3D scanning particle tracking velocimetry

In this article, we present an experimental setup and data processing schemes for 3D scanning particle tracking velocimetry (SPTV), which expands on the classical 3D particle tracking velocimetry (PTV) through changes in the illumination, image acquisition and analysis. 3D PTV is a flexible flow measurement technique based on the processing of stereoscopic images of flow tracer particles. The technique allows obtaining Lagrangian flow information directly from measured 3D trajectories of individual particles. While for a classical PTV the entire region of interest is simultaneously illuminated and recorded, in SPTV the flow field is recorded by sequential tomographic high-speed imaging of the region of interest. The advantage of the presented method is a considerable increase in maximum feasible seeding density. Results are shown for an experiment in homogenous turbulence and compared with PTV. SPTV yielded an average 3,500 tracked particles per time step, which implies a significant enhancement of the spatial resolution for Lagrangian flow measurements.     

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.     

Measuring evaporation of micro-fuel droplets using magnified DIH and DPIV

This paper details the use of magnified digital in-line holography (MDIH) and digital particle image velocimetry (DPIV) to measure the evaporation rates of fuel micro-droplets undergoing heating. The technique can be used to measure instantaneous evaporation along an individual droplet trajectory, or if applied to a series of droplets, the average evaporation over a number of successive measurement locations. The advantage of this technique over traditional optical techniques is greater spatial resolution and depth of field for the high magnification factors used. An application of the technique to the evaporation measurement of diesel fuel droplets ranging from 10 to 90 μm is presented. Results reveal that similar to larger droplets, temperature plays the dominant role in evaporation processes, with little sensitivity to initial droplet size found for a peak reactor temperature of 660 K.     

Spatially averaged time-resolved particle-tracking velocimetry in microspace considering Brownian motion of submicron fluorescent particles

A time-series measurement method is proposed to detect velocity fields in a microchannel taking into account Brownian motion of submicron tracer particles. The present study proposes spatially averaged time-resolved particle-tracking velocimetry (SAT–PTV), which can detect temporal variations of fluid flow and eliminate errors associated with Brownian motion without losing temporal resolution. Velocity vectors of tracer particles obtained by PTV are spatially averaged in each interrogation window of particle-image velocimetry, yielding full velocity field information with temporal resolution. Synthetic particle images, which include Brownian motion of submicron fluorescent particles in flow fields with linear velocity gradients, are generated to validate the ability of SAT–PTV to track particles. SAT–PTV correctly captures the velocity gradient profiles. The spatial resolution based on the size of the first interrogation window and the measurement depth of the microscope system is 6.7 m×6.7 m×1.9 m, within which several vectors are averaged. SAT–PTV is shown to measure the velocity field of a pulsating flow generated by an electrokinetic pump.An earlier version of this paper appeared in the Fourth International Symposium on Particle Image Velocimetry at Göttingen, Germany, 17–19 September 2001.     

A new particle tracking algorithm based on deterministic annealing and alternative distance measures

We describe a new particle tracking algorithm for the interrogation of double frame single exposure data, which is obtained with particle image velocimetry. The new procedure is based on an algorithm which has recently been proposed by Gold et al. (Gold et al., 1998) for solving point matching problems in statistical pattern recognition. For a given interrogation window, the algorithm simultaneously extracts: (i) the correct correspondences between particles in both frames and (ii) an estimate of the local flow-field parameters. Contrary to previous methods, the algorithm determines not only the local velocity, but other local components of the flow field, for example rotation and shear. This makes the new interrogation method superior to standard methods in particular in regions with high velocity gradients (e.g. vortices or shear flows). We perform benchmarks with three standard particle image velocimetry (PIV) and particle tracking velocimetry (PTV) methods: cross-correlation, nearest neighbour search, and image relaxation. We show that the new algorithm requires less particles per interrogation window than cross-correlation and allows for much higher particle densities than the other PTV methods. Consequently, one may obtain the velocity field at high spatial resolution even in regions of very fast flows. Finally, we find that the new algorithm is more robust against out-of-plane noise than previously proposed methods. Received: 1 March 1999 / Accepted: 29 July 1999     

Real-time image processing for particle tracking velocimetry

We present a novel high-speed particle tracking velocimetry (PTV) experimental system. Its novelty is due to the FPGA-based, real-time image processing “on camera”. Instead of an image, the camera transfers to the computer using a network card, only the relevant information of the identified flow tracers. Therefore, the system is ideal for the remote particle tracking systems in research and industrial applications, while the camera can be controlled and data can be transferred over any high-bandwidth network. We present the hardware and the open source software aspects of the PTV experiments. The tracking results of the new experimental system has been compared to the flow visualization and particle image velocimetry measurements. The canonical flow in the central cross section of a a cubic cavity (1:1:1 aspect ratio) in our lid-driven cavity apparatus is used for validation purposes. The downstream secondary eddy (DSE) is the sensitive portion of this flow and its size was measured with increasing Reynolds number (via increasing belt velocity). The size of DSE estimated from the flow visualization, PIV and compressed PTV is shown to agree within the experimental uncertainty of the methods applied.     

