Surrogate Fuels Research Articles

Impacts of low-temperature heat release on unstretched laminar burning velocity in advanced flex-fuel gasoline-ethanol engines

Improving brake thermal efficiency (BTE) of flex-fuel engines can contribute to decarbonizing conventional and hybridized vehicles running on spark-ignited (SI) engines. High compression ratio (CR) and burning velocity under exhaust gas recirculation (EGR) conditions can improve the engine BTE. Ethanol fuel, which has been attracting increasing attention recently, can improve brake thermal efficiency due to its high-octane number and fast laminar burning velocity (LBV). With higher CRs, low-temperature heat release (LTHR) occurs in the unburned mixture, which affects the compositions, oxidation, and in-cylinder temperature. Such thermodynamic changes on unstretched LBV have not been thoroughly investigated, especially in production-intent next-generation flex-fuel engines. This study aims to clarify the LTHR impacts on LBV under EGR conditions using ethanol-blended gasoline surrogate fuels. The testbed used a single-cylinder SI engine fueled by E0 (gasoline surrogate), E20 (20 % ethanol + 80 % E0 by volume), E40, E60, E85, and E100. Detailed chemical reaction calculations were conducted using the boundary conditions obtained from production-intent, strong-tumble flow, and flex-fuel engine experiments. This work predicted LTHR in an unburned mixture using a zero-dimensional detailed chemical reaction calculation. LBV is predicted by a 1D kinetic laminar flame code. As a result, LTHR proceeds before the main combustion. The results show that the temperature increase associated with LTHR increases the burning velocity, while the partial oxidants decrease the burning velocity. Moreover, this work examined higher CRs using detailed chemical reaction calculations, and the results show that fuels with higher ethanol blending ratios can increase LBV in the late combustion phase.

A methodology to replicate LTGC engine conditions in a single-zone model and validate gasoline surrogate formulations versus experimental measurements

In this work, surrogate fuels for three premium-grade gasoline fuels were formulated and its autoignition behavior was validated, together with that of an existing surrogate for E10 regular gasoline, by comparison to experimental data measured in the Sandia Low-Temperature Gasoline Combustion research engine facility. Surrogate fuels have been previously designed by matching fuel properties such as octane number, fuel/air stoichiometric ratio and hydrogen/carbon ratio. However, similar octane rating can be obtained with several blends that will show different ignition characteristics. To avoid this issue, the surrogate fuels shown in this paper have been formulated by matching the main components of the fuel detailed hydrocarbon analysis. Validation of the surrogates was performed by replicating engine experiments run with the real fuel in a 0-D internal combustion engine reactor model with detailed chemistry. A fitted heat-loss model had to be implemented to properly replicate the experimental in-cylinder thermodynamic conditions, which invalidates the adiabatic assumption sometimes applied to engine simulations. Furthermore, minor species present in the EGR and residuals, such as CO and NOx, had to be taken into account to replicate the experimental low-temperature heat release (LTHR) trends. Therefore, accounting for heat losses and detailed in-cylinder composition are essential to accurately simulate the engine data. The bottom dead center temperature deviation required to obtain the same combustion timing as in the experiments was used as a metric to evaluate the accuracy of simulations. The surrogates gave good performance for the whole explored range, and replication of LTHR is a key factor in matching the experimental data. Chemical kinetic analyses showed that replication of LTHR is controlled by the ability of the surrogate to properly balance radical generation from low-temperature chain branching decomposition with radical consumption from stable species. Surrogate components that act as a sink of radicals tend to suppress the LTHR of the fuel, which may lead to large deviations between simulations and experiments. Especial attention should be paid to these fuel species when selecting the surrogate components.

