ASYMMETRIC FLOW VELOCITY MEASUREMENT USING MULTIPATH ULTRASONIC FLOWMETER WITH ADAPTIVE WEIGHTING METHOD GUIDED BY TOMOGRAPHY
DOI:
https://doi.org/10.11113/jt.v82.13785Keywords:
Ultrasonic flowmeter, transit time method, dual-transducers, tomography, Hybrid USM-Tomo, Artificial Neural Network, Support Vector RegressionAbstract
A conventional transit time ultrasonic flowmeter (USM) has a high accuracy for symmetric flow profiles but inaccurate for asymmetric flow profiles. Flow profile shapes can also change over time and difficult to predict. USM with tomographic configuration (USM-Tomo) can adapt to the flow profile changes but result in low temporal resolution. Meanwhile, USM with an adaptive weighting method can measure asymmetric flow velocity but limited to specific asymmetric flow profiles. An alternative scheme to determine adaptive weighting in various asymmetric flow profiles, we proposed a hybrid USM-Tomo. This scheme proposes programmable acoustic path configuration that could set the path mode between USM and tomography. Reducing computation of time of flight in each acoustic can be done by applying the dual-transducers technique. An adaptive weighting of hybrid USM-Tomo is calculated based on the mapping function between the set of velocity on 6 parallel paths of USM and average flow velocity from USM-Tomo. The mapping function is determined using machine learning, i.e., Artificial Neural Network (ANN) and Support Vector Regression (SVR). In the measurement phase, the average flow velocity is determined using the mapping function with input 6 parallel acoustic paths. Based on various types of asymmetric flow profiles used in the experiment, the 6 parallel acoustic paths of USM could produce average flow velocity with error below 1% compared to USM-Tomo. Therefore, the proposed hybrid USM-Tomo scheme has potential to be an alternative scheme for flow meter in industrial application.
References
L. Zhang, L. Xu, and L. Rai. 2018. High-precision Ultrasonic Flowmeter for Mining Applications based on Velocity-area. Telkomnika. 16(1): 84-93.
V. Leontidis, C. Cuvier, G. Caignaert, P. Dupont, O. Roussette, S. Fammery, P. Nivet3, and A. Dazin. 2018. Experimental Validation of an Ultrasonic Flowmeter for Unsteady Flows Application. Meas. Sci. Technol. IOP Publishing. 29 (4): 045303.
A. Hallanger, C. Saetre, and K.-E. Froysa. 2018. Flow Profile Effects Due to Pipe Geometry in an Export Gas Metering Station-Analysis by CFD Simulations. Flow Meas. Instrum. 61: 56-65.
Q. Chen and J. Wu. 2014. Research on the Inherent Error of Ultrasonic Flowmeter in Non-ideal Hydrogen Flow Fields. Int. J. Hydrogen Energy. 39(11): 6104-6110.
C. Wang, T. Meng, H. Hu, and L. Zhang. 2012. Accuracy of the Ultrasonic Flow Meter Used in the Hydroturbine Intake Penstock of the Three Gorges Power Station. Flow Meas. Instrum. 25: 32-39.
AGA Report No. 9. 2007. Measurment of Gas by Multipath Ultrasonic Meters. Second Editon. American Gas Association, 400 North Capitol Street NW, Washiton.
H. Zhao, L. Peng, S. A. Stephane, H. Ishikawa, K. Shimizu, and M. Takamoto. 2014. CFD Aided Investigation of Multipath Ultrasonic Complex Flow Profile. IEEE Sens. J. 14(3): 897-907.
G. J. Brown and B. W. Griffith. 2013. A New Flow Conditioner for 4-Path Ultrasonic Flowmeters. 24-26.
L. C. Lynnworth and Y. Liu. 2006. Ultrasonic Flowmeters: Half-century Progress Report, 1955–2005. Ultrasonics. 44: e1371-e1378.
H. Zhao, L. Peng, T. Takahashi, and T. Hayashi. 2014. Support Vector Regression-Based Data Integration Method for Multipath Ultrasonic Flowmeter. IEEE Trans. Instrum. Meas. 1-9.
E. Liu, H. Tan, and S. Peng. 2017. A CFD Simulation for The Ultrasonic Flow Meter with a Header. Teh. Vjesn. 6(24): 1797-1801.
L. Zhang, H. Hu, T. Meng, C. Wang, N. Third, R. Road, and C. Dist. 2013. Effect of Flow Disturbance on Multi-Path Ultrasonic Flowmeters. IMEKO.
I. Gryshanova, I. Korobko, and P. Pogrebniy. 2016. Increasing of Accuracy of Multipath Ultrasonic Flow Meters by Intelligent Correction. Meas. Autom. Monit. 62(12): 411-416.
Y. Sun, T. Zhang, and Dandan Zheng. 2018. New Analysis Scheme of Flow-acoustic Coupling for Gas Ultrasonic Flowmeter with Vortex near the Transducer. Sensors. 18(4): 1151.
B. Wang, Y. Cui, W. Liu, and X. Luo. 2013. Study of Transducer Installation Effects on Ultrasonic Flow Metering Using Computational Fluid Dynamics. Adv. Mater. Res. 629: 676-681.
