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Constitutive behaviour of granular materials: from discrete element modelling to data-driven forecasting / TONGMING QU

Swansea University Author: TONGMING QU

  • E-Thesis – open access under embargo until: 4th April 2027

DOI (Published version): 10.23889/SUthesis.59813

Abstract

Granular materials are ubiquitous in engineering activities of our human beings. Constitutive modelling of granular materials, as one of the most fundamental problems in this field, has long received great attention. Over the past decades, analytical or phenomenological models are undoubtedly the mo...

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Published: Swansea 2022
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Feng, Yuntian ; Peric, Djordje
URI: https://cronfa.swan.ac.uk/Record/cronfa59813
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fullrecord <?xml version="1.0"?><rfc1807><datestamp>2022-04-13T13:02:38.6103310</datestamp><bib-version>v2</bib-version><id>59813</id><entry>2022-04-13</entry><title>Constitutive behaviour of granular materials: from discrete element modelling to data-driven forecasting</title><swanseaauthors><author><sid>2e6b64d244a47d39fb35bded13333783</sid><firstname>TONGMING</firstname><surname>QU</surname><name>TONGMING QU</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2022-04-13</date><abstract>Granular materials are ubiquitous in engineering activities of our human beings. Constitutive modelling of granular materials, as one of the most fundamental problems in this field, has long received great attention. Over the past decades, analytical or phenomenological models are undoubtedly the most common way to characterise the elastic-plastic behaviour of granular materials. However, although numerous attempts have been made, developing a unified theoretical model to capture the constitutive behaviour of granular materials remains an ongoing challenge.Instead of phenomenological models, numerical and data-driven surrogate models are two emerging alternatives to predict stress-strain responses of materials. Hierarchical multi-scale modelling and data-driven computing are two typical applications of these two constitutive modelling paradigms. Without the use of analytical models, the stress-strain mapping is directly provided by low-scale numerical modelling or data-driven forecasting in continuum-based numerical models. This thesis aims to partially address some open challenges for the constitutive modelling of granular materials from the two new research perspectives.In the discrete element modelling part, a total of 5 individual chapters are incorporated:(1)A novel flexible membrane algorithm has been proposed to simulate conventional triaxial testing for granular materials. The influence of flexible or rigid servo-wall conditions on the measured responses of granular materials in triaxial testing has been compared in detail via a series of numerical tests.(2)A hybrid analytical-computational calibration framework is proposed to calibrate particle-scale elastic parameters. The proposed calibration framework is tested through a collection of 2D and 3D discrete element models with both mono- and poly-disperse granular packings.(3)A physics-informed adaptive moment optimisation method is proposed to calibrate bond parameters in bonded particle models. A validation example of SiC ceramic is used to validate the proposed algorithm.(4)The ability of discrete element models with spheres to clumped particles in reproducing the constitutive behaviour of granular materials is explored through 4 perspectives. It is found that although discrete element models with spheres or clumped particles are capable of qualitatively describing the salient mechanical behaviour of granular materials, some qualitative deviations between experiments and the simulations are also observed, in terms of the stress-dilatancy behaviour and principal stress ratio against axial strain.(5)An adaptive granular representative volume element (RVE) model with an evolutionary periodic boundary is proposed for hierarchical multiscale analysis. The proposed adaptive RVE model avoids the reinitialisation of the RVE box that even undergoes extremely large shear deformation; meanwhile, a more e&#xFF;cient algorithm is presented to treat the interaction between boundary particles and other image particles.In the data-driven modelling part, a total of 2 individual chapters are involved:(1)A deep learning-based constitutive modelling strategy with the prediction model directly learning from triaxial testing data via discrete element modelling is explored. The predic-tion performance of two common recurrent neural networks (RNNs), i.e. Long short-term memory (LSTM) and gated recurrent unit (GRU) networks are compared in detail through hyperparameter investigations.(2)Micromechanical knowledge is used to discover critical microstructural variables associated with the constitutive behaviour of granular materials. Depending on the strategy to exploit a priori micromechanical knowledge, three di&#x2D9;erent training models are examined. The first strategy uses only the measurable external variables to make stress predictions; the second strategy utilises a directed graph to link all the external strain sequences and internal microstructural evolution variables into a single prediction model comprised of a set of sub-mappings, and the third strategy explicitly integrates the physically important non-temporal properties with external strain paths into training through an enhanced GRU.