A new hybrid uncertainty optimization method for structures using orthogonal series expansion

Wu, J; Luo, Z; Li, H; Zhang, N

A new hybrid uncertainty optimization method for structures using orthogonal series expansion

Wu, J

Luo, Z

Li, H

Zhang, N

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Publication Type:: Journal Article
Citation:: Applied Mathematical Modelling, 2017, 45 pp. 474 - 490
Issue Date:: 2017-05-01

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Field	Value	Language
dc.contributor.author	Wu, J https://orcid.org/0000-0002-4925-4242	en_US
dc.contributor.author	Luo, Z https://orcid.org/0000-0002-6953-3986	en_US
dc.contributor.author	Li, H https://orcid.org/0000-0003-4295-1288	en_US
dc.contributor.author	Zhang, N https://orcid.org/0000-0001-6408-0044	en_US
dc.date.available	2020-05-25T19:07:55Z
dc.date.issued	2017-05-01	en_US
dc.identifier.citation	Applied Mathematical Modelling, 2017, 45 pp. 474 - 490	en_US
dc.identifier.issn	0307-904X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/74856
dc.description.abstract	© 2017 Elsevier Inc. This paper proposes a new hybrid uncertain design optimization method for structures which contain both random and interval variables simultaneously. The optimization model is formulated with the feasible robustness and the reliability of the worst scenario. The hybrid uncertainty is quantified by using the orthogonal series expansion method that integrates the Polynomial Chaos (PC) expansion method and the Chebyshev interval method within a uniform framework. The design sensitivity of objective and constraints will be developed to greatly facilitate the use of gradient-based optimization algorithms. The numerical results show that this method will be more possible to seek the feasible solution.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP160102491
dc.relation	http://purl.org/au-research/grants/arc/DP150102751
dc.relation.ispartof	Applied Mathematical Modelling	en_US
dc.relation.isbasedon	10.1016/j.apm.2017.01.006	en_US
dc.subject.classification	Mechanical Engineering & Transports	en_US
dc.title	A new hybrid uncertainty optimization method for structures using orthogonal series expansion	en_US
dc.type	Journal Article
utslib.citation.volume	45	en_US
utslib.for	091307 Numerical Modelling and Mechanical Characterisation	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0103 Numerical and Computational Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	45	en_US

Abstract:

© 2017 Elsevier Inc. This paper proposes a new hybrid uncertain design optimization method for structures which contain both random and interval variables simultaneously. The optimization model is formulated with the feasible robustness and the reliability of the worst scenario. The hybrid uncertainty is quantified by using the orthogonal series expansion method that integrates the Polynomial Chaos (PC) expansion method and the Chebyshev interval method within a uniform framework. The design sensitivity of objective and constraints will be developed to greatly facilitate the use of gradient-based optimization algorithms. The numerical results show that this method will be more possible to seek the feasible solution.

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http://hdl.handle.net/10453/74856