Hybrid data-driven closure strategies for reduced order modeling

Anna Ivagnes, Giovanni Stabile, Andrea Mola, Traian Iliescu and Gianluigi Rozza

Applied Mathematics and Computation, 2023, vol. 448, issue C

Abstract: In this paper, we propose hybrid data-driven ROM closures for fluid flows. These new ROM closures combine two fundamentally different strategies: (i) purely data-driven ROM closures, both for the velocity and the pressure; and (ii) physically based, eddy viscosity data-driven closures, which model the energy transfer in the system. The first strategy consists in the addition of closure/correction terms to the governing equations, which are built from the available data. The second strategy includes turbulence modeling by adding eddy viscosity terms, which are determined by using machine learning techniques. The two strategies are combined for the first time in this paper to investigate a two-dimensional flow past a circular cylinder at Re=50,000. Our numerical results show that the hybrid data-driven ROM is more accurate than both the purely data-driven ROM and the eddy viscosity ROM.

Keywords: Model order reduction; Computational fluid dynamics; Stabilization; Supremizers; Data-driven approaches (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:448:y:2023:i:c:s0096300323000899

DOI: 10.1016/j.amc.2023.127920

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