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In the framework of the estimation of safety margins in nuclear accidentanalysis, a quantitative assessment of the uncertainties tainting the resultsof computer simulations is essential. Accurate uncertainty propagation(estimation of high probabilities or quantiles) and quantitative sensitivityanalysis may call for several thousand of code simulations. Complex computercodes, as the ones used in thermal-hydraulic accident scenario simulations, areoften too cpu-time expensive to be directly used to perform these studies. Asolution consists in replacing the computer model by a cpu inexpensivemathematical function, called a metamodel, built from a reduced number of codesimulations. However, in case of high dimensional experiments (with typicallyseveral tens of inputs), the metamodel building process remains difficult. Toface this limitation, we propose a methodology which combines several advancedstatistical tools: initial space-filling design, screening to identify thenon-influential inputs, Gaussian process (Gp) metamodel building with the groupof influential inputs as explanatory variables. The residual effect of thegroup of non-influential inputs is captured by another Gp metamodel. Then, theresulting joint Gp metamodel is used to accurately estimate Sobol' sensitivityindices and high quantiles (here $95\%$-quantile).The efficiency of themethodology to deal with a large number of inputs and reduce the calculationbudget is illustrated on a thermal-hydraulic calculation case simulating withthe CATHARE2 code a Loss Of Coolant Accident scenario in a Pressurized WaterReactor. A predictive Gp metamodel is built with only a few hundred of codesimulations and allows the calculation of the Sobol' sensitivity indices. ThisGp also provides a more accurate estimation of the 95%-quantile and associatedconfidence interval than the empirical approach, at equal calculation budget.Moreover, on this test case, the joint Gp approach outperforms the simple Gp.

Advanced methodology for uncertainty propagation in computer experiments with large number of inputs