Study of 3D-flow-field velocimetry for turbomachine
| Project/Area Number |
12650166
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Fluid engineering
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| Research Institution | Kyoto Institute of Technology |
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Principal Investigator |
MURATA Shigeru KYOTO INSTITUTE OF TECHNOLOGY, DEPARTMENT OF MECHANICAL AND SYSTEM ENGINEERING, ASSOCIATE PROFESSOR, 工芸学部, 助教授 (50174298)
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| Project Period (FY) |
2000 – 2002
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| Project Status |
Completed (Fiscal Year 2002)
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| Budget Amount *help |
¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2002: ¥200,000 (Direct Cost: ¥200,000)
Fiscal Year 2001: ¥700,000 (Direct Cost: ¥700,000)
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| Keywords | 3D-Flow-Field Analysis / Particle Images Velocimetry / Turbomachine / Particle Tracking Algorithm / Single Camera Observation / Derotating Observation / Depth-from-Defocus / Self-organizing Mapping / 自己組織化マツプ / 数値シミュレーション / 動画像解析 / depth-from-defocus / 3次元流動計測 / 流れの可視化 |
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| Research Abstract |
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.
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Report
(4 results)
Research Products
(5 results)
