An overview on the recent advances in computational fluid dynamics simulation of spouted beds
Bibliographic record
Abstract
Abstract Spouted beds are now widely applied in various physical operations and chemical reaction systems due to their unique structural and flow characteristics. Now there are growing interests to use computational fluid dynamics (CFD) to understand dense gas–solid two‐phase flows in spouted beds. Both Eulerian–Eulerian (two‐fluid model, TFM) and Eulerian–Lagrangian (discrete element method, DEM) approaches can be applied to the CFD modelling of spouted beds and spouted‐fluidised beds, and numerous verification studies have shown their reliabilities. This overview summarises the recent advances of the TFM and DEM approaches in the CFD modelling of spouted beds. The typical flow patterns of spouted beds can be well reproduced by both the TFM and the DEM approaches, and the simulated characteristic properties such as spout diameter, minimum spouting velocity and voidage profile are in good agreement with experimental data, indicating that CFD modelling based on the TFM and DEM approaches can serve as an important tool for predicting gas and solids behaviour in spouted beds. The TFM approach has been widely used in phenomenological studies that are mainly towards the understanding of the flow behaviour of the whole system. Although more computational capacity is required, the DEM approach offers a more natural way to simulate gas–solid flows, with each individual particle tracked in the simulation and can be applied readily for particle tracking, collision, mixing, circulation and mass transfer studies that are aimed at obtaining the profound particle‐scale understanding in fluid–solid multiple‐phase systems. However, each approach has its own limitations and defects. The complex nature of the TFM‐based simulation framework requires both proper descriptions of pseudo‐fluid properties like solid pressure, solid viscosity, solid friction stresses, etc. and suitable choice of drag models, and the boundary conditions also have effects on the simulation results. Moreover, the effects of these factors often interact with each other, which require even more subtle skills in conducting a simulation. In the DEM approach, the high computing source requirement limits its application of no more than several million particles. Furthermore, more insight investigation should be given to the acting mechanism of several forces on particles. One common challenge for these two approaches is to properly describe the inherent turbulence for both the solid and gas phases, especially for the spout region. Further fundamental and experimental studies on the kinematic properties of the two phases are needed to improve the accuracy of the CFD models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".