Measured Reynolds stress distributions and energy budgets of a fully pulsed round air jet

 Most particle-tracking velocimetry (PTV) algorithms are not suitable for calculating the velocity vectors of a fluid flow subjected to strong deformation, because these algorithms deal only with flows due to translation. Accordingly, it is necessary to develop a novel algorithm applicable to flows subjected to strong deformations such as rotation, shear, expansion and compression. This paper proposes a novel particle tracking algorithm using the velocity gradient tensor (VGT) which can deal with strong deformations and demonstrates that this algorithm is applicable to some basic fluid motions (rigidly rotating flow, Couette flow, and expansion flow). Furthermore, the performance of this algorithm is compared with the binary image cross-correlation method (BICC), the four-consecutive-time-step particle tracking method (4-PTV), and the spring model particle tracking algorithm (SPG) using simulations and experimental data. As a result, it is shown that this novel algorithm is useful and applicable for the highly accurate measurement and analysis of fluid flows subjected to strong deformations. Received: 9 February 1999/Accepted: 22 November 1999     

Efficient simulation of particle-laden turbulent flows with high mass loadings using LES

The paper is concerned with the simulation of particle-laden two-phase flows based on the Euler–Lagrange approach. The methodology developed is driven by two major requirements: (i) the necessity to tackle complex turbulent flows by eddy-resolving schemes such as large-eddy simulation; (ii) the demand to predict dispersed multiphase flows at high mass loadings. First, a highly efficient particle tracking algorithm was developed working on curvilinear, block-structured grids. Second, to allow the prediction of dense two-phase flows, the fluid–particle interaction (two-way coupling) as well as particle–particle collisions (four-way coupling) had to be taken into account. For the latter instead of a stochastic collision model, in the present study a deterministic collision model is considered. Nevertheless, the computational burden is minor owing to the concept of virtual cells, where only adjacent particles are taken into account in the search for potential collision partners. The methodology is applied to different test cases (plane channel flow, combustion chamber flow). The computational results are compared with experimental measurements and good agreement is found.     

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     

Rod-like particles matching algorithm based on SOM neural network in dispersed two-phase flow measurements

A matching algorithm based on self-organizing map (SOM) neural network is proposed for tracking rod-like particles in 2D optical measurements of dispersed two-phase flows. It is verified by both synthetic images of elongated particles mimicking 2D suspension flows and direct numerical simulations-based results of prolate particles dispersed in a turbulent channel flow. Furthermore, the potential benefit of this algorithm is evaluated by applying it to the experimental data of rod-like fibers tracking in wall turbulence. The study of the behavior of elongated particles suspended in turbulent flows has a practical importance and covers a wide range of applications in engineering and science. In experimental approach, particle tracking velocimetry of the dispersed phase has a key role together with particle image velocimetry of the carrier phase to obtain the velocities of both phases. The essential parts of particle tracking are to identify and match corresponding particles correctly in consecutive images. The present study is focused on the development of an algorithm for pairing non-spherical particles that have one major symmetry axis. The novel idea in the algorithm is to take the orientation of the particles into account for matching in addition to their positions. The method used is based on the SOM neural network that finds the most likely matching link in images on the basis of feature extraction and clustering. The fundamental concept is finding corresponding particles in the images with the nearest characteristics: position and orientation. The most effective aspect of this two-frame matching algorithm is that it does not require any preliminary knowledge of neither the flow field nor the particle behavior. Furthermore, using one additional characteristic of the non-spherical particles, namely their orientation, in addition to its coordinate vector, the pairing is improved both for more reliable matching at higher concentrations of dispersed particles and for higher robustness against loss of particle pairs between image frames.     

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.     

A model for tracking inertial particles in a lattice Boltzmann turbulent flow simulation

A computationally inexpensive model for tracking inertial particles through a turbulent flow is presented and applied to the turbulent flow through a square duct having a friction Reynolds number of Re_τ = 300. Prior to introducing particles into the model, the flow is simulated using a lattice Boltzmann computation, which is allowed to evolve until a steady state turbulent flow is achieved. A snapshot of the flow is then stored, and the trajectories of particles are computed through the flow domain under the influence of this static probability field. Although the flow is not computationally evolving during the particle tracking simulation, the local velocity is obtained stochastically from the local probability function, thus allowing the dynamics of the turbulent flow to be resolved from the point of view of the suspended particles. Particle inertia is modeled by using a relaxation parameter based on the particle Stokes number that allows for a particle velocity history to be incorporated during each time step. Wall deposition rates and deposition patterns are obtained and exhibit a high level of agreement with previously obtained DNS computational results and experimental results for a wide range of particle inertia. These results suggest that accurate particle tracking through complex turbulent flows may be feasible given a suitable probability field, such as one obtained from a lattice Boltzmann simulation. This in turn presents a new paradigm for the rapid acquisition of particle transport statistics without the need for concurrent computations of fluid flow evolution.     

