Construction of three-dimensional images of flow structure via particle tracking techniques

Construction of three-dimensional images of flow structure, based on the quantitative velocity field, is assessed for cases where experimental data are obtained using particle tracking technique. The experimental data are in the form of contiguous planes of particle images. These contiguous data planes are assumed to correspond to successive spatial realizations in steady flow, or to phase-referenced realizations in an unsteady flow.Given the particle images on contiguous planes, the in-plane velocity fields are determined. Then, the out-of-plane velocity field is obtained using a spectral interpolation method. Application of this method allows, in principle, construction of the three-dimensional vorticity field and the streamline patterns.A critical assessment is made of the uncertainties arising from the in-plane interpolation of the velocity field obtained from particle tracking and the evaluation of the out-of-plane velocity component. The consequences of such uncertainties on the reconstructed vorticity distributions and streamline patterns are addressed for two basic types of vortex flows: a columnar vortex, for which the streamlines are not closed and are spatially periodic in the streamwise direction; and for a spherical (Hill's) vortex exhibiting closed streamline patterns, and no spatial periodicity.     

A method for automatic particle tracking in a three-dimensional flow field

A technique has been developed whereby the three-dimensional motion of tracers in a fluid flow is automatically analysed. Simultaneous orthogonal views of the tracer-seeded flow were recorded by a single high speed cine camera through a split field mirror system, and subsequently converted to machine readable form by a video digitizer. Digital enhancement was used to separate the tracers from the contrasting background. Algorithms were developed to match the projections of individual tracers in the two views, obtain the three-dimensional coordinates, follow the tracers from frame to frame and compute the velocity vectors along the particle trajectories. Eulerian information was derived from the pooled velocity data points by interpolation on a regular spatial grid. Tests of the method on particle trajectories obtained in a small water tunnel have shown that the tracking is reliable even for rapidly changing and closely spaced paths.     

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     

A method to provide statistical measures of large-scale instantaneous particle clusters from planar images

Particle clusters are preferential accumulations of a solid, secondary phase that can be caused by turbulence. It is well known that particle clusters can influence the performance of systems employing suspension flows, such as pulverised fuel combustion systems. However, statistical analysis of clusters is limited by available methods to quantify them. In the current study, a method to identify planar slices of large-scale particle clusters from planar images of instantaneous particle distributions is presented. The method employs smoothing of instantaneous particle scatter images by a length scale, L _S , to produce pseudo-scalar fields of particle distributions. The scalar fields are compared with mean (not smoothed) images to produce cluster masks that are then multiplied by the original instantaneous image to produce a map of the locations of cluster slices. The sensitivity to the smoothing length scale is assessed parametrically for its influence on the statistical measures of the following parameters characterising slices of large-scale clusters in four representative flows: the physical locations of the cluster slice centroids; the area of the cluster slice; and the number of cluster slices per image. While the results are influenced by the selected value of smoothing length scale, L _S , the sensitivity is low in a physically reasonable range and the method performs well in this range for the four different flow cases. The method could be extended to provide volumetric measurements with suitable volumetric imaging systems.     

A quantitative study of three-dimensional Lagrangian particle tracking algorithms

A neural network particle finding algorithm and a new four-frame predictive tracking algorithm are proposed for three-dimensional Lagrangian particle tracking (LPT). A quantitative comparison of these and other algorithms commonly used in three-dimensional LPT is presented. Weighted averaging, one-dimensional and two-dimensional Gaussian fitting, and the neural network scheme are considered for determining particle centers in digital camera images. When the signal to noise ratio is high, the one-dimensional Gaussian estimation scheme is shown to achieve a good combination of accuracy and efficiency, while the neural network approach provides greater accuracy when the images are noisy. The effect of camera placement on both the yield and accuracy of three-dimensional particle positions is investigated, and it is shown that at least one camera must be positioned at a large angle with respect to the other cameras to minimize errors. Finally, the problem of tracking particles in time is studied. The nearest neighbor algorithm is compared with a three-frame predictive algorithm and two four-frame algorithms. These four algorithms are applied to particle tracks generated by direct numerical simulation both with and without a method to resolve tracking conflicts. The new four-frame predictive algorithm with no conflict resolution is shown to give the best performance. Finally, the best algorithms are verified to work in a real experimental environment.     

