Algorithm optimization for real-time volumetric particle tracking velocimetry under non-uniform illumination for airflow studies

Volumetric Particle Tracking Velocimetry (VPTV) is a scalable real-time method for measuring 3D velocity fields. Based on the previous work in our group, grayscale cameras equipped with Field Programmable Gate Arrays (FPGA) were used to reduce the data flow rate and achieve real-time 3D measurement. However, the centroids of particle segments were used in FPGA cameras for particle detection, which could be hindered when particles were streak-shaped due to non-uniform illumination and long exposure time. To improve particle detection, a hybrid VPTV system was proposed containing both regular grayscale cameras and FPGA grayscale cameras. In this hybrid system, the exposure time was extended, and the “good features to track (GFTT)” approach was used to find the corners of particle streaks. The Graphics Processing Units (GPU) were used to process the grayscale images and perform the GFTT. This approach resulted in a 65% enhancement of particle detection ratio compared to the previous algorithm. With GPU acceleration, the processing time per frame was accelerated 15 times compared to the CPU version. In addition, a data-analysis sequence correspondence (DSC) algorithm was developed to improve the correspondence task in this hybrid system. The DSC constructed datasets to train a logistic regression classifier to identify the correct correspondence using only two cameras in actual measurements. The results showed that this approach could achieve an 82% correct prediction ratio and thus can improve the correspondence task in VPTV.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Agricultural & Biological Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Zhao, Yu

Contributors dc:contributor

• Zhang, Yuanhui
• Grift, Tony
• Wang, Xinlei
• Sun, Yigang

• Building Environment
• Particle Tracking
• Real-time Visualization
• Flow Pattern

Statement dc:rights

• Copyright 2022 Yu Zhao

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/115570

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F115570