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Optimization of heat transfer and pressure drop of nano-antifreeze using statistical method of response surface methodology

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  • Al-Rashed, Abdullah A.A.A.

Abstract

In this paper, the coefficient of convective heat transfer and pressure drop of a non-Newtonian nanofluid in a horizontal tube has been predicted. For this study, the non-Newtonian Nanofluid of MWCNTs/EG-W was used as the working fluid and experimental correlations were used to calculate the thermal conductivity and viscosity of the nanofluid. The optimization has been predicted with the nanoparticles concentration in the range from 0.2 up to 1% and the temperature range from 25 up to 50 °C. The predicted results showed that the pressure drop in the tube increases by increasing temperature and increasing the volumetric percentage of nanoparticles. Finally, using the response surface methodology (RSM), the obtained data is optimized for the heat transfer coefficient and pressure drop. Thus, it was found that in the volume percentage of 0.725% and temperature of 49.672 °C, the highest coefficient of heat transfer occurs at the same time with the lowest drop in pressure.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:521:y:2019:i:c:p:531-542
    DOI: 10.1016/j.physa.2019.01.095
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    References listed on IDEAS

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    Cited by:

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    2. 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).

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