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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.
2022 ANS Winter Meeting and Technology Expo
November 13–17, 2022
Phoenix, AZ|Arizona Grand Resort
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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OPG, Microsoft to work toward net-zero future for Ontario
Ontario Power Generation and Microsoft Canada have formed a partnership aimed at combatting climate change and driving sustainable growth across the province of Ontario, the Canadian utility announced this week.
Under the partnership, Microsoft will procure clean energy credits (CECs) sourced from OPG’s nuclear and hydro assets in Ontario on an hourly basis. (OPG’s nuclear assets include the four-unit Darlington plant and six-unit Pickering facility.) According to the announcement, this will enable Microsoft to advance toward its 100/100/0 by 2030 goal, which commits the software firm to powering its data centers globally with zero-carbon energy, 24/7.
Tuesday, May 17, 2022|3:30–5:15PM EDT|Pointview
Benoit Forget (MIT)
Benjamin S. Collins (U. of Texas)
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Optimization of the Deep Neural Network Parameters for Generating Homogenized Fuel Assembly Data for Nodal Codes
Korawit Saeju (Ulsan Nat'l Institute of Science and Technology), Siarhei Dzianisau (Ulsan Nat'l Institute of Science and Technology), Muhammad Farid Khandaq (Ulsan Nat'l Institute of Science and Technology), Deokjung Lee (Ulsan Nat'l Institute of Science and Technology)
Genetic Algorithm-Based Optimisation of the Few-Group Structure for Lead Fast Reactors Analysis
M. Massone (Karlsruher Institut for Technologie), N. Abrate (Politecnico di Torino), G.F. Nallo (Politecnico di Torino), S. Dulla (Politecnico di Torino), P. Ravetto (Politecnico di Torino), D. Valerio (Politecnico di Torino)
Quantification of Neural Networks Uncertainties with Applications to SAFARI-1 Axial Neutron Flux Profiles
Lesego E. Moloko (South African Nuclear Energy Corp.), Pavel M. Bokov (South African Nuclear Energy Corp.), Xu Wu (NCSU), Kostadin N. Ivanov (NCSU)
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