An insight into the prediction of TiO2/water nanofluid viscosity through intelligence schemes

  • Mohammad Hossein AhmadiEmail author
  • Alireza Baghban
  • Mahyar Ghazvini
  • Masoud Hadipoor
  • Roghayeh GhasempourEmail author
  • Mohammad Reza Nazemzadegan


Viscosity can be mentioned as one of the most crucial properties of nanofluids due to its ability to describe the fluid resistance to flow, and as the result it affects other phenomena. The effects of nanofluids’ viscosity on different parameters can be enumerated as pressure drop, pumping power, feasibility of the nanofluid, and its convective heat transfer coefficient. In this investigation, the viscosity of TiO2/water nanofluid was compared and analyzed with experimental data. The primary goal of this investigation was to introduce a combination of experimental and modeling approaches to predict viscosity values using four different neural networks. Between MLP-ANN, ANFIS, LSSVM, and RBF-ANN methods, it was found that the LSSVM produced better results with the lowest deviation factor and reflected the most accurate responses between the proposed models. The regression diagram of experimental and estimated values shows an R2 coefficient of 0.995 and 0.993 for training and testing sections of the ANFIS model. These values for MLP-ANN, RBF-ANN, and LSSVM models were 0.998 and 0.999, 0.996 and 0.997, and 0.997 and 1.000 for their training and testing parts, respectively. Furthermore, the effect of different parameters was investigated using a sensitivity analysis which demonstrates that the average diameter can be considered as the most affecting parameter on the viscosity TiO2–water nanofluid with a relevancy factor of 0.992123.


Viscosity Neural networks LSSVM ANFIS TiO2–water nanofluids 



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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  • Mohammad Hossein Ahmadi
    • 1
    Email author
  • Alireza Baghban
    • 2
  • Mahyar Ghazvini
    • 3
  • Masoud Hadipoor
    • 4
  • Roghayeh Ghasempour
    • 3
    Email author
  • Mohammad Reza Nazemzadegan
    • 3
  1. 1.Faculty of Mechanical EngineeringShahrood University of TechnologyShahroodIran
  2. 2.Chemical Engineering DepartmentAmirkabir University of Technology, Mahshahr CampusMahshahrIran
  3. 3.Department of Renewable Energy and Environment, Faculty of New Sciences and TechnologiesUniversity of TehranTehranIran
  4. 4.Department of Petroleum Engineering, Ahwaz Faculty of Petroleum EngineeringPetroleum University of Technology (PUT)AhwazIran

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