Cluster-Event Biasing in Monte Carlo Applications to Systems Reliability

Khazen, Michael; Dubi, A.

doi:10.13182/nse02-a2282

Cited by 2 publications

(1 citation statement)

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“…In [16] the Cluster-Event biasing model was introduced and applied to the same problem yielding the benefit factor of the order of 10 4 . Computing probability of system's malfunction during 10 hours, the splitting has shown the benefit of about 100 for the optimal splitting factor ν * = 620.…”

Section: Single Surface Splitting Combined With Biasingmentioning

confidence: 99%

Monte Carlo variance reduction in applications to Systems Reliability using Phase Space Splitting

Khazen

Dubi

2004

Monte Carlo Methods and Applications

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Splitting is a widely known Monte Carlo variance reduction method (VRM). It has been successfully applied for a long time in Monte Carlo applications to neutral particles transport in Nuclear Engineering. In this field splitting is usually referred to as a Geometric Splitting method since it relies on geometric properties of the medium, namely distance and direction. Splitting is remarkable in the sense that it is a "safe" method. Unlike VRMs based on biasing it can not cause the sow tooth phenomenon in which a wrong result appears with very small statistical error. The only risk of using this method is increasing computation time. This makes the application of this method to such areas as safety-critical systems reliability and risk assessment a very attractive option.Suggested work concerns application of splitting to systems reliability problems. The system's structure based metric is used instead of the geometric approach. This metric takes into account importance of each system's component for the target event. Multiple disjunctive surfaces splitting, and splitting combined with the Cluster-Event biasing are considered. The optimal splitting parameters are predicted using the mathematical apparatus developed for applications of splitting in neutral particles transport. Provided numerical examples show considerable benefit of the method.

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Section: Single Surface Splitting Combined With Biasingmentioning

confidence: 99%