This work examines the aerodynamic efficiency improvement achieved by integrating C-type winglets into a small-scale Blended Wing Body (BWB) Unmanned Aerial Vehicle (UAV). The platform, designated S-3M, is an evolution of the RX-3 1:3 sub-scale demonstrator developed and flight-tested by the Laboratory of Fluid Mechanics and Turbomachinery (LFMT) during the DELAER project. The S-3M is redesigned for catapult launch and Intelligence–Surveillance–Reconnaissance (ISR) missions, supporting a useful payload of up to 5 kg. Strict dimensional, cost, and development constraints posed challenges in preserving aerodynamic efficiency and achieving sufficient stability margins. To meet these requirements, the design incorporates C-type winglets, tailored to enhance aerodynamic performance while providing stabilizing effects. Their integration enabled an increase in gross take-off weight (GTOW) and payload capacity, while ensuring adequate trimming without the need for a conventional horizontal tail. The aerodynamic development of the winglets and the overall configuration is supported by Computational Fluid Dynamics (CFD) analyses, followed by performance calculations. S-3M was manufactured by Carbon Fiber Technologies (CFT) and successfully flight-tested by LFMT, validating the design choices. Overall, the study demonstrates that C-type winglets can significantly improve efficiency and expand the operational envelope of BWB UAVs, highlighting the value of non-planar lifting surfaces in modern UAV design.

Current trends in the aerospace and UAV sectors emphasize integrating Artificial Intelligence (AI) technologies into the design process. AI technologies necessitate extensive data to capture the non-linearities in fluid phenomena. To address these needs, this work focuses on automating the data aggregation process for fixed-wing platforms, ranging from Micro–Mini to HALE-Strike UAVs, as classified by NATO. Specifically, this paper presents a framework for automating the tedious tasks required for geometry generation, mesh generation, and solution setup in a commercial Computational Fluid Dynamics (CFD) solver, for any arbitrary wing within the aforementioned design space. By combining various well-established open-source suites and commercial software via Python scripting, the preprocessing steps up to the solution require only a few minutes on a typical laptop workspace. Despite the rapid geometry acquisition, mesh generation, and solution setup through the pipeline, the guidelines and common practices for subsonic external flow simulations are still strictly followed. This results in solutions with a deviation of merely sub 5% from those of an experienced designer, even for the extremes of the flight envelope. The proposed framework significantly reduces design iteration times, enabling more efficient and innovative UAV development. Additionally, the framework’s ability to accumulate high-quality data for machine learning enhances predictive modeling and optimization capabilities across UAV design practices.