Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning Article Swipe
P. M. Suárez
,
Francisco Alcántara-Ávila
,
J. Rabault
,
Arnau Miró
,
Bernat Font
,
O. Lehmkuhl
,
R. Vinuesa
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2405.17210
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2405.17210
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.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2405.17210
- https://arxiv.org/pdf/2405.17210
- OA Status
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- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4399144144Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2405.17210Digital Object Identifier
- Title
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Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-05-27Full publication date if available
- Authors
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P. M. Suárez, Francisco Alcántara-Ávila, J. Rabault, Arnau Miró, Bernat Font, O. Lehmkuhl, R. VinuesaList of authors in order
- Landing page
-
https://arxiv.org/abs/2405.17210Publisher landing page
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https://arxiv.org/pdf/2405.17210Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2405.17210Direct OA link when available
- Concepts
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Turbulence, Reinforcement learning, Reinforcement, Flow (mathematics), Flow control (data), Computer science, Control (management), Mechanics, Physics, Artificial intelligence, Engineering, Structural engineering, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.numbers | 48 |
| abstract_inverted_index.present | 173 |
| abstract_inverted_index.results | 121, 130, 158 |
| abstract_inverted_index.reveals | 153 |
| abstract_inverted_index.several | 103 |
| abstract_inverted_index.($Re_D$) | 49 |
| abstract_inverted_index.Reynolds | 47 |
| abstract_inverted_index.\rev{For | 127 |
| abstract_inverted_index.analysis | 150 |
| abstract_inverted_index.authors' | 170 |
| abstract_inverted_index.control, | 140 |
| abstract_inverted_index.coupling | 86 |
| abstract_inverted_index.critical | 14 |
| abstract_inverted_index.exploits | 106 |
| abstract_inverted_index.involves | 67 |
| abstract_inverted_index.learning | 115 |
| abstract_inverted_index.multiple | 68 |
| abstract_inverted_index.periodic | 139 |
| abstract_inverted_index.speedup. | 126 |
| abstract_inverted_index.training | 125, 181 |
| abstract_inverted_index.transfer | 114 |
| abstract_inverted_index.Designing | 0 |
| abstract_inverted_index.adaptable | 109 |
| abstract_inverted_index.classical | 138 |
| abstract_inverted_index.conducted | 183 |
| abstract_inverted_index.cylinder, | 45 |
| abstract_inverted_index.framework | 95, 175 |
| abstract_inverted_index.including | 58 |
| abstract_inverted_index.instance, | 128 |
| abstract_inverted_index.introduce | 37 |
| abstract_inverted_index.learning. | 35 |
| abstract_inverted_index.potential | 23 |
| abstract_inverted_index.reduction | 31, 134 |
| abstract_inverted_index.structure | 163 |
| abstract_inverted_index.surfaces, | 77 |
| abstract_inverted_index.$Re_D=400$ | 152 |
| abstract_inverted_index.MARL-based | 174 |
| abstract_inverted_index.algorithm. | 100 |
| abstract_inverted_index.conducting | 193 |
| abstract_inverted_index.cylinders. | 186 |
| abstract_inverted_index.industrial | 15 |
| abstract_inverted_index.knowledge, | 171 |
| abstract_inverted_index.positioned | 71 |
| abstract_inverted_index.represents | 176 |
| abstract_inverted_index.strategies | 3, 28 |
| abstract_inverted_index.transition | 60 |
| abstract_inverted_index.$Re_D=400$, | 136 |
| abstract_inverted_index.advantages: | 104 |
| abstract_inverted_index.challenging | 11 |
| abstract_inverted_index.considering | 46 |
| abstract_inverted_index.demonstrate | 131 |
| abstract_inverted_index.discovering | 25 |
| abstract_inverted_index.facilitates | 113 |
| abstract_inverted_index.geometries, | 112 |
| abstract_inverted_index.invariance, | 108 |
| abstract_inverted_index.multi-agent | 92 |
| abstract_inverted_index.reduction.} | 146 |
| abstract_inverted_index.significant | 124 |
| abstract_inverted_index.breakthrough | 188 |
| abstract_inverted_index.implications. | 16 |
| abstract_inverted_index.outperforming | 137 |
| abstract_inverted_index.progressively | 196 |
| abstract_inverted_index.recirculation | 166 |
| abstract_inverted_index.reinforcement | 34 |
| abstract_inverted_index.instabilities. | 64 |
| abstract_inverted_index.turbulent-flow | 199 |
| abstract_inverted_index.configurations. | 200 |
| abstract_inverted_index.high-dimensional | 39 |
| abstract_inverted_index.cross-application | 117 |
| abstract_inverted_index.three-dimensional | 5 |
| abstract_inverted_index.zero-net-mass-flux | 69 |
| abstract_inverted_index.active-flow-control | 1 |
| abstract_inverted_index.reinforcement-learning | 93 |
| abstract_inverted_index.computational-fluid-dynamics | 88 |
| abstract_inverted_index.proximal-policy-optimization | 99 |
| abstract_inverted_index.proper-orthogonal-decomposition | 148 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |