Method Research on the Model Optimization of Whole-Core Fuel-Assembly Bowing Based on 3D Variational Algorithm and the Measurement Values

Abstract The phenomenon of fuel-assembly bowing would inevitably occur during operation, but the fuel-assembly bowing cannot be measured directly. Therefore, the challenge lies in obtaining the actual fuel-assembly bowing, which is a prerequisite for ensuring simulation accuracy. In this paper, a newly method for the model optimization of whole-core fuel-assembly bowing was proposed. The measurement values and theoretical whole-core model are combined in this method based on the 3D variational (3DVAR) and artificial neutral network (ANN) algorithm. In 3DVAR, the cost function is constructed to characterize the difference between the actual fuel-assembly bowing and theoretical model. The 3DVAR algorithm was applied to model optimization and the ANN algorithm was applied to establish the relationship between the whole-core model of fuel-assembly bowing and corresponding simulation of measurement values. For method verifications, the power-distribution measurements of a commercial NPP in China has been applied, and the model that need to be optimized was taken as the bowing amplitude of each fuel-assembly. The numerical results indicate that the relative errors of the power distribution in entire cycle could be reduced notably to meet the industrial criteria, and the maximum value is reduced from 13.4% to 7.7%. As conclude, the proposed method based on 3DVAR algorithm and the measurement values is efficient to optimize the model of fuel-assembly bowing.