Heat and Mass Transfer

, Volume 55, Issue 8, pp 2261–2276 | Cite as

Proposing a method for combining monitored multilayered perceptron (MLP) and self-organizing map (SOM) neural networks in prediction of heat transfer parameters in a double pipe heat exchanger with nanofluid

  • Nahid GhasemiEmail author
  • Heydar Maddah
  • Maryam Mohebbi
  • Reza Aghayari
  • Sohrab Rohani


The purpose of this study was to investigate the efficiency of using magnesium oxide nanoparticles, twisted tapes with different and modified twist ratios, and the different rotation velocity of perforated twisted tapes in the evaluation of Nusselt number, overall heat transfer coefficient and pressure drop in a double pipe heat exchanger. Subsequently, the results obtained from the experimental tests of Nusselt number, overall heat transfer coefficient and pressure drop using a modified twisted tape in a double pipe heat exchanger and a back propagation artificial neural network with a multilayered perceptron structure show the existence of a relationship between heat transfer parameters and input data such as cold and hot fluid temperatures, volume fractions of nanofluid, twisted tapes of different twist ratios, the rotation velocity of twisted tapes, Reynolds number of hot and cold fluids, viscosity and thermal conductivity of the nanofluid. Also, the non-monitored artificial neural network selected 33 neurons for use in the multilayered perceptron neural network. The network included the sigmoid function in the hidden layer and the Levenberg–Marquardt training algorithm with a three-layer topology of 9–33-1 with the correlation coefficients of 0.9987, 0.9933 and 0.9944 and the mean squared error of 0.66535, 4547.68 and 0.008768. This network was best for predicting heat transfer parameters such as Nusselt number, overall heat transfer coefficient and pressure drop.



Overall heat transfer coefficient (W m-2oC−1)


Nusselt number


Maximum error


Back propagation artificial neural network


Multilayered perceptron structure


Mean squared error




Artificial neural network


Self-organizing map



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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Nahid Ghasemi
    • 1
    Email author
  • Heydar Maddah
    • 1
  • Maryam Mohebbi
    • 2
  • Reza Aghayari
    • 1
  • Sohrab Rohani
    • 3
  1. 1.Department of Chemistry, Arak BranchIslamic Azad UniversityArakIran
  2. 2.Faculty of Food Science and Engineering, Tehran BranchIslamic Azad UniversityTehranIran
  3. 3.Department of Chemical and Biochemical EngineeringThe University of Western OntarioLondonCanada

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