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Record W4389541107 · doi:10.17118/11143/20862

A coupled pressure-based computional method for caviation in steady-state– a finite element modeling strategy applied to journal bearing

2023· article· en· W4389541107 on OpenAlexaff
Charles Aboussafy, Raynald Guilbault, Noël Brunetière

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsFinite element methodBearing (navigation)Steady state (chemistry)Computer scienceEngineeringStructural engineeringArtificial intelligenceChemistry

Abstract

fetched live from OpenAlex

Abstract: This study presents a cavitation model applied to the Navier-Stokes equations in a steady state condition. The development of the proposed model targets the study of systems with textured surfaces, where the Reynolds equation cannot be applied to produce accurate evaluations. Indeed, the Reynolds equation is effective in restricted ranges of sliding speeds and for limited ratios of the texture dimensions. The developed approach eliminates these limitations. Instead of the finite volume method common in CFD, the proposed model is based on a finite element discretization. The cavitation phase is modelled by a barometric formulation. Using a pseudo-compressibility model, it is possible to simulate the compressibility according to the bulk modulus of the fluid in all phases. Compared to models using Rayleigh–Plesset equation, the advantage of the proposed method is that no parameter must be correlated experimentally. The obtained preliminary results agree with both experimental and numerical evaluations extracted from reference papers. In particular, the predicted cavitation areas are in perfect agreement with the reference numerical results presented for journal 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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.915
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.029
GPT teacher head0.280
Teacher spread0.250 · 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
GenreMethods

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
Published2023
Admission routes1
Has abstractyes

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