水气两相流中稀疏气泡流动速度场的数字图像测量初探

由空压机提供的气体通过—排微小直径的喷嘴进入静止水体，形成水气两相流流场。在单相PIV和PTV技术的基础上，研究稀疏气液两相流情况下气泡的速度场分布。PIV算法采用快速傅立叶互相关分析法，而PTV算法需要获得每幅图像中每个气泡的形心，根据连续图像中的粒子对，计算速度。用PIV和PTV两种算法处理求出气泡的速度并对两种方法进行比较，其最终研究成果可应用于流体及多相流的流量测技术，提高我们进行低密度气液两相流相关研究的测量水平。同时为水气两相流的数值分析和理论研究提供流场测试的数据。     

采用PTV技术研究循环流化床内气固两相流动

采用PTV技术对循环流化床顶部颗粒稀疏流动区域进行了测量，其中采用先进的高速摄像技术获取流动的连续图像，并采用目前有望在气固两相流动测量中发挥较大作用的四种PTV算法：BICC法、VGT法、SPRING法和4-FRAME法，对所获取的图像进行颗粒配对处理，从而得到流场中运动颗粒的速度信息。所得到的结论为：本文所采用的PTV算法在图像处理中都产生少量的伪矢量，通过采取简单的伪矢量识别算法就可以剔除大部分伪矢量；本文实验条件下，测得循环流化床顶部区域内颗粒运动速度差别较小。本文工作为进一步详细实验测量研究奠定了理论与技术基础。     

风沙两相流PIV测量算法研究

在风沙两相流图像特征的基础上，提出了一种基于模式识别动态聚类方法中$K$-均值 算法的数字面具(Digital Mask)自动生成算法来求解风沙两相流动. 简化了传统生成Digital Mask过程中手动设置参数的操作，减少了人为误差，为批量处理风沙两相流PIV图像提供 了一种安全快捷的方法. 并将该算法应用于风沙两相流的PIV实际测量，分别得到了不同流 动状态下的气流、沙粒以及风沙气固两相流的速度场.     

风沙两相流测量技术研究进展

围绕风沙两相流的测量, 归纳了过去几十年来在风沙动力学研究中所使用的风速测量技术和输沙率测量装置.着重讨论了高频测量在目前风沙动力学研究中的必要性, 分析了传统风速和输沙率测量装置的优缺点.对新一代光学测量技术------PIV在风沙两相流测量中的应用进行了较为详细的探讨.指出PIV测速技术在风沙两相流研究中具有广泛的应用前景, 使用PIV测速技术可以得到风沙流结构、两相速度场等宏观信息, 同时也可以进行单个颗粒运动状态的研究.      

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。     

气液两相流中气泡运动速度场的PIV分析与研究

粒子图像测速技术（PIV）作为一种无扰、瞬态、全场速度测量方法，已被广泛应用于液体或气体的单相流流速场测定。对于两相流PIV技术，目前还处于起步阶段，本文应用PIV技术的基本原理，对静止液体中的气泡运动速度进行了分析，并对有关气液两相流测量问题进行了探讨。     

基于可见光与红外图像特征融合的目标跟踪

针对单一图像源下目标跟踪精度不高的问题,利用跟踪状态下的目标存在于可见光与红外图像中的特征对连续自适应均值移动跟踪算法做出改进。首先选取可见光图像的“颜色梯度背投影”作为改进的目标模型,选取红外图像的“灰度梯度背投影”作为改进的目标模型;然后根据可见光序列图像和红外序列图像各自进行连续自适应均值移动跟踪算法得到的对应的qi系数判定两种图像跟踪的效果,对两种图像的权重进行自适应调整,得到这两种图像的特征级融合图像和跟踪结果。实验结果表明,对于320像素×240像素的可见光和红外图像,基于可见光与红外图像特征融合的目标跟踪算法在复杂背景下能够较准确的跟踪目标,目标跟踪精度为0.5像素,跟踪速度为30~32 ms/帧。     

