首页> 外文会议>International congress on advances in nuclear power plants >LESSONS LEARNED FOR NUCLEAR SAFETY STUDIES FROM THE QUANTIFICATION OF INPUT PARAMETERS UNCERTAINTIES APPLIED TO CATHARE THERMAL-HYDRAULICS CODE WITHIN THE PREMIUM BENCHMARK
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LESSONS LEARNED FOR NUCLEAR SAFETY STUDIES FROM THE QUANTIFICATION OF INPUT PARAMETERS UNCERTAINTIES APPLIED TO CATHARE THERMAL-HYDRAULICS CODE WITHIN THE PREMIUM BENCHMARK

机译:从量化基准中适用于Cathare热工代码的输入参数不确定性中学到的用于核安全研究的经验教训

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In nuclear safely studies, Best-Estimate (BE) codes may be used provided that uncertainties are added to the relevant output parameters before comparing them with the acceptance criteria. The uncertainty of output parameters comes mainly from the lack of knowledge of the input parameters (initial and boundary conditions, parameters of physical models...). Moreover, the BEMUSE (Best Estimate Methods for Uncertainty and Sensitivity Evaluation) benchmark clearly showed that the quantification of input parameters uncertainty is a key point in uncertainty quantification. In the great majority of the studies, the application of the Best-Estimate Plus Uncertainty (BEPU) analysis relies on three steps with a probabilistic representation of uncertainty: 1. The determination of the input parameters statistical characteristics 2. The propagation of uncertainties from inputs to output parameters 3. The evaluation of the 95th percentile of the output with a high degree of confidence. The first step is usually based on expert judgments and comparing experimental data, especially for physical models parameters which cannot be directly measured. The user effect is also very important in the determination of the statistical characteristics: range of variation and probability law. To reduce this user effect and to help the experts in their evaluation, some mathematical methods have been specifically developed. However, determining the intrinsic uncertainty of such input parameters appears very complex. The PREMIUM (Post BEMUSE REflood Models Input Uncertainty Methods) benchmark is a follow-up activity of the BEMUSE programme dedicated to the quantification of uncertainties of the reflooding models in thermal-hydraulics system codes. For its contribution to this exercise, IRSN has used a quantification methodology called DIPE: Determination of Input Parameters uncertaintiEs. This paper presents a numerical application of the DIPE methodology related to the physical models involved in the prediction of core reflooding based on FEBA and PERICLES experiments. This work has been done with CATHARE 2 V2.5_2, a thermal-hydraulics system code developed by CEA, EDF, AREVA-NP and IRSN, used in particular in French safety studies. Lessons learned by IRSN from this exercise are discussed in the paper.
机译:在核安全研究中,可以使用最佳估计(BE)代码,条件是在将不确定性与接受标准进行比较之前将不确定性添加到相关的输出参数中。输出参数的不确定性主要来自缺乏对输入参数(初始和边界条件,物理模型的参数...)的了解。此外,BEMUSE(不确定性和敏感性评估的最佳估计方法)基准明确表明,输入参数不确定性的量化是不确定性量化的关键点。在绝大多数研究中,最佳估计加不确定度(BEPU)分析的应用取决于不确定性的概率表示的三个步骤:1.确定输入参数的统计特征2.从不确定性传播输入输出参数3.以高置信度评估输出的第95个百分点。第一步通常基于专家的判断并比较实验数据,尤其是对于无法直接测量的物理模型参数。用户效应在确定统计特征(变化范围和概率定律)中也非常重要。为了减少这种用户影响并帮助专家进行评估,专门开发了一些数学方法。但是,确定此类输入参数的内在不确定性似乎非常复杂。 PREMIUM(BEMUSE后洪水模型输入不确定性方法)基准是BEMUSE计划的后续活动,致力于量化热工液压系统代码中的洪水模型的不确定性。为了对此工作做出贡献,IRSN使用了一种称为DIPE的量化方法:确定输入参数的不确定性。本文介绍了基于PEBA和PERICLES实验的DIPE方法与物理模型相关的数值应用,这些物理模型参与了岩心回注的预测。这项工作是由CATHARE 2 V2.5_2完成的,CATHARE 2 V2.5_2是由CEA,EDF,AREVA-NP和IRSN开发的热工液压系统代码,尤其是在法国安全性研究中使用。本文讨论了IRSN从此练习中学到的经验教训。

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