D. Zheng, P. Zhang, and T. Xu. 2011. Study of Acoustic Transducer Protrusion and Recess Effects on Ultrasonic Flowmeter Measurement by Numerical Simulation. Flow Meas. Instrum. 22(5): 488-493.
G. Chen, G. Liu, B. Zhu, and W. Tan. 2015. 3D Isosceles Triangular Ultrasonic Path of Transit-time Ultrasonic Flowmeter : Theoretical Design and CFD Simulations. 15, 9: 4733-4742.
D. Kurniadi and A. Trisnobudi. 2006. A Multi-path Ultrasonic Transit Time Flow Meter Using a Tomography Method for Gas Flow Velocity Profile Measurement. Part. Part. Syst. Charact. 23(3–4): 330-338.
P. I. Moore, G. J. Brown, and B. P. Stimpson. 2000. Ultrasonic Transit-time Flowmeters Modelled with Theoretical Velocity Profiles : Methodology. Meas. Sci. Technol. 1802-1811.
K. Tawackolian. 2014. Investigation of a Ten-path Ultrasonic Flow Meter for Accurate Feedwater Measurements. Meas. Sci. Technol. 75304.
E. Mandard, D. Kouam, and R. Battault. 2008. Methodology for Developing a High-precision Ultrasound Flow Meter and Fluid Velocity Profile Reconstruction. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 55(1): 161-172.
H. Zhao, L. Peng, T. Takahashi, and T. Hayashi. 2014. ANN Based Data Integration for Multi-Path Ultrasonic Flowmeter. IEEE Sens. J. 14(2): 362-370.
L. Hu, L. Qin, K. Mao, W. Chen, and X. Fu. 2016. Optimization of Neural Network by Genetic Algorithm for Flowrate Determination in Multipath Ultrasonic Gas Flowmeter. 16(5): 1158-1167.
L. Qin, L. Hu, K. Mao, W. Chen, and X. Fu. 2016. Application of Extreme Learning Machine to Gas Flow Measurement with Multipath Acoustic Transducers. Flow Meas. Instrum. 49: 31-39.
L. Qin, L. Hu, K. Mao, W. Chen, and X. Fu. 2016. Flow Profile Identification with Multipath Transducers. Flow Meas. Instrum. 52(October): 148-156.
D. Zheng, D. Zhao, and J. Mei, 2015. Improved Numerical Integration Method for Flowrate of Ultrasonic Flowmeter Based on Gauss Quadrature for Non-ideal Flow Fields. Flow Meas. Instrum. 41: 28-35.
X. Tang, X. Xie, B. Fan, S. Member, and Y. Sun. 2018. A Fault-Tolerant Flow Measuring Method Based on PSO-SVM With Transit-Time Multipath Ultrasonic Gas Flowmeters. IEEE Trans. Instrum. Meas. 67(5): 992-1005.
J. Liu, B. Wang, Y. Cui, and H. Wang. 2015. Ultrasonic Tomographic Velocimeter for Visualization of Axial Flow Fields in Pipes. Flow Meas. Instrum. 41: 57-66.
C. L. Goh, A. R. Ruzairi, F. R. Hafiz, and Z. C. Tee. 2017. Ultrasonic Tomography System for Flow Monitoring : A Review. IEEE Sens. J. 17(17): 5382-5390.
K. Amri, L. F. Wiranata, S. Suprijanto, and D. Kurniadi. 2015. Fluid Flow Velocity Measurement Using Dual- Ultrasonic Transducer by Means of Simultaneously Transit Time Method. ICICI-BME Conference 2015. 1-4.
A. C. Kak and M. Slaney. 2001. Principles of Computerized Tomographic Imaging. IEEE Press.
S. Kv and B. K. Roy. 2012. An Intelligent Flow Measurement Technique using Ultrasonic Flow Meter with Optimized Neural Network. International Journal of Control and Automation. 5(4): 185-196.
M. T. Hagan and M. H. Beale. 2014. Neural Network Design. 2 edition. Martin Hagan.
A. J. Smola and B. Scholkopf. 2004. A Tutorial on Support Vector Regression. Stat. Comput. 14: 199-222.
S. Frank, C. Heilmann, and H. E. Siekmann. 1997. Point-velocity Methods for Flow-rate Measurements in Asymmetric Pipe Flow. Flow Meas Instrum. 7.
L. A. Salami. 1984. Asymmetric Flow Measurement in Circular Pipes. Transactions of the Institute of Measurement and Control. 6(4): 197-206.
G. J. Brown, D. R. Augenstein, and T. Cousins. 2006. An 8-Path Utrasonic Master Meter for Oil Custody Transfers. XVIII IMEKO WORLD Congr.
K. Amri, S. Suprijanto,E. Juliastuti and D. Kurniadi. 2017 Asymmetric Flow Velocity Profile Measurement using Multipath Ultrasonic Meter with Neural Network Technique. 5th International Conference on Instrumentation, Control, and Automation (ICA). 146-151.
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