Thermo-fluid dynamic modelling of a six-cylinder SI engine with a secondary air injection system

This paper deals with the modelling of one-dimensional (1D) unsteady flows in the complete exhaust system of spark-ignited (SI) engines with secondary air injection, to reduce the catalyst light-off time and to speed up the after-treatment system warm-up. In particular, the numerical code GASDYN, developed by the authors, has been extended to model the injection of air in the exhaust manifold, simulating the post-oxidation of pollutant emissions in the ducts. The main chemical reactions arising in the gas phase, in the upper part of the exhaust manifold, have been included, considering the oxidation of C3H6, C3H8, and CO, and the steam reforming of C3H6 and C3H8. The heat released in the gas due to the exothermal reactions has been taken into account to evaluate the exhaust gas temperature caused by the injection of air. The gas composition in the exhaust pipe system is imposed by the cylinder discharge process after the calculation of the combustion process via a thermodynamic multizone combustion model, based on a ‘fractal’ approach and augmented by kinetic emission sub-models for the prediction of emissions. A sub-model for the catalytic converter has been applied to simulate the pre-catalysts and the main catalysts from a fluid dynamic, thermal, and chemical point of view, pointing out the influence of secondary air injection parameters on the conversion efficiency of pollutants during the system warm-up. A Fiat–GM Powertrain, six-cylinder 3.2 l automotive SI engine (for Alfa Romeo) complying with Euro IV regulations has been modelled, in order to predict the chemical specie concentration along the exhaust system, allowing for reactions in gas and solid phases and taking account of the consequent heat released, which notably influences the wall and gas temperatures with and without secondary air injection. A large set of experimental data concerning this engine (in-cylinder pressures, pressure pulses, chemical composition along the system) has enabled a comprehensive comparison between predictions and measurements, in order to validate the model in different operating conditions.