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Locally weighted moving regression: A non-parametric method for modeling nanofluid features of dynamic viscosity

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  • Wei, Li
  • Arasteh, Hossein
  • abdollahi, Ali
  • Parsian, Amir
  • Taghipour, Abdolmajid
  • Mashayekhi, Ramin
  • Tlili, Iskander

Abstract

The objective of this study is to evaluate the variations of CuO/ ethanol nanofluid dynamic viscosity with temperature and shear rate experimentally. In this study, the nanoparticles average size, their mass fraction, temperature, and shear rate are varying from 10 to 50 nm, 0.005 to 5 wt. %, 25 to 70°C, and 2.6 to 64.6 s−1, respectively. The obtained results showed that as the nanoparticles mass fraction increases and the temperature decreases, the nanofluid dynamic viscosity enhances. Then to predict the nanofluid dynamic viscosity as a function of temperature and nanoparticles mass fraction, the locally weighted moving regression (LWMR) method is applied to the data alongside with applying a classic cubic interpolation for comparison. It was found that although both methods have acceptable performance in dealing with the trained data, the LWMR model results in a more precise prediction. In addition, the numerical viscosities obtained by both methods were evaluated at the trained dataset. Finally, it was concluded that the LWMR may be a suitable model for the investigation of nanofluid features.

Suggested Citation

  • Wei, Li & Arasteh, Hossein & abdollahi, Ali & Parsian, Amir & Taghipour, Abdolmajid & Mashayekhi, Ramin & Tlili, Iskander, 2020. "Locally weighted moving regression: A non-parametric method for modeling nanofluid features of dynamic viscosity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    • Handle: RePEc:eee:phsmap:v:550:y:2020:i:c:s0378437119322769
    DOI: 10.1016/j.physa.2019.124124
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    10. Bagherzadeh, Seyed Amin & Sulgani, Mohsen Tahmasebi & Nikkhah, Vahid & Bahrami, Mehrdad & Karimipour, Arash & Jiang, Yu, 2019. "Minimize pressure drop and maximize heat transfer coefficient by the new proposed multi-objective optimization/statistical model composed of “ANN + Genetic Algorithm” based on empirical data of CuO/pa," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    11. Bahrami, Mehrdad & Akbari, Mohammad & Bagherzadeh, Seyed Amin & Karimipour, Arash & Afrand, Masoud & Goodarzi, Marjan, 2019. "Develop 24 dissimilar ANNs by suitable architectures & training algorithms via sensitivity analysis to better statistical presentation: Measure MSEs between targets & ANN for Fe–CuO/Eg–Water nanofluid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 159-168.
    12. Bagherzadeh, Seyed Amin & D’Orazio, Annunziata & Karimipour, Arash & Goodarzi, Marjan & Bach, Quang-Vu, 2019. "A novel sensitivity analysis model of EANN for F-MWCNTs–Fe3O4/EG nanofluid thermal conductivity: Outputs predicted analytically instead of numerically to more accuracy and less costs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 406-415.
    13. Ershadi, Hamed & Karimipour, Arash, 2018. "Present a multi-criteria modeling and optimization (energy, economic and environmental) approach of industrial combined cooling heating and power (CCHP) generation systems using the genetic algorithm,," Energy, Elsevier, vol. 149(C), pages 286-295.
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