ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Explore membership for yourself or for your organization.
Conference Spotlight
2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
Latest Magazine Issues
Feb 2026
Jul 2025
Latest Journal Issues
Nuclear Science and Engineering
February 2026
Nuclear Technology
January 2026
Fusion Science and Technology
Latest News
Godzilla is helping ITER prepare for tokamak assembly
ITER employees stand by Godzilla, the most powerful commercially available industrial robot available. (Photo: ITER)
Many people are familiar with Godzilla as a giant reptilian monster that emerged from the sea off the coast of Japan, the product of radioactive contamination. These days, there is a new Godzilla, but it has a positive—and entirely fact-based—association with nuclear energy. This one has emerged inside the Tokamak Assembly Preparation Building of ITER in southern France.
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.