Reconstruction of turbulent fields based on super-resolution reconstruction method

It is an important issue to obtain detailed flow fields from limited flow fields data. The convolutional-neural-networks-based super-resolution reconstruction methods developed in recent years are effective methods to obtain detailed flow fields. The efficient sub-pixel convolutional neural network（ESPCN） method is used to reconstruct Rayleigh–Bénard（RB） convection numerical simulation data and turbulent boundary layer（TBL） experimental measured data, and obtain high resolution flow fields data. The reconstructed high resolution flow fields data obtained using ESPCN is then compared to the results from the traditional super-resolution reconstruction method, the bicubic interpolation method. The results indicate that the flow fields reconstructed by the ESPCN method and the bicubic method agree well with the original high-resolution flow fields data when the down-sampling ratio is small. But, when the down-sampling ratio is large, the accuracy of the flow fields reconstructed by the ESPCN method is significantly better than that constructed by the bicubic method. In addition, the ESPCN method has a better performance than the bicubic method in areas with large gradients.