ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
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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
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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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Nuclear Science and Engineering
June 2024
Nuclear Technology
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Latest News
Proving DRACO will deliver
The United States is now closer than it has been in over five decades to launching the first nuclear thermal rocket into space, thanks to DRACO—the Demonstration Rocket for Agile Cislunar Orbit.
Mingfu He, Youho Lee
Nuclear Technology | Volume 206 | Number 2 | February 2020 | Pages 358-374
Technical Paper | 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.