Optical analysis of particle image velocimetry data

1990 ◽  
Author(s):  
Patrick V. Farrell ◽  
D. Goetsch
Keyword(s):  
Particle Image Velocimetry ◽  
Particle Image ◽  
Particle Image Velocimetry Data ◽  
Optical Analysis ◽  
Image Velocimetry

scholarly journals Correlation Analysis of High-Resolution Particle Image Velocimetry Data of Screeching Jets

AIAA Journal ◽  
2019 ◽  
Vol 57 (2) ◽  
pp. 735-748 ◽  
Author(s):  
D. J. Tan ◽  
D. Honnery ◽  
A. Kalyan ◽  
V. Gryazev ◽  
S. A. Karabasov ◽  
...  
Keyword(s):  
Particle Image Velocimetry ◽  
High Resolution ◽  
Correlation Analysis ◽  
Particle Image ◽  
Particle Image Velocimetry Data ◽  
Image Velocimetry

scholarly journals Data assimilation method to de-noise and de-filter particle image velocimetry data

2019 ◽  
Vol 877 ◽  
pp. 196-213 ◽  
Author(s):  
Jurriaan J. J. Gillissen ◽  
Roland Bouffanais ◽  
Dick K. P. Yue
Keyword(s):  
Particle Image Velocimetry ◽  
Data Assimilation ◽  
Particle Image ◽  
Particle Image Velocimetry Data ◽  
Reconstruction Error ◽  
Integration Time ◽  
Piv Measurement ◽  
Image Velocimetry ◽  
The Difference ◽  
Filter Particle

We present a variational data assimilation method in order to improve the accuracy of velocity fields $\tilde{\boldsymbol{v}}$, that are measured using particle image velocimetry (PIV). The method minimises the space–time integral of the difference between the reconstruction $\boldsymbol{u}$ and $\tilde{\boldsymbol{v}}$, under the constraint, that $\boldsymbol{u}$ satisfies conservation of mass and momentum. We apply the method to synthetic velocimetry data, in a two-dimensional turbulent flow, where realistic PIV noise is generated by computationally mimicking the PIV measurement process. The method performs optimally when the assimilation integration time is of the order of the flow correlation time. We interpret these results by comparing them to one-dimensional diffusion and advection problems, for which we derive analytical expressions for the reconstruction error.

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