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        Multi-scale heat conduction models with improved equivalent thermal conductivity of TRISO fuel particles for FCM fuel

        Wang Mouhao,Bu Shanshan,Zhou Bing,Li Zhenzhong,Chen Deqi 한국원자력학회 2023 Nuclear Engineering and Technology Vol.55 No.3

        Fully Ceramic Microencapsulated (FCM) fuel is emerging advanced fuel material for the future nuclear reactors. The fuel pellet in the FCM fuel is composed of matrix and a large number of TRistructural- ISOtopic (TRISO) fuel particles which are randomly dispersed in the SiC matrix. The minimum layer thickness in a TRISO fuel particle is on the order of 105 m, and the length of the FCM pellet is on the order of 102 m. Hence, the heat transfer in the FCM pellet is a multi-scale phenomenon. In this study, three multi-scale heat conduction models including the Multi-region Layered (ML) model, Multi-region Non-layered (MN) model and Homogeneous model for FCM pellet were constructed. In the ML model, the random distributed TRISO fuel particles and coating layers are completely built. While the TRISO fuel particles with coating layers are homogenized in the MN model and the whole fuel pellet is taken as the homogenous material in the Homogeneous model. Taking the results by the ML model as the benchmark, the abilities of the MN model and Homogenous model to predict the maximum and average temperature were discussed. It was found that the MN model and the Homogenous model greatly underestimate the temperature of TRISO fuel particles. The reason is mainly that the conventional equivalent thermal conductivity (ETC) models do not take the internal heat source into account and are not suitable for the TRISO fuel particle. Then the improved ETCs considering internal heat source were derived. With the improved ETCs, the MN model is able to capture the peak temperature as well as the average temperature at a wide range of the linear powers (165 W/ cm~ 415 W/cm) and the packing fractions (20%e50%). With the improved ETCs, the Homogenous model is better to predict the average temperature at different linear powers and packing fractions, and able to predict the peak temperature at high packing fractions (45%e50%).

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