Aircraft maintenance planning plays a large role in ensuring operational efficiency and safety while minimising costs. Hangar maintenance scheduling can be trivial due to various uncertainties, such as non-routine tasks, resource availability, and unforeseen delays. Deterministic methods might struggle to account for these complexities and do not scale well with large, heterogeneous fleets, causing frequent and costly adjustments to the schedule. Previous research has focused on incorporating different uncertainties using robust scheduling methods. This research aims to develop and assess a stochastic and scalable aircraft hangar maintenance planning model that can provide insight in the robustness of the planning, next to incorporated uncertainties, to reduce the need for frequent planning revisions. The proposed method creates a schedule using a Genetic Algorithm (GA) that minimises maintenance costs and interval losses while adhering to operational constraints. After that, a Monte Carlo simulation is applied to assess the feasibility of the schedule under randomly generated check duration scenarios. Critical checks that can cause grounding of aircraft due to exceeded due dates are modified in a feedback loop to improve the robustness of the schedule. The maintenance optimisation is tested in a case study, provided by a European airline and discusses the trade-off between maintenance cost, interval loss, run time, and feasibility in hangar maintenance planning under uncertainty. The schedule is compared to a Mixed-Integer Linear Programming (MILP) benchmark model. Results show that the MILP outperforms the MILP in terms of cost and run time, but the GA might be useful in more complex scenarios. The simulations give insight in the robustness of the planning and show that delay propagation and grounding probabilities can be decreased by adjusting critical checks during re-optimisation. The overall grounding probability can go down by 9 to 40%, with 5 to 10% of checks fixed in time, respectively. This can lead to a more robust schedule, minimising revisions. An airline can use this framework as a decision-support tool to create variations on the planning and assess the impact of its decisions on the robustness of the schedule.

The aviation industry continues to grow at a steady annual rate of approximately 4.4%, intensifying global environmental concerns in light of international climate goals. In response, airlines are under increasing pressure to adopt sustainable innovations, with electrified aviation emerging as a promising pathway. One of the main challenges in electrified aviation is planning profitable flight schedules despite long turnaround times for battery recharging. To address this, a Flight Scheduling and Electrified Aircraft Routing (FSEAR) model was developed, advancing beyond models assuming full recharging or battery swaps. It integrates partial recharging through a recursive three-dimensional time-space-energy dynamic programming framework, combining multi-label dominance on profit and energy with a CO2 tax penalty for climate optimization. Based on the KLM Cityhopper network, three case studies with varying demand and distance profiles were developed. Results show that partial recharging increases profit by 22.5% to 27.8% compared to limiting operations to full recharging constraints. Emission reductions of 45.1% and 48.9% were achieved in close-range cases, while the long-range case showed a modest increase of 4.66%, reflecting a trade-off for enabling more profitable
operations with higher flight frequency and greater demand coverage. A consistent reduction in fleet size and a shift to fully all-electric compositions were also observed. This study demonstrates that partial recharging significantly enhances both the operational efficiency and environmental performance of electrified aviation, supporting lower-emission fleet compositions and enabling a more sustainable, cost-effective alternative to regional air transport.

Long haul flights account for over half of yearly aviation carbon emissions, while only taking up 6% of the total flights. To reduce the emissions of those flights, hydrogen combustion blended wing body (BWB) aircraft are a promising new technology. A conceptual design tool for hydrogen BWB aircraft is created to evaluate the effects of the cabin design and hydrogen tank placement on the aircraft level performance of the concepts. It is found that for a 350 passenger BWB concept a narrow cabin with 45% of the fuel placed next to the cabin results in the lowest fuel burn. For a 250 passenger BWB concept a narrow cabin with 40% of the fuel next to the cabin offers the lowest fuel burn.  The spread in performance is lower for the 250 passenger concepts than for the 350 passenger concepts, indicating the internal layout has a larger effect on the performance of the 350 passenger concepts. Optimising cruise conditions resulted in high optimal altitudes due to the low wing loading of LH2 BWBs, however limitations of the model with respect to the cruise altitude reduce the certainty of conclusions drawn. Conclusions drawn with respect to the sensitivity of the performance to design parameters are limited by uncertainty in the wave drag estimation of the concepts.

