DNS and LES of primary atomization of turbulent liquid jet injection into a gaseous crossflow environment

Anirudh Asuri Mukundan, Giovanni Tretola, Thibaut Menard, Marcus Hermann, Salvador Navarro-Martinez, Konstantina Vogiatzaki, Jorge Cesar Brandle de Motta, Alain Berlemont

Research output: Contribution to journalArticlepeer-review


In this paper, we study the primary atomization characteristics of liquid jet injected into a gaseous crossflow through direct numerical simulations (DNS) and large eddy simulations (LES). The DNS use a coupled level set volume of fluid (CLSVOF) sharp interface capturing method resolving all relevant scales to predict the drop size distribution (DSD) for drops larger than the grid spacing. The LES use a volume of fluid (VOF) diffused interface method modelling the sub grid droplets. The purpose of this paper is to provide a comparison of the results of drop data between DNS and LES. The simulations are performed for a liquid jet injection with liquid-gas momentum flux ratio of 6.6, liquid jet Reynolds number of 14,000 injected into a crossflowing air with Reynolds number 570,000 and Weber number of 330 at a liquid-to-gas density ratio of 10. Two distinct and simultaneous atomization/breakup mechanisms have been observed in the simulations: column/bag breakup and ligament/surface breakup. It was found that the DSDs obtained from the DNS and LES each follow a log-normal distribution based on their respective droplet diameter data. An overlap region exists between the individual DSDs from the DNS and LES when combined. The width of this overlap region decreases along the downstream direction. A log-normal distribution is found to be a good fit to the combined DSD incorporating both resolved and sub-grid droplets. This information is relevant for the secondary atomization simulations and modeling.
Original languageEnglish
Pages (from-to)1-9
JournalProceedings of the Combustion Institute
Publication statusPublished - 18 Sept 2020


  • Atomization
  • Crossflow
  • Interface capture
  • Stochastic fields
  • Drop size distribution


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