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Division Spotlight
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.
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!
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Latest News
Argonne’s METL gears up to test more sodium fast reactor components
Argonne National Laboratory has successfully swapped out an aging cold trap in the sodium test loop called METL (Mechanisms Engineering Test Loop), the Department of Energy announced April 23. The upgrade is the first of its kind in the United States in more than 30 years, according to the DOE, and will help test components and operations for the sodium-cooled fast reactors being developed now.
Yeni Li, Elisa Bertino, Hany S. Abdel-Khalik
Nuclear Technology | Volume 206 | Number 1 | January 2020 | Pages 82-93
Technical Paper | doi.org/10.1080/00295450.2019.1626170
Articles are hosted by Taylor and Francis Online.
Model-based defenses have been promoted over the past decade as essential defenses against intrusion and data deception attacks into the control network used to digitally regulate the operation of critical industrial systems such as nuclear reactors. The idea is that physics-based models could differentiate between genuine, i.e., unaltered by adversaries, and malicious network engineering data, e.g., flowrates, temperatures, etc. Machine learning techniques have also been proposed to further improve the differentiating power of model-based defenses by constantly monitoring the engineering data for any possible deviations that are not consistent with the physics. While this is a sound premise, critical systems, such as nuclear reactors, chemical plants, oil and gas plants, etc., share a common disadvantage: almost any information about them can be obtained by determined adversaries, such as state-sponsored attackers. Thus, one must question whether model-based defenses would be resilient under these extreme adversarial conditions. This paper represents a first step toward answering this question. Specifically, we introduce self-learning techniques, including both pure data-driven, e.g., deep neural networks, and physics-based techniques able to predict dynamic behavior for a nuclear reactor model. The results indicate that if attackers are technically capable, they can learn very accurate models for reactor behavior, which raises concerns about the effectiveness of model-based defenses.