The APPU project aims to lower the threshold of installing hydrogen-driven and boundary-layer-ingesting propulsion systems in the short term. This is done by replacing the Auxiliary Power Unit with an Auxiliary Power & Propulsion Unit (APPU). To study these concepts, the Airbus A321neo is taken as a reference baseline. The implementation of the APPU system on the A321neo aircraft necessitates a redesign of the existing empennage. The research objective of this thesis project is to investigate how an optimal empennage design for an aircraft equipped with an APPU system differs from an optimal empennage design for an aircraft without such a system. Multidisciplinary design optimization is used to minimize fuel weight for different aircraft configurations by optimizing the empennage geometry. Four different disciplines are identified. A weight discipline estimates the empennage weight based on the empirical Raymer equations. Two separate aerodynamic disciplines are implemented with different fidelity levels. The low-fidelity version is based on AVL's vortex lattice method, expanded with a constant friction coefficient drag model to account for viscous effects. The high-fidelity version is based on FlightStream's panel method. FlightStream proved to be infeasible for use in this study due to long run times and limited mesh robustness. Therefore, only the low-fidelity version is used to generate the final results. The static stability and control of the aircraft are ensured through a set of constraints that require specific stability and control derivatives to remain within defined limits. These derivatives are provided by the stability and control discipline that is based on AVL as well. The performance discipline applies the Breguet equation to convert the aircraft weight and aerodynamic performance into an estimate of the fuel load required to complete the design range. ParaPy is used as a multi-model-generator to provide the required input geometries for the disciplines. The study reveals that optimal empennage designs for aircraft equipped with an APPU system differ notably from optimal empennage designs for conventional aircraft. Both configurations benefit from high aspect ratios and reduced tailplane areas. However, APPU-equipped aircraft require a low cruciform tail to accommodate the hydrogen tank and reduced sweep angles to position the aerodynamic center aft without intersecting the propulsor plane. The low-fidelity aerodynamics discipline is unable to optimize the airfoils. AVL’s modeling of only the camber line makes it unsuitable for optimizing symmetric profiles, such as those on vertical tailplanes. Furthermore, the horizontal tailplane airfoils showed limited variation from the initial design. This is likely due to the dual-parameter definition of the camber line, as the class-shape transformation parameterization method is applied independently to both the upper and lower surfaces of the airfoils.

A rotary-wing aircraft, or helicopter, is crucial for maritime operations due to its Vertical Take-Off and Landing (VTOL) capabilities, making it essential for small-deck Naval ships. Helicopters serve multiple roles, enhancing the Royal Netherlands Navy’s (RNLN) missions. However, the rise of Unmanned Aerial Vehicles (UAVs) in the past decade has introduced a cheaper, expendable, and more efficient alternative for certain operations, as UAVs require no pilot and can operate continuously. UAVs are ideal for tasks related to improving situational awareness, thereby increasing mission effectiveness, reducing pilot risk, and lowering fuel consumption. The RNLN aims to utilize UAVs alongside crewed helicopters on new ships to maximize operational efficiency. Both helicopters and UAVs follow three mission phases: launch, mission, and recovery, with the recovery phase being the most challenging. During the recovery phase, a crewed helicopter approaches the sailing vessel in a standardized way for which operational limits are established. These limits are essential for rough weather situations and are determined through many flight tests, which is a time-consuming, expensive and hazardous task. In addition, these operational limits only apply to a standardized approach manoeuvre. No approaches for UAVs are prescribed yet, making it difficult to determine such limits, but opens the possibility to standardize newly optimal UAV-ship approaches for which operational limits can be established. Moreover, the ship needs to manoeuvre to obtain optimal wind conditions for an approaching helicopter, which is not desired for UAVs. This means that UAVs should be able to approach and land on the ship in any wind condition. This study investigated the effect of wind on optimized UAV-ship approaches, which can be used to aid and standardize maritime UAV-ship approaches. For this objective, a trajectory optimization framework was created that combined helicopter dynamics and performance, a ship, three wind models of increasing levels of accuracy and a solver to find optimal wind-affected approaches. Trajectories were optimized to be smooth and fast because this is most important for operational purposes. Experiments involved several wind directions, wind speeds and helicopter starting positions, affected by three wind models. This work shows that the created framework functions properly and can be used in a variety of situations. The optimized trajectories were evaluated by the Longest Common SubSequence (LCSS) similarity measure to investigate the effect of both wind direction, speed and model fidelity. With an output between 0 and 1, the LCSS algorithm provided intuitive results and clear trends. A conclusion is that the helicopter wants to exploit the wind as much as possible. It diverts its path to acquire a higher ground speed at the start and a stronger headwind to decelerate at the end of the approach. When introducing a wind gradient, it was observed that the helicopter adjusted its vertical manoeuvre with the same objective. In some wind cases, constraint limits were reached or violated, marking preliminary operational limits. Results were also interpreted with a different perspective to find the best starting positions for the helicopter to approach the vessel in a certain wind condition. These results showed a variety of optimal starting locations, based on the total objective function value, total energy consumption and by distance normalized quantities. Wind model fidelity did not influence this outcome significantly, but it showed that it is important to estimate performance parameters with higher-fidelity models for increased wind speeds. This could also be seen from the downdraft behind the hangar; while the optimized approaches did not deviate geometrically, performance indicators such as the power required were affected significantly.

