Deep Reinforcement Learning Strategies for Optimizing Flow Control in Wings

A computational fluid dynamics (CFD) framework to combine deep reinforcement learning (DRL)with active flow control (AFC) simulations is presented. This framework is first validated in a canonical geometry, the flow past a cylinder at Reynolds number 𝑅𝑒 = 100, achieving a drag reduction of roughly 10%. Then, the methodology is applied to a more complex case in order toexploit the DRL capabilities to control massive separations of turbulent flows. To do this, the flow past the SD7003 wing at 𝑅𝑒 = 60,000 and angle of attack 𝐴𝑜𝐴 = 14◦ is analyzed, where the DRL control achieves an increase in the aerodynamic efficiency of approximately 157%.