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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.”
Taro Ueki, Brian R. Nease
Nuclear Science and Engineering | Volume 153 | Number 2 | June 2006 | Pages 184-191
Technical Paper | doi.org/10.13182/NSE05-15
Articles are hosted by Taylor and Francis Online.
The performances of autoregressive processes and the autoregressive moving average process of order two and one [ARMA(2,1)] have been investigated concerning the confidence interval estimation in Monte Carlo eigenvalue calculation. Two reasons exist for these model choices. First, the Wold decomposition states that any zero-mean stationary stochastic process can be expressed as the sum of a deterministic process and a moving average process of infinite order. This justifies the application of autoregressive fitting and autoregressive moving average fitting to a centered k-effective series from stationary iteration cycles. Second, ARMA(2,1) fitting is a logically natural refinement of first-order autoregressive fitting since the noise propagation in iterated source methods can be reduced to an autoregressive moving average model of orders p and p - 1 [ARMA(p, p - 1)]. Numerical results are presented for the "k-effective of the world" problem. The results indicate that ARMA(2,1) fitting performs much better than the autoregressive fitting of low orders. Also presented are some related theoretical results; MacMillan's formula to confidence limits can be derived from the ARMA(p, p - 1) representation of source distribution; and the multiplicity of higher eigenmodes can make the decay of the autocorrelation of source distribution much different than predicted by the sum of exponential terms. The latter result indicates poor performance that time series methods would exhibit for the confidence interval estimation of the fission rate distribution in the critical reactor with symmetric component placement.
