Improved Profile Loss and Deviation Correlations for Axial-Turbine Blade Rows
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Bibliographic record
Abstract
Empirical correlations continue to play an important role in the early stages of turbine design and in the optimizing of the gas path. The recent development of highly-loaded low-pressure (LP) turbines has extended the design space beyond the range of geometric and aerodynamic parameters on which the existing correlations for profile losses and deviation are based. With the aid of a large database of measured profile losses, including in-house results and recent cases from the open literature, the profile loss predictions of Kacker and Okapuu have been reviewed. As a result, a revised correlation is proposed that appears to capture much better the loss behavior of axial-turbine airfoils of recent design, including very highly-loaded LP turbine airfoils. The opportunity was also taken to extend the range of applicability of the correlation for trailing-edge deviation originally proposed by Islam and Sjolander in 1999.
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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 it