The aerodynamic performance of an aircraft can be significantly enhanced by incorporating wingtip devices, such as winglets, which primarily reduce lift-induced drag caused by wingtip vortices. This study introduces a comprehensive optimization framework for designing winglets on a Class I fixed-wing mini-UAV, aiming to maximize aerodynamic efficiency and operational performance. Using surrogate-based optimization (SBO) techniques, this research developed winglet geometries with varying geometric parameters such as length, cant angle, and sweep angle with their performance being evaluated through high-fidelity Computational Fluid Dynamics (CFD) simulations. These simulations utilized Reynolds-Averaged Navier–Stokes (RANS) equations coupled with the Spalart–Allmaras turbulence model to capture the intricate flow dynamics around the UAV in different flight phases. The integration of SBO techniques allowed for an efficient exploration of the design space while reducing computational costs associated with iterative high-fidelity simulations. In particular, the proposed SBO framework optimized the UAV’s aerodynamic characteristics, including lift-to-drag ratio and drag reduction, followed by a stability and control analyses to ensure balanced performance for the optimal configurations. Dynamic stability evaluations revealed improved flight characteristics, maintaining control across operational envelopes. The results demonstrated a significant improvement in aerodynamic coefficients, range, endurance, and reduction in battery consumption throughout the entire UAV operational envelope, underscoring the potential of innovative winglet designs to enhance UAV performance across diverse mission profiles.

This paper presents a multi-fidelity optimization procedure for aircraft wing design, implemented in the early stages of the aircraft design process. Since wing shape is a key factor that influences aerodynamic performance, having an accurate estimate of its efficiency at the conceptual design phase is highly beneficial for aircraft designers. This study introduces a comprehensive optimization framework for designing the wing of a Class I fixed-wing mini-UAV with electric propulsion, focusing on maximizing aerodynamic efficiency and operational performance. Utilizing Class-Shape Transformation (CST) in combination with Surrogate-Based Optimization (SBO) techniques, the research first optimizes the airfoil shape to identify the most suitable airfoil for the UAV wing. Subsequently, SBO techniques are applied to generate wing geometries with varying characteristics, including aspect ratio ($AR$), taper ratio ($\lambda$), quarter-chord sweep angle (${\mathrm{\Lambda }}_{0.25}$), and tip twist angle ($\epsilon$). These geometries are then evaluated using both low- and high-fidelity aerodynamic simulations. The integration of SBO techniques enables an efficient exploration of the design space while minimizing the computational costs associated with iterative simulations. Specifically, the proposed SBO framework enhances the wing’s aerodynamic characteristics by optimizing the lift-to-drag ratio and reducing drag.

The aerodynamic performance of an aircraft can be enhanced by incorporating wingtip devices, or winglets, which primarily reduce lift-induced drag created by wingtip vortices. This study outlines an optimization procedure for implementing winglets on a Class I fixed-wing mini-UAV to maximize aerodynamic efficiency and performance. After the Conceptual and Preliminary design phases, a baseline UAV was developed without winglets, adhering to specific layout constraints (e.g., wingspan, length). Various winglet designs—plate and blended types with differing heights, cant angles, and sweep angles—were then created and assessed. A Computational Fluid Dynamics (CFD) analysis was conducted to evaluate the flow around both the winglet-free UAV and configurations with each winglet design. The simulations employed Reynolds-Averaged Navier-Stokes (RANS) equations coupled with the Spalart-Allmaras turbulence model, targeting the optimal winglet configuration for enhanced aerodynamic characteristics during cruise. Charts of lift, drag, pitching moment coefficients, and lift-to-drag ratios are presented, alongside flow contours illustrating vortex characteristics for both baseline and optimized configurations. Additionally, dynamic stability analyses examined how winglets impact the UAV’s stability and control. The results demonstrated a significant improvement in aerodynamic coefficients ($C_{Lmax}$, $L/D_{max}$, $C_{La}$, $C_{ma}$), leading to an increase in both range and endurance.

This paper presents a multi-fidelity optimization procedure for aircraft wing design, implemented in the early stages of the aircraft design process. Since wing shape is a key factor influencing aerodynamic performance, having an accurate estimate of its efficiency at the conceptual design phase is highly beneficial for aircraft designers. This study introduces a comprehensive optimization framework for designing the wing of a Class I fixed-wing mini-UAV with electric propulsion, focusing on maximizing aerodynamic efficiency and operational performance. Utilizing Class Shape Transformation (CST) in combination with Surrogate-Based Optimization (SBO) techniques, the research first optimizes the airfoil shape to identify the most suitable airfoil for the UAV wing. Subsequently, SBO techniques are applied to generate wing geometries with varying characteristics, including aspect ratio (AR), taper ratio (λ), quarter-chord sweep angle (Λ0.25), and tip twist angle (ε). These geometries are then evaluated using both low- and high-fidelity aerodynamic simulations. The integration of SBO techniques enables an efficient exploration of the design space while minimizing computational costs associated with iterative simulations. Specifically, the proposed SBO framework enhances the wing’s aerodynamic characteristics by optimizing the lift-to-drag ratio and reducing drag.

The increasing power density of Unmanned Aerial Vehicles (UAVs) has intensified the need for the efficient thermal management of their propulsion and electronic subsystems. This paper presents a systematic multi-fidelity methodology for the design and validation of a ducted forced convection cooling system for a Class-I mini-UAV. The approach combines analytical sizing and computational fluid dynamic (CFD) analyses. In the preliminary design phase, a surrogate-based optimization (SBO) framework was implemented to determine the optimal geometric characteristics of a NACA-type inlet duct, enabling the efficient exploration of the design space using a limited number of CFD simulations. SBO employed a Kriging surrogate model trained on a Design of Experiments (DoE) dataset to capture nonlinear interactions between duct geometry and performance metrics such as pressure recovery, total-pressure loss, and outlet flow uniformity. The optimized configuration was then refined and validated through detailed external and internal CFD studies under representative flight conditions. The optimized NACA duct configuration achieved an average increase of 10.5% in volume flow rate (VFR) and a 9.5% reduction in velocity distortion while maintaining a drag penalty below 1% compared to the benchmark Frick’s NACA duct. The presented methodology demonstrates that the early integration of surrogate-based optimization in UAV inlet design can significantly improve aerodynamic and thermal performance.