A Comparative Study of Existing and New Sphere Clump Generation Algorithms for Modeling Arbitrary Shaped Particles

This paper presents a comparative analysis of multiple algorithms for generating sphere clumps as approximations for irregularly-shaped particles in granular systems. Investigated algorithms both include four previously used techniques and two new ones developed in this study. They are often built on common concepts such as distance transforms, filling, packing techniques, and particle medial surface. The two new algorithms herein proposed arrange individual spheres in a clump shape using either a greedy volume coverage or a clustering approach based on a k-means machine learning technique. The performances of the various algorithms are evaluated in terms of both the number of spheres generated per clump and the volume error. The evaluation is conducted on diverse superquadric shapes, serving as ground-truth references, as well as on real rock pieces. Among considered clump generators, results show that existing algorithms may output dispersed results depending on user parameters that are difficult to calibrate, while both proposed algorithms generate realistic sphere clumps, with the volume coverage one being more convenient than the k-means based approach. As a matter of fact, the volume coverage technique is found to be the most effective approach among the studied algorithms in terms of sphere generation and volume precision.