Using Surface Asperities for Efficient Random Particle Overlap Detection in the Generation of Randomly Oriented and Located Particle Arrangements

Published

November 22, 2021

Author(s)

Edward Garboczi, Christa Torrence, Zachary Grasley, William Lawrimore

Abstract

A novel method of detecting overlap between two irregularly shaped virtual particles whose surfaces are described by analytical functions is presented in this study. Utilizing the analytical equation that describes the surface of the particle, the first and second spatial derivatives are used to locate the major surface asperities via the surface curvature. During the particle placement process, detecting overlap between two particles is a crucial, but slow, step. In this work, particle asperities are checked for intersection with nearby particles first, leading to significant improvements in efficiency, compared to previous methods. While the asperity check does not detect all cases of overlap between two particles, it leverages the probability that the overlap is most likely to occur at a surface asperity. Microstructures are generated using four particle size gradations and four volume fractions to demonstrate the decrease in computational time associated with using the asperity overlap detection versus using a surface point cloud. This overlap detection method provides significant increases in efficiency, averaging a speedup of 10 when generating a 40%volume fraction microstructure.

Citation

Powder Technology

Volume

399

Pub Type

Journals

Download Paper

https://doi.org/10.1016/j.powtec.2021.11.023

Local Download

Keywords

Overlap detection, Irregular particle placement, Spherical harmonics, Particle asperities

Citation

Garboczi, E. , Torrence, C. , Grasley, Z. and Lawrimore, W. (2021), Using Surface Asperities for Efficient Random Particle Overlap Detection in the Generation of Randomly Oriented and Located Particle Arrangements, Powder Technology, [online], https://doi.org/10.1016/j.powtec.2021.11.023, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932490 (Accessed April 19, 2024)