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:
-
- Univ. of Liverpool (United Kingdom)
- National Energy Technology Lab. (NETL), Morgantown, WV (United States)
- 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. https://doi.org/10.1007/s10035-019-0900-z
Higham, J. E., Vaidheeswaran, A., Benavides, K., and Shepley, P. Thu .
"Eigenparticles: characterizing particles using eigenfaces". United States. https://doi.org/10.1007/s10035-019-0900-z. https://www.osti.gov/servlets/purl/1582388.
@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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