Laminar flame speed correlations for methane, ethane, propane and their mixtures, and natural gas and gasoline for spark-ignition engine simulations

Amirante, Riccardo; Distaso, Elia; Tamburrano, Paolo; Reitz, Rolf D.

doi:10.1177/1468087417720018

Cited by 112 publications

(34 citation statements)

References 80 publications

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“…In this operating condition, the ignition delays of both fuels are again the same (Figure 8). However, the laminar flame speeds of both fuels show different behavior under high pressure and temperature [9,24]. Consequently, during the combustion process, both fuels could be faster or slower than each other.…”

Section: Combustion Analysismentioning

confidence: 99%

Investigation of Burn Duration and NO Emission in Lean Mixture with CNG and Gasoline

et al. 2019

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The results of experiments performed by gasoline and natural gas fuels in a single cylinder research engine were evaluated in this study. The main objective of this study is to compare exhaust gas emissions, efficiency, and burn durations for both fuels in stoichiometric and lean mixture. At the same time, cycle to cycle variation in these operating conditions should not exceed an acceptable value. In the ultra-lean mixture, gasoline fuel exceeded this determined limit before Compressed Natural Gas (CNG). Therefore, the reduction in NO was restricted by cyclic variations. In combustion analysis, although the burn duration of the gasoline in stoichiometric conditions was shorter than CNG, this situation reversed in favor of CNG in the ultra-lean mixtures. Contrary to some studies in the literature, the spark advance and ignition delay for CNG were the same or shorter than gasoline in this study. The primary reasons for this change are the high compression ratio and the different combustion chamber geometry. The increase in turbulence intensity has different effects on CNG and gasoline. As a result, it has been observed that NO emissions can meet the limits without a loss of efficiency for this engine operated with CNG under the ultra-lean mixture.

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Section: Combustion Analysismentioning

confidence: 99%

Investigation of Burn Duration and NO Emission in Lean Mixture with CNG and Gasoline

et al. 2019

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“…Heywood [4] based on gasoline research fuel experiments . Recently, Amirante et al [19] proposed a gasoline correlation by performing non-linear regression fitting algorithm on a wide range of commercial gasoline LFS experimental data. Several other authors have proposed numerically based correlations for specific gasoline surrogates [20]- [22].…”

Section: Laminar Flame Speed Correlationsmentioning

confidence: 99%

“…Thereafter, the best fit is obtained for each of 0 ( ), 1 ( ), 2 ( ), 1 ( , ), and 2 ( , ) in the form of the following Eqs. (17)(18)(19)(20)(21) where the coefficients are given in Table 4. The fitting procedure is described in detail in Appendix C. Figure 11: Comparison between the predicted and expected LFS values, on the left using Equation 1, and on the right using Equation (16), green lines are ±5 cm/s.…”

Section: Chapter 3: Laminar Flame Speed Correlationmentioning

confidence: 99%

Monte-Carlo based laminar flame speed correlation for gasoline

Harbi

Farooq

2020

Combustion and Flame

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Gasoline is a complex fuel containing hundreds of species, and, therefore, it is quite difficult to model all components present in gasoline. Alternatively, researchers tend to employ simpler surrogates that mimic targeted physical and chemical properties of gasoline. Two properties of gasoline, i.e., autoignition and laminar flame speed, play key role in the overall performance of spark-ignition and modern engines. For fuel-engine optimization, it is very important to have simple models which can accurately predict autoignition and laminar flame speed of gasoline. In this work, universal laminar flame speed correlation is proposed for typical gasolines. This correlation is based on Monte-Carlo simulations of randomly generated mixtures comprising of 21 gasoline-relevant molecules. Laminar flame speed of each molecule is numerically computed over a wide range of thermodynamic conditions using detailed chemical kinetic models, while flame speed of each mixture is estimated using a mixing rule. The proposed universal correlation is validated against experimentally-measured laminar flame speed of various gasoline fuels.

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“…The absence of large hydrocarbon chains and aromatics [31][32][33] makes natural gas a fuel cleaner than gasoline or diesel and able to guarantee lower gaseous emissions [32,34,35]. However, despite particle mass (PM) formation is small, the number concentrations of particles emitted by compressed natural gas (CNG) engines are not negligible when compared to those of Diesel engines [31,36,37].…”

Section: Introductionmentioning

confidence: 99%

Evolution of Soot Particle Number, Mass and Size Distribution along the Exhaust Line of a Heavy-Duty Engine Fueled with Compressed Natural Gas

et al. 2020

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An experimental study has been conducted to provide a characterization of the transformations that particle size distributions and the number density of soot particles can encounter along the exhaust line of a modern EURO VI compliant heavy-duty engine, fueled with compressed natural gas. Being aware of the particles history in the exhausts can be of utmost importance to understand soot formation and oxidation dynamics, so that, new strategies for further reducing these emissions can be formulated and present and future regulations met. To this purpose, particle samples were collected from several points along the exhaust pipe, namely upstream and downstream of each device the exhaust gases interact with. The engine was turbocharged and equipped with a two-stage after-treatment system. The measurements were carried out in steady conditions while the engine operated in stoichiometric conditions. Particle emissions were measured using a fast-response particle size spectrometer (DMS500) so that size information was analyzed in the range between 5 and 1000 nm. Particle mass information was derived from size distribution data using a correlation available in the literature. The reported results provide more insight on the particle emission process related to natural gas engines and, in particular, point out the effects that the turbine and the after-treatment devices produce on soot particles. Furthermore, the reported observations suggest that soot particles might not derive only from the fuel, namely, external sources, such as lubricant oil, might have a relevant role in soot formation.

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Laminar flame speed correlations for methane, ethane, propane and their mixtures, and natural gas and gasoline for spark-ignition engine simulations

Cited by 112 publications

References 80 publications

Investigation of Burn Duration and NO Emission in Lean Mixture with CNG and Gasoline

Investigation of Burn Duration and NO Emission in Lean Mixture with CNG and Gasoline

Monte-Carlo based laminar flame speed correlation for gasoline

Evolution of Soot Particle Number, Mass and Size Distribution along the Exhaust Line of a Heavy-Duty Engine Fueled with Compressed Natural Gas

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