Criticality benchmarking of ENDF/B-Ⅷ.0 and JEFF-3.3 neutron data libraries with RMC code

Abstract

New versions of ENDF/B and JEFF data libraries have been released during the past two years with significant updates in the neutron reaction sublibrary and the thermal neutron scattering sublibrary. In order to get a more comprehensive impression of the criticality quality of these two latest neutron data libraries, and to provide reference for the selection of the evaluated nuclear data libraries for the science and engineering applications of the Reactor Monte Carlo code RMC, the criticality benchmarking of the two latest neutron data libraries has been performed. RMC was employed as the computational tools, whose processing capability for the continuous representation ENDF/B-Ⅷ.0 thermal neutron scattering laws was developed. The RMC criticality validation suite consisting of 116 benchmarks was established for the benchmarking work. The latest ACE format data libraries of the neutron reaction and the thermal neutron scattering laws for ENDF/B-Ⅷ.0, ENDF/B-Ⅶ.1, and JEFF-3.3 were downloaded from the corresponding official sites. The ENDF/B-Ⅶ.0 data library was also employed to provide code-to-code validation for RMC. All the calculations for the four different data libraries were performed by using a parallel version of RMC, and all the calculated standard deviations are lower than 30pcm. Comprehensive analyses including the C/E values with uncertainties, the δk/σ values, and the metrics of χ2 and <|Δ|>, were conducted and presented. The calculated keff eigenvalues based on the four data libraries generally agree well with the benchmark evaluations for most cases. Among the 116 criticality benchmarks, the numbers of the calculated keff eigenvalues which agree with the benchmark evaluations within 3σ interval (with a confidence level of 99.6%) are 107, 109, 112, and 113 for ENDF/B-Ⅶ.0, ENDF/B-Ⅶ.1, ENDF/B-Ⅷ.0 and JEFF-3.3, respectively. The present results indicate that the ENDF/B-Ⅷ.0 neutron data library has a better performance on average.