A Low Reynolds Number Experimental Evaluation of Tubercles on a Low-Pressure Turbine Cascade
Bibliographic record
Abstract
Abstract Aircraft are operating at increasingly high-altitudes, where decreased air density and engine power settings have led to increasingly low Reynolds numbers in the low-pressure turbine portion of modern-day aeroengines. These operating conditions, in parallel with highly-loaded blade profiles, result in non-reattaching laminar boundary layer separation along the blade suction surface, increasing loss and decreasing engine performance. This work presents an experimental investigation into the potential for integrated leading-edge tubercles to improve blade performance in this operating regime. A turn-table cascade test-section was constructed and commissioned to test a purpose-designed, forward-loaded, low-pressure turbine blade profile at various incidences and Reynolds numbers. Baseline and tubercled blades were tested at axial chord Reynolds numbers at and between 15 000 and 60 000, and angles of incidence ranging from −5° to +10°. Experimental data collection included blade surface pressure measurements, total pressure loss in the blade wakes, hot-wire anemometry, surface hot-film measurements, and surface flow visualization using tufts. Test results showed that the implementation of tubercles did not lead to a performance enhancement. However, useful conclusions were drawn regarding the ability of tubercles to generate stream-wise vortices at ultra-low Reynolds numbers. Additional observations helped to characterize the suction surface boundary layer over the highly-loaded, low-pressure turbine blade profile when at off-design conditions. Recommendations were made for future work.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".