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2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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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.
Taro Ueki
Nuclear Science and Engineering | Volume 171 | Number 3 | July 2012 | Pages 220-230
Technical Paper | doi.org/10.13182/NSE11-35
Articles are hosted by Taylor and Francis Online.
An orthonormally weighted standardized time series (OWSTS) was investigated for the statistical error estimation of local tallies in Monte Carlo criticality calculation. Unlike the original implementation of a standardized time series, the computation of standard deviation via OWSTS can be made free of the grouping of iteration cycles into batches. The characteristic aspect of OWSTS is the application of an arbitrary number of weighting functions to a standardized series of tallies such that asymptotically independent and unbiased estimates are produced based on the statistics of Brownian bridge. In the present work, a trigonometric set of weighting functions is extended and applied to local power tallies in the three-dimensional model of a pressurized water reactor core. Numerical results demonstrate that the OWSTS error estimation is unbiased for a sufficiently large number of iteration cycles.
