Jack McAfee
Parametric sensitivity analysis to maximise auxetic effect of polymeric fibre based helical yarn.
McAfee, Jack; Faisal, Nadimul Haque
Abstract
Studies on designing polymeric fibres based helical auxetic yarn (HAY) to maximise their auxetic effect are yet to propose optimised design configurations for general impact mitigation applications. This study therefore presents optimal design parameters through analytical calculations and finite element (FE) method. Three main design parameters were considered which includes Poisson's ratio, core/wrap diameter ratio, and starting wrap angle. The Poisson's ratio of the HAY was calculated by measuring its total diameter at a given rate of strain. The investigation found here to be a starting wrap angle of a HAY (critical angle) that resulted in the highest possible exhibiting of the auxetic effect. The critical angle was determined to be 7°, and a maximum NPR of −12.04 was achieved with this design.
Citation
MCAFEE, J. and FAISAL, N.H. 2017. Parametric sensitivity analysis to maximise auxetic effect of polymeric fibre based helical yarn. Composite structures [online], 162, pages 1-12. Available from: https://doi.org/10.1016/j.compstruct.2016.11.077
Journal Article Type | Article |
---|---|
Acceptance Date | Nov 24, 2016 |
Online Publication Date | Nov 29, 2016 |
Publication Date | Feb 15, 2017 |
Deposit Date | Dec 2, 2016 |
Publicly Available Date | Nov 30, 2017 |
Journal | Composite structures |
Print ISSN | 0263-8223 |
Electronic ISSN | 1879-1085 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 162 |
Pages | 1-12 |
DOI | https://doi.org/10.1016/j.compstruct.2016.11.077 |
Keywords | Helical auxetic yarn (HAY); Design parameters; Negative Poisson's ratio (NPR); Sensitivity; Parametric analysis; Safety applications |
Public URL | http://hdl.handle.net/10059/1991 |
Contract Date | Dec 2, 2016 |
Files
MCAFEE 2016 Parametric sensitivity analysis
(2 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by-nc-nd/4.0/
You might also like
Applications of artificial intelligence in geothermal resource exploration: a review.
(2024)
Journal Article
Downloadable Citations
About OpenAIR@RGU
Administrator e-mail: publications@rgu.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search