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Record W7132974374

An Integrated Model For Thermal Analysis of an Aircraft Landing Gear Bogie Pivot Pin

2019· dissertation· W7132974374 on OpenAlexaff
Szu-Liang Wu

Bibliographic record

VenueTSpace · 2019
Typedissertation
Language
FieldEngineering
TopicMechanical Failure Analysis and Simulation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRunwayBogieFinite element methodOverheating (electricity)ThermalLanding gearHeat generationThermal conduction
DOInot available

Abstract

fetched live from OpenAlex

While operating on rough runways, commercial aircraft landing gear may experience overheating issues on the bogie pivot pin. This thesis is an integrated model that generates the landing gear dynamic behaviour and predicts the temperature of the pin. The integrated model consists of four individual components. Built in MATLAB Simulink, the dynamic model outputs the pin rotation angle and the dynamic loading with a given input runway profile and aircraft characteristics. The structural stress model is an ABAQUS finite element analysis model that determines the contact pressure distribution on the pin contact surface at any given dynamic loading. The dynamic variables and the pressure distribution are collected and used in the heat generation model to calculate the frictional heat generation and distribution. Finally, the heat conduction model in ABAQUS performs thermal analysis to obtain the operational temperature of the bogie pivot pin.

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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.340
Teacher spread0.310 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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