Sensitivity Developments for Sensitivity-Based RBDO With Correlated Input Variable and Varying Input Standard Deviation

2016 ◽  
Author(s):  
Hyunkyoo Cho ◽  
K. K. Choi ◽  
David Lamb
Keyword(s):  
Standard Deviation ◽  
Design Sensitivity ◽  
Performance Measure ◽  
Optimization Process ◽  
Probabilistic Constraints ◽  
Reliability Based Design ◽  
Design Iteration ◽  
Accurate Design ◽  
Fixed Constant ◽  
Input Correlation

In conventional reliability-based design optimization (RBDO), the input variable is statistically independent, and its standard deviation (STD) is a fixed constant. However, the independent input variable and the constant STD may not correctly describe reliability problems because, for certain problems, the input variable is correlated, and its STD varies as the mean changes. For this kind of design problem, the input correlation should be considered, and the input STD should be changed as the design iteration proceeds and the corresponding design variable — the input mean — changes. Specifically, the varying input STD causes the input variability to fluctuate so that the probability of failure (PoF) of the design becomes a moving target. Hence, RBDO with varying input STD suffers difficulty in convergence unless accurate design sensitivity is available. In sensitivity-based RBDO, the sensitivity for the most probable point (MPP) search and the design sensitivity of probabilistic constraints are required. In this paper, it is found that the input correlation significantly affects the sensitivity for the MPP search and that the varying STD has large impact on the design sensitivity for RBDO. Therefore, sensitivities considering both the input correlation and the varying input STD have been developed for an efficient and effective optimization process. For the reliability analysis method, the enriched performance measure approach (PMA+) is used, as it is efficient and more stable during the RBDO process. To represent the input correlation, a copula is used in the sensitivity derivations. Using a mathematical example, the accuracy and efficiency of the developed sensitivities are verified. The RBDO result for the mathematical example indicates that the developed methods provide accurate sensitivities in the optimization process.

A New Study on Reliability-Based Design Optimization

1999 ◽  
Vol 121 (4) ◽  
pp. 557-564 ◽  
Author(s):  
J. Tu ◽  
K. K. Choi ◽  
Y. H. Park
Keyword(s):  
Design Optimization ◽  
Reliability Index ◽  
Performance Measure ◽  
Probabilistic Constraints ◽  
Probabilistic Constraint ◽  
Reliability Based Design Optimization ◽  
Performance Measure Approach ◽  
Reliability Based Design ◽  
Index Approach ◽  
Special Cases

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.

Design Sensitivity Method for Sampling-Based RBDO With Varying Standard Deviation

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Hyunkyoo Cho ◽  
K. K. Choi ◽  
Ikjin Lee ◽  
David Lamb
Keyword(s):  
Standard Deviation ◽  
Design Variable ◽  
Score Function ◽  
Random Variable ◽  
Probability Of Failure ◽  
Design Sensitivity ◽  
Mean Value ◽  
Reliability Based Design ◽  
The Mean ◽  
Sensitivity Method

Conventional reliability-based design optimization (RBDO) uses the mean of input random variable as its design variable; and the standard deviation (STD) of the random variable is a fixed constant. However, the constant STD may not correctly represent certain RBDO problems well, especially when a specified tolerance of the input random variable is present as a percentage of the mean value. For this kind of design problem, the STD of the input random variable should vary as the corresponding design variable changes. In this paper, a method to calculate the design sensitivity of the probability of failure for RBDO with varying STD is developed. For sampling-based RBDO, which uses Monte Carlo simulation (MCS) for reliability analysis, the design sensitivity of the probability of failure is derived using a first-order score function. The score function contains the effect of the change in the STD in addition to the change in the mean. As copulas are used for the design sensitivity, correlated input random variables also can be used for RBDO with varying STD. Moreover, the design sensitivity can be calculated efficiently during the evaluation of the probability of failure. Using a mathematical example, the accuracy and efficiency of the developed design sensitivity method are verified. The RBDO result for mathematical and physical problems indicates that the developed method provides accurate design sensitivity in the optimization process.

Optimization of Jacket Type Offshore-Tower Under Uncertainties

2009 ◽  
Author(s):  
V. Togan ◽  
H. Karadeniz ◽  
A. T. Daloglu
Keyword(s):  
Performance Measure ◽  
Environmental Data ◽  
Probabilistic Constraints ◽  
Reliability Based Design Optimization ◽  
Performance Measure Approach ◽  
Reliability Based Design ◽  
Economical Design ◽  
Design Variables ◽  
Index Approach ◽  
And Performance

In this work, economical design implementation of a jacket tower, which is subjected to some uncertainties associated with the loads, the material properties, and environmental data etc., is presented. In order to fulfill the defined task, reliability based design optimization (RBDO) concept combining the reliability analysis and optimization is performed with reliability constraints including stress, buckling, and the lowest natural frequency. The probabilistic constraints are evaluated by using Reliability Index Approach (RIA) and Performance Measure approach (PMA). The mass of the tower is considered as being the objective function; the thickness and diameter of the cross-section of the jacket members are taken as being design variables of the optimization.

