQUAL_SPACING", "LOG_SPACING", "N_PER_DECADE", // or "LIST") -#define outputCondition "LIST" +#define outputCondition "EQUAL_SPACING" // Number of times the program outputs the fields (total number for "EQUAL_SPACING" // and "LOG_SPACING", number per decade for "N_PER_DECADE", ignored for "LIST") @@ -77,10 +54,19 @@ // User-defined list of time steps where the program should output. Only used if // outputCondition is "LIST" -#define outputList {0,200,1000,2000,4000,20000,40000,100000,200000,400000,600000,2000000} +#define outputList {} + +// Status is printed to the screen every skipPrintSteps +#define skipPrintSteps 10 // ================================================================================= // Set the flag determining if the total free energy is calculated for each output // ================================================================================= #define calcEnergy true + + + + + + diff --git a/tests/indexing_tests/indexTest/tex_files/allenCahn.tex b/tests/indexing_tests/indexTest/tex_files/allenCahn.tex new file mode 100644 index 000000000..d2b763351 --- /dev/null +++ b/tests/indexing_tests/indexTest/tex_files/allenCahn.tex @@ -0,0 +1,223 @@ +\documentclass[10pt]{article} +\usepackage{amsmath} +\usepackage{bm} +\usepackage{bbm} +\usepackage{mathrsfs} +\usepackage{graphicx} +\usepackage{wrapfig} +\usepackage{subcaption} +\usepackage{epsfig} +\usepackage{amsfonts} +\usepackage{amssymb} +\usepackage{amsmath} +\usepackage{wrapfig} +\usepackage{graphicx} +\usepackage{psfrag} +\newcommand{\sun}{\ensuremath{\odot}} % sun symbol is \sun +\let\vaccent=\v % rename builtin command \v{} to \vaccent{} +\renewcommand{\v}[1]{\ensuremath{\mathbf{#1}}} % for vectors +\newcommand{\gv}[1]{\ensuremath{\mbox{\boldmath$ #1 $}}} +\newcommand{\grad}[1]{\gv{\nabla} #1} +\renewcommand{\baselinestretch}{1.2} +\jot 5mm +\graphicspath{{./figures/}} +%text dimensions +\textwidth 6.5 in +\oddsidemargin .2 in +\topmargin -0.2 in +\textheight 8.5 in +\headheight 0.2in +\overfullrule = 0pt +\pagestyle{plain} +\def\newpar{\par\vskip 0.5cm} +\begin{document} +% +%---------------------------------------------------------------------- +% Define symbols +%---------------------------------------------------------------------- +% +\def\iso{\mathbbm{1}} +\def\half{{\textstyle{1 \over 2}}} +\def\third{{\textstyle{1 \over 3}}} +\def\fourth{{\textstyle{{1 \over 4}}}} +\def\twothird{{\textstyle {{2 \over 3}}}} +\def\ndim{{n_{\rm dim}}} +\def\nint{n_{\rm int}} +\def\lint{l_{\rm int}} +\def\nel{n_{\rm el}} +\def\nf{n_{\rm f}} +\def\DIV {\hbox{\af div}} +\def\GRAD{\hbox{\af Grad}} +\def\sym{\mathop{\rm sym}\nolimits} +\def\tr{\mathop{\rm tr}\nolimits} +\def\dev{\mathop{\rm dev}\nolimits} +\def\Dev{\mathop{\rm Dev}\nolimits} +\def\DEV{\mathop {\rm DEV}\nolimits} +\def\bfb {{\bi b}} +\def\Bnabla{\nabla} +\def\bG{{\bi G}} +\def\jmpdelu{{\lbrack\!\lbrack \Delta u\rbrack\!\rbrack}} +\def\jmpudot{{\lbrack\!\lbrack\dot u\rbrack\!\rbrack}} +\def\jmpu{{\lbrack\!\lbrack u\rbrack\!\rbrack}} +\def\jmphi{{\lbrack\!\lbrack\varphi\rbrack\!\rbrack}} +\def\ljmp{{\lbrack\!\lbrack}} +\def\rjmp{{\rbrack\!\rbrack}} +\def\sign{{\rm sign}} +\def\nn{{n+1}} +\def\na{{n+\vartheta}} +\def\nna{{n+(1-\vartheta)}} +\def\nt{{n+{1\over 2}}} +\def\nb{{n+\beta}} +\def\nbb{{n+(1-\beta)}} +%--------------------------------------------------------- +% Bold Face Math Characters: +% All In Format: \B***** . +%--------------------------------------------------------- +\def\bOne{\mbox{\boldmath$1$}} +\def\BGamma{\mbox{\boldmath$\Gamma$}} +\def\BDelta{\mbox{\boldmath$\Delta$}} +\def\BTheta{\mbox{\boldmath$\Theta$}} +\def\BLambda{\mbox{\boldmath$\Lambda$}} +\def\BXi{\mbox{\boldmath$\Xi$}} +\def\BPi{\mbox{\boldmath$\Pi$}} +\def\BSigma{\mbox{\boldmath$\Sigma$}} +\def\BUpsilon{\mbox{\boldmath$\Upsilon$}} +\def\BPhi{\mbox{\boldmath$\Phi$}} +\def\BPsi{\mbox{\boldmath$\Psi$}} +\def\BOmega{\mbox{\boldmath$\Omega$}} +\def\Balpha{\mbox{\boldmath$\alpha$}} +\def\Bbeta{\mbox{\boldmath$\beta$}} +\def\Bgamma{\mbox{\boldmath$\gamma$}} +\def\Bdelta{\mbox{\boldmath$\delta$}} +\def\Bepsilon{\mbox{\boldmath$\epsilon$}} +\def\Bzeta{\mbox{\boldmath$\zeta$}} +\def\Beta{\mbox{\boldmath$\eta$}} +\def\Btheta{\mbox{\boldmath$\theta$}} +\def\Biota{\mbox{\boldmath$\iota$}} +\def\Bkappa{\mbox{\boldmath$\kappa$}} +\def\Blambda{\mbox{\boldmath$\lambda$}} +\def\Bmu{\mbox{\boldmath$\mu$}} +\def\Bnu{\mbox{\boldmath$\nu$}} +\def\Bxi{\mbox{\boldmath$\xi$}} +\def\Bpi{\mbox{\boldmath$\pi$}} +\def\Brho{\mbox{\boldmath$\rho$}} +\def\Bsigma{\mbox{\boldmath$\sigma$}} +\def\Btau{\mbox{\boldmath$\tau$}} +\def\Bupsilon{\mbox{\boldmath$\upsilon$}} +\def\Bphi{\mbox{\boldmath$\phi$}} +\def\Bchi{\mbox{\boldmath$\chi$}} +\def\Bpsi{\mbox{\boldmath$\psi$}} +\def\Bomega{\mbox{\boldmath$\omega$}} +\def\Bvarepsilon{\mbox{\boldmath$\varepsilon$}} +\def\Bvartheta{\mbox{\boldmath$\vartheta$}} +\def\Bvarpi{\mbox{\boldmath$\varpi$}} +\def\Bvarrho{\mbox{\boldmath$\varrho$}} +\def\Bvarsigma{\mbox{\boldmath$\varsigma$}} +\def\Bvarphi{\mbox{\boldmath$\varphi$}} +\def\bone{\mathbf{1}} +\def\bzero{\mathbf{0}} +%--------------------------------------------------------- +% Bold Face Math Italic: +% All In Format: \b* . +%--------------------------------------------------------- +\def\bA{\mbox{\boldmath$ A$}} +\def\bB{\mbox{\boldmath$ B$}} +\def\bC{\mbox{\boldmath$ C$}} +\def\bD{\mbox{\boldmath$ D$}} +\def\bE{\mbox{\boldmath$ E$}} +\def\bF{\mbox{\boldmath$ F$}} +\def\bG{\mbox{\boldmath$ G$}} +\def\bH{\mbox{\boldmath$ H$}} +\def\bI{\mbox{\boldmath$ I$}} +\def\bJ{\mbox{\boldmath$ J$}} +\def\bK{\mbox{\boldmath$ K$}} +\def\bL{\mbox{\boldmath$ L$}} +\def\bM{\mbox{\boldmath$ M$}} +\def\bN{\mbox{\boldmath$ N$}} +\def\bO{\mbox{\boldmath$ O$}} +\def\bP{\mbox{\boldmath$ P$}} +\def\bQ{\mbox{\boldmath$ Q$}} +\def\bR{\mbox{\boldmath$ R$}} +\def\bS{\mbox{\boldmath$ S$}} +\def\bT{\mbox{\boldmath$ T$}} +\def\bU{\mbox{\boldmath$ U$}} +\def\bV{\mbox{\boldmath$ V$}} +\def\bW{\mbox{\boldmath$ W$}} +\def\bX{\mbox{\boldmath$ X$}} +\def\bY{\mbox{\boldmath$ Y$}} +\def\bZ{\mbox{\boldmath$ Z$}} +\def\ba{\mbox{\boldmath$ a$}} +\def\bb{\mbox{\boldmath$ b$}} +\def\bc{\mbox{\boldmath$ c$}} +\def\bd{\mbox{\boldmath$ d$}} +\def\be{\mbox{\boldmath$ e$}} +\def\bff{\mbox{\boldmath$ f$}} +\def\bg{\mbox{\boldmath$ g$}} +\def\bh{\mbox{\boldmath$ h$}} +\def\bi{\mbox{\boldmath$ i$}} +\def\bj{\mbox{\boldmath$ j$}} +\def\bk{\mbox{\boldmath$ k$}} +\def\bl{\mbox{\boldmath$ l$}} +\def\bm{\mbox{\boldmath$ m$}} +\def\bn{\mbox{\boldmath$ n$}} +\def\bo{\mbox{\boldmath$ o$}} +\def\bp{\mbox{\boldmath$ p$}} +\def\bq{\mbox{\boldmath$ q$}} +\def\br{\mbox{\boldmath$ r$}} +\def\bs{\mbox{\boldmath$ s$}} +\def\bt{\mbox{\boldmath$ t$}} +\def\bu{\mbox{\boldmath$ u$}} +\def\bv{\mbox{\boldmath$ v$}} +\def\bw{\mbox{\boldmath$ w$}} +\def\bx{\mbox{\boldmath$ x$}} +\def\by{\mbox{\boldmath$ y$}} +\def\bz{\mbox{\boldmath$ z$}} +%********************************* +%Start main paper +%********************************* +\centerline{\Large{\bf PRISMS PhaseField}} +\smallskip +\centerline{\Large{\bf Allen-Cahn Dynamics}} +\bigskip +Consider a free energy expression of the form: +\begin{equation} + \Pi(\eta, \grad \eta) = \int_{\Omega} f( \eta ) + \frac{\kappa}{2} \grad \eta \cdot \grad \eta ~dV +\end{equation} +where $\eta$ is the structural order parameter, and $\kappa$ is the gradient length scale parameter. + +\section{Variational treatment} +Considering variations on the primal field $\eta$ of the from $\eta+\epsilon w$, we have +\begin{align} +\delta \Pi &= \left. \frac{d}{d\epsilon} \int_{\Omega} f(\eta+\epsilon w) + \frac{\kappa}{2} \grad (\eta+\epsilon w) \cdot ~\grad (\eta+\epsilon w) ~dV \right\vert_{\epsilon=0} \\ +&= \int_{\Omega} w f_{,\eta} + \kappa \grad w \grad \eta ~dV \\ +&= \int_{\Omega} w \left( f_{,\eta} - \kappa \Delta \eta \right) ~dV + \int_{\partial \Omega} w \kappa \grad \eta \cdot n ~dS +\end{align} +Assuming $\kappa \grad \eta \cdot n = 0$, and using standard variational arguments on the equation $\delta \Pi =0$ we have the expression for chemical potential as +\begin{equation} + \mu = f_{,\eta} - \kappa \Delta \eta +\end{equation} + +\section{Kinetics} +Now the Parabolic PDE for Allen-Cahn dynamics is given by: +\begin{align} + \frac{\partial \eta}{\partial t} &= -M~(f_{,\eta} - \kappa \Delta \eta) +\end{align} +where $M$ is the constant mobility. +\section{Time discretization} +Considering forward Euler explicit time stepping, we have the time discretized kinetics equation: +\begin{align} + \eta^{n+1} &= \eta^{n} - \Delta t M~(f_{,\eta}^{n} - \kappa \Delta \eta^{n}) +\end{align} + +\section{Weak formulation} +In the weak formulation, considering an arbitrary variation $w$, the above equation can be expressed as a residual equation: +\begin{align} +\int_{\Omega} w \eta^{n+1} ~dV&= \int_{\Omega} w \eta^{n} - w \Delta t M~(f_{,\eta}^{n} - \kappa \Delta \eta^{n}) ~dV \\ +&= \int_{\Omega} w ( \underbrace{ \eta^{n} - \Delta t M~f_{,\eta}^{n} }_{r_{\eta}} ) + \grad w \underbrace{ (-\Delta t M \kappa)~ \cdot (\grad \eta^{n})}_{r_{\eta x}} ~dV \quad [\kappa \grad \eta \cdot n = 0 \quad \text{on} \quad \partial \Omega] +\end{align} +\vskip 0.25in +The above values of $r_{\eta}$ and $r_{\eta x}$ are used to define the residuals in the following parameters file: \\ +\textit{applications/allenCahn/parameters.h} + + +\end{document} \ No newline at end of file diff --git a/tests/indexing_tests/mechanics_doubleVec/CMakeLists.txt b/tests/indexing_tests/mechanics_doubleVec/CMakeLists.txt new file mode 100644 index 000000000..8eecee41b --- /dev/null +++ b/tests/indexing_tests/mechanics_doubleVec/CMakeLists.txt @@ -0,0 +1,34 @@ +## +# CMake script for the phaseField applications: +## + + +# Set the name of the project and target: +SET(TARGET "main") + +# Declare all source files the target consists of: +SET(TARGET_SRC + ${TARGET}.cc + # You can specify additional files here! + ) + +# Usually, you will not need to modify anything beyond this point... + +CMAKE_MINIMUM_REQUIRED(VERSION 2.8.8) + +FIND_PACKAGE(deal.II 8.0 QUIET + HINTS ${deal.II_DIR} ${DEAL_II_DIR} ../ ../../ $ENV{DEAL_II_DIR} + ) +IF(NOT ${deal.II_FOUND}) + MESSAGE(FATAL_ERROR "\n" + "*** Could not locate deal.II. ***\n\n" + "You may want to either pass a flag -DDEAL_II_DIR=/path/to/deal.II to cmake\n" + "or set an environment variable \"DEAL_II_DIR\" that contains this path." + ) +ENDIF() + +DEAL_II_INITIALIZE_CACHED_VARIABLES() +#set(DEAL_II_CXX_FLAGS_DEBUG "${DEAL_II_CXX_FLAGS_DEBUG} -Wno-maybe-uninitialized -Wno-deprecated-declarations -Wno-comment -Wno-unused-parameter -Wno-unused-variable -Wno-unused-but-set-variable") +#set(DEAL_II_CXX_FLAGS_RELEASE "${DEAL_II_CXX_FLAGS_DEBUG} -Wno-maybe-uninitialized -Wno-deprecated-declarations -Wno-comment -Wno-unused-parameter -Wno-unused-variable -Wno-unused-but-set-variable") +PROJECT(${TARGET}) +DEAL_II_INVOKE_AUTOPILOT() diff --git a/tests/indexing_tests/mechanics_doubleVec/ICs_and_BCs.h b/tests/indexing_tests/mechanics_doubleVec/ICs_and_BCs.h new file mode 100644 index 000000000..5da06e7b1 --- /dev/null +++ b/tests/indexing_tests/mechanics_doubleVec/ICs_and_BCs.h @@ -0,0 +1,102 @@ +template +class InitialCondition : public Function +{ +public: + unsigned int index; + Vector values; + InitialCondition (const unsigned int _index) : Function(1), index(_index) { + std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1); + } + double value (const Point &p, const unsigned int component = 0) const + { + double scalar_IC = 0; + // ===================================================================== + // ENTER THE INITIAL CONDITIONS HERE FOR SCALAR FIELDS + // ===================================================================== + // Enter the function describing conditions for the fields at point "p". + // Use "if" statements to set the initial condition for each variable + // according to its variable index. + + + // ===================================================================== + return scalar_IC; + } +}; + +template +class InitialConditionVec : public Function +{ +public: + unsigned int index; + //Vector values; + InitialConditionVec (const unsigned int _index) : Function(dim), index(_index) { + std::srand(Utilities::MPI::this_mpi_process(MPI_COMM_WORLD)+1); + } + void vector_value (const Point &p,Vector &vector_IC) const + { + // ===================================================================== + // ENTER THE INITIAL CONDITIONS HERE FOR VECTOR FIELDS + // ===================================================================== + // Enter the function describing conditions for the fields at point "p". + // Use "if" statements to set the initial condition for each variable + // according to its variable index. + + vector_IC(0) = 0.0; + vector_IC(1) = 0.0; + vector_IC(2) = 0.0; + + // ===================================================================== + } +}; + +// Sets the BCs for the problem variables +// "inputBCs" should be called for each component of each variable and should be in numerical order +// Four input arguments set the same BC on the entire boundary +// Two plus two times the number of dimensions inputs sets separate BCs on each face of the domain +// Inputs to "inputBCs": +// First input: variable number +// Second input: component number +// Third input: BC type (options are "ZERO_DERIVATIVE" and "DIRICHLET") +// Fourth input: BC value (ignored unless the BC type is "DIRICHLET") +// Odd inputs after the third: BC type +// Even inputs after the third: BC value +// Face numbering: starts at zero with the minimum of the first direction, one for the maximum of the first direction +// two for the minimum of the second direction, etc. +template +void generalizedProblem::setBCs(){ + + // ===================================================================== + // ENTER THE BOUNDARY CONDITIONS HERE + // ===================================================================== + // This function sets the BCs for the problem variables + // The function "inputBCs" should be called for each component of + // each variable and should be in numerical order. Four input arguments + // set the same BC on the entire boundary. Two plus two times the + // number of dimensions inputs sets separate BCs on each face of the domain. + // Inputs to "inputBCs": + // First input: variable number + // Second input: component number + // Third input: BC type (options are "ZERO_DERIVATIVE", "DIRICHLET", and "PERIODIC") + // Fourth input: BC value (ignored unless the BC type is "DIRICHLET") + // Odd inputs after the third: BC type + // Even inputs after the third: BC value + // Face numbering: starts at zero with the minimum of the first direction, one for the maximum of the first direction + // two for the minimum of the second direction, etc. + + inputBCs(0,0,"DIRICHLET",-1.0, "DIRICHLET",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0); + inputBCs(0,1,"DIRICHLET",0.0, "DIRICHLET",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0); + inputBCs(0,2,"DIRICHLET",0.0, "DIRICHLET",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0); + + inputBCs(1,0,"ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0); + inputBCs(1,1,"ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0); + inputBCs(1,2,"ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0); + + inputBCs(2,0,"DIRICHLET",-5.0, "DIRICHLET",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0); + inputBCs(2,1,"DIRICHLET",0.0, "DIRICHLET",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0); + inputBCs(2,2,"DIRICHLET",0.0, "DIRICHLET",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0); + + inputBCs(3,0,"DIRICHLET",-10.0, "DIRICHLET",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0, "ZERO_DERIVATIVE",0.0); + +} + + diff --git a/tests/indexing_tests/mechanics_doubleVec/equations.h b/tests/indexing_tests/mechanics_doubleVec/equations.h new file mode 100644 index 000000000..19a073a21 --- /dev/null +++ b/tests/indexing_tests/mechanics_doubleVec/equations.h @@ -0,0 +1,195 @@ +// List of variables and residual equations for the mechanics example