As airports approach the limits of their runway systems, increasing capacity through infrastructure expansion is not always feasible due to economic, environmental, and operational constraints. Consequently, there is growing interest in operational measures that improve the use of existing infrastructure. Two such concepts are Time-Based Separation (TBS), which reduces the loss of arrival capacity due to headwinds by dynamically adjusting separations, and RECAT-EU-PWS, which refines wake turbulence separation minima through a more detailed pairwise categorisation scheme. However, no openly available macro-level analysis currently evaluate the capacity gains associated with these concepts across different airport environments and operating conditions.

This thesis investigates to what extent the implementation of TBS and RECAT-EU-PWS can increase airport peak runway capacity. To address this question, ARCAS (Airport Runway Capacity Assessment Software) was developed as a discrete-event simulation model in Python. The model builds on previous runway dependency logic and was implemented in a flexible and modular framework capable of representing multiple runway configurations, traffic mixes, weather conditions, and separation schemes. Embedded within a Monte Carlo approach, ARCAS estimates peak capacity, generates capacity envelopes, and evaluates the effects of TBS and RECAT-EU-PWS under a range of representative scenarios.

The model was applied to several Spanish airport case studies representing single mixed, parallel segregated, intersecting, and converging/diverging runway systems. Verification and validation showed that the model provides a credible macro-level representation of runway performance, with Monte Carlo estimates stabilising at around 500 runs and peak-capacity deviations of up to 2% in when compared to other runway capacity models.

The results show that runway capacity is limited by different dominant bottlenecks depending on runway configuration. In single mixed and parallel segregated runways, the main drivers are traffic composition and weather, with the proportion of heavy aircraft emerging as the most significant source of capacity loss and headwind also exerting a strong influence. In intersecting and converging runway systems, by contrast, the dominant constraints are runway geometry and runway-blocking constraints. TBS was found to be technically beneficial only above configuration dependent headwind thresholds, with meaningful gains appearing at approximately 12 kts for single mixed runways, 4 kt for parallel segregated systems, 15 kts for intersecting layouts, and 25 kts for converging layouts. RECAT-EU-PWS likewise showed scenario specific effects: in single mixed operations, gains were observed across the entire analysed range of heavy aircraft shares, while in parallel segregated, intersecting, and converging layouts, meaningful benefits emerged only above heavy-aircraft shares of approximately 11%, 5%, and 5%, respectively.

A limitation of this work is that the analysis is intentionally restricted to a macro-level, runway-centred assessment of TBS and RECAT-EU-PWS. The current framework adopts a fixed categorical implementation of the 20-category wake turbulence scheme, does not explicitly represent complementary concepts such as ROCAT, and considers meteorological effects mainly through headwind conditions in the application of TBS. In addition, the model evaluates runway throughput once aircraft are already established in the approach stream, without accounting for upstream sequencing concepts such as Point Merge. The case-study validation is also limited to several Spanish airports, which can constrain the generalisation of the results, particularly for the intersecting-runway case where the selected scenario is not fully representative of typical operations. Finally, although the datasets used are of generally high quality, some parameters, such as departure runway occupancy times and departure speed profiles, had to be derived from previous work rather than obtained directly from local operational data.

Aerodynamic stall is a leading cause of fatal accidents in aviation. This has introduced regulatory changes to pilot training, where aerodynamic stall models are utilized in full flight simulators for mandatory stall recognition and recovery training. In this research, two longitudinal aerodynamic stall models of the Cessna Citation II were compared side by side to assess the fidelity of two different flow separation modeling options. The first model contains one flow separation state for the entire wing, while the second model models wing and stall-strip effects as two separate states. In previous research, the second model type showed approximately 30% improvement in matching flight test data. Pilot-in-the-loop tests were conducted in the SIMONA Research Simulator, where a Cessna Citation II test pilot actively flew quasi-steady stalls with both models. The tests revealed that the two-state flow separation model had better stall onset characteristics, but it lacked in recovery performance compared to the one-flow-separation model, often leading to higher oscillations. In addition, the tests showed that for an aircraft with reversible controls, an integrated control column with control loading and buffet feedback is required to further improve the model fidelity. The test data analysis revealed that elevator effectiveness variation, buffet cues, and the flow separation hysteresis time constant were the main contributors to oscillations in stall onset and recovery characteristic of the two-state flow separation model. However, the direct effects of having two flow separation states instead of one could not be fully isolated due to pitch model difference between the two stall models. The findings of this research provide guidance for future stall model development aimed at improving the fidelity and training effectiveness of full flight simulators.

