Trends in sensitivity analysis practice in the last decade

Ferretti, Federico; Saltelli, Andrea; Tarantola, Stefano

doi:10.1016/j.scitotenv.2016.02.133

Cited by 175 publications

(107 citation statements)

References 8 publications

(9 reference statements)

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“…However, the number of sensitivity analysis studies using this global method has been very small. Ferretti et al (2016) found that out of around 1.75 million research articles surveyed up to 2014, only 1 in 20 of studies mentioning "sensitivity analysis" also use or refer to "global sensitivity analysis". A common type of GSA is the variance-based method, which operates by apportioning the variance of the model's output into different sources of variation in the inputs.…”

Section: Different Approaches For Sensitivity Analysismentioning

confidence: 99%

Fast sensitivity analysis methods for computationally expensive models with multi-dimensional output

et al. 2018

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Abstract. Global sensitivity analysis (GSA) is a powerful approach in identifying which inputs or parameters most affect a model's output. This determines which inputs to include when performing model calibration or uncertainty analysis. GSA allows quantification of the sensitivity index (SI) of a particular input -the percentage of the total variability in the output attributed to the changes in that input -by averaging over the other inputs rather than fixing them at specific values. Traditional methods of computing the SIs using the Sobol and extended Fourier Amplitude Sensitivity Test (eFAST) methods involve running a model thousands of times, but this may not be feasible for computationally expensive Earth system models. GSA methods that use a statistical emulator in place of the expensive model are popular, as they require far fewer model runs. We performed an eight-input GSA, using the Sobol and eFAST methods, on two computationally expensive atmospheric chemical transport models using emulators that were trained with 80 runs of the models. We considered two methods to further reduce the computational cost of GSA: (1) a dimension reduction approach and (2) an emulator-free approach. When the output of a model is multi-dimensional, it is common practice to build a separate emulator for each dimension of the output space. Here, we used principal component analysis (PCA) to reduce the output dimension, built an emulator for each of the transformed outputs, and then computed SIs of the reconstructed output using the Sobol method. We considered the global distribution of the annual column mean lifetime of atmospheric methane, which requires ∼ 2000 emulators without PCA but only 5-40 emulators with PCA. We also applied an emulator-free method using a generalised additive model (GAM) to estimate the SIs using only the training runs. Compared to the emulator-only methods, the emulator-PCA and GAM methods accurately estimated the SIs of the ∼ 2000 methane lifetime outputs but were on average 24 and 37 times faster, respectively.

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Section: Different Approaches For Sensitivity Analysismentioning

confidence: 99%

Fast sensitivity analysis methods for computationally expensive models with multi-dimensional output

et al. 2018

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“…Sensitivity analysis aims to interrogate “how the output uncertainty of a model (numerical or otherwise) can be apportioned to different sources of uncertainty in the model input.” The most straightforward SA methods are the local sensitivity analysis (LSA) methods, which are defined based on partial derivatives or finite differences . Since they are computationally very cheap and easy, the majority of published literatures use LSA methods to interrogate the influences of input factors . However, one should note that the LSA indices depend on the nominal position of the base point, and are only informative around the base point.…”

Section: Introductionmentioning

confidence: 99%

Importance measure method for error sources of degradation systems based on the present worth of expected Taguchi quality loss

Sun

Cui

et al. 2020

Quality & Reliability Eng

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Deteriorating of the error sources and the deviation of the quality characteristic (QC) of the product from the target value are ubiquitous in practice. Studying the relative contribution of the error sources to the QC deviation is helpful for improving the product performance effectively. In this study, we propose an error importance measure (EIM) method to quantify the relative contribution of error sources to the Taguchi quality loss that is caused by the QC deviation. The present worth of the expected Taguchi quality loss (ETQL) is used to characterize the deteriorating QC deviation. We use Taylor series expansion to decompose the time‐dependent ETQL and the present worth of ETQL into a series of fractions, representing the individual and the interaction contributions of the error sources. Based on the decomposition, the time‐dependent and the time‐independent EIM indices for one and a group of error sources are defined, respectively. The evaluation procedure for the EIM indices is provided and the properties are discussed. Finally, we provide a realistic application case to an aircraft wheel door mechanism to demonstrate the effectiveness of the proposed EIM method.

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“…In fact, empirical parameters that are hard to measure experimentally would inevitably increase model freedom degree thus damage the results reliability at the same time. 12 Wagner firstly proposed the idea of global sensitivity, and meanwhile, a multicomponent-based global sensitivity analysis method was presented. 6 Modelers and practitioners have reached a consensus that sensitivity analysis (SA) method should be applied as a minimum, necessary component to answer the question of how uncertain of those factors are and where those uncertainties come from.…”

Section: Introductionmentioning

confidence: 99%

Global sensitivity analysis of uncertain parameters based on 2D modeling of solid oxide fuel cell

et al. 2019

Int J Energy Res

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Summary In this study, we propose an enhanced quasi–two‐dimensional and nonisothermal model for solid oxide fuel cell (SOFC) parametric simulation and optimization. The dependence of effective properties on microstructural parameters is fully considered in this model. Besides, an elementary effect (EE) approach based on Monte Carlo experiments is adopted to comprehensively evaluate the sensitivity of totally 24 parameters. A two sample Kolmogorov‐Smirnov (K‐S) test is carried out to evaluate the ability of EE method for robust and accurate sensitivity analysis. The investigation focuses on the important microstructural parameters of the composite anode/cathode function layers (AFL/CFL). With this research, relative volume fractions of conducting materials in the AFL/CFL are the most sensitive factors among all input parameters while particle radius is found to be the least sensitive microstructural parameters. The particle size ratio of electronic particles to ionic particles is found to be much more sensitive than particle size due to its significant effect on effective conductivity. The cathodic electrochemical parameters reflect cell performance more significantly than the anodic ones. To further elucidate the role of input factors, this study provides a principle for parametric sensitivity classification as well. Besides, impacts of current density variation on parametric sensitivity are comprehensively considered. Negative or positive effects of parameters on cell performance and their influencing mechanisms are also discussed. Furthermore, global sensitivity analysis for single parameters at different positions is performed to assess the probability of structural optimization along the channel length. Then, a feasible nonuniform distribution method in allusion to function layers is proposed for further improvement of cell performance according to global SA results of single parameters along the channel direction.

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Trends in sensitivity analysis practice in the last decade

Cited by 175 publications

References 8 publications

Fast sensitivity analysis methods for computationally expensive models with multi-dimensional output

Fast sensitivity analysis methods for computationally expensive models with multi-dimensional output

Importance measure method for error sources of degradation systems based on the present worth of expected Taguchi quality loss

Global sensitivity analysis of uncertain parameters based on 2D modeling of solid oxide fuel cell

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