Air cargo is a vital component in the worldwide supply chain and economy. In the KLM Ground Handling (GH) terminal, Unit Load Device (ULD) breakdown and shipment pickup are scheduled separately, with limited consideration of storage capacity and truck delays, which results in inefficiencies in schedules and higher storage requirements. To counteract these issues, this study proposes a scheduling model which synchronises ULD breakdown and shipment pickup and determines the effect of a truck slot confirmation system where drivers must confirm their presence some time before their scheduled slot, called the confirmation horizon. A Mixed-Integer Linear Programming (MILP) model extended from cross-docking literature is considered. Moreover, a Genetic Algorithm (GA) is developed to improve computational complexity. It is extended to a rolling horizon (RH) implementation, called the RH-GA, which can react to discovered delays. Using the baseline MILP (b-MILP) a comparison to the current scheduling method is performed. Moreover, experiments on realistic KLM GH scenarios are carried out using the RH-GA. The MILP failed to find feasible solutions within 8 hours for many small/mid-size scenarios. The GA always reached feasibility in tested scenarios and its runtime scaled linearly with scenario size. In the few scenarios which could be benchmarked, the synchronised GA outperformed the b-MILP. Moreover, introducing a non-zero confirmation horizon significantly improved the objective compared to no confirmation horizon. Across tested cases, mean peak inventory decreased by 9% and total tardiness by 2-3% relative to the 0-hour horizon case. Synchronised scheduling with a slot confirmation system can reduce storage requirements and improve on-time performance at the KLM GH terminal, indicating that further development and implementation of both the model and confirmation system may be valuable.

This thesis investigates the potential of Augmented Reality (AR) headsets to enhance field technicians’ performance (in terms of efficiency and effectivity) in Operations and Maintenance (O&M) processes in Offshore Wind Farms (OWF), with Vattenfall’s operations serving as the case study. The key deliverable of this report has been the Business Case (Chapter 9), which provides a qualitative and quantitative analysis of the AR enhanced business process innovation. A critical gap for such empirical case studies was identified in the field of industrial AR applications, especially in offshore wind contexts, as noted in Chapter 1. The research is further motivated by the generation of a generalisable multi-framework approach for business process innovation. This approach evaluates the business case of novel technologies such as AR in capital-intensive industries, fitting in Vattenfall’s (generalisable) Stage-Gate innovation model (Chapter 2). Furthermore, societal impact is granted through accelerating the energy transition, by providing the first step in a business process innovation that should lower the O&M costs (which is about 30% of the Levelised Cost of Energy (LCOE, full lifecycle cost) (Bosch et al., 2019)) of renewable energy, improving its competitiveness against fossil fuels (Chapter 2).

Predictive maintenance anticipates and prevents component failures by analysing operational data for early signs of degradation. Traditional industry-standard models for aircraft systems are often rule-based, missing complex patterns and limiting scalability. This research develops a deep learning (DL) fault detection pipeline for the engine bleed air system of wide-body twin-engine aircraft, leveraging real-world operational sensor and maintenance data. An interpretable feature engineering framework extracts physically informed features, including dual-engine comparisons, to train a gated recurrent unit (GRU) fault detection model for robust temporal modelling of healthy and faulty conditions. Bayesian optimisation is implemented for hyperparameter tuning. However, the scarcity of representative failure data, which is a common issue in aviation, limits the achievable performance and fidelity of DL models. To address this, a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) is employed to augment the dataset with synthetic failure data. A post-processing block labeling technique is introduced to enhance fidelity, and a novel fidelity savings metric translates model predictions into operational savings. GAN-based augmentation enhances recall, precision, and F1-score, and outperforms traditional augmentation. Further case study results show that the WGAN-GP-augmented model delivers four times the operational savings compared to the industry-standard model, 50\% more than the non-augmented GRU, and 31\% more than traditional augmentation.

This study addresses the initial phase of a multi-modal air cargo transport network, where trucks collect shipments from multiple origins and deliver them to the hub airport of an airline. Efficient coordination between ground transport and outbound flights is crucial for optimising truck load factors, reducing operational costs, and ensuring on-time cargo transfers at the hub. Poor synchronisation can cause delays and increased expenses, reducing the efficiency of the entire transport network. This paper presents a novel Mixed Integer Linear Programming (MILP) formulation and an Adaptive Large Neighbourhood Search (ALNS) framework for an integrated vehicle routing and dock-door scheduling problem that includes split delivery, incompatible products, time windows, and open routes, with the objective of minimising operational costs. The ALNS framework uses a dock-door-based route representation along with multiple insertion and removal operators to improve the solution to the problem at hand. A comparative analysis between the MILP and ALNS model shows that the ALNS model consistently outperforms the MILP model in computational efficiency and solution quality for larger and more complex instances. The ALNS model efficiently finds feasible solutions within significantly reduced computational times, making it practical for real-world applications. Moreover, using a case study of an airline, the ALNS-generated network demonstrates improvements in cost efficiency, fleet utilisation, and truck load factors compared to the airline’s historical routing data. Despite differences between the actual network data and the model-generated data, stemming from assumptions that create an idealised scenario that does not fully capture the complexities of real-world operations, the ALNS model offers significant enhancements in efficiency for the airline’s trucking network.

This research investigates the effects of mechanical loads on bottom-founded and floating offshore wind turbines (OWTs), specifically comparing different floater configurations (TLP and semi-submersible) with bottom-founded turbines (monopile). The study identifies three critical intersections for a fair comparison: the base of the blades, the main shaft, and the yaw system. These intersections allow for the prediction of stresses on components when specific material properties are known, which is often not the case in many turbines.

