Effects of Surface-Roughness Geometry on Separation-Bubble Transition
Bibliographic record
Abstract
This paper presents measurements of separation-bubble transition over a range of surfaces with randomly distributed roughness elements. The tested roughness patterns represent the typical range of roughness conditions encountered on in-service turbine blades. Through these measurements, the effects of size and spacing of the roughness elements, and the tendency of the roughness pattern toward protrusions or depressions (skewness), on the inception location and rate of transition are evaluated. Increased roughness height, increased spacing of the roughness elements, and a tendency of the roughness pattern toward depressions (negative skewness) are observed to promote earlier transition inception. The observed effects of roughness spacing and skewness are found to be small in comparison to that of the roughness height. Variation in the dominant mode of instability in the separated shear layer is achieved through adjustment of the streamwise pressure distribution. The results provide examples for the extent of interaction between viscous and inviscid stability mechanisms.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".