Abstract
Understanding the failure of brittle heterogeneous materials is essential in many applications. Heterogeneities in material properties are frequently modeled through random fields, which typically induces the need to solve finite element problems for a large number of realizations. In this context, we make use of reduced order modeling to solve these problems at an affordable computational cost. This paper proposes a reduced order modeling framework to predict crack propagation in brittle materials with random heterogeneities. The framework is based on a combination of the Proper Generalized Decomposition (PGD) method with Griffith’s global energy criterion. The PGD framework provides an explicit parametric solution for the physical response of the system. We illustrate that a non-intrusive sampling-based technique can be applied as a post-processing operation on the explicit solution provided by PGD. We first validate the framework using a global energy approach on a deterministic two-dimensional linear elastic fracture mechanics benchmark. Subsequently, we apply the reduced order modeling approach to a stochastic fracture propagation problem.
Original language | English |
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Pages (from-to) | 451-473 |
Number of pages | 23 |
Journal | Computational Mechanics |
Volume | 65 |
Issue number | 2 |
Early online date | 26 Oct 2019 |
DOIs | |
Publication status | Published - 1 Feb 2020 |
Funding
We acknowledge the support from the European Commission EACEA Agency, Framework Partnership Agreement Erasmus Mundus Action 1b, as a part of the EM Joint Doctorate Simulation in Engineering and Entrepreneurship Development (SEED). The work of S. Zlotnik and P. Díez was funded by the project DPI2017-85139-C2-2-R of the Spanish Ministry and by grant 2017-SGR-1278 from the Generalitat de Catalunya. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Keywords
- Brittle fracture
- Crack propagation
- Model order reduction
- Monte Carlo method
- Proper Generalized Decomposition
- Random fields