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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.

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.

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.

The contribution of fuel adsorption in engine oil and its subsequent desorption following combustion to the engine-out hydrocarbon (HC) emissions of a spark-ignited, air-cooled, V-twin utility engine was studied by comparing steady state and cycle-resolved HC emission measurements from operation with a standard full-blend gasoline, and with propane, which has a low solubility in oil. Experiments were performed at two speeds and three loads, and for different mean crankcase pressures. The crankcase pressure was found to impact the HC emissions, presumably through the ringpack mechanism, which was largely unaltered by the different fuels. The average and cycle-resolved HC emissions were found to be in good agreement, both qualitatively and quantitatively, for the two fuels. Further, the two fuels showed the same response to changes in the crankcase pressure. The solubility of propane in the oil is approximately an order of magnitude lower than for gasoline.

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.

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.

Gasoline fueled Homogeneous Charge Compression Ignition (HCCI) combustion with internal exhaust gas re-circulation using Negative Valve Overlap (NOL) was investigated by means of calculation and experiment in order to apply this technology to practical use with sufficient operating range and with acceptable emission and fuel consumption. In this paper we discuss the basic characteristics of NOL-HCCI with emphasis on the influence of intake valve timing on load range, residual gas fraction and induction air flow rate. Emission and fuel consumption under various operation conditions are also discussed. A water-cooled 250cc single cylinder engine with a direct injection system was used for this study. Three sets of valve timing were selected to investigate the effect of intake valve opening duration. Experimental results demonstrated that an engine speed of approximately 2000rpm yields an NMEP (Net Mean Effective Pressure) range from 200kPa to 400kPa.

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.

A comprehensive system level modeling approach is used to understand the effects of the various physical actuators during diesel HCCI transients. Control concepts during transient operations are simulated using a set of actuators suitable for combustion control in diesel HCCI engines (intake valve actuation, injection timing, cooled EGR, intake boost pressure and droplet size). The impact of these actuating techniques on the overall engine performance is quantified by investigating the amount of actuation required, timing of actuation and the use of a combination of actuators. Combined actuation improved actuation space that can be used to phase combustion timing better and in extending the operating range. The results from transient simulations indicate that diesel HCCI operation would benefit from the combined actuation of intake valve closure, injection timing, boost and cooled EGR.

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.

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.

A numerical study of the effects of injection parameters and operating conditions for diesel-fuel HCCI operation is presented with consideration of Variable Geometry Sprays (VGS). Methods of mixture preparation are explored that overcome one of the major problems in HCCI engine operation with diesel fuel and conventional direct injection systems, i.e., fuel loss due to wall impingement and the resulting unburned fuel. Low pressure injection of hollow cone sprays into the cylinder of a production engine with the spray cone angle changing during the injection period were simulated using the multi-dimensional KIVA-3V CFD code with detailed chemistry. Variation of the starting and ending spray angles, injection timing, initial cylinder pressure and temperature, swirl intensity, and compression ratio were explored. As a simplified case of VGS, two-pulse, hollow-cone sprays were also simulated.

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.