PIV for granular flows

 Particle image velocimetry (PIV) has been adapted for use in measuring particle displacement and velocity fields in granular flows. “Seeding” is achieved by using light and dark particles. The granular flow adjacent to a clear bounding wall is illuminated with a strobe, and the recorded images are analyzed using standard PIV techniques. The application is demonstrated by measuring convection rolls in a granular bed undergoing vertical oscillations. The PIV measured displacement is consistent with displacement of a marked layer of particles. Received: 29 January 1998/Accepted: 8 April 1999     

Intensity Capping: a simple method to improve cross-correlation PIV results

A common source of error in particle image velocimetry (PIV) is the presence of bright spots within the images. These bright spots are characterized by grayscale intensities much greater than the mean intensity of the image and are typically generated by intense scattering from seed particles. The displacement of bright spots can dominate the cross-correlation calculation within an interrogation window, and may thereby bias the resulting velocity vector. An efficient and easy-to-implement image-enhancement procedure is described to improve PIV results when bright spots are present. The procedure, called Intensity Capping, imposes a user-specified upper limit to the grayscale intensity of the images. The displacement calculation then better represents the displacement of all particles in an interrogation window and the bias due to bright spots is reduced. Four PIV codes and a large set of experimental and simulated images were used to evaluate the performance of Intensity Capping. The results indicate that Intensity Capping can significantly increase the number of valid vectors from experimental image pairs and reduce displacement error in the analysis of simulated images. A comparison with other PIV image-enhancement techniques shows that Intensity Capping offers competitive performance, low computational cost, ease of implementation, and minimal modification to the images.     

The influence of peak-locking errors on turbulence statistics computed from PIV ensembles

The influence of peak-locking errors on turbulence statistics computed from ensembles of PIV data is considered. PIV measurements are made in the streamwise–wall-normal plane of turbulent channel flow. The PIV images are interrogated in three distinct ways, generating ensembles of velocity fields with absolute, moderate, and minimal peak locking. Turbulence statistics computed for all three ensembles of data indicate a general sensitivity to peak locking in the single-point statistics, except for the mean velocity profile. Peak-locking errors propagate into the fluctuations of velocity, rendering single-point statistics inaccurate when severe peak locking is present. Multi-point correlations of both streamwise and wall-normal velocity are also found to be influenced by severe levels of peak locking. The displacement range of the measurement, defined by the PIV time delay, appears to affect the influence of peak-locking errors on turbulence statistics. Smaller displacement ranges, particularly those that produce displacement fluctuations that are less than one pixel in magnitude, yield inaccurate turbulence statistics in the presence of peak locking.     

A kilohertz frame rate cinemagraphic PIV system for laboratory-scale turbulent and unsteady flows

A kilohertz frame rate cinemagraphic particle image velocimetry (PIV) system has been developed for acquiring time-resolved image sequences of laboratory-scale gas and liquid-phase turbulent flows. Up to 8000 instantaneous PIV images per second are obtained, with sequence lengths exceeding 4000 images. The two-frame cross-correlation method employed precludes directional ambiguity and has a higher signal-to-noise ratio than single-frame autocorrelation or cross-correlation methods, facilitating acquisition of long uninterrupted sequences of valid PIV images. Low and high velocities can be measured simultaneously with similar accuracy by adaptively cross-correlating images with the appropriate time delay. Seed particle illumination is provided by two frequency-doubled Nd:YAG lasers producing Q-switched pulses at the camera frame rate. PIV images are acquired using a 16 mm high-speed rotating prism camera. Frame-to-frame registration is accomplished by imaging two pairs of crossed lines onto each frame and aligning the digitized image sequence to these markers using image processing algorithms. No flow disturbance is created by the markers because only their image is projected to the PIV imaging plane, with the physical projection device residing outside the flow field. The frame-to-frame alignment uncertainty contributes 2% to the overall velocity measurement uncertainty, which is otherwise comparable to similar film-based PIV methods. Received: 11 July 2000 / Accepted: 21 June 2001 Published online: 29 November 2001     

An extension of digital PIV-processing to double-exposed images

 In this paper digital processing techniques for PIV (Partical Image Velocimetry) using double-exposed particle images have been studied. It has been found that a pattern matching technique is significantly superior to the traditional autocorrelation method in the case that a large particle displacement between the double exposures is present on the image. In PIV using double-exposed images, the image shifting technique is usually used to solve the directional ambiguity problem. The performance of PIV using autocorrelation technique is dependent on the flow speed and the amount of image shift applied. This dependence, for example, causes a difficulty of autocorrelation in flows close to a solid boundary. The present study shows that a pattern matching technique eliminates such a difficulty. At the same signal-to-noise ratio, the pattern matching techndique has a better spatial resolution than that of autocorrelation. In concert with the pattern matching technique, PID (Particle Image Distortion) can be applied to double-exposed images, further improving the reliability and accuracy of velocity estimates of PIV in the presence of large velocity gradients. Generally speaking, PIP-matching and PID extend the validity of PIV using double-exposed images. The total processing time required by the PIV using the pattern matching technique and one PID iteration is of the same order as that required by the PIV using autocorrelation. Received: 7 July 1995 / Accepted: 11 September 1997     

