Passive safety systems are currently implemented or under consideration in several advanced nuclear plants. In the Best-Estimate Plus Uncertainty (BEPU) approach it is fundamental to qualify Best-Estimate thermal-hydraulic system codes and models for the phenomena typical of passive systems and to quantify the uncertainty of calculations. In the present paper, through RELAP5/MOD3.3 and RELAP5-3D codes, thermal-hydraulic analyses were carried out and the obtained results compared against the experimental data collected in the experimental test facility PERSEO. For the present analysis, Test 7 Part 2, conducted at the nominal primary pressure of 7 MPa, has been selected. An accuracy evaluation has been performed on the reference calculation carried out with the two codes. For the accuracy evaluation both a qualitative and quantitative approach have been considered. The uncertainty of both calculations has been quantified by means of the probabilistic method of propagation of input uncertainties. Uncertain input parameters have been selected considering initial and boundary conditions, physical parameters and model uncertainties. The Uncertainty Analysis has been carried out through the RAVEN framework developed by Idaho National Laboratory. Additionally, regression and correlation indices have been used to characterize the importance of the selected uncertain input parameters for the selected Figure Of Merit. The effect of sampling methodologies on the results has also been evaluated.

目前，一些先进的核电厂正在实施或正在考虑采用被动安全系统。在最佳估计加不确定度（BEPU）方法中，使最佳估计热工液压系统代码和模型符合被动系统的典型现象并量化计算的不确定性是至关重要的。在本文中，通过RELAP5 / MOD3.3和RELAP5-3D代码进行了热工液压分析，并将获得的结果与在实验测试设备PERSEO中收集的实验数据进行了比较。对于本分析，已选择在7 MPa的额定主压力下进行的测试7第2部分。已对使用这两个代码进行的参考计算进行了准确性评估。对于准确性评估，已经考虑了定性和定量方法。两种计算的不确定性已经通过输入不确定性传播的概率方法进行了量化。考虑到初始和边界条件，物理参数和模型不确定性，选择了不确定的输入参数。不确定性分析是通过爱达荷州国家实验室开发的RAVEN框架进行的。此外，回归和相关指数已用于表征所选品质因数的所选不确定输入参数的重要性。还评估了抽样方法对结果的影响。两种计算的不确定性已经通过输入不确定性传播的概率方法进行了量化。考虑到初始和边界条件，物理参数和模型不确定性，选择了不确定的输入参数。不确定性分析是通过爱达荷州国家实验室开发的RAVEN框架进行的。另外，回归和相关指数已用于表征所选品质因数的所选不确定输入参数的重要性。还评估了抽样方法对结果的影响。两种计算的不确定性已经通过输入不确定性传播的概率方法进行了量化。考虑到初始和边界条件，物理参数和模型不确定性，选择了不确定的输入参数。不确定性分析是通过爱达荷州国家实验室开发的RAVEN框架进行的。此外，回归和相关指数已用于表征所选品质因数的所选不确定输入参数的重要性。还评估了抽样方法对结果的影响。不确定性分析是通过爱达荷州国家实验室开发的RAVEN框架进行的。此外，回归和相关指数已用于表征所选品质因数的所选不确定输入参数的重要性。还评估了抽样方法对结果的影响。不确定性分析是通过爱达荷州国家实验室开发的RAVEN框架进行的。此外，回归和相关指数已用于表征所选品质因数的所选不确定输入参数的重要性。还评估了抽样方法对结果的影响。