Assessment of thermal conductivity of polyethylene glycol-carbon dot nanofluid through a combined experimental-data mining investigation

Abstract

The aim of this study is to determine the effect of nanoparticle concentration (φ) and temperature on the thermal conductivity of polyethylene glycol (PEG)-carbon dot nanofluid (NF). The considered range for temperature and φ is 20–60 °C and 0–7%, respectively. The results indicated an ascending trend of NF thermal conductivity with boosting both temperature and φ. The percentage of increase in thermal conductivity of NF with temperature and φ compared to the base fluid was in the range of 7.23–13.43% and 75.08–85.17%, respectively. Moreover, two efficient data-driven approaches, namely Multi-variate linear regression (MLR) and response surface methodology (RSM) schemes were developed to simulate the thermal conductivity of the PEG-carbon dot NF and fit the predictive relationships. The outcomes of modeling revealed that RSM model (R = 0.984 and RMSE = 0.013 W/m.K), due to taking into account the interaction between volume fraction and temperature, yielded more accuracy than MLR (R = 0.960 and RMSE = 0.021 W/m.K).

Keywords

• Carbon dot nanofluid
• Correlation
• Multi-linear regression
• Polyethylene glycol
• Response surface methodology
• Thermal conductivity