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.

Several studies have demonstrated that an integrated approach to the airline schedule recovery problem, optimising multiple facets simultaneously rather than using traditional sequential methods, yields improved solution quality; however, at the cost of model simplification, with most studies focusing solely on cost minimisation. This paper introduces a multi-objective Benders decomposition approach to a detailed integrated aircraft and crew recovery model that considers a heterogeneous fleet, individual crew members, and basic passenger considerations, thereby addressing both model simplifications and lack of multi-objective considerations. A lexicographic multi-objective optimisation scheme is integrated into Benders decomposition, allowing for the optimisation of multiple distinct objectives: minimising cancellations, minimising recovery costs, maximising on-time performance, and minimising changes made. Computational tests were conducted using real schedule data provided by Transavia, with results showing high-quality solutions within the model’s defined assumptions. However, Benders decomposition alone was insufficient to be applicable within live disruption management, with performance times ranging from 5 to 30 minutes depending on the scenario’s complexity. The model’s design and ability to consider crew on an individual level show strong promise in solution quality and applicability to the industry, as crew are often considered at both the pairing and individual levels. This provides a strong foundation for practical disruption management support tools if deficiencies in time performance can be addressed.

With the rapid growing aviation industry and the high amount of produced greenhouse gases, the search in more sustainable propulsion methods continues. A possible solution is the use of hydrogen fuel cells, significantly reducing the production of NOx and CO2. Although several studies are being performed for the performance and applications of the fuel cell, for which the Airbus ZEROe program is one of them, the additional air supply system lacks research. Due to the specific operating conditions of the fuel cell and the large difference between ambient conditions, the air supply system should be analysed with care as it can require a lot of energy over an entire flight. This thesis focuses on developing a model capable of analysing the performance of the air supply system, using the Airbus ZEROe fuel cell as a reference. Furthermore, different architectures have been compared in order to analyse the effect of different components. A model has been developed capable of sizing and analysing the performance for different air supply architectures. The compressor, turbine, and heat exchangers have beenmodelled such that they provide a physical representation of what is happening inside the air supply system. Furthermore, as the model requires geometrical parameters as an input, it can be easily adapted to optimise these components for different design conditions. Therefore, the model has been used to optimise four different architectures, consisting of a variation between single or double stage compressors and the inclusion of a recuperator. The optimisation has been performed for several design conditions, analysing not only the effect for different flight conditions but also the altitude, fuel cell pressure, and air mass flow. For the optimisation, it has been found that the architecture resulting in the lowest power required has a single stage compressor and a recuperator, having a power required of 77.5 kW for cruise. However, the addition of the recuperator adds a significant amount of mass, as the total air supply system mass is estimated around 315 kg. Compared to an architecture without the recuperator (168 kg) this is a significant difference. However, the power required for this architecture is significantly higher, with a value of 100 kW. Overall it was found that although adding mass, the recuperator significantly reduces the power required. Next to the recuperator, the addition of a second stage compressor has been analysed as well. Although adding mass, it has been seen that for the higher mass flow and pressure ratio cases, such as top of climb, the double stage compressor performs better than the single stage compressor, although this difference is not as large as for the addition of the recuperator. Apart from the optimisation, the off-design performance has been analysed as well for the four different architectures, taking the cruise-, takeoffand top of climb optimised architectures as a base. It was found that the off-design performance for the different architectures is mostly limited by the choice in design point. The main component that was affected the most by this is the turbine, as a turbine bypass valve is required. The turbine bypass valve regulates the mass flow passing through the turbine, controlling the expansion ratio for a fixed shaft speed. It has been observed that the valve is required for architectures that are optimised for high pressure ratios and low mass flows, as the valve was seen opened for lower pressure ratios and higher mass flows. Although the bypass valve is required, it was observed that the performance of these architectures was always better than an architecture that did not require the turbine bypass valve, such as those optimised for takeoff. Additionally, limiting operating conditions have been identified through off-design analysis. The most critical case for sizing the heat exchangers is the hot day scenario at the top of climb. Due to the high pressure ratio and the resulting high compressor outlet temperature, adequate cooling is required to achieve the necessary fuel cell temperature. It was found that, for other optimised points, architectures with a recuperator face more challenges in this regard compared to those without a recuperator, mainly because of their smaller liquid heat exchangers. Other identified limiting conditions include low mass flow operating points for the compressor, lower-than-design expansion ratios, and higher mass flows for the turbine. These conditions necessitate the addition of bypass valves to ensure proper operation.