Two-color digital PIV employing a single CCD camera

 An extension of two color particle image velocimetry (PIV) is described where the color images are recorded onto a single high-resolution (3060×2036 pixel) color CCD sensor. Unlike mono-color CCD sensors, this system not only eliminates the processing time and the subsequent digitization time of film-based PIV but also resolves the directional ambiguity of the velocity vector without using conventional image-shifting techniques. For comparing the spatial resolutions of film and CCD data, a calibration experiment is conducted by recording the speckle pattern onto 35 mm color film and using a CCD sensor under identical conditions. This technique has been successfully implemented for simulated turbine film-cooling flows in order to obtain a more detailed characterization of the coolant-injection phenomenon and its interaction with freestream disturbances. Received: 20 November 1996/Accepted: 29 January 1998     

On the accuracy of a MJPEG-based digital image compression PIV-system

 In this communication, the Digital Image Compression (DIC) – PIV system is introduced. The present system allows the measurement of mean and RMS velocities in turbulent flow fields, using JPEG digital image compression technique for on-line recording of thousands of images. The decompression and subsequent analysis of the images, performed by means of digital cross-correlation technique, is carried out off-line. Errors incurred by the application of the compression method are assessed and discussed. The effect of the compression is firstly analysed by linearly traversing (synthetic) computer-generated PIV-images at constant velocity. Secondly, accurate LDA measurements and data from direct numerical simulation (DNS) are used as a basis for the analysis in a low Reynolds number open water channel flow. The results show that excellent agreement between LDA and DIC–PIV measurements for mean and RMS velocities can be achieved using a compression factor up to 12. Received: 27 August 1996 / Accepted: 15 December 1998     

PIV measurement of flow around an arbitrarily moving body

This paper presents a PIV (particle image velocimetry) image processing method for measuring flow velocities around an arbitrarily moving body. This image processing technique uses a contour-texture analysis based on user-defined textons to determine the arbitrarily moving interface in the particle images. After the interface tracking procedure is performed, the particle images near the interface are transformed into Cartesian coordinates that are related to the distance from the interface. This transformed image always has a straight interface, so the interrogation windows can easily be arranged at certain distances from the interface. Accurate measurements near the interface can then be achieved by applying the window deformation algorithm in concert with PIV/IG (interface gradiometry). The displacement of each window is evaluated by using the window deformation algorithm and was found to result in acceptable errors except for the border windows. Quantitative evaluations of this method were performed by applying it to computer-generated images and actual PIV measurements.     

Spatial resolution of PIV for the measurement of turbulence

Recent technological advancements have made the use of particle image velocimetry (PIV) more widespread for studying turbulent flows over a wide range of scales. Although PIV does not threaten to make obsolete more mature techniques, such as hot-wire anemometry (HWA), it is justifiably becoming an increasingly important tool for turbulence research. This paper assesses the ability of PIV to resolve all relevant scales in a classical turbulent flow, namely grid turbulence, via a comparison with theoretical predictions as well as HWA measurements. Particular attention is given to the statistical convergence of mean turbulent quantities and the spatial resolution of PIV. An analytical method is developed to quantify and correct for the effect of the finite spatial resolution of PIV measurements. While the present uncorrected PIV results largely underestimate the mean turbulent kinetic energy and energy dissipation rate, the corrected measurements agree to a close approximation with the HWA data. The transport equation for the second-order structure function in grid turbulence is used to establish the range of scales affected by the limited resolution. The results show that PIV, due to the geometry of its sensing domain, must meet slightly more stringent requirements in terms of resolution, compared with HWA, in order to provide reliable measurements in turbulence.

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Background extraction from double-frame PIV images

This study presents a simple image pre-processing scheme to extract background information from double-frame particle image velocimetry (PIV) images. Everything that stays stagnant in the image (e.g., image background and light reflections from stationary objects) is assumed to be a source of disturbance and is removed by subtracting the second frame of the image pair from the first frame. This yields a single frame difference image, which is transferred back to a background extracted double-frame image. After the procedure the background in the image does not correlate with itself anymore and therefore a bias error in PIV analysis towards zero displacement is avoided. The simulations show that the procedure conserves the profile of tracer particle images when a displacement between the frames is larger than a particle image size. The performance of this procedure is emphasized with various examples, and extensions of the procedure are introduced. The extended procedure extracts background objects that move between the image frames, e.g. dispersed phase particles in a two-phase flow or laser light sheet reflections from moving objects.     

