Name: three-dimensional particle tracking velocimetry for turbulence applications: case of a jet flow
Uploaded: 2016-02-27T15:00:00.000+00:00
Duration: 13 min 2 s
Description: Watch this Scientific Journal Video about Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow at JoVE.com

这项工作是由机械科学与工程学院，伊利诺伊大学厄巴纳 - 香槟分校，部作为莱昂纳多P.查莫洛的开办包的一部分支持。

1.实验室安全<ol><li>查看所选照明光源的安全指导方针（ 如激光，LED产业，卤素）。 注意:在这个实验中，一组五个250瓦卤素聚光灯被用作照明。该光源的基本安全性和推荐方面描述如下。 <ol><li>避免与卤灯，它在高温下（〜3000色温）下操作的直接接触。 </li><li>采集数据时，为了避免加热的条件下考虑流动保持光ON而已。 </li><li>远离可燃所有材料靠近光源，包括任何种类的纸张。 </li></ol></li></ol> 2.实验装置<ol><li>选择合适的镜头<ol><li>选择低像差镜头，以避免校准问题。推荐镜头类型长焦或微透镜。 </li><li>确保镜头涵盖的obj中所需的字段的视图（FOV）等的距离，O，通过估计所需的放大倍率， 男 。 注意:倍率是相机芯片到FOV的长度的比例，和对象距离可以计算为O = F（1 / M + 1），其中f是透镜的焦距。在该实验中，摄像头芯片的长度为20.34毫米和相应的视场，或主镜，为50毫米的有限物距ø≤250毫米。 （物距被抑制由于在摄像机和视图分离器被安装在滑动件的有限长度。）的倍率为M = 20.34 / 50 = 0.41，和与对象的距离的给定范围近似焦距为f ≤72.7毫米。因此，微透镜具有60mm的焦距用于以f / 2.8D的焦率。 </li></ol></li><li>安装和调整与分流查看相机。 <ol><li>通过滑动MIR水平的审讯卷的主镜的中心沿着垂直安装后ROR和修正柱支架镜像。注意，此步骤是在安装次级镜之前进行。 </li><li>安装摄像机，并设置图像重合的中心与主镜的中心。 </li><li>调整摄像机视图的宽度和高度刚好盖住通过控制感兴趣区域（ROI）的区域主镜的录音软件设置。这个过程是为了减少图像尺寸和图像噪声。注意:在这个实验中，主反射镜的尺寸为5×5厘米2（1728点¯x1728像素）。 </li><li>设计一个（可定制）3D校准目标。它应包围整个调查音量。确保分路器的每个视图捕获所有目标标记，以便统一校准。 注:在本演示中，目标是3D印刷用Vero细胞回塑料。它有一个台阶状与尺寸35×35×30mm的3，用直径1毫米的白色目标点分隔符编2,5 mm开始，在5mm和10mm的垂直，流向和翼展方向。目标的精确几何形状是关键的，因为它伸入一个校准模型和摄像机位置相对于所述水槽。 </li></ol></li><li>将校准目标进审讯音量。 <ol><li>对相机面临的一个可调节高度的平台上标定目标位置。 </li><li>通过调整目标平台的高度相匹配与询问体积的中心校准目标的中心的高度。 注意:在本实施例中，校准目标的中心标记与射流喷嘴的中心20厘米的高度调平。气泡水平仪可以用于水平目标。 </li></ol></li><li>安装和调整四视图分路器的次镜。 <ol><li>找到从确保充分捕捉审讯的距离主镜。这为0.2μm此演示（FIGURË1）。 </li><li>在它的大致位置时，在来自主镜的各侧摄像机视图大致与副镜对准贴装副镜。通过将其固定到辅助镜的垂直安装柱固定副镜。 </li><li>重复此过程，对于其他三面镜子。检查所有次级镜的几何对称性相对于所述主​​镜。 </li><li>使通过调整次级反射镜的反射镜安装，以确保每个的四个视图包围整个校准目标最终的调整。检查镜&#39;的位置和角度的一个有效的方法是使用激光指示器来可视化各视图的图像的路径。 </li><li>检查用于通过移动一个反射镜子图像的重叠。 注意:如果只有一个视图改变，那么重叠区域可以忽略不计。否则，重复步骤2.4.2至2.4.5，直到重叠区域最小化。 </li></ol></li><li>将光源（S）直接面对审讯音量。确保将相机调光，以避免相机传感器损坏时覆盖。 <ol><li>检查光源是在整个调查体积均匀分布的。 </li><li>增强的光强度，如果需要的话，由下光源直接放置一个放大镜。注意:在这个实验中，使用焦距f 0 = 450nm 10mm的平凸放大镜加紧的照明。 </li></ol></li></ol> 3.建立优化<ol><li>打开并调整相机设置以提高图像质量。 <ol><li>调整镜头倍率，直到通过主镜的反射聚焦同样在所有四个视图次要镜子。 </li><li>检查从视图分离器的图像是对称的，并且通过从四个视图观察校准目标图像的对称性捕获询问体积。 </li><li>调整f数来捕获从摄像机最接近的和最远的校准目标点。 注意:这使得相机捕捉示踪粒子只有在审讯量的深度。在这个例子中，F数是11。 </li><li>设置所需的帧速率550赫兹（这取决于具体的应用，见简介）和录音软件相应最大限度的光敏感度。 </li></ol></li><li>通过一个直播相机视图观察分离器的每个视图的颗粒密度差检查主镜的每个视图照明。 注意:如果多个光源用来照亮所述询问体积，它是可能具有在分离器的每个视图的差异。在该实验中，顶部的两个次级镜，因为照明来自顶部接收较少的光。在水槽底部的使用平面镜可以帮助减少光变跨越的意见。 </li><li>采用3D-PTV光源前，请关闭房间内的灯光背景。 </li><li>调整相机的G＆B的水平，以更好地捕获粒子光散射。记录与各种G＆B水平几个短序列，发现通过观察粒子轨迹的分布和密度的最优的一个。 注意:在这个实验中，G＆B水平的范围是0-500，和黑色（B）的电平设定为500，以亮调光器的光散射，而，增益（G）设定介质，300，以适度放大图像信号和避免过度使图像变亮。 </li></ol> 4.