Abstract

Tomographic velocimetry as a 3D technique has attracted substantial research interests in recent years due to the pressing need for investigations of complex turbulent flows, which are inherently inhomogeneous. However, tomographic velocimetry usually suffers from high experimental costs, especially due to the formidable expenses of multiple high-speed cameras and the excitation laser source. To overcome this limitation, a cost-effective technique called endoscopic tomographic velocimetry has been developed in this work. As a single-camera system, nine projections of the target 3D luminous field at consecutive time instants can be registered from different orientations with one camera and customized fiber bundles, while this is possible only with the same number of cameras in a classical tomographic velocimetry system. Extensive numerical simulations were conducted with three inversion algorithms and two velocity calculation methods. According to RMS error analysis, it was found that the algebraic reconstruction technique outperformed the other two inversion algorithms, and the 3D optical flow method exhibited a better performance than cross correlation in terms of both accuracy and noise immunity. Proof-of-concept experiments were also performed to validate our developed system. The results suggested that an average reconstruction error of the artificially generated 3D velocity field was less than 6%, indicating good performance of the velocimetry system. Although this technique was demonstrated by reconstructing continuous luminous fields, it can be easily extended to discrete ones, which are typically adopted in particle image velocimetry. This technique is promising not only for flow diagnostics but other research areas such as biomedical imaging.

© 2019 Optical Society of America

Quantification of tomographic PIV uncertainty using controlled experimental measurements

Ning Liu, Yue Wu, and Lin Ma
Appl. Opt. 57(3) 420-427 (2018)

Assessment of plenoptic imaging for reconstruction of 3D discrete and continuous luminous fields

Hecong Liu, Qianlong Wang, and Weiwei Cai
J. Opt. Soc. Am. A 36(2) 149-158 (2019)

3D particle field reconstruction method based on convolutional neural network for SAPIV

Xiangju Qu, Yang Song, Ying Jin, Zhenyan Guo, Zhenhua Li, and Anzhi He
Opt. Express 27(8) 11413-11434 (2019)

3D SAPIV particle field reconstruction method based on adaptive threshold

Xiangju Qu, Yang Song, Ying Jin, Zhenhua Li, Xuezhen Wang, ZhenYan Guo, Yunjing Ji, and Anzhi He
Appl. Opt. 57(7) 1622-1633 (2018)

Regularized tomographic PIV for incompressible flows based on conservation of mass

Ning Liu and Lin Ma
Appl. Opt. 59(6) 1667-1677 (2020)