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
Uranium prices reach highest level since February 2024
The end-of-January spot price for uranium was $94.28 per pound, according to uranium fuel provider Cameco. That was the highest spot price posted by the company since the $95.00 per pound it listed at the end of February 2024. Spot prices during 2025 ranged from a low of $64.23 per pound at the end of March to a high of $82.63 per pound at the end of September.
Herschel P. Smith, John C. Wagner
Nuclear Science and Engineering | Volume 149 | Number 1 | January 2005 | Pages 23-37
Technical Paper | doi.org/10.13182/NSE05-A2474
Articles are hosted by Taylor and Francis Online.
Certain reactor transients cause a reduction in moderator temperature and, hence, increased attenuation of neutrons and decreased response of excore detectors. This decreased detector response is of concern because of the credit assumed for detector-initiated reactor trip to terminate the transient. Explicit modeling of this phenomenon presents the analyst with a difficult problem because of the dense and optically thick neutron absorption media, given the constraint that precise response characteristics must be known in order to account for this phenomenon. The solution in this study was judged to be the use of Monte Carlo techniques coupled with robust variance reduction to accelerate problem convergence. A fresh discussion on the motivation for variance reduction is included, followed by separate accounts of manual and automated applications of variance reduction techniques. Finally, the results of both manual and automated variance reduction techniques are presented and compared.