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Record W6966862082 · doi:10.4224/40003498

Ongoing test method development and characterization of RPAS vehicle response for complex airflow applications

2025· report· en· W6966862082 on OpenAlexaffvenueabout

Bibliographic record

VenueNPARC · 2025
Typereport
Languageen
Field
Topic
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsWind tunnelAirflowFlow (mathematics)Key (lock)TurbulenceProcess (computing)Clear-air turbulenceLimit (mathematics)

Abstract

fetched live from OpenAlex

The National Research Council Canada (NRC) is engaged in an ongoing effort to support government partners Transport Canada (TC) and Defence Research and Development Canada (DRDC) in the safe operation and regulation of Advanced Air Mobility (AAM) vehicles in the complex flows associated with urban and shipboard environments. The goals of the project were achieved: by evaluating the stability of a group of Small Remotely Piloted Aircraft Systems (sRPAS) in airflow conditions similar to the flow direction and turbulence in real Canadian cities using Complex Flow Testing (CFT); and by demonstrating the process of developing an engineered Flying Qualities (FQ) test designed to challenge the performance limits of selected sRPAS as if they were in complex flows use of a wind tunnel facility. The project demonstrated new testing capabilities, including new equipment for characterizing complex flows, new equipment for measuring vehicle performance, and new flow control devices designed to create representative flow fields for testing purposes. The key findings from the sRPAS CFT include: • Local normalized turbulence intensities of 0.15 and flow direction pitch-angles of -25◦ caused most of the vehicles to become unstable, or have degraded controllability, below the manufacturer’s sustained wind speed tolerance; and • Downdraft of between -25◦ and -45◦ reduced the wind speed limit for the tested sRPAS by as much as a local turbulence intensity of between 0.15 and 0.40, respectively. Key findings from the FQ testing demonstration include: • The Maximum Vertical Speed (MVS) test, designed to provide the maximum available vehicle RPM, was successful only for one of the vehicles tested, highlighting the importance of understanding the control system on vehicle characterization; and • The Mission Task Element (MTE) was able to challenge the vehicle performance including maximum RPM, roll angles, vertical acceleration, position maintenance, and Handling Qualities Rating (HQR), all of which were comparable between the Mission Task Element (MTE) and the Complex Flow Testing (CFT). The MTE and the CFT revealed multiple influences and relationships between vehicle performance and stability; however, no single performance parameter explained the wind speed limit results. This highlights that a more complex understanding of the vehicle systems and aerodynamics are required to be able to predict and test for safe conditions for all types of vehicles. As a result, the demonstration MTE did not allow the identification of vehicle performance limit. However, this test has allowed the identification of clear avenues for future study and additional considerations to modify the MTE design in the future.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.070
GPT teacher head0.362
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations0
Published2025
Admission routes3
Has abstractyes

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