TRACKER: AN OPENSOURCE PARTICLE TRACKING VELOCIMETRY (PTV) APPLICATION APPLIED TO MULTIPHASE FLOW REACTORS

被引:0
|
作者
Weber, Justin [1 ]
Bobek, Michael [2 ]
Rowan, Steven [2 ]
Yang, Jingsi [2 ]
Breault, Ronald [1 ]
机构
[1] US DOE, Natl Energy Technol Lab, Morgantown, WV 26505 USA
[2] Oak Ridge Inst Sci & Educ, Natl Energy Technol Lab, Morgantown, WV USA
来源
PROCEEDINGS OF THE ASME/JSME/KSME JOINT FLUIDS ENGINEERING CONFERENCE, 2019, VOL 5 | 2019年
关键词
particle imaging velocimetry; PTV; high speed video; particle tracking; multiphase flow;
D O I
暂无
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
The US Department of Energy's National Energy Technology Laboratory is pursuing the development of advanced energy conversion technologies, many of which use gas-solid reactors such as fluidized beds and risers. To understand these units and provide high fidelity particle velocities for model development and validation efforts, particle tracking velocimetry (PTV) is typically used and remains one of only a few ways to extract particle velocities from dense multiphase flow experiments. Combined with the rapidly improving cameras (higher frame rates, higher resolutions, and lower cost) and access to high performance computers, new particle tracking tools are needed. Tracker is an opensource, cross platform particle tracking velocimetry application for tracking objects in videos and image stacks. The goal of this project is to provide a tool that is, open source, continuously developed, does not rely on expensive software, parallel, has a graphical user interface (GUI), one continuous pipeline (from reading the file to post processing), well documented, and continuously tested and verified. The application has extensive preprocessing tools, two tracking methods including poly-projection and template matching, visualization tools, and post-processing tools. The techniques are tested using both synthetic data and real experimental images. The application is extremely flexible and is easily extended to other tracking techniques, with plans to add correlation-based algorithms and optical flow algorithms. The high-fidelity data being generated is now being used to validate computational fluid dynamic models that then will be used to predict the performance of these reactors, helping to achieve the US Department of Energy's goal of developing novel, compact gas-solid reactors.
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页数:7
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