校准<ol><li>放置在侦查卷中的校准目标添加示踪粒子之前，采取一些校正图像。使用调光器光源（ 如 LED闪光灯光）照射目标。 </li><li>除以校准图像分成四个独立的子图像，使一个文本文件包含指ENCE协调目标标记的位置。 OpenPTV软件（http://www.openptv.net）在这里被用于此目的。 注意:以后的处理等同于采用多个摄像机设置的用户。 </li><li>点击"创建校准"选项卡中保存图像，并在步骤4.2在软件的"CAL"文件夹中获得的文本文件后启动校准过程。 </li><li>点击"修改校准参数"选项卡，然后选择"校准定向参数"选项卡，每个拆分视图中心和校准目标的原点之间定义放大，旋转角度和距离。 注意:第一个行是从原点校准目标在X，Y，Z方向上的相机传感器的距离。第二行表示角度，单位为弧度，绕x，Y，Z轴。接着，一个3×3的数据表示旋转矩阵。然后，两个下列各行是x的针孔距离和<em&gt; y坐标从图像中心（毫米），焦距轴。最后一行包含水槽玻璃在相对 ​​于原点的目标在X，Y，Z方向上的位置。 </li><li>点击"检测"和"显示初始猜测"调查的"猜测"点与检测到的目标点相匹配。 </li><li>所有四个视图，直到&#39;猜&#39;点重复步骤4.4与所设定的校正图像的对齐。 </li><li>点击"取向"重构询问体积的方向。 注意:校准可以通过调整透镜畸变和仿射变换得到改善。现在，调查体积校准并准备处理数据。见笔者的附加 ​​说明论文36校准过程。 </li></ol> 5.流量设置/数据采集<ol><li>来自帧速率的估计在每个摄像机视图捕获颗粒的最大数量二维最大流速。在这个演示中，给定速度为U≈0.4米/秒，帧速率为550赫兹和4〜×4×4 立方厘米体积审讯。这导致〜每帧1000个粒子。 </li><li>打开相机，在步骤3中得到的最优化设置。 </li><li>添加播种颗粒和等待数平均停留时间，从而使流动达到稳定状态。如果需要加入更多的粒子，但避免高接种密度，在步骤5.1，这可能会导致模糊度估计。 注意:在该实例中，〜1.6克为1.1g / cm 3的密度为100微米银包覆空心陶瓷球体被用作种子用于流体介质（2×0.4×0.4米3）。 </li><li>记录流图像的期望数目。 注意:在这个实验中，在550赫兹9000图像用记录软件被抓获。如果相机和/或视图分路器移动（即使是轻微的运动可以严重影响结果）重复步骤2.4至5.3。 </li></ol> 6.数据处理（通过OpenPTV软件） <ol><li>除以在步骤5.4获得分成四个独立的子图像的原始图像。 </li><li>点击"开始"选项卡下的"初始化/重新启动"，从四个视图加载初始图像。 </li><li>右键单击"运行"目录，然后单击"主参数的摄像头，折射率，粒子识别，图像序列，观察量和标准对应的个数来控制数量。 <ol><li>定义的用于"常规"选项卡下的实验相机（意见）的数量。在这个实验中，设置的摄像机的数量为4。 </li><li>定义沿着"折射率"选项卡下的摄影机视图折射率。 </li><li>定义最小和像素检测以及灰度值的阈值MAX数量，优化了"粒子识别"选项卡下的粒子检测的所有四个视图数。德像素的最小和最大号tection和灰度阈值确定用于颗粒探测像素大小和亮度级别。它消除了噪音和颗粒失焦。 </li><li>定义&#39;的序列处理参数"下图像处理的数量。 </li><li>定义"观察音量"选项卡下的观察量。 </li><li>定义下的"标准对应"，包括立体匹配总频带参数（毫米）对应的相关性。 </li></ol></li><li>点击"预处理"标签下的"高通滤波器"。这加剧在所有四个观点粒子光散射。 </li><li>点击"粒子探测"，以确定检测到的颗粒在所有四个视图亚像素级的重心。重复步骤6.2和6.3，直到类似于在步骤5.1计算颗粒的预期数检测颗粒的数量。 </li><li>点击"对应关系"，建立立体correspondenCES在每个视图。 注意:要重建检测到粒子的三维位置，对应至少应该从三个意见确定。 </li><li>点击"3D位置"来获得基于所述校准检测粒子三维位置。 </li><li>点击"不带显示屏的序列&#39;重复步骤，从6.4到6.7的所有图像序列的过程。 注意:这将创建包含检测粒子与制表符分隔文本文件格式框架总结每个图像设定&#39;rt_is&#39;文件。 </li><li>右键单击"运行"目录，然后单击"跟踪参数"来定义半径球体，（ 如 dvxmin和dvymin毫米/帧）的参数，以搜索候选粒子进行跟踪。 </li><li>点击"跟踪不显示&#39;来定义步骤6.7获得重建的颗粒的标识（ID）。 注意:用于使用一个四帧predic跟踪相关的相邻帧的序列器预测-校正方案24。这个过程创造了包含在帧中检测到粒子的跟踪信息的每个图像集ptv_is文件;前两列分别示出了粒子的ID中的前一帧和下一帧。 </li><li>点击"显示轨迹"可视化轨迹中的每个摄影机视图。 </li></ol> 7.后处理（祈愿） 注:范围和后处理的类​​型取决于个人的需求和它，因此，可定制。这里，指向基底计算简单说明作为例子。 <ol><li>获得拉格朗日帧数据（通过Matlab的）。 <ol><li>提取每个粒子及其相关的ID从ptv_is文件的三维位置。它允许连接的图像序列中检测粒子重建轨迹。 </li><li>计算从给定帧速率的每个轨迹的速度和颗粒的加速度。在此示范，粒子的速度和加速度由低通用移动三次样条34,37滤波该位置信号来计算。 </li><li>使含三维位置，三维速度，加速度3D和时间戳以及每个轨迹的轨迹ID字段的结构阵列格式。在该数据格式中， 结构体数组的长度代表轨迹号。 </li></ol></li><li>获得欧拉帧数据（通过Matlab的）。 <ol><li>结构体阵列（步骤7.1.3）转换到使用每个颗粒的时间戳的时间之一。这将在步骤7.1.3得到同样的结构排列的结构，但结构体数组的长度现在代表帧号，这是在这个实验9000元。 </li><li>内插的时间结构数组为每个时间段三维网格，以获得欧拉坐标瞬时速度场。在这个演示中，有趣的GridDataction在Matlab用于执行内插。 </li></ol></li></ol>

