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Computational Mechanics
Erschienen in: Computational Mechanics 1/2023

29.04.2023 | Original Paper

Reducing internal variables and improving efficiency in data-driven modelling of anisotropic damage from RVE simulations

verfasst von: Julien Yvonnet, Qi-Chang He, Pengfei Li

Erschienen in: Computational Mechanics | Ausgabe 1/2023

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Abstract

The Data-Driven Harmonic Analysis of Damage (DDHAD) has been proposed recently to construct arbitrarily anisotropic damage models in quasi-brittle heterogeneous materials from calculations on Representative Volume Elements. During preliminary off-line calculations, numerical crack propagations are simulated and the effective damaged elasticity tensor is computed by numerical homogenization. A macroscopic damage model with internal variables is defined using harmonic analysis of the elasticity tensor. The method, called DDHAD, is in this work improved by two key novelties. First, a numerical framework is proposed to evaluate efficiently the model during on-line calculations by computing all the integral terms during off-line calculations and expressing the damage model in a compact matrix form. Second, a reduced model is introduced to further reduce the number of internal variables which is in general large in 3D. It is shown in an application to the damage of 3D printed lattice that even for a complex 3D induced anisotropy, the number of internal variables can drop to a very small number (3 or 4) with the aid of the proposed strategy.
Vorheriger Artikel Adaptive spatiotemporal dimension reduction in concurrent multiscale damage analysis
Nächster Artikel Deep learning and multi-level featurization of graph representations of microstructural data

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Metadaten
Titel
Reducing internal variables and improving efficiency in data-driven modelling of anisotropic damage from RVE simulations
verfasst von
Julien Yvonnet
Qi-Chang He
Pengfei Li
Publikationsdatum
29.04.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
Computational Mechanics / Ausgabe 1/2023
Print ISSN: 0178-7675
Elektronische ISSN: 1432-0924
DOI
https://doi.org/10.1007/s00466-023-02326-7

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