Performance Evaluation of Leading Edge Tubercles Applied to the Blades in a 2-D Compressor Cascade
Bibliographic record
Abstract
Abstract In the present paper, the performance of compressor blades modified with leading edge tubercles was evaluated and compared with that of a baseline profile at a high subsonic Mach number in a 2-D cascade. Specific tubercle geometries were selected based on an extensive literature survey and a Self-Organizing Map analysis. The compressor blade geometry of a popular aero-engine was reverse-engineered using laser-scanning. Baseline and tubercled compressor blades were 3-D printed and tested. Two sinusoidal tubercle shapes based on different amplitudes and wavelengths and one with a power law profile were selected. A 2-D compressor cascade was designed and commissioned to test these blades at high subsonic Mach number in the transonic wind tunnel at Royal Military College of Canada. Surface flow visualizations were performed with oil for observing and locating compressor blade stall for different sets of blades. Flow direction and the total pressure at the cascade exit were measured using a 5-hole, fast-response, traversing probe. Compressor blade performance was measured and compared with various tubercled blades at various angles of incidence, while maintaining periodicity at the inlet and exit planes. Total pressure loss coefficients were calculated for all 4 blades and compared for 6 positive angle of incidence. Power series tubercled profile resulted in slight improvements in the loss coefficient at 0° incidence and none of tubercled geometry compromized performance at the design point. The baseline blade stalled at 8° and tubercles were capable of delaying stall at this condition. Power series profile outperformed the baseline at all angle of incidence (AOI) with significant improvements at 8° AOI. Power series tubercled profiles performed better than other tubercled geometries at almost all AOI except 10° where sinusoidal tubercled profiles performed better. The presence of smaller valley and broader peaks is attributed with the performance improvement, supported by the flow visualization results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".