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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.

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.

Homogeneous Charge Compression Ignition (HCCI) combustion is being considered as a practical solution for diesel engines due to its high efficiency and low NOx and PM emissions. However, for diesel HCCI operation, there are still several problems that need to be solved. One is the spay-wall impingement issue associated with early injection, and a further problem is the extension of HCCI operation from low load to higher engine loads. In this study, a combination of Adaptive Injection Strategies (AIS) and a Two-Stage Combustion (TSC) strategy are proposed to solve the aforementioned problems. A multi-dimensional Computational Fluid Dynamics (CFD) code with detailed chemistry, the KIVA-CHEMKIN-GA code, was employed in this study, where Genetic Algorithms (GA) were used to optimize heavy-duty diesel engine operating parameters. The TSC concept was applied to optimize the combustion process at high speed (1737 rev/min) and medium load (57% load).

Hydraulically intensified medium pressure common rail (MPCR) electronic fuel injection systems are an attractive concept for heavy-duty diesel engine applications. They offer excellent packaging flexibility and thorough engine management system integration. Two different concepts were evaluated in this study. They are different in how the pressure generation and injection events are related. One used a direct principle, where the high-pressure generation and injection events occur simultaneously producing a near square injection rate profile. Another concept was based on an indirect principle, where potential energy (pressure) is first stored inside a hydraulic accumulator, and then released during injection, as a subsequent event. A falling rate shape is typically produced in this case. A unit pump, where the hydraulic intensifier is separated from the injector by a high-pressure line, and a unit injector design are considered for both concepts.

With recent increases in global fuel prices there has become a growing interest in expanding the use of diesel engines in the transportation industry. However, new engine development is costly and time intensive, requiring many hours of expensive engine tests. The ability to accurately predict an engine's performance based on existing models would reduce the expense involved in creating a new engine of different size. In the present study experimental results from two single-cylinder direct injection diesel engines were used to examine previously developed engine scaling models. The first scaling model was based on an equal spray penetration correlation. The second model considered both equal spray penetration and flame lift-off length. The engines used were a heavy-duty Caterpillar engine with a 2.44L displacement and a light-duty GM engine with a 0.48L displacement.

Lagrangian-Droplet and Eulerian-Fluid (LDEF) based spray models are widely used in engine and combustion system computations. Numerical grid and time-step-dependencies of Discrete Droplet Lagrangian spray models have been identified by previous researchers [1, 2]. The two main sources of grid-dependency are due to errors in predicting the droplet-gas relative velocity, and errors in describing droplet-droplet collision and coalescence processes. For reducing grid-dependency due to the relative velocity effects, results from gas jet theory are introduced along with a Lagrangian collision model [1, 3] and applied to model diesel sprays. The improved spray model is implemented in the engine simulation code KIVA-3V [4] and is tested under various conditions, including constant volume chambers and various engine geometries with vaporizing and combusting sprays with detailed chemistry.

In this paper, optimized single and double injection schemes were found using multi-dimensional engine simulation software (KIVA-3V) and a micro-genetic algorithm for a heavy duty diesel engine. The engine operating condition considered was at 1737 rev/min and 57 % load. The engine simulation code was validated using an engine equipped with a hydraulic-electronically controlled unit injector (HEUI) system. Five important parameters were used for the optimization - boost pressure, EGR rate, start-of-injection timing, fraction of fuel in the first pulse and dwell angle between first and second pulses. The optimum results for the single injection scheme showed significant improvements for the soot and NOx emissions. The start of injection timing was found to be very early, which suggests HCCI-like combustion. Optimized soot and NOx emissions were reduced to 0.005 g/kW-hr and 1.33 g/kW-hr, respectively, for the single injection scheme.

In the present paper optimization tools are used to recommend low-emission engine combustion chamber designs, spray targeting and swirl ratio levels for a heavy-duty diesel engine operated at high-load. The study identifies aspects of the combustion and pollution formation that are affected by mixing processes, and offers guidance for better matching of the piston geometry with the spray plume geometry for enhanced mixing. By coupling a GA (genetic algorithm) with the KIVA-CFD code, and also by utilizing an automated grid generation technique, multi-objective optimizations with goals of low emissions and fuel economy were achieved. Three different multi-objective genetic algorithms including a Micro-Genetic Algorithm (μGA), a Nondominated Sorting Genetic Algorithm II (NSGA II) and an Adaptive Range Multi-Objective Genetic Algorithm (ARMOGA) were compared for conducting the optimization under the same conditions.

