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Designing advanced, clean and fuel-efficient engines requires detailed understanding of fuel chemistry. While knowledge of fuel combustion chemistry has grown rapidly in recent years, the representation of conventional fossil fuels in full detail is still intractable. A popular approach is to use a model-fuel or surrogate blend that can mimic various characteristics of a conventional fuel. Despite the use of surrogate blends, there remains a gap between detailed chemistry and its utilization in computational fluid dynamics (CFD), due to the prohibitive computational cost of using thousands of chemical species in large numbers of computational cells. This work presents a set of software tools that help to enable the use of detailed chemistry in representing conventional fuels in CFD simulation. The software tools include the Surrogate Blend Optimizer and a suite of automated mechanism reduction strategies.

Flash boiling occurs in sprays when the ambient gas pressure is lower than the saturation pressure of the injected fuel. In the present work, a numerical study was conducted to investigate solid-cone spray behaviors under various flash-boiling conditions. A new spray cone angle correlation that is a function of injection parameters was developed and used for spray initialization at the nozzle exit to capture plume interactions and the global spray shape. The spray-breakup regime control was adjusted to enable catastrophic droplet breakup, characterized by Rayleigh-Taylor (RT) breakup, near the nozzle exit. The model was validated against experimental spray data from five different injectors, including both multi-hole and single-hole injectors, with injection pressure varying from 100 to 200 bar.

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.

3-D Computational Fluid Dynamics (CFD) simulations have been performed using a detailed reaction mechanism to capture the combustion and emissions behavior of an IFP Energies Nouvelles optical diesel engine. Simulation results for in-cylinder soot volume fraction (SVF) have been compared to experimental data reported by Pires da Cruz et al., for the engine operating in low-temperature combustion (LTC) mode with high EGR, and for varied operating conditions. For the simulations, a 4-component surrogate blend containing n-hexadecane, heptamethylnonane, 1-methylnaphthalene, and decalin was used to represents the chemical and physical properties of the standard European diesel used in the engine tests. A validated detailed surrogate mechanism containing 392 species and 2579 reactions was employed to model the chemistry of fuel combustion and emissions.

Automated mechanism reduction is important in enabling the use of kinetics data in engineering design. In this work, we report on a mechanism-reduction technique that serves as a practical tool for automated mechanism reduction when applied to engine-simulation, with particular focus on compression-ignition engines. For this application, a multi-zone engine model has been developed, which can capture the stratification in the engine due to crevice and boundary-layer cooling effects. The multi-zone model serves as the workhorse for the mechanism-reduction algorithm. The reduction process is designed to operate on model-solution data from a parametric matrix of runs, in which the multi-zone model is run under different conditions. A more accurate reduction can therefore be achieved while accounting for spatial variations in the engine, temporal variations over the compression cycle, and variations in operating conditions.

To address the growing need for accurate predictions of combustion phasing and emissions for development of advanced engines, a more accurate definition of model fuels and their associated chemical-kinetics mechanisms are necessary. Wide variations in street fuels require a model-fuel blending methodology to allow simulation of fuel-specific characteristics, such as ignition timing, emissions, and fuel vaporization. We present a surrogate-blending technique that serves as a practical modeling tool for determination of surrogate blends specifically tailored to different real-fuel characteristics, with particular focus on model fuels for gasoline engine simulation. We start from a palette of potential model-fuel components that are based on the characteristic chemical classes present in real fuels. From this palette, components are combined into a surrogate-fuel blend to represent a real fuel with specific fuel properties.

While gasoline surrogate development has progressed in the areas of more complex surrogate mixtures and in kinetic modeling tools and mechanism development, it is generally recognized that further development is still needed. This paper represents a small step in supporting this development by providing comparisons between experimental engine data and surrogate-based kinetic models. In our case, the HCCI engine data comes from a port-injected, single-cylinder research engine with intake-air heating for combustion phasing control. Timing sweeps were run at constant fuel rate for three market gasolines and five surrogate mixtures. Modeling was done using the CHEMKIN software with a gasoline mechanism set containing 1440 species and 6572 reactions. Five pure compounds were selected for the surrogate blends and include iso-octane, n-heptane, toluene, methylcyclohexane, and 1-hexene.

