MétaCan
Menu
Back to cohort
Record W2029131283 · doi:10.1115/1.4001920

A New Approach to Model Bolted Flange Joints With Full Face Gaskets

2011· article· en· W2029131283 on OpenAlexaff
Abdel‐Hakim Bouzid, Hichem Galai

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2011
Typearticle
Languageen
FieldEngineering
TopicEngineering Structural Analysis Methods
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsGasketFlangeStructural engineeringFinite element methodFlexibility (engineering)Bolted jointEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

The poor leakage performance of flanges with full face gaskets is attributed to the low reliability of the existing design methods, and in particular, their lack of assessing accurately the bolt and gasket load changes. The prediction of tightness of bolted joints relies very much on the level of precision of the gasket contact stress during operation. The accurate evaluation of this stress requires a flexibility analysis of the joint that includes the flange, gasket, and bolts, and the interaction between them. This paper analyzes the distribution of gasket stress and the load change in bolted joints with full face gaskets. It proposes a simple analytical approach capable of predicting flange rotation and bolt load change during operation. The method is based on the gasket-bolt-flange elastic interaction, including flange rotational flexibility. The proposed model is supported by comparison with numerical finite element analysis of different size flanges.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.022
GPT teacher head0.232
Teacher spread0.210 · 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

Citations24
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Pressure Vessel TechnologySame topicEngineering Structural Analysis MethodsFrench-language works237,207