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Sustainable air transportation

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.Air transportation is one of the most important services in the world, contributing greatly to the advancement of modern society. The use of commercial aviation has grown more than seven-fold since the first jet airliner flight in 1949, and is unmatched by any other major form of transportation.
However, air transportation has a local and a global impact on the environment. Aircraft emissions impact significantly on the Climate Change and the local and regional air quality levels. The other important environmental issue is aircraft noise.
Nowadays Air Traffic Management (ATM) and aircraft operations need to be modernised in order to reduce both the operating costs and the environmental impact.

 

Trajectory management for aircraft noise annoyance minimisation

We have developed an optimisation tool to compute noise annoyance optimal trajectories for specific scenarios. The involved airport, with its surrounding cartography, geography and meteorological data, define an scenario. In this scenario, a given trajectory produces a given amount of noise annoyance, in function of the emitted aircraft noise. This value, together with some operational costs, define one or several optimization criteria. Then, an optimization algorithm computes the best departing or approaching trajectory minimizing these criteria and satisfying a set of trajectory constraints which, in turn, depend on the dynamics of the aircraft and possible navigation or airspace constraints.
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This optimisation problem can be formally written as a continuous, constrained, non-linear optimal control problem. This oritinal infinite-dimensional problem is converted into a finite-dimensional one by discretising all the equations and numerically integrating the differential equations describing the dynamics of the aircraft. Then a Non Linear Programming (NLP) problem is obtained and it is solved by using comercial optimization packages.


Noise annoyance modeling

 

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Noise annoyance is the relation between a given acoustic situation and a given individual or set of persons affected by the noise and how cognitively or emotionally they evaluate this situation. In addition to acoustic elements, such as the loudness, the intensity, the spectra distribution and duration of the noise, there is a list of non-acoustic elements that should be taken into account to define a global annoyance figure. For example one should consider background noise, the time of the day, the type of day (weekend, working day...), the period of time between two consecutive flights, the type of affected zones but also personal and socio-economic elements.

Since noise annoyance is a subjective, complex and context-dependent concept we propose to model it by using fuzzy sets theory. Fuzzy set theory is a generalization of traditional set theory and provides a powerful means for the representation of imprecision and vagueness. A fuzzy system is an expert knowledge-based system that contains fuzzy algorithms in the form of a simple rule-base which provides a natural tool to model and process uncertainty.


Some results and current research

As an application example, we have used this tool to optimise departing trajectories at the airport of Girona (Catalonia, Spain). For this study two different aircraft were considered: an Airbus A340 and an Airbus A321 flying at night, morning and afternoon periods.

Our on-going research in this topic is focused on considering more than one aircraft event and optimize trajectories for a whole period of time. In this way, we forsee to spread the noise annoyance over the population in an optimal way.

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Improving ATM for solving capacity-demand imbalance situations in SESAR or NextGen scenarios

SESAR (in Europe) and NextGen (in the United States) are two ground breaking initiatives aiming at improving their respective Air Traffic Management (ATM) systems. In these new paradigms, two important concepts arise:

4D trajectories should become a reality and the airspace users (i.e. the aircraft operators) will be the owners of their trajectories. The ownership of the trajectories leads to a situation where if a capacity-demand imbalance exists, a negotiation process among airlines should be done to solve the potential conflicts. The network managers are not longer in charge of solving the imbalance in a centralised manner but of coordinating the negotiation between the airspace users. The airspace users will be involved in the process of balancing demand and capacity and a Collaborative Decision Making (CDM) will become mandatory at strategic level. In the future scenarios, it will be critical for airlines to know the associated cost of solving capacity-demand imbalances in the air transportation network. Therefore, if a negotiation process is established with concurrent airlines, those ones with more options, and with better information of the associated costs for each option, will be better placed.

 

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Our research aims at developing an optimisation framework for aircraft operators that have to negotiate with other airlines in order to solve a capacity-demand imbalance problem in the airspace. In this negotiation process, different slots might be traded. In this case, it would be essential for the airline to compute the optimal vertical profiles and speeds to be used for each of the possible options, that will result in a different final costs. When a regulation is set, the affected airspace users will initiate the negotiation process acting in different ways, which represent different options, according to their own interests and to the associated costs of each solution. Therefore, the proposed methodology is intended to assess the different options that a particular aircraft operator would have and to compute the associate cost for each of them in order to better perform in the negotiation.

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