The popularity of Remotely Piloted Aircraft Systems (RPAS) is growing at an increasing rate as an alternative to manned aviation for different purposes and applications. So far, most of their operations have been of military character, but the demand for civil and commercial applications is growing exponentially. RPAS have been allowed to operate in segregated airspace by restricting other users from entering the volume associated to the operation of the RPAS. This temporary solution based on accommodating their operations on a case-by-case basis is not feasible on the long-term. Moreover, the latest forecast carried out by SESAR Joint Undertaking (SJU) reveals that by 2050 RPAS will represent 20% of the fleet. The available airspace is a scarce resource and is already saturated, which means that additional unmanned operations would not be possible simultaneously. Integration in non-segregated airspace is the only manner in which the full benefits and capabilities of RPAS will be achieved, while maintaining the same levels of safety and efficiency as manned aviation. In order to achieve a safe, efficient and transparent integration, a common regulatory framework and new technologies are currently under investigation. However, neither operational nor performance standards of RPAS integration have been addressed properly. For that reason, this project is focused on the development of a methodology to determine minimum operational and performance requirements by assessing the impact of RPAS operations on the Air Traffic Management (ATM) Network. The scope is limited to the en-route phase as a starting point since none of the flight phases have been analysed yet. This project is carried out in collaboration with EUROCONTROL. RPAS are well known for presenting a wide range of performance characteristics, especially in terms of cruise speed and rate of climb. Thus, two different unmanned performance models have been chosen from the available performance models in the EUROCONTROL database: RP01 (MQ-9), which presents a similar performance to commercial aviation aircraft, and RP02 (RQ4A), which is considered a low-performance RPAS in terms of cruise speed and rate of climb. Building on a base scenario in the Paris Control Terminal Area (CTA),Monte Carlo simulations of the air traffic have been performed in order to randomly vary the main input variable: the set of en-route flights that is substituted by unmanned aircraft. Two different scenarios have been analysed separately in order to establish the requirements: one for same-performance RPAS, and the other for low-performance RPAS. In order to assess the impact, three different Key Performance Areas (KPAs) have been selected: capacity, efficiency and safety. These KPAs are in turn characterized by their corresponding Key Performance Indicators (KPIs), namely sector capacity, sector overload, flight time efficiency, flight path efficiency and number of potential conflicts. Based on an analysis of the current occupancy of the flight levels, operational requirements are defined in terms of altitude segregation. RPAS are not allowed to operate in the most occupied flight levels (FL): from FL300 to FL400. Therefore, depending on their operation and performance characteristics (i.e. ceiling), they will adapt their cruise FL to operate below FL300 or above FL400. The optimal value of performance requirements for same-performance RPAS has been found to be a minimum cruise speed of 390 kts, while for low-performance RPAS this minimum cruise speed is of 280 kts. Additionally, for low-performance RPAS, it is found that the rate of climb of 1,000 fpm results in the best performing case when changes in FL are required. The results show that the values of the key performance indicators for the base scenario are difficult to reach when RPAS are sharing the airspace. However, the establishment of minimum operational and performance requirements contributes to a significant reduction of the negative impact that the integration of RPAS operations implies. The development of this methodology and its application to the characteristics of each airspace sector will contribute to the full integration of RPAS in non-segregated airspace.

