Effects on thermophysical properties of carbon based nanofluids: Experimental data, modelling using regression, ANFIS and ANN. (October 2018) - Alrashed, Abdullah A.A.A.; Gharibdousti, Maryam Soltanpour; Goodarzi, Marjan; de Oliveira, Letícia Raquel; Safaei, Mohammad Reza; Bandarra Filho, Enio Pedone
Graphical abstract: Scanning electron microscopy (SEM) of diamond nanoparticles. Highlights: Two carboxyl based nanoparticles dispersed in water with usage of no surfactant. Effect of various parameters on nanofluids' properties was evaluated experimentally. ANFIS, ANN and regression were utilized to forecast nanofluids' properties. A detailed equation was concluded to predict properties of nanofluids. Suggested equation is in great consent with the former works & experimental data. Abstract: Viscosity, density and thermal conductivity of Diamond-COOH and MWCNT-COOH nanoparticles dispersed in water was studied without adding any surfactants or additives for a range of 20 °C < T < 50 °C and 0.0 < φ < 0.2 vol%. Accordingly, based on the experimental data, a new correlation was introduced that predicts the nanofluids' relative thermophysical properties. Besides the non-linear regression for minimum prediction error, an adaptive neuro-fuzzy inference system (ANFIS) and optimal artificial neural network (ANN) were developed. The model was fed by 120 experimental data. 70% of data points were included in the dataset training set and 30% were used as test set. The results of different theoretical models, predicted results and experimental data were compared together. The root-mean-square error (RMSE) and mean absolute percentage error (MAPE) were used to evaluate the results. The models explored the influence of material type, nanoparticle concentration and temperature on the thermophysical properties of nanofluids. As the results show the majority of theoretical models define the thermophysical properties accurately, if correct values of base fluid properties are fed to them. Yet, the current soft-computing methods show less error in comparison to the existing correlations. The ANN is recommended for future studies, as it provides the best fits to the experimental data.