Characteristics of Shallow Turbulent Near Wakes at Low Reynolds Numbers
Bibliographic record
Abstract
The present study deals with the noninvasive measurement of both velocity and concentration in the near region of shallow turbulent wakes using a laser-Doppler anemometer and a video-imaging technique. A 40 mm wide flat plate placed normal to the flow is used as the wake generator. The flow depths considered in the present study are small compared to the width of the channel and the generated wakes are categorized as shallow. Tests were conducted at two depths of flow (h=20 and 40 mm) and the boundary layer thickness of the approaching flow is comparable to the depth. The Reynolds number of the flow based on the approaching freestream boundary layer momentum thickness varies from 180 to 400, while, the Reynolds number based on the test body width was maintained nearly constant (≈4000). Measurements were carried out at three axial stations (2.5, 5, and 10 plate widths) downstream of the bluff body. At each axial station, the velocity measurements were carried at distances of h/4,h/2, and 3h/4 from the channel bottom and spanning the cross section of the wake. Appropriate length and velocity scales are identified to characterize the wake. The axial variation of the shallow wake half-width based on both the lateral velocity profile and the lateral concentration profile is also obtained. A shear parameter is introduced to analyze the relative effects of transverse shear and bed friction. [S0098-2202(00)01802-2]
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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 | 0.001 | 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".