Self-consistent clustering analysis for multiscale modeling at finite strains
Self-consistent clustering analysis for multiscale modeling at finite strains
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DOI：
10.1016/j.cma.2019.02.027
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发表时间：
2019-06
影响因子：
7.2
通讯作者：
Cheng Yu;O. L. Kafka;Wing Kam Liu
中科院分区：
文献类型：
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作者：
Cheng Yu;O. L. Kafka;Wing Kam Liu
精确、有效地模拟材料的微观结构相互作用和演化，预测材料的宏观行为，对于材料设计和制造过程控制具有重要意义。本文采用一种称为自洽聚类分析（SCA）的降阶方法来解决这一挑战。对一般弹粘塑性材料的大变形问题进行了修正。与传统的全场求解方法如有限元分析和快速傅立叶变换相比较，证明了预测多晶材料整体力学响应的准确性和效率。结果表明，降阶方法可以快速预测定量变化的组织性能关系。该方法的效用是通过进行并发多尺度模拟的大变形制造过程中，亚晶粒的空间分辨率，同时保持合理的计算费用。该方法可用于微结构敏感性能设计和工艺参数优化。
Accurate and efficient modeling of microstructural interaction and evolution for prediction of the macroscopic behavior of materials is important for material design and manufacturing process control. This paper approaches this challenge with a reduced-order method called self-consistent clustering analysis (SCA). It is reformulated for general elasto-viscoplastic materials under large deformation. The accuracy and efficiency for predicting overall mechanical response of polycrystalline materials is demonstrated with a comparison to traditional full-field solution methods such as finite element analysis and the fast Fourier transform. It is shown that the reduced-order method enables fast prediction of microstructure–property relationships with quantified variation. The utility of the method is demonstrated by conducting a concurrent multiscale simulation of a large-deformation manufacturing process with sub-grain spatial resolution while maintaining reasonable computational expense. This method could be used for microstructure-sensitive properties design as well as process parameters optimization.