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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
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!
Latest Magazine Issues
Apr 2025
Jan 2025
Latest Journal Issues
Nuclear Science and Engineering
May 2025
Nuclear Technology
Fusion Science and Technology
Latest News
TerraPower begins U.K. regulatory approval process
Seattle-based TerraPower signaled its interest this week in building its Natrium small modular reactor in the United Kingdom, the company announced.
TerraPower sent a letter to the U.K.’s Department for Energy Security and Net Zero, formally establishing its intention to enter the U.K. generic design assessment (GDA) process. This is TerraPower’s first step in deployment of its Natrium technology—a 345-MW sodium fast reactor coupled with a molten salt energy storage unit—on the international stage.
Hansol Kim, Joseph Seo, Yassin Hassan
Nuclear Technology | Volume 211 | Number 3 | March 2025 | Pages 452-475
Research Article | doi.org/10.1080/00295450.2024.2331897
Articles are hosted by Taylor and Francis Online.
This study presents a new approach to flow regime classification specifically tailored for typical wire-wrapped fuel assemblies in sodium fast reactors. Historically, the definition and understanding of flow regime boundaries have been extensively researched. However, many of these models suffer inaccuracy due to a lack of comprehensive data. In particular, the limited data, with only 36 data points for the laminar-to-transition boundary and 145 data points for the transition-to-turbulent boundary, often result in suboptimal models.
Recognizing the critical data gap, this study classified flow regimes based on a robust data set of over 5000 data points. A diverse range of algorithms was used to find the optimal classification model. These included logistic regression, artificial neural networks, support vector classifiers, Naïve Bayes, Gaussian Naïve Bayes, K-Nearest Neighbors, random forest, AdaBoost, GradientBoost, and XGBoost. A comparative analysis of these algorithms provides valuable insights.
This study presents a comprehensive set of machine learning algorithms to improve the accuracy and reliability of flow regime classification, which is a critical step in predicting friction factors and the efficient operation of sodium fast reactors.