Assessment of a PAH-based soot production model in laminar coflow methane diffusion flames doped by gasoline surrogate fuels

This article assesses the capability of the PAH-based soot model developed by the authors and validated in ethylene non-premixed flames to predict soot production in flames fueled with gasoline surrogates. The soot model was coupled to a flamelet model and the Rank-Correlated Full-Spectrum k model to simulate laminar coflow nitrogen-diluted methane/air diffusion flames doped with n-heptane/toluene and iso-octane/toluene mixtures. Consistent with our previous studies, the simulation was conducted using the Kaust Mechanism 1, pyrene as soot precursor, and the same set of model parameters. The model reproduced reasonably-well the peak soot volume fraction. However, the soot production onset was predicted much earlier than measurements owing to the early formation of pyrene induced by the presence of toluene. These discrepancies can be partially corrected by selecting a larger PAH than pyrene with a similar level of concentrations as soot precursor. For the present mechanism, anthanthrene was found to be the best candidate. Model results show that different mechanisms dominate the soot mass growth in ethylene and gasoline surrogate flames. While the HACA is more important in the former, PAH condensation largely prevails in the latter. This suggests that ethylene may be not the most relevant reference fuel for developing semi-empirical soot models for fires. Further investigations are required to confirm this conjecture.

Study on Chemical Kinetic Mechanism and Autoignition Characteristics of Isopentanol/Gasoline Surrogate Fuel

Abstract In this article, the detailed mechanism of isopentanol was simplified by direct relationship graph error propagation (DRGEP), generation rate analysis, reaction path optimization, and sensitivity analysis, and a comprehensive simplified mechanism of isopentanol/gasoline alternative fuels was obtained. Isopentanol/gasoline-characterized fuels with different blending ratios were investigated, and the results showed that blending of isopentanol promoted the autoignition of gasoline. It was found that blending isopentanol does not significantly affect the low-temperature reaction path of alkanes, but increases the reaction path flux from toluene to benzene. During combustion of isopentanol/gasoline alternative fuels, the isopentanol component exhibits a unique two-stage combustion phenomenon.

Development of laminar burning velocity prediction model and correlation of iso-octane air mixtures using artificial neural network

Surrogate fuels provide an economical alternative for forecasting the combustion properties of transportation fuels like diesel, gasoline, and kerosene. Iso-octane (2,2,4-trimethylpentane), a primary reference fuel for gasoline, is extensively used as a surrogate component. This study utilizes a Feed-Forward Artificial Neural Network (FFANN) with back-propagation (BP) to predict the laminar burning velocity (LBV) of iso-octane/air mixtures. The ANN model was developed using a dataset of 6339 data points, including 3788 experimental data points from literature since 2004 and 2551 simulation data points from reduced kinetic reaction mechanism (RKM) CHEMKIN simulations. The grid search cross-validation (CV) method was employed to optimize the ANN hyperparameters. Implemented in Python using Keras, the ANN model addresses research gaps by forecasting LBV for hydrocarbons beyond n-heptane and modelling LBV under conditions reflective of actual engine operations. Unlike prior studies, we optimized various ML models, their complexity, size, and hyperparameters to achieve high prediction accuracy. We also integrated a hybrid model combining Particle Swarm Optimization (PSO) with Genetic Algorithms (GA) for hyperparameter optimization, ensuring efficient configuration of the ANN. The constructed ANN model was compared to other ML models, including generalized linear regression (GLR), support vector machine (SVM), random forest (RF), and XGBoost regression. For the testing set (20 % of the total dataset), the ANN model outperformed all other ML models and empirical correlations, achieving a coefficient of determination (R2) of 0.9903, root mean square error (RMSE) of 1.911, and mean absolute error (MAE) of 1.206. Optimizing the ANN model with PSO further enhanced prediction accuracy, achieving a correlation coefficient of 0.9973 and a reduced mean absolute error of 0.753. To evaluate the ANN model's predictive ability, a reduced mechanism from the Lawrence Livermore National Laboratory (LLNL) was used. Compared to the LLNL RKM, the ANN predicted values showed lower percentage deviation from experimental data. Additionally, computation cost comparisons revealed that while RKM simulations in CHEMKIN took 244.2 s per case, the PSO-optimized ANN model required only 900 s for 150 cases. A 16-term correlation for laminar burning velocity (LBV) was derived from ANN predictions, serving as a practical tool to predict LBV based on pressure, temperature, and equivalence ratio. Unlike existing literature correlations, this newly developed correlation is valid across a wide range of operating conditions, offering a fundamental database for real-time combustion applications.

