Optimal design of velocity sensor for open channel flow using CFD
Keywords:
computational fluid dynamics, flow measurement, sensors, flow velocity, open channelAbstract
Abstract: In this study, computational fluid dynamics (CFD) was used to design the geometry of a new velocity sensor for measuring open channel flows. This sensor determined velocity by observing the travel of dye carried in the flow. Evaluation of this design required the development of fluid dynamics models to determine potential errors in fluid velocity measurement due to velocity changes caused by intrusion of the sensor in the fluid. It also required an analysis technique to determine the expected sensor response to the flow fields that resulted from the CFD modeling. These models were then used to improve the geometry of the sensor to minimize the measurement error. Starting with a simple design for the sensor geometry, the CFD analysis modeled the open channel flow around the sensor as turbulent using both the k-ω and k-ε Reynolds Averaged Navier-Stokes (RANS) turbulence models. The model predicted that the original sensor design would underestimate the free-stream velocities of open channels by 7.9% to 2.0% across a range from 0.1 m/s to 5.0 m/s. After using CFD to improve the sensor design, the velocity measurement error was limited to less than 4% across the same velocity range. Keywords: computational fluid dynamics, flow measurement, sensors, flow velocity, open channel DOI: 10.3965/j.ijabe.20171003.2147 Citation: Dvorak J S, Zhang N Q. Optimal design of velocity sensor for open channel flow using CFD. Int J Agric & Biol Eng, 2017; 10(3): 130–142.References
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[2] Muste M, Vermeyen T, Hotchkiss R, Oberg K. Acoustic velocimetry for riverine environments. J Hydraul Eng, 2007; 133(12): 1297–8.
[3] Rehmel M. Application of acoustic Doppler velocimeters for streamflow measurements. J Hydraul Eng, 2007; 133(12): 1433–8.
[4] Song Z,Wu T,Xu F,Li R. A simple formula for predicting settling velocity of sediment particles. Water Science and Engineering, 2008; 1(1): 37–43.
[5] Reis S, Seto E, Northcross A, Quinn N W T, Convertino M, Jones R L, et al. Integrating modelling and smart sensors for environmental and human health. Environmental Modelling & Software, 2015; 74: 238–46.
[6] Wong B P, Kerkez B. Real-time environmental sensor data: An application to water quality using web services. Environmental Modelling & Software, 2016; 84: 505–17.
[7] Luo H, Li G, Peng W, Song J, Bai Q. Real-time remote monitoring system for aquaculture water quality. Int J of Agric & Biol Eng, 2015; 8(6): 136–143.
[8] Jin N, Ma R, Lv Y, Lou X. A novel design of water environment monitoring system based on WSN. International Conference on Computer Design and Applications, IEEE, 2010; V2-593-V2-597.
[9] Gartia M R, Braunschweig B, Chang T-W, Moinzadeh P, Minsker B S, Agha G, et al. The microelectronic wireless nitrate sensor network for environmental water monitoring. Journal of Environmental Monitoring, 2012; 14(12): 3068–75.
[10] Mueller D S, Abad J D, Garcia C M, Gartner J W, Garcia M H, Oberg K A. Errors in acoustic Doppler profiler velocity measurements caused by flow disturbance. J Hydraul Eng, 2007; 133(12): 1411–20.
[11] Tokyay T, Constantinescu G, Gonzalez-Casto J A. Investigation of two elemental error sources in boat-mounted acoustic Doppler current profiler measurements by large eddy simulations. J Hydraul Eng, 2009; 135(11): 875–87.
[12] Levesque V A, Oberg K A. Computing discharge using the index velocity method. Techniques and Methods 3–A23. Reston, Va.: U.S. Geological Survey, 2012.
[13] Bigham D. Calibration and testing of a wireless suspended sediment sensor. MS Thesis. Manhattan, Kans.: Kansas State University, 2012.
[14] Zhang N, Dvorak J S, Zhang Y. A correlation-based optical flowmeter for enclosed flows. Transactions of the ASABE, 2013; 56(6): 1511–22.
[15] Dvorak J S, Bryant L E. An optical sprayer nozzle flow rate sensor. Transactions of the ASABE, 2015; 58(2): 251–259.
[16] Dvorak J S, Stombaugh T S, Wan Y. Nozzle Sensor for In-System Chemical Concentration Monitoring, 2016; 59(5): 1089–1099.
[17] ANSYS. ANSYS FLUENT Users Guide. Release 13.0. Canonsburg, Pa. 2010.
[18] Launder B E, Spalding D B. Lectures in mathematical models of turbulence. New York,: Academic Press; 1972. p 169.
[19] Shih T H, Liou W W, Shabbir A, Yang Z, Zhu J. A new k-ϵ eddy viscosity model for high reynolds number turbulent flows. Computers & Fluids, 1995; 24(3): 227–38.
[20] ANSYS. ANSYS FLUENT Theory Guide. Release 13.0. Canonsburg, Pa.2010.
[21] Wolfshtein M. The velocity and temperature distribution in one-dimensional flow with turbulence augmentation and pressure gradient. Int J Heat Mass Tran, 1969; 12(3): 301–18.
[22] Jongen T. Simulation and modeling of turbulent incompressible fluid flows: École polytechnique fédérale de Lausanne; 1998.
[23] Menter F R. Two-equation eddy-viscosity turbulence models for engineering applications. AIAA Journal, 1994; 32(8): 1598–605.
[24] Fourniotis N T, Toleris N E, Dimas A A, Demetracopoulos A C. Numerical computation of turbulence development in flow over sand dunes. Advances in Water Resources and Hydraulic Engineering. Berlin Heidelberg: Springer; 2009. pp. 843–8.
[25] Blasius H. Grenzschichten in Flüssigkeiten mit kleiner Reibung. Organ für angewantde mathematik, 1908; 56: 1–38.
[26] Blasius H. Das Ähnlichkeitsgesetz bei Reibungsvorgängen in Flüssigkeiten: Springer; 1913.
[27] Kármán T V. Über laminare und turbulente Reibung. ZAMM‐Journal of Applied Mathematics and Mechanics/Zeitschrift für Angewandte Mathematik und Mechanik, 1921; 1(4): 233–52.
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Published
2017-05-31
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Dvorak, J. S., & Zhang, N. (2017). Optimal design of velocity sensor for open channel flow using CFD. International Journal of Agricultural and Biological Engineering, 10(3), 130–142. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/2147
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Information Technology, Sensors and Control Systems
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