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BackgroundThere is a prominent contradiction between calculation efficiency and calculation accuracy when computational fluid dynamics (CFD) program is used in the thermal-hydraulic numerical simulation of reactors.PurposeThis study aims to alleviate the contradiction to achieve higher calculation results with higher calculation efficiency by finding a method to effectively optimize the results of the coarse-grid numerical simulation.MethodsFirst of all, the feedforward neural network (FNN) machine learning (ML) method was employed to compare two sets of numerical simulation results under a certain number of similar working conditions under coarse and fine grids. Then, an error function expression with general applicability for similar conditions was obtained to optimize the numerical simulation calculation results of the coarse grid. Finally, the optimization effect was evaluated by comparing the root mean square error (RMSE) between the calculation results of relevant physical quantities before and after optimization and the calculation results under fine grid.ResultsThe research results show that after the coarse grid calculation result is optimized by the error function obtained by the FNN method, the RMSE of the physical quantity related to the fine grid is significantly reduced.ConclusionsThe coarse-grid numerical simulation optimization technology based on neural network established in this study can effectively improve the calculation accuracy of the reactor's coarse-grid numerical simulation, and provide a method reference for realizing high-precision numerical simulation with high computational efficiency.