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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.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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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
Norway’s Halden reactor takes first step toward decommissioning
The government of Norway has granted the transfer of the Halden research reactor from the Institute for Energy Technology (IFE) to the state agency Norwegian Nuclear Decommissioning (NND). The 25-MWt Halden boiling water reactor operated from 1958 to 2018 and was used in the research of nuclear fuel, reactor internals, plant procedures and monitoring, and human factors.
Shengyuan Yan, Kai Yao, Fengjiao Li, Yingying Wei, Cong Chi Tran
Nuclear Technology | Volume 208 | Number 10 | October 2022 | Pages 1540-1552
Technical Paper | doi.org/10.1080/00295450.2022.2049965
Articles are hosted by Taylor and Francis Online.
The accurate assessment of human error probability (HEP) has an important impact on the safety of nuclear power plants. Therefore, it is necessary to develop a HEP model. This study analyzes the validity, sensitivity, and relationship between HEP and the indices of eye response and the subjective rating method. The analysis result showed that there is a correlation between HEP and the indices of eye response, subjective workload, and situation awareness level. Therefore, a back propagation neural network model was developed based on these indices. The correlation coefficient is more than 0.95 between the predicted data of the developed model and the target data. Also, the root mean square error was 0.0073, 0.0083, and 0.0077, and the determination coefficient was 0.965, 0.933, and 0.931 for the training, validation, and testing data sets, respectively. Therefore, the developed back propagation neural network model has reliable prediction accuracy for HEP.