Deep Reinforcement Learning for Active Flow Control around a Three-Dimensional Flow-Separated Wing at Re = 1,000
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914033400020992 |
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| author | Montalà, R. Font, B. Suárez, P. Rabault, J. Lehmkuhl, O. Vinuesa, R. Rodriguez, I. |
| author_facet | Montalà, R. Font, B. Suárez, P. Rabault, J. Lehmkuhl, O. Vinuesa, R. Rodriguez, I. |
| contents | This study explores the use of deep reinforcement learning (DRL) for active flow control (AFC) to reduce flow separation on wings at high angles of attack. Concretely, here the DRL agent controls the flow over the three-dimensional NACA0012 wing section at the Reynolds number Re = 1,000 and angle of attack AoA = 20 degrees, autonomously identifying optimal control actions through real-time flow data and a reward function focused on improving aerodynamic performance. The framework integrates the GPU-accelerated computational fluid dynamics (CFD) solver SOD2D with the TF-Agents DRL library via a Redis in-memory database, enabling rapid training. This work builds on previous DRL flow-control studies, demonstrating DRL potential to address complex aerodynamic challenges and push the boundaries of traditional AFC methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10195 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Reinforcement Learning for Active Flow Control around a Three-Dimensional Flow-Separated Wing at Re = 1,000 Montalà, R. Font, B. Suárez, P. Rabault, J. Lehmkuhl, O. Vinuesa, R. Rodriguez, I. Computational Engineering, Finance, and Science This study explores the use of deep reinforcement learning (DRL) for active flow control (AFC) to reduce flow separation on wings at high angles of attack. Concretely, here the DRL agent controls the flow over the three-dimensional NACA0012 wing section at the Reynolds number Re = 1,000 and angle of attack AoA = 20 degrees, autonomously identifying optimal control actions through real-time flow data and a reward function focused on improving aerodynamic performance. The framework integrates the GPU-accelerated computational fluid dynamics (CFD) solver SOD2D with the TF-Agents DRL library via a Redis in-memory database, enabling rapid training. This work builds on previous DRL flow-control studies, demonstrating DRL potential to address complex aerodynamic challenges and push the boundaries of traditional AFC methods. |
| title | Deep Reinforcement Learning for Active Flow Control around a Three-Dimensional Flow-Separated Wing at Re = 1,000 |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2509.10195 |