Simulation of gaseous pollutant dispersion around an isolated building using the <i>k–ω SST</i> (shear stress transport) turbulence model
Bibliographic record
Abstract
The dispersion of gaseous pollutant around buildings is complex due to complex turbulence features such as flow detachment and zones of high shear. Computational fluid dynamics (CFD) models are one of the most promising tools to describe the pollutant distribution in the near field of buildings. Reynolds-averaged Navier-Stokes (RANS) models are the most commonly used CFD techniques to address turbulence transport of the pollutant. This research work studies the use of [Formula: see text] closure model for the gas dispersion around a building by fully resolving the viscous sublayer for the first time. The performance of standard [Formula: see text] model is also included for comparison, along with results of an extensively validated Gaussian dispersion model, the U.S. Environmental Protection Agency (EPA) AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model). This study's CFD models apply the standard [Formula: see text] and the [Formula: see text] turbulence models to obtain wind flow field. A passive concentration transport equation is then calculated based on the resolved flow field to simulate the distribution of pollutant concentrations. The resultant simulation of both wind flow and concentration fields are validated rigorously by extensive data using multiple validation metrics. The wind flow field can be acceptably modeled by the [Formula: see text] model. However, the [Formula: see text] model fails to simulate the gas dispersion. The [Formula: see text] model outperforms [Formula: see text] in both flow and dispersion simulations, with higher hit rates for dimensionless velocity components and higher "factor of 2" of observations (FAC2) for normalized concentration. All these validation metrics of [Formula: see text] model pass the quality assurance criteria recommended by The Association of German Engineers (Verein Deutscher Ingenieure, VDI) guideline. Furthermore, these metrics are better than or the same as those in the literature. Comparison between the performances of [Formula: see text] and AERMOD shows that the CFD simulation is superior to Gaussian-type model for pollutant dispersion in the near wake of obstacles. AERMOD can perform as a screening tool for near-field gas dispersion due to its expeditious calculation and the ability to handle complicated cases. The utilization of [Formula: see text] to simulate gaseous pollutant dispersion around an isolated building is appropriate and is expected to be suitable for complex urban environment. IMPLICATIONS: Multiple validation metrics of [Formula: see text] turbulence model in CFD quantitatively indicated that this turbulence model was appropriate for the simulation of gas dispersion around buildings. CFD is, therefore, an attractive alternative to wind tunnel for modeling gas dispersion in urban environment due to its excellent performance, and lower cost.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".