Heat and Mass Transfer

, Volume 47, Issue 11, pp 1417–1425 | Cite as

Correlation of viscosity in nanofluids using genetic algorithm-neural network (GA-NN)

  • Hajir KarimiEmail author
  • Fakheri Yousefi
  • Mahmood Reza Rahimi


An accurate and efficient artificial neural network based on genetic algorithm (GA) is developed for predicting of nanofluids viscosity. The genetic algorithm (GA) is used to optimize the neural network parameters. The experimental viscosity in eight nanofluids in the range 238.15–343.15 K with the nanoparticle volume fraction up to 9.4% was used. The obtained results show that the GA-NN model has a good agreement with the experimental data with absolute deviation 2.48% and high correlation coefficient (R ≥ 0.98). The Results also reveals that GA-NN model outperforms to the conventional neural nets in predicting the viscosity of nanofluids with the overall percentage improvement of 39%. Furthermore, the results have also been compared with Einstein, Batchelor and Masoumi et al. models. The findings demonstrate that this model is an efficient method and have better accuracy.


Genetic Algorithm Mean Square Error Hide Neuron Base Fluid Effective Viscosity 
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.


  1. 1.
    Choi SUS (1995) Enhancing thermal conductivity of fluids with nanoparticles. In: Singer DA, Wang HP (eds) Development and applications of non-newtonian flows, ASME, FED-vol. 231/MD-vol. 66, New York, pp 99–106Google Scholar
  2. 2.
    Lee S, Choi SUS (1996) Application of metallic nanoparticle suspensions in advanced cooling systems. In: Recent advances in solids/structures and application of metallic materials. ASME PVP 342/MD 72, pp 227–234Google Scholar
  3. 3.
    Ren Y, Xie H, Cai A (2005) Effective thermal conductivity of nanofluids containing spherical nano particles. Appl Phys 38:3958Google Scholar
  4. 4.
    Mintsa HA, Roy G, Nguyen CT, Doucet D (2009) New temperature dependent thermal conductivity data for water based nanofluids. Int J Therm Sci 48:363–371CrossRefGoogle Scholar
  5. 5.
    Xiang-Qi W, Arun SM (2008) A review on nanofluids—part I theoretical and numerical investigations. Braz J Chem Eng 25(04):613–630Google Scholar
  6. 6.
    Xiang-Qi W, Arun SM (2008) A review on nanofluids—part II theoretical and numerical investigations. Braz J Chem Eng 25(04):631–648CrossRefGoogle Scholar
  7. 7.
    He Y, Jin Y, Chen H, Ding Y, Cang D, Lu H (2007) Heat transfer and flow behaviour of aqueous suspensions of TiO nanoparticles (nanofluids) flowing upward through a vertical pipe. J Heat Mass Transf 50:2272–2281zbMATHCrossRefGoogle Scholar
  8. 8.
    Nguyen CT, Desgranges F, Galanis N, Roy G, Mare T, Boucher S, Angue Mintsa H (2008) Viscosity data for Al2O3–water nanofluid-hysteresis: is heat transfer enhancement using nanofluids reliable. Int J Therm Sci 47:103–111CrossRefGoogle Scholar
  9. 9.
    Nguyen CT, Desgranges F, Roy G, Galanis N, Mare T, Boucher S, Mintsa HA (2007) Temperature and particle-size dependent viscosity data for water-based nanofluids–hysteresis phenomenon. Int J Heat Fluid Flow 28:1492–1506CrossRefGoogle Scholar
  10. 10.
    Namburu PK, Kulkarni DP, Misra D, Das DK (2007) Viscosity of copper oxide nanoparticles dispersed in ethylene glycol and water mixture. J Exp Therm Fluid Sci 32:397–402CrossRefGoogle Scholar
  11. 11.
    Chandrasekar M, Suresh S, Chandra Bose A (2010) Experimental investigations and theoretical determination of thermal conductivity and viscosity of Al2O3/water nanofluid. Exp Thermal Fluid Sci 34:210–216CrossRefGoogle Scholar
  12. 12.
    Duangthongsuk W, Wongwises S (2009) Measurement of temperature-dependent thermal conductivity and viscosity of TiO2-water nanofluids. Exp Thermal Fluid Sci 33:706–714CrossRefGoogle Scholar
