Model order reduction of transport-dominated systems with rotations using shifted proper orthogonal decomposition and artificial neural networks

0578913 - ÚT 2024 CZ eng C - Konferenční příspěvek (zahraniční konf.)
Kovárnová, A. - Isoz, Martin
Model order reduction of transport-dominated systems with rotations using shifted proper orthogonal decomposition and artificial neural networks.
SNA'23 Seminar on Numerical Analysis. Ostrava: Institute of Geonics of the CAS, 2023 - (Starý, J.; Sysala, S.; Sysalová, D.), s. 35-38. ISBN 978-80-86407-85-2.
[Seminar on Numerical Analysis. Ostrava (CZ), 23.01.2023-27.01.2023]
Institucionální podpora: RVO:61388998
Klíčová slova: model order reduction * CFD * shifted POD
Obor OECD: Applied mathematics
https://www.ugn.cas.cz/event/2023/sna/files/sna23-sbornik.pdf

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.
Trvalý link: https://hdl.handle.net/11104/0350354