Optimization of Thermal Conductivity of Al2O3 Nanofluid by Using ANN and GRG Methods

Common heat transfer fluids such as water, ethylene glycol, and engine oil have limited heat transfer capabilities due to their low heat transfer properties. Nanofluids are suspensions of nanoparticles in base fluids, a new challenge for thermal sciences provided by nanotechnology. In this study, we are to optimize and report the effects of various parameters such as the ratio of the thermal conductivity of nanoparticles to that of a base fluid, volume fraction, nanoparticle size, and temperature on the effective thermal conductivity of nanofluids using nonlinear optimization methods and artificial neural network. The results for nonlinear optimization methods show that Thermal conductivity of nanofluid enhanced by 32 percent. For the modeling of the Thermal conductivity of nanofluid, the feed-forward back-propagation ANN is employed. Result showed the maximum enhancement of 42 percent for thermal conductivity and this method is more acceptable since excellent agreement between the predictions and the experimental data is obtained with a MAE (mean absolute error) of 0.30%.