A new propagation analysis of statistical uncertainty in multi-group cross sections generated by Monte Carlo method

Highlights: • New formulations were derived for the propagation analysis. • The uncertainty of eigenvalue was quantified by the proposed method. • Validation works were conducted by the direct sampling method. • The propagated uncertainty can be precisely assessed by the proposed method. - Abstract: There are many studies on the Monte Carlo method to generate multi-group cross sections. However, there are not enough studies about the propagation of statistical uncertainty in multi-group cross sections. The purpose of this study is to generate Monte Carlo-based multi-group cross sections for the deterministic code and to evaluate the uncertainty of reactor physics parameters propagated from the statistical uncertainty in multi-group cross sections. To achieve this goal, new formulations for uncertainty propagations were developed. By applying the developed formulations, the propagated uncertainty of eigenvalue was quantified. The accuracy of the calculated uncertainty was validated by using the direct sampling method. Through this study, it is possible to accurately evaluate the uncertainty propagated from the statistical uncertainty in multi-group cross sections. This study can support the reliability of multi-group cross sections created by Monte Carlo method. In addition, the individual contribution of the statistical uncertainty of multi-group cross sections to the uncertainty of the eigenvalue can be calculated. It can provide the helpful information to produce accurate multi-group cross sections by Monte Carlo method.