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.
Human Factors, Instrumentation & Controls
Improving task performance, system reliability, system and personnel safety, efficiency, and effectiveness are the division's main objectives. Its major areas of interest include task design, procedures, training, instrument and control layout and placement, stress control, anthropometrics, psychological input, and motivation.
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.
Tuesday, October 5, 2021|10:40AM–12:20PM EDT
Joshua Hykes (Studsvik Scandpower)
Benoit Forget (MIT)
William Wieselquist (ORNL)
Joe Coale (NC State Univ.)
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Physics-Informed Deep Learning Neural Network Solution to the Neutron Diffusion Model
Mohamed H. Elhareef (Virginia Commonwealth Univ.), Zeyun Wu (Virginia Commonwealth Univ.), Yu Ma (Sun Yat-sen Univ.)
Improving Whole-Core Calculations by Bayesian Inference From Single-Assembly Measured Reactivity Weights
P.-L. Alzieu (CEA), G. Truchet (CEA), J. Tommasi (CEA)
Investigation Into the Use of Machine Learning Assisted Prediction of Nodal Parameters for Reduced Order Neutronic Simulation Models
Madhumitha Ravichandran (MIT), Cole A. Gentry (ORNL), Matteo Bucci (MIT)
Presentation Video — Presentation Video
Improved Rational Approximation for Spatially-Dependent Resonance Self-Shielding in CASMO5
Rodolfo Ferrer (Studsvik Scandpower), Joshua Hykes (Studsvik Scandpower)
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