With the continuous growth of the aviation sector, concerns regarding the effects of aircraft noise on the health and well-being of communities living in the vicinity of airports have been increasing. Aircraft noise annoyance is inherently subjective and its accurate prediction and quantification represent challenging tasks. There is a lack of consensus in the scientific community regarding which metrics are the best predictors for this type of annoyance. Additionally, many of the metrics employed in the field of (psycho)acoustics are typically computationally expensive. This study aims at developing a methodology which leverages machine learning techniques for instant predictions of various sound metrics and for annoyance rating predictions from input aircraft flyover recordings. The two-step framework involves a Convolutional Neural Network (CNN) for the former, followed by artificial intelligence (AI) models which use the CNN predictions as input for the latter, such as the Support Vector Machine and Random Forest. A listening experiment was conducted in order to gather labeled annoyance data from 60 aircraft flyover recordings of both landings and take-offs and a correlation analysis was subsequently made considering a large pool of sound metrics. The results show that, in general, metrics derived from Psychoacoustic Annoyance models, especially those of Zwicker and Di et al., present better performance as predictors compared to conventional metrics and most Sound Quality Metrics taken individually. Moreover, the AI framework achieves very promising results for both the annoyance ratings and metric predictions (overall, mean absolute errors of the annoyance ratings of approximately 0.4 and below, and R2 values above 0.85), highlighting the potential for bypassing the typically long overhead associated with computing SQMs (which involve expensive and complex algorithms). The drawn conclusions are dependent on the limited amount of data that was available. Hence, the results require further validation using more recordings of various types of aircraft.

Double-walled vessels are recognized as an effective storage solution for liquid hydrogen (LH2) due to their superior thermal insulation. Similar to a thermos flask, the vacuum space between the inner cryogenic vessel and the outer vacuum shell prevents convective heat transfer, which would otherwise result in the rapid boil-off of the stored LH2. A critical component of this technology is the inner support structure which supports the inner vessel within the outer vacuum shell. It must be robust enough to handle operational loads and potential crash events specific to aviation applications, prevent excessive thermo-mechanical stresses caused by the contracting inner vessel, and minimize heat transfer through conduction. Despite its importance, there is a noticeable lack of comprehensive designs and research addressing this component, particularly in the context of aviation.

The primary objective of this thesis is to develop a design methodology for creating effective inner support structures for double-walled vessel technology. The research is grounded in a practical context through a collaboration with AeroDelft, a student team at TU Delft currently retrofitting a Sling 4 aircraft for hydrogen-powered electric flight. As part of the next milestone in their Project Phoenix, AeroDelft aims to store 6 kg of LH2 in a double-walled vessel to meet the mission's requirements.

To begin, functional, operational, and constraint requirements were established for the inner support structure. A baseline design for the inner vessel and outer shell was then developed. Subsequently, four distinct inner support structure concepts were introduced, each with its own design intents and key considerations. These concepts were subjected to various analyses, including modal, thermo-mechanical, crash loads, and heat leakage, to assess their viability and ability to meet essential functional requirements. To determine the most performative design(s), the concepts were evaluated and compared based on the following performance metrics: gravimetric efficiency, heat leakage, safety, and feasibility of manufacturing and assembly.

