mlsanityData to 14 Oct 2026

High-lift Wing Separation Control via Bayesian Optimization and Deep Reinforcement Learning

arXiv 2605.11981 ↗First posted 12 May 2026physics.flu-dyn cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL with diffusion and flow models

Abstract (arXiv)

This study investigates active flow control (AFC) of a 30P30N high-lift wing at a Reynolds number Rec = 450,000 and angle of attack α = 23^circ using wallresolved large-eddy simulations (LES). Two optimization strategies are explored: open-loop Bayesian optimization (BO) and closed-loop deep reinforcement learning (DRL), both targeting the mitigation of stall and the improvement of aerodynamic efficiency via synthetic jets on the slat, main, and flap elements. The uncontrolled configuration was validated against literature data, confirming the reliability of the LES setup. The BO framework successfully identified steady jet velocities that increased efficiency by +10.9% through a -9.7% drag reduction while maintaining lift. In contrast, the DRL agent, despite leveraging instantaneous flow information from distributed sensors, achieved only minor improvements in lift and drag, with negligible efficiency gain. Training analysis indicated that the penalty-dominated reward constrained exploration. These results highlight the need for carefully designed rewards and computational acceleration strategies in DRL-based flow control at high Reynolds numbers.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL with diffusion and flow models, one of 15 directions in Deep reinforcement learning methods. Builds on 3 of its 30 references that are in the map. First posted on arXiv 12 May 2026.

Builds on

3 of 30 references.

References · 3

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2025Deep Reinforcement Learning for Active Flow Control around a Three-Dimensional Flow-Separated Wing at Re = 1,000cited by 6
  3. 2025Discovering Flow Separation Control Strategies in 3D Wings via Deep Reinforcement Learningcited by 1

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  1. 2024Towards Active Flow Control Strategies Through Deep Reinforcement Learning
  2. 2025Shocks Under Control: Taming Transonic Compressible Flow over an RAE2822 Airfoil with Deep Reinforcement Learning
  3. 2026Physics-guided surrogate learning enables zero-shot control of turbulent wings
  4. 2026Hypersonic Flow Control: Generalized Deep Reinforcement Learning for Hypersonic Intake Unstart Control under Uncertainty
  5. 2024Model-Based Reinforcement Learning for Control of Strongly-Disturbed Unsteady Aerodynamic Flows
  6. 2025Transfer learning-enhanced deep reinforcement learning for aerodynamic airfoil optimisation subject to structural constraints
  7. 2025Attention on flow control: transformer-based reinforcement learning for lift regulation in highly disturbed flows
  8. 2024Optimal Parallelization Strategies for Active Flow Control in Deep Reinforcement Learning-Based Computational Fluid Dynamics