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Record W2969807029 · doi:10.1177/1350650119870965

Investigation on backflow phenomenon in the aerostatic journal bearing

2019· article· en· W2969807029 on OpenAlexaff
Xinglong Chen, Gang Bao, James K. Mills

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology · 2019
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBackflowEccentricity (behavior)Dimensionless quantityMechanicsBearing (navigation)Reynolds equationWork (physics)Reynolds numberMaterials sciencePhysicsThermodynamicsEngineeringMechanical engineeringLawTurbulence

Abstract

fetched live from OpenAlex

In this work, the backflow phenomenon exhibited in the gas journal bearings is investigated. The effect of operational parameters such as eccentricity ratio ɛ, axial velocity of the bearing v z , misalignment angles [Formula: see text] and [Formula: see text] on the backflow behavior, and load force and axial friction force of the bearing are studied numerically. The differential transformation method and finite difference method are utilized to solve the dimensionless Reynolds equations required in the analysis. The performance parameters such as pressure distribution, gas flow rate, load force, and axial friction force are examined in the numerical simulations. The investigation reveals that when the bearing is operated in normal working zone, the load force F increases significantly with an increase in eccentricity ɛ; however, F may only increase slightly when the bearing operates in the backflow zone. It is suggested that the pressure P s should be set greater than 5.0, P c smaller than or equal to 3.0 to avoid the backflow behavior. The axial friction force [Formula: see text] is largely determined by the pressure [Formula: see text], and increases as pressure P c increases.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.199
Teacher spread0.183 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part J Journal of Engineering TribologySame topicTribology and Lubrication EngineeringFrench-language works237,207