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Osama AlSattam_Dissertation__ final format approved LW 04-24-2024.pdf (60.07 MB)

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Abstract Header

Noise Robust Particle Event Velocimetry with A Kalman Filter-Based Tracking

AlSattam, Osama A.
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1714134260767049

Abstract Details

2024, Doctor of Philosophy (Ph.D.), University of Dayton, Engineering.
Event-based pixel sensors asynchronously report changes in log-intensity in microsecond-order resolution. Its exceptional speed, cost effectiveness, and sparse event stream makes it an attractive imaging modality for particle tracking velocimetry. In this work, we propose a causal Kalman filter-based particle event velocimetry (KF-PEV). Using the Kalman filter model to track the events generated by the particles seeded in the flow medium, KF-PEV yields the linear least squares estimate of the particle track velocities corresponding to the flow vector field. KF-PEV processes events in a computationally efficient and streaming manner (i.e.~causal and iteratively updating). Our simulation-based benchmarking study with synthetic particle event data confirms that the proposed KF-PEV outperforms the conventional frame-based (FB) particle image/tracking velocimetry (PIV/PTV) as well as the state-of-the-art event-based (EB) particle velocimetry methods. In a real-world water tunnel event-based sensor data experiment performed on what we believe to be the widest field view ever reported, KF-PEV accurately predicted the expected flow field of the SD7003 wing, including details such as the lower velocity in the wake and the flow separation around the underside of an angled wing.
Keigo Hirakawa (Committee Chair)
97 p.
Aerospace Engineering; Electrical Engineering
PEV; PIV; PTV; KF-PEV; Particle Velocimetry; Event-Based; Event Camera

Recommended Citations

Citations

  • AlSattam, O. A. (2024). Noise Robust Particle Event Velocimetry with A Kalman Filter-Based Tracking [Doctoral dissertation, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1714134260767049

    APA Style (7th edition)

  • AlSattam, Osama. Noise Robust Particle Event Velocimetry with A Kalman Filter-Based Tracking. 2024. University of Dayton, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1714134260767049.

    MLA Style (8th edition)

  • AlSattam, Osama. "Noise Robust Particle Event Velocimetry with A Kalman Filter-Based Tracking." Doctoral dissertation, University of Dayton, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1714134260767049

    Chicago Manual of Style (17th edition)

dayton1714134260767049
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© 2024, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.
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