Original title: Model order reduction for particle-laden flows: systems with rotations and discrete transport operators
Authors: Kovárnová, A. ; Isoz, Martin
Document type: Papers
Conference/Event: Topical Problems of Fluid Mechanics 2023, Prague (CZ), 20230222
Year: 2023
Language: eng
Abstract: In the present work, we concentrate on particle-laden flows as an example of industry-relevant transport-dominated systems. Our previously-developed framework for data-driven model order reduction (MOR) of such systems, the shifted proper orthogonal decomposition with interpolation via artificial neural networks, is further extended by improving the handling of general transport operators. First, even with intrusive MOR approaches, the underlying numerical solvers can provide only discrete realizations of transports linked to the movement of individual particles in the system. On the other hand, our MOR methodology requires continuous transport operators. Thus, the original framework was extended by the possibility to reconstruct continuous approximations of known discrete transports via another artificial neural network. Second, the treatment of rotation-comprising transports was significantly improved.
Keywords: artificial neural networks; CFD-DEM; model order reduction; Open- FOAM; shifted POD
Project no.: TM04000048
Funding provider: GA TA ČR
Host item entry: Topical Problems of Fluid Mechanics 2023, ISBN 978-80-87012-83-3, ISSN 2336-5781

Institution: Institute of Thermomechanics AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://www2.it.cas.cz/fm2015/im/admin/showfile/data/my/Papers/2023/14-TPFM2023.pdf
Original record: https://hdl.handle.net/11104/0349998

Permalink: http://www.nusl.cz/ntk/nusl-538535


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