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A simulation method is presented for the analysis of combustion in spark ignition (SI) engines operated at elevated exhaust gas recirculation (EGR) level and employing multiple spark plug technology. The modeling is based on a zero-dimensional (0D) stochastic reactor model for SI engines (SI-SRM). The model is built on a probability density function (PDF) approach for turbulent reactive flows that enables for detailed chemistry consideration. Calculations were carried out for one, two, and three spark plugs. Capability of the SI-SRM to simulate engines with multiple spark plug (multiple ignitions) systems has been verified by comparison to the results from a three-dimensional (3D) computational fluid dynamics (CFD) model. Numerical simulations were carried for part load operating points with 12.5%, 20%, and 25% of EGR. At high load, the engine was operated at knock limit with 0%, and 20% of EGR and different inlet valve closure timing.

The fuel consumption and emissions of diesel engines is strongly influenced by the injection rate pattern, which influences the in-cylinder mixing and combustion process. Knowing the exact injection rate is mandatory for an optimal diesel combustion development. The short injection time of no more than some milliseconds prevents a direct flow rate measurement. However, the injection rate is deduced from the pressure change caused by injecting into a fuel reservoir or pipe. In an ideal case, the pressure increase in a fuel pipe correlates with the flow rate. Unfortunately, real measurement devices show measurement inaccuracies and errors, caused by non-ideal geometrical shapes as well as variable fuel temperature and fuel properties along the measurement pipe. To analyze the thermal effect onto the measurement results, an available rate measurement device is extended with a flexible heating system as well as multiple pressure and temperature sensors.

In scooter/motorbike engines coherent and stable tumble motion generation is still considered an effective mean in order to both reduce engine emissions and promote higher levels of combustion efficiency. The scientific research also assessed that squish motion is an effective mean for speeding up the combustion in a combustion process already fast. In a previous technical paper the authors demonstrated that for an engine having a high C/D ratio the squish motion is not only not necessary but also detrimental for the stability of the tumble motion itself, because there is a strong interaction between these two motions with the consequent formation of secondary vortices, which in turn penalizes the tumble breakdown and the turbulent kinetic energy production.

This paper presents part of the analytical work performed for the development and optimization of the 3.5L EcoBoost combustion system from Ford Motor Company. The 3.5L EcoBoost combustion system is a direct injected twin turbocharged combustion system employing side-mounted multi-hole injectors. Upfront 3D CFD, employing a Ford proprietary KIVA-based code, was extensively used in the combustion system development and optimization phases. This paper presents the effect of intake port design with various levels of tumble motion on the combustion system characteristics. A high tumble intake port design enforces a well-organized stable motion that results in higher turbulence intensity in the cylinder that in turn leads to faster burn rates, a more stable combustion and less fuel enrichment requirement at full load.

The proposed technique is a tailored deep neural network (DNN) training approach which uses an iterative process to support the learning of DNNs by targeting their specific misclassification and missed detections. The process begins with a DNN that is trained on freely available annotated image data, which we will refer to as the Base model, where a subset of the categories for the classifier are related to the automotive theater. A small set of video capture files taken from drives with test vehicles are selected, (based on the diversity of scenes, frequency of vehicles, incidental lighting, etc.), and the Base model is used to detect/classify images within the video files. A software application developed specifically for this work then allows for the capture of frames from the video set where the DNN has made misclassifications. The corresponding annotation files for these images are subsequently corrected to eliminate mislabels.

Ford Motor Company has recently implemented a Hardware-In-the-Loop (HIL) testing system for a new, highly complex, hybrid electric vehicle (HEV) Electronic Control Unit (ECU). The implementation of this HIL system has been quick and effective, since it is based on proven Commercial-Off-The-Shelf (COTS) automation tools for real-time that allow for a very flexible and intuitive design process. An overview of the HIL system implementation process and the derived development benefits will be shown in this paper. The initial concept for the use of this HIL system was a complete closed-loop vehicle simulation environment for Vehicle System Controller testing, but the paper will show that this concept has evolved to allow for the use of the HIL system for many facets of the design process.

