The Adverse Effect of Pre-Swirl on Ingestion in a Downstream Cavity
Bibliographic record
Abstract
Abstract Gas turbine designers demand accurate predictions of metal temperature to ensure acceptable operating life of components experiencing high thermal stress. Rotor–stator cavities ingest hot mainstream gas through rim seals when inadequately purged with relatively cool air bled off the compressor. Superfluous use of purge, and any associated windage increase, creates a parasitic loss in overall efficiency. Shear interaction caused by the difference in swirl between the purge and mainstream flow is a principal driver for ingestion; preswirled purge flow has the potential to alter the swirl gradient. This article presents the first assessment of purge conditioning in a downstream cavity. An experimental campaign was conducted in a new aero-engine representative 1.5-stage test facility designed to facilitate expedient changeover of modular components in the downstream stator assembly. Purge flow in the downstream cavity was supplied through a series of angled injectors contained in a single component at mid-radius. Three coswirled injection angles were tested. Measurements of CO2 gas concentration, static pressure, and swirl were taken in the cavity to examine the relationship between purge-mainstream swirl gradient and ingress downstream of a rotor blade. The aero-engine designer must balance caution when employing preswirl to reduce disc windage; coswirled purge increased the purge-mainstream swirl gradient and subsequently increased shear-driven ingestion.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".