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Hybrid data-driven closure strategies for reduced order modeling

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

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.

Suggested Citation

  • Ivagnes, Anna & Stabile, Giovanni & Mola, Andrea & Iliescu, Traian & Rozza, Gianluigi, 2023. "Hybrid data-driven closure strategies for reduced order modeling," Applied Mathematics and Computation, Elsevier, vol. 448(C).
    • Handle: RePEc:eee:apmaco:v:448:y:2023:i:c:s0096300323000899
    DOI: 10.1016/j.amc.2023.127920
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    References listed on IDEAS

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    1. Karasözen, Bülent & Yıldız, Süleyman & Uzunca, Murat, 2022. "Intrusive and data-driven reduced order modelling of the rotating thermal shallow water equation," Applied Mathematics and Computation, Elsevier, vol. 421(C).
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    Cited by:

    1. Liu, Zongtuan & Dong, Gang & Gui, Yunmiao, 2023. "Data-driven emergency evacuation decision for cruise ports under COVID-19: An improved genetic algorithm and simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).

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