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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.

Liquid fuel attached to the wall surface of the intake port, the piston and the combustion chamber is one of the main causes of the unburned hydrocarbon emissions from a port fueled SI engine, especially during transient operations. To investigate the liquid fuel film formation process and fuel film behavior during transient operation is essential to reduce exhaust emissions in real driving operations, including cold start operations. Optical techniques have been often applied to measure the fuel film in conventional reports, however, it is difficult to apply those previous techniques to actual engines during transient operations. In this study, using MEMS technique, a novel capacitance sensor has been developed to detect liquid fuel film formation and evaporation processes in actual engines. A resistance temperature detector (RTD) was also constructed on the MEMS sensor with the capacitance sensor to measure the sensor surface temperature.

Experimental methods and numerical analysis were used to investigate the mechanism of high-speed knocking that occurs in small two-stroke engines. The multi-ion probe method was used in the experiments to visualize flame propagation in the cylinder. The flame was detected by 14 ion probes grounded in the end gas region. A histogram was made of the order in which flames were detected. The characteristics of combustion in the cylinder were clarified by comparing warming up and after warming up and by extracting the features of the cycle in which knocking occurred. As a result, regions of fast flame propagation and regions prone to auto-ignition were identified. In the numerical analysis, flow and residual gas distribution in the cylinder, flame propagation and self-ignition were visualized by 3D CFD using 1D CFD calculation results as boundary conditions and initial conditions.

This study aimed to achieve both a high compression ratio and low knock intensity in a two-stroke engine. Previous research has suggested that knock intensity can be reduced by combining combustion chamber geometry and scavenging passaging design for the same engine specifications with a compression ratio of 13.7. In this report, we investigate whether low knock intensity can be achieved at compression ratios of 14.4 and 16.8 by adjusting the combustion chamber geometry and scavenging passage design. As a result, the mechanism by which combustion chamber geometry and scavenging passage design change knock intensity was clarified.

In internal combustion engine development, the ongoing research can be mainly classified into two categories based on the purpose: limiting exhaust emissions and searching for alternative fuels. One of the effective approaches reduce emissions is the improvement of thermal efficiency. Certain types of alternative fuels derived from renewable resources were estimated to confirm the thermal efficiency. This study uses a single-cylinder engine added with olefin and oxygenated additive fuel, such as 1-hexene, ethanol, and ETBE, to evaluate the parameters that affect thermal efficiency. Furthermore, the effects of various additive fuels are summarized and essential information is provided for determining next- generation fuel composition.

Lean combustion has been well known to be an effective method to improve the thermal efficiency. However, leaner mixture is prone to cause the unstable combustion and poorer unburned hydrocarbon (UTHC) emissions. Pre-chamber turbulent jet combustion has been proved to enhance the combustion stability under ultra-lean conditions. However, more NOx is formed during the combustion, resulting in the fact that the tailpipe NOx emission is too high to be still not available for the real application. In this report, in order to achieve a higher air excess ratio while keeping lower UTHC emissions, and especially NOx emission, a new combustion technique which combined pre-chamber jet combustion with fuel reforming was proposed and experimentally demonstrated on a pre-chamber engine.

Homogeneous charge compression ignition (HCCI) combustion is promising for not only high thermal efficiency but also reducing nitrogen oxides (NOx) and PM simultaneously. However, the operational range of the HCCI combustion is limited because of some issues, such as poor control of ignition timing and knocking by the excessive rate of pressure rise. In this study, a new combustion system based on the HCCI combustion process is proposed based on the authors' previous experimental work. This combustion system has a divided combustion chamber of two parts, one is small and the other is large. The most significant feature is the small chamber inside the piston. At first, combustion takes place in the small chamber, and then the burned gas is ejected into the large chamber to ignite the mixture in the large chamber.

Post-oxidation has been used to enhance the chemical reactions in the exhaust gas pipes, leading to the activations of the turbocharger and catalyst at cold state. In this research, a detailed study of the various mechanisms for post-oxidation is performed. For the post-oxidation activation, the unburned gas species (CO, THC, H2) in the exhaust manifold must be produced by some methodologies, such as scavenging, lambda-split, and post-injection. The required amount of O2 concentration can be either supplied by the scavenging (valve overlap tuning) or the secondary air injection (SAI) system. Mixing the species is also an important key to promoting post- oxidation, and an internal bypass adapter with a modified exhaust adapter shape was developed and evaluated.

