- Category Technical paper
- Edition SIA
- Date 12/04/2013
- Author G. Bernard, L. Saussol - LMS | O. Marchand, F. Lefevre, F. Carre - CRMT Centre de Recherche en Machines Thermiques
- Language English
Type PDF file (1.43 Mo)
(Downloadable immediately on receipt of online payment)
- Number of pages 11
- Code R-2013-06-18
- Fee from 8.00 € to 10.00 €
In order to meet emission standards (EuroVI), powertrain has become a very complex system leading to a strong interaction between powertrain components. Moreover, during the past decade, new engine strategies and technologies were developed to reduce pollutants’ emissions such as alternative fuels, electrical vehicle or after-treatment (SCR). In this context, system simulation is more and more used. Indeed, it allows to take into account interactions between subsystems but also integration of mechanical, and control components, reducing the use of expensive and intensive testing, and finally it makes it possible to accurately optimize powertrain architectures. System simulation is thus an interesting tool to study and apprehend new technologies as the ones developed to reduce pollutants emissions.
The work reported in this paper will focus on simulation applied to a heavy trucks Natural Gas (NG) spark ignition engine operating in lean fuel mixture. In-Cylinder process will be predicted in a physical way with reduced experimental data dependency and extrapolation will be correlated with additional experimental data. The characteristics of NG engine allow for strongly reducing pollutants formation within the combustion chamber as well as the fuel consumption. However, standard pollutant models were not able to reproduce the CO and NOx quantities measured during the engine tests as their order of magnitude is low. Therefore, specific developments on pollutants kinetics mechanism models have been done in order to increase their prediction. Finally, a comparison between simulation and test bench results has been made on a complete engine map showing a strong improvement in the prediction of the pollutant model.
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