Microstructure Statistics Property Relations of Anisotropic Polydisperse Particulate Composites using Tomography.

摘要

In this paper, a systematic method is presented for developing microstructure-statistics-property relations of anisotropic polydisperse particulate composites using micro-computer tomography (micro-CT). Micro-CT is used to obtain a detailed three-dimensional representation of the polydisperse microstructures, and a novel image processing pipeline is developed for identifying particles. In this work, particles are modeled as idealized shapes in order to guide the image processing steps and to provide a description of the discrete micro-CT dataset in continuous Euclidean space. N-point probability functions used to describe the morphology of the mixtures are calculated directly from real microstructures. The statistical descriptors are employed in the Hashin-Shtrikman variational principle to compute overall anisotropic bounds and self-consistent estimates of the thermal conductivity tensor. We make no assumptions of statistical isotropy nor ellipsoidal symmetry, and the microstructural description is obtained directly from micro-CT data. Various mixtures consisting of polydisperse ellipsoidal and spherical particles are prepared and studied to show how the morphology impacts the overall anisotropic thermal conductivity tensor.