A high-resolution velocity field predicted method in near wall boundary layer by CNN

A high-resolution velocity field prediction method in the boundary layer based on CNN-PTV is designed and verified in this paper. This method includes two stages, training process and prediction process. In the training process, a CNN model is trained by the particle image pairs. By optimizing the parameters, the exact ensemble particle movements are predicted by the CNN model. In the prediction process, a synthetic image including just a single particle is imported to the CNN model to estimate the flow information at this particular pixel space. Thus, a high-resolution velocity field is predicted. Comparing with the single-pixel ensemble correlation method, the CNN-PTV method has a higher precision. And the results of CNN-PTV method is insensitive to the frame numbers and particle density.