Experimental and Numerical Investigation on the Performance of a Family of Three HP Transonic Turbine Blades
Bibliographic record
Abstract
Experimental results concerning the performance of three high-pressure (HP) transonic turbine blades having fore-, aft- and mid-loadings have been presented previously by Corriveau and Sjolander [1]. Results from that study indicated that by shifting the loading towards the rear of the airfoil, improvements in loss performance of the order of 20% could be obtained near the design Mach number. In order to gain a better understanding of the underlying reasons for the improved loss performance of the aft-loaded blade, additional measurements were performed on the three cascades. Furthermore, 2-D numerical simulations of the cascade flow were performed in order to help in the interpretation of the experimental results. Based on the analysis of additional wake traverse data and base pressure measurements combined with the numerical results, it was found that the poorer loss performance of the baseline mid-loaded profile compared to the aft-loaded blade could be traced back to the former’s higher rear suction side curvature. The presence of higher rear suction surface curvature resulted in higher flow velocity in that region. Higher flow velocity at the trailing edge in turn contributed to reducing the base pressure. The lower base pressure at the trailing edge resulted in a stronger trailing edge shock system for the mid-loaded blade. This shock system increased the losses for the mid-loaded baseline profile when compared to the aft-loaded profile.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".