The accurate prediction of aerodynamic properties is an essential requirement for the design of applications that involve fluid flows, especially in the aerospace industry. The aerodynamic characteristics of fluid flows around a wing or an airfoil are usually forecasted using the numerical solution of the Reynolds-averaged Navier–Stokes equation. However, very heavy computational expenses and lengthy progression intervals are associated with this method. Advancements in computational power and efficiency throughout the present era have considerably reduced these costs; however, for many practical applications, performing numerical simulations is still a very computationally expensive and time-consuming task. The application of machine learning techniques has seen a sharp rise in various fields over recent years, including fluid dynamics, and they have proved their worth. In the present study, a famous machine learning model that is known as the back-propagation neural network was implemented for the prediction of the aerodynamic coefficients of airfoils. The most important aerodynamic properties of the coefficient of lift and the coefficient of drag were predicted by providing the model with the name, flow Reynolds number, Mach number and the angle of attack of the airfoils with respect to the incoming flows as input parameters. The dataset for the current study was obtained by performing CFD simulations using the RANS-based Spalart–Allmaras turbulence model on four different NACA series airfoils under varying aerodynamic conditions. The data that were obtained from the CFD simulations were divided into two subsets: 70% were used as training data and the remaining 30% were used as validation and testing data. The BPNN showed promising results for the prediction of the aerodynamic coefficients of airfoils under different conditions. An RMSE value of 3.57 $\times$ 10⁻⁷ was achieved for the best performance validation case with 28 epochs when there were 10 neurons in the hidden layer. The regression plot also depicted a close to perfect fit between the predicted and actual values for the regression curves.

Horseshoe vortex is a prominent feature in junction flows which not only changes the local flow patterns but is also responsible for aerodynamic loses in the region. In the past many investigations using wind tunnels and CFD have been made to study the junction flow. These studies have shown that RANS prediction of the junction flow demonstrate an acceptable qualitative match with the experimental results. The present study extends the CFD investigations to RANS predictions of the junction flow with boundary layer control using localized suction. Localized suction is an active technique that has proved successful, in the past through wind tunnel measurements, in elimination of the junction horseshoe vortex. The present investigations used these experimental results to evaluate the RANS predictions from the current study. During the course of this study, six cases of junction flows with and without localized suction were numerically simulated and compared with the wind tunnel measurements. For no suction case CFD and experiment have been found in good agreement with similar patterns observed for the vortex core in the symmetry plane. When suction was applied, a similar reduction in the vortex core was predicted by CFD as observed in the wind tunnel. It was concluded that RANS have made fairly accurate predictions in almost all the cases in comparison with the experimental data.