Departure management at major airports relies on Target Off-Block Time (TOBT), a human-declared readiness estimate that is prone to inaccuracy. Sensor-derived predictions from computer-vision turnaround monitoring offer a complementary signal, but their higher update frequency and distinct error profile risk destabilising the departure sequence.

This study evaluates whether Predicted End of Ground handling Time (PEGT) predictions can improve departure sequencing at Amsterdam Airport Schiphol without sacrificing schedule stability. A characterisation of operational PEGT data shows that PEGT becomes more accurate than TOBT within approximately 27 minutes of departure, but produces nearly twice as many updates and exhibits pessimistic bias in the final minutes before off-block. These properties motivate the design of selective acceptance filters.

Using a reconstructed rule-based Departure Manager and counterfactual replay of 21,152 departures across 31 operating days (August 2024), 230 configurations of five conjunctive, interpretable acceptance filters were evaluated via Latin hypercube sampling. Results show that unrestricted PEGT adoption reduces vacated slots by 22.6% but increases late resequencing by 18.6%, confirming that improved accuracy alone does not guarantee operational improvement.

However, selective filtering, predominantly through suppression of frequent and late-stage updates, identifies a regime of 55 configurations (24% of those tested) that simultaneously improve all five metrics relative to the TOBT-only baseline: resequencing (-0.6%), late resequencing (-6.6%), vacated slots (-13.3%), TSAT delay (-1.6%), and on-time performance (+0.2%). These configurations improve both the TOBT-only and naive unrestricted-PEGT baselines on every tested metric, demonstrating that composite use of TOBT and selectively filtered PEGT can transcend the baseline stability–slot adherence trade-off.

The results are based on one month of nominal operations at Amsterdam Airport Schiphol; generalisation to disrupted conditions and other departure management architectures requires further investigation.

Driven by the need to enhance safety, improve efficiency and address labour shortages, autonomous vessel operations are increasingly moving beyond open-water navigation toward more complex missions demanding integration of multiple control strategies. Dock-to-dock operations represent a critical mission encompassing the full spectrum of motion control challenges: long-distance transit, switching between control phases, and precise low-speed maneuvering in confined waters under environmental disturbances.

This thesis evaluates control strategies for autonomous dock-to-dock sailing on inland waterways. A benchmark system using industry standard PID controllers is compared to an all-in-one Reinforcement Learning (RL) controller and a third, hybrid system is proposed trading off the improved performance of the RL controller with the inherent stability guarantees of the benchmark system. Simulation results show all three controllers can successfully perform the mission. The RL controller docks significantly faster while rejecting higher lateral wind forces but struggles to generalise to unseen docking scenarios, while the hybrid system improves interpretability at the cost of performance. Furthermore, initial real-life testing of the benchmark system validates the simulation results.

Advancements in deep reinforcement learning (RL) open the door to the development of robust flight control systems (FCS) that have the potential to improve both safety and performance during off-nominal flight conditions. Simulation-based work on offline-RL FCS has already demonstrated robustness to adverse weather conditions, mechanical failures, and a wide range of operational conditions. However, it has neglected important dynamical phenomena that limit its applicability to reality. In anticipation of a future flight testing campaign of similar RL-based FCS, this research emulates the transition from simulation to reality by modelling prevalent sensor and actuator dynamics, and introduces a method to incorporate a long short-term memory (LSTM) artificial neural network (ANN) into the policy of a Soft Actor-Critic (SAC) agent. The approach is found to largely diminish the sensitivity of the controller to sensor noise and actuator dynamics, while increasing its robustness to delays in comparison with the ubiquitous feed forward deep neural network (DNN) and a traditional linear controller.

This thesis is on the assessment of handling qualities for a robust controller, previously designed using H∞ techniques for a longitudinal model of the Bo-105. A piloted simulator flight test was conducted in which a set of tasks was flown. These tasks included configuration of stick sensitivity, pitch and altitude tracking tasks, flown at different flight conditions to show robustness, as well as a bob-up and acceleration-deceleration task. The resulting handling quality rating showed that the controller, designed for a theoretical rating of level 1, achieved between level 1 and level 2 handling qualities between the tasks. Non-controller factors, such as stick force, missing cueing and performance limits, were commented on by the pilots to have an effect on the rating. Anti-windup methods, clamping and back-calculation, were used to improve controller response, and reduce oscillations observed during testing.

