Accurate prediction of thermal conductivity of ethylene glycol-based hybrid nanofluids using artificial intelligence techniques

Abstract

Accurate prediction of thermal conductivity of hybrid nanofluids is very important for industries such as microelectronics and cooling applications that heavily rely on the heat transfer. Many experimental investigations are conducted aiming at developing correlations to predict the relative thermal conductivity of hybrid nanofluids. However, the proposed correlations are limited to specific types of hybrid nanofluids. In this research, for the first time three soft computing techniques namely, Genetic programming (GP), Model tree (MT) and Multi linear regression (MLR) models, were developed and used to accurately predict the thermal conductivity of various ethylene glycol (EG)-based hybrid nanofluids. A total of 275 datasets from literature were collected and divided into the testing and training groups. The results obtained from the proposed approaches were compared with a number of performance metrics and empirical correlations. The performance criteria indicated that the GP model for the test dataset (R = 0.950, RMSE = 0.0225) had the best prediction performance for the relative thermal conductivity of hybrid nanofluids in comparison to MT (R = 0.928, RMSE =0.0301) and MLR (R = 0.787, RMSE =0.050), respectively. Sensitivity analysis showed that the nanoparticle volume fraction (R = 0.445, SI = 0.0667) was the most influential factor among all model input parameters.