Currently, a high workload is experienced by executive controllers operating in Area Control Center (ACC) Sector 3 of Dutch airspace. Due to a lack of predictive information prior to departure, the planner controller is unable to manipulate regional outbound traffic with the aim of reducing the experienced workload of the executive controller. The main contribution of this paper is the introduction of a decision support tool (DST), that supports the planner controller in manipulating regional outbound flights. The DST incorporates an adapted version of the Luchtverkeersleiding Nederland (LVNL) Workload Model (WLM), which is currently used by ACC supervisors for long-term workload management for executive controllers, to compute workload scores that support the planner controller’s decisions on the timing of take-off clearances for regional outbound flights. Within this research, only the postponement of take-off clearances was considered, as departures can be delayed when necessary, whereas advancing a departure time is often operationally infeasible. An experiment was conducted involving one planner controller and five executive controllers to explore the effect of DST use by the planner controller on the workload experienced by the executive controllers. Overall, the use of the DST demonstrates potential to reduce the workload experienced by executive controllers; however, further adaptations to the WLM, such as incorporating aircraft performance characteristics into the workload calculation, may enhance the effectiveness of the DST for medium-term planning in multi-airport environments.

Liquid hydrogen (LH2) is a promising option for deep aviation decarbonisation and could be deployed at airports within the next decade. Compared with Jet-A1, LH2 refuelling may require larger safety and exclusion zones that reduce stand availability and constrain apron access. Operational studies often assume these zones, while safety studies rarely turn consequence-driven distances into enforceable apron constraints and quantify delay impacts. This work provides an integrated safety-to-operations assessment for LH2 refuelling at compact airports. The objective is to identify the main safety distance drivers for open- apron LH2 releases during refuelling through a qualitative safety assessment and quantify how zone size and enforcement, refuelling logistics, and resource capacity affect departure delays as LH2 penetration increases.
A qualitative hazard and accident-pathway assessment (HAZID/HAZOP supported by bow-tie logic) was conducted to identify the main LH2 refuelling hazards and accident path- ways, and to determine how these hazards drive conservative safety-distance requirements. The outcomes were then used to select representative scenarios and define zoning modes for the operational analysis. These modes are translated into operational rules (e.g., stand closures and route restrictions) and implemented in a stochastic discrete-event simulation of a regional airport apron with LH2-specific turnaround processes. Experiments vary penetra- tion rate, stand layouts, refuelling logistics and transfer duration (S1–S5), and LH2/Jet-A1 truck-fleet sizing. Performance is assessed via statistical comparison of departure-delay met- rics.
The safety assessment identifies dispersion-and-ignition outcomes (flash fire and jet fire) as the dominant safety distance drivers for open-apron LH2 releases and motivates practical distance scales that define the zoning modes. In the operational simulation, zoning has lit- tle effect at low LH2 adoption, but delays increase sharply once conservative zones remove neighbouring stand capacity and push the apron into a capacity-limited regime. For smaller zones, performance is driven mainly by refuelling logistics and refueller availability rather than by routing restrictions. Undersized service fleets primarily worsen the tail of very late departures, with the binding constraint shifting between the LH2 and Jet-A1 fleets depend- ing on demand. Overall, safe scale-up requires joint design of zoning rules and refuelling resources to prevent structural apron bottlenecks.

Modern flight control systems must not only achieve high performance but also ensure robustness against uncertainties and actuator limitations, particularly in experimental aircraft, where flight validation is a key objective. The PH-LAB, a modified Cessna Citation II operated by TU Delft as a flying laboratory, provides a unique opportunity to validate control design methodologies on a full-size business jet platform. While prior work by Marques et al. 2025 introduced a structured H_∞ pitch rate controller for the aircraft using a first-order actuator model, no robust controller has yet been designed and assessed using a realistic actuator model representative of the fly-by-wire system used in the actual aircraft, alongside digital implementation to enable future flight testing. This thesis presents a significant intermediate step toward flight validation by developing and validating three versions of a structured H_∞ pitch rate controller: a baseline fully continuous-time design, a modified continuous-time version retuned to account for discretization effects, and a fully discrete-time implementation. All controllers were synthesized within the Loop Shaping Design Procedure framework, using a MATLAB/Simulink model of the aircraft. The proposed design meets all robustness and performance objectives, achieving Level 1 handling qualities while maintaining stability under uncertainties and digital effects. Performance is demonstrated through linear and nonlinear time-domain simulations, gap metric analysis, and evaluation against time-domain criteria, including tracking accuracy. These results confirm that implementing a discretized pitch rate controller using H_∞ LSDP with a second-order actuator model guarantees robustness against uncertainties and satisfies the Level 1 handling qualities. Hence, the control architecture is mature enough to proceed to flight testing. Beyond verifying the controller itself, this work contributes toward validating the design methodology in the ultimate setting, flight testing on a full-scale experimental aircraft. Future work will include piloted evaluations, lateral-directional controller extensions, and flight envelope expansion via gain scheduling.

