Flow structure and resistance over subaquaeous high‐ and low‐angle dunes
Bibliographic record
Abstract
Abstract A prominent control on the flow over subaqueous dunes is the slope of the downstream leeside. While previous work has focused on steep (~30°), asymmetric dunes with permanent flow separation, little is known about dunes with lower lee slope angles for which flow separation is absent or intermittent. Here we present a laboratory investigation where we systematically varied the dune lee slope, holding other geometric parameters and flow hydraulics constant, to explore effects on the turbulent flow field and flow resistance. Three sets of fixed dunes (lee slopes of 10°, 20°, and 30°) were separately installed in a 15 m long and 1 m wide flume and subjected to 0.20 m deep flow. Measurements consisted of high‐frequency, vertical profiles collected with a Laser Doppler Velocimeter. We show that the temporal and spatial occurrence of flow separation decreases with dune lee slope. Velocity gradients in the dune leeside depict a free shear layer downstream of the 30° dunes and a weaker shear layer closer to the bed for the 20° and 10° dunes. The decrease in velocity gradients leads to lower magnitude of turbulence production for gentle lee slopes. Aperiodic, strong ejection events dominate the shear layer but decrease in strength and frequency for low‐angle dunes. Flow resistance of dunes decreases with lee slope; the transition being nonlinear. Over the 10°, 20°, and 30° dunes, shear stress is 8%, 33%, and 90% greater than a flat bed, respectively. Our results demonstrate that dune lee slope plays an important but often ignored role in flow resistance.
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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) | 0.001 | 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".