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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.
A. Petruzzi
Nuclear Technology | Volume 205 | Number 12 | December 2019 | Pages 1554-1566
Technical Paper | doi.org/10.1080/00295450.2019.1632092
Articles are hosted by Taylor and Francis Online.
Predictive Modeling Methodology constitutes an innovative approach to perform uncertainty analysis (UA) that reduces the subjective and user-defined ways to manage experimental data and derive uncertainty of input parameters that characterize the Propagation of Input Uncertainties (PIU) and/or Propagation of Output Accuracies (POA) methods.
The Code with the capability of Adjoint Sensitivity and Uncertainty AnaLysis by Internal Data ADjustment and assimilation (CASUALIDAD) method can be developed as a fully deterministic method based on advanced mathematical tools to internally perform in the thermal-hydraulic system code the sensitivity analysis (SA) and the UA. The method is based upon powerful mathematical tools to perform the SA and upon the Data Adjustment and Assimilation methodology by which experimental observations are combined with code predictions and their respective errors through the application of the Bayes theorem and of the Principle of Maximum Likelihood to provide an improved estimate of the system state and of the associated uncertainty considering all input parameters that affect any prediction.
The methodology has been structured in two main steps. The first step generates the database of improved estimations (IEs) starting from the available set of experimental data and related qualified calculations. The second step deals with the use of the selected (from the obtained database) set of IEs for the uncertainty evaluation of the predicted nuclear power plant transient scenario.
The proposed methodology clearly interrelates in a consistent and robust framework the code validation issue with the evaluation of the uncertainty of code responses passing through the quantification of input uncertainty parameters of code models, thus constituting a step forward with respect to the subjectivity of the current methods based on PIU and/or POA.