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Thermal management has always played a significant role in reducing emissions and improving the fuel efficiency of the internal combustion engines (ICEs). With a momentous influence on the thermal behavior of the engines, the cooling system has a considerable impact on ICE performance. In this scenario, a method based on artificial neural network (ANN) of the cooling system was proposed in this work. Specific modeling methods were adopted for the various operating conditions and flow circuits of the cooling system. To describe these varied dynamic characteristics, four ANN sub-models were established to simulate the system at different temperature stages. As a closed-loop system, the temperature of the cooling system can be regarded as a result of all the experienced operating points. Therefore, integral parameters describing the trajectory of the system were selected as the input of the ANNs.

The start of combustion in a spark-ignited engine is highly dependent upon the conditions between the two spark plug electrodes at ignition. In addition to the air-to-fuel ratio in this gap, the gas flow is seen as most critical. In a combustion engine with a standard spark plug that protrudes into the combustion chamber, this gas flow is mainly dependent upon the tumble, swirl, or squish that is developed by the cylinder head and the piston movement. However, the air movement in the pre-chamber depends on the orientation of the orifices towards the main combustion chamber (MCC). This implies a less complex manipulation of local velocity in the electrode gap. This paper focuses on the effect of different pre-chamber designs on spark deflection by the inflowing gas. Therefore, a test rig was developed using the spark plug thread in the cylinder head of a motored engine.

As an important means of engine development and optimization, modelbuilding plays an increasingly important role in reducing carbon dioxide emissions of the internal combustion engines (ICEs). However, due to the non-linearity and high dimension of the engine system, a large amount of data is required to obtain high model accuracy. Therefore, a modelling approach combining the experimental data and prior knowledge was proposed in this study. With this method, an artificial neural network (ANN) model simulating the engine brake specific fuel consumption (BSFC) was established. With mean square error (MSE) and Kullback-Leibler divergence (KLD) serving as the fitness functions, the 86 experimental samples and constructed physical models were used to optimize the ANN weights through genetic algorithms.

Particulates and nitrogen oxides comprise the main emission components of the Diesel combustion and therefore are subject to exhaust emission legislation in respective applications. Yet, with ever more stringent emission standards and test-procedures, such as in passenger vehicle applications, resulting exhaust gas after-treatment systems are quite complex and costly. Hence, new technologies for emission control have to be explored. The application of non-thermal plasma (NTP) as a means to perform exhaust gas after-treatment is one such promising technology. In several publications dealing with NTP exhaust gas after-treatment the plasma state was generated via dielectric barrier discharges. Another way to generate a NTP is by a corona high-frequency discharge. Hence, in contrast to earlier publications, the experiments in this publication were conducted on an operated series-production Diesel engine with an industrial pilottype corona ignition system.