Development of Real Time Measuring Technique for Particulate Objects in Multi-Phase Flows Utilizing Image Analysis

利用图像分析开发多相流中颗粒物体的实时测量技术

基本信息

批准号：
06650223
负责人：
UEMURA Tomomasa
金额：
$ 1.34万
依托单位：
KANSAI UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1994
资助国家：
日本
起止时间：
1994 至 1995
项目状态：
已结题

项目摘要

This project aims to measure number and velocities of particulate objects such as a particles and tiny bubbles which are moving in crowded condition. For the purpose, the techniques of particle tracking velocimetry is most adequate, since the method can measure velocities of individual particles. But, existing algorithms of the method are commonly weak in the dense distribution of particles. In the present study, main effort is paid to develop a improved PTV method from the binary image correlation method which is effective for densely distributed particles.The method stands on pursuing a resemblance between clusters of particles calculating a special correlation coefficient which are optimized for binary data. The binary image correlation method can find particle to particle correspondences between two successive pictures in a very short time, then each particle trajectory can traced by analyzing sequential pictures.While continuously pursuing trajectories of moving particles, those particle can be identified. And even if some particles are obesrved as one particle in some pictures, they could be separated in different moment and pictures.Along with the above mentioned concept, an analyzing software is developed, and it is confirmed that the new analyzing system works far better than the former system for a condition of densely distributed particles. But, it still remains some issues of improvement such as faster analysis, reducing erroneous measurements and accumulating more experimental examples for stronger performance. In the future study, we are going to apply gray level analysis of the pictures to distinguish individual particle consisting large clusters.

该项目旨在测量颗粒物体的数量和速度，例如颗粒和小气泡，这些气泡在拥挤的情况下移动。为此，由于该方法可以测量单个颗粒的速度，因此粒子跟踪速度测定的技术是最适当的。但是，该方法的现有算法在颗粒的致密分布中通常较弱。在本研究中，从二进制图像相关方法中开发了改进的PTV方法，该方法有效，该方法对密集分布的粒子有效。该方法具有在计算特殊相关系数的粒子簇之间的相似度，该粒子簇对二进制数据进行了优化。二进制图像相关方法可以在很短的时间内找到两个连续图片之间的粒子对应关系，然后可以通过分析顺序图片来追溯每个粒子轨迹。虽然可以不断地追求移动粒子的轨迹，但可以识别这些粒子。即使在某些图片中将某些粒子作为一个粒子作为一个粒子，它们也可以在不同的时刻和图片中分开。随着上述概念，开发了一个分析软件，并且可以证实，新分析系统的效果要比以前的系统效果好得多，对于密集分布的粒子的条件。但是，仍然仍然是一些改进的问题，例如更快的分析，减少错误的测量结果并积累更多的实验示例，以提高性能。在将来的研究中，我们将应用图片的灰度分析，以区分组成大簇的个体粒子。