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In the framework of risk assessment in nuclear accident analysis,best-estimatecomputer codes, associated to a probabilistic modeling of theuncertain input variables,are used to estimate safety margins. A first step insuch uncertainty quantificationstudies is often to identify the criticalconfigurations (or penalizing, in thesense of a prescribed safety margin) ofseveral input parameters (called ``scenarioinputs''), under the uncertainty onthe other input parameters. However, the largeCPU-time cost of most of thecomputer codes used in nuclear engineering, as theones related tothermal-hydraulic accident scenario simulations, involve to develophighlyefficient strategies. This work focuses on machine learning algorithms bytheway of the metamodel-based approach (i.e., a mathematical model which isfittedon a small-size sample of simulations). To achieve it with a very largenumberof inputs, a specific and original methodology, called ICSCREAM(Identificationof penalizing Configurations using SCREening And Metamodel), isproposed. Thescreening of influential inputs is based on an advanced globalsensitivity analysistool (HSIC importance measures). A Gaussian processmetamodel is then sequentiallybuilt and used to estimate, within a Bayesianframework, the conditionalprobabilities of exceeding a high-level threshold,according to the scenario inputs.The efficiency of this methodology isillustrated on two high-dimensional (arounda hundred inputs) thermal-hydraulicindustrial cases simulating an accident of primarycoolant loss in a pressurizedwater reactor. For both use cases, the studyfocuses on the peak claddingtemperature (PCT) and critical configurations aredefined by exceeding the90%-quantile of PCT. In both cases, the ICSCREAMmethodology allows to estimate,by using only around one thousand of code simulations,the impact of thescenario inputs and their critical areas of values.

The ICSCREAM methodology: Identification of penalizing configurations in  computer experiments using screening and metamodel -- Applications in  thermal-hydraulics