Topology optimization framework for designing elastic wind tunnel models with additive manufacturing

• A level-set-based topology optimization framework is developed for elastic wind tunnel model design. • The framework reproduces both static compliance and modal characteristics of full-scale aircraft. • Manufacturability is ensured by penalty terms controlling overhang and edge sharpness in FDM printing. • The optimized model achieves less than 2 % error in eigenfrequency ratios and 0.999 similarity in compliance. • Experimental validation confirms good agreement in deformation, lift, and flutter behavior. Wind tunnel testing remains essential for aeroelastic analysis in aircraft development. However, creating scaled models that accurately replicate both the static and dynamic structural properties of full-scale aircraft is a significant challenge. Traditional fabrication methods are often time-consuming and expensive, highlighting the need for more efficient design and manufacturing processes. This study proposes a methodology for designing an elastic wind tunnel model with an internal structure optimized to mimic the static (compliance) and dynamic (eigenfrequencies) characteristics of a full-scale wing, while ensuring manufacturability through Fused Deposition Modeling (FDM) 3D printing. A level-set-based topology optimization framework was used to design the model's internal structure systematically. The optimization process, combining finite element method (FEM) analysis and automatic differentiation, aimed to match the target wing's eigenvalue ratios and compliance matrix. Penalty functions were added to prevent overhangs and sharp edges, ensuring the design's suitability for 3D printing. The numerically optimized model closely matched the scaled full-scale wing, with a maximum error of 1.7% in frequency ratios and a cosine similarity above 0.999 for the compliance matrices. Further aeroelastic analyses confirmed similar static deformation, lift distribution, and flutter characteristics. Experimental validation of a 3D-printed model showed good correlation in eigenfrequencies, accounting for material property differences inherent to additive manufacturing. The proposed methodology effectively combines topology optimization with additive manufacturing to create structurally equivalent aeroelastic models. Validated through both numerical and experimental results, this approach demonstrates its effectiveness and potential to advance aircraft development.