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.

中文翻译：

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带有演化周期边界的自适应粒度代表体积元模型，用于层次多尺度分析

分级多尺度分析通常使用微观代表体积元素（RVE）模型来捕获路径/历史相关的宏观响应，而不是使用现象学本构模型。但是，对于涉及大变形的问题，除非应用适当的重新初始化，否则由于RVE盒的逐渐变形，当前在地质力学中使用的RVE模型可能会失去代表性。这项工作基于最近提出的耦合分子动力学和材料点方法的RVE模型，结合演化周期边界（EPB）算法开发了自适应RVE模型，用于对经历大变形的颗粒材料进行分级多尺度分析。所提出的自适应RVE模型避免了RVE盒的重新初始化，该盒甚至经历了非常大的剪切变形。同时，它考虑了RVE模型的变形历史，并以更有效的方式处理了边界粒子与其他图像粒子之间的相互作用。以变​​形很大的数值实例来说明所提出的EPB算法增强的自适应粒度RVE模型。此外，将进一步讨论此新方法的一些关键功能，以进行澄清。以变​​形很大的数值实例来说明所提出的EPB算法增强的自适应粒度RVE模型。此外，将进一步讨论此新方法的一些关键功能，以进行澄清。以变​​形很大的数值实例来说明所提出的EPB算法增强的自适应粒度RVE模型。此外，将进一步讨论此新方法的一些关键功能，以进行澄清。