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Structural and Multidisciplinary Optimization

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23.11.2017 | RESEARCH PAPER

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

verfasst von: Jing Zheng, Zhen Luo, Chao Jiang, Bingyu Ni, Jinglai Wu

Erschienen in: Structural and Multidisciplinary Optimization | Ausgabe 6/2018

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Abstract

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.

Vorheriger Artikel Topology optimization of steady Navier-Stokes flow via a piecewise constant level set method

Nächster Artikel Topology optimization of fluid flow channel in cold plate for active phased array antenna

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Titel: Non-probabilistic reliability-based topology optimization with multidimensional parallelepiped convex model
verfasst von: Jing Zheng
Zhen Luo
Chao Jiang
Bingyu Ni
Jinglai Wu
Publikationsdatum: 23.11.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Structural and Multidisciplinary Optimization / Ausgabe 6/2018
Print ISSN: 1615-147X
Elektronische ISSN: 1615-1488
DOI: https://doi.org/10.1007/s00158-017-1851-9

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