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.

Disruptive events pose a significant challenge to airlines’ everyday operations due to the highly optimized nature of their schedules. Unforeseen events force airlines to rapidly reschedule and adjust their operations. Current disruption management methods rely mostly on reactive and static models that fail to capture the dynamic and probabilistic nature of airline recovery. This study presents a model-based reinforcement Reinforcement Learning (RL) method for aircraft recovery under disruption uncertainty that anticipates future potential disruptions. The Aircraft Recovery Problem (ARP) is formulated as a Markov Decision Process (MDP) and a framework is proposed in which an Approximate Dynamic Programming (ADP) algorithm that relies on Value Function Approximation (VFA) determines optimal recovery actions considering the immediate and future impact of each action. The uncertain disruptions are modelled as aircraft unavailabilities with a fixed probability of realizing. The aim of the model is to keep flight delays and cancellations at a minimum while exploiting stochastic information on potential aircraft unavailabilities. The model is tested on multiple scenarios with different objectives and levels of disruptions and is benchmarked against an exact optimization algorithm. Results indicate that a proactive approach outperforms reactive models, particularly in high-disruption scenarios with high aircraft utilization. The comparison with the exact benchmark shows that the RL method can achieve sub-optimal solutions with considerably less corrective actions. This framework offers a decision support tool that allows airline operators to find more resilient solutions in uncertain environments by incorporating probabilistic predictions on disruptions in the decision-making process

In recent years, the civil aviation sector has increasingly focused on optimizing propulsion designs to improve efficiency and reduce CO₂ emissions. Propellers have regained attention for their superior efficiency, particularly in applications like full-electric aircraft, though high noise emissions remain a significant challenge. This thesis investigates the aerodynamic and acoustic performance of a Boundary Layer Ingesting (BLI) propeller installed at the end of the fuselage of a transport plane, behind the vertical tail. The study focuses on the impact of unsteady blade loading on performance and explores whether increasing blade count or modifying blade sweep can mitigate drawbacks in a BLI configuration.
The development of numerical tools was the first step in this investigation. The aerodynamic tool, an enhanced Unsteady Non-Linear Vortex Lattice Method, accounts for viscosity and flow separation, while the acoustic solver uses Hanson's Helicoidal Surface Theory to model noise emissions from propellers in both isolated and installed conditions. Simulations were conducted on a modified 6-bladed XPROP-S. The velocity profiles at the propeller disk, used as inflow for the installed BLI analysis, were taken from CFD results of the fuselage with the vertical tail installed.
The complex inflow field led to periodic increases in blade loading, especially behind the vertical tail, causing oscillations in thrust and power. Increasing the blade count from two to six resulted in steadier performance and reduced force oscillations. In the installed configuration, the propellers exhibited a thrust-to-power ratio increase of 2% to 5%, with in-plane forces scaling with thrust and remaining within 3% of it. Noise emissions rose significantly after installation, with peak emissions shifting from the rotational plane to the propeller axis, where unsteady loading noise sources became more prominent. The noise level increase reached up to 46 dB during cruise for the 6-bladed baseline. While increasing the blade count reduced noise levels in isolated conditions due to lower propagation efficiency of steady loading noise, in installed conditions, unsteady loading noise sources counterbalanced this, leading to overall increased noise. Maximum noise emissions ranged from 32 dB (isolated, six blades) to 78 dB (installed, six blades). Sweeping the baseline blade backward at the tip and forward near the hub increased loading and thrust by up to 20%, particularly at the tips. In installed conditions, backward sweep reduced overall blade loading changes, while forward sweep increased them. Noise levels varied, with isolated conditions showing a slight increase and installed conditions experiencing up to a 4.7 dB reduction with backward sweep.
The analysis underscores the importance of accounting for unsteady blade loading and how the dominance of unsteady loading noise sources is influenced by loading oscillation amplitude, propeller geometry, and operating conditions.

The impact that rescheduling aircraft maintenance may have on both the network and maintenance roster is hard to grasp due to the inherent complexity of the problem. Various works have been dedicated to developing methods for recovering a disrupted airline schedule, with the primary focus being the recovery of the lines-of-flight in the days after the disruption. More recently, attention has been paid to the specific recovery of aircraft maintenance, constrained by the resources required. However, no comprehensive method exists that combines both network and maintenance scheduling in order to assess the effects of rescheduling maintenance in favor of flight execution. This paper presents a novel framework that provides decision-makers with the opportunity to assess the outcome of a choice they are about to make: what are the short- to medium-term effects of delaying airline maintenance in favor of operating a flight instead of canceling it? At its core lies a mixed-integer programming formulation, which considers a double space-time network, allowing the integral scheduling of network rotations and maintenance tasks in ground slots. A case study involving a heterogeneous fleet of 54 aircraft from a major European airline demonstrated the framework's effectiveness in scheduling 1,886 maintenance tasks and 471 flight rotations across 58 ground slots. The framework shows that preventing a rotation from cancellation by rescheduling maintenance results in approximately 13 schedule changes and 10 hours of additional delay within a two-week period, compared to the same schedule without the maintenance rescheduled. Thus, while the airline has prevented a rotation from cancellation, this came at the cost of multiple scheduling changes and additional network delays. It can be concluded that the initial formulation proves to be beneficial in assisting decision-makers in assessing the implications of delaying aircraft maintenance.

Inherent subjectivity, inefficiencies, and the substantial cost related to human-based visual inspection of high-pressure turbine (HPT) blades has driven research in alternative automated techniques. The
combination of computer vision (CV) and deep learning (DL) provides a compelling alternative. However, as DL models increase in capability, their large parameter spaces require substantial data to
achieve robust training. Therefore, this study explores the use of two Auxiliary Classifier Generative Adversarial Networks (AC-GANs) to augment proprietary datasets for two failure modes: (a) a 3-
channel red-green-blue (RGB) model for obstructed holes, and (b) a 4-channel RGB plus depth (RGBD) model for foreign object damage, with the depth reconstructed using monocular depth estimation. A Differential Evolution Optimizer (DEO) was used for hyperparameter optimization of a ResNet-18 classification target model. In the obstructed hole dataset, GAN-based augmentation showed significantly improved accuracy, recall, F1, and AUC-ROC (p < 0.05), competing with traditional methods using less augmentation. In the foreign object damage dataset, the inclusion of depth information significantly enhanced accuracy, precision, F1, and AUC-ROC performance (p < 0.05). However, the RGBD augmentation mainly resulted in a trade-off between precision and recall, without statistically significant differences (p > 0.05).

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.