Artificial Neural Network Technique for Estimating the Thermo-Physical Properties of Water-Alumina Nanofluid

With its superior thermo-physical characteristics to the carrier fluid, nanofluid is the most impactful heat transfer fluid. Thermal conductivity, density, viscosity, specific heat, coefficient of volumetric expansion, and other thermo-physical parameters play an important part in the thermal management of any heat transfer application. This thermal management governs the service life of an equipment or apparatus, which dissipates heat during its operation. If the equipment is well-managed thermally, then its service life will be extended. Otherwise the equipment stops functioning due to excess heat. Thermo-physical properties of nanofluid vary with the change in the concentration of nanoparticles. Estimation of the properties with the varying concentrations of the nanoparticles is time consuming and is economically not viable. There were many empirical models available in the literature for determining the thermo-physical properties of nanofluids. However, each model provides different values of thermo-physical properties and choosing the best model among the models available is a complex task. In this regard, to avoid the complication in choosing the best model, and in order to envisage the thermo-physical properties of the nanofluid, the Artificial Neural Network (ANN) technique was used. This technique is widely used among the researchers for various applications. The ANN approach was utilized in this work to estimate viscosity and thermal conductivity of water-based Al2O3 nanofluid for volume fractions between 0.01% and 0.1%. For thermal conductivity, mean square error (MSE) was observed as 4.504e-09 and for viscosity, it was observed as 6.4742e-09. Training times were 5 seconds and 4 seconds for thermal conductivity and viscosity datasets, respectively.