A STOKES NUMBER-BASED STOCHASTIC IMPROVEMENT FOR DISPERSION MODEL FOR LARGE EDDY SIMULATION

Lorenzo Angelilli*, Jacopo Liberatori, Pietro Paolo Ciottoli, Francisco E. Hernández-Pérez, Riccardo Malpica Galassi, Mauro Valorani, Hong G. Im

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

To improve the fidelity of large eddy simulation (LES) of spray jet dispersion, a dynamic subgrid dispersion model is proposed based on the Langevin-type stochastic framework to quantify the effective contribution of the stochastic component of the force as a function of the Stokes number related to the subgrid time scale, which is easily accessed by the LES closure model. The proposed model has two coefficients that require calibration, which were obtained following a rigorous calibration procedure based on forward uncertainty quantification algorithms. The performance of the model is assessed by comparison against a reference direct numerical simulation (DNS) test case. The comparisons for the spray analysis include averages of the number of droplets, mass source term, and droplet diameters conditioned on the vapor mass fraction, together with their Eulerian average at different axial locations. The results showed improved prediction of the particle clustering behavior near the nozzle exit observed in the DNS simulations.

Original languageEnglish (US)
Pages (from-to)35-55
Number of pages21
JournalAtomization and Sprays
Volume33
Issue number9
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 by Begell House, Inc.

Keywords

  • Brownian motion
  • calibration
  • Langevin equation
  • spray
  • uncertainty quantification

ASJC Scopus subject areas

  • General Chemical Engineering

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