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Appraising influence of COOH-MWCNTs on thermal conductivity of antifreeze using curve fitting and neural network

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  • Ghasemi, Ali
  • Hassani, Mohsen
  • Goodarzi, Marjan
  • Afrand, Masoud
  • Manafi, Sahebali

Abstract

Curve fitting and neural network modeling are suitable methods for modeling the complex relationship between various parameters in engineering problems. In this study, at the first, a curved fitting was performed on experimental data related to nano-antifreeze containing carbon nanotubes, which led to the presentation of a two-variable correlation to predict its thermal conductivity. After that, an artificial neural network was designed to evaluation of the effects of temperature and solid volume fraction on the thermal conductivity of nano-antifreeze. For modeling, the volume fraction and temperature were applied as input variables. By selecting 9 neurons for the hidden layer, the output of the neural network, which was thermal conductivity ratio, was obtained. The results showed that the proposed equation has good accuracy for engineering applications. However, comparative results showed that the neural network has a more accurate prediction than curve fitting for the thermal conductivity of the antifreeze containing multi walled carbon nanotubes (MWCNTs).

Suggested Citation

  • Ghasemi, Ali & Hassani, Mohsen & Goodarzi, Marjan & Afrand, Masoud & Manafi, Sahebali, 2019. "Appraising influence of COOH-MWCNTs on thermal conductivity of antifreeze using curve fitting and neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 36-45.
    • Handle: RePEc:eee:phsmap:v:514:y:2019:i:c:p:36-45
    DOI: 10.1016/j.physa.2018.09.004
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    1. 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.
    2. Hemmat Esfe, Mohammad & Hajmohammad, Hadi & Toghraie, Davood & Rostamian, Hadi & Mahian, Omid & Wongwises, Somchai, 2017. "Multi-objective optimization of nanofluid flow in double tube heat exchangers for applications in energy systems," Energy, Elsevier, vol. 137(C), pages 160-171.
    3. Yiamsawas, Thaklaew & Mahian, Omid & Dalkilic, Ahmet Selim & Kaewnai, Suthep & Wongwises, Somchai, 2013. "Experimental studies on the viscosity of TiO2 and Al2O3 nanoparticles suspended in a mixture of ethylene glycol and water for high temperature applications," Applied Energy, Elsevier, vol. 111(C), pages 40-45.
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    13. Wu, Huawei & Al-Rashed, Abdullah A.A.A. & Barzinjy, Azeez A. & Shahsavar, Amin & Karimi, Ali & Talebizadehsardari, Pouyan, 2019. "Curve-fitting on experimental thermal conductivity of motor oil under influence of hybrid nano additives containing multi-walled carbon nanotubes and zinc oxide," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    14. Al-Rashed, Abdullah A.A.A., 2019. "Optimization of heat transfer and pressure drop of nano-antifreeze using statistical method of response surface methodology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 531-542.
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