A Parametric Study Of The Parameters Governing Flow Incidence Angle Tolerance For Turbomachine Blades
Bibliographic record
Abstract
Performance metrics quantifying the efficiency of various turbomachinery blades aid in the development of an optimal blade design. In this study, a metric was created to investigate the performance of 48 different compressor blades created with Octave and MISES software. Three input parameters were varied: leading edge radius, the location of maximum blade thickness, and the number of blades in a blade row. The objective was to determine which of these parameters most strongly affects the average loss and incidence range for the blade row. After a sensitivity analysis of the three input parameters was conducted, it was found that the number of blades had the largest effect on blade performance, followed by the leading edge geometry and lastly the location of maximum thickness. Les indicateurs de performance qui mesurent les ef cacités de plusieurs pales utilisées dans les turbomachines aident le développement d’une conception optimale des pales. Dans cet article, un indicateur a été créé pour étudier la performance de 48 pales de compresseurs axiaux différents qui ont été faites avec les logiciels Octave et MISES. Trois paramètres d’entrés ont été examinés : le rayon du bord d’attaque, l’endroit de l’épaisseur maximale de la pale et le nombre de pales dans la rangée. L’objectif était de déterminer lequel parmi ces paramètres a l’effet le plus important sur la perte moyenne et la gamme de l’incidence de la rangée de pales. Après une analyse de sensibilité des trois paramètres, les résultats ont montré que le nombre de pales dans la rangée a eu l’effet le plus important sur la performance des pales, suivie par la géométrie du bord d’attaque et en n l’endroit de l’épaisseur maximale.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".