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Time-resolved particle image velocimetry (TR-PIV) is an advanced fluid mechanics experiment technology, which can simultaneously measure the velocity field from multi-frame images to analyze the evolution of fluid over time. Deep learning technology has made great progress in the field of particle image velocimetry (PIV). However, as far as we know, no deep learning method has been adopted to calculate the velocity field of TR-PIV images (i.e., TR-PIV). In this article, we propose a novel cascaded convolutional neural network (CNN) called CascLiteFlowNet-R-en for TR-PIV estimation task. Furthermore, to train and optimize the model, we generated a challenge TR-PIV dataset of multi-frame velocity fields. The velocity field here changes according to the frames to simulate the real fluid scene. Finally, the proposed model has been verified on synthetic and experimental particle images. The results show that our proposed method achieves excellent performance, with competitive calculation accuracy and high calculation efficiency.
Guo et al. (Sat,) studied this question.