  The semi-empirical nature of best-estimate models closing the balance
equations of thermal-hydraulic (TH) system codes is well-known as a significant
source 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 problem
based on a set of adequately chosen real experiments. One method from the TH
field, called CIRCE, addresses it. We present in the paper a generalization of
this method to several groups of experiments each having their own properties,
including different ranges for input conditions and different geometries. An
individual (log-)Gaussian distribution is therefore estimated for each group in
order to investigate whether the model uncertainty is homogeneous between the
groups, or should depend on the group. To this end, a multi-group CIRCE is
proposed where a variance parameter is estimated for each group jointly to a
mean parameter common to all the groups to preserve the uniqueness of the
best-estimate model. The ECME algorithm for Maximum Likelihood Estimation is
adapted to the latter context, then applied to relevant demonstration cases.
Finally, it is tested on a practical case to assess the uncertainty of critical
mass flow assuming two groups due to the difference of geometry between the
experimental setups.
