Influence of Laval tube geometry on airflow characteristics in yarn suction gun for polyester fully drawn yarn
Bibliographic record
Abstract
For the purpose of providing technique support for improvement of yarn suction gun,software CFX 12. 1 was used to simulate airflow patterns in the yarn suction gun with different geometrical parameters of the Laval tube,and the relation between the flow patterns and yarn suction performance was discussed. The influence mechanism of Laval tube geometry on the yarn performance was clarified. The simulation results and the experimental ones are in good agreement and the rational parameters were obtained as follows: the converging angle of Laval tube α = 90° and the diverging angle of Laval tubeβ = 6°. A rational converging angle of Laval tube accelerates the airflow smoothly in the Laval tube,and avoids more backflows and strong normal shock wave occurred in the Laval tube,which reduces kinetic energy loss. As a result,the suction efficiency increases. The suction efficiency can be raised through an appropriate diverging angle,which makes circumferential velocity component of airflow and air region of high speed and high density moderate. This increases drag force of air on the yarn and reduces friction of wall on the yarn and kinetic energy loss caused by the normal shock wave.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".