Flutter optimization of large transport aircraft

Sikes, G. D.; Tzong, George; Sharma, Añil

doi:10.2514/6.1992-4795

Cited by 3 publications

(1 citation statement)

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“…The aeroelastic optimization process, in essence, is a unified, multidisciplinary design process which can overcome barriers between different discipline groups while reducing the design cycle time. Previous 2 Shock and Vibration researches have mainly optimized aircraft structures from the perspectives of both static and dynamic constraints such as stress, displacement, modal frequency, and flutter constraints; see, for example, the previous publications by Sikes et al [7], Patil [8], Battoo and de Visser [9], Maute and Allen [10], Tischler, and Venkayya [11]. However, these researches have not taken into account the geometric nonlinearity induced by large deformation.…”

Section: Introductionmentioning

confidence: 99%

Aeroelastic Optimization Design for High-Aspect-Ratio Wings with Large Deformation

Xie

Meng

Wang³

et al. 2017

Shock and Vibration

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This paper presents a framework of aeroelastic optimization design for high-aspect-ratio wing with large deformation. A highly flexible wing model for wind tunnel test is optimized subjected to multiple aeroelastic constraints. Static aeroelastic analysis is carried out for the beamlike wing model, using a geometrically nonlinear beam formulation coupled with the nonplanar vortex lattice method. The flutter solutions are obtained using the -method based on the static equilibrium configuration. The corresponding unsteady aerodynamic forces are calculated by nonplanar doublet-lattice method. This paper obtains linear and nonlinear aeroelastic optimum results, respectively, by the ISIGHT optimization platform. In this optimization problem, parameters of beam cross section are chosen as the design variables to satisfy the displacement, flutter, and strength requirements, while minimizing wing weight. The results indicate that it is necessary to consider geometrical nonlinearity in aeroelastic optimization design. In addition, optimization strategies are explored to simplify the complex optimization process and reduce the computing time. Different criterion values are selected and studied for judging the effects of the simplified method on the computing time and the accuracy of results. In this way, the computing time is reduced by more than 30% on the premise of ensuring the accuracy.

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