Integrating Spatial and Temporal Modeling: A Deep Learning Framework for Super-Resolution Reconstruction and Prediction of Fluid Flows

In numerical simulations and experimental observations, flow field data are often limited by insufficient spatial resolution and incomplete time series, which in turn affect the accurate capture and modeling of flow structures and their evolution processes. To address this challenge, this paper proposes an end-to-end high-resolution flow field prediction framework that can rely solely on low-resolution input data to reconstruct the fine spatial structure at the current moment and predict the high-resolution evolution state at future moments. The framework consists of three types of deep neural network modules working together: the U-Net-based super-resolution reconstruction module (U-Net-SR) implements high-resolution spatial reconstruction at the current moment, the long short-term memory module (LSTM) models the temporal evolution trend of low-resolution sequences, and the U-Net-based multi-source fusion prediction module (U-Net-Pred) integrates the information from both to achieve accurate prediction of the highresolution flow field at future moments. This method is validated in three typical flow field tasks: laminar flow around a cylinder, laminar flow over a controlled pitch airfoil, and experimental turbulent flow field under cross-wind gusts passing through a flat plate airfoil. In multiple typical flow field tasks, the framework demonstrates excellent spatiotemporal modeling capabilities. Overall, the average Peak Signal-to-Noise Ratio(PSNR) of the prediction results generally remains in the range of 40–48 dB, and the Structural Similarity Index Measure(SSIM) index is mostly stable above 0.95, accurately capturing key structural features and maintaining good temporal evolution consistency. Compared with traditional single-path models that only handle spatial reconstruction or temporal prediction, the innovation of this framework lies in its dual U-Net collaborative mechanism: U-Net-SR provides the highresolution state at the current moment as a spatial benchmark, LSTM captures the overall evolution trend of the flow field, and U-Net-Pred completes cross-scale fusion and information collaboration in the feature space, thereby achieving better super-resolution prediction.