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.
Authors
- Stefanie Reese (ORCID: https://orcid.org/0000-0003-4760-8358)
- Tim Brepols (ORCID: https://orcid.org/0000-0002-1039-1776)
- Jian Zhang (ORCID: https://orcid.org/0000-0002-0431-0191)
- Qinghua Zhang (ORCID: https://orcid.org/0000-0002-7005-7788)
Institutions
- RWTH Aachen University (DE)
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
Funders
- China Scholarship Council