AbstractThe possibility of aeroelastic tailoring has been around since the early 1980s, most of their applications to aircraft structures have been `black metal' designs, which do not fully exploit the anisotropic properties of the composite materials. This somewhat conservative approach is at odds with the elastic tailoring capabilities offered by composite materials, which, by allowing modification of the bending and torsional stiffness coupling terms, lend themselves to innovative design solutions for improved aeroelastic performance.
The current work aims to develop a novel aeroelastic tailoring framework which can provide a tool for a rapid design process for robust and reliable composite aircraft wings. The terms robust and reliable are referred to as design sensitivity due to parametric variations in the composite material properties, ply orientation and structural parameters. To incorporate uncertainty in optimal designs requires a ‘probabilistic’ optimisation approach with an efficient uncertainty quantification method that can accurately evaluate the effect of parameter variations on the wing performance at a low computational cost. Polynomial Chaos Expansion and Random Sampling High Dimensional Model Representation methods used in the current work are capable of offering low computational cost for uncertainty quantification analysis. These methods are subsequently used in a Robust and Reliability-Based Design Optimisation approach in which a robust and reliable design configuration of a composite aircraft wing is obtained. An idealised ‘box-like’ Finite Element model representation for a high-aspect-ratio wing of a reference regional jet airliner is used to demonstrate the effectiveness of the approach.
A novel multi-level aeroelastic tailoring framework is introduced to obtain a robust and reliable composite aircraft wing design. The framework is capable of producing an optimised wing design with the best compromise between structural weight, robustness and structural reliability.
|Date of Award||28 Nov 2019|
|Supervisor||Alberto Pirrera (Supervisor) & Jonathan E Cooper (Supervisor)|