Digital Holographic Particle Tracking Velocimetry for 3-D Transient Flow around an Obstacle in a Narrow Channel

Digital holographic particle tracking velocimetry (PTV) is developed by single high-speed camera and single double pulsed laser with high frequency pulses. This system can directly capture 1000 hologram fringe images for 1 second through a camera computer memory. The 3-D particle location is made of the reconstruction by using a computer hologram algorithm in a personal computer. This system can successfully be applied to instantaneous 3-D velocity measurement in the water flow with a square obstacle, and can obtain an average of 300 instantaneous velocity vectors.     

Compressed holographic particle tracking velocimetry for microflow measurements

Optical Review - We have established a holographic particle tracking velocimetry (HPTV) technique based on compressed sensing (CS) for measurement of the velocity field of microflows in this study....     

一种单相机流场速度场三维测量方法

提出了一种用单CCD摄像机进行大型流场三维速度场的测量方法 ,该方法通过利用片光的厚度特征和粒子跟踪算法实现三维速度场的重建 ,通过对模拟脱硫塔中的流场测量证明 ,该方法可以测量定常或低速流场。     

A real-time video encoded particle imaging tracking technique for velocity measurement

A real-time video encoded particle imaging tracking technique (VPIT) for velocity measurement has been developed. It can currently capture images of a seeded particle flow field at up to a video rate of 25 pictures per second. The method as shown in this paper is suitable for measuring a slow sparsely seeded flow. A VPIT image presents a triplet image pattern. The image has been encoded into a single video frame with the time history of three events. This is achieved by synchronising the video (CCIR) signal from a CCD (charge coupled device) camera, operating in frame integration mode with a suitable light source. The principle of VPIT demonstrates how the direction and the magnitude of the velocity can be recorded for a sequence or track of particles. The VPIT triplet images resolve several common difficulties associated with the application of PIV. Firstly, the time history of the laser pulse can be ‘labeled’ on an individual particle image. Secondly, there is no velocity direction ambiguity in the VPIT image. Thirdly, it is possible to extract the acceleration of the particle from a single VPIT frame. Finally, for a sequence of captured frames, the problems of particle path tracking are simplified, because each VPIT image has a video encoded time sequence ‘labelled’ on it.     

Particle tracking velocimetry applied for fireworks

Particle tracking velocimetry (PTV) is applied for measuring the motions of luminous particles in fireworks. The objective of the study is to clarify the technical problems encountering in large-scale quantitative visualization in natural environment. The major problems are found to be uncontrolled background in nature, low pixel resolution relative to particle size, and large perspective. The ways to deal with these problems in current technological level are discussed. In the application, two cameras are located at 1.3 km from the launching point with 30-degree opening angle to implement 3-D PTV. The transient 3-D velocity distributions of around 200 m-scale diameter fireworks are obtained during the light emission from the explosion till burnout. Moreover, the evolution of the mean particle diameter that decreases continuously with the combustion is estimated with the measured velocity information by the particles equation of motion.     

A novel particle tracking velocimetry method for complex granular flow field

Particle tracking velocimetry(PTV) is one of the most commonly applied granular flow velocity measurement methods. However, traditional PTV methods may have issues such as high mismatching rates and a narrow measurement range when measuring granular flows with large bulk density and high-speed contrast. In this study, a novel PTV method is introduced to solve these problems using an optical flow matching algorithm with two further processing steps. The first step involves displacement correction, which is used to solve the mismatching problem in the case of high stacking density.The other step is trajectory splicing, which is used to solve the problem of a measurement range reduction in the case of high-speed contrast The hopper flow experimental results demonstrate superior performance of this proposed method in controlling the number of mismatched particles and better measuring efficiency in comparison with the traditional PTV method.     

Application of DPIV to study both steady state and transient turbomachinery flows

Digital particle imaging velocimetry (DPIV) is a powerful measurement technique, which can be used as an alternative or complementary approach to laser doppler velocimetry (LDV) in a wide range of research applications. The instantaneous planar velocity measurements obtained with PIV make it an attractive technique for use in the study of the complex flow fields encountered in turbomachinery. The planar nature of the technique also significantly reduces the facility run time over point-based techniques. Techniques for optical access, light sheet delivery, CCD camera technology and particulate seeding are discussed. Results from the successful application of the PIV technique to both the blade passage region of a transonic axial compressor and the diffuser region of a high speed centrifugal compressor are presented. Both instantaneous and time-averaged flow fields were obtained. The averaged flow field measurements are used to estimate the flow turbulence intensity. The instantaneous velocity vector maps obtained during compressor surge provide previously unobtainable insight into the complex flow field characteristics occurring during short lived surge events. These flow field maps illustrate the true power of the DPIV technique.     

