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Two advanced combustion models have been validated with the KIVA-3V Release 2 code in the context of two-stage combustion in a heavy duty diesel engine. The first model uses CHEMKIN to directly integrate chemistry in each computational cell. The second model accounts for flame propagation with the G-equation, and CHEMKIN predicts autoignition and handles chemistry ahead of and behind the flame front. A Damköhler number criterion was used in flame containing cells to characterize the local mixing status and determine whether heat release and species change should be a result of flame propagation or volumetric heat release. The purpose of this criterion is to make use of physical and chemical time scales to determine the most appropriate chemistry model, depending on the mixture composition and thermodynamic properties of the gas in each computational cell.

The authors used auxiliary gas injection (AGI) to increase in-cylinder mixing during the latter portion of combustion in a direct injection (DI) diesel engine in order to reduce soot emissions without affecting NOx. Experiments were conducted using various gas injection directions and compositions to explore the effect of these parameters. Simulations were employed to provide additional insight. AGI direction was found to have a profound impact on soot emissions. Researchers suggested that this was due to changes in the fuel spray-gas jet interaction with injection direction. Simulations supported this theory and suggested that the number of soot clouds affected by the gas jet may also be a factor. The oxygen content of the gas jet was also found to have an influence on emissions. Researchers found that, when the oxygen content of the gas jet was increased, soot emissions decreased. However, this was found to have a detrimental affect on NO.

Many commercial fuels, including gasoline and diesel, are multicomponent hydrocarbons. During the fuel vaporization process, the volatile components evaporate first, which dominate the region near the nozzle exit. The lately evaporated vapor with high penetration has high molecular weight. Thus, ignition and combustion of multicomponent fuels are not only influenced by distribution of fuel vapor mass fraction, but also by distribution of the components. This paper presents a spark ignition and combustion model with consideration of such multicomponent effects for GDI engines. Ignition kernel growth due to flame front propagation is considered in the model to eliminate the sensitivity of the numerical mesh size on the results.

Artificial neural networks (ANN) have been recognized as universal approximators for nonlinear continuous functions and actively applied in engine research in recent years [1, 2, 3, 4, 5, 6, 7 and 8]. This paper describes the methodology and results of using the ANN to model a turbocharged DI diesel engine. The engine was simulated using the CFD code (KIVA-ERC) over a wide range of operating conditions, and numerical simulation results were used to train the ANN. An efficient data collection methodology using the Design of Experiments (DOE) techniques was developed to select the most characteristic engine operating conditions and hence the most informative data to train the ANN. This approach minimizes the time and cost of collecting training data from either computational or experimental resources. The trained ANN was then used to predict engine parameters such as cylinder pressure, cylinder temperature, NOx and soot emissions, and cylinder heat transfer.

An integrated system model containing sub-models for a diesel engine, NOx and soot emissions, and a diesel particulate filter (DPF) has been used to simulate stead-state engine operating conditions. The simulation results have been used to investigate the effect of DPF loading and passive regeneration on engine performance and emissions. This work is the continuation of previous work done to create an overall diesel engine/exhaust system integrated model. As in the previous work, a diesel engine, exhaust system, engine soot emissions, and diesel particulate filter (DPF) sub-models have been integrated into an overall model using Matlab Simulink. For the current work new sub-models have been added for engine-out NOx emissions and an engine feedback controller. The integrated model is intended for use in simulating the interaction of the engine and exhaust aftertreatment components.

The use of low temperature combustion (LTC) modes has demonstrated abilities to lower diesel engine emissions while maintaining good fuel consumption. LTC is assumed to be a viable solution to assist in meeting stringent upcoming diesel engine emissions targets, particularly nitric oxides (NOx) and particulate matter (PM). However, LTC is currently limited to low engine loads and is not a feasible solution at higher loads on production engines. A mixed mode combustion strategy must be implemented to take advantage of the benefits offered from LTC at the low loads and speeds while switching to a conventional diesel combustion strategy at higher loads and speeds and thus allowing full range use of the engine under realistic driving conditions. Experiments were performed to characterize engine out emissions during transient engine operating conditions involving LTC combustion strategies.

An overall diesel engine and aftertreatment system model has been created that integrates diesel engine, exhaust system, engine emissions, and diesel particulate filter (DPF) models using MATLAB Simulink. The 1-D engine and exhaust system models were developed using WAVE. The engine emissions model combines a phenomenological soot model with artificial neural networks to predict engine out soot emissions. Experimental data from a light-duty diesel engine was used to calibrate both the engine and engine emissions models. The DPF model predicts the behavior of a clean and particulate-loaded catalyzed wall-flow filter. Experimental data was used to validate this sub-model individually. Several model integration issues were identified and addressed. These included time-step selection, continuous vs. limited triggering of sub-models, and code structuring for simulation speed. Required time-steps for different sub models varied by orders of magnitude.

An integrated system model containing sub-models for diesel engine, emissions, and aftertreatment devices has been developed. The objective is to study engine-device and device-device interactions. The emissions sub-models used are for NOx and PM (particulate matter) prediction. The aftertreatment sub-models used include a diesel oxidation catalyst (DOC) and a diesel particulate filter (DPF). Controllers have also been developed to allow for transient simulations, active DPF regeneration, and prevention/control of runaway DPF regenerations. The integrated system-level model has been used to simulate DPF regeneration via exhaust fuel injection ahead of the DOC. In addition, the controller model can use intake throttling to assist in active DPF regeneration if needed. Regeneration studies have been done for both steady engine load and with load transients. High to low engine load transients are of particular interest because they can lead to runaway DPF regeneration.

A single cylinder CFR research engine has been run in HCCI combustion mode for a range of temperatures and fuel compositions. The data indicate that the best HCCI operation, as measured by a combination of successful combustion with low ISFC, occurs at or near the rich limit of operation. Analysis of the pressure and heat release histories indicated the presence, or absence, and impact of the fuel's NTC ignition behavior on establishing successful HCCI operation. The auto-ignition trends observed were in complete agreement with previous results found in the literature. Furthermore, analysis of the importance of the fuel's octane sensitivity, through assessment of an octane index, successfully explained the changes in the fuels auto-ignition tendency with changes in engine operating conditions.

An approach to accurately capture overall behavior in a system level model of DI Diesel HCCI engine operation is presented. The modeling methodology is an improvement over the previous effort [36], where a multi-zone model with detailed chemical kinetics was coupled with an engine cycle simulation code. This multi-zone technique was found to be inadequate in capturing the fuel spray dynamics and its impact on mixing. An improved methodology is presented in this paper that can be used to model fully and partially premixed charge compression ignition engines. A Computational Fluid Dynamics (CFD) driven model is used where the effects of fuel injection, spray evolution, evaporation, and turbulent mixing are considered. The modeling approach is based on the premise that once the initial spray dynamics are correctly captured, the overall engine predictions during the combustion process can be captured with good accuracy.

Multi-dimensional computational efforts using comprehensive and skeletal kinetics have been made to investigate the cool flame region in HCCI combustion. The work was done in parallel to an experimental study that showed the impact of the negative temperature coefficient and the cool flame on the start of combustion using different fuels, which is now the focus of the simulation work. Experiments in a single cylinder CFR research engine with n-butane and a primary reference fuel with an octane number of 70 (PRF 70) were modeled. A comparison of the pressure and heat release traces of the experimental and computational results shows the difficulties in predicting the heat release in the cool flame region. The behavior of the driving radicals for two-stage ignition is studied and is compared to the behavior for a single-ignition from the literature. Model results show that PRF 70 exhibits more pronounced cool flame heat release than n-butane.