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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.

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.

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.

Gasoline consists of many different classes of hydrocarbons, such as paraffins, olefins, aromatics, and cycloalkanes. In this study, a surrogate gasoline reaction mechanism is developed, and it has one representative fuel constituent from each of these classes. These selected constituents are iso-octane, n-heptane, 1-pentene, toluene, and methyl-cyclohexane. The mechanism was developed in a step-wise fashion, adding submechanisms to treat each fuel component. Reactions important for low temperature oxidation (<1000K) and cross-reactions among different fuels are incorporated into the mechanism. The mechanism consists of 1328 species and 5835 reactions. A single-zone engine model is used to evaluate how well the mechanism captures autoignition behavior for conditions corresponding to homogeneous charge compression ignition (HCCI) engine operation.

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.

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.

At temperatures below 1100 K, the oxidation of nitric oxide (NO) impacts the oxidation of hydrocarbons, causing a sensitization effect in fuel combustion. This effect can be important in engine operations, especially those involving high levels of exhaust-gas recirculation (EGR). Many researchers have observed this NO sensitization for the oxidation of hydrocarbons in HCCI engines as well as stirred reactors. They used several model-fuel components relevant to gasoline, such as n-heptane, iso-octane, and toluene. As found in stirred reactor experiments, NO tends to increase the extent of oxidation for high-octane fuel components, such as isooctane and toluene. However, for the low-octane component n-heptane, NO has an inhibiting effect on hydrocarbon oxidation, particularly at low temperatures corresponding to the negative temperature coefficient (NTC) region.

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.

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.

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.

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.

Surrogate fuels used in simulations need to capture the physical, combustion and emission characteristics of the real diesel and gasoline fuels they represent. This requirement can result in complex surrogate fuels that are blends of components representing several chemical classes, such as normal-, cyclo- and iso-alkanes, alkenes and aromatics. With a palette of around 20 potential surrogate-fuel components we can identify a blend to represent the most important physical and chemical properties of a particular real fuel. However, a detailed chemical kinetics mechanism is required to use such a surrogate in a model of the in-cylinder combustion processes. The detailed mechanism must capture the relevant kinetic pathways for all of the surrogate-fuel components. To this end, we have assembled a large comprehensive kinetic mechanism that includes several thousands of species to represent the combustion behavior of a wide range of surrogate fuels for gasoline and diesel.

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.