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Record W2020600019 · doi:10.2514/1.27914

Smart Spring Control of Vibration on Helicopter Rotor Blades

2009· article· en· W2020600019 on OpenAlexafffund
Gregory Oxley, Fred Nitzsche, Dániel Feszty

Bibliographic record

VenueJournal of Aircraft · 2009
Typearticle
Languageen
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsCarleton University
FundersNational Research Council CanadaNational Technical University of Athens
KeywordsSpring (device)Structural engineeringDisplacement (psychology)StiffnessCoil springVibrationSmart materialActuatorControl theory (sociology)Moment of inertiaEngineeringMaterials sciencePhysicsAcousticsComputer scienceElectrical engineeringComposite materialClassical mechanics

Abstract

fetched live from OpenAlex

c1, c2 = Smart Spring viscous damping coefficients associated with primary and secondary load paths F = external (input) force applied to the Smart Spring k = effective dynamic stiffness of the Smart Spring k1, k2 = Smart Spring constants associated with primary and secondary load paths m = effective inertia of the Smart Spring m1, m2 = mass of external and internal sleeves in the Smart Spring N = contact force applied by piezoelectric stack T = Smart Spring period of actuation t = time x = displacement (output) yielded by the Smart Spring y = displacement associated with the Smart Spring secondary load path = dynamic stiffness complex coefficients = dry friction coefficient = Smart Spring control frequency, 2 =T ! = Smart Spring frequency of excitation

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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

Citations29
Published2009
Admission routes2
Has abstractyes

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