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Record W2063547655 · doi:10.1115/1.3118787

Short Term Relaxation Modeling of Valve Stem Packings

2009· article· en· W2063547655 on OpenAlexaff
Mohammed Diany, Abdel‐Hakim Bouzid

Bibliographic record

VenueJournal of Tribology · 2009
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsViscoelasticityCreepStiffnessMaterials scienceRotational symmetryRelaxation (psychology)Compression (physics)MechanicsFinite element methodStructural engineeringMechanical engineeringEngineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

The long term tightness performance of stuffing-box packings, used in valves, is conditioned by the capacity of its sealing material to maintain a contact pressure to a predetermined minimal threshold value. Due to the creep, this contact pressure decreases with time depending on the creep properties and the stiffness of the housing. Assessing relaxation is a key parameter in determining the tightness performance of a valve stem packing over time. An analytical model based on the packing viscoelastic behavior is developed to assess the contact pressures between the packing material and the stem and the housing and their variation with time. In parallel, an axisymmetric 2D finite element model was build to validate and support the analytical model. The valve stem packing relaxation performance is an important design parameter to consider when selecting compression packings.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.025
GPT teacher head0.250
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2009
Admission routes1
Has abstractyes

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