Consideration of the environmental impact of aircraft has become critical in commercial aviation. The continued growth of air traffic has caused increasing demands to reduce aircraft emissions, imposing new constraints on the design and development of future airplane concepts. In this paper, an aircraft design optimisation framework is used to design aircraft that minimise specific environmental metrics. Multidisciplinary design optimisation is used to optimise aircraft by simultaneously considering airframe, engine and mission. The environmental metrics considered in this investigation are CO2 emissions — which are proportional to fuel burn — and landing-takeoff NOx emissions. The results are compared to those of an aircraft with minimum direct operating cost. The design variables considered in the optimisation problems include aircraft geometry, engine parameters, and cruise settings. An augmented Lagrangian particle swarm optimiser and a genetic algorithm are used to solve the single objective and multi-objective optimisation problems, respectively.

Today, all sailplanes in any particular class look and perform more or less the same. This gives credence to the view that the necessary compromises in design and sailplane technology have reached a limit. Sailplane development is on a sort of plateau. Further, apart from perhaps boundary-layer control (BLC), there appears to be nothing in the foreseeable future that promises any significant improvement in performance. It is the purpose of this paper to, once again, encourage interest in Variable Geometry (VG) and address the controversies which prevailed in abundance during the 1970s and which apparently still survive today.

Current state-of-the-art trajectory prediction tools typically model aircraft as three-dimensional point-masses, and make a number of simplifying assumptions about the actual and anticipated dynamics states of the aircraft. They are typically based on predefined settings obtained from existing databases such as Eurocontrol’s Bada rather than real-time information, including on the environment, available onboard the aircraft. This significantly limits trajectory prediction performance. This paper proposes a high-accuracy four-dimensional trajectory prediction model for use onboard civil aircraft, as well as by ground-based systems, which addresses these limitations. It is designed for strategic traffic capacity optimisation and conflict-detection and resolution over time-horizons covering the entire duration of a flight. The model incorporates a number of features including a novel flight-control-system and an enhanced flight-script that incorporates new taxonomy and content thereby enabling better definition of aircraft intent. The accuracy of the model is characterised using operational data acquired during a real flight trial. Results show that the performance of the proposed model is significantly better than the current models. Its accuracy is better than the required navigation performance for departure, en route and Non-Precision-Approach phases of flight.

Aircraft upset and Loss of Control (LOC) is a leading cause of accidents in commercial and general aviation aircraft operations. A number of measures have been taken in the commercial segment to improve training and awareness of this problem and several organisations offer in-flight training to enhance awareness. In relative terms, in both commercial and general aviation sectors, the use of Full Flight Simulators (FFS) and Flight Training Devices (FTD) for this purpose is minimal. A key reason for this is the limited capability and coverage of flight models used in these devices. This paper provides examples of the limitations in civilian simulators in contrast to military efforts that have been using full envelope modelling for decades to enhance pilot training. The paper also presents techniques used in full-envelope modelling, their validity, and a regulatory vehicle that is in-line with current international guidelines for application to civilian trainer development.

A conventional inverse simulation does not accommodate control constraints; hence for aggressive manoeuvring flight conditions, where control inputs are close to the limits, these algorithms lose some of their applicability. A modification of the conventional inverse simulation technique that accommodates the onset of physical limits or constraints is proposed in this paper. In this way a process of constraints handling is incorporated into the inverse simulation algorithm. Therefore, the aim of this paper is to demonstrate that conventional inverse simulation can be improved in terms of the realism of the results by applying a predictive capability for applications involving manoeuvring flight. The paper gives details of the development of the predictive inverse simulation algorithm and helicopter model used and, by presenting examples of results calculated for pop-up and lateral realignment manoeuvres demonstrates that a ‘receding horizon’ predictive approach offers improvements in the realism of inverse simulation results.