Novel high-aspect ratio airframe designs pave the way for a more sustainable aviation future. Such configurations enhance the aerodynamic efficiency of an aircraft through induced drag reduction mechanisms. Further performance gains, mainly in terms of structural mass, are accomplished via composite materials airframes. Nevertheless, undesired phenomena such as geometric nonlinearities and aeroelastic couplings due to elevated flexibility may often rise, rendering the design and optimization of such airframes extremely intricate and prohibitive in terms of computational cost. Low-fidelity tools, often preferred on the early design stages, accelerate the design process, albeit suffering from reduced accuracy and ability to capture higher-order phenomena. Contrastingly, high-fidelity computational methods incur excessive computational cost and are therefore utilized at the later, detailed design stages. There arises, therefore, the need for a combination of the various fidelities involved in a cost-effective manner, in order to drive the design towards optimal configurations without significant performance losses. In our approach, variable fidelity analyses are initially conducted in order to shed light on their effect on the structural response of a high-aspect ratio composite materials reference wing. An optimization framework combining low and high-fidelity tools in a sequential manner is then proposed, aiming at attaining a minimum mass configuration subject to multidisciplinary design constraints. As demonstrated, reasonable mass reduction was obtained for a future aircraft wing configuration.

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.

The race towards cleaner and more efficient commercial aviation demands novel designs featuring improved aerodynamic and structural characteristics, the main pillars that drive aircraft efficiency. Among the many proposed and introduced, the increase in the aspect ratio of the wings enables greater fuel efficiency by reducing induced drag. Nevertheless, such structures are characterised by elevated flexibility, aggravating static and dynamic aeroelastic phenomena. Consequently, the preliminary and conceptual design and optimization stages using high-fidelity numerical tools is rendered extremely intricate and prohibitive in terms of computational cost. Low-fidelity tools, contrastingly, enable computational-burden alleviation. In our approach, a computational framework for the low-fidelity steady-state static aeroelastic optimization of a composite high-aspect-ratio commercial aircraft wing via surrogate modelling is proposed. The methodology starts with the development of the 3D panel method as well of the elements of the surrogate model. The design variables, objective function and constraints which formulate the optimization problem are then provided. Moreover, comparison against rigid aerodynamics indicate the significant load-alleviation capabilities of the present case study. The effect of structural nonlinearities is also explored. The optimization framework is executed and optimal laminates for the structural members are obtained. The optimal structure was deemed critical in panel buckling.

Efficient optimization is a prerequisite to realize the full potential of an aeronautical structure. The success of an optimization framework is predominately influenced by the ability to capture all relevant physics. Furthermore, high computational efficiency allows a greater number of runs during the design optimization process to support decision-making. The efficiency can be improved by the selection of highly optimized algorithms and by reducing the dimensionality of the optimization problem by formulating it using a finite number of significant parameters. A plethora of variable-fidelity tools, dictated by each design stage, are commonly used, ranging from costly high-fidelity to low-cost, low-fidelity methods. Unfortunately, despite rapid solution times, an optimization framework utilizing low-fidelity tools does not necessarily capture the physical problem accurately. At the same time, high-fidelity solution methods incur a very high computational cost. Aiming to bridge the gap and combine the best of both worlds, a multi-fidelity optimization framework was constructed in this research paper. In our approach, the low-fidelity modules and especially the equivalent-plate methodology structural representation, capable of drastically reducing the associated computational time, form the backbone of the optimization framework and a MIDACO optimizer is tasked with providing an initial optimized design. The higher fidelity modules are then employed to explore possible further gains in performance. The developed framework was applied to a benchmark airliner wing. As demonstrated, reasonable mass reduction was obtained for a current state of the art configuration.

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.