Friction velocity and power law velocity profile in smooth and rough shallow open channel flows
Bibliographic record
Abstract
This paper examines the mean velocity profiles in shallow, turbulent open channel flows. Velocity measurements were carried out in flows over smooth and rough beds using a laser-Doppler anemometer. One set of profiles, composed of 29 velocity distributions, was obtained in flows over a polished smooth aluminum plate. Three sets of profiles were obtained in flows over rough surfaces. The rough surfaces were formed by two sizes of sand grains and a wire mesh. The flow conditions over the rough surface are in the transitional roughness state. The measurements were obtained along the centerline of the flume at three different Froude numbers (Fr ~ 0.3, 0.8, 1.0). The lowest Froude number was selected to obtain data in the range of most other open channel testing programs and to represent a low subcritical Froude number. For each surface, the Reynolds number based on the boundary layer momentum thickness was varied from about 600 to 3000. In view of the recent questions concerning the applicability of the log-law and the debate regarding log-law versus power law, the turbulent inner region of the boundary layer is inspected. The fit of one type of power law for shallow flows over a smooth surface is considered. The appropriateness of extending this law to flows over rough surfaces is also examined. Alternate methods for determining the friction velocity of flows over smooth and rough surfaces are considered and compared with standard methods currently in use.Key words: power law, open channel flow, velocity profile, surface roughness.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".