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The cell-to-boundary method in the frame of memorization-based Monte Carlo algorithms. A new computational improvement in dynamic reliability

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  • Labeau, P.E
  • Zio, E

Abstract

Dynamic reliability aims at estimating failure risks associated with very rare scenarios, while accounting for the system dynamic evolution. In a Monte Carlo game devised for this purpose, the most time consuming operation consists in performing these dynamic calculations, which are repeated in thousands of histories. In order to save computer resources, the idea of memorizing information on the dynamic trajectories before the simulation was investigated. Two approaches were propounded: the cell-to-boundary (CTB) method, and algorithms based on the memorization of the most probable evolution (MPE) from each initial state. This paper presents a way to combine both methods, in order to further reduce the numerical workload of the simulation. A memorization of second-order MPEs is also propounded, to better investigate transients following the failure of a control means. These techniques are illustrated on the previously defined application of a PWR pressurizer.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:matcom:v:47:y:1998:i:2:p:347-360
    DOI: 10.1016/S0378-4754(98)00110-4
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Marseguerra, Marzio & Zio, Enrico, 2009. "Monte Carlo simulation for model-based fault diagnosis in dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 180-186.
    2. 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.
    3. 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).
    4. F Cadini & D Avram & E Zio, 2010. "System state estimation by particle filtering for fault diagnosis and prognosis," Journal of Risk and Reliability, , vol. 224(3), pages 149-158, September.

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