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 · 30 across the 2 of their papers we have counts for

collaborators

2 papers

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…

physics.comp-ph2017★ 4 cited

Adaptive coarse-graining for large-scale DEM simulations

Daniel Queteschiner, Thomas Lichtenegger, Simon Schneiderbauer +1

The large time and length scales and, not least, the vast number of particles involved in industrial-scale simulations inflate the computational costs of the Discrete Element Metho…