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Division Spotlight
Isotopes & Radiation
Members are devoted to applying nuclear science and engineering technologies involving isotopes, radiation applications, and associated equipment in scientific research, development, and industrial processes. Their interests lie primarily in education, industrial uses, biology, medicine, and health physics. Division committees include Analytical Applications of Isotopes and Radiation, Biology and Medicine, Radiation Applications, Radiation Sources and Detection, and Thermal Power Sources.
Meeting Spotlight
2024 ANS Winter Conference and Expo
November 17–21, 2024
Orlando, FL|Renaissance Orlando at SeaWorld
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
November 2024
Nuclear Technology
Fusion Science and Technology
Latest News
A proactive approach to reactor vessel aging management
Unit 2 at the Prairie Island nuclear power plant near Red Wing, Minn., underwent an outage in fall 2023, which included extensive work on the reactor vessel using a novel approach to replace baffle-former bolts and lower radial clevis insert bolts. The work relied on extensive analysis beforehand to determine which bolts to replace such that only the new bolts were structurally credited for performance of their safety function. This proactive approach eliminated the need for costly contingencies associated with inspections.
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.