MétaCan
Menu
Back to cohort
Record W2895979630 · doi:10.1115/1.4041691

Evaluation of Interfacial and Permeation Leaks in Gaskets and Compression Packing

2018· article· en· W2895979630 on OpenAlexaff
Ali Salah Omar Aweimer, Abdel‐Hakim Bouzid

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2018
Typearticle
Languageen
FieldEngineering
TopicEngineering Structural Analysis Methods
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsGasketLeakFlangeMaterials scienceLeakage (economics)PermeationComposite materialForensic engineeringEngineeringChemistry

Abstract

fetched live from OpenAlex

The quantities of leak rate through sealing systems are subjected to strict regulations because of the global concern on radiative materials. The maximum tolerated leak is becoming a design criterion in pressure vessel design codes, and the leak rate for an application under specific conditions is required to be estimated with reasonable accuracy. In this respect, experimental and theoretical studies are conducted to characterize gasket and packing materials to predict leakage. The amount of the total leak is the summation of the permeation leak through the sealing material and the interfacial leak generated between the sealing element and its mating surfaces. Unfortunately, existing models used to predict leakage do not separate these two types of leaks. This paper deals with a study based on experimental testing that quantifies the amount of these two types of leaks in bolted gasketed joints and packed stuffing boxes. It shows the contribution of interfacial leak for low and high contact surface stresses and the influence of the surface finish of 0.8 and 6.3 μm (32 and 250 μin) resulting from phonographic grooves in the case of a bolted flange joint. The results indicate that most leakage is interfacial, reaching 99% at the low stress while interfacial leak is of the same order of magnitude of permeation leak at high stresses reaching 10−6 and 10−8 mg/s in both packing and gaskets, respectively. Finally, particular focus is put on the technique of precompression to improve material sealing tightness.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.292
Teacher spread0.276 · 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 designBench or experimental
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

Citations11
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Nuclear Engineering and Radiation ScienceSame topicEngineering Structural Analysis MethodsFrench-language works237,207