Uncertainty Assessment of Octane Index Framework for Stoichiometric Knock Limits of Co-Optima Gasoline Fuel Blends.
Abstract not provided.
Vuilleumier, David↗
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Discovery of novel octane hyperboosting phenomenon in prenol biofuel/gasoline blends
This work describes the first documented case of an effect defined herein as "octane hyperboosting" by an oxygenated fuel compound, 3-methyl-2-buten-1-ol (prenol). Octane hyperboosting is characterized by the Research Octane Number (RON) of a mixture (e.g. an oxygenate biofuel blended into gasoline) exceeding the RON of the individual components in that mixture. This finding counters the widely held assumption that interpolation between the RON values of a pure compound and the base fuel provides the bounds for the RON performance of the blend. This is clearly distinct from the more commonly observed synergistic blending of oxygenates with gasoline, where the RON never exceeds the performance of the highest performing component. Octane hyperboosting was observed for blends of prenol and six different gasoline fuels with varying composition. Testing of compounds chemically similar to prenol yielded no qualitatively similar instances of octane hyperboosting, which suggests that the effect may not be widespread among fuel candidates. The phenomenon suggests an unexplored aspect of autoignition kinetics research for fuel blends, and may provide a new mechanism for significantly increasing fuel octane number, which is necessary for increasing combustion efficiency in spark ignition engines. This phenomenon also increases the potential candidate list of biofuels, as compounds hitherto discounted due to their lower pure component RON may exhibit hyperboosting behavior, and thereby enhanced performance, in blends.
octane, hyperboost, prenol, oxygenate, fuel proper↗
Capturing the Impact of Fuel Octane Number on Modern Gasoline Vehicles with Octane Indices
The need for high efficiency automotive engines has led to more complex air handling and fuel injection systems, higher compression ratios, more advanced combustion and aftertreatment systems, and the use of fuels with higher octane ratings. Higher octane number fuels have a lower propensity to knock. This work studies the influence of changing fuel octane rating on two modern production gasoline vehicles, one with a naturally aspirated, port injected engine and the other with a turbocharged, direct injected engine, using fuels with four different octane number grades (with 85, 87, 91, and 93 anti-knock indices) and operated over a variety of driving cycles and temperature conditions. Unlike previous studies, this effort develops and demonstrates a methodology that isolates fuel effects on fuel consumption and provides a clear view of the octane impact on existing vehicles. While fuel octane rating can also impact factors such as the allowable compression ratio and gear shifting strategies, this study examines fuel consumption changes that are solely attributable to octane rating on production vehicles. The developed approach uses results from drive cycle as well as steady-state tests along with response surface modeling to predict the engine combustion phasing and changes in efficiency for each fuel relative to the baseline 87 anti-knock index (AKI) fuel. The methodology is then used to capture the role of fuel octane rating on powertrain efficiency over Environmental Protection Agency (EPA) certification drive cycles and demonstrate the variation of that impact as a function of powertrain output power. Based on the vehicles and fuels tested in this study, the metrics indicate that the effects of octane on efficiency are limited to a portion of the operating modes represented in the driving cycles used for vehicle certification. In addition, an octane potential benefit index (OPBI) suggests that the vehicle with the turbocharged, direct injected engine has a greater potential to benefit from higher octane number fuel compared to the naturally aspirated, port injected engine and proposes a way to quantify this impact for production vehicles.