To accurately predict emissions as well as combustion phasing in a homogeneous charge compression ignition (HCCI) engine, detailed chemistry needs to be used in Computational Fluid Dynamics (CFD) modeling. In this work, CFD simulations of an Oak Ridge National Laboratory (ORNL) gasoline HCCI engine have been performed with full coupling to detailed chemistry. Engine experiments using an E30 gasoline surrogate blend were performed at ORNL, which included measurements of several trace species in the exhaust gas. CFD modeling using a detailed mechanism for the same fuel composition used in the experiments was also performed. Comparisons between data and model are made over a range of intake temperatures. The (experiment & model) surrogate blend consists of 33 wt % ethanol, 8.7 % n-heptane and 58.3 % iso-octane. The data and simulations involve timing sweeps using intake temperature to control combustion phasing at a constant fuel rate.

Detailed knowledge of hydrocarbon fuel combustion chemistry has grown tremendously in recent years. However, the gap between detailed chemistry and computational fluid dynamics (CFD) remains, because of the high cost of solving detailed chemistry in a large number of computational cells. This paper presents the results of applying a suite of techniques aimed at closing this gap. The techniques include use of a surrogate blend optimizer and a guided mechanism reduction methodology, as well as advanced methods for efficiently and accurately coupling the pre-reduced kinetic models with the multidimensional transport equations. The advanced methods include dynamic adaptive chemistry (DAC) and dynamic cell clustering (DCC) algorithms.

A methodology has been implemented to calculate local turbulent flame speeds for spark ignition engines accurately and on-the-fly in 3-D CFD modeling. The approach dynamically captures fuel effects, based on detailed chemistry calculations of laminar flame speeds. Accurately modeling flame propagation is critical to predicting heat release rates and emissions. Fuels used in spark ignition engines are increasingly complex, which necessitates the use of multi-component fuels or fuel surrogates for predictive simulation. Flame speeds of the individual components in these multi-component fuels may vary substantially, making it difficult to define flame speed values, especially for stratified mixtures. In addition to fuel effects, a wide range of local conditions of temperature, pressure, equivalence ratio and EGR are expected in spark ignition engines.

One of the best tools to explore complicated in-cylinder physics is computational fluid dynamics (CFD). In order to assess the accuracy and reliability of the CFD simulations, it is critical to perform validation studies over different engine operating conditions. Simulation-based design of SI engines requires predictive capabilities, where results do not need to be tuned for each operating condition. This requires the models adopted to simulate their respective engine physics to be reliable under a broad range of conditions. A detailed set of experimental data was obtained to validate the CFD predictions of SI engine combustion.

Gasoline Direct Injection (GDI) is a key technology in the automotive industry for improving fuel economy and performance of gasoline internal combustion engines. GDI engine performance and emission characteristics are mainly determined by the complex interaction of in-cylinder flow, mixture formation and subsequent combustion processes. In a GDI engine, mixture formation depends on spray characteristics. Spray evolution and mixture formation is critical to GDI engine operation. In this work, a multi-component surrogate fuel blend was used to represent the chemical and physical properties of the gasoline employed in the experimental engine tests. Multi-component spray models were also validated in this study against experimental spray injection measurements in a chamber. The spray-chamber data include spray-penetration lengths, transient spray velocities and droplet Sauter mean diameter (SMD) at different axial and radial distances from the spray tip, obtained using a PDPA system.

Advanced research in Spark-ignition (SI) engines has been focused on dilute-combustion concepts. For example, exhaust-gas recirculation is used to lower both fuel consumption and pollutant emissions while maintaining or enhancing engine performance, durability and reliability. These advancements achieve higher engine efficiency but may deteriorate combustion stability. One symptom of instability is a large cycle-to-cycle variation (CCV) in the in-cylinder flow and combustion metrics. Large-eddy simulation (LES) is a computational fluid dynamics (CFD) method that may be used to quantify CCV through numerical prediction of the turbulent flow and combustion processes in the engine over many engine cycles. In this study, we focus on evaluating the capability of LES to predict the in-cylinder flows and gas exchange processes in a motored SI engine installed with a transparent combustion chamber (TCC), comparing with recently published data.

While most published detailed reaction mechanisms for n-alkanes have been validated against shock-tube data that use pre-vaporized fuels, they have not been tested extensively using engine conditions. This is partly due to the complications of the effects of both spray and evaporation on ignition and on the gas-phase kinetics. In this study, CFD simulations of Ignition Quality Tests (IQT™) are used as a tool to validate the detailed reaction mechanisms, supplementing other validation tests that use more fundamental shock-tube data. The Ignition Quality Tester is a new ASTM standard for measuring the Cetane Number (CN) of fuels. Shock-tube data in the literature are limited for heavy n-alkanes of interest for engine fuels, which make CN data valuable for mechanism validation. The IQT employs a stationary combustion chamber that involves spray evaporation and mixing followed by combustion.