The modeling program Orcaflex is used to describe the motions and loading of TLP and semi-submersible floating offshore wind turbines (FOWTs). Bluewater Energy Services is currently designing a TLP platform for a wind turbine, and this design, along with a semi-submersible FOWT model, is compared with an IEA 15 MW bottom-fixed turbine. External loads such as waves and wind, generated from North Sea data, are considered. Additionally, the effects of design parameters like weight, waterline area, center of mass, and wind turbine generator (WTG) control settings are taken into account.

The study reveals that the semi-submersible platform is more susceptible to environmental loads, leading to some significant translational and rotational motions. Its stability relies on a large water surface area and a catenary mooring system, resulting in low system stiffness. In contrast, the bottom-fixed and TLP turbines exhibit lower motion fluctuations due to their higher system stiffness. The TLP experiences higher nacelle accelerations compared to the semi-submersible, except for heave acceleration, due to resonance with wave frequencies. Mechanical loadings are significantly influenced by wind speed and the turbine's controller. Before reaching the rated wind speed, mechanical loads increase with environmental loads, while post-rated wind speed, the loads stabilize or even decrease due to the controller's intervention.

Furthermore, the study identifies the driving factors for the lifetime of pitch, yaw, and main bearings. The pitch bearing's equivalent load is predominantly influenced by wind-induced moments, while the yaw bearing's load is largely governed by axial loads from the RNA's weight. The main upwind bearing's load is primarily affected by radial loads, with axial loads becoming more significant as wind loads increase.

The overall conclusion indicates that while platform motions influence system dynamics, their direct effect on mechanical loads is less significant compared to other factors such as wind loads and controller actions. The pitch controller plays a crucial role in managing mechanical loads, particularly for pitch bearings. Nevertheless, the relatively large mean angle of the semi-submersible platform impacts bearing lifetimes. The system's angle, combined with the weight of components, especially for the yaw bearing, is a critical factor in determining their lifetime. These findings are supported by existing literature, confirming the complex interplay between environmental conditions, system motions, and mechanical loadings in offshore wind turbines.

Battery state of health (SOH) estimation is one of the three main analytical tasks of a battery management system (BMS), when viewed from engineering maintenance and prognostics perspective. With the current global effort towards more suitable and greener processes, lithium-ion batteries have shown to be an important element in facilitating this transition. One industry where this can be noticed in particular is the transportation sector, where a strong shift towards battery electric vehicles (BEV) can be observed. Within the aviation sector, current research efforts include electrical flight. However, numerous challenges remain, that are typically observable within a safety critical domain such as aerospace. One these challenges includes the determination of uncertainty in battery SOH prediction. This would provide improved transparency on the capabilities and limitation of a model, when used as part of a battery system. Within this report we propose the use of a bidirectional gated recurrent unit (Bi-GRU) with learnable soft attention, to predict battery SOH based on charge measurements. Uncertainty analysis is enabled through the use of simultaneous quantile regression (SQR) and orthonormal certificate (OC), to be able to highlight and distinguish the aleatoric and epistemic uncertainty of the proposed model. We afterwards evaluate the model for point prediction accuracy using standard metrics, and evaluate the produced uncertainty using specialised test cases and calibration metrics. We achieved strong results using the proposed framework on a 2-phase fast charging dataset published by Toyota.

The increasing focus on sustainable living and the need to reduce dependency on fossil fuels has led to a growing interest in renewable energy sources. Among these, the wind energy sector has not only experienced significant growth in terms of numbers but also in size. Larger turbines lead to more severe leading-edge erosion and further increase operation and maintenance costs. To mitigate this problem, the new advanced leading-edge protection has become vital in the wind energy sector. This thesis focuses on the evaluation of two polymer coating materials (PA and PD) using a Pulsating Jet Erosion Test (PJET) setup.

The first aim of this thesis is to propose a novel analysis method to address the issue of volume interdependence in the PJET. To achieve this, a concept called "equivalent velocity" is introduced. The equivalent velocity represents the velocity at which a spherical droplet should impact a surface to exert the same kinetic energy per impingement as the actual water slug moving at the impact velocity. By utilizing this concept, the velocity-number of impacts plot takes into account the volume interdependence in erosion experiments.

The second aim is to utilize the PJET to analyze the erosion behavior of PA and PD coatings. The investigation focuses on understanding the relationship between impact velocity and the number of impacts until the incubation period and the breakthrough. The incubation period refers to the interval until the damage is visible and the breakthrough is the moment until the filler underneath the coating is exposed. Additionally, the erosion damage progression of the coatings was analyzed, and the lifetime prediction was evaluated using an existing long-term leading-edge rain erosion model.

The experimental results revealed that the ductile material (PD) exhibits a longer resistance to erosion compared to the stiff material (PA), with the mean number of impacts until breakthrough being 2 to 3 times higher for PD. Moreover, the long-term leading-edge rain erosion model highlights the importance of the accurate measurement of material properties, as lifetime prediction is very sensitive to ultimate tensile strength and Poisson’s ratio.

However, it is crucial to validate the equivalent velocity method through experiments and numerical modeling, while also improving the experimental method to allow for continuous observation of the erosion process in a controlled environment with temperature and humidity regulation. Conducting tests in a wider range of velocities is also recommended. Additionally, improvements for the rain erosion model are necessary to accommodate the utilization of the equivalent velocity.