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Study of Fluid Flow Measurement Using Positron Annihilation Technology

Published: 28 March 2019 Publication History

Abstract

Positron annihilation technology is used to study the behavior of flow and an algorithm is proposed to track tracer particle without a priori knowledge of the initial location of the particles. The features points is extracted from the original slice image group, the trajectory is linked by using Kuhn-Munkres algorithm and smoothed by using Kalman filter algorithm, and the 3D motion trajectory image and the average velocity value can be obtained finally. A series of experiments is performed to test the effectiveness of the approach. Experimental results show that positron annihilation technology is capable as a means of examining characteristics of flow regime.

References

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Parker, D.J., et al., 2008. Positron imaging techniques for process engineering: recentdevelopments at Birmingham. Meas. Sci. Technol. 19, 094004.
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Yang, Z., et al., 2006. Multiple-particle tracking--an Improvement for positron par-ticle tracking. Nucl. Instr. Meth. Phys. Res. Sec. A 564, 332.
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Griffiths, W., et al., 2011. The use of positron emission particle tracking (PEPT) to study the movement of inclusions in low-melting-point alloy castings. Metal.Mater. Trans. B 43B, 370.
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Volkwyn, T., et al., 2011. Studies of the effect of tracer activity on time-averagedpositron emission particle tracking measurements on tumbling mills at PEPTCape Town. Miner. Eng. 24, 261.
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Bickell, M., et al., 2012. A new line density tracking algorithm for PEPT and its appli-cation to multiple tracers. Nucl. Instr. Meth. Phys. Res. Sec. A 682, 36.
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Welch, G., Bishop, G. 1995. An Introduction to the Kalman Filter. University of North Carolina at Chapel Hill.
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    ICBET '19: Proceedings of the 2019 9th International Conference on Biomedical Engineering and Technology
    March 2019
    327 pages
    ISBN:9781450361309
    DOI:10.1145/3326172
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 28 March 2019

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    Author Tags

    1. Fluid flow measurement
    2. Kalman filter
    3. Kuhn-Munkres algorithm
    4. PEPT

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    Funding Sources

    • Natural Science Foundation of China
    • Aeronautical Science Foundation of China
    • Jiangsu Province Youth Funding
    • NUAA Innovation Projects

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    ICBET ' 19

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