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Particle image velocimetry (PIV), as a key technique in experimental fluid mechanics, is able to estimate complex velocity field through consecutive input particle images. In this study, an attention-mechanism incorporated deep recurrent network called ARaft-FlowNet has been proposed, on the basis of a previously established Recurrent All-Pairs Field Transforms optical flow model. The attention module is added to improve the network's capability of recognizing tracer particles' motion. Moreover, a parameterized dataset, ParaPIV-Dataset, is generated to explore the influence of particle parameters on deep learning networks, including particle diameter, image particle density, Gaussian noise, and peak intensity. The accuracy and generalizability of the newly proposed model has been evaluated and analyzed comprehensively. The results indicate that ARaft-FlowNet achieves state-of-the-art performance. Compared to previous methods, ARaft-FlowNet shows an accuracy improvement of 62.9%, 10.9%, and 9.4% in cylindrical flow, surface quasi-geostrophic flow, and DNS-turbulence flow. Meanwhile, the proposed model shows the strongest generalization and best capability to deal with complex flow fields with small-scale vortices. Additionally, tests on experimental turbulent jet data reveal that ARaft-FlowNet is able to deal with real PIV images with brightness variations and noise.
Han et al. (Sat,) studied this question.