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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.

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 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.

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.

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.

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.

In the present work the three-dimensional KIVA CFD code was used to simulate the combustion process in a HSDI diesel engine. State-of-the-art models, including the KH-RT spray breakup model, the RNG k-ε turbulence model, and a n-heptane reduced chemistry including reduced GRI NOx mechanism were used. The performances of two combustion models, KIVA-CHEMKIN and GAMUT (KIVA-CHEMKIN-G), coupled with 2-step and multi-step phenomenological soot models were compared. The numerical results were compared with available experimental data obtained from an optically accessible HSDI engine and good agreement was obtained. To assess the effects of the in-cylinder flow field on combustion and emissions, off-centered swirl flows were also considered. In these studies, the swirl center was initialized at different positions in the chamber for different cases to simulate the effects of different intake flow arrangements.

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).

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.

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.

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 newly developed turbocharging system, named MIXPC, is proposed and the performance of the proposed system applied to diesel engines is evaluated. The aim of this proposed system is to reduce the scavenging interference between cylinders, and to lower the pumping loss in cylinders and the brake specific fuel consumption. In addition, exhaust manifolds of simplified design can be constructed with small dimensions, low weight and a single turbine entry. A simulation code based on a second-order FVM+TVD (finite volume method + total variation diminishing) is developed and used to simulate engines with MIXPC. By simulating a 16V280ZJG diesel engine using the MPC turbocharging system and MIXPC, it is found that not only the average scavenging coefficient of MIXPC is larger than that of MPC, but also cylinders of MIXPC have more homogeneous scavenging coefficients than that of MPC, and the pumping loss and BSFC of MIXPC are lower than those of MPC.

The effect of piston ring pack crevice flow and lubricant oil vaporization on heavy-duty diesel engine deposits is investigated numerically using a multidimensional CFD code, KIVA3V, coupled with Chemkin II, and computational grids that resolve part of the crevice region appropriately. Improvements have been made to the code to be able to deal with the complex geometry of the ring pack, and sub-models for the crevice flow dynamics, lubricating oil vaporization and combustion, soot formation and deposition were also added to the code. Eight parametric cases were simulated under reacting conditions using detailed chemical kinetics to determine the effects of variations of lube-oil film thickness, distribution of the oil film thickness, number of injection pulses, and the main injection timing on engine soot deposition. The results show that crevice-borne hydrocarbon species play an important role in deposit formation on crevice surfaces.

Combustion in an HSDI diesel engine using different injectors to realize low emissions is modeled using detailed chemical kinetics in this study. Emission characteristics of the engine are investigated using injectors that have different included spray angles, ranging from 50 to 130 degrees. The engine was operated under PCCI conditions featuring early injection times, high EGR levels and high intake temperatures. The Representative Interactive Flamelet (RIF) model was used with the KIVA code for combustion and emission modeling. Modeling results show that spray targeting plays an important role in determining the in-cylinder mixture distributions, which in turn affect the resulting pollutant emissions. High soot emissions are observed for injection conditions that result in locally fuel rich regions due to spray impingement normal to the piston surface.

The heavy-duty diesel engine industry is required to meet stringent emission standards. There is also the demand for more fuel efficient engines by the customer. In a previous study on an engine with variable intake valve closure timing, the authors found that an early single injection and accompanying premixed charge compression ignition (PCCI) combustion provides advantages in emissions and fuel economy; however, unacceptably high peak pressures and rates of pressure-rise impose a severe operating constraint. The use of a Pressure Reactive Piston assembly (PRP) as a means to limit peak pressures is explored in the present work. The concept is applied to a heavy-duty diesel engine and genetic algorithms (GA) are used in conjunction with the multi-dimensional engine simulation code KIVA-3V to provide an optimized set of operating variables.

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.

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.

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.

Transient engine tests were performed to investigate behavior of transient emissions--hydrocarbon (HC) and oxides of Nitrogen (NOx)--in a 2.4L turbocharged four cylinder High Speed Direct Injection (HSDI) diesel engine which is coupled to a hydrostatic transient dynamometer. Emissions were measured from one exhaust port 5 cm downstream of the exhaust valve and from the exhaust pipe 14 cm below the wastegate of the turbocharger. These measurements were made with fast response HC and NOx measurement analyzers. The experiments were conducted by increasing torque at constant speed and by increasing speed at constant torque, in conventional diesel combustion regions. The emissions from the two locations are compared. The transient effects of Exhaust Gas Recirculation (EGR) rates and injection timing on HC and NOx are described and the effects of linear and step load change on emissions are compared.

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.