A computational optimization study was performed for a direct-injection diesel engine using a recently developed 1-D-KIVA3v-GA (1-Dimensional-KIVA3v-Genetic Algorithm) computer code. The code performs a full engine cycle simulation within the framework of a genetic algorithm (GA) code. Design fitness is determined using a 1-D (one-dimensional) gas dynamics code for the simulation of the gas exchange process, coupled with the KIVA3v code for three-dimensional simulations of spray, combustion and emissions formation. The 1-D-KIVA3v-GA methodology was used to simultaneously investigate the effect of eight engine input parameters on emissions and performance for four cases, which include cases at 2500 RPM and 1000 RPM, with both simulated at high-load and low-load conditions.

An integrated system based modeling approach has been developed to understand early Direct Injection (DI) Diesel Homogeneous Charge Compression Ignition (HCCI) process. GT-Power, a commercial one-dimensional (1-D) engine cycle code has been coupled with an external cylinder model which incorporates sub-models for fuel injection, vaporization, detailed chemistry calculations (Chemkin), heat transfer, energy conservation and species conservation. In order to improve the modeling accuracy, a multi-zone model has been implemented to account for temperature and fuel stratifications in the cylinder charge. The predictions from the coupled simulation have been compared with experimental data from a single cylinder Caterpillar truck engine modified for Diesel HCCI operation. A parametric study is conducted to examine the effect of combustion timing on four major control parameters. Overall the results show good agreement of the trends between the experiments and model predictions.

This paper presents three approaches that can be used for efficient multidimensional simulations of HCCI and DI engine combustion. The first approach uses a newly developed Adaptive Multi-grid Chemistry (AMC) model. The AMC model allows a fine mesh to be used to provide adequate resolution for the spray simulation, while dramatically reducing the number of cells that need to be computed by the chemistry solver. The model has been implemented into the KIVA3v2-CHEMKIN code and it was found that computer time was reduced by a factor of ten for HCCI cases and a factor of three to four for DI cases without losing prediction accuracy. The simulation results were compared with experimental data obtained from a Honda engine operated with n-heptane under HCCI conditions for which directly measured in-cylinder temperature and H2O mole fraction data are available.

Bulk gas temperature in an HCCI engine was measured using a novel optical sensing technique. A wavelength-agile absorption sensor using a fiber-coupled LED was used to measure the in-cylinder gas temperature. H2O absorption spectra spanning 1380-1420nm were recorded once every 63 μs using this sensor. The gas temperature was inferred from a least-squares fit of the integrated absorbance areas of H2O absorption features in this spectral region to those from simulated spectra. The primary source of the H2O was the humidity in the intake air. Measurements were made during the compression and early portion of the combustion phase of an n-heptane fueled HCCI engine. The measured pressure-temperature history was compared to kinetic calculations of the ignition delay, and showed the traversal of the negative temperature coefficient regime.

A multi-objective genetic algorithm methodology was applied to a heavy-duty diesel engine at three different operating conditions of interest. Separate optimizations were performed over various fuel injection nozzle parameters, piston bowl geometries and swirl ratios (SR). Different beginning of injection (BOI) timings were considered in all optimizations. The objective of the optimizations was to find the best possible fuel economy, NOx, and soot emissions tradeoffs. The input parameter ranges were determined using design of experiment methodology. A non-dominated sorting genetic algorithm II (NSGA II) was used for the optimization. For the optimization of piston bowl geometry, an automated grid generator was used for efficient mesh generation with variable geometry parameters. The KIVA3V release 2 code with improved ERC sub-models was used. The characteristic time combustion (CTC) model was employed to improve computational efficiency.

A computer model that is able to predict the occurrence of knock in spark ignition engines has been developed and implemented into the KIVA-3V code. Three major sub-models were used to simulate the overall process, namely the spark ignition model, combustion model, and end-gas auto-ignition models. The spark ignition and early flame development is modeled by a particle marker technique to locate the flame kernel. The characteristic-time combustion model is applied to simulate the propagation of the regular flame. The autoignition chemistry in the end-gas was modeled by a reduced chemical kinetics mechanism that is based on the Shell model. The present model was validated by simulating the experimental data in three different engines. The spark ignition and the combustion models were first validated by simulating a premixed Caterpillar engine that was converted to run on propane. Computed cylinder pressure agrees well with the experimental data.

