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Division Spotlight
Mathematics & Computation
Division members promote the advancement of mathematical and computational methods for solving problems arising in all disciplines encompassed by the Society. They place particular emphasis on numerical techniques for efficient computer applications to aid in the dissemination, integration, and proper use of computer codes, including preparation of computational benchmark and development of standards for computing practices, and to encourage the development on new computer codes and broaden their use.
Meeting Spotlight
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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 Technology
Fusion Science and Technology
Latest News
PPPL study points to better fusion plasma control
The combination of two previously known methods for managing plasma conditions can result in enhanced control of plasma in a fusion reactor, according to a simulation performed by researchers at the Department of Energy’s Princeton Plasma Physics Laboratory.
Changan Ren, He Li, Jichong Lei, Jie Liu, Wei Li, Kekun Gao, Guocai Huang, Xiaohua Yang, Tao Yu
Nuclear Technology | Volume 209 | Number 9 | September 2023 | Pages 1365-1372
Research Article | doi.org/10.1080/00295450.2023.2199098
Articles are hosted by Taylor and Francis Online.
With the advancement of artificial intelligence technology, intelligent diagnostic technology has been gradually implemented across various industries. This study proposes the use of convolutional neural networks–long short-term memory (CNNs-LSTM) for diagnosing faults in CPR1000 nuclear power plants (NPPs). To automatically extract data related to different types and levels of faults in the PCTRAN program, the study utilizes a self-developed AutoPCTRAN software and selects several key nuclear parameters as feature quantities. The study uses random sampling to create the training, validation, and test sets in an 8:1:1 ratio and identifies acceptable parameters to build the CNN-LSTM model. Test results show that the CNN-LSTM–based model for diagnosing CPR1000 NPP faults achieves a problem recognition rate of 99.6%, which validates the efficacy of the CNN-LSTM–based nuclear power fault diagnosis model.