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Title: Eigenparticles: characterizing particles using eigenfaces

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. As a result, when compared with a state-of-the-art Fourier based method, ‘Eigenparticles’ performs favorably, clearly distinguishing the different particles.

Authors:
ORCiD logo [1];  [2];  [2];  [3]
  1. Univ. of Liverpool (United Kingdom)
  2. National Energy Technology Lab. (NETL), Morgantown, WV (United States)
  3. Univ. of Sheffield (United Kingdom)
Publication Date:
Research Org.:
National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)
Sponsoring Org.:
USDOE Office of Fossil Energy (FE)
OSTI Identifier:
1582388
Resource Type:
Accepted Manuscript
Journal Name:
Granular Matter
Additional Journal Information:
[ Journal Volume: 21; Journal Issue: 3]; Journal ID: ISSN 1434-5021
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; Particle characterization; Particle shape; Principle components analysis; Eigenparticles; Eigenfeatures; Eigenfaces

Citation Formats

Higham, J. E., Vaidheeswaran, A., Benavides, K., and Shepley, P. Eigenparticles: characterizing particles using eigenfaces. United States: N. p., 2019. Web. doi:10.1007/s10035-019-0900-z.
Higham, J. E., Vaidheeswaran, A., Benavides, K., & Shepley, P. Eigenparticles: characterizing particles using eigenfaces. United States. doi:10.1007/s10035-019-0900-z.
Higham, J. E., Vaidheeswaran, A., Benavides, K., and Shepley, P. Thu . "Eigenparticles: characterizing particles using eigenfaces". United States. doi:10.1007/s10035-019-0900-z.
@article{osti_1582388,
title = {Eigenparticles: characterizing particles using eigenfaces},
author = {Higham, J. E. and Vaidheeswaran, A. and Benavides, K. and Shepley, P.},
abstractNote = {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. As a result, when compared with a state-of-the-art Fourier based method, ‘Eigenparticles’ performs favorably, clearly distinguishing the different particles.},
doi = {10.1007/s10035-019-0900-z},
journal = {Granular Matter},
number = [3],
volume = [21],
place = {United States},
year = {2019},
month = {5}
}

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