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The development of a new Nitric Oxide (NOx) reaction mechanism has been conducted by adding species, including hydrogen cyanide (HCN) and the CH radical to a reduced chemistry diesel combustion model. The additional chemical reactions were added to the ERC's reduced 12-step NOx mechanism, which consists of N, NO, N2O, and NO2. The new NOx mechanism was implemented into the KIVA/ERC-CHEMKIN code and was found to be able to predict the experimentally observed trend that the amount of engine-out NOx decreases as engine load is increased, which is not reproduced by the current reduced NOx mechanism. HCN and CH were found to be species that bridge CxHy products and N radicals via the reaction CH+N2→HCN+N under high equivalence ratio conditions, and Zeldovich NO formation is suppressed by the formation of HCN, a species in the Fenimore NO formation pathway. The additional species and reactions were also found to influence the prediction of engine-out soot emissions.

Improvements to combustion models for modeling spark ignition engines using the G-equation flame propagation model and detailed chemical kinetics have been performed. The improvements include revision of a PRF chemistry mechanism, precise calculation of “primary heat release” based on the sub-grid scale unburned/burnt volumes of flame-containing cells, modeling flame front quenching in highly stratified mixtures, introduction of a Damkohler model for assessing the combustion regime of flame-containing cells, and a better method of modeling the effects of the local residual value on the burning velocity. The validation of the revised PRF mechanism shows that the calculated ignition delay matches shock tube data very well. The improvements to the “primary heat release” model based on the cell unburned/burnt volumes more precisely consider the chemical kinetics heat release in unburned regions, and thus are thought to be physically reasonable.

An investigation of high speed direct injection (DI) compression ignition (CI) engine combustion fueled with gasoline (termed GDICI for Gasoline Direct-Injection Compression Ignition) in the low temperature combustion (LTC) regime is presented. As an aid to plan engine experiments at full load (16 bar IMEP, 2500 rev/min), exploration of operating conditions was first performed numerically employing a multi-dimensional CFD code, KIVA-ERC-Chemkin, that features improved sub-models and the Chemkin library. The oxidation chemistry of the fuel was calculated using a reduced mechanism for primary reference fuel combustion. Operation ranges of a light-duty diesel engine operating with GDICI combustion with constraints of combustion efficiency, noise level (pressure rise rate) and emissions were identified as functions of injection timings, exhaust gas recirculation rate and the fuel split ratio of double-pulse injections.

Several diffusion combustion scaling models were experimentally tested in two geometrically similar single-cylinder diesel engines with a bore diameter ratio of 1.7. Assuming that the engines have the same in-cylinder thermodynamic conditions and equivalence ratio, the combustion models primarily change the fuel injection pressure and engine speed in order to attain similar performance and emissions. The models tested include an extended scaling model, which scales diffusion flame lift-off length and jet spray penetration; a simple scaling model, which only scales spray penetration at equal mean piston speed; and a same speed scaling model, which holds crankshaft rotational velocity constant while also scaling spray penetration. Successfully scaling diffusion combustion proved difficult to accomplish because of apparent differences that remained in the fuel-air mixing and heat transfer processes.

Single-cylinder engine experiments were used to investigate a fuel reactivity controlled compression ignition (RCCI) concept in both light- and heavy-duty engines and comparisons were made between the two engine classes. It was found that with only small changes in the injection parameters, the combustion characteristics of the heavy-duty engine could be adequately reproduced in the light-duty engine. Comparisons of the emissions and performance showed that both engines can simultaneously achieve NOx below 0.05 g/kW-hr, soot below 0.01 g/kW-hr, ringing intensity below 4 MW/m2, and gross indicated efficiencies above 50 per cent. However, it was found that the peak gross indicated efficiency of the baseline light-duty engine was approximately 7 per cent lower than the heavy-duty engine. The energy balances of the two engines were compared and it was found that the largest factor contributing to the lower efficiency of the light-duty engine was increased heat transfer losses.

The potential of low temperature combustion to yield low NOx and soot while maintaining diesel-like thermal efficiencies has been demonstrated through countless studies. Methods of achieving low temperature combustion are just as numerous and they range from using high cetane number fuels, like diesel, with large amounts of exhaust gas recirculation, to completely premixing a high octane number fuel, like gasoline, and approaching an HCCI-like condition. The potential of operating a heavy-duty compression ignition engine fueled with conventional gasoline in a partially premixed combustion mode to have high thermal efficiency and low emissions has been demonstrated in this study. The objective of this work was to optimize the engine using computational tools. The KIVA3V-CHEMKIN code, a multi-dimensional engine CFD model was coupled to a Nondominated Sorting Genetic Algorithm (NSGA II), which is a multi-objective genetic algorithm.

