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Statistical estimation the thermal conductivity of MWCNTs-SiO2/Water-EG nanofluid using the ridge regression method

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  • Xiaohong, Dai
  • Huajiang, Chen
  • Bagherzadeh, Seyed Amin
  • Shayan, Masoud
  • Akbari, Mohammad

Abstract

Ridge regression is a regularization method which evaluated according to the experimental results of the hybrid nanofluid containing SiO2 and MWCNTs suspended in water and ethylene glycol as the base fluid concerned to the thermal conductivity versus different amounts of nanoparticles concentration and temperature. The novelty of this study is that this method is very useful for decreasing the variance of the fit and improves its future predictions Also, it is applicable especially in handling the small training data sets. Meanwhile, if the training data set is small, ridge regression can find a solution based on the cross validation and the ridge regression penalty. The findings showed that the fit is almost perfect because the fit line is almost identical with the Y=T line indicating the ideal fit. Also, the slope and y-intercept values of the fit line are 0.98 and 0.0076, respectively.

Suggested Citation

  • Xiaohong, Dai & Huajiang, Chen & Bagherzadeh, Seyed Amin & Shayan, Masoud & Akbari, Mohammad, 2020. "Statistical estimation the thermal conductivity of MWCNTs-SiO2/Water-EG nanofluid using the ridge regression method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    • Handle: RePEc:eee:phsmap:v:537:y:2020:i:c:s037843711931578x
    DOI: 10.1016/j.physa.2019.122782
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    Cited by:

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    2. Wei Wang & Linjian Li & Sheng Li & Fei Yin & Fang Liao & Tao Zhang & Xiaosong Li & Xiong Xiao & Yue Ma, 2022. "Average ordinary least squares‐centered penalized regression: A more efficient way to address multicollinearity than ridge regression," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(3), pages 347-368, August.

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