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
Reactor Physics
The division's objectives are to promote the advancement of knowledge and understanding of the fundamental physical phenomena characterizing nuclear reactors and other nuclear systems. The division encourages research and disseminates information through meetings and publications. Areas of technical interest include nuclear data, particle interactions and transport, reactor and nuclear systems analysis, methods, design, validation and operating experience and standards. The Wigner Award heads the awards program.
Meeting Spotlight
Utility Working Conference and Vendor Technology Expo (UWC 2024)
August 4–7, 2024
Marco Island, FL|JW Marriott Marco Island
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!
Latest Magazine Issues
Jul 2024
Jan 2024
Latest Journal Issues
Nuclear Science and Engineering
August 2024
Nuclear Technology
Fusion Science and Technology
Latest News
Oklo completes end-to-end demonstration of advanced fuel recycling
Oklo Inc. has announced that it has completed the first end-to-end demonstration of its advanced fuel recycling process as part of an ongoing $5 million project in collaboration with Argonne and Idaho National Laboratories. Oklo’s goal: scaling up its fuel recycling capabilities to deploy a commercial-scale recycling facility that would increase advanced reactor fuel supplies and enhance fuel cost effectiveness for its planned sodium fast reactors.
Keehoon Kim, Eric B. Bartlett
Nuclear Technology | Volume 108 | Number 2 | November 1994 | Pages 283-297
Technical Paper | Reactor Control | doi.org/10.13182/NT94-A35035
Articles are hosted by Taylor and Francis Online.
The objective of this research is to develop a fault-diagnostic advisor for nuclear power plant transients that is based on artificial neural networks. A method is described that provides an error bound and therefore a figure of merit for the diagnosis provided by this advisor. The data used in the development of the advisor contain ten simulated anomalies for the San Onofre Nuclear Power Generating Station. The stacked generalization approach is used with two different partitioning schemes. The results of these partitioning schemes are compared. It is shown that the advisor is capable of recognizing all ten anomalies while providing estimated error bounds on each of its diagnoses.