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Prediction of Sintering Density of Full-ceramic Microencapsulated Fuel Pellets Based on Joint BP Neural Network

After the Fukushima nuclear accident in Japan in 2011, people began to focus on the development and research of a new generation of nuclear fuel that can improve the safety of pressurized water reactors under accident conditions. Accident fault-tolerant fuel (ATF) is a new type of fuel developed to improve the ability of the reactor core to withstand serious accidents, which can ensure the safety of the reactor and the integrity of fuel elements under accident conditions. Full-ceramic microencapsulated fuel pellets show unique advantages in ATF field, and this study is carried out based on their sintering process. In this study, an algorithm based on joint BP neural network was designed for the first time to extract features from existing experimental data and summarize laws, so as to predict pellet density before sintering.