Ismail, Y, Yang, D and Ye, J (2016) Discrete element method for generating random fibre distributions in micromechanical models of fibre reinforced composite laminates. Composites Part B: Engineering, 90. pp. 485-492. ISSN 1359-8368
Abstract
A new approach is presented for generating random distribution of fibres in the representative volume element (RVE) of fibre reinforced composite laminates. The approach is based on discrete element method (DEM) and experimental data of fibre diameter distribution. It overcomes the jamming limit appeared in previous methods and is capable of generating high volume fractions of fibres with random distributions and any specified inter-fibre distances. Statistical analysis is then carried out on the fibre distributions generated within the RVEs, which show good agreement with experiments in all statistics analysed. The effective elastic properties of the generated RVEs are finally analysed by finite element method, which results show more reasonable agreement with the experimental results than previous methods.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 Elsevier Ltd. All rights reserved. This is an author produced version of a paper published in Composites Part B: Engineering. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | A. Fibres; B. Microstructures; Discrete element method; C. Computational modelling; C. Statistical properties/methods |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Civil Engineering (Leeds) > Institute for Resilient Infrastructure (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Jan 2016 10:58 |
Last Modified: | 14 Apr 2017 03:45 |
Published Version: | http://dx.doi.org/10.1016/j.compositesb.2016.01.03... |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.compositesb.2016.01.037 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:93334 |