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Multiple-Phase Trajectory Optimization for Formation Flight in Civil Aviation Master of Science Thesis M.E.G. van Hellenberg Hubar Technische Universiteit Delft Cover photo: Copyright AIRBUS S.A.S. 2014 – photo by S.Ramadier (http://aviationweek.com/blog/photo-airbus-a350-formation-flight) Multiple-Phase Trajectory Optimization for Formation Flight in Civil Aviation Master of Science Thesis by M.E.G. van Hellenberg Hubar in partial fulfillment of the requirements for the degree of Master of Science in Aerospace Engineering at the Delft University of Technology, to be defended publicly on Tuesday July 19, 2016 at 2:00 PM. Supervisor (Primary): Dr. ir. H.G. Visser Supervisor (Secondary): Dr. ir. S. Hartjes Thesis committee: Dr. ir. H.G. Visser TU Delft Dr. ir. S. Hartjes TU Delft Dr. ir. E. van Kampen TU Delft This thesis is confidential and cannot be made public until July 19, 2016. An electronic version of this thesis is available at http://repository.tudelft.nl/. Summary In this research the focus is on developing a tool that can optimize multiple-phase trajectories for com- mercial formation flights for minimum fuel consumption. In the past years, research has been conducted into formation flight and trajectory optimization, but not as a combined entity. Researchers have in- vestigated the aerodynamics behind formation flight and conducted flight tests to validate their results. The results show that a significant reduction in induced drag of 10-50% can be obtained for the trailing aircraft in a formation. Others, investigated the optimal routing for commercial aircraft and what the savings of formation flight could be when applied to certain flights. The trajectories of aircraft that join in formation, however, deviate from their optimal individual trajectories. What effect does formation flight have on the trajectories and where should the aircraft that will join in formation meet and split-up? These are questions that have not been answered yet. The research conducted in this study focuses on answering these questions by developing a tool that optimizes the trajectories of each aircraft in a formation such that the total fuel consumption is minimal. This question leads to the following research objective: ”To minimize the overall fuel consumption or Direct Operating Cost (DOC) of commercial aircraft flying in formation by developing a multiple-phase optimal control formulation.” From this research objective, the following research question is derived: ”Is it possible to develop a tool that optimizes the multiple-phase trajectories for two or three commer- cial aircraft flying in formation weighing the reduced fuel consumption, due to induced drag reduction, versus economic factors using a dynamic optimization technique?” When an aircraft flies through the sky it leaves an upwash behind it and when another aircraft flies through this upwash it experiences a reduction in induced drag, which leads to a reduction in fuel consumption. Different formation constellations and the relative positioning of the aircraft within the formation influence the amount of induced drag reduction. However, this study does not focus on this issue and during the experiments 25% reduction in induced drag is assumed for the trailing aircraft in a two-aircraft formation. There are multiple phases that are considered in the assembly and disassembly of a formation. For a two-aircraft formation, five phases are identified: the first two phases are from the departure points of both aircraft to the rendezvous point, the third is a joint phase from the rendezvous point to the split-up point and the fourth and fifth phases are from the split-up point to both destination airports. As the aircraft only fly in formation in phase 3, the reduction in induced drag is only valid for this phase. The structure of the developed optimization tool can be divided into three parts, the optimization framework, the input and the output. The optimization framework allows optimizing for both minimum fuel burn and minimum DOC and uses a MatLab based optimization software called GPOPS that is able to solve multiple-phase optimal control problems using pseudospectral methods. The framework contains the differential-algebraic equations that define the aircraft model as well. The solo phases are repre- sented by an intermediate point-mass dynamic model and the formation phases by a more simplified energy-state dynamic model. These dynamic aircraft performance models are linked to each other at the beginning and end of the phases. For the input, atmospheric data is required and three aircraft types are considered: the Boeing 737-300 (B733), the Boeing 747-400 (B744) and the McDonnell Douglas MD-11 (MD-11). When in formation, a formation flight induced drag fraction 휖 is implemented in the aircraft models. Several constraints are implemented in the tool as well (e.g. constraints on altitude, velocity, final weight, thrust level and duration of the formation segment). The output of the tool gives the state and control variables of all aircraft for all points in