ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
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.
2021 ANS Winter Meeting and Technology Expo
November 30–December 3, 2021
Washington, DC|Washington Hilton
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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Results of advanced nuclear survey released
The U.S. Nuclear Industry Council has released the results of its 2021 Advanced Nuclear Survey.
The results, issued late last month, include information from 17 USNIC-member advanced nuclear developers on topics such as federal and state policies, types of reactors in development, U.S./Canadian licensing, need for control room operators, Nuclear Regulatory Commission fees, Department of Energy programs, and high-assay low-enriched uranium (HALEU). In addition, 24 companies (the 17 member firms plus seven nonmember firms) provided their perspectives on the NRC’s planned 10 CFR Part 53 regulation of advanced reactors.
Wednesday, October 6, 2021|12:20–2:00PM (1:20–3:00PM EDT)|Salon A
Ryan G. McClarren (University of Notre Dame)
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Advanced Transient Diagnostic with Ensemble Digital Twin Modeling
Edward Chen (NC State Univ.), Linyu Lin (NC State Univ.), Nam T. Dinh (NC State Univ.)
Integration of Neural Networks With Numerical Solution of PDEs to Uncover Hidden Physics
A.S. Iskhakov (NC State Univ.), N. T. Dinh (NC State Univ.)
Application of Deep Learning Networks to Surrogate Modeling of Crud Deposition
B. Andersen (NC State Univ.), A. Godfrey (ORNL), J. Hou (NC State Univ.), D. Kropaczek (ORNL)
A Multi-Level Feature Extraction and Denoising Approach to Detect Subtle Variations in Industrial Control Systems
Arvind Sundaram (Purdue Univ.), Yeni Li (Purdue Univ.), Hany S. Abdel Khalik (Purdue Univ.)
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