Particle Image Velocimetry Investigation of the Coherent Structures in a Leading-Edge Slat Flow
Bibliographic record
Abstract
Air traffic volume is expected to triple in the U.S. and Europe by 2025, and as a result, the aerospace industry is facing stricter noise regulations. Apart from the engines, one of the significant contributors of aircraft noise is the deployment of high-lift devices, like leading-edge slats. The unsteady turbulent flow over a leading-edge slat is studied herein. In particular, particle image velocimetry (PIV) measurements were performed on a scale-model wing equipped with a leading-edge slat in the H.J. Irving–J.C.C. Picot Wind Tunnel. Two Reynolds numbers based on wing chord were studied: Re = 6 × 105 and 1.3 × 106. A snapshot proper orthogonal decomposition (POD) analysis indicated that differences in the time-averaged statistics between the two Reynolds numbers were tied to differences in the coherent structures formed in the slat cove shear layer. In particular, the lower Reynolds number flow seemed to be dominated by a large-scale vortex formed in the slat cove that was related to the unsteady flapping and subsequent impingement of the shear layer onto the underside of the slat. A train of smaller, more regular vortices was detected for the larger Reynolds number case, which seemed to cause the shear layer to be less curved and impinge closer to the tail of the slat than for the lower Reynolds number case. The smaller structures are consistent with Rossiter modes being excited within the slat cove. The impingement of the shear layers on and the proximity of the vortices to the slat and the main wing are expected to be strong acoustic dipoles in both cases.
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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".