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Thermal conductivity and viscosity are crucial thermophysical properties of nanofluids. They play a pivotal role in industries involved with heat transfer applications. Alumina (Al2O3) nanoparticles are known to be a good additive for thermophysical properties enhancement with favorable results in countless research. However, the measurement of thermophysical properties of nanofluids through experimental is expensive. Therefore, the random forest (RF), an advanced computational intelligence approach, is proposed to correctly predict the thermal conductivity and viscosity of alumina-based engine coolant nanofluids in this research. Experimental data from previous literature are utilized as input parameters for the development of the random forest models. The input parameters for the prediction of thermal conductivity are temperature and concentration, whereas the input parameters for the prediction of viscosity are temperature, concentration, and shear rate. Error metrics consisting of R-squared (R2), Adjusted R-squared (A-R2), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) are used to analyze and determine the performance of each random forest model. Based on the results, it is observed that all random forest models exhibit significantly high and consistent predictive accuracy with R2 of 0.9877 for thermal conductivity prediction and R2 of 0.9974 for viscosity prediction. The predictive accuracy of the random forest models can be enhanced by training it with multiple datasets which include several thermophysical properties of diversified nanofluids. [ABSTRACT FROM AUTHOR]

Numerical analysis is performed to investigate the unsteady natural convection flow of a nanofluid past a semiinfinite isothermal vertical plate. Five different types of water based nanofluids are considered in this investigation where Silver (Ag), Copper (Cu), Copper Oxide (CuO), Alumina (Al2O3) and Titanium Oxide (TiO2) are the nanoparticles. The governing non-dimensional partial differential equations are solved by employing an implicit finite-difference method of Crank-Nicolson type. Numerical results are computed for different values of pertinent parameters. The results for nanofluid temperature, velocity, local Skin friction and Nusselt number, average Skin friction and Nusselt number are discussed through graphs. The present numerical results for local Nusselt number have been compared with the well-established pure fluid correlation results for the limiting case and the comparison shows that the results are in excellent agreement. [ABSTRACT FROM AUTHOR]

In the present study, a numerical analysis is made for unsteady magnetohydrodynamic (MHD) natural convective boundary-layer flow past an impulsively started semi-infinite vertical plate with variable surface temperature and mass flux in the presence of thermal radiation and chemical reaction. The Crank-Nicolson implicit finite difference technique is implemented to solve the system of governing equations. Numerical results are obtained for different values of system parameters and analyzed through graphs. The velocity profiles of the present study have been compared with the available results for the limiting case and a good agreement is found between the results. [ABSTRACT FROM AUTHOR]