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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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DOE on track to deliver high-burnup SNF to Idaho by 2027
The Department of Energy said it anticipated delivering a research cask of high-burnup spent nuclear fuel from Dominion Energy’s North Anna nuclear power plant in Virginia to Idaho National Laboratory by fall 2027. The planned shipment is part of the High Burnup Dry Storage Research Project being conducted by the DOE with the Electric Power Research Institute.
As preparations continue, the DOE said it is working closely with federal agencies as well as tribal and state governments along potential transportation routes to ensure safety, transparency, and readiness every step of the way.
Watch the DOE’s latest video outlining the project here.
Yiqian Wu, Zhiyao Liu, Ming Jia, Cong Chi Tran, Shengyuan Yan
Nuclear Technology | Volume 206 | Number 1 | January 2020 | Pages 94-106
Technical Paper | doi.org/10.1080/00295450.2019.1620055
Articles are hosted by Taylor and Francis Online.
The development of a model for mental workload (MWL) prediction of an operator in nuclear power plants (NPPs) is necessary but challenging. In this study, the validity, sensitivity, and relationship between the four indices of eye tracking (i.e., pupil dilation, blink rate, fixation rate, and saccadic rate) and subjective rating method (i.e., the National Aeronautics and Space Administration-Task Load Index) of both experts and nonexperts when they are operating the state-oriented procedure system in NPPs are analyzed. An artificial neural network (ANN) is used to develop the MWL prediction model using the data of nonexperts. The correlation analysis results indicate that four eye tracking indices are sensitive to the subjective MWL, but there is no significant difference in the pupil diameter and saccadic rate between the experts and nonexperts. The validity of the proposed ANN-based prediction model is proven by the high correlation coefficient (higher than 0.95) between the original and predicted data. However, when the proposed ANN model was applied to the experts’ data, there was a significant difference between the original and predicted data. Therefore, the proposed prediction model can be applied to the experts’ data but with a certain adjustment to obtain the most possibly reasonable results.