Visual inference of flow flux via free surface PDE model and image sequence assimilation

Guo, Sheng; Xu, Chao; Yiu, Ka Fai Cedric

doi:10.1049/iet-csr.2018.0002

Cited by 1 publication

(1 citation statement)

References 38 publications

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“…swimming and flapping), special structures and kinematic strategies have been evolved to generate propulsion and navigate complex flow fields, which is associated with evolutionary advantages including energy savings [2][3][4][5] and flow enhancement [6]. Visualisation of the complex fluid media [7] induced by the biological locomotion is a promising way to yield intriguing insight into the physical mechanisms of biological intelligence. The discovered physical mechanisms can help to guide the design of intelligent biologically inspired robots [8,9].…”

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confidence: 99%

Filtering enhanced tomographic PIV reconstruction based on deep neural networks

Liang

Cai

et al. 2020

IET cyber-systems robotics

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Tomographic particle image velocimetry (Tomo‐PIV) has been successfully applied in measuring three‐dimensional (3D) flow field in recent years. Such technology highly relies on the reconstruction technique which provides the spatial particle distribution by using images from multiple cameras at different viewing angles. As the most popular reconstruction method, the multiplicative algebraic reconstruction technique (MART) has advantages in high computational speed and high accuracy for low particle seeding reconstruction. However, the accuracy is not satisfactory in the case of dense particle distributions to be reconstructed. To overcome this problem, a symmetric encode–decoder fully convolutional network is proposed in this paper to improve the reconstruction quality of MART. The input of the neural network is the particle field reconstructed by the MART approach, while the output is the regenerated image with the same resolution. Numerical evaluations indicate that those blurred or irregular particles can be significantly refined by the trained neural network. Most of the ghost particles can also be removed by this filtering method. The reconstruction accuracy can be improved by more than 10% without increasing the computational cost. Experimental evaluations indicate that the trained neural network can also provide similar satisfactory reconstruction and improved velocity fields.

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