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Characterising particle packings by principal component analysis

Y.T. Feng, Tingting Zhao, Min Wang, D.R.J. Owen, Yuntian Feng Orcid Logo, Roger Owen Orcid Logo

Computer Methods in Applied Mechanics and Engineering, Volume: 340, Pages: 70 - 89

Swansea University Authors: Yuntian Feng Orcid Logo, Roger Owen Orcid Logo

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DOI (Published version): 10.1016/j.cma.2018.05.018

Abstract

Particle packings play an important role in the discrete element modelling of particulate systems as different packings can lead to different physical behaviour, and therefore need to be properly characterised and controlled. Apart from a few conventional approaches, there is still a lack of more ge...

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Published in: Computer Methods in Applied Mechanics and Engineering
ISSN: 00457825
Published: 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa40287
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first_indexed 2018-05-22T13:14:57Z
last_indexed 2018-08-06T18:52:14Z
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spelling 2018-08-06T16:38:57.6036473 v2 40287 2018-05-22 Characterising particle packings by principal component analysis d66794f9c1357969a5badf654f960275 0000-0002-6396-8698 Yuntian Feng Yuntian Feng true false 0303b9485caf6fbc8787397a5d926d1c 0000-0003-2471-0544 Roger Owen Roger Owen true false 2018-05-22 CIVL Particle packings play an important role in the discrete element modelling of particulate systems as different packings can lead to different physical behaviour, and therefore need to be properly characterised and controlled. Apart from a few conventional approaches, there is still a lack of more general, comprehensive and quantitative approaches that can reveal some fundamental features of packings. The current work attempts to develop a novel packing characterising system based on two techniques: digitalised image representation of a packing and subsequent application of Principal Component Analysis to the resulting image. It will prove that the principal components or variances of a packing image can indeed qualify as the signature of the packing, and therefore can be utilised to characterise the packing. Furthermore, a dissimilarity coefficient or a similarity index will be defined which provides a single valued metric to quantitatively compare two packings. Comprehensive investigations for two sets of purposefully generated random packings are conducted to fully understand relationships of their principal variances with packing features. Various issues, including effects of grid resolutions and packing density on principal variances are discussed. Methods of how to apply principal variances to assess spatial homogeneity and isotropy of packings are proposed. The relationship between scaled packings and their principal variances is also considered. Journal Article Computer Methods in Applied Mechanics and Engineering 340 70 89 00457825 Particle packing; Digitalised image; Principal component analysis; Principal variance; Spatial homogeneity and isotropy 31 12 2018 2018-12-31 10.1016/j.cma.2018.05.018 COLLEGE NANME Civil Engineering COLLEGE CODE CIVL Swansea University 2018-08-06T16:38:57.6036473 2018-05-22T09:29:08.2297807 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Y.T. Feng 1 Tingting Zhao 2 Min Wang 3 D.R.J. Owen 4 Yuntian Feng 0000-0002-6396-8698 5 Roger Owen 0000-0003-2471-0544 6 0040287-31052018144419.pdf feng2018(2).pdf 2018-05-31T14:44:19.4870000 Output 19730067 application/pdf Accepted Manuscript true 2019-05-31T00:00:00.0000000 true eng
title Characterising particle packings by principal component analysis
spellingShingle Characterising particle packings by principal component analysis
Yuntian Feng
Roger Owen
title_short Characterising particle packings by principal component analysis
title_full Characterising particle packings by principal component analysis
title_fullStr Characterising particle packings by principal component analysis
title_full_unstemmed Characterising particle packings by principal component analysis
title_sort Characterising particle packings by principal component analysis
author_id_str_mv d66794f9c1357969a5badf654f960275
0303b9485caf6fbc8787397a5d926d1c
author_id_fullname_str_mv d66794f9c1357969a5badf654f960275_***_Yuntian Feng
0303b9485caf6fbc8787397a5d926d1c_***_Roger Owen
author Yuntian Feng
Roger Owen
author2 Y.T. Feng
Tingting Zhao
Min Wang
D.R.J. Owen
Yuntian Feng
Roger Owen
format Journal article
container_title Computer Methods in Applied Mechanics and Engineering
container_volume 340
container_start_page 70
publishDate 2018
institution Swansea University
issn 00457825
doi_str_mv 10.1016/j.cma.2018.05.018
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 1
active_str 0
description Particle packings play an important role in the discrete element modelling of particulate systems as different packings can lead to different physical behaviour, and therefore need to be properly characterised and controlled. Apart from a few conventional approaches, there is still a lack of more general, comprehensive and quantitative approaches that can reveal some fundamental features of packings. The current work attempts to develop a novel packing characterising system based on two techniques: digitalised image representation of a packing and subsequent application of Principal Component Analysis to the resulting image. It will prove that the principal components or variances of a packing image can indeed qualify as the signature of the packing, and therefore can be utilised to characterise the packing. Furthermore, a dissimilarity coefficient or a similarity index will be defined which provides a single valued metric to quantitatively compare two packings. Comprehensive investigations for two sets of purposefully generated random packings are conducted to fully understand relationships of their principal variances with packing features. Various issues, including effects of grid resolutions and packing density on principal variances are discussed. Methods of how to apply principal variances to assess spatial homogeneity and isotropy of packings are proposed. The relationship between scaled packings and their principal variances is also considered.
published_date 2018-12-31T03:51:18Z
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score 11.037144

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