Browse Publications Technical Papers 2009-01-1778

Fast Physical Emission Predictions for Off-line Calibration of Transient Control Strategies 2009-01-1778

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.
A new approach for predicting soot emissions has previously been presented [1] where the frame of the model was a multizone approach developed for NO formation prediction. Soot was, 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 soot oxidation. The post-flamefront oxidation is assumed to be dominated by surface oxidation, modelled with the Nagle and Strickland Constable oxidation model. The model showed good agreement with measured emissions for both NOx and soot over a wide range of operating conditions with conventional diesel combustion.
In this article some examples of transient control strategies have been tested and the engine performance and emissions have been measured with the main object to test the predictivity of the emission models to see if they are suitable for off-line calibration. The results show that the model predicts the transient emissions of both NO and soot rather well although it is hard to evaluate the soot predictions with high time resolution due to the rather slow measurements. Important model inputs such as air/fuel ratio and EGR rate is also more difficult to measure during transients than during steady state conditions and this can decrease the accuracy of the predictions. However, the advantages and disadvantages of different strategies can be predicted.


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