Measurements of Secondary Flows Downstream of a Turbine Cascade at Off-Design Incidence
Bibliographic record
Abstract
This paper presents experimental results of the secondary flows from two large-scale, low-speed, linear turbine cascades for which the incidence was varied. The aerofoils for the two cascades were designed for the same inlet and outlet conditions and differed mainly in their leading-edge geometries. Detailed flow field measurements were made upstream and downstream of the cascades and static pressure distributions were measured on the blade surfaces for three different values of incidence: 0, +10 and +20 degrees. The results from this experiment indicate that the strength of the passage vortex does not continue to increase with incidence, as would be expected from inviscid flow theory. The streamwise acceleration within the aerofoil passage seems to play an important role in influencing the strength of the vortex. The most recent off-design secondary loss correlation (Moustapha et al. [1]) includes leading-edge diameter as an influential correlating parameter. The correlation predicts that the secondary losses for the aerofoil with the larger leading-edge diameter are lower at off-design incidence; however, the opposite is observed experimentally. The loss results at high positive incidence have also high-lighted some serious shortcomings with the conventional method of loss decomposition. An empirical prediction method for secondary losses has been developed and will be presented in a subsequent paper.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".