Prediction of Viscosity Values of Nanofluids at Different pH Values by Alternating Decision Tree and Multilayer Perceptron Methods

Abstract

Due to the poor thermal properties of conventional thermal fluids such as water, oil and ethylene glycol, small solid particles are added to these fluids to enhance heat transfer. Since the viscosity change determines the rheological behavior of a liquid, it is very important to examine the parameters affecting the viscosity. Since the experimental viscosity measurement is expensive and time-consuming, it is more practical to estimate this parameter. In this study, CuO (copper oxide) nanoparticles were produced and then Scanning Electron Microscope (SEM) images analyses of the produced particles were made. Nanofluids were obtained by using pure water, ethanol and ethylene glycol materials together with the produced nanoparticles and the viscosity values were calculated by experimental setups at different density and temperatures. For the viscosity values of nanofluids, predictive models were created by using different computational intelligence methods. Mean square error (MSE), root mean square error (RMSE) and mean absolute percentage error (MAPE) error analyses were used to determine the accuracy of the predictive models. The multilayer perceptron method, which has the least error value in computational methods, was chosen as the best predicting method. The multilayer perceptron method, with an average accuracy of 51%, performed better than the alternating decision tree method. As a result, the viscosity increased with the increase in the pH of the nanofluids produced by adding CuO nanoparticles and decreased with the increase in the temperature of the nanofluids. The importance of this study is to create a predictive model using computational intelligence methods for viscosity values calculated with different pH values.