A revisit of different models for flow resistance in gravel-bed rivers and hydraulic flumes
Bibliographic record
Abstract
This work develops empirical equations for determining flow resistance through fitting a large number of empirically obtained data. Two models for determining flow resistance are developed to predict mean flow velocity by using an extensive database (N = 3507) from miscellaneous range of gravel-bed and mountainous rivers, representative of a wide hydraulic and geomorphologic condition. The first model is composed of six individual empirical equations which were fitted to the ratio of mean flow depth and hydraulic radius to coarser percentiles, D90 and D84, and median diameter, D50. Results indicate that the model which is fitted to the ratio of hydraulic radius to D84 is more preferable than the other equations. Since it is difficult to measure flow depth accurately in shallow water, another model which uses discharge as input was developed. A statistical analysis concluded results calculated using either the Ferguson equation or the Rickenmann and Recking equations are in good agreement with the measured values but better agreement is achieved by the newly parameterized equations presented here. In order to analyse the accuracy and the applicability of the proposed equations and the other recently developed flow resistance predictors, the equations were used to predict mean flow velocity of extensive database consisting of N = 1447 flume data. The newly developed equation and the Rickenmann and Recking equations which use discharge as input were also tested by using this new data set. One can see from the results that the new equation can predict mean flow velocity even more accurately than the Rickenmann and Recking equations. The results indicate better agreement between empirical data and calculated results for the newly parameterized equations than existing research.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".