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Due to increasingly strict emission regulations, the demand for internal combustion engine performance has enhanced. Combustion stability is one of the main research focuses due to its impacts on the emission level. Moreover, the combustion instability becomes more significant under the lean combustion concept, which is an essential direction of internal combustion engine development. The combustion instability is represented as the cycle-to-cycle variation. This paper presents a quasi-dimensional model system for predicting the cycle-to-cycle variation in 0D/1D simulation. The modeling is based on the cause-and-effect chain of cycle-to-cycle variation of spark ignition engines, which is established through the flow field analysis of large eddy simulation results [1]. In the model system, varying parameters are turbulent kinetic energy, the distribution of air-to-fuel equivalence ratio, and the in-cylinder velocity field.

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.

State-of-the-art spark-ignition engines mainly rely on the quasi-hemispherical flame propagation combustion method. Despite significant development efforts to obtain high energy conversion efficiencies while avoiding knock phenomena, achieved indicated efficiencies remain around 35 - 40 %. Further optimizations are enabled by significant excess air dilution or increased combustion speed. However, flammability limits and decreasing flame speeds with increasing air dilution prevent substantial improvements. Pre-Chamber (PC) initiated jet ignition combustion systems improve flame stability and shift flammability limits towards higher dilution levels due to increased turbulence and a larger flame area in the early Main-Chamber (MC) combustion stages. Simultaneously, the much-increased combustion speed reduces knock tendency, allowing the implementation of an innovative combustion method: PC-initiated jet ignition coupled with Spark-Assisted Compression Ignition (SACI).

Whether as an optimization problem or a development tool, neural networks help engineers to work more efficiently. This paper’s central aspect is to add metadata to the core files of the project simulation data. To understand the project and its simulation models, a pre-processing methodology and convolutional neural network architecture are presented. With the added labels, it is possible to access the content of the model files of an engine performance simulation tool without examining them. At first, a pre-processing approach and its design are introduced to extract and filter the desired data from the XML data structure. Then, the data is split into sequences and paired with labels. Expert knowledge is used to label the models. These labels are further paired with the extracted sequences.

High-efficient simulations are mandatory to manage the ever-increasing complexity of automotive powertrain system and reduce development time and costs. Integrating AI methods into the development process provides an ideal solution thanks to massive increase in computational power. Based on an 1D physical engine model of a turbo-charged direct injection gasoline engine with variable valve timing (VVT), a high-performance hybrid simulation model has been developed for increasing computing performance. The newly developed model is made of a physics-based low-pressure part including intake and exhaust peripheries and a neural-network-based high-pressure part for combustion chamber calculations. For the training and validation of the combustion chamber neural networks, a data set with 10.5 million operating points was generated in a short time thanks to the parallelizable combustion chamber simulations in stand-alone mode.

Prospective combustion engine applications require the highest possible energy conversion efficiencies for environmental and economic sustainability. For conventional Spark-Ignition (SI) engines, the quasi-hemispherical flame propagation combustion method can only be significantly optimized in combination with high excess air dilution or increased combustion speed. However, with increasing excess air dilution, this is difficult due to decreasing flame speeds and flammability limits. Pre-Chamber (PC) initiated jet ignition combustion systems significantly shift the flammability and flame stability limits towards higher dilution areas due to high levels of introduced turbulence and a significantly increased flame area in early combustion stages, leading to considerably increased combustion speeds and high efficiencies. By now, vehicle implementations of PC-initiated combustion systems remain niche applications, especially in combination with lean mixtures.

Future combustion engine applications require highest possible energy conversion efficiencies to reduce their environmental impact and be economically competitive. So far, spark-ignition (SI) engine combustion development mostly consisted of optimizing the hemispherical flame propagation combustion method. Thereby, a significant efficiency increase is only achievable in combination with high excess air dilution or increased combustion speed. However, with increasing excess air dilution, this is difficult due to decreasing flame speeds and flammability limits. Simultaneously, researchers have been investigating homogeneous charge compression ignition (HCCI) that achieves higher efficiencies due to its rapid volume reaction combustion and also enables high excess air dilution. However, the combustion is complex to control as it is initiated by auto-ignition (AI) processes. In-cylinder conditions reliably need to be reproduced to prevent damaging pre-ignitions.

