Policy-DRIFT: Dynamic Reward-Informed Flow Trajectory Steering

Skin-friction drag induced by wall-bounded turbulent flows accounts for a substantial fraction of energy consumption across commercial aerospace, wind energy, and marine transport. Its active reduction is one of the highest-value targets in engineering fluid dynamics. Deep reinforcement learning (DRL) has emerged as the leading approach for real-time flow control, yet its performance ceiling is set not by algorithmic capability but by reward structure, the naive scalar objective does not optimally reflect the underlying physics. Policy-DRIFT bypasses this ceiling by relocating reward information from policy gradients to generative model inference: a conditional flow matching model (CFM) constructs a physically-grounded manifold of realisable flow states spanning multiple control regimes, Terminal Reward Guidance (TRG) steers samples toward reward-maximising targets at inference, and a lightweight DRL policy, structurally decoupled from reward quality, tracks these full-field targets via root-mean-squared error (RMSE) minimisation. The test case is turbulent channel flow simulated using direct numerical simulation (DNS) at friction Reynolds number of $\mathrm{Re}_\tau = 180$, which is the canonical benchmark for wall-bounded turbulence. Policy-DRIFT achieves $49\%$ drag reduction approaching the theoretical upper bound, which is $\approx 16\%$ higher than the DRL benchmark, while consuming 37$\times$ less actuation energy. Our approach combines generative methods with active flow control, marking a paradigm shift towards controlling complex physical systems efficiently.