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Characterising 3D spherical packings by principal component analysis

Tingting Zhao, Yuntian Feng Orcid Logo, Yuanqiang Tan

Engineering Computations, Volume: ahead-of-print, Issue: ahead-of-print

Swansea University Author: Yuntian Feng Orcid Logo

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DOI (Published version): 10.1108/ec-05-2019-0225

Abstract

PurposeThe purpose of this paper is to extend the previous study [Computer Methods in Applied Mechanics and Engineering 340: 70-89, 2018] on the development of a novel packing characterising system based on principal component analysis (PCA) to quantitatively reveal some fundamental features of sphe...

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Published in: Engineering Computations
ISSN: 0264-4401
Published: Emerald 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa51481
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spelling 2019-08-19T10:40:28.5732945 v2 51481 2019-08-19 Characterising 3D spherical packings by principal component analysis d66794f9c1357969a5badf654f960275 0000-0002-6396-8698 Yuntian Feng Yuntian Feng true false 2019-08-19 CIVL PurposeThe purpose of this paper is to extend the previous study [Computer Methods in Applied Mechanics and Engineering 340: 70-89, 2018] on the development of a novel packing characterising system based on principal component analysis (PCA) to quantitatively reveal some fundamental features of spherical particle packings in three-dimensional.Design/methodology/approachGaussian quadrature is adopted to obtain the volume matrix representation of a particle packing. Then, the digitalised image of the packing is obtained by converting cross-sectional images along one direction to column vectors of the packing image. Both a principal variance (PV) function and a dissimilarity coefficient (DC) are proposed to characterise differences between different packings (or images).FindingsDifferences between two packings with different packing features can be revealed by the PVs and DC. Furthermore, the values of PV and DC can indicate different levels of effects on packing caused by configuration randomness, particle distribution, packing density and particle size distribution. The uniformity and isotropy of a packing can also be investigated by this PCA based approach.Originality/valueDevelop an alternative novel approach to quantitatively characterise sphere packings, particularly their differences. Journal Article Engineering Computations ahead-of-print ahead-of-print Emerald 0264-4401 19 10 2019 2019-10-19 10.1108/ec-05-2019-0225 http://dx.doi.org/10.1108/ec-05-2019-0225 COLLEGE NANME Civil Engineering COLLEGE CODE CIVL Swansea University 2019-08-19T10:40:28.5732945 2019-08-19T10:38:46.8033652 Tingting Zhao 1 Yuntian Feng 0000-0002-6396-8698 2 Yuanqiang Tan 3 51481__15047__ad12a354ecf54ccbb3f49b07f5e60781.pdf zhao2019(3).pdf 2019-08-19T10:40:28.5570000 Output 6246847 application/pdf Accepted Manuscript true 2019-10-19T00:00:00.0000000 true eng
title Characterising 3D spherical packings by principal component analysis
spellingShingle Characterising 3D spherical packings by principal component analysis
Yuntian Feng
title_short Characterising 3D spherical packings by principal component analysis
title_full Characterising 3D spherical packings by principal component analysis
title_fullStr Characterising 3D spherical packings by principal component analysis
title_full_unstemmed Characterising 3D spherical packings by principal component analysis
title_sort Characterising 3D spherical packings by principal component analysis
author_id_str_mv d66794f9c1357969a5badf654f960275
author_id_fullname_str_mv d66794f9c1357969a5badf654f960275_***_Yuntian Feng
author Yuntian Feng
author2 Tingting Zhao
Yuntian Feng
Yuanqiang Tan
format Journal article
container_title Engineering Computations
container_volume ahead-of-print
container_issue ahead-of-print
publishDate 2019
institution Swansea University
issn 0264-4401
doi_str_mv 10.1108/ec-05-2019-0225
publisher Emerald
url http://dx.doi.org/10.1108/ec-05-2019-0225
document_store_str 1
active_str 0
description PurposeThe purpose of this paper is to extend the previous study [Computer Methods in Applied Mechanics and Engineering 340: 70-89, 2018] on the development of a novel packing characterising system based on principal component analysis (PCA) to quantitatively reveal some fundamental features of spherical particle packings in three-dimensional.Design/methodology/approachGaussian quadrature is adopted to obtain the volume matrix representation of a particle packing. Then, the digitalised image of the packing is obtained by converting cross-sectional images along one direction to column vectors of the packing image. Both a principal variance (PV) function and a dissimilarity coefficient (DC) are proposed to characterise differences between different packings (or images).FindingsDifferences between two packings with different packing features can be revealed by the PVs and DC. Furthermore, the values of PV and DC can indicate different levels of effects on packing caused by configuration randomness, particle distribution, packing density and particle size distribution. The uniformity and isotropy of a packing can also be investigated by this PCA based approach.Originality/valueDevelop an alternative novel approach to quantitatively characterise sphere packings, particularly their differences.
published_date 2019-10-19T04:03:22Z
_version_ 1763753278985732096
score 11.013619

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