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Schlieren visualization was performed to investigate hydrogen injection into a quiescent chamber. The injection pressures investigated were 52 and 104 bar, and the chamber density ranged from 1.15 to 12.8 kg/m3, giving rise to underexpanded jets for all conditions. The expansion waves outside the nozzle were clearly visible with hydrogen, and the effect was confirmed with studies of nitrogen injected into a nitrogen environment. The distance between the expansion wave fronts was found to scale directly with the ratio of the exit pressure to the chamber pressure. The jet tip penetration rate was measured and was found to increase with injection pressure, and decrease with chamber density as expected. A mass- and momentum-preserving scheme was developed to relate the underexpanded jet to a subsonic jet of larger diameter.

Multidimensional models require physical inputs about the engine operating conditions. This paper explores the effects of unavoidable experimental uncertainties in the specification of important parameters such as the start of injection, duration of injection, amount of fuel injected per cycle, gas temperature at IVC, and the spray nozzle hole diameter. The study was conducted for a Caterpillar 3401 heavy-duty diesel engine for which extensive experimental data is available. The engine operating conditions include operation at high and low loads, with single and double injections. The computations were performed using a modified version of the KIVA3V code. Initially the model was calibrated to give very good agreement with experimental data in terms of trends and also to a lesser degree in absolute values, over a range of operating conditions and injection timings.

Engine design is a time consuming process in which many costly experimental tests are usually conducted. With increasing prediction ability of engine simulation tools, engine design aided by CFD software is being given more attention by both industry and academia. It is also of much interest to be able to use design information gained from an existing engine design of one size in the design of engines of other sizes to reduce design time and costs. Therefore it is important to study size-scaling relationships for engines over wide range of operating conditions. This paper presents CFD studies conducted for two production diesel engines - a light-duty GM-Fiat engine (0.5L displacement) and a heavy-duty Caterpillar engine (2.5L displacement). Previously developed scaling arguments, including an equal spray penetration scaling model and an extended, equal flame lift-off length scaling model were employed to explore the parametric scaling connections between the two engines.

Measurements of the instantaneous heat flux at three positions on the cylinder head surface, and the steady-state cylinder head temperatures at four positions on the cylinder head have been obtained. Engine tests were performed for a range of air-fuel ratios including regimes rich of stoichiometric, stoichiometric, and lean of stoichiometric. In addition, ignition timing was advanced in increments from 22° BTDC to 40° BTDC. All tests were run with the throttle either fixed in the wide open position, or fixed in a position that produced 75% of the maximum power with the standard ignition timing and an air-fuel ratio of 13.5. This was done to ensure that changes in air mass flow rate were not influencing the results. In addition, all tests were performed with a fuel mixture preparation being provided by system designed to deliver a homogeneous premixed charge to the inlet port. This was done to ensure that mixture preparation issues were not confounding the results.

A computational optimization study is performed for a heavy-duty direct-injection diesel engine using the recently developed KIVA-GA computer code. KIVA-GA performs full cycle engine simulations within the framework of a Genetic Algorithm (GA) global optimization code. Design fitness is determined using a one-dimensional gas -dynamics code for calculation of the gas exchange process, and a three-dimensional CFD code based on KIVA-3V for spray, combustion and emissions formation. The performance of the present Genetic Algorithm is demonstrated using a test problem with a multi-modal analytic function in which the optimum is known a priori. The KIVA-GA methodology is next used to simultaneously investigate the effects of six engine input parameters on emissions and performance for a high speed, medium load operating point for which baseline experimental validation data is available.

The scavenging process in a direct-injection two-stroke research engine was examined by using an electromagnetically controlled poppet valve to sample the trapped charge. A physical model was developed to characterize the scavenging based solely on the measured trapped gas composition. This method obviates the need to measure the post-combustion composition of the trapped charge, which significantly eases the sampling valve requirements. The valve that was developed proved to be very robust and was able to sample over 30% of the trapped mass at 3000 rpm. The measured scavenging efficiency was found to agree well with the non-isothermal two-zone perfect mixing limit of scavenging. The scavenging efficiency was found to increase with delivery ratio, and was nearly independent of speed.

An optimization study combining multidimensional CFD modeling and a global, evolutionary search technique known as the Genetic Algorithm has been carried out. The subject of this study was a 2-stroke, spark-ignited, direct-injection, single-cylinder research engine (SCRE). The goal of the study was to optimize the part load operating parameters of the engine in order to achieve the lowest possible emissions, improved fuel economy, and reduced wall heat transfer. Parameters subject to permutation in this study were the start-of-injection (SOI) timing, injection duration, spark timing, fuel injection angle, dwell between injections, and the percentage of fuel mass in the first injection pulse. The study was comprised of three cases. All simulations were for a part load, intermediate-speed condition representing a transition operating regime between stratified charge and homogeneous charge operation.

A multi-objective genetic algorithm coupled with the KIVA3V release 2 code was used to optimize the piston bowl geometry, spray targeting, and swirl ratio levels of a high speed direct injected (HSDI) diesel engine for passenger cars. Three modes, which represent full-, mid-, and low-loads, were optimized separately. A non-dominated sorting genetic algorithm II (NSGA II) was used for the optimization. High throughput computing was conducted using the CONDOR software. An automated grid generator was used for efficient mesh generation with variable geometry parameters, including open and reentrant bowl designs. A series of new spray models featuring reduced mesh dependency were also integrated into the code. A characteristic-time combustion (CTC) model was used for the initial optimization for time savings. Model validation was performed by comparison with experiments for the baseline engine at full-, mid-, and low-load operating conditions.

