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Fusion Science and Technology
July 2025
Latest News
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.
Andrea Murari, Guido Vagliasindi, Eleonora Arena, Paolo Arena, Luigi Fortuna, JET-EFDA Contributors
Fusion Science and Technology | Volume 58 | Number 2 | October 2010 | Pages 685-694
Selected Paper from the Sixth Fusion Data Validation Workshop 2010 (Part 1) | doi.org/10.13182/FST10-A10893
Articles are hosted by Taylor and Francis Online.
In practically all fields of science, measurements are affected by noise, which can sometimes be modeled with an appropriate probability distribution function. The results of measurements are therefore known only with uncertainties that sometimes can be significant. In many cases the noise source is independent of the system to be studied and the quantities to be measured. In this paper, a numerical approach to handle statistical uncertainties, due to an independent noise source, in a fuzzy logic system is developed. Numerical analysis and various tests with a benchmark show how statistical error bars can be interpreted as an independent "axis of complexity" with respect to the fuzzy boundaries of the membership functions. The uncertainties in the inputs can be transferred to the output and handled separately from the system intrinsic fuzzyness. The main advantages of this independent treatment of the measurement errors are shown in the case of a binary classification task: the regime confinement identification in high-temperature tokamak plasmas. Significant improvements in the correct prediction rate have been achieved with respect to the classification performed without considering the error bars in the measurements.