Building the nodal nuclear data dependences in a many-dimensional state-variable space

We present new methods for building the polynomial-regression based nodal nuclear data models. The data models can reflect dependences on a large number of state variables, and they can consider various history effects. Suitable multivariate polynomials that approximate the nodal data dependences are identified efficiently in an iterative manner. The history effects are analysed using a new sampling scheme for lattice calculations where the traditional base burnup and branch calculations are replaced by a large number of diverse burnup histories. The total number of lattice calculations is controlled so that the data models are built to a required accuracy.