PIV技术在气体界面不稳定性实验研究中的应用

采用粒子图像测速(Particle Image Velocimetry,PIV)技术,以乙二醇烟雾作为示踪粒子,实验测量了平面激波作用下SF6气柱-空气界面Richtmyer-Meshkov不稳定性演化图像和二维速度场.测量结果揭示了不稳定性流场的典型特征和细微结构,与高速摄影和数值模拟结果符合,说明建立的PIV技术适...     

A method to determine the measurement volume for particle shadow tracking velocimetry (PSTV)

Journal of Visualization - A method to determine the depth of detection (DOD) for particle shadow tracking velocimetry (PSTV) is presented. In contrast with other image-based velocimetry...     

基于高斯混合概率假设滤波的水下目标跟踪算法

为了解决传统水下目标跟踪中目标数目估计不准确、状态估计误差增长过快的问题,提出了一种基于高斯混合概率假设滤波的水下目标跟踪算法。该算法基于双基地观测模型,采用高斯混合概率假设滤波算法处理方位和时延信息,利用粒子群算法处理多普勒频率获得矢量速度,进一步提升算法的跟踪精度。结果表明,该算法能完成在杂波环境下对目标的跟踪,相比传统的关联算法,能够有效地实现目标个数估计和抑制状态误差增长的目的。     

Improved particle filters for multi-target tracking

We present a novel approach for improving particle filters for multi-target tracking. The suggested approach is based on drift homotopy for stochastic differential equations. Drift homotopy is used to design a Markov Chain Monte Carlo step which is appended to the particle filter and aims to bring the particle filter samples closer to the observations while at the same time respecting the target dynamics. We have used the proposed approach on the problem of multi-target tracking with a nonlinear observation model. The numerical results show that the suggested approach can improve significantly the performance of a particle filter.     

Advances in digital holographic micro-PTV for analyzing microscale flows

The accurate three-dimensional (3D) velocity field measurement technique has been receiving large attention in the study of microfluidics. DHM-PTV technique was developed by combining the digital holographic microscopy and particle tracking velocimetry technique. DHM-PTV is an ideal method for measuring three-component-three-dimensional (3C-3D) velocity field in a microscale flow with a fairly good spatial resolution. The advances in the DHM-PTV technique enable the measurement of various microscale flows, such as transport of red blood cells in a microtube and 3D flows in microfluidic devices. DHM-PTV is also applied in studying the motile behavior of swimming microorganisms. DHM-PTV would play an important role in ascertaining the undiscovered basic physics in various microscale and biofluid flow phenomena. In the current study, the basic principle of the DHM-PTV technique and its typical applications to microscale flows are introduced and discussed.     

Particle image analysis of open-channel flow at a backward facing step having a trench

Characteristics of open-channel flow at a backward facing step with a trench section were experimentally investigated. The experiments were performed using a straight open channel flume by varying the trench length. Two-dimensional velocity measurements by the particle tracking velocimetry (PTV) were conducted at the trench section using a high-resolution video camera of 12 fps. Mean and turbulent flow properties along with the water surface profile were measured for various aspect ratios of the trench. It was also observed that the flow at the trench begins to oscillate periodically for a specific aspect ratio of the trench.     

平面波经颅超声成像相位校正及散斑跟踪

准确的脑血流成像对脑功能监测和脑疾病的快速诊断具有重要意义,然而颅骨对超声传播的影响会导致成像质量下降、速度或位移估计不准确等问题.论文采用平面波相干复合结合散斑跟踪方法进行颅内散射目标成像和速度估计,以实现脑血流速度矢量检测;针对颅骨存在导致的超声相位畸变,利用数值仿真和体模实验研究了其对成像及散斑跟踪效果的影响,并...     

