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Research Papers: Design Automation

A Hybrid Loop Approach Using the Sufficient Descent Condition for Accurate, Robust, and Efficient Reliability-Based Design Optimization

[+] Author and Article Information
Behrooz Keshtegar

Department of Civil Engineering,
University of Zabol,
Zabol 9861335-856, Iran
e-mail: Bkeshtegar@uoz.ac.ir

Peng Hao

State Key Laboratory of Structural
Analysis for Industrial Equipment,
Department of Engineering Mechanics,
Dalian University of Technology,
Dalian 116023, China
e-mail: haopeng@dlut.edu.cn

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received November 23, 2015; final manuscript received July 6, 2016; published online September 13, 2016. Assoc. Editor: Xiaoping Du.

J. Mech. Des 138(12), 121401 (Sep 13, 2016) (11 pages) Paper No: MD-15-1777; doi: 10.1115/1.4034173 History: Received November 23, 2015; Revised July 06, 2016

For reliability-based design optimization (RBDO) problems, single loop approaches (SLA) are very efficient but prone to converge to inappropriate point for highly nonlinear constraint functions, and double loop approaches (DLA) are proven to be accurate but require more iterations to achieve stable results. In this paper, an adjusted advanced mean value (AAMV) method is firstly proposed to improve the robustness and efficiency of performance measure approach. The global convergence of the AAMV is guaranteed using sufficient descent condition for the reliability loop in RBDO. Then, a hybrid RBDO method is developed to improve the efficiency of DLA and accuracy of SLA, on the basis of sufficient descent condition and AAMV method, named as hybrid single and double loops (HSD) method. Three nonlinear concave and convex performance functions are used to illustrate the efficiency and robustness of the AAMV method; then the accuracy, robustness, and efficiency of the proposed HSD method are compared to current SLA and DLA through another three benchmark nonlinear RBDO examples. Results show that the AAMV is more robust and efficient than the existing reliability analysis methods. The HSD is more accurate than the SLA for highly nonlinear problems, and also exhibits a better performance than the DLA from the point of view of both robustness and efficiency.

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Figures

Grahic Jump Location
Fig. 1

Step size versus required number of iterations for various adjusted factors

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Fig. 2

Framework of the proposed MPTP search based on adjusted step size

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Fig. 3

Convergence histories of MPTP search using different reliability analysis methods for Example 2

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Fig. 4

Convergence histories of MPTP search using different reliability analysis methods for Example 3

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Fig. 5

Flowchart of the proposed HSD method for RBDO problems

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Fig. 6

Somatic view of conical structure

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