Application of Endwall Contouring to Transonic Turbine Cascades: Experimental Measurements at Design Conditions
Bibliographic record
Abstract
An experimental investigation of the endwall flows in two transonic linear turbine cascades was presented at the 2010 ASME Turbo Expo (GT2010–22760). Endwall contouring was subsequently implemented in these cascades to control the secondary flows, and reduce the total pressure losses. The current paper presents experimental results from these cascades to assess the effectiveness of endwall contouring in the transonic flow regime. The experimental results include blade loadings, total pressure losses, streamwise vorticity and secondary kinetic energy distributions. In addition, surface flow visualization results are presented in order to interpret the endwall limiting streamlines within the blade passages. The flat-endwall and contoured-endwall cascades produce very similar midspan loading distributions and profile losses, but exhibit different secondary flows. The endwall surface flow visualization results indicate weaker interaction between the secondary flows and the blade suction surface boundary layers in the contoured cascades. Overall, the implementation of endwall contouring results in smaller and less intense vortical structures, and the reduction of the associated secondary kinetic energy (SKE) and exit flow angle variations. However, the mass-averaged losses at the main measurement plane, located 40% axial chord lengths downstream of the cascade (1.4CX), do not corroborate the numerically predicted improvements for the contoured cascades. This is in part attributed to slower mixing rates of the secondary flows in the compressible flow regime. The mass-averaged results at 2.0CX, on the other hand, show smaller losses for the contoured cascades associated with smaller SKE dissipation downstream of the cascades. Accordingly, the mixed-out row losses also show improvements for the contoured cascades.
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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".