NatLabRockies · johnjasa · May 22, 2026 · May 22, 2026
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -35,6 +35,7 @@
 - Reduced import time for `h2integrate_model.py` by deferring imports of heavy dependencies until they are needed [PR 762](https://github.com/NatLabRockies/H2Integrate/pull/762)
 - Added multivariable purge gas stream output to `AmmoniaSynLoopPerformanceModel` [PR 760](https://github.com/NatLabRockies/H2Integrate/pull/760)
 - Add `constant` pricing mode for Grid cost models, allowing an explicit scalar price configuration alongside `per_timestep` and `per_year` modes. [PR 764](https://github.com/NatLabRockies/H2Integrate/pull/764)
+- Renamed `design_of_experiments` to `parameter_sweep` throughout the codebase to avoid confusion with "Department of Energy" (DOE) in the energy domain. The core code supports both the new `parameter_sweep` and legacy `design_of_experiments` YAML keys for backward compatibility. Updated all driver configs, examples, tests, and documentation. Added generator type descriptions to the parameter sweep docs page and updated the run-cases docs to recommend parameter sweeps over manual for-loops. [PR 768](https://github.com/NatLabRockies/H2Integrate/pull/768)
 
 ## 0.8 [April 15, 2026]
 

diff --git a/docs/_toc.yml b/docs/_toc.yml
@@ -15,7 +15,7 @@ parts:
       - file: user_guide/connecting_technologies
       - file: user_guide/defining_sites_and_resources
       - file: user_guide/design_optimization_in_h2i
-      - file: user_guide/design_of_experiments_in_h2i
+      - file: user_guide/parameter_sweep_in_h2i
       - file: user_guide/postprocessing_results
       - file: user_guide/recording_and_loading_data
       - file: user_guide/how_to_interface_with_user_defined_model

diff --git a/docs/user_guide/design_optimization_in_h2i.md b/docs/user_guide/design_optimization_in_h2i.md
@@ -9,7 +9,7 @@ We will lean on the `05_wind_h2_opt` example in the `examples` folder to illustr
 
 ```{note}
 When we say "driver" in this context, we are referring to the optimizer that is used to solve the optimization problem as detailed in this [OpenMDAO doc page](https://openmdao.org/newdocs/versions/latest/basic_user_guide/single_disciplinary_optimization/first_optimization.html).
-Drivers could also refer to a design of experiments (DOE) or other types of analysis that are not strictly optimizers.
+Drivers could also refer to a parameter sweep (called "Design of Experiments" or DOE in OpenMDAO) or other types of analysis that are not strictly optimizers.
 ```
 
 

diff --git a/docs/user_guide/how_to_run_several_cases_in_sequence.md b/docs/user_guide/how_to_run_several_cases_in_sequence.md
@@ -2,8 +2,18 @@
 
 ## Overview
 
-If you have several different cases you want to run with different input parameters, these cases can be set up in an input spreadsheet rather than directly modifying the `tech_config`.
-This is done using the functions in `h2integrate/tools/run_cases.py`
+```{tip}
+For most use cases where you want to sweep over different values of **design variables** (e.g., system capacities, number of clusters), we recommend using the built-in [parameter sweep capability](parameter_sweep_in_h2i.md).
+It handles case generation, parallel execution, and result recording automatically.
+```
+
+The approach described on this page is intended for **advanced users** who need to modify **technology configuration** parameters between runs — values that are not exposed as design variables in the `driver_config.yaml`.
+This is done using the functions in `h2integrate/tools/run_cases.py`.
+
+```{warning}
+The for-loop approach shown below does not automatically record results to a SQL file, does not support parallel execution, and requires manual post-processing.
+Use the [parameter sweep](parameter_sweep_in_h2i.md) whenever possible.
+```
 
