Modeling advection-dominated flows with space-local reduced-order models

• Novel space-local POD approach for advection-dominated flows. • Local basis improves generalizability and sparsity of ROMs. • Overlapping subdomains ensure smooth solutions. • ROMs conserve energy and allow larger simulation time steps Reduced-order models (ROMs) are often used to accelerate the simulation of large physical systems. However, traditional ROM techniques, such as proper orthogonal decomposition (POD)-based methods, often struggle with advection-dominated flows due to the slow decay of singular values. This results in high computational costs and potential instabilities. This paper proposes a novel approach using space-local POD to address the challenges arising from the slow singular value decay. Instead of global basis functions, our method employs local basis functions that are applied across the domain, analogous to the finite element method, but with a data-driven basis. By dividing the domain into subdomains and applying the space-local POD, we obtain a sparse representation that generalizes better outside the training regime. This allows the use of a larger number of basis functions compared to standard POD, without prohibitive computational costs. To ensure smoothness across subdomain boundaries, we introduce overlapping subdomains inspired by the partition of unity method. Our approach is validated through simulations of the 1D and 2D advection equation. We demonstrate that using our space-local approach, we obtain a ROM that generalizes better to flow conditions not included in the training data. In addition, we show that the constructed ROM inherits the energy conservation and non-linear stability properties from the full-order model. Finally, we find that using a space-local ROM allows for larger time steps.

Authors

Institutions

Publication Details

Journal
Computers & Fluids
Published
2025-11-20
DOI
https://doi.org/10.1016/j.compfluid.2025.106911
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Modeling advection-dominated flows with space-local reduced-order models

Benjamin Sanderse, Wouter Edeling, Toby van Gastelen
Computers & Fluids
Model Reduction and Neural Networks
article

Modeling advection-dominated flows with space-local reduced-order models

Benjamin Sanderse, Wouter Edeling, Toby van Gastelen
article en

Abstract

• Novel space-local POD approach for advection-dominated flows. • Local basis improves generalizability and sparsity of ROMs. • Overlapping subdomains ensure smooth solutions. • ROMs conserve energy and allow larger simulation time steps Reduced-order models (ROMs) are often used to accelerate the simulation of large physical systems. However, traditional ROM techniques, such as proper orthogonal decomposition (POD)-based methods, often struggle with advection-dominated flows due to the slow decay of singular values. This results in high computational costs and potential instabilities. This paper proposes a novel approach using space-local POD to address the challenges arising from the slow singular value decay. Instead of global basis functions, our method employs local basis functions that are applied across the domain, analogous to the finite element method, but with a data-driven basis. By dividing the domain into subdomains and applying the space-local POD, we obtain a sparse representation that generalizes better outside the training regime. This allows the use of a larger number of basis functions compared to standard POD, without prohibitive computational costs. To ensure smoothness across subdomain boundaries, we introduce overlapping subdomains inspired by the partition of unity method. Our approach is validated through simulations of the 1D and 2D advection equation. We demonstrate that using our space-local approach, we obtain a ROM that generalizes better to flow conditions not included in the training data. In addition, we show that the constructed ROM inherits the energy conservation and non-linear stability properties from the full-order model. Finally, we find that using a space-local ROM allows for larger time steps.

Computers & FluidsVol. 305
Centrum Wiskunde & Informatica (NL), Eindhoven University of Technology (NL)
Openalex Percentile: Top 72%
Model Reduction and Neural Networks
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Modeling advection-dominated flows with space-local reduced-order models — Benjamin Sanderse, Wouter Edeling, et al. · Computers & Fluids (2025) | TGRS Research Map | TGRS