A new two-frame particle tracking algorithm using match probability

A new particle tracking algorithm using the concept of match probability between two consequent image frames has been developed to obtain an instantaneous 2-dimensional velocity field. Our proposed algorithm for correctly tracking particle paths from only two image frames is based on iterative estimation of match probability and no-match probability as a measure of the matching degree. A computer simulation has been carried out to study the performance of the developed algorithm by comparing with the conventional 4-frame Particle Tracking Velocimetry (PTV) method. The effect of various thresholds used in the developed algorithm on the recovery ratio and the error ratio were also investigated. Although the new algorithm relies on the iterative updating process of match probability which is time consuming, computation time is relatively short compared to that of the 4-frame PTV method. Additionally, the new 2-frame PTV algorithm recovers more velocity vectors and has a higher dynamic range and a lower error ratio.This work was supported in part by non-directed research fund, Korea Research Foundation, 1993 and Hyundai Maritime Research Institute.     

A vision-based hybrid particle tracking velocimetry (PTV) technique using a modified cascade correlation peak-finding method

A novel technique for particle tracking velocimetry is presented in this paper to overcome the issue of overlapping particle images encountered in the flows with high particle density or under volumetric illumination conditions. To achieve this goal, algorithms for particle identification and tracking are developed based on current methods and validated with both synthetic and experimental image sets. The results from synthetic image tests show that the particle identification algorithm is able to resolve overlapped particle images up to 50?% under noisy conditions, while keeping the root mean square peak location error under 0.07?pixels. The algorithm is also robust to the size changes up to a size ratio of 5. The tracking method developed from a classic computer vision matching algorithm is capable of capturing a velocity gradient up to 0.3 while maintaining the error under 0.2?pixels. Sensitivity tests were performed to describe the optimum conditions for the technique in terms of particle image density, particle image sizes and velocity gradients, also its sensitivity to errors of the PIV results that guide the tracking process. The comparison with other existing tracking techniques demonstrates that this technique is able to resolve more vectors out of a dense particle image field.     

Three-dimensional particle tracking method using FPGA-based real-time image processing and four-view image splitter

We present a cost-effective solution of the three-dimensional particle tracking velocimetry (3D-PTV) system based on the real-time image processing method (Kreizer et al. Exp Fluids 48:105–110, 2010) and a four-view image splitter. The image processing algorithm, based on the intensity threshold and intensity gradients estimated using the fixed-size Sobel kernel, is implemented on the field-programmable gate array integrated into the camera electronics. It enables extracting positions of tracked objects, such as tracers or large particles, in real time. The second major component of this system is a four-view split-screen device that provides four views of the observation volume from different angles. An open-source ray-tracing software package allows for a customized optical setup for the given experimental settings of working distances and camera parameters. The specific design enables tracking in larger observation volumes when compared to the designs published up to date. The present cost-effective solution is complemented with open-source particle tracking software that receives raw data acquired by the real-time image processing system and returns trajectories of the identified particles. The combination of these components simplifies the 3D-PTV technique by reducing the size and increasing recording speed and storage capabilities. The system is capable to track a multitude of particles at high speed and stream the data over the computer network. The system can provide a solution for the remotely controlled tracking experiments, such as in microgravity, underwater or in applications with harsh experimental conditions.     

A color-coded particle tracking velocimeter with application to natural convection

This paper introduces a novel concept of the image-processing technique to visualize a two-dimensional flow field and to generate streaks with embedded time marks in real time. Grey levels of a pixel are used in this work to encode the temporal information rather than the intensity of particle trajectories used in conventional image-processing systems. To do this, the incoming video image is first binarized by a step function predefined in an input look-up table, with the height of the step varying with time to bear temporal marks. A real-time arithmetic-logic unit and an image-frame buffer are used to accumulate the images. These processes result in continuous streaks with embedded time marks which have many advantages over those recorded by conventional techniques. Pseudo-color output look-up tables and color monitor provide more direct and intuitive means for human visual perception. This color-coded particle tracking velocimetry was applied to investigate natural convection in an enclosure heated from below with an isothermal extrusion. By quantifying the color-coded streaks, velocity vectors were obtained at a discrete number of points with less labor. The present technique provides an elaborate tool for simultaneous qualitative and quantitative realizations of steady and slowly varying flow fields.A version of this paper was presented at the twenty-second Midwestern Mechanics Conference, University of Missouri-Rolla, USA, October 6–9, 1991     