Hydrogen-fueled aircraft promise to achieve significantly reduced climate impact compared to their kerosene counterparts. However, while hydrogen has a significantly higher gravimetric density than kerosene, its volumetric density is much lower, meaning that even in a liquid state, it takes up approximately four times the volume. This results in significant challenges in integrating hydrogen storage in aircraft. It has been suggested that blended wing body (BWB) aircraft may provide a better-suited alternative for hydrogen integration compared to the conventional tube-and-wing (TAW) configuration.
Prior research has investigated hydrogen-fueled BWB aircraft, highlighting different potential tank integration strategies. To the author's knowledge, no direct comparison of these hydrogen configurations highlights their relative performance impact compared to a consistent kerosene baseline aircraft. Therefore, this work compares different hydrogen tank configurations identified in prior research to a kerosene BWB baseline under consistent top-level aircraft requirements (TLARs), including a design range of 2500 nmi and 239 passengers. Technology assumptions for a 2050 entry into service (EIS) are applied. The analysis is complemented through the development of hydrogen and kerosene TAW configurations that fulfill the same TLARs.
The comparison is performed in three steps. First, a BWB is designed for each tank configuration using conceptual design methods. Second, the four tank layouts are compared and the layout is identified that imposes the lowest energy penalty on the BWB. Third, the integration penalty of the hydrogen BWB is compared to that of the hydrogen TAW.
A BWB employing a combined tank configuration, with hydrogen tanks located beside and aft of the passenger cabin, is found to experience the lowest integration penalty. The BWB experiences a 12.5% penalty in block energy for the design mission, compared to a penalty of 10.5% experienced by the TAW. This indicates that the BWB is less well suited to hydrogen integration than the TAW under the specified TLARs. In addition, sensitivity studies are conducted to evaluate the impact of integration assumptions on the BWB. These show that reasonable variations in the assumptions do not change the conclusion of this study. The findings do not eliminate hydrogen-fueled BWB aircraft as a viable alternative to hydrogen-fueled TAW designs. In fact, literature shows that BWB configurations still offer an inherent efficiency advantage, although their higher integration penalty must be considered in the overall trade-off.

This thesis presents the design, development, and experimental validation of a perching drone equipped with an underactuated robotic gripper and tactile sensing for grasping onto structures. Perching extends drone endurance for applications such as long-term monitoring, while tactile sensing enables precise alignment when visual data is unreliable. A control strategy combining position-based control with tactile feedback is implemented using DIGIT tactile sensors for contact-aware adjustments. Two per-pixel inference models convert RGB images into tactile information: a sensitive contact model for binary contact detection and a depth reconstruction model that estimates surface normals, which is then used to determine the contact surface orientation. After outlier filtering, the depth model achieves a mean absolute error of 5.32° in orientation estimation. Experiments demonstrate reliable grasping with up to 12 cm of position error and successful correction of both position and orientation using simulated tactile input. These results highlight the potential of tactile-based strategies for robust aerial manipulation in uncertain environments.

