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Education, Training & Workforce Development
The Education, Training & Workforce Development Division provides communication among the academic, industrial, and governmental communities through the exchange of views and information on matters related to education, training and workforce development in nuclear and radiological science, engineering, and technology. Industry leaders, education and training professionals, and interested students work together through Society-sponsored meetings and publications, to enrich their professional development, to educate the general public, and to advance nuclear and radiological science and engineering.
2023 ANS Winter Conference and Expo
November 12–15, 2023
Washington, D.C.|Washington Hilton
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Nuclear Science and Engineering
Fusion Science and Technology
NRC moves ahead on HALEU enrichment, rulemaking, and guidance
The Nuclear Regulatory Commission is requesting comments on the regulatory basis for a proposed rule for light water reactor fuel designs featuring high-assay low-enriched uranium (HALEU), including accident tolerant fuel (ATF) designs, and on draft guidance for the environmental evaluation of ATFs containing uranium enriched up to 8 percent U-235. Some of the HALEU feedstock for those LWR fuels and for advanced reactor fuels could be produced within the first Category II fuel facility licensed by the NRC—Centrus Energy’s American Centrifuge Plant in Piketon, Ohio. On September 21, the NRC approved the start of enrichment operations in the plant’s modest 16-machine HALEU demonstration cascade.
Tianyu Liu, Noah Wolfe, Christopher D. Carothers, Wei Ji, X. George Xu
Nuclear Science and Engineering | Volume 185 | Number 1 | January 2017 | Pages 232-242
Technical Note | doi.org/10.13182/NSE16-33
Articles are hosted by Taylor and Francis Online.
XSBench is a proxy application used to study the performance of nuclear macroscopic cross-section data construction, which is usually the most time-consuming process in Monte Carlo neutron transport simulations. In this technical note we report on our experience in optimizing XSBench to Intel multicore central processing units (CPUs), many integrated core coprocessors (MICs), and Nvidia graphics processing units (GPUs). The continuous-energy cross-section construction in the Monte Carlo simulation of the Hoogenboom-Martin large problem is used in our benchmark. We demonstrate that through several tuning techniques, particularly data prefetch, the performance of XSBench on each platform can be desirably improved compared to the original implementation on the same platform. It is shown that the performance gain is 1.46× on the Westmere CPU, 1.51× on the Haswell CPU, 2.25× on the Knights Corner (KNC) MIC, and 5.98× on the Kepler GPU. The comparison across different platforms shows that when using the high-end Haswell CPU as the baseline, the KNC MIC is 1.63× faster while the high-end Kepler GPU is 2.20× faster.