Advances in particle packing algorithms for generating the medium in the Discrete Element Method

Abstract

Several theoretical contributions in order to establish a particle packing methodology are presented. In this respect, a generic formulation of a new method for packing particles based on a constructive advancing front method, which uses Monte Carlo techniques for the generation of particle dimensions, is also shown. The method can be used to obtain virtual dense packings of particles with several geometrical shapes. It employs continuous, discrete and empirical statistical distributions in order to generate the dimensions of particles. The packing algorithm is very flexible and allows alternatives for: 1-The direction of the advancing front (inwards or outwards), 2-The selection of the local advancing front, 3-The method for placing a mobile particle in contact with others and 4-The overlap checks. The algorithm also allows obtaining highly porous media when it is slightly modified. Practical applications of the formulations are presented in the end of this paper.

Point Cloud Generation for Meshfree Methods: An Overview

“…The method is also used in a couple of commercial meshfree codes [62, 140], which also report parallel implementations. This work has even been generalized to fill volume domains with objects of different shapes and sizes instead of points [56,116,117,163], such as spheres, ellipsoids, and coils, for the purpose of discrete element method (DEM) simulations.…”

Statistical modeling of random hail impact

“…To avoid overlapping between particles during modeling of the sparse particle field, a distance limitation ⏐ ⏐ X p − X q ⏐ ⏐ ≥ δ between particles was used instead of the conventional overlapping detection process. This greatly improves the modeling efficiency, because the conventional method adds particles into the spatial domain one by one, and overlapping detection and adjustment of overlapped particles are required for each newly added particle [16]. To clearly show the efficiency improvement, the proposed modeling approach (as detailed in Sections 2.1 and 2.2) was compared with the conventional modeling method, i.e., adding particles to the spatial domain one by one [16].…”

“…This greatly improves the modeling efficiency, because the conventional method adds particles into the spatial domain one by one, and overlapping detection and adjustment of overlapped particles are required for each newly added particle [16]. To clearly show the efficiency improvement, the proposed modeling approach (as detailed in Sections 2.1 and 2.2) was compared with the conventional modeling method, i.e., adding particles to the spatial domain one by one [16]. In this work, the time consumptions (T C ) of the two methods for modeling random hail fields were obtained by using the same high-performance computer (Processor: Inter(R) Xeon(R) Gold 6132 CPU @2.60 GHz; Total number of processors: 56; Installed memory (RAM): 128 GB).…”

“…From the literature, limited methods that are able to establish a random hail field comprising multiple random hailstones are emerged. At present, generating a field consisting of random particles relies on discrete element simulation (DEM) [14], Voronoi cells [15], and some overlapping detection methods such as adding particles to the spatial domain one by one [16] and simulating growth of particles at random points in a space [17]. These methods have been successfully applied to model microscopic crystals and particles [18], mesoscopic sand particles and powders [19] and macroscopic gravel particles [20,21], etc.…”

Research on the Effect of Particle Distribution on the Crack Propagation in Shaped Energy Blasting Based on the Smoothed Particle Hydrodynamics

“…Although there are many ways to ensure the uniformity of particle distribution in large or simple geometric domains, for example [17,18], the particles tend to distribute uniformly during the SPH simulation process if appropriate kernel function and smoothing length are adopted, but the traditional SPH method, typically, faces some difficulty in ensuring a uniform distribution of particles for models with complex geometries or those with a very small geometric domain. e existing repulsive force method [19] and the virtual particle method [20] primarily solve the problem of the boundary particles missing which happens due to these particles being "truncated" by the boundary in the support domain.…”