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Hierarchical multi-scale models for mechanical response prediction of highly filled elastic–plastic and viscoplastic particulate composites

J.Y.S. Li-Mayer, D. Lewis, S. Connors, A. Glauser, D.M. Williamson, Hari Arora Orcid Logo, M.N. Charalambides

Computational Materials Science, Volume: 181, Start page: 109734

Swansea University Author: Hari Arora Orcid Logo

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Abstract

Though a vast amount of literature can be found on modelling particulate reinforced composites and suspensions, the treatment of such materials at very high volume fractions (>90%), typical of high performance energetic materials, remains a challenge. The latter is due to the very wide particle s...

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Published in: Computational Materials Science
ISSN: 0927-0256
Published: Elsevier BV 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa53950
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first_indexed 2020-04-15T19:43:17Z
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spelling 2020-07-07T08:26:45.9937389 v2 53950 2020-04-15 Hierarchical multi-scale models for mechanical response prediction of highly filled elastic–plastic and viscoplastic particulate composites ed7371c768e9746008a6807f9f7a1555 0000-0002-9790-0907 Hari Arora Hari Arora true false 2020-04-15 MEDE Though a vast amount of literature can be found on modelling particulate reinforced composites and suspensions, the treatment of such materials at very high volume fractions (>90%), typical of high performance energetic materials, remains a challenge. The latter is due to the very wide particle size distribution needed to reach such a high value of In order to meet this challenge, multiscale models that can treat the presence of particles at various scales are needed. This study presents a novel hierarchical multiscale method for predicting the effective properties of elasto-viscoplastic polymeric composites at high . Firstly, simulated microstructures with randomly packed spherical inclusions in a polymeric matrix were generated. Homogenised properties predicted using the finite element (FE) method were then iteratively passed in a hierarchical multi-scale manner as modified matrix properties until the desired filler was achieved. The validated hierarchical model was then applied to a real composite with microstructures reconstructed from image scan data, incorporating cohesive elements to predict debonding of the filler particles and subsequent catastrophic failure. The predicted behaviour was compared to data from uniaxial tensile tests. Our method is applicable to the prediction of mechanical behaviour of any highly filled composite with a non-linear matrix, arbitrary particle filler shape and a large particle size distribution, surpassing limitations of traditional analytical models and other published computational models. Journal Article Computational Materials Science 181 109734 Elsevier BV 0927-0256 Micromechanical model, Particle reinforced viscoplastic polymer, Plastic bonded explosives, Particle debonding 1 8 2020 2020-08-01 10.1016/j.commatsci.2020.109734 COLLEGE NANME Biomedical Engineering COLLEGE CODE MEDE Swansea University 2020-07-07T08:26:45.9937389 2020-04-15T15:59:22.3341270 J.Y.S. Li-Mayer 1 D. Lewis 2 S. Connors 3 A. Glauser 4 D.M. Williamson 5 Hari Arora 0000-0002-9790-0907 6 M.N. Charalambides 7 53950__17068__de69278305e14c5595452d093bf7b7c0.pdf 53950.pdf 2020-04-15T16:02:28.7249484 Output 2413649 application/pdf Accepted Manuscript true 2021-04-27T00:00:00.0000000 Released under the terms of a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND). true eng
title Hierarchical multi-scale models for mechanical response prediction of highly filled elastic–plastic and viscoplastic particulate composites
spellingShingle Hierarchical multi-scale models for mechanical response prediction of highly filled elastic–plastic and viscoplastic particulate composites
Hari Arora
title_short Hierarchical multi-scale models for mechanical response prediction of highly filled elastic–plastic and viscoplastic particulate composites
title_full Hierarchical multi-scale models for mechanical response prediction of highly filled elastic–plastic and viscoplastic particulate composites
title_fullStr Hierarchical multi-scale models for mechanical response prediction of highly filled elastic–plastic and viscoplastic particulate composites
title_full_unstemmed Hierarchical multi-scale models for mechanical response prediction of highly filled elastic–plastic and viscoplastic particulate composites
title_sort Hierarchical multi-scale models for mechanical response prediction of highly filled elastic–plastic and viscoplastic particulate composites
author_id_str_mv ed7371c768e9746008a6807f9f7a1555
author_id_fullname_str_mv ed7371c768e9746008a6807f9f7a1555_***_Hari Arora
author Hari Arora
author2 J.Y.S. Li-Mayer
D. Lewis
S. Connors
A. Glauser
D.M. Williamson
Hari Arora
M.N. Charalambides
format Journal article
container_title Computational Materials Science
container_volume 181
container_start_page 109734
publishDate 2020
institution Swansea University
issn 0927-0256
doi_str_mv 10.1016/j.commatsci.2020.109734
publisher Elsevier BV
document_store_str 1
active_str 0
description Though a vast amount of literature can be found on modelling particulate reinforced composites and suspensions, the treatment of such materials at very high volume fractions (>90%), typical of high performance energetic materials, remains a challenge. The latter is due to the very wide particle size distribution needed to reach such a high value of In order to meet this challenge, multiscale models that can treat the presence of particles at various scales are needed. This study presents a novel hierarchical multiscale method for predicting the effective properties of elasto-viscoplastic polymeric composites at high . Firstly, simulated microstructures with randomly packed spherical inclusions in a polymeric matrix were generated. Homogenised properties predicted using the finite element (FE) method were then iteratively passed in a hierarchical multi-scale manner as modified matrix properties until the desired filler was achieved. The validated hierarchical model was then applied to a real composite with microstructures reconstructed from image scan data, incorporating cohesive elements to predict debonding of the filler particles and subsequent catastrophic failure. The predicted behaviour was compared to data from uniaxial tensile tests. Our method is applicable to the prediction of mechanical behaviour of any highly filled composite with a non-linear matrix, arbitrary particle filler shape and a large particle size distribution, surpassing limitations of traditional analytical models and other published computational models.
published_date 2020-08-01T04:08:24Z
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score 10.897445