Effect of aspect ratio on developing and developed narrow open channel flow with rough bed
Bibliographic record
Abstract
This study attempts to unravel the effect of aspect ratio on the turbulence characteristics in developing and fully developed narrow open channel flows. In this regard, experiments were conducted in a rough bed open channel flow. Instantaneous 3D velocities were acquired using an acoustic Doppler velocimeter at various locations along the centerline of the flume. The variables of interest include the mean components of the flow velocity, turbulence intensity, wall normal Reynolds shear stress, correlation coefficient, turbulence kinetic energy, and anisotropy. A new correlation between the equivalent roughness and velocity shift from the smooth wall logarithmic equation as a function of aspect ratio is proposed. Aspect ratio was found to influence the velocity characteristics throughout the depth in the developing flow region, while the effects are confined to the outer layer for fully developed flows. New equations to describe the variation of turbulence intensities and turbulent kinetic energy are proposed for narrow open channel flows. Reynolds stress anisotropy analysis reveals that level of anisotropy in narrow open channel flow is less than in wide open channel flows. Finally, a linear regression model is proposed to predict flow development length in narrow open channel flows with a rough bed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".