Experimental and CFD Study of Exhaust Ejectors With Bent Mixing Tubes
Bibliographic record
Abstract
This paper presents preliminary results of a study performed on bent ejectors having applications in the aerospace industry. The ejectors consisted of a simple round primary nozzle and a bent mixing tube. The effects of bend angles on pumping characteristics were studied. A cold flow wind tunnel was used to provide the primary flow of air at ambient temperature at a maximum mass flow rate of 1.6 kg/sec. Velocity and pressure measurements were taken upstream of the nozzle and at the exit of mixing tube. The study was done with and without swirl in the primary flow. It was observed that swirl in primary flow improved the pumping performance of the bent. CFD analysis was performed using a commercial CFD code and the results were compared with the experiments. It was observed that the bend in the ejector had a significant effect on the pumping performance. The entrainment ratio decreased and then increased with the degree of bend. An ejector with a 45° bend in the geometry had 28% less entrainment ratio than a same size straight ejector. It was found that swirl in the primary flow improves the performance of the bent ejector. A maximum 17% increase in the entrainment ratio was observed with swirl in the case of a 67.5° bent ejector. In both 22.5° and 67.5° bent ejectors the pressure rise was slightly improved by 20° swirl in the primary flow. CFD was generally successful in predicting the trends as observed in the experiment results. CFD predictions of pumping were within ± 7% of the experiments.
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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) | 0.001 | 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 | 0.001 |
| 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".