Flow deceleration as a method of determining drag coefficient over roughened flat beds
Bibliographic record
Abstract
The drag coefficient (CD) is a fundamental parameter in the determination of shear stress or the drag force between a fixed object and fluid moving over it. In natural settings, conventional methods of defining it are largely impractical and so either smooth‐bed CD or constant values are used irrespective of bed roughness or flow strength. This paper deals with the determination of CD over naturally roughened beds. The work was carried out in two annular flumes of known, constant water mass. In an otherwise balanced system, flow deceleration is a manifestation of the total drag force exerted at the rigid boundaries (Newton's second law). The inversion of this relationship is used to yield the bed drag coefficients. The advantages of this method include its accurate use over rough and irregular beds, as shown by experiments over patchy and homogeneous gravel beds and over a wide range of Reynolds numbers. The value of CD was found to converge to the constant value of 3 × 10−3 determined by Sternberg [1968] at intermediate velocities, and a reduction in the drag coefficient occurred at high velocities. Results showed that patch spacing did not influence the shear stress value in the case of one‐grain‐thick gravel patches. A modification of the equipment for field use may give advantages where traditional methods fail due to difficulties in obtaining accurate velocity profile measurements.
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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 | 0.001 | 0.001 |
| 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".