Theses

Stricter emissions regulations are accelerating the shift toward “net-zero” powertrains, including battery-electric, fuel-cell and zero-carbon-fuelled internal combustion engine powertrains. In hard-to-abate sectors such as heavy-duty transport, marine, and construction, hydrogen and ammonia have emerged as promising alternatives due to their potential for carbon-neutral combustion and compatibility with existing internal combustion engine infrastructure.

Effective use of these fuels requires high-efficiency engines, the development of which depends on predictive combustion models. These models rely on chemical kinetic mechanisms to accurately simulate key processes such as flame propagation, autoignition, and species evolution. Nevertheless, chemical kinetic mechanisms are generally validated against experimental data at a broad range of thermochemical conditions and few studies in the literature focus on developing mechanisms specifically for engine applications – relevant pressures, temperatures, mixture dilutions and stoichiometries. Therefore, this remains an underexplored research area that can deliver significant improvements in the predictive accuracy of engine models.

This thesis focuses on improving the predictive accuracy of chemical kinetic mechanisms by developing a fundamentally new approach to chemical kinetic mechanism optimisation and its application to the optimisation of mechanisms for hydrogen and ammonia. The optimised mechanisms were further validated against experimental engine data.

A comprehensive data collection of hydrogen experimental data at engine-relevant thermochemical conditions was assembled and used to identify the literature mechanism that observes the best predictive fit to the data. A new optimisation framework was then developed, which utilises a multi-objective particle swarm optimisation algorithm to search the uncertainty hyperspace of selected pre-exponential rate coefficients, aiming to reduce simulation errors, while maximising the number of experimental datapoints modelled within experimental uncertainty. The optimised mechanism, “Bath-H2”, achieved a 35% reduction in the normalised root mean square error between model predictions and experimental measurements, while increasing predictions within experimental uncertainty by 19% compared to the baseline mechanism. Post-optimisation flux analyses revealed changes in the production and consumption pathways of hydroxyl radicals that led to the improved prediction of experimental data at conditions observed in internal combustion engines.

The optimised mechanism was further validated against experimental data from a hydrogen-fuelled Cooperative Fuel Research (CFR) engine operating in HCCI mode. The modelling results revealed that the mechanism matches the predictive accuracy of literature mechanisms for conditions without NO injection in the intake manifold, while significantly outperforming them in the cases involving NO addition. Uncertainty analyses showed that the optimised mechanism predicts autoignition within experimental uncertainty.

The optimisation framework was further applied to the development of an ammonia oxidation mechanism by including additional experimental data on hydrogen-ammonia blends and ammonia-only systems. The optimised mechanism, “Bath-NH3”, exhibited a 2.9% reduction in the normalised root mean square error between model predictions and experimental measurements at the expense of a 1.0% decrease in the number of predictions within experimental uncertainty against the baseline mechanism. Post-optimisation flux analyses revealed changes in the production and consumption pathways of OH radicals that led to the improved prediction of experimental data at intermediate temperatures and pressures for hydrogen-ammonia fuel blends. Further validation against the CFR experimental engine data revealed that Bath-NH3 predicted autoignition in the CFR engine with increased accuracy compared to the baseline.

The growing cost and time requirements of powertrain design and optimisation, due to the increasing complexity of vehicle powertrains, put an ever-greater emphasis on the need for truly predictive combustion models to streamline powertrain development processes. This work highlights the importance of chemical kinetic mechanisms for the progression of predictive combustion modelling, develops two mechanisms with improved predictive accuracy and identifies several promising directions that can enhance both the applicability of the optimised mechanisms and the efficiency of the optimisation framework presented.