Component‐Based Model‐Order Reduction With Mortar Tied Contact for Nonlinear Quasi‐Static Mechanical Problems

ABSTRACT In this work, we present a model‐order reduction technique for nonlinear structures assembled from components. The reduced‐ order model is constructed by reducing the substructures with proper orthogonal decomposition and connecting them by a mortar‐tied contact formulation. The substructure projection matrices are computed by the proper orthogonal decomposition (POD) method from snapshots computed on the substructure level. The snapshots are computed using Latin hypercube sampling based on a parametrization of the boundary conditions. In numerical examples, we show the accuracy and efficiency of the method for nonlinear problems involving material and geometric nonlinearities as well as non‐matching meshes. The method can predict solutions of new systems with varying boundary conditions and material behaviors.

Authors

Institutions

Publication Details

Journal
International Journal for Numerical Methods in Engineering
Published
2025-04-27
DOI
https://doi.org/10.1002/nme.70041
Citations
4
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
5.29

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Component‐Based Model‐Order Reduction With Mortar Tied Contact for Nonlinear Quasi‐Static Mechanical Problems

Stefanie Reese, Tim Brepols, Stephan Ritzert, Jannick Kehls
4 citations
International Journal for Numerical Methods in Engineering
Model Reduction and Neural Networks
5.29
article

Component‐Based Model‐Order Reduction With Mortar Tied Contact for Nonlinear Quasi‐Static Mechanical Problems

Stefanie Reese, Tim Brepols, Stephan Ritzert, Jannick Kehls
article en
4 citations

Abstract

ABSTRACT In this work, we present a model‐order reduction technique for nonlinear structures assembled from components. The reduced‐ order model is constructed by reducing the substructures with proper orthogonal decomposition and connecting them by a mortar‐tied contact formulation. The substructure projection matrices are computed by the proper orthogonal decomposition (POD) method from snapshots computed on the substructure level. The snapshots are computed using Latin hypercube sampling based on a parametrization of the boundary conditions. In numerical examples, we show the accuracy and efficiency of the method for nonlinear problems involving material and geometric nonlinearities as well as non‐matching meshes. The method can predict solutions of new systems with varying boundary conditions and material behaviors.

International Journal for Numerical Methods in EngineeringVol. 126(8)
University of Siegen (DE), RWTH Aachen University (DE)
Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 5%
Model Reduction and Neural Networks
5.29
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.