Reinforcement Learning-Based Closed-Loop Airfoil Flow Control

TL;DR

This paper explores how reinforcement learning can be used to develop effective closed-loop flow control strategies for airfoils, demonstrating significant improvements in lift-to-drag ratio through parameter optimization and 3D validation.

Contribution

It introduces a systematic analysis of RL-based flow control, highlighting the importance of control parameters and demonstrating scalability to 3D airfoil flows.

Findings

Control parameters significantly affect performance.

Optimal update intervals broaden actuation frequency spectrum.

Pretrained RL controllers with physics-informed profiles improve flow control.

Abstract

We systematically investigated a reinforcement learning (RL)-based closed-loop active flow control strategy to enhance the lift-to-drag ratio of a wing section with an NLF(1)-0115 airfoil at an angle of attack 5 degree. The effects of key control parameters, including actuation location, observed state, reward function, and control update interval, are evaluated at a chord-based Reynolds number of Re=20,000. Results show that all parameters significantly influence control performance, with the update interval playing a particularly critical role. Properly chosen update intervals introduce a broader spectrum of actuation frequencies, enabling more effective interactions with a wider range of flow structures and contributing to improved control effectiveness. The optimally trained RL controller is further evaluated in a three-dimensional numerical setup at the same Reynolds number. Actuation is applied using both spanwise-uniform and spanwise-varying control profiles. The results demonstrate that the pretrained controller, combined with a physics-informed spanwise distribution, achieves substantial performance gains. These findings extend the feasibility and scalability of a pretrained RL-based control strategy to more complex airfoil flows.