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Division Spotlight
Materials Science & Technology
The objectives of MSTD are: promote the advancement of materials science in Nuclear Science Technology; support the multidisciplines which constitute it; encourage research by providing a forum for the presentation, exchange, and documentation of relevant information; promote the interaction and communication among its members; and recognize and reward its members for significant contributions to the field of materials science in nuclear technology.
Meeting Spotlight
International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 2025)
April 27–30, 2025
Denver, CO|The Westin Denver Downtown
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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Latest News
Argonne’s METL gears up to test more sodium fast reactor components
Argonne National Laboratory has successfully swapped out an aging cold trap in the sodium test loop called METL (Mechanisms Engineering Test Loop), the Department of Energy announced April 23. The upgrade is the first of its kind in the United States in more than 30 years, according to the DOE, and will help test components and operations for the sodium-cooled fast reactors being developed now.
Christopher Wallace, Curtis McEwan, Graeme West, William Aylward, Stephen McArthur
Nuclear Technology | Volume 206 | Number 5 | May 2020 | Pages 697-705
Technical Paper | doi.org/10.1080/00295450.2019.1697174
Articles are hosted by Taylor and Francis Online.
This paper summarizes a novel approach to improved localization of fuel defects by fusing existing data sources and methods within a neural network model to make accurate and quantifiable identification earlier than existing processes. The approach is demonstrated through application to a CANDU reactor and utilizes a small, manually labeled set of delayed neutron data augmented with neutronic power data to train a neural network to estimate the probability of a fuel channel containing a defect. Results demonstrate that the model is often capable of identifying likely defects earlier than existing methods and could support earlier decision making to enable a reduction in cost and time required to localize defects. The approach described has broader application to other reactor types given the general difficulty of detecting fuel defects via fission product measurement and the large quantities of ancillary parameters normally already recorded that can be leveraged using machine learning techniques.