Evaluation of Auto-Ignition Characteristics of Diesel Surrogate Fuels with the Same Derived Cetane Numbers at Changed Thermodynamic Environments

Evaluation of Auto-Ignition Characteristics of Diesel Surrogate Fuels with the Same Derived Cetane Numbers at Changed Thermodynamic Environments

Descriptors-based machine-learning prediction of cetane number using quantitative structure–property relationship

The physicochemical properties of liquid alternative fuels are important but difficult to measure/predict, especially when complex surrogate fuels are concerned. In the present work, machine learning is used to develop quantitative structure–property relationship models. The fuel chemical structure is represented by molecular descriptors, allowing the linking of important features of the fuel composition and key properties of fuel utilization. Feature selection is employed to select the most relevant features that describe the chemical structure of the fuel and several machine learning algorithms are tested to construct interpretable models. The effectiveness of the methodology is demonstrated through the development of accurate and interpretable predictive models for cetane numbers, with a focus on understanding the link between molecular structure and fuel properties. In this context, matrix-based descriptors and descriptors related to the number of atoms in the molecule are directly linked with the cetane number of hydrocarbons. Furthermore, the results showed that molecular connectivity indices play a role in the cetane number for aromatic molecules. Also, the methodology is extended to predict the cetane number of ester and ether molecules, leveraging the design of alternative fuels towards fully sustainable fuel utilization.

Surrogate Model for the Effect of Charge State in Lithium-ion Battery Fires

Recently, the demand for eco-friendly energy sources, including batteries, has increased rapidly owing to growing climate and environmental problems. However, battery fire accidents are also increasing, and many researchers are focusing on the safe use of batteries and their flammability characteristics. Owing to the multiple battery types, conducting experiments is difficult in all cases, so various methods to model battery fires are being attempted. This study focuses on the effect of charge states and models surrogate fuels for battery thermal runaway. Three surrogate fuels were selected based on the battery components, and a method of increasing the oxygen concentration was adapted to model the effect that occurs when the charge states of a battery fire increases. As a result, the temperature and pressure increased in all cases, and it appears that the explosion caused by the battery thermal runaway can be simulated by the model. One of the results of this study is that ethane exposed to a high temperature not only contributes to the physical explosion of the battery but also increases the risk of chemical explosion as it decomposes in to H<sub>2</sub> and CH<sub>4</sub> .

EXPERIMENTAL AND REGRESSION ANALYSIS OF COMPRESSION IGNITION ENGINE POWERED BY DIESEL SURROGATE FUEL FROM USED LUBRICATING OIL USING MICROWAVE PYROLYSIS

Fossil fuels are depleting rapidly, and the search for alternative fuels has become inevitable. The researchers investigated the usage of surrogate fuels for internal combustion engines due to the high demand for diesel fuel in all sectors. Diesel surrogate fuels (DSFs) from used lubricating oil seem to be a solution for the future. The disposal of used lube oils poses a major environmental issue that affects the entire planet. This study aims to anticipate the exhaust emissions, brake-specific fuel consumption (BSFC), and brake thermal efficiency (BTE) of a compression ignition engine powered by blends. The blends used are B10 (DSF10% + diesel 90%), B20 (DSF20% + diesel 80%), B30 (DSF30% + diesel 70%), and B50 (DSF50% + diesel 50%) of DSF with diesel through microwave-assisted pyrolysis of used lubricating oil as fuel. Experimental findings were applied to predict each condition's performance characteristics. To estimate the performance characteristics, regression analysis was employed using experimental data in Microsoft Excel and Design Expert-12 software. The experimental data were compared to the simulated data. It was observed that fuel obtained from used lubricating oil using microwave-assisted pyrolysis could be used to power diesel engines without making any changes to the existing engines because the chemical characteristics of the diesel surrogate fuel were almost identical to those of diesel. The BTE of a compression ignition engine with DSF was close when compared to diesel up to full loading condition. At 1.924 kW brake power, the DSF30% has a higher efficiency than the other three blends. It was also observed that the rise in brake power also raises BSFC, thereby lowering thermal efficiency.

