An adaptive granular representative volume element model with an evolutionary periodic boundary for hierarchical multiscale analysis

The hierarchical multiscale analysis normally utilizes a microscopic representative volume element (RVE) model to capture path/history‐dependent macroscopic responses instead of using phenomenological constitutive models. However, for problems involving large deformation, the current RVE model used...

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Bibliographic Details
Published in:International journal for numerical methods in engineering 2021-05, Vol.122 (9), p.2239-2253
Main Authors: Qu, Tongming, Feng, Yuntian, Wang, Min
Format: Article
Language:English
Subjects:
adaptive representative volume element
Constitutive models
Coupling (molecular)
discrete element method
ENGINEERING
Evolutionary algorithms
evolutionary periodic boundary
Geomechanics
Granular materials
Mathematical models
Molecular dynamics
Multiscale analysis
Shear deformation
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Summary:The hierarchical multiscale analysis normally utilizes a microscopic representative volume element (RVE) model to capture path/history‐dependent macroscopic responses instead of using phenomenological constitutive models. However, for problems involving large deformation, the current RVE model used in geomechanics may lose representative properties due to the progressive distortion of the RVE box, unless a proper reinitialization is applied. This work develops an adaptive RVE model in conjunction with an evolutionary periodic boundary (EPB) algorithm for hierarchical multiscale analysis of granular materials undergoing large deformation based on a recent RVE model proposed for coupling molecular dynamics and the material point method. The proposed adaptive RVE model avoids the reinitialization of the RVE box that even undergoes extremely large shear deformation; meanwhile, it accounts for the deformation history of the RVE model and treats the interaction between boundary particles and other image particles in a more efficient way. Numerical examples with extremely large deformation are used to illustrate the adaptive granular RVE model enhanced by the proposed EPB algorithm. Furthermore, some key features of this new methodology are further discussed for clarification.
ISSN:0029-5981
1097-0207
DOI:10.1002/nme.6620