Diagnosis of Solid Distribution in Vessels Stirred with Multiple PBTs and Comparison of Two Modelling Approaches
Bibliographic record
Abstract
Abstract The features of solids concentration distribution were investigated in two baffled vessels of different scale. The vessels were of high aspect ratio and were stirred with multiple PBTs. Both steady‐ and unsteady‐state experiments were performed. The experimental data were compared with the previsions of the one‐dimensional sedimentation‐dispersion model and of CFD tools. The former approach provides good estimates only of the average, steady‐state vertical profiles, while the latter describes the local variations much more accurately. Both approaches give fairly good estimates of the transient solids concentration distribution. The dynamic CFD simulations allowed us also to tune the value of the turbulent Schmidt number as a relevant parameter. Finally, both simulation approaches have confirmed that the particle settling velocity in a stirred liquid is a correct parameter to be used instead of the terminal settling velocity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".