# An Automated Framework for Streamlined CFD-Based Design and Optimization of Fixed-Wing UAV Wings
## Appendix A.2.4 Feature Recognition  
Traditional approaches to pre-processing often rely on extensive manual interventions, including the creation of multiple custom PIDs to apply localized meshing parameters. While this approach allows for fine control over mesh refinement, it introduces significant challenges:  
* Time-Intensive Processes: Manual custom PID creation demands meticulous geometry manipulation, which is labor-intensive and prone to errors. * Lack of Robustness: The process is highly dependent on the operator's expertise, reducing the ability to automate and standardize the meshing for diverse geometries. * Automation Limitations: Manual adjustments are difficult to replicate in Python-based environments, limiting the scalability and efficiency of the pre-processing module. To overcome these challenges, the pre-processing module leverages the feature-recognition capabilities provided by ANSA. The custom _feature-recognition_ method automates the identification and tagging of critical geometric elements on the wing, such as the leading edge, trailing edge, and wingtip sharp features. This automated approach ensures that appropriate meshing parameters will be later applied to the relevant recognized regions without the need for excessive custom PID definitions, thereby streamlining the workflow and enhancing the framework's ability to mesh arbitrary wing geometries consistently. defined earlier in the _feature-recognition_ method, three key aspects of the wing geometry are identified: the leading edge (LE), trailing edge (TE), and wingtip. The wing-wall session not only includes the meshing parameters for the wing surface but also applies specialized treatments for the leading and trailing edges. Similarly, the wingtip session incorporates specific adaptations to handle the meshing process of the sharp edges around the wingtip airfoil. Finally, the far-field session generates the surface mesh on the far-field boundaries. All parameters considering the surface mesh sessions are summarized in Table 4.  
The first three mesh parameter categories--namely \"Mesh type,\" \"Mesh Sizing,\" and \"Curvature Refinement\"--are shared across all sessions and define the fundamental or global parameters needed for surface grid creation. The mesh type specifies the type of algorithm and elements used for meshing. In this case, the ANSA CFD meshing algorithm is employed, generating first-order quad elements where possible and first-order trias in regions where quads cannot be formed. This algorithm generates unstructured curvilinear grids with first-order quadrangle elements on the specified PIDs. The mesh sizing parameters dictate the growth rate of surface elements and impose a constraint on the maximum element length. The leading and trailing edges of the wing are meshed using an anisotropic distribution of quad elements, which ensures a finer resolution near the sharp or highly curved features. To ensure smooth transitions and controlled spacing, constant-height zones are defined both above and below the leading edge. This region of elements forms a fine structured mesh near the stagnation point, where large gradients are expected. Moving further away from this constant-height zone, the element height increases according to the specified growth rate, ensuring a gradual transition to coarser elements. Finally, a maximum element aspect ratio is enforced to prevent the excessive stretching of elements, maintaining high-quality discretization. The result of this treatment can be observed in Figure A4. Similarly, the meshing treatment for the trailing edge ensures that the trimmed surface is discretized with four rows of elements, forming an anisotropic structured mesh. Moving away from the trimmed surface on the upper and lower wing surfaces, the element height increases in accordance with the specified growth factor, while the generated elements form a structured grid.