time and with these variables, the trajec- tories can be replicated and the fuel burn, distance flown and flight time per aircraft can be obtained. Furthermore, the implementation of schedule disturbances and wind contributed to make the tool more iii realistic. The developed tool has been validated by comparing the results with the aircraft performance manuals of the manufacturers and by replicating a case study of other research reported in the literature and comparing the results. Several experiments were conducted to investigate the benefits of formation flight. A common result all experiments showed is that in a formation the leading aircraft, due to the detour it must fly, con- sumes more than compared to its corresponding solo flight. Often, however, the reduction of the trailing aircraft is high enough to render the total fuel consumption for all aircraft in the formation lower than the total solo consumption. Also, the flight times and track distances increase when formation flight is applied. This is due to the fact that aircraft must fly a detour to meet-up. Existing scheduled flights have been replicated and the developed tool showed that combining two daily KLM flights, KLM can save over 1.2 million kilograms of fuel per year and when two SkyTeam flights (one KLM and one Delta) join in formation they can yearly save over 2.3 million kilograms of fuel. Furthermore, experiments demon- strated that combining different aircraft types in a formation affect the formation, and the sequence of the aircraft in a formation influences the results as well. The experiments showed that when combin- ing a B744 with a MD-11, the aircraft will only join in formation when the B744 is the trailing aircraft. When the B744 is designated to lead the formation, the results showed that flying in formation is not beneficial. This is due to different performance characteristics of the aircraft. In another experiment, the results of departure delay for one of the aircraft in the formation are investigated. This revealed that not only the on-time aircraft slows down and the delayed aircraft speeds up, but the rendezvous point location shifts as well. The location of the rendezvous point shifts towards the split-up point, while it also shifts sideways towards the delayed flight. To investigate the effects of wind on the formation, another experiment was conducted. A wind-field was modelled across the North-Atlantic Ocean and a set of eastbound flights was compared with a set of westbound flights. The westbound flights, which encounter headwinds, divert North to encounter less wind and feature a larger formation segment (and a relatively larger reduction in fuel burn) compared to the eastbound flights. Finally, an experiment containing three aircraft in formation was conducted. The results of this experiment were not further discussed, but the experiment showed that the tool is able to optimize the trajectories for more than two aircraft joining in formation. All experiments are optimized for minimum fuel consumption because it is less straightforward to determine the DOC, as it consists of costs that are different per airline and flight. A sensitivity analysis was conducted to explore the effect of shifting the optimization focus (from optimal for fuel to optimal for time) for a set of flights. This showed that, although the results are dependent of the flight prop- erties (route, aircraft type, wind, etc.), compared to the corresponding solo flights, formation flight can offer significant reductions in fuel consumption without increasing the flight time. Another sensitivity analysis was conducted to investigate what the effect would be on the results, when the induced drag reduction due to formation flight is altered. For this analysis, the assumed reduction in induced drag on the trailing aircraft varied from 0-50% of the total induced drag. This resulted in a change in results in terms of trajectories (shift in rendezvous point location), fuel burn and flight time. However, these changes are as expected (a higher reduction in drag results in less fuel burn and vice versa), which means the developed tool is robust for different values of induced drag reductions. In this study an optimization tool was developed to optimize the trajectories of multiple aircraft that join in formation for minimum fuel consumption or DOC. This tool can be developed further to enable, for example, airlines to explore the benefits of formation flight; on a greater scale including an entire flight schedule. Furthermore, the results of the performed experiments indicate that formation flight can lead to significant fuel reduction compared to flying solo. These results could prompt further development of aviation regulations to allow formation flight, as this can be a short-term fuel saving solution. iv Acknowledements A little less than one year ago, before I started my thesis, I was sceptic because I thought that this project would mean that I would have to spend almost a year, working all alone, on a subject that would not necessarily interest me.

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