 ## Setting up a variation of parameters in a .csv
 

diff --git a/docs/user_guide/how_to_set_up_an_analysis.md b/docs/user_guide/how_to_set_up_an_analysis.md
@@ -237,7 +237,7 @@ Once the configs are loaded into H2I and the model is instantiated, you can dire
 This is an advanced approach that isn't necessarily recommended for basic users, but showcases the level of flexibility possible with H2I.
 
 ```{note}
-The same behavior shown here with a manual for-loop can be achieved by using the [design of experiments capability](design_of_experiments_in_h2i.md).
+The same behavior shown here with a manual for-loop can be achieved more easily by using the [parameter sweep capability](parameter_sweep_in_h2i.md), which is the recommended approach for most users.
 ```
 
 ```{code-cell} ipython3

diff --git a/...ser_guide/design_of_experiments_in_h2i.md → docs/user_guide/parameter_sweep_in_h2i.md b/...ser_guide/design_of_experiments_in_h2i.md → docs/user_guide/parameter_sweep_in_h2i.md
@@ -11,18 +11,31 @@ kernelspec:
   name: python3
 ---
 
-# Design of experiments in H2I
+# Parameter sweeps in H2I
 
-One of the key features of H2Integrate is the ability to perform a design of experiments (DOE) for hybrid energy systems.
+One of the key features of H2Integrate is the ability to perform **parameter sweeps** across hybrid energy systems.
+A parameter sweep systematically varies one or more design variables across a range of values and evaluates the system at each combination, making it easy to explore the design space.
 
-The design of experiments process uses the `driver_config.yaml` file to define the design sweep, including the design variables, constraints, and objective functions.
+```{note}
+Under the hood, H2Integrate uses OpenMDAO's [Design of Experiments (DOE) Driver](https://openmdao.org/newdocs/versions/latest/_srcdocs/packages/drivers/doe_generators.html) to perform parameter sweeps.
+We use the term **parameter sweep** in H2Integrate to avoid confusion with the U.S. Department of Energy (DOE), which is frequently referenced in our domain.
+If you see "DOE" or "Design of Experiments" in OpenMDAO documentation, it refers to the same capability called "parameter sweep" here.
+```
+
+The parameter sweep is configured through the `driver_config.yaml` file, which defines the sweep settings, design variables, constraints, and objective functions.
 Detailed information on setting up the `driver_config.yaml` file can be found in the
-[user guide](https://h2integrate.readthedocs.io/en/latest/user_guide/design_optimization_in_h2i.html)
+[user guide](https://h2integrate.readthedocs.io/en/latest/user_guide/design_optimization_in_h2i.html).
+
+```{tip}
+Parameter sweeps are the **recommended way** to run multiple cases with different design variable values.
+If you need to change technology configuration values (not just design variables), see the
+[advanced for-loop approach](how_to_run_several_cases_in_sequence.md) instead.
+```
 
 ## Driver config file
 
-The driver config file defines the analysis type and the optimization or design of experiments settings.
-For completeness, here is an example of a driver config file for a design of experiments problem:
+The driver config file defines the analysis type and the parameter sweep settings.
+For completeness, here is an example of a driver config file for a parameter sweep:
 
 ```{literalinclude} ../../examples/22_site_doe/driver_config.yaml
 :language: yaml
@@ -31,7 +44,8 @@ For completeness, here is an example of a driver config file for a design of exp
 
 ## Types of Generators
 
-H2Integrate currently supports the following types of generators:
+H2Integrate supports the following generator types to create the set of cases for a parameter sweep.
+Each generator produces combinations of design variable values using a different sampling strategy.
 
 - ["uniform"](#uniform): uses the `UniformGenerator` generator
 - ["fullfact"](#fullfactorial): uses the `FullFactorialGenerator` generator
@@ -45,9 +59,11 @@ Documentation for each generator type can be found on [OpenMDAO's documentation
 (uniform)=
 ### Uniform
 
