How-to-Control-Hydrodynamic-Force-on-Fluidic-Pinball-via-Deep-Reinforcement-Learning

Code and data of paper "How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learning", which is introduced in Physics of Fluids.

Data generated by the open-loop method with constant rotation speed is available in https://pan.westlake.edu.cn:443/link/672D2CD0F2DE1DB7EC237A7B3C37765C Password: AhzU

Code can be implemented by Processing for CFD solver and by Python for DRL.

Requirements and Installation:

1. Processing 3.5.4 for Linux
2. Tensorflow 2.10.0
3. Gym 0.19.0
4. xvfb-run for Linux

Implementation:

train DRL and begin interaction with CFD

Python test_changed.py -env CFD -fil None

Supplement: There are slight clerical errors in the publish paper as time is limited. We apologize that these errors may affect readers' understanding. Therefore, we have updated the version to correct the errors, and if you find any other details of errors in the future, please contact us. We will be very grateful and make the necessary modifications in a timely manner. The latest version can be found at https://arxiv.org/abs/2304.11526.