Higham, JE ORCID: 0000-0001-7577-0913, Vaidheeswaran, A, Benavides, K and Shepley, P ORCID: 0000-0003-2888-6758
(2019)
Eigenparticles: characterizing particles using eigenfaces.
GRANULAR MATTER, 21 (3).
45-.
Text
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Abstract
The shape characteristics of particles have a pinnacle role in microsopic and macroscopic features of a system. Several studies have highlighted the need for considering deviations from a spherical representation of particles for accurate modeling of granular and multiphase flow systems. Using a shape factor, sphericity or roundness parameter alone is proven to be inadequate to capture the physical phenomena. In the present study we propose a novel metric based on the pattern recognition method Eigenfaces, coining the technique ‘Eigenparticles’. Using this technique we create a single statistical distribution of basis shapes to describe the morphological composition. The proposed technique is successfully validated with test shapes and applied to real particles. When compared with a state-of-the-art Fourier based method, ‘Eigenparticles’ performs favorably, clearly distinguishing the different particles.
Item Type: | Article |
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Uncontrolled Keywords: | Particle characterization, Particle shape, Principle components analysis, Eigenparticles, Eigenfeatures, Eigenfaces |
Depositing User: | Symplectic Admin |
Date Deposited: | 23 Sep 2019 08:44 |
Last Modified: | 15 Mar 2024 14:56 |
DOI: | 10.1007/s10035-019-0900-z |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/3055612 |