Prediction of nanofluids viscosity using random forest (RF) approach

摘要

Accurate estimation of viscosity, one of the most important thermo-physical properties of nanofluids, is essential in heat transfer fluid applications in many industries. In this paper, for the first time, the random forest (RF), a robust artificial intelligence method is utilized to accurately estimate the viscosity of Newtonian nanofluids. To develop the model five input parameters were used, namely the temperature, solid volume fraction, viscosity of the base fluid, nanoparticle size, and density of nanoparticle. Further, 2890 datasets were collected from 50 references representing a wide range of experimental settings. The model's predictive performance was assessed against those of a multilayer perceptron (MLP) model, a support vector regression (SVR) and various classical and empirical models. The models' performance were analyzed using various statistical performance indicators and graphical plots. Performance criteria assessment, using the testing dataset, showed that the RF model provided the best prediction of the viscosity of nanofluids (R = 0.989, RMSE = 0.139, MAPE = 4.758%) in comparison to those of the MLP (R = 0.915, RMSE = 0.377, MPE = 16.194%) and the SVR (R = 0.941, RMSE = 0.315, MAPE = 7.895%). Moreover, a sensitivity analysis demonstrated that the volume fraction and density of nanoparticles were the most and second most significant factors affecting the viscosity of nanofluid, respectively.