Stochastic finite element analysis framework for modelling thermal conductivity of particulate modified polymer composites

Abstract

Abstract Polymers possess desirable properties such as low specific gravity and good corrosion resistance, which make them attractive for a variety of applications. However, polymers typically lack in mechanical, thermal and electrical properties. Therefore, considerable research efforts are spent to improve polymer properties using particulate fillers. A multitude of filler materials and shapes are available, such as fiber/rod, plate-like or spherical geometries. Composite morphologies may also differ based on particle distribution, dispersion and alignment, all of which may be affected by filler loading and manufacturing routes. Given the significant efforts associated with experimental analyses, modeling approaches are expedient for providing a fundamental understanding for particulate modified polymers. In the present study a numerical modeling framework was developed for predicting the effective properties, specifically, thermal conductivity, of particulate modified polymers considering filler-matrix and particle-to-particle interfacial effects. Employing a stochastic finite element analysis, composites with randomly distributed spherical particles were modeled and validated against experimental data. The developed modeling framework, which can accommodate a broad range of filler shapes and composite morphologies, provides an effective means for elucidating experimental studies and guiding the design of particulate modified polymer composites.