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Record W4383502151 · doi:10.4050/f-0079-2023-17968

Experimental Demonstration of the Lifting Capability of a Towed Payload Using Multiple Fixed-wing UAVs

2023· article· en· W4383502151 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Control Systems
Canadian institutionsSafran Electronics (Canada)Université de Sherbrooke
Fundersnot available
KeywordsPayload (computing)Fixed wingAerospace engineeringLift (data mining)Flight testRotor (electric)Automotive engineeringWingEngineeringComputer scienceSimulationMarine engineeringElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents an experimental demonstration of the Giant Rotor System (GRS), a heavy-lifting aircraft concept based on the Electric Power Reconfigurable Rotor (EPR²) concept and subsequent studies. The GRS is the first system to successfully demonstrate, under real outdoor flight conditions, the lifting of a payload with two off-the-shelf tethered fixed-wing unmanned aerial vehicles (UAVs). The non-optimized system demonstrated hover flight and slow vertical lifting (less than 1 m/s) capabilities while lifting a 20 kg payload with two 3.2 kg UAVs and a total of 2.1 kW. The lifting efficiency achieved by the GRS is approximately 4 times better than that of any conventional rotorcraft or heavy-lift VTOL system. The results of this study are promising and bring us closer to the reality of using available commercial airplanes for vertical lifting applications. The experimental setup, the control scheme, the flight test results, and a comparison of the GRS performance to that of conventional rotorcraft are described in this paper.

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 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 score0.413
Threshold uncertainty score0.306

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 head0.015
GPT teacher head0.215
Teacher spread0.200 · 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