Bibliographic record
Abstract
The development of turbulence criteria to provide early guidance for the design of vertiports is presented in this paper. For any aircraft, winds, in particular crosswinds and gusty winds, are top of mind for all pilots engaging in take-off and landing maneuvers. It is anticipated that the same will be true for VTOL and eVTOLs landing on vertiports, in particular as new vertiports are built closer and closer to urban centres. First, a review of the current design criteria for vertiports around the world related to wind is presented, highlighting the commonality between the guidance and the gaps in their content. Second, the controllability criteria that VTOL and eVTOLs will likely need to meet in the pursuit of an airworthiness certification are reviewed and their pertinence with regards to vertiport design are discussed. Third, the characters of the wind and their impact on eVTOL flights at or near take-off and landing infrastructure is explored. Finally, a set of turbulence criteria for vertiports and a turbulence index are proposed. The index includes a scale for conditions ranging from favorable for take-off and landing; to more and more demanding conditions; up to turbulence conditions to be firmly avoided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".