Large Eddy Simulation of Near-Bed Flow and Turbulence over Roughness Elements in the Shallow Open-Channel
Bibliographic record
Abstract
Turbulent flows in rough open-channels have complex structures near the channel-bed. The near-bed flow can cause bed erosion, channel instability, and damages to fish habitats. This paper aims to improve our understanding of the structures. Transverse square bars placed at the channel-bed form two-dimensional roughness elements. Turbulent flows over the bars are predicted using large eddy simulation (LES). The predicted flow quantities compare well with experimental data. The LES model predicts mean-flow velocity profiles that resemble those in the classic turbulent boundary layer over a flat plate and profiles that change patterns in the vicinity of roughness elements, depending on the pitch-to-roughness height ratio λ/k. The relative turbulence intensity and normalized Reynolds shear stress reach maxima of 15% and 1.2%, respectively, at λ/k = 8, compared to 9% and 0.2% at λ/k = 2. The predicted bottom boundary layers constitute a large portion of the total depth, indicating roughness effect on the flow throughout the water column. Fluid exchange between the roughness cavity and outer region occurs due to turbulence fluctuations. The fluctuations increase in intensity with increasing λ/k ratio. This ratio dictates the number of eddies in the cavity as well as their locations and shapes. It also controls turbulence stress distributions. LES can be used to explore strategies for erosion control, channel restoration, and habitat protection.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".