Support Vector Regression-Based Data Integration Method for Multipath Ultrasonic Flowmeter

This paper presents a support vector regression (SVR)-based data integration method for a 4-path ultrasonic flowmeter, which is able to estimate accurately the mean cross-sectional flow velocity under complex flow profiles. While installed in the pipeline with complex configurations, such as single-elbow or out-plane double-elbow, the performance of multipath ultrasonic flowmeter will degenerate due to the strong nonlinear relationships between the flow velocities on different individual sound paths and the mean flow velocity on the cross section, particularly when the straight pipe length is not guaranteed. The presented SVR-based method is of an excellent nonlinear mapping and generalization ability. The cases while the Reynolds number in the range of 3.25 × 103 - 3.25 × 105 were simulated using computational fluid dynamics and the flow profiles located on the cross sections of 5 and 10 times pipe diameter downstream a single elbow and an out-plane doubleelbow were extracted to construct the data set for SVR training and test. It is found that the error of the estimated crosssectional mean flow velocity obtained by the SVR-based data integration method is within ±0.5% without the requirement of a flow conditioner, which is significantly better than the results from the traditional integration method with constant weights. The presented SVR-based data integration method is helpful to extend the limitation of straight pipe length for the installation of multipath ultrasonic flowmeter, which is attractive for the practical applications of multipath ultrasonic flowmeter.

[1]  M. L. Sanderson,et al.  Guidelines for the use of ultrasonic non-invasive metering techniques , 2002 .

[2]  Kyung-shik Shin,et al.  An application of support vector machines in bankruptcy prediction model , 2005, Expert Syst. Appl..

[3]  G J Brown,et al.  AN 8-PATH ULTRASONIC MASTER METER FOR OIL CUSTODY TRANSFERS , 2006 .

[4]  Toshihiro Yamamoto,et al.  ANN Based Data Integration for Multi-Path Ultrasonic Flowmeter , 2014, IEEE Sensors Journal.

[5]  Gregor J. Brown,et al.  Ultrasonic transit-time flowmeters modelled with theoretical velocity profiles: methodology , 2000 .

[6]  Tsyh Tyan Yeh,et al.  Special Ultrasonic Flowmeters for In-Situ Diagnosis of Swirl and Cross Flow | NIST , 2001 .

[7]  L. Salami,et al.  Application of a computer to asymmetric flow measurement in circular pipes , 1984 .

[8]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[9]  F. Peters,et al.  Effects of upstream installations on the reading of an ultrasonic flowmeter , 2004 .

[10]  Lihui Peng,et al.  Data Integration Method for Multipath Ultrasonic Flowmeter , 2012, IEEE Sensors Journal.

[11]  Kyoung-jae Kim,et al.  Financial time series forecasting using support vector machines , 2003, Neurocomputing.

[12]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[13]  Tsyh Tyan Yeh,et al.  An intelligent ultrasonic flowmeter for improved flow measurement and flow calibration facility , 2001, IMTC 2001. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Cat. No.01CH 37188).

[14]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[15]  E. Luntta,et al.  Neural network approach to ultrasonic flow measurements , 1999 .

[16]  Hisashi Ninokata,et al.  Numerical investigation of bent pipe flows at transitional Reynolds number , 2011 .

[17]  N. Crawford,et al.  A numerical investigation of the flow structures and losses for turbulent flow in 90° elbow bends , 2009 .

[18]  Alexandre Voser,et al.  Analyse und Fehleroptimierung der mehrpfadigen akustischen Durchflussmessung in Wasserkraftanlagen , 1999 .

[19]  Suad Jakirlić,et al.  Modeling Rotating and Swirling Turbulent Flows: A Perpetual Challenge , 2002 .

[20]  A. Hilgenstock,et al.  Analysis of installation effects by means of computational fluid dynamics—CFD vs experiments? , 1996 .