The analyses demonstrated that all four design concepts meet the essential functional requirements, providing a solid foundation for further development or potential prototyping. While the focus was on a vessel designed for a small aircraft, the methodology and design insights are applicable to larger systems. Hence, this research contributes to advancing cryogenic storage solutions for hydrogen-powered aviation.

System integrators across industry adhere to the so called V-model to manage their product development process. The V-model starts with the conceptualization of a system architecture, guided by functional and non-functional requirements and ending with the verification and validation of the designed product. In the conceptual design stage, crucial decisions impacting the performance of the final product are made. However, due to limited knowledge about the behavior of emerging technologies, unknown effects of connecting various components, and the low technology readiness levels (TRL) of components, the architectural design space for innovative design projects becomes large and uncertain. This uncertainty is traditionally managed through engineering judgement and large safety factors, which restrict design space exploration and often lead to sub-optimal solutions.

To address this, an uncertainty-based system architecture design exploration and optimization framework is developed in which system architecture design inputs and models, commonly used in model-based systems engineering, are treated as stochastic. This is integrated into a multidisciplinary multi-objective surrogate based optimization framework aimed at finding robust optimal vehicle system architectures.

The proposed framework has been successfully applied to the design of a heavy duty electric vehicle drive train. This resulted in increased system-specific knowledge of the vehicle’s performance in real-world applications by finding the sensitivity of the optimal design solution to operational- and model-uncertainties, thereby facilitating better informed design decisions, enabling trade-offs and reducing project risks.

Manned-Unmanned teaming with Loyal Wingmen is part of every current future fighter concept. However, while much effort is put into design and research, it remains to be seen whether the inclusion of Wingmen yields a better mission outcome. Additionally, there is much uncertainty about the ideal design and the Concept of Operations. This work aims to change that by addressing all three elements. Using Knowledge-Based Engineering, a set of Wingmen is designed for the Suppression and Destruction of Enemy Air Defenses. In the frame of a parameter study, physics-based combat simulations are conducted using Agent-Based Modeling. The results are analyzed via the derived Measures of Effectiveness of Mission Effectiveness, Survivability, Cost, and Lethality. The performance of formations including Wingmen is compared to the performance of modern tactics. The results show significant improvements in mission outcomes when Wingmen are deployed. Optimum results are achieved with Wingmen featuring slightly reduced Radar Cross-Section and balanced Specific Excess Power. The Concept of Operations has an effect, too. Fewer Wingmen carrying a larger number of weapons yield higher overall survivability than more but smaller Wingmen. The inverse holds for overall lethality. The results provide valuable insights into the optimal design and function of the Loyal Wingman concept.

Trajectory optimization has proven to be a powerful tool in solving a wide variety of optimal control problems in the aerospace field. However, in many cases, numerical complexities prevent the analysis of optimal trajectories for high-fidelity models, particularly due to the inherent difficulty of transcribing high-order dynamic systems. This research project proposes a methodology incorporating reduced-order modeling that retains the most critical dynamic characteristics from a full-order model while allowing the resulting simplification to be manageable for a trajectory optimization solver. The study applies this methodology to evaluate optimal landing trajectories for the UNIFIER19 C7A, a hybrid-electric aircraft equipped with a distributed electric propulsion system that was previously developed under the UNIFIER19 project. Results show that the reduced-order models generated for the aircraft can be used to generate flyable trajectories, verified by tracking the resulting landing approach paths using the base high-fidelity model. It is envisioned that this methodology will also be applicable to other aircraft models and mission phases.

