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Traditional particle-characterization techniques require particles to be dispersed from their original form, which destroys important morphologic information in materials such as aggregates or porous materials. X-ray computed microtomography provides a powerful tool for non-destructive analysis. However, robust techniques for comprehensive material characterization of these images have not been developed. In this paper we present a new algorithm for the computer analysis of particulate materials from high-resolution tomography images. The key aspect of the algorithm is the assignment of every solid-phase voxel in the image to its associated particle in a physically representative manner, which is in essence a particle-scale computer reconstruction of the material. Once this digital reconstruction is obtained, a vast amount of morphologic information can be extracted, including parameters obtained by traditional particle-analysis techniques (e.g., particle-size distribution and porosity) as well as parameters not usually available (e.g., spatial correlations in particle size, particle aspect ratios, surface areas, and orientations, particle contacts, particle coordination numbers, and more). Additionally, the computer reconstruction allows for complex manipulations such as the comparison of a specific parameter for two different particle-size classes within the material. The paper includes validation of the algorithm using computer-generated packings, as well as an example using microtomography data from a real material.

Collective rearrangement is an important class of algorithms for the computer generation of random sphere packings, especially for those of low porosity. In this paper we examine how the choice of boundary conditions affects internal packing structure. The results help to quantify the depth to which non-periodic boundaries influence internal structure. More interesting results are obtained for periodic boundary conditions, showing that for small packings, self-assembly into ordered packings is possible, even without a seed structure or undue influence from the algorithm. Furthermore, the structure depends on the shape of the periodic domain. Finally, we propose examining the transition from random to ordered packings to better characterize the random-close-packed (RCP) limit and its associated packing structures.