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Carbon-neutral (CN) fuels will be part of the solution to reducing global warming effects of the transportation sector, along with electrification. CN fuels such as hydrogen, ammonia, biofuels, and e-fuels can play a primary role in some segments (aviation, shipping, heavy-duty road vehicles) and a secondary role in others (light-duty road vehicles). The composition and properties of these fuels vary substantially from existing fossil fuels. Fuel effects on performance and emissions are complex, especially when these fuels are blended with fossil fuels. Predictively modeling the combustion of these fuels in engine and combustor CFD simulations requires accurate representation of the fuel blends. We discuss a methodology for matching the targeted fuel properties of specific CN fuels, using a blend of surrogate fuel components, to form a fuel model that can accurately capture fuel effects in an engine simulation.

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.

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.

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.

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.

This study focuses on Lagrangian spray models that are commonly used in engine CFD simulations. In modeling sprays, droplet collision is one of the physical phenomena that must be accounted for. There are two main parts of droplet collision models for sprays - detecting colliding pairs of droplets and predicting the outcomes of these collisions. For the first part, we focus on the efficiency of the algorithm. We present an implementation of the arbitrary adaptive collision mesh model of Hou and Schmidt [1], and examine its efficiency in dealing with large simulations. Through theoretical analysis and numerical tests, we show that the computational cost of this model scales pseudo-linearly with respect to the number of parcels in the sprays. Regarding the second part, we examine the variations in existing phenomenological models used for predicting binary droplet collision outcomes. A quantitative accuracy metric is used to evaluate the models with respect to the experimental data set.

Surface film formation is an important phenomenon during spray impingement in a combustion chamber. The film that forms on the chamber walls and piston bowl produces soot post-combustion. While some droplets stick to the wall surface, others splash and interact with the gas present inside the combustion chamber. Accurate prediction of both the film thickness and splashed mass is crucial for surface film model development since it leads to a precise estimation of the amount of soot and other exhaust gases formed. This information could guide future studies aimed at a comprehensive understanding of the combustion process and might enable development of engines with reduced emissions. Dynamic structure Large Eddy Simulation (LES) turbulence model implemented for in-cylinder sprays [1] has shown to predict the flow structure of a spray more accurately than the Reynolds-averaged Navier-Stokes turbulence model.

In most large-eddy simulation (LES) applications to two-phase engine flows, the liquid-air interactions need to be accounted for as source terms in the respective governing equations. Accurate calculation of these source terms requires the relative velocity “seen” by liquid droplets as they move across the flow, which generally needs to be estimated using a turbulent dispersion model. Turbulent dispersion modeling in LES is very scarce in the literature. In most studies on engine spray flows, sub-grid scale (SGS) models for the turbulent dispersion still follow the same stochastic approach originally proposed for Reynolds-averaged Navier-Stokes (RANS). In this study, an SGS dispersion model is formulated in which the instantaneous gas velocity is decomposed into a deterministic part and a stochastic part. The deterministic part is reconstructed using the approximate deconvolution method (ADM), in which the large-scale flow can be readily calculated.

Although turbocharging can extend the high load limit of low temperature combustion (LTC) strategies such as reactivity controlled compression ignition (RCCI), the low exhaust enthalpy prevalent in these strategies necessitates the use of high exhaust pressures for improving turbocharger efficiency, causing high pumping losses and poor fuel economy. To mitigate these pumping losses, the divided exhaust period (DEP) concept is proposed. In this concept, the exhaust gas is directed to two separate manifolds: the blowdown manifold which is connected to the turbocharger and the scavenging manifold that bypasses the turbocharger. By separately actuating the exhaust valves using variable valve actuation, the exhaust flow is split between two manifolds, thereby reducing the overall engine backpressure and lowering pumping losses. In this paper, results from zero-dimensional and one-dimensional simulations of a multicylinder RCCI light-duty engine equipped with DEP are presented.

