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An optimized Line Sampling method for the estimation of the failure probability of nuclear passive systems

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  • Zio, E.
  • Pedroni, N.

Abstract

The quantitative reliability assessment of a thermal–hydraulic (T–H) passive safety system of a nuclear power plant can be obtained by (i) Monte Carlo (MC) sampling the uncertainties of the system model and parameters, (ii) computing, for each sample, the system response by a mechanistic T–H code and (iii) comparing the system response with pre-established safety thresholds, which define the success or failure of the safety function. The computational effort involved can be prohibitive because of the large number of (typically long) T–H code simulations that must be performed (one for each sample) for the statistical estimation of the probability of success or failure.

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  • 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.
    • Handle: RePEc:eee:reensy:v:95:y:2010:i:12:p:1300-1313
    DOI: 10.1016/j.ress.2010.06.007
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    5. Helton, Jon C. & Sallaberry, Cedric J., 2009. "Conceptual basis for the definition and calculation of expected dose in performance assessments for the proposed high-level radioactive waste repository at Yucca Mountain, Nevada," Reliability Engineering and System Safety, Elsevier, vol. 94(3), pages 677-698.
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    7. Helton, Jon C. & Sallaberry, Cedric J., 2009. "Computational implementation of sampling-based approaches to the calculation of expected dose in performance assessments for the proposed high-level radioactive waste repository at Yucca Mountain, Nev," Reliability Engineering and System Safety, Elsevier, vol. 94(3), pages 699-721.
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    9. 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.
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    Cited by:

    1. 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.
    2. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2016. "Advanced RESTART method for the estimation of the probability of failure of highly reliable hybrid dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 117-126.
    3. 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.
    4. Kong Fah Tee & Andrew Utomi Ebenuwa, 2019. "Combination of line sampling and important sampling for reliability assessment of buried pipelines," Journal of Risk and Reliability, , vol. 233(2), pages 139-150, April.
    5. Olatubosun, Samuel Abiodun & Zhang, Zhijian, 2019. "Dependency consideration of passive system reliability by coupled stress-strength interference/functional relations of parameters approach," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 549-560.
    6. Ajenjo, Antoine & Ardillon, Emmanuel & Chabridon, Vincent & Cogan, Scott & Sadoulet-Reboul, Emeline, 2023. "Robustness evaluation of the reliability of penstocks combining line sampling and neural networks," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    7. Jin, Kyungho & Kim, Hyeonmin & Ryu, Seunghyoung & Kim, Seunggeun & Park, Jinkyun, 2022. "An approach to constructing effective training data for a classification model to evaluate the reliability of a passive safety system," Reliability Engineering and System Safety, Elsevier, vol. 222(C).

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