Boundary layer dynamics and drag reduction in flows of high cohesive sediment suspensions
Bibliographic record
Abstract
Drag reduction has been observed in suspension flows of low clay concentrations in previous studies. Here, velocity profiles and bed shear stresses, expressed as shear velocities, are measured using epoxy‐coated hot‐film sensors to evaluate drag reduction and controlling factors in suspension flows of high clay concentrations (4 and 8 g l–1). The directly measured shear velocity in the viscous sublayer is found to be reduced by as much as 70% relative to the profile‐derived shear velocity in the logarithmic layer. Drag reduction is found to increase with increasing clay concentration and decreasing flow strength. Density profile data indicate that the suspension flows were not stratified, and examinations of particle size distributions suggest that flocculation was not significant in causing the observed drag reduction. Measurements of the velocity profiles and of the shear velocity in the viscous sublayer indicate significant thickening of the inner wall layer and show turbulence damping in the viscous sublayer. These effects become stronger for higher concentrations and lower flow strength, suggesting that they are responsible for drag reduction in flows of clay suspension. Empirical relationships have been derived that can be used to predict the magnitude of drag reduction and the reduced shear stress in mud suspensions for both laboratory and field cohesive sediment transport studies.
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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".