# An Automated Framework for Streamlined CFD-Based Design and Optimization of Fixed-Wing UAV Wings
## 1 Introduction
The framework presented in this study addresses these challenges directly by automating the generation and analysis of both conventional and BWB UAV meshes, emphasizing their potential importance in future UAV developments. Several studies have explored the potential for fully automating the time-intensive phase of aerodynamic evaluation [14; 15; 16]. These works highlight specific algorithmic strategies to accelerate aspects of the design process, such as focusing on the solution phase alone, automating calculations (e.g., decomposing forces into lift and drag), or employing high-performance computing. Other research efforts have adopted a more holistic approach to address the problem comprehensively. Specifically, in [17], the authors developed a framework to automatically handle mesh generation and CFD analysis. However, their analysis is limited to simple trapezoidal subsonic UAV wing planforms. Additionally, the study in [18] examined the efficiency of multi-fidelity algorithms that combine low-fidelity panel methods with CFD for BlendedWing Body (BWB) UAVs. Their approach began with a baseline geometry, which was deformed using Free-Form Deformation (FFD). This study, though, offers constrained degrees of freedom within the design space, as the configuration cannot deviate from a given baseline, e.g., providing no degrees of freedom in terms of wingspan. Furthermore, in [19], the authors applied FFD to locally morph computational grids and perform optimization studies using RANS CFD simulations. Similarly, in [20], researchers focused on validating an automated CFD chain, creating a Python-based framework that integrated multiple software tools for geometry manipulation. Both the above works, however, are limited to a specific baseline configuration, e.g., the Common Research Model in [19]. Moreover, studies [21; 22] introduced machine learning methodologies for optimizing UAVs, including wings, fuselage, and engine configurations, by using high-level parameters (e.g., aspect ratio, wingspan, taper ratio, or thrust for engines) and targeting specific Unmanned Combat Aerial Vehicle (UCAV) designs. These works employed neural networks trained on low-fidelity panel data generated from frameworks capable of sampling design spaces, creating geometry models, and conducting analyses. Although these studies present a promising foundation that could potentially integrate CFD data, they do not eventually involve CFD results, which limits the fidelity and applicability of their optimization frameworks. To cover the aforementioned research gaps, this study aims to develop an innovative, robust, and streamlined framework employing high-end commercial software to enable fully automated, high-fidelity CFD analyses of UAV wings. More specifically, the key objectives of this work are as follows:  
* Provide a general algorithm for automation of CFD-based investigations. All aspects are based on an extensive literature survey at every part of the study, combining well-established common practices with proposed novel algorithms that unify all the components. Due to budget constraints, the authors could not develop the framework to work with all possible software alternatives. However, in case other research groups want to employ a different software, they will only have to emphasize specific parameters, and the overall architecture will remain as suggested. * In contrast to existing frameworks that focus on given platforms or narrow design spaces, examine an extensive design space, allowing its application to the vast majority of UAV configurations [8]. * Accommodate wings for both conventional and tailless configurations (flying wings, BWBs), while incorporating a set of 35 design parameters that can completely control the wing planform and its airfoils. * Apart from presenting the pipeline, suggest robust evaluation criteria and metrics in an automated format.