Manuel Villegas Díaz

In this study, the environmental realities of enhancing departure aircraft procedures, based on optimal Continuous Climb Operations (CCOs), are discussed for a long-range wide-body aircraft. The findings are driven by the advantage offered by the analysis of real Flight Data Recorder (FDR) data against the simulated data, obtained through the use of orthogonal polynomials such as Chebyshev polynomials, in partnership with Gauss-type integration rules. Therein, the calculation of improved trajectories aims at reducing the negative environmental aspects of civil aviation on urbanized areas around airports and enhancing the operational cost efficiency. The multi-objective trajectory optimization has not only been applied to multiple environmental factors particularly; fuel consumption and noise impacts, but also has been constrained by Air Traffic Control (ATC) operational restrictions from a real scenario. The problem, tackled through a multi-objective optimization process based on CCO principles by a Chebyshev–Gauss–Lobatto (CGL) Pseudospectral Method, relies on a numerical framework that bears out the benefits in terms of fuel consumption and noise emissions. Therefore, this study promotes the need for investigating, not the implementation of a CCO generally assumed as an uninterrupted climb departure, but an optimal CCO.

Demand & Capacity Management solutions are key SESAR (Single European Sky ATM Research) research projects to adapt future airspace to the expected high air traffic growth in a Trajectory Based Operations (TBO) environment. These solutions rely on processes, methods and metrics regarding the complexity assessment of traffic flows. However, current complexity methodologies and metrics do not properly take into account the impact of trajectories’ uncertainty to the quality of complexity predictions of air traffic demand. This paper proposes the development of several Bayesian network (BN) models to identify the impacts of TBO uncertainties to the quality of the predictions of complexity of air traffic demand for two particular Demand Capacity Balance (DCB) solutions developed by SESAR 2020, i.e., Dynamic Airspace Configuration (DAC) and Flight Centric Air Traffic Control (FCA). In total, seven BN models are elicited covering each concept at different time horizons. The models allow evaluating the influence of the “complexity generators” in the “complexity metrics”. Moreover, when the required level for the uncertainty of complexity is set, the networks allow identifying by how much uncertainty of the input variables should improve.

Performing Continuous Climb Operation (CCO) procedures enable the reduction of the environmental footprint and the improvement of the trajectory efficiency when individually operated. However, its operation may affect negatively the overall operational efficiency at Terminal Manoeuvring Areas (TMAs). The estimation of capacity is a matter of paramount importance to all airport planning and analyzing the capacity effects of this particular operational technique on a certain scenario will definitely help on evaluating its potential applicability. In this paper, departure runway capacity at the Adolfo Suárez Madrid-Barajas airport was operationally evaluated when introducing CCOs. The considered trajectories consisted of multiobjective optimized CCOs based on the optimal control theory, using the pseudospectral direct numerical method. These scenarios allowed addressing of the incremental variations of CCOs versus conventional departures, through fast time simulation, with the objective to assess the effects on the operations.