Published July 2014 | Version v1
Journal article

Estimating the occurrence of foreign material in Advanced Gas-cooled Reactors: A Bayesian Monte Carlo approach

Creators

Description

Highlights: • The amount of a specific type of foreign material found in UK AGRs has been estimated. • The estimate is based on very few instances of detection in numerous inspections. • A Bayesian Monte Carlo approach was used. • The study supports safety case claims on coolant flow impairment. • The methodology is applicable to any inspection campaign on any plant system. - Abstract: The current occurrence of a particular sort of foreign material in eight UK Advanced Gas-cooled Reactors has been estimated by means of a parametric approach. The study includes both variability, treated in analytic fashion via the combination of standard probability distributions, and the uncertainty in the parameters of the model of choice, whose posterior distribution was inferred in Bayesian fashion by means of a Monte Carlo route consisting in the conditional acceptance of sets of model parameters drawn from a prior distribution based on engineering judgement. The model underlying the present study specifically refers to the re-loading and inspection routines of UK Advanced Gas-cooled Reactors. The approach to inference here presented, however, is of general validity and can be applied to the outcome of any inspection campaign on any plant system, and indeed to any situation in which the outcome of a stochastic process is more easily simulated than described by a probability density or mass function

Availability note (English)

Available from http://dx.doi.org/10.1016/j.anucene.2014.01.028

Additional details

Identifiers

DOI
10.1016/j.anucene.2014.01.028;
PII
S0306-4549(14)00046-2;

Publishing Information

Journal Title
Annals of Nuclear Energy (Oxford)
Journal Volume
69
Journal Page Range
p. 1-6
ISSN
0306-4549
CODEN
ANENDJ

Optional Information

Copyright
Copyright (c) 2014 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.