application + +// ================================================================================= +// Define the variables in the model +// ================================================================================= +// The number of variables +#define num_var 4 + +// The names of the variables, whether they are scalars or vectors and whether the +// governing eqn for the variable is parabolic or elliptic +#define variable_name {"u","u2","u3","c"} +#define variable_type {"VECTOR","VECTOR","VECTOR","SCALAR"} +#define variable_eq_type {"ELLIPTIC","PARABOLIC","ELLIPTIC","ELLIPTIC"} + +// Flags for whether the value, gradient, and Hessian are needed in the residual eqns +#define need_val {false,false,false,false} +#define need_grad {true,true,true,true} +#define need_hess {false,false,false,false} + +// Flags for whether the residual equation has a term multiplied by the test function +// (need_val_residual) and/or the gradient of the test function (need_grad_residual) +#define need_val_residual {false,false,false,false} +#define need_grad_residual {true,true,true,true} + +// Flags for whether the value, gradient, and Hessian are needed in the residual eqn +// for the left-hand-side of the iterative solver for elliptic equations +#define need_val_LHS {false,false,false,false} +#define need_grad_LHS {true,true,true,true} +#define need_hess_LHS {false,false,false,false} + +// Flags for whether the residual equation for the left-hand-side of the iterative +// solver for elliptic equations has a term multiplied by the test function +// (need_val_residual) and/or the gradient of the test function (need_grad_residual) +#define need_val_residual_LHS {false,false,false,false} +#define need_grad_residual_LHS {true,false,true,true} + +// ================================================================================= +// Define the model parameters and the residual equations +// ================================================================================= +// Parameters in the residual equations and expressions for the residual equations +// can be set here. For simple cases, the entire residual equation can be written +// here. For more complex cases with loops or conditional statements, residual +// equations (or parts of residual equations) can be written below in "residualRHS". + +// Define Mechanical properties +// Mechanical symmetry of the material and stiffness parameters +#define MaterialModels {"ISOTROPIC"} +#define MaterialConstants {{2.0,0.3}} + + +// ================================================================================= +// residualRHS +// ================================================================================= +// This function calculates the residual equations for each variable. It takes +// "modelVariablesList" as an input, which is a list of the value and derivatives of +// each of the variables at a specific quadrature point. The (x,y,z) location of +// that quadrature point is given by "q_point_loc". The function outputs +// "modelResidualsList", a list of the value and gradient terms of the residual for +// each residual equation. The index for each variable in these lists corresponds to +// the order it is defined at the top of this file (starting at 0). +template +void generalizedProblem::residualRHS(const std::vector > & modelVariablesList, + std::vector > & modelResidualsList, + dealii::Point > q_point_loc) const { + +//u +vectorgradType ux = modelVariablesList[0].vectorGrad; +vectorgradType u2x = modelVariablesList[1].vectorGrad; +vectorgradType u3x = modelVariablesList[2].vectorGrad; +scalargradType cx = modelVariablesList[3].scalarGrad; +vectorgradType Rux, Rux2; + + +//compute strain tensor +dealii::VectorizedArray E[dim][dim], S[dim][dim]; +for (unsigned int i=0; i E2[dim][dim], S2[dim][dim]; +for (unsigned int i=0; i(CIJ_list[0], E, S); + +computeStress(CIJ_list[0], E2, S2); + +//compute residual +for (unsigned int i=0; icurrentFieldIndex". +template +void generalizedProblem::residualLHS(const std::vector > & modelVarList, + modelResidual & modelRes, + dealii::Point > q_point_loc) const { + +//u +vectorgradType ux = modelVarList[0].vectorGrad; +vectorgradType u2x = modelVarList[1].vectorGrad; +vectorgradType u3x = modelVarList[2].vectorGrad; +scalargradType cx = modelVarList[3].scalarGrad; +vectorgradType Rux; + +dealii::VectorizedArray E[dim][dim], S[dim][dim]; +if (this->currentFieldIndex == 0){ + //compute strain tensor + for (unsigned int i=0; i(CIJ_list[0], E, S); + +//compute residual +for (unsigned int i=0; icurrentFieldIndex < 3){ + modelRes.vectorGradResidual = Rux; +} +else { + modelRes.scalarGradResidual = cx; +} +} + +// ================================================================================= +// energyDensity (needed only if calcEnergy == true) +// ================================================================================= +// This function integrates the free energy density across the computational domain. +// It takes "modelVariablesList" as an input, which is a list of the value and +// derivatives of each of the variables at a specific quadrature point. It also +// takes the mapped quadrature weight, "JxW_value", as an input. The (x,y,z) location +// of the quadrature point is given by "q_point_loc". The weighted value of the +// energy density is added to "energy" variable and the components of the energy +// density are added to the "energy_components" variable (index 0: chemical energy, +// index 1: gradient energy, index 2: elastic energy). +template +void generalizedProblem::energyDensity(const std::vector > & modelVarList, + const dealii::VectorizedArray & JxW_value, + dealii::Point > q_point_loc) { + +} + + + + diff --git a/tests/indexing_tests/mechanics_doubleVec/formulation_mechanics.pdf b/tests/indexing_tests/mechanics_doubleVec/formulation_mechanics.pdf new file mode 100644 index 000000000..57037ff2e Binary files /dev/null and b/tests/indexing_tests/mechanics_doubleVec/formulation_mechanics.pdf differ diff --git a/tests/indexing_tests/mechanics_doubleVec/main.cc b/tests/indexing_tests/mechanics_doubleVec/main.cc new file mode 100644 index 000000000..b52f5a86b --- /dev/null +++ b/tests/indexing_tests/mechanics_doubleVec/main.cc @@ -0,0 +1,51 @@ +// Linear elasticity example application + +// Header files +#include "../../../include/dealIIheaders.h" + +#include "parameters.h" +#include "../../../src/models/coupled/generalized_model.h" +#include "equations.h" +#include "ICs_and_BCs.h" +#include "../../../src/models/coupled/generalized_model_functions.h" + +//main +int main (int argc, char **argv) +{ + Utilities::MPI::MPI_InitFinalize mpi_initialization(argc, argv,numbers::invalid_unsigned_int); + try + { + deallog.depth_console(0); + generalizedProblem problem; + + problem.setBCs(); + problem.buildFields(); + problem.init (); + problem.solve(); + } + catch (std::exception &exc) + { + std::cerr << std::endl << std::endl + << "----------------------------------------------------" + << std::endl; + std::cerr << "Exception on processing: " << std::endl + << exc.what() << std::endl + << "Aborting!" << std::endl + << "----------------------------------------------------" + << std::endl; + return 1; + } + catch (...) + { + std::cerr << std::endl << std::endl + << "----------------------------------------------------" + << std::endl; + std::cerr << "Unknown exception!" << std::endl + << "Aborting!" << std::endl + << "----------------------------------------------------" + << std::endl; + return 1; + } + + return 0; +} diff --git a/tests/indexing_tests/mechanics_doubleVec/parameters.h b/tests/indexing_tests/mechanics_doubleVec/parameters.h new file mode 100644 index 000000000..f1f19dc7d --- /dev/null +++ b/tests/indexing_tests/mechanics_doubleVec/parameters.h @@ -0,0 +1,52 @@ +// Parameter list for the mechanics example application +// All strictly numerical parameters should be set in this file + +// ================================================================================= +// Set the number of dimensions (1, 2, or 3 for a 1D, 2D, or 3D calculation) +// ================================================================================= +#define problemDIM 3 + +// ================================================================================= +// Set the length of the domain in all three dimensions +// ================================================================================= +// Each axes spans from zero to the specified length +#define spanX 100.0 +#define spanY 100.0 +#define spanZ 100.0 + +// ================================================================================= +// Set the element parameters +// ================================================================================= +// The number of elements in each direction is 2^(refineFactor) * subdivisions +// For optimal performance, use refineFactor primarily to determine the element size +#define subdivisionsX 1 +#define subdivisionsY 1 +#define subdivisionsZ 1 +#define refineFactor 4 + +// Set the polynomial degree of the element (suggested values: 1 or 2) +#define finiteElementDegree 1 + +// ================================================================================= +// Set the elliptic solver parameters +// ================================================================================= +// The solver type (currently the only recommended option is conjugate gradient) +#define solverType SolverCG + +// The tolerance for convergence (L2 norm of the residual) +#define solverTolerance 1.0e-10 + +// The maximum number of solver iterations per time step +#define maxSolverIterations 1000 + +// ================================================================================= +// Set the output parameters +// ================================================================================= +// Each field in the problem will be output is writeOutput is set to "true" +#define writeOutput true + + + + + + diff --git a/tests/indexing_tests/mechanics_doubleVec/tex_files/mechanics.tex b/tests/indexing_tests/mechanics_doubleVec/tex_files/mechanics.tex new file mode 100644 index 000000000..c041b432e --- /dev/null +++ b/tests/indexing_tests/mechanics_doubleVec/tex_files/mechanics.tex @@ -0,0 +1,215 @@ +\documentclass[10pt]{article} +\usepackage{amsmath} +\usepackage{bm} +\usepackage{bbm} +\usepackage{mathrsfs} +\usepackage{graphicx} +\usepackage{wrapfig} +\usepackage{subcaption} +\usepackage{epsfig} +\usepackage{amsfonts} +\usepackage{amssymb} +\usepackage{amsmath} +\usepackage{wrapfig} +\usepackage{graphicx} +\usepackage{psfrag} +\newcommand{\sun}{\ensuremath{\odot}} % sun symbol is \sun +\let\vaccent=\v % rename builtin command \v{} to \vaccent{} +\renewcommand{\v}[1]{\ensuremath{\mathbf{#1}}} % for vectors +\newcommand{\gv}[1]{\ensuremath{\mbox{\boldmath$ #1 $}}} +\newcommand{\grad}[1]{\gv{\nabla} #1} +\renewcommand{\baselinestretch}{1.2} +\jot 5mm +\graphicspath{{./figures/}} +%text dimensions +\textwidth 6.5 in +\oddsidemargin .2 in +\topmargin -0.2 in +\textheight 8.5 in +\headheight 0.2in +\overfullrule = 0pt +\pagestyle{plain} +\def\newpar{\par\vskip 0.5cm} +\begin{document} +% +%---------------------------------------------------------------------- +% Define symbols +%---------------------------------------------------------------------- +% +\def\iso{\mathbbm{1}} +\def\half{{\textstyle{1 \over 2}}} +\def\third{{\textstyle{1 \over 3}}} +\def\fourth{{\textstyle{{1 \over 4}}}} +\def\twothird{{\textstyle {{2 \over 3}}}} +\def\ndim{{n_{\rm dim}}} +\def\nint{n_{\rm int}} +\def\lint{l_{\rm int}} +\def\nel{n_{\rm el}} +\def\nf{n_{\rm f}} +\def\DIV {\hbox{\af div}} +\def\GRAD{\hbox{\af Grad}} +\def\sym{\mathop{\rm sym}\nolimits} +\def\tr{\mathop{\rm tr}\nolimits} +\def\dev{\mathop{\rm dev}\nolimits} +\def\Dev{\mathop{\rm Dev}\nolimits} +\def\DEV{\mathop {\rm DEV}\nolimits} +\def\bfb {{\bi b}} +\def\Bnabla{\nabla} +\def\bG{{\bi G}} +\def\jmpdelu{{\lbrack\!\lbrack \Delta u\rbrack\!\rbrack}} +\def\jmpudot{{\lbrack\!\lbrack\dot u\rbrack\!\rbrack}} +\def\jmpu{{\lbrack\!\lbrack u\rbrack\!\rbrack}} +\def\jmphi{{\lbrack\!\lbrack\varphi\rbrack\!\rbrack}} +\def\ljmp{{\lbrack\!\lbrack}} +\def\rjmp{{\rbrack\!\rbrack}} +\def\sign{{\rm sign}} +\def\nn{{n+1}} +\def\na{{n+\vartheta}} +\def\nna{{n+(1-\vartheta)}} +\def\nt{{n+{1\over 2}}} +\def\nb{{n+\beta}} +\def\nbb{{n+(1-\beta)}} +%--------------------------------------------------------- +% Bold Face Math Characters: +% All In Format: \B***** . +%--------------------------------------------------------- +\def\bOne{\mbox{\boldmath$1$}} +\def\BGamma{\mbox{\boldmath$\Gamma$}} +\def\BDelta{\mbox{\boldmath$\Delta$}} +\def\BTheta{\mbox{\boldmath$\Theta$}} +\def\BLambda{\mbox{\boldmath$\Lambda$}} +\def\BXi{\mbox{\boldmath$\Xi$}} +\def\BPi{\mbox{\boldmath$\Pi$}} +\def\BSigma{\mbox{\boldmath$\Sigma$}} +\def\BUpsilon{\mbox{\boldmath$\Upsilon$}} +\def\BPhi{\mbox{\boldmath$\Phi$}} +\def\BPsi{\mbox{\boldmath$\Psi$}} +\def\BOmega{\mbox{\boldmath$\Omega$}} +\def\Balpha{\mbox{\boldmath$\alpha$}} +\def\Bbeta{\mbox{\boldmath$\beta$}} +\def\Bgamma{\mbox{\boldmath$\gamma$}} +\def\Bdelta{\mbox{\boldmath$\delta$}} +\def\Bepsilon{\mbox{\boldmath$\epsilon$}} +\def\Bzeta{\mbox{\boldmath$\zeta$}} +\def\Beta{\mbox{\boldmath$\eta$}} +\def\Btheta{\mbox{\boldmath$\theta$}} +\def\Biota{\mbox{\boldmath$\iota$}} +\def\Bkappa{\mbox{\boldmath$\kappa$}} +\def\Blambda{\mbox{\boldmath$\lambda$}} +\def\Bmu{\mbox{\boldmath$\mu$}} +\def\Bnu{\mbox{\boldmath$\nu$}} +\def\Bxi{\mbox{\boldmath$\xi$}} +\def\Bpi{\mbox{\boldmath$\pi$}} +\def\Brho{\mbox{\boldmath$\rho$}} +\def\Bsigma{\mbox{\boldmath$\sigma$}} +\def\Btau{\mbox{\boldmath$\tau$}} +\def\Bupsilon{\mbox{\boldmath$\upsilon$}} +\def\Bphi{\mbox{\boldmath$\phi$}} +\def\Bchi{\mbox{\boldmath$\chi$}} +\def\Bpsi{\mbox{\boldmath$\psi$}} +\def\Bomega{\mbox{\boldmath$\omega$}} +\def\Bvarepsilon{\mbox{\boldmath$\varepsilon$}} +\def\Bvartheta{\mbox{\boldmath$\vartheta$}} +\def\Bvarpi{\mbox{\boldmath$\varpi$}} +\def\Bvarrho{\mbox{\boldmath$\varrho$}} +\def\Bvarsigma{\mbox{\boldmath$\varsigma$}} +\def\Bvarphi{\mbox{\boldmath$\varphi$}} +\def\bone{\mathbf{1}} +\def\bzero{\mathbf{0}} +%--------------------------------------------------------- +% Bold Face Math Italic: +% All In Format: \b* . +%--------------------------------------------------------- +\def\bA{\mbox{\boldmath$ A$}} +\def\bB{\mbox{\boldmath$ B$}} +\def\bC{\mbox{\boldmath$ C$}} +\def\bD{\mbox{\boldmath$ D$}} +\def\bE{\mbox{\boldmath$ E$}} +\def\bF{\mbox{\boldmath$ F$}} +\def\bG{\mbox{\boldmath$ G$}} +\def\bH{\mbox{\boldmath$ H$}} +\def\bI{\mbox{\boldmath$ I$}} +\def\bJ{\mbox{\boldmath$ J$}} +\def\bK{\mbox{\boldmath$ K$}} +\def\bL{\mbox{\boldmath$ L$}} +\def\bM{\mbox{\boldmath$ M$}} +\def\bN{\mbox{\boldmath$ N$}} +\def\bO{\mbox{\boldmath$ O$}} +\def\bP{\mbox{\boldmath$ P$}} +\def\bQ{\mbox{\boldmath$ Q$}} +\def\bR{\mbox{\boldmath$ R$}} +\def\bS{\mbox{\boldmath$ S$}} +\def\bT{\mbox{\boldmath$ T$}} +\def\bU{\mbox{\boldmath$ U$}} +\def\bV{\mbox{\boldmath$ V$}} +\def\bW{\mbox{\boldmath$ W$}} +\def\bX{\mbox{\boldmath$ X$}} +\def\bY{\mbox{\boldmath$ Y$}} +\def\bZ{\mbox{\boldmath$ Z$}} +\def\ba{\mbox{\boldmath$ a$}} +\def\bb{\mbox{\boldmath$ b$}} +\def\bc{\mbox{\boldmath$ c$}} +\def\bd{\mbox{\boldmath$ d$}} +\def\be{\mbox{\boldmath$ e$}} +\def\bff{\mbox{\boldmath$ f$}} +\def\bg{\mbox{\boldmath$ g$}} +\def\bh{\mbox{\boldmath$ h$}} +\def\bi{\mbox{\boldmath$ i$}} +\def\bj{\mbox{\boldmath$ j$}} +\def\bk{\mbox{\boldmath$ k$}} +\def\bl{\mbox{\boldmath$ l$}} +\def\bm{\mbox{\boldmath$ m$}} +\def\bn{\mbox{\boldmath$ n$}} +\def\bo{\mbox{\boldmath$ o$}} +\def\bp{\mbox{\boldmath$ p$}} +\def\bq{\mbox{\boldmath$ q$}} +\def\br{\mbox{\boldmath$ r$}} +\def\bs{\mbox{\boldmath$ s$}} +\def\bt{\mbox{\boldmath$ t$}} +\def\bu{\mbox{\boldmath$ u$}} +\def\bv{\mbox{\boldmath$ v$}} +\def\bw{\mbox{\boldmath$ w$}} +\def\bx{\mbox{\boldmath$ x$}} +\def\by{\mbox{\boldmath$ y$}} +\def\bz{\mbox{\boldmath$ z$}} +%********************************* +%Start main paper +%********************************* +\centerline{\Large{\bf