Computational fluid dynamics (CFD) plays an important role in the research of naval and ocean engineering. In recent years, the introduction of deep learning technology has brought new opportunities to fluid mechanics, which not only reduces the consumption of computational resources and time costs but also improves the accuracy of numerical simulations and experimental measurements. Nevertheless, recent works have attempted to incorporate temporal information into super-resolution reconstruction, most mainstream methods still focus on single-frame spatial reconstruction, failing to fully leverage the and spatiotemporal correlations of fluid flow. To address this issue, we propose a time-resolved three-dimensional flow-field reconstruction framework, called spatiotemporal feature interaction-based super-resolution transformer framework, based on a multi-domain fusion transformer. First, a multi-domain fusion transformer is designed to fuse the multi-scale features of the flow field by capturing different spatial and frequency-domain characteristics. Second, we employ a spatiotemporal feature interaction module to extract the inherent spatiotemporal relationships between consecutive flow fields. This allows the model to reconstruct a series of N high-resolution flow fields when fed with a continuous sequence of N low-resolution flow fields. In addition, corresponding turbulence super-resolution datasets are constructed for model training and testing, covering different types of fluid flows. Finally, to further verify the generalization performance of the model, a flow case around the National Advisory Committee for Aeronautics 0012 and 4412 hydrofoil are established based on CFD simulations. Experimental results show that the proposed time-resolved three-dimensional flow-field reconstruction framework not only achieves high-precision results in reconstructing various fluid fields but also obtains the optimal super-resolution reconstruction performance in the hydrofoil flow case, demonstrating excellent flow-field reconstruction capability and strong generalization.
Wang et al. (Thu,) studied this question.