Novel Geometric Parameters for Assessing Flow Over Realistic Versus Idealized Urban Arrays
Bibliographic record
Abstract
Abstract Urban heterogeneity, such as the variation of street layouts, building shapes, and building heights, cannot be fully represented by density parameters commonly used in idealized urban environmental analyses. To address this shortcoming and better model flow fields over complex urban neighborhoods, we propose two novel descriptive geometric parameters, alignedness and building facet entropy, which quantify the connectivity of inter‐building spaces along the prevailing wind direction and the variation of building facet orientations, respectively. We then conducted large eddy simulations over 101 urban layouts, including realistic urban configurations with uniform building height as well as idealized building arrays with variable heights, and evaluated the resulting bulk flow properties. Urban canopy flow over realistic neighborhoods resembles staggered building arrays for low urban densities but becomes similar to aligned configurations beyond λp ∼ 0.25 where the realistic flow is less sensitive to changes in density. We further show that compared to traditional density parameters (such as plan and frontal area densities), the mean alignedness, a measure of connectivity of flow paths in street canyons, better predicts canopy‐averaged flow properties. Furthermore, for realistic urban flow, the dispersive momentum flux shows a clear increasing trend with building density, and a decreasing trend with alignedness, which is in contrast with idealized cases that exhibit no clear trend. This distinct behavior further highlights the necessity of evaluating flow over realistic urban layouts for flow parameterization. This study provides an improved method of describing urban layouts for flow characterization that can be applied in neighborhood‐scale urban canopy parameterization.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".