dc.date.accessioned | 2019-04-09T07:19:58Z | |
dc.date.available | 2019-08-20T22:47:08Z | |
dc.date.created | 2019-02-20T19:37:14Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Rabault, Jean Kuchta, Miroslav Jensen, Atle Reglade, Ulysse Cerardi, Nicolas . Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control. Journal of Fluid Mechanics. 2019 | |
dc.identifier.uri | http://hdl.handle.net/10852/67610 | |
dc.description.abstract | We present the first application of an Artificial Neural Network trained through a Deep Reinforcement Learning agent to perform active flow control. It is shown that, in a 2D simulation of the Kármán vortex street at moderate Reynolds number (Re = 100), our Artificial Neural Network is able to learn an active control strategy from experimenting with the mass flow rates of two jets on the sides of a cylinder. By interacting with the unsteady wake, the Artificial Neural Network successfully stabilizes the vortex alley and reduces drag by about 8 %. This is performed while using small mass flow rates for the actuation, on the order of 0.5 % of the mass flow rate intersecting the cylinder cross section once a new pseudo-periodic shedding regime is found. This opens the way to a new class of methods for performing active flow control. | en_US |
dc.language | EN | |
dc.publisher | Cambridge University Press | |
dc.title | Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control | en_US |
dc.type | Journal article | en_US |
dc.creator.author | Rabault, Jean | |
dc.creator.author | Kuchta, Miroslav | |
dc.creator.author | Jensen, Atle | |
dc.creator.author | Reglade, Ulysse | |
dc.creator.author | Cerardi, Nicolas | |
cristin.unitcode | 185,15,13,15 | |
cristin.unitname | Mekanikk | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 2 | |
dc.identifier.cristin | 1679366 | |
dc.identifier.bibliographiccitation | info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal of Fluid Mechanics&rft.volume=&rft.spage=&rft.date=2019 | |
dc.identifier.jtitle | Journal of Fluid Mechanics | |
dc.identifier.doi | 10.1017/jfm.2019.62 | |
dc.identifier.urn | URN:NBN:no-70794 | |
dc.type.document | Tidsskriftartikkel | en_US |
dc.type.peerreviewed | Peer reviewed | |
dc.source.issn | 0022-1120 | |
dc.identifier.fulltext | Fulltext https://www.duo.uio.no/bitstream/handle/10852/67610/1/template.pdf | |
dc.type.version | AcceptedVersion | |