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Multi-Scale Analysis of Reinforced Composites / MALEBOGO TSHEKO

Swansea University Author: MALEBOGO TSHEKO

DOI (Published version): 10.23889/SUThesis.70851

Abstract

The thesis centres on multi-scale modelling of heterogeneous solids, where the macroscopic behaviour is intricately linked to the microscopic structure. To manage the substantial memory and computational power demands of multi-scale modelling, a discrete Representative Volume Element (RVE) boundary...

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Published: Swansea 2025
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: W. G. Wulf, and D. Peric.
URI: https://cronfa.swan.ac.uk/Record/cronfa70851
first_indexed 2025-11-06T13:55:24Z
last_indexed 2025-11-07T07:35:32Z
id cronfa70851
recordtype RisThesis
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spelling 2025-11-06T14:45:26.2092482 v2 70851 2025-11-06 Multi-Scale Analysis of Reinforced Composites 106126f1baab23ec2c0583dee38a19f7 MALEBOGO TSHEKO MALEBOGO TSHEKO true false 2025-11-06 The thesis centres on multi-scale modelling of heterogeneous solids, where the macroscopic behaviour is intricately linked to the microscopic structure. To manage the substantial memory and computational power demands of multi-scale modelling, a discrete Representative Volume Element (RVE) boundary approach based on a finite element model of the microstructure is employed. RVE refers to a small unit of a material that represents the whole material in terms of structure and properties. A new homogenisation method is explored in this research, and subsequently, the results obtained from this approach are rigorously compared to analytical methods using multiple verifying examples. This comparative analysis provides insights into the accuracy and reliability of the multi-scale modelling technique in predicting material behaviours across different scales.Homogenous, isotropic and transversely isotropic 3D solids are investigated. Then, a homogenisation tool employing the least squares method is used to determine the effective elastic properties of the composite. A more advanced tool, a Neural Network, utilises data from the least squares method to predict the elastic properties of the composite without the necessity of re-modelling the RVE and conducting additional tests.The trained neural network, which utilises data derived from least squared method plays a crucial role in predicting elastic properties of composites with very high accuracy. By integrating the neural network’s predictions with an optimisation algorithm, they can effectively tailor materials to meet specific criteria such as cost-efficiency and lightweight construction. This integrated approach not only enhances design precision but also reduces the need for extensive re-modelling of the RVE and repetitive testing. Ultimately, it fosters the development of innovative materials that strike an optimal balance between performance and resource utilisation in various engineering applications. E-Thesis Swansea Multi-scale, Composites, Homogenisation, Solid Mechanics,Machine learning, Neural Network, Optimisation, Isotropy, orthotropy, VolumeRepresentative Element, Matrix, Inclusion, Gmsh 10 10 2025 2025-10-10 10.23889/SUThesis.70851 COLLEGE NANME COLLEGE CODE Swansea University W. G. Wulf, and D. Peric. Doctoral Ph.D Botswana government Botswana government 2025-11-06T14:45:26.2092482 2025-11-06T13:49:48.3339370 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering MALEBOGO TSHEKO 1 70851__35572__8a46314dbbdb48e5b831a5575554ff54.pdf 2024_Tsheko_M.final.70851.pdf 2025-11-06T13:54:38.3661575 Output 34421320 application/pdf E-Thesis – open access true Copyright: The author, Malebogo Tsheko, 2024 true eng
title Multi-Scale Analysis of Reinforced Composites
spellingShingle Multi-Scale Analysis of Reinforced Composites
MALEBOGO TSHEKO
title_short Multi-Scale Analysis of Reinforced Composites
title_full Multi-Scale Analysis of Reinforced Composites
title_fullStr Multi-Scale Analysis of Reinforced Composites
title_full_unstemmed Multi-Scale Analysis of Reinforced Composites
title_sort Multi-Scale Analysis of Reinforced Composites
author_id_str_mv 106126f1baab23ec2c0583dee38a19f7
author_id_fullname_str_mv 106126f1baab23ec2c0583dee38a19f7_***_MALEBOGO TSHEKO
author MALEBOGO TSHEKO
author2 MALEBOGO TSHEKO
format E-Thesis
publishDate 2025
institution Swansea University
doi_str_mv 10.23889/SUThesis.70851
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 The thesis centres on multi-scale modelling of heterogeneous solids, where the macroscopic behaviour is intricately linked to the microscopic structure. To manage the substantial memory and computational power demands of multi-scale modelling, a discrete Representative Volume Element (RVE) boundary approach based on a finite element model of the microstructure is employed. RVE refers to a small unit of a material that represents the whole material in terms of structure and properties. A new homogenisation method is explored in this research, and subsequently, the results obtained from this approach are rigorously compared to analytical methods using multiple verifying examples. This comparative analysis provides insights into the accuracy and reliability of the multi-scale modelling technique in predicting material behaviours across different scales.Homogenous, isotropic and transversely isotropic 3D solids are investigated. Then, a homogenisation tool employing the least squares method is used to determine the effective elastic properties of the composite. A more advanced tool, a Neural Network, utilises data from the least squares method to predict the elastic properties of the composite without the necessity of re-modelling the RVE and conducting additional tests.The trained neural network, which utilises data derived from least squared method plays a crucial role in predicting elastic properties of composites with very high accuracy. By integrating the neural network’s predictions with an optimisation algorithm, they can effectively tailor materials to meet specific criteria such as cost-efficiency and lightweight construction. This integrated approach not only enhances design precision but also reduces the need for extensive re-modelling of the RVE and repetitive testing. Ultimately, it fosters the development of innovative materials that strike an optimal balance between performance and resource utilisation in various engineering applications.
published_date 2025-10-10T06:59:22Z
_version_ 1861427881896312832
score 11.100739

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