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Safety margins confidence estimation for a passive residual heat removal system

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  • Zio, Enrico
  • Di Maio, Francesco
  • Tong, Jiejuan

Abstract

For licensing purposes, safety cases of nuclear power plants (NPPs) must be presented at the Regulatory Authority with the necessary confidence on the outcomes of the models used to analyze the plant safety behavior. In the present work, we consider the problem of providing a quantitative indication of the confidence in the safety margin estimation by a model with uncertain inputs giving in output the maximum outlet water temperature of the residual heat removal system (RHRs) in accident scenarios of the high temperature reactor-pebble modular (HTR-PM). The quantitative evaluation is carried out by means of a computational procedure of literature, based on order statistics (OS). The procedure is analyzed with respect to some of its key parameters defining the sample size and the number of uncertain inputs.

Suggested Citation

  • Zio, Enrico & Di Maio, Francesco & Tong, Jiejuan, 2010. "Safety margins confidence estimation for a passive residual heat removal system," Reliability Engineering and System Safety, Elsevier, vol. 95(8), pages 828-836.
    • Handle: RePEc:eee:reensy:v:95:y:2010:i:8:p:828-836
    DOI: 10.1016/j.ress.2010.03.006
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    References listed on IDEAS

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    1. Burgazzi, Luciano, 2007. "Thermal–hydraulic passive system reliability-based design approach," Reliability Engineering and System Safety, Elsevier, vol. 92(9), pages 1250-1257.
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    1. Di Maio, Francesco & Nicola, Giancarlo & Borgonovo, Emanuele & Zio, Enrico, 2016. "Invariant methods for an ensemble-based sensitivity analysis of a passive containment cooling system of an AP1000 nuclear power plant," Reliability Engineering and System Safety, Elsevier, vol. 151(C), pages 12-19.
    2. Di Maio, Francesco & Rai, Ajit & Zio, Enrico, 2016. "A dynamic probabilistic safety margin characterization approach in support of Integrated Deterministic and Probabilistic Safety Analysis," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 9-18.
    3. Wang, Wei & Cammi, Antonio & Di Maio, Francesco & Lorenzi, Stefano & Zio, Enrico, 2018. "A Monte Carlo-based exploration framework for identifying components vulnerable to cyber threats in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 24-37.
    4. Di Maio, Francesco & Baronchelli, Samuele & Zio, Enrico, 2014. "Hierarchical differential evolution for minimal cut sets identification: Application to nuclear safety systems," European Journal of Operational Research, Elsevier, vol. 238(2), pages 645-652.
    5. Di Maio, Francesco & Picoco, Claudia & Zio, Enrico & Rychkov, Valentin, 2017. "Safety margin sensitivity analysis for model selection in nuclear power plant probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 122-138.
    6. 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.
    7. Francesco Di Maio & Samuele Baronchelli & Enrico Zio, 2015. "A Computational Framework for Prime Implicants Identification in Noncoherent Dynamic Systems," Risk Analysis, John Wiley & Sons, vol. 35(1), pages 142-156, January.
    8. Matteo Vagnoli & Francesco Di Maio & Enrico Zio, 2018. "Ensembles of climate change models for risk assessment of nuclear power plants," Journal of Risk and Reliability, , vol. 232(2), pages 185-200, April.
    9. 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.
    10. Francesco Di Maio & Nicola Pedroni & Barnabás Tóth & Luciano Burgazzi & Enrico Zio, 2021. "Reliability Assessment of Passive Safety Systems for Nuclear Energy Applications: State-of-the-Art and Open Issues," Energies, MDPI, vol. 14(15), pages 1-17, August.
    11. Chi, Lixun & Su, Huai & Zio, Enrico & Qadrdan, Meysam & Li, Xueyi & Zhang, Li & Fan, Lin & Zhou, Jing & Yang, Zhaoming & Zhang, Jinjun, 2021. "Data-driven reliability assessment method of Integrated Energy Systems based on probabilistic deep learning and Gaussian mixture Model-Hidden Markov Model," Renewable Energy, Elsevier, vol. 174(C), pages 952-970.
    12. Dimaio, F. & Scapinello, O. & Zio, E. & Ciarapica, C. & Cincotta, S. & Crivellari, A. & Decarli, L. & Larosa, L., 2021. "Accounting for Safety Barriers Degradation in the Risk Assessment of Oil and Gas Systems by Multistate Bayesian Networks," Reliability Engineering and System Safety, Elsevier, vol. 216(C).

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