1995 Fiscal Year Final Research Report Summary
Development of Real Time Measuring Technique for Particulate Objects in Multi-Phase Flows Utilizing Image Analysis
| Project/Area Number |
06650223
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
Fluid engineering
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| Research Institution | KANSAI UNIVERSITY |
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Principal Investigator |
UEMURA Tomomasa Kansai University Department of Industrial Engineering, Professor, 工学部, 教授 (70029536)
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| Project Period (FY) |
1994 – 1995
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| Keywords | Image Analysis / PTV / PIV / Binary Image Correlation / Particle Velocimetry / High Density Ditribution |
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| Research Abstract |
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.
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