The European Commission aerospace industry set-up the Clean Sky programme to mitigate the impacts of air transport on the environment and on fuel resources. The work reported in this thesis is part of the stream of activities carried out under the Clean Sky programme towards the realisation of the ACARE objectives. The Clean Sky JTI and air transport is a collaborative effort involving industry, research organ- isations and academia to introduce novel technologies to improve the environmental impact of aviation. As part of the activities, more environmentally friendly aircraft trajectories are studied under the SGO Integrated Technology Demonstrator (ITD). The classical aircraft trajectory optimisation approach defines the problem by using the aircraft dynamics and engine performance thus neglecting the airframe systems power
requirement. Airframe systems are vital in commercial aircraft and their operation in- curs a fuel penalty (herein singularly referred to as ’penalties’ thereafter) on the aircraft engines from which the energy is extracted. Presently, the airframe systems penalties have not been considered within the classical trajectory optimisation problem definition. However, the effects of these penalties on typical trajectories flown today are significant which necessitated the need to identify methods of making the overall secondary power system work more efficiently.
In this work therefore, the effects of secondary power off-take penalties on aircraft trajec- tory optimisation were studied. The study focused on identifying these penalties on cur- rent aircraft trajectories when compared with trajectories optimised without considera- tions to power off-takes. The work involves the development and evaluation of GATAC through optimised trajectory simulations. The models required for GATAC evaluation include the Aircraft Dynamics Model (ADM3), Aircraft Systems Model (ASM4), Air Traffic Management (ATM), atmospheric, engine and emissions models. I the author personally developed the ant-icing model algorithm within the ASM. The purpose of the anti-icing model is to provide quantitative estimates of fuel penalty due to anti-icing which would allow the evaluation of fuel burn, noise and emissions from aircraft fitted with future air navigation systems in line with the Clean Sky activities (see Fig. 1.8). The figure shows the set-up for identification, development and evaluation of key tech- nologies through the use of demonstration models for the concept of greener and lighter next generation aircraft.
At the time of this work, the weather model was yet to be developed, therefore, an artificial cloud algorithm was implemented within the scope of the icing cases ran. The
3Developed by the Cranfield University Clean Sky team.
Fig. 1.8: Scope of Clean Sky Activities [6]
artificial icing model could simulate hundreds of icing scenarios based on the Appendix C icing conditions; however, only 48 cases (see Table 5.1) were of significance to this work. The simulations were ran and analysed, and the results have been presented in section 6. The primary case study for the analysis is:
A short haul flight from London (Heathrow) to Amsterdam (Schiphol) was used as the case study route.
A medium size, 180 passenger twin turbo-fan engine aircraft.
Each segment was optimised with and without consideration to power off-take penalties.
NSGAMO5 was used.
GATAC tool was used for generating the optimised trajectories.
The subsidiary cases include the departure, cruise and arrival under icing conditions. In each case, the trajectory was optimised based on trade-off between fuel burn and
5Non-dominated Search Genetic Algorithm Multi-objective Optimiser developed by Cranfield Uni-
flight time. Consequently, a multi-objective trajectory optimisation based on Pareto ef- ficiency method was used to minimise fuel burn, emissions and noise using GATAC tool. A vertical profile mission was simulated with the aim to obtain trajectories optimised for minimum fuel burn, flight time and emissions. A 3D mission was also simulated for the three objectives as well as minimum noise. It is worth noting that in Pareto method, a trajectory optimised for one parameter would not be the same as that optimised for another parameter. The objectives functions are in conflict such that minimising one objective leads to increase in the other; hence, a compromise trade-off is sought. This trade-off is defined as the Pareto optimum. To narrow down the search domain, certain constraints such as altitude, speed and stopping criteria were imposed relative to the mission and baseline aircraft profile.
A total of 60,000m ground distance was covered during departure from the ground level to FL100. The cruise simulation covered a range of 360,000m operating from FL100 to FL390 while the arrival covered a distance of 60,000m from FL100 to the ground level. In addition to minimum fuel and minimum time optimality problem, cases for noise mitigation were simulated in the 3D case departure. This is because during depar- ture and arrival noise is of a great concern to the people living around airports. Above FL100, noise is actually no longer an issue to the ground population, therefore, was not considered in the cruise case. Noise minimisation was also not considered in the arrival case because arrival is viewed as a reversal of departure in this work. In each case, 250 generations were ran for a population of 100, and initialisation factor of 50 which gives a total of 30,000 generations. Usually, a simulation of this nature would require very long running time except unless done on a grid system. Consequently, a high speed cluster of five computers each running three daemons6 was used in order to
speed up the simulation. A typical cruise case takes between 36 to 48 hours run time using the cluster. However, in departure and arrival which require less computational effort, local multiple daemons in the host computer were used to attain convergence within reasonable time.
The results (see section 6) showed that flying theoretically optimised trajectories with systems can cause unexpected fuel penalties up to 11%. By including the penalties of airframe systems within the optimisation loop however, fuel savings of up to 4% could be achieved. Thus, this work helped in evaluating the relationship between fuel burnt during flight and the amount of emissions generated by the classical and weather optimised aircraft trajectories. The approach used in this work could therefore, assist commercial aircraft operators manage excessive fuel burn and emissions imposed by power off-takes while operating in real weather conditions.