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Remembering ANS member Gil Brown
Brown
The nuclear community is mourning the loss of Gilbert Brown, who passed away on July 11 at the age of 77 following a battle with cancer.
Brown, an American Nuclear Society Fellow and an ANS member for nearly 50 years, joined the faculty at Lowell Technological Institute—now the University of Massachusetts–Lowell—in 1973 and remained there for the rest of his career. He eventually became director of the UMass Lowell nuclear engineering program. After his retirement, he remained an emeritus professor at the university.
Sukesh Aghara, chair of the Nuclear Engineering Department Heads Organization, noted in an email to NEDHO members and others that “Gil was a relentless advocate for nuclear energy and a deeply respected member of our professional community. He was also a kind and generous friend—and one of the reasons I ended up at UMass Lowell. He served the university with great dedication. . . . Within NEDHO, Gil was a steady presence and served for many years as our treasurer. His contributions to nuclear engineering education and to this community will be dearly missed.”
C. van der Hoeven, E. Schneider, L. Leal
Nuclear Science and Engineering | Volume 179 | Number 1 | January 2015 | Pages 1-21
Technical Paper | doi.org/10.13182/NSE13-78
Articles are hosted by Taylor and Francis Online.
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.