mandar-tabib-1 / active_flow_control_past_cylinder_using_drl

Active control of the flow past a cylinder at varying Reynolds number

This research project is a direct continuation of the work by Darshan Thummar. The repository is structured as follows:

• test_cases: OpenFOAM simulation setups

Getting started

To run a test case, create a run folder (ignored by version control), copy the case from test_cases to run, and execute the Allrun script. To run with Singularity, the image has to be built fist; see Singularity and SLURM.

mkdir -p run
cp -r test_cases/cylinder2D_base run/
cd run/cylinder2D_base
# for execution with singularity
./Allrun.singularity
# for execution for local OpenFOAM installation
./Allrun

Singularity and SLURM

Singularity is a container tool that allows making results reproducible and performing simulations, to a large extent, platform independent. The only remaining dependencies are Singularity itself and Open-MPI (see next section for further comments). To build the image, run:

sudo singularity build of_v2012.sif docker://andreweiner/of_pytorch:of2012-py1.7.1-cpu

To run a simulation with Singularity, use the dedicated Allrun.singularity scripts. TU Braunschweig's HPC uses the SLURM scheduler. The repository contains an annotated example jobscript. The script expects the Singularity image in the top level directory of this repository and the simulation folder in run. To submit a job, run:

sbatch jobscript name_of_simulation

To show all running jobs of a user, use squeue -u $USER. Another helpful command is quota -s to check the available disk space.

Starting a Training

Choose a setup:

cd DRL_py_beta

Before you can start you have to download the baseline_data from here: baseline_data(400MB)

Then you have to copy the content of that folder into the ./env/baseline_case/baseline_data folder

Start the Training:

sbatch python_job.sh

Evaluate a specific Episode

Inside a Setup Folder:

If you choose the sample 52 for example:

cp ./env/base_case/agentRotatingWallVelocity_start_without_training ./env/run/sample_52

cp ./results/models/policy_51.pt ./env/run/sample_52/policy.pt

Now edit the jobscript file in that newly created folder in line 11:

From:

cd ./env/run/sample_*/

To:

cd ./env/run/sample_52/

Now to start the evaluation:

sbatch ./env/run/sample_52/jobscript.sh

Resetting the Setup

Make sure you have downloaded and saved all needed data

Choose a setup:

cd DRL_py_beta

Reset:

sbatch cleanup.sh

Report

The report for this study : https://doi.org/10.5281/zenodo.5634050

BibTex citation :

@misc{fabian_gabriel_2021_5634050,

  author       = {Fabian Gabriel},
  
  title        = {{Aktive Regelung einer Zylinderumströmung bei 
                   variierender Reynoldszahl durch bestärkendes
                   Lernen}},
                   
  month        = oct,
  
  year         = 2021,
  
  publisher    = {Zenodo},
  
  doi          = {10.5281/zenodo.5634050},
  
  url          = {https://doi.org/10.5281/zenodo.5634050}
}