In the year 2050, global anthropogenic radiative forcing from aircraft emissions are projected to increase significantly. Recent studies have considered climate optimized flight trajectories to be a promising measure to mitigate non-CO2 emissions’ environmental impact, which is highly sensitive to locus and time of emissions. Estimating the maximum mitigation potential from these trajectories requires accounting of air traffic regulations. As designing regulated climate optimal trajectories necessitates solving a hybrid optimal control system with unknown mode sequence and associated switching times, there is a need to build an efficient and systematic control technique. In this thesis, a bi-level optimal control algorithm is proposed for designing climate optimal cruise trajectories, the lower level calculates the optimal switching times and control inputs of a fixed mode sequence and, the upper level updates the mode sequence with mode insertion which lower the cost locally. The problem for trajectory optimization is formulated here as a hybrid optimal control problem with a switched system and with a variable mode sequence, where step-climb and descent modes are included in the mode sequence. Optimal Control problems for minimizing operating cost and climate cost with fictitious climate cost functions (CCF), varying with altitude, are solved to study the performance of the algorithm. The algorithm is implemented within the Trajectory Optimization Module (TOM) by building a bi-level framework. The framework was validated by solving the operating cost optimal control problem. The maximum error between the cost reduction estimated by the algorithm and the actual cost reduction was found to be less than 15%. With high probability it can be stated that the bi-level framework is able to calculate an optimal mode sequence as the framework allow for zero entry modes in the mode sequence i.e. modes of zero duration. Although, careful consideration is required while selecting a mode for insertion as the framework is highly dependent on the sequence of the set of modes. Despite a satisfactory performance of the bi-level optimal control technique there are few challenges which limits the scope of this technique. The maximum error was found to increase for optimal control problems with AirClim CCFs. The dependence of the AirClim CCFs on position of the aircraft influences the locus of the trajectory at each flight level. Because of this the the trajectories calculated in each iteration of the framework are found to be inconsistent. A flight trajectory guided by waypoints is proposed as a solution for future studies to handle the inconsistency between trajectories. As future studies are expected to focus on finding optimal mode definitions for designing climate optimal trajectories, the bi-level optimal control algorithm can act as an intermediary tool with which the researchers can systematically investigate cost benefits along the trajectories.

To overcome the disadvantages of radar vectors and to improve efficiency and safety of terminal airspace operations, EUROCONTROL has proposed the so-called Point Merge System (PMS) technique for merging inbound traffic flows. As the PMS provides better predictability, it can potentially offer significant fuel and environmental benefits as well as a considerable reduction of inbound delays. The main goal of this project is to explore the potential benefits/drawbacks (environmental impact, capacity, delay, fuel efficiency and safety) of implementing PMS-based arrival management at Amsterdam Airport Schiphol (AAS). In this research, a PMS route network is designed for two runway configurations at AAS. Using Mixed Integer Linear Programming (MILP) , an optimal PMS scheduling model has been designed. A case study is performed using current flight data to demonstrate the performance of the scheduling model and to find the potential advantages of implementing PMS-based arrival management at AAS.

Condensation trails, or contrails in short, are the white lines that can often be seen trailing high-altitude jet aircraft. Due to their interference with the local energy balance of the atmosphere they contribute to anthropogenic climate change. Research has shown strategies with great contrail mitigation potential at relatively small fuel and/or time cost in free flight. This thesis attempts to quantify contrail mitigation potential in practical and realistic scenario by introducing flight planning as tool for mitigation. A tool was developed that plans and simulates flights from the Netherlands to several destinations in North America. From the results it is clear that at least 50% of contrails can be mitigated at less than 2% additional fuel through flight planning. The results have confirmed the hypothesis that large shares of contrails can be mitigates against a few percent additional fuel consumption and flight time.

A Hybrid Optimal Control (HOC) framework which can be used in conjunction with existing optimal control software is presented. By using hybrid optimal control theory, trajectory optimization problems for systems that are both discrete time and continuous time from a mathematical perspective can be solved. A case study on multi-aircraft formation flying for civil aviation is performed which demonstrates the capabilities of the designed method. Whilst previous research has dealt with either high accuracy trajectories for small formation flying problems or low accuracy modelling of very large formation flying trajectories, in this thesis HOC is used to achieve a high accuracy optimal trajectory for formations of three aircraft and larger. Given the number of flights that are performed every day, the impact of saving fuel just by optimizing their trajectories using formation flying can be significant on a global scale, both environmentally and economically.

Loss of Control is the primary contributor to aviation fatalities. To prevent this type of accident, flight envelope protection is considered to be a necessary development. The calculation of the Safe Flight Envelope provides a bound on the states that can safely be approached by the aircraft. Although theoretically accurate, some states may not be reachable under the influence of disturbances (e.g. turbulence). In this thesis a stochastic extension to the reachability analysis is applied to a simplified aircraft model. The probabilistic reachability analysis yields the transition probability from a state to the target set. By comparing the deterministic and probabilistic Safe Flight Envelope, it becomes clear that the Safe Flight Envelope can shrink considerably under the influence of turbulence. It is shown that for a 3 sigma (99.7%) confidence interval, the envelope can shrink by as much as 50.8% compared to the deterministic envelope. Furthermore, it is found that for high roll angles, some parts of the deterministic envelope have a 0% transition probability under the influence of turbulence, further emphasizing the importance of probabilistic envelopes.