A hybrid digital particle tracking velocimetry technique

A novel approach to digital particle tracking velocimetry (DPTV) based on cross-correlation digital particle image velocimetry (DPIV) is presented that eliminates the need to interpolate the randomly located velocity vectors (typical of tracking techniques) and results in significantly improved resolution and accuracy. In particular, this approach allows for the direct measurement of mean squared fluctuating gradients, and thus several important components of the turbulent dissipation. The effect of various parameters (seeding density, particle diameter, dynamic range, out-of-plane motion, and gradient strength) on accuracy for both DPTV and DPIV are investigated using a Monte Carlo simulation and optimal values are reported. Validation results are presented from the comparison of measurements by the DPTV technique in a turbulent flat plate boundary layer to laser Doppler anemometer (LDA) measurements in the same flow as well as direct numerical simulation (DNS) data. The DPIV analysis of the images used for the DPTV validation is included for comparison. Received: 29 August 1994/Accepted: 31 May 1996     

Effect of particle number density in in-line digital holographic particle velocimetry

A digital in-line holographic particle tracking velocimetry (HPTV) system was developed to measure 3D (three-dimensional) velocity fields of turbulent flows. The digital HPTV (DHPTV) procedure consists of four steps: recording, numerical reconstruction, particle extraction and velocity extraction. In the recording step, a digital CCD camera was used as a recording device. Holograms contained many unwanted images or noise. To get clean holograms, digital image processing techniques were adopted. In the velocity extraction routine, we improved the HPTV algorithm to extract 3D displacement information of tracer particles. In general, the results obtained using HPTV were not fully acceptable due to technical limitations such as low spatial resolution, small volume size, and low numerical aperture (NA). The problems of spatial resolution and NA are closely related with a recording device. As one experimental parameter that can be optimized, we focused on the particle number density. Variation of the reconstruction efficiency and recovery ratio were compared quantitatively with varying particle number density to check performance of the developed in-line DHPTV system. The reconstruction efficiency represented the particle number distribution acquired through the numerical reconstruction procedure. In addition the recovery ratio showed the performance of 3D PTV algorithm employed for DHPTV measurements. The particle number density in the range of C _o = 13–17 particles/mm³ was found to be optimum for the DHPTV system tested in this study.     

Digital particle image velocimetry/thermometry and application to the wake of a heated circular cylinder

 Digital particle image velocimetry/thermometry (DPIV/T) is a technique whereby the velocity and temperature fields are obtained using thermochromic liquid crystal (TLC) seeding particles in water. In this paper, the uncertainty levels associated with temperature and velocity measurements using DPIV/T are studied. The study shows that large uncertainties are encountered when the temperature is measured from individual TLC particles. Therefore, an averaging procedure is presented which can reduce the temperature uncertainties. The uncertainty is reduced by computing the average temperature of the particles within the common specified sampling window used for standard DPIV. Using this procedure, the velocity and temperature distributions of an unsteady wake behind a heated circular cylinder are measured experimentally at Re=610. The instantaneous DPIV/T measurements are shown to be useful for computing statistical flow quantities, such as mean and velocity-temperature correlations. Received: 3 January 2000/Accepted: 26 June 2000     

A single-camera coupled PTV–LIF technique

 A single-camera coupled particle tracking velocimetry–laser-induced fluorescence (PTV–LIF) technique and validation results from an experiment in a neutrally buoyant turbulent round jet are presented. The single-camera implementation allows the use of a 12-bit 60 frame-per-second 1024 × 1024 pixel digital CCD camera capable of streaming images in real time to hard disk resulting in very accurate PTV and LIF with excellent spatial and temporal resolution. The technique is capable of determining the turbulent scalar flux, as well as the Reynolds stress and mean and fluctuating velocity and concentration fields. Details of dye choice, corrections for attenuation due to dye, particles and water, photobleaching, vignetting, CCD calibration, and illumination power and geometry corrections are presented. Detailed results from the validation experiment confirm the accuracy and resolution of the technique, and in particular, the ability to measure . Bootstrap 95% uncertainty intervals are presented for the calculated statistics. Received: 28 July 2000/Accepted: 8 November 2000     

