Flow Separation in Airfoils with Rough Leading Edges
Bibliographic record
Abstract
In this study we consider the flow over airfoils with leading-edge roughness, designed to mimic the ice depositions that may occur on an aircraft in flight. The focus of this investigation is the effect of the angle of attack on the mean-flow three-dimensionality. In our previous work (Kumar et al., Journal of Turbulence, Vol. 22, No. 11, 2021, pp. 735–760), we found stationary spanwise inhomogeneities in the form of alternating regions of fast- and slow-moving fluid, which were termed “flow channels.” In the present study we investigate further this phenomenon. We observe the formation of hairpin vortices downstream of the roughness elements, which eventually merge; this causes the formation of wider channels that remain coherent and affect the trailing-edge separation. With increasing angle of attack, the intensity of flow channeling can increase or decrease depending on the topology of the leading-edge roughness. Its effect on the trailing-edge separation remains, however, significant. The mean-separation line is highly distorted, and the separation length can vary by up to 30% of the chord length along the span.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".