# An Automated Framework for Streamlined CFD-Based Design and Optimization of Fixed-Wing UAV Wings
## 5 Framework Performance
### Large Scale Deployment  
To evaluate the scalability and robustness of the proposed framework, 1165 wing geometries are sampled from the design space outlined in Section 2. The sampling process employs a Latin Hypercube Sampling (LHS) technique to ensure a uniform distribution of design variables across the multidimensional design space [67]. These sampled wings represent a diverse range of conventional and unconventional configurations, including both conventional wings and more complex blended geometries typically found in tailless, flying wing, or BWB UAVs. Figure 17 provides a visual depiction of the distributions for the high-level planform parameters, illustrating the diversity and coverage achieved in the sampling process. For each wing configuration, the corresponding flow velocity is sampled according to the methodology described in Section 2. The angle of attack range spans from \(-4^{\circ}\) to \(+16^{\circ}\), with an additional \(4^{\circ}\) added in cases where stall has not yet occurred. This approach ensures a comprehensive aerodynamic characterization for each wing. The final dataset comprises 12,858 computational analyses, with an average of 11 simulation runs per configuration. This extensive dataset provides a robust foundation for evaluating the framework's performance in terms of computational time, robustness, and mesh quality. From the total number of analyses, 96.75% were successfully completed, while the remaining  
Figure 17: Distribution of the sampled planform high-level parameters. Each subfigure corresponds directly to a specific design variable from Table 2 (e.g., chord lengths \(c_{i}\), spanwise positions \(b_{i}\), sweep angles \(\Lambda_{i}\), twist angles \(i_{i}\), and dihedral angles \(\gamma_{i}\)). 3.25% encountered issues related to mesh resolution (e.g., \(y^{+}\) values) or convergence failure across the majority of runs. Such outcomes offer valuable insights into areas requiring improvement in mesh generation and solver setup for specific configurations. Table 10 summarizes execution time statistics across the framework's main phases, excluding geometry generation, which is negligible in comparison to other modules. From these data, it can be concluded that the median execution time for the pipeline from mesh generation to solution execution is approximately 539 s (9 min), with a standard deviation of 150 s. These results emphasize the framework's capability to deliver high-fidelity aerodynamic results within a short timeframe, significantly enhancing the efficiency of the design process. Figure 18 depicts the distribution of the two most time-consuming phases--mesh generation and simulation execution. The distribution plots reveal that mesh generation exhibits less variability compared to simulation execution, which is influenced by the complexity of each configuration. While as described, \(y^{+}\) is not an absolute measure of grid fidelity, it serves as a practical first step in assessing the quality of boundary layer resolution. In contrast, metrics such as the number of prism layers within the boundary layer provide a more comprehensive but inherently complex three-dimensional evaluation, which should be examined as a secondary step by the designer at critical spanwise locations on the wing surface. Equation (13) shows the criteria used to define a high-quality mesh. \[y^{+}_{avg\text{\#}wing\_surface}<1\text{ }and\ y^{+}_{max\text{\#}wing\_surface}<2 \tag{13}\]  
Among the 12,858 cases, 93.2% met these quality constraints, indicating a robust and consistent mesh generation process. Outliers, representing 0.5% of cases with abnormal