Int J Performability Eng ›› 2017, Vol. 13 ›› Issue (6): 864-877.doi: 10.23940/ijpe.17.06.p8.864877

• Original articles • Previous Articles     Next Articles

An Efficient Moving Optimal Radial Sampling Method for Reliability-Based Design Optimization

Xiaoke Lia, Zhenzhong Chenb, Wuyi Minga, Haobo Qiuc, *, Jun Maa, *, and Wenbin Hea   

  1. aHenan Key Laboratory of Mechanical Equipment Intelligent Manufacturing, School of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
    bCollege of Mechanical Engineering, Donghua University, Shanghai 201620, China
    cThe State Key Laboratory of Digital Manufacturing Equipment and Technology,Huazhong University of Science & Technology,Wuhan 430074, China

Abstract: Reliability-based design optimization (RBDO) plays an essential role in structure and system design. However, its application in practical engineering is hindered by the huge computational cost in performance function evaluation. In this paper, a moving optimal radial sampling (MORS) method is proposed for RBDO problems to substantially improve the computational efficiency of Monte Carlo simulation (MCS). In MORS, the failure probability and its gradient are calculated using radius based importance sampling (RBIS) method. The initial radius is selected according to the target reliability, which can also be used to check the feasibility of probabilistic constraints afterwards. The arc search scheme in enhanced performance measure approach (PMA+) and linear interpolation scheme are used to calculate the optimal radius of RBIS. After the failure probability and its gradient are calculated, the optimal design is obtained using sequential approximation programming (SAP). The computational capability of the proposed MORS method is demonstrated using a honeycomb crashworthiness design application, a nonlinear mathematical problem and a speed reducer design. The comparison results show that the proposed MORS-SAP method is very efficient and accurate.

Submitted on July 25, 2017; Revised on August 30, 2017; Accepted on September 15, 2017(This paper was presented at the Third International Symposium on System and Software Reliability.
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