Abstract

Particle image velocimetry (PIV), as a widely used technique for measuring flow velocity fields and characterizing flow behavior, is widely used across various fluid mechanics applications, such as aerodynamics and fluid mechanics. However, the existing PIV methods focus on spatial resolution, i.e., the ability to extract small-scale motions, while ignoring the situation of large-scale flow motion. In this paper, a novel deep learning framework called Transformer is introduced, which is based on the self-attention mechanism and can capture global information. First, in order to enhance the ability to sense location information, convolutional neural network (CNN) is utilized to extract features from original particle images. Second, self-attention mechanism is applied to capture global information based on a set of image features. Third, velocity fields of the particle images are estimated in the encoder-decoder network. Finally, a number of both synthetic and experimental particle images are used to verify the proposed method. The experimental results indicate that the proposed method is superior to cross-correlation and optical flow methods on measurement accuracy. Moreover, it has better performance than traditional deep learning-based PIV methods on large-scale flow motion estimation.

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