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BEPU robustness analysis via perturbed law-based sensitivity indices

Author

Listed:
  • Bertrand Iooss
  • Vanessa Vergès
  • Vincent Larget

Abstract

The “best-estimate plus uncertainty†(BEPU) methodology is the term used in the nuclear engineering community when dealing with uncertainty quantification issues in realistic numerical simulation models. One of the most critical hypothesis in these studies is the choice of the probability distributions of uncertain input variables which are propagated through the model. Bringing stringent justifications to the BEPU approach, especially in a safety study, requires quantifying the impact of potential uncertainty on the input variable distribution. To solve this problem, this paper deepens the robustness analysis based on the “Perturbed Law-based sensitivity Indices†(PLI). The PLI quantifies the impact of a perturbation of an input distribution on the quantity of interest (as a quantile of a model output or a safety margin) in the BEPU study. The mathematical formalism of the PLI is applied to two particular quantities of interest: the quantile and the superquantile. For both quantities, the PLI can be easily computed using a unique Monte-Carlo sample containing model inputs and output. Numerical tests are developed in order to define validity criteria of a PLI-based robustness analysis. The practical use of the method is illustrated on thermal-hydraulic computer experiments, simulating a cold leg Intermediate Break Loss Of Coolant Accident (IBLOCA) in a pressurized water nuclear reactor.

Suggested Citation

  • Bertrand Iooss & Vanessa Vergès & Vincent Larget, 2022. "BEPU robustness analysis via perturbed law-based sensitivity indices," Journal of Risk and Reliability, , vol. 236(5), pages 855-865, October.
  • Handle: RePEc:sae:risrel:v:236:y:2022:i:5:p:855-865
    DOI: 10.1177/1748006X211036569
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    References listed on IDEAS

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    1. 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).
    2. Zio, E. & Pedroni, N., 2012. "Monte Carlo simulation-based sensitivity analysis of the model of a thermal–hydraulic passive system," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 90-106.
    3. Pasanisi, Alberto & Keller, Merlin & Parent, Eric, 2012. "Estimation of a quantity of interest in uncertainty analysis: Some help from Bayesian decision theory," Reliability Engineering and System Safety, Elsevier, vol. 100(C), pages 93-101.
    4. Iooss, Bertrand & Le Gratiet, Loïc, 2019. "Uncertainty and sensitivity analysis of functional risk curves based on Gaussian processes," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 58-66.
    5. Tatiana Labopin-Richard & Fabrice Gamboa & Aur'elien Garivier & Bertrand Iooss, 2014. "Bregman superquantiles. Estimation methods and applications," Papers 1405.6677, arXiv.org, revised Jan 2016.
    6. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
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