Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917943500079104 |
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| author | Suárez, P. Alcántara-Ávila, F. Rabault, J. Miró, A. Font, B. Lehmkuhl, O. Vinuesa, R. |
| author_facet | Suárez, P. Alcántara-Ávila, F. Rabault, J. Miró, A. Font, B. Lehmkuhl, O. Vinuesa, R. |
| contents | Designing active-flow-control (AFC) strategies for three-dimensional (3D) bluff bodies is a challenging task with critical industrial implications. In this study we explore the potential of discovering novel control strategies for drag reduction using deep reinforcement learning. We introduce a high-dimensional AFC setup on a 3D cylinder, considering Reynolds numbers ($Re_D$) from $100$ to $400$, which is a range including the transition to 3D wake instabilities. The setup involves multiple zero-net-mass-flux jets positioned on the top and bottom surfaces, aligned into two slots. The method relies on coupling the computational-fluid-dynamics solver with a multi-agent reinforcement-learning (MARL) framework based on the proximal-policy-optimization algorithm. MARL offers several advantages: it exploits local invariance, adaptable control across geometries, facilitates transfer learning and cross-application of agents, and results in a significant training speedup. \rev{For instance, our results demonstrate $16\%$ drag reduction for $Re_D=400$, outperforming classical periodic control, which yields up to $6\%$ reduction.} A proper-orthogonal-decomposition (POD) analysis at $Re_D=400$ reveals that the DRL control results in a stable wake structure with longer recirculation bubble. To the authors' knowledge, the present MARL-based framework represents the first time where training is conducted in 3D cylinders. This breakthrough paves the way for conducting AFC on progressively more complex turbulent-flow configurations. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_17210 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning Suárez, P. Alcántara-Ávila, F. Rabault, J. Miró, A. Font, B. Lehmkuhl, O. Vinuesa, R. Fluid Dynamics Designing active-flow-control (AFC) strategies for three-dimensional (3D) bluff bodies is a challenging task with critical industrial implications. In this study we explore the potential of discovering novel control strategies for drag reduction using deep reinforcement learning. We introduce a high-dimensional AFC setup on a 3D cylinder, considering Reynolds numbers ($Re_D$) from $100$ to $400$, which is a range including the transition to 3D wake instabilities. The setup involves multiple zero-net-mass-flux jets positioned on the top and bottom surfaces, aligned into two slots. The method relies on coupling the computational-fluid-dynamics solver with a multi-agent reinforcement-learning (MARL) framework based on the proximal-policy-optimization algorithm. MARL offers several advantages: it exploits local invariance, adaptable control across geometries, facilitates transfer learning and cross-application of agents, and results in a significant training speedup. \rev{For instance, our results demonstrate $16\%$ drag reduction for $Re_D=400$, outperforming classical periodic control, which yields up to $6\%$ reduction.} A proper-orthogonal-decomposition (POD) analysis at $Re_D=400$ reveals that the DRL control results in a stable wake structure with longer recirculation bubble. To the authors' knowledge, the present MARL-based framework represents the first time where training is conducted in 3D cylinders. This breakthrough paves the way for conducting AFC on progressively more complex turbulent-flow configurations. |
| title | Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2405.17210 |