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P., Wilcox, N. A., Tatterson, G. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Application+of+image+processing+to+the+analysis+of+three-dimensional+flow+fields.">Application of image processing to the analysis of three-dimensional flow fields.</a> Opt Eng. 23, 283-287 (1984).</li><li>Racca, R., Dewey, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+method+for+automatic+particle+tracking+in+a+three-dimensional+flow+field.">A method for automatic particle tracking in a three-dimensional flow field.</a> Exp Fluids. 6, 25-32 (1988).</li><li>Maas, H. G., Gruen, D., Papantoniou, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Particle+tracking+velocimetry+in+three-dimensional+flows.">Particle tracking velocimetry in three-dimensional flows.</a> Exp Fluids. 15, 133-146 (1993).</li><li>Kasagi, N., Matsunaga, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Three-dimensional+particle+tracking+velocimetry+measurement+of+turbulence+statistics+and+energy+budget+in+a+backward-facing+step+flow.">Three-dimensional particle tracking velocimetry measurement of turbulence statistics and energy budget in a backward-facing step flow.</a> Int J Heat Fluid Fl. 16, 477-485 (1995).</li><li>Virant, M., Dracos, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=3D+PTV+and+its+application+on+Lagrangian+motion.">3D PTV and its application on Lagrangian motion.</a> Meas Sci Technol. 8, 1539 (1997).</li><li>Willneff, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> A spatio-temporal matching algorithm for 3 D particle tracking velocimetry. , (2003).</li><li>Rosi, G. A., Sherry, M., Kinzel, M., Rival, D. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Characterizing+the+lower+log+region+of+the+atmospheric+surface+layer+via+large-scale+particle+tracking+velocimetry.">Characterizing the lower log region of the atmospheric surface layer via large-scale particle tracking velocimetry.</a> Exp Fluid. 55, 1-10 (2014).</li><li>Fu, S., Biwole, P. H., Mathis, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Particle+Tracking+Velocimetry+for+indoor+airflow+field:+A+review.">Particle Tracking Velocimetry for indoor airflow field: A review.</a> Build Environ. 87, 34-44 (2015).</li><li>Kolaas, J., Jensen, A., Mielnik, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visualization+and+measurements+of+flows+in+micro+silicon+Y-channels.">Visualization and measurements of flows in micro silicon Y-channels.</a> Eur Phys J E. 36, 1-11 (2013).</li><li>G&#252;lan, U., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Experimental+study+of+aortic+flow+in+the+ascending+aortavia+Particle+Tracking+Velocimetry.">Experimental study of aortic flow in the ascending aortavia Particle Tracking Velocimetry.</a> Exp Fluids. 53, 1469-1485 (2012).</li><li>Mei, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Velocity+fidelity+of+flow+tracer+particles.">Velocity fidelity of flow tracer particles.</a> Exp Fluids. 22, 1-13 (1996).</li><li>Tropea, C., Yarin, A. L., Foss, J. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Springer handbook of experimental fluid mechanics. 1, (2007).</li><li>Melling, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tracer+particles+and+seeding+for+particle+image+velocimetry.">Tracer particles and seeding for particle image velocimetry.</a> Meas Sci Technol. 8, 1406 (1997).</li><li>Hering, F., Leue, C., Wierzimok, D., J&#228;hne, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Particle+tracking+velocimetry+beneath+water+waves.+Part+I:+visualization+and+tracking+algorithms.">Particle tracking velocimetry beneath water waves. Part I: visualization and tracking algorithms.</a> Exp Fluids. 23, 472-482 (1997).</li><li>Biferale, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lagrangian+structure+functions+in+turbulence:+A+quantitative+comparison+between+experiment+and+direct+numerical+simulation.">Lagrangian structure functions in turbulence: A quantitative comparison between experiment and direct numerical simulation.</a> Phys Fluids. 20, 065103 (2008).</li><li>L&#252;thi, B., Tsinober, A., Kinzelbach, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lagrangian+measurement+of+vorticity+dynamics+in+turbulent+flow.">Lagrangian measurement of vorticity dynamics in turbulent flow.</a> J Fluid mech. 528, 87-118 (2005).</li><li>Hoyer, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=3d+scanning+particle+tracking+velocimetry.">3d scanning particle tracking velocimetry.</a> Exp Fluids. 39, 923-934 (2005).</li><li>Kim, J. -. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Three-dimensional particle tracking velocimetry for turbulence applications. , (2015).</li><li>L&#252;thi, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Some+aspects+of+strain,+vorticity+and+material+element+dynamics+as+measured+with+3D+particle+tracking+velocimetry+in+a+turbulent+flow.">Some aspects of strain, vorticity and material element dynamics as measured with 3D particle tracking velocimetry in a turbulent flow.</a> ETH Z&#252;rich. , (2002).</li><li>Pope, S. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Turbulent flows. , (2000).</li></ol>

