Probabilistic Approach to Predict Abnormal Combustion in Spark Ignition Engines King Abdullah University of Science and Tech

Format:

Conference/Event

Author/Creator:

Mubarak Ali, Mubarak Ali, author.

Contributor:

Hernández Pérez, Francisco

Im, Hong

Luong, Minh Bau

Sow, Aliou

Conference Name:

International Powertrains, Fuels & Lubricants Meeting (2018-09-17 : Heidelberg, Germany)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2018

Summary:

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 and others [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, 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

Notes:

Vendor supplied data

Publisher Number:

2018-01-1722

Access Restriction:

Restricted for use by site license