The application of an in-line, stereoscopic, PIV system to 3-component velocity measurements

Grant, Ian; Fu, Sijie; Pan, Xiaojun; Wang, Xu

doi:10.1007/bf00189710

Cited by 24 publications

(11 citation statements)

References 21 publications

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“…To address this issue, efforts have been made to extend the spatial range of measurement of PIV to the third spatial dimension. Ingenious set-ups have been implemented to extract the so-called out-of-plane component from combined planar views of the flow field: angular (Gauthier and Riethmuller 1988), translational (Prasad and Adrian 1993), single camera setup (Reese et al 1995), Scheimpflug arrangement (Prasad and Jensen 1995), in-line (Grant et al 1995), multilayer (Raffel et al 1996, Abe et al 1998, scanning (Brücker 1997), multiplane (Kähler and Kompenhans 2000).…”

Section: Introductionmentioning

confidence: 99%

Defocusing digital particle image velocimetry and the three-dimensional characterization of two-phase flows

Pereira¹,

Gharib

2002

Meas. Sci. Technol.

162

126

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Defocusing digital particle image velocimetry (DDPIV) is the natural extension of planar PIV techniques to the third spatial dimension. In this paper we give details of the defocusing optical concept by which scalar and vector information can be retrieved within large volumes. The optical model and computational procedures are presented with the specific purpose of mapping the number density, the size distribution, the associated local void fraction and the velocity of bubbles or particles in two-phase flows. Every particle or bubble is characterized in terms of size and of spatial coordinates, used to compute a true three-component velocity field by spatial three-dimensional cross-correlation. The spatial resolution and uncertainty limits are established through numerical simulations. The performance of the DDPIV technique is established in terms of number density and void fraction. Finally, the velocity evaluation methodology, using the spatial cross-correlation technique, is described and discussed in terms of velocity accuracy.

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Section: Introductionmentioning

confidence: 99%

Defocusing digital particle image velocimetry and the three-dimensional characterization of two-phase flows

Pereira¹,

Gharib

2002

Meas. Sci. Technol.

162

126

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“…Most approaches may be classified as stereographic (Chang et al 1984), holographic (Thompson 1989) or multiple plane (light sheet) in methodology (Utami and Ueno 1984) with some studies combining the techniques (Grant et al 1991). Variants of the stereogrammetry method making the method more robust and simple to apply (Grant and Pan 1995) have been reported.…”

Section: Particle Image Velocimetrymentioning

confidence: 99%

“…(ii) In a first experimental application, a vortex generator was used to produce a wing-tip type vortex A Nd:YAG laser illuminated a plane normal to the mean flow. The images, captured on 35 mm film, were processed using the multi-layer, feed-forwards neural method (Grant and Pan 1995). The image pairs were found to be correctly matched on 91.7% of occasions ( figure 9(a)).…”

Section: A Case Study: Multi-layer Feed-forwards Networkmentioning

confidence: 99%

The use of neural techniques in PIV and PTV

Grant

Pan

1997

Meas. Sci. Technol.

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The neural network method uses ideas developed from the physiological modelling of the human brain in computational mechanics. The technique provides mechanisms analogous to biological processes such as learning from experience, generalizing from learning data to a wider set of stimuli and extraction of key attributes from excessively noisy data sets. It has found frequent application in optimization, image enhancement and pattern recognition, key problems in particle image velocimetry (PIV). The development of the method and its principal categories and features are described, with special emphasis on its application to PIV and particle tracking velocimetry (PTV). The application of the neural network method to important categories of the PIV image analysis procedure is described in the present paper. These are image enhancement, fringe analysis, PTV and stereo view reconciliation. The applications of common generic net types, feed-forwards and recurrent, are discussed and illustrated by example. The key strength of the neural technique, its ability to respond to changing circumstances by self-modification or regulation of its processing parameters, is illustrated by example and compared with conventional processing strategies adopted in PIV.

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“…For this purpose, particle image velocimetry (PIV) (Adrian 1991;Willert and Gharib 1991) is an effective technique due to its capability of measuring 2-D flow fields instantaneously, which can produce statistics of the mean velocity field, Reynolds stresses, turbulent kinetic energy (TKE), dissipation rate, etc. PIV can also measure distribution of 3 velocity components with stereoscopic or holographic imaging techniques (Arroyo and Greated 1991;Prasad and Adrian 1993;Grant et al 1995;Zhang et al 1997). However, PIV systems are generally not suitable for field experiments.…”

Section: Introductionmentioning

confidence: 99%

A dual-beam dual-camera method for a battery-powered underwater miniature PIV (UWMPIV) system

et al. 2012

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A battery-powered in situ Underwater Miniature PIV (UWMPIV) has been developed and deployed for field studies. Instead of generating high-energy laser pulses as in a conventional PIV system, the UWMPIV employs a low-power Continuous Wave (CW) laser (class IIIb) and an oscillating mirror (galvanometer) to generate laser sheets. In a previous version of the UWMPIV, the time between exposures of a pair of particle images, Dt, could not be reduced without loss of illumination strength. This limitation makes it unsuitable for high-speed flows. In this paper, we present a technique to solve this problem by adopting two CW lasers with different wavelength and two CCD cameras in a second-generation UWMPIV system. Several issues including optical alignment, non-uniform distribution of Dt due to the varying speed of the scanning beam and local flow velocities are discussed. The timing issue is solved through a simple calibration procedure that involves the reconstruction of maps of laser beam arrival time. Comparison of the performance between the new method and a conventional PIV system is presented. Measurements were performed in a laboratory open-channel flume. Excellent agreement was found between the new method and the standard PIV measurement in terms of the extracted vertical profiles of mean velocity, RMS fluctuation, Reynolds stress and dissipation rate of turbulent kinetic energy.

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The application of an in-line, stereoscopic, PIV system to 3-component velocity measurements

Cited by 24 publications

References 21 publications

Defocusing digital particle image velocimetry and the three-dimensional characterization of two-phase flows

Defocusing digital particle image velocimetry and the three-dimensional characterization of two-phase flows

The use of neural techniques in PIV and PTV

A dual-beam dual-camera method for a battery-powered underwater miniature PIV (UWMPIV) system

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