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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.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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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
Excelsior University student section awarded community education grant
The American Nuclear Society Student Section at Excelsior University in Albany, N.Y., was awarded a $5,000 grant from the ANS Student Section Strategic Fund initiative for its program, Empowering Tomorrow’s Nuclear Innovators: A Collaborative Approach to Nuclear Technology Education and Awareness.
Chaung Lin, Tsung-Ming Lin
Nuclear Technology | Volume 127 | Number 1 | July 1999 | Pages 102-112
Technical Paper | Materials for Nuclear Systems | doi.org/10.13182/NT99-A2987
Articles are hosted by Taylor and Francis Online.
Neural networks such as the radial basis function network, adaptive neuro-fuzzy inference systems, and the multilayer feedforward neural network were adopted to model the steam generator water level, which was intended to be the analytic redundancy in the signal validation system. The training data were the simulation results of the small-demand turbine power variations around the steady state. The test data were from two small-load maneuvers: the load reduction from 100 to 50% of the rated power, and one feedwater pump trip event. The network training required only a short time, and the simulation results show that the neural networks are suitable for the modeling of steam generator water level.