Oblique, Stratified Winds about a Shelter Fence. Part II: Comparison of Measurements with Numerical Models
Bibliographic record
Abstract
Abstract To evaluate Reynolds-averaged Navier–Stokes (RANS) models of disturbed micrometeorological winds, steady-state computations using a second-order closure are compared with observations (see Part I) in which the surface layer wind was disturbed by a long, thin porous fence (height h = 1.25 m; thickness dx ≈ 1 mm). Starting with the case of neutral stratification and normal incidence, it is shown that low-resolution RANS simulations (streamwise grid interval Δx/h = 1) produce reasonably good transects of mean wind speed, though with an ambiguity (or nonuniqueness) of at least 10%–15% of the amplitude of the relative wind curve, mainly arising from sensitivity to the choice of the solution mesh. For nearly perpendicular flows, the measured influence of stratification (stable or unstable) is to diminish the amplitude of the relative wind curve (i.e., windbreak is less effective), an effect that is replicated very well by the simulations. Obliquity of the incident wind, like stratification, also correlates with poorer shelter, but the computed response of the relative wind curve to obliquity is excessive. As for higher-order wind statistics, computed transects of velocity standard deviations compare poorly to those observed. Therefore, if this disturbed flow may be taken as representative, then caution must be recommended should it be thought that RANS-type models might be suitable (i.e., accurate, as well as convenient) for computing the disturbed wind statistics (typically mean velocity, shear stress tensor, and turbulent kinetic energy dissipation rate) that are needed to “drive” modern dispersion models in the complex wind regimes that must be confronted in such contexts as urban dispersion, or the wind migration of pollen.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".