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Record W2945197178 · doi:10.1139/tcsme-2018-0134

Research on dynamic characteristics of gas film of spherical hybrid gas bearings based on computational fluid dynamics

2019· article· en· W2945197178 on OpenAlexvenueno aff
Chenhui Jia, Zhiwu Cui, Shijun Guo, Wensuo Ma

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsComputational fluid dynamicsMechanicsEccentricity (behavior)TurbulenceMaterials scienceBearing (navigation)Coupling (piping)Flow (mathematics)Reynolds equationForce dynamicsFluid bearingRotational speedDynamic simulationMechanical engineeringSimulationReynolds numberComputer sciencePhysicsEngineeringComposite materialLubrication

Abstract

fetched live from OpenAlex

A realizable k–ε turbulence model for spherical spiral groove hybrid gas bearing films was established based on computational fluid dynamics (CFD). A six degrees of freedom passive grid was used to calculate the gas film pressure distribution, bearing capacity, and dynamic characteristic coefficients numerically. The gas flow field dynamic and static pressure coupling mechanism was studied. The effects of the rotation speed, gas film thickness eccentricity ratio, and gas supply pressure on the dynamic and static pressure bearing capacity, and dynamic characteristic coefficients during operation were analyzed as a method of research into the mechanical mechanisms of gas bearing stability. The CFD calculation analysis can simulate the complex gas flow in the transient flow field of the gas film and determine reasonable operation parameters to optimize the dynamic and static pressure coupling effects, which can improve the gas film bearing capacity, dynamic characteristics, and operational stability of gas bearings.

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 score0.724
Threshold uncertainty score0.638

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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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
Published2019
Admission routes1
Has abstractyes

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