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.
Division Spotlight
Nuclear Installations Safety
Devoted specifically to the safety of nuclear installations and the health and safety of the public, this division seeks a better understanding of the role of safety in the design, construction and operation of nuclear installation facilities. The division also promotes engineering and scientific technology advancement associated with the safety of such facilities.
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.
Ryan J. Hoover, Kenji Shimada
Nuclear Technology | Volume 210 | Number 11 | November 2024 | Pages 2204-2214
Research Article | doi.org/10.1080/00295450.2024.2312022
Articles are hosted by Taylor and Francis Online.
Transient mitigation for nuclear power plants is essential for safe operation. The fourth industrial revolution brings with it the potential for data-based predictive maintenance and identifying remaining time of life for degrading components. An improvement to predictive maintenance would be to address continued operation with faulty components between the time of identification and eventual replacement. The ability to perform data analysis and contemporary digital control systems allows for data-driven control system actions. A methodology is developed herein to train a neural network that can map desired system performance and current plant component capability to control system settings. Simulations of plant transients were recorded and used to train a neural network. This neural network was tested with different target performance goals. The results show that the trained neural network recommended settings that affected the control system response so as to meet the target performance goals.