Research on fast prediction of boiling flow parameters in rod bundle of NPP based on efficient CFD-ROM methods

Historically, Computational Fluid Dynamics (CFD) has been widely used to verify the flow dynamics in rod bundle channels. Nevertheless, the iterative calculation and time consumption make it impractical for the application of digital twin that require efficiency. In order to tackle this difficulty, we suggest utilizing a data-driven order reduced model (ROM) to rapidly predict flow fields of rod bundle channels. Previous research on Reduced Order Models (ROM) for rod bundle channel has primarily focused on the single-phase state, with little emphasis on the two-phase state. Thus, the study focuses on the ROM of rod bundle channel in a two-phase state. First, a Computational Fluid Dynamics (CFD) data set is processed using the Proper Orthogonal Decomposition (POD) algorithm to identify important modes. Then, a Back-Propagation Neural Network (BPNN) model is trained as the agent model, due to its strong ability to fit non-linear relationship between input and output. The structure of BPNN is optimized. Ultimately, the ROM model is utilized to predict the flow field of a rod bundle channel under new boundary conditions. The comparison with CFD calculation confirms the effectiveness of the Reduced Order Model (ROM), showcasing its high precision in predicting the temperature, velocity, and void fraction fields within the rod bundle channel. In addition, the ROM model reaches a computation speed that is roughly 10^4 times quicker than standard CFD simulations. Nonetheless, it is noted that the model's predictive accuracy diminishes under conditions of slight subcooled boiling, due to the lack of samples under the condition. An improvement strategy: Subdivide the sampling space region according to various change stages, implement partition sampling.