Physics-guided surrogate learning enables zero-shot control of turbulent wings
2026-04-10 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors address the difficulty of reducing drag caused by turbulent airflow over airplane wings, which is important because drag wastes fuel. They used reinforcement learning, a method where a computer learns by trial and error, but trained it on simpler channel flows instead of full wings to save time and computing power. Then, without extra training, they applied this approach to a real wing shape and achieved significant drag reduction. This method worked better than previous controls and was much faster to train, making advanced flow control more practical.
turbulent boundary layeraerodynamic dragreinforcement learningNACA4412 wingskin-friction dragzero-shot controladverse pressure gradientflow controlopposition controlReynolds number
Authors
Yuning Wang, Pol Suarez, Mathis Bode, Ricardo Vinuesa
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 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.