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Division Spotlight
Operations & Power
Members focus on the dissemination of knowledge and information in the area of power reactors with particular application to the production of electric power and process heat. The division sponsors meetings on the coverage of applied nuclear science and engineering as related to power plants, non-power reactors, and other nuclear facilities. It encourages and assists with the dissemination of knowledge pertinent to the safe and efficient operation of nuclear facilities through professional staff development, information exchange, and supporting the generation of viable solutions to current issues.
Meeting Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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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July 2025
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Latest News
NextGen MURR Working Group established in Missouri
The University of Missouri’s Board of Curators has created the NextGen MURR Working Group to serve as a strategic advisory body for the development of the NextGen MURR (University of Missouri Research Reactor).
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.