+Generates random samples drawn uniformly between the lower and upper bounds of each design variable. Good for initial exploration when you have no prior knowledge of the design space.
+
 ```yaml
 driver:
-  design_of_experiments:
+  parameter_sweep:
     flag: True
     generator: "uniform" #type of generator to use
     num_samples: 10 #input is specific to this generator
@@ -57,50 +73,50 @@ driver:
 (fullfactorial)=
 ### FullFactorial
 
+Evaluates **every combination** of evenly spaced levels for each design variable. Provides complete coverage of the design space but grows exponentially with the number of variables (e.g., 3 variables with 4 levels = 64 cases).
+
 ```yaml
 driver:
-  design_of_experiments:
+  parameter_sweep:
     flag: True
     generator: "fullfact" #type of generator to use
     levels: 2 #input is specific to this generator
 ```
 
-The **levels** input is the number of evenly spaced levels between each design variable lower and upper bound.
-
-You can check the values that will be used for a specific design variable by running:
-
-```python
-import numpy as np
-
-design_variable_values = np.linspace(lower_bound,upper_bound,levels)
-```
+The **levels** input is the number of evenly spaced levels between each design variable lower and upper bound, inclusive.
 
 (plackettburman)=
 ### PlackettBurman
 
+A screening method that uses a fractional factorial approach to identify which design variables have the **largest effect** on the outputs. Requires far fewer runs than a full factorial sweep, making it useful for narrowing down which variables matter most before performing a more detailed study.
+
 ```yaml
 driver:
-  design_of_experiments:
+  parameter_sweep:
     flag: True
     generator: "plackettburman" #type of generator to use
 ```
 
 (boxbehnken)=
 ### BoxBehnken
 
+A response surface method that samples at the **midpoints of edges** and the center of the design space (never at the corners). Useful for fitting quadratic response surface models with fewer runs than a full factorial at three levels. Best suited for 3 or more design variables.
+
 ```yaml
 driver:
-  design_of_experiments:
+  parameter_sweep:
     flag: True
     generator: "boxbehnken" #type of generator to use
 ```
 
 (latinhypercube)=
 ### LatinHypercube
 
+A space-filling method that divides each variable's range into equal-probability intervals and samples exactly once from each interval. Provides good coverage of the design space with fewer samples than a full factorial.
+
 ```yaml
 driver:
-  design_of_experiments:
+  parameter_sweep:
     flag: True
     generator: "latinhypercube" #type of generator to use
     num_samples:  10 #input is specific to this generator
@@ -111,11 +127,11 @@ driver:
 (csv)=
 ### CSV
 
-This method is useful if there are specific combinations of designs variables that you want to sweep. An example is shown here:
+Allows you to specify **exact combinations** of design variable values in a CSV file. This is useful when you have specific scenarios you want to evaluate rather than a systematic sampling of the design space.
 
 ```yaml
 driver:
-  design_of_experiments:
+  parameter_sweep:
     flag: True
     generator: "csvgen" #type of generator to use
     filename: "cases_to_run.csv" #input is specific to this generator
@@ -132,7 +148,7 @@ You should check the csv file for potential formatting issues before running a s
 This `csvgen` generator example reflects the work to produce the `examples/20_solar_electrolyzer_doe`
 example.
 
-We use the `examples/20_solar_electrolyzer_doe/driver_config.yaml` to run a design of experiments for
+We use the `examples/20_solar_electrolyzer_doe/driver_config.yaml` to run a parameter sweep for
 varying combinations of solar power and hydrogen electrolyzer capacities.
 
 ```{literalinclude} ../../examples/20_solar_electrolyzer_doe/driver_config.yaml
@@ -148,7 +164,7 @@ The different combinations of solar and electrolyzer capacities are listed in th
 :language: text
 ```
 
-Next, we'll import the required models and functions to complete run a successful design of experiments.
+Next, we'll import the required models and functions to run a parameter sweep.
 
