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Nuclear Criticality Safety
NCSD provides communication among nuclear criticality safety professionals through the development of standards, the evolution of training methods and materials, the presentation of technical data and procedures, and the creation of specialty publications. In these ways, the division furthers the exchange of technical information on nuclear criticality safety with the ultimate goal of promoting the safe handling of fissionable materials outside reactors.
2020 ANS Virtual Winter Meeting
November 16–19, 2020
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U.S. reactor technologies to be featured at IAEA conference
A virtual side event at the 64th General Conference of the International Atomic Energy Agency will spotlight U.S. reactor technologies. The free event, US Reactor Technologies: Flexible Energy Security for Real-World Challenges, will be held this Thursday, September 24, from 9:00 a.m. to 10:30 a.m. (EDT).
The event will highlight the capabilities of small modular reactors and other innovative reactors for addressing countries’ current needs. It will also examine anticipated challenges in the future, as well as underscore the need to act now.
The event is sponsored by the U.S. Department of Energy’s Office of Nuclear Energy. Advanced registration is required.
Mingfu He, Youho Lee
Nuclear Technology | Volume 206 | Number 2 | February 2020 | Pages 358-374
Technical Paper | dx.doi.org/10.1080/00295450.2019.1626177
Articles are hosted by Taylor and Francis Online.
Considering the highly nonlinear behavior and phenomenological complexity of critical heat flux (CHF), this study proposes a novel method to predict CHF on microstructure surface using machine learning technologies. An extensive literature survey was conducted to collect experimental data on microstructure surfaces. Data on horizontal silicon specimens of cylindrical pillars with square arrangements were selected for both training and testing various machine learning methods, including ν-support vector machine, back-propagation neural network, radial basis function neural network, general regression neural network, and deep belief network (DBN). Among the tested machine learning methods, DBN is shown to provide the best accuracy for CHF prediction. The obtained parametric CHF behavior of DBN with respect to pillar diameter, spacing, and height agrees with the physical understanding of CHF on microstructure surfaces. The presented approach is expected to support the design optimization of microstructure for CHF maximization.