Hybrid data-driven closure strategies for re duce d order modeling

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 clo-sures, 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 , 0 0 0 . 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.(c) 2023 Elsevier Inc. All rights reserved.

Automated summary

In this paper, hybrid data-driven ROM closures for fluid flows are proposed. These closures combine two fundamentally different strategies: purely data-driven closures and physically based, eddy viscosity data-driven closures. The hybrid model is applied to investigate a two-dimensional flow past a circular cylinder at Re = 50,000, and the numerical results show that it is more accurate than both the purely data-driven ROM and the eddy viscosity ROM.