Reinforcement Learning applied to flight control has shown to have several benefits over classical, linear flight controllers, as it eliminates the need for gain scheduling and it could provide fault-tolerance. The application to civil aviation in practice, however, is non-existent as there are multiple safety concerns. This research demonstrates the evaluation of longitudinal Handling Qualities of the Soft Actor-Critic Deep Reinforcement Learning framework with the aim to translate the unpredictable black box of Reinforcement Learning into classical flight control terminology. The framework is applied to a pitch rate command system of a jet aircraft and shows robustness to off-nominal flight conditions, center of gravity shifts and biased sensor noise. Accurate tracking performance is achieved, while adhering to Level 1 longitudinal Handling Qualities for all conditions.

The increasing demand and complexity of air traffic management (ATM) systems necessitate significant advancements in automation to ensure safety and efficiency. Artificial intelligence (AI) and machine learning (ML) are emerging as promising solutions to manage this growing complexity, offering enhanced decision-making and predictive capabilities. However, the effectiveness of ML models in ATM heavily relies on the availability of extensive, high-quality data. In many cases, such data is scarce or incomplete, which presents a major challenge for training robust models. Synthetic data generation (SDG) represents a viable solution to address this data scarcity, enabling the creation of realistic, diverse datasets that can effectively fill data gaps and unlock the ML value proposition. The Terminal Maneuvering Area (TMA) is a crucial segment of airspace where aircraft perform complex maneuvers including rare go-arounds, and holding patterns. The TMA is characterized by high traffic density and diverse trajectory types, necessitating precise, granular data to model these dynamic scenarios accurately. The main research objective of this thesis project was to investigate the applicability of Generative Adversarial Networks (GANs) in generating synthetic 4-dimensional aircraft landing trajectories capable of capturing traffic patterns in this airspace. An unconditional recurrent-based network (TimeGAN) was applied and complemented with an investigation into the merit of conditioning to reap the rewards of transfer learning between classes. The resulting synthetic trajectories were evaluated in terms of data diversity, fidelity and usefulness. The main challenge identified during this research was the imbalance in data classes, which affected the models' ability to accurately capture data patterns, particularly in less frequent scenarios. Generating synthetic data based on separate groupings showed promise in addressing these imbalances, although this approach was sensitive to the number of clusters chosen. Smoothing filters frequently proved necessary to ensure realistic landings were devoid of unwanted noise. Overall, this work proves the capability of TimeGAN in generating diverse, realistic trajectories that are difficult to differentiate from real historical data.

Wind turbines play an increasingly imporant role in the energy production of our time. In order to optimize the performance of wind turbine blades, this thesis work aims at assessing the possibility of using panel methods for gradient based optimization of the aerodynamics of wind turbine blades. Specifically, the method employed has used Dirichlet boundary condition, a fixed wake for optimization and a free wake model for validation. The panel method developed has been validated against the MIRAS software and CFD results. The results of the optimization are compared against the Glauert optimum blade. The blade is parameterized using NACA profiles and the twist and chord are used as design
variables. Two optimizations have been performed: an unconstrained optimization, which has shown to take advantage of limitations of the panel method model; a second optimization is performed applying a thrust constraint and with tighter bounds on the design variables, which is capable of achieving realistic results. The main conclusion is that realistic blade designs can be achieved using a fixed wake panel method for aerodynamic optimization, although ultimately the performance of these designs should be assessed using
higher fidelity models.

In this paper, we introduce the Air Freight Flight Schedule Development Problem (AFFSDP). In this problem, a cargo-only carrier has to adjust an existing flight schedule based on changes in the predicted demand. The result of this research is a model that combines airport selection, fleet routing and cargo routing, together with the use of a (random) mandatory flight list and timetable setting for all other optional flights. The model is formulated as a Mixed Integer Linear Program (MILP). To reduce the pre-processing time of the model two meta-heuristics have been implemented that reduced the run time from several hours to minutes. To improve the performance of the model, all fractional numbers have been reshaped to integer numbers. The symmetry of the model was brought to a minimum by implementing a path-preference model. The model was tested with 3 aircraft types, for 3 different demand scenarios and 3 different freedoms. This sums up to 27 unique test cases. Overall, the results show that against a benchmark solution the total serviced demand could be increased by 20-50%.

Large-scale helicopters have unique characteristics of maneuverability and low-speed performance compared to fixed-wing aircraft. They can take off and land vertically, hover in place for extended periods of time, and move in all six directions, making them occupy important niches in both military and civil aviation. However, these advantages come at a cost: helicopters are inherently unstable with complicated dynamics, and generally more unsafe than commercial air travel. The fatality rate of non-military helicopters is about 1.44 per 100,000 ight hours [9]. This high number is partially explained by the more risky nature of helicopter missions, but is still considerably high compared to the fatality rate of commercial aviation in general.

L1 adaptive control is a relatively new technique that attempts to tackle the robustness shortcoming of the MRAC controller by applying a low pass filter to the control input. It can be used as an augmentation loop using baseline controllers such as backstepping or PID. This controller has been implemented as an augmentation loop with both these baseline controllers in three models: a quadcopter, a hexacopter and a Bolkow Bo-105 helicopter 3 degree of freedom model. It is then compared to the baseline where it's shown that this implementation does not provide any significant advantages over the baseline controllers.