2022 Volume 10 Issue 2 Pages 150-161
This paper introduces a multi-sized particle sampling method within an arbitrary 2D shape using power tessellation. Our method aims to improve packing density as to sample as many particles as possible in a limited area. We propose a porosity-driven optimization technique to ensure no overlap between particles while increasing the packing density. With such properties, our method is applicable to physically-based simulations, such as the Discrete Element Method and its related framework. Additionally, this technology allows users to set the target particle size distribution by a pre-designed cumulative distribution function and restrict the errors between 10% and 20% after the optimization. We demonstrate that our multi-sized particle sampling algorithms significantly improve packing density compared to Poisson disk sampling and SPH-based blue noise sampling.
