This paper discusses the use of the dimension-wise expansion model for cross-section parameterization. The components of the model were approximated with tensor products of orthogonal polynomials. As we demonstrate, the model for a specific cross-section can be built in a systematic way directly from data without any a priori knowledge of its structure. The methodology is able to construct a finite basis of orthogonal polynomials that is required to approximate a cross-section with pre-specified accuracy. The methodology includes a global sensitivity analysis that indicates irrelevant state parameters which can be excluded from the model without compromising the accuracy of the approximation and without repetition of the fitting process. To fit the dimension-wise expansion model, Randomised Quasi-Monte-Carlo Integration and Sparse Grid Integration methods were used. To test the parameterization methods with different integrations embedded we have used the OECD PBMR 400 MW benchmark problem. It has been shown in this paper that the Sparse Grid Integration achieves pre-specified accuracy with a significantly (up to 1-2 orders of magnitude) smaller number of samples compared to Randomised Quasi-Monte-Carlo Integration.
- Global sensitivity analysis
- Monte-Carlo Integration
- Neutron cross-section parameterization
- Sparse Grid Integration