Intelligent initial point selection for MPP search in reliability-based design optimization
- PDF / 903,729 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 80 Downloads / 155 Views
RESEARCH PAPER
Intelligent initial point selection for MPP search in reliability-based design optimization Yongsu Jung 1 & Hyunkyoo Cho 2 & Ikjin Lee 1 Received: 24 December 2019 / Revised: 23 February 2020 / Accepted: 12 March 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In this paper, intelligent initial point selection for performance measure approach (PMA) of reliability-based design optimization (RBDO) is proposed to improve computational efficiency of the most probable point (MPP) search. Unlike existing PMA algorithms concentrating on enhancement of the optimization algorithm for MPP search, the proposed method focuses on how to intelligently select an initial point which is close to the true MPP so that fast convergence can be achieved. Since the proposed method provides a new initial point for MPP search, it can be combined with any existing PMA algorithms. To obtain the initial point, the first-order Taylor series expansion with respect to a design vector is applied to MPP in U-space obtained from the previous RBDO iteration. Thus, the Jacobian matrix of the MPP vector with respect to the design vector is derived in an analytical way with no additional function evaluation. The derived Jacobian matrix is validated through numerical study. Comparative study with two existing initial point strategies for MPP search—the origin in U-space and the previous MPP in U-space under the condition of design closeness—shows that the proposed initial point significantly improves efficiency of MPP search in any PMA algorithm with various types of performance functions and input distributions. Keywords Reliability-based design optimization (RBDO) . Performance measure approach (PMA) . Sensitivity analysis . Most probable point (MPP)
1 Introduction Over the past few decades, reliability-based design optimization (RBDO) has been developed to account for uncertainties in a system such as environmental conditions, machining and manufacturing tolerances, and material anisotropy and inhomogeneity. Since system performances have randomness as well caused by various input uncertainties, unexpected failure of the system may arise if those uncertainties are not considered in the system design. Numerous studies on RBDO have been reported to obtain a reliable and cost-effective optimum design (Shin and Lee 2015; Hu et al. 2016; Sun et al. 2017; Lee et al. 2019; Duan et al. 2020). Responsible Editor: Pingfeng Wang * Ikjin Lee [email protected] 1
Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea
2
Department of Mechanical Engineering, Mokpo National University, Muan-gun 58554, South Korea
There have been various attempts to develop effective reliability analysis for RBDO including sampling-based methods (Dubourg et al. 2011; Lee et al. 2011) using Monte Carlo simulation (MCS) that requires a large number of analyses (Rubinstein and Kroese 2016). On the other hand, analytical methods which approximate limit-state functions at the
Data Loading...
