A Large Eddy Simulation to determine the effect of trees on wind and turbulence over a suburban surface
Bibliographic record
Abstract
A proper modeling of flow and turbulence within and over urban canopies is key to properly predict air pollution and dispersion in cities. Trees are an integral part of the urban landscape. In many suburban neighborhoods, tree cover is 10 to 30% and trees are often taller than buildings. The effect of trees on drag, mean wind and turbulence in cities is not accounted for in current weather, air pollution and dispersion models. Our goal is to use high-resolution Large Eddy Simulations (LES) over a realistic urban canopy to inform about the effects of trees drag, mean wind and turbulence in the urban roughness sublayer (RSL). The simulated area is part of the Sunset-Neighborhood in Vancouver, Canada. In this area, long-term wind and turbulence measurements are available from instruments on a 28m-tall tower. Further, a high precision airborne Light Detection and Ranging (LiDAR) point cloud provides data to represent both buildings and trees at high spatial resolution in a realistic configuration. Trees are described by location-specific leaf area density (LAD) profiles. LES simulations are performed over a 512 x 512m characteristic subset of the city that contains the tower location and source area. In the LES, buildings are accounted for through an immersed boundary method, adopting a zero level-set distance function to localize the surface location, whereas drag forces from trees are parametrized as a function of the height-dependent LAD. Spectra of streamwise and vertical velocity components compare well between tower data and the model data, confirming the good performances of LES in simulations of flow over fully rough surfaces. We show how the presence of trees affects mean velocity and computed momentum flux profiles, significantly decreasing dispersive terms in the bulk of the flow, which are usually found to play a role in pressure driven boundary layer flows. The impact of trees on integral length scales in the flow is discussed.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".