Parameter optimization of coriolis mass flow meter in laminar flow regime using Doe-Taguchi method

Year 2023, Volume: 9 Issue: 4, 1026 - 1040, 04.08.2023

Vikram Kolhe Suyash Pawar Vishal Chaudharı Ravindra Edlabadkar Sandipkumar Sonawane

https://doi.org/10.18186/thermal.1335677

Cited By: 1

Abstract

The paper outlines the progression of a mathematical model using the Taguchi approach to analyze the performance of a Coriolis mass flow meter (CMFM). The sensor position, exci-tation frequency, and flow rate parameters were optimized using the Taguchi method for the meter’s maximum time-lag output. An orthogonal array of experiments was designed, and the time lag results were obtained for two tube configurations (viz. Omega and Diamond) and parameter levels. The obtained data was analyzed using analysis of variance (ANOVA) to understand the relationship between the variables and the time lag. The results showed that the Omega tube configuration exhibited a lower percentage error compared to the Diamond tube configuration. Additionally, an increase in flow rate led to a decrease in the error. The regression models fitted the experimental data well, with high R2 values indicating a good fit. The ANOVA showed the factors’ importance in affecting the time lag and the levels of interac-tion between the best individual parameters for maximizing the outcome. The most important factors affecting the Omega and Diamond tube configurations’ maximum performance have been identified as the flow rate and sensor position, respectively. This study offers a system-atic method for optimizing sensor parameters and provides light on how CMFMs behave in laminar flow. The experimental setup and mathematical model also serve as a basis for future research and advancements in CMFM design and functionality.

Keywords

Coriolis Mass Flow Meter, Error Analysis, Analysis of Variance, Taguchi Approach, Laminar Flow, Tube Configuration

