程耿东

个人信息Personal Information

教授

博士生导师

硕士生导师

性别:男

毕业院校:丹麦技术大学

学位:博士

所在单位:力学与航空航天学院

电子邮箱:chenggd@dlut.edu.cn

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On predicting the effective elastic properties of polymer nanocomposites by novel numerical implementation of asymptotic homogenization method

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论文类型:期刊论文

发表时间:2016-01-01

发表刊物:COMPOSITE STRUCTURES

收录刊物:SCIE、EI

卷号:135

页面范围:297-305

ISSN号:0263-8223

关键字:Polymer nanocomposite; Effective elastic properties; Asymptotic homogenization

摘要:The main issue of this paper is an attempt to predict the elastic properties of PVC/ABS/nano-CaCO3 polymer nanocomposites by coupling data of microstructure analysis and mechanical tests with a micro-mechanics analysis. The novel numerical implementation of asymptotic homogenization (NIAH) method is compared with the representative volume element (RVE) method under Dirichlet and Neumann boundary conditions. Numerical simulation results show that the NIAH method is more reasonable and accurate for simulating the micro-mechanical properties of periodic composite materials. In order to account for two kinds of particles spatial distribution in nanocomposites, statistical asymptotic homogenization (AH) method is performed by assigning randomness to the spatial distribution for the two kinds of particles in the unit cell. With increase of the number of the unit cell particles, the results of NIAH method converge to a stable value. We also show that in the case of fixed-size of the unit cell, the average mechanical properties of the obtained results converge to the stable value with the increase of the number of random configurations realization. In this paper, we use the unit cell containing the minimum number of particles for different weight ratio of two kinds of particles to obtain the effective mechanical properties by averaging simulation results of many samples of different configurations in the unit cell. Based on the results obtained, it can be concluded that spatial distribution for the two kinds of particles in the unit cell has a significant impact on the material macroscopic elastic properties. Furthermore, the method is more efficient to predict the mean values of the elastic properties than the experimental dispersions. Crown Copyright (C) 2015 Published by Elsevier Ltd. All rights reserved.