Název: Shifted proper orthogonal decomposition and artificial neural networks for time-continuous reduced order models of transport-dominated systems
Autoři: Kovárnová, A. ; Krah, P. ; Reiss, J. ; Isoz, Martin
Typ dokumentu: Příspěvky z konference
Konference/Akce: Topical problems of fluid mechanics 2022, Praha (CZ), 20220216
Rok: 2022
Jazyk: eng
Abstrakt: Transport-dominated systems are pervasive in both industrial and scientific applications. However, they provide a challenge for common mode-based model order reduction (MOR) approaches, as they often require a large number of linear modes to obtain a sufficiently accurate reduced order model (ROM). In this work, we utilize the shifted proper orthogonal decomposition (sPOD), a methodology tailored for MOR of transport-dominated systems, and combine it with an interpolation based on artificial neural networks (ANN) to obtain a time-continuous ROM usable in engineering practice. The resulting MOR framework is purely data-driven, i.e., it does not require any information on the full order model (FOM) structure, which extends its applicability. On the other hand, compared to the standard projection-based approaches to MOR, the dimensionality reduction utilizing sPOD and ANN is significantly more computationally expensive since it requires a solution of high-dimensional optimization problems.
Klíčová slova: CFD-DEM; model order reduction; OpenFOAM; shifted POD
Číslo projektu: EF15_003/0000493
Poskytovatel projektu: GA MŠk
Zdrojový dokument: Topical Problems of Fluid Mechanics 2022, ISBN 978-80-87012-77-2, ISSN 2336-5781

Instituce: Ústav termomechaniky AV ČR (web)
Informace o dostupnosti dokumentu: Dokument je dostupný na externích webových stránkách.
Externí umístění souboru: http://www2.it.cas.cz/fm2015/im/admin/showfile/data/my/Papers/2022/16-TPFM2022.pdf
Původní záznam: https://hdl.handle.net/11104/0334779

Trvalý odkaz NUŠL: http://www.nusl.cz/ntk/nusl-510652


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