Liquid transportation fuels require costly and time-consuming tests to characterize metrics, such as Research Octane Number (RON) for gasoline. If fuel sale restrictions requiring use of standard ...Cooperative Fuel Research (CFR) testing procedures do not apply, these tests may be avoided by using multivariate statistical models to predict RON and other quantities. Existing techniques inform these models using information about existing, similar fuels—for example, training a model for gasoline RON with a large number of characterized gasoline samples. While this yields the most accurate predictive models for these fuels, this approach lacks the ability to predict characteristics of fuels outside the training data set. Here we show that an accurate statistical model for the RON of gasoline and gasoline-like fuels can be constructed by ensuring the representation of key functional groups in the spectroscopic data set are used to train the model. We found that a principal component regression model for RON based on IR absorbance and informed using neat and 134 mixtures of n-heptane, isooctane, toluene, ethanol, methylcyclohexane, and 1-hexene could predict RON for the 10 Coordinating Research Council (CRC) Fuels for Advanced Combustion Engine (FACE) gasolines and 12 FACE gasoline blends with ethanol within 34.8±36.1 on average and 51.2 in the worst case. We next studied the effect of adding 28 additional minor components found in the FACE gasolines to the statistical model, and determined that it was necessary to add additional representatives of the branched alkane and aromatics classes to reduce model error. For example, adding 2,3-dimethylpentane and xylene to the previous model allowed it to predict RON for the 22 target fuels within 0.3±4.4 on average and 7.9 in the worst case. However, we determined that the specific choice of fuel in those classes mattered less than ensuring the representation of the relevant functional group. This work builds upon previous efforts by creating models informed by neat and surrogate fuels—rather than complex real fuels—that could predict the performance of complex unknown fuels.
Design and optimization of higher efficiency, lower-emission internal combustion engines are highly dependent on fuel chemistry. Resolving chemistry for complex fuels, like gasoline, is challenging. ...A solution is to study a fuel surrogate: a blend of a small number of well-characterized hydrocarbons to represent real fuels by emulating their thermophysical and chemical kinetics properties. In the current study, an existing gasoline surrogate formulation algorithm is further enhanced by incorporating novel chemometric models. These models use infrared spectra of hydrocarbon fuels to predict octane numbers and are valid for a wide array of neat hydrocarbons and mixtures of such. This work leverages 14 hydrocarbon species to form tailored surrogate palettes for the fuels for advanced combustion engine (FACE) gasolines, including candidate component species not previously considered, namely, n-pentane, 2-methylpentane, 1-pentene, cyclohexane, and o-xylene. We evaluate the performance of “full” and “reduced” surrogates for the 10 fuels for advanced combustion engine gasolines, containing between 8–12 and 4–7 components, respectively. These surrogates match the target properties of the real fuels, on average, within 5%. This close agreement demonstrates that the algorithm can design surrogates matching the wide array of target properties, such as octane numbers, density, hydrogen-to-carbon ratio, distillation characteristics, and proportions of carbon–carbon bond types. We also compare our surrogates to those available in literature (FACE gasolines A, C, F, G, I, and J). Our surrogates for these fuels, on average, better match RON, MON, and distillation characteristics within 0.5%, 0.7%, and 0.9%, respectively, with literature surrogates at 1.2%, 1.1%, and 1.8% error, respectively. However, our surrogates perform slightly worse for density, hydrogen-to-carbon ratio, and carbon–carbon bond types at errors of 3.3%, 6.8%, and 2.2%, respectively, with literature surrogates at 1.3%, 2.3%, and 1.9%, respectively. Overall, the approach demonstrated here offers a promising method to better design surrogates for gasoline-like fuels with a wide array of properties.
This work uses support vector machine regression to correlate infrared absorption spectra to a metric representing low temperature combustion engine (LTC) performance, the LTC index: a singular value ...encapsulating achievable engine loads, combustion phasing, and efficiency. 313 total fuels informed the model, including mixtures and surrogate gasoline fuels containing n-heptane, isooctane (i.e., 2,2,4-trimethylpentane), toluene, ethanol, methylcyclohexane, xylene(s), 2-methybutane, and 2-methylhexane. We predicted LTC indices of the FACE (Fuels for Advanced Combustion Engines) gasolines A–J within ±6.0 units. The proposed methodology can be used to both predict gasoline LTC performance and also identify important hydrocarbon components that most improve (or reduce) LTC engine performance.
•We describe a previously introduced new fuel performance metric for LTC engines.•Driving cycle simulations provide real-world engine operating conditions.•We modeled HCCI engines to determine fuel ...operating envelopes.•We calculated the LTC performance index for oxygenated reference fuels mixtures.•The LTC fuel performance index is not well-correlated to octane number.
A new metric for ranking the suitability of fuels in LTC engines was recently introduced, based on the fraction of potential fuel savings achieved in the FTP-75 light-duty vehicle driving cycle. In the current study, this LTC fuel performance index was calculated computationally and analyzed for a number of fuel blends comprised of n-heptane, isooctane, toluene, and ethanol in various combinations and ratios corresponding to octane numbers from 0 to 100. In order to calculate the LTC index for each fuel, computational driving cycle simulations were first performed using a typical light-duty passenger vehicle, providing pairs of engine speed and load points. Separately, for each fuel blend considered, single-zone naturally aspirated HCCI engine simulations with a compression ratio of 9.5 were performed in order to determine the operating envelopes. These results were combined to determine the varying improvement in fuel economy offered by fuels, forming the basis for the LTC fuel index. The resulting fuel performance indices ranged from 36.4 for neat n-heptane (PRF0) to 9.20 for a three-component blend of n-heptane, isooctane, and ethanol (ERF1). For the chosen engine and chosen conditions, in general lower-octane fuels performed better, resulting in higher LTC fuel index values; however, the fuel performance index correlated poorly with octane rating for less-reactive, higher-octane fuels.
