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Heat and Mass Transfer

, Volume 53, Issue 2, pp 673–685 | Cite as

Prediction of friction factor of pure water flowing inside vertical smooth and microfin tubes by using artificial neural networks

  • A. Çebi
  • E. Akdoğan
  • A. Celen
  • A. S. Dalkilic
Original

Abstract

An artificial neural network (ANN) model of friction factor in smooth and microfin tubes under heating, cooling and isothermal conditions was developed in this study. Data used in ANN was taken from a vertically positioned heat exchanger experimental setup. Multi-layered feed-forward neural network with backpropagation algorithm, radial basis function networks and hybrid PSO-neural network algorithm were applied to the database. Inputs were the ratio of cross sectional flow area to hydraulic diameter, experimental condition number depending on isothermal, heating, or cooling conditions and mass flow rate while the friction factor was the output of the constructed system. It was observed that such neural network based system could effectively predict the friction factor values of the flows regardless of their tube types. A dependency analysis to determine the strongest parameter that affected the network and database was also performed and tube geometry was found to be the strongest parameter of all as a result of analysis.

Keywords

Artificial Neural Network Hide Layer Mass Flow Rate Friction Factor Hydraulic Diameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

List of symbols

\({\dot{\text{m}}}\)

Mass flow rate (kg/s)

∆P

Pressure drop (mbar)

Ac

Cross section of the pipe (m2)

D

Hydraulic diameter (mm)

De

Equivalent hydraulic diameter (mm)

Di

Inner diameter (mm)

Do

Outer diameter (mm)

e

Fin height (mm)

f

Friction factor (−)

L

Length of the pipe (m)

n

Number of fins around the inner perimeter (−)

p

Fin pitch (mm)

t

Pipe wall thickness (mm)

V

Average velocity (m/s)

α

Helix angle (°)

ρ

Average density (kg/m3)

Abbreviations

AI

Artificial intelligence

ANN

Artificial neural network

BA

Bayesian regulation

LM

Levenberg–Marquardt

MLP

Multi-layer perceptron

MSE

Mean square error

PSO

Particle swarm optimization

R

Correlation coefficient

RBF

Radial basis function

RP

Resilient backpropagation

SCG

Scaled conjugate gradient

Notes

Acknowledgments

This study has been financially supported by Yildiz Technical University Scientific Research Projects Coordination Department, Project Number 2013-06-01-KAP01. The authors also wish to thank Wieland-Wilke AG (Ulm, Germany) for valuable donation of the microfin tube used in the present study.

