To Predict the Effective Thermal Conductivity of Nanofluids Filled With Metallic Nanoparticles Considering Nonlinear Effect of Volume Fraction in Series-Resistormodel

Nanofluids, a mixture of nanoparticles in base fluids, have drawn keen attention in heat transfer applications due to their high thermal conductivity. Pertinent parameters, like fluid and the nanoparticle thermal conductivity, particle size and volume fraction and many others have shown significant but complex and remarkable effects on thermal performance of nanofluids, which is commonly characterized by the thermal conductivity enhancement. We have here, developed a series-resistor model considering various parameters majorly the impact of nanolayer thickness, size, nanoparticle volume fraction and ratio of particle to fluid thermal conductivity. Artificial neural network (ANN) technique has also been used to evaluate the ETC of nanofluids and then the results have been compared with experimental results present in literature and the results evaluated using ANN technique