Aerodynamic Optimization of Adaptive Wing Configurations with different Leading-Edge Shapes

The adjoint method is efficient for computing derivatives, enabling gradient-based optimization to manage systems with many design variables. Therefore, this paper aims to investigate the aerodynamic optimization design of morphing wings with different leading-edge shapes. The wing shapes include a clean UAS-S45 wing and different tubercle-based wing shapes. This study emphasizes optimizing the various shapes in the leading edge to delay stall and increase the aerodynamic performance of the wing. Firstly, baseline wing design studies were carried out, followed by experimental wind tunnel validation to ensure accuracy and methodology validation. After establishing the baseline, optimization was conducted using a multidisciplinary design optimization (MDO) approach with DAFoam, a Reynolds-averaged Navier–Stokes solver. The optimization process employs a Free Form Deformation approach to produce different variants of the leading-edge shapes. Numerical investigations of flow characteristics, performed using computational fluid dynamics (CFD) computations, validate the numerical scheme against experimental data. The optimization strategy combines the Interior Point OPTimizer (IPOPT) within the adjoint solver framework with ICEM to generate high-quality numerical meshes. The experimental and numerical results showed the advantages of tubercles in maintaining aerodynamic effectiveness, particularly at high angles of attack. The study confirms that tubercle-profiled leading edges improve post-stall aerodynamic behavior by mitigating abrupt flow separation and facilitating smoother transitions during stall. For optimized tubercle-based leading edges, the peaks and valleys generate alternating regions of high and low vorticity, forming counter-rotating vortices that enhance mixing, improving aerodynamic performance. The peak configuration, in particular, exhibits smooth airflow acceleration over the crest of the tubercle, creating higher velocity regions along the leading edge and downstream, demonstrating enhanced flow attachment. While conventional bio-inspired designs already offer significant performance benefits at high angles of attack, further optimization of tubercle shapes for morphing leading edges has demonstrated additional improvements in aerodynamic efficiency at low angles of attack. However, achieving convergence during optimization remains challenging due to mesh deformation and movement complexities in these geometries. Future work will aim to expand the dataset, refine the optimization process, and enable more detailed analyses to unlock the full potential of tubercle-based designs.