The aerospace industry increasingly employs composite materials due to their superior strength-to-weight ratios and corrosion resistance. However, the heterogeneous and anisotropic nature of composites introduces complex failure mechanisms such as delamination, matrix cracking and fiber breakage, which make modeling of degradation processes challenging. In addition, variability in material quality due to manufacturing processes, along with operational uncertainties, further challenges the reliable prediction of remaining useful life of life (RUL) of composites. As a result uncertainty management is essential for enabling informed decision-making in prognostics and health management (PHM).

This thesis presents a novel uncertainty management approach specifically targeting past state uncertainties that stem from variability in composite manufacturing processes, such as embedded defects and material quality inconsistencies. By leveraging advanced ultrasonic imaging through Dolphicam technology, internal structural variations in aerospace-grade carbon fiber-reinforced polymer (CFRP) composites were quantified. Subsequently, a similarity-informed methodology was developed, employing both spatial pyramid histogram (SPH) and convolutional neural network (CNN)-based embedding techniques, to group specimens based on internal structural quality. This approach assumes that structurally similar composites exhibit analogous degradation behavior under fatigue loading, enabling targeted and more reliable prognostic modeling.

Experimental validation involved fabricating CFRP laminates with intentionally embedded defects, followed by detailed nondestructive inspections (NDI) and fatigue testing with digital image correlation (DIC)-based strain measurements. The similarity-informed prognostic model demonstrated significant improvements in prediction accuracy and uncertainty quantification compared to conventional methods, confirming the viability and advantages of integrating similarity-based past state uncertainty management in aerospace composite structures to improve reliability in prognostics.

Overall, the results underscore the value of high-fidelity NDI for material quality characterization and highlight the potential of similarity learning techniques to reduce prognostic uncertainty. By enhancing the credibility of RUL predictions, this work contributes to more reliable decision-making capabilities and supports the advancement of predictive maintenance strategies for aerospace composite structures.

Wildfires are an increasingly severe and global threat. Vegetation loss has a long-term effect on ecosystems, with losses causing increased risks of disease and pest infestations, while also contributing to carbon emissions. Aerial firefighting is an essential tool for suppressing and containing wildfires. However, with wildfires becoming more likely and impactful, current firefighting aircraft are unsuitable due to their limited operational capabilities. Rotorcraft do not possess the water capacity or range to effectively combat these fires. On the other hand, fixed-wing aircraft such as the Canadair CL-415 have greater speed, range and capacity, but their firefighting capabilities are limited by their inability to hover. Rotorcraft, however, are capable of Vertical Take-Off and Landing (VTOL), allowing far greater operational flexibility to combat fires. As such, there exists a need for a new aircraft with the water capacity, range, and speed of fixed-wing aircraft, and the VTOL capabilities, adaptability, and precision of rotorcraft.

The FireEagle aims to bridge the gap between modern firefighting rotorcraft and fixed-wing aircraft by combining both of their operational capabilities and performances. This is achieved by carrying 12 000 litres of water while reaching speeds greater than 500 km/h in cruise, with an on-station endurance of 2.5 hours. The FireEagle will have VTOL capabilities and a deployable water snorkel, so it can be refilled from diverse water sources. A modular payload system allows for mission flexibility: by mounting different attachments, operations such as firefighting, cargo transport, law enforcement, passenger transport and military operations can be conducted. Moreover, the integration of a Prognostics and Health Management (PHM) framework quantifies the probability of success of a given mission, allowing operators to reliably estimate the aircraft’s ability to operate in the given mission conditions, while also transitioning towards predictive maintenance.

Reconciling the high payload and cruise speed requirements with the VTOL capability was the greatest challenge faced. Guided by a trade-off between several concepts and inspired by the CH-47, a compound helicopter design approach was chosen, with tandem rotors allowing it to achieve VTOL capabilities while carrying 12 000 litres of water. To ensure the speed requirement is met, forward thrust is provided by two dedicated propellers mounted on the wing tips. In cruise, the wing is optimized to provide 75% of the total lift to exploit its efficiency compared to the rotors at high speed, while the vertical stabilisers are split to allow access to the modular payload attachment system. ensure reliable operations from take-off to landing, a new framework for PHM is proposed. From discussions with industry experts and extensive literature review, a multitude of sensors have been positioned within critical systems such as the engines and rotors. For each type of sensor, a data analytics model is used to predict the remaining useful life of the individual systems, which leads to a mission success rate of the aircraft.