Work

An efficient physics-based model order reduction for geometrically nonlinear solid mechanics

Year: 2025

Type: article

Abstract: Model order reduction simplifies detailed and complex Finite Element (FE) models by solving a reduced set of equations, typically through projection methods. This work proposes a physics-based model order reduction technique that circumvents the need for training data to solve quasi-static geometrically non-linear solid mechanics utilizing the concept of modal derivatives. This method comprises two key components. Firstly, the modified Gram–Schmidt process is incorporated to ensure an orthogonal projection in the reduction procedure. Secondly, a greedy selection algorithm that constructs the projection function with the most significant modal derivatives. This proposed method is applied to various test cases, showcasing its validity and efficacy in diverse scenarios. • Physics-based Model Order Reduction for geometrically nonlinear solid mechanics. • Modified Gram–Schmidt orthogonalization for an orthogonal Galerkin projection. • Greedy selection to limit the projection size while maintaining accuracy.

Source: Finite Elements in Analysis and Design

DOI: 10.1016/j.finel.2025.104351

Authors: Phani Ram Babbepalli, Joris J.C. Remmers, Olaf van der Sluis

Institution: 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: $2,990

Funders: ITEA, Rijksdienst voor Ondernemend Nederland, ITEA3