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Modeling of the discharge coefcient of diferential pressure fowmeters: approximation by using radial-basis function neural networks
https://doi.org/10.32446/0368-1025it.2024-9-19-26
Abstract
The discharge coefficient of flow transducers of liquids and gases of differential pressure flowmeters plays an important role in flow rate measurement. The problem of modeling and calculating the discharge coefficient of differential pressure flowmeters directly affects the accuracy of flow rate measurement of these devices. The results of modeling the discharge coefficient of the differential pressure flowmeter in the form of radial-basis neural networks are presented. The described structure of the neural network calculates the values of the discharge coefficient with an angular pressure tapping method. The article evaluates the error of approximation of the discharge coefficient by radial-basis function networks and provides recommendations for building such networks to solve problems of modeling the characteristics of differential pressure flowmeters. The article discusses the main advantages and disadvantages of using such networks as discharge coefficients of the differential pressure flowmeters. The research showed that the use of such networks is justified by their properties to approximate the discharge coefficient and their efficiency in measuring gas and liquid flow rates.
Supporting agencies: The author declares that no funds, grants, or other support were received during the preparation of this manuscript.
References
1. Kremlevsky P.P. Rashodomery i schetchiki kolichestva: Spravochnik [Flow meters and counters of substances: Handbook] Leningrad, Mashinostroenie Publ., 1989, 701 p. (In Russ.)
2. Gallagher J. E. Natural Gas Measurement Handbook. Gulf Publishing Company. 2007. 425 p.
3. LaNasa P. J., Upp E. L. Fluid Flow Measurement. Butterworth-Heinemann. 2014. 296 p.
4. Reader-Harris M. Orifice plates and Venturi tubes. London: Springer International Publishing. 2015. 386 p.
5. Stolz J. A Universal Equation for the Calculation of Discharge Coefficient of Orifice Plates. Amsterdam: North-Holland, 1978. 32 p.
6. Reader-Harris M.J., Sattary J.A., Spearman E.P. The Orifice Plate Discharge Coefficient Equation. East Kilbride, Glasgow: NEL, Progress Report No. PR14: EUEC/17 (EEC005). 1992.
7. Cristancho D.E., Hall K.R., Coy L.A., Iglesias-Silva G.A. An alternative formulation of the standard orifice equation for natural gas // Flow Measurement and Instrumentation. 2010. Vol. 21. P. 299 – 301.
8. Reader-Harris M.J., Barton N., Hodges D. The effect of contaminated orifice plates on the discharge coefficient // Flow Measurement and Instrumentation. 2012. Vol. 25. P. 2 – 7.
9. Baker O., Lau P., Tawackolian K. Reynolds number dependence of an orifice plate // Flow Measurement and Instrumentation. 2013. Vol. 30. P. 123 – 132.
10. Dayev Z.A., Kairakbaev A.K. Modeling of coefficient of contraction of differential pressure flowmeters. Flow Measurement and Instrumentation. 2019. Vol. 66. P. 128 – 131.
11. Hua C., Geng Y. Wet gas metering technique based on slotted orifice and swirlmeter in series // Flow Measurement and Instrumentation. 2013. Vol. 30. P. 138-143.
12. Dayev Z.A. Theoretical modelling of natural gas unsteady flow rate measurement using variable differential pressure method // Flow Measurement and Instrumentation. 2018. Vol. 62. P. 33 – 36.
13. Düz H. Effect of conical angle on the hydraulic properties of orifice plate flows: A numerical approach // Flow Measurement and Instrumentation. 2021. Vol. 81. №102026.
14. Sravani, V., Venkata, S.K. Parametric analysis of orifice plates on measurement of flow: A review // Ain Shams Engineering Journal. 2022. Vol. 13. №101639.
15. Wu Y., Ma Y., Lyu J., Niu F., Jiao Q. Experimental study on effect of structural parameters on pressure drop characteristics of multi-orifice plates. Flow Measurement and Instrumentation. 2023. Vol. 91. №102346.
16. Borg D., Suetake M., Brandão D. A neural network developed in a Foundation Fieldbus environment to calculate flow rates for compressible fluid // Flow Measurement and Instrumentation. 2014. Vol. 40. P. 142-148.
17. Shaban H., Tavoularis S. Measurement of gas and liquid flow rates in two-phase pipe flows by the application of machine learning techniques to differential pressure signals // International Journal of Multiphase Flow. 2014. Vol. 67. P. 106 – 117.
18. Dayev Zh.A. Application of artificial neural networks instead of the orifice plate discharge coefficient // Flow Measurement and Instrumentation. 2020. Vol. 71. №101674.
19. Dayev Z. A., Kairakbaev A. K. Fuzzy interpretation of the discharge coefficient for the orifice plate of automated flow rate measuring systems. Applied Artificial Intelligence, 35, 1128–1146 (2021). https://doi.org/10.1080/08839514.2021.1975378
20. Dayev Z., Shopanova G., Toksanbaeva B. et al. Modeling the flow rate of dry part in the wet gas mixture using decision tree/kernel/non-parametric regression-based soft-computing techniques. Flow Measurement and Instrumentation, 86, 102195 (2022). https://doi.org/10.1016/j.fl owmeasinst.2022.102195
21. Haykin S. Neural Networks. A Comprehensive Foundation. Prentice Hall (1999).
22. Shaik M. E., Islam M. M., Hossain Q. S. A review on neural network techniques for the prediction of road traffic accident severity. Asian Transport Studies, 7, 100040 (2021). https://doi.org/10.1016/j.eastsj.2021.100040
23. Wu H., Cai X., Feng M. The evaluation of course teaching effect based on improved RBF neural network. Systems and Soft Computing, 6, 200085 (2024). https://doi.org/10.1016/j.sasc.2024.200085
24. Nassajian Mojarrad F., Han Veiga M., Hesthaven J. S., Öffner P. A new variable shape parameter strategy for RBF approximation using neural networks. Computers & Mathematics with Applications, 143, 151–168 (2023). https://doi.org/10.1016/j.camwa.2023.05.005
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For citations:
Dayev Zh.А. Modeling of the discharge coefcient of diferential pressure fowmeters: approximation by using radial-basis function neural networks. Izmeritel`naya Tekhnika. 2024;(9):19-26. (In Russ.) https://doi.org/10.32446/0368-1025it.2024-9-19-26
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