# An Automated Framework for Streamlined CFD-Based Design and Optimization of Fixed-Wing UAV Wings
## 3 Framework Breakdown
### Mesh Module
#### 3.2.3 Inflation Layer Mesh  
This study employs a wall-resolving approach to accurately capture near-wall phenomena, which are critical for predicting aerodynamic characteristics. The near-wall region is dominated by the boundary layer, transitioning from the viscous sublayer dominated \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Wing-Wall Session**} & \multicolumn{2}{c}{**Wingtip Session**} & \multicolumn{2}{c}{**Far-Field Session**} \\ \hline \multirow{3}{*}{**Mesh type**} & **Algorithm** & CFD & CFD & CFD \\  & **Element Type** & Quads and Trias & Quads and Trias & Quads and Trias \\  & **Order** & 1 st & 1 st & 1 st \\ \hline \multirow{3}{*}{**Mesh Sizing**} & **Growth Rate** & 1.2 & 1.2 & 1.2 \\  & **Max Length** & \(f(\textit{MAC})\) & \(f(\textit{c3})\) & \(f(\textit{c1})\) \\ \hline \multirow{3}{*}{**Curvature Refinement**} & **Distortion Angle** & 0 & 0 & 15 \\  & **Min length** & \(f(\textit{c3})\) & \(f(\textit{c3})\) & - \\ \hline \multirow{3}{*}{**Leading Edge Treatment**} & **Distribution** & Anisotropic & & \\ \cline{2-5}  & **Min first height** & \(f(\textit{c3})\) & - & - \\ \cline{2-5}  & **Growth Factor** & 1.15 & & \\ \cline{2-5}  & **Max Aspect** & 1 & & \\ \hline \multirow{3}{*}{**Trailing Edge Treatment**} & **Distribution** & Anisotropic & & \\ \cline{2-5}  & **Min Aspect** & 0.05 & - & - \\ \cline{1-1}  & **Growth Factor** & 1.15 & & \\ \cline{1-1}  & **Max Aspect** & 0.7 & & \\ \hline \multicolumn{1}{c}{**Sharp Edge Treatment**} & **Max Length** & - & \(f(\textit{c3})\) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters that control surface mesh. by laminar flow, where fluid particles move in orderly layers, to turbulent flow, characterized by chaotic fluctuations. These transitions significantly affect performance metrics such as skin friction and separation zones, making precise boundary layer modeling a necessity [44; 45]. To address these challenges, the near-wall treatment focuses on resolving flow structures adjacent to the wall, guided by the non-dimensional wall distance \(y^{+}\)[46]. Depending on the turbulence modeling approach, the meshing strategy ensures the first cell lies at a specific \(y^{+}\), which in CFD applications is called first-layer \(y^{+}\), or simply \(y^{+}\). As mentioned at the beginning of Section 3, this study employs the k-omega SST turbulence model [38]. Considering the latter, grid refinements target average \(y^{+}\) values less than one (\(y^{+}<1\)), which is essential for resolving the viscous sublayer. The ANSA software allows us to have more control over specific features of the inflation layer; these features are listed below. * The first two layers are of the same height, equal to the first layer height calculation discussed in Appendix A.3. * A growth factor is applied for a specific number of layers that are calculated according to the total boundary layer height (see Appendix A.3). * To ensure the full coverage of the physical boundary layer and a smooth transition from the inflation layer to the volume mesh, additional layers are added on top for redundancy. The inflation layer height is calculated iteratively, starting with an initial thickness of twice the first layer height. Successive layers are generated using a growth factor, typically set to 1.2 for smooth transitions. The iterative process continues until the total inflation thickness equals or exceeds the estimated boundary layer thickness (\(\delta\)). When the total inflation thickness exceeds the flat plate boundary layer height, the number of layers is returned and adjusted by a safety factor (1.1) to ensure adequate coverage, according to suggested practices [41]. Finally, additional layers are placed on top for redundancy. The following inflation layer (Figure 7) is produced automatically using the proposed framework. Figure 7: An example of the inflation layer that resulted from the automatic algorithm. The zoomed-in views in Figure 7 highlight critical aspects of the grid structure at the symmetry plane.