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The semi-empirical nature of best-estimate models closing the balanceequations of thermal-hydraulic (TH) system codes is well-known as a significantsource of uncertainty for accuracy of output predictions. This uncertainty,called model uncertainty, is usually represented by multiplicative(log-)Gaussian variables whose estimation requires solving an inverse problembased on a set of adequately chosen real experiments. One method from the THfield, called CIRCE, addresses it. We present in the paper a generalization ofthis method to several groups of experiments each having their own properties,including different ranges for input conditions and different geometries. Anindividual (log-)Gaussian distribution is therefore estimated for each group inorder to investigate whether the model uncertainty is homogeneous between thegroups, or should depend on the group. To this end, a multi-group CIRCE isproposed where a variance parameter is estimated for each group jointly to amean parameter common to all the groups to preserve the uniqueness of thebest-estimate model. The ECME algorithm for Maximum Likelihood Estimation isadapted to the latter context, then applied to relevant demonstration cases.Finally, it is tested on a practical case to assess the uncertainty of criticalmass flow assuming two groups due to the difference of geometry between theexperimental setups.

A generalization of the CIRCE method for quantifying input model uncertainty in presence of several groups of experiments