Solar sailing is a propellant-free propulsion method, leveraging the momentum of Sun-emitted photons to generate thrust. In Earth orbit, the small and constrained magnitude of the solar-sail thrust with respect to the planetary gravity
implies the need for many revolutions to accomplish an orbital transfer. Solving the resulting optimization problem requires algorithms capable of handling very large sets of decision variables. This thesis focuses on the development of a Differential Dynamic
Programming (DDP) optimization algorithm, introducing adaptive parameter tuning and novel methodologies to tackle constrained and variable-duration problems. The DDP solver is characterized (in terms of hyper-parameter sensitivity and convergence properties)
and validated against a state-of-the-art direct optimization method. The devised algorithm is applied to time-optimal Earth-centered solar-sail transfers at GEO and LEO altitudes, successfully optimizing transfer durations of up to 1000 revolutions: solutions
display distinct acceleration and drift phases, apogee reversals to optimize orbit circularization, and altitude-dependent requirements on attitude control. A variable-duration transfer problem is solved by initializing DDP using a regression performed on
the previously optimized solutions.

This thesis investigates the use of optimization techniques to determine the value of multimodal (Air+Rail) networks. By using Mixed Integer Linear Programming (MILP), the mathematical model derived especially for this use case determines the optimal way for airlines to route their passengers on ultra-short haul networks. The model does not only consider operational costs, but also addresses the importance of sustainability and the value of time, and includes the value to be found in capturing passengers at cities where there is no airport in the near vicinity. This research demonstrates that by (partially) routing passengers on rail networks, rather than air, flights can be reduced on the short haul network. This results in higher profit, shorter average travel times and reduced average emissions, all whilst capturing a larger market. This research contributes to the knowledge on multimodal air+rail transport by showcasing the potential benefits it can have for airline alliances in a quantitative way, incentivizing airlines to shift towards rail partnerships for their ultra-shorthaul network and adopting more sustainable practices.

The deployment and subsequent development of an Advanced Air Mobility (AAM) transportation system is expected to take an incredible amount of resources in terms of planning, time and capital. Due to the system not yet being operational anywhere, and consequently, the lack of clear operational boundaries set, researchers and developers are left with an enormous design space. Currently, parallel independent developments are taking place in all aspects of the system, and different perspectives have enabled a wide range of concepts to be formulated in each. Whereas independent assessments provide crucial insights into the individual components of the system themselves, they fail to capture the inherent interdependencies. They also do not capture the growth of the aircraft fleet in correlation with the growth of the vertiport network. This gives rise to the need for a framework which is capable of establishing a preliminary vertiport network to allow for the study of the aircraft, fleet and total system performance in a coherent manner, and the scalable correlated evolution thereof. Consequently, this study aims to develop a unified framework for the formation of a scalable vertiport allocation plan in conjunction with system-performance-based heterogeneous fleet sizing. The scalable vertiport allocator employs a distance-based agglomerative clustering algorithm to determine the clusters in the ultimate vertiport network and the k-means clustering algorithm to determine the preliminary location of the vertiport per cluster. This is followed by a commute-distance based vertiport elimination procedure to establish each vertiport network to be assessed. The optimal fleet at each stage of network growth is established through a parameter sweep conducted across the fleet size and composition. The system-based performance metrics of the combinations of vertiport network and fleet are then assessed using an on-demand agent-based simulation. The framework is applied to New York (NY) state and models of existing multi-rotor (MR) and tilt-rotor (TR) aircraft are utilized as test case. The test case results show the applicability of the framework in the establishment of a scalable preliminary vertiport allocation plan to maximize system commute distance, and the correlated growth of the optimal fleet based on the maximum system performance.

The W-type is a motion-compensated offshore crane capable of hoisting up to 2 mt of cargo, thereby supporting the maintenance processes for the rapidly growing offshore wind farms. However, the rated cargo mass is limited by the dynamic amplification factor (DAF), which is a governing parameter in the structural design of offshore cranes. During the design phase, the industry currently makes use of a one-dimensional spring-mass model to estimate the DAF. In this study, a simplified flexible multibody dynamic model was developed, and validated with experimental data, to simulate the DAF instead. It was found that the previous method underpredicts the DAF by up to 56%, because it neglects the effects of crane inertia and structural damping. Besides, the active controller of the luffing cylinders was modelled for the first time, which reduced the DAF up to 13%. Therefore, the simple spring-mass model is not deemed accurate for this application.

This study explores the integration of machine learning with space mapping techniques to enhance the mapping of optimal control sequences between low- and high-fidelity flight mechanic models. Space mapping is a methodology that simplifies the control optimisation process by approximating a high-fidelity model using less computationally demanding low-fidelity models, which are then iteratively corrected to converge towards high-fidelity outputs. The main research question investigates how the integration of space mapping with sequence-to-sequence neural networks can improve control sequence mapping compared to traditional model predictive control (MPC) methods, particularly in managing the trajectory differences in non-linear flight regimes.

