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The correlation between statistical descriptors of heterogeneous materials
Computer Methods in Applied Mechanics and Engineering, Volume: 384, Start page: 113948
Swansea University Authors:
Shaoqing Cui, Jinlong Fu , Hywel Thomas
, Chenfeng Li
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DOI (Published version): 10.1016/j.cma.2021.113948
Abstract
Heterogeneous materials such as rocks and composites are comprised of multiple material phases of different sizes and shapes that are randomly distributed through the medium. The random microstructure is typically described by using various statistical descriptors, which include volume fraction, two...
Published in: | Computer Methods in Applied Mechanics and Engineering |
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ISSN: | 0045-7825 |
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Elsevier BV
2021
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URI: | https://cronfa.swan.ac.uk/Record/cronfa57162 |
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The random microstructure is typically described by using various statistical descriptors, which include volume fraction, two-point correlation function, and tortuosity, to name a few. Capturing different morphological features, a large number of statistical descriptors are proposed in different research fields, such as material science, geoscience and computational engineering. It is well known that these statistical descriptors are not independent from each other, but until recently it remains unclear what descriptors are more similar or more different. In particular, it is extremely difficult to look for quantified relations between various descriptors, since they are often defined in very different formats. The lack of quantified understanding of descriptors’ relations can cause uncertainties or even systematic errors in heterogeneous materials studies. To address this issue, we propose a novel and generic correlation analysis strategy and establish, for the first time, the quantified relations between various statistical descriptors for heterogeneous materials. Based on data science techniques, our approach consists of three operational steps: data regularization, dimension reduction and correlation analysis. A total of 41 statistical descriptors are collected and analysed in this study, which is readily extensible to include other new descriptors. The generic and quantified correlation results are compared with other established descriptor relations that are obtained from analytical analysis or physical intuition, and good agreements are observed in all cases. The quantified relations between various descriptors are summarized in a single correlation graph, which provides useful guiding information for the characterization, reconstruction and property prediction of heterogeneous materials.</abstract><type>Journal Article</type><journal>Computer Methods in Applied Mechanics and Engineering</journal><volume>384</volume><journalNumber/><paginationStart>113948</paginationStart><paginationEnd/><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0045-7825</issnPrint><issnElectronic/><keywords>Heterogeneous material, Random media, Morphology, Characterization, Reconstruction, Property prediction</keywords><publishedDay>1</publishedDay><publishedMonth>10</publishedMonth><publishedYear>2021</publishedYear><publishedDate>2021-10-01</publishedDate><doi>10.1016/j.cma.2021.113948</doi><url/><notes/><college>COLLEGE NANME</college><department>Science and Engineering - Faculty</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>FGSEN</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2021-12-14T14:30:36.5999080</lastEdited><Created>2021-06-17T11:17:22.2833507</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering</level></path><authors><author><firstname>Shaoqing</firstname><surname>Cui</surname><orcid/><order>1</order></author><author><firstname>Jinlong</firstname><surname>Fu</surname><orcid>0000-0003-2964-4777</orcid><order>2</order></author><author><firstname>Song</firstname><surname>Cen</surname><order>3</order></author><author><firstname>Hywel</firstname><surname>Thomas</surname><orcid>0000-0002-3951-0409</orcid><order>4</order></author><author><firstname>Chenfeng</firstname><surname>Li</surname><orcid>0000-0003-0441-211X</orcid><order>5</order></author></authors><documents><document><filename>57162__20188__682616e9928a41ed97a90cd2ac0c7a52.pdf</filename><originalFilename>57162.pdf</originalFilename><uploaded>2021-06-17T15:22:53.1289466</uploaded><type>Output</type><contentLength>1537942</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><embargoDate>2022-06-12T00:00:00.0000000</embargoDate><documentNotes>Released under the terms of a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by-nc-nd/4.0/</licence></document></documents><OutputDurs/></rfc1807> |
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2021-12-14T14:30:36.5999080 v2 57162 2021-06-17 The correlation between statistical descriptors of heterogeneous materials 88a9a34dc92416ac83ea8ff485d06ade Shaoqing Cui Shaoqing Cui true false e870d228a5035d2ef500eacbfc9f0302 0000-0003-2964-4777 Jinlong Fu Jinlong Fu true false 08ebc76b093f3e17fed29281f5cb637e 0000-0002-3951-0409 Hywel Thomas Hywel Thomas true false 82fe170d5ae2c840e538a36209e5a3ac 0000-0003-0441-211X Chenfeng Li Chenfeng Li true false 2021-06-17 FGSEN Heterogeneous materials such as rocks and composites are comprised of multiple material phases of different sizes and shapes that are randomly distributed through the medium. The random microstructure is typically described by using various statistical descriptors, which include volume fraction, two-point correlation function, and tortuosity, to name a few. Capturing different morphological features, a large number of statistical descriptors are proposed in different research fields, such as material science, geoscience