Optimizing flow control with deep reinforcement learning: Plasma actuator placement around a square cylinder

Abstract

This study introduces a deep reinforcement learning-based flow control approach to enhance the efficiency of multiple plasma actuators on a square cylinder. The research seeks to adjust the control inputs of these actuators to diminish both drag and lift forces on the cylinder, ensuring flow stability in the process. The proposed model uses a two-dimensional direct numerical simulation of flow past a square cylinder to represent the environment. The control approach involves adjusting the AC voltage across three specific configurations of the plasma actuators. Initially tested at a Reynolds number (ReD) of 100, this strategy was later applied at ReD of 180. We observed a 97% reduction in the mean drag coefficient at ReD = 100 and a 99% reduction at ReD = 180. Furthermore, the findings suggest that increasing the Reynolds number makes it harder to mitigate vortex shedding using plasma actuators on just the cylinder's rear surface. However, an optimized configuration of these actuators can fully suppress vortex shedding under the proposed control scheme.