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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 OpenAlexaff
Waqas Khan, Meyer Nahon

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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

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 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

Citations80
Published2013
Admission routes1
Has abstractyes

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