Development of wall turbulence control law for friction drag reduction using reinforcement learning

Abstract

Recently, reinforcement learning has attracted much attention in the area of flow control because of its ability to obtain a long-term optimal policy. So far, it has been applied only to low-dimensional flows with simple actuations. In this study, we apply reinforcement learning to wall turbulence control in order to determine complex spatio-temporal distribution of wall blowing and suction for reducing skin friction drag based on near-wall sensing information. It is demonstrated that the control policy obtained from the present study exhibits unique relationship between sensing information and a control input, and achieves better drag reduction performance than the well-known opposition control.