Jehlik, Forrest↗
Exploring the Relationship Between Octane Sensitivity and Heat-of-Vaporization
The latent heat-of-vaporization (HoV) of blends of biofuel and hydrocarbon components into gasolines has recently experienced expanded interest because of the potential for increased HoV to increase fuel knock resistance in direct-injection (DI) engines. Several studies have been conducted, with some studies identifying an additional anti-knock benefit from HoV and others failing to arrive at the same conclusion. Consideration of these studies holistically shows that they can be grouped according to the level of fuel octane sensitivity variation within their fuel matrices. When comparing fuels of different octane sensitivity significant additional anti-knock benefits associated with HoV are sometimes observed. Studies that fix the octane sensitivity find that HoV does not produce additional anti-knock benefit. New studies were performed at ORNL and NREL to further investigate the relationship between HoV and octane sensitivity. Three fuels were formulated for the ORNL study with matched RON and octane sensitivity, but with differing HoV. Experiments with these fuels in a 1.6-liter GTDI engine showed that the fuels exhibited very similar combustion phasing under knock-limited spark advance (KLSA) conditions. Fuels having a range of RON, octane sensitivity, and HoV were tested at NREL in a single-cylinder GDI engine under conditions where octane sensitivity has little effect on knock resistance. KLSA was found to be well correlated with RON. These results reinforce the concept that HoV anti-knock effects can be viewed as a contributor to octane sensitivity. From this viewpoint, HoV effects manifest themselves as increases in octane sensitivity.
30 DIRECT ENERGY CONVERSION↗
Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design
Octane sensitivity (OS), defined as the research octane number (RON) minus the motor octane number (MON) of a fuel, has gained interest among researchers due to its effect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially within advanced compression ignition engines. RON/MON must be experimentally tested to determine OS, requiring time, funding, and specialized equipment. Thus, predictive models trained with existing experimental data and molecular descriptors (via quantitative structure-property relationships (QSPRs)) would allow for the preemptive screening of compounds prior to performing these experiments. Here, the present work proposes two methods for predicting the OS of a given compound: using artificial neural networks (ANNs) trained with QSPR descriptors to predict RON and MON individually to compute OS (derived octane sensitivity (dOS)), and using ANNs trained with QSPR descriptors to directly predict OS. Twenty-five ANNs were trained for both RON and MON and their test sets achieved an overall 6.4% and 5.2% error, respectively. Twenty-five additional ANNs were trained for both dOS and OS; dOS calculations were found to have 15.3% error while predicting OS directly resulted in 9.9% error. A chemical analysis of the top QSPR descriptors for RON/MON and OS is conducted, highlighting desirable structural features for high-performing molecules and offering insight into the inner mathematical workings of ANNs; such chemical interpretations study the interconnections between structural features, descriptors, and fuel performance showing that connectivity, structural diversity, and atomic hybridization consistently drive fuel performance.
09 BIOMASS FUELS↗
Chemical kinetics of octane sensitivity in a spark-ignition engine (Chemical Kinetics of Octane Sensitivity in a Spark Ignition Engine)
This article uses a chemical kinetic modeling approach to study the influences of fuel molecular structure on Octane Sensitivity (OS) in Spark Ignition (SI) engines. Octane Sensitivity has the potential to identify fuels that can be used in next-generation high compression, turbocharged SI engines to avoid unwanted knocking conditions and extend the range of operating conditions that can be used in such engines. While the concept of octane numbers of different fuels has been familiar for many years, the variations of their values and their role in determining Octane Sensitivity have not been addressed previously in terms of the basic structures of the fuel molecules. In particular, the importance of electron delocalization on low temperature hydrocarbon reactivity and its role in determining OS in engine fuel is described here for the first time. Finally, the role of electron delocalization on fuel reactivity and Octane Sensitivity is illustrated for a very wide range of engine fuel types, including n-alkane, 1-olefin, n-alcohol, and n-alkyl benzenes, and the unifying features of these fuels and their common trends, using existing detailed chemical kinetic reaction mechanisms that have been collected and unified to produce an overall model with unprecedented capabilities.
