Active flow control of a turbulent separation bubble through deep reinforcement learning
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913342259462144 |
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| author | Font, Bernat Alcántara-Ávila, Francisco Rabault, Jean Vinuesa, Ricardo Lehmkuhl, Oriol |
| author_facet | Font, Bernat Alcántara-Ávila, Francisco Rabault, Jean Vinuesa, Ricardo Lehmkuhl, Oriol |
| contents | The control efficacy of classical periodic forcing and deep reinforcement learning (DRL) is assessed for a turbulent separation bubble (TSB) at $Re_τ=180$ on the upstream region before separation occurs. The TSB can resemble a separation phenomenon naturally arising in wings, and a successful reduction of the TSB can have practical implications in the reduction of the aviation carbon footprint. We find that the classical zero-net-mas-flux (ZNMF) periodic control is able to reduce the TSB by 15.7%. On the other hand, the DRL-based control achieves 25.3% reduction and provides a smoother control strategy while also being ZNMF. To the best of our knowledge, the current test case is the highest Reynolds-number flow that has been successfully controlled using DRL to this date. In future work, these results will be scaled to well-resolved large-eddy simulation grids. Furthermore, we provide details of our open-source CFD-DRL framework suited for the next generation of exascale computing machines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_20295 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Active flow control of a turbulent separation bubble through deep reinforcement learning Font, Bernat Alcántara-Ávila, Francisco Rabault, Jean Vinuesa, Ricardo Lehmkuhl, Oriol Fluid Dynamics The control efficacy of classical periodic forcing and deep reinforcement learning (DRL) is assessed for a turbulent separation bubble (TSB) at $Re_τ=180$ on the upstream region before separation occurs. The TSB can resemble a separation phenomenon naturally arising in wings, and a successful reduction of the TSB can have practical implications in the reduction of the aviation carbon footprint. We find that the classical zero-net-mas-flux (ZNMF) periodic control is able to reduce the TSB by 15.7%. On the other hand, the DRL-based control achieves 25.3% reduction and provides a smoother control strategy while also being ZNMF. To the best of our knowledge, the current test case is the highest Reynolds-number flow that has been successfully controlled using DRL to this date. In future work, these results will be scaled to well-resolved large-eddy simulation grids. Furthermore, we provide details of our open-source CFD-DRL framework suited for the next generation of exascale computing machines. |
| title | Active flow control of a turbulent separation bubble through deep reinforcement learning |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2403.20295 |