Multirotor UAVs have become an essential tool in a wider range of applications, including among others disaster management, and search and rescue (SAR) operations. Typically, these systems operate outdoors, with their guidance and positioning being based primarily on GPS. This work is focused on the design and optimization of a multirotor UAV specifically tailored for indoor SAR applications, where GPS signal is unavailable, and obstacles are prevalent. The design incorporates a lightweight frame structure, in order to increase the UAV’s payload capability. This is necessary, since the UAV requires multiple obstacle recognition and avoidance sensors, as well as thermal and optical cameras, to successfully accomplish its mission objectives in a GPS-denied environment. Towards this goal, various trade studies were conducted including different motor/propeller configurations and airframe FEM analyses. The aerodynamic performance of the UAV is evaluated also, using dedicated CFD analyses that incorporate the effect of propellers. Lastly, a prototype of the designed configuration is produced using additive manufacturing methods and initial flight tests of the UAV are performed.

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.

In the recent years a rapid increase of multirotor UAVs in the commercial market is observed resulting in a large number of motor/propeller concepts and thrust architectures. The limited availability of data for the aerodynamic performance of the motor/propeller system often leads to a non-optimal operation on multirotor UAVs design points. Since experimental investigations are both cost- and time-demanding, the accurate CFD modeling of UAV propellers is crucial and highly supportive in the early design phases of multirotor UAVs. In the current study, a CFD framework is employed for the performance investigation of a small-scale three-blade propeller on a lightweight micro quadrotor UAV, designed for indoor search and rescue operations. More specifically, two widely implemented methods for propeller modeling are examined, namely the Multiple Reference Frame (MRF) and the Sliding Mesh (SM). Several operating points are investigated, corresponding to different propeller rotating speeds (RPM) and Reynolds numbers. The accuracy of each method is evaluated by comparing the CFD results with those obtained from literature experimental data. Finally, the uncertainty of the computational methods is quantified through Richardson’s extrapolation method.

Designing an efficient and optimized multirotor UAV requires laborious trade-off analyses, involving numerous design variables and mission requirement parameters, especially during the early conceptual design phase. The large number of unknown parameters, as well as the associated design effort often leads to non-optimal designs, for the sake of time efficiency. This work presents the implementation of a machine learning (ML) framework to assist and expedite the conceptual design phase of multirotor UAVs. The framework utilizes information from a comprehensive database of commercial lightweight multirotor UAVs. The database contains an extensive collection of crucial sizing parameters, performance metrics, and features associated with foldability and indoor guidance (e.g., obstacle avoidance sensors). These attributes specifically pertain to multirotor UAVs weighing less than 2kg, which exhibit diverse design and performance characteristics. The proposed ML framework employs multiple regression models (e.g. k-nearest neighbors regression, multi-layer perceptron regression) to predict the sizing parameters during a multirotor UAV’s conceptual design phase. This enables designers to make quick informed decisions, while also significantly reducing computational time and effort. Finally, the ML framework’s predictive capability is validated by comparing the predicted values with real-world data from an “unseen” test dataset.

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.