Loading…
A comprehensive Lagrangian flame-kernel model to predict ignition in SI engines
A Lagrangian model to predict the first stages of the combustion process in SI engines, when the size of flame kernel is small compared with the mesh size, and flame development is influenced by heat transfer from the spark, local flow, turbulence and air/fuel mixture distribution is presented. The...
Saved in:
Published in: | International journal of computer mathematics 2014-01, Vol.91 (1), p.157-174 |
---|---|
Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | A Lagrangian model to predict the first stages of the combustion process in SI engines, when the size of flame kernel is small compared with the mesh size, and flame development is influenced by heat transfer from the spark, local flow, turbulence and air/fuel mixture distribution is presented. The spark channel is initially represented by a set of Lagrangian particles that are convected by the mean flow. Flame kernels are launched locally for all the particles satisfying an ignition criterion based on the local Karlovitz number. For each of them, equations of energy and mass are solved accounting for electrical power transferred from the electrical circuit, local turbulence and flame speed. The proposed model has been validated with experimental data provided by Herweg et al.; a computational mesh reproducing the geometrical details of the optical, pre-chamber SI engine was built, including the electrodes. Initially, cold-flow simulations were carried out to verify the validity of the computed flow-field and turbulent distribution at ignition time. Then, the combustion process was simulated accounting for the effects of different engine speeds, air/fuel ratio and spark-plug position. Encouraging results were achieved for a wide range of operating conditions. |
---|---|
ISSN: | 0020-7160 1029-0265 |
DOI: | 10.1080/00207160.2013.829213 |