Numerical study of gas–solid flow characteristics of cylindrical fluidized beds based on coarse‐grained <scp>CFD</scp> ‐ <scp>DEM</scp> method
Bibliographic record
Abstract
Abstract The existing researches lack the comprehensive comparison of the performance of two‐fluid model (TFM) and computational fluid dynamics‐discrete element model (CFD‐DEM) using a cylindrical fluidized bed as a research object. In addition, the applicability of rotational periodic boundary conditions in CFD‐DEM simulations of cylindrical fluidized beds is still unclear. Therefore, taking cylindrical fluidized bed as the object and studying the performance of different simulation methods can provide guidance for the selection of simulation methods in subsequent related studies. In the present study, TFM and coarse‐grained CFD‐DEM were used in simulations of the fluidized bed to evaluate the performance of different numerical methods. Furthermore, the applicability of rotating periodic boundary conditions in CFD‐DEM simulations was investigated. The results show that TFM and coarse‐grained CFD‐DEM perform in general agreement in predicting macro variables (e.g., overall pressure drop and bed height). However, radial void fraction distribution and void fraction probability density function (PDF) distribution of CFD‐DEM agreed better with the experimental data. CFD‐DEM simulations with rotational periodic boundary conditions applied showed lower predicted void fraction PDF peaks at packed bed heights and poorly modelling particle mixing in the central of cylindrical fluidized bed due to changes in the boundary conditions as well as the number of particle parcels. Therefore, both TFM and CFD‐DEM can obtain reasonable macro variables, but CFD‐DEM predicted more accurate gas–solid two‐phase distribution. The CFD‐DEM with rotating periodic boundary conditions could not reasonably predict the pressure drop and gas–solid two‐phase distribution inside the cylindrical fluidized bed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".