Computational Comparison of an Air-Air Ejector System Utilizing a Primary S-Bend Transition Duct Employing the Realizable k-ε Turbulence Model
Bibliographic record
Abstract
A computational study of an air-air ejector system, utilizing a primary S-bend transition duct, was compared with experimental results. Two primary ducts, differing in offset, consisted of an annular-to-circular-to-oblong transition which incorporated a total area increase of 62.4% with the duct performing 16% diffusion. The ducts were analyzed both alone and in ejector configuration under varying degrees of inlet swirl. The ejector geometry consisted of the duct with a downstream mixing tube. Several mixing tubes, of oblong cross sectional shape, differing in both length and area, were tested in various parametric configurations. Ejector performance was established on the basis of pumping capability, duct back pressure, and outlet effective area. Experimental work commenced on a cold flow test rig, with duct inlet conditions being measured with four 3-hole pitot probes. The duct outlet profiles were measured using a 7-hole probe which traversed the entire exit area. Three conditions of inlet swirl were analyzed: 0°, 20° and 40°. Experimental results showed an increase in pumping performance with increased inlet swirl, mixing tube length, area ratio and standoff. An optimum standoff value of 0.25Dh2 was observed. CFD simulations were based on experimental mass flow inlet conditions utilizing the realizable k-ε turbulence model. CFD results showed that the realizable k-ε turbulence model was quite capable of modeling the complex flow for the associated geometry, and correctly predicted flow features as well as performance trends for all geometrical configurations tested. However, the CFD was unable to properly predict the duct inlet static pressure leading to erroneous back pressure results.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".