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Record W2552466332 · doi:10.1139/juvs-2014-0019

Design and manufacture of propellers for small unmanned aerial vehicles

2016· article· en· W2552466332 on OpenAlexafffundvenue
Brian Rutkay, Jeremy Laliberté

Bibliographic record

VenueJournal of Unmanned Vehicle Systems · 2016
Typearticle
Languageen
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsCarleton University
FundersMolson Foundation
KeywordsPropellerThrustWind tunnelAutomotive engineeringFuel efficiencyMarine engineeringEngineeringWind powerPower consumptionProcess (computing)Computer sciencePower (physics)Mechanical engineeringAerospace engineering

Abstract

fetched live from OpenAlex

The objective of this research was to develop a process for the design and manufacture of mission- and aircraft-specific propellers for small unmanned aerial vehicles. This objective was met by creating a computer program to design a propeller that meets user-defined aircraft performance requirements within the limitations of the electric motor, user-selected materials, and manufacturing methods. A comprehensive review of prior UAV propeller design and additive manufacturing for small propellers is also presented in this paper. The use of additive manufacturing (3D printing) in making flightworthy propellers was explored through material testing, manufacturing trials, and by testing the propellers under simulated flight conditions in a wind tunnel. It was found that the propeller performance generated nearly the predicted design thrust but the efficiency and power consumption could not be accurately measured with the present test setup. While flight testing was not completed at this time, ground and wind tunnel testing were sufficient to demonstrate the feasibility of producing flightworthy propellers using additive manufacturing.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.202
Teacher spread0.185 · 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
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

Citations14
Published2016
Admission routes3
Has abstractyes

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