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Monte Carlo simulation-based sensitivity analysis of the model of a thermal-hydraulic passive system

机译:基于蒙特卡罗模拟的热工被动系统模型灵敏度分析

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摘要

Thermal-Hydraulic (T-H) passive safety systems are potentially more reliable than active systems, and for this reason are expected to improve the safety of nuclear power plants. However, uncertainties are present in the operation and modeling of a T-H passive system and the system may find itself unable to accomplish its function. For the analysis of the system functional failures, a mechanistic code is used and the probability of failure is estimated based on a Monte Carlo (MC) sample of code runs which propagate the uncertainties in the model and numerical values of its parameters/variables. Within this framework, sensitivity analysis aims at determining the contribution of the individual uncertain parameters (i.e., the inputs to the mechanistic code) to (ⅰ) the uncertainty in the outputs of the T-H model code and (ⅱ) the probability of functional failure of the passive system. The analysis requires multiple (e.g., many hundreds or thousands) evaluations of the code for different combinations of system inputs: this makes the associated computational effort prohibitive in those practical cases in which the computer code requires several hours to run a single simulation. To tackle the computational issue, in this work the use of the Subset Simulation (SS) and Line Sampling (LS) methods is investigated. The methods are tested on two case studies: the first one is based on the well-known Ishigami function [1]; the second one involves the natural convection cooling in a Gas-cooled Fast Reactor (GFR) after a Loss of Coolant Accident (LOCA) [2].
机译:热液(T-H)被动安全系统可能比主动系统更可靠,因此,有望提高核电厂的安全性。但是,在T-H无源系统的操作和建模中存在不确定性,并且系统可能发现自己无法完成其功能。为了分析系统功能故障,使用了一个机械代码,并根据蒙特卡罗(MC)代码运行样本估算了故障概率,该样本传播了模型中的不确定性及其参数/变量的数值。在此框架内,敏感性分析旨在确定各个不确定参数(即,对机械代码的输入)对(ⅰ)TH模型代码输出中的不确定性和(ⅱ)功能失效的可能性的贡献。被动系统。对于系统输入的不同组合,分析需要对代码进行多次(例如,成百上千次)评估:这使得在计算机代码需要几个小时来运行一次模拟的实际情况下,相关的计算工作变得无法进行。为了解决计算问题,在这项工作中,研究了子集仿真(SS)和线采样(LS)方法的使用。在两个案例研究中测试了这些方法:第一个基于众所周知的Ishigami函数[1];第二种是损失冷却剂事故(LOCA)后,在气冷快堆(GFR)中进行自然对流冷却[2]。

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