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Record W2102351229 · doi:10.1139/juvs-2014-0024

Modeling and flight testing of wing shaping for roll control of an unmanned aerial vehicle

2015· article· en· W2102351229 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Unmanned Vehicle Systems · 2015
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsnot available
Fundersnot available
KeywordsAileronWaypointAirframeAutopilotWingTestbedFlight testAerospace engineeringFlight control surfacesEngineeringFlight simulatorAerodynamicsAeronauticsComputer scienceSimulationControl theory (sociology)Control (management)Artificial intelligence

Abstract

fetched live from OpenAlex

In this paper, an approach is described to implement autonomous (waypoint tracking) flight in a testbed airframe, which uses wing twist for roll control. These flights were performed using an existing commercial autopilot. Aileron effectiveness was identified as a parameter that could be modified to maintain roll control during autonomous flight. A modeling process was then developed to calculate the aileron effectiveness for a wing shaping demonstrator aircraft utilizing numerically determined aerodynamic properties. Simulations and flight tests with the testbed aircraft were performed that demonstrated suitability of the approach for autonomous flight. In-flight aileron doublets were used to validate the aileron effectiveness predicted by the numerical model, which matched within 7%.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.038
GPT teacher head0.246
Teacher spread0.208 · 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