Airflow in the urban environment: RPAS use-case study
Bibliographic record
Abstract
The objectives of the 4th year of the Remotely Piloted Aircraft Systems Operator Guidance and Safety Assurance Tools for the Urban Environment project were: • to validate urban airflow characteristics identified during previous years in a real urban use-case; and • to extend the findings that have been provided to date for development of regulations for urban RPAS operations. The use-case consisted of four days of flights in downtown Montréal to measure urban airflow using a small RPAS (sRPAS). For prevailing south-westerly winds, the location of the use-case buildings and flight-paths were downstream of many high-rise buildings, which made the site a good choice for the urban airflow measurements. The sRPAS was instrumented with an airflow measurement system. The use-case provided airflow and flight log data useful for evaluating urban airflow characteristics and effects on sRPAS stability. During the use-case flights, fixed rooftop anemometer stations along the flight path were used to measure urban airflow at altitudes within close proximity to downtown Montréal rooftops. A model-scale wind tunnel test of the Montréal site was also conducted to provide more resolution on the characteristics of urban flows at the use-case site and to provide validation data. Key findings from the use-case and model-scale wind tunnel simulations included: • High wind speeds and turbulence intensities of up to 27% were found in the urban flow field of Montréal. The magnitude and relative locations of the maximum turbulence intensities in Montréal are consistent with observations from simulations of other Canadian cities. This adds to the body of knowledge that has shown that building-tops and building-corners at heights where sRPAS may fly can present regions of high turbulence intensity and high wind speeds. • The sRPAS use-case results have shown that the sub-scale simulation of the Montréal site flow field accurately modelled the flow characteristics along the flight-paths for steady wind conditions. • The application of the Urban Instability Avoidance Method was applied to the use-case data from the current project. The method predicted a more restrictive reference wind speed from a local upwind weather station hourly report compared to the manufacturer’s specified sustained wind speed tolerance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".