Gasoline PPC: A Parametric Study of Late Cycle Mixing Conditions using a Predictive Two-zone SRM Modeling Tool 2013-01-2621
The relatively new combustion concept known as partially premixed combustion (PPC) has high efficiency and low emissions. However, there are still challenges when it comes to fully understanding and implementing PPC. Thus a predictive combustion tool was used to gain further insight into the combustion process in late cycle mixing. The modeling tool is a stochastic reactor model (SRM) based on probability density functions (PDF). The model requires less computational time than a similar study using computational fluid dynamics (CFD). A novel approach with a two-zone SRM was used to capture the behavior of the partially premixed or stratified zones prior to ignition. This study focuses on PPC mixing conditions and the use of an efficient analysis approach. It was done in three steps: a validation of the two-zone SRM against CFD and experimental data, a parametric study using a design of experiment (DOE) approach to late cycle mixing conditions, and analyses of fuel mass distribution with time-resolved probability density functions (TPDF). Results from the investigation show that the two-zone SRM is suitable for prediction of the PPC conditions and is able to run simulations at an average of 25 min/cycle. The findings of the parametric study showed, that a higher mixing intensity is preferable to longer mixing duration before the start of combustion as it decreases pressure rise rate without penalizing combustion efficiency. The TPDF plots offer a good alternative when presenting mixture fraction distributions. However, they may be more suited to smaller amounts of data than are presented in this investigation.
Citation: Lundgren, M., Tuner, M., Johansson, B., Bjerkborn, S. et al., "Gasoline PPC: A Parametric Study of Late Cycle Mixing Conditions using a Predictive Two-zone SRM Modeling Tool," SAE Technical Paper 2013-01-2621, 2013, https://doi.org/10.4271/2013-01-2621. Download Citation
Marcus Lundgren, Martin Tuner, Bengt Johansson, Simon Bjerkborn, Karin Frojd, Arne Andersson, Fabian Mauss, Bincheng Jiang