Statistical and Intelligent Analysis of Viscosity behavior of MgO-MWCNT (25–75%)/10W40 Hybrid Nanolubricant Using Artificial Neural Network Modeling and Response Surface Methodology

Using of artificial neural networks and different evolutionary algorithms to predict the viscosity and thermal conductivity of silica-alumina-MWCN/water nanofluid

“…This method can predict all points with MOD below 1%, but the RSM is only able to predict 70% of all points with MOD below 1%. In the study by Esfe et al [ 17 ], which was carried out using the ANN, they investigated the μ nf of MWCNT-MgO (25–75%)/10W40 oil HNF. The initial conditions determined in this study include the Temp range of 5–55 °C, the γ range of 6665–11997 s −1 and φ range of 0–1%.…”

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

“…It was noticed that most of these studies investigate the ANN approach and compare the results with correlation-based results as in Refs. [ [33] , [34] , [35] , [36] ]. Other studies have investigated the ANN alongside other machine learning approaches including Boosted Regression Tree (BRT), and Support Vector Machine (SVM) [ 37 ], or other statistical tools such as response surface methodology (RSM) [ 38 ].…”

“…VF 0.9971 The BRT showed slightly better results than ANN and SVM. [ 33 ] MWCNT-MgO-OB ANN, Correlation Temp., VF, SR 0.9990 ANN showed better performance compared to correlation (R 2 correlation = 0.9321) [ 45 ] MWCNT-ZnO hybrid-OB ANN Temp., VF, SR 0.9900 Single configuration was studied [ 46 ] WO 3 -MWCNT-EGB ANN Temp., VF 0.9980 Different training algorithms are evaluated alongside “trainlm” [ 34 ] MWCNT-ZnO-OB Correlation, ANN Temp., VF, SR 0.9921 A single hidden layer with six neurons was studied only. The ANN showed better performance than the correlation [ 35 ] TiO 2 -Al 2 O 3 -EGB Correlation, ANN Temp., VF, Density 0.9982 Slight better performance for the ANN over correlation.…”