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Mathematical and artificial brain structure-based modeling of heat conductivity of water based nanofluid enriched by double wall carbon nanotubes

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  • Hemmat Esfe, Mohammad
  • Afrand, Masoud

Abstract

Mathematical modeling based on response surface method and also an artificial brain structure-based model was applied to predict thermal properties of a water-based nanofluid earned by a set of laboratory results. Functionalized DWCNTs were used to enrich the conventional fluid (Water in the present study). Using response surface method a two-variable experimental based correlation proposed as a function of concentration and temperature. Also, artificial neural network employed as a brain structure-based approach with two inputs (temperature and concentrations) and thermal conductivity ratio as the desired output. Using mathematical and artificial based methods help to improve the economic efficiency of experimental studies.

Suggested Citation

  • Hemmat Esfe, Mohammad & Afrand, Masoud, 2020. "Mathematical and artificial brain structure-based modeling of heat conductivity of water based nanofluid enriched by double wall carbon nanotubes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    • Handle: RePEc:eee:phsmap:v:540:y:2020:i:c:s0378437119303668
    DOI: 10.1016/j.physa.2019.04.002
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    References listed on IDEAS

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    1. Sheikholeslami, Mohsen & Bandpy, Mofid Gorji & Ashorynejad, Hamid Reza, 2015. "Lattice Boltzmann Method for simulation of magnetic field effect on hydrothermal behavior of nanofluid in a cubic cavity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 58-70.
    2. Sheikholeslami, Mohsen & Ganji, Davood Domiri, 2015. "Entropy generation of nanofluid in presence of magnetic field using Lattice Boltzmann Method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 273-286.
    3. Hajmohammadi, M.R. & Toghraei, I., 2018. "Optimal design and thermal performance improvement of a double-layered microchannel heat sink by introducing Al2O3 nano-particles into the water," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 328-344.
    4. Karimipour, Arash & Hemmat Esfe, Mohammad & Safaei, Mohammad Reza & Toghraie Semiromi, Davood & Jafari, Saeed & Kazi, S.N., 2014. "Mixed convection of copper–water nanofluid in a shallow inclined lid driven cavity using the lattice Boltzmann method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 150-168.
    5. Hemmat Esfe, Mohammad & Abbasian Arani, Ali Akbar & Esfandeh, Saeed & Afrand, Masoud, 2019. "Proposing new hybrid nano-engine oil for lubrication of internal combustion engines: Preventing cold start engine damages and saving energy," Energy, Elsevier, vol. 170(C), pages 228-238.
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