Model order reduction for particle-laden flows: systems with rotations and discrete transport operators

0573854 - ÚT 2024 CZ eng C - Konferenční příspěvek (zahraniční konf.)
Kovárnová, A. - Isoz, Martin
Model order reduction for particle-laden flows: systems with rotations and discrete transport operators.
Topical Problems of Fluid Mechanics 2023. Praha: Ústav termomechaniky AV ČR, v. v. i., 2023 - (Šimurda, D.; Bodnár, T.), s. 96-103. ISBN 978-80-87012-83-3. ISSN 2336-5781.
[Topical Problems of Fluid Mechanics 2023. Prague (CZ), 22.02.2023-24.02.2023]
Grant CEP: GA TA ČR(CZ) TM04000048
Institucionální podpora: RVO:61388998
Klíčová slova: model order reduction * shifted POD * artificial neural networks * CFD-DEM * Open- FOAM
Obor OECD: Applied mechanics
http://www2.it.cas.cz/fm2015/im/admin/showfile/data/my/Papers/2023/14-TPFM2023.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/11104/0349998