Effective scheduling is challenging in Component Maintenance, Repair, and Overhaul (CMRO) operations due to the complexity of dynamically allocating resources across multiple jobs with varying priorities and technical constraints. Current industry practices typically rely on static, manual scheduling, resulting in suboptimal resource allocation and insufficient adaptability to operational disruptions. Most existing studies approach specific job shop problems by incorporating individual features, such as job prioritization or resource constraints, without considering the combined operational complexities of CMRO shops. Therefore, this research presents a scheduling model using the Flexible Job Shop Scheduling Problem (FJSSP), tailored to dynamic CMRO environments. The model uses Mixed Integer Linear Programming (MILP) to simultaneously schedule technicians and machines while accounting for skill requirements, resource constraints, and job prioritization. The approach balances multiple objectives, including tardiness and earliness, to enhance shop performance metrics such as Turnaround Time (TAT) and On Time Delivery (OTD) rates. Outcomes from a case study applied to real-world data from CMRO shops demonstrate significant operational improvements, achieving a reduction in TAT of up to 34% and an improvement in OTD by approximately 23% relative to historical shop performance. Furthermore, the model incorporates schedule robustness measures, minimizing deviations from planned schedules, despite operational uncertainties. Additionally, comparative analysis with a traditional heuristic dispatching rule model confirms the superior performance of the proposed optimization framework. This framework can be broadly applied to improve scheduling efficiency and stability in CMRO shops and similar workshop environments.

This thesis presents a novel framework for solving the Multi-Skill Resource-Constrained Multi-Modal Project Scheduling Problem with maximum time lags, addressing the challenges of scalability, deadline adherence, and uncertainty in job durations. The research is conducted through a case study with the maintenance department of a large European airline, using real-life maintenance scheduling data. To improve scalability, the framework integrates batching techniques that segment the scheduling horizon and a priority-rule-based heuristic to hot start the solver, significantly reducing computational runtimes for large problem instances. A range of objective functions, including single and multi-objective formulations, are explored to evaluate their impact on scheduling performance. The results demonstrate
that multi-objective formulations provide the best balance between throughput and deadline adherence and consistently outperform a priority-based heuristic. A clear trade-off is observed between optimizing for maximum tardiness and average tardiness, where minimizing maximum tardiness improves deadline adherence at the cost of lower throughput, while minimizing average tardiness has a more consistent throughput but allows slightly more deadline misses. To address job duration uncertainty, adaptive buffering strategies based on historical job performance are introduced and shown to outperform static buffers by tailoring slack times to individual job characteristics. In the examined case study, the combination of an adaptive buffering strategy with a multi-objective function combining makespan and
average weighted tardiness offers the most effective trade-off between robustness and efficiency. Overall, the framework proves to be scalable, adaptable, and well-suited to real-world scheduling environments with high variability and complex constraints.

As with many aspects of modern life, wind nowcasting and forecasting are integral parts of aviation and Air Traffic Management (ATM). This study investigates the use of a Denoising Diffusion Probabilistic Model (DDPM) for nowcasting (inpainting) and forecasting (image-to-video) of wind fields using aircraft-derived meteorological data. The DDPM, implemented with a U-Net backbone, demonstrated strong performance in nowcasting tasks, outperforming previous models such as the Lagrangian transportation-based Meteo-Particle (MP) model and
Physically Inspired Neural Network (PINN) approach with a 29% improvement in magnitude error and a 62% reduction in directional error. The nowcasting model achieved a magnitude
error of 2.03 m/s and a directional error of 4.2°, based on 190 test samples from late 2024. A key contribution lies in the DDPMs ability to produce more consistent and lower-variance predictions than prior methods. The RMSE improved on the PINN results by 29%, to 3.99 m/s. Despite these successes, forecasting proved significantly more challenging, with no meaningful results achieved. The study used ECMWF CERRA reanalysis data for training
and evaluated model performance with simulated aircraft tracks on known wind fields and with real aircraft-derived data from the The Royal Netherlands Meteorological Institute (KNMI)’s EMADDC dataset split into model input and validation subsets. High computational demands restricted testing capabilities, and uncertainty quantification and severe weather conditions remain challenging for the model.

The Europe-Asia-Oceania air route is experiencing rapid growth, traditionally served by direct legacy flights but increasingly dominated by hub-based carriers. These airlines leverage large, single-hub models to capture transfer traffic. However, limited research exists on how to efficiently design and operate multi-hub networks for international connectivity. Existing models either oversimplify hub capacities or focus solely on fleet planning and re-timing, lacking an integrated, schedule-based approach. This research develops an integrated decision support model that combines a capacitated multi-allocation p-hub location problem with airline schedule design under operational constraints and competitive dynamics. A multi-step iterative method connects hub assignment and scheduling using a genetic algorithm to ensure feasibility, connectivity, and profitability. The findings reveal that adding hubs initially boosts network efficiency and profitability, though marginal benefits diminish after a certain number of hubs. The integrated model significantly narrows the gap between theoretical and actualized profitability, showing a 7.6\% increase in daily profit for the scheduling model over iterations. This research offers a crucial decision-support tool for long-term airline network planning, particularly in rapidly expanding aviation markets. Its ability to jointly optimize hub locations and flight schedules under operational constraints provides substantial utility for diverse airline network planning scenarios, leading to more viable and profitable network configurations.

