Steady‐state distribution of air‐core in a hydrocyclone
Bibliographic record
Abstract
ABSTRACT The structure and movements of the air‐core in a hydrocyclone play a key role in the performance and efficiency of separation. The technique of computational fluid dynamics has been used to evaluate the air‐core characteristics and their influence on the internal flow field for the case of a 140 mm hydrocyclone. Through computational analysis for developed flow in a hydrocyclone, the shape, diameter, and steady‐state distribution of the air‐core were analyzed and presented. The steady‐state distribution of the air‐core could be divided into three distinctive parts. Two parts of the air core, on the cylindrical and spigot sections are very stable, and their shape and diameters do not vary over time. For the third part, on the conical section, the dense transient‐state situation of the air‐core has no clear rule. This results in the dense transient internal flow field, meanwhile, the internal flow fields of the other two parts are stable. Based on the calculation, the analysis of steady state of the air‐core has been extended to operational conditions, including inlet velocities, viscosity, and atmospheric pressure. Those operation factors do not have any influence on the steady‐state situation of the air‐core in the developed flow of hydrocyclone. However, diameters of air core change with those factors. Any increase in the inlet velocity will increase the air‐core diameter.
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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 | 0.000 | 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) | 0.001 | 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 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".