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Division Spotlight
Fusion Energy
This division promotes the development and timely introduction of fusion energy as a sustainable energy source with favorable economic, environmental, and safety attributes. The division cooperates with other organizations on common issues of multidisciplinary fusion science and technology, conducts professional meetings, and disseminates technical information in support of these goals. Members focus on the assessment and resolution of critical developmental issues for practical fusion energy applications.
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
Vogtle-3 shuts down for valve issue
One of the new Vogtle units in Georgia was shut down unexpectedly on Monday last week for a valve issue that has since been investigated and repaired. According to multiple local news outlets, Georgia Power reported on July 17 that Unit 3 was back in service.
Southern Company spokesperson Jacob Hawkins confirmed that Vogtle-3 went off line at 9:25 p.m. local time on July 8 “due to lowering water levels in the steam generators caused by a valve issue on one of the three main feedwater pumps.”
Humberto E. Garcia, Richard B. Vilim
Nuclear Technology | Volume 141 | Number 1 | January 2003 | Pages 69-77
Technical Paper | Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies | doi.org/10.13182/NT03-A3351
Articles are hosted by Taylor and Francis Online.
Two basic approaches can be mentioned to model physical systems. One approach derives a model structure from the known physical laws. However, obtaining a model with the required fidelity may be difficult if the system is not well understood. A second approach is to employ a black-box structure to learn the implicit input-output relationships from measurements in which no particular attention is paid to modeling the underlying processes. A method that draws on the respective strengths of each of these two approaches is described. The technique integrates known first-principles knowledge derived from physical modeling with measured input-output mappings derived from neural processing to produce a computer model of a dynamical process. The technique is used to detect operational changes of mechanical equipment by statistically comparing, using a likelihood test, the predicted model output for the given measured input with the actual process output. Experimental results with a peristaltic pump are presented.