MétaCan
Menu
Back to cohort
Record W4382059360 · doi:10.4050/f-0079-2023-18150

Full Scale Gear Tooth Bending Fatigue Tests Obtained Early in the Development of a Rotorcraft Transmission

2023· article· en· W4382059360 on OpenAlexaff
Yuriy Gmirya, Peter Palcic, Dave Binney, Wei Hu, Erin Carter

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsPinionSpiral bevel gearStructural engineeringNon-circular gearLanding gearTorqueEngineeringPolygon meshBevel gearAutomotive engineeringBendingComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

A new gear testing method is introduced to reduce development cost and time. It allows component-level testing of individual gear meshes and new gear designs. Instrumented gear sets are tested at full load while the rest of gearbox components are still being built. Since the fatigue strength of the gears is determined earlier in the development cycle, design deficiencies are identified and understood earlier. In this new method, an individual gear mesh installed in a stiff facility housing is used to mimic the contact pattern and bending stress demonstrated by the same mesh in an actual aircraft housing. Analytical gear models are used to identify the displacement difference between the stiff test facility and the aircraft housing. The test stand is designed so it can be adjusted accurately to provide gear and pinion positions that are representative of the deflected positions under load in the aircraft housing. A spiral bevel mesh and a split torque double helical reduction stage with multiple meshes are evaluated using the developed method. The contact pattern and strain survey results of the gear meshes are correlated with predicted results.

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 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 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 score0.337
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.239
Teacher spread0.221 · 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 teacher head, 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicGear and Bearing Dynamics AnalysisFrench-language works237,207