# An Automated Framework for Streamlined CFD-Based Design and Optimization of Fixed-Wing UAV Wings
## 1 Introduction  
In recent decades, fixed-wing UAVs have found an increasingly diverse range of applications, including precision agriculture [1], cargo transport [2], and environmental monitoring [3]. UAVs can also be used for beach monitoring [4] and search and rescue situations in various environments [5] (i.e., forests and mountains, as well as highly densely populated areas). This growth is guided by advancements in autonomy, propulsion systems, and materials, enabling UAVs to meet evolving operational demands while maintaining adaptability for a broad spectrum of missions. Despite these advancements, the design methodologies for fixed-wing UAVs largely resemble the \"traditional\" design methods employed for manned aircrafts [6, 7]. The latter have evolved through a systematic, iterative, human intensive approach segmented intothree primary stages: conceptual, preliminary, and detailed design. The conceptual phase lays the groundwork by defining high-level system requirements and exploring multiple configurations to address mission-specific criteria. At this phase, which is marked by significant uncertainty, rapid prototyping and multiple testing of various configurations are key to advances in the next stages. The need for the exploration of the design space before progressing requires the utilization of low-fidelity and semi-empirical methods to assess the impact of alterations, typically in geometry. The preliminary phase refines these configurations through focused analyses of key components, ensuring alignment with performance, stability, and manufacturability goals. Computational Fluid Dynamics (CFD) analyses are fundamental for assessing aerodynamic performance and optimizing UAV designs, especially in this stage. High-fidelity methods, such as Reynolds-Averaged Navier-Stokes (RANS) simulations, require detailed meshes that can be laborious to generate and validate, adding significant overhead to the design process. It is acknowledged that experimental testing, particularly with rapid prototyping (RP), remains fundamental in fixed-wing UAV development. However, conducting wind tunnel tests often requires significant infrastructure, including specialized facilities, calibration procedures, and trained personnel, making it resource-intensive--especially for large-scale UAVs in the tactical, MALE, and HALE categories [8]. In contrast, CFD provides a standardized and reproducible method for systematically analyzing multiple design variations in design offices, acting as a third way of analyzing engineering problems, in addition to theoretical relations and experiments [9]. Consequently, CFD is often the primary tool for aerodynamic assessment, while experimental testing serves as a crucial validation step where resources permit. Finally, the detail design phase involves the details of the selected configuration, including the construction drawings, resolving all remaining uncertainties to deliver a prototype. This structured, iterative process, while effective, underscores the resource-intensive nature of fixed-wing UAV development. This is even more evident when addressing specialized applications or unconventional configurations. Especially concerning the latter, and following the design trends of modern aviation, novel configurations are investigated [2; 10; 11] and new lift and drag estimation methods are introduced to assist in the early stages of development [12]. Among these novel configurations, Blended Wing Body (BWB) designs are gaining significant attention due to their potential to improve aerodynamic efficiency, increase payload capacity, and reduce fuel consumption. Unlike conventional UAV designs, BWBs integrate the fuselage and wings into a unified aerodynamic shape, which presents unique advantages and challenges in aerodynamic analysis. These designs are particularly relevant for missions demanding long endurance and high efficiency, such as surveillance or cargo delivery, making them a prime focus for modern research frameworks [13].