Hydrogen is emerging as a viable energy carrier for the decarbonization of internal combustion engines (ICEs), representing a necessary step toward the long-term sustainability of this technology. In particular, hydrogen direct injection (DI) operation is receiving increased attention due to its inherent advantages over port fuel injection (PFI), such as reduced risks of abnormal combustion, higher specific power, and improved thermal efficiency. However, the mixture preparation process in DI operation generally leads to a stratified charge, especially under intermediate-to-late injection strategies, which in turn strongly affects ignition, combustion performance, and engine-out emissions. Therefore, investigating mixture formation, its key influencing parameters, and the resulting effects on the combustion process is essential for the proper design and optimization of hydrogen-fuelled DI ICEs.
In this context, computational fluid dynamics (CFD) emerges as a powerful tool to address this research gap. Nevertheless, the numerical simulation of hydrogen DI ICEs presents several challenges, mainly related to the high pressure ratios across the injector nozzle, which generate under-expanded hydrogen jets with complex shock structures, as well as to the combustion behaviour of lean air–hydrogen mixtures characterized by thermo-diffusive instabilities. Consequently, the development of a high-fidelity and computationally efficient CFD methodology is a key requirement.
In this work, a retrofitted single-cylinder engine (SCE) equipped with a hollow-cone injector is simulated over the entire engine cycle, considering operation under a moderately late DI strategy. First, the proposed 3D-CFD methodology is validated against the engine experimental data to assess its predictivity. The same operating condition is then investigated through multi-cycle simulations to evaluate numerical stability and analyse convergence behaviour. The results show that the air–hydrogen mixture is highly stratified at ignition timing, yet the methodology accurately captures the in-cylinder pressure and heat release rate evolution, also across multiple engine cycles.