scholarly journals On the feasibility of selective spatial correlation to accelerate convergence of PIV image analysis based on confidence statistics

2020 ◽  
Vol 61 (10) ◽  
Author(s):  
M. Edwards ◽  
R. Theunissen ◽  
C. B. Allen ◽  
D. J. Poole
Keyword(s):  
Cross Correlation ◽  
Particle Image ◽  
Field Of View ◽  
Steady Flows ◽  
Spatial Displacement ◽  
Temporal Fluctuations ◽  
Correlation Operator ◽  
Image Velocimetry ◽  
Accelerate Convergence ◽  
Image Pairs

Abstract This paper presents a method which allows for a reduced portion of a particle image velocimetry (PIV) image to be analysed, without introducing numerical artefacts near the edges of the reduced region. Based on confidence intervals of statistics of interest, such a region can be determined automatically depending on user-imposed confidence requirements, allowing for already satisfactorily converged regions of the field of view to be neglected in further analysis, offering significant computational benefits. Temporal fluctuations of the flow are unavoidable even for very steady flows, and the magnitude of such fluctuations will naturally vary over the domain. Moreover, the non-linear modulation effects of the cross-correlation operator exacerbate the perceived temporal fluctuations in regions of strong spatial displacement gradients. It follows, therefore, that steady, uniform, flow regions will require fewer contributing images than their less steady, spatially fluctuating, counterparts within the same field of view, and hence the further analysis of image pairs may be solely driven by small, isolated, non-converged regions. In this paper, a methodology is presented which allows these non-converged regions to be identified and subsequently analysed in isolation from the rest of the image, while ensuring that such localised analysis is not adversely affected by the reduced analysis region, i.e. does not introduce boundary effects, thus accelerating the analysis procedure considerably. Via experimental analysis, it is shown that under typical conditions a 44% reduction in the required number of correlations for an ensemble solution is achieved, compared to conventional image processing routines while maintaining a specified level of confidence over the domain. Graphic abstract

Influence of Concentration and Number of Image Pairs in μ-PIV Experiments

2007 ◽  
Author(s):  
Stefano Chiavaroli ◽  
David Newport ◽  
Gian Luca Morini ◽  
Christine Barrot Lattes ◽  
Lucien Baldas ◽  
...  
Keyword(s):  
Processing Time ◽  
Cross Correlation ◽  
Particle Concentration ◽  
Particle Image ◽  
Accurate Result ◽  
Velocity Measurements ◽  
Micro Particle Image Velocimetry ◽  
Image Velocimetry ◽  
Micro Flow ◽  
Image Pairs

Micro Particle Image Velocimetry (μ-PIV) is a non-intrusive technique widely used nowadays to experimentally obtain the velocity field of a micro flow. The main goal of this research was to examine the influence of particle concentration and the number of images acquired, on the accuracy of the μ-PIV velocity measurement. For this reason, a comparison between experimental and analytical values was made. It has been demonstrated that the influence of the seeding concentration on the accuracy of the velocity measurements, into the investigated range, can be considered insignificant. On the other hand, the number of images selected for the cross-correlation is more important for the accuracy of the measurements. By increasing the quantity of images processed it is possible to artificially increase the seeding concentration and reduce the scatter. However, this considerably increases the processing time for the experiment. A trade-off is required between obtaining a highly accurate result without losing precious experimental down time. When the range of the concentration is fixed, it is possible to set the maximum inaccuracy allowance tolerated for the experiment. There is a compromise between a better precision and adequate time to process the data.

PIV Measurements Within the Waves of Wavy and Wavy-Annular Horizontal Two-Phase Flow

2005 ◽  
Author(s):  
R. E. Foster ◽  
T. A. Shedd
Keyword(s):  
Particle Image ◽  
Image Processing Algorithm ◽  
Two Phase ◽  
Piv Measurements ◽  
Post Process ◽  
Image Velocimetry ◽  
Processed Form ◽  
Image Pairs ◽  
Particle Images ◽  
The Waves

A novel technique of microscopic Particle Image Velocimetry (PIV) is presented for two-phase annular, wavy-annular and stratified flow. Seeding of opaque particles in a water/dye flow allows the acquisition of instantaneous film velocity data in the film cross-section at the center of the tube in the form of digital image pairs. An image processing algorithm is also described that allows numerical velocities to be distilled from particle images by commercial PIV software. The approach yields promising results for stratified and wavy-annular flows, however highly bubbly flows remain difficult to image and post-process. Initial data images are presented in raw and processed form.

A New Image-Based Soil Deformation Measurement System

2012 ◽  
Vol 629 ◽  
pp. 488-492
Author(s):  
Yan Jiao Zhao ◽  
Yu Xin Wang ◽  
Guo He ◽  
Hong Hua Zhao
Keyword(s):  
Neural Network ◽  
Measurement System ◽  
Cross Correlation ◽  
Particle Image ◽  
Research Field ◽  
Deformation Measurement ◽  
Practical Significance ◽  
Soil Deformation ◽  
Correlation Algorithm ◽  
Image Velocimetry

A Soil Deformation Measurement System using OPENCV library and FFTW library in C++ was developed in this paper. The system applied camera calibration based on neural network and Fasst Fourier Transform (FFT) cross-correlation algorithm for Particle Image Velocimetry (PIV). It is used to obtain soil deformation data, such as displacements, velocity and strain, and visualize the deformation. Experiments show that this system could acquire deformation data from soil images accurately, efficiently and continuously, which provides a strong proof that image processing technology has practical significance and application value in the research field of geotechnical engineering.

Applications of Micro-PIV Technique in Visualization of Flow in Microspace

2007 ◽  
Author(s):  
Ruijin Wang ◽  
Jianzhong Lin ◽  
Yifeng Wang
Keyword(s):  
Cross Correlation ◽  
Particle Image ◽  
Characteristic Length ◽  
Tracer Particle ◽  
Image Process ◽  
Micro Piv ◽  
Correlation Algorithm ◽  
Piv Technique ◽  
Image Velocimetry ◽  
Brown Motion

A micro-resolution particle image velocimetry (micro-PIV) technique for flow visualization in microspace is presented here. The micro-PIV system was constructed through adding an epi-fluorescence microscope, improving the light source and choosing suitable tracing particle. According to smaller characteristic length of the flow in microscale and higher precision prolepsis, an image process technique based on cross correlation algorithm was conducted. To eliminate the main error caused by Brown motion of tracer particle, an approach by averaging the velocities of the ensemble particles in same interrogation plot was brought forward. Micro-PIV measure-ments of three typical flows (in a micromixer, near barriers and in a micro-jet) were carried out. The experimental results show that the micro-PIV system is suitable to both steady and unsteady flow in microscale. It is helpful to design micro-devices and analysis on data collected from such micro-devices.

Particle Image Velocimetry: Automatic Fringe Analysis By Cross-Correlation

1988 ◽  
Vol 27 (3) ◽  
Author(s):  
Atsushi Kirita ◽  
Christopher J. D. Pickering ◽  
Neil A. Halliwell
Keyword(s):  
Particle Image Velocimetry ◽  
Cross Correlation ◽  
Particle Image ◽  
Fringe Analysis ◽  
Image Velocimetry
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