 ```{code-cell} ipython3
 # Import necessary methods and packages
@@ -169,9 +185,9 @@ EXAMPLE_DIR = Path("../../examples/20_solar_electrolyzer_doe").resolve()
 config = load_yaml(EXAMPLE_DIR / "20_solar_electrolyzer_doe.yaml")
 
 driver_config = load_yaml(EXAMPLE_DIR / config["driver_config"])
-csv_config_fn = EXAMPLE_DIR / driver_config["driver"]["design_of_experiments"]["filename"]
+csv_config_fn = EXAMPLE_DIR / driver_config["driver"]["parameter_sweep"]["filename"]
 config["driver_config"] = driver_config
-config["driver_config"]["driver"]["design_of_experiments"]["filename"] = csv_config_fn
+config["driver_config"]["driver"]["parameter_sweep"]["filename"] = csv_config_fn
 
 config["technology_config"] = load_yaml(EXAMPLE_DIR / config["technology_config"])
 config["plant_config"] = load_yaml(EXAMPLE_DIR / config["plant_config"])

diff --git a/docs/user_guide/postprocessing_results.md b/docs/user_guide/postprocessing_results.md
@@ -175,7 +175,7 @@ The `electricity_base_unit` argument (default: `"MW"`) controls the units used f
 ### Summarizing scalar results to CSV
 
 While `save_case_timeseries_as_csv` exports time-series data, the `convert_sql_to_csv_summary` function extracts **scalar** results from one or more SQL recorder files and writes them to a single CSV summary.
-This is especially useful when running parameter sweeps or DOE studies, where each row in the output corresponds to a different case.
+This is especially useful when running parameter sweeps, where each row in the output corresponds to a different case.
 
 The function:
 
@@ -212,9 +212,9 @@ model.post_process()
 summary_df = convert_sql_to_csv_summary(model.recorder_path)
 ```
 
-#### Summarizing parallel DOE results
+#### Summarizing parallel parameter sweep results
 
-When a DOE or parallel study is executed, H2Integrate writes one SQL file per process (e.g. `cases.sql_0`, `cases.sql_1`).
+When a parameter sweep or parallel study is executed, H2Integrate writes one SQL file per process (e.g. `cases.sql_0`, `cases.sql_1`).
 Pass the base name and the function handles the rest:
 
 ```python

diff --git a/examples/20_solar_electrolyzer_doe/20_solar_electrolyzer_doe.yaml b/examples/20_solar_electrolyzer_doe/20_solar_electrolyzer_doe.yaml
@@ -1,5 +1,5 @@
 name: H2Integrate_config
-system_summary: Run DOE over solar and electrolyzer capacities
+system_summary: Run parameter sweep over solar and electrolyzer capacities
 driver_config: driver_config.yaml
 technology_config: tech_config.yaml
 plant_config: plant_config.yaml
diff --git a/examples/20_solar_electrolyzer_doe/driver_config.yaml b/examples/20_solar_electrolyzer_doe/driver_config.yaml
@@ -1,10 +1,10 @@
-description: Driver config for CSVGen design of experiments
+description: Driver config for CSVGen parameter sweep
 name: driver_config
 general:
   folder_output: ex_20_out
   create_om_reports: true
 driver:
-  design_of_experiments:
+  parameter_sweep:
     flag: true
     debug_print: true
     generator: csvgen

diff --git a/examples/20_solar_electrolyzer_doe/run_csv_doe.py b/examples/20_solar_electrolyzer_doe/run_csv_doe.py
@@ -1,4 +1,4 @@
-"""Minimal working example from the DOE user guide docs."""
+"""Minimal working example from the parameter sweep user guide docs."""
 
 from h2integrate import H2IntegrateModel
 from h2integrate.core.dict_utils import update_defaults
@@ -9,7 +9,7 @@
 config = load_yaml("20_solar_electrolyzer_doe.yaml")
 
 driver_config = load_yaml(config["driver_config"])
-csv_config_fn = driver_config["driver"]["design_of_experiments"]["filename"]
+csv_config_fn = driver_config["driver"]["parameter_sweep"]["filename"]
 
 try:
     model = H2IntegrateModel(config)
@@ -43,7 +43,7 @@
     new_csv_filename.name,
 )
 driver_config["driver"].update(updated_driver)
-print(f"New DOE driver CSV file: {new_csv_filename}")
+print(f"New parameter sweep driver CSV file: {new_csv_filename}")
 
