MAWS (Modelling of Adaptive Wing Structures)
| Dates | 01 January 2012 - 31 July 2014 |
|---|---|
| Funder | Clean Sky |
| Contact person | Prof. Jonathan Cooper |
The primary aim of the MAWS project is to develop an inverse design technique to determine the internal stiffness distribution in the wing-tip in order to meet an in-flight defined aerodynamic shape as well as providing inherent gust loads alleviation.
Objectives
Development of a Reduced Order Model (ROM) inverse approach to determine the stiffness requirements of aircraft wings, and hence the internal structure, in order to meet defined aerodynamic shapes (e.g. giving maximum L/D ratio) including wash-in, wash-out and neutral aerodynamic lift.
- Definition of an adaptive spar/rib concept to enable the variation in stiffness requirements to be met;
- Evaluation of the selective shape capability of the concept and optimisation of the design for a range of flight conditions; and
- Definition of the limitations of the concept and ROM approach, including power requirements.
Scope
Development of an aerodynamic shape optimisation approach based upon:
- Lamar’s method applicable for any point in the flight envelope;
- Development of beam FE model and aerodynamic panels for a Regional TurboJet wing;
- Development of an optimisation approach to obtain the minimum mass wing that achieves the required structural deflections at any point on the flight envelope subject to various structural constraints;
- Investigation into three different approaches of adaptive stiffness morphing: rotating spars, moving spars and moving spar caps to achieve required changes in bending and torsional stiffness along with moving the leading edge and trailing edge shape.
- Development of optimisation approach to determined required amount of morphing needed to achieve required aerodynamic shape throughout the flight envelope;
- Demonstration of approach on Regional TurboJet wing model; and
- Evaluation of the effects of uncertainty on the morphing optimisation process using Polynomial Chaos Expansion and Bayesian methods.