Abstract

Best estimate plus uncertainty for the safety assessment of nuclear power plant transient requires, among others, estimating the probability density function (PDF) of physical model parameters in thermal-hydraulic system codes. In that context, Bayesian calibration based on experimental data from separate-effect test facilities are increasingly popular to inform the PDF of a single thermal-hydraulic phenomenon. These calibrations are, however, time intensive, especially when considering multiple time-dependent outputs. Calibrating on many tests with different boundary conditions and potentially different phenomena to derive PDFs applicable to complex transients appears intractable, even using hierarchical modeling. In this paper, we start investigating this problem by considering a set of Flooding Experiments with Blocked Arrays reflood tests with different boundary conditions. We use TRACE v5.0p3 to model time- and space-dependent temperature profiles, pressure drops, and liquid carry-over. Global sensitivity analysis helps screen out noninfluential parameters and gain a detailed understanding of the modeled physics of reflood. The analysis shows that, for all tests, the outputs were sensitive to a similar set of influential model parameters. In turn, Bayesian calibration yields similar posterior PDFs for the influential parameters, and forward propagation of these posterior PDFs yields similar confidence intervals. As such, the information of the investigated tests can well be represented by a unique posterior PDF. Such simplifications, although not general, are welcome to help manage the intensive calibration effort necessary for dealing with complex thermal-hydraulic transients encountered in nuclear power plants.

摘要

核电厂瞬态安全评估的最佳估计加不确定性除其他外，需要估计热工水力系统代码中物理模型参数的概率密度函数 (PDF)。在这种情况下，基于来自分离效应测试设施的实验数据的贝叶斯校准越来越流行，以告知 PDF 单个热水力现象。然而，这些校准是时间密集型的，尤其是在考虑多个与时间相关的输出时。即使使用分层建模，对具有不同边界条件和潜在不同现象的许多测试进行校准以导出适用于复杂瞬态的 PDF 似乎也很棘手。在本文中，我们通过考虑一组具有不同边界条件的 Blocked Arrays 再洪水测试的洪水实验来开始研究这个问题。我们使用 TRACE v5.0p3 对时间和空间相关的温度曲线、压降和液体残留进行建模。全局敏感性分析有助于筛选出无影响的参数，并详细了解模拟的再洪水物理特性。分析表明，对于所有测试，输出对一组类似的有影响的模型参数很敏感。反过来，贝叶斯校准会为有影响的参数产生相似的后验概率密度函数，并且这些后验概率概率分布的前向传播会产生相似的置信区间。因此，所调查测试的信息可以很好地由唯一的后验 PDF 表示。这样的简化，虽然不是一般的，