Hybrid Analysis Method for Reliability-Based Design Optimization

2003 ◽  
Vol 125 (2) ◽  
pp. 221-232 ◽  
Author(s):  
Byeng D. Youn ◽  
Kyung K. Choi ◽  
Young H. Park
Keyword(s):  
Design Optimization ◽  
Mean Value ◽  
Performance Measure ◽  
Equality Constraint ◽  
Probabilistic Constraints ◽  
Probabilistic Constraint ◽  
Reliability Based Design Optimization ◽  
Performance Function ◽  
Reliability Based Design ◽  
Advanced Mean Value

Reliability-based design optimization (RBDO) involves evaluation of probabilistic constraints, which can be done in two different ways, the reliability index approach (RIA) and the performance measure approach (PMA). It has been reported in the literature that RIA yields instability for some problems but PMA is robust and efficient in identifying a probabilistic failure mode in the optimization process. However, several examples of numerical tests of PMA have also shown instability and inefficiency in the RBDO process if the advanced mean value (AMV) method, which is a numerical tool for probabilistic constraint evaluation in PMA, is used, since it behaves poorly for a concave performance function, even though it is effective for a convex performance function. To overcome difficulties of the AMV method, the conjugate mean value (CMV) method is proposed in this paper for the concave performance function in PMA. However, since the CMV method exhibits the slow rate of convergence for the convex function, it is selectively used for concave-type constraints. That is, once the type of the performance function is identified, either the AMV method or the CMV method can be adaptively used for PMA during the RBDO iteration to evaluate probabilistic constraints effectively. This is referred to as the hybrid mean value (HMV) method. The enhanced PMA with the HMV method is compared to RIA for effective evaluation of probabilistic constraints in the RBDO process. It is shown that PMA with a spherical equality constraint is easier to solve than RIA with a complicated equality constraint in estimating the probabilistic constraint in the RBDO process.

Stable Evaluation of Probabilistic Constraint in Reliability-Based Design Optimization for Large Deformation Problem

2001 ◽  
Author(s):  
Young H. Park
Keyword(s):  
Design Optimization ◽  
Large Deformation ◽  
Design Sensitivity ◽  
Performance Measure ◽  
Probabilistic Constraint ◽  
Efficient Design ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Deformation Problem ◽  
Large Deformation Problem

Abstract In this paper, Reliability-Based Design Optimization (RBDO) is carried out using two distinct ways, the Reliability Index Approach (RIA) and the Performance Measure Approach (PMA). It has been theoretically explained that RIA shows instability but PMA is stable and efficient in identifying a probabilistic failure mode in the RBDO process. The PMA is compared to RIA with regard to the stable evaluation of a probabilistic constraint in the RBDO using a large deformation problem. In addition, an efficient Design Sensitivity Analysis (DSA) method is developed to support reliability analysis and reliability-based optimization for a hyper-elastic structure with factional contact using a meshfree method. A numerical result is presented to demonstrate the comparative study between RIA and PMA.

An Investigation of Nonlinearity of Reliability-Based Design Optimization Approaches

2002 ◽  
Author(s):  
Kyung K. Choi ◽  
Byeng D. Youn
Keyword(s):  
Design Optimization ◽  
Reliability Analysis ◽  
Probability Distributions ◽  
Performance Measure ◽  
Probabilistic Constraints ◽  
Random Parameters ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Highly Nonlinear ◽  
Standard Normal

Deterministic optimum designs that are obtained without consideration of uncertainty could lead to unreliable designs, which call for a reliability approach to design optimization, using a Reliability-Based Design Optimization (RBDO) method. A typical RBDO process iteratively carries out a design optimization in an original random space (X-space) and reliability analysis in an independent and standard normal random space (U-space). This process requires numerous nonlinear mapping between X- and U-spaces for a various probability distributions. Therefore, the nonlinearity of RBDO problem will depend on the type of distribution of random parameters, since a transformation between X- and U-spaces introduces additional nonlinearity to reliability-based performance measures evaluated during the RBDO process. Evaluation of probabilistic constraints in RBDO can be carried out in two different ways: the Reliability Index Approach (RIA) and the Performance Measure Approach (PMA). Different reliability analysis approaches employed in RIA and PMA result in different behaviors of nonlinearity of RIA and PMA in the RBDO process. In this paper, it is shown that RIA becomes much more difficult to solve for non-normally distributed random parameters because of highly nonlinear transformations involved. However, PMA is rather independent of probability distributions because of little involvement of the nonlinear transformation.

A Hybrid Reliability Approach for Reliability-Based Design Optimization

2010 ◽  
Author(s):  
Po Ting Lin ◽  
Yogesh Jaluria ◽  
Hae Chang Gea
Keyword(s):  
Design Optimization ◽  
Reliability Index ◽  
Optimization Problems ◽  
Performance Measure ◽  
Accurate Solution ◽  
Probabilistic Constraints ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Index Approach ◽  
Hybrid Reliability

Reliability-based Design Optimization problems have been solved by two well-known methods: Reliability Index Approach (RIA) and Performance Measure Approach (PMA). RIA generates first-order approximate probabilistic constraints using the measures of reliability indices. For infeasible design points, the traditional RIA method suffers from inaccurate evaluation of the reliability index. To overcome this problem, the Modified Reliability Index Approach (MRIA) has been proposed. The MRIA provides the accurate solution of the reliability index but also inherits some inefficiency characteristics from the Most Probable Failure Point (MPFP) search when nonlinear constraints are involved. In this paper, the benchmark examples have been utilized to examine the efficiency and stability of both PMA and MRIA. In our study, we found that the MRIA is capable of obtaining the correct optimal solutions regardless of the locations of design points but the PMA is much efficient in the inverse reliability analysis. To take advantages of the strengths of both methods, a Hybrid Reliability Approach (HRA) is proposed. The HRA uses a selection factor that can determine which method to use during optimization iterations. Numerical examples from the proposed method are presented and compared with the MRIA and the PMA.

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