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A rigorous model for prediction of viscosity of oil-based hybrid nanofluids

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  • Jamei, Mehdi
  • Ahmadianfar, Iman

Abstract

Oil-based hybrid nanofluids play an important role in heat transfer in cooling systems and lubrication. Therefore, various experimental investigations are conducted to estimate their viscosity. However, such measurements can be carried out on limited types of oil-based hybrid nanofluids and often are time consuming and expensive. The main objective of this paper is to develop a rigorous data-driven method based on an advanced genetic programming (GP) called multigene genetic programming (MGGP) to predict the viscosity of Newtonian oil-based hybrid nanofluids which has not previously been used in this area. A comparative analysis was performed using the gene expression programming (GEP), multi-variate linear regression (MLR) methods and various correlations. 679 experimental data points with different nanoparticles and oil-based fluids were collected from literature to develop the Artificial Intelligent (AI) models. The new approach showed superior performance in estimating of the relative viscosity of oil-based hybrid nanofluids in comparison with all correlations methods. Furthermore, the MGGP results for the test dataset (R=0.991, RMSE=0.05, PI=0.643) were more accurate than those obtained from the GEP (R=0.975, RMSE=0.083, PI=0.696) and MLR (R=0.912, RMSE =0.153, PI=1), respectively. The sensitivity analysis was also performed demonstrating that the volume fraction (PIs=0.849, DV1=10.079%), temperature (PIs=0.463, DV2=9.966%) and nanoparticles size (PIs=0.420, DV3=6.092%) are the most significant factors in assessing relative viscosity, respectively.

Suggested Citation

  • Jamei, Mehdi & Ahmadianfar, Iman, 2020. "A rigorous model for prediction of viscosity of oil-based hybrid nanofluids," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    • Handle: RePEc:eee:phsmap:v:556:y:2020:i:c:s0378437120304283
    DOI: 10.1016/j.physa.2020.124827
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    References listed on IDEAS

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    1. Karimipour, Arash & Bagherzadeh, Seyed Amin & Taghipour, Abdolmajid & Abdollahi, Ali & Safaei, Mohammad Reza, 2019. "A novel nonlinear regression model of SVR as a substitute for ANN to predict conductivity of MWCNT-CuO/water hybrid nanofluid based on empirical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 89-97.
    2. Kamiński, Marcin & Ossowski, Rafał Leszek, 2014. "Prediction of the effective parameters of the nanofluids using the generalized stochastic perturbation method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 10-22.
    3. Hemmati-Sarapardeh, Abdolhossein & Varamesh, Amir & Husein, Maen M. & Karan, Kunal, 2018. "On the evaluation of the viscosity of nanofluid systems: Modeling and data assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 313-329.
    4. Kulwinder Parmar & Rashmi Bhardwaj, 2015. "River Water Prediction Modeling Using Neural Networks, Fuzzy and Wavelet Coupled Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(1), pages 17-33, January.
    5. Moradikazerouni, Alireza & Hajizadeh, Ahmad & Safaei, Mohammad Reza & Afrand, Masoud & Yarmand, Hooman & Zulkifli, Nurin Wahidah Binti Mohd, 2019. "Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: Optimal artificial neural network and curve-fitting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 138-145.
    6. Tian, Zhe & Rostami, Sara & Taherialekouhi, Roozbeh & Karimipour, Arash & Moradikazerouni, Alireza & Yarmand, Hooman & Zulkifli, Nurin Wahidah Binti Mohd, 2020. "Prediction of rheological behavior of a new hybrid nanofluid consists of copper oxide and multi wall carbon nanotubes suspended in a mixture of water and ethylene glycol using curve-fitting on experim," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
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