References

  1. 1.
    Adhikari B, Jindal VK (2000) Artificial neural networks: a new tool for prediction of pressure drop of non-Newtonian fluid foods through tubes. J Food Eng 46:43–51. doi: 10.1016/S0260-8774(00)00072-8 CrossRefGoogle Scholar
  2. 2.
    Nasseh S, Mohebbi A, Jeirani Z, Sarrafi A (2007) Predicting pressure drop in venturi scrubbers with artificial neural networks. J Hazard Mater 143:144–149. doi: 10.1016/j.jhazmat.2006.09.005 CrossRefGoogle Scholar
  3. 3.
    Zdaniuk GJ, Chamra LM, Keith D (2007) Walters, Correlating heat transfer and friction in helically-finned tubes using artificial neural networks. Int J Heat Mass Transf 50:4713–4723. doi: 10.1016/j.ijheatmasstransfer.2007.03.043 CrossRefzbMATHGoogle Scholar
  4. 4.
    Amanifard N, Nariman-Zadeh N, Borji M, Khalkhali A, Habibdoust A (2008) Modelling and Pareto optimization of heat transfer and flow coefficients in microchannels using GMDH type neural networks and genetic algorithms. Energy Convers Manag 49:311–325. doi: 10.1016/j.enconman.2007.06.002 CrossRefGoogle Scholar
  5. 5.
    Yuhong Z, Wenxin H (2009) Application of artificial neural network to predict the friction factor of open channel flow. Commun Nonlinear Sci Numer Simul 14:2373–2378. doi: 10.1016/j.cnsns.2008.06.020 CrossRefGoogle Scholar
  6. 6.
    Salmasi F, Khatibi R, Ghorbani MA (2012) A study of friction factor formulation in pipes using artificial 36:121–138. doi: 10.3906/muh-1008-30 Google Scholar
  7. 7.
    Fadare DA, Ofidhe UI (2009) Artificial neural network model for prediction of friction factor in pipe flow. J Appl Sci Res 5:662–670. http://www.aensionline.com/jasr/jasr/2009/662-670.pdf
  8. 8.
    Nasr MRJ, Khalaj AH (2010) Heat transfer coefficient and friction factor prediction of corrugated tubes combined with twisted tape ınserts using artificial neural network. Heat Transf Eng 31:59–69. doi: 10.1080/01457630903263440 CrossRefGoogle Scholar
  9. 9.
    Beigzadeh R, Rahimi M (2012) Prediction of heat transfer and flow characteristics in helically coiled tubes using artificial neural networks. Int Commun Heat Mass Transf 39:1279–1285. doi: 10.1016/j.icheatmasstransfer.2012.06.008 CrossRefGoogle Scholar
  10. 10.
    Cong T, Su G, Qiu S, Tian W (2013) Applications of ANNs in flow and heat transfer problems in nuclear engineering: a review work. Prog Nucl Energy 62:54–71. doi: 10.1016/j.pnucene.2012.09.003 CrossRefGoogle Scholar
  11. 11.
    Balcilar M, Dalkilic AS, Wongwises S (2011) Artificial neural network techniques for the determination of condensation heat transfer characteristics during downward annular flow of R134a inside a vertical smooth tube. Int Commun Heat Mass Transf 38:75–84. doi: 10.1016/j.icheatmasstransfer.2010.10.009 CrossRefGoogle Scholar
  12. 12.
    Balcilar M, Dalkilic AS, Suriyawong A, Yiamsawas T, Wongwises S (2012) Investigation of pool boiling of nano fl uids using arti fi cial neural networks and correlation development techniques. Int Commun Heat Mass Transf 39:424–431. doi: 10.1016/j.icheatmasstransfer.2012.01.008 CrossRefGoogle Scholar
  13. 13.
    Balcilar M, Dalkilic AS, Agra O, Atayilmaz SO, Wongwises S (2012) A correlation development for predicting the pressure drop of various refrigerants during condensation and evaporation in horizontal smooth and micro- fi n tubes. Int Commun Heat Mass Transf 39:937–944. doi: 10.1016/j.icheatmasstransfer.2012.05.005 CrossRefGoogle Scholar
  14. 14.
    Kayaci N, Balcilar M, Tabatabaei M, Celen A, Yıldız O, Dalkilic AS et al (2013) Determination of the single-phase forced convection heat transfer characteristics of TiO2 nanofluids flowing in smooth and Micro-Fin tubes by means of CFD and ANN analyses. Curr Nanosci 12(6):61–80Google Scholar
  15. 15.
    Balcilar M, Aroonrat K, Dalkilic AS, Wongwises S (2013) A numerical correlation development study for the determination of Nusselt numbers during boiling and condensation of R134a inside smooth and corrugated tubes. Int Commun Heat Mass Transf 48:141–148. doi: 10.1016/j.icheatmasstransfer.2013.08.012 CrossRefGoogle Scholar
  16. 16.
    Balcilar M, Aroonrat K, Dalkilic AS, Wongwises S (2013) A generalized numerical correlation study for the determination of pressure drop during condensation and boiling of R134a inside smooth and corrugated tubes. Int Commun Heat Mass Transf 49:78–85. doi: 10.1016/j.icheatmasstransfer.2013.08.010 CrossRefGoogle Scholar
  17. 17.
    Balcilar M, Dalkilic AS, Aroonrat K, Wongwises S (2014) Neural network based analyses for the determination of evaporation heat transfer characteristics during downward flow of r134a ınside a vertical smooth and corrugated tube. Arab J Sci Eng 39:1271–1290. doi: 10.1007/s13369-013-0659-1 CrossRefGoogle Scholar
  18. 18.
    Balcilar M, Dalkilic AS, Sonmez AC, Wongwises S (2014) Classification of in-tube boiling R134a data belonging to the smooth and corrugated tubes. Int Commun Heat Mass Transf 53:185–194. doi: 10.1016/j.icheatmasstransfer.2014.02.020 CrossRefGoogle Scholar
  19. 19.
    Brognaux LJ, Webb RL, Chamra LM, Chung BY (1997) Single-phase heat transfer in micro-fin tubes. Int J Heat Mass Transf 40:4345–4357. doi: 10.1016/S0017-9310(97)00078-1 CrossRefGoogle Scholar
  20. 20.
