  In the framework of the estimation of safety margins in nuclear accident
analysis, a quantitative assessment of the uncertainties tainting the results
of computer simulations is essential. Accurate uncertainty propagation
(estimation of high probabilities or quantiles) and quantitative sensitivity
analysis may call for several thousand of code simulations. Complex computer
codes, as the ones used in thermal-hydraulic accident scenario simulations, are
often too cpu-time expensive to be directly used to perform these studies. A
solution consists in replacing the computer model by a cpu inexpensive
mathematical function, called a metamodel, built from a reduced number of code
simulations. However, in case of high dimensional experiments (with typically
several tens of inputs), the metamodel building process remains difficult. To
face this limitation, we propose a methodology which combines several advanced
statistical tools: initial space-filling design, screening to identify the
non-influential inputs, Gaussian process (Gp) metamodel building with the group
of influential inputs as explanatory variables. The residual effect of the
group of non-influential inputs is captured by another Gp metamodel. Then, the
resulting joint Gp metamodel is used to accurately estimate Sobol' sensitivity
indices and high quantiles (here $95\%$-quantile).The efficiency of the
methodology to deal with a large number of inputs and reduce the calculation
budget is illustrated on a thermal-hydraulic calculation case simulating with
the CATHARE2 code a Loss Of Coolant Accident scenario in a Pressurized Water
Reactor. A predictive Gp metamodel is built with only a few hundred of code
simulations and allows the calculation of the Sobol' sensitivity indices. This
Gp also provides a more accurate estimation of the 95%-quantile and associated
confidence interval than the empirical approach, at equal calculation budget.
Moreover, on this test case, the joint Gp approach outperforms the simple Gp.
