用贝叶斯优化和强化学习控制机翼失速，提升气动效率
High-lift Wing Separation Control via Bayesian Optimization and Deep Reinforcement Learning
- 用贝叶斯优化找稳态喷流参数，实现10.9%效率提升
- 强化学习因奖励函数设计问题，效率提升不明显
- 适合关注高雷诺数流动控制的工程师与研究人员
本研究在雷诺数Re$_c$ = 450,000、攻角α = 23°条件下，采用壁面解析的大涡模拟（LES）对30P30N高升力机翼进行主动流动控制（AFC）研究。对比了开环贝叶斯优化（BO）与闭环深度强化学习（DRL）两种策略，均通过襟翼、主翼和前缘缝翼上的合成喷流来抑制失速并提升气动效率。未控工况与文献数据吻合，验证了模拟可靠性。BO框架成功识别出稳定喷流速度，使效率提升+10.9%，同时实现-9.7%的阻力降低且保持升力不变。相比之下，虽利用分布式传感器获取瞬时流场信息，但DRL代理仅获得微弱的升阻改善，效率增益可忽略。训练分析表明，惩罚主导的奖励函数限制了探索能力。结果凸显在高雷诺数下，强化学习需更合理的设计奖励机制与计算加速策略。
原文摘要 · Abstract (English)
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 $α$ = 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. The uncontrolled configuration was validated against literature data, confirming the reliability of the LES setup. The BO framework successfully identified steady jet velocities that increased efficiency by +10.9% through a -9.7% drag reduction while maintaining lift. In contrast, the DRL agent, despite leveraging instantaneous flow information from distributed sensors, achieved only minor improvements in lift and drag, with negligible efficiency gain. Training analysis indicated that the penalty-dominated reward constrained exploration. These results highlight the need for carefully designed rewards and computational acceleration strategies in DRL-based flow control at high Reynolds numbers.
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