In the pursuit of sustainable aviation, with a sharp focus on reducing emissions through innovative designs and enhanced flight mechanics, the computational cost of high-fidelity models becomes a significant limitation. These models, crucial for capturing complex interactions in advanced aircraft designs, often require simplification to reduce computational demands. This research proposes a novel approach by combining the strengths of machine learning, particularly sequence-to-sequence neural networks like Gated Recurrent Units (GRUs) and transformers, with space mapping techniques to bridge the gap between low- and high-fidelity models effectively.

The study delves into two main machine learning architectures: GRUs and transformers. GRUs excel in managing sequences with fewer changes, maintaining stable predictions with minimal error. Transformers on the other hand are well suited at handling complex sequences with frequent changes, thanks to their ability to process entire sequences simultaneously through self-attention mechanisms. This capability makes transformers particularly suitable for dynamic scenarios where anticipating future states is crucial.

A significant contribution of this study is the implementation of the Prior Knowledge Input-Difference (PKI-D) architecture, which uses the low-fidelity model output as a baseline that the neural network corrects, providing a robust framework for the machine learning models to accurately predict trajectory adjustments. This architecture not only enhances the predictive accuracy but also optimises computational efficiency by reducing the dependency on extensive high-fidelity simulations.

Comparative analyses reveal that MPC methods typically provides superior mapping performance for trajectories requiring no anticipation, while the hybrid machine learning-space mapping approach offers improved performance comparably or better in complex scenarios requiring advanced anticipation. This study highlights the critical role of active learning in adapting the machine learning models to new data dynamically, a feature that proves essential in maintaining accuracy over prolonged operational periods.

In conclusion, this research demonstrates that integrating space mapping with machine learning can significantly enhance the mapping of control sequences in aerospace applications. It provides a starting point for future studies to explore tailor made machine learning solutions using extremely small data sets in situations where data availability is sparse. This research could further open up avenues where the advanced capabilities of machine learning can be applied to problems in aerospace engineering previously inaccessible.

The aviation industry is exploring unconventional aircraft designs like the Flying V in its drive to reduce carbon emissions and improve fuel efficiency. The Flying V, featuring a crescent wing with leading edge kink, currently suffers from an unstable nose-up pitch tendency. This study investigates vortex control methods within this kink region to increase lift on the outboard wing and delay the pitch-break. A full-span, modular wind tunnel model is used to investigate the effects of parabolic and diamond juncture fillets, as well as full-chord fences, on the pitching moment characteristics. Stereoscopic Particle Image Velocimetry (SPIV) and oil flow visualization are used to analyse vortex behaviour and flow topology. Results reveal that the parabolic fillet outperforms the diamond fillet in generating lift at higher angles of attack due to its ability to promote vortex formation and delay breakdown. The installation of full-chord fences increases lift on the outboard wing and positively influences the pitching moment, though none of the tested configurations increased the pitch-break angle.

Liquid Hydrogen (LH2) appears as one of the leading solutions for sustainable aviation, with rear-fuselage tanks being one of the preferred options for fuel integration in conventional tube & wing designs. Studies have already identified some limitations of these concepts concerning the larger horizontal tailplane required. However, no studies so far considered the industry approach of sharing a single tailplane in an aircraft family for this type of aircraft.

The thesis draws some first guidelines for the design of LH2 aircraft families with rear tanks, with a special focus on the performance penalties due to tailplane commonality and its comparison to conventional designs. For this purpose, a methodology has been developed that systematically sizes the tailplane of an aircraft considering potential family members already in the preliminary design stage. The results revealed that lower performance penalty due to tailplane commonality can be expected for an LH2 family compared to kerosene powered designs.

Aircraft redesign and flight path optimisation offer promising methods of rethinking how we fly. As the effects of aviation on the planet are becoming better understood, focus has shifted from cost minimisation to climate impact mitigation. In this study, simultaneous aircraft design and trajectory optimisation is used to investigate the trade-off between direct operating costs (DOC) and the global average temperature response (ATR) associated with a typical medium range flight. It is shown for a representative mid-latitude atmosphere and narrowbody aircraft that the ATR can be reduced by 49% with approximately 0.5% increase in operating costs through 2-dimensional flight path optimisation. With simultaneous wing planform optimisation, the DOC-ATR trade-off is even more favourable, leading to a 56% ATR reduction with no increase in operating costs. Results indicate that contrail avoidance is a highly cost-effective method of minimising the climate effects of aviation.

