Octane Numbers of Ethanol− and Methanol−Gasoline Blends Estimated from Molar Concentrations
ABSTRACT When expressed using volumetric concentrations (as is industry practice), the addition of relatively small amounts of ethanol or methanol (e.g., 10% by volume) to gasoline appears to result in disproportionately large, nonlinear increases in research octane number (RON) and motor octane number (MON). As a result, volumetric “blending octane numbers” are of limited value for estimating the octane number of alcohol−gasoline blends because they vary with alcohol content and base gasoline composition. We show that RON and MON increases with alcohol content are approximately linear when expressed using molar concentrations. Moreover, molar-based blending octane numbers are effectively equal to the octane numbers of the pure alcohols for most base gasolines. A limited dependence on gasoline composition was observed, namely, greater-than-predicted octane numbers for ethanol−gasoline blends with unusually high isoparaffin content. We suggest that octane numbers of methanol−gasoline and ethanol−gasoline blends can be estimated conveniently and more accurately from their molar composition by linear interpolation between the octane numbers of the base gasoline and the pure alcohol.
SourceAvailable from: Gautam T. Kalghatgi[Show abstract] [Hide abstract]
ABSTRACT: SAE 2014-01-2609 As SI engines strive for higher efficiency they are more likely to encounter knock and fuel anti-knock quality, which is currently measured by RON and MON, becomes more important. However, the RON and MON scales are based on primary reference fuels (PRF) – mixtures of iso-octane and n-heptane – whose autoignition chemistry is significantly different from that of practical fuels. Hence RON or MON alone can truly characterize a gasoline for its knock behavior only at their respective test conditions. The same gasoline will match different PRF fuels at different operating conditions. The true anti-knock quality of a fuel is given by the octane index, OI = RON –KS where S = RON – MON, is the sensitivity. K depends on the pressure and temperature evolution in the unburned gas during the engine cycle and hence is different at different operating conditions and is negative in modern engines. In this paper we propose that the gasolines are ranked against toluene /n-heptane mixtures (toluene reference fuel, TRF). The gasoline is assigned a Toluene Number (TN), which is the volume percent of toluene in the TRF which matches the gasoline for knock in the CFR RON test. Since TRFs will have comparable sensitivity to gasolines for the same RON, they will have comparable OI at different engine conditions and the TN will describe the knock behavior of the gasoline reasonably well at all different conditions. The paper describes the method and shows supporting experimental data. The other advantage of such a method is that it will enable a better quantitative description of fuels, such as ethanol mixtures, with RON > 100 and hence beyond the maximum of the current octane scale.SAE International Journal of Fuels and Lubricants 01/2014; 7(3):663-672.
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ABSTRACT: Gasoline is the most widely used fuel for light duty automobile transportation, but its molecular complexity makes it intractable to experimentally and computationally study the fundamental combustion properties. Therefore, surrogate fuels with a simpler molecular composition that represent real fuel behavior in one or more aspects are needed to enable repeatable experimental and computational combustion investigations. This study presents a novel computational methodology for formulating surrogates for FACE (fuels for advanced combustion engines) gasolines A and C by combining regression modeling with physical and chemical kinetics simulations. The computational methodology integrates simulation tools executed across different software platforms. Initially, the palette of surrogate species and carbon types for the target fuels were determined from a detailed hydrocarbon analysis (DHA). A regression algorithm implemented in MATLAB was linked to REFPROP for simulation of distillation curves and calculation of physical properties of surrogate compositions. The MATLAB code generates a surrogate composition at each iteration, which is then used to automatically generate CHEMKIN input files that are submitted to homogeneous batch reactor simulations for prediction of research octane number (RON). The regression algorithm determines the optimal surrogate composition to match the fuel properties of FACE A and C gasoline, specifically hydrogen/carbon (H/C) ratio, density, distillation characteristics, carbon types, and RON. The optimal surrogate fuel compositions obtained using the present computational approach was compared to the real fuel properties, as well as with surrogate compositions available in the literature. Experiments were conducted within a Cooperative Fuels Research (CFR) engine operating under controlled autoignition (CAI) mode to compare the formulated surrogates against the real fuels. Carbon monoxide measurements indicated that the proposed surrogates accurately reproduced the global reactivity of the real fuels across various combustion regimes.Fuel 03/2015; 143:290-300. DOI:10.1016/j.fuel.2014.11.022 · 3.41 Impact Factor
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ABSTRACT: Gaseous fuels, such as Liquefied Petroleum Gas (LPG) and Natural Gas (NG), thanks to their excellent mixing capabilities and high knocking resistance, allow complete and cleaner combustion than gasoline in Spark Ignition (SI) engines, resulting in lower pollutant emissions, above all if particulate matter is considered. In previous works [1, 2] the authors proved how the simultaneous combustion of gasoline and gaseous fuel (NG or LPG) may strongly reduce both fuel consumption and pollutant emissions with respect to pure gasoline operation without a significant power loss. These very encouraging results were obtained thanks to the strong knock resistance increase obtained adding gaseous fuel to gasoline, which allowed the use of stoichiometric mixtures and better spark advances, even at full load. The introduction of such a kind of combustion in series production engines would however require the use of properly calibrated simulation models, capable to adequately predict the performance and efficiency of engines fuelled by gaseous fuel-gasoline mixtures; in particular, specific combustion models are needed, together with reliable knock onset prediction sub-model. The total absence of such sub-models in the scientific literature induced the authors to investigate the knocking resistance of gasoline-propane mixtures and calibrate a proper knock onset prediction sub-model to be implemented in the zero dimensional thermodynamic models usually employed for engine performance optimization. To this purpose several light knocking in-cylinder pressure cycles have been recorded on a CFR engine, fuelled by gasoline, propane and their mixtures, varying the most important knock-related parameters: compression ratio, spark advance, inlet mixture temperature and fuel mixture composition. The collected data have been used to calibrate two different models, compared in terms of knock onset prediction accuracy: the Knock Integral model (KI) and the Ignition Delay model (ID). Both models revealed a good reliability in predicting the onset of knocking phenomena, with maximum errors around 4 crank angle degrees. The Knock Integral model showed a slightly higher accuracy, which, together with its lower computational effort, makes it preferable for the implementation in the commonly employed thermodynamic engine models. http://www.sciencedirect.com/science/article/pii/S1743967114202558