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On Broadening Failure Rate Distributions in PRA Uncertainty Analyses

机译:关于扩大PRA不确定度分析中的故障率分布

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Several recent nuclear power plant probabilistic risk assessments (PRAs) have utilized broadened Reactor Safety Study (RSS) component failure rate population variability curves to compensate for such things as expert “overvaluation bias” in the estimates upon which the curves are based.A simple two‐components of variation empirical Bayes model is proposed for use in estimating the between‐expert variability curve in the presence of such biases. Under certain conditions this curve is a population variability curve. Comparisons are made with the existing method.The popular procedure appears to be generally much more conservative than the empirical Bayes method in removing such biases. In one case the broadened curve based on the popular method is more than two orders of magnitude broader than the empirical Bayes curve. In another case it is found that the maximum justifiable degree of broadening of the RSS curve is to increase α from 5 to 12, which is significantly less than the 20 value recommended in the popular
机译:最近的几项核电厂概率风险评估(PRA)利用了扩大的反应堆安全研究(RSS)组件故障率总体变异性曲线,以补偿曲线所依据的估计中的专家“高估偏差”等因素。提出了一个简单的变异经验贝叶斯双分量模型,用于估计存在此类偏差的专家间变异性曲线。在某些条件下,该曲线是总体变异性曲线。与现有方法进行比较。在消除这种偏见方面,流行的程序似乎通常比经验贝叶斯方法保守得多。在一种情况下,基于流行方法的加宽曲线比经验贝叶斯曲线宽两个数量级以上。在另一种情况下,发现RSS曲线的最大合理拓宽程度是将α从5%提高到12%,这明显低于流行中推荐的20%值

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