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Research Papers: Fuel Combustion

A Comprehensive Kinetics Library for Simulating the Combustion of Automotive Fuels

[+] Author and Article Information
Chitralkumar V. Naik

ANSYS Inc.,
5930 Cornerstone Ct W. #230,
San Diego, CA 92121
e-mail: chitral.naik@ansys.com

Karthik V. Puduppakkam

ANSYS Inc.,
5930 Cornerstone Ct W. #230,
San Diego, CA 92121
e-mail: karthik.puduppakkam@ansys.com

Ellen Meeks

ANSYS Inc.,
5930 Cornerstone Ct W. #230,
San Diego, CA 92121
e-mail: ellen.meeks@ansys.com

1Corresponding author.

Contributed by the Internal Combustion Engine Division of ASME for publication in the Journal of Energy Resources Technology. Manuscript received March 11, 2019; final manuscript received March 12, 2019; published online April 4, 2019. Assoc. Editor: Hameed Metghalchi.

J. Energy Resour. Technol 141(9), 092201 (Apr 04, 2019) (8 pages) Paper No: JERT-19-1144; doi: 10.1115/1.4043250 History: Received March 11, 2019; Accepted March 14, 2019

We have developed a surrogate blending methodology to identify surrogates with a desired degree of complexity. Along with estimation methods for various physical and chemical properties for fuel blends, we have assembled and developed a rich library of over 60 fuel components. The components cover a carbon number range from 1 to 20, and chemical classes including linear and branched alkanes, olefins, aromatics with one and two rings, alcohols, esters, and ethers. With these, surrogates can be formulated to represent most gasoline, diesel, gaseous fuels, renewable fuels, and several additives. As part of the library, we have assembled self-consistent and detailed reaction mechanisms for all the components, as well as for emissions including NOx and polycyclic aromatic hydrocarbons and a detailed soot-surface mechanism. An extensive validation suite has been used to improve the kinetics database such that good predictions and agreement to data are achieved for the fuel components and fuel-component blends, within experimental uncertainties. This effectively eliminates the need to tune specific rate parameters when employing the kinetics mechanisms in combustion simulations. For engine simulations, the master mechanisms have been reduced using a combination of available reduction methods while strictly controlling the error tolerances for targeted predictions. This approach has resulted in small mechanisms for efficiently incorporating the validated kinetics into computational fluid dynamics (CFD) applications. The surrogate formulation methodology, the comprehensive fuel library, and mechanism reduction strategies suggested in this work allow the use of CFD to explore design concepts and fuel effects in engines with reliable predictions.

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Figures

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Fig. 1

Hierarchy of the master mechanisms in the library of fuel kinetics mechanisms

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Fig. 2

Predicted laminar flame speeds of H2/O2/He mixture with an equivalence ratio of 1 at 1–25 atm and 295 K, in comparison with the experimental data of Burke et al. [52]

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Fig. 3

Predicted ignition times of TRF80/air mixture at an equivalence ratio of 1 and pressure of 40 bars, in comparison with the experimental data of Javed et al. [53]

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Fig. 4

Predicted ignition times of stoichiometric FACE-G gasoline/air mixtures at 40 bars, in comparison with the experimental data of Sarathy et al. [15]. Surrogate used in simulation was optimized for the test fuel.

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Fig. 5

Predicted ignition times of HRD-76 diesel/air ignition times at an equivalence ratio of 0.5 and pressure of 20 atm, in comparison with the experimental data of Gowdagiri et al. [54]. Surrogate used in simulation was optimized for the test fuel.

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Fig. 6

Predicted ignition times of Jet-A/air ignition times at an equivalence ratio of 0.5 and pressure of 20 atm, in comparison with the experimental data of Wang and Oehlschlaeger [55]. Surrogate used in simulation was optimized for the test fuel.

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Fig. 7

Predicted soot-particle size distribution in toluene “G7” flame at 0.9 cm separation distance compared with that reported in Ref. [39]

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Fig. 8

Typical trend in the extent of mechanism reduction by applying multiple methods in iterative fashion, while satisfying the accuracy target set in Chemkin-Pro Reaction Workbench [40]. Note that mechanism reduction under different conditions may yield different trends.

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Fig. 9

Simulated and measured pressure curves for the reference conditions in an IFPEN optical diesel engine

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Fig. 10

Crank angle for 10% and 50% heat release points (CA10 and CA50) for parametric variation in operating conditions in an IFPEN optical diesel engine

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Fig. 11

Evolution of in-cylinder planar averaged soot volume fraction in an IFPEN optical diesel engine

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Fig. 12

Simulated and measured pressure curves for the reference conditions in an ORNL engine with FACE-9 diesel

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Fig. 13

Exhaust soot mass for the reference conditions and for the hotter inlet temperature in an ORNL diesel engine

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Fig. 14

Soot size distribution under reference conditions in an ORNL diesel engine

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