Spatiotemporal super-resolution forecasting of high-speed turbulent flows

This paper implements a spatiotemporal neural network architecture based on the U-Net prototype with four branches, UBranch, to perform both spatial reconstruction and temporal forecasting of flow fields. A high-speed turbulent flow featuring shock-wave turbulent boundary layer interaction is utilized to demonstrate the forecasting in two-dimensional flow frames. The main elements of UBranch consist of convolutional neural networks, which are fast and lightweight for such functions, in a form that bypasses the use of complex and time-consuming long-short-term memory networks. The proposed model can provide the following four future time frames when fed with a sequence of two-dimensional flow images with reasonable accuracy and low root mean square error, and, in parallel, it can indicate the maximum pressure points, which is of primary importance for shock-wave turbulent boundary layer interaction. Apart from the temporal operation, UBranch can also perform spatial super-resolution tasks, reconstructing a low-resolution image to a finer field with increased accuracy. Calculated peak signal-to-noise ratios reach 29.0 for spatiotemporal and 35.0 for spatial-only tasks.