Multi-objective optimization of thermophysical properties of f–Al₂O₃ nano-dispersions in heat transfer oil

Nanofluids are proven to be the next-generation smart fluids with tunable thermal and viscous properties. Nanomaterial concentration plays a vital role in determining the heat transfer and viscous transport characteristics. An optimum concentration is generally required to regulate a feasible and economical heat transfer operation. This research involves the modeling and optimizing different temperature-dependent thermal and viscous parameters for varying concentrations of nanofluids. The nanofluids consist of functionalized alumina (f–Al₂O₃) nano-dispersions in thermal oil (highly refined mineral oil). The experimentally measured temperature-dependent nanofluids' properties are used to optimize thermophysical parameters using Response Surface Methodology. Two case studies/scenarios are considered in the present research, where the primary objective is to maximize thermal conductivity for heat transfer applications and minimize nanoparticle loadings for economical operation. The input parameters include temperature and nanoparticle loadings. The output parameters or response include thermal conductivity, viscosity, density, and specific heat of nanofluids. For case study 1, the optimal findings for the thermal conductivity, viscosity, density, and specific heat are 0.146061 W/m °C, 0.031889 Pa.s, 838.529 kg/m³ and 1533.9 j/kg °C, respectively. For case study 2, the optimal findings for thermal conductivity, viscosity, density, and specific heat are 0.13476 W/m °C, 0.0226062 Pa.s, 831.071 kg/m³ and 1791.14 j/kg °C, respectively. Although the optimal value for thermal conductivity decreased in case study 2, the nanoparticle weight % was reduced from 1 to 0.322473%.