Knock Tendency Prediction in Highly Charged SI Engines

2017-32-0130

11/05/2017

Event
JSAE/SAE Small Engine Technologies Conference & Exhibition
Authors Abstract
Content
The continually increasing stringent requirements in terms of emissions and performance lead to the demand for further development of gasoline engines, in order to satisfy the regulations and to be competitive in the market.
One of the main limitations in simultaneously improving the efficiency and performance of SI engines is the knock behaviour. This phenomenon limits either the possibility to adopt a higher compression ratio, which would be beneficial for the engine efficiency, or it causes a poor combustion timing which leads to a higher fuel consumption and a lag in performance. As a result, having the possibility to judge the risk of knock during the design phase can be beneficial to increase the potentials of the engine.
In this work, a methodology for the prediction of the knock tendency in spark ignition engines using a 3D-CFD software has been developed. This method, evaluating the local conditions in the combustion chamber, allows to predict also the area where the autoignition will occur. For this study, two approaches, based on the work of Douaud & Eyzat [3] and the Kinetics-fit model [4], have been considered and compared.
Both approaches were evaluated at low-end torque and peak power, which represent the most significant points to describe the performance of an engine at full load, showing a good accordance with the theory behind the knock.
In conclusion, a comparison between the results in the two operating points has been performed, obtaining an objective criterion, independent of the engine operating conditions, for the prediction of the knock tendency.
Meta TagsDetails
DOI
https://doi.org/10.4271/2017-32-0130
Pages
9
Citation
Bevilacqua, V., Boeger, M., Corvaglia, G., Penzel, M. et al., "Knock Tendency Prediction in Highly Charged SI Engines," SAE Technical Paper 2017-32-0130, 2017, https://doi.org/10.4271/2017-32-0130.
Additional Details
Publisher
Published
Nov 5, 2017
Product Code
2017-32-0130
Content Type
Technical Paper
Language
English