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Division Spotlight
Human Factors, Instrumentation & Controls
Improving task performance, system reliability, system and personnel safety, efficiency, and effectiveness are the division's main objectives. Its major areas of interest include task design, procedures, training, instrument and control layout and placement, stress control, anthropometrics, psychological input, and motivation.
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.”
Alexander G. Parlos, Amir F. Atiya, Kil T. Chong, Wei K. Tsai
Nuclear Technology | Volume 97 | Number 1 | January 1992 | Pages 79-96
Technical Paper | Nuclear Reactor Safety | doi.org/10.13182/NT92-A34628
Articles are hosted by Taylor and Francis Online.
The nonlinear identification of process dynamics encountered in nuclear power plant components is addressed, in an input/output sense, using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the model structure to be identified. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard backpropagation learning algorithm is modified, and it is used for the supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The response of a representative steam generator is predicted using a neural network, and it is compared to the response obtained from a sophisticated computer model based on first principles. The transient responses compare well, although further research is warranted to determine the predictive capabilities of these networks during more severe operational transients and accident scenarios.