A nonlinear manifold model order reduction approach for thermo‐mechanically coupled plasticity

Abstract This study employs a nonlinear manifold mode order reduction (MOR) approach to address the high computational cost inherent in multi‐physical nonlinear models, which are discretized by the finite element method (FEM), with a particular focus on thermo‐mechanically coupled plasticity under finite strain conditions. Utilizing nodal snapshots obtained from a full‐order simulation, a projection matrix is constructed through singular value decomposition (SVD), facilitating the construction of a reduced system within a lower‐dimensional space. This reduction process entails the multiplication of the discretized residual vector and stiffness matrix by a linear projection matrix, complemented by the incorporation of a nonlinear projection component to ensure accurate reconstruction within the nonlinear part. Subsequently, a comprehensive three‐dimensional benchmark test is conducted to assess the accuracy and efficacy of the reduced‐order model.

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Publication Details

Journal
PAMM
Published
2024-11-06
DOI
https://doi.org/10.1002/pamm.202400070
Citations
2
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.56

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article

A nonlinear manifold model order reduction approach for thermo‐mechanically coupled plasticity

Stefanie Reese, Tim Brepols, Jian Zhang, Qinghua Zhang
2 citations
PAMM
Model Reduction and Neural Networks
0.56
article

A nonlinear manifold model order reduction approach for thermo‐mechanically coupled plasticity

Stefanie Reese, Tim Brepols, Jian Zhang, Qinghua Zhang
article en
2 citations

Abstract

Abstract This study employs a nonlinear manifold mode order reduction (MOR) approach to address the high computational cost inherent in multi‐physical nonlinear models, which are discretized by the finite element method (FEM), with a particular focus on thermo‐mechanically coupled plasticity under finite strain conditions. Utilizing nodal snapshots obtained from a full‐order simulation, a projection matrix is constructed through singular value decomposition (SVD), facilitating the construction of a reduced system within a lower‐dimensional space. This reduction process entails the multiplication of the discretized residual vector and stiffness matrix by a linear projection matrix, complemented by the incorporation of a nonlinear projection component to ensure accurate reconstruction within the nonlinear part. Subsequently, a comprehensive three‐dimensional benchmark test is conducted to assess the accuracy and efficacy of the reduced‐order model.

PAMMVol. 24(4)
RWTH Aachen University (DE)
China Scholarship Council
Openalex Percentile: Top 34%
Model Reduction and Neural Networks
0.56
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