Reactivity Controlled Compression Ignition combustion (RCCI) has been demonstrated at mid to high loads [1, 2, 3, 4, 5, 6] as a method to operate an internal combustion engine that produces low NOx and low PM emissions with high thermal efficiency. The current study investigates RCCI engine operation at loads of 2 and 4.5 bar gross IMEP at engine speeds between 800 and 1700 rev/min. This load range was selected to cover the range from the previous work of 6 bar gIMEP down to an off-idle load at 2 bar. The fueling strategy for the low load investigation consisted of in-cylinder fuel blending using port-fuel-injection of gasoline and early cycle, direct-injection of either diesel fuel or gasoline doped with 3.5% by volume 2-EHN (2-ethylhexyl nitrate). At these loads, engine operating conditions such as inlet air temperature, port fuel percentage, and engine speed were varied to investigate their effect on combustion.

Many studies have demonstrated ability of low temperature combustion to yield low NOx and soot while maintaining diesel-like thermal efficiencies. Methods of achieving low temperature combustion are numerous and range from using high cetane number fuels, like diesel, with large amounts of exhaust gas recirculation, to completely premixing a high octane number fuel, like gasoline, and approaching an HCCI-like condition. Both of the aforementioned techniques have relatively short combustion duration that results in very a rapid rate of heat release, and hence very rapid rates of pressure rise. This has been one of the major challenges for premixed, low temperature combustion at mid and high load. Reactivity Controlled Compression Ignition (RCCI) has been introduced recently, which is a dual fuel partially premixed combustion concept.

A multi-component fuel model is used to represent gasoline in computational fluid dynamics (CFD) simulations of a dual-fuel engine that combines premixed gasoline injection with diesel direct injection. The simulations employ detailed-kinetics mechanisms for both the gasoline and diesel surrogate fuels, through use of an advanced and efficient chemistry solver. The objective of this work is to elucidate kinetics effects of dual-fuel usage in Reactivity Controlled Compression Ignition (RCCI) combustion. The model is applied to simulate recent experiments on highly efficient RCCI engines. These engine experiments used a dual-fuel RCCI strategy with port-fuel-injection of gasoline and early-cycle, multiple injections of diesel fuel with a conventional diesel injector. The experiments showed that the US 2010 heavy-duty NO and soot emissions regulations were easily met without aftertreatment, while achieving greater than 50% net indicated thermal efficiency.

Biodiesel-fueled engine simulations were performed using the KIVA3v-Release 2 code coupled with Chemkin-II for detailed chemistry. The model incorporates a reduced mechanism that was created from a methyl decanoate/methyl-9-decenoate mechanism developed at the Lawrence Livermore National Laboratory. A combination of Directed Relation Graph, chemical lumping, and limited reaction rate tuning was used to reduce the detailed mechanism from 3299 species and 10806 reactions to 77 species and 209 reactions. The mechanism was validated against its detailed counterpart and predicted accurate ignition delay times over a range of relevant operating conditions. The mechanism was then combined with the ERC PRF mechanism to include n-heptane as an additional fuel component. The biodiesel mechanism was applied in KIVA using a discrete multi-component model with accurate physical properties for the five common components of real biodiesel fuel.

Many recent studies have shown that the Reactivity Controlled Compression Ignition (RCCI) combustion strategy can achieve high efficiency with low emissions. However, it has also been revealed that RCCI combustion is difficult at high loads due to its premixed nature. To operate at moderate to high loads with gasoline/diesel dual fuel, high amounts of EGR or an ultra low compression ratio have shown to be required. Considering that both of these approaches inherently lower thermodynamic efficiency, in this study natural gas was utilized as a replacement for gasoline as the low-reactivity fuel. Due to the lower reactivity (i.e., higher octane number) of natural gas compared to gasoline, it was hypothesized to be a better fuel for RCCI combustion, in which a large reactivity gradient between the two fuels is beneficial in controlling the maximum pressure rise rate.

Manufacturers of heavy-duty diesel engines are facing increasingly stringent, emission standards. These standards have motivated new research efforts towards improving the performance of diesel engines. The objective of the present program is to develop a comprehensive analytical model of the diesel combustion process that can be used to explore the influence of design changes. This will enable industry to predict the effect of these changes on engine performance and emissions. A major benefit of the successful implementation of such models is that engine development time and costs would be reduced through their use. The computer model is based on the three-dimensional KIVA-II code, with state-of-the-art submodels for spray atomization, drop breakup / coalescence, multi-component fuel vaporization, spray/wall interaction, ignition and combustion, wall heat transfer, unburned HC and NOx formation, and soot and radiation.

Real transportation fuels, such as gasoline and diesel, are mixtures of thousands of different hydrocarbons. For multidimensional engine applications, numerical simulations of combustion of real fuels with all of the hydrocarbon species included exceeds present computational capabilities. Consequently, surrogate fuel models are normally utilized. A good surrogate fuel model should approximate the essential physical and chemical properties of the real fuel. In this work, we present a novel methodology for the formulation of surrogate fuel models based on local optimization and sensitivity analysis technologies. Within the proposed approach, several important fuel properties are considered. Under the physical properties, we focus on volatility, density, lower heating value (LHV), and viscosity, while the chemical properties relate to the chemical composition, hydrogen to carbon (H/C) ratio, and ignition behavior. An error tolerance is assigned to each property for convergence checking.