PIV速度场坏矢量的本征正交分解处理技术

介绍了一种针对粒子图像测速(PIV)基于本征正交分解(POD)的速度场后处理技术.该技术改变了现在后处理技术将速度场坏矢量识别和修正分开实现的局面,通过迭代方法有效地实现了速度场坏点统一的识别和修复算法.算法利用POD分解的低阶模态信息重构出可以用于坏矢量识别的参考速度场,利用该参考速度场对全流场进行坏点识别并完成修正.通过对一套光滑的PIV速度场数据引入高斯分布的随机误差,测试验证了该POD方法的优越性.在坏矢量识别方面新方法较归一化中值检验有更高的正确性,能识别大面积出现的坏矢量区域.在坏矢量修补的插值算法中,新方法的计算效率又高于传统Gappy POD方法,且计算精度优于常见的矢量场内插数学方法.特别是在数据缺失的大连通区域,该方法对物理流场有很好的预测效果.     

壁面湍流发卡涡包空间模态的TRPIV实验研究

运用高时间分辨率粒子图像测速(Time-resolved PIV简称TRPIV),测量得到平板湍流边界层流向/法向平面内瞬时速度矢量空间分布的时间序列;采用空间局部平均速度结构函数的概念,识别和提取湍流边界层中大尺度发卡涡包结构的空间特征。发现在湍流边界层中不同法向位置多个正负发卡涡包结构同时交替存在。这些分布在不同法向高度的发卡涡包结构之间通过倾斜的涡量剪切层相联系,构成了湍流边界层中内、外区紧密相连、相互作用的一种稳态的分布方式。     

AN ARITHMETIC METHOD OF SEGMENTING MOVING AEOLIAN SAND PARTICLES＇ IMAGES FROM BACKGROUND INFORMATION OF DIGITAL HIGH-SPEED PHOTOGRAPHY IMAGES

为了深入理解多因素驱动下风沙颗粒起动的动态演化规律,需要准确地获得沙质床面附近沙粒群起跳的方式、速度和运动轨迹.以连续强激光源为照明的数字高速摄影技术是研究这类问题的有效手段,但由于风沙运动的高速摄影图像具有运动沙粒和静止床面的对比度小、相邻两帧图像相似性小等特点,原始图像叠加算法难以有效实现目标与背景的分割.该文提出了基于相邻的风沙运动图像灰度差值变化原理的图像分割算法.实例显示,只要选择合适的相邻图像灰度差值阈值和自适应二值化处理方法就能实现图像中运动沙粒与床面分割.当起跳沙粒浓度较低情况下,基于MATLAB平台的最小距离匹配的粒子追踪算法（PTV算法）能较为准确地恢复床面附近沙粒的运动轨迹.     

Cold flow experiments in an entrained flow gasification reactor with a swirl-stabilized pulverized biofuel burner

Short particle residence time in entrained flow gasifiers demands the use of pulverized fuel particles to promote mass and heat transfer, resulting high fuel conversion rate. The pulverized biomass particles have a wide range of aspect ratios which can exhibit different dispersion behavior than that of spherical particles in hot product gas flows. This results in spatial and temporal variations in temperature distribution, the composition and the concentration of syngas and soot yield. One way to control the particle dispersion is to impart a swirling motion to the carrier gas phase. This paper investigates the dispersion behavior of biomass fuel particles in swirling flows. A two-phase particle image velocimetry technique was applied to simultaneously measure particle and gas phase velocities in turbulent isothermal flows. Post-processed PIV images showed that a poly-dispersed behavior of biomass particles with a range of particle size of 112–160 µm imposed a significant impact on the air flow pattern, causing air flow decelerated in a region of high particle concentration. Moreover, the velocity field, obtained from individually tracked biomass particles showed that the swirling motion of the carrier air flow gives arise a rapid spreading of the particles.     

