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Record W4240779069 · doi:10.1115/1.4023425

Prediction of Leak Rates Through Porous Gaskets at High Temperature

2013· article· en· W4240779069 on OpenAlexaff
Lotfi Grine, Abdel‐Hakim Bouzid

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2013
Typearticle
Languageen
FieldMathematics
TopicGas Dynamics and Kinetic Theory
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsGasketLeakLeakage (economics)Materials sciencePorous mediumPorosityMechanicsMass flow rateComposite materialVoid (composites)Permeability (electromagnetism)ThermodynamicsChemistryMembrane

Abstract

fetched live from OpenAlex

The ability of a gasket to maintain tightness under different operating conditions has been studied extensively in recent years. However, most of the research studies conducted on leakage predictions was performed at room temperature. The aim of this work is to predict leakage through gaskets taking into account the effect of the temperature on the fluid properties and gasket internal structural characteristics. The analytical model of slip flow regime to evaluate the mass leak rates through a porous gasket developed by Grine and Bouzid (2011, “Correlation of Gaseous Mass Leak Rates Through Micro and Nano-Porous Gaskets,” ASME J. Pressure Vessel Technol.) was used in this study. The results from the model were validated and compared with the experimental data obtained from tests conducted on the Universal Gasket Rig with two different gases (helium and nitrogen). The leak rates measured are in the range of 1 to 0.0001 mg/s, which are measurable using the pressure rise technique. As a second objective, the influence of the gasket displacements caused by stress and temperature on the flow leakage was studied. A relationship between displacement or void thickness and leakage is clearly demonstrated. The slip flow regime model is capable of predicting leakage at temperature with reasonable accuracy.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.250
Teacher spread0.236 · 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 designTheoretical or conceptual
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

Citations9
Published2013
Admission routes1
Has abstractyes

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