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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{JAMEI:2020:PASMA,

• author = "Mehdi Jamei and Iman Ahmadianfar",
• title = "A rigorous model for prediction of viscosity of oil-based hybrid nanofluids",
• journal = "Physica A: Statistical Mechanics and its Applications",
• volume = "556",
• pages = "124827",
• year = "2020",
• ISSN = "0378-4371",
• DOI = "doi:10.1016/j.physa.2020.124827",
• URL = "http://www.sciencedirect.com/science/article/pii/S0378437120304283",
• keywords = "genetic algorithms, genetic programming, Relative viscosity, Oil-based hybrid nanofluids, Artificial intelligence, Multigene genetic programming, Gene expression programming",
• 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=10.079percent), temperature (PIs=0.463, DV2=9.966percent) and nanoparticles size (PIs=0.420, DV3=6.092percent) are the most significant factors in assessing relative viscosity, respectively",

}

Genetic Programming entries for Mehdi Jamei Iman Ahmadianfar

Citations