Urban air mobility (UAM) is an emerging innovation and research area and is one of the key pillars enabling
future vertical mobility. UAM specifically focuses on air travel within and around urban areas. In recent years, more studies have been performed on the acceptability and performance of UAM vehicles. However, a research gap is present on the dynamic rollover tendencies for UAM lift plus cruise vehicles, a type of Vertical Take-Off and Landing (VTOL) multirotor vehicle. Dynamic rollovers are known to be a risk for helicopters, another type of VTOL. A dynamic rollover occurs when the center of gravity of an aircraft rotates around a pivot point. The pivot point is the contact point of the surface and the aircraft’s landing gear. This causes the aircraft to roll and eventually crash. There are multiple situations in which a rotary aircraft can roll over, such as sloped landings or due to strong crosswinds.

This thesis research aims to investigate the susceptibility of dynamic rollover for a lift plus cruise vehicle during landing through computational simulations. Investigating this susceptibility is achieved by identifying the flight conditions that influence the dynamic rollover behaviour of the vehicle and testing design alterations to the lift plus cruise model. To model the rollover susceptibility, a base scenario is introduced in which an eight-rotor lift plus cruise vehicle makes a vertical landing of eight meters and reaches a landing surface. During this descent, the vehicle encounters a wind gust. The model was developed using the equations of motion with five degrees of freedom, which include: roll, pitch, surge, sway and heave.

From this research it can be concluded that the wind gust influences the rollover susceptibility of the vehicle. The stronger the wind gust, the more prone the vehicle is to roll over and the more time the wind gust has to develop, the less chance the vehicle has to roll over. In comparison to the helicopter, the multirotor is more stable and less prone to rolling over. Design alterations have been applied to the model to investigate whether improvements are possible in the design of the lift plus cruise vehicle to decrease its tendency to roll over. It was found that a smaller disc loading is beneficial, as well as a smaller lateral distance between the rotors. To reduce the wingspan of the vehicle and therefore, reduce the angle at which the rotor can touch the ground. When investigating a reduction in the number of rotors, in all cases, a reduction would not benefit the susceptibility to rollover in a positive sense. The influence of the landing gear structure is small, therefore, the development of a new type of landing gear does not have a priority.

In conclusion, this thesis explores the factors influencing rollover susceptibility in a UAM multirotor vehicle. By systematically analysing the dynamics of vehicle behaviour under various conditions, key insights into rollovers were uncovered.

The introduction of the shipping container revolutionised global trade by significantly reducing handling costs, improving efficiency and enabling intermodal transportation. This development paved the way for the expansion of international trade and the development of highly interconnected global supply chains. Congestion, sustainability concerns, and vulnerability to disruption have become major obstacles. Recent examples of such obstacles are the COVID-19 pandemic and the blockage of the Suez Canal. Synchromodality has been identified as a potential solution for mitigating some of these concerns.

As a relatively new concept, synchromodality has mainly been studied at a theoretical level, focusing on its definition and potential. As the concept of synchromodality seems to gain attention from a broader public, more recent research has also focused on the more quantitative side. These quantitative studies primarily model the transport planning side of synchromodality. In these studies, some aspects in the supply chain have been overlooked so far, mainly the impact on critical infrastructure such as container terminals.

This research addresses this overlooked area in the existing academic landscape. Aspects such as the loading, unloading and stacking of containers are explicitly modelled in combination with synchromodal transport planning optimisation. This allows for an assessment of how synchromodality influences container terminal operations and how constraints and the dynamics at container terminals influence the transport planning. A multi-agent system approach is used as a framework for this model, as this presents a good option to model the different stakeholders involved in synchromodal transport.

The performance was analysed based on key performance metrics, including container relocation frequency, dwell times, and cost efficiency. The findings indicate that the integration of synchromodal transport planning with container terminal operations yields significant improvements. An iterative feedback loop between the transport planning agent and terminal agents facilitates more effective decision-making, leading to feasible transport planning, smoother operations, and improved resource utilisation.

Scenario analysis yielded further interesting results in terms of how a synchromodal planner would adapt to disruptions. The two most interesting findings are a decrease in the number of transshipments and a modal shift towards faster, more flexible, but also more expensive and more polluting transport modes.

