MétaCan
Menu
Back to cohort
Record W4402680709 · doi:10.4050/f-0080-2024-1137

Wind Tunnel Testing of RPAS in Representative Urban Flow Fields

2024· article· en· W4402680709 on OpenAlexaffabout

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsCarleton UniversityNational Research Council Canada
Fundersnot available
KeywordsWind tunnelEnvironmental scienceFlow (mathematics)Marine engineeringComputer scienceEngineeringAerospace engineeringMechanicsPhysics

Abstract

fetched live from OpenAlex

This paper describes wind tunnel testing of small remotely piloted aircraft systems (RPAS) to understand better the maximum wind speeds in which they can be safely operated. Urban flow fields can contain complex flow structures such as speed changes, direction changes, shear layers, turbulence and vorticity; all of these can impact the safety of urban RPAS operations. The work described in this paper is part of an ongoing effort to provide Canadian regulators with knowledge to guide safe RPAS operations in urban environments. In the wind tunnel, flow fields representative of urban flows were created using simple flow manipulators like bluff bodies and vanes. The flow manipulators and the resulting flow fields, in relation to representative urban flows, are described in this paper. Wind tunnel testing of a number of RPAS in these representative airflows was conducted to evaluate the sustained wind speed limit at which the vehicle could maintain a stable hover. These tests enabled a step in the understanding of the wind speed limit for various RPAS in different flows. The paper shows a clear impact of turbulence level on the maximum safe operating wind speed of RPAS.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.226

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.257
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

Explore more

Same topicVehicle emissions and performanceFrench-language works237,207