## 4 Results and Discussion
### Numerical Validation  
After the comparison of the lift-to-drag ratio (_L/D_) for the optimized S809 airfoil generated by the coupling of the CST and PARSEC parameterization methods with a genetic algorithm generated by XFOIL, the optimized S809 airfoil originated by the CST and GA gave a significantly better lift-to-drag ratio in comparison with the PARSEC method. Therefore, the optimized NREL S809 airfoil originating from the CST method was simulated at different flow conditions, using CFD for validation of the results with the experimental data provided by The Ohio State University (OSU) [26]. An individual comparison based on (CST and GA) optimized geometry was validated in the CFD-CFX environment. TheS809 airfoil generated by the coupling of CST parametrization and GA optimization was further validated numerically. The simulation was run for the optimized S809 airfoil at angles of attack of 0\({}^{\circ}\) and 6.2\({}^{\circ}\) and at the same boundary conditions shown in Table 3 in order to compare the results with the existing experimental data from the reliable, NREL-based data source provided by OSU. Some of the forces applied to the airfoil were usually resolved into two forces and one moment. For the total components, the net forces acting normal with the incoming flow stream are known as the lift forces, and the net forces acting parallel to the incoming flow stream are known as the drag forces. Table 7 shows the lift and drag coefficients of the baseline and optimized airfoils and their computational fluid dynamics simulation data. The _L/D_ ratio for the optimized airfoil was approximately 12.4% higher than that of the experimental data of the baseline S809 airfoil. It is also clearly shown that the \(C_{l}\) computed by the turbulence model for an optimized airfoil at a 6.2\({}^{\circ}\) angle of attack was 12.2% better than the experimental results of the original airfoil. Figure 14 shows the pressure distribution (\(Cp\)) over the upper and lower surfaces of the baseline and the optimized airfoil calculated by CFD analysis. It can be observed from the \(Cp\) graph that the pressure calculated by CFD had a lower negative pressure on the lower surface of the optimized airfoil. As a result, lift generation was greater in the optimized airfoil compared with the baseline, and this implied better aerodynamic properties for the blade design of the wind turbine. Figure 15 shows the simulation outcomes of the static pressure over the upper and lower surfaces of the baseline and optimized S809 airfoils in a wind speed of 7 m/s at angles of attack of 0\({}^{\circ}\) and 6.2\({}^{\circ}\). The baseline and optimized airfoils with pressure contours are shown in Figure 15a,b, respectively. The left side of the figure displays the pressure contour at 0\({}^{\circ}\), while the right side shows the pressure contour at a 6.2\({}^{\circ}\) angle of attack. Figure 15 clearly depicts that more negative pressure was observed over the upper surface of the baseline airfoil than the optimized airfoil, and hence more lift was generated in the optimized S809 airfoil. Consequently, the airfoil was pushed upward effectively, normal to the incoming flow stream. As a result, the S809 airfoil produced better aerodynamic properties for the design of the blade of the wind turbine. The velocity contours shown in Figure 16 are consistent with the results at angles of attack of 0\({}^{\circ}\) and 6.2\({}^{\circ}\). The baseline and optimized airfoils with velocity contours are shown in Figure 16a,b, respectively. The left side of the figure shows the velocity contour at an angle of attack of 0\({}^{\circ}\), while the right side shows the velocity contour at 6.2\({}^{\circ}\). The lower surface of both the baseline and optimized airfoils experienced a slightly lower velocity compared with the upper surface of the airfoil. The stagnation point of the airfoil changed by changing the AOA, and it mostly lied near the leading in the lower surface of the airfoil.