PRISMS-PF}} +\smallskip +\centerline{\Large{\bf Mechanics (Infinitesimal Strain)}} +\bigskip +Consider a strain energy expression of the form: +\begin{equation} + \Pi(\varepsilon) = \int_{\Omega} \frac{1}{2} \varepsilon:C:\varepsilon ~dV +\end{equation} +where $\varepsilon$ is the infinitesimal strain tensor, $C_{ijkl}=\lambda \delta_{ij} \delta_{kl}+\mu ( \delta_{ik} \delta_{jl}+ \delta_{il} \delta_{jk} )$ is the fourth order elasticity tensor and ($\lambda$, $mu$) are the Lame parameters. + +\section{Variational treatment} +Considering variations on the displacement $u$ of the from $u+\epsilon w$, we have +\begin{align} +\delta \Pi &= \left. \frac{d}{d\epsilon} \int_{\Omega} \frac{1}{2} \varepsilon_{\epsilon}:C:\varepsilon_{\epsilon} ~dV \right\vert_{\epsilon=0} \\ +&= -\int_{\Omega} \grad w : C : \varepsilon ~dV + \int_{\partial \Omega} w \cdot (C : \varepsilon \cdot n) ~dS\\ +&= -\int_{\Omega} \grad w : \sigma ~dV + \int_{\partial \Omega} w \cdot (\sigma \cdot n) ~dS\\ +&= -\int_{\Omega} \grad w : \sigma ~dV + \int_{\partial \Omega} w \cdot t ~dS +\end{align} +where $\sigma = C : \varepsilon$ is the stress tensor and $t=\sigma \cdot n$ is the surface traction.\\ + +The minimization of the variation, $\delta \Pi=0$, gives the weak formulation of the governing equation of mechanics: + +\begin{align} +\int_{\Omega} \grad w : \sigma ~dV - \int_{\partial \Omega} w \cdot t ~dS = 0 +\end{align} + +If surface tractions are zero: \\ +\begin{align} +R &= \int_{\Omega} \grad w : \sigma ~dV = 0 +\end{align} +We solve for $R=0$ using a gradient scheme which involves the following linearization: +\begin{align} +R~|_{u}+ \frac{\partial R}{\partial u} \Delta u &= 0 \\ +\Rightarrow \frac{\partial R}{\partial u} \Delta u &= -R~|_{u} +\end{align} +This is the linear system $Ax=b$ which we solve implicitly using the Conjugate Gradient scheme. For clarity, here in the left hand side (LHS) $A=\frac{\partial R}{\partial u}$, $x=\Delta u$ and the right hand side (RHS) is $b=-R~|_{u}$. + +\end{document} \ No newline at end of file diff --git a/tests/regression_tests/regress_test_results.txt b/tests/regression_tests/regress_test_results.txt index 16fa9fc6f..0e783f293 100644 --- a/tests/regression_tests/regress_test_results.txt +++ b/tests/regression_tests/regress_test_results.txt @@ -127,3 +127,51 @@ Results of the 3D regression test (2017-01-16 14:59): Difference from reference: 3.46944695195e-17 Result: Pass +Results of the 3D regression test (2017-01-24 12:45): +Difference from reference: 3.46944695195e-17 +Result: Pass + +Results of the 3D regression test (2017-01-27 13:46): +Difference from reference: 3.46944695195e-17 +Result: Pass + +Results of the 3D regression test (2017-01-27 13:47): +Difference from reference: 3.46944695195e-17 +Result: Pass + +Results of the 3D regression test (2017-01-27 14:02): +Difference from reference: 3.46944695195e-17 +Result: Pass + +Results of the 3D regression test (2017-01-27 14:15): +Difference from reference: 3.46944695195e-17 +Result: Pass + +Results of the 3D regression test (2017-01-27 14:29): +Difference from reference: 3.46944695195e-17 +Result: Pass + +Results of the 3D regression test (2017-01-27 16:22): +Difference from reference: 3.46944695195e-17 +Result: Pass + +Results of the 3D regression test (2017-01-30 08:54): +Difference from reference: 3.46944695195e-17 +Result: Pass + +Results of the 3D regression test (2017-01-30 11:33): +Difference from reference: 3.46944695195e-17 +Result: Pass + +Results of the 3D regression test (2017-01-30 11:54): +Difference from reference: 3.46944695195e-17 +Result: Pass + +Results of the 3D regression test (2017-01-30 12:54): +Difference from reference: 3.46944695195e-17 +Result: Pass + +Results of the 3D regression test (2017-01-30 13:46): +Difference from reference: 3.46944695195e-17 +Result: Pass + diff --git a/tests/unit_tests/main.cc b/tests/unit_tests/main.cc index cc7973172..70c817851 100644 --- a/tests/unit_tests/main.cc +++ b/tests/unit_tests/main.cc @@ -6,7 +6,7 @@ int main(int argc, char **argv) { - //init MPI + //init MPI Utilities::MPI::MPI_InitFinalize mpi_initialization(argc, argv, numbers::invalid_unsigned_int); std::cout << "Beginning unit tests..." << std::endl; @@ -19,7 +19,6 @@ int main(int argc, char **argv) pass = computeInvM_tester_2D.test_computeInvM(argc, argv); tests_passed += pass; - // Unit tests for the method "outputResults" total_tests++; unitTest<2,double> outputResults_tester_2D; diff --git a/tests/unit_tests/test_invM.h b/tests/unit_tests/test_invM.h index 2dd9f61bd..4fa4c7aaf 100644 --- a/tests/unit_tests/test_invM.h +++ b/tests/unit_tests/test_invM.h @@ -4,12 +4,20 @@ class testInvM: public MatrixFreePDE { public: testInvM(){ - //init the MatrixFreePDE class for testing - this->initForTests(); - //call computeInvM() - this->computeInvM(); - invMNorm=this->invM.l2_norm(); + // Initialize the test field object (needed for computeInvM()) + Field test_field(SCALAR,PARABOLIC,"c"); + this->fields.push_back(test_field); + + //init the MatrixFreePDE class for testing + this->initForTests(); + + //call computeInvM() + this->computeInvM(); + invMNorm=this->invM.l2_norm(); + + // Need to clear fields or there's an error in the destructor + this->fields.clear(); }; double invMNorm; @@ -34,6 +42,6 @@ template char buffer[100]; sprintf (buffer, "Test