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Record W2046725847 · doi:10.1115/imece2012-86621

Characteristic Aircraft Landing Gear Thermo-Tribo-Mechanical Model

2012· article· en· W2046725847 on OpenAlexafffund
Laurent Heirendt, Hugh H. T. Liu, Phillip Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsDow Chemical (Canada)University of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaSafran
KeywordsLanding gearRunwayPiston (optics)Shock absorberShock (circulatory)Work (physics)ThermalMechanical engineeringMechanicsThermal shockAerospace engineeringStructural engineeringComputer scienceEngineeringMaterials sciencePhysicsMeteorology

Abstract

fetched live from OpenAlex

A methodology for studying the characteristic thermal response of a landing gear (LG) shock absorber is presented. Rough runways induce high loads on the shock absorber bearings and because of high relative sliding speeds of the shock absorber piston, heat is dissipated which is known to have led to structural damage. In this work, an overall model has been developed that is used to outline the characteristics of the thermal behavior and identify the heat sources and sinks in the landing gear shock absorber. The developed thermo-tribo-mechanical model (TTM model) is subdivided into four parts, all using simplified but representative equations. Emphasis is placed on developing a methodological framework and studying the evolution of the average temperature in the TZI (thermal zone of interest) while taxiing and taking-off.

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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.030
GPT teacher head0.258
Teacher spread0.228 · 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

Citations3
Published2012
Admission routes2
Has abstractyes

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