Modeling and optimization of thermal conductivity of synthesized MWCNT/water nanofluids using response surface methodology for heat transfer applications

Abstract

This paper reports on the experimental examination and optimization of a response surface methodology (RSM)-based predictive model for the thermal conductivity of aqueous multi-walled carbon nanotube (MWCNT)-based nanofluids for heat transfer applications. The design matrix was created with nanofluid temperature (°C) and nanoparticle concentration (mass/%) as independent variables, while thermal conductivity was considered as a response variable. Magnetic stirring and ultrasonication were used to produce nanofluid samples. The thermal conductivity of the prepared samples was measured, and quadratic models were selected through regression analysis. ANOVA was performed to validate the models. The maximum thermal conductivity value, i.e., 0.988 W m⁻¹ K⁻¹, was achieved at MWCNT particle content 0.5 mass/% and 60 °C temperature. A comprehensive optimization study was also performed for maximizing thermal conductivity. The optimal values for the thermal conductivity of nanofluids were found to be 0.8845 W m⁻¹ K⁻¹, whereas the optimal values for the control factors, i.e., nanofluid temperature and nanoparticles' concentration, were estimated to be 60 °C and 0.5 mass/%, respectively. The coefficient of determination R² for the thermal conductivity of the developed model was found to be 0.9866, which confirmed the suitability of the developed models. The optimized MWCNT/water nanofluid shows potential as an effective heat transfer fluid, particularly for solar thermal and hybrid photovoltaic/thermal applications.