An experimental and new study on thermal conductivity and zeta potential of Fe3O4/water nanofluid: Machine learning modeling and proposing a new correlation

• Published in: Powder technology Vol. 420; p. 118388
• Main Authors: Sahin, Fevzi, Genc, Omer, Gökcek, Murat, Çolak, Andaç Batur
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.04.2023
• Subjects: Artificial neural network; Fe3O4; Nanofluid; Thermal conductivity; Zeta potential; Fe3O4; Thermal conductivity; Artificial neural network; Nanofluid; Zeta potential
• ISSN: 0032-5910; 1873-328X
• DOI: 10.1016/j.powtec.2023.118388

It is important to predict the thermophysical properties of nanofluids, which have higher heat transfer performance compared to the base fluid, without the need for experimental studies. In this study, two different artificial neural networks were created to predict the thermal conductivity and zeta potential of Fe3O4/water nanofluid. The thermal conductivity and zeta potential of the Fe3O4/water nanofluid prepared at three different concentrations were experimentally measured. An innovative mathematical correlation is proposed to calculate thermal conductivity based on temperature and concentration using the obtained experimental data. Considering that the correlations in the literature can generally be calculated according to concentration, the novelty of the proposed model stands out. The calculated values for thermal conductivity and zeta potential of the created artificial neural network and the new mathematical correlation were compared with the results of the experiments. In addition, a comprehensive performance analysis was made by calculating different performance parameters. The R values of the neural network models were above 0.99 and mean squared error values were obtained as 1.47E-05 and 1.58E-06, respectively. In addition, the mean deviation values calculated for the thermal conductivity of the network model were 0.03%, while it was 0.05% for the new mathematical correlation. The study results showed that ANN models can predict the thermal conductivity and zeta potential of Fe3O4/water nanofluid with high accuracy. The proposed new mathematical correlation was also found to have higher error rates compared to the ANN model, although it was able to calculate thermal conductivity values with high accuracy. [Display omitted] •Fe3O4/water nanofluid prepared in three different concentrations.•Thermal conductivity and zeta potential values were experimentally measured.•Two different artificial neural networks were created.•Thermal conductivity and zeta potential values were predicted.•Experimental values are found compatible with ANN outputs.