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基于高速纹影/阴影成像的流场测速技术研究进展

朱海军 王倩 梅笑寒 吴羽 赵长颖

朱海军,王倩,梅笑寒,等. 基于高速纹影/阴影成像的流场测速技术研究进展[J]. 实验流体力学,2022,36(2):49-73 doi: 10.11729/syltlx20210110
引用本文: 朱海军,王倩,梅笑寒,等. 基于高速纹影/阴影成像的流场测速技术研究进展[J]. 实验流体力学,2022,36(2):49-73 doi: 10.11729/syltlx20210110
ZHU H J,WANG Q,MEI X H,et al. A review on flow field velocimetry based on high-speed schlieren/shadowgraph systems[J]. Journal of Experiments in Fluid Mechanics, 2022,36(2):49-73. doi: 10.11729/syltlx20210110
Citation: ZHU H J,WANG Q,MEI X H,et al. A review on flow field velocimetry based on high-speed schlieren/shadowgraph systems[J]. Journal of Experiments in Fluid Mechanics, 2022,36(2):49-73. doi: 10.11729/syltlx20210110

基于高速纹影/阴影成像的流场测速技术研究进展

doi: 10.11729/syltlx20210110
基金项目: 国家自然科学基金(51976121,52011530187)
详细信息
    作者简介:

    朱海军:(1996—),男,安徽合肥人,硕士研究生。研究方向:基于阴影的三维流场测速技术。通信地址:上海市闵行区东川路800号上海交通大学闵行校区机械与动力工程学院工程热物理研究所(200240)。E-mail:haijun.zhu@sjtu.edu.cn

    通讯作者:

    E-mail:qianwang@sjtu.edu.cn

  • 中图分类号: TK31

A review on flow field velocimetry based on high-speed schlieren/shadowgraph systems

  • 摘要: 本文对近年基于纹影/阴影成像的二维和三维速度场测量方法进行了综述,主要内容包括纹影成像的基本原理、硬件设备和测速算法的研究进展。在二维测速方面,介绍了纹影/阴影PIV算法、光流算法及改进算法的原理、适用场景以及优缺点。纹影特性改进光流测速算法可以实现高精度、高空间分辨率的速度场计算,适用范围相对较广。在三维粒子追踪测速方面,主要介绍了层析阴影成像、双视角平行光段阴影成像、双视角汇聚光段阴影成像三种系统的光路设置,并对各自采用的粒子重构和追踪算法进行了比较。双视角阴影成像系统的光路布置更为简洁,降低了对硬件设备的要求,在高速测量中更具优势。梳理了近年来三维粒子追踪测速算法的发展脉络,重点介绍了“先追踪–后重构”和“时间–空间耦合”的双视角三维粒子追踪测速算法。时间–空间耦合的三维粒子追踪测速算法充分利用了时间和空间信息,将时序信息引入立体匹配过程中,显著提升了双视角阴影成像系统在粒子图像密度较高时的重构正确率和追踪准确率,其整体性能优于多种人工智能算法。测速算法在上述方面取得的研究进展,结合短曝光、高帧频的图像采集优势,使得纹影/阴影成像成为一种新型的高帧频、高精度的速度测量技术,在复杂湍流及高瞬态流场实验研究中具有广泛的应用前景。
  • 图  1  纹影成像原理示意图

    Figure  1.  Schematic of schlieren imaging principle

    图  2  Z型纹影仪光路设置

    Figure  2.  Setup of Z-type schlieren apparatus

    图  3  互相关算法原理[45]

    Figure  3.  The principle of correlation algorithm[45]

    图  4  热羽流纹影图像及MQD互相关算法得到的平均速度场[45]

    Figure  4.  Schlieren image of thermal plume and the average velocity estimation via MQD correlation algorithm[45]

    图  5  超声速氦气射流平均速度场[47]

    Figure  5.  Average velocity estimations of supersonic helium jet [47]

    图  6  超声速剪切层平均速度场[48]

    Figure  6.  Average velocity estimations of supersonic shear layer [48]

    图  7  金字塔算法优化光流算法的基本流程

    Figure  7.  The pipeline of pyramid algorithm optimized optical flow

    图  8  不同鲁棒函数及其影响函数

    Figure  8.  Various robust function and corresponding influence functions

    图  9  不同权重系数下纹影特性测速算法计算结果与光流算法结果[57]

