Within the Terminal Maneuvering Area (TMA), the conventional step-down technique is still dominating most of the airports in the world, especially at the busy day-time periods.
New procedures aiming at noise abatement have been proposed to considerably reduce the noise exposure from the approaching aircraft to the ground level, such as the Continuous Descent Approach (CDA) and the advanced CDA techniques. Both of them have been implemented into Amsterdam Airport Schiphol for a number of years at the relatively quiet night-time periods. After the implementation of a (advanced) CDA, the noise impact on the environment around the perimeter of the airport has been dramatically reduced. Simultaneously, a reduction of fuel cost has also been achieved due to the cleaner approach configurations and lower throttle settings. However, because of the unfeasibility of the aircraft position prediction, landing intervals between aircraft has to be increased from 1.8 to 4 minutes with the subject to avoid the conflicts between scheduled aircraft. As a consequence, the airspace is not enough to handle all the arriving and departing aircraft at busy periods with the usage of a (advanced) CDA. In order to overcome the shortcomings, one additional system has to be taken into operation. The Airborne Separation Assistance System (ASAS) is integrated to provide the capability of minimizing and maintaining separation distances.
Considering these existing trajectory-based procedures, noise abatement are likely realized
- By keeping the aircraft as high as possible;
- By keeping the aircraft at high altitudes as long as possible;
- By keeping the configuration of the aircraft as clean as possible;
- By keeping the engine throttle settings of the aircraft as low as possible.
It is undeniable that the noise emission to the environment around the aircraft and the noise impact on the ground level after propagation can be perceptible reduced by the listed realizations. Nevertheless, these procedural designs are still far from the field of optimization. A good example is the Terminal Area Energy Management (TAEM) for Reusable Launch Vehicles (RLV). Optimum trajectories are found from the initial point to the final point with various optimization algorithms. Interval analysis is on the top the list due to its guaranteed capability of locating the global minimum for given problems.
Therefore, if we model the aircraft approach within TMA into a dynamic global optimization problem, either the noise exposure or the atmospheric pollutant emission or both can be selected as the cost function to be minimized. Aircraft aerodynamics, aircraft performance and engine performance have to be given combined with some necessary assumptions and simplifications. Equations of motion and performance capabilities of the aircraft provide equality and inequality constraints to limit the design space.
A Boeing 747-100/200 model in the Faculty of Aerospace Engineering (Technology University of Delft) will be used to represent a heavy and wide-body civil aircraft. This model is able to present the most difficulties with respect to flying segmented flight paths because of its dynamic characteristics. The Division of Control & Simulation has the model in SIMONA so that flight tests for the designed trajectories can be carried through in the simulator and the final performances can be given and compared with the step-down and (advanced) CDA techniques.
Some of the main challenges can be summarized as:
- It turns out to be a multi-objective optimization problem when the noise exposure and the atmospheric pollutant emission are both taken into account during the trajectories optimization. Because of the possible conflicts between these two objectives, compromise has to be made in such a way that the solutions are mathematically equal. Namely, solutions which can not be improved in one objective without degrading the performance of the other objective will be selected as the acceptable results.
- Algorithms using interval analysis are able to find the global optimum for a finite-dimensional static optimization problem. However, dynamic optimization is far more complex when the objective is to optimize time-dependent control functions. By the use of parameterizations of these control functions, an initial infinite-dimensional dynamic optimization problem can be transformed into a finite-dimensional quasi-static optimization problem. A flexible way to parameterize the control functions needs to be developed in order to better take advantages of the given global optimization algorithm.
- Within the entire optimization, intervals rather than real numbers will be added, subtracted, multiplied and divided. Look-up tables built with real numbers for acoustic and engine models have to work in an interval environment. Given an interval input, these look-up tables have to provide an interval output to carry on the calculation. That is, various categories of interpolation for intervals are necessarily required.
Unconventional approach methods are badly needed to make the TMA quieter and cleaner. The goal will be fulfilled after the implementation of trajectories optimization on CleanEra aircraft.