A hybrid offline-online model order reduction approach for damage propagation problems

Accurately modelling damage propagation in composites with 3D explicit finite element methods requires high-dimensional models, making simulations computationally prohibitive. Conventional reduced-order models (ROMs) trained offline are ineffective for fracture problems, since stress redistribution and crack growth cannot be anticipated a priori. In this work, a hybrid offline–online ROM that couples elastic-only offline training with adaptive online enrichment of the reduced basis during damage evolution is introduced. Proper Orthogonal Decomposition (POD) is combined with Energy-Conserving Mesh Sampling and Weighting (ECSW) and Gappy data reconstruction to achieve efficient time integration with 3D solid and cohesive elements. Unlike existing domain decomposition approaches, the proposed framework does not require prior knowledge of crack paths and can refine the basis anywhere in the domain as damage develops. The method is demonstrated on open-hole tensile tests at two distinct length scales, capturing delamination, fibre failure and failure stress with good accuracy when compared to full-order simulations and experiments. Improved computational savings are achieved, with efficiency gains increasing with model size. These results establish the hybrid ROM as a scalable and general approach for modelling distributed, path-dependent fracture in composite materials. • Introduces a hybrid offline–online reduced-order modelling framework for distributed damage in composites. • Elastic-only offline training enables inexpensive basis construction without prior crack path knowledge. • Adaptive online enrichment refines the basis in situ to capture stress redistribution and path-dependent damage. • Simultaneously models weak and strong discontinuities (with CDM and CZM) using the same reduced order setting. • Demonstrated on open-hole tensile specimens at multiple length scales, with improved computational efficiency.

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Publication Details

Journal
Computers & Structures
Published
2025-11-25
DOI
https://doi.org/10.1016/j.compstruc.2025.108046
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

A hybrid offline-online model order reduction approach for damage propagation problems

Jagan Selvaraj, Stephen R. Hallett
Computers & Structures
Model Reduction and Neural Networks
article

A hybrid offline-online model order reduction approach for damage propagation problems

Jagan Selvaraj, Stephen R. Hallett
article en

Abstract

Accurately modelling damage propagation in composites with 3D explicit finite element methods requires high-dimensional models, making simulations computationally prohibitive. Conventional reduced-order models (ROMs) trained offline are ineffective for fracture problems, since stress redistribution and crack growth cannot be anticipated a priori. In this work, a hybrid offline–online ROM that couples elastic-only offline training with adaptive online enrichment of the reduced basis during damage evolution is introduced. Proper Orthogonal Decomposition (POD) is combined with Energy-Conserving Mesh Sampling and Weighting (ECSW) and Gappy data reconstruction to achieve efficient time integration with 3D solid and cohesive elements. Unlike existing domain decomposition approaches, the proposed framework does not require prior knowledge of crack paths and can refine the basis anywhere in the domain as damage develops. The method is demonstrated on open-hole tensile tests at two distinct length scales, capturing delamination, fibre failure and failure stress with good accuracy when compared to full-order simulations and experiments. Improved computational savings are achieved, with efficiency gains increasing with model size. These results establish the hybrid ROM as a scalable and general approach for modelling distributed, path-dependent fracture in composite materials. • Introduces a hybrid offline–online reduced-order modelling framework for distributed damage in composites. • Elastic-only offline training enables inexpensive basis construction without prior crack path knowledge. • Adaptive online enrichment refines the basis in situ to capture stress redistribution and path-dependent damage. • Simultaneously models weak and strong discontinuities (with CDM and CZM) using the same reduced order setting. • Demonstrated on open-hole tensile specimens at multiple length scales, with improved computational efficiency.

Computers & StructuresVol. 320
University of Bristol, Engineering and Physical Sciences Research Council
Openalex Percentile: Top 71%
Model Reduction and Neural Networks
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