Stochastic multiscale modeling of heat conductivity of Polymeric clay nanocomposites

Abstract

We propose a stochastic multi-scale method to quantify the most significant input parameters influencing the heat conductivity of polymeric nano-composites (PNCs) with clay reinforcement. Therefore, a surrogate based global sensitivity analysis is coupled with a hierarchical multi-scale method employing computational homogenization. The effect of the conductivity of the fibers and the matrix, the Kapitza resistance, volume fraction and aspect ratio on the ’macroscopic’ conductivity of the composite is systematically studied. We show that all selected surrogate models yield consistently the conclusions that the most influential input parameters are the aspect ratio followed by the volume fraction. The Kapitza Resistance has no significant effect on the thermal conductivity of the PNCs. The most accurate surrogate model in terms of the R2 value is the moving least square (MLS).