 # Step 3
 config["driver_config"] = driver_config

diff --git a/examples/21_iron_examples/iron_mapping/driver_config.yaml b/examples/21_iron_examples/iron_mapping/driver_config.yaml
@@ -3,7 +3,7 @@ description: This analysis runs a hybrid plant to match the first example in H2I
 general:
   folder_output: ex_out
 driver:
-  design_of_experiments:
+  parameter_sweep:
     flag: true
     debug_print: false
     generator: csvgen

diff --git a/examples/22_site_doe/22_solar_site_doe.yaml b/examples/22_site_doe/22_solar_site_doe.yaml
@@ -1,5 +1,5 @@
 name: H2Integrate_config
-system_summary: Run DOE over solar capacities and site locations
+system_summary: Run parameter sweep over solar capacities and site locations
 driver_config: driver_config.yaml
 technology_config: tech_config.yaml
 plant_config: plant_config.yaml
diff --git a/examples/22_site_doe/driver_config.yaml b/examples/22_site_doe/driver_config.yaml
@@ -1,10 +1,10 @@
-description: Driver config for CSVGen design of experiments
+description: Driver config for CSVGen parameter sweep
 name: driver_config
 general:
   folder_output: ex_22_out
   create_om_reports: false
 driver:
-  design_of_experiments:
+  parameter_sweep:
     flag: true
     debug_print: true
     generator: csvgen

diff --git a/examples/27_site_doe_diff/27_wind_solar_site_doe.yaml b/examples/27_site_doe_diff/27_wind_solar_site_doe.yaml
@@ -1,5 +1,5 @@
 name: H2Integrate_config
-system_summary: Run DOE over solar capacities and site locations
+system_summary: Run parameter sweep over solar capacities and site locations
 driver_config: driver_config.yaml
 technology_config: tech_config.yaml
 plant_config: plant_config.yaml
diff --git a/examples/27_site_doe_diff/driver_config.yaml b/examples/27_site_doe_diff/driver_config.yaml
@@ -1,10 +1,10 @@
-description: Driver config for CSVGen design of experiments
+description: Driver config for CSVGen parameter sweep
 name: driver_config
 general:
   folder_output: ex_27_out
   create_om_reports: false
 driver:
-  design_of_experiments:
+  parameter_sweep:
     flag: true
     debug_print: false
     generator: csvgen

diff --git a/examples/test/test_all_examples.py b/examples/test/test_all_examples.py
@@ -1746,7 +1746,7 @@ def test_windard_pv_battery_dispatch_example(subtests, temp_copy_of_example):
 @pytest.mark.parametrize(
     "example_folder,resource_example_folder", [("20_solar_electrolyzer_doe", None)]
 )
-def test_csvgen_design_of_experiments(subtests, temp_copy_of_example):
+def test_csvgen_parameter_sweep(subtests, temp_copy_of_example):
     example_folder = temp_copy_of_example
 
     with pytest.raises(UserWarning) as excinfo:
@@ -1760,7 +1760,7 @@ def test_csvgen_design_of_experiments(subtests, temp_copy_of_example):
     # load the driver config file
     driver_config = load_driver_yaml("driver_config.yaml")
     # specify the filepath to the csv file
-    csv_fpath = Path(driver_config["driver"]["design_of_experiments"]["filename"]).absolute()
+    csv_fpath = Path(driver_config["driver"]["parameter_sweep"]["filename"]).absolute()
     # run the csv checker method, we want it to write the csv file to a new filepath so
     # set overwrite_file=False
     new_csv_filename = check_file_format_for_csv_generator(

diff --git a/h2integrate/converters/solar/test/test_pysam_solar.py b/h2integrate/converters/solar/test/test_pysam_solar.py
@@ -323,7 +323,7 @@ def test_pvwatts_singleowner_notilt_different_site(basic_pysam_options, plant_co
     """
 
     driver_config = {
-        "driver": {"design_of_experiments": {"flag": True}},
+        "driver": {"parameter_sweep": {"flag": True}},
         "design_variables": {
             "site": {
                 "latitude": {},