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
Accelerator Applications
The division was organized to promote the advancement of knowledge of the use of particle accelerator technologies for nuclear and other applications. It focuses on production of neutrons and other particles, utilization of these particles for scientific or industrial purposes, such as the production or destruction of radionuclides significant to energy, medicine, defense or other endeavors, as well as imaging and diagnostics.
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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Nuclear Science and Engineering
August 2025
Nuclear Technology
July 2025
Fusion Science and Technology
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).
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.