State-dependent convergence of Galerkin-based reduced-order models for Couette flow

In this study, we explore the effect of basis functions on the performance and convergence of the Galerkin projection-based reduced-order model (ROM) in the minimal flow unit of Couette flow. POD (proper orthogonal decomposition) modes obtained from direct numerical simulation, and controllability and balanced truncation modes from the linearised Navier–Stokes equations (LNSEs) with different base flows (laminar base flow and turbulent mean flow) and an eddy viscosity model are considered. In the neighbourhood of the laminar base state, the ROMs based on the modes from the LNSEs with the laminar base flow and molecular viscosity are found to perform very well as they are able to capture the linear stability of the laminar base flow for each plane Fourier component only with a single degree of freedom. In particular, the ROM based on the balanced truncation modes models the linear dynamics involving transient growth around the laminar base flow most effectively, consistent with previous studies. In contrast, for turbulent state, the ROM based on POD modes is found to reproduce its statistics and coherent dynamics most effectively. The ROMs based on the modes from the LNSE with turbulent mean flow and an eddy viscosity model performs better compared with any other ROMs using the modes from the LNSE. These observations suggest that the performance and convergence of a ROM are highly state-dependent. In particular, this state dependence is strongly correlated with the information and dynamics that each of the basis functions contain. Discussions supporting these observations are also provided in relation to the flow physics involved and the form of coherent structures in Couette flow.

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Publication Details

Journal
Journal of Fluid Mechanics
Published
2026-03-03
DOI
https://doi.org/10.1017/jfm.2026.11263
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

State-dependent convergence of Galerkin-based reduced-order models for Couette flow

Igor A. Maia, Yongyun Hwang, Zhi Zong, André V. G. Cavalieri
Journal of Fluid Mechanics
Model Reduction and Neural Networks
article

State-dependent convergence of Galerkin-based reduced-order models for Couette flow

Igor A. Maia, Yongyun Hwang, Zhi Zong, André V. G. Cavalieri
article en

Abstract

In this study, we explore the effect of basis functions on the performance and convergence of the Galerkin projection-based reduced-order model (ROM) in the minimal flow unit of Couette flow. POD (proper orthogonal decomposition) modes obtained from direct numerical simulation, and controllability and balanced truncation modes from the linearised Navier–Stokes equations (LNSEs) with different base flows (laminar base flow and turbulent mean flow) and an eddy viscosity model are considered. In the neighbourhood of the laminar base state, the ROMs based on the modes from the LNSEs with the laminar base flow and molecular viscosity are found to perform very well as they are able to capture the linear stability of the laminar base flow for each plane Fourier component only with a single degree of freedom. In particular, the ROM based on the balanced truncation modes models the linear dynamics involving transient growth around the laminar base flow most effectively, consistent with previous studies. In contrast, for turbulent state, the ROM based on POD modes is found to reproduce its statistics and coherent dynamics most effectively. The ROMs based on the modes from the LNSE with turbulent mean flow and an eddy viscosity model performs better compared with any other ROMs using the modes from the LNSE. These observations suggest that the performance and convergence of a ROM are highly state-dependent. In particular, this state dependence is strongly correlated with the information and dynamics that each of the basis functions contain. Discussions supporting these observations are also provided in relation to the flow physics involved and the form of coherent structures in Couette flow.

Journal of Fluid MechanicsVol. 1030
Instituto Tecnológico de Aeronáutica (BR), Imperial College London (GB)
Instituto Tecnológico de Aeronáutica, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Engineering and Physical Sciences Research Council, Division of Graduate Education, Division of Mathematical Sciences
Openalex Percentile: Top 99%
Model Reduction and Neural Networks
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