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Aircraft contrails are considered as one of the emissions towards climate change. Trajectory optimization is one of the methods that can be used to reduce contrail formation and a strategy that can readily be implemented. However, most of the trajectory optimization studies were performed only with clean engines, and engine degradation was not considered. This work provides a novel approach to aircraft trajectory optimization problem by including engine degradation and real aircraft trajectories within the optimization framework. Impact of engine degradation on long range optimum aircraft trajectories were assessed by quantifying the difference in fuel burn and contrail formation, when an aircraft is flying on a trajectory which has been optimized for clean engines and degraded engines. For this study, Contrail prediction model was developed with one clean and two degraded engines, integrated with aircraft performance model in an optimization framework to generate optimum aircraft trajectories. The optimum trajectories generated with degraded engines were compared with the trajectories generated with clean engines. The results have shown, for long range aircraft the persistent contrails can be reduced by 2.1% with low level degraded engines having 50°C EGT increase and 3.84% with highly degraded engines having 100°C EGT increase. However, to achieve zero persistent contrail for the same route there is a significant penalty of 2.3% and 4.2% increase of fuel with low level degraded engines and highly degraded engines. Therefore, impact of engine degradation on contrail optimum aircraft trajectories are significant and in order to reduce contrails aircraft need to fly on an optimized trajectory specifically customized for the degraded engine performance.

As alternative fuels are designated for future energy applications, flexible combustor designs require considerable development to ensure stable operation with reduced NOx emissions. A non-premixed variable swirl burner was used to experimentally appraise changes in NO production pathways, with CH4 NH3, and H2 flames, alongside intermediate fuel blends. Maintaining an equivalent thermal power and flame temperature between fuels, preheated reactants (500 K) were supplied to the burner, with parametric changes made to pressure (1–6 bara) and swirl number (0.8–2.0). NO production was characterized, alongside variations in flame structure and topology, with a correlation demonstrated for exhaust emissions. NO production was shown to be sensitive to combustor pressure, providing an expected increase for CH4 and H2 flames. Emission profiles from both NH3 and H2 flames are shown to be significantly augmented by a change in swirl number. As NH3 fractions were increased in the H2 blend, a decaying trend in NO emissions was observed with an increase in pressure, and as a function of mixture ratio. However, this behaviour was markedly augmented by a change in swirl number and suggests that further reductions may be possible at increased pressure. At the low swirl/high pressure condition the NH3/H2 blend outperformed pure H2, providing lower NO concentrations. Emissions data were normalised using the traditional dry/O2 correction, alongside mass scaled by thermal power, with a comparison provided. The corresponding differences in emission formation pathways were investigated, alongside high-speed OH* chemiluminescence to further elucidate findings.

Advanced non-destructive monitoring scheme is necessary for modern-day lightweight composite structures used in aerospace industry, due to their susceptibility to barely visible damages from minor impact loads. Acoustic emission (AE) based monitoring of these structures has received significant attention in the past few years primarily due to their possibility of use in operating structures under service loads. However, localization and characterization of damages using AE is still an open area of research. The exploration of the space of signal features collected by a distributed sensor network and its reliable mapping to damage metrics (such as location, nature, intensity) is still far from conclusive. This problem becomes more critical for composite structures with complex features/geometry where the localized effects of discontinuity in geometric or mechanical properties do not make it appropriate to rely on simple signal features (such as time difference of arrival, peak amplitude, etc.) to identify damage. In this work, the AE signal features (which are spatially and temporally correlated) have been mapped to the damage properties empirically with a training dataset using metamodeling techniques. This is used in the online monitoring phase to infer the probabilistic description of the acoustic emission source within a hierarchical Bayesian inference framework. The methodology is tested on a carbon fibre composite panel with stiffeners that is subjected to impact and dynamic fatigue loading. The study presents a generalized machine learning-based automated AE damage detection methodology which both localizes and characterizes damage under varying operational loads.