Coherent structure identification from wavelet analysis of particle image velocimetry data

 A new technique based on wavelet transform is applied to bidimensional velocity fields obtained by particle image velocimetry (PIV) measurements, in order to extract and characterize swirling motion associated with coherent structures. The proposed technique is based on the selectivity property of the wavelet transform and permits the detection of regions of the flow field associated with coherent structures and their spatial localization. Furthermore, being the method based on the analysis of the local energy content at separated scales, it is possible to extract the typical wavenumber associated with structures and therefore the typical length-scale. The procedure is validated by the application to velocity vector fields obtained from PIV measurements in different flow conditions and turbulence levels. Results are compared with those obtained by other more standard procedures, and the advantages and limitations of the proposed method are then discussed. Received: 16 October 2000 / Accepted: 18 June 2001 Published online: 29 November 2001     

Measurement of fully-developed turbulent pipe flow with digital particle image velocimetry

A new and unique high-resolution image acquisition system for digital particle image velocimetry (DPIV) in turbulent flows is used for the measurement of fully-developed turbulent pipe flow at a Reynolds number of 5300. The flow conditions of the pipe flow match those of a direct numerical simulation (DNS) and of measurements with conventional (viz., photographic) PIV and with laser-Doppler velocimetry (LDV). This experiment allows a direct and detailed comparison of the conventional and digital implementations of the PIV method for a non-trivial unsteady flow. The results for the turbulence statistics and power spectra show that the level of accuracy for DPIV is comparable to that of conventional PIV, despite a considerable difference in the interrogation pixel resolution, i.e. 32 × 32 (DPIV) versus 256 × 256 (PIV). This result is in agreement with an earlier analytical prediction for the measurement accuracy. One of the advantages of DPIV over conventional PIV is that the interrogation of the DPIV images takes only a fraction of the time needed for the interrogation of the PIV photographs.     

Analysis of interpolation schemes for image deformation methods in PIV: effect of noise on the accuracy and spatial resolution

As testified by a previous article (Astarita and Cardone in Exp Fluids 38:233–243, 2005), a critical point that can influence significantly the accuracy of image deformation methods (IDM) for particle image velocimetry (PIV) is the interpolation scheme (IS) used in the reconstruction of deformed images. In the cited paper the effect of noise has been neglected and for this reason in this follow-up paper the influence of the IS, in the presence of noise, on both accuracy and spatial resolution is studied. Performance assessment is conducted using synthetic images with particles of Gaussian shape and with constant and sinusoidal displacement fields. Both the local and the top hat moving average approaches are investigated and the modulation transfer function, the total and bias errors have been used to evaluate the performances of IDMs for PIV applications. The results show that, when a high noise level is present in the images, the influence of the IS is less relevant than what was shown by Astarita and Cardone (Exp Fluids 38:233–243, 2005).     

Error quantification of 3D homogeneous and isotropic turbulence measurements using 2D PIV

Particle image velocimetry (PIV) has become a popular non-intrusive tool for measuring various types of flows. However, when measuring three dimensional flows with 2D PIV, there is inherent measurement error due to out-of-plane motion. Errors in the measured velocity field propagate to turbulence statistics. Since this can distort the overall flow characteristics, it is important to understand the effect of this out-of-plane error. In this study, the effect of out-of-plane motion on turbulence statistics is quantified. Using forced isotropic turbulence direct numerical simulation (DNS) flow field data provided by the Johns Hopkins turbulence database (JHTDB), synthetic image tests are performed. Turbulence statistics such as turbulence kinetic energy, dissipation rate, Taylor microscale, Kolmogorov scale, and velocity correlations are calculated. Various test cases were simulated while controlling three main parameters which affect the out-of-plane motion: PIV interrogation window size, camera inter-frame time, and laser sheet thickness. The amount of out-of-plane motion was first quantified, and then the error variation according to these parameters was examined. This information can be useful when examining fully three dimensional flows such as homogeneous and isotropic turbulence via 2D PIV.     

