Probabilistic Approach to Predict Abnormal Combustion in Spark Ignition Engines

Mohammed Jaasim, Minh Bau Luong, Aliou Sow, Francisco Hernandez Perez, Hong G. Im

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations


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. [1]. 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, P. 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.
Original languageEnglish (US)
Title of host publicationSAE Technical Paper Series
PublisherSAE International
StatePublished - Sep 10 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was funded by King Abdullah University of Science and Technology (KAUST) and the computations utilized the KAUST supercomputing facility. The authors thank convergent science for providing the licenses for the code.


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