Dynamic viscosity prediction of nanofluids using artificial neural network (ANN) and genetic algorithm (GA) - Topal, Halil Ibrahim; Erdogan, Beytullah; Kocar, Oguz; Onur, Tugba Ozge; Öztop, Hakan Fehmi (Springer Heidelberg, 2024)
Nanofluids exhibit remarkable thermophysical properties, making them highly promising candidates for heat transfer applications. Viscosity is a crucial property among the thermophysical properties of nanofluids, significantly influencing heat transfer rates and pressure loss computations. In this study, the dynamic viscosity of water-based nanofluids containing Al2O3, TiO2, and ZnO nanoparticles was experimentally measured over a wide range of volumetric concentrations (0.1-1.0%) and temperatures (20-50 degrees C). Then, the dynamic viscosity of nanofluids is predicted with a multi-layer perceptron artificial neural network (ANN). Moreover, the genetic algorithm (GA) is adopted for obtaining the dynamic viscosity value of nanofluids. Finally, the results obtained from the designed ANN model and GA are compared. The results show the feasibility of predicting the dynamic viscosity with the designed ANN model. The proposed ANN model holds promises to meet demands for the detection of the dynamic viscosity of the nanofluids instead of using theoretical estimation equations or experiments which require substantial expertise or time.
Keywords: Artificial neural network; Genetic algorithm; Nanofluid; Dynamic viscosity; Aluminum oxide; Titanium dioxide; Zinc oxide