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Donalds, Barnard call for streamlining NRC’s regulatory process
“To be frank, any emissions-related climate goals are moonshots without nuclear energy, and next-generation nuclear technology is something that the United States can and SHOULD lead on.” So writes U.S. Rep. Byron Donalds (R., Fla.) and Christopher Barnard, vice president of external affairs for the American Conservation Coalition, in an essay published by RealClear Energy.
Good news: Donalds, one of the strongest advocates for nuclear energy in the U.S. House, and Barnard, publisher and coauthor of Green Market Revolution, begin their essay by noting some recent positive developments for nuclear power. They characterize the initial criticality of Vogtle-3, the first new nuclear reactor built in the United States in about 30 years, as “a monumental achievement for the American nuclear industry.” They praise the Biden administration’s allocation of funds to keep established nuclear plants operational.
Kaushik Banerjee, William R. Martin
Nuclear Science and Engineering | Volume 174 | Number 1 | May 2013 | Pages 30-45
Technical Paper | doi.org/10.13182/NSE11-94
Articles are hosted by Taylor and Francis Online.
Monte Carlo point detector and surface crossing flux tallies are two widely used tallies, but they suffer from an unbounded variance. As a result, the central limit theorem cannot be used for these tallies to estimate confidence intervals. By construction, kernel density estimator (KDE) tallies can be directly used to estimate flux at a point, but the variance of this point estimate does not converge as 1/N, which is not unexpected for a point quantity. However, an improved approach is to modify both point detector and surface crossing flux tallies directly by using KDE within a variance reduction approach and taking advantage of the fact that KDE estimates the underlying probability density function. This methodology is illustrated by several numerical examples and shows numerically that both the surface crossing tally and the point detector tally converge as 1/N (in variance), and both are asymptotically unbiased.