Analytical dynamic model of coefficient of friction of air pipeline under pressure
Vasyl Dmytriv 1  
,   Ihor Dmytriv 1  
,   Ivan Horodetskyy 2,   Taras Dmytriv 1
More details
Hide details
Lviv Polytechnic National University
Lviv National Agrarian University
Vasyl Dmytriv   

Lviv Polytechnic National University
Submission date: 2019-08-20
Acceptance date: 2019-11-18
Online publication date: 2019-11-20
Publication date: 2019-11-20
Diagnostyka 2019;20(4):89–94
To transport of the air in the pipeline, an analytical model is developed that takes into account the gas velocity, its kinematic and dynamic characteristics - density, viscosity depending on the pressure in a given space of the pipeline. The analytical model makes it possible to calculate the coefficient of friction of gas transportation in the pipeline at intervals of the absolute pressure from 220 to 2 KPa and M < 1 Mach numbers, depending on the diameter and length of the pipeline and physical and technological characteristics of the gas. The K1* aspect ratio is proposed, which characterizes in time the ratio of the dynamic force of movement of gas to the static pressure related to the diameter of the pipeline. The coefficient of air friction was modeled according to the vacuum pressure as a parameter of density and air flow. Air flow was taken from 1.917 · 10-3 m3/sec to 44.5 · 10-3 m3/sec respectively, diameters from 0.030 to 0.070 m and Mach number was M = 0.005-0.13. At the pipeline internal diameters of 22, 30, 36 mm accordingly for pressure losses from 2 to 14 kPa the coefficient of air friction varies from 0.006 to 54.527 respectively.
He S, Ariyaratne C. Wall shear stress in the early stage of unsteady turbulent pipe flow. Journal of Hydraulic Engineering. 2011; 137(5): 606–610.
Sundstrom LRJ, Cervantes MJ. On the similarity of pulsating and accelerating turbulent pipe flows. Flow, Turbulence and Combustion. 2018; 100(2): 417-436.
Kong R, Kim S. Characterization of horizontal air-water two-phase flow. The 16th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-16), August 30-September 4, Chicago, USA. 2015: 5559-5572.
Offor UH, Alabi SB. An accurate and computationally efficient explicit friction factor model. Advances in Chemical Engineering and Science. 2016; 6: 237-245.
Medina YC, Fonticiella OMC, Morales OFG. Design and modelation of piping systems by means of use friction factor in the transition turbulent zone. Mathematical Modelling of Engineering Problems. 2017; 4(4): 162-167.
Azizi N, Homayoon R, Hojjati MR. Predicting the Colebrook-White friction factor in the pipe flow by new explicit correlations. Journal of Fluids Engineering. 2018; 141(5).
Pimenta BD, Robaina AD, Peiter MX, Mezzomo W, Kirchner JH, Ben LHB. Performance of explicit approximations of the coefficient of head loss for pressurized conduits. Brazilian Journal of Agricultural and Environmental Engineering (Revista Brasileira de Engenharia Agrícola e Ambiental). 2018; 22(5): 301-307.
Tarek A, Ganat and Meftah Hrairi. Gas–liquid two-phase upward flow through a vertical pipe: influence of pressure drop on the measurement of fluid flow rate. Energies. MDPI, Open Access Journal. 2018; 11(11):1-23.
Brkic D, Praks P. Unified friction formulation from laminar to fully rough turbulent flow. Applied Sciences. 2018; 8(11): 2036.
Ortiz-Vidal LE, Mureithi N, Rodriguez OMH. Friction factor in two-phase gas-liquid pipe flow. 8-th International Conference on Multiphase Flow ICMF-2013, May 26-31. Jeju, Korea 2013.
Lukman S, Oke IA. Accurate solutions of colebrook-white’s friction factor formulae. Nigerian Journal of Technology (NIJOTECH). Nigeria. 2017; 36(4): 1039–1048.
Salmasi F, Khatibi R, Ghorbani MA. A study of friction factor formulation in pipes using artificial intelligence techniques and explicit equations. Turkish Journal of Engineering and Environmental Sciences. 2012; 36(2): 121-138.
Offor UH, Alabi SB. Artificial neural network model for friction factor prediction. Journal of Materials Science and Chemical Engineering. 2016; 4: 77-83.
Brkic D, Sojbasic C. Intelligent Flow Friction Estimation. Computational Intelligence and Neuroscience. 2016.
Loitsianskyi LG. Mechanics of fluid and gas. State Publishing House of technical and theoretical literature. Moscow, Russia. 1952.