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Reliability Assessment of Passive Safety Systems for Nuclear Energy Applications: State-of-the-Art and Open Issues

Author

Listed:
  • Francesco Di Maio

    (Energy Department, Politecnico di Milano, 20156 Milan, Italy)

  • Nicola Pedroni

    (Dipartimento di Energia, Politecnico di Torino, 10121 Turin, Italy)

  • Barnabás Tóth

    (NUBIKI Nuclear Safety Research Institute Ltd., 1121 Budapest, Hungary)

  • Luciano Burgazzi

    (ENEA Agenzia Nazionale per le Nuove Tecnologie, L’energia e lo Sviluppo Economico Sostenibile, 40121 Bologna, Italy)

  • Enrico Zio

    (Energy Department, Politecnico di Milano, 20156 Milan, Italy
    Centre for Research on Risk and Crises (CRC), MINES ParisTech, PSL Research University, 75006 Paris, France)

Abstract

Passive systems are fundamental for the safe development of Nuclear Power Plant (NPP) technology. The accurate assessment of their reliability is crucial for their use in the nuclear industry. In this paper, we present a review of the approaches and procedures for the reliability assessment of passive systems. We complete the work by discussing the pending open issues, in particular with respect to the need of novel sensitivity analysis methods, the role of empirical modelling and the integration of passive safety systems assessment in the (static/dynamic) Probabilistic Safety Assessment (PSA) framework.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4688-:d:607118
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    References listed on IDEAS

    as
    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. Makai, M. & Pál, L., 2006. "Best estimate method and safety analysis II," Reliability Engineering and System Safety, Elsevier, vol. 91(2), pages 222-232.
    3. Arul, A. John & Iyer, N. Kannan & Velusamy, K., 2009. "Adjoint operator approach to functional reliability analysis of passive fluid dynamical systems," Reliability Engineering and System Safety, Elsevier, vol. 94(12), pages 1917-1926.
    4. Nagel, Joseph B. & Rieckermann, Jörg & Sudret, Bruno, 2020. "Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: Application to urban drainage simulation," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    5. Storlie, Curtis B. & Swiler, Laura P. & Helton, Jon C. & Sallaberry, Cedric J., 2009. "Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1735-1763.
    6. Echard, B. & Gayton, N. & Lemaire, M. & Relun, N., 2013. "A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 232-240.
    7. Nayak, A.K. & Jain, Vikas & Gartia, M.R. & Prasad, Hari & Anthony, A. & Bhatia, S.K. & Sinha, R.K., 2009. "Reliability assessment of passive isolation condenser system of AHWR using APSRA methodology," Reliability Engineering and System Safety, Elsevier, vol. 94(6), pages 1064-1075.
    8. 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.
    9. Burgazzi, Luciano, 2007. "Thermal–hydraulic passive system reliability-based design approach," Reliability Engineering and System Safety, Elsevier, vol. 92(9), pages 1250-1257.
    10. Sallaberry, C.J. & Helton, J.C. & Hora, S.C., 2008. "Extension of Latin hypercube samples with correlated variables," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 1047-1059.
    11. Zio, E. & Pedroni, N., 2009. "Functional failure analysis of a thermal–hydraulic passive system by means of Line Sampling," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1764-1781.
    12. 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.
    13. 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.
    14. Pourgol-Mohamad, Mohammad & Mosleh, Ali & Modarres, Mohammad, 2010. "Methodology for the use of experimental data to enhance model output uncertainty assessment in thermal hydraulics codes," Reliability Engineering and System Safety, Elsevier, vol. 95(2), pages 77-86.
    15. Zio, E. & Pedroni, N., 2010. "An optimized Line Sampling method for the estimation of the failure probability of nuclear passive systems," Reliability Engineering and System Safety, Elsevier, vol. 95(12), pages 1300-1313.
    16. Fauriat, W. & Gayton, N., 2014. "AK-SYS: An adaptation of the AK-MCS method for system reliability," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 137-144.
    17. Alison L. Gibbs & Francis Edward Su, 2002. "On Choosing and Bounding Probability Metrics," International Statistical Review, International Statistical Institute, vol. 70(3), pages 419-435, December.
    18. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    19. Helton, J.C. & Johnson, J.D. & Sallaberry, C.J. & Storlie, C.B., 2006. "Survey of sampling-based methods for uncertainty and sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1175-1209.
    20. Wu, Xu & Kozlowski, Tomasz & Meidani, Hadi, 2018. "Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 422-436.
    21. Cadini, F. & Gioletta, A. & Zio, E., 2015. "Improved metamodel-based importance sampling for the performance assessment of radioactive waste repositories," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 188-197.
    22. Burgazzi, Luciano, 2008. "About time-variant reliability analysis with reference to passive systems assessment," Reliability Engineering and System Safety, Elsevier, vol. 93(11), pages 1682-1688.
    23. 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.
    24. 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.
    25. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    26. Pedroni, N. & Zio, E. & Apostolakis, G.E., 2010. "Comparison of bootstrapped artificial neural networks and quadratic response surfaces for the estimation of the functional failure probability of a thermal–hydraulic passive system," Reliability Engineering and System Safety, Elsevier, vol. 95(4), pages 386-395.
    27. Zio, E. & Pedroni, N., 2009. "Building confidence in the reliability assessment of thermal-hydraulic passive systems," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 268-281.
    28. Zdravko I. Botev & Dirk P. Kroese, 2008. "An Efficient Algorithm for Rare-event Probability Estimation, Combinatorial Optimization, and Counting," Methodology and Computing in Applied Probability, Springer, vol. 10(4), pages 471-505, December.
    29. Burgazzi, Luciano, 2007. "State of the art in reliability of thermal-hydraulic passive systems," Reliability Engineering and System Safety, Elsevier, vol. 92(5), pages 671-675.
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    1. Gyunyoung Heo, 2022. "Advancements in Probabilistic Safety Assessment of Nuclear Energy for Sustainability," Energies, MDPI, vol. 15(2), pages 1-2, January.
    2. 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).

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