A kilohertz frame rate cinemagraphic PIV system for laboratory-scale turbulent and unsteady flows

A kilohertz frame rate cinemagraphic particle image velocimetry (PIV) system has been developed for acquiring time-resolved image sequences of laboratory-scale gas and liquid-phase turbulent flows. Up to 8000 instantaneous PIV images per second are obtained, with sequence lengths exceeding 4000 images. The two-frame cross-correlation method employed precludes directional ambiguity and has a higher signal-to-noise ratio than single-frame autocorrelation or cross-correlation methods, facilitating acquisition of long uninterrupted sequences of valid PIV images. Low and high velocities can be measured simultaneously with similar accuracy by adaptively cross-correlating images with the appropriate time delay. Seed particle illumination is provided by two frequency-doubled Nd:YAG lasers producing Q-switched pulses at the camera frame rate. PIV images are acquired using a 16 mm high-speed rotating prism camera. Frame-to-frame registration is accomplished by imaging two pairs of crossed lines onto each frame and aligning the digitized image sequence to these markers using image processing algorithms. No flow disturbance is created by the markers because only their image is projected to the PIV imaging plane, with the physical projection device residing outside the flow field. The frame-to-frame alignment uncertainty contributes 2% to the overall velocity measurement uncertainty, which is otherwise comparable to similar film-based PIV methods. Received: 11 July 2000 / Accepted: 21 June 2001 Published online: 29 November 2001     

Phase discrimination method for simultaneous two-phase separation in time-resolved stereo PIV measurements

A phase discrimination method for two-phase PIV is presented that is capable of simultaneously separating the two phases from time-resolved stereoscopic PIV images taken in a particle-laden jet. The technique developed expands on previous work done by Khalitov and Longmire (Exp Fluids 32:252–268, 2002), where by means of image processing techniques, a raw two-phase PIV image can be separated into two single-phase images according to particle size and intensity distributions. The technique is expanded through the use of three new image processing algorithms to separate particles of similar size (up to an order of magnitude better than published work) for fields of view much larger than previously considered. It also addresses the known problem of noisy background images produced by high-speed CMOS cameras, which makes the particle detection and separation from the noisy background difficult, through the use of a novel fast Fourier transform background filter.     

Simultaneous velocity field measurements in two-phase flows for turbulent mixing of sprays by means of two-phase PIV

This paper describes a novel derivative of the PIV method for measuring the velocity fields of droplets and gas phases simultaneously using fluorescence images rather than Mie scattering images. Two-phase PIV allows the simultaneous and independent velocity field measurement of the liquid phase droplets and ambient gas in the case of two-phase flow mixing. For phase discrimination, each phase is labelled by a different fluorescent dye: the gas phase is seeded with small liquid droplets, tagged by an efficient fluorescent dye while the droplets of the liquid phases are tagged by a different fluorescent dye. For each phase, the wavelength shift of fluorescence is used to separate fluorescence from Mie scattering and to distinguish between the fluorescence of each phase. With the use of two cross-correlation PIV cameras and adequate optical filters, we obtain two double frame images, one for each phase. Thus standard PIV or PTV algorithms are used to obtain the simultaneous and independent velocity fields of the two phases. Because the two-phase PIV technique relies on the ability to produce two simultaneous and independent images of the two phases, the choice of the labelling dyes and of the associated optical filter sets is relevant for the image acquisition. Thus a spectroscopic study has been carried out to choose the optimal fluorescent dyes and the associated optical filters. The method has been evaluated in a simple two-phase flow: droplets of 30–40 μm diameter, produced by an ultrasonic nozzle are injected into a gas coflow seeded with small particles. Some initial results have been obtained which demonstrate the potential of the method.     

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.     

