航空发动机中气液两相流的可视化检测

赵愉; 岳士弘; 张洋洋; 王化祥

doi:10.13700/j.bh.1001-5965.2017.0060

航空发动机中气液两相流的可视化检测

doi: 10.13700/j.bh.1001-5965.2017.0060

天津大学电气与自动化工程学院, 天津 300072

基金项目:

国家自然科学基金 61174014

详细信息

作者简介:
赵愉女, 硕士研究生。主要研究方向:EIT应用

岳士弘男, 博士, 教授。主要研究方向:智能信息处理技术和EIT应用

通讯作者:
岳士弘, E-mail: shyue1999@tju.edu.cn

中图分类号: V233.3⁺4;TE311
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- 被引次数: 0
出版历程
- 收稿日期: 2017-02-13
- 录用日期: 2017-05-19
- 网络出版日期: 2017-11-20

Visible measurement of gas-liquid two-phase flow in aircraft engine

School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China

Funds:

National Natural Science Foundation of China 61174014

More Information

Corresponding author: YUE Shihong, E-mail: shyue1999@tju.edu.cn

摘要

摘要:
针对电阻抗断层成像（EIT）技术中FCM聚类算法的灵敏度系数信息缺失以及测量电压的利用率低两方面问题，提出一种新的成像算法。在该算法中引入灵敏度系数矩阵信息修正各个剖分单元的电压。同时提出了将测量电压数据按照其权系数进行处理的方法，该方法可应用于所有EIT经典反演算法之中。理论和数值仿真结果均表明，与已有的FCM聚类算法相比，优化后算法对两相流型的定位准确度更高，得到的重建图像的空间分辨率与之前相比相对误差降低了5%~15%，相关系数提高了5%~20%。
- 电阻抗断层成像(EIT) /
- FCM聚类算法 /
- 测量电压 /
- 灵敏度系数矩阵 /
- 两相流型
Abstract:
This paper focuses on the lack of the sensitivity coefficient information and the low utilization of the measurement voltage in FCM clustering algorithm in electrical impedance tomography (EIT) technology, and proposes a new imaging algorithm. In the new algorithm, sensitivity matrix information is introduced to correct the voltage of each subdivision unit. And at the same time, we propose to handle the measurement voltage according to its weight coefficient in the total voltage value, and this method can be applied to all EIT classical inversion algorithms. Both the theoretical analysis and numerical simulation results demonstrate that the new algorithm is more accurate in locating two-phase flow patterns than the existing FCM clustering algorithm, the spatial resolution deviation of reconstructed image has been reduced by 5% to 15%, and the correlation coefficient has been increased by 5% to 20%.
- electrical impedance tomography (EIT) /
- FCM clustering algorithm /
- measurement voltage /
- sensitivity matrix /
- two-phase flow patterns

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图 1 正问题/逆问题求解过程

Figure 1. Solution procedure of forward/inverse problem

下载: 全尺寸图片幻灯片

图 2 测量数据权系数化说明

Figure 2. Weight coefficient of measurement data

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图 3 权系数化前后的测量值

Figure 3. Measured values before and after coefficient weighting

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图 4 流型分布

Figure 4. Distribution of flow patterns

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图 5 LBP、原始图像和优化后图像的相关系数对比

Figure 5. Comparison among correlation coefficient of LBP, original image and optimized image

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图 6 LBP、原始图像、优化后图像的相对误差对比

Figure 6. Comparison among relative errors of LBP, original image and optimized image

下载: 全尺寸图片幻灯片

表 1 不同算法的4个模型重建图像

Table 1. Reconstructed images for four models by different algorithms

模型序号	两相流模型	LBP算法	FCM-EIT算法	优化后FCM-EIT算法
1
2
3
4

下载: 导出CSV

表 2 Landweber和Tikhonov算法及权系数法的模型重建图像

Table 2. Model reconstructed images by Landweber and Tikhonov algorithms and coefficient weighting

泡状介质分布模型	Landweber/Tikhonov算法成像	权系数法处理后成像

下载: 导出CSV

表 3 LBP、原始图像、优化后图像的相关系数

Table 3. Correlation coefficients of LBP, original image and optimized image

模型序号	LBP算法	原始FCM算法	优化后FCM算法
1	0.414 1	0.364 7	0.415 4
2	0.373 8	0.295 9	0.390 2
3	0.166 9	0.877 7	0.922 2
4	0.345 5	0.405 1	0.568 4

下载: 导出CSV

表 4 LBP、原始图像、优化后图像的相对误差

Table 4. Relative errors of LBP, original image and optimized image

模型序号	LBP算法	原始FCM算法	优化后FCM算法
1	0.173 4	0.276 3	0.201 1
2	0.207 7	0.267 8	0.163 9
3	0.859 8	0.072 8	0.050 5
4	0.301 2	0.297 8	0.219 5

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