This study presents an aerostructural optimization framework for the preliminary design of a low-Reynolds-number composite UAV wing, aiming to simultaneously enhance aerodynamic efficiency and structural performance. While previous work has primarily addressed aerodynamic optimization in isolation, the present approach integrates Computational Fluid Dynamics (CFD) and Finite Element Method (FEM) analyses within a surrogate-based optimization (SBO) framework. The design space includes both aerodynamic parameters—aspect ratio, taper ratio, sweep angle, and twist—and structural variables related to the internal wing layout and component thicknesses. To reduce the computational cost associated with high-fidelity simulations, Kriging surrogate models are employed in conjunction with an Expected Improvement (EI) infill strategy, enabling efficient exploration of the coupled design space. The framework is evaluated through multiple independent optimization runs using different initial sampling strategies, demonstrating consistent convergence toward feasible high-performance designs. The surrogate models exhibit strong predictive capability, as confirmed by Root Mean Square Error (RMSE) and Leave-One-Out (LOO) cross-validation metrics. The results indicate that aerodynamic variables, particularly aspect ratio and twist, are the primary drivers of range performance. However, structural variables—most notably skin thickness—strongly influence constraint satisfaction, especially with respect to buckling and strength requirements, and therefore play a key role in defining the feasible design space. The optimal configuration achieves a maximum range of approximately 203 km while satisfying all strength, stiffness, and aerodynamic constraints. Overall, the proposed methodology provides an efficient and robust tool for the early-stage aerostructural design of low-Reynolds-number UAV wings.
Nikolaou et al. (Thu,) studied this question.