Geometrically nonlinear high-fidelity aerostructural optimization for highly flexible wings

Over the past decade, advances in MDO have enabled the aerodynamic and structural design of aircraft wings to be simultaneously optimized using high-fidelity models. Using RANS CFD and detailed structural finite element models in these optimizations enables an accurate trade-off between cruise drag and structural mass. Modeling the coupling of aerodynamics and structures allows the optimizer to aeroelastically tailor the wing, taking advantage of flexibility for improved performance. These capabilities make MDO a key enabling technology for the next generation of flexible and efficient high-aspect-ratio transport aircraft. However, as their aspect ratios increase, these wings increasingly exhibit geometrically nonlinear behavior that linear structural analysis methods cannot model. This work demonstrates the first simultaneous optimization of a wing's aerodynamic shape and structural sizing using high-fidelity geometrically nonlinear models. To enable this we implement a novel geometrically nonlinear shell element, an efficient nonlinear solver, and a constitutive model for stiffened shells. We then couple these nonlinear structural analysis tools to CFD through a geometrically nonlinear transfer scheme. Using these capabilities, we optimize a single-aisle commercial transport aircraft wing with 547 design variables and 1277 constraints. Although the optimized designs exhibit extreme flexibility-an aspect ratio above 19 and deflections exceeding 30% semispan-geometric nonlinearity has minimal impact on aerodynamic performance, planform design, and overall aircraft mass. However, the Brazier effect causes internal loads that linear analysis misses, requiring geometrically nonlinear analysis to produce a feasible design. The developed framework enables the pursuit of next-generation high-aspect-ratio wing designs by providing the computational foundation needed to exploit extreme wing flexibility as a design opportunity rather than a constraint.