The laminar burning speed is an important intrinsic property of an air-fuel mixture determining key combustion characteristics such as turbulent flame propagation. It is a function of the mixture composition (mixture fraction and residual gas mass fraction) and of the thermodynamic conditions. Experimental measurements of Laminar Flame Speeds (LFS) are common in literature, but initial pressure and temperature are limited to low values due to the test conditions: typical pressure values for LFS detection are lower than 25 bar, and temperature rarely exceeds 550 K. Actual trends in spark ignition engines are to increase specific power output by downsizing and supercharging, thus the flame front involves even more higher pressure and temperature since the beginning of combustion.

Two oxygenated fuels were evaluated on a single-cylinder diesel engine and compared to three hydrocarbon diesel fuels. The oxygenated fuels included canola biodiesel (canola methyl esters, CME) and CME blended with dibutyl succinate (DBS), both of which are or have the potential to be bio-derived. DBS was added to improve the cold flow properties, but also reduced the cetane number and net heating value of the resulting blend. A 60-40 blend of the two (60% vol CME and 40% vol DBS) provided desirable cold flow benefits while staying above the U.S. minimum cetane number requirement. Contrary to prior vehicle test results and numerous literature reports, single-cylinder engine testing of both CME and the 60-40 blend showed no statistically discernable change in NOx emissions relative to diesel fuel, but only when constant intake oxygen was maintained.

Combustion in modern spark-ignition (SI) engines is increasingly knock-limited with the wide adoption of downsizing and turbocharging technologies. Fuel autoignition conditions are different in these engines compared to the standard Research Octane Number (RON) and Motor Octane Numbers (MON) tests. The Octane Index, OI = RON - K(RON-MON), has been proposed as a means to characterize the actual fuel anti-knock performance in modern engines. The K-factor, by definition equal to 0 and 1 for the RON and MON tests respectively, is intended to characterize the deviation of modern engine operation from these standard octane tests. Accurate knowledge of K is of central importance to the OI model; however, a single method for determining K has not been well accepted in the literature.

In a recent study, quantitative measurements were presented of in-cylinder spatial distributions of mixture equivalence ratio in a single-cylinder light-duty optical diesel engine, operated with a non-reactive mixture at conditions similar to an early injection low-temperature combustion mode. In the experiments a planar laser-induced fluorescence (PLIF) methodology was used to obtain local mixture equivalence ratio values based on a diesel fuel surrogate (75% n-heptane, 25% iso-octane), with a small fraction of toluene as fluorescing tracer (0.5% by mass). Significant changes in the mixture's structure and composition at the walls were observed due to increased charge motion at high swirl and injection pressure levels. This suggested a non-negligible impact on wall heat transfer and, ultimately, on efficiency and engine-out emissions.

Self-piercing rivet (SPR) joints are a key joining technology for lightweight materials, and they have been widely used in automobile manufacturing. Manual visual crack inspection of SPR joints could be time-consuming and relies on high-level training for engineers to distinguish features subjectively. This paper presents a novel machine learning-based crack detection method for SPR joint button images. Firstly, sub-images are cropped from the button images and preprocessed into three categories (i.e., cracks, edges and smooth regions) as training samples. Then, the Artificial Neural Network (ANN) is chosen as the classification algorithm for sub-images. In the training of ANN, three pattern descriptors are proposed as feature extractors of sub-images, and compared with validation samples. Lastly, a search algorithm is developed to extend the application of the learned model from sub-images into the original button images.

This paper presents a methodology for the 3D CFD simulation of the intake and compression processes of four stroke internal combustion engines.The main feature of this approach is to provide very accurate initial conditions by means of a cost-effective initialization step. Calculations are applied to a low stroke-to-bore SI engine, operated at full load and maximum engine speed. It is demonstrated that initial conditions for this kind of engines have an important influence on flow field development, particularly in terms of mean velocities close to the firing TDC. Simulation results are used to discuss the choice of a set of parameters for the flow field characterization of low stroke-to-bore engines, as well as to provide an insight into the flow patterns during the overlapping period.

The software design of this new engine control unit is based on a unique and homogenous torque structure. All input signals are converted into torque equivalents and a torque coordinator determines their influence on the final torque delivered to the powertrain. The basic torque structure is independent on the type of fuel and can be used for gasoline, diesel, or CNG injection systems. This allows better use of custom specific algorithms and facilitates reusability, which is supported by the graphical design tool that creates all modules using automatic code generation. Injection specific algorithms can be linked to the software by simply setting a software switch.

