A wind tunnel examination of shear stress partitioning for an assortment of surface roughness distributions
Bibliographic record
Abstract
Surface roughness aids to ameliorate wind erosion by extracting a portion of the wind's momentum thereby reducing the quantity of stress on the surface. This paper evaluates the effect of different spatial arrangements of surface roughness on the partition of average drag forces and distribution of stress at the surface. A new tiered force balance was used in a wind tunnel to independently and simultaneously measure the drag on arrays of roughness elements and the drag on the intervening surface. In addition, Irwin sensors recorded point measurements of surface shear stress within the arrays. Roughness arrays consisted of small cylinders in four different spatial arrangements, one being staggered and three being simplifications of natural roughness configurations, at four roughness densities. Results from the tiered force balance and Irwin sensors indicate that the roughness configuration has a small impact on the average (R) and maximum (R″) drag partition. The protection of the surface increased with roughness density regardless of the roughness arrangement. Point measurements of shear stress revealed that the roughness configuration had a small impact on the distribution of shear stress at the surface, and that the maximum shear stress scaled to the average shear stress. Drag partition measurements were compared to the ratios predicted by the Raupach et al. (1993) model and a good degree of agreement was found for all configurations when using measured values of the β and m parameter.
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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 | 0.000 | 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".