30 DIRECT ENERGY CONVERSION↗
Development of a Supercharged Octane Number and a Supercharged Octane Index
Gasoline knock resistance is characterized by the Research and Motor Octane Number (RON and MON), which are rated on the CFR octane rating engine at naturally aspirated conditions. However, modern automotive downsized boosted spark ignition (SI) engines generally operate at higher cylinder pressures and lower temperatures relative to the RON and MON tests. Using the naturally aspirated RON and MON ratings, the octane index (OI) characterizes the knock resistance of gasolines under boosted operation by linearly extrapolating into boosted “beyond RON” conditions via RON, MON, and a linear regression K factor. Using OI solely based on naturally aspirated RON and MON tests to extrapolate into boosted conditions can lead to significant errors in predicting boosted knock resistance between gasolines due to non-linear changes in autoignition and knocking characteristics with increasing pressure conditions. Here, a new “Supercharged Octane Number” (SON) method was developed on the CFR engine at increased intake pressures, which improved the correlation to boosted knock-limited automotive SI engine data over RON for several surrogate fuels and gasolines, including five “Co-Optima” RON 98 fuels and an E10 regular grade gasoline. Furthermore, the conventional OI was extended to a newly introduced Supercharged Octane Index (OI S ) based on SON and RON, which significantly improved the correlation to fuel knock resistance measurements from modern boosted SI engine knock-limited spark advance tests. This demonstrated the first proof of concept of a SON and OI S to better characterize a fuel’s knock resistance in modern boosted SI engines.
42 ENGINEERING↗
Combustion Characteristics of Various Fuels during Research Octane Number Testing on an Instrumented CFR F1/F2 Engine
The Cooperative Fuels Research (CFR) engine is the long-established standard for characterization of fuel knock resistance in spark-ignition internal combustion engines. Despite its measurements of RON and MON being widely used, there is little understanding of what governs the CFR octane rating for fuels of various chemical compositions compared to primary reference fuels (iso-octane and n-heptane). Detailed combustion characteristics were measured on a highly instrumented CFR F1/F2 engine during RON testing of fuels with significantly different chemical composition. The results revealed differences in the cylinder pressure and temperature conditions, as well as knocking characteristics.
auto-ignition↗
Rapid prediction of fuel research octane number and octane sensitivity using the AFIDA constant-volume combustion chamber
Current research octane number (RON) and motor octane number (MON) gasoline performance characterization techniques use dated, complex engine testing methodology and limit researchers’ ability to easily characterize small volumes of experimental fuels. A novel methodology is presented that correlates measured ignition delay (ID) time to RON in an Advanced Fuel Ignition Delay Analyzer (AFIDA) constant-volume combustion chamber device at a single pressure/temperature condition, with an r2 of 0.99 and standard error (SE) of 1.0. The correlation of the slope of the ID time between two additional temperature points to octane sensitivity (S) produces an r2 of 0.97 and SE of 0.69; however, fuels with S>12 are indistinguishable. These results are based on methodology calibration using 31 primary and toluene reference fuels containing 0%-40% ethanol with RON values ranging from 85 to 113. Validation of these methods using a 102-sample fuel matrix spanning an array of base fuels and additive chemistry designed to test the robust applicability of the method, along with pump gasoline and high-octane surrogate blend samples, demonstrates an r2 of 0.94 and SE of 1.3 for the RON correlation over all samples, whereas the equivalent S correlation produces an r2 of 0.78 and SE of 1.2 by excluding two additives, 3-pentanone and diisobutylene, which displayed poor S correlation results. This novel AFIDA analysis method can be performed in 1 h and with 40 mL of fuel, offering significant improvements in time and volume requirements over traditional techniques.
33 ADVANCED PROPULSION SYSTEMS↗
Prediction of Research/Motor Octane Number and Octane Sensitivity Using Artificial Neural Networks
Octane sensitivity (OS), dfined as the research octane number (RON) minus the motor octane number (MON) of a given fuel, has gained interest among researchers due to its apparent ffect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially with respect to advanced compression ignition engines. RON/MON must be experimentally tested to determine OS; however, the experimental methods utilized require a substantial amount of time, a significant monetary investment, and specialized equipment. To this end, predictive computational models trained with existing experimental data and molecular properties would allow for the preemptive screening of compounds prior to performing these experiments. The present work proposes two methods for predicting the OS of a given compound: using artficial neural networks (ANNs) trained with quantitative structure-property relationship (QSPR) descriptors to predict RON and MON individually to compute OS from RON/MON predictions (derived octane sensitivity, dOS), and using an ANN trained with QSPR descriptors to directly predict OS. ANNs trained to predict RON and MON achieved test set root-mean-square errors (RMSEs) of 10.499 and 7.551 respectively. dOS calculations were found to have a test set RMSE of 6.432 while predicting OS directly resulted in a test set RMSE of 7.019, showing it is more bene cial to obtain OS from RON/MON predictions than predicting it directly. Furthermore, relationships between individual QSPR descriptors and RON/MON/OS are discussed, highlighting correlations between specfic molecular features and these properties.
