Particle image velocimetry: A review
01 Jan 1997-Vol. 211, Iss: 1, pp 55-76
TL;DR: The evolution of particle image velocimetry (PIV) from its various roots is discussed in this paper, where the importance of these roots and their influence on different trends in the speciality are described.
Abstract: The evolution of particle image velocimetry (PIV) from its various roots is discussed. The importance of these roots and their influence on different trends in the speciality are described. The state-of-the-art of the technique today is overviewed and illustrated by reference to recent, seminal publications describing both the development and application of PIV.
Citations
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TL;DR: The size specifications for suitable tracer particles for particle image velocimetry (PIV), particularly with respect to their flow tracking capability, are discussed and quantified for several examples.
Abstract: The size specifications for suitable tracer particles for particle image velocimetry (PIV), particularly with respect to their flow tracking capability, are discussed and quantified for several examples. A review of a wide variety of tracer materials used in recent PIV experiments in liquids and gases indicates that appropriately sized particles have normally been used. With emphasis on gas flows, methods of generating seeding particles and for introducing the particles into the flow are described and their advantages are discussed.
1,122 citations
Cites methods from "Particle image velocimetry: A revie..."
...During the last decade, the technique of particle image velocimetry (PIV, reviewed recently by Grant (1997)) has seen rapidly increasing application for non-intrusive diagnostic investigations of complex flow fields, both on its own and as a complement to laser Doppler anemometry (LDA)....
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TL;DR: In this article, a peak-normalization method was introduced to make the mean bias error negligible in comparison with the root-mean-square (RMS) error, and a peak compensation technique was also introduced to reduce the RMS error.
Abstract: 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.
393 citations
Cites methods from "Particle image velocimetry: A revie..."
...As a quantitative flow visualization tool, the recently emerging PIV has already been challenged in a wide range of applications (Grant 1994, 1997)....
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01 Jan 2005
199 citations
Additional excerpts
...In particular, Particle Image Velocimetry [12] is a standard technique for making a flow visible and measurable by injecting many small particles that scatter light and show the fluid motion....
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TL;DR: In this paper, a submersible Particle Image Velocimetry (PIV) system with a sample area of 20 × 20 cm2 was used to measure the topology of the bottom boundary layer of the ocean.
Abstract: Turbulence characteristics in the coastal ocean bottom boundary layer are measured using a submersible Particle Image Velocimetry (PIV) system with a sample area of 20 × 20 cm2. Measurements are performed in the New York Bight at elevations ranging from 10 cm to about 1.4 m above the seafloor. Recorded data for each elevation consists of 130 s of image pairs recorded at 1 Hz. After processing, the data at each elevation consist of 130 instantaneous spatial velocity distributions within the sample area. The vertical distribution of mean velocity indicates the presence of large-scale shear even at the highest measurement station. The flow also undergoes variations at timescales longer than the present data series. Spatial spectra of the energy and dissipation are calculated from individual vector maps. The data extend well beyond the peak in the dissipation spectrum and demonstrate that the turbulence is clearly anisotropic even in the dissipation range. The vector maps are also patched together to...
192 citations
Cites methods from "Particle image velocimetry: A revie..."
...The method is essentially similar to that proposed by Stewart and Grant (1962) and has also been used extensively for estimating the dissipation rate in turbulent laboratory flows....
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...PIV has already been implemented in a variety of forms (e.g., Adrian 1991; Grant 1997), but in most laboratory applications the fluid is seeded with microscopic tracer particles, and a selected sample area is illuminated with a laser light sheet....
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TL;DR: A new particle tracking software algorithm designed to accurately track the motion of low-contrast particles against a background with large variations in light levels is presented, based on a polynomial fit of the intensity around each feature point.
Abstract: We present a new particle tracking software algorithm designed to accurately track the motion of low-contrast particles against a background with large variations in light levels. The method is based on a polynomial fit of the intensity around each feature point, weighted by a Gaussian function of the distance from the centre, and is especially suitable for tracking endogeneous particles in the cell, imaged with bright field, phase contrast or fluorescence optical microscopy. Furthermore, the method can simultaneously track particles of all different sizes, and allows significant freedom in their shape. The algorithm is evaluated using the quantitative measures of accuracy and precision of previous authors, using simulated images at variable signal-to-noise ratios. To these we add a new test of the error due to a non-uniform background. Finally the tracking of particles in real cell images is demonstrated. The method is made freely available for non-commencial use as a software package with a graphical user-inferface, which can be run within the Matlab programming environment.
178 citations
Cites background from "Particle image velocimetry: A revie..."
...It is also useful for studying the transport of fluorescentlylabelled single-molecules and organelles within the cell [6, 7], and is widely used for particle imaging velocimetry [8, 9]....
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References
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TL;DR: A review of these methods can be found in articles by Lauterborn & Vogel (1984), Adrian (1986a), Hesselink (1988), and Dudderar et al..
Abstract: An important achievement of modern experimental fluid mechanics is the invention and development of techniques for the measurement of whole, instantaneous fields of scalars and vectors. These techniques include tomographic interferometry (Hesselink 1988) and planar laser-induced fluorescence for scalars (Hassa et al 1987), and nuclear-magnetic-resonance imaging (Lee et al 1987), planar laser-induced fluorescence, laser-speckle velocimetry, particle-tracking velocimetry, molecular-tracking velocimetry (Miles et al 1989), and particle-image velocimetry for velocity fields. Reviews of these methods can be found in articles by Lauterborn & Vogel (1984), Adrian (1986a), Hesselink (1988), and Dudderar et al (1988), in books written by Merzkirch (1987) and edited by Chiang & Reid (1988) and Gad-el-Hak (1989).
3,413 citations
TL;DR: In this article, it was shown that a sphere moving through a very viscous liquid with velocity V relative to a uniform simple shear, the translation velocity being parallel to the streamlines and measured relative to streamline through the centre, experiences a lift force 81·2μVa2k½/v½ + smaller terms perpendicular to the flow direction, which acts to deflect the particle towards the streamline moving in the direction opposite to V.
Abstract: It is shown that a sphere moving through a very viscous liquid with velocity V relative to a uniform simple shear, the translation velocity being parallel to the streamlines and measured relative to the streamline through the centre, experiences a lift force 81·2μVa2k½/v½ + smaller terms perpendicular to the flow direction, which acts to deflect the particle towards the streamlines moving in the direction opposite to V. Here, a denotes the radius of the sphere, κ the magnitude of the velocity gradient, and μ and v the viscosity and kinematic viscosity, respectively. The relevance of the result to the observations by Segree & Silberberg (1962) of small spheres in Poiseuille flow is discussed briefly. Comments are also made about the problem of a sphere in a parabolic velocity profile and the functional dependence of the lift upon the parameters is obtained.
2,912 citations
2,131 citations
TL;DR: In this article, the directional ambiguity associated with PIV and LSV is resolved by implementing local spatial cross-correlations between two sequential single-exposed particle images, and the recovered velocity data are used to compute the spatial and temporal vorticity distribution and the circulation of the vortex ring.
Abstract: 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.
1,976 citations
TL;DR: Preferential concentration describes the accumulation of dense particles within specific regions of the instantaneous turbulence field as mentioned in this paper, which occurs in dilute particle-laden flows with particle time constants of the same order as an appropriately chosen turbulence time scale.
969 citations