Eigenparticles: characterizing particles using eigenfaces
The shape characteristics of particles have a pinnacle role in microsopic and macroscopic features of a system. Several studies have highlighted the need for considering deviations from a spherical representation of particles for accurate modeling of granular and multiphase flow systems. Using a shape factor, sphericity or roundness parameter alone is proven to be inadequate to capture the physical phenomena. In the present study we propose a novel metric based on the pattern recognition method Eigenfaces, coining the technique ‘Eigenparticles’. Using this technique we create a single statistical distribution of basis shapes to describe the morphological composition. The proposed technique is successfully validated with test shapes and applied to real particles. As a result, when compared with a state-of-the-art Fourier based method, ‘Eigenparticles’ performs favorably, clearly distinguishing the different particles.