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Record W2047201894 · doi:10.5139/ijass.2011.12.2.93

Review of Active Rotor Control Research in Canada

2011· article· en· W2047201894 on OpenAlexafffundabout
Dániel Feszty, Fred Nitzsche

Bibliographic record

VenueInternational Journal of Aeronautical and Space Sciences · 2011
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaMitacsOntario Centres of Excellence
KeywordsEngineeringWind tunnelContext (archaeology)Control (management)Rotor (electric)TowerStiffnessSystems engineeringAerospace engineeringMechanical engineeringComputer scienceStructural engineeringGeography

Abstract

fetched live from OpenAlex

The current status of Canadian research on rotor-based actively controlled technologies for helicopters is reviewed in this paper. First, worldwide research in this field is overviewed to put Canadian research into context. Then, the unique hybrid control concept of Carleton University is described, along with its key element, the "stiffness control" concept. Next, the smart hybrid active rotor control system (SHARCS) projected's history and organization is presented, which aims to demonstrate the hybrid control concept in a wind tunnel test campaign. To support the activities of SHARCS, unique computational tools, novel experimental facilities and new know-how had to be developed in Canada, among them the state-of-the-art Carleton Whirl Tower facility or the ability to design and manufacture aeroelastically scaled helicopter rotors for wind tunnel testing. In the second half of the paper, details are provided on the current status of development on the three subsystems of SHARCS, i.e. that of the actively controlled tip, the actively controlled flap and the unique stiffness-control device, the active pitch link.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.574
Threshold uncertainty score0.847

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.003

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 head0.059
GPT teacher head0.307
Teacher spread0.248 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations13
Published2011
Admission routes3
Has abstractyes

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