Particle tracking velocimetry with an ant colony optimization algorithm

A new concept algorithm based on the ant colony optimization is developed for the use in 2-D and 3-D particle tracking velocimetry (PTV). In the particle matching process of PTV, the ant colony optimization is usually aimed at minimization of the sum of the distances between the first-frame and second-frame particles. But this type of minimization often goes unsuccessfully in the regions where the particles are located very close to each other. In order to avoid this flaw, a new type of minimization is attempted using a physical property corresponding to the flow consistency or the quasi-rigidity of particle distribution patterns. Specifically, the ant colony optimization is now aimed at minimization of the sum of the relaxation of neighbor particles. In the present study, the new algorithm is applied to sets of 2-D and 3-D synthetic particle images as well as the experimental images with successful results.     

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 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 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     

基于Boltzmann模型方程的气体运动论统一算法研究

模型方程出发,研究确立含流态控制参数可描述不同流域气体流动特征的气体分子速度分布函数方程; 研究发展气体运动论离散速度坐标法, 借助非定常时间分裂数值计算方法和NND差分格式, 结合DSMC方法关于分子运动与碰撞去耦技术, 发展直接求解速度分布函数的气体运动论耦合迭代数值格式; 研制可用于物理空间各点宏观流动取矩的离散速度数值积分方法, 由此提出一套能有效模拟稀薄流到连续流不同流域气体流动问题统一算法. 通过对不同Knudsen数下一维激波内流动、二维圆柱、三维球体绕流数值计算表明, 计算结果与有关实验数据及其它途径研究结果(如DSMC模拟值、N-S数值解)吻合较好, 证实气体运动论统一算法求解各流域气体流动问题的可行性. 尝试将统一算法进行HPF并行化程序设计, 基于对球体绕流及类``神舟'返回舱外形绕流问题进行HPF初步并行试算, 显示出统一算法具有很好的并行可扩展性, 可望建立起新型的能有效模拟各流域飞行器绕流HPF并行算法研究方向. 通过将气体运动论统一算法推广应用于微槽道流动计算研究, 已初步发展起可靠模拟二维短微槽道流动数值算法; 通过对Couette流、Poiseuille流、压力驱动的二维短槽道流数值模拟, 证实该算法对微槽道气体流动问题具有较强的模拟能力, 可望发展起基于Boltzmann模型方程能可靠模拟MEMS微流动问题气体运动论数值计算方法研究途径.      

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.     

The static pressure field as a driving mechanism for the streamwise corner flow in the presence of an inclined transverse plane

In this work, the authors proposed a microscopic particle tracking system based on the previous work (Tien et al. in Exp Fluids 44(6):1015–1026, 2008). A three-pinhole plate, color-coded by color filters of different wavelengths, is utilized to create a triple exposure pattern on the image sensor plane for each particle, and each color channel of the color camera acts as an independent image sensor. This modification increases the particle image density of the original monochrome system by three times and eliminates the ambiguities caused by overlap of the triangle exposure patterns. A novel lighting method and a color separation algorithm are proposed to overcome the measurement errors due to crosstalk between color filters. A complete post-processing procedure, including a cascade correlation peak-finding algorithm to resolve overlap particles, a calibration-based method to calculate the depth location based on epipolar line search method, and a vision-based particle tracking algorithm is developed to identify, locate and track the Lagrangian motions of the tracer particles and reconstruct the flow field. A 10X infinity-corrected microscope and back-lighted by three individual high power color LEDs aligning to each of the pinhole is used to image the flow. The volume of imaging is 600 × 600 × 600 μm³. The experimental uncertainties of the system verified with experiments show that the location uncertainties are less than 0.10 and 0.08 μm for the in-plane and less than 0.82 μm for the out-of-plane components, respectively. The displacement uncertainties are 0.62 and 0.63 μm for the in-plane and 0.77 μm for the out-of-plane components, respectively. This technique is applied to measure a flow over a backward-facing micro-channel flow. The channel/step height is 600/250 μm. A steady flow with low particle density and an accelerating flow with high particle density are measured and compared to validate the flow field resolved from a two-frame tracking method. The Reynolds number in the current work varies from 0.033 to 0.825. A total of 20,592 vectors are reconstructed by time-averaged tracking of 156 image pairs from the steady flow case, and roughly 400 vectors per image pair are reconstructed by two-frame tracking from the accelerating flow case.     

