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Division Spotlight
Nuclear Nonproliferation Policy
The mission of the Nuclear Nonproliferation Policy Division (NNPD) is to promote the peaceful use of nuclear technology while simultaneously preventing the diversion and misuse of nuclear material and technology through appropriate safeguards and security, and promotion of nuclear nonproliferation policies. To achieve this mission, the objectives of the NNPD are to: Promote policy that discourages the proliferation of nuclear technology and material to inappropriate entities. Provide information to ANS members, the technical community at large, opinion leaders, and decision makers to improve their understanding of nuclear nonproliferation issues. Become a recognized technical resource on nuclear nonproliferation, safeguards, and security issues. Serve as the integration and coordination body for nuclear nonproliferation activities for the ANS. Work cooperatively with other ANS divisions to achieve these objective nonproliferation policies.
Meeting Spotlight
ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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
El Salvador: Looking to nuclear
In 2022, El Salvador’s leadership decided to expand its modest, mostly hydro- and geothermal-based electricity system, which is supported by expensive imported natural gas and diesel generation. They chose to use advanced nuclear reactors, preferably fueled by thorium-based fuels, to power their civilian efforts. The choice of thorium was made to inform the world that the reactor program was for civilian purposes only, and so they chose a fuel that was plentiful, easy to source and work with, and not a proliferation risk.
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.