The increasing complexity of the UAV aerodynamic design, imposed by novel configurations and requirements, has highlighted the need for efficient tools for high-fidelity simulation, especially for optimization purposes. The current work presents an automated CFD framework, tailored for fixed-wing UAVs, designed to streamline the geometry generation of wings, mesh creation, and simulation execution into a Python-based pipeline. The framework employs a parameterized meshing module capable of handling a broad range of wing geometries within an extensive design space, thereby reducing manual effort and achieving pre-processing times in the order of five minutes. Incorporating GPU-enabled solvers and high-performance computing environments allows for rapid and scalable aerodynamic evaluations. An automated methodology for assessing the CFD results is presented, addressing the discretization and iterative errors, as well as grid resolution, especially near wall surfaces. Comparisons with the results produced by a specialized mechanical engineer with over five years of experience in aircraft-related CFD indicate high accuracy, with deviations below 3% for key aerodynamic metrics. A large-scale deployment further demonstrates consistency across diverse wing samples. A Bayesian Optimization case study then illustrates the framework’s utility, identifying a wing design with an 8% improvement in the lift-to-drag ratio, while maintaining an average $y^{+}$ value below 1 along the surface. Overall, the proposed approach streamlines fixed-wing UAV design processes and supports advanced aerodynamic optimization and data generation.

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.