A swept-volume method of calculating the volume swept by the flame during each time step is developed and used to improve the calculation of fuel reaction rates. The improved reaction rates have been applied to the ignition model and coupled with the level set G-equation combustion model. In the ignition model, a single initial kernel is formed after which the kernel is convected by the gas flow and its growth rate is determined by the flame speed and thermal expansion due to the energy transfer from the electrical circuit. The predicted ignition kernel size was compared with the available experimental data and good agreements were achieved. Once the ignition kernel reaches a size when the fully turbulent flame is developed, the G-equation model is switched on to track the mean turbulent flame front propagation.

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.

Natural gas is a promising alternative fuel for internal combustion engines due to its rich reserves and low price, as well as good physical and chemical properties. Its low carbon structure and high octane number are beneficial for CO2 reduction and knock mitigation, respectively. Diesel and natural gas dual fuel combustion is a viable pathway to utilize natural gas in diesel engines. To achieve high efficiency and low emission combustion in a practical diesel engine over a wide range of operating conditions, understanding the performance responses to engine system parameter variations is needed. The controllability of two combustion strategies, diesel pilot ignition (DPI) and single injection reactivity controlled compression ignition (RCCI), were evaluated using the multi-dimension CFD simulation in this paper.

In this paper, large eddy simulation (LES) coupled with two uncertainty quantification (UQ) methods, namely latin-hypercube sampling (LHS) and polynomial chaos expansion (PCE), have been used to quantify the effects of model parameters and spray boundary conditions on diesel and gasoline spray simulations. Evaporating, non-reacting spray data was used to compare penetration, mixture fraction and spray probability contour. Two different sets of four uncertain variables were used for diesel and gasoline sprays, respectively. UQ results showed good agreement between experiments and predictions. UQ statistics indicated that discharge coefficient has stronger impact on gasoline than diesel sprays, and spray cone angle is important for vapor penetration of both types of sprays. Additionally, examination of the gasoline spray characteristics showed that plume-to-plume interaction and nozzle dribble are important phenomena that need to be considered in high-fidelity gasoline spray simulations.

Computational fluid dynamics of gas-fueled large-bore spark ignition engines with pre-chamber ignition can speed up the design process of these engines provided that 1) the reliability of the results is not affected by poor meshing and 2) the time cost of the meshing process does not negatively compensate for the advantages of running a computer simulation. In this work a flame propagation model that runs with arbitrary hybrid meshes was developed and coupled with the KIVA4-MHI CFD solver, in order to address these aims. The solver follows the G-Equation level-set method for turbulent flame propagation by Tan and Reitz, and employs improved numerics to handle meshes featuring different cell types such as hexahedra, tetrahedra, square pyramids and triangular prisms. Detailed reaction kinetics from the SpeedCHEM solver are used to compute the non-equilibrium composition evolution downstream and upstream of the flame surface, where chemical equilibrium is instead assumed.

Three time integration schemes and four finite volume interpolation schemes for the convection term in momentum equation were tested under turbulent planar gas jet and Sandia non-reacting vaporizing Spray-H cases. The three time integration schemes are the first-order Euler implicit scheme, the second-order backward scheme, and the second-order Crank-Nicolson scheme. The four spatial interpolation schemes are cubic central, linear central, upwind, and vanLeer schemes. Velocity magnitude contour, centerline and radial mean velocity and Reynolds stress profiles for the planar turbulent gas jet case, and fuel vapor contour and liquid and vapor penetrations for the Diesel spray case predicted by the different numerical schemes were compared. The sensitivity of the numerical schemes to mesh resolution was also investigated. The non-viscosity based dynamic structure subgrid model was used. The numerical tool used in this study was OpenFOAM.

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.

While Low Temperature Combustion (LTC) strategies such as Reactivity Controlled Compression Ignition (RCCI) exhibit high thermal efficiency and produce low NOx and soot emissions, low load operation is still a significant challenge due to high unburnt hydrocarbon (UHC) and carbon monoxide (CO) emissions, which occur as a result of poor combustion efficiencies at these operating points. Furthermore, the exhaust gas temperatures are insufficient to light-off the Diesel Oxidation Catalyst (DOC), thereby resulting in poor UHC and CO conversion efficiencies by the aftertreatment system. To achieve exhaust gas temperature values sufficient for DOC light-off, combustion can be appropriately phased by changing the ratio of gasoline to diesel in the cylinder, or by burning additional fuel injected during the expansion stroke through post-injection.

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.