A cluster-based incremental potential approach for reduced order homogenization of bones.

IF 2.2 4区 医学 Q3 ENGINEERING, BIOMEDICAL
Xiaozhe Ju, Chunli Xu, Yangjian Xu, Lihua Liang, Junbo Liang, Weiming Tao
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引用次数: 0

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 104 against conventional FE computations and that beyond 103 against the original pRBMOR approach are observed.

基于集群的增量势能法,用于骨骼的降阶均质化。
我们开发了一种基于聚类的模型阶次缩减方法(称为 C-pRBMOR),用于高效地对骨骼进行均质化,该方法与大量通用标准材料(GSM)模型兼容。为此,我们将基于混合增量势公式的 pRBMOR 方法扩展为聚类版本,以显著提高计算效率。骨骼的微观建模属于 GSM 框架的混合增量类,源于两种势能。C-pRBMOR 方法的离线阶段包括在 RVE 中对微域进行空间分解的聚类分析和对微观塑性应变场进行时空分解。此外,还对两种不同的聚类方法和两种模式识别算法进行了比较研究。为了进行在线分析,从有效的增量变分公式中导出了一个群集增强版的减变量演化方程,使得需要数值求解的非线性方程组非常小。几个数值示例显示了 C-pRBMOR 方法的有效性。与传统的 FE 计算相比,C-pRBMOR 方法的加速率超过了 104,与原始的 pRBMOR 方法相比,加速率超过了 103。
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来源期刊
International Journal for Numerical Methods in Biomedical Engineering
International Journal for Numerical Methods in Biomedical Engineering ENGINEERING, BIOMEDICAL-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
4.50
自引率
9.50%
发文量
103
审稿时长
3 months
期刊介绍: All differential equation based models for biomedical applications and their novel solutions (using either established numerical methods such as finite difference, finite element and finite volume methods or new numerical methods) are within the scope of this journal. Manuscripts with experimental and analytical themes are also welcome if a component of the paper deals with numerical methods. Special cases that may not involve differential equations such as image processing, meshing and artificial intelligence are within the scope. Any research that is broadly linked to the wellbeing of the human body, either directly or indirectly, is also within the scope of this journal.
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