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DC poleHodnotaJazyk
dc.contributor.authorHeidler, Václav
dc.contributor.authorPecka, Aleš
dc.contributor.authorBublík, Ondřej
dc.contributor.authorVimmr, Jan
dc.date.accessioned2023-06-26T10:00:08Z-
dc.date.available2023-06-26T10:00:08Z-
dc.date.issued2022
dc.identifier.citationHEIDLER, V. PECKA, A. BUBLÍK, O. VIMMR, J. NEURAL NETWORK PREDICTION OF THE FLOW FIELD IN A PERIODIC DOMAIN WITH HYPERNETWORK PARAMETRIZATION. In ECCOMAS conference proceeding. Barcelona: Scipedia S.L., 2022. s. 1-10. ISBN: neuvedeno , ISSN: 2696-6999cs
dc.identifier.issn2696-6999
dc.identifier.uri2-s2.0-85146928981
dc.identifier.urihttp://hdl.handle.net/11025/52986
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherScipedia S.L.en
dc.relation.ispartofseriesECCOMAS conference proceedingen
dc.rights© Scipedia S.L.en
dc.titleNeural network prediction of the flow field in a periodic domain with hypernetwork parametrizationen
dc.typekonferenční příspěvekcs
dc.typeConferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThis paper is concerned with fast flow field prediction in a blade cascade for variable blade shapes as well as variable Reynolds number using the machine-learning architecture called convolutional neural network. To generate flow field for a specific Reynolds number, an encoder-decoder convolutional neural network, also called U-Net, is used. The values 500, 1000 and 1500 of the Reynolds number are chosen as the training set. Three U-Nets were trained on CFD results for 100 blade profiles, each U-Net for a different Reynolds number. In order to get a prediction for variable Reynolds number, a so-called hypernetwork in employed. The hypernetwork essentially interpolates between the two trained U-Nets. The architecture of the hypernetwork is fully-connected feedforward neural network with one input neuron correspond-ing to the Reynolds number, one hidden layer and the output layer corresponds to the weights for the interpolated U-Net. The concept of the hypernetwork-based parametrization is tested on a problem of compressible fluid flow through a blade cascade with three unseen blade profiles and unseen Reynolds number.en
dc.subject.translatedblade cascadeen
dc.subject.translatedcompressible fluid flowen
dc.subject.translatedconvolution neural networken
dc.subject.translatedhypernetworken
dc.subject.translatedReynolds parametrizationen
dc.subject.translatedU-Neten
dc.identifier.doi10.23967/eccomas.2022.192
dc.type.statusPeer-revieweden
dc.identifier.obd43939209
dc.project.IDGA21-31457S/Použití neuronových sítí pro rychlou predikci proudového pole v úlohách interakce tekutiny s tělesemcs
Vyskytuje se v kolekcích:Konferenční příspěvky / Conference papers (NTIS)
Konferenční příspěvky / Conference Papers (KME)
OBD

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