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Study of 3D-flow-field velocimetry for turbomachine

涡轮机三维流场测速研究

基本信息

批准号：
12650166
负责人：
MURATA Shigeru
金额：
$ 0.58万
依托单位：
Kyoto Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2000
资助国家：
日本
起止时间：
2000 至 2002
项目状态：
已结题

项目摘要

In this research, the image analyzing system with a single camera was developed for the PIV measurement of the unsteady velocity distribution in 3D space and it was applied to the unsteady flow measurement of the strong shear flow between a static and rotating disks to demonstrate its utility.First, a derotating observation system with a reflective prism was constructed to measure the velocity distribution relative to the rotating object, such as an impeller or a fan of turbomachine at the speed up to 300RPM. A 3CCD camera and the Michelson interferometer with two color filters were employed in the observation system to measure the depth of tracer particles distributed in 3D space. The particle depth for each tracer particle was measured using a pair of green and red frames simultaneously captured with different focus settings. The depth detection was performed by means of the technique of a depth-from-defocus. It was confirmed in numerical simulations and experiments that the depth detection based on a depth-from-defocus could be carried out with the error ratio of 3% to the measurable depth range if the spatial resolution of original images was higher.Secondly, a 3D particle tracking algorithm based on self-organizing maps neural network was developed for 3D Particle Image Velocimetry (3D-PTV). The tracking performance was checked in numerical simulations for the uniform flow and the rotating flow in a cubic cavity.Furthermore, the present observation system with a single camera was successfully applied to the velocity distribution measurement for the strong shear flow in the gap between a static and rotating disks. In this experiment, it was shown that the distribution of tangential velocity component along the axial coordinate could be measured with the flow visualization images captured from the axial direction using the present method.

本研究开发了单摄像机图像分析系统，用于三维空间非定常速度分布的PIV测量，并将其应用于静、旋转圆盘间强剪切流的非定常流场测量，以验证其实用性。首先，构建了一个带反射棱镜的反旋观测系统，用于测量相对于旋转物体的速度分布，例如叶轮或转速高达300 RPM的涡轮风扇。观测系统采用3CCD摄像机和带双色滤光片的迈克尔逊干涉仪测量示踪粒子在三维空间中的分布深度。每个示踪剂颗粒的颗粒深度使用一对绿色和红色帧来测量，这对帧是用不同的焦点设置同时捕获的。深度检测是通过从散焦的深度的技术来执行的。数值模拟和实验结果表明，在原始图像空间分辨率较高的情况下，基于散焦恢复深度的深度检测方法可以实现对可测深度范围的3%误差。其次，提出了一种基于自组织映射神经网络的三维粒子图像测速（3D-PTV）跟踪算法。通过对均匀流和旋转流的数值模拟，验证了该系统的跟踪性能，并将该系统成功地应用于静、旋转圆盘间强剪切流差距的速度分布测量。实验结果表明，利用本方法可以从轴向获取流动显示图像，测量切向速度分量沿着轴向的分布。