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Complex models of nodal nuclear data

Paper in proceedings, 2011

During the core simulations, nuclear data are required at various nodal thermal-hydraulic and
fuel burnup conditions. The nodal data are also partially affected by thermal-hydraulic and
fuel burnup conditions in surrounding nodes as these change the neutron energy spectrum
in the node. Therefore, the nodal data are functions of many parameters (state variables),
and the more state variables are considered by the nodal data models the more accurate and
flexible the models get. The existing table and polynomial regressionmodels, however, cannot
reflect the data dependences on many state variables. As for the table models, the number
of mesh points (and necessary lattice calculations) grows exponentially with the number
of variables. As for the polynomial regression models, the number of possible multivariate
polynomials exceeds the limits of existing selection algorithms that should identify a few
dozens of the most important polynomials. Also, the standard scheme of lattice calculations
is not convenient for modelling the data dependences on various burnup conditions since it
performs only a single or few burnup calculations at fixed nominal conditions.
We suggest a new efficient algorithm for selecting the most important multivariate polynomials
for the polynomial regression models so that dependences on many state variables can
be considered. We also present a new scheme for lattice calculations where a large number
of burnup histories are accomplished at varied nodal conditions. The number of lattice calculations
being performed and the number of polynomials being analysed are controlled and
minimised while building the nodal data models of a required accuracy.

lattice calculations

group cross sections

polynomial regression

nuclear nodal data models