Validation of rooftop wind measurements in the urban environment: Comparison between wind tunnel results and field data
Bibliographic record
Abstract
Urban airflow characteristics from a full-scale field test were compared to those from a model-scale wind tunnel test to validate the use of model-scale data in the development of urban airflows guidance and recommendations. Rooftop-mounted anemometer measurements were acquired during a field study conducted in Montréal, Canada in 2023, and wind-tunnel data were acquired at the same relative rooftop-anemometer locations using a 1:300 scale model. The comparison between the field study and wind-tunnel test was enabled by identifying a compatible set of reference conditions. The airflow properties, including the mean wind speed, turbulence intensity, and flow angularity, had better agreement between field data and wind-tunnel data for the buildings that were in the core of the urban environment, where building wakes are the dominant flow feature. Flow speed and turbulence intensity were often higher in the field than in the wind tunnel, although the general trends in these parameters were predicted adequately overall. The use of airport-weather station data as the reference conditions in the field was shown to be a practical approach in the absence of a local reference in the city. A comparison of the velocity spectra between the field test and the wind-tunnel test showed good agreement over the range of full-scale frequencies that are related to typical building-widths and to the size of future urban air mobility vehicles.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".