High-resolution flow field recovery with pooling decomposition and vortex attention

In experimental measurements of flow fields, the acquired data often fail to adequately resolve the fine structural characteristics of complex flows due to limitations in current technical conditions and measurement techniques. This constraint is particularly pronounced in analyzing turbulent random fluctuations and identifying small-scale vortices, hindering in-depth investigations into flow field dynamics. To address this challenge, this study proposes a novel super-resolution reconstruction method for flow fields based on pooling decomposition and a vortex attention mechanism. By introducing an innovative attention mechanism for high-order flow field quantities, the model effectively focuses on vortices within the flow field and extracts high-order information to guide velocity field reconstruction. Simultaneously, a pooling decomposition strategy is employed to specifically learn the null-space components of the flow field while preserving range-space components to enhance reconstruction performance. Notably, the introduction of a physics-constrained loss function ensures kinetic energy spectrum consistency in the reconstructed results. Experimental results demonstrate that the proposed model significantly improves the accuracy of flow field data reconstruction, exhibiting outstanding performance in turbulent kinetic energy spectrum recovery and small-scale vortex reconstruction, along with excellent cross-condition generalization capability. This research provides a new technical approach to overcome the resolution limitations of experimental measurements and achieve high-precision analysis of complex flow structures.