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• W3090502885 abstract "Significance Reinforcement learning (RL) has been applied effectively in games and robotic manipulation. We demonstrate the effectiveness of RL in experimental fluid mechanics by applying it to reduce the drag of circular cylinders in turbulent flow, a canonical fluid–structure interaction problem. Although physics agnostic, RL managed to reduce the drag by <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML overflow=scroll> <mml:mn>30</mml:mn> <mml:mi>%</mml:mi> </mml:math> or reach another specified optimum point very quickly. Following this discovery, we used high-fidelity simulations to probe the underlying physical mechanisms so that the discovered control techniques can be generalized to other similar flow problems. More broadly, RL-guided active control can lead to efficient exploration of additional flow-control strategies in experimental fluid mechanics, potentially paving the way for accelerating scientific discovery and different designs in flow-related engineering problems." @default.
• W3090502885 created "2020-10-08" @default.
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• W3090502885 date "2020-10-05" @default.
• W3090502885 modified "2023-10-16" @default.
• W3090502885 title "Reinforcement learning for bluff body active flow control in experiments and simulations" @default.
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• W3090502885 doi "https://doi.org/10.1073/pnas.2004939117" @default.
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