Abstract
We numerically assess time-evolving particle size segregation in a lab-scale gas-solid fluidized bed. Based on expensive short-
term unresolved CFD-DEM simulations we deduce characteristic flow patterns for extremely fast data-based recurrence CFD
(rCFD) simulations.
For that purpose, we further developed rCFD to account for segregating particle fractions, hence extrapolating short-term
databases not only in time but also in terms of poly-dispersity. While for the case of mild segregation rCFD predictions agree
very well with corresponding CFD-DEM simulations, the method fails for more substantial segregation.
In terms of computational performance, rCFD simulations are more than four orders of magnitude faster than corresponding
CFD-DEM simulations, eventually allowing for real-time simulations of segregation in poly-disperse fluidized beds.
Keywords: gas-solid fluidized bed, recurrence CFD, particle size segregation
term unresolved CFD-DEM simulations we deduce characteristic flow patterns for extremely fast data-based recurrence CFD
(rCFD) simulations.
For that purpose, we further developed rCFD to account for segregating particle fractions, hence extrapolating short-term
databases not only in time but also in terms of poly-dispersity. While for the case of mild segregation rCFD predictions agree
very well with corresponding CFD-DEM simulations, the method fails for more substantial segregation.
In terms of computational performance, rCFD simulations are more than four orders of magnitude faster than corresponding
CFD-DEM simulations, eventually allowing for real-time simulations of segregation in poly-disperse fluidized beds.
Keywords: gas-solid fluidized bed, recurrence CFD, particle size segregation
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 12th International Conference on Multiphase Flows |
| Subtitle of host publication | ICMF 2025, Toulouse, France, May 12-16, 2025 |
| Number of pages | 2 |
| Edition | 1 |
| Publication status | Published - 2025 |
Fields of science
- 203 Mechanical Engineering
- 211104 Metallurgy
- 204007 Thermal process engineering
- 103043 Computational physics
- 203024 Thermodynamics
- 204006 Mechanical process engineering
- 103032 Fluid mechanics
- 203016 Measurement engineering
JKU Focus areas
- Sustainable Development: Responsible Technologies and Management
- Digital Transformation