On the stability of projection-based model order reduction for convection-dominated laminar and turbulent flows
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Abstract (arXiv)
In the literature on projection-based nonlinear model order reduction for fluid dynamics problems, it is often claimed that due to modal truncation, a projection-based reduced-order model (PROM) does not resolve the dissipative regime of the turbulent energy cascade and therefore is numerically unstable. Efforts at addressing this claim have ranged from attempting to model the effects of the truncated modes to enriching the classical subspace of approximation in order to account for the truncated phenomena. This paper challenges this claim. Exploring the relationship between projection-based model order reduction and semi-discretization and using numerical evidence from three relevant flow problems, it argues in an orderly manner that the real culprit behind most if not all reported numerical instabilities of PROMs for turbulence and convection-dominated turbulent flow problems is the Galerkin framework that has been used for constructing the PROMs. The paper also shows that alternatively, a Petrov-Galerkin framework can be used to construct numerically stable PROMs for convection-dominated laminar as well as turbulent flow problems that are numerically stable and accurate, without resorting to additional closure models or tailoring of the subspace of approximation. It also shows that such alternative PROMs deliver significant speedup factors.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Jan 2020.
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Citing papers per year: 2023: 1; 2024: 3; 2025: 0; 2026: 2.
6 papers.
- 2023A Multifidelity deep operator network approach to closure for multiscale systemscited by 11
- 2024Reduced-order modeling of unsteady fluid flow using neural network ensemblescited by 4
- 2024The role of interface boundary conditions and sampling strategies for Schwarz-based coupling of projection-based reduced order modelscited by 4
- 2024Learning Latent Space Dynamics with Model-Form Uncertainties: A Stochastic Reduced-Order Modeling Approachcited by 2
- 2026KROM: Kernelized Reduced Order Modelingcited by 1
- 2026A Quadratic Order Reduction -- Gaussian Process Ordinary Differential Equation framework for the inference of Large Continuous Dynamical Systemscited by 0
- 2026A Quadratic Order Reduction -- Gaussian Process Ordinary Differential Equation framework for the inference of Large Continuous Dynamical Systems11 Jun 2026
- 2026KROM: Kernelized Reduced Order Modeling27 Feb 2026
- 2024The role of interface boundary conditions and sampling strategies for Schwarz-based coupling of projection-based reduced order models7 Oct 2024
- 2024Learning Latent Space Dynamics with Model-Form Uncertainties: A Stochastic Reduced-Order Modeling Approach30 Aug 2024
- 2024Reduced-order modeling of unsteady fluid flow using neural network ensembles8 Feb 2024
- 2023A Multifidelity deep operator network approach to closure for multiscale systems15 Mar 2023