Turbulence statistics from optical whole-field measurements in particle-laden turbulence

Obtaining turbulence statistics in particle-laden flows using optical whole-field measurements is complicated due to the inevitable data loss. The effects of this data loss are first studied using synthetic data and it is shown that the interpolation of missing data leads to biased results for the turbulence spectrum and its derived quantities. It is also shown that the use of overlapping interrogation regions in images with a low image density can lead to biased results due to oversampling. The slotting method is introduced for the processing of particle image velocimetry (PIV) data fields with missing data. Next to this, it is extended to handle unstructured data. Using experimental data obtained by a dual-camera PIV/PTV (particle tracking velocimetry) system in particle-laden grid turbulence, the performance of the new approach is studied. Some preliminary two-phase results are presented to indicate the significant improvement in the statistics, as well as to demonstrate the unique capabilities of the system.An erratum to this article can be found at

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Flow structure of microbubble-laden turbulent channel flow measured by PIV combined with the shadow image technique

The turbulence structure of a horizontal channel flow with microbubbles is experimentally investigated using combined particle image velocimetry (PIV) in order to clarify the mechanism of drag reduction caused by microbubbles. A new system which simultaneously measures the liquid phase and the dispersed bubbles is proposed, based on a combination of particle tracking velocimetry (PTV), laser-induced fluorescence (LIF) and the shadow image technique (SIT). To accurately obtain the velocity of the liquid phase, tracer particles which overlap with the bubble shadow images are almost entirely eliminated in the post-processing. Finally, the turbulence characteristics of the flow field are presented, including measurements for both phases, and the bubble effect on the turbulence is quantified.     

A method for automatic estimation of instantaneous local uncertainty in particle image velocimetry measurements

The uncertainty of any measurement is the interval in which one believes the actual error lies. Particle image velocimetry (PIV) measurement error depends on the PIV algorithm used, a wide range of user inputs, flow characteristics, and the experimental setup. Since these factors vary in time and space, they lead to nonuniform error throughout the flow field. As such, a universal PIV uncertainty estimate is not adequate and can be misleading. This is of particular interest when PIV data are used for comparison with computational or experimental data. A method to estimate the uncertainty from sources detectable in the raw images and due to the PIV calculation of each individual velocity measurement is presented. The relationship between four error sources and their contribution to PIV error is first determined. The sources, or parameters, considered are particle image diameter, particle density, particle displacement, and velocity gradient, although this choice in parameters is arbitrary and may not be complete. This information provides a four-dimensional “uncertainty surface” specific to the PIV algorithm used. After PIV processing, our code “measures" the value of each of these parameters and estimates the velocity uncertainty due to the PIV algorithm for each vector in the flow field. The reliability of our methodology is validated using known flow fields so the actual error can be determined. Our analysis shows that, for most flows, the uncertainty distribution obtained using this method fits the confidence interval. An experiment is used to show that systematic uncertainties are accurately computed for a jet flow. The method is general and can be adapted to any PIV analysis, provided that the relevant error sources can be identified for a given experiment and the appropriate parameters can be quantified from the images obtained.     

3D holographic PIV with a forward-scattering laser sheet and stereoscopic analysis

 A new 3D PIV system combining holography and stereoscopic PIV is presented. The double pulsed holographic recording relies on the forward scattering of particles in the laser sheet. The holographic images of the particles are used for a stereoscopic PIV analysis. An imaging system with a rightangle prism is used to acquire a stereoscopic pair of images. The application of the system to the vortex flow from an inclined delta wing shows the prospects and limitations of the technique. Received: 23 December 1996/Accepted: 1 June 1997     

Particle image velocimetry measurements of the separated flow behind a rearward facing step

Particle image velocimetry (PIV) was used to measure mean flow and turbulence characteristics in the separated flow behind a rearward facing step. The confidence limits characterising estimates of mean velocity and turbulence intensity obtained by PIV are discussed. The relevance of background turbulence levels and sample size in determining these limits is considered.     

Measurement of the conditioned turbulence and temperature field of a premixed Bunsen burner by planar laser Rayleigh scattering and stereo particle image velocimetry

The turbulence and temperature field of Bunsen-type turbulent lean methane/air flames has been investigated using planar laser Rayleigh scattering (PLRS) and stereo particle image velocimetry (stereo PIV). Temporally averaged reaction progress variable plots have been computed from PLRS measurements in order to provide a basis with regards to the verification of computational fluid dynamics (CFD) models. Turbulence was characterised by stereo PIV in one plane for all three velocity components. Averaged velocity fields have been calculated, as well as Reynolds-decomposed fluctuation vector fields. Conditioned root mean square (RMS) values of the turbulent fluctuations in terms of unburnt and burnt gas could be determined by making use of the information gained from a threshold setting procedure in the PIV raw images. Furthermore, several length scales were measured indirectly from PIV vector plots. In this context, all integral length scales being accessible with stereo PIV were computed separately for the burnt and unburnt regions and were compared to each other. It could be observed that all integral length scales increased in the burnt zone. Additionally, the conditioned Taylor and Kolmogorov lengths have been extracted from the PIV field data, derived either from the zero-radius curvature of the correlation function or from common turbulence theory relations.