An attention-mechanism incorporated deep recurrent optical flow network for particle image velocimetry
An attention-mechanism incorporated deep recurrent optical flow network for particle image velocimetry
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DOI：
10.1063/5.0155124
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发表时间：
2023-07
影响因子：
4.6
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中科院分区：
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粒子图像测速技术(PIV)作为实验流体力学中的一项关键技术，能够通过连续输入的粒子图像来估计复杂的速度场。在本研究中，在先前建立的递归全对场变换光流模型的基础上，提出了一种包含深层递归网络的注意机制--ARARFT-FlowNet。加入注意力模块，提高了网络对示踪粒子运动的识别能力。此外，还生成了一个参数数据集ParaPIV-DataSet，用于研究粒子直径、图像粒子密度、高斯噪声和峰值强度等粒子参数对深度学习网络的影响。对新模型的精度和泛化能力进行了全面的评价和分析。结果表明，ARIFT-FlowNet达到了最先进的性能。与以往的方法相比，ARARFT-FlowNet在柱状流、地面准地转流和DNS-湍流中的精度分别提高了62.9%、10.9%和9.4%。同时，该模型具有较强的泛化能力和较强的处理小尺度旋涡复杂流场的能力。此外，对实验湍流射流数据的测试表明，Araft-FlowNet能够处理具有亮度变化和噪声的真实PIV图像。
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.