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September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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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.”
Lubomír Bureš, Stefano Caruso
Nuclear Science and Engineering | Volume 191 | Number 1 | July 2018 | Pages 66-84
Technical Paper | doi.org/10.1080/00295639.2018.1442059
Articles are hosted by Taylor and Francis Online.
Knowledge of the radionuclide inventory in spent nuclear fuel is important for back-end operations such as fuel transport and storage, but it is also relevant for the postclosure safety case for a deep geological repository. Extensive depletion calculations using neutron transport solvers can be time consuming and resource intensive in the case of characterization of a large number of fuel assemblies. Issues of computational demand are further amplified when the inventory of only a single pin from the assembly is desired.
A new approach to speeding up the computational time without significant loss of accuracy is proposed in this work, consisting of simplification of the modeled geometry by means of stochastic optimization. The development of this novel methodology, the Acropolis methodology, is described in detail in this paper. Additionally, extensive benchmark and validation exercises were carried out to present and discuss the advantages and limitations of the proposed method.