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Spent Fuel Performance Assessment for a Repository System Located in a Salt Dome

机译:位于盐丘中的储存库系统的乏燃料性能评估

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摘要

The results of the uncertainty analysis for the Brine Intrusion Scenario are expressed in terms of subjective probability distributions and statistical tolerance limits. Due to the generic nature of the repository, no findings from exploratory work were available to update the subjective probability distributions at the level of scenario and parameter uncertainties. The outcome of this analysis can be regarded as the first step of an iterative process to enhance the performance of the repository. The statistical tolerance limits (e.g. for the released dose rate) can be tightened by increasing the sample size and hence by increasing the number of model runs to be performed. Despite the relatively small sample size, the sensitivity analysis identified the main contributors to the uncertainty of the results of the consequence analysis (e.g. dose rate). This way it permits useful insights and recommendations as to where the state of knowledge should be improved in order to reduce the uncertainty of the results most effectively. The model results are subject to numerous uncertainties and can therefore only be given in form of subjective probability distributions. The model results are to be compared to protection goals. These goals are, however, formulated deterministically, i.e. they ignore the inevitable uncertainty of the computed results, which they are to be compared to, and are therefore incomplete. They need to be supplemented by additional requirements. An example of such an additional requirement for the maximum annual dose rate would be. for instance: "At a confidence level of at least 95%, the maximum annual dose rate needs to be below the limit value with at least 95% subjective probability".
机译:盐水入侵场景的不确定性分析结果以主观概率分布和统计公差极限表示。由于存储库的一般性质,没有探索性工作的发现可用于在场景和参数不确定性级别上更新主观概率分布。该分析的结果可以视为提高存储库性能的迭代过程的第一步。可以通过增加样本大小并因此通过增加要执行的模型运行次数来加强统计公差极限(例如,对于释放剂量率)。尽管样本量相对较小,但敏感性分析确定了导致后果分析结果不确定性(例如剂量率)的主要因素。这样,就可以在哪些方面应改进知识状态提供有用的见解和建议,以便最有效地减少结果的不确定性。模型结果存在许多不确定性,因此只能以主观概率分布的形式给出。模型结果将与保护目标进行比较。但是,这些目标是确定性地制定的,即它们忽略了计算结果不可避免的不确定性,将其与之进行比较,因此是不完整的。它们需要补充其他要求。这样的最大年剂量率的附加要求的一个例子是。例如:“在置信度至少为95%时,最大年剂量率必须低于极限值,且主观概率至少为95%”。

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