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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.10185 |
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| _version_ | 1866912584811151360 |
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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 | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10185 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Discovering Flow Separation Control Strategies in 3D Wings via Deep Reinforcement Learning Montalà, R. Font, B. Suárez, P. Rabault, J. Lehmkuhl, O. Vinuesa, R. Rodriguez, I. Computational Engineering, Finance, and Science 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. |
| title | Discovering Flow Separation Control Strategies in 3D Wings via Deep Reinforcement Learning |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2509.10185 |