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Fuel Combustion

Simulating Flame Lift-Off Characteristics of Diesel and Biodiesel Fuels Using Detailed Chemical-Kinetic Mechanisms and Large Eddy Simulation Turbulence Model

[+] Author and Article Information
Sibendu Som1

Argonne National Laboratory, Energy Systems Division, 9700 S. Cass Avenue, Argonne, IL 60439ssom@anl.gov

Douglas E. Longman

Argonne National Laboratory, Energy Systems Division, 9700 S. Cass Avenue, Argonne, IL 60439

Zhaoyu Luo, Max Plomer, Tianfeng Lu

 University of Connecticut, Storrs, CT 06269

Peter K. Senecal, Eric Pomraning

Convergent Science, Inc., Middleton, WI 53562

1

Corrseponding author.

J. Energy Resour. Technol 134(3), 032204 (Aug 06, 2012) (10 pages) doi:10.1115/1.4007216 History: Received January 18, 2012; Revised May 29, 2012; Published August 06, 2012; Online August 06, 2012

Combustion in direct-injection diesel engines occurs in a lifted, turbulent diffusion flame mode. Numerous studies indicate that the combustion and emissions in such engines are strongly influenced by the lifted flame characteristics, which are in turn determined by fuel and air mixing in the upstream region of the lifted flame, and consequently by the liquid breakup and spray development processes. From a numerical standpoint, these spray combustion processes depend heavily on the choice of underlying spray, combustion, and turbulence models. The present numerical study investigates the influence of different chemical kinetic mechanisms for diesel and biodiesel fuels, as well as Reynolds-averaged Navier–Stokes (RANS) and large eddy simulation (LES) turbulence models on predicting flame lift-off lengths (LOLs) and ignition delays. Specifically, two chemical kinetic mechanisms for n-heptane (NHPT) and three for biodiesel surrogates are investigated. In addition, the renormalization group (RNG) k-ε (RANS) model is compared to the Smagorinsky based LES turbulence model. Using adaptive grid resolution, minimum grid sizes of 250 μm and 125 μm were obtained for the RANS and LES cases, respectively. Validations of these models were performed against experimental data from Sandia National Laboratories in a constant volume combustion chamber. Ignition delay and flame lift-off validations were performed at different ambient temperature conditions. The LES model predicts lower ignition delays and qualitatively better flame structures compared to the RNG k-ε model. The use of realistic chemistry and a ternary surrogate mixture, which consists of methyl decanoate, methyl nine-decenoate, and NHPT, results in better predicted LOLs and ignition delays. For diesel fuel though, only marginal improvements are observed by using larger size mechanisms. However, these improved predictions come at a significant increase in computational cost.

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Copyright © 2012 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Grid generated in CONVERGE at 0.4 ms ASI for combusting sprays described in Table 1. The field of view is 108 mm each side.

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Figure 2

Measured [11] (a) flame LOL and (b) ignition delay vs. ambient temperature calculated by using Chalmers and Lu mechanisms, respectively, for NHPT

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Figure 3

Measured [11] and predicted flame LOL vs. ambient temperature for NHPT and DF #2, calculated using Chalmers and Lu mechanisms, respectively

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Figure 4

Measured [11] and predicted (a) spray penetration vs. time and (b) vapor penetration vs. time, under nonreacting conditions at an ambient temperature of 900 K for biodiesel fuel

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Figure 5

Flame LOL predicted by the three mechanisms at 1.5 ms from SOI compared with the OH-chemiluminescence data from Sandia [11]. The average equivalence ratio at flame lift-off location is also indicated. The images from simulations plot the OH mass-fraction contours at the same axial and radial scale as the experimental images.

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Figure 6

Measured [11] and predicted spray penetration, with UConn-123 and ERC-bio mechanisms, respectively, as a function of time at an ambient temperature of 1000 K under reacting conditions

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Figure 7

Computed liquid fuel penetration and temperature contours predicted by using UConn-123 and ERC-bio mechanisms, respectively, for the spray flames plotted in Fig. 6

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Figure 8

Images comparing the equivalence ratio calculated using RANS and LES models, respectively, against the experimental data from Sandia [11] under nonreacting conditions

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Figure 9

Comparison of predicted temperature contours calculated using RANS (RNG k-ε) and LES (Smagorinsky) turbulence models, respectively, at different times ASI

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Figure 10

Measured [11] and predicted ignition delay vs. ambient temperature for NHPT calculated by using RANS and LES turbulence models, respectively

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Figure 11

(a) Scalability and (b) computational efficiency per node for the diesel and biodiesel surrogate mechanisms discussed in the context of Sec. 3

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