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A self-calibrating model for Diesel engine simulations is presented. The overall model consists of a zero-dimensional direct injection stochastic reactor model (DI-SRM) for engine in-cylinder processes simulations and a package of optimization algorithms (OPAL) suitable for solving various optimization, automatization and search problems. In the DI-SRM, based on an extensive model parameters study, the mixing time history that affects the level of in-cylinder turbulence was selected as a main calibration parameter. As targets during calibration against the experimental data, in-cylinder pressure history and engine-out emissions, including nitrogen oxides and unburned hydrocarbons were chosen. The calibration task was solved using DI-SRM and OPAL working as an integrated tool. Within OPAL, genetic algorithms (GA) were used to determine model constants necessary for calibrating. Engine-out emissions in DI-SRM were calculated based on the reduced mechanism of n-heptane.

Worldwide, there is the demand to reduce harmful emissions from non-road vehicles to fulfill European Stage V+ and VI (2022, 2024) emission legislation. The rules require significant reductions in nitrogen oxides (NOx), methane (CH4) and formaldehyde (CH2O) emissions from non-road vehicles. Compressed natural gas (CNG) engines with appropriate exhaust aftertreatment systems such as three-way catalytic converter (TWC) can meet these regulations. An issue remains for reducing emissions during the engine cold start where the CNG engine and TWC yet do not reach their optimum operating conditions. The resulting complexity of engine and catalyst calibration can be efficiently supported by numerical models. Hence, it is required to develop accurate simulation models which can predict cold start emissions. This work presents a real-time engine model for transient engine-out emission prediction using tabulated chemistry for CNG.