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Probabilistic Approach to Predict Abnormal Combustion in Spark Ignition Engines
ISSN: 0148-7191, e-ISSN: 2688-3627
Published September 10, 2018 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
This study presents a computational framework to predict the outcome of combustion process based on a given RANS initial condition by performing statistical analysis of Sankaran number, Sa, and ignition regime theory proposed by Im et al. . A criterion to predict strong auto-ignition/detonation a priori is used in this study, which is based on Sankaran-Zeldovich criterion. In the context of detonation, Sa is normalized by a sound speed, and is spatially calculated for the bulk mixture with temperature and equivalence ratio stratifications. The initial conditions from previous pre-ignition simulations were used to compute the spatial Sa distribution followed by the statistics of Sa including the mean Sa, the probability density function (PDF) of Sa, and the detonation probability, PD. Sa is found to be decreased and detonation probability increased significantly with increase of temperature. The statistic mean Sa calculated for the entire computational domain and the predicted Sa from the theory were found to be nearly identical. The predictions based on the adapted Sankaran-Zel’dovich criterion and detonation probability agree well with the results of the previous high fidelity pre-ignition simulations.
- Mohammed Jaasim Mubarak Ali - King Abdullah University of Science & Tech.
- Minh Bau Luong - King Abdullah University of Science & Tech.
- Aliou Sow - King Abdullah University of Science & Tech.
- Francisco Hernandez Perez - King Abdullah University of Science & Tech.
- Hong Im - King Abdullah University of Science & Tech.
CitationMubarak Ali, M., Luong, M., Sow, A., Hernandez Perez, F. et al., "Probabilistic Approach to Predict Abnormal Combustion in Spark Ignition Engines," SAE Technical Paper 2018-01-1722, 2018, https://doi.org/10.4271/2018-01-1722.
Data Sets - Support Documents
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