    Siddique M, Alhazmy M (2008) Experimental study of turbulent single-phase flow and heat transfer inside a micro-finned tube. Int J Refrig 31:234–241. doi: 10.1016/j.ijrefrig.2007.06.005 CrossRefGoogle Scholar
  21. 21.
    Meyer JP, Olivier JA (2011) Transitional flow inside enhanced tubes for fully developed and developing flow with different types of inlet disturbances: part II-heat transfer. Int J Heat Mass Transf 54:1598–1607. doi: 10.1016/j.ijheatmasstransfer.2010.11.026 CrossRefGoogle Scholar
  22. 22.
    Çebi A, Celen A, Dalkilic AS, Wongwises S (2013) Frıctıon factor characterıstıcs for upward sıngle-phase flows ınsıde smooth and mıcrofın tubes of a double-pıpe heat exchanger for heatıng/coolıng condıtıons. J Enhanc Heat Transf 20:413–425CrossRefGoogle Scholar
  23. 23.
    Çebi A (2014) Experimental determination of pressure drop of pure water flowing ınside vertical smooth and microfin tubes. Master of Sciences Thesis, Yildiz Technical University, 2014Google Scholar
  24. 24.
    Moffat RJ (1988) Describing the uncertainties in experimental results. Exp Therm. Fluid Sci 1:3–17. doi: 10.1016/0894-1777(88)90043-X CrossRefGoogle Scholar
  25. 25.
    Celen A, Dalkilic AS, Wongwises S (2013) Experimental analysis of the single phase pressure drop characteristics of smooth and microfin tubes. Int Commun Heat Mass Transf 46:58–66. doi: 10.1016/j.icheatmasstransfer.2013.05.010 CrossRefGoogle Scholar
  26. 26.
    Blasius PRH (1913) Das AehnlichkeitsgesetzbeiReibungsvorgangen in flüssigkeiten. Forschungsheft 131:1–41Google Scholar
  27. 27.
    Churchill SW (1973) Empirical expressions for the shear stressing turbulent flow in commercial pipe. AlChE J 19:375–376CrossRefGoogle Scholar
  28. 28.
    Manadilli G (1997) Replace implicit equations with sigmoidal functions. Chem Eng J 104:129–132Google Scholar
  29. 29.
    Swamee PK, Jain AK (1976) Explicit equation for pipe flow problems. J Hydraul Div ASCE 102:657–664Google Scholar
  30. 30.
    Moody LF (1944) Friction factors for pipe flows. Trans ASME 66:671–684Google Scholar
  31. 31.
    Sonnad JR, Goudar CT (2006) Turbulent flow friction factor calculation using a mathematically exact alternative to the Colebrook-White equation. J Hydraul Eng ASCE 132:863–867CrossRefGoogle Scholar
  32. 32.
    Kökkülünk G, Akdoǧan E, Ayhan V (2013) Prediction of emissions and exhaust temperature for direct injection diesel engine with emulsified fuel using ANN. Turkish J Electr Eng Comput Sci 21:2141–2152. doi: 10.3906/elk-1202-24 CrossRefGoogle Scholar
  33. 33.
    Elçiçek H, Akdoğan E, Karagöz S (2014) The use of artificial neural network for prediction of dissolution kinetics. Sci World J 2014:1–9CrossRefGoogle Scholar
  34. 34.
    Krzywanski J, Nowak W (2012) Modeling of heat transfer coefficient in the furnace of CFB boilers by artificial neural network approach. Int J Heat Mass Transf 55:4246–4253. doi: 10.1016/j.ijheatmasstransfer.2012.03.066 CrossRefGoogle Scholar
  35. 35.
    Marner WJ, Bergles AE, Chenoweth JM (1983) On the presentation of performance data for enhanced tubes used in sheil-and-tube heat exchangers. J Heat Transfer 105:358–365CrossRefGoogle Scholar
  36. 36.
    Nasr MRJ, Khalaj AH, Mozaffari SH (2010) Modeling of heat transfer enhancement by wire coil inserts using artificial neural network analysis. Appl Therm Eng 30:143–151. doi: 10.1016/j.applthermaleng.2009.07.014 CrossRefGoogle Scholar
  37. 37.
    Nguyen D, Widrow B (1990) Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. In: IJCNN ınternational joint conference on neural networks, San Diego, CA, USA, 1990, pp 21–26Google Scholar
  38. 38.
    Jiao B, Lian Z, Gu X (2008) A dynamic inertia weight particle swarm optimization algorithm. Chaos, Solitons Fractals 37:698–705. doi: 10.1016/j.chaos.2006.09.063 CrossRefzbMATHGoogle Scholar
  39. 39.
    Nickabadi A, Ebadzadeh MM, Safabakhsh R (2011) A novel particle swarm optimization algorithm with adaptive inertia weight. Appl Soft Comput J 11:3658–3670. doi: 10.1016/j.asoc.2011.01.037 CrossRefGoogle Scholar
  40. 40.
    Bansal JC, Singh PK, Saraswat M, Verma A, Jadon SS, Abraham A (2011) Inertia weight strategies in particle swarm. In: 2011 Third world congress on nature and biologically inspired computing, pp 640–647Google Scholar
  41. 41.
    Feng Y, Teng G, Wang A, Yao Y (2007) Chaotic ınertia weight in particle swarm optimization. In: Innovative computing, ınformation and control ICIC, pp 7–10Google Scholar
  42. 42.
    Chen S, Montgomery J, Bolufé-Röhler A (2015) Measuring the curse of dimensionality and its effects on particle swarm optimization and differential evolution. Appl Intell 42:514–526. doi: 10.1007/s10489-014-0613-2 CrossRefGoogle Scholar
  43. 43.
    Shayya WH, Sablani SS (1998) An artificial neural network for non-iterative calculation of the friction factor in pipeline flow. Comput Electron Agric 21:219–228. doi: 10.1016/S0168-1699(98)00032-5 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • A. Çebi
    • 1
  • E. Akdoğan
    • 2
  • A. Celen
    • 1
  • A. S. Dalkilic
    • 1
  1. 1.Heat and Thermodynamics Division, Department of Mechanical Engineering, Faculty of Mechanical EngineeringYildiz Technical UniversityIstanbulTurkey
  2. 2.Department of Mechatronics Engineering, Faculty of Mechanical EngineeringYildiz Technical UniversityIstanbulTurkey

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