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Engine valve flow coefficients are not only used to characterize the performance of valve/port designs, but also for modelling gas exchange in 0D/1D engine simulation. Flow coefficients are usually estimated with small pressure ratios and at ambient air conditions. In contrast, the ranges for pressure ratio, pressure and temperature level during engine operation are much more extensive. In this work the influences of these three parameters on SI engine poppet valve flow coefficients are investigated using 3D CFD and measurements for validation. While former investigations already showed some pressure ratio dependencies by measurement, here the use of 3D CFD allows a more comprehensive analysis and a deeper understanding of the relevant effects. At first, typical ranges for the three mentioned parameters during engine operation are presented.

Engine valve flow coefficients are used to describe the flow throughput performance of engine valve/port designs, and to model gas exchange in 0D/1D engine simulation. Valve flow coefficients are normally determined at a stationary flow test bench, separately for intake and exhaust side, in the absence of the piston. However, engine operation differs from this setup; i. a. the piston might interact with valve flow around scavenging top dead center, and instead of steady boundary conditions, valve flow is nearly always subjected to pressure pulsations, due to pressure wave reflections within the gas exchange ports. In this work the influences of piston position and flow pulsation on valve flow coefficients are investigated for different SI engine geometries by means of 3D CFD and measurements at an enhanced flow test bench.

Longitudinal models are used to evaluate different vehicle-engine concepts with respect to driving behavior and emissions. The engine is generally map-based. An explicit calculation of both fluid dynamics inside the engine air path and cylinder combustion is not considered due to long computing times. Particularly for dynamic certification cycles (WLTC, US06 etc.), dynamic engine effects severely influence the quality of results. Hence, an evaluation of transient engine behavior with map-based engine models is restricted to a certain extent. The coupling of detailed 1D-engine models is an alternative, which rapidly increases the model computation time to approximately 300 times higher than that of real time. In many technical areas, the Fourier transformation (FT) method is applied, which makes it possible to represent superimposed oscillations by their sinusoidal harmonic oscillations of different orders.

In order to meet increasingly stringent exhaust emission regulations, new engine concepts need to be developed. Lean combustion systems for stationary running large gas engines can reduce raw NOx-emissions to a very low level and enable the compliance with the exhaust emission standards without using a cost-intensive SCR-aftertreatment system. Experimental investigations in the past have already confirmed that a strong reduction of the charge air temperature even below ambient conditions by using an absorption chiller can significantly reduce NOx emissions. However, test bench operation of large gas engines is costly and time-consuming. To increase the efficiency of the engine development process, the possibility to use 0D/1D engine simulation prior to test bench studies of new concepts is investigated using the example of low temperature charge air cooling. In this context, a reliable prediction of engine efficiency and NOx-emissions is important.

Due to increasingly strict emission regulations, lean combustion concept has become an essential direction of internal combustion engine development to reduce engine emissions. However, lean combustion will lead high combustion instability and unpredictive engine emissions. The combustion instability is represented as the high cycle-to-cycle variation. Therefore, understanding the mechanism of cycle-to-cycle variation is crucial for the internal combustion engine design. This paper investigates the cause-and-effect chain of cycle-to-cycle variation of spark ignition engines using 3D CFD simulations with CONVERGE v3.0. The cyclic variations were simulated through Large Eddy Simulations, and the simulations based on Reynolds-averaged Navier–Stokes were used as supplements. The analysis focuses on two key factors that determine the combustion process: the turbulent intensity and the homogeneity of the air/fuel mixture.

As engine knock limits the efficiency of spark ignition engines and consequently further reduction of CO2 emissions, SI engines are typically designed to operate at the knock boundary. Therefore, a precise knock model is necessary to consider this phenomenon in an engine process simulation. The basis of the introduced 0D/1D knock model is to predict when the unburnt mixture auto-ignites, since auto-ignitions precede knocking events. The knock model further needs to evaluate the auto-ignition, because not every auto-ignition results in engine knock. As the introduced model’s prediction of the auto-ignition onset is already validated at extensive variations of operating conditions, this publication focusses on its evaluation. For this, two new, independent criteria are developed that take the pre-reactions of the unburnt mixture before the start of combustion into account to calculate a respective threshold for the auto-ignition onset at the knock boundary.

Engine knock is limiting the efficiency of spark ignition engines and consequently further reduction of CO2 emissions. Thus, an combustion process simulation needs a well working knock model to take this phenomenon into account. As knocking events result from auto-ignitions, the basis of a knock model is the accurate modeling of the latter. For this, the introduced 0D/1D knock model calculates the Livengood-Wu integral to estimate the state of the pre-reactions of the unburnt mixture and considers the two-stage auto-ignition of gasoline fuels, which occurs at specific boundary conditions. The model presented in this publication is validated against measurement data of a single cylinder engine. For this purpose, more than 12 000 knocking working cycles are investigated, covering extensively varied operating conditions for a wide-ranging validation.

