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Statistical developments for target and conditional sensitivity analysis: Application on safety studies for nuclear reactor

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  • Marrel, Amandine
  • Chabridon, Vincent

Abstract

In the framework of uncertainty treatment in numerical simulation, Global sensitivity analysis (GSA) aims at determining (qualitatively or quantitatively) how the variability of the uncertain inputs affects the model output. However, from reliability and risk management perspectives, GSA might be insufficient to capture the influence of the inputs on a restricted domain of the output (e.g., critical event).

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  • Marrel, Amandine & Chabridon, Vincent, 2021. "Statistical developments for target and conditional sensitivity analysis: Application on safety studies for nuclear reactor," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    • Handle: RePEc:eee:reensy:v:214:y:2021:i:c:s0951832021002465
    DOI: 10.1016/j.ress.2021.107711
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    References listed on IDEAS

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    1. Rubinstein, Reuven Y., 1986. "The score function approach for sensitivity analysis of computer simulation models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 28(5), pages 351-379.
    2. Wei, Pengfei & Lu, Zhenzhou & Song, Jingwen, 2015. "Variable importance analysis: A comprehensive review," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 399-432.
    3. Sudret, B. & Mai, C.V., 2015. "Computing derivative-based global sensitivity measures using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 241-250.
    4. Marrel, Amandine & Iooss, Bertrand & Laurent, Béatrice & Roustant, Olivier, 2009. "Calculations of Sobol indices for the Gaussian process metamodel," Reliability Engineering and System Safety, Elsevier, vol. 94(3), pages 742-751.
    5. Chabridon, Vincent & Balesdent, Mathieu & Bourinet, Jean-Marc & Morio, Jérôme & Gayton, Nicolas, 2018. "Reliability-based sensitivity estimators of rare event probability in the presence of distribution parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 164-178.
    6. Manel Baucells & Emanuele Borgonovo, 2013. "Invariant Probabilistic Sensitivity Analysis," Management Science, INFORMS, vol. 59(11), pages 2536-2549, November.
    7. Sudret, Bruno, 2008. "Global sensitivity analysis using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 964-979.
    8. Kucherenko, Sergei & Song, Shufang & Wang, Lu, 2019. "Quantile based global sensitivity measures," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 35-48.
    9. Székely, Gábor J. & Rizzo, Maria L., 2013. "The distance correlation t-test of independence in high dimension," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 193-213.
    10. Konakli, Katerina & Sudret, Bruno, 2016. "Global sensitivity analysis using low-rank tensor approximations," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 64-83.
    11. Jean-Claude Fort & Thierry Klein & Nabil Rachdi, 2016. "New sensitivity analysis subordinated to a contrast," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(15), pages 4349-4364, August.
    12. Shun Yao & Xianyang Zhang & Xiaofeng Shao, 2018. "Testing mutual independence in high dimension via distance covariance," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(3), pages 455-480, June.
    13. Song, Shufang & Lu, Zhenzhou & Qiao, Hongwei, 2009. "Subset simulation for structural reliability sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 658-665.
    14. Maume-Deschamps, Véronique & Niang, Ibrahima, 2018. "Estimation of quantile oriented sensitivity indices," Statistics & Probability Letters, Elsevier, vol. 134(C), pages 122-127.
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    Cited by:

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    2. Fan, Ruijia & Chang, Guofeng & Xu, Yiming & Zhang, Yuanzhi & Wei, Pengnan, 2024. "Enhancing PEM fuel cell dynamic performance: Co-optimization of cathode catalyst layer composition and operating conditions using a novel surrogate model," Renewable Energy, Elsevier, vol. 231(C).
    3. Barr, John & Rabitz, Herschel, 2023. "Kernel-based global sensitivity analysis obtained from a single data set," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    4. Ma, Yuan-Zhuo & Jin, Xiang-Xiang & Zhao, Xiang & Li, Hong-Shuang & Zhao, Zhen-Zhou & Xu, Chang, 2024. "Reliability-oriented global sensitivity analysis using subset simulation and space partition," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    5. Liu, Jiaxin & Yu, Deping & Yang, Taibo & Liu, Caixue & Wang, Guangjin & Liu, Xiaoming, 2023. "Discovering the causes for the change of the vibration characteristics of the core support barrel in PWR nuclear power plants: A combined investigation based on ex-core neutron noise analysis and nume," Reliability Engineering and System Safety, Elsevier, vol. 234(C).

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