The Flying-V, a novel aircraft design developed at Delft University of Technology, presents a revolutionary flying wing transport aircraft with a remarkable 20% reduction in energy consumption compared to traditional twin-aisle planes.
This thesis project delves into the study of optimizing the Flying-V's landing performance, emphasizing the necessity of reducing pitch attitude. High-lift devices, particularly split flaps, were explored for this purpose. Wind tunnel tests were carried out on a scaled-down model of the half- wing, in the Open Jet Facility of TU Delft. The tests yielded two successful flap configurations— a single-flap and a double-flap.
These were analyzed further using a flight performance tool to make a final selection on the flap configuration. The single-flap option proved effective in reducing landing pitch attitude by 3 degrees, significantly lowering obscured segment by 20 to 30 m and the pilot's eye altitude by 1 m. This is a quite desirable outcome for the landing performance of the Flying-V which significantly improves pilot’s vision.

Developments in computational capabilities and the always increasing demand for higher performance of internal flow applications has meant that Computational Fluid Dynamics (CFD) has become an essential tool within the design process. A key point of interest is to couple the fluid solver with numerical optimisation techniques in order to obtain a more automated design process that can handle a vast amount of different design aspects. The open source CFD suite SU2 has emerged as an enabler for aerodynamic shape optimisation involving a large number of design variables, due to its efficient, accurate and flexible discrete adjoint solver. 
For the adjoint-based aerodynamic design optimisation of internal flow applications the deformation of the volumetric mesh has to be performed in an robust and efficient manner. Often small wall clearance gaps and periodic domains are encountered in internal flow domains, which could potentially lead to the deterioration of the mesh. Sliding boundary node methods can be applied in order to maintain the mesh quality in case of small wall clearance gaps. Additionally, periodic boundaries can be displaced in a periodic manner following the applied deformation in order to prevent low quality cells near the periodic interface. Therefore, it would be of interest to implement the sliding boundary node methods and periodic conditions in a Radial Basis Function (RBF) interpolation method, one of the most robust mesh deformation methods available. 
Additionally, the computational efficiency should be considered, since high computational times should be prevented for large and complex three-dimensional cases with a high number of design variables. 
The aim of this thesis project is therefore to develop a robust and computationally efficient mesh deformation method suitable within the discrete adjoint optimisation framework of SU2 for internal flow applications by means of developing an implementation of the RBF interpolation method including sliding boundary node algorithms, periodic boundary conditions and data reductions methods.
The sliding is achieved by replacing the interpolation condition for the sliding nodes with a planar slip condition. Or alternatively, by freely displacing the sliding nodes based on the known deformation and subsequently projecting the nodes back onto the boundary. The periodic displacement of the boundaries is ensured by making the distance function of the RBF periodic. The periodic nodes are then treated as internal nodes to allow them to move.
The developed RBF-SliDe tool is able to generate higher minimum mesh qualities compared with the regular RBF interpolation method. The sliding of the boundary nodes reduces the degree of skewing of the mesh elements in case of drastic deformations, resulting in a higher minimum mesh quality. Furthermore, the introduction of the periodic displacement prevents low quality skewed or compressed mesh elements, as the periodic boundaries move along with the deformation. 
The Aachen turbine stator blade is considered as a realistic three-dimensional test case. For this stator blade an optimised geometry was available, which was obtained with an adjoint-based aerodynamic optimisation performed with SU2. Therefore, the resulting minimum mesh quality is compared to the one obtained with the more conventional linear elasticity equation method as used in SU2. The minimum mesh quality obtained with the RBF-SliDe tool is nearly three times higher compared to the minimum mesh quality of the linear elasticity equations methods. This highlights the potential of the periodic sliding RBF interpolation method in terms of preserving the mesh quality.

Considerations that decide producibility of a design are an important part of the design process, and must be included in the early design stages to ensure that these designs can be realised. Not including these considerations carries the risk of incurring additional costs and delays at later stages of product development because of design changes, or can lead to limiting oneself to conservative design choices to reduce the associated risk.
The current process in the industry accounts for these production considerations in design through a manual process that is iterative and time-consuming, and hence forms a bottleneck in being able to trade-off multiple design concepts. Attempts at accounting for these production considerations in an automated way are associated with the limitations of either only considering the manufacturing cost, being specific solutions that work only in certain scenarios, or being dependent on some commercial software tools, which are not fully suitable for use in context of automation and/or at the conceptual design stage. Additionally, the aspects of manufacturing and assembly are usually not considered at the same time in these studies.
Therefore, this thesis aims at developing a methodology that enables the automated inclusion of production considerations in the conceptual design process of aircraft structures, while overcoming shortcomings of the state-of-the-art....