Influence of a Novel 3D Leading Edge Geometry on the Aerodynamic Performance of Low Pressure Turbine Blade Cascade Vanes
Bibliographic record
Abstract
This paper addresses the issue of aerodynamic performance of a novel 3D leading edge modification to a reference low pressure turbine blade. An analysis of tubercles found in nature and used in some engineering applications was employed to synthesize new leading edge geometry. A sinusoidal wave-like geometry characterized by wavelength and amplitude was used to modify the leading edge along the span of a 2D profile, rendering a 3D blade shape. The rationale behind using the sinusoidal leading edge was that they induce streamwise vortices at the leading edge which influence the separation behaviour downstream. Surface pressure and total pressure measurements were made in experiments on a cascade rig. These were complemented with computational fluid dynamics studies where flow visualization was also made from numerical results. The tests were carried out at low Reynolds number of 5.5 × 104 on a well-researched profile representative of conventional low pressure turbine profiles. The performance of the new 3D leading edge geometries was compared against the reference blade revealing a downstream shift in separated flow for the LE tubercle blades; however, total pressure loss reduction was not conclusively substantiated for the blade with leading edge tubercles when compared with the performance of the baseline blade. Factors contributing to the total pressure loss are discussed.
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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 | 0.001 | 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".