Vector post-processing algorithm for phase discrimination of two-phase PIV

A simple phase separation method using vector post-processing techniques is evaluated to measure velocity fields in a bubble plume. To provide for validation, fluorescent seeding is used, and two sets of synoptic images are obtained: mixed-phase images containing bubbles and fluorescent particles, and fluid-phase images containing only fluorescent particles. A third dataset is derived by applying a digital mask to remove bubbles from the mixed-phase images. All datasets are processed using cross-correlation particle image velocimetry (PIV). The resulting vector maps for the raw, mixed-phase data contain both bubble and continuous-phase velocity vectors. To separate the phases, a vector post-processing algorithm applies a maximum velocity threshold for the continuous-phase velocities coupled with the vector median filter to identify remaining bubble-velocity vectors and remove them from the mixed-phase velocity field. To validate the phase separation algorithm, the post-processed fluid-phase vectors are compared to PIV results obtained from both the optically separated and digitally masked data. The comparison among these methods shows that the post-processed mixed-phase data have small errors in regions near some bubbles, but for dilute environmental flows (low void fraction and slip velocity approximately equal to the entrained fluid velocity), the algorithm predicts well both instantaneous and time average statistical quantities. The method is reliable for flows having 10% or less of the field of view occupied by bubbles. The resulting instantaneous data provide information on plume wandering and eddy-size distributions within the bubble plume. By comparison among the datasets, it is shown that the patchiness of the vector-post processed and image masked data limit the diameter of identifiable eddy structures to the average distance between bubbles in the image, and that both datasets give identical probability density functions of eddy size. The optically filtered data have better data coverage and predict a greater probability of larger eddies as compared to the other two datasets.     

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.     

Local gas and liquid phase velocity measurement in a miniature stirred vessel using PIV combined with a new image processing algorithm

A method which combines standard two-dimensional particle image velocimetry (PIV) with a new image processing algorithm has been developed to measure the average local gas bubble velocities, as well as the local velocities of the liquid phase, within small stirred vessel reactors. The technique was applied to measurements in a gas–liquid high throughput experimentation (HTE) vessel of 45 mm diameter, but it is equally suited to measurements in larger scale reactors. For the measurement of liquid velocities, 3 μm latex seeding particles were used. For gas velocity measurements, a separate experiment was conducted which involved doping the liquid phase with fluorescent Rhodamine dye to allow the gas–liquid interfaces to be identified. The analysis of raw PIV images enabled the detection of bubbles within the laser plane, their differentiation from obscuring bubbles in front of the laser plane, and their use in lieu of tracer particles for gas velocity analysis using cross-correlation methods. The accuracy of the technique was verified by measuring the velocity of a bubble rising in a vertical glass column. The new method enabled detailed velocity fields of both phases to be obtained in an air–water system. The overall flow patterns obtained showed a good qualitative agreement with previous work in large scale vessels. The downward liquid velocities above the impeller were greatly reduced by the addition of the gas, and significant differences between the flow patterns of the two-phases were observed.     

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.     

A novel architecture for a super-resolution PIV algorithm developed for the improvement of the resolution of large velocity gradient measurements

Three different particle image processing algorithms have been developed for the improvement of PIV velocity measurements characterized by large velocity gradients. The objectives of this study are to point out the limitations of the standard processing methods and to propose a complete algorithm to enhance the measurement accuracy. The heart of the PIV image processing is a direct cross-correlation calculation in order to obtain complete flexibility in the choice of the size and the shape of the interrogation window (IW). An iterative procedure is then applied for the reduction of the size of IW at each measurement location. This procedure allows taking into account the local particle concentration in the image. The results of this first iterative processing, applied to synthetic images, show both a significant improvement of measurement accuracy and an increase of the spatial resolution. Finally, a super-resolution algorithm is developed to further increase the spatial resolution of the measurement by determining the displacement of each particle. The computer time for a complete image processing is optimized by the introduction of original data storage in Binary Space Partitions trees. It is shown that measurement errors for large velocity gradient flows are similar to those obtained in simpler cases with uniform translation displacements. This last result validates the ability of the developed super-resolution algorithm for the aerodynamic characterization of large velocity gradient flows.