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DC poleHodnotaJazyk
dc.contributor.authorKovář, Patrik
dc.contributor.authorFürst, Jiří
dc.date.accessioned2024-02-06T14:18:23Z
dc.date.available2024-02-06T14:18:23Z
dc.date.issued2023
dc.identifier.citationApplied and Computational Mechanics. 2023, vol. 17, no. 2, p. 153-168.en
dc.identifier.issn1802-680X (Print)
dc.identifier.issn2336-1182 (Online)
dc.identifier.urihttp://hdl.handle.net/11025/55269
dc.format16 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherUniversity of West Bohemiaen
dc.rights© 2017 University of West Bohemia. All rights reserved.en
dc.subjectkompresorová kaskádacs
dc.subjectempirické korelacecs
dc.subjectstrojové učenícs
dc.subjectneuronové sítě vyššího řáducs
dc.titleCompressor cascade correlations modelling at design points using artificial neural networksen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedIn recent years, the flow analysis by means of computational fluid dynamics (CFD) has become a useful design and optimization tool. Unfortunately, despite advances in the computational power, numerical simulations are still very time consuming. Thus, empirical correlation models keep their importance as a tool for early stages of axial compressor design and for prediction of basic performance parameters. These correlations were developed based on experimental data obtained from 2D measurements performed on cases of classical airfoils such as the NACA 65-series or C.4 profiles. There is insufficient amount of experimental data for other families of airfoils, but CFD simulations can be used instead and their results correlated using artificial neural networks (ANN), as described in this work. Unlike the classical deep learning approach using perceptrons, this work presents neural networks employing higher order neural units.en
dc.subject.translatedcompressor cascadeen
dc.subject.translatedempirical correlationsen
dc.subject.translatedmachine learningen
dc.subject.translatedhigher order neural networksen
dc.identifier.doihttps://doi.org/10.24132/acm.2023.828
dc.type.statusPeer revieweden
Vyskytuje se v kolekcích:Volume 17, number 2 (2023)
Volume 17, number 2 (2023)

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  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
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  4. Applied and Computational Mechanics
  5. Volume 17 (2023)
  6. Volume 17, number 2 (2023)