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Record W3043617427 · doi:10.1108/ilt-12-2019-0537

Numerical model of two-phase mechanical face seal with shallow grooves based on finite volume method

2020· article· en· W3043617427 on OpenAlexaff
Wenbin Gao, Weifeng Huang, Tao Wang, Ying Liu, Zhihao Wang, Yuming Wang

Bibliographic record

VenueIndustrial Lubrication and Tribology · 2020
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSeal (emblem)Groove (engineering)CavitationMechanical engineeringPhase (matter)BoilingFinite volume methodFlow (mathematics)Materials scienceVolume (thermodynamics)MechanicsEngineeringStructural engineeringPhysics

Abstract

fetched live from OpenAlex

Purpose By modeling and analyzing the two-phase mechanical seal of the fan-shaped groove end face, which is prone to phase change, an effective method to study the flow field of the mechanical seal when both cavitation and boiling exist simultaneously is found. Design/methodology/approach Based on the finite volume method, a fluid model was developed to investigate a two-phase mechanical seal. The validity of the proposed model was verified by comparing with some classical models. Findings By modeling and analyzing the two-phase mechanical seal of the fan-shaped groove end face, which is prone to phase change, the analysis of the gap flow field of the mechanical seal was realized when cavitation and boiling existed simultaneously. Originality/value Based on the model proposed for different conditions, the pressure and phase states in the shallow groove sealing gap were compared. The phase change rate between the mechanical seal faces was also investigated. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2019-0537/

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 score0.969
Threshold uncertainty score0.748

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 head0.057
GPT teacher head0.283
Teacher spread0.226 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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