Active control for the flow around various geometries through deep reinforcement learning

Abstract

Based on the deep reinforcement learning (DRL) method, the active flow control strategy obtained from artificial neural networks (ANNs) is applied to reducing the drag force of various blunt bodies. The control strategy is realized by the agent described by ANNs model which maps appropriate environment sensing signals and control actions, and ANNs are constructed by exploring the controlled system through proximal policy optimization method. The drag reduction effect for ellipse, square, hexagon and diamond geometries under double- and triple-jets control is systematically studied, and the robustness of DRL jet control method is verified. The numerical results show that the drag reduction effect of triple-jets control is significantly better than that of double-jets control when Reynolds number is 80 and angle of attack is 0, and under the triple-jets control situation, the DRL agent can significantly reduce the drag by approximately 11.50%, 10.56%, 8.35%, and 2.78% for ellipse, square, hexagon and diamond model, respectively. In addition, based on the ellipse model, the drag reduction effect of the active control strategy under different AOA and different Reynolds numbers are further studied. When the AOA of ellipse configuration are 5°, 10°, 15° and 20° and the Reynolds number remains 80, the control strategies of DRL achieve the drag reduction of 5.44%, 0.59%, 11.67% and 0.28%, respectively. Meanwhile, when the AOA is 0, the drag reduction reaches 10.84% and 23.63% under the condition of the Reynolds number is 160 and 320, respectively. The significant control effect shows that the reinforcement learning method coupled with the ANNs shows a powerful ability to identical system when facing control problem with high-dimensional nonlinear characteristics. The ability to identify complex systems also shows that DRL methods can be further applied to active flow control under conditions of higher Reynolds number.