Robust topology optimization for structures under interval uncertainty

Wu, J; Gao, J; Luo, Z; Brown, T

Robust topology optimization for structures under interval uncertainty

Wu, J

Gao, J

Luo, Z

Brown, T

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Publication Type:: Journal Article
Citation:: Advances in Engineering Software, 2016, 99 pp. 36 - 48
Issue Date:: 2016-09-01

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Field	Value	Language
dc.contributor.author	Wu, J https://orcid.org/0000-0002-4925-4242	en_US
dc.contributor.author	Gao, J https://orcid.org/0000-0002-4760-2768	en_US
dc.contributor.author	Luo, Z https://orcid.org/0000-0002-6953-3986	en_US
dc.contributor.author	Brown, T https://orcid.org/0000-0002-0155-9151	en_US
dc.date.available	2020-05-25T19:10:10Z
dc.date.issued	2016-09-01	en_US
dc.identifier.citation	Advances in Engineering Software, 2016, 99 pp. 36 - 48	en_US
dc.identifier.issn	0965-9978	en_US
dc.identifier.uri	http://hdl.handle.net/10453/43779
dc.description.abstract	© 2016 Elsevier Ltd. All rights reserved. This paper proposes a new non-probabilistic robust topology optimization approach for structures under interval uncertainty, as a complementarity of the probabilistic robust topology optimization methods. Firstly, to avoid the nested double-loop optimization procedure that is time consuming in computations, the interval arithmetic is introduced to estimate the bounds of the interval objective function and formulate the design problem under the worst scenario. Secondly, a type of non-intrusive method using the Chebyshev interval inclusion function is established to implement the interval arithmetic. Finally, a new sensitivity analysis method is developed to evaluate the design sensitivities for objective functions like structural mean compliance with respect to interval uncertainty. It can overcome the difficulty due to non-differentiability of intervals and enable the direct application of gradient-based optimization algorithms, e.g. the Method of Moving Asymptotes (MMA), to the interval uncertain topology optimization problems. Several examples are used to demonstrate the effectiveness of the proposed method.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP150102751
dc.relation.ispartof	Advances in Engineering Software	en_US
dc.relation.isbasedon	10.1016/j.advengsoft.2016.05.002	en_US
dc.subject.classification	Applied Mathematics	en_US
dc.title	Robust topology optimization for structures under interval uncertainty	en_US
dc.type	Journal Article
utslib.citation.volume	99	en_US
utslib.for	091307 Numerical Modelling and Mechanical Characterisation	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	99	en_US

Abstract:

© 2016 Elsevier Ltd. All rights reserved. This paper proposes a new non-probabilistic robust topology optimization approach for structures under interval uncertainty, as a complementarity of the probabilistic robust topology optimization methods. Firstly, to avoid the nested double-loop optimization procedure that is time consuming in computations, the interval arithmetic is introduced to estimate the bounds of the interval objective function and formulate the design problem under the worst scenario. Secondly, a type of non-intrusive method using the Chebyshev interval inclusion function is established to implement the interval arithmetic. Finally, a new sensitivity analysis method is developed to evaluate the design sensitivities for objective functions like structural mean compliance with respect to interval uncertainty. It can overcome the difficulty due to non-differentiability of intervals and enable the direct application of gradient-based optimization algorithms, e.g. the Method of Moving Asymptotes (MMA), to the interval uncertain topology optimization problems. Several examples are used to demonstrate the effectiveness of the proposed method.

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http://hdl.handle.net/10453/43779