Stabilized POD reduced order models for convection-dominated incompressible flows

Abstract We present a comparative computational study of two stabilized Reduced Order Models (ROMs) for the simulation of convection-dominated incompressible flow (Reynolds number of the order of a few thousands). Representative solutions in the parameter space, which includes either time only or time and Reynolds number, are computed with a Finite Volume method and used to generate a reduced basis via Proper Orthogonal Decomposition (POD). Galerkin projection of the Navier–Stokes equations onto the reduced space is used to compute the ROM solution. To ensure computational efficiency, the number of POD modes is truncated and ROM solution accuracy is recovered through two stabilization methods: (i) adding a global constant artificial viscosity to the reduced dimensional model, and (ii) adding a different value of artificial viscosity for the different POD modes. We test the stabilized ROMs for fluid flow in an idealized medical device consisting of a conical convergent, a narrow throat, and a sudden expansion. Both stabilization methods significantly improve the ROM solution accuracy over a standard (non-stabilized) POD-Galerkin model.

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Publication Details

Journal
Computational and Applied Mathematics
Published
2025-07-25
DOI
https://doi.org/10.1007/s40314-025-03344-2
Citations
1
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
1.32

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article

Stabilized POD reduced order models for convection-dominated incompressible flows

Annalisa Quaini, Pierfrancesco Siena, Michele Girfoglio, Gianluigi Rozza
1 citations
Computational and Applied Mathematics
Model Reduction and Neural Networks
1.32
article

Stabilized POD reduced order models for convection-dominated incompressible flows

Annalisa Quaini, Pierfrancesco Siena, Michele Girfoglio, Gianluigi Rozza
article en
1 citations

Abstract

Abstract We present a comparative computational study of two stabilized Reduced Order Models (ROMs) for the simulation of convection-dominated incompressible flow (Reynolds number of the order of a few thousands). Representative solutions in the parameter space, which includes either time only or time and Reynolds number, are computed with a Finite Volume method and used to generate a reduced basis via Proper Orthogonal Decomposition (POD). Galerkin projection of the Navier–Stokes equations onto the reduced space is used to compute the ROM solution. To ensure computational efficiency, the number of POD modes is truncated and ROM solution accuracy is recovered through two stabilization methods: (i) adding a global constant artificial viscosity to the reduced dimensional model, and (ii) adding a different value of artificial viscosity for the different POD modes. We test the stabilized ROMs for fluid flow in an idealized medical device consisting of a conical convergent, a narrow throat, and a sudden expansion. Both stabilization methods significantly improve the ROM solution accuracy over a standard (non-stabilized) POD-Galerkin model.

Computational and Applied MathematicsVol. 44(7)
Scuola Internazionale Superiore di Studi Avanzati (IT), University of Houston (US), University of Palermo (IT)
National Science Foundation, Istituto Nazionale di Alta Matematica "Francesco Severi", Scuola Internazionale Superiore di Studi Avanzati, Gruppo Nazionale per il Calcolo Scientifico
Openalex Percentile: Top 17%
Model Reduction and Neural Networks
1.32
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Stabilized POD reduced order models for convection-dominated incompressible flows — Annalisa Quaini, Pierfrancesco Siena, et al. · Computational and Applied Mathematics (2025) | TGRS Research Map | TGRS