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The correlation between statistical descriptors of heterogeneous materials

Shaoqing Cui, PhD student Fu, Song Cen, Hywel Thomas Orcid Logo, Chenfeng Li Orcid Logo

Computer Methods in Applied Mechanics and Engineering, Volume: 384, Start page: 113948

Swansea University Authors: Shaoqing Cui, PhD student Fu, Hywel Thomas Orcid Logo, Chenfeng Li Orcid Logo

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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...

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Published in: Computer Methods in Applied Mechanics and Engineering
ISSN: 0045-7825
Published: Elsevier BV 2021
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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&#x2019; 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><CollegeCode>COLLEGE CODE</CollegeCode><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>PhD student</firstname><surname>Fu</surname><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>
spelling 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 PhD student Fu PhD student 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 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 COLLEGE CODE 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 PhD student Fu 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
PhD student 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
author_id_str_mv 88a9a34dc92416ac83ea8ff485d06ade
e870d228a5035d2ef500eacbfc9f0302
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author_id_fullname_str_mv 88a9a34dc92416ac83ea8ff485d06ade_***_Shaoqing Cui
e870d228a5035d2ef500eacbfc9f0302_***_PhD student Fu
08ebc76b093f3e17fed29281f5cb637e_***_Hywel Thomas
82fe170d5ae2c840e538a36209e5a3ac_***_Chenfeng Li
author Shaoqing Cui
PhD student Fu
Hywel Thomas
Chenfeng Li
author2 Shaoqing Cui
PhD student Fu
Song Cen
Hywel Thomas
Chenfeng Li
format Journal article
container_title Computer Methods in Applied Mechanics and Engineering
container_volume 384
container_start_page 113948
publishDate 2021
institution Swansea University
issn 0045-7825
doi_str_mv 10.1016/j.cma.2021.113948
publisher Elsevier BV
college_str Faculty of Science and Engineering
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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
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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 2021-10-01T05:14:05Z
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score 11.357488

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