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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.
Man Gyun Na
Nuclear Technology | Volume 128 | Number 3 | December 1999 | Pages 327-340
Technical Paper | Reactor Operations and Control | doi.org/10.13182/NT99-A3035
Articles are hosted by Taylor and Francis Online.
A neuro-fuzzy method is used to estimate the departure from nucleate boiling (DNB) protection limit using the measured average temperature and pressure of a reactor core. The neuro-fuzzy system parameters are optimized by two learning methods. A genetic algorithm is used to optimize the antecedent parameters of the neuro-fuzzy inference system, and a least-squares algorithm is used to solve the consequent parameters. Two neuro-fuzzy inference systems are used according to the pressure and temperature regions. The proposed method, which is applied to the Yonggwang 3 and 4 nuclear power plants, has a 6.09% larger thermal margin than the conventional Westinghouse OTT DNB protection logic. This simple algorithm can provide good information for nuclear power plant operation and diagnosis by estimating the DNB protection limit each time step.