and computational engineering. It is well known that these statistical descriptors are not independent from each other, but until recently it remains unclear what descriptors are more similar or more different. In particular, it is extremely difficult to look for quantified relations between various descriptors, since they are often defined in very different formats. The lack of quantified understanding of descriptors’ relations can cause uncertainties or even systematic errors in heterogeneous materials studies. To address this issue, we propose a novel and generic correlation analysis strategy and establish, for the first time, the quantified relations between various statistical descriptors for heterogeneous materials. Based on data science techniques, our approach consists of three operational steps: data regularization, dimension reduction and correlation analysis. A total of 41 statistical descriptors are collected and analysed in this study, which is readily extensible to include other new descriptors. The generic and quantified correlation results are compared with other established descriptor relations that are obtained from analytical analysis or physical intuition, and good agreements are observed in all cases. The quantified relations between various descriptors are summarized in a single correlation graph, which provides useful guiding information for the characterization, reconstruction and property prediction of heterogeneous materials. Journal Article Computer Methods in Applied Mechanics and Engineering 384 113948 Elsevier BV 0045-7825 Heterogeneous material, Random media, Morphology, Characterization, Reconstruction, Property prediction 1 10 2021 2021-10-01 10.1016/j.cma.2021.113948 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University 2021-12-14T14:30:36.5999080 2021-06-17T11:17:22.2833507 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Shaoqing Cui 1 Jinlong Fu 0000-0003-2964-4777 2 Song Cen 3 Hywel Thomas 0000-0002-3951-0409 4 Chenfeng Li 0000-0003-0441-211X 5 57162__20188__682616e9928a41ed97a90cd2ac0c7a52.pdf 57162.pdf 2021-06-17T15:22:53.1289466 Output 1537942 application/pdf Accepted Manuscript true 2022-06-12T00:00:00.0000000 Released under the terms of a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License true eng http://creativecommons.org/licenses/by-nc-nd/4.0/ |
title |
The correlation between statistical descriptors of heterogeneous materials |
spellingShingle |
The correlation between statistical descriptors of heterogeneous materials Shaoqing Cui Jinlong Fu Hywel Thomas Chenfeng Li |
title_short |
The correlation between statistical descriptors of heterogeneous materials |
title_full |
The correlation between statistical descriptors of heterogeneous materials |
title_fullStr |
The correlation between statistical descriptors of heterogeneous materials |
title_full_unstemmed |
The correlation between statistical descriptors of heterogeneous materials |
title_sort |
The correlation between statistical descriptors of heterogeneous materials |
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88a9a34dc92416ac83ea8ff485d06ade e870d228a5035d2ef500eacbfc9f0302 08ebc76b093f3e17fed29281f5cb637e 82fe170d5ae2c840e538a36209e5a3ac |
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88a9a34dc92416ac83ea8ff485d06ade_***_Shaoqing Cui e870d228a5035d2ef500eacbfc9f0302_***_Jinlong Fu 08ebc76b093f3e17fed29281f5cb637e_***_Hywel Thomas 82fe170d5ae2c840e538a36209e5a3ac_***_Chenfeng Li |
author |
Shaoqing Cui Jinlong Fu Hywel Thomas Chenfeng Li |
author2 |
Shaoqing Cui Jinlong Fu Song Cen Hywel Thomas Chenfeng Li |
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Computer Methods in Applied Mechanics and Engineering |
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description |
Heterogeneous materials such as rocks and composites are comprised of multiple material phases of different sizes and shapes that are randomly distributed through the medium. The random microstructure is typically described by using various statistical descriptors, which include volume fraction, two-point correlation function, and tortuosity, to name a few. Capturing different morphological features, a large number of statistical descriptors are proposed in different research fields, such as material science, geoscience and computational engineering. It is well known that these statistical descriptors are not independent from each other, but until recently it remains unclear what descriptors are more similar or more different. In particular, it is extremely difficult to look for quantified relations between various descriptors, since they are often defined in very different formats. The lack of quantified understanding of descriptors’ relations can cause uncertainties or even systematic errors in heterogeneous materials studies. To address this issue, we propose a novel and generic correlation analysis strategy and establish, for the first time, the quantified relations between various statistical descriptors for heterogeneous materials. Based on data science techniques, our approach consists of three operational steps: data regularization, dimension reduction and correlation analysis. A total of 41 statistical descriptors are collected and analysed in this study, which is readily extensible to include other new descriptors. The generic and quantified correlation results are compared with other established descriptor relations that are obtained from analytical analysis or physical intuition, and good agreements are observed in all cases. The quantified relations between various descriptors are summarized in a single correlation graph, which provides useful guiding information for the characterization, reconstruction and property prediction of heterogeneous materials. |
published_date |
2021-10-01T04:12:41Z |
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1763753864879669248 |
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11.021648 |