均质化(气候)
聚类分析
非线性系统
计算
应用数学
区域分解方法
计算机科学
算法
数学优化
数学
有限元法
结构工程
物理
工程类
人工智能
生物多样性
生态学
生物
量子力学
作者
Xiaozhe Ju,Chunli Xu,Yangjian Xu,Lihua Liang,Junbo Liang,Weiming Tao
摘要
Abstract We develop a cluster‐based model order reduction (called C‐pRBMOR) approach for efficient homogenization of bones, compatible with a large variety of generalized standard material (GSM) models. To this end, the pRBMOR approach based on a mixed incremental potential formulation is extended to a clustered version for a significantly improved computational efficiency. The microscopic modeling of bones falls into a mixed incremental class of the GSM framework, originating from two potentials. An offline phase of the C‐pRBMOR approach includes both a clustering analysis spatially decomposing the micro‐domain within an RVE and a space–time decomposition of the microscopic plastic strain fields. A comparative study on two different clustering approaches and two algorithms for mode identification is additionally conducted. For an online analysis, a cluster‐enhanced version of evolution equations for the reduced variables is derived from an effective incremental variational formulation, rendering a very small set of nonlinear equations to be numerically solved. Several numerical examples show the effectiveness of the C‐pRBMOR approach. A striking acceleration rate beyond 10 4 against conventional FE computations and that beyond 10 3 against the original pRBMOR approach are observed.
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