## 3 Results and Discussion
### Wind Tunnel Test Results--Comparison of Numerical and Experimental Data  
A total of 11 distinct test sets were conducted in the wind tunnel, for various angles of attack and flap deflections. As the angle of attack increased, the deflection of the SMTE flap decreased. The flap deflection values at each angle of attack were obtained from a prior aerodynamic study. To ensure the consistency of the results, Computational Fluid Dynamics (CFD) simulations were conducted using OpenFOAM, replicating the flow conditions  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Air Density (Kg/m3)** & **Temperature (\({}^{\circ}\)C)** & **Relative Humidity (\%)** & **Air Speed (m/s)** & **Pressure (kPa)** \\ \hline
1.19 & 22.6 & 34 & 28.3 & 101.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Air properties inside the wind tunnel test section.  
Figure 20: UAS-S45 morphing wing section installed in the wind tunnel test section.  
and scaling of the wind tunnel experiments. The computational domain dimensions were selected to match the wind tunnel test section, with a height of 0.62 m, a width of 0.91 m and a length of 1.83 m. Therefore, the effects of the tunnel walls on the flow field are inherently captured in the CFD model.  
The simulations were conducted using the same free-stream velocity as in the experiments, 28.3 m/s, corresponding to the same Reynolds number based on the reference chord length. The fluid was modeled as incompressible, and the Reynolds-Averaged Navier-Stokes (RANS) equations were solved to capture the steady-state flow behavior. To account for turbulence effects, the Spalart-Allmaras turbulence model was employed.  
After conducting a mesh independence study, the final computational grid consisted of approximately 4,465,431 cells. Boundary conditions included uniform velocity inlet, static pressure outlet and no-slip conditions on solid surfaces. These simulations allowed for a direct comparison between the experimental and CFD results.  
The results from both the wind tunnel tests and CFD simulations, along with their associated errors, are presented in Table 6.  
Table 6 shows that the numerical and experimental results are in very good agreement for both \(\mathrm{C_{L}}\) and \(\mathrm{C_{D}}\). The maximum error occurred in the \(\mathrm{C_{D}}\) results, reaching up to 7.3%, while the maximum error for \(\mathrm{C_{L}}\) is 4.06%. These differences arise from various uncertainty factors in the wind tunnel tests, with drag force being more affected than lift force. However, both errors for \(\mathrm{C_{L}}\) and \(\mathrm{C_{D}}\) remain within an acceptable range and demonstrate strong agreement with the numerical results. Figure 21 presents the variations in \(\mathrm{C_{L}}\) and \(\mathrm{C_{D}}\) with the angle of attack along with their relative errors.  
As shown in Figure 21, both data sets exhibit an increasing trend with the angle of attack. However, the rate at which \(\mathrm{C_{L}}\) increases with the variation in the angle of attack is higher than that of \(\mathrm{C_{D}}\). This observation also validates the findings of our recent aerodynamic study [36], which reported similar variation trends for both \(\mathrm{C_{L}}\) and \(\mathrm{C_{D}}\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**AAO** **(\(\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{\mathrm{\mathrm{\mathrm{\mathrm{  \mathrm{ }}}}}}}}}}}}}\) (\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ }}}}}}}}}}}}}}})} **(\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\