The effect of injection rate shape on spray evolution and emission characteristics is investigated and a methodology for active control of fuel injection is proposed. Extensive validation of advanced vaporization and primary jet breakup models was performed with experimental data before studying the effects of systematic changes of injection rate shape. Excellent agreement with the experiments was obtained for liquid and vapor penetration lengths, over a broad range of gas densities and temperatures. Also the predicted flame lift-off lengths of reacting diesel fuel sprays were in good agreement with the experiments. After the validation of the models, well-defined rate shapes were used to study the effect of injection rate shape on liquid and vapor penetration, flame lift-off lengths and emission characteristics.

Numerical simulations are performed to investigate the fuel/air mixing preparation in a gasoline direct injection (GDI) engine. A two-valve OHV engine with wedge combustion chamber is investigated since automobiles equipped with this type of engine are readily available in the U.S. market. Modifying and retrofitting these engines for GDI operation could become a viable scenario for some engine manufactures. A pressure-swirl injector and wide spacing injection layout are adapted to enhance mixture preparation. The primary interest is on preparing the mixture with adequate equivalence ratio at the spark plug under a wide range of engine operating conditions. Two different engine operating conditions are investigated with respect to engine speed and load. A modified version of the KIVA-3V multi-dimensional CFD code is used. The modified code includes the Linearized Instability Sheet Atomization (LISA) model to simulate the development of the hollow cone spray.

In this study, a new combustion model for simulating the diesel combustion process is introduced. This model was verified by comparing numerical simulations to experimental data for a six-mode test cycle using a Caterpillar 3400 series engine. Additional comparisons are made for baseline cases for both a Caterpillar 3500 series engine and a Sandia optical access engine. In the combustion model, reactions limited by diffusion are modeled using a probability density function (PDF) model. For kinetically limited (premixed) combustion, an Arrhenius rate is used. To include effects of temperature fluctuations, this reaction rate is weighted by a temperature probability density function. A transport equation for premixed fuel was implemented to transition between the premixed and diffusion burning modes. The ratio of fuel in a computational cell that is premixed is used to determine the combustion mode.

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.

A phenomenological nozzle flow model has been developed and implemented in both the FIRE and KIVA-II codes to simulate the effects of the nozzle geometry on fuel injection and spray processes. The model takes account of the nozzle passage inlet configuration, flow losses and cavitation, the injection pressure and combustion chamber conditions and provides initial conditions for multidimensional spray modeling. The discharge coefficient of the injector, the effective injection velocity and the initial drop or injected liquid ‘blob’ sizes are calculated dynamically during the entire injection event. The model was coupled with the wave breakup model to simulate experiments of non-vaporizing sprays under diesel conditions. Good agreement was obtained in liquid penetration, spray angle and drop size (Sauter Mean Diameter). The integrated model was also used to model combustion in a Cummins single-cylinder optical engine with good agreement.

The present study uses a numerical model to investigate the effects of flow turbulence on premixed iso-octane HCCI engine combustion. Different levels of in-cylinder turbulence are generated by using different piston geometries, namely a disc-shape versus a square-shape bowl. The numerical model is based on the KIVA code which is modified to use CHEMKIN as the chemistry solver. A detailed reaction mechanism is used to simulate the fuel chemistry. It is found that turbulence has significant effects on HCCI combustion. In the current engine setup, the main effect of turbulence is to affect the wall heat transfer, and hence to change the mixture temperature which, in turn, influences the ignition timing and combustion duration. The model also predicts that the combustion duration in the square bowl case is longer than that in the disc piston case which agrees with the measurements.

Experimental data is used in conjunction with multi-dimensional modeling in a modified version of the KIVA-3V code to characterize the emissions behavior of a high-speed, direct-injection diesel engine. Injection pressure and EGR are varied across a range of typical small-bore diesel operating conditions and the resulting soot-NOx tradeoff is analyzed. Good agreement is obtained between experimental and modeling trends; the HSDI engine shows increasing soot and decreasing NOx with higher EGR and lower injection pressure. The model also indicates that most of the NOx is formed in the region where the bulk of the initial heat release first takes place, both for zero and high EGR cases. The mechanism of NOx reduction with high EGR is shown to be primarily through a decrease in thermal NOx formation rate.

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.

A dual fuel engine simulation model was formulated and the combustion process of a diesel/natural gas dual fuel engine was studied using an updated KIVA-3V Computational Fluid Dynamic (CFD) code. The dual fuel engine ignition and combustion process is complicated since it includes diesel injection, atomization and ignition, superimposed with premixed natural gas combustion. However, understanding of the combustion process is critical for engine performance optimization. Starting from a previously validated Characteristic-Timescale diesel combustion model, a natural gas combustion model was implemented and added to simulate the ignition and combustion process in a dual fuel bus engine. Available engine test data were used for validation of both the diesel-only and the premixed spark-ignition operation regimes. A new formulation of the Characteristic-Timescale combustion model was then introduced to allow smooth transition between the combustion regimes.

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.

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.

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.