In the framework of risk assessment in nuclear accident analysis, best-estimatecomputer codes, associated to a probabilistic modeling of the uncertain input variables,are used to estimate safety margins. A first step in such uncertainty quantificationstudies is often to identify the critical configurations (or penalizing, in thesense of a prescribed safety margin) of several input parameters (called ``scenarioinputs''), under the uncertainty on the other input parameters. However, the largeCPU-time cost of most of the computer codes used in nuclear engineering, as theones related to thermal-hydraulic accident scenario simulations, involve to develophighly efficient strategies. This work focuses on machine learning algorithms bythe way of the metamodel-based approach (i.e., a mathematical model which is fittedon a small-size sample of simulations). To achieve it with a very large numberof inputs, a specific and original methodology, called ICSCREAM (Identificationof penalizing Configurations using SCREening And Metamodel), is proposed. Thescreening of influential inputs is based on an advanced global sensitivity analysistool (HSIC importance measures). A Gaussian process metamodel is then sequentiallybuilt and used to estimate, within a Bayesian framework, the conditionalprobabilities of exceeding a high-level threshold, according to the scenario inputs.The efficiency of this methodology is illustrated on two high-dimensional (arounda hundred inputs) thermal-hydraulic industrial cases simulating an accident of primarycoolant loss in a pressurized water reactor. For both use cases, the studyfocuses on the peak cladding temperature (PCT) and critical configurations aredefined by exceeding the 90%-quantile of PCT. In both cases, the ICSCREAMmethodology allows to estimate, by using only around one thousand of code simulations,the impact of the scenario inputs and their critical areas of values.
翻译:在核事故分析的风险评估框架内,与不确定投入变量的概率模型相关的最高级计算机代码被用于估算安全边际。这种不确定性量化研究的第一步往往是在其它输入参数的不确定性下,确定若干输入参数(称为“预估安全边距”)的关键配置(或惩罚)。然而,在核工程中所使用的大多数计算机代码(即与热水流事故假设情景模拟相关的部分)的大规模CPU时间成本,涉及制定高效的战略。这项工作的重点是通过基于元模型的方法(即一个数学模型,在设定安全边距时,在设定安全边距时)确定若干输入参数(即所谓的“预估量” )的关键配置(即,在其他输入参数参数的不确定性参数的不确定性下,一个特定和原始的方法,称为ICCREAM(仅使用SRIG和Metal模型对配置进行惩罚),用于筛选具有影响力的输入数据,然后以全球高级敏感度分析工具(HSICE)为基础,在使用高成本模型的模型中,使用一个高成本模型,一个高成本模型,在使用高水平的模型中,使用一个基压模型,在使用高成本模型的模型中,在使用。