ICSCREAM方法：利用筛查和元模型识别计算机实验中的惩罚配置——热力学-流体力学应用
The ICSCREAM methodology: Identification of penalizing configurations in computer experiments using screening and metamodel -- Applications in thermal-hydraulics
AI总结：
ICSCREAM方法通过全局敏感性分析和元模型技术，高效识别热力学-流体力学事故模拟中的临界配置，减少计算成本。
摘要：
在核事故分析的风险评估框架中，最佳估计计算机代码被用于估计安全余量，这些代码与对不确定输入变量的概率建模相关。此类不确定性量化研究的第一步通常是识别几个输入参数（称为“场景输入”）的临界配置（或惩罚，即根据规定的安全余量）。然而，核工程中大多数计算机代码的计算时间成本很高，例如与热力学-流体力学事故场景模拟相关的代码，因此需要开发高效的策略。本文专注于通过元模型方法（即基于小样本模拟拟合的数学模型）的机器学习算法。为了在大量输入的情况下实现这一目标，提出了一种特定且原创的方法，称为ICSCREAM（利用筛查和元模型识别惩罚配置）。输入筛选基于先进的全局敏感性分析工具（HSIC重要性度量）。然后逐步构建高斯过程元模型，并在贝叶斯框架下用于估计，根据场景输入，超过高水平阈值的条件概率。该方法的效率在两个高维（约一百个输入）的热力学-流体力学工业案例中得到验证，模拟了压水堆中主冷却剂损失的事故。对于这两种使用案例，研究重点是峰值包层温度（PCT）和临界配置由PCT的90%分位数超过定义。在两种情况下，ICSCREAM方法通过仅使用约一千次代码模拟，能够估计场景输入及其临界值区域的影响。
英文摘要：
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.
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