Prediction of the Hitec Molten Salt Convective Heat Transfer Performance Using Artificial Neural Networks

  • Ahmed Ibrahim ElShafeiEmail author
  • Omar Khaled Sallam
  • Mohammed A. Boraey
  • Amr Guaily
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1153)


Hitec molten salt is a ternary eutectic mixture salt that is used as an energy storage medium in concentrated solar power plants to improve the system performance and reduce the operational cost. Thus, the heat transfer performance represented in Nusselt number has been investigated numerically under different inlet temperature and velocity conditions with constant uniform side heat flux. Also, friction factor and mass flow rate are studied numerically. CFD input/output data with 40 studied cases are used as a training dataset of a 2-layer Neural Network for thermo-hydro fields’ accelerated results predictions. Bayesian regularized Neural Network showed a satisfactory agreement for thermo-hydro fields’ predictions compared to the CFD results for the testing dataset not included in the training of the network.


Artificial Neural Network (ANN) Hitec molten salt Heat transfer Forced convection heat transfer 


  1. 1.
    Capocelli, M., Caputo, G., De Falco, M., Balog, I., Piemonte, V.: Numerical modeling of a novel thermocline thermal storage for concentrated solar power. J. Sol. Energy Eng. 141(5), 051001 (2019)CrossRefGoogle Scholar
  2. 2.
    Bin, L., Yu-ting, W., Chong-fang, M., Meng, Y., Hang, G.: Turbulent convective heat transfer with molten salt in a circular pipe. Int. Commun. Heat Mass Transf. 36(9), 912–916 (2009)CrossRefGoogle Scholar
  3. 3.
    Li, P.J., Cheng, Y.T., Liu, S.: Factors affecting the performance of molten salt heat receiver. In: Advanced Materials Research, vol. 365, pp. 8–13 (2012)Google Scholar
  4. 4.
    Lu, Y.W., Li, X.L., Du, W.B., Wu, Y.T., Ma, C.F.: Laminar natural convection heat transfer characteristics of molten salt around the horizontal cylinder. Energy Procedia 69, 681–688 (2015)CrossRefGoogle Scholar
  5. 5.
    Ferng, Y.M., Lin, K.Y., Chi, C.W.: CFD investigating thermal-hydraulic characteristics of FLiNaK salt as a heat exchange fluid. Appl. Therm. Eng. 37, 235–240 (2012)CrossRefGoogle Scholar
  6. 6.
    Qiu, Y., Li, M.J., Li, M.J., Zhang, H.H., Ning, B.: Numerical and experimental study on heat transfer and flow features of representative molten salts for energy applications in turbulent tube flow. Int. J. Heat Mass Transf. 135, 732–745 (2019)CrossRefGoogle Scholar
  7. 7.
    ANSYS FLUENT User’s Guide, “Ansys Fluent Theory Guide,” ANSYS Inc., November 2013Google Scholar
  8. 8.
    MacKay, D.J.: A practical Bayesian framework for backpropagation networks. Neural Comput. 4(3), 448–472 (1992)CrossRefGoogle Scholar
  9. 9.
    Okut, H.: Bayesian regularized neural networks for small n big p data. In: Artificial Neural Networks-Models and Applications (2016).

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ahmed Ibrahim ElShafei
    • 1
    Email author
  • Omar Khaled Sallam
    • 2
  • Mohammed A. Boraey
    • 1
    • 3
  • Amr Guaily
    • 1
    • 4
  1. 1.Smart Engineering Systems Research Center (SESC)Nile UniversityGizaEgypt
  2. 2.School of Engineering and Applied SciencesNile UniversityGizaEgypt
  3. 3.Mechanical Power Engineering DepartmentZagazig UniversityZagazigEgypt
  4. 4.Department of Engineering Math and Physics, Faculty of EngineeringCairo UniversityGizaEgypt

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