Multi Objective Optimization on OpenFOAM cases

If you're using this piece of software, please care enough to cite it in your publications

Relying on ax-platform to experiment around 0-code parameter variation and multi-objective optimization of OpenFOAM cases.

Objectives and features

Parameter values are fetched through a YAML/JSON configuration file. Absolutely no code should be needed, add parameters to the YAML file and they should be picked up automatically
The no-code thing is taken to the extreme, through a YAML config file, you can (need-to):
- Specify the template case
- Specify how the case is ran
- Specify how/where parameters are substituted
- Specify how your metrics are computed

The scripts

paramVariation.py runs a Parameter Variation Study with the provided config and outputs trial data as a CSV file. Trial parameters are generated with SOBOL.
multiObjOpt.py runs a multi-objective optimization study (can use the same config file) and captures a JSON snapshot of the experiment. You can also run it with a single objective.

How do I use this?

The only requirement of usage (aside from being able to install dependency software) is that your template case needs to be ready to be parameterized.

# Clone the repository
git clone https://github.com/FoamScience/OpenFOAM-Multi-Objective-Optimization multiOptFoam
cd multiOptFoam
# Install dependencies
pip3 install -r requirements.txt
# Run parameter variation with config.yaml on pitzDaily case
./paramVariation.py
# Or run a multi-objective optimization
./multiObjOpt.py

Docs and Sample configuration

You can find a quick tutorial in the docs page. The sample config file and case are also documented.

Contribution is welcome!