Validation of urban airflow measurements through a combined field test and wind tunnel study
Bibliographic record
Abstract
A combined field test and wind-tunnel study was conducted to support the ongoing development of operator guidance and recommendations for Remotely Piloted Aircraft Systems (RPAS) operations in urban airflows. The goal of the overall study was to acquire urban-airflow data in a real environment in order to validate equivalent airflow characteristics from model-scale testing. The field test was designed to measure urban airflow characteristics using anemometers mounted on a small RPAS. The RPAS was flown along various flight paths in downtown Montréal in 2023. Following the field test, airflow measurements were taken at the equivalent spatial locations in a wind tunnel using a 1:300-scale model of the same test site. Data-processing routines for the RPAS airflow measurements included accounting for the body-motion of the vehicle and applying custom calibration equations for the RPAS-mounted sonic anemometers. Comparing the RPAS and wind-tunnel measurements required a common coordinate system, a digital model of the city, and identification of compatible reference conditions. The distribution of mean flow speed and turbulence intensity from the field test compared well with the wind-tunnel results, including the shape of the distribution and location of the maxima. Additionally, the variation in flow characteristics along a flight path, such as mean flow speed and turbulence intensity, compared favourably with wind-tunnel results acquired at the same relative locations. This work demonstrates the suitability of model-scale testing for studying urban flow fields.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".