In:
Operations Research, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 38, No. 2 ( 1990-04), p. 265-277
Abstract:
This paper designs prediction models to estimate the chance of the most severe nuclear accidents (such as complete core melts) for the population of U.S. and worldwide nuclear reactors. We formally introduce the notion of random escalation of incident severity. We then develop a class of models that views accidents of high severity as members of a subpopulation of incidents of lower severity; a random escalation model (REM) uses Bayesian methods to update unobservable failure rates and other model parameters. The priors for failure rates are based on extensive engineering judgment about the probabilities of core melt. Predictive distributions for time to time to core melt are calculated from the model, based on operational experience and accident data accumulated to date; the results are compared with those of N. C. Rasmussen, H. W. Lewis, P. C. Groer and others. The paper includes three theorems that reveal the structure of separable densities for parameter updating, the invariance of REMs under severity level classification and the reproducibility of Poisson-Binomial REMs. In an appendix, we examine the special assumptions that are required to specify the current U.S. Nuclear Regulatory Commission risk model.
Type of Medium:
Online Resource
ISSN:
0030-364X
,
1526-5463
DOI:
10.1287/opre.38.2.265
Language:
English
Publisher:
Institute for Operations Research and the Management Sciences (INFORMS)
Publication Date:
1990
detail.hit.zdb_id:
2019440-7
detail.hit.zdb_id:
123389-0
SSG:
3,2
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