most citedMachine learning approaches to close the filtered two-fluid model for gas-solid flows: Models for subgrid drag force and solid phase stress

26 citations · 26 across the 1 of their papers we have counts for

collaborators

2 papers

physics.class-ph2026

Sliding contact fraction in gravity-driven dense cohesionless granular flows

Patrick Richard, Riccardo Artoni, Clovis Lambert +2

Using large-scale, three-dimensional discrete element simulations, we investigate the kinematic properties of granular chute flows of cohesionless grains, together with the evoluti…

physics.flu-dyn2023★ 26 cited

Machine learning approaches to close the filtered two-fluid model for gas-solid flows: Models for subgrid drag force and solid phase stress

Baptiste Hardy, Stefanie Rauchenzauner, Pascal Fede +4

Gas-particle flows are commonly simulated through two-fluid model at industrial-scale. However, these simulations need very fine grid to have accurate flow predictions, which is pr…