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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.”
Mihály Makai, Zoltán Szatmáry
Nuclear Science and Engineering | Volume 177 | Number 1 | May 2014 | Pages 52-67
Technical Paper | doi.org/10.13182/NSE12-97
Articles are hosted by Taylor and Francis Online.
In the Monte Carlo (MC) method, statistical noise is usually present, and it may become dominant in the calculation of a distribution, usually by iteration, but it is less important in calculating integrals. The subject of the present work is the role of statistical noise in iterations involving stochastic simulation (the MC method). Convergence is checked by comparing two consecutive solutions in the iteration. The statistical noise may randomize or pervert the convergence. We study the probability of convergence and the correct estimation of the variance in a simplified model problem. We also study the statistical properties of the solution to a deterministic problem with a stochastic source obtained from a stochastic calculation. There are iteration strategies resulting in nonconvergence or a randomly stopped iteration.
