Development and validation of echo PIV

The combination of ultrasound echo images with digital particle image velocimetry (DPIV) methods has resulted in a two-dimensional, two-component velocity field measurement technique appropriate for opaque flow conditions including blood flow in clinical applications. Advanced PIV processing algorithms including an iterative scheme and window offsetting were used to increase the spatial resolution of the velocity measurement to a maximum of 1.8 mm×3.1 mm. Velocity validation tests in fully developed laminar pipe flow showed good agreement with both optical PIV measurements and the expected parabolic profile. A dynamic range of 1 to 60 cm/s has been obtained to date.     

Volumetric-correlation PIV to measure particle concentration and velocity of microflows

Volumetric-correlation particle image velocimetry (VPIV) is a new technique that provides a 3-dimensional 2-component velocity field from a single image plane. This single camera technique is simpler and cheaper to implement than multi-camera systems and has the capacity to measure time-varying flows. Additionally, this technique has significant advantages over other 3D PIV velocity measurement techniques, most notably in the capacity to measure highly seeded flows. Highly seeded flows, often unavoidable in industrial and biological flows, offer considerable advantages due to higher information density and better overall signal-to-noise ratio allowing for optimal spatial and temporal resolution. Here, we further develop VPIV adding the capability to measure concentration and increasing the robustness and accuracy of the technique. Particle concentrations are calculated using volumetric auto-correlations, and subsequently the velocities are calculated using volumetric cross-correlation corrected for variations in particle concentration. Along with the ability to calculate the particle concentration profile, our enhanced VPIV produces significant improvement in the accuracy of velocity measurements. Furthermore, this technique has been demonstrated to be insensitive to out-of-plane flows. The velocity measurement accuracy of the enhanced VPIV exceeds that of standard micro-PIV measurements, especially in near-wall regions. The 3D velocity and particle-concentration measurement capability of VPIV are demonstrated using both synthetic and experimental results.     

Influence of individual variations of particle image intensities on high-resolution PIV

Individual variations of intensity of tracer particles, e.g., due to out-of-plane displacements between exposures, strongly limit the achievable accuracy of correlation-based PIV processing. The RMS error originated by this effect correlates with the spatial resolution that can be achieved with the processing algorithm making especially high-resolution algorithms like iterative image deformation affected by this error. Both aspects are shown, the gain of resolution by iterative image deformation and the loss of accuracy due to individual variations of particle intensities.     

A model-based validation framework for PIV and PTV

The utility of particle image velocimetry (PIV) for measurement of velocity fields in many fluid flows is well established. This has created interest in overcoming difficulties with the technique when applied to increasingly larger fields of view where there exists a significant range of velocities and spatial velocity gradients are large. In this regard, a major deficiency with standard cross-correlation PIV is the inherent link between the density of vectors in the measurement field and the maximum measurable displacement. Several schemes exist to reduce this link. These iterative hierarchical/multiresolution schemes are strongly dependent on vector validation routines to remove spurious vectors. Here the design of a new framework for vector validation is described. This framework is general enough for use with both regular and irregularly spaced vector fields to make it applicable to particle image velocimetry (PIV), particle tracking velocimetry (PTV), and hybrid methods. It is based on the determination of a smoothed displacement field that robustly characterizes the measured field such that invalid vectors are attenuated more than valid vectors. In this particular study a thin-plate spline model is incorporated within an iterative regularized weighted least-squares routine to find a smoothed version of the displacement field that maintains pertinent velocity gradient information. The utility of the methodology is demonstrated for a complex flow profile containing four vortices where the valid displacement ranges from ?1/4 to +1/4 of the area of interest (AOI) dimension. Results indicate that this validation strategy can discriminate between valid and invalid vectors remarkably well over a range of parameter settings. In the example presented a flow field with significant velocity gradients and having a high number of invalid vectors (25%) is accurately validated.     

Analysis of weighting windows for image deformation methods in PIV

The use of a weighting window (WW) in the evaluation of the cross-correlation coefficient and in the iterative procedure of image deformation method for particle image velocimetry (PIV) applications can be used to both stabilise the process and to increase the spatial resolution. The choice of the WW is a parameter that influences the complete PIV algorithm. Aim of this paper is to examine the influence of this aspect on both the accuracy and spatial resolution of the PIV algorithm. Results show an overall accordance between the theoretical approach and the simulation both with synthetic and real images. The choice of the combination of WW influences significantly the spatial resolution and accuracy of the PIV algorithm.

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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     

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.     

Digital particle image velocimetry

Digital particle image velocimetry (DPIV) is the digital counterpart of conventional laser speckle velocitmetry (LSV) and particle image velocimetry (PIV) techniques. In this novel, two-dimensional technique, digitally recorded video images are analyzed computationally, removing both the photographic and opto-mechanical processing steps inherent to PIV and LSV. The directional ambiguity generally associated with PIV and LSV is resolved by implementing local spatial cross-correlations between two sequential single-exposed particle images. The images are recorded at video rate (30 Hz or slower) which currently limits the application of the technique to low speed flows until digital, high resolution video systems with higher framing rates become more economically feasible. Sequential imaging makes it possible to study unsteady phenomena like the temporal evolution of a vortex ring described in this paper. The spatial velocity measurements are compared with data obtained by direct measurement of the separation of individual particle pairs. Recovered velocity data are used to compute the spatial and temporal vorticity distribution and the circulation of the vortex ring.     

Interfacial PIV to resolve flows in the vicinity of curved surfaces

PIV measurements near a wall are generally difficult due to low seeding density, low velocity, high velocity gradient, and strong reflections. Such problems are often compounded by curved boundaries, which are commonly found in many industrial and medical applications. To systematically solve these problems, this paper presents two novel techniques for near-wall measurement, together named Interfacial PIV, which extracts both wall-shear gradient and near-wall tangential velocity profiles at one-pixel resolution. To deal with curved walls, image strips at a curved wall are stretched into rectangles by means of conformal transformation. To extract the maximal spatial information on the near-wall tangential velocity field, a novel 1D correlation function is performed on each horizontal pixel line of the transformed image template to form a “correlation stack”. This 1D correlation function requires that the wall-normal displacement component of the particles be smaller than the particle image diameter in order to produce a correlation signal. Within the image regions satisfying this condition, the correlation function yields peaks that form a tangential velocity profile. To determine this profile robustly, we propose to integrate gradients of tangential velocity outward from the wall, wherein the gradient at each wall-normal position is measured by fitting a straight line to the correlation peaks. The capability of Interfacial PIV was validated against Particle Image Distortion using synthetic image pairs generated from a DNS velocity field over a sinusoidal bed. Different velocity measurement schemes performed on the same correlation stacks were also demonstrated. The results suggest that Interfacial PIV using line fitting and gradient integration provides the best accuracy of all cases in the measurements of velocity gradient and velocity profile near wall surfaces.     

Improvements to PIV image analysis by recognizing the velocity gradients

Two iterative PIV image processing methods are introduced, which utilize displacement and deformation of the interrogation areas to maximize the correlation. The velocity gradients used for the window deformation are iteratively estimated directly from the images and no velocity values are required from neighbouring interrogation areas, as with numerical differentiation. The improved accuracy and resolution of the velocity gradient estimation compared to numerical differentiation is shown using synthetic images. The performance in a real application is shown using experimental reference images.