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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
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!
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Latest News
Virginia utility considers SMRs
Dominion Energy Virginia has issued a request for proposals from leading nuclear companies to study the feasibility of putting a small modular reactor at its North Anna nuclear power plant.
While the utility says it is not a commitment to build an SMR at the site, the RFP is “an important first step in evaluating the technology and the North Anna site to support Dominion Energy customers’ future energy needs consistent with the company’s most recent Integrated Resource Plan.”
Zhichao Guo, Robert E. Uhrig
Nuclear Technology | Volume 99 | Number 1 | July 1992 | Pages 36-42
Technical Paper | Nuclear Reactor Safety | doi.org/10.13182/NT92-A34701
Articles are hosted by Taylor and Francis Online.
A hybrid artificial neural network is used to model the thermodynamic behavior of the Tennessee Valley Authority’s Sequoyah nuclear power plant using data for heat rate measurements acquired over a 1-yr period. The modeling process involves the use of a selforganizing network to rearrange the original data into several classes by clustering. Then, the centroids of these clusters are used as the training patterns for an artificial neural network that utilizes backpropagation training to adjust the weights on the connections between artificial neurons. This procedure greatly reduces the training time and reduces the system error. Comparison of the calculated heat rates with those predicted by the artificial neural network gives an error of <0.1%. A sensitivity analysis is then performed by taking the partial derivative of the heat rate with respect to each individual input to secure a sensitivity coefficient. These coefficients identified the input variables that were most important to improving the heat rate and efficiency. The methodology reported is an alternative to the conventional modeling procedures used in other heat rate monitoring systems. It has the advantage that the artificial neural network model is based on actual plant data that cover the dynamic range normally occurring over an annual cycle of operation, and it is not subject to linearization or empirical approximations. This process could be utilized by existing heat rate monitoring systems.