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Record W4200499229 · doi:10.32920/16811278

Early On-Set Prediction Of Vortex-Ring State Of Quadrotors

2021· preprint· en· W4200499229 on OpenAlexaff
Joel A. McQuaid

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsVortexThrustVortex ringDownwashAerospace engineeringWakeDescent (aeronautics)AerodynamicsMechanicsControl theory (sociology)Wake turbulenceRing (chemistry)Rotor (electric)PhysicsVortex sheddingEngineeringComputer scienceMechanical engineeringReynolds number

Abstract

fetched live from OpenAlex

The OpenFOAM CFD package was used to initially investigate the aerodynamic effects of vortex-ring state of a quadrotor, then to study various quadrotor flight maneuvers to minimize the thrust loses of vortex-ring state and followed by possible detection methods for a drone entering vortex-ring state. Vortex-ring state is characteristic of a decrease in the effective angle of attack of incoming airflow due to a rotor descending into its downwash. This causes significant loses in the thrust of the rotor, which typically leads to severe flight upsets for rotorcraft. A quadrotor was studied at varying descent velocities to investigate wake roll-up at the rotor tips and the subsequent effects on rotor thrust and power. The quadrotor was then subjected to non-vertical descent angles to investigate thrust loss mitigation approaches due to vortex-ring state. A method of detecting the on-set of vortex-ring state is proposed using various differential pressure measurements on the quadrotor. It has been shown that by monitoring the pressure difference between the top of the quadrotor body and the bottom of one of the quadrotor legs, a pressure drop can be seen just prior to the on-set of vortex-ring state. This pressure drop was shown to occur during descending flight regimes and may prove to be an effective pre-vortex-ring state warning system.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.589

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 head0.012
GPT teacher head0.213
Teacher spread0.201 · 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 designBench or experimental
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

Citations0
Published2021
Admission routes1
Has abstractyes

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