The focus of the present study was to characterize the fuel reactivity of high octane number fuels (i.e., low fuel reactivity), namely gasoline, ethanol, and methanol when mixed with cetane improvers under lean, premixed combustion conditions. Two commercially available cetane improvers, 2-ethylhexyl nitrate and di-tert-butyl peroxide, were used in the study. First, blends of the primary reference fuels iso-octane and n-heptane were port injected under fixed operating conditions. The resulting combustion phasings were used to generate effective PRF number maps. Then, blends of the aforementioned base fuels and cetane improvers were tested under the same lean premixed conditions as the PRF blends. Based on the combustion phasing results of the base fuel and cetane improver mixture, the effective PRF number, or octane number, could be determined.

Homogeneous low temperature combustion is believed to be a promising approach to resolve the conflict of goals between high efficiency and low exhaust emissions. Disadvantageously for this kind of combustion, the whole process depends on chemical kinetics and thus is hard to control. Reactivity controlled combustion can help to overcome this difficulty. In the so-called RCCI (reactivity controlled compression ignition) combustion concept a small amount of pilot diesel that is injected directly into the combustion chamber ignites a highly diluted gasoline-air mixture. As the gasoline does not ignite without the diesel, the pilot injection timing and the ratio between diesel and gasoline can be used to control the combustion process. A phenomenological multi-zone model to predict RCCI combustion has been developed and validated against experimental and 3D-CFD data. The model captures the main physics governing ignition and combustion.

In multidimensional modeling, fuels have been represented predominantly by single components, such as octane for gasoline. Several bicomponent studies have been performed, but these are still limited in their ability to represent real fuels, which are blends of as many as 300 components. This study outlines a method by which the fuel composition is represented by a distribution function of the fuel molecular weight. This allows a much wider range of compositions to be modeled, and only requires including two additional “species” besides the fuel, namely the mean and second moment of the distribution. This approach has been previously presented but is applied here to multidimensional calculations. Results are presented for single component droplet vaporization for comparison with single component fuel predictions, as well as results for a multicomponent gasoline and a diesel droplet.

Fuel spray atomization and breakup processes within a direct-injection spark-ignition (DISI) engine and outside the engine were modeled using a modified KIVA-3V code with improved spray models. The structures of the predicted sprays were qualitatively compared with planar images. The considered sprays were created by a prototype pressure-swirl injector and the planar images were obtained by laser sheet imaging in an optical DISI engine. In the out-of-engine case, the spray was injected into atmospheric air, and was modeled in a two dimensional bomb. In the engine case, the injection started from 270° ATDC, and full 3-D computations in the same engine were performed. In both cases, two liquid injection pressure conditions were applied, that is, 3.40 MPa and 6.12 MPa. The model gives good prediction of the tip penetration, and external spray shape, but the internal structure prediction has relatively lower accuracy, especially near the spray axis.

A modified version of the multi-dimensional KIVA-II code is used to model the effects of multiple injection schemes and exhaust gas recirculation (EGR) on direct injected diesel engine NOx and soot emissions. The computational results, which also considered double and triple injection schemes and varying EGR amounts, are compared with experimental data obtained from a single cylinder version of a Caterpillar heavy-duty truck engine. The study is done at high load (75% of peak torque at 1600 rpm) where EGR is known to produce unacceptable increases in soot (particulate). The effect of soot and spray model formulations are considered. This includes a new spray model based on Rayleigh-Taylor instabilities for liquid breakup. A soot oxidation model that accounts for turbulent mixing and kinetic effects were found to give accurate results. The results showed excellent agreement between predicted and measured in-cylinder pressure, and heat release data for the various cases.

Two-Color imaging optics were developed and used to observe soot emission processes in a modern heavy-duty diesel engine. The engine was equipped with a common rail, electronically-controlled, high-pressure fuel injection system that is capable of up to four injection pulses per engine cycle. The engine was instrumented with an endoscope system for optical access for the combustion visualization. Multidimensional combustion and soot modeling results were used for comparisons to enhance the understanding and interpretation of the experimental data. Good agreement between computed and measured cylinder pressures, heat release and soot and NOx emissions was achieved. In addition, good qualitative agreement was found between in-cylinder soot concentration (KL) and temperature fields obtained from the endoscope images and those obtained from the multidimensional modeling.

A computational study was performed to evaluate the effects of bowl geometry, fuel spray targeting and swirl ratio under highly diluted, low-temperature combustion conditions in a heavy-duty diesel engine. This study is used to examine aspects of low-temperature combustion that are affected by mixing processes and offers insight into the effect these processes have on emissions formation and oxidation. The foundation for this exploratory study stems from a large data set which was generated using a genetic algorithm optimization methodology. The main results suggest that an optimal combination of spray targeting, swirl ratio and bowl geometry exist to simultaneously minimize emissions formation and improve soot and CO oxidation rates. Spray targeting was found to have a significant impact on the emissions and fuel consumption performance, and was furthermore found to be the most influential design parameter explored in this study.