ashao/openfoam-multi-objective-optimization

A tiny project to use ax-platform for multi-objective optimization on OpenFOAM cases

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!

By either filing issues or opening pull requests, you can contribute to the development of this project, which I would appreciate.