References

• REFERENCES
• [1] Kumar V, Anklin M. Numerical Simulations of Coriolis Flow Meters for Low Reynolds Number. Flows J Metrol Soc India 2011;26:225–235. [CrossRef]
• [2] Gupta P, Srinivasan K, Prabhu S. Tests on various configurations of Coriolis mass flowmeters. Measurement 2006;39:296–307. [CrossRef]
• [3] Sharma S, Patil P, Vasudev A, Jain S. Performance evaluation of an indigenously designed copper (U) tube Coriolis mass flow sensors. Measurement 2010;43:11651172. [CrossRef]
• [4] Patil P, Sharma S, Jain S. Response surface modeling of vibrating omega tube (Copper) electromechanical Coriolis mass flow sensor. Expert Syst Appl 2012;39:4418–4426. [CrossRef]
• [5] Patil P, Sharma S, Jain S. Performance evaluation of a copper omega type Coriolis mass flow sensor with an aid of ANFIS tool. Expert Syst Appl 2012;39:5019–5024. [CrossRef]
• [6] Patil P, Sharma S, Jain S. Prediction modeling of coriolis type mass flow sensor using neural network. Instrum Exp Tech 2011;54:435439. [CrossRef]
• [7] Patil P, Sharma S, Paliwal V, Kumar A. ANN modelling of Cu type omega vibration based mass flow sensor. Proced Technol 2014;14:260–265. [CrossRef]
• [8] Patil P, Sharma S, Jaiswal H, Kumar A. Modeling influence of tube material on vibration based EMMFs using ANFIS. Procedia Mater Sci 2014;6:10971103. [CrossRef]
• [9] Kolhe V, Edlabadkar R. Performance evaluation of Coriolis mass flow meter in laminar flow regime. Flow Meas Instrum 2021;77:113. [CrossRef]
• [10] Sharma S, Bhattacharya M, Khaliquzzama M, Sapra A. Development of a mass flow rate meter based on Coriolis effect. Int J Mech Eng Educ 2015;29:132146. [CrossRef]
• [11] Baker R. Coriolis flowmeters: Industrial practice and published information. Flow Meas Instrum 1994;5:229246. [CrossRef]
• [12] Baker R. Flow Measurement Handbook. Cambridge: Cambridge University Press; 2000.
• [13] Pei X, Li X, Xu H, Zhang X. Flow-induced vibration characteristics of the U-type Coriolis mass flowmeter with liquid hydrogen. J Zhejiang Univ Sci A 2022;23:495–504. [CrossRef]
• [14] Ghalme S, Mankar A, Bhalerao Y. Parameter optimization in milling of glass fiber reinforced plastic (GFRP) using DOE‑Taguchi method. Springer Plus 2016;5:1376. [CrossRef]
• [15] Ghalme S, Mankar A, Bhalerao Y. Optimization of wear loss in silicon nitride (Si3N4)–hexagonal boron nitride (hBN) composite using DoE–Taguchi method. Springer plus. 2016;5:1671. [CrossRef]
• [16] Ghalme S, Mankar A, Bhalerao Y. Original Integrated Taguchi-simulated annealing (SA) approach for analyzing wear behaviour of silicon nitride. J Appl Res Technol 2018;15:624–632. [CrossRef]
• [17] Khan M, Nadeem S. Theoretical treatment of bio-convective Maxwell nanofluid over an exponentially stretching sheet. Can J Phys 2019;98:732741. [CrossRef]
• [18] Nadeem S, Khan M, Muhammad N, Ahmad S. Mathematical analysis of bio-convective micropolar nanofluid. J Comput Des Eng 2019;6:233242. [CrossRef]
• [19] Khan M, Nadeem S. Muhammad N. Micropolar fluid flow with temperature-dependent transport properties. Heat Transf 2020;49:23752389. [CrossRef]
• [20] Ahmad S, Nadeem S. Muhammad N, Khan M. Cattaneo-Christov heat flux model for stagnation point flow of micropolar nanofluid toward a nonlinear stretching surface with slip effects. J Therm Anal Calorim 2021;143:11871199. [CrossRef]
• [21] Ahmad S, Khan M, Nadeem S. Mathematical analysis of heat and mass transfer in a Maxwell fluid with double stratification. Phys Scr 2020;96:025202. [CrossRef]
• [22] Khan M, Ullah N, Nadeem S. Transient flow of Maxwell nanofluid over a shrinking surface: Numerical solutions and stability analysis. Surf Interfaces 2021;22:100829. [CrossRef]
• [23] Khan M, Nadeem S, Saleem A. Mathematical analysis of heat and mass transfer in a Maxwell Fluid. Proc Inst Mech Eng C J Mech Eng Sci 2020;235:49674976. [CrossRef]
• [24] Khan M, Nadeem S. A comparative study between linear and exponential stretching sheet with double stratification of a rotating Maxwell nanofluid flow. Surf Interf 2021;22: 100886. [CrossRef]
• [25] Nadeem S, Khan M, Nadeem A. Transportation of slip effects on nanomaterial micropolar fluid flow over exponentially stretching. Alex Eng J 2020;59:34433450.
• [26] Haider J, Muhammad N. Computation of thermal energy in a rectangular cavity with a heated top wall. Int J Mod Phys B 2022;36:2250212.
• [27] Haider J, Ahammad N, Khan M, Guedri K, Galal A. Insight into the study of natural convection heat transfer mechanisms in a square cavity via finite volume method. Int J Mod Phys B 2022;37:2350038.
• [28] Raza M, Haider J, Ahammad N, Guedri K, Galal A. Insightful study of the characterization of the Cobalt oxide nanomaterials and hydrothermal synthesis. Int J Mod Phys B 2022;37:2350101.
• [29] Nadeem S, Haider J, Akhtar S, Ali S. Numerical simulations of convective heat transfer of a viscous fluid inside a rectangular cavity with heated rotating obstacles. Int J Mod Phys B 2022;36:2250200.
• [30] Asghar S, Haider J, Muhammad N. The modified KdV equation for a nonlinear evolution problem with perturbation technique. Int J Mod Phys B 2022;36:2250160.
• [31] Haider J, Ahmad S. Dynamics of the Rabinowitsch fluid in a reduced form of elliptic duct using finite volume method. Int J Mod Phys B 2022;36:2250217.
• [32] Rahman J, Mannan A, Ghoneim M, Yassen M, Haider, J. Insight into the study of some nonlinear evolution problems: Applications based on Variation Iteration Method with Laplace. Int J Mod Phys B 2022;37:2350030.
• [33] Haider J, Muhammad N. Mathematical analysis of flow passing through a rectangular nozzle. Int J Mod Phys B 2022;36:2250176.
• [34] Zahid M, Younus A, Ghoneim M, Yassen M, Haider J. Quaternion-valued exponential matrices and its fundamental properties. Int J Mod Phys B 2022;37:2350027.
• [35] Haider J, Asghar S, Nadeem S. Travelling wave solutions of the third-order KdV equation using Jacobi elliptic function method. Int J Mod Phys B 2022;37:2350117.