result for 'computeInvM' in %u dimension(s): %u\n", dim, pass); std::cout << buffer; - + return pass; } diff --git a/tests/unit_tests/test_setRigidBodyModeConstraints.h b/tests/unit_tests/test_setRigidBodyModeConstraints.h index d199334cb..079d71c68 100644 --- a/tests/unit_tests/test_setRigidBodyModeConstraints.h +++ b/tests/unit_tests/test_setRigidBodyModeConstraints.h @@ -38,7 +38,7 @@ bool unitTest::test_setRigidBodyModeConstraints(std::vector rigidBod test.call_setRigidBodyModeConstraints(rigidBodyModeComponents,num_constraints); // Add up the total number of constraints across all processors - int global_num_constraints = rigidBodyModeComponents.size(); + unsigned int global_num_constraints = rigidBodyModeComponents.size(); Utilities::MPI::sum(global_num_constraints,MPI_COMM_WORLD); // Check if calculated value equals expected value diff --git a/version b/version index 9459d4ba2..524cb5524 100644 --- a/version +++ b/version @@ -1 +1 @@ -1.1 +1.1.1 diff --git a/version_changes.txt b/version_changes.txt index ba7b57016..659f2dbe5 100644 --- a/version_changes.txt +++ b/version_changes.txt @@ -1,3 +1,20 @@ +==================== +Version 1.1.1: +=================== +Patch to v1.1, released in January 2017. This patch fixes a number of related indexing bugs lurking in previous versions of the code. From a user +perspective, nothing should change (unless you directly hit one of these bugs). + +Bug fixes: +- Fixed indexing bug that caused a seg fault if the elliptic equation variable wasn't given last. +- Fixed indexing bug that prevented the use of multiple vector variables. +- Fixed indexing bug that prevented the use of vector variables for parabolic equations. +- Fixed indexing bug that prevented the solution of multiple elliptic equations. + +Known issues: +- For some compilers that lack full C++11 support (including the Intel compilers on the Stampede cluster), errors are generated because + we initialize some vectors with C-style arrays. This will be fixed in the near future. + + ==================== Version 1.1: =================== @@ -9,7 +26,9 @@ Added functionality: the value of that variable to zero at the origin. - Added more options for controlling the spacing between outputs. Users can now choose between equal spacing, log spacing, and a specified number of outputs per decade or they can input a list of time steps at which to output. +- Added the ability to control how often the progress of a run prints to the screen. Set “skipPrintSteps” in parameters.h. - Ability to change the output file format between “.vtu” and “.vtk”. Used in the application allenCahn_pfield. +- When mesh adaptivity is turned on, the mesh will adapt before the first time step. Changes to the pre-built applications: - New application: singlePrecipitateKKS is a 3D simulation of 1/8 of a precipitate. Governing equations are similar to precipitateEvolution @@ -17,19 +36,27 @@ Changes to the pre-built applications: - New application: allenCahn_pfield uses the PField capability of PRISMS IntegrationTools to load the initial condition from .vtk files - New application: precipitateEvolution_pfunction uses the PFunction capability of PRISMS IntegrationTools to load parameters (both and functions) into the code. +- New applications: CHiMaD_benchmark1a and CHiMaD_benchmark2a are phase field benchmark problems for spinodal decomposition and Ostwald + ripening, respectively. More about the benchmark problems can be found at: https://pages.nist.gov/chimad-phase-field/ - Added formulation files for the CHAC_anisotropy and CHAC_anisotropyRegularized applications. - Boundary conditions for grainGrowth changed to periodic. +- Boundary condition for the mechanics application changed for higher deformation. - Moduli changed in precipitateEvolution so that the evolution is dominated by the misfit, not the interfacial energy. Bug fixes: +- Fixed constraint bug for purely elliptic problems. The solution in the domain was correct, but the boundary value for Dirichlet BCs was + double what it was supposed to be. - Removed unnecessary LAPACK check in the CMakeLists.txt files. Other changes: - Installation instructions updated, both in the README and in the user guide. +- The “init” function was split into an “init” function and a “reinit” function. The latter is used for reinitializing the system for + adaptive meshing. +- Removed call of “norm_sqr”, which no longer exists in deal.II v8.5. Known issues: -- For some compilers (including the Intel compilers on the Stampede cluster), errors are generated because we initialize some vectors with - C-style arrays. This will be fixed in the near future. +- For some compilers that lack full C++11 support (including the Intel compilers on the Stampede cluster), errors are generated because + we initialize some vectors with C-style arrays. This will be fixed in the near future. ==================== Version 1.0: @@ -49,7 +76,7 @@ Version 0.9.3: Patch to version 0.9.2, released in July 2016. Most changes are specific to coupled Cahn-Hilliard-Allen-Cahn-Mechanics calculations. Bug fixes: -- Made necessary fixes for compatability Deal.II v8.4. +- Made necessary fixes for compatibility Deal.II v8.4. Added functionality: - Support for concentration-dependent stress-free transformation strains for coupled Cahn-Hilliard-Allen-Cahn-Mechanics calculations.