Design Optimization Methodology for Future Hybrid Wing Structures

Lower wing skins are a key battleground for metal-based structures for commercial aircraft as they represent the heaviest part of a metallic wing box.  Constellium is developing solutions to meet this challenge for both the near, medium and long terms, with increasing levels of ambition the longer the term.  These range from advanced 2xxx alloys (e.g. 2027-T351 and its optimizations) through AIRWARE alloys such as AW232, to exploiting the potential of bonded structures including hybrid laminates.

In this context, a design optimization methodology has been developed with the aim to design and optimise hybrid wing structures that satisfy strength, fatigue and damage tolerance criteria. To this aim the concept of Fibre Metal Laminates was exploited in a lay-up optimization scheme as it is a key technology for future lower wing structures.

The methodology enables the exploration of the design space for Fibre Metal Laminates by finding lay-up solutions that meet all criteria. In doing so, it reveals the contribution of individual criteria to the obtained design solutions, and it can illustrate the design improvement potential by comparing the design solution with second best solutions and with the solution that satisfies subordinate criteria. This functionality is in particular important in the identification of design potential of novel constituents, like the mentioned advanced alloys.

The method is capable of designing a lower wing skin consisting of Fibre Metal Laminates with an upper wing made of aluminium, while assuring compatibility between wing cross-sections.

The optimization methodology may incorporate the fundamental theories and methods developed over the past decade for predicting fatigue and damage tolerance properties of Fibre Metal Laminates. However, here it is demonstrated how the optimization procedure can be improved in reduction of computational time by considering approximations of the evaluation methodologies while maintaining accuracy.

The influence of the optimization setting, approximations and different design criteria have been investigated extensively, and the results to support and demonstrate the design methodology are presented.