A kinetic study on the blending behavior of sustainable and conventional aviation fuels: Soot formation processes

Together with electrification, the introduction of short-term carbon cycle alternatives to fossil fuels represents one of the most promising strategies towards a significative reduction in GHG emissions. For sectors such as air transport this option is particularly appealing, as aviation strongly depends on the availability of high energy-density carriers. These needs justify the current interest in sustainable aviation fuels (SAF). In particular, paraffinic kerosenes from Fischer-Tropsch (FT) processes, hydro-processed esters and fatty acids (HEFA) and alcohol-to-jet (ATJ) are within the SAF that are attracting major interests. Their introduction as drop-in fuels, pure or in blends with conventional jet-fuels from fossils, may also have a positive impact on non-CO2 emissions such as soot, whose formation is known to strongly correlate with fuel composition. This study addresses a set of three standard (Jet-A, JP5 and JP8) and three sustainable jet fuels (HEFA-Camelina, FT-SPK, ATJ) unraveling the mechanisms behind their soot emission tendency and that of their blends. Suitable surrogate fuels are formulated from a palette of 11 components using an in-house code targeting physical and chemical properties of the real fuels. The sooting behavior of selected jet-fuels and blends (HEFA/Jet-A, FT-SPK/Jet-A, ATJ/Jet-A) is then calculated and compared with soot volume fraction data in laminar counterflow diffusion flames. A targeted kinetic analysis highlights kinetic pathways governing soot inception in standard jet-fuels in comparison to their sustainable alternatives supporting explanation of the observed trends. Morphological aspects such as the average particle diameters and particles size distribution from HEFA, Jet-A and HEFA/Jet-A flames are also discussed, identifying characteristic behaviors informing effective blending strategies and motivating targeted experimental studies.

Surrogates for Liquid-Liquid Extraction.

Fuel surrogates are mixtures that mimic the properties of real fuels with only a small number of components, simplifying the calculation and simulation of fuel-related processes. This work extends a previously published surrogate optimization algorithm toward the generation of fuel surrogates with a focus on liquid-liquid extraction characteristics. For this purpose, experimental liquid-liquid equilibrium data from batch extraction experiments are incorporated into the calculation procedure as an additional constraint. The use of the method is demonstrated by optimizing a surrogate for the catalytic reformate. Application of the surrogate to an extraction process and comparison with experimental data demonstrate that the resulting surrogate accurately depicts the properties of the real mixture with regard to liquid-liquid extraction performance. This demonstrates that the use of such surrogates is of particular interest for mixtures used as extracting agents for biofuels.

A hybrid GA-ANN and correlation approach to developing a laminar burning velocity prediction model for isooctane/blends-air mixtures

