Generation of Improved Isotopic Molybdenum Covariances from Elemental Cross-Section Data Using SAMMY

There is a need for improved molybdenum isotope covariance data for use in modeling a new uranium-molybdenum fuel form to be produced at the Y-12 National Security Complex (Y-12). Covariance data correlate the uncertainty in an isotopic cross section at a particular energy to uncertainties at other energies. While high-fidelity covariance data exist for key isotopes, the low-fidelity covariance data available for most isotopes, including the natural molybdenum isotopes considered in this work, are derived from integral measurements without meaningful correlation between energy regions. This paper provides a framework for using the Bayesian R-matrix code SAMMY to derive improved isotopic resonance region covariance data from elemental experimental cross-section data. These resonance-wise covariance data were combined with integral uncertainty data from the Atlas of Neutron Resonances, uncertainty data generated via a dispersion method, and high-energy uncertainty data previously generated with the Empire-KALMAN code to produce an improved set of covariance data for the natural molybdenum isotopes. The improved covariance data sets, along with the associated resonance parameters, were inserted into JENDL4.0 data files for the molybdenum isotopes for use in data processing and modeling codes. Additionally, a series of critical experiments featuring the new U(19.5%)-10Mo fuel form produced at Y-12 was designed. Along with existing molybdenum sensitive critical experiments, these were used to compare the performance of the new molybdenum covariance data against the existing low-fidelity evaluation. The new covariance data were found to result in reduced overall bias, reduced bias due to the molybdenum isotopes, and improved goodness of fit of computational to experimental results.