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Modelling Size of Inclusions in Air Entrainment Models for Eulerian RANS Simulations

EasyChair Preprint no. 11236

13 pagesDate: November 3, 2023


In this paper, we present a new air entrainment model which predicts the bubble diameter for entrained air based on
Hinze theory. In this theory, after bdubbles are created at the free-surface, they are successively broken down into
smaller and smaller structures due to turbulent breakup. When turbulence is no longer intense enough, the bubble
diameter stabilises. By assuming this turbulent breakup occurs close to the free surface and rapidly compared with
other flow characteristics, it is possible to determine the diameter of stable bubbles when they are entrained below the
free-surface. This model is implemented in a multifluid RANS solver with an interfacial area transport equation to
account for bubble diameter polydispersion. The diameter calculated from Hinze theory determines how the interfacial
area transport equation must be adapted to account for air entrainment in the simulations.
Air entrainment is first generally described before introducing the model which is developed in neptune_cfd, a finite
volume RANS solver developed by EDF, CEA, IRSN and Framatome which allows for the numerical resolution of
separate Reynolds averaged Eulerian equations (mass, momentum and energy) for n phases coupled by interfacial
transfer terms. Results obtained are then compared with experimental data in several cases representative of air
entrainment phenomena. A special focus is made on mesh convergence and on the model relation with the mesh.

Keyphrases: Bubbly flow, Computational Fluid Dynamics, Interfacial area transport, Multiphase flows, Turbulence

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Federico Baraglia and Jérôme Laviéville and Nicolas Mérigoux and Olivier Simonin},
  title = {Modelling Size of Inclusions in Air Entrainment Models for Eulerian RANS Simulations},
  howpublished = {EasyChair Preprint no. 11236},

  year = {EasyChair, 2023}}
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