A detailed reaction mechanism for the combustion of biodiesel fuels has recently been developed by Westbrook and co-workers. This detailed mechanism involves 5037 species and 19990 reactions, which prohibits its direct use in computational fluid dynamic (CFD) applications. In the present work, various mechanism reduction methods included in the Reaction Workbench software were used to derive a semi-detailed biodiesel combustion mechanism, while maintaining the accuracy of the master mechanism for a desired set of engine conditions. The reduced combustion mechanism for a five-component biodiesel fuel was employed in the FORTÉ CFD simulation package to take advantage of advanced chemistry solver methodologies and advanced spray models. Simulations were performed for a Volvo D12C heavy diesel engine fueled by RME fuel using a 72° sector mesh. Predictions were validated against measured in-cylinder parameters and exhaust emission concentrations.

Aromatics constitute a significant portion of refinery fuels. Characterizing the impact of various aromatic components on combustion and emissions facilitates formulation of surrogate fuels for engine simulations. The impact of blending aromatics in fuel surrogates is usually nonlinear for ignition characteristics responsible for knocking in spark engines and for combustion phasing in diesel engines. In this work, we have characterized the behavior of nine aromatics components under engine-relevant conditions. A self-consistent and validated detailed kinetics mechanism has been developed for gasoline and diesel surrogates that contains toluene, ethylbenzene, n-propylbenzene, n-butylbenzene, isomers of xylene, 1,2,4-trimethylbenzene, and 1-methylnaphthalene. Numerical experiments using 0-D and 1-D models have been performed to study the relative behavior of these aromatics for different reacting conditions.

Accurate modeling of the characteristics of diesel-engine combustion leads to more efficient design. Accurate modeling in turn depends on correctly capturing spray dynamics, turbulence, and fuel chemistry. This work presents a computational fluid dynamics (CFD) investigation of a well characterized small-bore direct injection diesel engine at Sandia National Laboratories’ Combustion Research Facility. The engine has been studied for two piston-bowls geometries and various injection timings. Simulation of these conditions test the predictive capabilities of our approach to diesel engine modeling using Ansys Forte. An experimental database covering a wide range of operating conditions is provided by the Engine Combustion Network for this engine, which is used to validate our modeling approach. Automatic and solution-adaptive meshing is used, and the recommended settings are discussed.

An important goal for CFD simulation in engine design is to be able to predict the combustion behavior as operating conditions are varied and as hardware is modified. Such predictive capability allows virtual prototyping and optimization of design parameters. For low-temperature combustion conditions, such as with high rates of exhaust-gas recirculation, reliable and accurate predictions have been elusive. Soot has been particularly difficult to predict, due to the dependence of soot formation on the fuel composition and the kinetics detail of the fuel combustion. Soot evolution in diesel engines is impacted by fuel and chemistry effects, as well as by spray dynamics and turbulence. In this work, we present a systematic approach to accurately simulate combustion and emissions in a high-performance BMW diesel engine. This approach has been tested and validated against experimental data for a wide range of operating conditions.

3-D Computational Fluid Dynamics (CFD) simulations have been performed using a detailed reaction mechanism to capture the combustion and emissions behavior of an IFP Energies nouvelles optical gasoline direct injection engine. Simulation results for in-cylinder soot volume fraction have been compared to experimental data provided by Pires da Cruz et al. [1] The engine was operated at low-load and tests were performed with parametric variations of the operating conditions including fuel injection timing, inlet temperature, and addition of fuel in the intake port. Full cycle simulations were performed including intake and exhaust ports, valve and piston motion. A Cartesian mesh was generated using automatic mesh generation in the FORTÉ CFD software. For the simulations, a 7-component surrogate blend was used to represent the chemical and physical properties of the European gasoline used in the engine tests.

Accurately predicting the evolution of soot mass and soot particle numbers under engine conditions is critical to advanced engine design. A detailed soot-chemistry model that can capture soot under gasoline and diesel conditions without tuning is necessary for such predictions. Building confidence in the predictive usage of the chemistry in engine simulations requires validating the soot kinetics over a wide range of operating conditions and fuels, using data from different experimental techniques, and using sources from laboratory flames to engines. This validation study focuses on a soot-chemistry model that considers multiple nucleation, growth, and oxidation reaction pathways. It involves 14 gas-phase precursors and considers the effect of different soot-particle surface sites.