Non-probabilistic reliability-based topology optimization with multidimensional parallelepiped convex model

Zheng, J; Luo, Z; Jiang, C; Ni, B; Wu, J

Non-probabilistic reliability-based topology optimization with multidimensional parallelepiped convex model

Zheng, J

Luo, Z

Jiang, C Ni, B Wu, J

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Publication Type:: Journal Article
Citation:: Structural and Multidisciplinary Optimization, 2018, 57 (6), pp. 2205 - 2221
Issue Date:: 2018-06-01

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Field	Value	Language
dc.contributor.author	Zheng, J https://orcid.org/0000-0002-1480-3862	en_US
dc.contributor.author	Luo, Z https://orcid.org/0000-0002-6953-3986	en_US
dc.contributor.author	Jiang, C	en_US
dc.contributor.author	Ni, B	en_US
dc.contributor.author	Wu, J https://orcid.org/0000-0002-4925-4242	en_US
dc.date.issued	2018-06-01	en_US
dc.identifier.citation	Structural and Multidisciplinary Optimization, 2018, 57 (6), pp. 2205 - 2221	en_US
dc.identifier.issn	1615-147X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125229
dc.description.abstract	© 2017, Springer-Verlag GmbH Germany. In this paper, a new non-probabilistic reliability-based topology optimization (NRBTO) method is proposed to account for interval uncertainties considering parametric correlations. Firstly, a reliability index is defined based on a newly developed multidimensional parallelepiped (MP) convex model, and the reliability-based topology optimization problem is formulated to optimize the topology of the structure, to minimize material volume under displacement constraints. Secondly, an efficient decoupling scheme is applied to transform the double-loop NRBTO into a sequential optimization process, using the sequential optimization & reliability assessment (SORA) method associated with the performance measurement approach (PMA). Thirdly, the adjoint variable method is used to obtain the sensitivity information for both uncertain and design variables, and a gradient-based algorithm is employed to solve the optimization problem. Finally, typical numerical examples are used to demonstrate the effectiveness of the proposed topology optimization method.	en_US
dc.relation.ispartof	Structural and Multidisciplinary Optimization	en_US
dc.relation.isbasedon	10.1007/s00158-017-1851-9	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Design Practice & Management	en_US
dc.title	Non-probabilistic reliability-based topology optimization with multidimensional parallelepiped convex model	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	57	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	01 Mathematical 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	closed_access	*
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	57	en_US

Abstract:

© 2017, Springer-Verlag GmbH Germany. In this paper, a new non-probabilistic reliability-based topology optimization (NRBTO) method is proposed to account for interval uncertainties considering parametric correlations. Firstly, a reliability index is defined based on a newly developed multidimensional parallelepiped (MP) convex model, and the reliability-based topology optimization problem is formulated to optimize the topology of the structure, to minimize material volume under displacement constraints. Secondly, an efficient decoupling scheme is applied to transform the double-loop NRBTO into a sequential optimization process, using the sequential optimization & reliability assessment (SORA) method associated with the performance measurement approach (PMA). Thirdly, the adjoint variable method is used to obtain the sensitivity information for both uncertain and design variables, and a gradient-based algorithm is employed to solve the optimization problem. Finally, typical numerical examples are used to demonstrate the effectiveness of the proposed topology optimization method.

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http://hdl.handle.net/10453/125229