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Conference Spotlight
2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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Latest News
IAEA again raises global nuclear power projections
Noting recent momentum behind nuclear power, the International Atomic Energy Agency has revised up its projections for the expansion of nuclear power, estimating that global nuclear operational capacity will more than double by 2050—reaching 2.6 times the 2024 level—with small modular reactors expected to play a pivotal role in this high-case scenario.
IAEA director general Rafael Mariano Grossi announced the new projections, contained in the annual report Energy, Electricity, and Nuclear Power Estimates for the Period up to 2050 at the 69th IAEA General Conference in Vienna.
In the report’s high-case scenario, nuclear electrical generating capacity is projected to increase to from 377 GW at the end of 2024 to 992 GW by 2050. In a low-case scenario, capacity rises 50 percent, compared with 2024, to 561 GW. SMRs are projected to account for 24 percent of the new capacity added in the high case and for 5 percent in the low case.
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.