Performance of digital image velocimetry processing techniques

Digital particle image velocimetry (DPIV)-processing techniques have become increasingly more sophisticated in recent years. However, much work is still done using standard traditional methods of analysis. This paper investigates several traditionally based techniques for cross-correlation image processing in terms of computational efficiency and measurement accuracy. Direct spatial domain correlation, standard fast Fourier transform (FFT) correlation, a dynamic FFT correlation technique, and a new hybrid correlation method are discussed and evaluated. In addition, a particle-tracking velocimetry scheme based on that of Cowen and Monismith (1997) is examined in the same context as the DPIV methods. A detailed examination of the behaviors of each correlation method reveals that direct spatial domain correlation is more accurate than FFT-based methods, with the standard FFT correlation showing the weakest performance. Using the more robust methods (dynamic FFT and hybrid correlation), accuracy can be improved significantly over the standard FFT method in many cases, while still remaining computationally efficient. The particle-tracking algorithm studied was found to yield comparable accuracy to the DPIV routines and can provide much higher spatial- resolution possibilities. Received: 3 September 1999 / Accepted: 21 June 2001 Published online: 29 November 2001     

Two-dimensional Gaussian regression for sub-pixel displacement estimation in particle image velocimetry or particle position estimation in particle tracking velocimetry

An explicit solution of two-dimensional Gaussian regression for the estimation of particle displacement from the correlation function in particle image velocimetry (PIV) or particle position from the images in particle tracking velocimetry (PTV) with sub-pixel accuracy is introduced. The accuracy and the ability of the methods to avoid pixel locking due to non-axially orientated, elliptically shaped particle images or correlation peaks are investigated using simulated and experimentally obtained images.     

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.     

Assessment of tomographic PIV in wall-bounded turbulence using direct numerical simulation data

Simulations of tomographic particle image velocimetry (Tomo-PIV) are performed using direct numerical simulation data of a channel flow at Reynolds number of Re _τ = 934, to investigate the influence of experimental parameters such as camera position, seeding density, interrogation volume size and spatial resolution. The simulations employ camera modelling, a Mie scattering illumination model, lens distortion effects and calibration to realistically model a tomographic experiment. Results are presented for camera position and orientation in three-dimensional space, to obtain an optimal reconstruction quality. Furthermore, a quantitative analysis is performed on the accuracy of first and second order flow statistics, at various voxel sizes normalised using the viscous inner length scale. This enables the result to be used as a general reference for wall-bounded turbulent experiments. In addition, a ratio relating seeding density and the interrogation volume size is proposed to obtain an optimal reference value that remains constant. This can be used to determine the required seeding density concentration for a certain interrogation volume size.     

Pseudo turbulence in PIV breaking-wave measurements

 The particle image velocimetry (PIV) technique was employed to measure the instantaneous velocity distribution under nonbreaking and breaking water waves. The corresponding turbulence intensity was calculated by the ensemble average of repeated measurements. The pseudo turbulence found was large enough to affect the accuracy of the turbulence measurements. We follow Prasad et al.'s (1992) approach to demonstrate that the pseudo turbulence is related to the bias error, which is the discrepancy between the true position of the particle image and the position calculated from the pixel array data with inadequate pixel resolution. To reduce the bias error (or the pseudo turbulence), we first calculate it from a turbulence-free flow with the same experimental set-up as that used for the targeted experiments (i.e., we use the same size of field of view, seeding particles, seeding density, lens aperture, and laser wavelength in both experiments). Then we minimize the bias error from the turbulence measurements in the actual experiments. To demonstrate the procedure, the evolution of a breaking wave is investigated. Received: 30 January 1998/Accepted: 28 October 1999     

A dual-camera cinematographic PIV measurement system at kilohertz frame rate for high-speed, unsteady flows

A digital dual-camera cinematographic particle image velocimetry (CPIV) system has been developed to provide time-resolved, high resolution flow measurements in high-Reynolds number, turbulent flows. Two high-speed CMOS cameras were optically combined to acquire double-pulsed CPIV images at kilohertz frame rates. Bias and random errors due to camera misalignment, camera vibration, and lens aberration were corrected or estimated. Systematic errors due to the camera misalignment were reduced to less than 2 pixels throughout the image plane using mechanical alignment, resulting in 3.1% positional uncertainty of velocity measurements. Frame-to-frame uncertainties caused by mechanical vibration were eliminated with the aid of digital image calibration and frame-to-frame camera registration. This dual-camera CPIV system is capable of resolving high speed, unsteady flows with high temporal and spatial resolutions. It also allows time intervals between the two exposures down to 4 μs, enabling the measurements of speed flows 5–10 times higher than possible with frame-straddling using similar cameras. A turbulent shallow cavity was then chosen as the experimental object investigated by this dual-camera CPIV technique.     

