An Empirical Prediction Method For Secondary Losses In Turbines—Part II: A New Secondary Loss Correlation
Bibliographic record
Abstract
A new empirical prediction method for design and off-design secondary losses in turbines has been developed. The empirical prediction method is based on a new loss breakdown scheme, and as discussed in Part I, the secondary loss definition in this new scheme differs from that in the conventional one. Therefore, a new secondary loss correlation for design and off-design incidence values has been developed. It is based on a database of linear cascade measurements from the present authors’ experiments as well as cases available in the open literature. The new correlation is based on correlating parameters that are similar to those used in existing correlations. This paper also focuses on providing physical insights into the relationship between these parameters and the loss generation mechanisms in the endwall region. To demonstrate the improvements achieved with the new prediction method, the measured cascade data are compared to predictions from the most recent design and off-design secondary loss correlations (Kacker, S. C. and Okapuu, U., 1982, ASME J. Turbomach., 104, pp. 111-119, Moustapha, S. H., Kacker, S. C., and Tremblay, B., 1990, ASME J. Turbomach., 112, pp. 267–276) using the conventional loss breakdown. The Kacker and Okapuu correlation is based on rotating-rig and engine data, and a scaling factor is needed to make their correlation predictions apply to the linear cascade environment. This suggests that there are additional and significant losses in the engine that are not present in the linear cascade environment.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".