Influence of Channel Aspect Ratio and Curvature on Flow, Secondary Circulation, and Bed Shear Stress in a Rectangular Channel Bend
Bibliographic record
Abstract
Flow within an alluvial channel bend is significantly affected by channel geometry, including curvature ratio (bend radius/channel width, R/B) and aspect ratio (channel width/flow depth, B/H). High curvature bends (R/B≤3) can experience substantially more erosion than milder curvature bends. This study employs a three-dimensional Reynolds-Averaged Navier-Stokes (RANS) model to investigate the effects of curvature ratio and aspect ratio on bend flow with respect to a high curvature (R/B=1.5) base case in a 135° bend. Experimental data are used to validate the RANS model predictions for the high curvature base case with a flat bed (FB) and an equilibrium deformed bed (DB). Five curvature ratios (1.5, 3, 5, 8, and 10) and four aspect ratios (5.00, 6.67, 9.09, and 12.50) are investigated. Results show that a decrease in R/B or B/H for the FB cases results in a strong increase in total circulation of the regions associated with the primary cell of cross-stream circulation (Γ+), an increase in maximum bed shear stress, and an increase in the contribution of the cross-stream component to the total magnitude of bed shear stress. The values of R/B and B/H also affect the structure of the cross-stream flow. The primary cell of cross-stream circulation splits into two clockwise-rotating cells at low R/B values and the cell situated closer to the inner wall induces strong ejections of vorticity. At high R/B values, a secondary counter-clockwise rotating cell forms at the outer bank. At lower B/H values, the primary cell splits into two clockwise-rotating cells. This study shows that the position and size of regions of high bed shear stress, and thus the capacity of the flow to entrain sediment, depend strongly on bend curvature.
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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 | 0.000 |
| 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".