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dc.date.accessioned2020-08-12T18:13:10Z
dc.date.available2021-05-13T22:45:45Z
dc.date.created2020-05-13T17:25:03Z
dc.date.issued2020
dc.identifier.citationTang, Hongwei Rabault, Jean Kuhnle, Alexander Wang, Yan Wang, Tongguang . Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning. Physics of Fluids. 2020
dc.identifier.urihttp://hdl.handle.net/10852/78291
dc.description.abstractThis paper focuses on the active flow control of a computational fluid dynamics simulation over a range of Reynolds numbers using deep reinforcement learning (DRL). More precisely, the proximal policy optimization (PPO) method is used to control the mass flow rate of four synthetic jets symmetrically located on the upper and lower sides of a cylinder immersed in a two-dimensional flow domain. The learning environment supports four flow configurations with Reynolds numbers 100, 200, 300, and 400, respectively. A new smoothing interpolation function is proposed to help the PPO algorithm learn to set continuous actions, which is of great importance to effectively suppress problematic jumps in lift and allow a better convergence for the training process. It is shown that the DRL controller is able to significantly reduce the lift and drag fluctuations and actively reduce the drag by ∼5.7%, 21.6%, 32.7%, and 38.7%, at Re = 100, 200, 300, and 400, respectively. More importantly, it can also effectively reduce drag for any previously unseen value of the Reynolds number between 60 and 400. This highlights the generalization ability of deep neural networks and is an important milestone toward the development of practical applications of DRL to active flow control.
dc.languageEN
dc.publisherAmerican Institute of Physics
dc.titleRobust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning
dc.typeJournal article
dc.creator.authorTang, Hongwei
dc.creator.authorRabault, Jean
dc.creator.authorKuhnle, Alexander
dc.creator.authorWang, Yan
dc.creator.authorWang, Tongguang
cristin.unitcode185,15,13,15
cristin.unitnameMekanikk
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2
dc.identifier.cristin1810854
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Physics of Fluids&rft.volume=&rft.spage=&rft.date=2020
dc.identifier.jtitlePhysics of Fluids
dc.identifier.volume32
dc.identifier.issue5
dc.identifier.doihttps://doi.org/10.1063/5.0006492
dc.identifier.urnURN:NBN:no-81405
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn1070-6631
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/78291/1/5.0006492.pdf
dc.type.versionPublishedVersion
cristin.articleid053605


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