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Fuel loading process begins at Palisades
The Palisades nuclear power plant has drawn closer to restart, as plant staff began the process of loading fuel into the reactor vessel on Sunday morning.
The commencement of fuel loading places the Covert, Mich., facility in Mode 6—or the refueling stage—under the plant’s technical specifications, plant owner and operator Holtec International said in a news release. The Palisades reactor core consists of 204 fuel assemblies that include new fuel and partially used fuel from the plant’s most recent operating cycles. According to Holtec, the fuel loading is being conducted in accordance with plant procedures and technical specifications.
Priscila Palma Sanchez, Adimir dos Santos
Nuclear Science and Engineering | Volume 195 | Number 5 | May 2021 | Pages 555-562
Technical Note | doi.org/10.1080/00295639.2020.1854541
Articles are hosted by Taylor and Francis Online.
In order to ensure safety in a nuclear power plant, operation and protection systems must take into account safety parameters, whether to guide operators or to trip the reactor in emergency cases. Especially in a boron-free small modular reactor (SMR) where reactivity and power are controlled exclusively by rod banks, the power distribution is mostly influenced by its movements affecting the power peaking factor (PPF), which is an important parameter to be considered. The PPF relates the maximum local linear power density to the average power density in a fuel rod indicating a high neutron flux that can cause fuel rod damage. In this technical note, 2117 samples from simulations of an idealized boron-free SMR controlled exclusively by rod banks were used to generate a Support Vector Machine (SVM) model capable of estimating the PPF as a function of control rod bank positions. Such model could be used to predict the maximum PPF in the reactor core by carrying out simple calculation. Residing in a SVM parameter grid search and a 10-cross-validation process in the training set to reach an optimized and robust model, the results have shown a root-mean-squared error of about 0.1% consistent for both training and testing sets.