Hybrid-attention-based Swin-Transformer super-resolution reconstruction for tomographic particle image velocimetry

Abstract

Research on three-dimensional (3D) flow velocity fields holds significant importance in aerodynamic performance design, energy power, and biomedicine. Nevertheless, current techniques for measuring three-dimensional flow velocity fields, such as tomographic particle image velocimetry (Tomo-PIV), have challenges in achieving accurate and high-resolution measurements of tiny structures in flow fields. Consequently, a 3D flow field super-resolution (SR) reconstruction method based on Swin-Transformer framework (SWINFlow-3D) has been proposed in this paper. SWINFlow-3D comprises stacked residual channel attention Swin-transformer blocks, each containing multiple Swin-Transformer standard layers, incorporating a hybrid attention mechanism that allows for integrating relevant information from several channels and gives greater importance to critical information. Second, a loss function for SR reconstruction of the flow field has been introduced, taking into account the physical constraints such as divergence and curl. Furthermore, the characteristics obtained by interpolation downsampling methods are different from those of real experiments. To address this limitation, we construct a dataset based on cross correlation downsampling. Simulation experiments are carried out on Johns Hopkins Turbulence Database isotropic turbulence data and cylindrical wake data. The results are subsequently compared with those of the interpolation approach and 3D flow field SR reconstruction method, and our model yields the best results for all the metrics. Ultimately, to ascertain the accuracy and practical applicability of the model in practical tests, we conduct experiments on jet data and cylindrical wake recorded by Tomo-PIV. The experimental results demonstrate that SWINFlow-3D with the loss function presented in this study can be used to effectively reconstruct the 3D flow field and flow features, exhibiting strong generalizability.