# Discovering Flow Separation Control Strategies in 3D Wings via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2509.10185 · HTML: https://mlsanity.com/paper/2509.10185/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2025.

## Built on by (most cited first, 1 of 1)

- [High-lift Wing Separation Control via Bayesian Optimization and Deep Reinforcement Learning](https://mlsanity.com/paper/2605.11981/) (2026)

## Abstract (arXiv)

In this work, deep reinforcement learning (DRL) is applied to active flow control (AFC) over a threedimensional SD7003 wing at a Reynolds number of Re = 60,000 and angle of attack of AoA = 14 degrees. In the uncontrolled baseline case, the flow exhibits massive separation and a fully turbulent wake. Using a GPU-accelerated CFD solver and multi-agent training, DRL discovers control strategies that enhance lift (79%), reduce drag (65%), and improve aerodynamic efficiency (408%). Flow visualizations confirm reattachment of the separated shear layer, demonstrating the potential of DRL for complex and turbulent flows.