Fast Physical Prediction of NO and Soot in Diesel Engines

2009-01-1121

04/20/2009

Event
SAE World Congress & Exhibition
Authors Abstract
Content
A clear trend in engine development is that the engines are becoming more and more complex both regarding components and component-systems as well as controlling them. These complex engines have great potential to minimize emissions but they also have a great number of combinations of setting. Systematic testing to find these optimum settings is getting more and more challenging. A possible remedy is to roughly optimize these settings offline with predictive models and then only perform the fine tuning in the engine test bed. To be able to do so, two things are needed; firstly a engine model that will predict how the different setting affect engine performance and secondly how the engine performance affects the emissions.
This article shows a new approach for predicting soot emissions. The frame of the model is a multizone approach developed for NO formation prediction. Soot is, in the presented model, predicted by assuming that a roughly constant fraction of the fuel remains as soot on the lean side of the flame and thereafter modelling the conditions for post-flamefront oxidation. The post-flamefront oxidation is assumed to be dominated by surface oxidation, modelled with the Nagle and Strickland Constable oxidation model. By using this simplified emissions model and by replacing time demanding equations with look-up tables, the calculation time needed to predict both NO and soot was reduced to approximately 0.15 seconds per engine cycle.
The resulting model shows good agreement with measured emissions for both NOx and soot over a wide range of operating conditions with conventional diesel combustion.
Meta TagsDetails
DOI
https://doi.org/10.4271/2009-01-1121
Pages
12
Citation
Westlund, A., and Ångström, H., "Fast Physical Prediction of NO and Soot in Diesel Engines," SAE Technical Paper 2009-01-1121, 2009, https://doi.org/10.4271/2009-01-1121.
Additional Details
Publisher
Published
Apr 20, 2009
Product Code
2009-01-1121
Content Type
Technical Paper
Language
English