Experimental Validation of Viscosity Blending Rules and Extrapolation for Sustainable Aviation Fuel

Hauck, F. R.; Yang, Zhibin; Kosir, Shane T.; Heyne, Joshua S.; Landera, Alexander; George, Anthe

doi:10.2514/6.2020-3671

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…The poor results of the base model are comprehensible due to the mentioned low-temperature behavior of jet fuels. To model the kinematic viscosity with the QSPR method for fuels as mixtures of pure components, the Grunberg–Nissan mixing rule is recommended in the literature, see eq This is a logarithmic mixing rule that calculates the viscosity of the fuel ν mix as the sum of the logarithmic viscosities of the pure compounds ν i weighted by the mass fraction w i .…”

Section: Resultsmentioning

confidence: 99%

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Probabilistic Mean Quantitative Structure–Property Relationship Modeling of Jet Fuel Properties

et al. 2021

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We present a novel probabilistic mean quantitative structure–property relationship (M-QSPR) method for the prediction of jet fuel properties considering two-dimensional gas chromatography measurements. Fuels are represented as one mean pseudo-structure that is inferred by a weighted average over structures of 1866 molecules that could be present in the individual fuel. The method allows training of models on both data of pure components and of fuels and does not require mixing rules for the calculation of the bulk property. This drastically increases the number of available training data and allows the direct learning of the mixing behavior. For the modeling, we use a Monte-Carlo dropout neural network, a probabilistic machine learning algorithm, that estimates prediction uncertainties due to possible unidentified isomers and dissimilarity of training and test data. Models are developed to predict the freezing point, flash point, net heat of combustion, and temperature-dependent properties such as density, viscosity, and surface tension. We investigate the effect of the presence of fuels in the training data on the predictions for up to 82 conventional fuels and 50 synthetic fuels. The results of the predictions are compared on three metrics that quantify accuracy, precision, and reliability. These metrics allow a comprehensive estimation of the predictive capability of the models. For the prediction of density, surface tension, and net heat of combustion, the M-QSPR method yields highly accurate results even without the presence of fuels in the training data. For properties with nonlinear behavior over temperature and complex fuel component interactions, like viscosity and freezing point, the presence of fuels in the training data was found to be essential for the method.

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Section: Resultsmentioning

confidence: 99%

“…To model the kinematic viscosity with the QSPR method for fuels as mixtures of pure components, the Grunberg−Nissan mixing rule is recommended in the literature, see eq 16. 46 w wwG ln( ) ln( )…”

Section: Freezing Pointmentioning

confidence: 99%

Probabilistic Mean Quantitative Structure–Property Relationship Modeling of Jet Fuel Properties

et al. 2021

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“…For simple or complex mixtures blending rules can be used to make viscosity predictions provided viscosity data is available for the constituent liquids. Several algebraic blending rules to predict the viscosity of mixtures based on their components' properties have been developed, but quantification of systematic and random error of blending rules is rarely discussed in detail (Centeno et al, 2011;Hauck et al, 2020;Hernández et al, 2021). Recently Hernandez et al (Hernández et al, 2021) compared 30 different blending rules for petroleum-based fuels using 303 experiment data from biodiesel and petroldiesel binary blends.…”

Section: Introductionmentioning

confidence: 99%

Error quantification of the Arrhenius blending rule for viscosity of hydrocarbon mixtures

et al. 2022

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Six hundred and seventy-five measurements of dynamic viscosity and density have been used to assess the prediction error of the Arrhenius blending rule for kinematic viscosity of hydrocarbon mixtures. Major trends within the data show that mixture complexity–binary to hundreds of components—and temperature are more important determinants of prediction error than differences in molecular size or hydrogen saturation between the components of the mixtures. Over the range evaluated, no correlation between prediction error and mole fractions was observed, suggesting the log of viscosity truly is linear in mole fraction, as indicated by the Arrhenius blending rule. Mixture complexity and temperature also impact molar volume and its prediction. However, a linear regression between the two model errors explains less than 20% of the observed variation, indicating that mixture viscosity and/or molar volume are not linear with respect to temperature and/or mixture complexity. Extensive discussion of the intermolecular forces and the geometric arrangement of molecules and vacancies in liquids, which ultimately determines its viscosity, is brought into context with the implicit approximations behind the Arrhenius blending rule. The complexity of this physics is not compatible with a simple algebraic correction to the model. However, sufficient data is now available to determine confidence intervals around the prediction of fuel viscosity based on its component mole fractions and viscosities. At −40°C, when all identified components are pure molecules the modeling error is 13.2% of the predicted (nominal) viscosity times the root mean square of the component mole fractions.

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A GC × GC Tier α combustor operability prescreening method for sustainable aviation fuel candidates

Yang

Kosir

Stachler

et al. 2021

Fuel

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Experimental Validation of Viscosity Blending Rules and Extrapolation for Sustainable Aviation Fuel

Cited by 5 publications

References 17 publications

Probabilistic Mean Quantitative Structure–Property Relationship Modeling of Jet Fuel Properties

Probabilistic Mean Quantitative Structure–Property Relationship Modeling of Jet Fuel Properties

Error quantification of the Arrhenius blending rule for viscosity of hydrocarbon mixtures

A GC × GC Tier α combustor operability prescreening method for sustainable aviation fuel candidates

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