The Safety of Advanced Air Mobility and The Effects of Wind in the Urban Canyon
Bibliographic record
Abstract
The safe integration of vertical takeoff and landing aircraft (VTOLs) into an urban setting requires careful consideration of the effects of the wind flows through the urban canyon, which are complex and cannot be predicted with the reliability level necessary to enable safe operations. The assessment of wind in the urban canyon is critical to the siting and design of vertiports and safe operations of VTOLs. Vertiport design guidance related to the effects of wind flows in the urban canyon is limited and draws from helipad design guidance. The guidance recommends that wind conditions be assessed across a vertiport, but the guidance does not suggest how the study should be conducted nor does it provide design thresholds for wind. RWDI, the National Research Council Canada and Transport Canada have joined forces to identify and study the wind characteristics that would be important for the aerodynamic performance of VTOL and how these characteristics would be influenced by buildings and structures in the urban environment. In the framework of this research effort, an experimental approach was developed, centered on several sites in Canadian cities, representing a range of urban densities and surrounding topographies, as wind conditions are highly dependent on the built environment, including building footprint, building features, combinations of buildings and the local climatology. The work involved a quantitative approach in which physical models of several urban sites were built, instrumented and tested in boundary layer wind tunnels. In the wind tunnel experiments, "red flag" wind conditions were identified and measured. Detailed data capturing these flow features were collected around roof tops of lower and taller buildings, representing prospective vertiport locations and along prospective flight paths. This paper provides the details of the wind tunnel testing conducted for one of the urban sites; a site with an existing helipad at a hospital and a low-rise parking garage. The wind tunnel measurements were compared to published helipad design thresholds and combined with long term meteorological conditions representative of the site to determine how often vertiport operations will be limited by unfavourable urban wind conditions on an annual basis and by season. The wind tunnel study provides a case study and demonstrates how to evaluate the safety of a vertiport now, based on limited availability of VTOL aerodynamic performance capabilities and into the future as more information becomes available on VTOL performance in turbulent winds.
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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 | 0.001 |
| 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".