Estimation of particle dynamics in 2-D fluidized beds using particle tracking velocimetry

The experimental characterization of particle dynamics in fluidized beds is of great importance in fostering an understanding of solid phase motion and its effect on particle properties in granulation processes. Commonly used techniques such as particle image velocimetry rely on the cross-correlation of illumination intensity and averaging procedures. It is not possible to obtain single particle velocities with such techniques. Moreover, the estimated velocities may not accurately represent the local particle velocities in regions with high velocity gradients. Consequently, there is a need for devices and methods that are capable of acquiring individual particle velocities. This paper describes how particle tracking velocimetry can be adapted to dense particulate flows. The approach presented in this paper couples high-speed imaging with an innovative segmentation algorithm for particle detection, and employs the Voronoi method to solve the assignment problem usually encountered in densely seeded flows. Lagrangian particle tracks are obtained as primary information, and these serve as the basis for calculating sophisticated quantities such as the solid-phase flow field, granular temperature, and solid volume fraction. We show that the consistency of individual trajectories is sufficient to recognize collision events.     

Estimation of particle dynamics in 2-D fluidized beds using particle tracking velocimetry

The experimental characterization of particle dynamics in fluidized beds is of great importance in fostering an understanding of solid phase motion and its effect on particle properties in granulation processes.Commonly used techniques such as particle image velocimetry rely on the cross-correlation of illumination intensity and averaging procedures.It is not possible to obtain single particle velocities with such techniques.Moreover,the estimated velocities may not accurately represent the local particle velocities in regions with high velocity gradients.Consequently,there is a need for devices and methods that are capable of acquiring individual particle velocities.This paper describes how particle tracking velocimetry can be adapted to dense particulate flows.The approach presented in this paper couples high-speed imaging with an innovative segmentation algorithm for particle detection,and employs the Voronoi method to solve the assignment problem usually encountered in densely seeded flows.Lagrangian particle tracks are obtained as primary information,and these serve as the basis for calculating sophisticated quantities such as the solid-phase flow field,granular temperature,and solid volume fraction.We show that the consistency of individual trajectories is sufficient to recognize collision events.     

Characterization of Single-Phase Flow Through Carbonate Rocks: Quantitative Comparison of NMR Flow Propagator Measurements with a Realistic Pore Network Model

We report here the quantitative comparisons between the measured NMR flow propagator of a carbonate rock and the flow propagator calculated with a porous network extracted from the micro-CT image of the twin plug. We developed a numerical model based on a particle tracking algorithm in pore space. The particle tracking in throats is described using the first arrival time distribution. As pores have an important volume fraction in the sample considered, we implemented a time-delay mechanism for particle transport in the pores. We consider that the nodes have volume and there is a transport of the tracking particles inside the nodes, which leads to an “apparent” time-delay. Simulations of flow propagator show good agreement with low field NMR experiments performed on the twin plug of the sample used for pore network extraction with a single adjustable parameter (that describes the dynamics in the pores). These results lead us to a better understanding of the connection between pore structure and the behavior of NMR flow propagator in fluid-saturated rocks and are essential in interpreting the experimental data and correlating NMR parameters to petrophysical properties.     

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.     

X-ray-based assessment of the three-dimensional velocity of the liquid phase in a bubble column

Information regarding the local liquid velocity in bubble columns is of great interest for research into its performance. Common optical methods fail in bubble flows that have large void fractions because of the different refraction indices of the liquid and gaseous phases. The new X-ray particle tracking velocimetry (XPTV) described here solves the problem by the use of X-rays instead of light. X-rays penetrate a gas/liquid flow in straight lines. XPTV enables us to measure the three-dimensional velocity of the liquid phase. This method was applied and validated in two bubble columns. The same method is also applicable to opaque liquids. Received: 14 August 2000/Accepted: 12 January 2001     

New insights into particle image velocimetry data using fuzzy-logic-based correlation/particle tracking processing

Digital particle image velocimetry (DPIV) data processing has been developed to the point where DPIV image data are processed via auto- or cross-correlation techniques in near real time and the results are displayed on screen as they are processed. Correlation techniques are highly desirable, since they provide velocity measurements on a regular grid, which are readily comparable to CFD predictions of the flow field. In high-speed flows, particle lag effects are always of concern; however, the correlation operation does not provide any means for minimization or elimination of systematic errors in the recorded particle image data. In this paper, we present a combined correlation processing/particle tracking technique providing “super-resolution” velocity measurements. Fuzzy-logic principles are employed to maximize the information recovery in the correlation operation and to determine the correct particle pairings in the tracking operation. The combined correlation/particle tracking technique is applied to DPIV data obtained in the diffuser region of a high-speed centrifugal compressor producing velocity vector maps with an average density of 6 vectors/mm². Inspection of the particle tracking results revealed large particles that were not following the flow. Using preknowledge of the flow field, the biased velocity estimates arising from large particles in the flow were removed, thereby improving the accuracy of the measurements. Received: 21 October 1999/Accepted: 19 August 2000     

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.