Estimation of Bed Shear Stress in Shallow Transitional Flows under Condition of Incipient Motion of Sand Particles Using Turbulence Characteristics
Bibliographic record
Abstract
In this experimental study, using an ADV, experiments were performed in three different shallow water flows under hydraulically transitional flow condition to estimate the bed shear stress using turbulence characteristics. Vertical distributions of all shear and normal Reynolds stresses as well as TKE were evaluated and simplified in order to estimate bed shear stress under incipient motion of four groups of sand particles. To determine bed shear stress, as the main approach, the linear portion of the −u′w′ profiles were extended towards the channel bed. The necessity of the approach of the vector addition of −u′w′ and −v′w′ in this experimental study was examined. It was found that the bed shear stress can be effectively estimated by multiplying the values of u′20, v′20, w′20 and TKE0 by 0.17, 0.33, 1.24 and 0.2, respectively. However, it was found that these values were slightly proportional to the shear Reynolds number. Additionally, the one-point measurement approach was assessed. The TKE method which applies all three components of Reynolds normal stresses was preferred to the u′2, v′2 and w′2 methods. Results showed that, u′20, v′20 and w′20 have values of 60.5, 31.3 and 8.2 percent of the total, respectively.
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 | 0.000 |
| 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 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".