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Division Spotlight
Nuclear Installations Safety
Devoted specifically to the safety of nuclear installations and the health and safety of the public, this division seeks a better understanding of the role of safety in the design, construction and operation of nuclear installation facilities. The division also promotes engineering and scientific technology advancement associated with the safety of such facilities.
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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Latest News
Commercial nuclear innovation "new space" age
In early 2006, a start-up company launched a small rocket from a tiny island in the Pacific. It exploded, showering the island with debris. A year later, a second launch attempt sent a rocket to space but failed to make orbit, burning up in the atmosphere. Another year brought a third attempt—and a third failure. The following month, in September 2008, the company used the last of its funds to launch a fourth rocket. It reached orbit, making history as the first privately funded liquid-fueled rocket to do so.
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.