As a practical and effective tool for quantitative in-plane deformation measurement of a planar object surface, two-dimensional digital image correlation (2D DIC) is now widely accepted and commonly used in the field of experimental mechanics. It directly provides full-field displacements to sub-pixel accuracy and full-field strains by comparing the digital images of a test object surface acquired before and after deformation. In this review, methodologies of the 2D DIC technique for displacement field measurement and strain field estimation are systematically reviewed and discussed. Detailed analyses of the measurement accuracy considering the influences of both experimental conditions and algorithm details are provided. Measures for achieving high accuracy deformation measurement using the 2D DIC technique are also recommended. Since microscale and nanoscale deformation measurement can easily be realized by combining the 2D DIC technique with high-spatial-resolution microscopes, the 2D DIC technique should find more applications in broad areas.

https://iopscience.iop.org/article/10.1088/0957-0233/20/6/062001/pdf

Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review

Digital particle image velocimetry (DPIV) is a non-intrusive analysis technique that is very popular for mapping flows quantitatively. To get accurate results, in particular in complex flow fields, a number of challenges have to be faced and solved: The quality of the flow measurements is affected by computational details such as image pre-conditioning, sub-pixel peak estimators, data validation procedures, interpolation algorithms and smoothing methods. The accuracy of several algorithms was determined and the best performing methods were implemented in a user-friendly open-source tool for performing DPIV flow analysis in Matlab.

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PIVlab – Towards User-friendly, Affordable and Accurate Digital Particle Image Velocimetry in MATLAB

Image processing methods for particle image velocimetry (PIV) are reviewed. The discussion focuses on iterative methods aimed at enhancing the precision and spatial resolution of numerical interrogation schemes. Emphasis is placed on the efforts made to overcome the limitations of the correlation interrogation method with respect to typical problems such as in-plane loss of pairs, velocity gradient compensation and correlation peak locking. The discussion shows how the correlation signal benefits from simple operations such as the window-offset technique, or the continuous window deformation, which compensates for the in-plane velocity gradient. The image interrogation process is presented within the discussion of the image matching problem and several algorithms and implementations currently in use are classified depending on the choice made about the particle image pattern matching scheme. Several methods that differ in their implementations are found to be substantially similar. Iterative image deformation methods that account for the continuous particle image pattern transformation are analysed and the effect of crucial choices such as image interpolation method, displacement prediction correction and correlation peak fit scheme are discussed. The quantitative performance assessment made through synthetic PIV images yields order-of-magnitude improvement on the precision of the particle image displacement at a sub-pixel level when the image deformation is applied. Moreover, the issue of spatial resolution is addressed and the limiting factors of the specific interrogation methods are discussed. Finally, an attempt for a flow-adaptive spatial resolution method is proposed. The method takes into account the local velocity derivatives in order to perform a local increase of the interrogation spatial density and a refinement of the local interrogation window size. The resulting spatial resolution is selectively enhanced. The method's performance is analysed and compared with some precursor techniques, namely the conventional cross-correlation analysis with and without the effect of a window discrete offset and deformation. The suitability of the method for the measurement in turbulent flows is illustrated with the application to a turbulent backward facing step flow.

https://iopscience.iop.org/article/10.1088/0957-0233/13/1/201/pdf

Iterative image deformation methods in PIV

Sub-pixel precision image matching for measuring surface displacements on mass movements using normalized cross-correlation

A novel method is presented to quantify the uncertainty of PIV data. The approach is a posteriori, i.e. the unknown actual error of the measured velocity field is estimated using the velocity field itself as input along with the original images. The principle of the method relies on the concept of super-resolution: the image pair is matched according to the cross-correlation analysis and the residual distance between matched particle image pairs (particle disparity vector) due to incomplete match between the two exposures is measured. The ensemble of disparity vectors within the interrogation window is analyzed statistically. The dispersion of the disparity vector returns the estimate of the random error, whereas the mean value of the disparity indicates the occurrence of a systematic error. The validity of the working principle is first demonstrated via Monte Carlo simulations. Two different interrogation algorithms are considered, namely the cross-correlation with discrete window offset and the multi-pass with window deformation. In the simulated recordings, the effects of particle image displacement, its gradient, out-of-plane motion, seeding density and particle image diameter are considered. In all cases good agreement is retrieved, indicating that the error estimator is able to follow the trend of the actual error with satisfactory precision. Experiments where time-resolved PIV data are available are used to prove the concept under realistic measurement conditions. In this case the ‘exact’ velocity field is unknown; however a high accuracy estimate is obtained with an advanced interrogation algorithm that exploits the redundant information of highly temporally oversampled data (pyramid correlation, Sciacchitano et al (2012 Exp. Fluids 53 1087–105)). The image-matching estimator returns the instantaneous distribution of the estimated velocity measurement error. The spatial distribution compares very well with that of the actual error with maxima in the highly sheared regions and in the 3D turbulent regions. The high level of correlation between the estimated error and the actual error indicates that this new approach can be utilized to directly infer the measurement uncertainty from PIV data. A procedure is shown where the results of the error estimation are employed to minimize the measurement uncertainty by selecting the optimal interrogation window size.

https://iopscience.iop.org/article/10.1088/0957-0233/24/4/045302/pdf

PIV uncertainty quantification by image matching

The goal of the present study is to quantify and reduce, when possible, errors in two-dimensional digital particle image velocimetry (DPIV). Two major errors, namely the mean bias and root-mean-square (RMS) errors, have been studied. One fundamental source of these errors arises from the implementation of cross correlation (CC). Other major sources of these errors arise from the peak-finding scheme, which locates the correlation peak with a sub-pixel accuracy, and noise within the particle images. Two processing techniques are used to extract the particle displacements. First, a CC method utilizing the FFT algorithm for fast processing is implemented. Second, a particle image pattern matching (PIPM) technique, usually requiring a direct computation and therefore more time consuming, is used. Using DPIV on simulated images, both the mean-bias and RMS errors have been found to be of the order of 0.1 pixels for CC. The errors of PIPM are about an order of magnitude less than those of CC. In the present paper the authors introduce a peak-normalization method which reduces the error level of CC to that of PIPM without adding much computational effort. A peak-compensation technique is also introduced to make the mean-bias error negligible in comparison with the RMS error. Noise in an image suppresses the mean-bias error but, on the other hand, significantly amplifies the RMS error. A digital video signal usually has a lower noise level than that of an analogue one and therefore provides a smaller error in DPIV.

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H Huang

Papers

On errors of digital particle image velocimetry