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Since the mechanisms leading to cyclic combustion variabilities in direct injection gasoline engines are still poorly understood, advanced computational studies are necessary to be able to predict, analyze and optimize the complete engine process from aerodynamics to mixing, ignition, combustion and heat transfer. In this work the Scale-Adaptive Simulation (SAS) turbulence model is used in combination with a parameterized lagrangian spray model for the purpose of predicting transient in-cylinder cold flow, injection and mixture formation in a gasoline engine. An existing CFD model based on FLUENT v15.0 [1] has been extended with a spray description using the FLUENT Discrete Phase Model (DPM). This article will first discuss the validation of the in-cylinder cold flow model using experimental data measured within an optically accessible engine by High Speed Particle Image Velocimetry (HS-PIV).

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.