A novel statistical spring-bead based network model for self-sensing smart polymer materials
A novel statistical spring-bead based network model for self-sensing smart polymer materials
复制标题
一种新型统计弹簧珠网络模型，用于自感知智能聚合物材料
DOI：
--
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Lenore L. Dai
中科院分区：
文献类型：
--
作者：
Jinjun Zhang;B. Koo;Yingtao Liu;J. Zou;A. Chattopadhyay;Lenore L. Dai
本文提出了一种多尺度建模方法来模拟负载敏感智能聚合物材料的自感知行为。在纳米尺度的分子动力学模拟和宏观尺度的有限元模型之间建立了一个基于统计弹珠的网络模型。对所建立的网络模型进行了参数研究，考察了热固性交联度对自敏感材料力学响应的影响。实验结果和仿真结果的比较表明，多尺度框架能够以足够的精度捕捉全局力学响应，网络模型也能够模拟智能聚合物的自感知现象。最后，采用分子动力学模拟和基于网络模型的模拟来评价单调载荷作用下自感知材料的损伤萌生。
This paper presents a multiscale modeling approach to simulating the self-sensing behavior of a load sensitive smart polymer material. A statistical spring-bead based network model is developed to bridge the molecular dynamics simulations at the nanoscale and the finite element model at the macroscale. Parametric studies are conducted on the developed network model to investigate the effects of the thermoset crosslinking degree on the mechanical response of the self-sensing material. A comparison between experimental and simulation results shows that the multiscale framework is able to capture the global mechanical response with adequate accuracy and the network model is also capable of simulating the self-sensing phenomenon of the smart polymer. Finally, the molecular dynamics simulation and network model based simulation are implemented to evaluate damage initiation in the self-sensing material under monotonic loading.