Three-dimensional particle cluster tracking algorithm using affine transformation

In Particle Image Velocimetry (PIV), the cross correlation tracking technique is widely used to analyze the particle images. The technique assumes that the fluid motion, within small regions of the flow field, is given by parallel movements over short time intervals. However, actual flow fields may have some distorted motion, such as rotation and shear. Therefore, if the distortion of the flow field is not negligible, the fluid motion can not be tracked well using the cross correlation technique. The author proposed a new particle tracking technique, based on the particle cluster matching using linear Affine Transformation. The algorithm can be applied to flow fields which exhibit characteristics such as rotation and shear. The deformation of the cluster pattern is expressed by the linear Affine Transformation. The parameter of the transformation can be determined using the least square technique from the particle positions. The effectiveness of the tracking techniques, including 3D cross correlation, Spring Model and Affine Transformation, were evaluated with synthetic data of three-dimensional flow field. The cross correlation technique could be applicable to the small deformation cases. When the deformation of particle pattern between two images are very large, the pattern deformation could not be expressed by the Affine Transformation, i.e., linear transformation, resulting in mis-tracking. However, the Spring Model technique was found to be more effective even in the larger deformation condition, because the Spring Model does not assume the linear transformation.     

Study on concurrent measurements of velocity and density distributions for high-speed CO2 flow

Velocity and density distributions of a high-speed and initial CO₂ jet flow have been analyzed concurrently by a developed three-dimensional digital speckle tomography and a particle image velocimetry (PIV). Two high-speed cameras have been used for the tomography and one for the PIV since a shape of a nozzle for the jet flow is bilaterally symmetric and the starting flow is fast and unsteady. The speckle movements between no flow and CO₂ jet flow have been obtained by a cross-correlation tracking method so that those distances can be transferred to deflection angles of laser rays for density gradients. The three-dimensional density fields for the high-speed CO₂ jet flow have been reconstructed from the deflection angles by the real-time tomography method, and the two-dimensional velocity fields have been calculated by the PIV method concurrently and instantaneously.     

Volume three-dimensional flow measurements using wavelength multiplexing

Optically distinguishable seeding particles that emit light in a narrow bandwidth, and a combination of bandwidths, were prepared by encapsulating quantum dots. The three-dimensional components of the particles' displacement were measured within a volume of fluid with particle tracking velocimetry (PTV). Particles are multiplexed to different hue bands in the camera images, enabling an increased seeding density and (or) fewer cameras to be used, thereby increasing the measurement spatial resolution and (or) reducing optical access requirements. The technique is also applicable to two-phase flow measurements with PTV or particle image velocimetry, where each phase is uniquely seeded.     

基于积分直方图的快速粒子滤波跟踪算法

基于直方图的粒子滤波已成功地用于解决计算机视觉中的目标跟踪问题,但是,在观测似然计算上的低效限制了它们的实时应用。针对该问题,提出了一种快速的粒子跟踪方法。其建立在积分直方图技术的基础上,使得每个候选样本的观测似然能够由少量的查找表运算有效地计算出来。该方法使用了大量的粒子以确保鲁棒性,同时确保具备实时跟踪的能力。实验结果表明该方法在计算效率上优于通常的粒子滤波跟踪方法。     

Applications of Defocusing DPIV to Bubbly Flow Measurement

Defocusing digital particle image velocimetry (DDPIV) was used to investigate a bubbly flow in the wake of a hydrofoil. DDPIV is a three component volumetric velocimetry technique that operates at full video rate. Complex, three‐dimensional, and time‐dependent flows can be measured. To measure the bubble sizes, an extension to DDPIV was made to infer bubble sizes from their intensities. Both bubble size distributions and bubble velocity fields were simultaneously measured. Results indicate that DDPIV can reliably measure bubble sizes in the range of 100 microns, as well as resolving their aggregate motion.     

Rolling velocity and relative motion of particle detector in local granular flow

The velocity of a particle detector in granular flow can be regarded as the combination of rolling and sliding velocities. The study of the contribution of rolling velocity and sliding velocity provides a new explanation to the relative motion between the detector and the local granular flow. In this study, a spherical detector using embedded inertial navigation technology is placed in the chute granular flow to study the movement of the detector relative to the granular flow. It is shown by particle image velocimetry (PIV) that the velocity of chute granular flow conforms to Silbert's formula. And the velocity of the detector is greater than that of the granular flow around it. By decomposing the velocity into sliding and rolling velocity, it is indicated that the movement of the detector relative to the granular flow is mainly caused by rolling. The rolling detail shown by DEM simulation leads to two potential mechanisms based on the position and drive of the detector.