📝 SummarXiv

Abstract

Mastering turbulence remains one of physics' most intractable challenges, with its chaotic, multi-scale dynamics driving energy dissipation across transport and energy systems. Here we report REACT (Reinforcement Learning for Environmental Adaptation and Control of Turbulence), a fully autonomous reinforcement learning framework that achieves real-time, adaptive, closed-loop turbulence control in real-world environments. Deployed on a road vehicle model equipped solely with onboard sensors and servo-actuated surfaces, REACT learns directly from sparse experimental measurements in a wind tunnel environment, bypassing intractable direct numerical simulations and empirical turbulence models. The agent autonomously converges to a policy that reduces aerodynamic drag while achieving net energy savings. Without prior knowledge of flow physics, it discovers that dynamically suppressing spatio-temporally coherent flow structures in the vehicle wake maximizes energy efficiency, achieving two to four times greater performance than model-based baseline controllers. Through a physics-informed training that recasts data in terms of dimensionless physical groups and parametric input spaces, REACT synthesizes offline a single generalizable agent that transfers across speeds without retraining. These results move agentic learning beyond simulation to robust, interpretable real-world control of high-Reynolds turbulence, opening a path to self-optimizing physical systems in transport, energy and environmental flows.