Separation-Bubble-Transition Measurements on a Low-Re Airfoil Using Particle Image Velocimetry
Bibliographic record
Abstract
This paper presents experimental results on separation-bubble transition at low Reynolds number and low freestream turbulence, measured on an airfoil using particle image velocimetry (PIV). The two-dimensional PIV measurements have been performed over the suction surface of a low-Reynolds-number airfoil in a water tow-tank facility. Reynolds numbers, based on airfoil chord length and towing speed, of 40,000 and 65,000 have been examined at various angles of incidence, providing a range of streamwise pressure distributions and transitional separation-bubble geometries. The types of bubbles observed range from a short and thick bubble with separation near the leading edge of the airfoil, to a long and thin bubble with separation far downstream of the suction peak. The PIV measurements facilitate visualization of the vortex dynamics associated with separation-bubble transition. The growth of instability waves within the separated shear layer and eventual breakdown into turbulence is documented through the instantaneous vector fields. For all cases examined, large-scale vortex shedding and multiple reverse-flow zones are observed in the reattachment region. A technique for estimating the location of transition onset based on statistical turbulence quantities is presented, and comparisons are made to existing transition models.
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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) | 0.000 | 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 | 0.001 |
| 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".