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Division Spotlight
Nuclear Installations Safety
Devoted specifically to the safety of nuclear installations and the health and safety of the public, this division seeks a better understanding of the role of safety in the design, construction and operation of nuclear installation facilities. The division also promotes engineering and scientific technology advancement associated with the safety of such facilities.
Meeting Spotlight
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
G7 pledges support for nuclear at Italy meeting
The Group of Seven (G7) recommitted its support for nuclear energy in the countries that opt to use it at a Ministerial Meeting on Climate in Italy last month.
In a statement following the April meeting, the group committed to support multilateral efforts to strengthen the resilience of nuclear supply chains, referencing the goal set by 25 countries during last year’s COP28 climate conference in Dubai to triple global nuclear generating capacity by 2050.
Bingbing Ji, Zhiping Chen, Jia Liu, Liangzhi Cao, Zhuojie Sui, Hongchun Wu
Nuclear Science and Engineering | Volume 195 | Number 12 | December 2021 | Pages 1247-1264
Technical Paper | doi.org/10.1080/00295639.2021.1923338
Articles are hosted by Taylor and Francis Online.
Because of the complexity of the nuclear reactor system, traditional statistical sampling methods, such as random sampling and Latin hypercube sampling, often lead to unstable uncertainty quantification results of the reactor physics analysis. In order to make the analysis results robust, traditional sampling methods require a large number of samples, which brings a huge computation cost. For this reason, this paper proposes a new sampling scheme based on the moment matching method to generate efficient samples for the uncertainty quantification of reactor physics calculations. A linear programming model is established to minimize the deviations of the first- and second-order moments. The generated samples can better reflect the statistical characteristics of the real distribution than classical sampling methods. A series of numerical experiments is carried out to demonstrate the superiority of the proposed moment matching sampling method, which can quickly provide more reliable uncertainty quantification results with a small sample size.