Aerodynamic Performance Evaluation of Leading Edge Tubercles on Low Pressure Turbine Blades at Very Low Reynolds Numbers and Higher Turbulence
Bibliographic record
Abstract
Abstract At higher altitude, an aero-engine low pressure turbine is susceptible to a substantial decrease in performance commonly called the Reynolds lapse. Alleviating the negative impact of decreasing Reynolds number on performance could lead to enhanced high-altitude operations. This paper presents results and recommendations from an experimental application of leading edge tubercles as passive flow control devices for a purpose-designed, aft-loaded LPT profile. These bio-inspired geometric features have been shown to delay stall and improve post-stall operability of airfoils and wings. In the present study, tubercle geometry was based on an optimized tubercle geometry that was designed previously for another application with guidance provided by a neural network technique called Self-Organizing Maps, trained by available published data. In addition to low turbulence tests, the performance of the baseline and tubercled blade were also investigated at higher turbulence that is more representative of engine operation. The experimental LPT performance evaluation took place in a cascade rig and included measurement of mid-span surface pressure, signal variance of blade surface hot film data, and cascade-exit mixed-out losses. Conclusions were drawn after consideration of wake traverses, exit losses, surface pressure measurements, and hot film signal variance at various axial-chord Reynolds numbers between 15 000 and 90 000. The suction-surface pressure provided information on blade loading and flow separation while hot-film provided information on the state of the boundary layer. Some reduction in exit losses and increased performance at higher Reynolds numbers was noted. Valuable insight has been gained on the challenges associated with very low Reynolds number testing and recommendations have been 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 | 0.000 | 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".