• DOI:10.48550/arXiv.2207.10531
• Corpus ID: 250921282

Hybrid Data-Driven Closure Strategies for Reduced Order Modeling

@article{Ivagnes2022HybridDC,
  title={Hybrid Data-Driven Closure Strategies for Reduced Order Modeling},
  author={Anna Ivagnes and Giovanni Stabile and Andrea Mola and Traian Iliescu and Gianluigi Rozza},
  journal={Appl. Math. Comput.},
  year={2022},
  volume={448},
  pages={127920}
}

• Anna Ivagnes, G. Stabile, +2 authors G. Rozza
• Published 21 July 2022
• Engineering
• Appl. Math. Comput.

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=50000. 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.