DOE OSTI2016
A central problem in performing accurate nuclear reactor simulations is the ability to capture the changes in the few group microscopic cross sections as functions of system state, e.g. moderator density, fuel temperature, or neutron spectrum, and time and/or burnup of the fuel. For most reactor simulation scenarios, this results in huge data stream, which must be interpolated as needed by downstream calculations (e.g., core-wide simulation). This is typically accomplished by using sophisticated interpolating functions to capture the few-group variations over the wide range of conditions expected during operation. To address handling this big data, this paper explores the use of dimensionality reduction (DR) techniques to identify the true dimensionality of the data prior to its use in the calculations. This could significantly reduce the computational cost required for data processing/manipulation, thus reducing a significant portion of the total calculations time. By way of an example, in the current version of the ORIGEN-ARP module, the assembly-averaged microscopic cross sections at different conditions, e.g., fuel enrichments, irradiation history and water density, which are calculated using the TRITON sequence and interpolated on-demand by the ARP module to model assembly depletion for a wide range of assembly types (e.g. Westinghouse 17x17 and GE 9x9) over a wide range of conditions. This approach typically results in ∼1 GB per assembly type, which although not insurmountable, can be impractical for distribution. and it is the premise of this paper that most of this data is heavily correlated and hence can take great benefit of condensation via DR. To demonstrate DR, the proposed algorithm is applied to the nuclear data streams generated by SCALE 6.1. Using the depletion sequence (TRITON), microscopic cross sections are calculated for a mixed oxide (MOX) fuel assembly design at a representative range of fuel enrichments and fuel burnup. In conventional core simulators, multi dimensional interpolation of these cross sections is used to provides neutronics feedback due to local conditions without returning to expensive TRITON calculations for every change. This paper shows that dimensionality reduction can help compress the size of the cross section data without sacrificing the accuracy of their reconstruction at all conditions of interest. This work explores the application of dimensionality reduction algorithms to nuclear data streams typically employed in support of conventional full-core reactor calculations which rely on interpolation of pre-calculated microscopic cross sections. In particular, analysis of the few group MOX cross sections revealed that a large degree of dependencies exist between the few-group cross sections, which can be revealed via range-finding algorithms. Specifically, correlations between different reaction types for the same isotope have been identified, and correlations between different enrichments and correlations across different isotopes have been identified. This paper demonstrates that the reduction errors can be quantified, and they are uncorrelated with the range of variations for the various cross sections. In future work, we will focus on identifying the smallest rank that takes into account all types of correlations across the range of conditions used to model cross section variations. (authors)
22 GENERAL STUDIES OF NUCLEAR REACTORS↗