Influence of Airflow Separation on the Turbulent Flow Structure Over Wind-Generated Water Waves
Bibliographic record
Abstract
An experimental study is conducted to investigate the airside flow behavior within the crest-trough region over wind generated water waves. Two-dimensional velocity fields in a plane perpendicular to the surface were measured using particle image velocimetry (PIV). The experiments were conducted in a wind wave flume 0.45 m wide, 0.9 m high and 3 m long. The measurements were made at a fetch of 2.1 m and at the wind speeds of 3.7 and 4.4 m s−1. An algorithm was developed to segregate separated and non-separated velocity fields within the measured dataset. The results show lower magnitudes of the streamwise velocity and higher magnitudes of Reynolds stress and turbulent kinetic energy for the separated flow fields than that for the non-separated flow fields, indicating that the flow separation significantly enhances turbulence in the near surface region. The enhanced Reynolds stress is positive which indicates that the flow separation increases downward momentum transfer from wind to the wave. The two dimensional plot of instantaneous velocity showed that the separation vortices are restricted to the region bounded by the wave crest and trough. The presented results demonstrate that the flow separation plays a significant role in the interfacial transport processes and therefore, it can be concluded that the understanding of the airflow field within the crest-trough region is vital to improve our knowledge about the air-water heat, mass and momentum exchange.
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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".