Passive in-line catalyzed hydrocarbon (HC) traps have been used by some manufacturers in the automotive industry to reduce regulated tailpipe (TP) emissions of non-methane organic gas (NMOG) during engine cold-start conditions. However, most NMOG molecules produced during gasoline combustion are only weakly adsorbed via physisorption onto the zeolites typically used in a HC trap. As a consequence, NMOG desorption occurs at low temperatures resulting in the use of very high platinum group metal (PGM) loadings in an effort to combust NMOG before it escapes from a HC trap. In the current study, a 2.0 L direct-injection (DI) Ford Focus running on gasoline fuel was evaluated with full useful life aftertreatment where the underbody converter was either a three-way catalyst (TWC) or a HC trap. A new HC trap technology developed by Ford and Umicore demonstrated reduced TP NMOG emissions of 50% over the TWC-only system without any increase in oxides of oxygen (NOx) emissions.

Over the last decade, many methods have been proposed for estimating the in-cylinder combustion pressure or the torque from instantaneous crankshaft speed measurements. However, such approaches are typically computationally expensive. In this paper, an entirely different approach is presented to allow the real-time estimation of the in-cylinder pressures based on crankshaft speed measurements. The technical implementation of the method will be presented, as well as extensive results obtained for a V-6 S.I. engine while varying spark timing, engine speed, engine load and EGR. The method allows to estimate the in-cylinder pressure with an average estimation error of the order of 1 to 2% of the peak pressure. It is very general in its formulation, is statistically robust in the presence of noise, and computationally inexpensive.

There is keen interest in understanding the origins of engine-out unburned hydrocarbons emitted during SI engine cold start. This is especially true for the first few firing cycles, which can contribute disproportionately to the total emissions measured over standard drive cycles such as the US Federal Test Procedure (FTP). This study reports on the development of a novel methodology for capturing and quantifying unburned hydrocarbon emissions (HC), CO, and CO2 on a cycle-by-cycle basis during an engine cold start. The method was demonstrated by applying it to a 4 cylinder 2 liter GTDI (Gasoline Turbocharged Direct Injection) engine for cold start conditions at an ambient temperature of 22°C. For this technique, the entirety of the engine exhaust gas was captured for a predetermined number of firing cycles.

Nowadays, an automotive DI Diesel engine is demanded to provide an adequate power output together with limit-complying NOx and soot emissions so that the development of a specific combustion concept is the result of a trade-off between conflicting objectives. In other words, the development of a low-emission DI diesel combustion concept could be mathematically represented as a multi-objective optimization problem. In recent years, genetic algorithm and CFD simulations were successfully applied to this kind of problem. However, combining GA optimization with actual CFD-3D combustion simulations can be too onerous since a large number of simulations is usually required, resulting in a high computational cost and, thus, limiting the suitability of this method for industrial processes.

The aim of this work is to propose modifications to the managing of the 1st generation Common Rail injectors in order to reduce actuation time towards multiple injection strategies. The current Common Rail injector driven by 1st ECU generation is capable of operating under stable conditions with a minimum dwell between two consecutive injections of 1.8 ms. This limits the possibility in using proper and efficient injection strategies for emission control purposes. A previous numerical study, performed by the electro-fluid-mechanical model built up by Matlab-Simulink environment, highlighted different area where injector may be improved with particular emphasis on electronic driving circuit and components design. Experiments carried out at injector Bosch test-bench showed that a proper control of the solenoid valve allowed reducing drastically the standard deviation during the pilot pulses.

Lean NOx Trap (LNT) is an aftertreatment device typically used to reduce oxides of nitrogen (NOx) emissions for a lean burn engine. NOx is stored in the LNT during the lean operation of an engine. When the air-fuel ratio becomes rich, the stored NOx is released and catalytically reduced by the reductants such as CO, H2 and HC. Tailpipe NOx emissions can be significantly reduced by properly modulating the lean (storage) and rich (purge) periods. A control-oriented lumped parameter model is presented in this paper. The model captures the key steady state and transient characteristics of an LNT and includes the effects of the important engine operating parameters. The model can be used for system performance evaluation and control strategy development.

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.