Surrogate fuels offer a cost-effective way to predict the combustion properties of transportation fuels like diesel, gasoline, kerosene, etc. Iso-octane (2,2,4-trimethylpentane) is a key gasoline reference and surrogate component. Researchers explore alternative fuels and their combustion characteristics to enhance efficiency and reduce emissions. The rising interest lies in the blending of isooctane with various alternative fuels, aiming for cleaner and more efficient combustion. In this current study, a machine learning method called Feed-Forward Artificial Neural Network (FFANN) with back-propagation (BP) was employed to forecast the laminar burning velocity (LBV) of isooctane/blends – air mixtures. A total of eleven blends including ammonia, hydrogen, methane, methanol, ethanol, butanol, n-heptane, 2-methyl furan (2-MF), 2,5-dimethylfuran (2,5-DMF), 2-methyl tetrahydrofuran (2-MTHF), and syngas were examined. The artificial neural network (ANN) model was created using a dataset consisting of 2234 data points gathered from the past experimental literature since 1983. To enhance the ANN's predictive capability, a combination of the random search CV technique and selective testing approach was utilized for optimizing ANN hyperparameters, while the genetic algorithm (GA) was deployed to optimize the ANN's weight values. The development of the ANN model was carried out within the Python software environment, utilizing the Keras application programming interface. The constructed GA-ANN model was compared to a variety of other machine learning (ML) models developed within this study, including generalized linear regression (GLR), support vector machine (SVM), random forest (RF), artificial neural network (ANN), and XGBoost regression. When evaluated on the testing set, which constitutes 15% of the complete dataset, the GA-ANN model demonstrated superior performance compared to all other ML models utilized in this research, achieving an impressive prediction accuracy with the coefficient of determination (R2) of 0.9910, root mean square error (RMSE) of 0.8231, and a mean absolute error (MAE) of 0.643. Additionally, a common LBV correlation for all isooctane/blend-air mixtures was created using the extended Gulder’s LBV formulation with the input parameters including pressure, temperature, equivalence ratio, and molar fraction of blend. This correlation results showed very minimum deviation from the experimental with an RMSE value of less than 0.38541.

Comparative Analysis of Jet Fuel Surrogates Based on a Comprehensive Kinetic Scheme

Comparative Analysis of Jet Fuel Surrogates Based on a Comprehensive Kinetic Scheme

Method for generating kinetically relevant fuel surrogates based on chemical functional group compositions

Fuel surrogates, simplified representations of complex fuels, play a crucial role in understanding fuel behavior and developing efficient combustion technologies. Previously published surrogates, specifically those designed to model shock tube speciation data, exhibit chemical functional group fragmentation similar in both type and quantity to their parent fuels, suggesting the importance of matching chemical functional groups for surrogates. This paper presents a data-driven optimization procedure for formulating fuel surrogates exclusively by matching chemical functional groups to their parent fuels. The resulting surrogates enable accurate modeling of speciation results, provide valuable insights into reaction kinetics, and are shown to consistently reproduce the ignition quality of their parent fuels, as represented by the Derived Cetane Number (DCN). The method automatically selects the appropriate number and type of components and optimizes their compositions to achieve similar chemical functional group compositions as the target fuel. The applicability of the method is demonstrated by formulating surrogates for two reference jet fuels (Jet-A POSF-10325, F-24), one synthetic alcohol-to-jet fuel (ATJ POSF11498), and a blend of ATJ/F-24 (50/50 vol ratio). Additionally, comparisons of rate of production, rate of progress, and sensitivity analyses in chemical kinetics modeling of the fuels and their surrogates highlight the consistency in outputs between surrogates with different compositions but similar chemical functional group fragmentation.

Development of Physical-Chemical Surrogate Models and Skeletal Mechanisms for the Combustion Simulation of Several Jet Fuels

Abstract The physical–chemical surrogate models for S-8, Jet-A, and RP-3 fuels to capture their physical and kinetics properties have been developed in this study. n-dodecane (nC12H26), 2,5-dimethylhexane (C8H18-25), and toluene (C6H5CH3) were chosen as candidate surrogate components and formulated by the function group based surrogate fuel methodology. Some important physical properties and spray characteristics for S-8, Jet-A, and RP-3 surrogate models were validated. The results indicate that present surrogate models can well emulate various physical properties to accurately reproduce the spray characteristics. Then, a minimal and high-precision surrogate skeletal mechanism that can be suitable for computational fluid dynamics (CFD) simulations was developed and validated against some fundamental combustion experiments for each surrogate component. Furthermore, the performances of surrogate models that contain the surrogate formulation and associated skeletal mechanisms were validated against the experimental data on ignition delay times (IDTs), species concentration profiles, and laminar flame speeds (Su0) in a wide range of conditions. Finally, the surrogate fuels were used to combustion CFD simulations to model the spray combustion process in a constant volume combustion chamber. It can be seen that the agreements between the simulation and experiment in fundamental and spray combustion characteristics are reasonably good, which proves that present surrogate models are accurate and robust to be applied in CFD simulations.

