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Conference Spotlight
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.
Moosung Jae, Antony D. Milici, William E. Kastenberg, George E. Apostolakis
Nuclear Technology | Volume 104 | Number 1 | October 1993 | Pages 13-36
Technical Paper | Nuclear Reactor Safety | doi.org/10.13182/NT93-A34867
Articles are hosted by Taylor and Francis Online.
A framework for assessing severe accident management strategies is presented using a new analytical tool, namely, influence diagrams. This framework includes multiple and sequential decisions, sensitivity analysis, and uncertainty propagation, and is applied to a proposed set of strategies for a pressurized water reactor station blackout sequence. The influence diagram associated with these strategies is constructed and evaluated. Each decision variable, represented by a node in the influence diagram, has an uncertainty distribution associated with it. Using the mean value of these distributions, a best estimate assessment is performed, and each strategy is ranked with respect to the conditional frequency of early containment failure (ECF). For the preferred alternative, the sensitivity of the results to values of the input variables is investigated. The sensitivity of the ranking itself is then considered. The distributions of the uncertain variables are also propagated through the influence diagram to rank the alternatives with respect to the uncertainty associated with the calculated conditional frequency of ECF. Finally, the sensitivity of the variance of the output distribution, given the preferred decision alternative, to the uncertainty of the input variables is investigated.