Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning

Yazeed AbuShanab; Wahib A Al-Ammari; Samer Gowid; Ahmad K Sleiti

doi:10.1016/j.heliyon.2023.e16716

Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning

Heliyon. 2023 May 26;9(6):e16716. doi: 10.1016/j.heliyon.2023.e16716. eCollection 2023 Jun.

Authors

Yazeed AbuShanab¹, Wahib A Al-Ammari¹, Samer Gowid¹, Ahmad K Sleiti¹

Affiliation

¹ Department of Mechanical & Industrial Engineering, College of Engineering, Qatar University, Qatar.

Abstract

This study focuses on predicting the dynamic viscosity of nanofluids, specifically Polyalpha-Olefin-hexagonal boron nitride (PAO-hBN) using machine learning models. The primary goal of this research is to assess and contrast the effectiveness of three distinct machine learning models: Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The main objective is the identification of a model that demonstrates the highest level of accuracy in predicting a nanofluid's viscosity namely, PAO-hBN nanofluids. The models were trained and validated using 540 experimental data points, where the mean square error (MSE) and the coefficient of determination R² were utilized for performance evaluation. The results demonstrated that all three models could predict the viscosity of PAO-hBN nanofluids accurately, but the ANFIS and ANN models outperformed the SVR model. The ANFIS and ANN models had similar performance, but the ANN model was preferred due to its faster training and computation time. The optimized ANN model had an R² of 0.99994, which indicates a high level of accuracy in predicting the viscosity of PAO-hBN nanofluids. The elimination of the shear rate parameter from the input layer improved the accuracy of the ANN model to an absolute relative error of less than 1.89% over the full temperature range (-19.7 °C-70 °C) compared to 11% in the traditional correlation-based model. These results suggest that the use of machine learning models can significantly improve the accuracy of predicting the viscosity of PAO-hBN nanofluids. Overall, this study demonstrated that the use of machine learning models, specifically ANN, can be effective in predicting PAO-hBN nanofluids' dynamic viscosity. The findings provide a new perspective on how to predict the thermodynamic properties of nanofluids with high accuracy, which could have important applications in various industries.

Keywords: Adaptive neuro-fuzzy inference; Artificial neural network; Correlation; Dynamic viscosity; Nano-fluids; Prediction; System.