Deep reinforcement learning for active flow control in a turbulent separation bubble Article Swipe
Bernat Font
,
Francisco Alcántara-Ávila
,
Jean Rabault
,
Ricardo Vinuesa
,
O. Lehmkuhl
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41467-025-56408-6
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41467-025-56408-6
The control efficacy of deep reinforcement learning (DRL) compared with classical periodic forcing is numerically assessed for a turbulent separation bubble (TSB). We show that a control strategy learned on a coarse grid works on a fine grid as long as the coarse grid captures main flow features. This allows to significantly reduce the computational cost of DRL training in a turbulent-flow environment. On the fine grid, the periodic control is able to reduce the TSB area by 6.8%, while the DRL-based control achieves 9.0% reduction. Furthermore, the DRL agent provides a smoother control strategy while conserving momentum instantaneously. The physical analysis of the DRL control strategy reveals the production of large-scale counter-rotating vortices by adjacent actuator pairs. It is shown that the DRL agent acts on a wide range of frequencies to sustain these vortices in time. Last, we also introduce our computational fluid dynamics and DRL open-source framework suited for the next generation of exascale computing machines.
Related Topics
Concepts
Grid
Turbulence
Momentum (technical analysis)
Forcing (mathematics)
Reinforcement learning
Vortex
Bubble
Computer science
Flow (mathematics)
Flow control (data)
Reduction (mathematics)
Control (management)
Mechanics
Control theory (sociology)
Artificial intelligence
Physics
Mathematics
Parallel computing
Geometry
Atmospheric sciences
Computer network
Finance
Economics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41467-025-56408-6
- OA Status
- gold
- Cited By
- 15
- References
- 84
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407203234
All OpenAlex metadata
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https://openalex.org/W4407203234Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41467-025-56408-6Digital Object Identifier
- Title
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Deep reinforcement learning for active flow control in a turbulent separation bubbleWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-06Full publication date if available
- Authors
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Bernat Font, Francisco Alcántara-Ávila, Jean Rabault, Ricardo Vinuesa, O. LehmkuhlList of authors in order
- Landing page
-
https://doi.org/10.1038/s41467-025-56408-6Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1038/s41467-025-56408-6Direct OA link when available
- Concepts
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Grid, Turbulence, Momentum (technical analysis), Forcing (mathematics), Reinforcement learning, Vortex, Bubble, Computer science, Flow (mathematics), Flow control (data), Reduction (mathematics), Control (management), Mechanics, Control theory (sociology), Artificial intelligence, Physics, Mathematics, Parallel computing, Geometry, Atmospheric sciences, Computer network, Finance, EconomicsTop concepts (fields/topics) attached by OpenAlex
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15Total citation count in OpenAlex
- Citations by year (recent)
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2025: 15Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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