New insights into particle image velocimetry data using fuzzy-logic-based correlation/particle tracking processing

2001 ◽  
Vol 30 (4) ◽  
pp. 434-447 ◽  
Author(s):  
M. P. Wernet
Keyword(s):  
Fuzzy Logic ◽  
Particle Image Velocimetry ◽  
Particle Tracking ◽  
Particle Image ◽  
Particle Image Velocimetry Data ◽  
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

Experimental Observation of Convection during Equiaxed Solidification of Transparent Alloys

2006 ◽  
Vol 508 ◽  
pp. 157-162 ◽  
Author(s):  
Sven Eck ◽  
J.P. Mogeritsch ◽  
Andreas Ludwig
Keyword(s):  
Particle Image Velocimetry ◽  
Size Distribution ◽  
Particle Tracking ◽  
Sedimentation Rate ◽  
Particle Image ◽  
Number Density ◽  
Experimental Conditions ◽  
Equiaxed Solidification ◽  
Image Velocimetry ◽  
Experimental Runs

3D samples of NH4Cl-H2O solutions were solidified under defined experimental conditions. The occurring melt convection was investigated by Particle Image Velocimetry (PIV). The occurrence of NH4Cl crystals was observed optically and first attempts were made to quantitatively measure its number density, size distribution and sedimentation rate by PIV and Particle Tracking (PT). In order to prove the reproducibility of the results several experimental runs with equal and slightly modified conditions were analyzed.

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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