Consistency of the full and reduced order models for evolve-filter-relax regularization of convection-dominated, marginally-resolved flows

Maria Strazzullo, Michele Girfoglio, Francesco Ballarin, Traian Iliescu, Gianluigi Rozza

Risultato della ricerca: Contributo in rivistaArticolo in rivista

Abstract

Numerical stabilization is often used to eliminate (alleviate) the spurious oscillations generally produced by full order models (FOMs) in under-resolved or marginally-resolved simulations of convection-dominated flows. In this article, we investigate the role of numerical stabilization in reduced order models (ROMs) of marginally-resolved, convection-dominated incompressible flows. Specifically, we investigate the FOM-ROM consistency, that is, whether the numerical stabilization is beneficial both at the FOM and the ROM level. As a numerical stabilization strategy, we focus on the evolve-filter-relax (EFR) regularization algorithm, which centers around spatial filtering. To investigate the FOM-ROM consistency, we consider two ROM strategies: (i) the EFR-noEFR, in which the EFR stabilization is used at the FOM level, but not at the ROM level; and (ii) the EFR-EFR, in which the EFR stabilization is used both at the FOM and at the ROM level. We compare the EFR-noEFR with the EFR-EFR in the numerical simulation of a 2D incompressible flow past a circular cylinder in the convection-dominated, marginally-resolved regime. We also perform model reduction with respect to both time and Reynolds number. Our numerical investigation shows that the EFR-EFR is more accurate than the EFR-noEFR, which suggests that FOM-ROM consistency is beneficial in convection-dominated, marginally-resolved flows.
Lingua originaleEnglish
pagine (da-a)3148-3178
Numero di pagine31
RivistaInternational Journal for Numerical Methods in Engineering
Volume123
DOI
Stato di pubblicazionePubblicato - 2022

Keywords

  • Navier–Stokes equations
  • evolve‐filter‐relax stabilization
  • marginally‐resolved convection‐dominated flows
  • proper orthogonal decomposition
  • reduced order modeling

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