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Record W4397007893 · doi:10.1139/tcsme-2023-0180

A vibration model of a flexible multiple shaft gear system with the tip relief modification

2024· article· en· W4397007893 on OpenAlexvenueno aff
Yajun Xu, Xinbin Li, Jing Liu, Ruikun Pang

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsVibrationStructural engineeringEngineeringMechanical engineeringComputer sciencePhysicsAcoustics

Abstract

fetched live from OpenAlex

The fixed-shaft gear system (FSGS) is one critical part in gear transmission systems (GTSs), whose vibrations can significantly influence performances of GTSs. To reduce the vibrations of rigid FSGS caused by installation and manufacture errors, the tooth profile modifications without the shaft deformation were widely introduced. This paper establishes a flexible multiple shaft gear system (FMSGS) model of an unmanned underwater vehicle, which can obtain vibrations of rotor at different axial positions. However, the previous rigid FSGS model cannot obtain those vibrations. In the FMSGS model, the Timoshenko beam elements are applied to the establishment of shaft segments considering flexibility. The meshing stiffness for helical gears with different ranges of tooth profile modification are quantitatively expressed by a gear slicing method. Based on the kinetic relationship between the gear pairs, shaft segment, and bearings, the dynamic equations of FMSGS are derived. By comparing the time-domain waveforms and spectra of FMSGS, the effects of the tooth profile modification amounts and lengths on the vibrations of FMSGS are revealed. The proposed FMSGS model is validated by an experiment. This study extends the dynamic methods of FSGSs, and some suggestions on the vibration control of FSGSs are given.

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: none
Teacher disagreement score0.983
Threshold uncertainty score0.442

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 head0.012
GPT teacher head0.189
Teacher spread0.177 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicGear and Bearing Dynamics AnalysisFrench-language works237,207