Transient dynamics of stall and reattachment at low Reynolds number
Bibliographic record
Abstract
Wind tunnel experiments are performed to investigate stall and reattachment transients for an aerofoil and wing model at low chord Reynolds numbers ( $8\times 10^4\leqslant {{Re}}_c\leqslant 1\times 10^5$ ) where a laminar separation bubble (LSB) may form on the suction surface. Direct force measurements and particle image velocimetry (PIV) are employed simultaneously to characterise the transient aerodynamic loading and flow field development. The imposed changes in operating conditions leading to stall and reattachment include changes in angle of attack at multiple pitch rates and changes in Reynolds number. The evolution of the lift coefficient is consistent with dynamic stall at higher Reynolds numbers, with a reduction in time delay between the passing of the static stall condition and the loss of lift for increasing pitch rate. During an increase in angle of attack, the separation bubble moves upstream prior to rapidly bursting, whereas for a decrease of Reynolds number, the LSB undergoes a more gradual monotonic increase in length prior to bursting. In contrast to notable differences in the aerodynamic loading and flow field development for different types of transients leading to LSB bursting, the process of LSB formation is less sensitive to the type of imposed change in operating conditions. Spanwise PIV measurements on the aerofoil and wing models indicate that the spanwise flow development is also insensitive to the type of imposed transient during LSB bursting and formation.
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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 | 0.000 |
| 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) | 0.001 | 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".