作者宣称，他们没有竞争的经济利益。

<html> 3D-PTV具有很大的潜力，以解开各种湍流，如在低层大气中25，室内空气分布26，或脉动流的大规模动荡运动的复杂的物理主动脉拓扑28在许多其他之中。但是，它的优点和局限性，以及经验的理解是必不可少最大限度地发挥其潜力。试错的初步测试和优化设置，包括帧速率，照明光源，G＆B级和图像滤波方法详尽的迭代，直接与重建一套（ 例如示踪）粒子的拉格...

湍流喷流是在工程应用中普遍存在。这种流动的详细描述是在从大型环保排放系统涵盖电子微尺度器件的实际问题广泛至关重要。由于其对一些广阔的应用前景影响，喷气流量已经深入研究了1 - 4。几个实验技术，包括热线测速仪4 - 8，激光多普勒测速仪（LDV）4，9 - 12，和粒子图像测速（PIV）12 - 16，都被用来描述飞机在大范围的雷诺数和边界流向条件。近日，采用3D-PTV研究湍流/非湍流界面的喷气流17，18已做了一些研究。 3D-PTV是特别合适的描述复杂湍流音响的技术从不同的角度的视场。它允许粒子轨迹在使用多视点立体参考拉格朗日框架体积内重建。该技术最早是由19张和介绍了由RACCA和杜威20进一步发展。此后，许多方面做了改进的3D-PTV算法和实验设置21 - 24。与这些成绩和以往的作品，该系统已被成功地用于研究各种流体的现象，如在4米x 2米×2米25，室内气流场26的一个结构域的大型流体运动，脉动流27和主动脉血流28 。 一个三维PTV测量的工作原理包括数据采集系统设置，记录/前处理，校准，3D的对应，时间跟踪和后处理。精确的校准允许的粒子位置的精确检测秒。在三个以上的图像视图检测到的粒子的对应允许基于对极几何形状的三维粒子位置的重建。从连续的图像帧的连锁导致定义粒子轨迹S（t）的一个时间跟踪。所述3D-PTV系统的优化是必需最大化多粒子可追溯性的可能性。 优化的第一步是获取合适的数据采集系统，包括高速摄像机，照明光源和播种颗粒的功能。与询问体积的大小沿所述照相机分辨率定义像素尺寸，因此，所需要的籽晶颗粒大小，这应该是比单一像素大。检测的颗粒的质心通过取由亮度21加权粒子像素的平均位置具有子像素精度估计。摄像机的帧率是密切associat编与雷诺数和检测到的颗粒链接的能力。较高的帧速率可以用于解决更快流量或粒子的数量较多，因为当图像之间的平均位移超过所述颗粒的平均分离的跟踪变得更加困难。 快门速度，光圈和灵敏度有三个因素在图像采集来考虑。快门速度应该足够快，以最小化颗粒，从而降低颗粒质心位置的不确定性围绕模糊。相机光圈应调整到询问体积的景深，以减少的容积之外探测粒子的概率。由于摄像机的最大感光度是固定的，作为帧速率的增加，所需要的必要的光以照亮颗粒应相应增加。不像PIV，复合光学设置以及高功率激光器没有严格要求在3D-PTV，只要光源足够粪便从示踪粒子到相机羊羔。连续LED或卤素灯属于跳过同步21，需要良好的成本效益的方案。 在3D-PTV，像其他光流的测量技术，示踪粒子速度被假设为在本地瞬时流体速度29。然而，这仅仅是对空的直径和惯性的理想示踪剂的情况下示踪颗粒应足够大，以由照相机捕获。有限微粒的保真度可以通过斯托克斯数式 T来确定， 即颗粒的弛豫时间尺度的比率和感兴趣紊流结构的时间尺度。一般来说，S T应当比1小很多对于式 T≤0.1流跟踪误差在1％以下30。在深入讨论可以在梅等人发现29 - 31 </sup&gt;。推荐的粒径为3D-PTV实验取决于光源和摄像机的灵敏度而变化。卤素或LED灯作为照明源，相对较大的颗粒被使用（ 例如 50-200微米）32，而较小的颗粒（ 例如 ，1-50微米）33，34可以用高功率激光可使用（ 例如 80-100瓦CW激光）。具有高反射率对于一个给定波长的光粒子，就像在卤素灯银涂层，可以放大自己的印记成图像。接种密度为成功的3D-PTV测量的另一个重要参数。少数颗粒导致低轨迹数，而颗粒的数量过多会导致在建立对应和跟踪歧义。在建立对应歧义包括重叠粒子和检测沿定义极线多名候选人。在跟踪的过程中，模糊性由于高seedin<html>是因为颗粒的相对短平均间隔的发生g密度。 第二步骤是在记录/预处理最佳设置，以提高图像质量。照相的设置，如增益和黑电平（G B）的，在优化图像质量方面发挥了重要的作用。黑电平限定在图像的最暗部分的亮度水平，而增益放大的图像的亮度。对G＆B水平的轻微变化可以显著影响可追溯性的可能性。事实上，高G＆B可能过亮的图像，并最终损坏相机传感器。为了说明这一点，在流程再造G＆B水平的影响也研究过这个文章。在预处理步骤中，图像被过滤，用一个高通滤波器，以从颗粒强调光散射。像素大小和灰度调整以最大化询问体积内的颗粒探测。 t"的&gt;优化的第三步是在立体成像，这是基于对极几何，照相机参数（焦距，主点，并且失真系数），以及折射率的变化的精确校准。此过程是必不可少的，以尽量减少在3D基准目标点的重构误差。极几何使用相对距离和倾斜角度（摄像机和询问体积之间）从目标图像。沿着通过询问体积相机视图折射率变化可以基于质谱的方法来考虑等 21上 。在该实验中，以规则分布的目标点的三维阶梯状结构被用作靶。 在3D-PTV实验中，虽然只需要两个图像，以确定一个三维粒子位置，典型地更多的摄像机被用来减少模糊度21。昂贵的设置有多个高速摄像机另一种是viEW分离器，由Hoyer 等人提出的。35，用于使用3D-PTV的和由Gulean 等人 28的生物医学应用最近应用。该视图分路器由一个金字塔形的镜子（关于此主镜）和四个调节后视镜（关于此副镜）组成。在这项工作中，一个四视图分离器和一个摄像机被用来从四个摄像机模仿立体成像。该系统是用于表征一个管喷射的中间流场与直径d H = 1cm到重新 ≈7,000拉格朗日和欧拉在约14.5-18.5直径下游射流原点帧。

