Model Guided Application for Investigating Particle Number (PN) Emissions in GDI Spark Ignition Engines 2019-26-0062
Model guided application combining physico-chemical internal combustion engine simulation with advanced analytics offers a robust and practical framework to develop and test particle number (PN) emissions reduction strategies. The digital engineering workflow in this paper integrates the Stochastic Reactor Model (SRM) Engine Suite with parameter estimation techniques to simulate particle formation and dynamics in gasoline direct injection (GDI) spark ignition (SI) engines. The evolution of the particle population characteristics at engine-out and through the sampling system is investigated. The particle population balance model is extended beyond soot to include sulphates and volatile organic fractions. This particle model is coupled with the gas phase chemistry precursors and is solved using a sectional method. To account for particle losses in the measurement system, a reactor network that accounts for the effects of dilution is implemented. The combustion chamber is divided into a wall zone and a bulk zone and the fuel impingement on the cylinder wall is simulated. The bulk zone consists of the majority of the in-cylinder charge and the wall zone is responsible for resolving the distribution of equivalence ratios near the wall which is crucial for the formation of soot. In this work, the SRM Engine Suite is calibrated to a single-cylinder test engine operated at 12 steady state load-speed points. First, the flame propagation model is calibrated using the experimental in-cylinder pressure profiles and gas phase emissions. Then, the population balance model parameters are calibrated based on the experimental data for particle size distributions from the 12 representative operating conditions. Good agreement was obtained for the in-cylinder gas pressure profiles, and gas phase emissions such as NOx, whereas the model captures the trends in unburned hydrocarbon emissions well. The model guided application identifies the critical dilution ratios to ensure a robust PN measurement procedure.
Kok Foong Lee, Nick Eaves, Sebastian Mosbach, David Ooi, Jiawei Lai, Amit Bhave, Andreas Manz, Jan Niklas Geiler, Jennifer Anna Noble, Dumitru Duca, Cristian Focsa
CMCL Innovations, Univ of Cambridge, Robert Bosch GmbH, University Of Lille
Symposium on International Automotive Technology 2019