4.7 Article

Data-driven variational multiscale reduced order models

Journal

Publisher

ELSEVIER SCIENCE SA
DOI: 10.1016/j.cma.2020.113470

Keywords

Reduced order model; Variational multiscale; Data-driven model

Funding

  1. National Science Foundation [DMS-2012253, BMMB-1929731]
  2. U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing (ASCR) [DE-SC0019290]
  3. U.S. Department of Energy (DOE) [DE-SC0019290] Funding Source: U.S. Department of Energy (DOE)

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A new data-driven reduced order model (ROM) framework is proposed in this study, which is based on the hierarchical structure of the variational multiscale (VMS) methodology and utilizes data to enhance ROM accuracy. By separating scales, identifying closure terms, and modeling them with available data, the new framework shows significantly higher accuracy compared to standard ROMs.
We propose a new data-driven reduced order model (ROM) framework that centers around the hierarchical structure of the variational multiscale (VMS) methodology and utilizes data to increase the ROM accuracy at a modest computational cost. The VMS methodology is a natural fit for the hierarchical structure of the ROM basis: In the first step, we use the ROM projection to separate the scales into three categories: (i) resolved large scales, (ii) resolved small scales, and (iii) unresolved scales. In the second step, we explicitly identify the VMS-ROM closure terms, i.e., the terms representing the interactions among the three types of scales. In the third step, we use available data to model the VMS-ROM closure terms. Thus, instead of phenomenological models used in VMS for standard numerical discretizations (e.g., eddy viscosity models), we utilize available data to construct new structural VMS-ROM closure models. Specifically, we build ROM operators (vectors, matrices, and tensors) that are closest to the true ROM closure terms evaluated with the available data. We test the new data-driven VMS-ROM in the numerical simulation of four test cases: (i) the 1D Burgers equation with viscosity coefficient nu = 10(-3); (ii) a 2D flow past a circular cylinder at Reynolds numbers Re = 100, Re = 500, and Re = 1000; (iii) the quasi-geostrophic equations at Reynolds number Re = 450 and Rossby number Ro = 0.0036; and (iv) a 2D flow over a backward facing step at Reynolds number Re = 1000. The numerical results show that the data-driven VMS-ROM is significantly more accurate than standard ROMs. (C) 2020 Elsevier B.V. All rights reserved.

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