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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.”
Jichong Lei, Zhenping Chen, Jiandong Zhou, Chao Yang, Changan Ren, Wei Li, Chao Xie, Zining Ni, Gan Huang, Leiming Li, Jinsen Xie, Tao Yu
Nuclear Technology | Volume 208 | Number 7 | July 2022 | Pages 1223-1232
Technical Note | doi.org/10.1080/00295450.2021.2018270
Articles are hosted by Taylor and Francis Online.
The reactor core design involves the search for and detailed calculation of a large number of schemes. Four different machine learning algorithms were used in this technical note: the C4.5 algorithm (an algorithm of decision trees), Support Vector Machine, Random Forest, and Multi-layer Perceptron, respectively. Uranium enrichment, the number of fuel rods containing burnable poison, and the concentration of burnable poison were taken as independent variables in the calculation. The k-eff unevenness coefficient, the radial power nonuniformity coefficient, the radial flux nonuniformity coefficient, and the core life were taken as the number of core parameters fulfilled (CPF). Machine learning models were constructed through learning the training data set, which consisted of a large number of assembly and core schemes whose nuclear design parameters were already known. Using the models, the CPF values for the unknown core data set (the test data set) were quickly predicted. The results show that the cross-validation accuracy of each algorithm was above 94% and that the C4.5 algorithm had the highest accuracy for the overall prediction of the CPF values. For the CPF value prediction of the test data set, the time for the training data set was within 10s, while the Random Forest algorithm has the highest prediction accuracy for CPF = 4 or CPF ≠ 4.