Nonlinear Dynamic Inversion (NDI) control techniques provide a conceptually simple and modular control framework, making it an attractive technique for designing flight control laws with shorter design cycles. However, its lack of inherent robustness guarantees shifts the burden of the design from the synthesis to the analysis part. Conversely, H-infinity Loop-Shaping provides controllers with robust stability guarantees. This work proposes a novel framework leveraging the H-infinity Loop-Shaping Design Procedure to optimize a structured linear variant of Incremental Nonlinear Dynamic Inversion (INDI) control, a Hybrid IDI controller. The Hybrid IDI controller consists of a blend between classical model-based DI and sensor-based IDI. The proposed methodology is validated through the design of a pitch-rate controller for NASA's X-29 experimental aircraft. Results demonstrate that the approach achieves robustness guarantees comparable to standard full-order H-infinity controllers while maintaining the simplicity and modular architecture of NDI-like structures, thereby combining the advantages of both techniques.

Flared folding wing tips (FFWTs) improve aerodynamic efficiency but present aeroelastic challenges. This study develops an FFWT aeroelastic model using: (i) multibody constraint formulation for kinematics, (ii) non-linear static analysis for equilibrium under aerodynamic loading, and (iii) linearised dynamic analysis for time-dependent behaviour.

The multibody formulation defines the wing tip’s motion through hinge constraints, while the non-linear static analysis examines the effects of flare angles on equilibrium fold angles and reaction forces. The wing root bending moment (WRBM) decreases by 17% compared to a locked configuration but increases by 23.4% as the flare angle grows from 0o to 20o. For flare angles below 10o, the solver characteristics and initial equilibrium positions at lower velocities can lead to numerical issues such as zero-division errors and poorly conditioned matrices.

The linearised dynamic model, based on the static solution, is evaluated with different configurations: a locked hinge, a free hinge, and a locked-free hinge. Smaller flare angles allow higher fold angles but introduce minor anomalies in the inner wing tip’s response, while larger flare angles improve numerical stability yet cause more persistent oscillations. The locked-free case assesses hinge release during a gust encounter, where releasing the hinge at peak gust intensity leads to larger persistent oscillations. Artificial numerical diffusion and structural damping effectively reduce numerical noise in reaction moments, revealing underlying trends and improving stability.

Bird strike simulations require accurate information about the nonlinear material properties of the bird and structure, dynamic changes in contact between bird and structure, and large displacements and rotations of the structural elements. The soft-body nature of birds allows it to flow fluidly upon impact, making accurate simulation validation difficult. Setting up a model requires evaluating and considering a large number of parameters. To simplify the process of obtaining validated bird strike models, a set of guidelines for setting up the simulations, covering the major parameters that influence the validation of the models, is established. This was done for an EWVT-SPH (Element Weighted Voronoi Tessellation Smoothed Particle Hydrodynamics) bird model by following the building block approach laid out by the aerospace standards of SAE-G28 for both rigid and complaint target structures.

Wake losses in wind farms, caused by closely spaced turbines, reduce downstream power production and overall efficiency. These losses can be mitigated through wind farm flow control strategies, specifically wake steering, which involves yaw control to misalign turbines with incoming wind, and redirecting wakes to enhance overall power output. This thesis aims to develop a real-time, model-based controller that addresses operational and environmental challenges in large-scale wind farms. The controller adapts to off-design conditions such as changing atmospheric factors and offline turbines, making it more effective than traditional controllers based on pre-optimized lookup tables. The research modifies engineering wake models to account for heterogeneous wind conditions, improves the computational efficiency of the Serial-Refine optimization strategy for real-time use, and incorporates tuning of wake model parameters to ensure adaptability. Additionally, the impact of offline turbines on optimal yaw misalignment angles is examined. Results indicate that adapting wake models using sensor data improves power predictions in non-uniform wind environments, while the distributed optimization framework for real-time yaw adjustments performs comparably to centralized methods. Incorporating offline turbines into the control strategy further boosts power output, leading to increased revenue for the wind farm. This research advances wind farm flow control by developing control policies that are tailored to dynamic, real-world site conditions.

