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Record W2899198958 · doi:10.1115/detc2018-85559

Studying the Effect of Freeplay on Nose Landing Gear Shimmy Using a Fully Nonlinear Model

2018· article· en· W2899198958 on OpenAlexaff
Mohsen Rahmani, Kamran Behdinan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSpeed wobbleNonlinear systemLanding gearTorqueControl theory (sociology)Stability (learning theory)InstabilityComputer scienceEngineeringAerospace engineeringMechanicsPhysicsClassical mechanicsControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

Shimmy is a common instability of landing gear systems which has been known for a long time. Yet, it is often studied using simplified dynamic models in which the chief system nonlinearities are neglected. Particularly, the influence of worn components and loose joints manifesting itself as a freeplay nonlinearity has been only touched upon in few works. The present paper utilizes a fully nonlinear landing gear dynamic model to obtain nonlinear stability boundaries and to study the onset, severity, frequency jumps, and mode shifts of the system as a result of the torque link freeplay. Using stability maps in the parameter space and time histories of the oscillations the degrading effect of excessive clearance and wear in the torque links is demonstrated, which in turn offers insights for designing shimmy-free landing gears.

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)0.001
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.036
GPT teacher head0.300
Teacher spread0.264 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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