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 TOMOGRA-PHY (WCIPT6) • Previous Articles Next Articles

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:2012-05-02 Published:2012-04-28
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)

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

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)

Abstract

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

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

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.

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.

References

1 Yan,Y.,“Mass flow measurement of bulk solids in pneumatic pipelines”,Meas.Sci.Technol.,7(12),1687-1706(1996).
2 Thorn,R.,Beck,M.S.,Green,R.G.,“Non-intrusive methods of velocity measurement in pneumatic conveying”,J.Phys.E Sci.Instrum.,15(11),1131-1139(1982).
3 Green,R.G.,Rahmat,M.F.,Dutton,K.,Evansy,K.,Goudey,A., Henry,M.,“Velocity and mass flow rate profiles of dry powders in a gravity drop conveyor using an electrodynamic tomography system”, Meas.Sci.Technol.,8(4),429-436(1997).
4 Wang,X.,Guo,L.J.,Zhang,X.M.,Guo,F.D.,“Experimental study of liquid slug velocity in horizontal pipeline”,Journal of Engineering Thermophysics,27(1),71-74(2006).(in Chinese)
5 Xu,L.A.,Yang,H.L.,Zhang,T.,Li,W.,“Clamped-on ultrasound cross-correlation flowmeter and its application to liquid/solid two-phases flow measurement”,Chinese Journal of Scientific Instrument,14(3),257-262(1993).(in Chinese)
6 Kennedy,J.,Eberhart,R.C.,“Particle swarm optimization”,In:Proceedings of IEEE international conference on neural networks,Perth, Australia,4,1942-1948(1995).
7 Li,A.G.,Tan,Z.,Bao,F.M.,He,S.P.,“Particle swarm optimization”, Computer Engineering and Applications,38(21),1-3(2002).(in Chinese)
8 Shi,X.H.,Liang,Y.C.,Lee,H.P.,Lu,C.,Wang,L.M.,“An improved GA and a novel PSO-GA-based hybrid algorithm”,Infor.Process. Lett.,93,255-261(2005).
9 Liu,B.,Wang,L.,Jin,Y.H.,Tang,F.,Huang,D.X.,“Improved particle swarm optimization combined with chaos”,Chaos,Solitons Fractals,25(5),1261-1271(2005).
10 Jutten,C.,Herault,J.,“Blind separation of sources”,Signal Process.,24(1),1-10(1991).
11 Karhunen,J.,“Neural approaches to independent component analysis and source separation”,In:Proc.ESANN,Bruges,Belgium,49-266(1996).
12 Haritopoulos,M.,Yin,H.,Allison,N.,“Image denoising using self-organizing map-based nonlinear independent component analysis”, Neural Networks,15(8-9),1085-1098(2002).
13 Burel,G.,“Blind separation of sources:A nonlinear neural algorithm”,Neural Networks,5(6),937-947(1992).
14 Wei,Y.,Liu,Z.X.,Li,N.,Sun,D.B.,“Nonlinear blind source separation using improved particle swarm optimization”,Aerospace Electron.Infor.Eng.Control,28(1),138-142(2006).
15 Yang,H.H.,Amari,S.,Cichocki,A.,“Information theoretic approach to blind separation non-linear mixture”,Signal Process.,64 (2),291-300(1998).

[1]	Danlei Chen, Yiqing Luo, Xigang Yuan. Cascade refrigeration system synthesis based on hybrid simulated annealing and particle swarm optimization algorithm [J]. Chinese Journal of Chemical Engineering, 2023, 58(6): 244-255.
[2]	Lusheng Zhai, Bo Xu, Haiyan Xia, Ningde Jin. Simultaneous measurement of velocity profile and liquid film thickness in horizontal gas-liquid slug flow by using ultrasonic Doppler method [J]. Chinese Journal of Chemical Engineering, 2023, 58(6): 323-340.
[3]	Danlei Chen, Yiqing Luo, Xigang Yuan. Refrigeration system synthesis based on de-redundant model by particle swarm optimization algorithm [J]. Chinese Journal of Chemical Engineering, 2022, 50(10): 412-422.
[4]	Xia Chen, Yan Wang, Lianying Wu, Weitao Zhang, Yangdong Hu. Testing and validation of a self-diffusion coefficient model based on molecular dynamics simulations [J]. Chinese Journal of Chemical Engineering, 2021, 36(8): 138-145.
[5]	Yang Lü, Kai Lu, Youxiang Bai, Yulong Ma, Yongsheng Ren. Fouling characteristics of 90° elbow in high salinity wastewater from coal chemical industry [J]. Chinese Journal of Chemical Engineering, 2021, 35(7): 143-151.
[6]	M. Ijaz Khan, Seifedine Kadry, Yuming Chu, M. Waqas. Modeling and numerical analysis of nanoliquid (titanium oxide, graphene oxide) flow viscous fluid with second order velocity slip and entropy generation [J]. Chinese Journal of Chemical Engineering, 2021, 29(3): 17-25.
[7]	Yi Li, Zhi Ning, Ming Lü. Experimental study on fusion and break-up motion after droplet collision [J]. Chinese Journal of Chemical Engineering, 2020, 28(3): 712-720.
[8]	Shuqi Fang, Xinyue Zhang, Jingyi Zhang, Chun Chang, Pan Li, Jing Bai. Evaluation on the natural gas hydrate formation process [J]. Chinese Journal of Chemical Engineering, 2020, 28(3): 881-888.
[9]	A. Al-Farraji, Haidar Taofeeq. Effect of elevated temperature and silica sand particle size on minimum fluidization velocity in an atmospheric bubbling fluidized bed [J]. Chinese Journal of Chemical Engineering, 2020, 28(12): 2985-2992.
[10]	Zhen Tian, Xi Li, Youwei Cheng, Lijun Wang. Interaction of two in-line bubbles of equal size rising in viscous liquid [J]. Chinese Journal of Chemical Engineering, 2020, 28(1): 54-62.
[11]	Yongzheng Li, Xiaolai Zhang, Guangwei Zhai, Haitao Zhang, Tao Li, Qiwen Sun, Weiyong Ying. LDV measurements of particle velocity distribution in an annular stripper [J]. Chinese Journal of Chemical Engineering, 2019, 27(10): 2293-2303.
[12]	Nikolaos A. Avgerinos, Dionissios P. Margaris. A three-dimensional CFD study of the hydrodynamic behavior of equal and unequal-sized in-line methane bubbles at high pressure [J]. Chin.J.Chem.Eng., 2018, 26(9): 1792-1802.
[13]	Tenglong Su, Fengling Yang, Meiting Li, Kanghui Wu. Characterization on the hydrodynamics of a covering-plate Rushton impeller [J]. Chin.J.Chem.Eng., 2018, 26(6): 1392-1400.
[14]	Pouria Amani, Elham Mohammadi, Sahar Akhgar. Design aspect of a novel L-shaped pulsed column for liquid-liquid extraction applications: Energy consumption and the characteristics velocity concept [J]. Chin.J.Chem.Eng., 2018, 26(4): 723-730.
[15]	Dazhang Yang, Jianhua Liu, Xiaoxue E, Linlin Jiang. Model for seawater fouling and effects of temperature, flow velocity and surface free energy on seawater fouling [J]. Chin.J.Chem.Eng., 2016, 24(5): 658-664.

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

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments