A random algorithm for 3D modeling of solid particles considering elongation, flatness, sphericity, and convexity
Songling Han, Changming Wang, Xiaoyang Liu, Bailong Li, Ruiyuan Gao, Shuo Li
Abstract
Generating particles with specific shape characteristics is regarded as a critical issue in the research of granular materials. Improving the particle generation method to consider more comprehensive shape descriptors becomes a central challenge in this field. We described a novel solution for parametrically generate non-convex particles to meet this challenge. First, to conveniently capture particle characteristics, this work established estimation functions of 3D shape parameters (elongation, flatness, sphericity, and convexity). Then, the present study proposed a novel stochastic algorithm for generating non-convex particles. (This algorithm successfully controls the above particle shape parameters.) Finally, this work verified the mechanical properties of the generated particles are similar to those of realistic-shaped particles, by comparing the numerical results of three-dimensional compression of granular materials. The proposed algorithm has a good performance in controlling particle shape parameters and generate particles quickly.