Incomplete similarity of the turbulent boundary layer over an aerofoil in the presence of synthetic jets
Bibliographic record
Abstract
This study is concerned with the near-wall flow structure over a NACA 0025 aerofoil at a constant chord-based Reynolds number of 100 000 across various angles of attack, where an array of 12 circular-orifice synthetic jet actuators (SJAs) was used to reattach the flow under conditions of flow separation. The SJAs were operated in burst-mode at two distinct momentum coefficients, a 50 % duty cycle and a modulation frequency of 200 Hz, targeting the separated shear layer frequency. Particle image velocimetry was conducted using three side-by-side cameras to capture the velocity fields along the aerofoil surface at the centreline. At zero angle of attack, the velocity profiles exhibited characteristics of a turbulent boundary layer, following the law of the wall in the inner layer while deviating from the logarithmic law in the outer layer. At higher angles of attack, while some logarithmic behaviour could still be detected close to the wall, a wide region of the velocity profiles became predominantly linear, exhibiting a behaviour differing from both a canonical turbulent boundary layer and a turbulent wall jet. The entire shear flow was decomposed into three regions: the boundary layer, the jet layer and the mixing layer that extended between the two. The mixing layer was analysed by applying several scaling laws to the time-averaged velocity components, where it was revealed that the characteristic velocity of the two velocity components is different. An asymptotic solution was obtained under a low spreading rate at infinite Reynolds number, providing a theoretical basis for the experimental observations.
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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 | 0.000 |
| 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 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".