Air Ejector Pumping Enhancement Through Pulsing Primary Flow
Bibliographic record
Abstract
Improving the performance of an ejector is a flow control problem. Passive methods such as changing the geometry of the mixing tube showed that, for a simple mixing tube geometry of a concentric cone-tube combination, the diameter of the tube had to be at least 4 times the diameter of the primary nozzle. Thus for a 5.13 mm dia. primary jet, a 22.7 mm dia. tube was 27% better than a 17.41 mm dia. tube. A standard Venturi mixing tube with 17.41 mm dia. throat was 100% better. Also the shape of the entrance cone had only a little effect and could be substituted by other shapes. A tube without an entrance shape was found to be still reasonably efficient. Both experiments and Computer Fluid Dynamics (CFD) analysis show that pulsing the primary jet flow, an active method of flow control, improved ejector performance. The physics of this improvement has been discussed. Pumping effectiveness of the ejector was found to be proportional to the square of the pulsation strength. The details of the many pulsators tested are discussed. The majority of the improvement appears to be due to the initial toroidal vortex the pulsation produces. The improvement was strongest at 127-131 Hz, less than half the fundamental frequency of 746 Hz of the system. The pumping effectiveness increased by up to 4.5 times that for a steady jet. Different types of pulse shapes tested indicate that a sinusoidal pulse superimposed on a steady flow is very efficient. For pulses which have only positive pulse velocities, a narrow pulse was more efficient. The data also showed that a strong synthetic jet actuator gave ejector performance as good as a pulsed jet with primary flow.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".