A multi-dimensional Computational Fluid Dynamics (CFD) code with detailed chemistry, the KIVA-CHEMKIN-GA code, was employed in this study, where Genetic Algorithms (GA) were used to optimize heavy-duty diesel engine operating parameters. A two-stage combustion (TSC) concept was explored to optimize the combustion process at high speed (1737 rev/min) and medium load (57% load). Two combustion modes were combined in this concept. The first stage is ideally Homogeneous Charge Compression Ignition (HCCI) combustion and the second stage is diffusion combustion under high temperature and low oxygen concentration conditions. This can be achieved for example by optimization of two-stage combustion using multiple injection or sprays from two different injectors.

Fuel Injection System (FIS) research on injection pressure, timing control, and rate shaping, and studies on the modeling of injector nozzle flows and their effect on fuel spray characteristics are usually conducted separately. Only recently has the fuel injection and spraying process been studied as a complete system, i.e., including both the high-pressure fuel delivery and its effect on the nozzle flow characteristics, including nozzle cavitation. A methodology for coupling the fuel injection system and its effect on spray characteristics is presented here. The method is applied to an example case of a conventional pump-line-nozzle system. Mathematical models for characterizing the flows from the pump to the nozzle are formulated and solved using the Method of Characteristics and finite difference techniques. The nozzle internal flow is modeled using zero-dimensional flow models, in which the nozzle cavitation and its effect on the nozzle exit flow are accounted for.

An improved discrete particle ignition kernel (DPIK) model and the G-equation combustion model have been developed and implemented in KIVA-3V. In the ignition model, the spark ignition kernel growth is tracked by Lagrangian markers and the spark discharge energy and flow turbulence effects on the ignition kernel growth are considered. The predicted ignition kernel size was compared with the available measurements and good agreement was obtained. Once the ignition kernel grows to a size where the turbulent flame is fully developed, the level set method (G-equation) is used to track the mean turbulent flame propagation. It is shown that, by ignoring the detailed turbulent flame brush structure, fine numerical resolution is not needed, thus making the models suitable for use in multidimensional modeling of SI engine combustion.

A level set G-equation model has been developed to model the combustion process in spark ignition engines. The spark ignition process was modeled using an improved version of the Discrete Particle Ignition Kernel (DPIK) model. The two models were implemented into the KIVA-3V code to simulate SI engine combustion under both premixed and direct injection conditions. In the ignition model, the ignition kernel growth is tracked by Lagrangian markers, and spark discharge energy and flow turbulence effects on the kernel growth are considered. Once the ignition kernel grows to a size where the turbulent flame is fully developed, the G-equation model is used to track the mean turbulent flame evolution. When combined with a characteristic time scale combustion model, the models were also used to simulate stratified combustion in DISI engines, where the triple flame structure must be considered.

The combustion and emissions of a diesel/natural gas dual-fuel engine are studied. Available engine experimental data demonstrates that the dual-fuel configuration provides a potential alternative to diesel engine operation for reducing emissions. The experiments are compared to multi-dimensional model results. The computer code used is based on the KIVA-3V code and consists of updated sub-models to simulate more accurately the fuel spray atomization, auto-ignition, combustion and emissions processes. The model results show that dual-fuel engine combustion and emissions are well predicted by the present multi-dimensional model. Significant reduction in NOx emissions is observed in both the experiments and simulations when natural gas is substituted for diesel fuel. The HC emissions are under predicted by numerical model as the natural gas substitution is increased.

Detailed chemical kinetics was implemented in the KIVA-3V multidimensional CFD code to study the combustion process in Homogeneous Charge Compression Ignition (HCCI) engines. The CHEMKIN code was implemented such that the chemistry and flow solutions were coupled. Detailed reaction mechanisms were used to simulate the fuel chemistry of ignition and combustion. Effects of turbulent mixing on the reaction rates were also considered. The model was validated using the experimental data from two modified heavy-duty diesel engines, including a Volvo engine and a Caterpillar engine operated at the HCCI mode. The results show that good levels of agreement were obtained using the present KIVA/CHEMKIN model for a wide range of engine conditions, including various fuels, injection systems, engine speeds, and EGR levels. Ignition timings were predicted well without the need to adjust any kinetic constants.