Numerical Analysis of the Geometrical Effects on the Airflow Characteristics of an Air Suction Gun
Bibliographic record
Abstract
The yarn suction efficiency of an air suction gun is closely related to the airflow patterns, which are strongly affected by the geometry of the gun. To obtain basic data for the optimum design of a gun, we investigated the airflow patterns in the gun with different geometrical parameters by numerical simulation and discussed the relation between the flow patterns and yarn suction force Fm. Compressed-air inflow angle plays an important role in generating a helical flow by controlling circumferential velocity component vc in a yarn propulsion tube. This helical airflow greatly promotes yarn suction capacity. Fm has a closer relationship to the distribution of air velocity than air pressure, and strongly depends on vc. The airflow patterns are weakly dependent on a passage diverging angle of nozzle and a converging angle of de Laval tube. A reduction in throat diameter of de Laval tube causes a rapid extension of the supersonic flow area near the throat accompanied by increasing axial velocity component in the de Laval tube. However, it leads to decreases in vc in the yarn propulsion tube and air velocity in the yarn inhalation tube, which hinders the promotion of Fm.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".