Developing particle image velocimetry software based on a deep neural network. Majewski, W., Wei, R., & Kumar, V. Journal of Flow Visualization and Image Processing, 27(4):359-376, Begell House Inc., 2020. ![Developing particle image velocimetry software based on a deep neural network [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 12 downloads As an experimental technique for fluid mechanics, particle image velocimetry (PIV) can extract global and quantitative velocity field from images. With the development of artificial intelligence, designing PIV method based on deep learning is quite promising and worth exploring. First, in this paper, the authors introduce the optical flow neural network based on one proposed in the computer vision community. Second, a data set including particle images and the ground truth fluid motion is generated to train the parameters of the networks. This leads to a deep neural network for PIV which can provide estimation of dense motion (down to maximum one vector for one pixel) with the high degree of efficiency. The featuring of particle image extracted by the neural network is also investigated in this paper. It is found that feature matching improves the accuracy of esti-mation. The proposed network model is firstly evaluated by a synthetic image sequence of turbulent flow. An experiment measuring the flow over an aerofoil is used to validate the practicability. The experimental results indicate that compared with the traditional cross correlation method, the proposed deep neural network has advantages in accuracy, spatial resolution, and efficiency.
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abstract = {As an experimental technique for fluid mechanics, particle image velocimetry (PIV) can extract global and quantitative velocity field from images. With the development of artificial intelligence, designing PIV method based on deep learning is quite promising and worth exploring. First, in this paper, the authors introduce the optical flow neural network based on one proposed in the computer vision community. Second, a data set including particle images and the ground truth fluid motion is generated to train the parameters of the networks. This leads to a deep neural network for PIV which can provide estimation of dense motion (down to maximum one vector for one pixel) with the high degree of efficiency. The featuring of particle image extracted by the neural network is also investigated in this paper. It is found that feature matching improves the accuracy of esti-mation. The proposed network model is firstly evaluated by a synthetic image sequence of turbulent flow. An experiment measuring the flow over an aerofoil is used to validate the practicability. The experimental results indicate that compared with the traditional cross correlation method, the proposed deep neural network has advantages in accuracy, spatial resolution, and efficiency.},
bibtype = {article},
author = {Majewski, Wojciech and Wei, Runjie and Kumar, Vivek},
doi = {10.1615/JFlowVisImageProc.2020033180},
journal = {Journal of Flow Visualization and Image Processing},
number = {4}
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Downloads: 12
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