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Uncertainty and sensitivity analysis of a PWR LOCA sequence using parametric and non-parametric methods

Author

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  • Zugazagoitia, Eneko
  • Queral, Cesar
  • Fernández-Cosials, Kevin
  • Gómez, Javier
  • Durán, Luis Felipe
  • Sánchez-Torrijos, Jorge
  • Posada, José María

Abstract

The Best Estimate Plus Uncertainty (BEPU) approach is being used worldwide for nuclear power plants licensing. This method relies on the use of best estimate models to simulate sequences, evaluating the uncertainties involved. To assess these uncertainties, several methodologies have been developed such as the non-parametric Wilks/Wald method, parametric methods that reconstruct a distribution from the data, or the binomial approach. Additionally, sensitivity analyses can be performed to obtain the correlation of the output-inputs. Finally, a variability analysis of the most influential parameters made to find a combination of parameters that can lead to damage is also useful. In this paper, all previous techniques are described, studied and applied by performing a large Monte Carlo set of simulations of a loss of coolant accident in a pressurized water reactor assessing two figures of merit. The comparison of the different methods show that the most conservative is the Wilks/Wald method; the least conservative is the parametric approach, and in between, the binomial one. The impact of the sample size is also studied for all methods, showing different behaviors for the different approaches.

Suggested Citation

  • Zugazagoitia, Eneko & Queral, Cesar & Fernández-Cosials, Kevin & Gómez, Javier & Durán, Luis Felipe & Sánchez-Torrijos, Jorge & Posada, José María, 2020. "Uncertainty and sensitivity analysis of a PWR LOCA sequence using parametric and non-parametric methods," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:reensy:v:193:y:2020:i:c:s0951832019300651
    DOI: 10.1016/j.ress.2019.106607
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    References listed on IDEAS

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    1. Sanchez-Saez, F. & Sánchez, A.I. & Villanueva, J.F. & Carlos, S. & Martorell, S., 2018. "Uncertainty analysis of a large break loss of coolant accident in a pressurized water reactor using non-parametric methods," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 19-28.
    2. Di Maio, Francesco & Bandini, Alessandro & Zio, Enrico & Alberola, Sofia Carlos & Sanchez-Saez, Francisco & Martorell, Sebastián, 2016. "Bootstrapped-ensemble-based Sensitivity Analysis of a trace thermal-hydraulic model based on a limited number of PWR large break loca simulations," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 122-134.
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    Cited by:

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    2. Liu, Yang & Wang, Dewei & Sun, Xiaodong & Liu, Yang & Dinh, Nam & Hu, Rui, 2021. "Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    3. Antonello, Federico & Buongiorno, Jacopo & Zio, Enrico, 2022. "A methodology to perform dynamic risk assessment using system theory and modeling and simulation: Application to nuclear batteries," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    4. Mazgaj, Piotr & Darnowski, Piotr & Kaszko, Aleksej & Hortal, Javier & Dusic, Milorad & Mendizábal, Rafael & Pelayo, Fernando, 2022. "Demonstration of the E-BEPU methodology for SL-LOCA in a Gen-III PWR reactor," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    5. Reyes-Fuentes, Melisa & del-Valle-Gallegos, Edmundo & Duran-Gonzalez, Julian & Ortíz-Villafuerte, Javier & Castillo-Durán, Rogelio & Gómez-Torres, Armando & Queral, Cesar, 2021. "AZTUSIA: A new application software for Uncertainty and Sensitivity analysis for nuclear reactors," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    6. Li, Shen & Kim, Do Kyun & Benson, Simon, 2021. "A probabilistic approach to assess the computational uncertainty of ultimate strength of hull girders," Reliability Engineering and System Safety, Elsevier, vol. 213(C).

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