A novel particle tracking algorithm using polar coordinate system similarity

A new algorithm using polar coordinate system similarity (PCSS) for tracking particle in particle tracking velocimetry (PTV) is proposed. The essence of the algorithm is to consider simultaneously the changes of the distance and angle of surrounding particles relative to the object particle. Monte Carlo simulations of a solid body rotational flow and a parallel shearing flow are used to investigate flows measurable by PCSS and the influences of experimental parameters on the implementation of the new algorithm. The results indicate that the PCSS algorithm can be applied to flows subjected to strong rotation and is not sensitive to experimental parameters in comparison with the conventional binary image cross-correlation (BICC) algorithm. Finally, PCSS is applied to images of a real experiment. The project supported by the National Natural Science Foundation of China (50206019) The English text was polished by Yunming Chen.     

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.     

Development of an efficient statistical volumes of fluid–Lagrangian particle tracking coupling method

The breakup of a liquid jet into irregular liquid structures and droplets leading to the formation of a dilute spray has been simulated numerically. To overcome the shortcomings of certain numerical methods in specific flow regimes, a combined approach has been chosen. The intact liquid core, its primary breakup and the dense spray regime are simulated using the volumes of fluid (VOF) method in combination with LES, whereas the Lagrangian particle tracking (LPT) approach in the LES context is applied to the dilute spray regime and the secondary breakup of droplets. A method has been developed to couple both simulation types on a statistical basis. This statistical coupling approach (SCA) reflects the dominating physical mechanisms of the two‐phase flow in each regime to a high degree. The main benefit of the SCA is computational efficiency as compared with the more straightforward approach where one follows each structure, denoted here as the direct coupling approach. The computational benefits stem from the reduction of computational time since the VOF simulation is run only until statistical convergence and not during the whole spray development. A second benefit using the SCA is the possibility to use the stochastic parcel method in the LPT simulation whereby a large number of droplets may be handled. The coupling approach is applied to the atomization of a fuel jet in a high pressure chamber, demonstrating the gain of efficiency of the SCA as compared with direct coupling approach. Copyright © 2014 The Authors. International Journal for Numerical Methods in Fluids published by John Wiley & Sons Ltd.     

A new artificial neural network tracking technique for particle image velocimetry

 The analysis of Particle Image Velocimetry (PIV) data requires effective algorithms to track efficiently the particles suspended in the fluid flow. The artificial neural network algorithm method described here presents a new approach to solve this problem. Contrary to the classic cross correlation method, this new method does not require a large number of particles per frame, it can handle flows with large velocity gradients, and is suited for tracking images with multiple exposures as well as tracking through consecutive images. The algorithm was tested on synthetic and available experimental data to provide a thorough performance analysis. Received: 28 May 1996/Accepted: 25 December 1996     

Stereo-PIV using self-calibration on particle images

A stereo-PIV (stereo particle image velocimetry) calibration procedure has been developed based on fitting a camera pinhole model to the two cameras using single or multiple views of a 3D calibration plate. A disparity vector map is computed on the real particle images by cross-correlation of the images from cameras 1 and 2 to determine if the calibration plate coincides with the light sheet. From the disparity vectors, the true position of the light sheet in space is fitted and the mapping functions are corrected accordingly. It is shown that it is possible to derive accurate mapping functions, even if the calibration plate is quite far away from the light sheet, making the calibration procedure much easier. A modified 3-media camera pinhole model has been implemented to account for index-of-refraction changes along the optical path. It is then possible to calibrate outside closed flow cells and self-calibrate onto the recordings. This method allows stereo-PIV measurements to be taken inside closed measurement volumes, which was not previously possible. From the computed correlation maps, the position and thickness of the two laser light sheets can be derived to determine the thickness, degree of overlap and the flatness of the two sheets.     