To reduce the environmental impact of the maritime sector, the International Maritime Organization (IMO) has established emission limits and ambitious sustainability targets. While superyachts are currently exempt from many of these regulations, they must align with IMO’s objectives to establish the yachting industry as a responsible sector and adapt to the increasing application of these emission limits. Focusing only on new builds is not sufficient to meet ambitious targets, and sustainable refit of existing yachts is also essential. This applies not only to motor yachts but also to sailing yachts, which face unique challenges due to limited space which further complicates integrating alternative fuels like methanol or hydrogen, given their lower energy density. Despite various studies exploring alternative energy solutions, there remains a research gap in understanding the trade-offs associated with different energy configurations installable in a refit to minimize energy demand and emissions. To address this, an assessment model that evaluate technical feasibility, emissions, docking time, and Total Cost of Ownership (TCO) for different energy configurations is developed. Results from the assessment model, applied to two Royal Huisman sailing yachts of different size across various owner profiles, reveal that integrating alternative energy solutions requires significant reductions in energy demand. Feasible configurations range from using a single fuel HVO configuration for conservative owners to hybrid solutions involving HVO and methanol for more open to changes owners. Ultimately, owners willing to implement various energy reduction methods can achieve several feasible and sustainable configurations.

The concept of Distributed Electric Propulsion (DEP) aircraft holds great promise in achieving the goals outlined in Flightpath 2050. This involves utilizing electricity to power propellers and enhancing overall flight performance through interactions between the wings and propellers. This thesis investigates the stability and control aspects of DEP aircraft compared to conventional aircraft. A tool is proposed to estimate the minimum horizontal tail and aileron size for different configurations, considering propeller effects. Analyzing three configurations (turboprop excluding propeller effects, turboprop including propeller effects, and DEP including propeller effects), propellers are found to destabilize the aircraft but enhance take-off rotation and provide additional damping during roll. The DEP aircraft exhibits a 19% smaller normalized horizontal tail size than the turboprop (with propeller effects), attributed to lower propeller destabilizing moments and a shorter fuselage. Despite a lower roll requirement, the DEP aircraft needs the same normalized aileron size due to larger rolling moment of inertia. A sensitivity analysis suggests a T-Tail is optimal for DEP aircraft stability, and adjusting battery placement and reserve fuel improves overall performance.

The adoption of hydrogen as an alternative fuel in aircraft has the potential to reduce the climate effect of aviation significantly. However, hydrogen leakage during production, storage, or use can offset these benefits by altering atmospheric chemistry and composition, particularly through interactions with methane, ozone, and stratospheric water vapour. This study employs surrogate models based on recurrent and convolutional neural networks to simulate the climate effects of hydrogen leaks, achieving rapid projections 30,000 times faster than conventional climate models, with an error margin of less than 5%. This efficiency enables the quantification of uncertainties related to hydrogen leakage rates and atmospheric chemistry through Monte Carlo simulations, allowing for an assessment of their contributions to radiative forcing under various Shared Socioeconomic Pathway (SSP) climate scenarios. By 2100, the radiative forcing from aviation-induced hydrogen leaks is projected to reach 43.9 ± 21.2 mW m-2 (±1σ) under the stringent climate change mitigation projection SSP1-2.6, accounting for 35% of aviation’s total radiative forcing. Under the more conservative scenario SSP3-7.0, higher methane levels reduce the oxidative capacity of the atmosphere, lowering the projection to 17.7 ± 6.9 mW m-2 (±1σ) which corresponds to approximately 5% of the aviation’s total radiative forcing for this scenario. These findings demonstrate that hydrogen leaks have the potential to substantially contribute to aviation’s total radiative forcing, with the magnitude of their impact heavily influenced by background climate conditions. It will be important to minimise hydrogen leakage to fully harness the climate benefits of transitioning to hydrogen as a fuel for aircraft.

This study investigates the impact of CO$_2$ emission limitations on the connectivity and profitability of a hub-and-spoke airline network, with a specific focus on Schiphol Airport and KLM Royal Dutch Airlines. As sustainability becomes increasingly critical, the aviation industry faces mounting pressure to reduce its environmental footprint without compromising operational efficiency. This research employs a Mixed Integer Linear Programming (MILP) model to simulate airline operations under CO$_2$ constraints. Using synthetic flight data and OpenAP for accurate emissions estimation, the model optimises fleet composition, flight scheduling, and passenger flow while balancing profitability and emissions reduction. The study explores the potential of fleet renewal to mitigate the negative effects of emission caps on both connectivity and revenue. The findings demonstrate that significant emission reductions can be achieved through strategic fleet planning, with minimal impact on the airline's network and financial performance. This research provides a viable framework for integrating environmental considerations into long-term airline planning, contributing to the ongoing efforts to reduce aviation's environmental impact while maintaining its economic and operational benefits.

