Journal of Thermal Analysis and Calorimetry

, Volume 134, Issue 3, pp 2275–2286 | Cite as

Prediction and modeling of MWCNT/Carbon (60/40)/SAE 10 W 40/SAE 85 W 90(50/50) nanofluid viscosity using artificial neural network (ANN) and self-organizing map (SOM)

  • Heydar Maddah
  • Reza Aghayari
  • Mohammad Hossein AhmadiEmail author
  • Mohammad Rahimzadeh
  • Nahid Ghasemi


The present study investigated and predicted the relative viscosity of multiwall carbon nanotube/carbon (60/40)/SAE 10 W 40/(Society of Automotive Engineers) SAE 85 W 90(50/50) at different temperatures and the different volumetric fraction by applying artificial neural networks based on experimental data. Several samples of nanofluid were provided by adding nanoparticles in 0%, 0.1%, 0.3%, 0.5%, 0.8% and 1% volumetric concentrations. Dynamic viscosity of the nanofluid was measured in the temperature range of 25–50 °C. Initially, a self-organizing 6 × 6 hexagonal network was used. A total of 36 neurons were chosen. The winner neuron was neuron 25, having assigned the most data to itself. Then 25 neurons were used for the neural network, which had a very good performance. Temperature and concentration were considered as input variables, while the relative viscosity was the output parameter of the neural network. Mean-square error, correlation coefficient and standard deviation were utilized in order to assess the results. Based on the obtained results, the best model was double-layer perceptron neural network with 25 neurons. The mean square error, correlation coefficient and standard deviation were equal to 2.0193e−008, 1 and 0.00021082, respectively. Therefore, the model is able to predict relative viscosity with appropriate accuracy.


Nanofluid Viscosity Artificial neural networks Self-organizing map 


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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2018

Authors and Affiliations

  • Heydar Maddah
    • 1
  • Reza Aghayari
    • 1
  • Mohammad Hossein Ahmadi
    • 2
    Email author
  • Mohammad Rahimzadeh
    • 3
  • Nahid Ghasemi
    • 4
  1. 1.Department of ChemistryPayame Noor University (PNU)TehranIran
  2. 2.Faculty of Mechanical EngineeringShahrood University of TechnologyShahroodIran
  3. 3.Department of Mechanical EngineeringGolestan UniversityGorganIran
  4. 4.Department of Chemistry, Arak BranchIslamic Azad UniversityArakIran

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