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Meeting Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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
August 2025
Nuclear Technology
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July 2025
Latest News
The U.S. Million Person Study of Low-Dose-Rate Health Effects
There is a critical knowledge gap regarding the health consequences of exposure to radiation received gradually over time. While there is a plethora of studies on the risks of adverse outcomes from both acute and high-dose exposures, including the landmark study of atomic bomb survivors, these are not characteristic of the chronic exposure to low-dose radiation encountered in occupational and public settings. In addition, smaller cohorts have limited numbers leading to reduced statistical power.
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.