Modeling study of the ignition characteristics of directly injected 1,3-Cyclohexadiene in surrogate Gasoline/Air under RCCI combustion conditions

In this work a validated chemical kinetic model of the combustion of multicomponent gasoline surrogate fuels containing 680 reactions of 152 species has been used to simulate conditions reminiscent of those of Reactivity Controlled Compression Ignition (RCCI) in order to evaluate 1,3-cyclohexadiene (CYC6H8) as the high reactivity directly injected fuel and a gasoline surrogate fuel as the low reactivity premixed fuel. A single-zone modeling approach was adopted using a modified Cantera example describing the simulation of a gaseous Diesel-type internal combustion engine. The adaptable chemical structure of CYC6H8 to cis-1,3,5-hexatriene (lC6H8), load, start of injection and premixed fuel mass fraction were varied in order to analyze the potential of CYC6H8 as an ignition enhancer. Results have been compared to n-heptane and show that CYC6H8 and its isomer lC6H8 have the potential to be used as the high reactivity directly injected fuel in dual-fuel RCCI combustion, especially at high load conditions. For increased load, the ignition promoting effect is more pronounced at lower premixed fuel mass fractions and less affected by a delayed start of injection. The ignition promoting effect of lC6H8 is more pronounced than CYC6H8 for a decreasing premixed fuel mass fraction. Results from the kinetic analysis indicate that at increased concentrations of lC6H8, the thermal effects are becoming more significant.

Formulation of a Jet Fuel Surrogate and Its Kinetic Chemical Mechanism by Emulating Physical and Chemical Properties of Real Jet Fuel

The application of jet fuel in gas turbines and diesel engines adheres to the Army’s single-fuel forward policy, streamlining supply chains. To ensure precise engine combustion numerical studies, surrogate fuels and mechanisms should faithfully replicate real fuel properties and combustion traits. In this work, a new four-component jet fuel surrogate containing 39.05% n-dodecane/21.79% isocetane/11.49% decalin/27.67% toluene by mole fraction is formulated based on a property optimizer. The new-formulated fuel surrogate can satisfactorily emulate the chemical and physical properties of real jet fuel, including cetane number (CN), threshold sooting index (TSI), molecular weight (MW), lower heating value (LHV), the ratio of hydrogen and carbon (H/C), liquid density, viscosity, and surface tension. Furthermore, a reduced and robust kinetic chemical mechanism (containing 124 species and 590 reactions) that could be directly employed in practical engine combustion simulations has also been developed for the proposed surrogate jet fuel. The mechanism is validated through comprehensive experimental data, including ignition delay time (IDT) determined in shock tubes and rapid compression machines (RCMs), species mole fractions measured in premixed flames and jet stirred reactors (JSRs), and laminar flame speeds. Generally, the property deviations of the jet fuel surrogate are less than 2% except for MW (10.73%), viscosity (5.88%), and surface tension (8.71%). The comparison results between the predictions and measurements are in good agreement, indicating that the current kinetic mechanism is capable of reflecting the oxidation process of real jet fuel. The current mechanism can accurately capture variations in the ignition delay time in the negative temperature coefficient (NTC) region as well. In the future, the proposed surrogate jet fuel could be applied in practical engine computational fluid dynamic (CFD) simulations.

