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

Ricard Montalà; Bernat Font; Oriol Lehmkuhl; Ricardo Vinuesa; Ivette Rodriguez

physics.flu-dynMay 12, 2026

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

Authors: Ricard Montalà, Bernat Font, Oriol Lehmkuhl, Ricardo Vinuesa, Ivette Rodriguez

Organizations: 1TUAREG, Universitat Politècnica de Catalunya (UPC), Terrassa, Spain · 2Mechanical Engineering, Delft University of Technology (TU Delft), Delft, Netherlands · 3CASE, Barcelona Supercomputing Center (BSC), Barcelona, Spain · Department of Aeroespace Enigneering, University of Michigan, Ann Arbor, USA

Abstract

This study investigates active flow control (AFC) of a 30P30N high-lift wing at a Reynolds number Re $_c$ = 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.

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Heuristic Learning for Active Flow Control Using Coding Agents

Paul Garnier, Jonathan Viquerat, Elie Hachem

Mines Paris - PSL University · Centre for Material Forming (CEMEF) · CNRS · Independent Researcher

Active flow control involves nonlinear dynamics, partial observations, and computationally expensive simulations, making controller design particularly challenging. Deep reinforcement learning (DRL) has emerged as a powerful framework for such problems, but its success typically relies on large numbers of simulator interactions and produces neural-network policies whose decision process often remains difficult to interpret. In this work, we investigate a different paradigm: instead of optimizing neural-network parameters, we use modern coding agents to search directly for explicit executable feedback laws. We introduce a constrained heuristic-learning protocol in which an agent iteratively proposes, evaluates, and revises controller implementations while interacting exclusively through the public benchmark interface. The proposed framework is evaluated on 13 active flow-control benchmarks spanning one, two, and three-dimensional problems and compared against the strongest available DRL baselines under identical simulation budgets. The discovered heuristic controllers match or outperform the best DRL policy in 10 of the 13 environments while remaining compact, interpretable, and directly inspectable. Beyond aggregate performance, the resulting controllers reveal physically meaningful feedback mechanisms, transfer successfully across more challenging configurations, and remain competitive under varying Reynolds and Rayleigh numbers, actuator counts, and observation sparsity. These results suggest that heuristic learning through coding agents constitutes a credible and complementary alternative to conventional reinforcement learning, combining competitive performance with physically interpretable controller representations. Prompts and source code are available at https://github.com/DonsetPG/fluid-heuristic-learning.

Deep Reinforcement Learning Heuristics Coding Agents Nonlinear Dynamics

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Abstract

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