In conclusion, this research demonstrates that the integration of synchromodal transport planning and container terminal operations improves the efficiency and adaptability of synchromodal logistics networks. Through these advances, this research contributes to ongoing efforts in the planning of synchromodal transport and the optimisation of container terminals, offering valuable information for both academia and industry.

Returnable Transport Items (RTIs) play a critical role in sustainable logistics by enabling asset reuse across supply chains. However, damaged RTIs must be efficiently transported to repair centers and reintegrated into the network to maintain service quality and cost-efficiency. This thesis addresses the challenge of optimizing such repair-integrated transport flows within RTI pooling systems, using Container Centralen as a real-world case study. An Integer Linear Programming (ILP) model is developed to jointly determine the optimal allocation of broken RTIs to repair centers and the subsequent redistribution of repaired items to depots, under constraints such as repair capacity, transport time, and demand fulfillment. The model is implemented in Python using Gurobi and embedded in a user-friendly dashboard for decision support. Through simulation experiments based on historical data and realistic scenarios, the integrated approach demonstrates significant improvements over heuristic strategies, including reduced transport distances, better repair center utilization, and improved service reliability. The findings highlight the practical and theoretical value of integrated logistics modeling for RTI networks and propose several avenues for future research, including the extension to multiple RTI types, incorporation of uncertainty, and scalability enhancements. This work contributes to both operations research literature and practical decision-making in circular supply chain management.

Airside service roads are essential for the movement of ground support vehicles that enable efficient aircraft turnaround and overall airport operations. Despite their importance, no standardised method exists for evaluating their capacity and performance, limiting the ability of airports to optimise this operational infrastructure. However, the heterogeneity of service road traffic — characterised by vehicles with varying speeds, sizes, and manoeuvrability — complicates capacity estimation and Level of Service (LoS) assessment. This research shows that a combination of microscopic traffic simulation and Multiple Linear Regression (MLR) provides a robust framework for estimating Passenger Car Unit (PCU) values and evaluating service road performance. Using five common airside road configurations, the method estimates PCU values for eleven service vehicle types and establishes LoS boundaries expressed in PCU per hour. The results reveal that PCU values vary significantly across intersection types, with four-way priority intersections exhibiting the highest sensitivity to traffic heterogeneity. Roundabouts are found to facilitate smoother traffic flow. Two three-way intersection types provide similar PCU values, but traditional priority rules facilitate substantially better performance. The results of the component-level models are upscaled to a service road network model, presenting a method for describing network performance. These findings provide a standardised method for assessing airside service roads, enabling airports to improve infrastructure planning and mitigate congestion amid rising aviation demands.

Airport surface movement operations present a promising opportunity for enhancing airport sustainability, as up to 25% of airport emissions originate from taxiing aircraft. This study explores the use of Electric Towing Vehicles (ETVs) to tow aircraft to runways, eliminating the operational use of aircraft engines during taxiing. Given the high cost of ETVs, their effective deployment is essential. This research adopts a Multi-Agent Pickup and Delivery (MAPD) framework to optimize ETV operations, addressing both the task assignment and path planning aspects of this problem. A novel task assignment method based on Ant Colony Optimization (ACO) is proposed, capable of efficiently scheduling towing and charging tasks for ETVs. The proposed method outperforms benchmark Mixed Integer Linear Programming (MILP) methods in terms of runtime and scalability, while delivering a competitive solution quality. The ACO-based task assignment method is integrated with a state-of-the-art motion planning algorithm based on Priority Based Search (PBS) and Safe Interval Motion Planning (SIMP). The resulting MAPD solution method is evaluated through its application in a high-fidelity simulation of Amsterdam Airport Schiphol (AAS). The simulation results indicate that deploying 22 ETVs at AAS can achieve a 57% reduction in the annual addressable CO2 emissions, while ensuring the economic feasibility of the operational concept.

