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The 4th Workshop of the Engine Combustion Network (ECN) was held September 5-6, 2015 in Kyoto, Japan. This manuscript presents a summary of the progress in experiments and modeling among ECN contributors leading to a better understanding of soot formation under the ECN “Spray A” configuration and some parametric variants. Relevant published and unpublished work from prior ECN workshops is reviewed. Experiments measuring soot particle size and morphology, soot volume fraction (fv), and transient soot mass have been conducted at various international institutions providing target data for improvements to computational models. Multiple modeling contributions using both the Reynolds Averaged Navier-Stokes (RANS) Equations approach and the Large-Eddy Simulation (LES) approach have been submitted. Among these, various chemical mechanisms, soot models, and turbulence-chemistry interaction (TCI) methodologies have been considered.

This paper is part of a larger body of experimental and computational work devoted to studying the role of close-coupled post injections on soot reduction in a heavy-duty optical engine. It is a continuation of an earlier computational paper. The goals of the current work are to develop new CFD analysis tools and methods and apply them to gain a more in depth understanding of the different in-cylinder environments into which fuel from main- and post-injections are injected and to study how the in-cylinder flow, thermal and chemical fields are transformed between start of injection timings. The engine represented in this computational study is a single-cylinder, direct-injection, heavy-duty, low-swirl engine with optical components. It is based on the Cummins N14, has a cylindrical shaped piston bowl and an eight-hole injector that are both centered on the cylinder axis. The fuel used was n-heptane and the engine operating condition was light load at 1200 RPM.

In this work computational and experimental approaches are combined to characterize in-cylinder flow structures and local flow field properties during operation of the Sandia 1.9L light-duty optical Diesel engine. A full computational model of the single-cylinder research engine was used that considers the complete intake and exhaust runners and plenums, as well as the adjustable throttling devices used in the experiments to obtain different swirl ratios. The in-cylinder flow predictions were validated against an extensive set of planar PIV measurements at different vertical locations in the combustion chamber for different swirl ratio configurations. Principal Component Analysis was used to characterize precession, tilting and eccentricity, and regional averages of the in-cylinder turbulence properties in the squish region and the piston bowl.

In a recent study, quantitative measurements were presented of in-cylinder spatial distributions of mixture equivalence ratio in a single-cylinder light-duty optical diesel engine, operated with a non-reactive mixture at conditions similar to an early injection low-temperature combustion mode. In the experiments a planar laser-induced fluorescence (PLIF) methodology was used to obtain local mixture equivalence ratio values based on a diesel fuel surrogate (75% n-heptane, 25% iso-octane), with a small fraction of toluene as fluorescing tracer (0.5% by mass). Significant changes in the mixture's structure and composition at the walls were observed due to increased charge motion at high swirl and injection pressure levels. This suggested a non-negligible impact on wall heat transfer and, ultimately, on efficiency and engine-out emissions.

In a recent experimental study the in-cylinder spatial distribution of mixture equivalence ratio was quantified under non-combusting conditions by planar laser-induced fluorescence (PLIF) of a fuel tracer (toluene). The measurements were made in a single-cylinder, direct-injection, light-duty diesel engine at conditions matched to an early-injection low-temperature combustion mode. A fuel amount corresponding to a low load (3.0 bar indicated mean effective pressure) operating condition was introduced with a single injection at -23.6° ATDC. The data were acquired during the mixture preparation period from near the start of injection (-22.5° ATDC) until the crank angle where the start of high-temperature heat release normally occurs (-5° ATDC). In the present study the measured in-cylinder images are compared with a fully resolved three-dimensional CFD model, namely KIVA3V-RANS simulations.

Premixed charge compression ignition (PCI) strategies offer the potential for simultaneously low NOx and soot emissions with diesel-like efficiency. However, these strategies are generally confined to low loads due to inadequate control of combustion phasing and heat-release rate. One PCI strategy, dual-fuel reactivity-controlled compression ignition (RCCI), has been developed to control combustion phasing and rate of heat release. The RCCI concept uses in-cylinder blending of two fuels with different auto-ignition characteristics to achieve controlled high-efficiency clean combustion. This study explores fuel reactivity stratification as a method to control the rate of heat release for PCI combustion. To introduce fuel reactivity stratification, the research engine is equipped with two fuel systems. A low-pressure (100 bar) gasoline direct injector (GDI) delivers iso-octane, and a higher-pressure (600 bar) common-rail diesel direct-injector delivers n-heptane.

The effects of spray targeting on mixing, combustion, and pollutant formation under a low-load, late-injection, low-temperature combustion (LTC) diesel operating condition are investigated by optical engine measurements and multi-dimensional modeling. Three common spray-targeting strategies are examined: conventional piston-bowl-wall targeting (152° included angle); narrow-angle floor targeting (124° included angle); and wide-angle piston-bowl-lip targeting (160° included angle). Planar laser-induced fluorescence diagnostics in a heavy-duty direct-injection optical diesel engine provide two-dimensional images of fuel-vapor, low-temperature ignition (H2CO), high-temperature ignition (OH) and soot-formation species (PAH) to characterize the LTC combustion process.

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.

Sources of unburned hydrocarbon (UHC) emissions are examined for a highly dilute (10% oxygen concentration), moderately boosted (1.5 bar), low load (3.0 bar IMEP) operating condition in a single-cylinder, light-duty, optically accessible diesel engine undergoing partially-premixed low-temperature combustion (LTC). The evolution of the in-cylinder spatial distribution of UHC is observed throughout the combustion event through measurement of liquid fuel distributions via elastic light scattering, vapor and liquid fuel distributions via laser-induced fluorescence, and velocity fields via particle image velocimetry (PIV). The measurements are complemented by and contrasted with the predictions of multi-dimensional simulations employing a realistic, though reduced, chemical mechanism to describe the combustion process.

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.

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.

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.

In-cylinder flow and combustion processes simulated with the standard k-ε turbulence model and with an alternative model-employing a non-linear, quadratic equation for the turbulent stresses-are contrasted for both motored and fired engine operation at two loads. For motored operation, the differences observed in the predictions of mean flow development are small and do not emerge until expansion. Larger differences are found in the spatial distribution and magnitude of turbulent kinetic energy. The non-linear model generally predicts lower energy levels and larger turbulent time scales. With fuel injection and combustion, significant differences in flow structure and in the spatial distribution of soot are predicted by the two models. The models also predict considerably different combustion efficiencies and NOx emissions.

Low-temperature combustion (LTC) strategies for diesel engines are of increasing interest because of their potential to significantly reduce particulate matter (PM) and nitrogen oxide (NOx) emissions. LTC with late fuel injection further offers the benefit of combustion phasing control because ignition is closely coupled to the fuel injection event. But with a short ignition-delay, fuel jet mixing processes must be rapid to achieve adequate premixing before ignition. In the current study, mixing and pollutant formation of late-injection LTC are studied in a single-cylinder, direct-injection, optically accessible heavy-duty diesel engine using three laser-based imaging diagnostics. Simultaneous planar laser-induced fluorescence of the hydroxyl radical (OH) and combined formaldehyde (H2CO) and polycyclic aromatic hydrocarbons (PAH) are compared with vapor-fuel concentration measurements from a non-combusting condition.

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.