Synthetic jet control of the airfoil based on deep reinforcement learning

This article proposes a closed-loop active flow control framework based on proximal policy optimization (PPO) algorithm and a synthetic jet to suppress severe flow separation of EH1590 airfoil at high angles of attack. Aerodynamic characteristics of airfoils under different jet parameters are obtained through computational fluid dynamics (CFD) simulation, and a deep neural network surrogate model is trained to achieve rapid prediction. Based on this alternative model, the PPO algorithm is used to train the agent, taking the pressure distribution of the flow field around the airfoil as the state, to optimize and improve the weighted objectives of lift-to-drag ratio and jet energy consumption. Finally, the agent is tested in both fixed and variable angle of attack tasks, and its adaptability and control effectiveness in a higher-resolution CFD environment are verified by coupling with CFD. The results show that at a fixed angle of attack of 15°, the lift-to-drag ratio increased from 4.56 to 7.34. Under variable angles of attack, the agent can automatically adapt and maintain a high lift-to-drag ratio. The error between the lift-to-drag ratio calculated directly by coupling with CFD and the test results of the surrogate model is within 5%, which verifies the effectiveness of the training strategy based on the surrogate model in transferring to the real flow field. This study provides an efficient and feasible technical path for the reinforcement learning application of airfoil flow control under high Reynolds number and high angle of attack conditions.