Physics-guided surrogate learning enables zero-shot control of turbulent wings

TL;DR

This paper introduces a physics-guided surrogate learning approach that enables zero-shot control of turbulent wings, significantly reducing drag without additional training.

Contribution

It demonstrates that local turbulence structures can be exploited to transfer control policies from simplified flows to realistic wing geometries, reducing training costs.

Findings

Achieved 28.7% skin-friction drag reduction

Reduced total drag by 10.7%

Outperformed state-of-the-art opposition control by 40% in friction drag

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.