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Division Spotlight
Education, Training & Workforce Development
The Education, Training & Workforce Development Division provides communication among the academic, industrial, and governmental communities through the exchange of views and information on matters related to education, training and workforce development in nuclear and radiological science, engineering, and technology. Industry leaders, education and training professionals, and interested students work together through Society-sponsored meetings and publications, to enrich their professional development, to educate the general public, and to advance nuclear and radiological science and engineering.
Meeting Spotlight
ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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
Norway’s Halden reactor takes first step toward decommissioning
The government of Norway has granted the transfer of the Halden research reactor from the Institute for Energy Technology (IFE) to the state agency Norwegian Nuclear Decommissioning (NND). The 25-MWt Halden boiling water reactor operated from 1958 to 2018 and was used in the research of nuclear fuel, reactor internals, plant procedures and monitoring, and human factors.
Akiyuki Seki, Masanori Yoshikawa, Ryota Nishinomiya, Shoichiro Okita, Shigeru Takaya, Xing Yan
Nuclear Technology | Volume 210 | Number 6 | June 2024 | Pages 1003-1014
Research Article | doi.org/10.1080/00295450.2023.2273566
Articles are hosted by Taylor and Francis Online.
In the case of a new nuclear reactor, existing evaluation experience is limited; thus, accidents and troubles may occur as a result of such lack of experience. To deal with such situations, it is desirable to use a virtual nuclear plant to reproduce behaviors under various conditions and identify unknown anomalies from the behaviors. Then, when an abnormal situation occurs, one can quickly determine the cause of the abnormality to operate plant equipment and return the plant to a stable condition as quickly as possible. Two types of deep neural network (DNN) systems have been constructed to support the identification of unknown anomalies and the determination of their causes. One is a surrogate system that can estimate physical quantities of a nuclear power plant in a computational time of several orders less than a physical simulation model. The other is an abnormal situation identification system that can estimate the state of the disturbance causing an anomaly from physical quantities of a nuclear power plant. Both systems are trained and tested using data obtained from the analytical code for incore and plant dynamics (ACCORD), which reproduces the steady and dynamic behavior of the actual High Temperature Engineering Test Reactor (HTTR) under various scenarios. The DNN models are built by adjusting the main hyperparameters. Through these procedures, these systems are shown to be able to perform with a high degree of accuracy.