Knocking is one of today’s main limitations in the ongoing efforts to increase efficiency and reduce emissions of spark-ignition engines. Especially for synthetic fuels or any alternative fuel type in general with a much steeper increase of the knock frequency at the KLSA, such as hydrogen, precise knock prediction is crucial to exploit their full potential. This paper therefore proposes a post-processing tool enabling further investigations to continuously gain better understanding of the knocking phenomenon. In this context, evaluation of local auto-ignitions preceding knock is crucial to improve knowledge about the stochastic occurrence of knock but also identify critical engine design to further optimize the geometry. In contrast to 0D simulations, 3D CFD simulations provide the possibility to investigate local parameters in the cylinder during the combustion.

To meet the requirements of strict CO2 emission regulations in the future, internal combustion engines must have excellent efficiencies for a wide operating range. In order to achieve this goal, various technologies must be applied. Additionally, fuels other than gasoline should also be considered. In order to investigate the potential of the efficiency improvement, a SI engine was designed and optimized using 0D/1D methods. Some of the advanced features of this engine model include: High stroke-to-bore-ratio, variable valve timings with Miller cycle, EGR, cylinder deactivation, high turbulence concept, variable compression ratio and extreme downsizing. The fuel of choice was gasoline. With the proper application of technologies, the fuel consumption at the most relevant operating window could be decreased by approximately 10% in comparison to a state-of-the-art spark-ignited direct-injection four-cylinder passenger car engine.

The industry is working intensively on the precision of thermal management. By using complex thermal management strategies, it is possible to make engine heat distribution more accurate and dynamic, thereby increasing efficiency. Significant efforts are made to improve the cooling efficiency of the engine water jacket by using 3D CFD. As well, 1D simulation plays a significant role in the design and analysis of the cooling system, especially for considering transient behaviour of the engine. In this work, a practice-oriented universal method for creating a 1D water jacket model is presented. The focus is on the discretization strategy of 3D geometry and the calculation of heat transfer using Nusselt correlations. The basis and reference are 3D CFD simulations of the water jacket. Guidelines for the water jacket discretization are proposed. The heat transfer calculation in the 1D-templates is based on Nusselt-correlations (Nu = Nu(Re, Pr)), which are derived from 3D CFD simulations.

Predictive physical modeling is an established method used in the development process for automotive components and systems. While accurate predictions can be issued after tuning model parameters, long computation times are expected depending on the complexity of the model. As requirements for components and systems continuously increase, new optimization approaches are constantly being applied to solve multidimensional objectives and resulting conflicts optimally. Some of those approaches are deemed not feasible, as the computational times for required single predictions using conventional simulation models are too high. To address this issue it is proposed to use data-driven model such as neural networks. Previous efforts have failed due to sparse data sets and resulting poor predictive ability. This paper introduces an AI Toolchain used for data-driven modeling of combustion engine components. Two methods for generating scalable and fully variable datasets will be shown.

Intensified emission regulations as well as consumption demands lead to an increasing significance of unburned hydrocarbon (UHC) emissions for diesel engines. On the one hand, the quantity of hydrocarbon (HC) raw emissions is important for emission predictions as well as for the exhaust after treatment. On the other hand, HC emissions are also important for predicting combustion efficiency and thus fuel consumption, since a part of unreleased chemical energy of the fuel is still bound in the HC molecules. Due to these reasons, a simulation model for predicting HC raw emissions was developed for diesel engines based on a phenomenological two-zone model. The HC model takes three main sources of HC emissions of diesel engines into account: Firstly, it contains a sub-model that describes the fuel dribble out of the injector after the end of injection. Secondly, HC emissions from cold peripheral zones near cylinder walls are determined in another sub-model.

As a consequence of reduced fuel consumption, direct injection gasoline engines have already prevailed against port fuel injection. However, in-cylinder fuel homogenization strongly depends on charge motion and injection strategies and can be challenging due to the reduced available time for mixture formation. An insufficient homogenization has generally a negative impact on the combustion and therefore also on efficiency and emissions. In order to reach the targets of the intensified CO2 emission reduction, further increase in efficiency of SI engines is essential. In this connection, 0D/1D simulation is a fundamental tool due to its application area in an early stage of development and its relatively low computational costs. Certainly, inhomogeneities are still not considered in quasi dimensional combustion models because the prediction of mixture formation is not included in the state of the art 0D/1D simulation.

Intensified emission regulations as well as consumption demands lead to an increasing significance of carbon monoxide (CO) emissions for diesel engines. On the one hand, the quantity of CO raw emissions is important for emission predictions as well as for the exhaust gas after treatment. On the other hand, CO emissions are also important for predicting combustion efficiency and thus fuel consumption, since a part of unreleased chemical energy of the fuel is still bound in the CO molecules. Due to these reasons, a simulation model for predicting CO raw emissions was developed for diesel engines based on a phenomenological two-zone model. The CO model takes three main sources of CO emissions of diesel engines into account: Firstly, it contains a sub model that describes CO from local understoichiometric areas. Secondly, CO emissions from overmixed regions are considered.