Deep reinforcement learning for active flow control in a turbulent separation bubble
Abstract 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 fea...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56408-6 |
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