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 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 | 0.001 |
| 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".