Interval Uncertainty Reduction and Single-Disciplinary Sensitivity Analysis With Multi-Objective Optimization

Li, M.; Williams, Nathan; Azarm, Shapour

doi:10.1115/1.3066736

Cited by 21 publications

(13 citation statements)

References 39 publications

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“…This method however did not address the interaction between design parameters, and only the main effect was analyzed. A hybrid approach [26,27] was developed to analyze the sensitivity of parameterized interval variables via multi-objective optimization problems, where the variation of an output with a given interval input was estimated by optimization. The searching procedure for optimization can be computationally expensive for highly nonlinear and coupled systems.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity Analysis in Quantified Interval Constraint Satisfaction Problems

Wang

Cheng

et al. 2015

Journal of Mechanical Design

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Interval is an alternative to probability distribution in quantifying uncertainty for sensitivity analysis (SA) when there is a lack of data to fit a distribution with good confidence. It only requires the information of lower and upper bounds. Analytical relations among design parameters, design variables, and target performances under uncertainty can be modeled as interval-valued constraints. By incorporating logic quantifiers, quantified constraint satisfaction problems (QCSPs) can integrate semantics and engineering intent in mathematical relations for engineering design. In this paper, a global sensitivity analysis (GSA) method is developed for feasible design space searching problems that are formulated as QCSPs, where the effects of value variations and quantifier changes for design parameters on target performances are analyzed based on several proposed metrics, including the indeterminacy of target performances, information gain of parameter variations, and infeasibility of constraints. Three examples are used to demonstrate the proposed approach.

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Section: Introductionmentioning

confidence: 99%

“…Different from other interval based SA approaches [25][26][27], our approach considers both the main and interaction effects for feasible design problems with continuous design parameters. Our SA approach for interval-valued variables is global.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis in Quantified Interval Constraint Satisfaction Problems

Wang

Cheng

et al. 2015

Journal of Mechanical Design

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“…Other work has explored the use of target sets to decompose the design space and identify optimal solutions [39]. Further opportunities can be found in robust design, where research has characterized the effect of design variable variations on performance [40] while applying regionalized sensitivity analysis [41] and internal-reduction measures [42].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Effectiveness of Using Graveyard Data When Generating Design Alternatives

Foster¹,

Ferguson

2013

Journal of Computing and Information Science in Engineering

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The objective of this paper is to demonstrate that unique alternative designs can be efficiently found by searching the discarded data (or graveyard) from a multiobjective genetic algorithm (MOGA). Motivation for using graveyard data to generate design alternatives arises from the computational cost associated with real-time design space exploration of multiobjective optimization problems. The effectiveness of this approach is explored by comparing (1) the uniqueness of alternatives found using graveyard data and those generated using an optimization-based search, and (2) how alternative generation near the Pareto frontier is impacted. Two multiobjective case study problems are introduced-a two bar truss and an I-beam design optimization. Results from these studies indicate that using graveyard data allows for the discovery of alternative designs that are at least 70% as unique as alternatives found using an optimization-based alternative identification approach, while saving a significant number of functional evaluations. Additionally, graveyard data are shown to be better suited for alternative generation near the Pareto frontier than standard sampling techniques. Finally, areas of future work are also discussed.

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“…Similarly, the authors showed that quality control [18], improved failureprediction models [19,20] can lead to substantial reduction of failure probability of aerospace structures. More recently, Li et al [21] considered systems that have interval uncertainty in their inputs. They developed a multiobjective optimization model to obtain optimal reduction of parameter uncertainty that provide the maximum improvement in system performance with the least amount of investment.…”

mentioning

confidence: 99%

Effects of Structural Tests on Aircraft Safety

2010

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This paper presents a methodology to investigate the effects of structural tests on aircraft safety. In particular, the paper focuses on the effect of the number of coupon tests and structural element tests on the final distribution of failure stress. The mean failure stress is assumed to be predicted by a failure criterion (e.g., Tsai-Wu), and the initial distribution of this mean failure stress reflects the uncertainty in the analysis procedure that uses coupon test data to predict structural failure. In addition to the uncertainty in the mean failure stress, there is also uncertainty in its variability due to the finite number of coupon tests. Bayesian updating is used to update the failure stress distribution based on results of the element tests. A Monte Carlo simulation of a large number of uncertainties and the possible test results are used to obtain the probability of structural failure in a certification test or in actual flight. Incorporating the Bayesian updating into the Monte Carlo simulation loop is computationally prohibitive; therefore, a surrogate procedure is devised to overcome the computational challenge. A structural design following the Federal Aviation Administration regulations is considered, and the tradeoffs between the number of tests and the weight and probability of failure in the certification test and in service are explored. To make this tradeoff analysis computationally affordable, response surface approximations are used to relate the knockdown factor to the probability of failure in service and in the certification test. It is found that it is possible to do a simultaneously probabilistic design and satisfy the Federal Aviation Administration regulations for deterministic design.

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Interval Uncertainty Reduction and Single-Disciplinary Sensitivity Analysis With Multi-Objective Optimization

Cited by 21 publications

References 39 publications

Sensitivity Analysis in Quantified Interval Constraint Satisfaction Problems

Sensitivity Analysis in Quantified Interval Constraint Satisfaction Problems

Exploring the Effectiveness of Using Graveyard Data When Generating Design Alternatives

Effects of Structural Tests on Aircraft Safety

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