A SUPER-RESOLUTION LATTICE BOLTZMANN METHOD BASED ON CONVOLUTIONAL NEURAL NETWORK

For fluid problems, such as water inrush disaster in tunnels, shape design and optimization of aircraft and automobile, computational fluid dynamics (CFD) is commonly used to predict flow characteristics and analyze structural performance. However, high-precision CFD simulations require significant computational resources. In recent years, the super-resolution flow field reconstruction method based on machine learning has made significant progress in fluid mechanics. In this study, a novel super-resolution flow field reconstruction model SRLBM is firstly proposed, which is based on the lattice Boltzmann method (LBM) combined with convolutional neural networks, to reconstruct mesoscopic distribution functions from low-resolution to high-resolution, thereby restoring macroscopic velocity fields and vorticity fields. First, the two-dimensional flow around a cylinder at different Reynolds numbers is simulated using LBM, and compared with published data regarding various aspects to validate the accuracy of LBM. Then, the data from the two-dimensional cylinder flow are utilized as the training dataset for SRLBM, and the reconstruction performance of SRLBM under different scaling factors are compared. The results show that SRLBM can effectively restore high-resolution distribution functions for different scaling factors. At a scaling factor of 8, compared to bicubic interpolation reconstruction methods, the error of SRLBM is reduced by nearly 60% regarding distribution functions, and is reduced by nearly 70% regarding the macroscopic fields. Even at a scaling factor of 32, the macroscopic field restored by SRLBM is generally consistent with the results from the direct numerical simulation. Incorporating solid volume fraction and distribution function as input channels can effectively enhance the predictability of SRLBM, which can reduce the relative error in the cylinder region by nearly 40% when the scaling factor is 32. The SRLBM demonstrates a good generalization ability, when the scale factor is 8, the error in the reconstructed high-resolution flow field within a certain range of Reynolds numbers is below 3%. Therefore, after adequate training, SRLBM has the potential to become an effective method for reconstructing high-resolution complex flow field.