Statistical 3D Analysis and Modeling of Complex Particle Systems based on Tomographic Image Data

Abstract

Abstract Geometrically complex particle systems containing individual particles characterized by disperse sizes and irregular non-spherical shapes exist in a wide range of application areas. One example are so-called active particle systems which form an important component of electrodes in lithium-ion batteries. Apart from that, particle systems are also analyzed in the context of mining treatment processes in which the relevant particles are not only characterized by their disperse sizes and shapes but also by different material properties. These two examples serve to illustrate methods for the analysis and stochastic modeling of the 3D morphology of geometrically complex particle systems using tomographic image data. These methods are based on the phase- and/or particle-based segmentation of the voxel-based image data. Subsequently, parametric stochastic microstructure models are calibrated to real data by fitting geometrical image characteristics, whereby a significant reduction of complexity is achieved. Suplementary information about the material can also be integrated into the models, when additional imaging techniques, such as scanning electron microscopy, are included.