Thermal conductivity estimation of nanofluids with TiO2 nanoparticles by employing artificial neural networks

Applying nanofluids in energy-related technologies and thermal mediums can lead to remarkable enhancement in their efficiency and performance due to their modified thermophysical properties. Among thermophysical properties, thermal conductivity (TC) performs principal role in heat transfer ability of nanofluids. Artificial neural networks (ANNs) have shown promising performance in modeling nanofluids’ TC. In this article, two types of ANNs are used for estimating TC of nanofluids with TiO2 nanoparticles. In this regard, effective factors including particle size, temperature, volume fraction of solid particles and TC of the base fluids are applied at the input of the model. Based on the comparison between the estimated data and the corresponding actual ones, it is concluded that employing multi-layer perceptron (MLP) is superior compared with group method of data handling (GMDH). In the optimal conditions of the networks, the R-squared value of the models based on both MLP and GMDH was 0.999. Moreover, average absolute relative deviations of the mentioned models were around 0.23% and 0.32%, respectively.