An image processing method for feature extraction and dynamic tracking of particle clusters in CFBs

A new image processing method based on the high-speed camera is proposed to identify, locate, and track clusters. The instantaneous characteristic parameters of particle clusters in the riser of the circulating fluidized bed (CFB) can be acquired, such as solids holdup, vertical velocity, lateral displacement, aspect ratio and near-circularity. Experiments were carried out with glass bead particles, river sand particles and FCC particles. The time series of images of gas–solid flow in a CFB riser with a 100 mm × 25 mm cross-section and 3.2 m in length were obtained using high-speed cameras. The k-means++ clustering algorithm is utilized to identify the clusters, centroid is applied to locate the clusters, and the cross-correlation algorithm is employed to track the specific clusters and number them to get the instantaneous characteristic parameters. The results illustrate that the shapes of clusters in the center area are closest to circle, moving upwards at a uniform speed, while the clusters in the side-wall area are mostly elongated or long chain-like, moving slowly downwards. In the transition area, the clusters are more complex, moving upwards at a constant speed, and having large lateral displacement. The results show that the image processing method used in this study is successful in acquiring the dynamic and structural parameters of the clusters simultaneously.     

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.     

Tomographic particle tracking velocimetry using telecentric imaging

The goal of this article is to discuss 3D Particle Tracking Velocimetry (PTV) in a tomographic reconstructed voxel space with at least doubling the spatial resolution compared to classical 3D PTV. For this purpose, a new tomographic reconstruction technique based on telecentric imaging in combination with the epipolar geometry is presented. The method overcomes the need for memory intensive weighting matrices or cost intensive iterations, which are necessary in iterative algebraic reconstruction techniques. A characteristic of tomographic reconstruction is the reconstruction of ghost particles. As the aim of PTV is the reconstruction of true particle paths, this article focuses on the removal of ghost particles and ghost trajectories. The method is validated via a synthetic turbulent flow field and via the benchmark experiment of a vortex ring.     

Aerosols dispersion modelling using probabilistic particle tracking

A method is proposed which can facilitate parallel computations of particle transport in complex environments, such as urban landscapes. A two stage‐approach is used, where in the first stage, physical simulations of various aerosol release scenarios are conducted on a high‐performance distributed computing facility, such as a Beowulf cluster or a computing grid, and stored in a database as a set of transfer probabilities. In this stage, the method provides a partially decoupled parallel implementation of a tightly coupled physical system. In the second stage, various aerosol release scenarios can be analysed in a timely manner, using obtained probability distributions and a simpler stochastic simulator, which can be executed on a commodity computer, such as a workstation or a laptop. The method presents a possibility of solving the inverse problem of determining the release source from the available deposition data. Using the proposed approach and developed graphical tools, a case of aerosol dispersion in a typical urban landscape has been studied. A considerable speedup of analysis time for different aerosol dispersion scenarios has been demonstrated. The method is appropriate for the development of express risk analysis systems. Copyright © 2006 John Wiley & Sons, Ltd.     

New tracking algorithm for particle image velocimetry

The cross correlation tracking technique is widely used to analyze image data, in Particle Image Velocimetry (PIV). The technique assumes that the fluid motion, within small regions of the flow field, is parallel over short time intervals. However, actual flow fields may have some distorted motion, such as rotation, shear and expansion. Therefore, if the distortion of the flow field is not negligible, the fluid motion can not be tracked well using the cross correlation technique. In this study, a new algorithm for particle tracking, called the Spring Model technique, has been proposed. The algorithm can be applied to flow fields which exhibit characteristics such as rotation, shear and expansion.The algorithm is based on pattern matching of particle clusters between the first and second image. A particle cluster is composed of particles which are assumed to be connected by invisible elastic springs. Depending on the deformation of the cluster pattern (i.e., the particle positions), the invisible springs have some forces. The smallest force pattern in the second image is the most probable pattern match to the correspondent original pattern in the first image. Therefore, by finding the best matches, particle movements can be tracked between the two images. Three-dimensional flow fields can also be reconstructed with this technique.The effectiveness of the Spring Model technique was verified with synthetic data from both a two-dimensional flow and three-dimensional flow. It showed a high degree of accuracy, even for the three-dimensional calculation. The experimental data from a vortex flow field in a cylinder wake was also measured by the Spring model technique.