Evaluation of GDI spray structures under flash boiling and strong collapse conditions with diverse fuels

GDI (Gasoline Direct Injection) sprays are a matter of continuous research due to their potential for higher thermal efficiency and power output. Injections in GDI engines tend to happen at relatively high temperatures, which, combined with volatile fuels, are a perfect environment for flashing sprays, which are present in recently developed GDI engine passenger cars. Phenomena like flash boiling and especially spray collapse are still being investigated due to their complexity and impact on the engine performance. In the present research, the impact of different levels of flash boiling in the sprays is analyzed. Four surrogate mono-component fuels were tested with different volatile properties. Two optical techniques were carried out simultaneously: light extinction images (DBI) and frontal MIE scattering. The spray width was used to represent the radial expansion and study its relation with the spray collapse due to flash boiling. The degree of collapse was quantified by means of the instantaneous spray width variation for which three levels of collapse were identified. A novel MIE frontal approach was employed to study the spray surface evolution and the collapsing time by monitoring the nozzle tip clouding (opaquing) by the fuel. Results showed an increment of the width with the ratio of saturation to ambient pressure (Rp), as well as a strong correlation between the spray surface and the Rp at each fuel temperature when the spray is stabilized. Based on the MIE technique, three collapse instants were identified throughout the injection time depending on the Rp.

Soot nucleation in diffusion flames and the role of aromatic hydrocarbons

We perform spatially resolved measurements of light scattering of soot in atmospheric pressure counterflow diffusion flames to complement previously reported data on soot pyrometry, temperature and gaseous species up to three-ring polycyclic aromatic hydrocarbons (PAHs). We compare two flames: a baseline ethylene flame and a toluene-seeded flame in which an aliquot of ethylene in the feed stream is replaced with 3500 ppm of pre-vaporized toluene. The goal is twofold: directly adding an aromatic fuel to bypass the formation of the first aromatic ring, widely regarded as the main bottleneck to soot formation from aliphatic fuels, and assessing the impact of a common component of surrogates of transportation fuels on soot formation. The composition of the fuel and oxidizer streams are adjusted to ensure invariance of the temperature-time history, thereby decoupling the chemical effects of the fuel substitution from other factors. The doping approach enables the comparison of very similar flames with respect to combustion products, radicals and critical precursors to aromatic formation (C2–C5 species), in addition to the temperature-time history. Doping with toluene boosts the aromatic content and soot volume fraction relative to the baseline ethylene flame, but, surprisingly, the soot number density and nucleation rate are affected modestly. As a result, the observed difference in volume fraction in the toluene-doped flame is reflective of larger initial particles at the onset of soot nucleation. The nucleation rate when soot first appears near the flame is of the same order as the dimerization rate of single-ring aromatics, in contrast with the expectation that the dimerization of larger PAHs initiates the process. Even though in and of itself nucleation contributes modestly to the overall soot loading, nucleation conditions the overall soot loading by affecting the size of the initial particle, which ultimately affects subsequent growth.

Reactive molecular dynamics simulations of multicomponent models for RP-3 jet fuel in combustion at supercritical conditions: A comprehensive mechanism study

The aeroengine combustors frequently operate at temperatures and pressures higher than the critical values of the fuels. In this work, the combustion mechanisms and kinetic parameters of two multicomponent models for RP-3 jet fuel are investigated using reactive force field molecular dynamics (ReaxFF-MD). This study reveals that CC bond dissociation, H-abstraction, and isomerization dominate the initial combustion, with the main products of C1–C3 species, •OH and •OOH radicals. The reactions related to the formation and consumption of the radicals play a crucial role in the fuel combustion process. Some radicals rupture into smaller species by releasing C2H4 species. The cracked small molecules are further oxidized and react with radicals to produce large amounts of H2O molecules. The apparent activation energies of the two models are predicted to be 36.40 and 38.04 kcal/mol, judged by the variations in the number of total reactants at different temperatures. The simulation results are in good agreement with the experimental data at high pressures. Finally, the reactants and major product distributions of the two- and three-component fuels under fuel-rich, stoichiometric and fuel-lean combustion are studied. A comprehensive mechanism is developed by simulating the supercritical combustion of two surrogate fuels.