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Hybrid phenomenological and mathematical-based modeling approach for diesel engine emission predictio
ISSN: 0148-7191, e-ISSN: 2688-3627
To be published on April 14, 2020 by SAE International in United States
Due to the negative health effects associated with engine pollutants, environmental problems caused by combustion engine emissions and the current strict emission standards, it is essential to better understand and model the emission formation process in order to reduce them. Further development of emission models, improves the accuracy of the model-based optimization approach, which is used as a decisive tool for combustion system development and engine-out emission reduction. The numerical approaches for emission simulation are closely coupled to the combustion model. Using a detailed emission model, considering the 3D mixture preparation simulation incl. chemical reactions, demands high computational effort. Phenomenological models, used in 1-D approaches for model-based system optimization can deliver heat release rate and using a two-zone approach can estimate the NOx emissions. Due to the lack in modeling of 3D mixture preparation phenomena, such models are not capable to predict soot or HC emissions. However, employing physical-based air-path and combustion modeling, these models can predict the engine behavior outside of the training points. Mathematical models are very fast and accurate enough in the training area to simulate the engine-out emissions. However, they are not capable of extrapolation, if the engine operating conditions deviate from the training conditions. Combining the phenomenological combustion model with mathematical emission prediction model can provide multiple advantages. This so called hybrid emission modeling approach includes a predictive air-path and combustion model to calculate the characteristic combustion parameters, like ignition delay, flame temperature, etc. for prediction of emissions by mathematical approaches. The mathematical model which uses such input characteristics parameters from the combustion process is first trained and then used for prediction of NOx, soot, HC and CO emissions. Using phenomenological combustion model, the effects of changing the engine operating conditions outside the training area on characteristic parameters coming from the combustion model can be simulated. Using these characteristic parameters, the engine emissions can be well predicted by the mathematical model, considering engine operating conditions. In this paper, the novel hybrid emission model is presented and its advantages and limitations comparing to physical and mathematical models for diesel emission modeling are discussed. Accuracy, computational effort and application fields are shown and future development potentials are discussed.