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Record W3172871564 · doi:10.1177/0954406221996408

Random vibration analysis of thin-walled elastic rings under multiple moving loads

2021· article· en· W3172871564 on OpenAlexaff
Jalal Taheri Kahnamouei, Jianming Yang

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · 2021
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsFinite element methodVibrationDiscretizationStructural engineeringParametric statisticsNewmark-beta methodRing (chemistry)Monte Carlo methodRandom vibrationBeam (structure)MathematicsEngineeringPhysicsMathematical analysisAcoustics

Abstract

fetched live from OpenAlex

This paper investigates the random vibration of a thin-walled ring subjected to multiple moving loads from the inside of the ring. A ring gear in a planetary gear train with three equally-spaced planets is taken as an example. The ring is discretized with the finite element method of curved beam elements. The supports of the ring gear are treated as three linear springs to mimic a general bolt connection. The stochastic Newmark algorithm is used to solve the equations and obtain the response's mean and variance. Monte Carlo simulations are also conducted to verify the results from the stochastic Newmark scheme. A parametric study is conducted to examine the effect of design parameters on the responses.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.386
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.008
GPT teacher head0.205
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

Citations5
Published2021
Admission routes1
Has abstractyes

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