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.

Airborne wind energy systems offer a promising approach for renewable energy generation. However, the noise emissions associated with these systems should be understood and minimized as well to promote their integration and (social) acceptance. This research aims to identify the noise sources of airborne wind energy systems, systems with leading-edge inflatable (LEI) kites and fixed-wing kites in particular, and establish a foundation for future research in this field. Analytical simulations using the Brooks, Pope, and Marcolini model and the Amiet model were combined with experimental measurements for this research.

The analysis of the fixed-wing kite revealed prominent peaks around 1500 Hz and 2000 Hz in the noise spectra, with the higher frequency peak observed at higher kite velocities. Analytical predictions indicated laminar boundary layer vortex shedding and tether vortex shedding as the main noise sources. The study also investigated the directivity of the turbulent boundary layer trailing edge noise, which revealed dipoles that exhibited slight deformations at higher frequencies.

For the LEI kite, noise analysis identified peaks in the sound pressure level around 300-400 Hz and 1000-2000 Hz. Analytical predictions highlighted turbulent boundary layer trailing edge noise and vortex shedding from the tether and bridle lines as the dominant noise sources.

By considering the implications of these findings, the noise impact of airborne wind energy systems can be minimized, fostering their sustainable deployment and acceptance.

To improve the safety of commercial air transport, pilots are required to train on simulators to recognize the characteristics of an impeding stall and subsequently correctly recover from it. To prevent negative training, it is important that the accuracy of the used simulation models is high. A manner to model the nonlinear, unsteady aerodynamic effects during the stall is by using Kirchhoff's theory of flow separation. However, widespread difficulties exist in correctly estimating the stall-related parameters in these models. It is not always possible to simply gather more flight data to solve this problem. Therefore, the research in this paper aims to increase model accuracy by making optimal use of already existing flight data via introduction of the slice-based modeling method. This is done by analyzing the change in the parameter estimate values when applying the system identification procedure to sliced partitions of simulated flight data. These partitions incrementally increase in size with time from the stall. The simulation data is generated to be representative of the available flight test data. The change in parameter estimates was analyzed for both the pre-stall and post-stall phase. The estimated value for each partition was compared to the actual parameter value setting in the simulation model used to create the data. Manually, an optimal window was found for each parameter for which the estimated value and actual value were equal. For the stall-related parameters this window is often not more than 10 s wider than the stall. For the linear stability and control derivatives it is found that using more data generally results in a better estimate. These window sizes were used in the estimation for each separate parameter on the real flight test data. Even though this method represents a prototype, in more than half of the validation cases a decrease in MSE of 10 % to 35 % could be achieved. This shows that the new slice-based modeling method is able to improve the accuracy of nonlinear stall models without the need to gather more flight data. Additionally, the parameter estimate behaviour analysis technique and slice-based modeling method may have applications that reach beyond the realm of stall modeling.

In the conceptual design phase, OEMs perform technology assessment to identify feasible solutions for their new aircraft. When the OEM is unable to assess a certain technology themselves, they consult Tier-1 suppliers to involve
them in the design process. The suppliers have higher fidelity analysis tools that can help the OEM with their assessment, but the characteristics of such tools (e.g. high detail of input, computational time to execute) prevent an in-depth exploration of the design space. This thesis proposes a framework that enables a closer collaboration between the OEM and their supplier. The collaboration is streamlined through a web-based collaborative environment. Front-loading principles are applied to capitalise on existing data, reducing the efforts required by the supplier to service the OEM. The framework was successfully applied to an internally developed test case, and a use case within the ongoing collaborative research project DEFAINE, concerning the design of an aileron.