Modified Accuracy of RANS Modeling of Urban Pollutant Flow within Generic Building Clusters Using a High-Quality Full-Scale Dispersion Dataset
Bibliographic record
Abstract
To improve the reliability of the computational fluid dynamics (CFD) models of wind-driven pollutant dispersion within urban settings, a re-calibration study is conducted to optimize the standard k−ε model. A modified optimization framework based on the genetic algorithm is adapted to alleviate the computational expenses and to further identify ranges for each empirical coefficient to achieve the most reliable and accurate predictions. A robust objective function is defined, incorporating both the flow parameters and pollutant concentration through several linear and logarithmic measures. The coefficients are trained using high-quality and full-scale tracer experiments in a mock urban arrangement simulating a building array. The proposed ranges are 0.14≤Cμ≤0.15, 1.30≤Cε1≤1.46, 1.68≤Cε2≤1.80, 1.12≤σε≤1.20, and 0.87≤σk≤1.00. A thorough evaluation of the predicted flow and concentration fields indicates the modified closure is effective. The fraction of predictions within the acceptable ranges from measurements has increased by 8% for pollutant concentration and 27% for turbulence kinetic energy. The generality of the calibrated model is further tested by modeling additional cases with different meteorological conditions, in which the calculated validation metrics attest to the noteworthy improvements in predictions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".