<table><tbody><tr><td>Mikrotron 4CXP 4 lanes CXP-6 CoaXPress</td><td>ImageOps</td><td>CAMMC4082</td><td>High-speed camera</td></tr><tr><td>Active Silicon FireBird CoaX Frame Grabber</td><td>ImageOps</td><td>FBD-4XCXP6</td><td>Frame Grabber</td></tr><tr><td>100 &amp;mu;m silver-coated hollow ceramic spheres</td><td>Potters Industries LLC</td><td>AG-SL150-30-TRD</td><td>Seeding Paritcles</td></tr><tr><td>StreamPix6</td><td>Upstate Technical Equipment CO.,INC</td><td>MISNOR-STP-6-S-CL</td><td>Camera appliation</td></tr><tr><td>Four-view splitter</td><td>Photrack AG</td><td></td><td>Customized part and necessary if performing 3D-PTV with one camera</td></tr><tr><td>250 Watts Spotlight Halogen</td><td>General Electrics</td><td>23719</td><td>Light source</td></tr><tr><td>OpenPTV (Software)</td><td>OpenPTV (http://www.openptv.net)</td><td></td><td>Open source particle tracking software (Note: available as a service for anyone who wants to use it without all the installation mess or computer power availability problems).</td></tr></tbody></table>

three-dimensional particle tracking velocimetry for turbulence applications: case of a jet flow

3D-PTV是一个定量的流量测量技术，该技术的目的是跟踪使用图像序列的立体记录三个维度的一组颗粒的拉格朗日路径。其基本组件，功能，约束和3D-PTV拓扑结构由高速摄像头，支持四视图分路器的优化技巧进行了描述和本文中讨论。该技术是在重新 ≈7000施加到圆形射流的中间流场（5 &lt;X / D &lt;25）。在欧拉帧拉格朗日流动特征和湍流量围绕射流原点的下游10的直径，并在从所述喷射芯各种径向距离估计。拉格朗日属性包括轨迹，速度和选定的颗粒的加速度，以及流路，其从所述的Frenet-Serret等式获得的曲率。在位于十的横平面围绕喷射心轴的三维速度和湍流场的推定直径射流的下游与文献相比较，并在从喷射芯各种径向距离得到大规模流向速度运动的功率谱。

的照片，并将设置的示意图示于图 1和2。校准目标，在基准标记上反射的视图分离器和3D校准重建在图 3中示出。公认的校准目标的RMS为7.3微米，5.7微米和141.7微米的流向X，翼展方向y和深度z方向。在z -协调的相对较高的RMS是由于相对于那些在其它方向和相比x和y坐标与z轴四个视图相对小角度的减小目标...

