A Monte Carlo Based Turbulent Flame Propagation Model for Predictive SI In-Cylinder Engine Simulations Employing Detailed Chemistry for Accurate Knock Prediction 2012-01-1680
This paper reports on a turbulent flame propagation model combined with a zero-dimensional two-zone stochastic reactor model (SRM) for efficient predictive SI in-cylinder combustion calculations. The SRM is a probability density function based model utilizing detailed chemistry, which allows for accurate knock prediction. The new model makes it possible to - in addition - study the effects of fuel chemistry on flame propagation, yielding a predictive tool for efficient SI in-cylinder calculations with all benefits of detailed kinetics.
The turbulent flame propagation model is based on a recent analytically derived formula by Kolla et al. It was simplified to better suit SI engine modelling, while retaining the features allowing for general application. Parameters which could be assumed constant for a large spectrum of situations were replaced with a small number of user parameters, for which assumed default values were found to provide a good fit to a range of cases. Only one parameter, the turbulence intensity, needed tuning to obtain excellent agreement for various cases. The laminar flame speed is obtained from a laminar flame speed library generated using detailed chemistry.
The flame development was calculated from the turbulent flame speed under the assumption of a spherical flame. A Monte Carlo geometry calculation was applied to cater for arbitrary cylinder geometries and spark plug positions, modelling the geometrical properties of the flame with high precision. In later stages of the project, a polygon based description of the flame surface was used, to achieve faster computational times than those of the Monte Carlo model.
Citation: Bjerkborn, S., Frojd, K., Perlman, C., and Mauss, F., "A Monte Carlo Based Turbulent Flame Propagation Model for Predictive SI In-Cylinder Engine Simulations Employing Detailed Chemistry for Accurate Knock Prediction," SAE Int. J. Engines 5(4):1637-1647, 2012, https://doi.org/10.4271/2012-01-1680. Download Citation
Simon Bjerkborn, Karin Frojd, Cathleen Perlman, Fabian Mauss
LOGE AB, BTU Cottbus
SAE 2012 International Powertrains, Fuels & Lubricants Meeting
SAE International Journal of Engines-V121-3EJ, SAE International Journal of Engines-V121-3