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Division Spotlight
Materials Science & Technology
The objectives of MSTD are: promote the advancement of materials science in Nuclear Science Technology; support the multidisciplines which constitute it; encourage research by providing a forum for the presentation, exchange, and documentation of relevant information; promote the interaction and communication among its members; and recognize and reward its members for significant contributions to the field of materials science in nuclear technology.
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.
Se Woo Cheon, Soon Heung Chang
Nuclear Technology | Volume 102 | Number 2 | May 1993 | Pages 177-191
Technical Paper | Nuclear Reactor Safety | doi.org/10.13182/NT93-A34815
Articles are hosted by Taylor and Francis Online.
Expert systems that have neural networks for their knowledge bases are called connectionist expert systems. Several powerful advantages of connectionist expert systems over conventional rule-based expert systems are discussed. The backpropagation network (BPN) algorithm is applied to the connectionist expert system for the identification of transients in nuclear power plants. In this approach, the transient is identified by mapping or associating patterns of symptom input vectors to patterns representing transient conditions. The general mapping capability of the neural network allows one to identify a transient easily. A number of case studies are performed with emphasis on the applicability of the neural network to the classification problems. Based on the case studies, the BPN algorithm can identify the transient well, although untrained, incomplete, sensor-failed, or time-varying symptoms are given. Also, multiple transients are easily identified with a given symptom input vector.
