Stochastic Finite Element Analysis Framework for Modelling Electrical Properties of Particle-Modified Polymer Composites

Nanomaterials

Pierre Mertiny¹  Hamidreza Ahmadi Moghaddam¹ 

[1] Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada;

关键词: stochastic finite element analysis;    Monte Carlo simulation;    particulate polymer composites;    electrical conductivity;    percolation threshold;    piezoresistivity;   

DOI  :  10.3390/nano10091754

来源: DOAJ

【 摘 要 】

Properties such as low specific gravity and cost make polymers attractive for many engineering applications, yet their mechanical, thermal, and electrical properties are typically inferior compared to other engineering materials. Material designers have been seeking to improve polymer properties, which may be achieved by adding suitable particulate fillers. However, the design process is challenging due to countless permutations of available filler materials, different morphologies, filler loadings and fabrication routes. Designing materials solely through experimentation is ineffective given the considerable time and cost associated with such campaigns. Analytical models, on the other hand, typically lack detail, accuracy and versatility. Increasingly powerful numerical techniques are a promising route to alleviate these shortcomings. A stochastic finite element analysis method for predicting the properties of filler-modified polymers is herein presented with a focus on electrical properties, i.e., conductivity, percolation, and piezoresistivity behavior of composites with randomly distributed and dispersed filler particles. The effect of temperature was also explored. While the modeling framework enables prediction of the properties for a variety of filler morphologies, the present study considers spherical particles for the case of nano-silver modified epoxy polymer. Predicted properties were contrasted with data available in the technical literature to demonstrate the viability of the developed modeling approach.

【 授权许可】

Unknown