Wall Pressure and Temperature Distribution in Bent Oblong Exhaust Ejectors
Bibliographic record
Abstract
In aerospace industry ejectors are employed to reduce infrared signatures of hot exhaust gases and ducts. The ambient air, entrained from the surrounding, acts as a cushion between hot exhaust and the ejector walls. This reduces the temperature and hence the infrared signature of exhaust ducts. In many applications the ejectors are bent upward to hide the hot engine from heat seeking missiles. Due to the bend, the hot gases from the turbine hit the side walls leading to hot spots on the ejector walls. This study was aimed to see the effectiveness of a series of bent oblong ejectors as infrared signature suppressors. Wall temperatures were measured with infrared thermal imaging camera and pressures were measured with static wall taps. The wall static pressure shows rise in pressure along the length of the ejector. It also identifies areas of flow separation and the areas where the primary flow hits the ejector walls and produces hot spots. Wall temperature distribution shows that the oblong nozzle has a detrimental effect by creating hot spots on the ejector surface. Wall temperature of the ejectors increased with the degree of bend. The normalized maximum wall temperature (T*w(max)) of 67.5° bent ejector was 45% higher than the straight ejector. The swirl in the primary flow also increased the wall temperature. On a straight ejector the T*w(max) with the 30° swirl was 35% higher than the no swirl case.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".