Kinetic modeling of engine combustion: an uncertainty analysis

Song Cheng, Yi Yang, Michael J. Brear

Research output: Journal article publicationConference articleAcademic researchpeer-review


Prediction of engine combustion to some specified degree of accuracy requires kinetic models with sufficiently certain parameters. It is an open question as to whether a kinetic model that has been extensively validated by high-quality kinetic experiments is capable of reproducing engine combustion phenomenon to within typical levels of measurement uncertainty. This paper therefore introduces a method that can be used to evaluate the uncertainty in simulating engine combustion processes using the uncertainties that are embedded in the chemical mechanism. The method is based on a deterministic framework of uncertainty quantification, and is termed bound-to-bound data collaboration. This method can be used to quantify the engine modeling uncertainty propagated from the uncertainties in the kinetic mechanisms, and also allows examination of the impact of engine operating conditions on engine modeling uncertainty.

Original languageEnglish
Journal11th Asia-Pacific Conference on Combustion
Publication statusPublished - 2017
Externally publishedYes
Event11th Asia-Pacific Conference on Combustion, ASPACC 2017 - University of Sydney, Sydney, Australia
Duration: 10 Dec 201714 Dec 2017

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Energy Engineering and Power Technology
  • Fuel Technology
  • General Chemical Engineering


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