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A concept paper on dynamic reliability via Monte Carlo simulation

Author

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  • Marseguerra, M.
  • Zio, E.
  • Devooght, J.
  • Labeau, P.E.

Abstract

The methodologies employed in the probabilistic safety assessment (PSA) of hazardous engineering systems have reached a high level of maturity. However, a few issues are still worth of further investigation in order to increase the confidence in the results obtained. In this view, in the past 5–10 years, researchers in the field of reliability have proposed a more `dynamic' approach to PSA with the aim of addressing issues concerning the possible mutual interactions between the hardware system states and the plant physical evolution. Nonetheless, some objections have been raised against such a dynamic approach, especially against its practical complexity and the lack of a clear definition of the domain of its applicability. In this paper, an attempt to define more precisely the field of application for a dynamic approach is propounded on the basis of the concept of accident duration. The qualitative discussion is supported with examples of postulated severe accidents in nuclear power plants like those investigated in level-2 PSA. The application of Monte Carlo simulation as a tool capable, in principle, of handling all the features of dynamic PSA is illustrated. Monte Carlo algorithms are illustrated aiming at improving the stochastic part of the analysis and the deterministic integration as well, so as to allow for a considerable reduction in the computation times.

Suggested Citation

  • Marseguerra, M. & Zio, E. & Devooght, J. & Labeau, P.E., 1998. "A concept paper on dynamic reliability via Monte Carlo simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 47(2), pages 371-382.
    • Handle: RePEc:eee:matcom:v:47:y:1998:i:2:p:371-382
    DOI: 10.1016/S0378-4754(98)00112-8
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    Citations

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    Cited by:

    1. Labeau, P.E & Zio, E, 1998. "The cell-to-boundary method in the frame of memorization-based Monte Carlo algorithms. A new computational improvement in dynamic reliability," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 47(2), pages 347-360.
    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. Marseguerra M. & Zio E., 1998. "Weight updating in forced Monte Carlo approach to dynamic PSA," Monte Carlo Methods and Applications, De Gruyter, vol. 4(4), pages 359-374, December.
    4. Daniele Codetta-Raiteri & Luigi Portinale, 2014. "Approaching dynamic reliability with predictive and diagnostic purposes by exploiting dynamic Bayesian networks," Journal of Risk and Reliability, , vol. 228(5), pages 488-503, October.
    5. Vasilyev, A. & Andrews, J. & Dunnett, S.J. & Jackson, L.M., 2021. "Dynamic Reliability Assessment of PEM Fuel Cell Systems," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    6. Chiacchio, F. & D’Urso, D. & Manno, G. & Compagno, L., 2016. "Stochastic hybrid automaton model of a multi-state system with aging: Reliability assessment and design consequences," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 1-13.
    7. Zhang, Huilong & Innal, Fares & Dufour, François & Dutuit, Yves, 2014. "Piecewise Deterministic Markov Processes based approach applied to an offshore oil production system," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 126-134.
    8. Maidana, Renan G. & Parhizkar, Tarannom & Gomola, Alojz & Utne, Ingrid B. & Mosleh, Ali, 2023. "Supervised dynamic probabilistic risk assessment: Review and comparison of methods," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    9. Mohammad Nadjafi & Mohammad Ali Farsi, 2021. "Reliability analysis of system with timing functional dependency using fuzzy-bathtub failure rates," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(5), pages 919-930, October.
    10. Mandelli, D. & Parisi, C. & Alfonsi, A. & Maljovec, D. & Boring, R. & Ewing, S. & St Germain, S. & Smith, C. & Rabiti, C. & Rasmussen, M., 2019. "Multi-unit dynamic PRA," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 303-317.
    11. Durga Rao, K. & Gopika, V. & Sanyasi Rao, V.V.S. & Kushwaha, H.S. & Verma, A.K. & Srividya, A., 2009. "Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 872-883.
    12. Babykina, Génia & Brînzei, Nicolae & Aubry, Jean-François & Deleuze, Gilles, 2016. "Modeling and simulation of a controlled steam generator in the context of dynamic reliability using a Stochastic Hybrid Automaton," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 115-136.

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