A review of global sensitivity analysis for uncertainty structure

Xiao, Sun; Lv, ZhenZhou; Wang, Wei

doi:10.1360/sspma2016-00516

Cited by 4 publications

(4 citation statements)

References 52 publications

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“…x 1 ∼ N 9.9, 5 2 . This example has been studied by Kaymaz [22]. The contribution indexes of samples of four methods, i.e., MCS, IS, LS and SS, are computed and the results are given in Tab.…”

Section: Example 2: Nonlinear Examplementioning

confidence: 99%

“…Recently, the joint use of simulation methods and surrogate model methods, which is called 'active learning method', has been proposed, i.e., Kriging model with MCS [16], Kriging with IS [17] and Kriging with SS [18,19]. Also, the sensitivity analysis for variables has been attracted more and more attentions [20][21][22][23][24][25][26][27][28]. Sensitivity analysis can help researchers to identify the main factors affecting the uncertainty of the output response of a model [21,22] .…”

Section: Introductionmentioning

confidence: 99%

“…Also, the sensitivity analysis for variables has been attracted more and more attentions [20][21][22][23][24][25][26][27][28]. Sensitivity analysis can help researchers to identify the main factors affecting the uncertainty of the output response of a model [21,22] . There are many commonly used local and global sensitivity indexes, and also the corresponding analysis methods for these sensitivity measures are developed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sensitivity of Sample for Simulation-Based Reliability Analysis Methods

Yuan¹,

JianGu²,

Liu³

2021

Computer Modeling in Engineering &Amp; Sciences

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In structural reliability analysis, simulation methods are widely used. The statistical characteristics of failure probability estimate of these methods have been well investigated. In this study, the sensitivities of the failure probability estimate and its statistical characteristics with regard to sample, called 'contribution indexes' , are proposed to measure the contribution of sample. The contribution indexes in four widely simulation methods, i.e., Monte Carlo simulation (MCS), importance sampling (IS), line sampling (LS) and subset simulation (SS) are derived and analyzed. The proposed contribution indexes of sample can provide valuable information understanding the methods deeply, and enlighten potential improvement of methods. It is found that the main differences between these investigated methods lie in the contribution indexes of the safety samples, which are the main factors to the efficiency of the methods. Moreover, numerical examples are used to validate these findings.

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Section: Example 2: Nonlinear Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sensitivity of Sample for Simulation-Based Reliability Analysis Methods

Yuan¹,

JianGu²,

Liu³

2021

Computer Modeling in Engineering &Amp; Sciences

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“…Moreover, the moment‐independent sensitivity index has an invariance of transformation, which means the transformed moment‐independent index is unchanged. This property ensures that the moment‐independent sensitivity index can be solved efficiently when the distribution of the model output variables spans several orders of magnitude . Moment‐independent sensitivity analysis has been implemented, and the influence of parameter correlation has been addressed.…”

Section: Introductionmentioning

confidence: 99%

Global moment‐independent sensitivity analysis of single‐stage thermoelectric refrigeration system

Zhang

Liu

et al. 2019

Int J Energy Res

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A mathematical model of single-stage thermoelectric refrigeration system (SSTRS) was built considering the influence of external heat transfer. Based on the relationship between the input system design variables and the output cooling capacity, the coefficient of performance and the influence of the fluctuation of the design variables on the stability of the thermoelectric refrigeration system output performance parameters were studied using a moment-independent sensitivity analysis. The Latin hypercube sampling method was used to simulate the fluctuation of the design variables, and their moment-independent sensitivity indices were calculated by the Monte Carlo method. The design variables were then sorted according to their importance to the output parameters, and the key design variables affecting the coefficient of performance and the cooling capacity were obtained. These results can provide useful guidance for the design and optimisation of SSTRS.

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