Department of Mechanical Engineering and Materials Science, Rice University, MS-321, Houston, TX 77251-1892, USA.
Philos Trans A Math Phys Eng Sci. 2013 May 20;371(1993):20120494. doi: 10.1098/rsta.2012.0494. Print 2013 Jun 28.
This paper reviews and enhances numerical models for determining thermal, elastic and electrical properties of carbon nanotube-reinforced polymer composites. For the determination of the effective stress-strain curve and thermal conductivity of the composite material, finite-element analysis (FEA), in conjunction with the embedded fibre method (EFM), is used. Variable nanotube geometry, alignment and waviness are taken into account. First, a random morphology of a user-defined volume fraction of nanotubes is generated, and their properties are incorporated into the polymer matrix using the EFM. Next, incremental and iterative FEA approaches are used for the determination of the nonlinear properties of the nanocomposite. For the determination of the electrical properties, a spanning network identification algorithm is used. First, a realistic nanotube morphology is generated from input parameters defined by the user. The spanning network algorithm then determines the connectivity between nanotubes in a representative volume element. Then, interconnected nanotube networks are converted to equivalent resistor circuits. Finally, Kirchhoff's current law is used in conjunction with FEA to solve for the voltages and currents in the system and thus calculate the effective electrical conductivity of the nanocomposite. The model accounts for electrical transport mechanisms such as electron hopping and simultaneously calculates percolation probability, identifies the backbone and determines the effective conductivity. Monte Carlo analysis of 500 random microstructures is performed to capture the stochastic nature of the fibre generation and to derive statistically reliable results. The models are validated by comparison with various experimental datasets reported in the recent literature.
本文综述并增强了用于确定碳纳米管增强聚合物复合材料热、弹和电性能的数值模型。为了确定复合材料的有效应力-应变曲线和热导率，使用有限元分析（FEA）结合嵌入式纤维方法（EFM）。考虑了纳米管的可变几何形状、取向和波纹。首先，生成用户定义的纳米管体积分数的随机形态，并使用 EFM 将其特性纳入聚合物基体中。然后，采用增量和迭代 FEA 方法来确定纳米复合材料的非线性特性。为了确定电性能，使用跨越网络识别算法。首先，从用户定义的输入参数生成现实的纳米管形态。跨越网络算法然后确定代表性体积元素中纳米管之间的连通性。然后，将相互连接的纳米管网络转换为等效电阻电路。最后，结合 FEA 使用基尔霍夫电流定律求解系统中的电压和电流，从而计算纳米复合材料的有效电导率。该模型考虑了电子跳跃等电传输机制，并同时计算渗流概率、识别骨干和确定有效电导率。通过对 500 个随机微观结构进行蒙特卡罗分析，捕捉纤维生成的随机性，并得出统计上可靠的结果。通过与最近文献中报道的各种实验数据集进行比较来验证模型。