Watch this Scientific Journal Video about Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow at JoVE.com

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

这里描述了基于高速摄像头，四视图分路器上的三维粒子跟踪测速（3D-PTV）系统。该技术是下游在雷诺数Re≈7000施加到从圆形管在十个直径附近的喷流。

湍流应用三维粒子跟踪测速:一喷流病例

3D-PTV is a quantitative flow measurement technique that aims to track the Lagrangian paths of a set of particles in three dimensions using stereoscopic ...

three-dimensional-particle-tracking-velocimetry-for-turbulence

Universidad San Sebastian

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JoVE Journal

JoVE Core

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Physics

Civil Engineering

Unit 1

Flow in Pipes

Motion in Two or Three Dimensions

SCIENTISTS IN ACTION

12.1K 次观看.  University of Illinois at Urbana-Champaign. 这里描述了基于高速摄像头，四视图分路器上的三维粒子跟踪测速（3D-PTV）系统。该技术是下游在雷诺数Re≈7000施加到从圆形管在十个直径附近的喷流。 

视频: Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Determining 3D Flow Fields via Multi-camera Light Field Imaging

In the field of fluid mechanics, the resolution of computational schemes has outpaced experimental methods and widened the gap between predicted and observed phenomena in fluid flows. Thus, a need exists for an accessible method capable of resolving three-dimensional (3D) data sets for a range of problems. We present a novel technique for performing quantitative 3D imaging of many types of flow fields. The 3D technique enables investigation of complicated velocity fields and bubbly flows. Measurements of these types present a variety of challenges to the instrument. For instance, optically dense bubbly multiphase flows cannot be readily imaged by traditional, non-invasive flow measurement techniques due to the bubbles occluding optical access to the interior regions of the volume of interest. By using Light Field Imaging we are able to reparameterize images captured by an array of cameras to reconstruct a 3D volumetric map for every time instance, despite partial occlusions in the volume. The technique makes use of an algorithm known as synthetic aperture (SA) refocusing, whereby a 3D focal stack is generated by combining images from several cameras post-capture 1. Light Field Imaging allows for the capture of angular as well as spatial information about the light rays, and hence enables 3D scene reconstruction. Quantitative information can then be extracted from the 3D reconstructions using a variety of processing algorithms. In particular, we have developed measurement methods based on Light Field Imaging for performing 3D particle image velocimetry (PIV), extracting bubbles in a 3D field and tracking the boundary of a flickering flame. We present the fundamentals of the Light Field Imaging methodology in the context of our setup for performing 3DPIV of the airflow passing over a set of synthetic vocal folds, and show representative results from application of the technique to a bubble-entraining plunging jet.

A technique for performing quantitative three-dimensional (3D) imaging for a range of fluid flows is presented. Using concepts from the area of Light Field Imaging, we reconstruct 3D volumes from arrays of images. Our 3D results span a broad range including velocity fields and multi-phase bubble size distributions.

通过多摄像头的光场成像的三维流场测定

In the field of fluid mechanics, the resolution of computational schemes has outpaced experimental methods and widened the gap between predicted and ...

科研

工程学

High-speed Particle Image Velocimetry Near Surfaces

Multi-dimensional and transient flows play a key role in many areas of science, engineering, and health sciences but are often not well understood. The complex nature of these flows may be studied using particle image velocimetry (PIV), a laser-based imaging technique for optically accessible flows. Though many forms of PIV exist that extend the technique beyond the original planar two-component velocity measurement capabilities, the basic PIV system consists of a light source (laser), a camera, tracer particles, and analysis algorithms. The imaging and recording parameters, the light source, and the algorithms are adjusted to optimize the recording for the flow of interest and obtain valid velocity data. 
Common PIV investigations measure two-component velocities in a plane at a few frames per second. However, recent developments in instrumentation have facilitated high-frame rate (&gt; 1 kHz) measurements capable of resolving transient flows with high temporal resolution. Therefore, high-frame rate measurements have enabled investigations on the evolution of the structure and dynamics of highly transient flows. These investigations play a critical role in understanding the fundamental physics of complex flows.
A detailed description for performing high-resolution, high-speed planar PIV to study a transient flow near the surface of a flat plate is presented here. Details for adjusting the parameter constraints such as image and recording properties, the laser sheet properties, and processing algorithms to adapt PIV for any flow of interest are included.

A procedure for studying transient flows near boundaries using high-resolution, high-speed particle image velocimetry (PIV) is described here. PIV is a non-intrusive measurement technique applicable to any optically accessible flow by optimizing several parameter constraints such as the image and recording properties, the laser sheet properties, and analysis algorithms.

表面附近的高速粒子图像测速

Multi-dimensional and transient flows play a key role in many areas of science, engineering, and health sciences but are often not well understood. The ...

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Image analysis has been increasingly used for the measurement of river flows due to its capabilities to furnish detailed quantitative depictions at a relatively low cost. This manuscript describes an application of particle tracking velocimetry (PTV) to a bed-load experiment with lightweight sediment. The key characteristics of the investigated sediment transport conditions were the presence of a covered flow and of a fixed rough bed above which particles were released in limited number at the flume inlet. Under the applied flow conditions, the motion of the individual bed-load particles was intermittent, with alternating movement and stillness terms. The flow pattern was preliminarily characterized by acoustic measurements of vertical profiles of the stream-wise velocity. During process visualization, a large field of view was obtained using two action-cameras placed at different locations along the flume. The experimental protocol is described in terms of channel calibration, experiment realization, image pre-processing, automatic particle tracking, and post-processing of particle track data from the two cameras. The presented proof-of-concept results include probability distributions of the particle hop length and duration. The achievements of this work are compared to those of existing literature to demonstrate the validity of the protocol.

The manuscript presents a protocol for the conduction of bed-load sediment transport experiments where the moving particles are tracked by image analysis. The experimental facility, the procedures for run realization and data processing, and finally some proof-of-concept results are presented here.

基于图像的拉格朗日粒子跟踪在床载荷实验

Image analysis has been increasingly used for the measurement of river flows due to its capabilities to furnish detailed quantitative depictions at a ...

