Robust and Reliability-based Design Optimization of High Aspect Ratio Wings Including Geometric Nonlinearities

Abstract

High aspect ratio wings (HARW) can provide higher lift-over-drag ratios leading to longer
ranges through the resulting reduction in the induced drag. However, the increased structural
flexibility inherent in HARWs renders them more susceptible to significant deflections under
identical operating conditions compared to wings with lower aspect ratios. This increased
flexibility can adversely impact the dynamic and aeroelastic behavior of the wings,
potentially precipitating aeroelastic instabilities at lower velocities than those observed in
comparatively stiffer wing structures. Furthermore, the presence of geometric nonlinearities
in very flexible wings complicates the prediction of their static and dynamic behaviors,
rendering conventional linear modelling approaches inadequate. Besides, the design of high
aspect ratio wings also confronts operational constraints, particularly within airport
environments, which can be solved by the introduction of folding wingtips that can reduce
the span for airport compatibility. Further work has shown that the use of floating folding
wingtips enables significant gust loads alleviation and improved roll behaviour, however, a
large deformation of the folding wingtip can also introduce geometric nonlinearities that
significantly affect the overall aircraft performance. Therefore, it is of paramount importance
to incorporate the impact of geometric nonlinearities on the aeroelastic response in the realm
of HARW design. In the context of optimizing HARWs incorporating floating folding
wingtips, or not, there are currently few robust or reliability-based optimization studies that
considers input parameter uncertainties within the design process.
This thesis develops a robust and reliability-based design optimization process to consider
input parameter uncertainties in high aspect ratio wing designs including geometric nonlinear
effects. Wing weight minimization /maximize lift over drag maximization/ maximization of
range is considered under aeroelastic constraints with stress limitations, the jig twist and
stiffness distribution along the wing considered as design variables. Robust Design
Optimization (RDO) and Reliability-based Design Optimization (RBDO) are integrated
together to enhance the performance while satisfying a certain confidence in reliability and
robustness requirements. To consider the effect of parameter uncertainties in the design
process, surrogate models are introduced to conduct uncertainty quantification at a low
computational cost. Combined with constrained optimization algorithms, genetic algorithms,
a robust and reliability-based design optimization method assisted by surrogate models is
developed to produce an optimal design subjected to constraints with a good balance between
performance robustness and structural reliability.

Date of Award	18 Jun 2024
Original language	English
Awarding Institution	University of Bristol
Supervisor	Francesca Pianosi (Supervisor) & Jonathan E Cooper (Supervisor)