基于粒子图像分割的混合PIV-PTV算法

李拓; 张清福; 潘翀; 陈爽; 申俊琦; 王宏伟; 李晓辉; 黄湛; 王晋军

doi:10.7638/kqdlxxb-2023.0031

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基于粒子图像分割的混合PIV-PTV算法

研究论文 | 更新时间：2026-05-28

- 基于粒子图像分割的混合PIV-PTV算法
- Hybrid PIV-PTV algorithm based on particle image segmentation
- 空气动力学学报 2024年42卷第2期页码：68-75
- 作者机构：
  
  1. 北京航空航天大学流体力学教育部重点实验室,北京,100191
  2. 北京航空航天大学宁波创新研究院先进飞行器与空天动力创新研究中心,宁波,315800
  3. 中国空气动力研究与发展中心设备设计与测试技术研究所,绵阳,621000
  4. 中国航天空气动力技术研究院,北京,100074
- 作者简介：
  
  李拓（2000—），男，陕西蓝田人，硕士研究生，研究方向：实验空气动力学. E-mail：li_tuo@buaa.edu.cn
  陈爽*（1979—），研究员，研究方向：流场光学诊断. E-mail：chenshuang56@126.com
- 基金信息：
  
  国家自然科学基金（61935008,91952301）;国家重点研发计划（2020YFA0405700）
- DOI：10.7638/kqdlxxb-2023.0031
  中图分类号： V211.71
- 纸质出版：2024
- 稿件说明：
移动端阅览
李拓, 张清福, 潘翀, 等. 基于粒子图像分割的混合PIV-PTV算法[J]. 空气动力学学报, 2024, 42(2): 68−75.

LI Tuo, ZHANG Qingfu, PAN Chong, et al. Hybrid PIV-PTV algorithm based on particle image segmentation[J]. 2024, 42(2): 68-75.
李拓, 张清福, 潘翀, 等. 基于粒子图像分割的混合PIV-PTV算法[J]. 空气动力学学报, 2024, 42(2): 68−75. DOI： 10.7638/kqdlxxb-2023.0031

LI Tuo, ZHANG Qingfu, PAN Chong, et al. Hybrid PIV-PTV algorithm based on particle image segmentation[J]. 2024, 42(2): 68-75. DOI： 10.7638/kqdlxxb-2023.0031.

摘要

粒子图像测速法（particle image velocimetry

PIV）因其非接触场测量的特性，已成为空气动力学领域的主要测量工具。复杂流动的速度场往往具有非均匀性，示踪粒子难以在待测空间均匀分布。因此，在应用PIV互相关算法处理粒子稀疏区时，需要采用更大的查询窗口以降低测量的不确定度，但会带来空间分辨率低的实际问题。而粒子追踪测速法（particle tracking velocimetry

PTV）追踪单个示踪粒子的跨帧位移，具有比PIV更高的空间分辨率，但难以适用于粒子浓度高的稠密区。针对PIV、PTV各自的优点，本文发展了一种基于粒子图像分割的混合PIV-PTV测速技术。首先定义了基于维诺多边形的粒子局部浓度量度，用以计算示踪粒子在粒子图像上的局部浓度场；其次通过设定的浓度阈值对粒子进行二分类，使用基于高斯核函数的支持向量机寻找出最优的分类边界，从而实现对粒子图像的粒子稀疏区和稠密区的划分；最后对两个区域分别使用PIV和PTV进行速度场计算，并合并为完整的速度场输出。仿真结果表明，上述方法可实现对粒子图像中的示踪粒子稀疏区和稠密区的自动划分，有效提高速度场测量的空间分辨率。将该方法应用在马赫数

=6的湍流边界层近壁测量中，可有效解决高速条件下粒子因强剪切难以进入边界层近壁区的问题，显著提高对近壁流动的解析能力。

Abstract

Particle image velocimetry (PIV) has become a major measurement tool in the field of aerodynamics due to its characteristic of non-intrusive field measurement. The velocity field distribution of complex flows is often non-uniform and it is difficult to uniformly seed tracer particles

which makes accurate PIV measurements difficult. Therefore

when applying the PIV cross-correlation algorithm to deal with the particle-sparse area

it is necessary to use a larger query window to reduce the uncertainty of measurement

at expense of low spatial resolution. On the other hand

particle tracking velocimetry (PTV) tracks the cross-frame displacement of a single tracer particle

which has a higher spatial resolution than PIV

but it is difficult to apply it to dense areas with high particle concentrations. To solve this problem

a hybrid PIV-PTV velocity measurement technique based on particle image segmentation is developed. Firstly

the local concentration field of the tracer particles is calculated

which is based on the Voronoi polygon. Then the particles are binary classified by the set concentration threshold

and the support vector machine based on Gaussian kernel function is used to find the optimal classification boundary

so as to realize the division of the particle dense area and sparse area of the particle image. Finally

both PIV and PTV were used to calculate the velocity fields in the two regions respectively

and the two results were combined into a complete output of the velocity field. The results show that the above method can automatically divide the particle sparse and dense regions in the particle image

and can effectively improve the spatial resolution of the velocity field measurement. When the method is applied the near-wall measurement of the turbulent boundary layer at Mach 6

it effectively overcomes the problem that the tracer particles are difficult to enter the near-wall region of the boundary layer due to the strong shear of high speed flow

and significantly improve the analytical ability of the near-wall flow.

关键词

Keywords

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