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Division Spotlight
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.
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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Nuclear Science and Engineering
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Nuclear Technology
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Fusion Science and Technology
Latest News
Uncertainty contributes to lowest uranium spot prices in 18 months
A combination of plentiful supply and uncertain demand resulted in spot pricing for uranium closing out March below $64 per pound, with dips down to about $63.50 during mid-March—the lowest futures prices in 18 months, according to tracking by analysis firm Trading Economics. Spot prices have also fallen steadily since the beginning of 2024. Meanwhile, long-term prices have held steady at about $80 per pound at the end of March, according to Canadian front-end uranium mining, milling, and conversion company Cameco.
Jianghua Wei, Yuntao Song, Kaizhong Ding, Yonghua Chen, Hui Yuan, Zhoushun Guo
Fusion Science and Technology | Volume 80 | Number 7 | October 2024 | Pages 843-855
Research Article | doi.org/10.1080/15361055.2024.2312027
Articles are hosted by Taylor and Francis Online.
Proton therapy for tumor treatment is a typical application of nuclear technology. For proton therapy systems, robotic patient positioning systems (PPSs) are increasingly used because of their high flexibility and efficiency. Most robotic PPSs are developed based on industrial robots, which have good repeatability but low absolute position accuracy (1 to 3 mm) and do not satisfy the requirement of highly precise treatment. In this study, an optimized algorithm, named the Back Propagation Neural Network (BPNN) algorithm based on particle swarm optimization, is proposed to improve the performance of absolute positioning accuracy. A comparison of the training for the traditional BPNN and the optimized algorithm is presented. A series of experiments with different payload weights and tools is implemented to validate the performance of the proposed method. The training results show that the proposed method can improve the average predicted positioning error from 0.55 to 0.38 mm. The results of the experiment with a calibration tool show that the average position error is reduced from 4.10 to 0.32 mm. The results of the experiment with a carbon fiber couch top show that the average and maximal positioning errors are 0.35 and 0.77 mm, respectively. All the results verify the feasibility of the proposed method in this study in improving the position accuracy of the robotic PPS.