Optimization of spiral guide vane for tangential inlet cyclone separator based on <scp>CFD</scp> and response surface methodology
Bibliographic record
Abstract
Abstract The inlet geometry influences the development of short‐circuit flow and the performance of the tangential inlet cyclone. The spiral guide vane (a special inlet geometry) within annular space of the cyclone is proposed to guide initial flows, decrease short‐circuit flow rate and enhance performance. This study is to investigate the effects of spiral guide vanes on the flow field and performance of the cyclone, and then determine an optimal spiral guide vane geometry by computational fluid dynamics (CFD) and response surface methodology. The influence of spiral guide vane dimensions (length, width, and helix angle) is investigated at first, and two key factors (width and helix angle) are identified. Then the width, helix angle, and inlet velocity are selected to calculate sample points, and predicted models of separation efficiency and pressure drop are developed. Finally, Pareto solutions are gained to determine the best‐performing spiral guide vane design for the cyclone. When the spiral guide vane helix angle is 2.84°, the spiral guide vane width is 0.013 m, and the inlet velocity is 12.32 m/s, the studied tangential inlet cyclone has the best performance. Compared with original design without spiral guide vane, the collection efficiency of fine particles (1 μm) for the optimized cyclone increases by almost 13%, the separation efficiency increases by 2.30%, and the pressure drop decreases by 8.41%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".