# An Automated Framework for Streamlined CFD-Based Design and Optimization of Fixed-Wing UAV Wings
## 7 Conclusions  
This study presents the development and validation of an automated CFD pre-processing framework designed to streamline high-fidelity aerodynamic simulations tailored for fixed-wing UAV applications. By integrating the geometric modeling, mesh generation, and simulation setup into a cohesive workflow, this framework significantly reduces manual effort and computational inefficiencies. By leveraging Python APIs and a combination of open-source and commercial tools, tshe framework achieves a fully parameterized and automated pipeline, reducing pre-processing times to mere minutes and making it suitable for rapid iterations in design workflows. Moreover, a methodology for estimating the reliability of the framework's results is proposed, incorporating considerations for discretization error, potential early solution termination, non-dimensional \(y^{+}\) values on the wing's surface, and grid resolution within the boundary layer. Adhering to best practices and guidelines established by CFD experts and practitioners, the framework demonstrated high accuracy compared to expert-generated results, as well as high consistency across a large number of sampled wings. Additionally, various grid independence studies have been performed, covering a wide range of the design space. The framework's integration with high-performance computing (HPC) environments featuring GPU solvers minimizes the labor-intensive work needed to prepare a geometry for CFD simulation. This results in pre-processing durations in the order of 5-7 min and the complete execution of the framework in less than 10-15 min for a single wing at a given angle of attack. Moreover, the framework is integrated with an off-the-shelf optimizer, functioning as a robust black-box function. Using Bayesian Optimization, the framework identified an optimal wing configuration that achieved an 8% improvement in \(L/D\), along with a 2% increase in \(C_{L}\), and a modest reduction in \(C_{M}\). These improvements, while incremental, highlight the framework's adaptability and demonstrate the advantages of encapsulating all modules within a unified Python pipeline. The framework's reliable mesh generation ensures seamless execution of cases without computational issues, even under varying conditions. In summary, the framework represents a significant step forward in streamlining the pre-processing and analysis phases of UAV design. It provides a scalable, efficient, and accurate solution for high-fidelity aerodynamic evaluations, while establishing a robust foundation for future advancements in optimization. Future work could address the following areas to enhance the framework's capabilities and expand its scope of use. The framework can be extended to accommodate more complex non-planar features, such as wings with winglets, fins, fences, and tail configurations. Incorporating these components can potentially enable the analysis of complete fixed-wing UAV and aircraft configurations. Moreover, the framework provides a great opportunity to generate large-scale datasets suitable for machine learning applications of aerodynamic shape optimization for UAVs. Conceptualization, C.P. and P.P. ; methodology, C.P. and G.E. ; software, C.P., G.E. and D.T. ; validation, C.P. and P.P. ; investigation, C.P. and G.E. ; resources, P.P. and C.P. ; data curation, C.P., G.E. and D.T. ; writing--original draft preparation, C.P., G.E. and D.T. ; writing--review and editing, C.P. and P.P. ; visualization, C.P., G.E. and D.T. ; supervision, P.P. ; funding acquisition, P.P. All authors have read and agreed to the published version of the manuscript. The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the Basic Research Financing (Horizontal support for all Sciences), National Recovery and Resilience Plan (Project Number: 016429, Project Acronym: INDIANA). Data Availability StatementData available on request due to restrictions related to the details of the baseline platform.