</abstract><type>E-Thesis</type><journal/><volume/><journalNumber/><paginationStart/><paginationEnd/><publisher/><placeOfPublication>Swansea</placeOfPublication><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic/><keywords>Discrete element method, Granular materials, Constitutive modelling, Deep learning, Parameter calibration</keywords><publishedDay>4</publishedDay><publishedMonth>4</publishedMonth><publishedYear>2022</publishedYear><publishedDate>2022-04-04</publishedDate><doi>10.23889/SUthesis.59813</doi><url/><notes>ORCiD identifier: https://orcid.org/0000-0003-3058-8282</notes><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><supervisor>Feng, Yuntian ; Peric, Djordje</supervisor><degreelevel>Doctoral</degreelevel><degreename>Ph.D</degreename><degreesponsorsfunders>Zienkowicz Scholarships; China Scholarship Council Scholarship</degreesponsorsfunders><apcterm/><lastEdited>2022-04-13T13:02:38.6103310</lastEdited><Created>2022-04-13T10:57:29.2919205</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Uncategorised</level></path><authors><author><firstname>TONGMING</firstname><surname>QU</surname><order>1</order></author></authors><documents><document><filename>Under embargo</filename><originalFilename>Under embargo</originalFilename><uploaded>2022-04-13T12:52:23.5180320</uploaded><type>Output</type><contentLength>68069072</contentLength><contentType>application/pdf</contentType><version>E-Thesis &#x2013; open access</version><cronfaStatus>true</cronfaStatus><embargoDate>2027-04-04T00:00:00.0000000</embargoDate><documentNotes>Constitutive behaviour of granular materials: from discrete element modelling to data-driven forecasting &#xA9; 2022 by Tongming Qu is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. Third party content is excluded for use under the license terms.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2022-04-13T13:02:38.6103310 v2 59813 2022-04-13 Constitutive behaviour of granular materials: from discrete element modelling to data-driven forecasting 2e6b64d244a47d39fb35bded13333783 TONGMING QU TONGMING QU true false 2022-04-13 Granular materials are ubiquitous in engineering activities of our human beings. Constitutive modelling of granular materials, as one of the most fundamental problems in this field, has long received great attention. Over the past decades, analytical or phenomenological models are undoubtedly the most common way to characterise the elastic-plastic behaviour of granular materials. However, although numerous attempts have been made, developing a unified theoretical model to capture the constitutive behaviour of granular materials remains an ongoing challenge.Instead of phenomenological models, numerical and data-driven surrogate models are two emerging alternatives to predict stress-strain responses of materials. Hierarchical multi-scale modelling and data-driven computing are two typical applications of these two constitutive modelling paradigms. Without the use of analytical models, the stress-strain mapping is directly provided by low-scale numerical modelling or data-driven forecasting in continuum-based numerical models. This thesis aims to partially address some open challenges for the constitutive modelling of granular materials from the two new research perspectives.In the discrete element modelling part, a total of 5 individual chapters are incorporated:(1)A novel flexible membrane algorithm has been proposed to simulate conventional triaxial testing for granular materials. The influence of flexible or rigid servo-wall conditions on the measured responses of granular materials in triaxial testing has been compared in detail via a series of numerical tests.(2)A hybrid analytical-computational calibration framework is proposed to calibrate particle-scale elastic parameters. The proposed calibration framework is tested through a collection of 2D and 3D discrete element models with both mono- and poly-disperse granular packings.(3)A physics-informed adaptive moment optimisation method is proposed to calibrate bond parameters in bonded particle models. A validation example of SiC ceramic is used to validate the proposed algorithm.(4)The ability of discrete element models with spheres to clumped particles in reproducing the constitutive behaviour of granular materials is explored through 4 perspectives. It is found that although discrete element models with spheres or clumped particles are capable of qualitatively describing the salient mechanical behaviour of granular materials, some qualitative deviations between experiments and the simulations are also observed, in terms of the stress-dilatancy behaviour and principal stress ratio against axial strain.(5)An adaptive granular representative volume element (RVE) model with an evolutionary periodic boundary is proposed for hierarchical multiscale analysis. The proposed adaptive RVE model avoids the reinitialisation of the RVE box that even undergoes extremely large shear deformation; meanwhile, a more eÿcient algorithm is presented to treat the interaction between boundary particles and other image particles.In the data-driven modelling part, a total of 2 individual chapters are involved:(1)A deep learning-based constitutive modelling strategy with the prediction model directly learning from triaxial testing data via discrete element modelling is explored. The predic-tion performance of two common recurrent neural networks (RNNs), i.e. Long short-term memory (LSTM) and gated recurrent unit (GRU) networks are compared in detail through hyperparameter investigations.(2)Micromechanical knowledge is used to discover critical microstructural variables associated with the constitutive behaviour of granular materials. Depending on the strategy to exploit a priori micromechanical knowledge, three di˙erent training models are examined. The first strategy uses only the measurable external variables to make stress predictions; the second strategy utilises a directed graph to link all the external strain sequences and internal microstructural evolution variables into a single prediction model comprised of a set of sub-mappings, and the third strategy explicitly integrates the physically important non-temporal properties with external strain paths into training through an enhanced GRU. E-Thesis Swansea Discrete element method, Granular materials, Constitutive modelling, Deep learning, Parameter calibration 4 4 2022 2022-04-04 10.23889/SUthesis.59813 ORCiD identifier: https://orcid.org/0000-0003-3058-8282 COLLEGE NANME COLLEGE CODE Swansea University Feng, Yuntian ; Peric, Djordje Doctoral Ph.D Zienkowicz Scholarships; China Scholarship Council Scholarship 2022-04-13T13:02:38.6103310 2022-04-13T10:57:29.2919205 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised TONGMING QU 1 Under embargo Under embargo 2022-04-13T12:52:23.5180320 Output 68069072 application/pdf E-Thesis – open access true 2027-04-04T00:00:00.0000000 Constitutive behaviour of granular materials: from discrete element modelling to data-driven forecasting © 2022 by Tongming Qu is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. Third party content is excluded for use under the license terms. true eng https://creativecommons.org/licenses/by/4.0/
