An Empirical Prediction Method for Secondary Losses in Turbines: Part I — A New Loss Breakdown Scheme and Penetration Depth Correlation
Bibliographic record
Abstract
Despite its wide use in meanline analyses, the conventional loss breakdown scheme is based on a number of assumptions that are known to be physically unsatisfactory. One of these assumptions states that the loss generated in the airfoil surface boundary layers is uniform across the span. The loss results at high positive incidence presented in a previous paper (Benner et al. [1]) indicate that this assumption causes the conventional scheme to produce erroneous values of the secondary loss component. A new empirical prediction method for secondary losses in turbines has been developed, and it is based on a new loss breakdown scheme. In the first part of this two-part paper, the new loss breakdown scheme is presented. Using data from the current authors’ off-design cascade loss measurements (Benner et al. [1]), it is shown that the secondary losses obtained with the new scheme produce a trend with incidence that is physically more reasonable. Unlike the conventional loss breakdown scheme, the new scheme requires a correlation for the spanwise penetration depth of the passage vortex separation line at the trailing edge. One such correlation exists (Sharma and Butler [2]); however, it was based on a small database. An improved correlation for penetration distance has been developed from a considerably larger database, and it is detailed in this 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".