Offshore wind turbines (OWTs) play a critical role in renewable energy, requiring efficient methods to predict fatigue loads on their support structures under harsh environmental conditions. Traditional fatigue assessment methods are effective but costly and impractical at scale. Surrogate models (SMs) offer a cost-effective alternative, but site-specific SMs often fail to generalize across turbines in varying environments and locations. This study explores whether platform-generic SMs trained on a Simulation Database can serve as reliable replacements for site-specific SMs trained on Supervisory Control and Data Acquisition (SCADA) and Structural Health Monitoring (SHM) data for predicting fatigue loads in OWT support structures. The SMs, developed using both deterministic and Bayesian neural networks (NNs), were evaluated in three progressively complex model setups featuring SCADA signals, nacelle accelerations, and tower top strain gauges. Results showed that site-specific SMs generally achieved lower mean average percentage error (MAPE), with deterministic NNs at 47.1m lowest astronomical tide (LAT) in the fore-aft (FA) direction reducing errors from 16.8% to 7.2% and in the side-side (SS) direction from 27.0% to 4.5% as additional signals were introduced. Platform-generic SMs exhibited higher MAPEs due to the broader scope of the model and slight mismatches between simulated and real turbine dynamics, especially in the FA direction. Nonetheless, in the SS direction at 47.1m LAT, platform-generic SMs showed promising performance, achieving errors as low as 7.5% in one configuration. Although Bayesian NNs did not consistently lower errors compared with deterministic approaches, they provided valuable insights into how far test data deviated from the training distribution, helping identify the potential limits of the model. This capability has significant practical implications, as it can potentially serve as a tool for detecting sensor malfunctions or identifying irregular data, ensuring more reliable predictions in real-world applications. Overall, platform-generic SMs still require refinement before they can fully replace site-specific SMs, and Bayesian NNs should not replace deterministic NNs but rather serve as a valuable supplement.

Airports play a significant role in economic development while presenting considerable energy consumption with demands from various stakeholders. Their important environmental impact can be reduced through an optimised energy system design. In this study, a systematic framework for the generation and comparison of airport energy systems is developed. Mixed integer linear programming and multi-objective optimization are employed to generate different system configurations, which are then compared using multi-criteria analysis. The airport energy demands are estimated based on a simple model for the building and the airfield handling area, using a limited set of parameters available for any airport. First results for Amsterdam Schiphol and five other European hub airports indicate that fully renewable energy systems can be competitive, especially when integrating battery storage for daily energy management. Self-sufficiencies of around 80% can be economically viable, relying on energy systems employing photovoltaic cells, a CO2 network, batteries and grid electricity.

Since the 1970s, helicopters have been vital in disaster response but face limitations in cost, infrastructure, and maneuverability. This thesis presents the design and optimization of an emergency response flyer tailored for the GoAERO competition. Multirotor configurations with hybrid-electric propulsion are investigated to overcome the limitations of existing fully electric VTOL technologies.

A Multidisciplinary Design Optimization (MDO) framework is applied to hybrid-electric quadrotor, hexarotor, and octorotor configurations, optimizing the balance between rotor count, mass, and performance. The objective is to minimize Maximum Takeoff Mass (MTOM) while enhancing dynamic performance and maximizing the payload-to-system mass ratio. This study addresses a key research gap by integrating early-stage handling qualities (HQ) evaluation and exploring trade-offs between optimized rotor count configurations. Aerodynamic forces and energy consumption are estimated using momentum theory, while system dynamics are analyzed through Newton-Euler equations and eigenvalue assessments.

Results show that the hexarotor configuration achieves the fastest convergence, balancing design complexity and design space exploring. Configurations maintain a disk loading between 43–63[kg/m2] with hover efficiency between 5 and 5.8. A higher rotor count improves hover efficiency and disk loading, making performance comparable to eHang and Vahana while achieving nearly twice the cruise speed, rivaling helicopters.

At a hybridization factor (HF) of 0.1, MTOM is reduced by 75%, with only a 3% mass variation between configurations. However, at higher HF, mass increases by 20% due to relatively low energy density of batteries further impacting structural support demands, underscoring the need for a more detailed support structure model. Stability analysis confirms neutral hover stability, while a real, negative eigenvalue at cruise identifies the surge subsidence mode, governed by system mass.

Rotor count significantly impacts design flexibility. Hexacopters and octocopters offer better disk loading homogeneity, whereas quadrotors face constrained rotor sizing and elevated blade and disk loading, limiting efficiency.
Quadrotors excel in payload-to-mass ratio and simplicity, making them ideal for productivity missions but less suited for maneuvering-intensive tasks with lower disk loading and control authority, highlighting the importance of early HQ considerations.

This thesis makes a valuable contribution to the advancement of eVTOL research by addressing early-stage HQ assessments where on the basis of other literature and configuration optimization, the hexacopter emerges as the most viable for the GoAERO competition, striking the best balance between mass, handling qualities, and mission performance.