Exploiting DEM to Link Thermal Conduction and Elastic Stiffness in Granular Materials

Abstract

Estimating the effective thermal conductivity (ETC) of granular materials is important in various engineering disciplines. The ETC of a granular material is not unique, rather it depends upon the material’s packing characteristics (i.e., porosity and coordination number). Directly measuring the ETC of granular materials with a particular packing density and subjected to specific stress conditions is experimentally challenging. There is a need to develop reliable, indirect experimental methods to measure the ETC of granular materials. Here, we explore the possibility of linking the ETC of granular materials to their elastic moduli. This study used a thermal pipe network model implemented in a discrete element method (DEM) code to generate ETC data for ideal, virtual two-phase granular samples with stagnant pore fluid. Parametric studies considered the sensitivity of the ETC to the sample packing. Data from small deformation probes were used to explore links between the samples’ elastic moduli and their ETCs. The results provide a theoretical background for the development of an indirect experimental method to predict the ETC or trends in the variation in the ETC by considering stiffness data that are relatively straightforward to acquire. The study shows how DEM can be used as a sophisticated thought experiment to explore novel ideas for developing experimental procedures.