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Record W2806955162 · doi:10.1139/tcsme-2017-0128

Analysis of dynamic characteristics of power split spiral bevel gear transmission system based on teeth geometric contact analysis

2018· article· en· W2806955162 on OpenAlexvenueno aff
Hao Dong, Zhiyu Liu, Xiaolong Zhao, Yahui Hu

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSpiral bevel gearBevel gearVibrationAccelerationStiffnessContact analysisDynamic load testingStructural engineeringControl theory (sociology)Power (physics)Spiral (railway)EngineeringMechanicsFinite element methodAcousticsPhysicsComputer scienceMechanical engineeringClassical mechanics

Abstract

fetched live from OpenAlex

To solve the dynamic load distribution mechanism of the power split transmission system of spiral bevel gears, according to the characteristics of the closed loop of power flow, the deformation coordination condition is deduced. Through the gear teeth geometric contact analysis technique, the time-varying meshing stiffness conditions in the model are solved. The linear time-varying dynamic model of the torsional vibration of the bevel gear split transmission system is established by the lumped mass parameter method. Considering the influence of time-varying mesh stiffness excitation conditions and damping, the dynamic differential equations are treated in a dimensionless way. The dynamic load change and dynamic response characteristics of the system are obtained by numerical solution, and the influence of parameters such as speed and damping on dynamic power flow and dynamic characteristics of the system is revealed. The results show that with an increase of meshing damping ratio, dynamic power flow of each gear pair changes little, and the vibration acceleration and its root mean square value of each gear pair of the system are smaller. With an increase in speed, vibration acceleration and its average amplitude increase.

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 score0.796
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.004
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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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
Published2018
Admission routes1
Has abstractyes

Explore more

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