Optimization of viscosity of titania nanotubes ethylene glycol/water-based nanofluids using response surface methodology

• Published in: Fuel (Guildford) 2023-09, Vol.347, p.128334, Article 128334
• Main Authors: Bin Mahfouz, Abdullah, Ali, Abulhassan, Mubashir, Muhammad, Hanbazazah, Abdulkader S., Alsaady, Mustafa, Show, Pau Loke
• Format: Article
• Language: English
• Subjects: Nanofluid; Optimization; Response surface methodology; Titania nanotubes; Viscosity
• ISSN: 0016-2361; 1873-7153
• DOI: 10.1016/j.fuel.2023.128334

[Display omitted] •Development of RSM model to predict TiO2 based nanofluid viscosity.•The model was validated using experimental data and the error was 0.7–1.9%.•Conducted the sensitivity analysis of procerss parameters.•Optimise max nanofluid concentration (0.56%), shear rate (2 5 2) and temperature 38 °C.•Min nanofluid concentration (0.04%), shear rate (179 1/s) and temperature 63.4 °C. Nanofluids have gained significant importance in heat transfer applications due to their unique thermal properties, such as higher thermal conductivity, higher convective heat transfer coefficients, and improved thermal stability. They offer the potential for more efficient heat transfer in various engineering applications, including cooling systems, heat exchangers, and thermal energy storage systems. Additionally, their use can lead to reduced energy consumption and improved performance in various industrial processes, making them an attractive option for researchers and engineers in heat transfer. Their thermophysical properties must be known to gain the maximum benefits of using nanofluids as process fluids. Despite the significance of viscosity as a characteristic of nanofluids, adequate research on viscosity modeling cannot offer accurate predictions. The current work aims to use Response Surface Methodology to develop a prediction model for the viscosity of a nanofluid comprised of Titanium (TiO2) nanotubes dissolved in ethylene glycol and water. Different mass concentrations of nanotubes ranged from 0% to 1%, the shear rate was set to 150–500 s−1, and the temperature range was kept from 25 to 65 °C. Moreover, a comprehensive optimization study was conducted using the proposed model. The significance of process input parameters was assessed, and the findings showed that the nanofluid concentration is the most significant input, followed by temperature and shear rate. Two case studies were explored for both maximization and minimization of viscosity. The optimization results showed that the optimum condition, for viscosity maximization (0.00371 Pa·s), of input parameters such as nanofluid concentration, temperature, and shear rate are 0.675%, 205 1/s, and 25 °C for the maximum viscosity 0.00371 Pa·s. For viscosity minimization, the optimum input parameters were nanofluid concentration, temperature, and shear rate are 0.04%, 179 1/s, and 63.4 °C, respectively.