System Reliability-Based Design Optimization Under Tradeoff Between Reduction of Sampling Uncertainty and Design Shift

Bae, Sangjune; Kim, Nam Hoon; Jang, Seung-gyo

doi:10.1115/1.4041859

Cited by 7 publications

(4 citation statements)

References 23 publications

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“…Nassar and Austin [4] addressed the importance of constructing consistent evaluation criteria to measure the importance of every performance requirement in a multi-objective formulation for trade-offs, regardless of whether it is a system-level option or a component-level one. The trade-off considered by Bae et al in [5] is the impact of design variable selection on the final optimal solutions. In their design problem formulation, the probabilities of failures of the components and the system were included as constraints.…”

Section: Introductionmentioning

confidence: 99%

Gradient-Based Trade-Off Design for Engineering Applications

Royster,

Hou

2023

Designs

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The goal of the trade-off design method presented in this study is to achieve newly targeted performance requirements by modifying the current values of the design variables. The trade-off design problem is formulated in the framework of Sequential Quadratic Programming. The method is computationally efficient as it is gradient-based, which, however, requires the performance functions to be differentiable. A new equation to calculate the scale factor to control the size of the design variables is introduced in this study, which can ensure the new design achieves the targeted performance objective. Three formal approaches are developed in this study for trade-off design to handle various design scenarios, which include one that can handle cases with linearly dependent constraints and with more constraints than the number of design variables. Three engineering design problems are presented as examples to validate and demonstrate the use of these trade-off approaches to find the best way to adjust the design variables to meet the revised performance requirements.

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Section: Introductionmentioning

confidence: 99%

Gradient-Based Trade-Off Design for Engineering Applications

Royster,

Hou

2023

Designs

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“…The system designer usually quantifies system reliability by the probability that a system works properly without failures. The reliability may be estimated either by a physics-based approach [1][2][3][4] or a statistics-based approach [5,6]. A physicsbased approach predicts the reliability using computational models derived from physics principles, and the computational models are called limit-state functions.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Approach to Recovering Missing Component Dependence for System Reliability Prediction via Synergy Between Physics and Data

2021

Volume 3B: 47th Design Automation Conference (DAC)

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Predicting system reliability is often a core task in systems design. System reliability depends on component reliability and dependence of components. Component reliability can be predicted with a physics-based approach if the associated physical models are available. If the models do not exist, component reliability may be estimated from data. When both types of components coexist, their dependence is often unknown, and the component states are therefore assumed independent by the traditional method, which can result in a large error. This work proposes a new system reliability method to recover the missing component dependence, thereby leading to a more accurate estimate of the joint probability density (PDF) of all the component states. The method works for series systems whose load is shared by its components that may fail due to excessive loading. For components without physical models available, the load data are recorded upon failure, and equivalent physical models are created; the model parameters are estimated by the proposed Bayesian approach. Then models of all component states become available, and the dependence of component states, as well as their joint PDF, can be estimated. Four examples are used to evaluate the proposed method, and the results indicate that the proposed method can produce more accurate predictions of system reliability than the traditional method that assumes independent component states.

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“…Picheny et al 6 studied the influence of sample sizes and target probability of failure on the conservative estimate. Bae et al 7,8 showed a tradeoff between making design conservative and using more samples to reduce sampling uncertainty. However, they assumed that the samples are normally distributed.…”

Section: Introductionmentioning

confidence: 99%

“…Bae et al. 7,8 showed a tradeoff between making design conservative and using more samples to reduce sampling uncertainty. However, they assumed that the samples are normally distributed.…”

Section: Introductionmentioning

confidence: 99%

Reduced allowable strength of composite laminate for unknown distribution due to limited tests

Zhang

Kim

Palliyaguru

et al. 2020

Journal of Composite Materials

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In design under uncertainty, random distributions are often determined by expensive sampling tests. A key question is whether to invest in more samples or live with a reduced performance by fewer samples due to large uncertainty. The question is particularly difficult to answer when the type of distribution is unknown. This paper investigates the tradeoff between performance and conservativeness in estimating B-basis allowables, using experiments on composite plates with holes. Two approaches that do not require a distribution type are examined: (1) bootstrap confidence intervals and (2) Hanson-Koopmans non-parametric method. Based on the study, it is found that the Hanson-Koopmans method was more conservative than the bootstrap method because the latter penalized allowables for small-size samples. For a small number of samples (less than 29), conservative estimations are preferred over accuracy to account for the large uncertainty. Based on this observation, the bootstrap-assisted Hanson-Koopmans method is proposed to enhance the conservativeness. For the tested cases, the performance penalty using the bootstrap-assisted Hanson-Koopmans method for a small number of samples is found to be substantial.

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System Reliability-Based Design Optimization Under Tradeoff Between Reduction of Sampling Uncertainty and Design Shift

Cited by 7 publications

References 23 publications

Gradient-Based Trade-Off Design for Engineering Applications

Gradient-Based Trade-Off Design for Engineering Applications

A Bayesian Approach to Recovering Missing Component Dependence for System Reliability Prediction via Synergy Between Physics and Data

Reduced allowable strength of composite laminate for unknown distribution due to limited tests

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