Broadband absorption spectroscopy, by way of FTIR, was used to investigate the vapor cloud of a single millimeter sized liquid droplet suspended by a syringe as it evaporates at standard conditions. ...Single beam data were collected every 8 seconds resulting in a time-resolved record. Species concentrations were tracked using their resonant absorption peaks and correlated with a multidimensional numerical model. The numerical model combined a Gaussian beam transmission through a temporally changing spherical vapor cloud with radial concentration gradients, informed by the D 2 law and interpreted using the Abel transform. There was fair agreement with temporal evaporation trends for single component runs. Multicomponent experiments of ethanol and isooctane showed synergistic blending effects and preferential evaporation of ethanol. Droplets were also suspended by a thermocouple to track the droplet temperature over time as they were subject to evaporative cooling. This work is the foundation of a basic technique for collecting useful data to inform a complex transport problem.
This work investigates the impact of injector temperature on the characteristics of high-pressure n-dodecane sprays under conditions relevant to heavy-duty diesel engines. Sprays are injected from a ...pair of single-hole diesel injectors belonging to the family of “Spray C” and “Spray D” Engine Combustion Network (ECN) injectors. Low and high injector temperature conditions are achieved by activating or deactivating a cooling jacket. We quantify spray spreading angle and penetration using high-speed shadowgraphy and long-distance microscopy imaging. We evaluate differences in fuel/air mixture formation at key timings through one-dimensional modeling. Injections from a cooled injector penetrate faster than those from a higher temperature injector, especially for an injector already prone to cavitation (Spray C). When uncooled, Spray C exhibited a time-varying spreading angle at early times during the injection event, which exacerbates the reduction in initial penetration rate relative to the cooled injector. Changes in fuel density alone cannot account for the observed trends, and we show that implementing a transient spreading angle in the model (guided by the time-sequenced images) is an effective solution to match experimental penetration characteristics. Time- and axially resolved radial mixture fractions derived from the model reveal that failure to account for early spreading angle transients and their impact on penetration and mixture formation leads to erroneous mixture fraction distributions at key timings associated with first- and second-stage ignition. Such oversights could lead the community toward incorrect model calibrations.
Design and optimization of higher efficiency, lower-emission internal combustion engines are highly dependent on fuel chemistry. Resolving chemistry for complex fuels, like gasoline, is challenging. ...A solution is to study a fuel surrogate: a blend of a small number of well-characterized hydrocarbons to represent real fuels by emulating their thermophysical and chemical kinetics properties. In the current study, an existing gasoline surrogate formulation algorithm is further enhanced by incorporating novel chemometric models. These models use infrared spectra of hydrocarbon fuels to predict octane numbers, and are valid for a wide array of neat hydrocarbons and mixtures of such. This work leverages 14 hydrocarbon species to form tailored surrogate palettes for the Fuels for Advanced Combustion Engine (FACE) gasolines, including candidate component species not previously considered: n-pentane, 2-methylpentane, 1-pentene, cyclohexane, and o-xylene. We evaluate the performance of "full" and "reduced" surrogates for the 10 fuels for advanced combustion engine (FACE) gasolines, containing between 8-12 and 4-7 components, respectively. These surrogates match the target properties of the real fuels, on average, within 5 %. This close agreement demonstrates that the algorithm can design surrogates matching the wide array of target properties: octane numbers, density, hydrogen-to-carbon ratio, distillation characteristics, and proportions of carbon-carbon bond types. We also compare our surrogates to those available in literature (FACE gasolines A, C, F, G, I and J). Overall, the approach demonstrated here offers a promising method to better design surrogates for gasoline-like fuels with a wide array of properties.
Liquid transportation fuels require costly and time-consuming tests to characterize metrics, such as Research Octane Number (RON) for gasoline. If fuel sale restrictions requiring use of standard ...Cooperative Fuel Research testing procedures do not apply, these tests may be avoided by using multivariate statistical models to predict RON and other quantities. Here we show that an accurate statistical model for the RON of gasoline and gasoline-like fuels can be constructed by ensuring the representation of key functional groups in the spectroscopic data set are used to train the model. We found that a principal component regression model for RON based on IR absorbance and informed using neat and 134 mixtures of n-heptane, isooctane, toluene, ethanol, methylcyclohexane, and 1-hexene could predict RON for the 10 Coordinating Research Council Fuels for Advanced Combustion Engine (FACE) gasolines and 12 FACE gasoline blends with ethanol within 34.8+/-36.1 on average and 51.2 in the worst case. We next studied the effect of adding 28 additional minor components found in the FACE gasolines to the statistical model, and determined that it was necessary to add additional representatives of the branched alkane and aromatics classes to reduce model error. For example, adding 2,3-dimethylpentane and xylene to the previous model allowed it to predict RON for the 22 target fuels within 0.3+/-4.4 on average and 7.9 in the worst case. However, we determined that the specific choice of fuel in those classes mattered less than ensuring the representation of the relevant functional group. This work builds upon previous efforts by creating models informed by neat and surrogate fuels---rather than complex real fuels---that could predict the performance of complex unknown fuels.
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