The future air traffic management (ATM) concept envisaged by the Single European Sky ATM Research – SESAR – and the USA equivalent NextGen, mark a paradigm shift from the current reactive approach of ATM towards holistic strategic collaborative decision making. The core of the future ATM concept relies on common situational awareness over potentially large time-horizons, based upon the user operational intent. This is beyond human capabilities and requires the support of automation tools to predict aircraft state throughout the operation and provide support to optimal decision making long before any potential conflict may arise. This is achieved with trajectory predictors and conflict detectors and resolvers respectively. Numerous tools have been developed, typically geared towards addressing specific airborne applications. However, a comprehensive literature search suggests that none of the tools was designed to predict trajectories throughout the entire operation of an aircraft, i.e. gate-to-gate. Yet, such functionality is relevant in the holistic optimisation of aircraft operations. To address this gap, this paper builds on an existing en route trajectory prediction (TP) model and develops novel techniques to predict aircraft trajectories for the transitions between the ground- and enroute-phases of operation and for the ground-phase, thereby enabling gate-to-gate (or enroute -to-enroute) TP. The model is developed on the basis of Newtonian physics and operational procedures. Real recorded data obtained from a flight data record (FDR) were used to estimate some of the input parameters required by the model. The remaining parameters were taken from the BADA 3.7 model. Performance results using these flight data demonstrate that the proposed TP model has the potential to accurately predict gate-to-gate trajectories and to support future ATM applications such as gate-to-gate synchronisation.

Advances in space activity are linked to reductions in launch cost. Air-breathing propulsion-assisted flight systems offer the potential for revolutionary change of the space operations paradigm. Horizontal launch of a space-access system provides mission flexibility, responsiveness, and affordability. One way to reduce launch cost is to increase the Mach number at which a launch vehicle is staged from a carrier aircraft. Without exceeding the engine and airframe design limits, the pre-compressor cooling technology allows an operational aircraft to operate at Mach numbers and altitudes beyond its basic operational limits. This is an essential, near-term technology for reducing launch cost to place small-weight payloads in low Earth orbit. The advantage of this technology is assessed with a modified McDonnell Douglas QF-4C aircraft. Payloads are unachievable or marginal with an unmodified QF-4C. However, payloads weighing around 150 pounds are plausible with this aircraft when incorporating the water injection pre-compressor cooling (WIPCC) technology.

In this study, development of a new fuel flow rate model for the climbing phase of flight was achieved using a genetic algorithm (GA) method. Two modelling approaches were performed using real flight data records (FDRs) from a medium-weight transport-category aircraft. The first model considered the dependency of fuel consumption only with respect to altitude, whereas the effects of both altitude and true airspeed (TAS) were included in the second model. The proposed models are improvements on existing models because the relationship between fuel flow rate, flight altitude, and TAS can be deduced using the derived formulations. Both modelling approaches were found to provide accurate results after performing an error analysis for fuel flow rate values. It was clear that incorporating the TAS effect into the second model enhanced the accuracy of the model, but the first model was also found to be appropriate for practical usage.

This paper describes the analysis and the minimum weight optimisation of a fuselage composite stiffened panel made from carbon/epoxy material and stiffened by five omega stringers. The panel investigated inside the European project MAAXIMUS is studied using a fast tool, which relies on a semi-analytical procedure for the analysis and on genetic algorithms for the optimisation. The semi-analytical approach is used to compute the buckling load and to study the post-buckling response. Different design variables are considered during the optimisation, such as the stacking sequences of the skin and the stiffener, the geometry and the cross-section of the stiffener. The comparison between finite element and fast tool results reveals the ability of the formulation to predict the buckling load and the post-buckling response of the panel. The reduced CPU time necessary for the analysis and the optimisation makes the procedure an attractive strategy to improve the effectiveness of the preliminary design phases.

An investigation is presented to elucidate the performance of a staggered, spanwise cambered biplane. The spanwise camber yielded wings forming a ‘∧’ or ‘∨’ when viewed streamwise. The configuration is examined in terms of its aerodynamic and stability characteristics. The feasibility of negating the requirement for a conventional empennage is explored. Geometric variation encompassed front and back wing anhedral/dihedral angles yielding 49 combinations. Evaluation of the geometry was accomplished using both wind tunnel testing and numerical simulation. The results indicated that front wing dihedral in conjunction with aft wing anhedral was most beneficial, such that the benefit of wake spacing was maximised. Aerodynamic benefit was indicated compared to a conventional empennage geometry. The greatest disparity in behaviour of the fore and aft wing anhedral/dihedral distribution was in the high lift regime, where the nature of the stall varied. Simulations to establish the viability of the geometry in terms of controllability were also conducted and indicated that the configuration is viable.

This paper presents an improved algorithm for foreign object debris (FOD) detection on the runway with several innovative techniques. The detection scheme incorporates four steps of geometric adjustment, background subtraction, clutter suppression and camouflage elimination. After geometric adjustment, the background model is built for each pixel with a set of RGB colour values taken in the past at the same location or in the neighborhood in the step of background subtraction. The background model samples are substituted randomly with an unfixed update period. Furthermore, the steps of clutter suppression and camouflage elimination are added to modify the segmentation map after background subtraction in order to increase the detection probability and decrease the false alarm rate. The overall algorithm is applied to the test data and real data on the runway. The results show that the RGB-based algorithm performs better than the classical gray-based techniques.