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.     

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.     

Flow structure of microbubble-laden turbulent channel flow measured by PIV combined with the shadow image technique

The turbulence structure of a horizontal channel flow with microbubbles is experimentally investigated using combined particle image velocimetry (PIV) in order to clarify the mechanism of drag reduction caused by microbubbles. A new system which simultaneously measures the liquid phase and the dispersed bubbles is proposed, based on a combination of particle tracking velocimetry (PTV), laser-induced fluorescence (LIF) and the shadow image technique (SIT). To accurately obtain the velocity of the liquid phase, tracer particles which overlap with the bubble shadow images are almost entirely eliminated in the post-processing. Finally, the turbulence characteristics of the flow field are presented, including measurements for both phases, and the bubble effect on the turbulence is quantified.     

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.     

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 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.     

Particle imaging techniques for microfabricated fluidic systems

This paper presents the design and implementation of velocimetry techniques applicable to the analysis of microfluidic systems. The application of both micron-resolution particle image velocimetry (micro-PIV) and particle tracking velocimetry (PTV) to the measurement of velocity fields within micromachined fluidic channels is presented. The particle tracking system uses epifluorescent microscopy, CCD imaging, and specialized image interrogation algorithms to provide microscale velocity measurement resolution. The flow field in a straight channel section is measured using cross-correlation micro-PIV and compared to the analytical solution for a measured mass flow rate. Velocity field measurements of the flow at the intersection of a cross-channel are also presented and compared with simulations from a commercially available flow solver, CFD-ACE+. Discussions regarding flow seeding, imaging optics, and the flow setup for measuring flows in microfabricated fluidic devices are presented. A simple process for estimating measurement uncertainty of the in-plane velocity measurements caused by three-dimensional Brownian motion is described. A definition for the measurement depth for PTV measurements is proposed. The agreement between measured and predicted values lends further support to the argument that liquid microflows with characteristic dimensions of order 50-μm dimension channels follow macroscale flow theory.     

Turbulence statistics from optical whole-field measurements in particle-laden turbulence

Obtaining turbulence statistics in particle-laden flows using optical whole-field measurements is complicated due to the inevitable data loss. The effects of this data loss are first studied using synthetic data and it is shown that the interpolation of missing data leads to biased results for the turbulence spectrum and its derived quantities. It is also shown that the use of overlapping interrogation regions in images with a low image density can lead to biased results due to oversampling. The slotting method is introduced for the processing of particle image velocimetry (PIV) data fields with missing data. Next to this, it is extended to handle unstructured data. Using experimental data obtained by a dual-camera PIV/PTV (particle tracking velocimetry) system in particle-laden grid turbulence, the performance of the new approach is studied. Some preliminary two-phase results are presented to indicate the significant improvement in the statistics, as well as to demonstrate the unique capabilities of the system.An erratum to this article can be found at

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Development and validation of echo PIV

The combination of ultrasound echo images with digital particle image velocimetry (DPIV) methods has resulted in a two-dimensional, two-component velocity field measurement technique appropriate for opaque flow conditions including blood flow in clinical applications. Advanced PIV processing algorithms including an iterative scheme and window offsetting were used to increase the spatial resolution of the velocity measurement to a maximum of 1.8 mm×3.1 mm. Velocity validation tests in fully developed laminar pipe flow showed good agreement with both optical PIV measurements and the expected parabolic profile. A dynamic range of 1 to 60 cm/s has been obtained to date.     

两相流PIV粒子图像处理方法的研究

本文在单相PIV技术的基础上研究了两相流动PIV图像处理方法，采用摸板匹配法和灰度加权标定法对两相粒子进行了识别、区分和标定，采用灰度互相关法对区分后的单相粒子图像进行了处理，应用基于以上方法编制的Windows应用软件，首先对由美国Minnesota大学复杂流动实验室提供的两相流动粒子图片进行了处理，通过对比分析可见，应用本文所采用的方法能对两相粒子进行有效的识别和区分，然后以搅拌槽内液固两相流场为例对此方法进行了应用。     

