Future combustion engine applications require highest possible energy conversion efficiencies to reduce their environmental impact and be economically competitive. So far, spark-ignition (SI) engine combustion development mostly consisted of optimizing the hemispherical flame propagation combustion method. Thereby, a significant efficiency increase is only achievable in combination with high excess air dilution or increased combustion speed. However, with increasing excess air dilution, this is difficult due to decreasing flame speeds and flammability limits. Simultaneously, researchers have been investigating homogeneous charge compression ignition (HCCI) that achieves higher efficiencies due to its rapid volume reaction combustion and also enables high excess air dilution. However, the combustion is complex to control as it is initiated by auto-ignition (AI) processes. In-cylinder conditions reliably need to be reproduced to prevent damaging pre-ignitions. Consequently, HCCI has only been applied for low load operation. The spark-assisted compression ignition (SACI) represents a compromise between SI combustion and HCCI. Thereby, a flame propagation is initiated that triggers a controlled volume reaction in the remaining charge. By now, there is only one (mainly stoichiometric) application of SACI combustion in the market. In combination with lean mixtures, SACI allows overall efficiencies >40%. A market launch of a lean mixture SACI engine is therefore desirable. This requires a fast-running and predictive physical model to conduct robust concept studies in the early development process. This paper addresses the development of a quasi-dimensional burn rate model for the SACI combustion method. The modeling approach combines the well-established two-zone entrainment model (for flame propagation) with a multi pseudo-zone volume reaction model based on a distributed AI integral, which is linked to a detailed two-stage AI model. The model is integrated into the so-called cylinder module developed at IFS (Institute of Automotive Engineering Stuttgart). A validation versus measurement data shows a satisfactory prediction of the burn rate and pressure curve.
