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Improving fuel efficiency while meeting relevant emission limits set by emissions legislation is among the main objectives of engine development. Simultaneously the development costs and development time have to be steadily reduced. For these reasons, the high demands in terms of quality and validity of measurements at the engine test bench are continuously rising. This paper will present a new methodology for efficient testing of an industrial combustion engine in order to improve the process of decision making for combustion-relevant component setups. The methodology includes various modules for increasing measurement quality and validity. Modules like stationary point detection to determine steady state engine behavior, signal quality checks to monitor the signal quality of chosen measurement signals and plausibility checks to evaluate physical relations between several measurement signals ensure a high measurement quality over all measurements.

Due to the increasing number of engine setting parameters to be optimized, model based calibration techniques have been introduced to medium speed engine testing to keep the number of engine tests low. Polynomials in combination with d-optimal test plans have been proven to be a good choice for modeling the stationary behavior of selected engine outputs. Model approaches like artificial neural networks (ANNs) have been rarely used for medium speed purposes since they require quite high amounts of testing data for model training. To evaluate the potential of these model approaches radial basis function networks, a subclass of neural networks, as well as Gaussian processes have been investigated as alternatives to polynomials. A manageable amount of tests according to an adapted d-optimal test plan was carried out at a test bench.

A spray model for pressure-swirl atomizers that is based on a linearized instability analysis of liquid sheets has been combined with an ignition and combustion model for stratified charge spark ignition engines. The ignition model has been advanced, such that the presence of dual spark plugs can now be accounted for. Independent validation of the spray model is achieved by investigating a pressure-swirl injector inside a pressure bomb containing air at ambient temperature. In a second step, the complete model is used to estimate the performance of a small marine DISI Two-Stroke engine operating in stratified charge mode. Simulation results and experimental data are compared for several different injection timings and the agreement is generally good such that there is confidence in the predictive quality of the combustion model. Finally the model is applied in a conceptual study to investigate possible benefits of split injections.

A phenomenological multi-zone model for prediction of heat release and exhaust emissions in DI Diesel engines is developed in an attempt to diminish the common trade-off between model accuracy and computing efficiency. This task is achieved by uncoupling the kinetic emission models from the detailed calculations of spray formation and heat release, which allows to substantially reduce the number of zones in that the kinetics of NOx- and soot-formation have to be solved. The predicted results of the model are compared with experimental data obtained from a turbo-charged, high-speed Diesel engine. The results for heat release and pressure histories as well as the estimated nitric oxide emissions are in good agreement with the test data, indicating that an appropriate simplification of the complex combustion process has been established. The soot model is capable of predicting trends, but there are further improvements necessary in order to produce quantitatively correct results.