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.

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.

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.

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.

In order to operate effectively, exhaust gas aftertreatment (EAT) systems require a certain temperature level. The trend towards higher grades of hybridisation causes longer switch-off phases of the internal combustion engine (ICE) during which the EAT components cool down. Additionally, efficiency enhancements of the ICE result in lower exhaust gas temperatures. In combination with further strengthening of the legal requirements regarding tailpipe emissions, new approaches are desired to ensure reliable emission reductions under all conditions. One possibility to achieve a faster warm-up of the EAT system is to place it upstream of the turbine, where temperatures are higher. Although, the extra thermal inertia and larger volume upstream of the turbine delay the throttle response, even a light hybridisation is sufficient for compensating the dynamic loss.

The introduction of real driving emission measurements increases the need of improved transient engine behavior while keeping the emissions to a minimum. A possible way of enhancing the transient engine behavior is the targeted usage of scavenging. Scavenging is realized by an inlet- and exhaust-valve overlap. Fresh scavenging air flows directly from intake manifold through the cylinder into the exhaust manifold. Therefore, the mass flow at the turbine increases and causes a reduced turbo lag, which results in a more dynamic engine behavior. The unburned oxygen causes a decrease of the three-way catalyst (TWC) conversion rate. To keep the TWC operation close to stoichiometry, a rich combustion is performed. The rich combustion products (most notably carbon monoxide) mix in the exhaust manifold and react with oxygen so that the conversion rate of the TWC is ensured.

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.

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.

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.

In this research, simulation and experimental investigation of H2 emission formation and its influence during the post-oxidation phenomenon were conducted on a turbo-charged spark ignition engine. During the post-oxidation phenomenon phase, rich air-fuel ratio (A/F) is used inside the cylinder. This rich excursion gives rise to the production of H2 emission by various reactions inside the cylinder. It is expected that the generation of this H2 emission can play a key role in the actuation of the post-oxidation and its reaction rate if enough temperature and mixing strength are attained. It is predicted that when rich combustion inside the cylinder will take place, more carbon monoxide (CO)/ Total Hydro Carbon (THC)/ Hydrogen (H2) contents will arrive in the exhaust manifold. This H2 content facilitates in the production of OH radical which contributes to the post-oxidation reaction and in-turn can aid towards increasing the enthalpy.

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.

On the way to emission-free mobility, future fuels must be CO2 neutral. To achieve this, synthetic fuels are being developed. In order to better assess the effects of the new fuels on the engine process, simulation models are being developed that reproduce the chemical and physical properties of these fuels. In this paper, the fuel DMC+ is examined. DMC+ (a mixture of dimethyl carbonate (DMC) and methyl formate (MeFo) mainly, characterized by the lack of C-C Bonds and high oxygen content) offers advantages with regard to evaporation heat, demand of oxygen and knock resistance. Furthermore, its combustion is almost particle free. With the aid of modern 0D/1D simulation methods, an assessment of the potential of DMC+ can be made. It is shown that the simulative conversion of a state-of-the-art gasoline engine to DMC+ fuel offers advantages in terms of efficiency in many operating points even if the engine design is not altered.

For long haul transport, diesel engine due to its low fuel consumption and low operating costs will remain dominant over a long term. In order to achieve CO2 neutrality, the use of electricity-based, synthetic fuels (e-fuels) provides a solution. Especially the group of oxymethylene ethers (OME) is given much attention because of its soot-free combustion. However, the new fuel properties and the changed combustion characteristics place new demands on engine design. Meanwhile, the use of new fuels also creates new degrees of freedom to operate diesel engines. In this work, the application of dimethoxymethane (OME1) is investigated by means of 1D simulation at three operating points in a truck diesel engine. The subsystems of fuel injection, air path and exhaust gas are sequentially adjusted for the purpose of low emissions, especially for low nitrogen oxides (NOx).