A modified method for morphology quantification and generation of 2D granular particles

• Published in: Granular matter Vol. 24; no. 1
• Main Authors: Chen, Jun, Li, Ruilin, Mo, Pin-Qiang, Zhou, Guoqing, Cai, Shangyue, Chen, Daqing
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-02-2022; Springer Nature B.V
• Subjects: Algorithms; Aspect ratio; Complex Fluids and Microfluidics; Contours; Elliptic fitting; Engineering Fluid Dynamics; Engineering Thermodynamics; Foundations; Geoengineering; Heat and Mass Transfer; Hydraulics; Industrial Chemistry/Chemical Engineering; Least squares; Materials Science; Mechanical properties; Morphology; Multiscale analysis of particulate micro- and macro-processes; Original Paper; Physical properties; Physics; Physics and Astronomy; Soft and Granular Matter; Surface layers; Particle generation method; Locally weighted regression smoothing method; Particle morphology; The least-squares fitting ellipse
• ISSN: 1434-5021; 1434-7636
• DOI: 10.1007/s10035-021-01149-8

The morphologies of individual particles vary greatly, which intimately affect the physical and mechanical properties of granular assemblies. In this paper, a modified morphology quantification and generation method of granular particles based on two-dimensional images is proposed. By defining the ratio of the long axis and the short axis of the least-squares fitting ellipse of the particle contour as the aspect ratio, we characterize the deviations between the particle contour and the least-squares fitting ellipse as the angularity. Meanwhile, the surface texture is calculated by using the locally weighted regression smoothing method. Five types of particles are used to verify the quantization method, and the results demonstrate a comprehensive description of particle morphology. As the inversion of the quantification, a generation method of granular particles is further introduced and then compared with the traditional Fourier generation algorithm. The algorithms proposed in this work are computationally efficient and could be used for rapid quantification and generation of particle morphology in particulate matter research.
Graphic abstract