There is a growing need for low-emissions concepts due to stricter emission regulations, more stringent homologation cycles, and the possibility of a ban on new engines by 2035. Of particular concern are the conditions during a cold start, when the Three-Way Catalyst is not yet heated to its light-off temperature. During this period, the catalyst remains inactive, thereby failing to convert pollutants. Reducing the time needed to reach this temperature is crucial to comply with the more stringent emissions standards. The post oxidation by means of secondary air injection, illustrated in this work, is a possible solution to reduce the time needed to reach the above-mentioned temperature. The strategy consists of injecting air into the exhaust manifold via secondary air injectors to oxidize unburned fuel that comes from a rich combustion within the cylinder.

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.

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.

Following Part 1 of the previous study, this paper reports the structure’s exciting force and summarize the overall research results. An experimental study was conducted to clarify the relationship between engine combustion and vibration, and to establish technology to suppress it. This study focused on the vehicle interior noise caused by combustion in which vibration transmission is the main component at high speed and high load region. A phenomenon in which both the combustion’s exciting force and the structure’s exciting force are combined is defined as vehicle interior noise caused by combustion. Conventionally, combustion and vibration are often discussed in terms of the average cycle, but considering the nonstationary property of vibration, in this paper analyzed the structure’s exciting force characteristics for vibration in cycle-by-cycle. Analysis was conducted using the combustion indicators clarified in the previous study.

To realize stable combustion in lean or diluted conditions, reducing cycle-to-cycle variations of flow and fuel distribution is important. In this study, the effect of initial flow field was examined by simultaneous Time-Resolved PIV and visualization on two cross-sections in a fully optical-access engine under motoring and firing conditions with homogeneous pre-mixture. As a result, Omega index was defined and plotted on the correlation map between turbulence kinetic energy and CA10 (duration from ignition timing to 10% to the total accumulated heat). The omega index describes the strength of a horizontal flow field that resembles the shape of the Greek letter Omega. The plots with high Omega index were found frequently in the CA10 retarded cycles. On the other hand, the plots with low Omega index have simple tumble flows and the correlation was clearly found.

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.

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.

Ignition delay times are major information needed to allow the simulation of auto-ignition and knocking combustion in internal combustion engines (ICEs). Due to their variance over changing boundary conditions (BC) and limitations of measurement processes, a common way to obtain them is via reaction kinetic simulations. To facilitate and accelerate the simulation process with varying operating conditions and gas composition definitions, an efficient tool that uses Cantera’s Python interface has been created. It allows the end-user to easily calculate the ignition delay data needed for engine simulation without the necessity for in-depth knowledge of the underlying processes. All calculations are based on the creation of a homogeneously mixed gaseous mixture corresponding to engine-based environmental conditions. Depending on the desired fuel, oxidizer, temperature, pressure, water, and exhaust gas recirculation (EGR) rate, the resulting reactant composition is computed.

Knocking is still one of today’s major limitations regarding efficiency-increasing measures for SI combustion engines. Due to the complex stochastic nature of the phenomenon, not only prediction and consideration within the engine development is of relevance. A further challenge is control of the phenomenon during engine operation, with the aim of maximizing the efficiency while preventing engine damage and maintaining the driver comfort. Conventional knock control is characterized by detecting knock events and subsequently adjusting the spark timing depending on whether knock was detected. This paper proposes a new knock control concept based on the prediction and direct control of the knock frequency, compared to the conventional reactive and indirect control of the knock frequency. For the prediction of the knock frequency, a calculation approach based on three different parameters is utilized.

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.

Some SI (spark-ignition) engines fueled with gasoline for industrial machineries are designed based on the conventional diesel engine in consideration of the compatibility with installation. Such diesel engine-based SI engines secure a combustion chamber by a piston bowl instead of a pent-roof combustion chamber widely applied for SI engines for automobiles. In the development of SI engines, because knocking deteriorates the power output and the thermal efficiency, it is essential to clarify causes of knocking and predict knocking events. However, there has been little research on knocking in diesel engine-based SI engines. The purpose of this study is to elucidate knocking phenomena in a gasoline engine with a re-entrant piston bowl and swirl flow numerically and experimentally. In-cylinder visualization and pressure analysis of knock onset cycles have been experimentally performed. Locations of autoignition have been predicted by 3D-CFD analysis with detailed chemical reactions.