On the resolution limit of digital particle image velocimetry

This work analyzes the spatial resolution that can be achieved by digital particle image velocimetry (DPIV) as a function of the tracer particles and the imaging and recording system. As the in-plane resolution for window-correlation evaluation is related by the interrogation window size, it was assumed in the past that single-pixel ensemble-correlation increases the spatial resolution up to the pixel limit. However, it is shown that the determining factor limiting the resolution of single-pixel ensemble-correlation are the size of the particle images, which is dependent on the size of the particles, the magnification, the f-number of the imaging system, and the optical aberrations. Furthermore, since the minimum detectable particle image size is determined by the pixel size of the camera sensor in DPIV, this quantity is also considered in this analysis. It is shown that the optimal magnification that results in the best possible spatial resolution can be estimated from the particle size, the lens properties, and the pixel size of the camera. Thus, the information provided in this paper allows for the optimization of the camera and objective lens choices as well as the working distance for a given setup. Furthermore, the possibility of increasing the spatial resolution by means of particle tracking velocimetry (PTV) is discussed in detail. It is shown that this technique allows to increase the spatial resolution to the subpixel limit for averaged flow fields. In addition, PTV evaluation methods do not show bias errors that are typical for correlation-based approaches. Therefore, this technique is best suited for the estimation of velocity profiles.     

A model-based validation framework for PIV and PTV

The utility of particle image velocimetry (PIV) for measurement of velocity fields in many fluid flows is well established. This has created interest in overcoming difficulties with the technique when applied to increasingly larger fields of view where there exists a significant range of velocities and spatial velocity gradients are large. In this regard, a major deficiency with standard cross-correlation PIV is the inherent link between the density of vectors in the measurement field and the maximum measurable displacement. Several schemes exist to reduce this link. These iterative hierarchical/multiresolution schemes are strongly dependent on vector validation routines to remove spurious vectors. Here the design of a new framework for vector validation is described. This framework is general enough for use with both regular and irregularly spaced vector fields to make it applicable to particle image velocimetry (PIV), particle tracking velocimetry (PTV), and hybrid methods. It is based on the determination of a smoothed displacement field that robustly characterizes the measured field such that invalid vectors are attenuated more than valid vectors. In this particular study a thin-plate spline model is incorporated within an iterative regularized weighted least-squares routine to find a smoothed version of the displacement field that maintains pertinent velocity gradient information. The utility of the methodology is demonstrated for a complex flow profile containing four vortices where the valid displacement ranges from ?1/4 to +1/4 of the area of interest (AOI) dimension. Results indicate that this validation strategy can discriminate between valid and invalid vectors remarkably well over a range of parameter settings. In the example presented a flow field with significant velocity gradients and having a high number of invalid vectors (25%) is accurately validated.     

基于光流算法的粒子图像测速技术研究和验证

光流测量技术作为一种新的空气动力学实验技术,以其像素级分辨率的矢量场测量优势获得广泛的应用。光流测量技术使用光流约束方程,配合平滑限定条件,可以进行速度场测量,获得高分辨率的全局矢量场。本文首先通过研究积分最小化光流测速理论和算法,采用C++编写光流速度测量程序;然后通过三种典型的人工位移图像对光流计算程序进行了验证,并将结果和标准位移分布进行比对分析,以指导如何在实际应用中获得高精度光流速度场;最后进行小型风洞后向台阶实验,利用高速相机拍摄示踪粒子图像,使用光流计算程序获得速度矢量场,同采用互相关算法的粒子图像测速计算结果相比较,体现出光流计算方法像素级分辨率的矢量场测量优势。     

PIV measurement of flow around an arbitrarily moving body

This paper presents a PIV (particle image velocimetry) image processing method for measuring flow velocities around an arbitrarily moving body. This image processing technique uses a contour-texture analysis based on user-defined textons to determine the arbitrarily moving interface in the particle images. After the interface tracking procedure is performed, the particle images near the interface are transformed into Cartesian coordinates that are related to the distance from the interface. This transformed image always has a straight interface, so the interrogation windows can easily be arranged at certain distances from the interface. Accurate measurements near the interface can then be achieved by applying the window deformation algorithm in concert with PIV/IG (interface gradiometry). The displacement of each window is evaluated by using the window deformation algorithm and was found to result in acceptable errors except for the border windows. Quantitative evaluations of this method were performed by applying it to computer-generated images and actual PIV measurements.     

