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Reactor Physics
The division's objectives are to promote the advancement of knowledge and understanding of the fundamental physical phenomena characterizing nuclear reactors and other nuclear systems. The division encourages research and disseminates information through meetings and publications. Areas of technical interest include nuclear data, particle interactions and transport, reactor and nuclear systems analysis, methods, design, validation and operating experience and standards. The Wigner Award heads the awards program.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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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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Fusion Science and Technology
Latest News
Argonne research aims to improve nuclear fuel recycling and metal recovery
Servis
Scientists at Argonne National Laboratory are investigating a used nuclear fuel recycling technology that could lead to a scaled-down and more efficient approach to metal recovery, according to a recent news article from the lab. The research, led by Argonne radiochemist Anna Servis with funding from the Department of Energy’s Advanced Research Projects Agency–Energy (ARPA-E), could have an impact beyond the nuclear fuel cycle and improve other high-value metal processing, such as rare earth recovery, according to Argonne.
The research: Servis’s work is being carried out under ARPA-E’s CURIE (Converting UNF Radioisotopes Into Energy) program. The specific project—Radioisotope Capture Intensification Using Rotating Packed Bed Contactors—started in 2023 and is scheduled to end in January 2026.
Bin Long, Ying Liu, Fulin Zeng, Jijun Zhou, Yuqian Yang
Fusion Science and Technology | Volume 78 | Number 5 | July 2022 | Pages 379-388
Technical Paper | doi.org/10.1080/15361055.2022.2033061
Articles are hosted by Taylor and Francis Online.
Edge-coherent mode (ECM) is one of the most promising modes in the tokamak fusion experiment, such as the Experimental Advanced Superconducting Tokamak (EAST). This paper presents an efficient convolution neural network model called NoiseNet for ECM recognition from the cross-power spectral data. NoiseNet suppresses the overfitting by applying noise in both the horizontal and vertical directions to the output of each layer of the convolution. And the improvement of the receptive field enables the convolution layer to better learn the difference between the ECM and the turbulence in the data. Experiments show that NoiseNet has better performance in ECM recognition with fewer parameters, and thus improved efficiency, than other major models, such as AlexNet, ResNet, and DenseNet. NoiseNet achieves a test accuracy of 93.94% on the ECM data sets. In addition, compared with the traditional method, this method does not depend on the empirical threshold and its generalization ability will improve with the increase in the amount of data.