Flow fields prediction for data-driven model of 5 × 5 fuel rod bundles based on POD-RBFNN surrogate model

Abstract

The fuel rod bundles are a crucial component of nuclear reactors. Research the flow characteristics of the fuel rod bundles can be computationally expensive, despite some progress made in the past few decades. In this paper, the fast prediction of flow fields for 5 × 5 fuel rod bundles is implemented based on a data-driven algorithm. First, a refined model of the 5 × 5 fuel rod bundles with spacer grid is established, and meshes are generated using a hybrid meshes technology. Then, the flow characteristics of the 5 × 5 fuel rod bundles are extensively analyzed using Computational Fluid Dynamics (CFD) methods. A comparison between the CFD results and experimental data reveals a strong agreement, thereby validating our research findings. Subsequently, a reduced-order model (ROM) called the proper orthogonal decomposition (POD)-radial basis function neural network (RBFNN) surrogate model is proposed. the POD algorithm is utilized to extracting the dominant flow modes, and the RBFNN is employed to predict the POD mode coefficients of non-sample points. By linearly combining the predicted POD mode coefficients with the POD modes, rapid prediction of flow fields for non-sample points can be achieved. Compared to the full-scale CFD simulations, it finds that the proposed POD-RBFNN surrogate model not only significantly improved efficiency, but also maintained a high level of predictive accuracy. It is believed that the research results hold important value for CFD calculations within nuclear reactor cores.