09 BIOMASS FUELS↗
Catalyst design to direct high-octane gasoline fuel properties for improved engine efficiency
The paraffin-to-olefin (P/O) ratio in gasoline fuel is a critical metric affecting fuel properties and engine efficiency. In the conversion of dimethyl ether (DME) to high-octane hydrocarbons over BEA zeolite catalysts, the P/O ratio can be controlled through catalyst design. Here, we report bimetallic catalysts that balance the net hydrogenation and dehydrogenation activity during DME homologation. The Cu-Zn/BEA catalyst exhibited greater relative dehydrogenation activity attributed to higher ionic site density, resulting in a lower P/O ratio (6.6) versus the benchmark Cu/BEA (9.4). The Cu-Ni/BEA catalyst exhibited increased hydrogenation due to reduced Ni species, resulting in a higher P/O ratio (19). The product fuel properties were estimated with an efficiency merit function and compared against finished gasolines and a typical alkylate blendstock. Merit values for the hydrocarbon product from all three BEA catalysts exceeded those of the comparison fuels (0–5.3), with the product from Cu-Zn/BEA exhibiting the highest merit value (9.7).
Catalyst design↗
Isolating the effects of reactivity stratification in reactivity-controlled compression ignition with iso-octane and n -heptane on a light-duty multi-cylinder engine
Explore the source record for details and available documents.
Wissink, Martin L.↗
Combustion chemistry of iso-octane/ethanol blends: Effects on ignition and reaction pathways
Not Available
Barraza-Botet, Cesar L. (ORCID:0000000200941172)↗
Effects of High Octane Ethanol Blends on Four Legacy Flex-Fuel Vehicles, and a Turbocharged GDI Vehicle
The U.S. Department of Energy (DOE) is supporting engine and vehicle research to investigate the potential of high-octane fuels to improve fuel economy. Ethanol has very high research octane number (RON) and heat of vaporization (HoV), properties that make it an excellent spark ignition engine fuel. The prospects of increasing both the ethanol content and the octane number of the gasoline pool has the potential to enable improved fuel economy in future vehicles with downsized, downsped engines. This report describes a small study to explore the potential performance benefits of high octane ethanol blends in the legacy fleet. There are over 17 million flex-fuel vehicles (FFVs) on the road today in the United States, vehicles capable of using any fuel from E0 to E85. If a future high-octane blend for dedicated vehicles is on the horizon, the nation is faced with the classic chicken-and-egg dilemma. If today’s FFVs can see a performance advantage with a high octane ethanol blend such as E25 or E30, then perhaps consumer demand for this fuel can serve as a bridge to future dedicated vehicles. Experiments were performed with four FFVs using a 10% ethanol fuel (E10) with 88 pump octane, and a market gasoline blended with ethanol to make a 30% by volume ethanol fuel (E30) with 94 pump octane. The research octane numbers were 92.4 for the E10 fuel and 100.7 for the E30 fuel. Two vehicles had gasoline direct injected (GDI) engines, and two featured port fuel injection (PFI). Significant wide open throttle (WOT) performance improvements were measured for three of the four FFVs, with one vehicle showing no change. Additionally, a conventional (non-FFV) vehicle with a small turbocharged direct-injected engine was tested with a regular grade of gasoline with no ethanol (E0) and a splash blend of this same fuel with 15% ethanol by volume (E15). RON was increased from 90.7 for the E0 to 97.8 for the E15 blend. Significant wide open throttle and thermal efficiency performance improvement was measured for this vehicle, which achieved near volumetric fuel economy parity on the aggressive US06 drive cycle, demonstrating the potential for improved fuel economy in forthcoming downsized, downsped engines with high-octane fuels.
