Research on Multi-scale and Multi-directional Negative Pressure Suction–type Separation of Fresh Tea Leaves Based on CFD and Bench Tests
Bibliographic record
Abstract
In order to break through the bottlenecks of unreasonable negative pressure port arrangement and poor pressure adaptability in existing air suction sorting equipment, this study designed four negative pressure port arrangement schemes: noncoaxially adjacent, coaxially adjacent, noncoaxially opposed, and coaxially opposed . A porous rotary disc adsorption structure with a diameter of 220 mm was adopted. Combined with computational fluid dynamics (CFD) technology and the Realizable k-ε turbulence model, the flow field distribution was simulated and analyzed under three conditions: constant pressure of 380 Pa, constant pressure of 320 Pa, and differential pressure of 380/320 Pa. A bench test was established to verify the sorting rate using single leaves, one-bud-one-leaf, and one-bud-two-leaf samples. The results show that the differential pressure of 380/320 Pa is the optimal adsorption pressure, as it balances suction power and flow field uniformity. The noncoaxially adjacent scheme is the optimal arrangement, featuring a stable flow field without obvious eddies. Under the differential pressure condition, the sorting rates of single leaves and one-bud-one-leaf samples reach 87% and 70%, respectively, which are significantly better than those of other schemes. This study provides theoretical support and engineering references for the research and development of multiscale air suction sorting equipment for fresh tea leaves.
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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 | 0.001 | 0.001 |
| 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".