用物理启发的代理模型，零样本控制机翼湍流，大幅降阻。
Physics-guided surrogate learning enables zero-shot control of turbulent wings
- 基于机翼边界层统计特性，在通道流中训练控制策略。
- 零样本部署实现28.7%摩擦阻力降低，总阻力降10.7%。
- 训练成本降四数量级，适合真实飞行器快速部署。
机翼表面湍流边界层是飞机阻力的主要来源，但其控制因多尺度动力学和空间异质性而困难，尤其在逆压梯度下。强化学习虽在理想流动中超越现有方法，但在真实几何上的应用受限于计算成本与泛化能力。本文通过利用壁面湍流的局部结构，使策略在匹配机翼边界层统计特性的通道流中训练，并直接部署至 $Re_c=2\times10^5$ 的 NACA4412 机翼上，实现零样本控制。该方法使摩擦阻力降低28.7%，总阻力降低10.7%，在摩擦阻力方面较最先进反对控制提升40%，总阻力提升5%。训练成本相较机翼上训练降低四个数量级，实现可扩展的气动控制。
原文摘要 · Abstract (English)
Turbulent boundary layers over aerodynamic surfaces are a major source of aircraft drag, yet their control remains challenging due to multiscale dynamics and spatial variability, particularly under adverse pressure gradients. Reinforcement learning has outperformed state-of-the-art strategies in canonical flows, but its application to realistic geometries is limited by computational cost and transferability. Here we show that these limitations can be overcome by exploiting local structures of wall-bounded turbulence. Policies are trained in turbulent channel flows matched to wing boundary-layer statistics and deployed directly onto a NACA4412 wing at $Re_c=2\times10^5$ without further training, being the so-called zero-shot control. This achieves a 28.7% reduction in skin-friction drag and a 10.7% reduction in total drag, outperforming the state-of-the-art opposition control by 40% in friction drag reduction and 5% in total drag. Training cost is reduced by four orders of magnitude relative to on-wing training, enabling scalable flow control.
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