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Division Spotlight
Accelerator Applications
The division was organized to promote the advancement of knowledge of the use of particle accelerator technologies for nuclear and other applications. It focuses on production of neutrons and other particles, utilization of these particles for scientific or industrial purposes, such as the production or destruction of radionuclides significant to energy, medicine, defense or other endeavors, as well as imaging and diagnostics.
Meeting Spotlight
2027 ANS Winter Conference and Expo
October 31–November 4, 2027
Washington, DC|The Westin Washington, DC 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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Nuclear Science and Engineering
December 2024
Nuclear Technology
Fusion Science and Technology
November 2024
Latest News
Siting of Canadian repository gets support of tribal nation
Canada’s Nuclear Waste Management Organization (NWMO) announced that Wabigoon Lake Ojibway Nation has indicated its willingness to support moving forward to the next phase of the site selection process to host a deep geological repository for Canada’s spent nuclear fuel.
Yang Zhou, Ming Jiang, Xiaolin Yuan, Guizhong Zuo, Yue Chen, Jilei Hou, Kai Jia, Peng Liu, Zhixin Cheng
Fusion Science and Technology | Volume 80 | Number 8 | November 2024 | Pages 1001-1011
Research Article | doi.org/10.1080/15361055.2023.2275089
Articles are hosted by Taylor and Francis Online.
A molecular pump is a high vacuum acquisition piece of equipment that provides a clean vacuum environment for the Experimental Advanced Superconducting Tokamak (EAST) device. Its running state affects the smooth development of the EAST experiment. Because of fatigue degradation of internal components of the molecular pump, vacuum leakage may occur during long-term operation, causing secondary hazards to the device. In order to improve the accuracy of molecular pump fault prediction, based on the long short-term memory network (LSTM), the deep long short-term memory network (DE-LSTM) and the bidirectional long short-term memory network (Bi-LSTM) are combined. The deep bidirectional long short-term memory network (DE-Bi-LSTM) algorithm is proposed, and the piecewise linear degradation model is introduced to predict fault of the molecular pump. By collecting the vibration signals leaked in the atmosphere and running to the fault time series on the destructive test platform simulating molecular pump fault, data were extracted in the time domain. Finally, the obtained feature vector set was used as the input of the DE-Bi-LSTM algorithm through data standardization to train the model and realize the prediction of molecular pump fault. The experimental results show that the proposed method is optimal to LSTM, DE-LSTM, and Bi-LSTM in predicting performance.