Investigations of the law‐of‐the‐wall over sparse roughness elements
Bibliographic record
Abstract
This paper examines the application of the law‐of‐the‐wall or gradient method for calculating the shear velocity, roughness length, and displacement height over three increasing roughness densities replicated with three different sized cubes within a recirculating wind tunnel. We compare these aerodynamic parameter estimates with estimates of the same parameters derived from other established methods: Reynolds stress analysis and the outer‐layer velocity‐defect law. By using more than one roughness height for the same roughness density (λ), dependencies of these parameters on roughness element height were also evaluated. Using the vertical wind speed logarithmic profile layer (determined graphically), resulted in shear velocity estimates that are greater by more than a factor of two than those determined using hot‐film anemometry. The law‐of‐the‐wall method provided a good estimate of the roughness length when applied to only that portion of the wind speed profile identified by Reynolds stress measurements to be within the constant stress layer; however, the shear velocity was overestimated by an average of 43% compared with that measured directly by hot‐film anemometry. The best prediction of both of the roughness length and shear velocity, compared to estimates using Reynolds stress analysis, was obtained using the outer‐layer velocity‐defect law. We advocate that the velocity‐defect law method be used in wind tunnel testing for calculating the shear velocity and roughness length from velocity profiles over sparsely spaced roughness elements, or when flow is highly heterogeneous, instead of the law‐of‐the‐wall.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".