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Record W2061284563 · doi:10.1109/carpi.2010.5624423

Wind turbine control using a gearless epicyclic transmission

2010· article· en· W2061284563 on OpenAlexaff
Xiao Qing, Vikram Chopra, S.H.H. Zargarbashi, Jorge Angeles

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsAngular velocityTurbineMechanism (biology)BacklashControl theory (sociology)Blade pitchAlternatorController (irrigation)StiffnessWind powerEngineeringAutomotive engineeringComputer sciencePhysicsMechanical engineeringStructural engineeringControl (management)Power (physics)Electrical engineeringArtificial intelligenceClassical mechanics

Abstract

fetched live from OpenAlex

A recurrent problem in energy production by means of wind turbines is how to keep a constant angular velocity at the shaft driving the alternator in the presence of a randomly varying turbine angular velocity. The latter is caused by the random nature of the wind velocity. Proposed in this paper is an innovative two-degree-of-freedom mechanism, with the morphology of a differential gear train, but without gears. The latter are replaced by cam-roller pairs, which offer many advantages over gears: low friction, low backlash and much higher stiffness. The mechanism, dubbed pitch-roll wrist, was originally developed as a robotic wrist for pitch-roll generation. It is shown in the paper that the mechanism can effectively control its pitch velocity in the presence of a randomly varying angular velocity, which emulates that produced by wind on the shaft of a wind turbine. The pitch velocity, in turn, emulates that of the shaft driving the alternator. The control algorithm, based on a PID controller, was tested on an experimental testbed. Results show an effective angular velocity control.

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)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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