Influence of blockage ratios in shaping wind dynamics in urban environments
Bibliographic record
Abstract
Analytical urban canopy models (UCMs) based on Prandtl’s mixing length theory usually ignore the blockage effects caused by building structures, which greatly reduces their accuracy in representing wind flow and turbulence variations within urban boundary layers. This study employs large-eddy simulations under neutral atmospheric stratification to investigate the effects of various blockage ratios on wind dynamics in urban environments. Detailed analyses are conducted on variations in instantaneous flow fields, mean velocity, Reynolds shear stress, and vorticity around buildings. Results indicate that higher blockage ratios restrict airflow above buildings, leading to increased local wind speeds and intensified turbulence within the urban canopy layer. In contrast, lower blockage ratios allow smoother airflow over the canopy, minimizing interactions between the airflow and buildings. Vorticity analysis suggests that higher blockage ratios induce smaller, denser vortices in the wake region, while lower blockage ratios generate longer, more dispersed vortices near the rooftop. Furthermore, this study introduces a modified friction velocity that reduces the bias in velocity by about 17% at a low blockage ratio of 4.44%, resulting in a more accurate representation of the velocity distribution around buildings. As a result, for neutral stratification at a specific moment, known parameters such as atmospheric boundary layer height can be used to predict velocity without additional simulations, thus significantly reducing the computational costs.
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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) | 0.000 | 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".