Hummingbirds generate bilateral vortex loops during hovering: evidence from flow visualization

This article derives a method to estimate and correct the bias error of the shift vector’s absolute length in the presence of curved streamlines. The main idea is to identify the most likely streamline with constant curvature from the second-order shift vector and its gradient. The work establishes a theoretical framework including the systematic errors of the first-order and second-order shift vector’s absolute value and angle. Synthetic images of a stationary vortex are used to validate the proposed method. The curvature-correction is also applied to a synthetic flow field with non-constant curvature to demonstrate its potential for more realistic flow fields. The results reveal that second-order accurate vector fields suffer from a biased shift vector length depending on the streamline’s curvature and on the shift vector length. The bias error is negligible for vector fields with a shift vector length below the streamline curvature radius. For large shift vectors or strong curvatures, the bias error can be significantly reduced with the developed method. The approach is very general and can be applied to any vector field obtained from window-correlation particle image velocimetry (PIV), single-pixel ensemble-correlation PIV, particle tracking velocimetry or optical flow methods. It also works for all 3D extensions of the techniques, such as 3D-PTV or tomographic PIV.     

Two-color digital PIV employing a single CCD camera

 An extension of two color particle image velocimetry (PIV) is described where the color images are recorded onto a single high-resolution (3060×2036 pixel) color CCD sensor. Unlike mono-color CCD sensors, this system not only eliminates the processing time and the subsequent digitization time of film-based PIV but also resolves the directional ambiguity of the velocity vector without using conventional image-shifting techniques. For comparing the spatial resolutions of film and CCD data, a calibration experiment is conducted by recording the speckle pattern onto 35 mm color film and using a CCD sensor under identical conditions. This technique has been successfully implemented for simulated turbine film-cooling flows in order to obtain a more detailed characterization of the coolant-injection phenomenon and its interaction with freestream disturbances. Received: 20 November 1996/Accepted: 29 January 1998     

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.     

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     

Eulerian and Lagrangian views of a turbulent boundary layer flow using time-resolved tomographic PIV

Coherent structures and their time evolution in the logarithmic region of a turbulent boundary layer investigated by means of 3D space–time correlations and time-dependent conditional averaging techniques are the focuses of the present paper. Experiments have been performed in the water tunnel at TU Delft measuring the particle motion within a volume of a turbulent boundary layer flow along a flat plate at a free-stream velocity of 0.53 m/s at Re _θ = 2,460 based on momentum thickness by using time-resolved tomographic particle image velocimetry (PIV) at 1 kHz sampling rate and particle tracking velocimetry (PTV). The obtained data enable an investigation into the flow structures in a 3D Eulerian reference frame within time durations corresponding to 28 δ/U. An analysis of the time evolution of conditional averages of vorticity components representing inclined hairpin-like legs and of Q2- and Q4-events has been performed, which gives evidence to rethink the early stages of the classical hairpin development model for high Reynolds number TBLs. Furthermore, a PTV algorithm has been applied on the time sequences of reconstructed 3D particle image distributions identifying thousands of particle trajectories that enable the calculation of probability distributions of the three components of Lagrangian accelerations.     

两相流显微PIV/PTV系统的开发

开发了一个能同时测量两相流中两相速度和细颗粒尺寸分布的显微PIV/PTV系统,其硬件系统包括大功率连续激光器、显微镜、高速摄像机;软件系统由改进的球形颗粒图像识别算法、各种图像处理算法和各种先进的PIV/PTV算法组成。其中改进的圆弧识别算法能够进行更准确地进行曲线分割而能对充满噪音并相互重叠的颗粒图像给出较好的识别结果。应用该PIV系统,可以在微秒和微米数量级上捕获细颗粒/气泡图像,并能较准确地同时得到两相速度、颗粒尺寸和浓度分布。对焚香可吸入颗粒物进行了速度和尺寸的同时测量,得到了较满意的结果。