Missions to Near-Earth Objects (NEOs) are a growing trend in spaceflight. The interest for such bodies is not only justified by the threat that they represent for the Earth, but also because they are potential sources of extra-terrestrial materials, provide windows to the past of the Solar System and to planetary formation, and offer a good opportunity to demonstrate deep-space mission technologies. The Asteroid Impact Mission (AIM) is the next European Space Agency mission to a NEO, which has among its main objectives the deployment of the Asteroid Spectral Imaging Mission Cubesat (ASPECT) in the vicinity of the binary asteroid Didymos during the Payload Delivery Phase (PDP). However, this deployment is affected by the uncertainties and errors coming from the strongly perturbed environment, the previous phase dispersions, and the navigation and command inaccuracies. The objective of this thesis is therefore to assess the viability of the cubesat deployment in terms of the safety of the reference trajectories and the accuracy of the deployment conditions achieved. The reference trajectories that target the cubesat’s commissioning orbit injection point were designed by implementing a two-arc hyperbolic trajectory design problem in a direct-shooting optimisation architecture in the commercial heuristics optimisation software known as Mixed Integer Distributed Ant Colony Optimization (MIDACO). These trajectories were obtained in an ideal, unperturbed scenario. The safety and accuracy of the trajectories obtained were tested in a series of Monte Carlo campaigns in which the uncertainties, errors and dispersions present in the system were used as Monte Carlo variables in a high-level Guidance, Navigation and Control (GNC) simulator implemented in Simulink 2016b. After identifying the pericentre of the target Self-Stabilised Terminator Orbit (SSTO) as the best point to carry out the deployment, five different families of trajectories that bring the spacecraft within the safety deployment margins were found. The total velocity increment required for these trajectories were found to vary from 1.2 to 3.4 m/s. These relatively high velocity increments are caused by the combination of the constraints imposed in the design process that emanate from the mission operational requirements. The first Monte Carlo set of simulations showed that the optimum number of guidance correction manoeuvres is assessed to be three. The timing of these manoeuvres is case dependent, but one correction must take place in the first arc flown, and the other two during the second arc and before the deployment. Results of the second Monte Carlo campaign showed that the trajectories were unsafe according to the mission safety requirements on the minimum distance to the main asteroid and on the margin to the escape velocity. Of these two requirements, the escape velocity margin was the main cause of these safety violations, with several trajectories falling below the margin of 2 cm/s at certain epochs. This was due to the initial state vector dispersion coming from the previous phase and, to a lesser extent, due to the potential thrust failures. In the final Monte Carlo campaign, the ASPECT injection success rates reached a maximum value of 75% for the trajectory that targets the SSTO with a semi-major axis equal to 5 km. The cause of this low success rate was attributed to a combination of high initial position dispersions and velocity navigation errors. Due to this velocity navigation inaccuracies, the corrections made by the guidance subsystem are not capable of compensating the dispersions in initial position. These corrections further deteriorate the final deployment conditions, since their calculation only takes into account deviations in position, and not in velocity. The results of this study highlight the need of finding ways of reducing the position dispersion with respect to the nominal trajectory prior to the autonomous PDP. Furthermore, they indicate that the prediction of success rates can be improved by refining the navigation performance models, since these are considered conservative when compared to the real scenario.

Airports around the world continue to face issues related to the environmental impact of aviation. Mitigation measures have therefore been implemented at many of these airports. Attaining an optimal combination of these mitigation measures is a complex process and because of these complexities, this process does not always result in the most efficient solution in terms of environmental impact. It is expected that some of the inefficiencies of mitigation measures can be eliminated by using a process that is based on three main principles: to use mathematical optimisation in order to select the best mitigation options, to evaluate multiple performance areas simultaneously, and to evaluate multiple mitigation options at multiple levels of aggregation simultaneously. These three principles have therefore been implemented as capabilities in an integrated decision support system, to determine whether such a system could help improve the airport environmental management process. Based on the results obtained with the developed support system, the benefits resulting from each of the three capabilities are demonstrated. But ultimately, it is shown that especially the combination of these three capabilities, integrated into a single support system, contributes to improving the airport environmental management process.@en