The aviation industry confronts challenges from increased air traffic and environmental concerns. Enhancing engine efficiency becomes crucial due to persistent greenhouse gas emissions from air travel, particularly in long flights with high CO2 emissions. Higher by-pass ratios (BPR) can address this by boosting engine propulsive efficiency, yet integrating modern large ultra-high bypass ratio (UHBR) engines onto the aircraft introduces complexities affecting structural weight and aerodynamics. To evaluate innovative engine-aircraft integration concepts, precise early-stage design and weight assessment methods are needed. Existing conceptual weight estimation methods for pylons lack the ability and precision to do so, as they mainly rely on statistical methods applied to previous generation engines which do not represent current trends. This thesis proposes a physics-based design approach for pylon structures using Knowledge-Based Engineering (KBE) principles, enhancing the evaluation of diverse engine-aircraft integration designs. The proposed methodology revolves around 4 key ingredients. First, the study introduces a completely new parameterization of the pylon structure involving the mathematical description of 'zero-thickness geometry' and structural elements, which leads to a total of 80 parameters to characterize the pylon structure.
This parameterization is implemented in a ParaPy Python application, featuring the 'PylonDesigner' superclass that controls the geometry generation process, performs a weight evaluation process, and contains dedicated attributes and functions for structural analysis and sizing optimization. Specialized classes are implemented to create the geometry of different pylon types using the ParaPy Geometry library. The generated pylon structural geometry is analyzed using the commercially available finite element code Abaqus. To enable a proper coupling between the ParaPy and Abaqus, an application programming interface (API) has been implemented. Using this API, the meshed pylon geometry is processed part-by-part, after which the full structure is assembled. Boundary conditions are then applied, and the analysis is defined including the loads. During the analysis, the pylon is subject to a total of 20 limit loads cases covering different maneuvers, thrust settings and gusts, and 4 ultimate load cases representing the critical fan-blade off event. The results from the structural analysis in Abaqus and a weight evaluation procedure using the geometry in ParaPy are used as inputs for a sizing optimization procedure making use of the Scipy Optimize Sequential Least Squares Programming (SLSQP) algorithm. The objective of this optimization is to minimize the structural weight of the pylon, while subject to constraints on the maximum allowable stress in each component. Validation utilizes two engine-aircraft integration cases: the pylon supporting LEAP-1B engine on Boeing B737-MAX and LEAP-1A engine on Airbus A320 neo. The method approximates pylon weight effectively when employing a 'FEM weight-to-realistic weight' conversion factor. In conclusion, this methodology holds potential in assessing UHBR turbofan engine design and weight penalties, primarily for wing-mounted engines using box-beam structures. Further development is required to address validation challenges and explore various pylon architectures, extending the model to fuselage-mounted struts, and integrating rotor dynamic simulations. Coupling with an engine model shows promise for evaluating variable engine design and integration parameters.

Over the last five decades, the majority of commercial aircraft consisted of the traditional tube-and-wing configuration. This traditional configuration is approaching a fuel efficiency asymptote. Besides that, with the increasing number of passengers and cargo transported by air every year, and environmental impact as an important factor in aircraft design, there is a necessity for a solution that is able to boost aircraft efficiency. Currently, the faculty of Aerospace Engineering at TU Delft is working on a promising aircraft configuration, namely the Flying-V. This is a specific type of flying wing that is tailless, V-shaped, and consists of two cylindrical pressurised cabins located in the leading edge of the wing. Wind tunnel experiments show that the aircraft is longitudinally statically stable up to an angle of attack of 20¶, after that pitch break occurs. Besides that, research performed on the aerodynamic coefficients obtained using the Vortex Lattice Method and results from the maiden flight test of a scale model of the aircraft conclude that the Dutch roll mode is unstable. Therefore, this research defines a set of key stability and handling quality requirements based on civil aviation authorities combined with military standards for cruise- and approach conditions. These key requirements are consequently assessed with a simulation model of the aircraft using aerodynamic coefficients obtained from the Vortex Lattice Method and wind tunnel experiments. In an attempt to make the key stability and handling quality characteristics of the TU Delft Flying-V adhere to the defined requirements, this thesis aims to contribute to this research field by designing a nonlinear Incremental Nonlinear Dynamic Inversion (INDI) flight control system that is applied to the simulation model of the aircraft. Finally, the performance of the aircraft using this flight control system is assessed and proposals for aerodynamic design changes and control layout design changes are given.