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Division Spotlight
Operations & Power
Members focus on the dissemination of knowledge and information in the area of power reactors with particular application to the production of electric power and process heat. The division sponsors meetings on the coverage of applied nuclear science and engineering as related to power plants, non-power reactors, and other nuclear facilities. It encourages and assists with the dissemination of knowledge pertinent to the safe and efficient operation of nuclear facilities through professional staff development, information exchange, and supporting the generation of viable solutions to current issues.
Meeting Spotlight
International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 2025)
April 27–30, 2025
Denver, CO|The Westin Denver Downtown
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
Argonne’s METL gears up to test more sodium fast reactor components
Argonne National Laboratory has successfully swapped out an aging cold trap in the sodium test loop called METL (Mechanisms Engineering Test Loop), the Department of Energy announced April 23. The upgrade is the first of its kind in the United States in more than 30 years, according to the DOE, and will help test components and operations for the sodium-cooled fast reactors being developed now.
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.