Real-time reinforcement learning for turbulent state-dependent control in a bluff-body wake

Abstract Controlling turbulent dynamics remains a major challenge because their chaotic, multiscale nature strongly affects many fluid systems. Here, we present REACT, an autonomous reinforcement learning framework for real-time, state-dependent control of turbulent wake dynamics in a wind tunnel. Deployed on an Ahmed-body model equipped only with onboard sensors and servo-actuated surfaces, REACT learns from sparse wind-tunnel measurements, bypassing empirical turbulence models. The agent autonomously converges to a policy that reduces aerodynamic drag while delivering net energy savings. Without prior knowledge of flow physics, it discovers that dynamically suppressing spatiotemporally coherent wake structures maximizes energy efficiency, achieving two to four times greater performance than model-based baseline controllers. We contrast REACT’s dynamics-aware, state-dependent policy with quasi-steady, mean-flow-oriented policies learned by standard reinforcement learning baselines, which yield lower drag reduction and do not suppress coherent instabilities in this turbulent-wake regime. Finally, by training in a nondimensional state-reward space with amplitudes approximately invariant to Reynolds number, and conditioning on Reynolds number for temporal adaptation, REACT learns an offline policy that remains effective across Re = 86,400-518,400 without retraining. These results demonstrate autonomous closed-loop reinforcement learning control in a high-Reynolds-number wind-tunnel environment and suggest a path toward data-driven, state-dependent control of turbulent flows.