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Division Spotlight
Fusion Energy
This division promotes the development and timely introduction of fusion energy as a sustainable energy source with favorable economic, environmental, and safety attributes. The division cooperates with other organizations on common issues of multidisciplinary fusion science and technology, conducts professional meetings, and disseminates technical information in support of these goals. Members focus on the assessment and resolution of critical developmental issues for practical fusion energy applications.
Meeting Spotlight
ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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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Mar 2025
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Nuclear Science and Engineering
April 2025
Nuclear Technology
Fusion Science and Technology
Latest News
U.S. and Japan collaborate on high-burnup fast reactor fuel safety tests at INL
Idaho National Laboratory recently conducted a safety test on high-burnup fast reactor fuel from historic irradiation testing at the lab’s Experimental Breeder Reactor-II (EBR-II). According to the Department of Energy, which announced the work March 12, it’s the first such safety test to be performed in over 20 years.
Advances in Thermal Hydraulics (ATH 2024)
Technical Session
Tuesday, November 19, 2024|10:00–11:45AM EST|Coral C
Session Chair:
Subash Sharma (UML)
Alternate Chair:
Ethan Wong (Imperial College London)
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Assessment of Physics-Informed Neural Networks (PINNs) with Transfer Learning (TL) for Incompressible Turbulent Flows
10:00–10:20AM EST
Y.H. Wong (Imperial College London), Y. Duan (Imperial College London), L. Lampunio (Imperial College London), M.D. Eaton (Imperial College London), M.J.Bluck (Imperial College London)
Paper
Prediction and Uncertainty Quantification of Critical Heat Flux -- A Comparison Between Generative Conditional VAEs and DNNs
10:20–10:40AM EST
Farah Alsafadi (NCSU), Aidan Furlong (NCSU), Xu Wu (NCSU)
Quantifying Model Uncertainty of Neural-Network Based Turbulence Closures
10:40–11:00AM EST
Cody Grogan (Utah State), Som Dutta (Utah State), Mauricio Tano (INL), Somayajulu L.N. Dhulipala (INL), Izabela Gutowska (Oregon State)
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