Scalable Particle Generation for Granular Shape Study

The shape of granular matter (particle) is crucial for understanding their properties and assembly behavior. Existing studies often rely on intuitive or machine-derived shape descriptors (e.g. sphericity and Corey shape factors) and are usually carried out on single, individual particles with specific shape features, lacking statistical evaluation on a large number of particles. Meanwhile, it is also questionable whether the pre-selected shape descriptors would sufficiently capture the rich morphological information provided by the particle. In this paper, we first propose a two-step particle generation pipeline to evaluate the quality of the previous shape descriptors. To overcome the scarcity issue of particle samples, we explicitly use a Metaball-Imaging algorithm to transform pixel data into a lower-dimensional space and propose a conditional generative method to design 3D realistic style particles. Meanwhile, we also design a new shape estimator to provide shape constraints to guide the conditional generation process. Building on this, we then propose "attribute twins" --- particles that share identical shape features but differ in actual morphologies. Attribute twins provide essential particle samples to investigate whether existing shape descriptors are sufficient to represent the effects of particle shape. In a series of simulations focusing on the drag force experienced by settling particles in a fluid, we use these distilled attribute twins under different constraints of single or multiple shape descriptors. Our results shed light on the limitations of current shape descriptors in representing the influence of particle shape in this physical process and highlight the need for improved shape descriptors in the future.