MétaCan
Menu
Back to cohort
Record W1963781090 · doi:10.2514/6.2005-2270

Smart Spring Control of Vibration and Noise in Helicopter Blades

2005· article· en· W1963781090 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venue46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference · 2005
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsCarleton University
FundersNational Research Council CanadaNational Technical University of Athens
KeywordsSpring (device)VibrationNoise (video)Vibration controlAcousticsComputer scienceEngineeringControl theory (sociology)Structural engineeringControl (management)PhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Helicopters are versatile tools for many applications but unfortunately create significant vibration and noise. Traditional passive and active approaches to suppress these vibrations are either effective only at specific frequencies or limited by electromechanical capabilities of smart actuators to overcome the severe aerodynamic conditions. The Smart Spring is a device that avoids these limitations by only altering the structural impedance properties of the blade root. It is desired to implement a numerical tool to analyze the effectiveness of the Smart Spring and to perform efficient parametric studies. In the paper, the Smart Spring is modeled in the SMARTROTOR aeroelastic/aeroacoustic helicopter code as part of the pitch link. It is verified that the Smart Spring is an effective tool for suppressing vibratory loads passing through the pitch link to the airframe. Copyright

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 categoriesMeta-epidemiology (narrow)
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 score0.815
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
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 head0.007
GPT teacher head0.218
Teacher spread0.210 · 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