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The present paper shows the use of a 1-D wave action model in the design process of a sequential parallel turbocharged engine. Even though little information was available at the beginning of the design process, a wave action model was used because of its capability of predicting the behaviour of the new engine. Main issues that were studied by means of simulations are: system architecture, turbochargers matching, prediction of the altitude effect on the turbocharging system, optimization of the transition between different modes, and control system design. The paper also summarises the limitations of the model, mainly concerning combustion process modelling, which were later identified once experimental information was available.

Despite the development in NOx aftertreatment for Diesel engines, EGR is a cost-effective solution to fulfill current and future emission regulations. There is a wide bibliography discussing the global effects of EGR on combustion and emissions. However, little has been published concerning the effects of the unsuitable EGR and air distribution among cylinders. Since current HSDI engines operate with EGR rates as high as 50% the effect of the unequal EGR distribution becomes important. In addition, cylinder-to-cylinder charge dispersion becomes a critical aspect on the control of low temperature combustion systems. In concordance with the aspects outlined before, the aim of this paper is to study the effects of the EGR cylinder to cylinder distribution on the engine performance and emissions. To cope with this objective, experiments have been conducted in a HSDI engine with two different EGR systems.

The present paper aims at developing a novel methodology to create a one-dimensional simulation model for an automotive turbocharged gasoline engine. The gas-path modeling of the engine, which includes a variable nozzle turbine (VNT) and variable valve timing (VVT) strategies, is described in detail. The model calibration procedure is mainly distinguished by isolating the different engine parts, decoupling the turbocharger, using PI controls to find fitting parameters and checking and validating mean and crank-angle resolved variables. To handle model limitations, it requires experimental data and a previous combustion analysis of some steady operating points. The methodology is completed with the determination of fitting correlations to estimate heat losses and pressure drops in engine systems. It also includes the training of an Artificial Neural Network (ANN) to predict the combustion process and the integration into the model and final validation.