A. Wadi Al-Fatlawi - Department of Mechanical Engineering, University of Kufa, Najaf, Iraq
J. Hashemi - Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
S. Hossain - Institute for Energy Research, Jiangsu University, Zhenjiang, ۲۱۲۰۱۳, P.R. China
M. El Haj Assad - Department of Sustainable and Renewable Energy Engineering, University of Sharjah, Sharjah ۲۷۲۷۲, United Arab Emirates

A computational fluid dynamic (CFD) and machine learning approach is used to investigate heat transfer on NASA airfoils of type NACA ۰۰۱۲. Several different models have been developed to examine the effect of laminar flow, Spalart flow, and Allmaras flow on the NACA ۰۰۱۲ airfoil under varying aerodynamic conditions. Temperature conditions at high and low temperatures are discussed in this article for different airfoil modes, which are porous mode and non-porous mode. Specific parameters included permeability of ۱۱.۳۶ x ۱۰-۱۰ m۲, porosity of ۰.۶۴, an inertia coefficient of ۰.۳۷, and a temperature range between ۲۰۰ K and ۴۰۰ K. The study revealed that a temperature increase can significantly increase lift-to-drag. Additionally, employing both a porous state and temperature differentials further contributes to enhancing the lift-to-drag coefficient. The neural network also successfully predicted outcomes when adjusting the temperature, particularly in scenarios with a greater number of cases. Nevertheless, this study assessed the accuracy of the system using a SMOTER model. It has been shown that the MSE, MAE, and R for the best performance validation of the testing case were ۰.۰۰۰۳۱۴, ۰.۰۰۰۸, and ۰.۹۹۸۹۶۰, respectively, at K = ۳. However, the study shows that epoch values greater than ۲۰۰۰ increase computational time and cost without improving accuracy. This indicates that the SMOTER model can be used to classify the testing case accurately; however, higher epoch values are not necessary for optimal performance.

Computational modeling, Aerodynamics, Subsonic flow around airfoils, Heat transfer, Machine learning, CFD