Closed-loop plasma flow control of a turbulent cylinder wake flow using machine learning at Reynolds number of 28 000

Abstract

Machine learning is increasingly used for active flow control. In this experimental study, alternating-current dielectric barrier discharge plasma actuators are deployed for the closed-loop intelligent control of the flow around a cylinder at a Reynolds number of 28 000 based on the velocity feedback from two hot-wire sensors placed in the wake. Variations in the cylinder drag are monitored by a load cell, and the temporal response of the wake flow field is visualized by a high-speed particle image velocimetry system working at 1 kHz. The high-speed control law is operated using a field programmable gate array optimized by genetic programing (GP). The results show that the peak drag reduction achieved by machine learning is of similar magnitude to that of conventional steady actuation (∼15%), while the power saving ratio is 35% higher than with conventional techniques because of the reduced power consumption. Analysis of the best GP control laws shows that the intensity of plasma actuation should be kept at a medium level to maximize the power-saving ratio. When compared with the baseline uncontrolled flow, the best controlled cases constrain the meandering motion of the cylinder wake, resulting in a narrow stabilized velocity deficit zone in the time-averaged sense. According to the results of proper orthogonal decomposition and dynamic mode decomposition, Karman vortex shedding is promoted under the best GP control.