Abstract

In the framework of risk assessment in nuclear accident analysis, best-estimate computer codes associated with probabilistic modeling of uncertain input variables are used to estimate safety margins. Often, a first step in such uncertainty quantification studies is to identify the critical configurations (or penalizing, in the sense of a prescribed safety margin) of several input parameters (called scenario inputs) under the uncertainty of the other input parameters. However, the large CPU-time cost of most of the computer codes used in nuclear engineering, as the ones related to thermal-hydraulic accident scenario simulations, involves developing highly efficient strategies. This work focuses on machine learning algorithms by way of a metamodel-based approach (i.e., a mathematical model that is fitted on a small sample of simulations). To achieve it with a very large number of inputs, a specific and original methodology called Identification of penalizing Configurations using SCREening And Metamodel (ICSCREAM) is proposed. The screening of influential inputs is based on an advanced global sensitivity analysis tool (Hilbert-Schmidt Independence Criterion importance measures). A Gaussian process metamodel is then sequentially built and used to estimate within a Bayesian framework the conditional probabilities of exceeding a high-level threshold according to the scenario inputs. The efficiency of this methodology is illustrated with two high-dimensional (around a hundred inputs) thermal-hydraulic industrial cases simulating an accident of primary coolant loss in a pressurized water reactor. For both use cases, the study focuses on the peak cladding temperature (PCT), and critical configurations are defined by exceeding the 90%-quantile of the PCT. In both cases, using only around one thousand code simulations, the ICSCREAM methodology allows one to estimate the impact of the scenario inputs and their critical areas of values.

摘要

在核事故分析中的风险评估框架中，与不确定输入变量的概率建模相关的最佳估计计算机代码用于估计安全裕度。通常，此类不确定性量化研究的第一步是在其他输入参数的不确定性下确定几个输入参数（称为场景输入）的关键配置（或在规定的安全裕度意义上的惩罚）。然而，核工程中使用的大多数计算机代码（如与热工水力事故情景模拟相关的代码）的大量 CPU 时间成本涉及开发高效策略。这项工作侧重于通过基于元模型的方法（即，适合小样本模拟的数学模型）的机器学习算法。为了通过大量输入来实现它，提出了一种特定的原始方法，称为使用筛选和元模型（ICSCREAM）识别惩罚配置。有影响的输入的筛选基于先进的全局敏感性分析工具（希尔伯特-施密特独立标准重要性度量）。然后依次构建高斯过程元模型，并用于在贝叶斯框架内根据场景输入估计超过高级阈值的条件概率。该方法的效率通过两个高维（大约一百个输入）热工水力工业案例来说明，这些案例模拟了压水反应堆中一次冷却剂损失的事故。对于这两种用例，该研究的重点是峰值包层温度 (PCT)，和关键配置是通过超过 PCT 的 90% 分位数来定义的。在这两种情况下，仅使用大约一千个代码模拟，ICSCREAM 方法就允许人们估计场景输入的影响及其关键价值领域。