Numerical Analysis of Fuel Impacts on Advanced Compression Ignition Strategies for Multi-Mode Internal Combustion Engines 2020-01-1124
Multi-mode combustion strategies may provide a promising pathway to improve thermal efficiency in light-duty spark ignition (SI) engines by enabling switchable combustion modes, wherein an engine may operate under advanced compression ignition (ACI) at low load and spark-assisted ignition at high load. The extension from the SI mode to the ACI mode requires accurate control of intake charge conditions, e.g., pressure, temperature and equivalence ratio, in order to achieve stable combustion phasing and rapid mode-switches. This study presents results from computational fluid dynamics (CFD) analysis to gain insights into mixture charge formation and combustion dynamics pertaining to auto-ignition processes. The computational study begins with a discussion of thermal wall boundary condition that significantly impacts the combustion phasing. The validated model setup with the properly optimized boundary condition was verified across broad range of engine load conditions with varying air-excess ratios, intake air charge temperatures, and two RON 98 fuels (Alkylate and E30). The overall trend in the reactivity of ACI combustion was found to be heavily impacted by the wall temperature condition, which has not been highlighted in previous experimental ACI engine studies. This suggests that major uncertainties in the CFD study of ACI engines may result from the unknown wall heat transfer rate. In addition, the obtained results reveal a distinct range of wall temperatures required for each of the fuels employed in this study, suggesting that fuel properties impact the mixture charge reactivity in response to a change in thermal wall boundary condition.
Citation: Kim, S., Kim, J., Shah, A., Scarcelli, R. et al., "Numerical Analysis of Fuel Impacts on Advanced Compression Ignition Strategies for Multi-Mode Internal Combustion Engines," SAE Technical Paper 2020-01-1124, 2020, https://doi.org/10.4271/2020-01-1124. Download Citation
Sayop Kim, Joohan Kim, Ashish Shah, Riccardo Scarcelli, Toby Rockstroh