Urban canopy parameterization of the non-local building effects with variable building height
Bibliographic record
Abstract
Variability of building height induces flow heterogeneity and directly controls the depth of the roughness sub-layer, the strength of mutual sheltering, and the overlapping of urban canopy flow, which poses challenges for accurate modeling. Large-eddy simulations over 96 building arrays with varying density, height variability (standard deviation of building height), and horizontal arrangements were conducted to reveal the impact on the urban flow. Results demonstrate a strong non-local building effect on the flow due to height variability, where flow around high buildings possesses high wind speed, dispersive momentum flux, and other distinctive flow patterns, whereas around low buildings, the flow pattern is less unique. The complex flow behavior is beyond the capacity of the current multi-layer urban canopy model (MLUCM) where turbulent constants and drag effects were considered in a simplified way. The increased height variability and urban density also blur the interface of urban canopy, further making MLUCM estimates model constants heavily based on a clear urban canopy inappropriate. Based on the original model, we comprehensively tested potential contributing factors such as the estimation of displacement height, height-dependent drag coefficients, and the extended roughness sublayer. The modified model provides a better overall agreement with the LES results, especially above the mean building height where the prediction of the extended urban canopy layer is largely improved.
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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".