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Materials Science & Technology
The objectives of MSTD are: promote the advancement of materials science in Nuclear Science Technology; support the multidisciplines which constitute it; encourage research by providing a forum for the presentation, exchange, and documentation of relevant information; promote the interaction and communication among its members; and recognize and reward its members for significant contributions to the field of materials science in nuclear technology.
Materials in Nuclear Energy Systems (MiNES 2023)
December 10–14, 2023
New Orleans, LA|New Orleans Marriott
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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Nuclear Science and Engineering
Fusion Science and Technology
Granholm visits Clinch River Site to show support for SMRs
Energy secretary Jennifer Granholm visited the Clinch River Nuclear Site in Oak Ridge, Tenn., on December 5 to highlight the Biden administration’s support for the Tennessee Valley Authority’s advanced nuclear technology program.
Granholm indicated that the administration is willing to provide funding for the nation’s first commercial small modular reactor at the site. “Excited to see a shovel in the ground, hopefully in a few more years,” she said. “TVA is leading on small modular reactors with this site. Everybody’s looking to TVA to make sure that this can actually happen.”
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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