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's objectives are to promote the advancement of knowledge and understanding of the fundamental physical phenomena characterizing nuclear reactors and other nuclear systems. The division encourages research and disseminates information through meetings and publications. Areas of technical interest include nuclear data, particle interactions and transport, reactor and nuclear systems analysis, methods, design, validation and operating experience and standards. The Wigner Award heads the awards program.
Nuclear and Emerging Technologies for Space (NETS 2023)
May 7–11, 2023
Idaho Falls, ID|Snake River Event Center
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
Wanted: Information leading to a neutron source for fusion energy R&D
The Department of Energy’s Office of Science published a notice in the March 27 Federal Register calling for input on technological approaches to a Fusion Prototypic Neutron Source (FPNS) for materials irradiation research under DOE-SC’s Fusion Energy Sciences program, as well as partnership models that could accelerate the construction and delivery of the facility. The request for information (RFI) calls for responses by May 11.
Wednesday, October 6, 2021|1:20–3:00PM EDT
Patrick Brantley (LLNL)
Ryan G. McClarren (University of Notre Dame)
Joseph Fustero (NC State Univ.)
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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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