Effects of guide holes on the performance of a vertical turbo air classifier
Bibliographic record
Abstract
Abstract The wide usage of guide parts makes it one of the most interesting research hot spots in the field of fluid machinery. To improve the flow field distribution of the vertical turbo air classifier, the guide holes in the air intake region are designed. The flow fields of the classifiers with and without guide holes are simulated using ANSYS‐FLUENT. The gas‐phase simulation results show that the guide holes have a ‘diversion’ effect on the airflow, decreasing the tangential velocity and increasing the radial velocity of the airflow. After the airflow passes through the guide holes, the small guide hole sizes cause the large radial velocity and the strong ‘diversion’ effect, which can improve the flow field distribution. The well‐distributed flow field of the elutriation region and annular region are obtained when the guide hole size is 7 mm × 7 mm. The discrete phase simulation results show that the cut size of the classifier without the guide holes is 9.1 μm. The cut size is 13.4 μm when the guide hole size is 7 mm × 7 mm. With the increase in the guide hole size, the cut size increases. However, when the guide hole size is larger than 10.5 mm × 10.5 mm, the cut size is almost kept unchanged, which is 22.9 μm.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".