09 BIOMASS FUELS↗
Octane-On-Demand: Onboard Separation of Oxygenates from Gasoline
Increasing standards for emissions and fuel economy have driven the need for downsizing of the internal combustion engines. To accommodate this change and prevent the possibility of knocking, fuels with higher octane numbers are needed. However, octane requirements are not uniform across the whole vehicle drive cycle, which leads to inefficient use of the high-octane fuel components. In approach that has been shown to substantially increase fuel economy, a dual-fuel solution, also called octane-on-demand, can be used and supply fuel with the required octane rating by metering in the appropriate ratio of high and low octane fuels, as dictated by the driving conditions. Barriers associated with introducing two fuels in the marketplace for an octane-on-demand approach can be mitigated by the use of an onboard separation system, which separates a single fuel, such as market E10 gasoline, into the high-octane oxygenate, like ethanol, and the a lower-octane base fuel. Currently investigated onboard separation systems rely on pervaporation membranes, which lose efficiency as the oxygenate is removed, and do not separate the entirety of it, leading to loses of this valuable component. Here, we present chemical separation strategies, such as liquid-solid and liquid-liquid extraction that provide advantages over membrane separation. This paper introduces applications of tailored chemical reactions, solid sorbent materials, and ionic liquids that are shown to have a high oxygenate recovery, and utility extended beyond ethanol to potential future oxygenate additives, such as isomers of butanol.
ethanol separation, octane-on-demand, SAMMS, Ionic↗
Isobutanol Octane Blending Model with Gasoline: Cooperative Research and Development Final Report, CRADA Number CRD-17-00689
The purpose of this agreement between the National Renewable Energy Laboratory (NREL) and Gevo, under the DOE Small Business Vouchers Pilot, is to develop a predictive octane blending model for isobutanol and blendstocks for oxygenated blending (BOBs). Validated models of ethanol octane blending effects are currently used in refinery blending models. While it is known that isobutanol increases octane when blended into BOBs, the effect is non-linear, and dependent on hydrocarbon blendstock properties. Because no predictive model currently exists for isobutanol and BOBs, blenders must perform expensive and time-consuming tests to determine the octane effect on each batch of finished fuel. NREL will make basic measurements of research octane number (RON) and motor octane number (MON) of finished gasoline-isobutanol blends prepared in a broad range of BOBs. The key properties and detailed composition of BOBs utilized will be measured by NREL. Using these measured fuel properties, NREL will develop a predictive octane blending model for isobutanol in BOBs that can be used by terminals to further reduce barriers to market penetration of isobutanol.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Predicting octane number from microscale flame dynamics
The standard method for measuring the octane number of fuels requires large sample volumes (~1L) and access to a Cooperative Fuel Research (CFR) engine. This method reliably quantifies the knock resistance of fuels in spark ignition engines, however the large sample volume requirement prevents testing of new experimental fuels (often produced in quantities of just ~1 mL), and the large equipment size impedes mobile, decentralized testing of remote fuel supplies. When direct measurements of octane number are impractical, other methods are needed. Micro flow reactors have shown promise in measuring ignition characteristics that are sensitive to octane number, and they are compact and operate on small volumes (~1 mL). This study uses simulations to demonstrate that measurements of the unsteady flame dynamics in a micro flow reactor can provide valuable data for accurate octane number predictions. Simulations of the flow reactor are used to obtain ignition characteristics for over 200 ethanol-toluene primary reference fuels (ETPRF) and 21 biofuel blends. A feed forward neural network is trained using the micro flow reactor ignition characteristics, fuel properties, and known research octane number (RON) and motor octane number (MON) for the ETPRF fuels. Here, the neural network is able to predict the RON and MON of the biofuel blends to within 2 octane number on average. Prediction results are compared to other methods available in the literature. Additional neural network models are trained that show improved prediction accuracy as additional fuel training data becomes available.
02 PETROLEUM↗