Comparison of Mixture and Multifluid Models for In-Nozzle Cavitation Prediction

Michele Battistoni; Sibendu Som; Douglas E. Longman

doi:10.1115/1.4026369

Journal of Engineering for Gas Turbines and Power | Volume 136 | Issue 6 | research-article

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Research Papers: Gas Turbines: Combustion, Fuels, and Emissions

Comparison of Mixture and Multifluid Models for In-Nozzle Cavitation Prediction

Michele Battistoni ¹, Sibendu Som and Douglas E. Longman

[+] Author and Article Information

Michele Battistoni

Department of Industrial Engineering,
University of Perugia,
Perugia, 06125ItalyVisiting Researcher
Energy Systems Division,
Argonne National Laboratory,
Argonne, IL 60439
e-mail: michele.battistoni@unipg.it

Sibendu Som

Energy Systems Division,
Argonne National Laboratory,
Argonne, IL 60439
e-mail: ssom@anl.gov

Douglas E. Longman

Energy Systems Division,
Argonne National Laboratory,
Argonne, IL 60439
e-mail: dlongman@anl.gov

¹Corresponding author.

Contributed by the Combustion and Fuels Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received December 3, 2013; final manuscript received December 16, 2013; published online February 4, 2014. Editor: David Wisler. The United States Government retains, and by accepting the article for publication, the publisher acknowledges that the United States Government retains, a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for United States government purposes.

J. Eng. Gas Turbines Power 136(6), 061506 (Feb 04, 2014) (12 pages) Paper No: GTP-13-1436; doi: 10.1115/1.4026369 History: Received December 03, 2013; Revised December 16, 2013

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Abstract

Fuel injectors often feature cavitation because of large pressure gradients, which in some regions lead to extremely low pressures. The main objective of this work is to compare the prediction capabilities of two multiphase flow approaches for modeling cavitation in small nozzles, like those used in high-pressure diesel or gasoline fuel injectors. Numerical results are assessed against quantitative high resolution experimental data collected at Argonne National Laboratory using synchrotron X-ray radiography of a model nozzle. One numerical approach uses a homogeneous mixture model with the volume of fluid (VOF) method, in which phase change is modeled via the homogeneous relaxation model (HRM). The second approach is based on the multifluid nonhomogeneous model and uses the Rayleigh bubble-dynamics model to account for cavitation. Both models include three components, i.e., liquid, vapor, and air, and the flow is compressible. Quantitatively, the amount of void predicted by the multifluid model is in good agreement with measurements, while the mixture model overpredicts the values. Qualitatively, void regions look similar and compare well with the experimental measurements. Grid converged results have been achieved for the prediction of mass flow rate while grid-convergence for void fraction is still an open point. Simulation results indicate that most of the vapor is produced at the nozzle entrance. In addition, downstream along the centerline, void due to expansion of noncondensable gases has been identified. The paper also includes a discussion about the effect of turbulent pressure fluctuations on cavitation inception.

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Figures

Fig. 2

X-ray measurements of cavitation in a 500 μm polycarbonate nozzle, obtained via (a) radiographic imaging and (b) raster-scan microprobe radiography using a monochromatic synchrotron source at 8–10 keV. Images reproduced with permission from Duke et al. [12].

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Fig. 1

Nozzle geometry tested by Duke et al. [12]

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Fig. 3

Examples of grids generated in CONVERGE (a) and FIRE (b) with 150 μm base grid size and 9 μm minimum cell size

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Fig. 4

Predicted void fraction integrated along the transverse direction using mixture and multifluid codes, compared to experimental data [12]. Dimensions are in millimeters. Flow is from bottom to top.

$Predicted void fraction integrated along the transverse direction using mixture and multifluid codes, compared to experimental data [12]. Dimensions are in millimeters. Flow is from bottom to top.$

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$Predicted void fraction integrated along the transverse direction using mixture and multifluid codes, compared to experimental data [12]. Dimensions are in millimeters. Flow is from bottom to top.$

Fig. 5

Computed total volume fraction α_g (vapor + air) along the channel axis evaluated in one cell layer thick slices, compared to the experimental data [12]

$Computed total volume fraction αg (vapor + air) along the channel axis evaluated in one cell layer thick slices, compared to the experimental data [12]$

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$Computed total volume fraction αg (vapor + air) along the channel axis evaluated in one cell layer thick slices, compared to the experimental data [12]$

Fig. 6

Cell size effects on total void fraction α_g along the channel

$Cell size effects on total void fraction αg along the channel$

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$Cell size effects on total void fraction αg along the channel$

Fig. 7

Predicted velocity (a), pressure (b), volume fractions (c), (d), and (e), and mass fractions (f) and (g). Left column shows mixture model results, right column shows multifluid model results. Color scale range is shown above each row.

$Predicted velocity (a), pressure (b), volume fractions (c), (d), and (e), and mass fractions (f) and (g). Left column shows mixture model results, right column shows multifluid model results. Color scale range is shown above each row.$

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Fig. 9

Outlet pressure effects under equal CN value. Pressure profiles along the nozzle wall (a) and along the axis (b) are shown using the mixture model code.

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Fig. 8

Pressure profiles along nozzle wall (a) and nozzle centerline (b)

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Fig. 10

Outlet pressure effects under equal CN value. Predicted total volume fractions α_g using the mixture model code. Dissolved air is set to the baseline value of 2 × 10⁻⁵ mass fraction (cf. Table 2).

$Outlet pressure effects under equal CN value. Predicted total volume fractions αg using the mixture model code. Dissolved air is set to the baseline value of 2 × 10−5 mass fraction (cf. Table 2).$

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Fig. 11

Effect of dissolved air mass fraction Y₃ on total void fraction α_g predicted along the channel using the mixture model code

$Effect of dissolved air mass fraction Y3 on total void fraction αg predicted along the channel using the mixture model code$

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$Effect of dissolved air mass fraction Y3 on total void fraction αg predicted along the channel using the mixture model code$

Fig. 12

Effect of the dissolved air mass fraction Y₃ on the vapor production and on the total void fraction patterns using the mixture model code. Left column shows vapor mass fraction Y₂ and right column shows total void fraction α_g. Color scale range is shown above each column.

$Effect of the dissolved air mass fraction Y3 on the vapor production and on the total void fraction patterns using the mixture model code. Left column shows vapor mass fraction Y2 and right column shows total void fraction αg. Color scale range is shown above each column.$

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Fig. 13

Effect of turbulent pressure fluctuations on total void fraction α_g predicted along the channel using the multifluid model code

$Effect of turbulent pressure fluctuations on total void fraction αg predicted along the channel using the multifluid model code$

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$Effect of turbulent pressure fluctuations on total void fraction αg predicted along the channel using the multifluid model code$

Fig. 14

Effect of turbulent pressure fluctuations on cavitation development using the multifluid model code

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Battistoni M, Som S, Longman DE. Comparison of Mixture and Multifluid Models for In-Nozzle Cavitation Prediction. ASME. J. Eng. Gas Turbines Power. 2014;136(6):061506-061506-12. doi:10.1115/1.4026369.

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Comparison of Mixture and Multifluid Models for In-Nozzle Cavitation Prediction

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