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Geometrically Nonlinear High-fidelity Aerostructural Optimization Including Geometric Design Variables

AIAA 2023-3316
Session: Aeroelastic and Aero-Structures Optimization
Published Online:https://doi.org/10.2514/6.2023-3316
Abstract:

View Video Presentation: https://doi.org/10.2514/6.2023-3316.vid

Over the past decade, advances inMDOhave enabled the optimization of aircraft wings using high-fidelity simulations of their coupled aerodynamic and structural behavior. Using RANS CFD and detailed structural finite element wingbox models, the aerodynamic shape and internal structural sizing of a wing can be optimized concurrently to tailor the aeroelastic behavior of the wing and optimally trade-off drag and structural mass. This capability makes MDO a key enabling technology for the next generation of efficient high-aspect-ratio transport aircraft. However, as their aspect-ratios increase, these wings increasingly exhibit geometrically nonlinear behavior that cannot be correctly modeled by typical linear structural analysis methods. This paper builds on our previous work, demonstrating the first simultaneous optimization of a wing’s aerodynamic shape and structural sizing using high-fidelity geometrically nonlinear models. Our methods are implemented in the open-source finite element library, TACS, and include a geometrically nonlinear shell element formulation, an efficient nonlinear solver, and a constitutive model for stiffened shells. We then demonstrate the ability to couple these nonlinear structural analysis tools to a high-fidelity RANS CFD solver using a geometrically nonlinear load and displacement transfer scheme. Finally we use this capability to perform a series of fuel-burn minimizations of a single-aisle commercial transport aircraft wing featuring 578 design variables and 1287 constraints. Our results suggest that geometrically nonlinear effects noticeably change the optimum trade-off between drag and structural mass at aspect-ratios typical of modern transport aircraft, even if the attained fuel-burn is similar.