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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Powering the future: How the DOE is fueling nuclear fuel cycle research and development
As global interest in nuclear energy surges, the United States must remain at the forefront of research and development to ensure national energy security, advance nuclear technologies, and promote international cooperation on safety and nonproliferation. A crucial step in achieving this is analyzing how funding and resources are allocated to better understand how to direct future research and development. The Department of Energy has spearheaded this effort by funding hundreds of research projects across the country through the Nuclear Energy University Program (NEUP). This initiative has empowered dozens of universities to collaborate toward a nuclear-friendly future.
L. L. Carter, T. L. Miles, S. E. Binney
Nuclear Science and Engineering | Volume 113 | Number 4 | April 1993 | Pages 324-338
Technical Paper | doi.org/10.13182/NSE93-A15332
Articles are hosted by Taylor and Francis Online.
Statistical uncertainties for neutron transport calculations using the Monte Carlo method are typically evaluated during the calculation by using the first and second moments of the tallies. There are concerns that these statistical uncertainties may be substantially nonconservative in some classes of problems, especially reactor eigenvalue problems with the additional complication of a generation-to-generation source. Optimization of the Monte Carlo random walks may introduce further nonconservatism. Calculations are reported that quantify the reliability of the uncertainty by comparing an ensemble of Monte Carlo predictions of means and uncertainties to the true means for a liquid-metal fast reactor. It was found that the number of samples falling beyond a 90% confidence limit interval was typically not far from the expected 10%. However, 2 samples out of ∼300 were beyond four standard deviations, while for a normal distribution there is <1 chance in 10 000 that a sample will be beyond four standard deviations.