Optimizing OpenFOAM's GAMG settings via Bayesian optimization

This project aims to use Bayesian optimization for finding the optimal solver settings in OpenFOAM. The basis of the project can be found in the repository Learning of optimized solver settings for CFD applications

Getting started - local execution

Dependencies

The instructions and tests are tailored to:

• OpenFOAM-v2406
• Python 3.10

Newer versions might work as well but were not explicitly tested.

To set up a suitable virtual environment, run:

# repository top-level
python3 -m venv bopt
source bopt/bin/activate
pip install --upgrade pip
pip install -r requirements.txt

Running an optimization

source bopt/bin/activate
python3 run.py example_config_local.yaml &> log.example_run

The script eval_runs.py contains a rudimentary example for visualizing the outcome.

Getting started - cluster execution

Allocating a workspace

The instructions in this subsection are specific to TU Dresden's Barnard system. The following command creates a workspace named general_testing that is valid for 90 days.

ws_allocate -F horse -r 7 -m firstname.lastname@tu-dresden.de -n general_testing -d 90
cd /data/horse/ws/$USER-general_testing

For more details on the workspace allocation, refer to the quick start guide.

Dependencies

First, clone the repository to your workspace:

git clone https://github.com/JanisGeise/BayesOpt_solverSettings
cd BayesOpt_solverSettings

To set up a suitable virtual environment, run:

# repository top-level
module load release/24.04 GCCcore/12.2.0 Python/3.10.8
python -m venv bopt
source bopt/bin/activate
pip install --upgrade pip
pip install -r requirements.txt

For more details on the HPC system, refer to the documentation:

• Python virtual environments
• OpenFOAM on Barnard

Running an optimization

The driver script has to be started via a jobscirpt. A suitable jobscript looks as follows (don't forget to substitute the mail address):

#!/bin/bash
#SBATCH --nodes=1
#SBATCH --tasks-per-node=1
#SBATCH --time=08:00:00
#SBATCH --job-name=gamg_opt
#SBATCH --mail-type=start,end
#SBATCH --mail-user=<your.email>@tu-dresden.de

module load release/24.04 GCCcore/12.2.0 Python/3.10.8
source bopt/bin/activate
python run.py example_config_slurm.yaml &> log.example_run

To submit the job, run:

sbatch jobscript

The resources required for running a simulation are specified in batch_settings of the general configuration file (refer to example_config_slurm.yaml).

Test case

The current test case is a 2D, laminar flow past a cylinder taken from the flow_data repository.

More test cases etc. will follow

Still TODO

• early stopping
• test different optimization configs for ax