CFD simulation of gas–solid bubbling fluidized bed: A new method for adjusting drag law
Bibliographic record
Abstract
Abstract In computational fluid dynamics modelling of gas–solid two phase flow, drag force is one of the dominant mechanisms for interphase momentum transfer. Despite the profusion of drag models, none of the available drag functions gives accurate results in their own original form. In this work the drag correlations of Syamlal and O'Brien (Syamlal and O'Brien, Int. J. Multiphase Flow. 1988; 14(4):473–481), Gidaspow (Gidaspow, Appl. Mech. Rev. 1986; 39:1–23), Wen and Yu (Wen and Yu, Chem. Eng. Prog. Symp. Ser. 1966; 62(2):100–111), Arastoopour et al. (Arastoopour et al., Powder Technol. 1990; 62(2): 163–170), Gibilaro et al. (Gibilaro et al., Chem. Eng. Sci. 1985; 40:1817–1823), Di Felice (Di Felice, Int. J. Multiphase Flow. 1994; 20(1):153–159), Zhang‐Reese (Zhang and Reese, Chem. Eng. Sci. 2003; 58(8):1641–1644) and Hill et al. (Hill et al., J. Fluid Mech. 2001; 448:243–278) are reviewed using a multi‐fluid model of FLUENT V6.3.26 (FLUENT, 2007. Fluent 6.3 User's Guide, 23.5 Eulerian Model, Fluent, Inc.) software with the resulting hydrodynamics parameters being compared with experimental data. The main contribution of this work is to propose an easy to implement and efficient method for adjustment of Di Felice drag law which is more efficient compared to the one proposed by Syamlal‐O'Brien. The new method adopted in this work showed a quantitative improvement compared to the adjusted drag model of Syamlal‐O'Brien. Prediction of bed expansion and pressure drop showed excellent agreement with results of experiments conducted in a Plexiglas fluidized bed. A mesh size sensitivity analysis with varied interval spacing showed that mesh interval spacing with 18 times the particle diameter and using higher order discretization methods produces acceptable results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".