arXiv：cs.AI· Yuning Wang, Pol Suarez, Mathis Bode, Ricardo Vinuesa·· 13 小时前
物理引导代理学习实现湍流机翼零样本控制
Physics-guided surrogate learning enables zero-shot control of turbulent wings
AI 导读
研究提出利用壁面湍流局部结构的物理引导代理学习方法，在湍流槽道流中训练的策略无需额外训练即可直接部署到 NACA4412 机翼段，实现零样本迁移，在 $Re_c=2\times10^5$ 下总阻力降低 11.1%。
正文
Abstract: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 section at $Re_c=2\times10^5$ without additional training on the wing, constituting zero-shot deployment. The transferred policies reduce total drag by 11.1\%, while state-of-the-art opposition control reduces it by 8.9\%. With the actuation cost taken into account, the corresponding net-energy savings are 10.9\% and 8.8\%, respectively. Surrogate training provides an estimated improvement of four orders of magnitude in exploration efficiency over direct on-wing training, enabling scalable flow control.
| Subjects: | Fluid Dynamics (physics.flu-dyn); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2604.09434 [physics.flu-dyn] |
| (or arXiv:2604.09434v3 [physics.flu-dyn] for this version) | |
| https://doi.org/10.48550/arXiv.2604.09434 arXiv-issued DOI via DataCite |
Submission history
From: Yuning Wang [view email]
[v1]
Fri, 10 Apr 2026 15:50:44 UTC (43,645 KB)
[v2]
Mon, 13 Apr 2026 02:40:35 UTC (31,127 KB)
[v3]
Fri, 9 Oct 2026 14:16:19 UTC (34,226 KB)
来源：arXiv：cs.AI · arxiv.org