Název: Model order reduction of transport-dominated systems with rotations using shifted proper orthogonal decomposition and artificial neural networks
Autoři: Kovárnová, A. ; Isoz, Martin
Typ dokumentu: Příspěvky z konference
Konference/Akce: Seminar on Numerical Analysis, Ostrava (CZ), 20230123
Rok: 2023
Jazyk: eng
Abstrakt: 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.
Klíčová slova: CFD; model order reduction; shifted POD
Zdrojový dokument: SNA'23 Seminar on Numerical Analysis, ISBN 978-80-86407-85-2

Instituce: Ústav termomechaniky AV ČR (web)
Informace o dostupnosti dokumentu: Dokument je dostupný na externích webových stránkách.
Externí umístění souboru: https://www.ugn.cas.cz/event/2023/sna/files/sna23-sbornik.pdf
Původní záznam: https://hdl.handle.net/11104/0350354

Trvalý odkaz NUŠL: http://www.nusl.cz/ntk/nusl-539231


Záznam je zařazen do těchto sbírek:
Věda a výzkum > AV ČR > Ústav termomechaniky
Konferenční materiály > Příspěvky z konference