As green transition in aviation continues to be pushed, SAFs becomes increasingly important. Hydrotreated Pyrolysis Oils (HPO) could potentially expand the ways SAFs could be produced. This thesis looks at the flame speed as well as NOx and CO emissions of this potential SAF when blended with Jet-A1. Experiment was performed in a bunsen burner setup, with flame speed determined from chemiluminescence images and emission data obtained from a gas analyzer. The results showed higher NOx emission, especially when equivalence ratio is close to stoichiometric condition. Higher CO was also observed for rich conditions, while no significant change in flame speed was seen. Experiment was also performed for Jet-A1 & hydrogen blend, which saw higher NO and CO emission at stoichiometric condition, and much higher flame speed.

Organic Rankine Cycle (ORC) turbines for combined cycle engines offer significant potential for improving fuel efficiency. However, turbine weight remains a critical design factor, as the turbogenerator can account for up to one-third of the power unit mass. The turbine design tool TurboSim, developed by Majer and Pini, is computationally expensive. This study aims to develop an accurate and computationally efficient symbolic regression surrogate model to predict ORC radial-inflow turbine efficiency and weight. While a single multi-output model was initially explored, separate models for each working fluid provided better accuracy. Four fluid categories—refrigerants (R134a), hydrocarbons (butane, cyclopentane, toluene), alcohols (ethanol), and siloxanes (MM)—were analyzed, showing that working fluid has a minor effect on efficiency but a significant impact on turbine weight. A parametric study identified key design variables, with impeller radius ratio and hub-to-tip radius ratio having the greatest impact. While efficiency predictions were generally reliable, weight predictions exhibited greater variability, with lower R² values and higher MSE for some fluids. The results highlight the potential of symbolic regression for rapid and interpretable turbine design optimization. However, further research is needed to improve weight prediction consistency.

Research aimed at improving engine maintenance practices is vital to ensuring the aviation industry's high safety standards. Both preventive and corrective maintenance approaches result in either higher costs or increased failures. Predictive maintenance aims to achieve an optimal balance by using time series and historical failure data to anticipate future problems and plan engine maintenance activities. While extensive research has been done on the application of AI models for predictive maintenance using simulated datasets, limited research has been done using real world engine data. Simulated datasets lack the complexity of real world systems which undermines the applicability of their findings to real world situations. This paper addresses the gap between theory and industry by utilizing eight years of operating and maintenance data from the GEnx-1B, provided by KLM Engine Services. We expand classic residual based methods with a novel approach, sister engine analysis, to improve the performance of an LSTM-based fault detection model. The data is labeled based on maintenance reports, which provide the most accurate link to the underlying degradation state of an engine. These reports reveal a significant bias toward high pressure turbine degradation in the GEnx-1B. Furthermore, our findings indicate that incorporating sister engine analysis enhances the average F1 score of an AI-based fault detection model by 7.7% and that AI methods outperform the current industry standard when provided with the same input data. The use of physically relevant model features and LIME analysis ensures the model's behavior is interpretable. These results are significant as they help build trust in AI-based solutions for predictive maintenance, which is crucial for their broader adoption.

UAVs have become increasingly popular, finding applications in diverse areas such as military operations, search and rescue, delivery services, wireless communication, and aerial surveillance. A critical aspect of UAV operations is autonomous landing, especially on moving targets like ships, which presents significant challenges due to the dynamic and unpredictable maritime environment. This research explores the potential of reinforcement learning as a strategy for achieving optimal guidance during the autonomous landing process of the VSQP. The study illustrates that the reinforcement learning learning framework can effectively steer the VSQP, ensuring safe and accurate landings on a moving ship, and outperforming the benchmark controller in a more intelligent manner. The proposed approach not only improves landing performance but also extends the operational capability of the VSQP.

This study investigated the effect of tangential strain on the stability of perturbed laminar lean premixed hydrogen flames in a counterflow reactants-to-products configuration. Laminar premixed flames are highly susceptible to intrinsic instabilities, including hydrodynamic and thermodiffusive instabilities, which can cause perturbations in the flame front to grow. However, the impact of tangential strain, introduced by velocity gradients along the flame front in a counterflow setup, had not been fully explored in the context of flame stability. The present work bridged this gap by combining the counterflow setup used in previous studies of NOx reduction with the perturbation analysis commonly employed in flame stability research.

Direct Numerical Simulations were performed to study the behaviour of sinusoidal perturbations imposed on the flame front, focusing on the growth rates of these perturbations under two different strain rates (2000 1/s and 4000 1/s). The results indicated that the tangential strain improved the stability of the flame front, as the strain rate led to a reduction in both the maximum observed growth rate and the growth rate after the perturbation reached its peak. The growth rates observed were in the non-linear regime, characterised by continual variation over time. At higher strain rates, the flame front stabilised more quickly, suggesting a strong correlation between strain rate and perturbation growth dynamics. The strain-induced velocity gradients displaced the flame front, reducing its effective curvature and wavelength, further contributing to the stabilisation process.

In addition to the strain rate, the study investigated the effects of varying the amplitude and wavelength of initial perturbations. It was found that the amplitude of the initial perturbation had little impact on the maximum growth rate, but variations in the initial wavelength significantly influenced both the growth dynamics and the maximum growth rate of the perturbations.

Overall, the results provided a comprehensive understanding of how tangential strain affected flame stability and highlighted the importance of wavelength variations in determining perturbation growth rates. These findings provide insights into potential strategies to improve flame stability in practical combustion systems aimed at reducing emissions, such as in lean hydrogen combustion.