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Record W2065767335 · doi:10.1109/tmech.2013.2264105

Toward an Accurate Physics-Based UAV Thruster Model

2013· article· en· W2065767335 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE/ASME Transactions on Mechatronics · 2013
Typearticle
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsMcGill University
Fundersnot available
KeywordsThrustPropellerAerodynamicsAerospace engineeringPropulsionTorqueControl theory (sociology)Simple (philosophy)Fixed wingComputer sciencePhysicsWingMarine engineeringEngineeringControl (management)

Abstract

fetched live from OpenAlex

Small unmanned aerial vehicles (UAVs) come in many types, the most common being fixed-wing and rotorcraft. Most of these are powered by brushless dc motors driving fixed-pitch propellers. Since the thrusters are typically quite powerful, relative to the weight of the aircraft, the motion of these UAVs is usually dominated by the thruster dynamics. It therefore becomes particularly important to have a good model of the thruster, which can be assembled based on simple measurements of the system properties, rather than from exhaustive testing. This paper presents such a model. The governing equations are assembled by considering, in succession, the motor electrodynamics and the propeller aerodynamics. The results of the model are compared to experimental test results for a particular thruster assembly. Agreement between the two is excellent-with an error of 4.7% in thrust and 7.6% in torque under static conditions-thereby demonstrating the validity of the proposed approach.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.909
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.060
GPT teacher head0.280
Teacher spread0.220 · 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