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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.
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June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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Framatome signs contracts with Sizewell C
French nuclear developer Framatome is slated to deliver key equipment for Sizewell C Ltd.’s two large reactors planned for the United Kingdom’s Suffolk coast.
The agreement, reportedly worth multiple billions of euros, was announced this week and will involve Framatome from the design phase until commissioning. The company also agreed to a long-term fuel supply deal. Framatome is 80.5 percent owned by France’s EDF and 19.5 percent owned by Mitsubishi Heavy Industries.
M. Humberstone, B. Wood, J. Henkel, J. W. Hines
Nuclear Technology | Volume 173 | Number 1 | January 2011 | Pages 35-45
Technical Paper | NPIC&HMIT Special / Nuclear Plant Operations and Control | doi.org/10.13182/NT11-A11482
Articles are hosted by Taylor and Francis Online.
Models used for system monitoring must strike a balance between stability and elasticity. Ideally, a model should adapt to new operating conditions without losing the ability to differentiate faults from nominal conditions. To this end, an adaptive nonparametric model (ANPM) has been developed for integrated monitoring, diagnostic, and prognostic use on small to medium size reactors. This paper gives an overview of the development of the ANPM with two example applications. The ANPM's original intent is to adapt a nonparametric model's memory matrix from data created using a first principle model (FPM) to the system's actual unfaulted data. This would be useful for monitoring new system designs from first construction and operation when the only available data are from FPMs. The FPM's data are used to build the best possible models initially, but during the system's operation, new data can be collected that are more accurate for future empirical model predictions. The use of the ANPM is demonstrated on two systems. The first system is a heat exchanger model that is modeled in SIMULINK with both a low-fidelity and a high-fidelity simulation. The second system is a flow loop, a physical system at The University of Tennessee that is also modeled in SIMULINK. The results of testing the ANPM on nonfaulted conditions for the heat exchanger model and the flow loop are given. Areas of future work and development are outlined.