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Technical Paper

N-Heptane Ignition Delay Time Model for Two Stage Combustion Process

2017-09-04
2017-24-0071
Ignition delay time is key to any hydrocarbon combustion process. In that sense, this parameter has to be known accurately, and especially for internal combustion engine applications. Combustion timing is one of the most important factors influencing overall engine performances like power output, combustion efficiency, emissions, in-cylinder peak pressure, etc. In the case of low temperature combustion (LTC) mode (e.g. HCCI mode), this parameter is controlled by chemical kinetics. In this paper, an ignition delay time model including 7 direct reactions and 13 species coupled with a temperature criterion is described. This mechanism has been obtained from the previous 26-step n-heptane reduced mechanism, focusing on the low temperature region which is the most important phase during the two stage combustion process. The complete model works with 7 reactions until the critical temperature is reached, leading to the detection of the ignition delay time value.
Technical Paper

A Mixing Timescale Model for PDF Simulations of LTC Combustion Process in Internal Combustion Engines

2019-09-09
2019-24-0113
Transported probability density function (PDF) methods are currently being pursued as a viable approach to model the effects of turbulent mixing and mixture stratification, especially for new alternative combustion modes as for example Homogeneous Charge Compression ignition (HCCI) which is one of the advanced low temperature combustion (LTC) concepts. Recently, they have been applied to simple engine configurations to demonstrate the importance of accurate accounting for turbulence/chemistry interactions. PDF methods can explicitly account for the turbulent fluctuations in species composition and temperature relative to mean value. The choice of the mixing model is an important aspect of PDF approach. Different mixing models can be found in the literature, the most popular is the IEM model (Interaction by Exchange with the Mean). This model is very similar to the LMSE model (Linear Mean Square Estimation).
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