On the estimation of viscosities of Newtonian nanofluids

Abstract One of the important properties of nanofluids is their viscosities which is required in various applications. Despite the importance of this property, there is no general model for prediction of viscosities of nanofluids. Hence, developing accurate and general models for prediction of this property is of great importance. The purpose of this work was to develop four accurate and reliable models based on Multilayer Perceptron Neural Networks (MLP-NNs), Least Square Support Vector Machine (LSSVM), Adaptive Neuro Fuzzy Inference System (ANFIS) and Gene Expression Programming (GEP) techniques for estimation of viscosities of Newtonian nanofluids with different nanoparticles and different base fluids. An extensive number of 1140 data points of viscosities of nanofluids were utilized for development of the models. The results of the proposed models were examined with the predictions of various techniques. Moreover, the accuracy of the proposed models was also examined by comparing their results with predictions of several well-known literature correlations. Results show that the developed models provide accurate and reliable estimations and outperform literature correlations and present better results. Moreover, the results of CSA-LSSVM model are better and accurate than the results of other developed models.