Flow and Pollutant Dispersion Model in a 2D Urban Street Canyons Using Computational Fluid Dynamics
Bibliographic record
Abstract
A two-dimensional model is used to simulate temperature distribution, wind speed and pollutants dispersion within an isolated two-dimensional street canyon using SIMPLE algorithm in ANSYS Fluent version 16.2. The simulation is based on the Reynolds-averaged Navier–Stokes equations coupled with a series of standard, RNG and realizable k-ε turbulence models. Simulation domain consisted of a street canyon with two buildings enclosing a street with the aspect ratio of 1. The wind is assumed to be perpendicular to the direction of the street and the source of the pollution is assumed to be liner. The results showed that the RNG k-ε turbulence model is the most optimum model by comparing with the calculated data under different wind speed patterns and pollutant dispersion model. The improvement of turbulent viscosity term of the RNG k-ε turbulence model provides a more accurate and reliable numerical solution for the present study regarding to the pollution dispersion in a street canyon. The simulation results also showed that the dimensionless pollutant concentrations, P, is larger on the leeward side of the buildings and decrease exponentially from floor to top of the upstream buildings. Furthermore, the results showed that the pollutant concentrations on the leeward side of building are more than that on the windward side due to the pollutant transportation of vortex circulation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".