cylindercontrol

This repository includes codes for numerical cylinder flow control cases.

Low Reynolds (Re=100) cylinder flow control with rotary oscillations by DRL optimization algorithm

The folder rotaryRe100DRL contains a DRL agent based on the original OpenAI baselines implementation of the PPO algorithm.

The graphical abstract is available at https://youtu.be/9X8XtHk0R84

Building CFD environment and the control coupling codes

• In order to run training before you have to download and build TFlows CFD solver here from the development_branch.

• The solver Processor can be built with the follolwing command:

make MPI=yes FCOMP=mpif90.openmpi CGNS=no CGNS_MPI=openmpi DIR_CASE=<folder_with_the_agent>

• Change the text inside the brackets with the folder name containing User_Mod folder coupling the Fortran code with the DLR agent. After successfull build create a symbolic link to the Processor binaries inside the DIR_CASE.

Running training

• Run python module to start the agent

cd OpenAI-baselines-CustomEnvs
python -m baselines.run

• Training log with be written to a train_data subfolder with correspondong current models of the controllers

• Use tensorboard for monitoring the training progress

Testing trained model

• Use the follwing code to test a trained model

python -m baselines.run --load_path=<absolute_path_to_the_trained_model>

• Use pretrained model to see the control in action

python -m baselines.run --load_path=<absolute_path>/cylindercontrol/rotaryRe100DRL/OpenAI-baselines-CustomEnvs/trained_models/VirtualEnv/20200622-190846_dns_re100_entropy0.01_3-cd-0.2cl_50long/checkpoints/00050

Other issues

• Original python conda environment used for the project can be restored using environment.yml file, see directions here

• Most of the training parametres can be modified in /CustomEnvs/CustomEnvs.py and /CustomEnvs/VirtualEnv/VirtualEnv.py files.