With the further tightening of emission regulations and the introduction of real driving emission tests (RDE), the simulative prediction of emissions is becoming increasingly important for the development of future low-emission internal combustion engines. In this context, gas-exchange simulation can be used as a powerful tool for the evaluation of new design concepts. However, the simplified description of the combustion chamber can make the prediction of complex in-cylinder phenomena like emission formation quite challenging. The present work focuses on the prediction of gaseous pollutants from a spark-ignition (SI) direct injection (DI) engine with 1D–0D gas-exchange simulations. The accuracy of the simulative prediction regarding gaseous pollutant emissions is assessed based on the comparison with measurement data obtained with a research single cylinder engine (SCE). Multiple variations of engine operating parameters – for example, load, speed, air-to-fuel ratio, valve timing – are taken into account to verify the predictivity of the simulation toward changing engine operating conditions. Regarding the unburned hydrocarbon (HC) emissions, phenomenological models are used to estimate the contribution of the piston top-land crevice as well as flame wall-quenching and oil-film fuel adsorption-desorption mechanisms. Regarding CO and NO emissions, multiple approaches to describe the burned zone kinetics in combination with a two-zone 0D combustion chamber model are evaluated. In particular, calculations with reduced reaction kinetics are compared with simplified kinetic descriptions. At engine warm operation, the HC models show an accuracy mainly within 20%. The predictions for the NO emissions follow the trend of the measurements with changing engine operating parameters and all modeled results are mainly within ±20%. Regarding CO emissions, the simplified kinetic models are not capable to predict CO at stoichiometric conditions with errors below 30%. With the usage of a reduced kinetic mechanism, a better prediction capability of CO at stoichiometric air-to-fuel ratio could be achieved.

Reynolds-averaged Navier–Stokes (RANS) three-dimensional (3D) computational fluid dynamics (CFD) simulations of gaseous emissions from combustion engines are very demanding due to the complex geometry, the emissions formation mechanisms, and the transient processes inside the cylinders. The validation of emission simulation is challenging because of modeling simplifications, fundamental differences from reality (e.g., fuel surrogates), and difficulty in the comparison with measured emission values, which depend on the measuring position. In this study, detailed gaseous emission data were acquired for a spark ignition (SI) direct-injection (DI) single-cylinder engine (SCE) fueled with a toluene reference fuel (TRF) surrogate to allow precise comparison with simulations. Multiple devices in different sampling locations were used for the measurement of average emission concentration, as well as hydrocarbon (HC) cycle- and species-resolved values. A RANS 3D-CFD methodology to predict gaseous pollutants was developed and validated with this experimental database. For precise validation, the emission comparison was performed in the exact same locations as the pollutants were measured. Additionally, the same surrogate fuel used in the measurements was defined in the simulation. To focus on the emission prediction, the pressure and heat release traces were reproduced by calibrating a G-equation flame propagation model. The differences of simulation results with measurements were within 4% for CO₂, while for O₂ and NO, the deviations were within 26%. CO emissions were generally overestimated probably because of inaccuracies in mixture formation. For HC emissions, deviations up to 50% were observed possibly due to inexact estimation of the influence of the piston-ring crevice geometry. The reasonable prediction accuracy in the RANS context makes the method a useful framework for the analysis of emissions from SI engines, as well as for mechanism validation under engine relevant conditions.

Considering the strict regulations on the transport sector emissions, predictive models for engine emissions are essential tools to optimize high-efficient low-emission internal combustion engines (ICE) for vehicles. This aspect is of major importance, especially for developing new combustion concepts (e.g. lean, pre-chamber) or using alternative fuels. Among the gaseous emissions from spark-ignition (SI) engines, unburned hydrocarbons (uHC) are the most challenging species to model due to the complexity of the formation mechanisms. Phenomenological models are successfully used in these cases to predict emissions with a reduced computational effort. In this work, uHC phenomenological model approaches by the authors are further developed to improve the model predictivity for multiple variations including engine design, engine operating parameters, as well as different fuels and ignition methods. The model accounts for uHC contributions from piston top-land crevice, wall flame quenching, oil film fuel adsorption/desorption and features a tabulated-chemistry approach to describe uHC post-oxidation. With the support of 3D-CFD simulations, multiple novel modelling assumptions are developed and verified. The model is validated against an extensive measurement database obtained with two small-bore single-cylinder engines (SCE) fuelled with gasoline-like fuel, one with SI and one with pre-chamber, as well as against data from two different ultra-lean large-bore engines fuelled with natural gas (one equipped with a pre-chamber and one dual-fuel with a diesel pilot). The model correctly predicts the trends and absolute values of uHC emissions for all the operating conditions and the engines with an accuracy on average of 11.4%. The results demonstrate the general applicability of the model to different engine designs, the correct description of the main mechanisms contributing to fuel partial oxidation, and the potential to be extended to predict unburned fuel emissions with other fuels.