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

Mehdi Jamei and Iman Ahmadianfar

Physica A: Statistical Mechanics and its Applications, 2020, vol. 556, issue C

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.

Keywords: Relative viscosity; Oil-based hybrid nanofluids; Artificial intelligence; Multigene genetic programming; Gene expression programming (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437120304283
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:556:y:2020:i:c:s0378437120304283

DOI: 10.1016/j.physa.2020.124827

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().