Modeling of the Urban Gust Environment with Application to Autonomous Flight
Bibliographic record
Abstract
Desirable surveillance applications of unmanned aerial vehicles (UAV) around urban areas provides motivation for the investigation of turbulent wind generated by buildings which can potentially cause instability in aircraft flight. It has been shown that the urban canyons an d single buildings which form the basic components of the urban geometry have classifiable flows with signi ficant degrees of circulation and shear, a potential danger to light aircraft. A first generation methodology is p roposed by which wind data important to flight planning in an urban environment is selected, accessed and applied. It is assumed the urban environment can be represented as a combination of discrete single buildings and canyons each easily amenable to computational fluid dynamics (CFD). A set of single buildings an d canyons typical to the North American urban environment is selected to provide a set of wind data. A selection algorithm is proposed which will determine if the flow at a location in a given urban environment fits a member of the wind data set. Results from simulations for the Aerosonde UAV in a simple urban environment are presented and analyzed.
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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 | 0.000 | 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) | 0.001 | 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".