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In the present paper, a recently developed centrifugal compressor model is briefly summarized. It provides a refined geometrical schematization of the device, especially of the impeller, starting from a reduced set of linear and angular dimensions. A geometrical module reproduces the 3D geometry of the impeller and furnishes the data employed to solve the 1D flow equations inside the rotating and stationary ducts constituting the complete device. The 1D compressor model allows to predict the performance maps (pressure ratio and efficiency) with good accuracy, once the tuning of a number of parameters is realized to characterize various flow losses and heat exchange. To overcome the limitations related to the model tuning, unknown parameters are selected with reference to 5 different devices employing an optimization procedure (modeFRONTIER™).

The modern engine development process is characterized by shorter development cycles and a reduced number of prototypes. However, simultaneously exhaust after-treatment and emission testing is becoming increasingly more sophisticated. It is expected that predictive simulation tools that encompass the entire powertrain can potentially improve the efficiency of the calibration process. The testing of an ECU using a HiL system requires a real-time model. Additionally, if the initial parameters of the ECU are to be defined and tested, the model has to be more accurate than is typical for ECU functional testing. It is possible to enhance the generalization capability of the simulation, with neuronal network sub-models embedded into the architecture of a physical model, while still maintaining real-time execution. This paper emphasizes the experimental investigation and physical modeling of the port fuel injected SI engine.

The modern engine design process is characterized by shorter development cycles and a reduced number of prototypes. However, simultaneously exhaust after-treatment and emission testing is becoming increasingly more sophisticated. The introduction of predictive real-time simulation tools that represent the entire powertrain can likely contribute to improving the efficiency of the calibration process. Engine models, which are purely based on physical first principles, are usually not capable of real-time applications, especially if the simulation is focused on cold start and warm-up behavior. However, the initial data definition for the ECU using a Hardware-in-the-Loop (HiL)-Simulator requires a model with both real-time capability and sufficient accuracy. The use of artificial intelligence systems becomes necessary, e.g. neural networks. Methods, structures and the realization of a hybrid real-time model are presented in this paper, which combines physical and neural network models.

A uniform flow distribution at converter inlet is one of the fundamental requirements to meet high catalytic efficiency. Commonly used tools for optimization of the inlet flow distribution are flow measurements as well as CFD analysis. This paper puts emphasis on the experimental procedures and results. The interaction of flow measurements and CFD is outlined. The exhaust gas flow is transient, compressible and hot, making in-situ flow measurements very complex. On the other hand, to utilize the advantages of flow testing at steady-state and cold conditions the significance of these results has to be verified first. CFD analysis under different boundary conditions prove that - in a first approach - the flow situation can be regarded as a sequence of successive, steady-state situations. Using the Reynolds analogy a formula for the steady-state, cold test mass flow is derived, taking into account the cylinder displacement and the rated speed.

Gasoline particulate filters (GPF) recently entered the market, and are already regarded a state-of-the-art solution for gasoline exhaust aftertreatment systems to enable EU6d-TEMP fulfilment and beyond. Especially for coated GPF applications, the prognosis of the emission conversion performance over lifetime poses an ambitious challenge, which significantly influences future catalyst diagnosis calibrations. The paper presents key-findings for the different GPF application variants. In the first part, experimental GPF ash loading results are presented. Ash accumulates as thin wall layers and short plugs, but does not penetrate into the wall. However, it suppresses deep bed filtration of soot, initially decreasing the soot-loaded backpressure. For the emission calibration, the non-linear backpressure development complicates the soot load monitoring, eventually leading to compromises between high safety against soot overloading and a low number of active regenerations.