Browsing by Author "Nissanka, I.D"

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    PublicationOpen Access
    A Numerical Investigation of Valve Timing and Intake Pressure Effects on Performance and Emissions in a Hydrogen Port Fuel Injection Engine
    (Faculty of Engineering, 2025-09-09) Wickramaarachchi, I; Nissanka, I.D; Wijeyakulasuriya, S
    Hydrogen internal combustion engines (H2ICEs) offer a viable low-emission alternative for decarbonizing transport, especially where full electrification is not practical. Among fueling strategies, port fuel injection (PFI) is particularly attractive due to its compatibility with existing engine platforms and simplicity compared to direct injection (DI). Performance and emissions in hydrogen PFI engines are strongly influenced by valve timing and intake boosting strategies. This study presents a computational framework to investigate the coupled effects of valve timing and intake pressure on the performance, thermal efficiency, and NOx emissions of a hydrogen PFI engine under fuel-lean conditions (ϕ = 0.59). A modified Sandia optical engine geometry was simulated using CONVERGE CFD v4.1, employing detailed chemistry and adaptive mesh refinement. Latin Hypercube Sampling (LHS) was employed to generate 373 design cases that span a wide parametric space. Results show that intake boosting significantly improves performance, achieving a 220% increase in indicated power (up to 43.55 kW) and an 11% improvement in thermal efficiency (up to 48.7%) over the baseline configuration. However, these gains are accompanied by elevated NOx emissions, particularly at higher valve overlaps. Conversely, the configuration that achieved the lowest NOx emissions reduced them by 76% compared to the baseline, albeit at the expense of lower power and efficiency. The three configurations representing the most favorable outcomes for power, efficiency, and emissions within the studied parameter space highlight the inherent trade-offs among these objectives. These results provide practical guidance for calibrating hydrogen PFI engines and establish a solid foundation for future studies incorporating formal optimization methods.