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基于Matlab的电容层析成像敏感场的仿真研究 被引量:5

Simulation Study on Sensitivity Field of Electrical Capacitance Tomography System on Matlab
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摘要 电容层析成像是监测两相流动的一种新技术。它可重建两相流在其流经管道横截面上的相分布图像,而先决条件就是要得到敏感场的分布。本文对电容传感器进行了数学描述,建立了电容敏感场分布的数学模型及其有限元模型,介绍了敏感场的计算方法,然后利用有限元法,用Matlab工具实现了敏感场的仿真,并且研究了敏感场分布的规律。实验证明:本文建立的电容敏感场分布符合实际,而且在速率方面也能满足很好的要求。 Electrical capacitance tomography is a new technique for monitoring two-phase flow. It can reconstruct cross-sectional phase distribution images of the pipeline through which two-phase flow runs. Before image reconstruction the sensitivity distribution of the tomography system must be defined. After proposing the mathematical formulation of capacitance transducers,this paper builds the mathematical and finite element model of the capacitance sensitivity field distribution respectively of ECT system.h 'also introduces the theory and the constitution of ECT system and the computing method of the sensitivity field. Then it realizes the simulation of the sensitivity field and studies the rules of the distribution of the sensitivity field by FEM on Matlab.h can be proven that the sensitivity field proposed is in accordance with the practice and it also attains a good standard in the velocity.
作者 曹琳琳
出处 《微计算机信息》 北大核心 2006年第04S期289-291,288,共4页 Control & Automation
关键词 两相流 电容层析成像 敏感场 有限元法 MATLAB two-phase flow electrical capacitance tomography(ECT) sensitivity field finite element method(FEM) Matlab
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  • 1王兴,颜华.电容层析成像技术及发展现状[J].沈阳工业大学学报,2001,23(6):497-500. 被引量:12
  • 2Xie C G, Huang S M,Hoyle B S,et al.Eleetrieal Capacitance tomography for flow imaging:System model for development of image reconstruction algorithms and design of primary sensors.lEE Proceedings-G,1992,139(1):89-98.
  • 3Khan S H,Abdullah F, Finite element modeling of muhielectrode eapaeitive systems for flow imaging.lEE Proceedings-G,1993,140(3):316-222.
  • 4Huang S M,Xie C G,Thom R,et at,Design of sensor electronics for electrical capacitance tomography.IEE Proceedings-G,1992,139(2):83-98.
  • 5张利萍,李宏光.灰色神经网络预测算法在DMF回收过程中的应用[J].微计算机信息,2005,21(1):183-184. 被引量:27

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