Effect of inlet structure parameters of dispersed phase on flow field characteristics in a liquid–liquid cyclone reactor
Bibliographic record
Abstract
Abstract The shear strength and concentration distribution characteristics in a liquid–liquid cyclone reactor (LLCR) were studied using the RSM turbulent model. The influence of tangential slot height–number ( h–n ), axial centre position of the slot ( c ), and distance between the upper end of the slot and the lower end of the guide vane ( d ) on the mixing performance of two liquids in LLCR were studied. The mixing performance was quantified using dispersed tangential velocity gradient G and mean dispersion uniformity ( β ) respectively. The research results indicated that adjustments of structural parameters showed influence on the shear strength in LLCR. By reducing d , realigning the centres of tangential slots, buffer chambers, and dispersed‐phase inlets, and increasing the number of inlets, the β of LLCR was optimized and highest decrease was 0.09, thereby improving mixing efficiency. Moreover, models with moderate h–n ( h–n = 30–4, 24–5) exhibit both reduced β and enhanced mixing efficiency after optimizing parameter c , particularly when tangential slots and guide vanes are closely integrated without overlapping interference ( d = 0, 3, 5). In summary, the optimal models for mixing efficiency obtained by changing the tangential slot structure are 30–4–82–112 and 30–4–72–102.
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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) | 0.000 | 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 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".