Optimizing Flow Control with Deep Reinforcement Learning: Plasma Actuator Placement around a Square Cylinder

TL;DR

This study employs deep reinforcement learning to optimize plasma actuator placement around a square cylinder, significantly reducing drag and vortex shedding at different Reynolds numbers, demonstrating the effectiveness of AI-driven flow control.

Contribution

It introduces a novel DRL-based active flow control method for plasma actuators on a square cylinder, achieving near-complete drag reduction and vortex suppression.

Findings

97% drag reduction at Re_D=100

99% drag reduction at Re_D=180

Complete vortex suppression with optimized plasma placement

Abstract

The present study proposes an active flow control (AFC) approach based on deep reinforcement learning (DRL) to optimize the performance of multiple plasma actuators on a square cylinder. The investigation aims to modify the control inputs of the plasma actuators to reduce the drag and lift forces affecting the cylinder while maintaining a stable flow regime. The environment of the proposed model is represented by a two-dimensional direct numerical simulation (DNS) of a flow past a square cylinder. The control strategy is based on the regulation of the supplied alternating current (AC) voltage at three distinct configurations of the plasma actuators. The effectiveness of the designed strategy is first investigated for Reynolds number, $Re_{D} = 100$, and further applied for $Re_{D} = 180$. The applied active flow control strategy is able to reduce the mean drag coefficient by 97\% at $Re_{D} = 100$ and by 99\% at $Re_D=180$. Furthermore, the results from this study show that with the increase in Reynolds number, it becomes more challenging to eliminate vortex shedding with plasma actuators located only on the rear surface of the cylinder. Nevertheless, the proposed control scheme is able to completely suppress it with an optimized configuration of the plasma actuators.