环境科学

Methods for Measuring the Orientation and Rotation Rate of 3D-printed Particles in Turbulence

Experimental methods are presented for measuring the rotational and translational motion of anisotropic particles in turbulent fluid flows. 3D printing technology is used to fabricate particles with slender arms connected at a common center. Shapes explored are crosses (two perpendicular rods), jacks (three perpendicular rods), triads (three rods in triangular planar symmetry), and tetrads (four arms in tetrahedral symmetry). Methods for producing on the order of 10,000 fluorescently dyed particles are described. Time-resolved measurements of their orientation and solid-body rotation rate are obtained from four synchronized videos of their motion in a turbulent flow between oscillating grids with R&#955; = 91. In this relatively low-Reynolds number flow, the advected particles are small enough that they approximate ellipsoidal tracer particles. We present results of time-resolved 3D trajectories of position and orientation of the particles as well as measurements of their rotation rates.

We use 3D printing to fabricate anisotropic particles in the shapes of jacks, crosses, tetrads, and triads, whose alignments and rotations in turbulent fluid flow can be measured from multiple simultaneous video images.

测量的方向和旋转速率方法3D打印在湍流颗粒

Experimental methods are presented for measuring the rotational and translational motion of anisotropic particles in turbulent fluid flows. 3D printing ...

Micro-particle Image Velocimetry for Velocity Profile Measurements of Micro Blood Flows

Micro-particle image velocimetry (&mu;PIV) is used to visualize paired images of micro particles seeded in blood flows. The images are cross-correlated to give an accurate velocity profile. A protocol is presented for &mu;PIV measurements of blood flows in microchannels. At the scale of the microcirculation, blood cannot be considered a homogeneous fluid, as it is a suspension of flexible particles suspended in plasma, a Newtonian fluid. Shear rate, maximum velocity, velocity profile shape, and flow rate can be derived from these measurements. Several key parameters such as focal depth, particle concentration, and system compliance, are presented in order to ensure accurate, useful data along with examples and representative results for various hematocrits and flow conditions.

Micro-particle image velocimetry (&mu;PIV) is used to visualize paired images of micro particles seeded in blood flows which are cross-correlated to give an accurate velocity profile. Shear rate, maximum velocity, velocity profile shape, and flow rate, each of which has clinical applications, can be derived from these measurements.

微观粒子图像测速技术，微血液流动的速度剖面测量

Micro-particle image velocimetry (&mu;PIV) is used to visualize paired images of micro particles seeded in blood flows. The images are cross-correlated to ...

生物工程

Visually Based Characterization of the Incipient Particle Motion in Regular Substrates: From Laminar to Turbulent Conditions

Two different experimental methods for determining the threshold of particle motion as a function of geometrical properties of the bed from laminar to turbulent flow conditions are presented. For that purpose, the incipient motion of a single bead is studied on regular substrates that consist of a monolayer of fixed spheres of uniform size that are regularly arranged in triangular and quadratic symmetries. The threshold is characterized by the critical Shields number. The criterion for the onset of motion is defined as the displacement from the original equilibrium position to the neighboring one. The displacement and the mode of motion are identified with an imaging system. The laminar flow is induced using a rotational rheometer with a parallel disk configuration. The shear Reynolds number remains below 1. The turbulent flow is induced in a low-speed wind tunnel with open jet test section. The air velocity is regulated with a frequency converter on the blower fan. The velocity profile is measured with a hot wire probe connected to a hot film anemometer. The shear Reynolds number ranges between 40 and 150. The logarithmic velocity law and the modified wall law presented by Rotta are used to infer the shear velocity from the experimental data. The latter is of special interest when the mobile bead is partially exposed to the turbulent flow in the so-called hydraulically transitional flow regime. The shear stress is estimated at onset of motion. Some illustrative results showing the strong impact of the angle of repose, and the exposure of the bead to shear flow are represented in both regimes.

Two different methods for characterizing the incipient particle motion of a single bead as a function of the sediment bed geometry from laminar to turbulent flow are presented.

从层流到湍流条件下的规则基底表面初始粒子运动的可视化表征

Two different experimental methods for determining the threshold of particle motion as a function of geometrical properties of the bed from laminar to ...

Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods

It is well known that the flow field over a delta wing is dominated by a pair of counter rotating leading edge vortices (LEV). However, their mechanism is not well understood. The flow visualization technique is a promising non-intrusive method to illustrate the complex flow field spatially and temporally. A basic flow visualization setup consists of a high-powered laser and optic lenses to generate the laser sheet, a camera, a tracer particle generator, and a data processor. The wind tunnel setup, the specifications of devices involved, and the corresponding parameter settings are dependent on the flow features to be obtained.
Normal smoke wire flow visualization uses a smoke wire to demonstrate the flow streaklines. However, the performance of this method is limited by poor spatial resolution when it is conducted in a complex flow field. Therefore, an improved smoke flow visualization technique has been developed. This technique illustrates the large-scale global LEV flow field and the small-scale shear layer flow structure at the same time, providing a valuable reference for later detailed particle image velocimetry (PIV) measurement.
In this paper, the application of the improved smoke flow visualization and PIV measurement to study the unsteady flow phenomena over a delta wing is demonstrated. The procedure and cautions for conducting the experiment are listed, including wind tunnel setup, data acquisition, and data processing. The representative results show that these two flow visualization methods are effective techniques for investigating the three-dimensional flow field qualitatively and quantitatively.

