Reliability-Based Design Optimization Using Enhanced Dimension Reduction Method With Variable Sampling Points

Author(s):  
Sunmin Yook ◽  
Gabseong Lee ◽  
Sang-Joon Yoon ◽  
Jae-Yong Park ◽  
Dong-Hoon Choi

Reliability-Based Design Optimization (RBDO) is an effective method to handle an optimization problem constrained by reliability performance. In spite of its great benefits, one of the most challenging issues for implementing RBDO is associated with very intensive computational demands of Reliability Analysis (RA). Moreover, an accurate and efficient RA method is indispensible to apply RBDO to practical engineering design problems. Among various RA methods, an enhanced Dimension Reduction (eDR) method is the most popular one due to the high computational efficiency. It is very desirable to obtain an accurate and efficient RA result by using the minimum number of sampling points. But, it is difficult to determine it. That is because it depends on the nonlinearity of a constraint from approximating a model and the degree of uncertainty from integrating a design factor. In this research, eDR method with variable sampling points has been studied and proposed to resolve the early mentioned difficulties. The main idea of the suggested method is to employ a different number of axial sampling points for each random design factor. It is according to the nonlinearity of a constraint and the degree of uncertainty of each random design factor. For each random variable, it begins to use three points first and decides to stop or increase the axial sampling points based upon the proposed criteria in this study. In case of increasing sampling points, it is incremented by one sampling point and ended up five sampling points at most. As it shown in the result, the efficiency of eDR method with variable sampling points for each random variable is superior to the one with fixed sampling points without sacrificing any accuracy. Through the three representative RA problems, it is verified that the proposed RA method generates the result 26.5% more efficiently on average than the conventional eDR method with fixed sampling points. Furthermore, the Performance Measure Approach (PMA) was used to evaluate the performance of RBDO using the new RA method. For the comparison, three mathematical and one engineering RBDO problems were solved by both eDR method with variable sampling points and conventional one with fixed sampling points. Finally, the comparison results clearly demonstrate that RBDO using the suggested RA method is superior to the conventional one in terms of accuracy and efficiency.

2019 ◽  
Vol 19 (3) ◽  
pp. 221-230 ◽  
Author(s):  
Gh. Kharmanda ◽  
I. R. Antypas

Introduction. The integration of reliability and optimization concepts seeks to design structures that should be both economic and reliable. This model is called Reliability-Based Design Optimization (RBDO). In fact, the coupling between the mechanical modelling, the reliability analyses and the optimization methods leads to very high computational cost and weak convergence stability. Materials andMethods. Several methods have been developed to overcome these difficulties. The methods called Reliability Index Approach (RIA) and Performance Measure Approach (PMA) are two alternative methods. RIA describes the probabilistic constraint as a reliability index while PMA was proposed by converting the probability measure to a performance measure. An Optimum Safety Factor (OSF) method is proposed to compute safety factors satisfying a required reliability level without demanding additional computing cost for the reliability evaluation. The OSF equations are formulated considering RIA and PMA and extended to multiple failure case.Research Results. Several linear and nonlinear distribution laws are applied to composite yarns studies and then extended to multiple failure modes. It has been shown that the idea of the OSF method is to avoid the reliability constraint evaluation with a particular optimization process.Discussion and Conclusions. The simplified implementation framework of the OSF strategy consists of decoupling the optimization and the reliability analyses. It provides designers with efficient solutions that should be economic satisfying a required reliability level. It is demonstrated that the RBDO compared to OSF has several advantages: small number of optimization variables, good convergence stability, small computing time, satisfaction of the required reliability levels.


2006 ◽  
Vol 129 (4) ◽  
pp. 449-454 ◽  
Author(s):  
Alan P. Bowling ◽  
John E. Renaud ◽  
Jeremy T. Newkirk ◽  
Neal M. Patel ◽  
Harish Agarwal

In this investigation a robotic system’s dynamic performance is optimized for high reliability under uncertainty. The dynamic capability equations (DCE) allow designers to predict the dynamic performance of a robotic system for a particular configuration and reference point on the end effector (i.e., point design). Here the DCE are used in conjunction with a reliability-based design optimization (RBDO) strategy in order to obtain designs with robust dynamic performance with respect to the end-effector reference point. In this work a unilevel performance measure approach is used to perform RBDO. This is important for the reliable design of robotic systems in which a solution to the DCE is required for each constraint call. The method is illustrated on a robot design problem.


1999 ◽  
Vol 121 (4) ◽  
pp. 557-564 ◽  
Author(s):  
J. Tu ◽  
K. K. Choi ◽  
Y. H. Park

This paper presents a general approach for probabilistic constraint evaluation in the reliability-based design optimization (RBDO). Different perspectives of the general approach are consistent in prescribing the probabilistic constraint, where the conventional reliability index approach (RIA) and the proposed performance measure approach (PMA) are identified as two special cases. PMA is shown to be inherently robust and more efficient in evaluating inactive probabilistic constraints, while RIA is more efficient for violated probabilistic constraints. Moreover, RBDO often yields a higher rate of convergence by using PMA, while RIA yields singularity in some cases.


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