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Division Spotlight
Thermal Hydraulics
The division provides a forum for focused technical dialogue on thermal hydraulic technology in the nuclear industry. Specifically, this will include heat transfer and fluid mechanics involved in the utilization of nuclear energy. It is intended to attract the highest quality of theoretical and experimental work to ANS, including research on basic phenomena and application to nuclear system design.
Meeting Spotlight
2024 ANS Winter Conference and Expo
November 17–21, 2024
Orlando, FL|Renaissance Orlando at SeaWorld
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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Nov 2024
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Nuclear Science and Engineering
December 2024
Nuclear Technology
Fusion Science and Technology
November 2024
Latest News
Japanese researchers test detection devices at West Valley
Two research scientists from Japan’s Kyoto University and Kochi University of Technology visited the West Valley Demonstration Project in western New York state earlier this fall to test their novel radiation detectors, the Department of Energy’s Office of Environmental Management announced on November 19.
Technical Session|Special Topics
Tuesday, August 22, 2023|9:00–10:20AM EDT|Columbia 5-8
Session Chair:
Han Bao
Alternate Chair:
Majdi I. Radaideh
Session Organizer:
Kurshad Muftuoglu
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Machine-Learning-Aided Approach for Predicting the Thermal Expansion Behaviors in Advanced Test Reactor Capsules
9:00–9:20AM EDT
Takanori Kajihara (INL), Han Bao (INL), Nicolas E. Woolstenhulme (INL), Colby B. Jensen (INL), Daniel B. Chapman (INL), Sunming Qin (INL), Austin D. Fleming (INL)
Paper
Investigation of Machine Learning Regression Techniques to Predict Critical Heat Flux over a Large Parameter Space
9:20–9:40AM EDT
Emil Helmryd Grosfilley (Uppsala Univ.), Gustav Robertson (Uppsala Univ.), Jerol Soibam (Mälardalen Univ.), Jean-Marie Le Corre (Westinghouse Electric Sweden)
Using Machine Learning to Assess Spill Fire Data for use in Fire PRA
9:40–10:00AM EDT
Elvan Sahin (Virginia Tech), Mehran Islam (Virginia Tech), Brian Y. Lattimer (Virginia Tech), Juliana P. Duarte (Univ. Wisconsin, Madison)
Machine Learning from LES Data to Improve Coarse Grid RANS Simulations
10:00–10:20AM EDT
Arsen S. Iskhakov (NCSU), Taylor Grubbs (NCSU), Nam T. Dinh (NCSU), Victor Coppo Leite (Penn State), Elia Merzari (Penn State)
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