To support the transition toward climate-neutral mobility and power generation, internal combustion engines (ICEs) must operate efficiently on renewable, carbon-neutral fuels. Hydrogen, methanol, and ammonia-hydrogen blends are promising candidates due to their favorable production pathways and combustion properties. However, their knock behavior differs significantly from conventional fuels, requiring dedicated simulation tools.
This work presents a modeling framework based on quasi-dimensional (QD) engine simulation, including two separate knock prediction models. The first model predicts the knock boundary of a given operating point and combines an auto-ignition model with a knock criterion. The overall methodology was originally developed for gasoline and is here adapted to hydrogen, methanol, and ammonia-hydrogen blends. For this purpose, the relevant fuel properties were incorporated into the auto-ignition model, and a suitable knock criterion was identified that applies to all investigated fuels. The model was validated using experimental data from single-cylinder engine tests. In addition, two entirely new modeling approaches were developed to predict statistical knock values, specifically knock frequency and knock intensity. Each model was calibrated once per fuel and subsequently validated across a wide range of conditions.
The results show that the adapted knock boundary model and the new statistical model accurately capture the knock behavior of hydrogen, methanol, and ammonia-hydrogen blends. The methodology enables predictive knock analysis using QD simulation and supports the development of robust, high-efficiency ICEs for future carbon-neutral applications.