A simplified approach to computing flow and bedload along gravel dune-like bedforms
Bibliographic record
Abstract
River dunes are important bedforms. Problems associated with the development and evolution of dune bedforms include increased flood risks, channel erosion, and damage to fish habitats. The purpose of this paper is to investigate the near-bed flow structure and bedload transport along gavel dune-like bedforms. The velocity field is computed using a relatively simple multi-layer hydrodynamic model, with a parameterization of flow separation in the leeside of dunes. The computation is of high efficiency and avoids uncertainties caused by flow separation. Fractional transport rates for a sediment mixture of sands and gravel are calculated using surface-based techniques. The computed flow velocities and bed shear stresses are in good comparison with acoustic Doppler velocimeter measurements. Bedload transport is shown to increase non-linearly with distance toward the dune crest and reach the maximum at the crest. This implies that dune-length averaged bed shear stress is not suitable for bedload calculations. At low discharges, the bed shear stress is the limiting factor, resulting in insignificant bedload. At high discharges when the bed shear stress exceeds a threshold, the effect of sediment-grain hiding and sediment-size availability are important for bedload calculations. The discharge–transport relationship is highly non-linear. This paper has demonstrated selective transport and potential dune surface coarsening. The simplified modelling approach has a good potential for application to field conditions.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".