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Division Spotlight
Thermal Hydraulics
The division provides a forum for focused technical dialogue on thermal hydraulic technology in the nuclear industry. Specifically, this will include heat transfer and fluid mechanics involved in the utilization of nuclear energy. It is intended to attract the highest quality of theoretical and experimental work to ANS, including research on basic phenomena and application to nuclear system design.
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.”
Mingfu He, Youho Lee (Univ of New Mexico)
Proceedings | Advances in Thermal Hydraulics 2018 | Orlando, FL, November 11-15, 2018 | Pages 449-459
The critical heat flux (CHF) sets the upper limit of efficient heat removal for pool boiling. Microstructures fabricated on a heat transfer substrate can effectively increase the limit of heat removal and delay the boiling crisis. The exact physics mechanisms behind microstructure enhancement still remain ambiguous and CHF prediction on microstructured surfaces is not well resolved even if numerous related studies and experiments have been performed. In this study, the deep belief network (DBN) is proposed to predict CHF and study parametric trends of CHF by collecting relevant CHF datasets from published papers. Performance comparisons with other four common machine learning techniques and three modified Zuber models accounting for the effects of microstructures are conducted for exploring complicated and nonlinear relation between CHF and microstructures. Different from the training process of other regression modelling problems, a special model convergence, which is defined in Subsection 3.1, is required to be incorporated into the CHF model of DBN for exhibiting accurate parametric trends of CHF and improving the prediction accuracy. Numerical results demonstrate that DBN can achieve the best performance of CHF prediction in terms of prediction accuracy. The presented methodology provides new insights for CHF modelling in pool boiling enhanced by microstructures.