A new method for wing shape optimisation is presented, in which the wing shape is optimised not only for the best aerodynamic properties but also for minimum structural weight. An advanced structural sizing method for wing structural weight estimation is developed. The method is based on elementary, physics based analyses of simplified structure to determine the amount of required material to resist the applied loads. The sizing loads are determined based on the airworthiness regulations using advanced aerodynamic analysis tools. In this new weight estimation method, the effect of wing outer (aerodynamic) shape on the wing-box structural weight is taken into account. A mathematical equation is derived, which relates the wing-box structural weight to the wing outer shape. The weight estimation method is validated for several existing passenger aircraft. A series of optimisations is performed to optimise the wing shape for a general objective function, which includes both aerodynamic and structural properties of the wing. The aircraft fuel weight is used as the objective function. Different structures are tested both metal and composite ones. The latter includes different composite layups. In order to find the effect of the internal structure on the optimum wing outer shape, the design lift coefficient is assumed to be constant for all the test cases. The results of the optimisations showed that the optimum wing shape is depending on the structural concept (metal alloys versus different composite layups) for different internal structures.

This paper presents an innovative optimisation method for aircraft fuselage structural design. Detailed local finite element analyses of panel buckling are further processed such that they can be applied as failure constraints in the global level optimisation. The high computational costs involved with the finite element analyses are limited by advanced use of surrogate modelling methods. This yields high flexibility and efficiency in the local level optimisation procedure and allows for efficient gradient based search methods as well as more costly direct search optimisations like genetic algorithms (GAs). The method is demonstrated on a composite fuselage barrel design case considering common structural sizing variables like thicknesses and stringer dimensions. Optimised barrel designs are obtained where the constraints that are derived from the panel buckling analyses are active. The total computational cost for the complete local and global level optimisation procedures is in the order of days on common-performance hardware.

ITA and EMBRAER are currently executing the research project Automation of Aircraft Structural Assembly (AASA) whose goal is to implement a robotic cell for automating the riveting process of aeronautical structures. The proposal described herein complements the AASA project, adds other manufacturing processes, namely sanding and polishing of aircraft surfaces. To implement the additional processes AASA project resources and facilities were used (robots and metrology systems) and devices designed and /or acquired to allow sharing of these resources. Among these, an Automatic Tooling Support for AERonautics structures (ATS_AER) was designed and built; also, a robot tool changer with high load capacity was acquired. The outcome of this research project is the evaluation of the feasibility of automating the processes of sanding and polishing metal surfaces in the aircraft manufacture using robots. The operating method adopted for surface treatment employed the ‘U’ type trajectory optimised to be run by a KUKA robot KR 500. The sanding process has been applied to aluminum metal sheet specimen sized 2•18ft2 (0•20m2) and used commercial 600 and 800 sandpaper. The automated sanding process yielded an average value of RA 0•48 ± 0•08 which is 25% more efficient when compared to the traditional, manual process whose average value of RA is 0•75 ± 0•51.

In this study, a new aero-propulsive model (APM) was derived from the flight manual data of a transport aircraft using Genetic Algorithms (GAs) to perform accurate trajectory predictions. This new GA-based APM provided several improvements to the existing models. The use of GAs enhanced the accuracy of both propulsive and aerodynamic modelling. The effect of compressible drag rise above the critical Mach number, which was not included in previous models, was considered along with the effects of compressibility and profile camber in the aerodynamic model. Consideration of the thrust dependency with respect to Mach number and the altitude in the propulsive model expression was observed to be a more practical approach. The proposed GA model successfully predicted the trajectory for the descent phase, as well, which was not possible in previous models. Close agreement was observed when comparing the time to climb and time to descent values obtained from the model with the flight manual data.

A cockpit instrumentation system provides various elements of information for pilots. However, logical inference based on a cockpit instruments fault tree (FT) and reliability sometimes cannot give a correct diagnosis of failures. In addition, in flight control systems (FCS), a fault identification method based on the multiple-model (MM) estimator cannot find the basic fault cause. To deal with these problems, a hybrid approach which is capable of integrating inference and fault identification is proposed. In this approach, the event nodes of the FT which have correlations to the FCS are separated into modules. Each module corresponds to a fault mode of the FCS. To use these correlations, fault inference and the MM estimator can share fault diagnosis information. Simulation results show that the proposed approach is helpful in detecting the root cause of failure and is more correct than single fault diagnosis method.