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Record W4295953390 · doi:10.4050/f-0078-2022-0043

Housing and Bearing Deformation Interaction of Large Size Bearings

2022· article· en· W4295953390 on OpenAlexaff
Biqiang Xu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsBearing (navigation)Finite element methodStiffnessStructural engineeringDeflection (physics)Contact mechanicsEngineeringThrust bearingContact theoryMechanical engineeringComputer sciencePhysicsThrust

Abstract

fetched live from OpenAlex

The traditional studies [1, 3] of bearing deflection and load cannot consider the interaction of bearing deflections at radial, axial and tilting directions. Swashplate bearings, rotor bearings, and etc. are usually of large size and mounted on flexible bearing housing, it makes the housing and bearing deform in all directions. The interaction between housing and bearing races is no longer negligible. On the other hand, modeling contact status between rolling elements and bearing race way in the housing-bearing assembly finite element model is too complicated and not practical for engineering analyses. In this study, multi-scale finite element model is developed for the system with rolling element bearings to investigate the interaction of housing and bearing race deformations. Rolling elements are simplified as spring element to make the system level finite element model executable. Hertzian contact theory is employed to determine roller compression non-linear stiffness. Clearance and preload can be considered in the stiffness curve. The result provides correct stiffness and load distribution for housing design. FEA submodel is used for local detailed roller contact analysis. It provides detailed bearing contact analysis of interested individual roller for detailed bearing design.

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 score0.155
Threshold uncertainty score0.191

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.202
Teacher spread0.197 · 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
Published2022
Admission routes1
Has abstractyes

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