The Velocity Measurement of Two-phase Flow Based on Particle Swarm Optimization Algorithm and Nonlinear Blind Source Separation

›› 2012, Vol. 20 ›› Issue (2): 346-351.

• SELECTED PAPERS FROM THE 6TH WORLD CONGRESS ON INDUSTRIAL PROCESS TOMOGRAPHY (WCIPT6) • 上一篇下一篇

The Velocity Measurement of Two-phase Flow Based on Particle Swarm Optimization Algorithm and Nonlinear Blind Source Separation

吴新杰¹, 崔春阳¹, 胡晟¹, 李志宏², 吴成东³

1. College of Physics, Liaoning University, Shenyang 110036, China;
2. Key Laboratory of Condition Monitoring and Control for Power Plant Equipment, Ministry of Education, North China Electric Power University, Beijing 102206, China;
3 School of Information Science and Engineering, Northeastern University, Shenyang 110006, China

收稿日期:2011-12-14 修回日期:2012-01-10 出版日期:2012-04-28 发布日期:2012-05-02
通讯作者: WU Xinjie
基金资助:
Supported by the National Natural Science Foundation of China (50736002,61072005);the Youth Backbone Teacher Project of University,Ministry of Education,China;the Scientific Research Foundation of the Department of Science and Technology of Liaoning Province (20102082);the Changjiang Scholars and Innovative Team Development Plan (IRT0952)

The Velocity Measurement of Two-phase Flow Based on Particle Swarm Optimization Algorithm and Nonlinear Blind Source Separation

WU Xinjie¹, CUI Chunyang¹, HU Sheng¹, LI Zhihong², WU Chengdong³

1. College of Physics, Liaoning University, Shenyang 110036, China;
2. Key Laboratory of Condition Monitoring and Control for Power Plant Equipment, Ministry of Education, North China Electric Power University, Beijing 102206, China;
3 School of Information Science and Engineering, Northeastern University, Shenyang 110006, China

Received:2011-12-14 Revised:2012-01-10 Online:2012-04-28 Published:2012-05-02
Supported by:
Supported by the National Natural Science Foundation of China (50736002,61072005);the Youth Backbone Teacher Project of University,Ministry of Education,China;the Scientific Research Foundation of the Department of Science and Technology of Liaoning Province (20102082);the Changjiang Scholars and Innovative Team Development Plan (IRT0952)

摘要/Abstract

摘要： In order to overcome the disturbance of noise,this paper presented a method to measure two-phase flow velocity using particle swarm optimization algorithm,nonlinear blind source separation and cross correlation method.Because of the nonlinear relationship between the output signals of capacitance sensors and fluid in pipeline,nonlinear blind source separation is applied.In nonlinear blind source separation,the odd polynomials of higher order are used to fit the nonlinear transformation function,and the mutual information of separation signals is used as the evaluation function.Then the parameters of polynomial and linear separation matrix can be estimated by mutual information of separation signals and particle swarm optimization algorithm,thus the source signals can be separated from the mixed signals.The two-phase flow signals with noise which are obtained from upstream and downstream sensors are respectively processed by nonlinear blind source separation method so that the noise can be effectively removed.Therefore,based on these noise-suppressed signals,the distinct curves of cross correlation function and the transit times are obtained,and then the velocities of two-phase flow can be accurately calculated.Finally,the simulation experimental results are given.The results have proved that this method can meet the measurement requirements of two-phase flow velocity.

关键词: particle swarm optimization, nonlinear blind source separation, velocity, cross correlation method

Abstract: In order to overcome the disturbance of noise,this paper presented a method to measure two-phase flow velocity using particle swarm optimization algorithm,nonlinear blind source separation and cross correlation method.Because of the nonlinear relationship between the output signals of capacitance sensors and fluid in pipeline,nonlinear blind source separation is applied.In nonlinear blind source separation,the odd polynomials of higher order are used to fit the nonlinear transformation function,and the mutual information of separation signals is used as the evaluation function.Then the parameters of polynomial and linear separation matrix can be estimated by mutual information of separation signals and particle swarm optimization algorithm,thus the source signals can be separated from the mixed signals.The two-phase flow signals with noise which are obtained from upstream and downstream sensors are respectively processed by nonlinear blind source separation method so that the noise can be effectively removed.Therefore,based on these noise-suppressed signals,the distinct curves of cross correlation function and the transit times are obtained,and then the velocities of two-phase flow can be accurately calculated.Finally,the simulation experimental results are given.The results have proved that this method can meet the measurement requirements of two-phase flow velocity.

Key words: particle swarm optimization, nonlinear blind source separation, velocity, cross correlation method

吴新杰, 崔春阳, 胡晟, 李志宏, 吴成东. The Velocity Measurement of Two-phase Flow Based on Particle Swarm Optimization Algorithm and Nonlinear Blind Source Separation[J]. , 2012, 20(2): 346-351.

WU Xinjie, CUI Chunyang, HU Sheng, LI Zhihong, WU Chengdong. The Velocity Measurement of Two-phase Flow Based on Particle Swarm Optimization Algorithm and Nonlinear Blind Source Separation[J]. , 2012, 20(2): 346-351.

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The Velocity Measurement of Two-phase Flow Based on Particle Swarm Optimization Algorithm and Nonlinear Blind Source Separation

The Velocity Measurement of Two-phase Flow Based on Particle Swarm Optimization Algorithm and Nonlinear Blind Source Separation

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