  13. 13.
    Einstein A (1906) Eine neue bestimmung der molekuldimensionen. Ann Phys 19:289–306CrossRefGoogle Scholar
  14. 14.
    Masoumi N, Sohrabi N, Behzadmehr A (2009) A new model for calculating the effective viscosity of nanofluids. J Phys D Appl Phys 42:055501 (6 pp)Google Scholar
  15. 15.
    Hosseini MS, Mohebbi A, Ghader S (2010) Correlation of shear viscosity of nanofluids using the local composition theory. Chin J Chem Eng 18:102–110CrossRefGoogle Scholar
  16. 16.
    Maiga SEB, Nguyen CT, Galanis N, Roy G (2004) Heat transfer behaviours of nanofluids in a uniformly heated tube. Superlattices Microstruct 35:543–557CrossRefGoogle Scholar
  17. 17.
    Kulkarni DP, Das DK, Chukwu G (2006) Temperature dependent rheological property of copper oxide nanoparticles suspension (Nanofluid). J Nanosci Nanotechnol 6:1150–1154CrossRefGoogle Scholar
  18. 18.
    Karimi H, yousefi F (2007) Correlation of vapour liquid equilibria of binary mixtures using artificial neural networks. Chin J Chem Eng 15:765–771CrossRefGoogle Scholar
  19. 19.
    Karimi H, Saghatoleslami N, Rahimi MR (2010) Prediction of water activity coefficient in TEG-water system using diffusion neural network (DNN). Chem Biochem Eng Q 24:167–176Google Scholar
  20. 20.
    Kurt H, Kayfeci M (2009) Prediction of thermal conductivity of ethylene glycol-water solution by using artificial neural networks. Appl Energy 86:862244–862248CrossRefGoogle Scholar
  21. 21.
    Sablani SS, Kacimov A, Perret J, Mujumdar AS, Campo A (2005) Non- Iterative estimation of heat transfer coefficients using neural network models. Int J Heat Mass Transf 48:665–790zbMATHCrossRefGoogle Scholar
  22. 22.
    Kurt H, Atik K, Ozkaymak M, Binark AK (2006) The artificial neural network approach for evolution of temperature and density profiles of salt gradient solar pond. J Energy Inst 80:46–51CrossRefGoogle Scholar
  23. 23.
    Kalogirou S (2000) Applications of artificial neural networks for energy systems. Appl Energy 67(1–2):17–35CrossRefGoogle Scholar
  24. 24.
    Jain L, Fanelli AM (2000) Recent advances in artificial neural networks. Design and applications. CRC Press, Boca RatonGoogle Scholar
  25. 25.
    Hormik K, Stinchhcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 68:359–366CrossRefGoogle Scholar
  26. 26.
    Goldberg D (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, MassachusettszbMATHGoogle Scholar
  27. 27.
    Tavman I, Turgut A, Chirtoc M, Schuchmann HP, Tarman S (2008) Experimental investigation of viscosity and thermal conductivity of suspensions containing nanosized ceramic particles. Int Sci J 34:99–104Google Scholar
  28. 28.
    Ravi P, David S, Jinlin W (2006) Measurements of nanofluid viscosity and its implications for thermal applications. Appl Phys Lett 89:133108CrossRefGoogle Scholar
  29. 29.
    Haisheng C, Yulong D, Yurong H, Chunqing T (2007) Rheological behaviour of ethylene glycol based Titania nanofluids. Chem Phys Lett 444:333–337CrossRefGoogle Scholar
  30. 30.
    Huang CF, Moraga C (2004) A diffusion-neural-network for learning from small samples Intrna. J Aprox Reason 35:137–161MathSciNetzbMATHCrossRefGoogle Scholar
  31. 31.
    Batchelor GK (1977) The effect of Brownian motion on the bulk stress in the suspension of spherical particles. J Fluid Mech 83:97–117MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Hajir Karimi
    • 1
    Email author
  • Fakheri Yousefi
    • 2
  • Mahmood Reza Rahimi
    • 1
  1. 1.Chemical Engineering DepartmentYasouj UniversityYasoujIran
  2. 2.Department of ChemistryYasouj UniversityYasoujIran

Personalised recommendations