title Constitutive behaviour of granular materials: from discrete element modelling to data-driven forecasting
spellingShingle Constitutive behaviour of granular materials: from discrete element modelling to data-driven forecasting
TONGMING QU
title_short Constitutive behaviour of granular materials: from discrete element modelling to data-driven forecasting
title_full Constitutive behaviour of granular materials: from discrete element modelling to data-driven forecasting
title_fullStr Constitutive behaviour of granular materials: from discrete element modelling to data-driven forecasting
title_full_unstemmed Constitutive behaviour of granular materials: from discrete element modelling to data-driven forecasting
title_sort Constitutive behaviour of granular materials: from discrete element modelling to data-driven forecasting
author_id_str_mv 2e6b64d244a47d39fb35bded13333783
author_id_fullname_str_mv 2e6b64d244a47d39fb35bded13333783_***_TONGMING QU
author TONGMING QU
author2 TONGMING QU
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doi_str_mv 10.23889/SUthesis.59813
college_str Faculty of Science and Engineering
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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 Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
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description Granular materials are ubiquitous in engineering activities of our human beings. Constitutive modelling of granular materials, as one of the most fundamental problems in this field, has long received great attention. Over the past decades, analytical or phenomenological models are undoubtedly the most common way to characterise the elastic-plastic behaviour of granular materials. However, although numerous attempts have been made, developing a unified theoretical model to capture the constitutive behaviour of granular materials remains an ongoing challenge.Instead of phenomenological models, numerical and data-driven surrogate models are two emerging alternatives to predict stress-strain responses of materials. Hierarchical multi-scale modelling and data-driven computing are two typical applications of these two constitutive modelling paradigms. Without the use of analytical models, the stress-strain mapping is directly provided by low-scale numerical modelling or data-driven forecasting in continuum-based numerical models. This thesis aims to partially address some open challenges for the constitutive modelling of granular materials from the two new research perspectives.In the discrete element modelling part, a total of 5 individual chapters are incorporated:(1)A novel flexible membrane algorithm has been proposed to simulate conventional triaxial testing for granular materials. The influence of flexible or rigid servo-wall conditions on the measured responses of granular materials in triaxial testing has been compared in detail via a series of numerical tests.(2)A hybrid analytical-computational calibration framework is proposed to calibrate particle-scale elastic parameters. The proposed calibration framework is tested through a collection of 2D and 3D discrete element models with both mono- and poly-disperse granular packings.(3)A physics-informed adaptive moment optimisation method is proposed to calibrate bond parameters in bonded particle models. A validation example of SiC ceramic is used to validate the proposed algorithm.(4)The ability of discrete element models with spheres to clumped particles in reproducing the constitutive behaviour of granular materials is explored through 4 perspectives. It is found that although discrete element models with spheres or clumped particles are capable of qualitatively describing the salient mechanical behaviour of granular materials, some qualitative deviations between experiments and the simulations are also observed, in terms of the stress-dilatancy behaviour and principal stress ratio against axial strain.(5)An adaptive granular representative volume element (RVE) model with an evolutionary periodic boundary is proposed for hierarchical multiscale analysis. The proposed adaptive RVE model avoids the reinitialisation of the RVE box that even undergoes extremely large shear deformation; meanwhile, a more eÿcient algorithm is presented to treat the interaction between boundary particles and other image particles.In the data-driven modelling part, a total of 2 individual chapters are involved:(1)A deep learning-based constitutive modelling strategy with the prediction model directly learning from triaxial testing data via discrete element modelling is explored. The predic-tion performance of two common recurrent neural networks (RNNs), i.e. Long short-term memory (LSTM) and gated recurrent unit (GRU) networks are compared in detail through hyperparameter investigations.(2)Micromechanical knowledge is used to discover critical microstructural variables associated with the constitutive behaviour of granular materials. Depending on the strategy to exploit a priori micromechanical knowledge, three di˙erent training models are examined. The first strategy uses only the measurable external variables to make stress predictions; the second strategy utilises a directed graph to link all the external strain sequences and internal microstructural evolution variables into a single prediction model comprised of a set of sub-mappings, and the third strategy explicitly integrates the physically important non-temporal properties with external strain paths into training through an enhanced GRU.
published_date 2022-04-04T04:17:24Z
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score 11.036706

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