MODELLING PIPE FLOW USING PYTHON
Keywords:
Pipe flow, Friction factor, Python Programming, Moody’s diagram, Reynolds numberAbstract
In every wake of life, the flow of fluids through pipes is encountered. The major problem encountered while analysing pipe flow problems is obtaining friction factor. Though Moody's diagram helps evaluate the friction factor, the obtained solution is error-prone due to errors in reading the graph. So, to remove the mistakes using hand calculations and improper use of diagram, an attempt has been made in this research article to automate the process of pipe flow modelling. The Colebrook-White equation has been iteratively solved to obtain the friction factor. The modelling is done using Python as it is easy to use and has a vast library backup. The robustness of the developed program has been demonstrated by plotting Moody’s diagram using the code. Three different pipe flow problems through a single pipeline are solved using the developed program, and the obtained results are precisely equal to those shown in the literature.
References
I. N. Rott, Note on the History of the Reynolds Number, Annu. Rev. Fluid Mech. 22 (1990) 1–12. doi:10.1146/annurev.fl.22.010190.000245.
II. M.T. Schobeiri, Basic Physics of Laminar-Turbulent Transition, in: Turbomach. Flow Phys. Dyn. Perform., Springer Berlin Heidelberg, Berlin, Heidelberg, 2012: pp. 515–544. doi:10.1007/978-3-642-24675-3_19.
III. G. Biswas, V. Eswaran, Turbulent flows: fundamentals, experiments and modeling, CRC Press, 2002.
IV. A. Cioncolini, L. Santini, An experimental investigation regarding the laminar to turbulent flow transition in helically coiled pipes, Exp. Therm. Fluid Sci. 30 (2006) 367–380. doi:https://doi.org/10.1016/j.expthermflusci.2005.08.005.
V. Y. Zhou, Turbulence theories and statistical closure approaches, Phys. Rep. 935 (2021) 1–117. doi:https://doi.org/10.1016/j.physrep.2021.07.001.
VI. E.A. Al-Khdheeawi, D.S. Mahdi, Apparent viscosity prediction of water-based muds using empirical correlation and an artificial neural network, Energies. 12 (2019) 1–10. doi:10.3390/en12163067.
VII. B. Guerrero, M.F. Lambert, R.C. Chin, Extreme wall shear stress events in turbulent pipe flows: spatial characteristics of coherent motions, J. Fluid Mech. 904 (2020) A18. doi:10.1017/jfm.2020.689.
VIII. H.A. Milukow, A.D. Binns, J. Adamowski, H. Bonakdari, B. Gharabaghi, Estimation of the Darcy–Weisbach friction factor for ungauged streams using Gene Expression Programming and Extreme Learning Machines, J. Hydrol. 568 (2019) 311–321. doi:https://doi.org/10.1016/j.jhydrol.2018.10.073.
IX. J. Nikuradse, Regularity of turbulent flow in smooth pipes, 1949.
X. J. Nikuradse, others, Laws of flow in rough pipes, (1950).
XI. M. LaViolette, On the History, Science, and Technology Included in the Moody Diagram, J. Fluids Eng. 139 (2017). doi:10.1115/1.4035116.
XII. P. Dumka, S. Sharma, H. Gautam, D.R. Mishra, Finite Volume Modelling of an Axisymmetric Cylindrical Fin using Python, Res. Appl. Therm. Eng. 4 (2021) 1–11.
XIII. Y.C. Huei, Benefits and introduction to python programming for freshmore students using inexpensive robots, in: Proc. IEEE Int. Conf. Teaching, Assess. Learn. Eng. Learn. Futur. Now, TALE 2014, 2015: pp. 12–17. doi:10.1109/TALE.2014.7062611.
XIV. G. Moruzzi, Python Basics and the Interactive Mode, in: Essent. Python Phys., Springer International Publishing, Cham, 2020: pp. 1–39. doi:10.1007/978-3-030-45027-4_1.
XV. P. Dumka, A. Singh, G.P. Singh, D.R. Mishra, Kinematics of Fluid : A Python Approach, Int. J. Res. Anal. Rev. 9 (2022) 131–135.
XVI. M. Cywiak, D. Cywiak, SymPy, in: Multi-Platform Graph. Program. with Kivy Basic Anal. Program. 2D, 3D, Stereosc. Des., Apress, Berkeley, CA, 2021: pp. 173–190. doi:10.1007/978-1-4842-7113-1_11.
XVII. A. Meurer, C.P. Smith, M. Paprocki, O. Čertík, S.B. Kirpichev, M. Rocklin, Am.T. Kumar, S. Ivanov, J.K. Moore, S. Singh, T. Rathnayake, S. Vig, B.E. Granger, R.P. Muller, F. Bonazzi, H. Gupta, S. Vats, F. Johansson, F. Pedregosa, M.J. Curry, A.R. Terrel, Š. Roučka, A. Saboo, I. Fernando, S. Kulal, R. Cimrman, A. Scopatz, SymPy: Symbolic computing in python, PeerJ Comput. Sci. 2017 (2017) 1–27. doi:10.7717/peerj-cs.103.
XVIII. R. Johansson, Numerical python: Scientific computing and data science applications with numpy, SciPy and matplotlib, Second edition, Apress, Berkeley, CA, 2018. doi:10.1007/978-1-4842-4246-9.
XIX. P.S. Pawar, D.R. Mishra, P. Dumka, M. Pradesh, OBTAINING EXACT SOLUTIONS OF VISCO- INCOMPRESSIBLE PARALLEL FLOWS USING PYTHON, Int. J. Eng. Appl. Sci. Technol. 6 (2022) 213–217.
XX. P. Dumka, P.S. Pawar, A. Sauda, G. Shukla, D.R. Mishra, Application of He’s homotopy and perturbation method to solve heat transfer equations: A python approach, Adv. Eng. Softw. 170 (2022) 103160. doi:10.1016/j.advengsoft.2022.103160.
XXI. E. Bisong, Matplotlib and Seaborn, in: Build. Mach. Learn. Deep Learn. Model. Google Cloud Platf., Apress, Berkeley, CA, 2019: pp. 151–165. doi:10.1007/978-1-4842-4470-8_12.
XXII. D.I.H. Barr, C. WHITE, A.A. SMITH, T.E. STANTON, APPLICATION OF SIMILITUDE THEORY TO CORRELATION OF UNIFORM FLOW DATA., Proc. Inst. Civ. Eng. 37 (1967) 487–509.
XXIII. G. Biswas, S.K. Som, Introduction to Fluid Mechanics and Fluid Machines, Tata McGraw-Hill Education, 2003.
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 International Education and Research Journal (IERJ)
This work is licensed under a Creative Commons Attribution 4.0 International License.
