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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
2025 ANS Annual Conference
June 15–18, 2025
Chicago, IL|Chicago Marriott Downtown
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
Smarter waste strategies: Helping deliver on the promise of advanced nuclear
At COP28, held in Dubai in 2023, a clear consensus emerged: Nuclear energy must be a cornerstone of the global clean energy transition. With electricity demand projected to soar as we decarbonize not just power but also industry, transport, and heat, the case for new nuclear is compelling. More than 20 countries committed to tripling global nuclear capacity by 2050. In the United States alone, the Department of Energy forecasts that the country’s current nuclear capacity could more than triple, adding 200 GW of new nuclear to the existing 95 GW by mid-century.
Tetsuo Tamaoki, Masuo Sato, Ryoichi Takahashi
Nuclear Technology | Volume 100 | Number 3 | December 1992 | Pages 378-389
Technical Paper | Reactor Operation | doi.org/10.13182/NT92-A34732
Articles are hosted by Taylor and Francis Online.
An advanced diagnostic method is proposed that uses automated pattern recognition for reactor noise. The method enables intensive diagnosis of known anomalies and extensive detection of unknown plant states. It also enables automatic learning of reference noise patterns for an unknown plant state and monitoring of the subsequent state change by regarding the new reference patterns as those for a known plant state. Application results for the method used on artificial noise data produced by a fast breeder reactor noise simulator are presented. A diagnostic system based on the proposed method will make it possible to automatically accumulate and make the most of anomaly data from actual power plants, although it is still difficult to identify the cause of an abnormality automatically.
