跟踪器:一个开源的粒子跟踪测速(PTV)应用程序,应用于多相流反应器

J. Weber, M. Bobek, S. Rowan, Jing-Shyang Yang, R. Breault
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引用次数: 2

摘要

美国能源部的国家能源技术实验室正在寻求发展先进的能源转换技术,其中许多使用气固反应器,如流化床和立管。为了了解这些单元并为模型开发和验证工作提供高保真的颗粒速度,通常使用颗粒跟踪速度测量(PTV),并且仍然是从密集多相流实验中提取颗粒速度的少数几种方法之一。结合快速改进的相机(更高的帧率,更高的分辨率和更低的成本)和高性能计算机的使用,需要新的粒子跟踪工具。Tracker是一个开源的跨平台粒子跟踪测速应用程序,用于跟踪视频和图像堆栈中的物体。这个项目的目标是提供一个开源的、持续开发的、不依赖于昂贵的软件的、并行的、具有图形用户界面(GUI)、一个连续的管道(从读取文件到后处理)、有良好文档的、持续测试和验证的工具。该应用程序具有广泛的预处理工具,包括多投影和模板匹配在内的两种跟踪方法,可视化工具和后处理工具。使用合成数据和真实实验图像对这些技术进行了测试。该应用程序非常灵活,很容易扩展到其他跟踪技术,并计划添加基于相关的算法和光流算法。生成的高保真数据现在被用于验证计算流体动力学模型,然后将用于预测这些反应堆的性能,帮助实现美国能源部开发新型紧凑型气固反应堆的目标。
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Tracker: An Opensource Particle Tracking Velocimetry (PTV) Application Applied to Multiphase Flow Reactors
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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