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Atomization and Sprays

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ISSN Druckformat: 1044-5110

ISSN Online: 1936-2684

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CALIBRATION OF THE CONSTANTS IN THE KELVIN-HELMHOLTZ RAYLEIGH-TAYLOR (KH-RT) BREAKUP MODEL FOR DIESEL SPRAY UNDER WIDE CONDITIONS BASED ON ADVANCED DATA ANALYSIS TECHNIQUES

Volumen 32, Ausgabe 6, 2022, pp. 1-27
DOI: 10.1615/AtomizSpr.2022040203
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ABSTRAKT

The Kelvin-Helmholtz (KH) Rayleigh-Taylor (RT) breakup model has been widely utilized in the simulations of diesel spray, whereas the calibrations of the five constants in the KH-RT model highly depend on the operator's experience. To overcome the shortcoming, advanced data analysis techniques were introduced into this study by employing genetic algorithm (GA) and global sensitivity analysis for the simulations using the Reynolds-averaged Navier-Stokes (RANS) turbulence model. First, a genetic algorithm was used to optimize the five constants for the representative cases of diesel spray under different ambient temperatures and pressures. Based on the optimal solutions obtained from the GA optimizations and global sensitivity analysis, the dominant parameters affecting the predicted liquid penetrations of diesel spray, including CRT and Cb, are identified. By fitting these optimal solutions, two correlations for CRT and Cb are derived as: CRT = 3.2 × ρamb-0.32Тamb-0.2 and Cb = 4.24 × ln(ρamb) + 4.74. The change of the breakup length constant (Cb) for the introduction of the RT mechanism with the ambient density (ρamb) and the variation of the optimal child droplet size constant of the RT mechanism (CRT) with the ambient temperature and pressure (Tamb and ρamb) can be understood from the instability of the RT mechanism and the derivation of the RT breakup model, respectively. Extensive validations indicate that the derived correlations are suitable for diesel spray under wide ranges of ambient pressure, ambient temperature, injection pressure, and nozzle diameter for various experimental data sources in the literature.

Figures

  • Schematic diagram of the KH–RT model for the spray breakup process
  • Schematic diagram of the three stages in the evolution of the liquid penetration
  • Three computational meshes with different grid densities used in the simulation
  • Comparison of the predictions from the computational model using three computational meshes
under the conditions of Case 1 with pamb = 16:6 MPa
  • Evolution of the three optimization targets using genetic algorithm for Case 1 with pamb = 16:6
MPa
  • Variation ranges of the five parameters in the final generation obtained by genetic algorithm relative
to their preset ranges for Case 2 with Tamb = 300 K and Case 7 with Tamb = 767 K
  • Average absolute value of the Spearman’s rank correlation coefficient of the five parameters in
the KH-RT model on the predicted liquid penetration based on global sensitivity analysis for Case 2 with
Tamb = 300 K and Case 7 with Tamb = 767 K
  • Overall variation trend of CRT and Cb under different ambient pressures in the optimal solutions
for (a) Case 2 with Tamb = 300 K and (b) Case 7 with Tamb = 767 K
  • Variation of the optimal value of CRT under various ambient pressures and temperatures for Case
2 with Tamb = 300 K, Case 1 with Tamb = 451 K, and Case 7 with Tamb = 767 K
  • Variation of the optimal value of Cb under various ambient densities for Cases 1, 2, and 7
  • Validations of the predicted liquid penetration under various ambient pressures at non-evaporating
conditions (symbols are experimental data and lines are predictions)
  • Comparison of the simulated and measured spray contour for Case 3
  • Validations of the predicted liquid penetration under various ambient pressures at evaporating
conditions
  • Validations of the predicted liquid penetration under various ambient temperatures (symbols are
experimental data and lines are predictions)
  • Validations of the predicted liquid penetration under various injection pressures at nonevaporating conditions (symbols are experimental data and lines are predictions)
  • Validations of the predicted liquid penetration under various ambient temperatures (symbols are
experimental data and lines are predictions)
  • Validations of the predicted liquid penetration under various nozzle diameters (symbols are experimental data and lines are predictions)
  • Validations of the predicted liquid penetration under various injection pressures at evaporating
conditions (symbols are experimental data and lines are predictions)
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