Abstract
In the first part-parametric study of the present study, the effects of different mean temperature, T0, within and outside the negative temperature (NTC) regime together with both thermal and equivalence ratio stratification on the ignition of n-heptane/air mixture under homogeneous-charge compression ignition (HCCI) conditions were investigated . For the details of twenty-three DNS cases, readers are
referred to the part I of the present study .
In this second part of the present study, the chemical aspects of thermally- and/or compositionallystratified n-heptane/air mixture are examined by analyzing the available data to obtain the insight of the ignition process in HCCI combustion. Based on the temporal evolution of important species and the overall reaction pathways of n-heptane oxidation mechanism, the effects of T0, T and on the ignition characteristics of HCI combustion are further elucidated. Chemical explosive mode analysis (CEMA) is adopted to understand the spatial ignition characteristics of the lean n-heptane/air mixture by identifying controlling species and elementary reactions at different locations and times. CEMA has been applied to various DNS problems such as turbulent lifted jet flames in heated coflows, turbulent
reacting jet flames in cross flows, and ignition of hydrocarbon fuel/air mixtures under HCCI conditions. From these studies, CEMA has been proved as a reliable computational flame diagnostics tool to systematically detect important species and reactions for premixed flames and limited phenomena including ignition and extinction.
Original language | English (US) |
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Title of host publication | 10th Asia-Pacific Conference on Combustion, ASPACC 2015 |
Publisher | Combustion Institute |
State | Published - Jan 1 2015 |
Bibliographical note
KAUST Repository Item: Exported on 2020-12-24Acknowledgements: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2015R1A2A2A01007378). This research used the resources of the Supercomputing Laboratory at King Abdullah University of Science and Technology (KAUST).