Numerical Simulation of Turbulent Flow and Pollutant Dispersion in Urban Street Canyons
Bibliographic record
Abstract
In this study, we have developed a numerical model based on an open source Computational Fluid Dynamics (CFD) package OpenFOAM, in order to investigate the flow pattern and pollutant dispersion in urban street canyons with different geometry configurations. In the new model, the pollutant transport driven by airflow is modeled by the scalar transport equation coupling with the momentum equations for airflow, which are deduced from the Reynolds Averaged Navier-Stokes (RANS) equations. The turbulent flow calculation has been calibrated by various two-equation turbulence closure models to select a practical and efficient turbulence model to reasonably capture the flow pattern. Particularly, an appropriate value of the turbulent Schmidt number has been selected for the pollutant dispersion in urban street canyons, based upon previous studies and careful calibrations against experimental measurements. Eventually, the numerical model has been validated against different well-known laboratory experiments in regard to various aspect ratios (a relationship between the building height and the width of the street canyon), and different building roof shapes (flat, shed, gable and round). The comparisons between the numerical simulations and experimental measurements show a good agreement on the flow pattern and pollutant distribution. This indicates the ability of the new numerical model, which can be applied to investigate the wind flow and pollutant dispersion in urban street canyons.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".