  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.