    Figure  9.  Results of schlieren motion estimation with diffirent weight parameters and optical flow [57]

    图  10  渐进非凸优化步骤[59]

    Figure  10.  The pipeline of graduate non-convex algorithm[59]

    图  11  扩散碰撞火焰流场算法优化前后权重系数取值分布[59]

    Figure  11.  Weight parameter map of schlieren motion estimation of a diffusion collision flame field without and with graduate non-convex optimization [59]

    图  12  甲烷/氢气混合碰撞火焰瞬时速度场和涡量场[59]

    Figure  12.  Results of methane-hydrogen mixed collision flame [59]

    图  13  纹影特性测速算法优化前后速度场局部残差比较[59]

    Figure  13.  Comparison of local residual of schlieren motion estimation without and with graduate non-convex optimization [59]

    图  14  气液两相流中的气泡运动速度和连续相二维运动速度[62]

    Figure  14.  Bubble velocity and continuous phase velocity in gas-liquid two phase flow[62]

    图  15  平面微型通道中的液滴及内部示踪粒子阴影图像以及液滴内部二维速度[63]

    Figure  15.  Shadow image of droplet and tracer particle , and 2D velocity in droplet in planar micro-channel [63]

    图  16  常用三维流场测速技术测试区域体积、深度和帧频统计[4-6, 74-104]

    Figure  16.  Map of test volume, test depth and frame rate of 3D flow field velocimetry [4-6, 74-104]

    图  17  不同三维粒子追踪算法策略示意图[67]

    Figure  17.  Schematic of different 3D particle tracking strategy [67]

    图  18  层析阴影成像系统实验装置以及喷雾液滴阴影图像和破碎液滴在2个不同时刻的三维分布[106]

    Figure  18.  Schematic of tomographic shadowgraphy setup, shadow image of spray droplets and 3D distribution of broken drop-lets velocity field in two moments[106]

    图  19  双视角阴影成像系统以及粒子二维/三维运动轨迹[107]

    Figure  19.  Schematic of two-view collimated light path shadowgraphy setup and 2D/3D particle trajectory[107]

    图  20  双视角汇聚光段阴影成像系统以及双视角小孔成像相机模型[75]

    Figure  20.  Schematic of two-view converging light path shadowgraphy setup and model of two-view CCD camera based on pin-hole imaging theory[75]

    图  21  肥皂泡破裂阴影图像及破碎边缘特征点三维运动轨迹[75]

    Figure  21.  Shadow image of broken soap bubble and 3D trajectory of selected particles[75]

    图  22  先追踪–后重构的双视角三维粒子追踪算法示意图[105]

    Figure  22.  Pipeline of image space-based tracking strategy [105]

    图  23  液滴飞溅计算结果(从上至下:液滴飞溅阴影图像、二次液滴三维运动轨迹、二次液滴的3个速度分量统计结果)[105]

    Figure  23.  Results of droplet splashing (top: shadow image of droplet splashing; middle: 3D trajectory of secondary droplets; bottom: three velocity components of secondary droplets)[105]

    图  24  4个视角立体匹配示意图[4]

    Figure  24.  Schematic of stereotype particle match in four perspectives[4]

    图  25  时间–空间耦合的双视角三维粒子追踪算法示意图[116]

    Figure  25.  Schematic of spatial-temporal 3D particle tracking method[116]

    图  26  二维预测约束条件和重构约束条件[116]

    Figure  26.  2-Dimensional prediction constraint and particle reconstruc-tion constraint [116]

    图  27  各向同性强制湍流流场中的三维运动轨迹以及涡环流场三维速度矢量[116]

    Figure  27.  3D trajectory of tracer particles in isotropic forced turbulence flow field and 3D velocity vectors of tracer particles in vortex flow field [116]

    图  28  不同三维粒子追踪算法的重构正确率和追踪正确率[116]

    Figure  28.  Reconstruction correctness and tracking correctness of different 3D PTV strategies [116]

    图  29  热羽流及示踪粒子阴影图像以及粒子三维运动轨迹[74]

    Figure  29.  Shadow images of thermal plume and tracer particles, and 3D trajectory of tracer particles[74]

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  • 收稿日期:  2021-08-30
  • 录用日期:  2021-12-14
  • 修回日期:  2021-11-08
  • 网络出版日期:  2022-03-02
  • 刊出日期:  2022-05-19

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