Towards Active Flow Control Strategies Through Deep Reinforcement Learning
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910689957773312 |
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| author | Montalà, Ricard Font, Bernat Suárez, Pol Rabault, Jean Lehmkuhl, Oriol Rodriguez, Ivette |
| author_facet | Montalà, Ricard Font, Bernat Suárez, Pol Rabault, Jean Lehmkuhl, Oriol Rodriguez, Ivette |
| contents | This paper presents a deep reinforcement learning (DRL) framework for active flow control (AFC) to reduce drag in aerodynamic bodies. Tested on a 3D cylinder at Re = 100, the DRL approach achieved a 9.32% drag reduction and a 78.4% decrease in lift oscillations by learning advanced actuation strategies. The methodology integrates a CFD solver with a DRL model using an in-memory database for efficient communication between |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05536 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Active Flow Control Strategies Through Deep Reinforcement Learning Montalà, Ricard Font, Bernat Suárez, Pol Rabault, Jean Lehmkuhl, Oriol Rodriguez, Ivette Machine Learning Fluid Dynamics This paper presents a deep reinforcement learning (DRL) framework for active flow control (AFC) to reduce drag in aerodynamic bodies. Tested on a 3D cylinder at Re = 100, the DRL approach achieved a 9.32% drag reduction and a 78.4% decrease in lift oscillations by learning advanced actuation strategies. The methodology integrates a CFD solver with a DRL model using an in-memory database for efficient communication between |
| title | Towards Active Flow Control Strategies Through Deep Reinforcement Learning |
| topic | Machine Learning Fluid Dynamics |
| url | https://arxiv.org/abs/2411.05536 |