Imaging laser Doppler velocimetry

Imaging laser Doppler velocimetry (ILDV) is a novel flow measurement technique, which enables the measurement of the velocity in an imaging plane. It is an evolution of heterodyne Doppler global velocimetry (HDGV) and may be regarded as the planar extension of the classical dual-beam laser Doppler velocimetry (LDV) by crossing light sheets in the flow instead of focused laser beams. Seeding particles within the flow are illuminated from two different directions, and the light scattered from the moving particles exhibits a frequency shift due to the Doppler effect. The frequency shift depends on the direction of the illumination and the velocity of the particle. The superposition of the two different frequency-shifted signals on the detector creates interference and leads to an amplitude modulated signal wherein the modulation frequency depends on the velocity of the particle. This signal is detected using either a high-speed camera or alternatively a smart pixel imaging array. This detector array performs a quadrature detection on each pixel with a maximum demodulation frequency of 250 kHz. To demonstrate the feasibility of the technique, two experiments are presented: The first experiment compares the measured velocity distribution of a free jet using ILDV performed with the smart pixel detector array and a high-speed camera with a reference measurement using PIV. The second experiment shows an advanced setup using two smart pixel detector arrays to measure the velocity distribution on a rotating disk, demonstrating the potential of the technique for high-velocity flow measurements.     

The application of a 3D PTV algorithm to a mixed convection flow

A 3D particle-tracking velocimetry (PTV) algorithm is applied to the wake flow behind a heated cylinder. The method is tested in advance with respect to its accuracy and performance. In the accuracy tests, its capability to locate particles in 3D space is tested. It appears that the algorithm can determine the particle position with an accuracy of less than 0.5 camera pixels, equivalent to 0.3 mm in the present test situation. The performance tests show that for particles located in a 2D plane, the algorithm can track the particles with a vector yield reaching 100%, which means that a velocity vector can be determined for almost all particles detected. The calculated velocity vectors for this situation have a standard deviation of less than 1%. The performance is also tested on a mixed convection flow behind a heated cylinder in which the 2D flow transits into a 3D flow. As there is no exact solution of such a flow available, the 3D PTV results are compared with visualisation results. The results show that the 3D PTV method can capture the main features of the 3D transition of the 2D vortex street.     

Improving the dynamic range of particle tracking velocimetry systems

In applying a video-based particle image velocimetry (PTV) system in a complex fluid flow, it is common to find both regions of fast and slow moving flow intermixing-particularly in highly turbulent or reversing flows. When one attempts to track the movement of particles in such a flow with a wide velocity range (and hence, separation distance between particle images), resolution problems are encountered. Inability to cover a wide range of velocities is actually a limitation of PTV. A method is introduced here that extends the dynamic range of PTV when implemented on a video-based system. It combines the use of multiple frames and multiple exposures on a single frame. The method is subsequently verified by tracking dots painted on a spinning flat disc.     

A comparative analysis of the uncertainty of astigmatism-μPTV,stereo-μPIV,and μPIV

Astigmatism or wavefront deformation, microscopic particle tracking velocimetry (A-μPTV) (Chen et al. in Exp Fluids 47:849–863, 2009; Cierpka et al. in Meas Sci Technol 21:045401, 2010b) is a method to determine the complete 3D3C velocity field in micro-fluidic devices with a single camera. By using an intrinsic calibration procedure that enables a robust and precise calibration on the basis of the measured data itself (Cierpka et al. in Meas Sci Technol 22:015401, doi:, 2011), accurate results without errors due to spatial averaging or bias due to the depth of correlation can be obtained. This method takes all image aberrations into account, allows for the use of the whole CCD sensor, and is easy to apply without expert knowledge. In this paper, a comparative study is presented to assess the uncertainties of two state-of-the-art methods for 3C3D velocity field measurements in microscopic flows: stereoscopic micro-particle image velocimetry (S-μPIV) and astigmatism micro-particle tracking velocimetry (A-μPTV). First, the main parameters affecting all methods’ measurement uncertainty are identified, described, and quantified. Second, the test case of the flow over a backward-facing step is analyzed using all methods. For comparison, standard 2D2C μPIV measurements and numerical flow simulations are shown as well. Advantages and disadvantages of both methods are discussed.