Deep reinforcement learning-based active flow control of vortex-induced vibration of a square cylinder

Abstract

We mitigate vortex-induced vibrations of a square cylinder at a Reynolds number of 100 using deep reinforcement learning (DRL)-based active flow control (AFC). The proposed method exploits the powerful nonlinear and high-dimensional problem-solving capabilities of DRL, overcoming limitations of linear and model-based control approaches. Three positions of jet actuators including the front, the middle, and the back of the cylinder sides were tested. The DRL agent as a controller is able to optimize the velocity of the jets to minimize drag and lift coefficients and refine the control strategy. The results show that a significant reduction in vibration amplitude of 86%, 79%, and 96% is achieved for the three different positions of the jet actuators, respectively. The DRL-based AFC method is robust under various reduced velocities. This study successfully demonstrates the potential of DRL-based AFC method in mitigating flow-induced instabilities.