A statistical analysis of developing knock intensity in a mixture with temperature inhomogeneities
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Luong, Minh Bau
Desai, Swapnil

Pérez, Francisco E. Hernández
Sankaran, Ramanan

Johansson, Bengt

Im, Hong G.

KAUST Department
Clean Combustion Research CenterComputational Reacting Flow Laboratory (CRFL)
Mechanical Engineering Program
Physical Science and Engineering (PSE) Division
Date
2020-07-27Online Publication Date
2020-07-27Print Publication Date
2020-07Embargo End Date
2022-07-27Submitted Date
2019-11-07Permanent link to this record
http://hdl.handle.net/10754/664485
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Knock formation and its intensity for a stoichiometric ethanol/air mixture under a representative endgas auto-ignition condition in IC engines with temperature inhomogeneities are investigated using multidimensional direct numerical simulations (DNS) with a 40-species skeletal mechanism of ethanol. Two- and three-dimensional simulations are performed by systematically varying temperature fluctuations and its most energetic length scale, lT. The volumetric fraction of the mixture regions that have the propensity to detonation development, FD, is proposed as a metric to predict the amplitude of knock intensity.It isfound that with increasing lT, FD shows a good agreement with the heat release fraction of the mixture regions with pressure greater than equilibrium pressure, FH. The detonation peninsula is well captured by FD and FH when plotting them as a function of the volume-averaged ξ , ξ, (ξ = a/Ssp is the ratio of the acoustic speed, a to the ignition front speed, Ssp). Decreasing lT is found to significantly reduce the super-knock intensity. The results suggest that decreasing lT, as in engines with tumble desigCitation
Luong, M. B., Desai, S., Pérez, F. E. H., Sankaran, R., Johansson, B., & Im, H. G. (2020). A statistical analysis of developing knock intensity in a mixture with temperature inhomogeneities. Proceedings of the Combustion Institute. doi:10.1016/j.proci.2020.05.044Sponsors
This work was sponsored by King Abdullah University of Science and Technology and used the resources of the KAUST Supercomputing Laboratory.Publisher
Elsevier BVAdditional Links
https://linkinghub.elsevier.com/retrieve/pii/S1540748920300924ae974a485f413a2113503eed53cd6c53
10.1016/j.proci.2020.05.044