Here, we present a protocol to observe unsteady vortical flows over a delta wing using a modified smoke flow visualization technique and investigate the mechanism responsible for the oscillations of the leading-edge vortex breakdown locations.

基于流动可视化方法的三角洲机翼流动结构实验研究

It is well known that the flow field over a delta wing is dominated by a pair of counter rotating leading edge vortices (LEV). However, their mechanism is ...

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Numerous problems in scientific and engineering fields involve understanding the kinematics of particles in turbulent flows, such as contaminants, marine micro-organisms, and/or sediments in the ocean, or fluidized bed reactors and combustion processes in engineered systems. In order to study the effect of turbulence on the kinematics of particles in such flows, simultaneous measurements of both the flow and particle kinematics are required. Non-intrusive, optical flow measurement techniques for measuring turbulence, or for tracking particles, exist but measuring both simultaneously can be challenging due to interference between the techniques. The method presented herein provides a low cost and relatively simple method to make simultaneous measurements of the flow and particle kinematics. A cross section of the flow is measured using a particle image velocimetry (PIV) technique, which provides two components of velocity in the measurement plane. This technique utilizes a pulsed-laser for illumination of the seeded flow field that is imaged by a digital camera. The particle kinematics are simultaneously imaged using a light emitting diode (LED) line light that illuminates a planar cross section of the flow that overlaps with the PIV field-of-view (FOV). The line light is of low enough power that it does not affect the PIV measurements, but powerful enough to illuminate the larger particles of interest imaged using the high-speed camera. High-speed images that contain the laser pulses from the PIV technique are easily filtered by examining the summed intensity level of each high-speed image. By making the frame rate of the high-speed camera incommensurate with that of the PIV camera frame rate, the number of contaminated frames in the high-speed time series can be minimized. The technique is suitable for mean flows that are predominantly two-dimensional, contain particles that are at least 5 times the mean diameter of the PIV seeding tracers, and are low in concentration.

The technique described herein offers a low cost and relatively simple method to simultaneously measure particle kinematics and turbulence in flows with low particle concentrations. The turbulence is measured using particle image velocimetry (PIV), and particle kinematics are calculated from images obtained with a high-speed camera in an overlapping field-of-view.

流成像技术同时测量湍流和粒子运动学

Numerous problems in scientific and engineering fields involve understanding the kinematics of particles in turbulent flows, such as contaminants, marine ...

Particle Image Velocimetry Investigation of Hemodynamics via Aortic Phantom

Aortic valve dysfunction and stroke have recently been reported in transcatheter aortic valve implantation (TAVI) patients. Thrombus in the aortic sinus and neo-sinus due to hemodynamic changes has been suspected. In vitro experiments help investigate the hemodynamic characteristics in the cases where an in vivo assessment proves to be limited. In vitro experiments are also more robust, and the variable parameters are controlled readily. Particle image velocimetry (PIV) is a popular velocimetry method for in vitro studies. It provides a high-resolution velocity field such that even small-scale flow features are observed. The purpose of this study is to show how PIV is used to investigate the flow field in the aortic sinus after TAVI. The in vitro setup of the aortic phantom, TAVI for PIV, and the data acquisition process and post-processing flow analysis are described. The hemodynamic parameters are derived, including the velocity, flow stasis, vortex, vorticity, and particle residence. The results confirm that in vitro experiments and PIV help investigate the hemodynamic features in the aortic sinus.

Particle Image Velocimetry Investigation of Hemodynamics via Aortic Phantom

The present protocol describes particle image velocimetry (PIV) measurements performed to investigate the sinus flow through the in vitro setup of the transcatheter aortic valve (TAV). The hemodynamic parameters based on velocity are also determined.

通过主动脉幻影进行血液动力学的粒子图像测速

Aortic valve dysfunction and stroke have recently been reported in transcatheter aortic valve implantation (TAVI) patients. Thrombus in the aortic sinus ...

A Protocol for Real-time 3D Single Particle Tracking

Real-time three-dimensional single particle tracking (RT-3D-SPT) has the potential to shed light on fast, 3D processes in cellular systems. Although various RT-3D-SPT methods have been put forward in recent years, tracking high speed 3D diffusing particles at low photon count rates remains a challenge. Moreover, RT-3D-SPT setups are generally complex and difficult to implement, limiting their widespread application to biological problems. This protocol presents a RT-3D-SPT system named 3D Dynamic Photon Localization Tracking (3D-DyPLoT), which can track particles with high diffusive speed (up to 20 &#181;m2/s) at low photon count rates (down to 10 kHz). 3D-DyPLoT employs a 2D electro-optic deflector (2D-EOD) and a tunable acoustic gradient (TAG) lens to drive a single focused laser spot dynamically in 3D. Combined with an optimized position estimation algorithm, 3D-DyPLoT can lock onto single particles with high tracking speed and high localization precision. Owing to the single excitation and single detection path layout, 3D-DyPLoT is robust and easy to set up. This protocol discusses how to build 3D-DyPLoT step by step. First, the optical layout is described. Next, the system is calibrated and optimized by raster scanning a 190 nm fluorescent bead with the piezoelectric nanopositioner. Finally, to demonstrate real-time 3D tracking ability, 110 nm fluorescent beads are tracked in water.

This protocol details the construction and operation of a real-time 3D single particle tracking microscope capable of tracking nanoscale fluorescent probes at high diffusive speeds and low photon count rates.

一种实时3D 单粒子跟踪协议

Real-time three-dimensional single particle tracking (RT-3D-SPT) has the potential to shed light on fast, 3D processes in cellular systems. Although ...