Numerical Investigation of Mixing Tabs in Multi-Ring Entraining Diffuser in Air-Air Ejectors
Bibliographic record
Abstract
This research studied the effect of mixing tabs used in entraining diffusers in subsonic short air-air ejectors. Although numerous studies exist on mixing tabs in nozzles, none was found that placed tabs in an entraining diffuser system used with an ejector. Given that mixing tabs led to improvements at the nozzle, they were analyzed in entraining diffusers for this research. Numerical studies of tab performance were carried out using two-equation κ-ɛ turbulence models with enhanced wall treatment available in ANSYS Fluent on an ejector obtained from a prior thesis [1]. Only triangular tabs were considered on the ejector. Tab number was varied with 4, 8 and 12 tabs. Increasing tab number from 4 to 8 led to increased diffuser pumping while increasing to 12 tabs led to marginal decrease, although higher than the base case. Addition of 4 tabs led to unchanged overall pumping, while 8 and 12 tabs led to reduced overall ejector pumping performance. Tab blockage ratio was also varied at 1%, 2% and 4% blockage per tab. Increasing blockage ratios was associated with increasing diffuser pumping, but worsened pumping at standoff, however, total pumping was unchanged. A location study identified that the placement of tabs just before the diffuser is not as effective as tabs placed at the nozzle exit for similar blockage ratios. Additionally, tabs placed in the primary flow region were associated with higher back pressure penalties than those in the secondary flow region.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".