Work

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

Year: 2025

Type: article

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.

Source: Computers & Fluids

DOI: 10.1016/j.compfluid.2025.106911

Authors: T. Van Gastelen, W. Edeling, B. Sanderse

Institutions: Centrum Wiskunde & Informatica, Eindhoven University of Technology

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,700

Funders: Technische Universiteit Eindhoven, Nederlandse Organisatie voor Wetenschappelijk Onderzoek