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Efficient micromechanical analysis of fiber-reinforced composites subjected to cyclic loading through time homogenization and reduced-order modeling

机译:通过时间均质化和降阶建模对纤维增强复合材料进行循环载荷的有效微力学分析

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摘要

In this paper, a number of techniques used to accelerate the solution of finite element problems involving a large number of load cycles are explored and applied to the micromechanical analysis of fiber-reinforced composites. The microscopic domain consists of unidirectional linear-elastic fibers embedded in a viscoelastic/viscoplastic polymeric matrix. Time homogenization is applied to divide the original equilibrium problem in macro- and microchronological parts. The size of the problem is further reduced by a combination of Proper Orthogonal Decomposition (POD) and the Empirical Cubature Method (ECM), resulting in a hyper-reduced model. A novel technique for history recovery combining Gappy Data reconstruction with a k-means clustering algorithm is proposed, as well as an adaptive strategy combining time homogenization and POD without an offline training phase. The performance of each acceleration technique is assessed and the resultant speed-ups obtained by combining them are presented. (C) 2018 Elsevier B.V. All rights reserved.
机译:在本文中,探索了许多用于加速解决涉及大量载荷循环的有限元问题的技术,并将其应用于纤维增强复合材料的微机械分析。微观域由嵌入粘弹性/粘塑性聚合物基体中的单向线性弹性纤维组成。时间均化用于将原始平衡问题分为宏观和微观两个部分。通过适当的正交分解(POD)和经验容器法(ECM)的组合,问题的大小进一步减小,从而产生了模型的简化。提出了一种结合Gappy数据重构和k-means聚类算法的历史恢复新技术,以及一种将时间均化和POD相结合而无需离线训练阶段的自适应策略。评估了每种加速技术的性能,并给出了将它们组合起来所得到的加速结果。 (C)2018 Elsevier B.V.保留所有权利。

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