AI RESEARCH
High-lift Wing Separation Control via Bayesian Optimization and Deep Reinforcement Learning
arXiv CS.AI
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ArXi:2605.11981v1 Announce Type: cross This study investigates active flow control (AFC) of a 30P30N high-lift wing at a Reynolds number Re$_c$ = 450,000 and angle of attack $\alpha$ = 23$^\circ$ using wallresolved large-eddy simulations (LES). Two optimization strategies are explored: open-loop Bayesian optimization (BO) and closed-loop deep reinforcement learning (DRL), both targeting the mitigation of stall and the improvement of aerodynamic efficiency via synthetic jets on the slat, main, and flap elements.