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Record W2145023518 · doi:10.1115/pvp2002-1078

Determination of Gasket Stress Levels During Thermal Transients

2002· article· en· W2145023518 on OpenAlexaff
Warren Brown, M. Derenne, Abdel‐Hakim Bouzid

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Structural Analysis Methods
Canadian institutionsÉcole de Technologie SupérieurePolytechnique Montréal
Fundersnot available
KeywordsGasketFlangeFinite element methodJoint (building)Bolted jointThermalMaterials scienceStructural engineeringStress (linguistics)Temperature measurementTransient (computer programming)Thermal shockInternal pressureLeakage (economics)Thermal analysisPressure vesselMechanical engineeringComputer scienceEngineeringComposite materialPhysicsThermodynamics

Abstract

fetched live from OpenAlex

The leakage of bolted flange joints at high temperature or during transient thermal shock is a well recognised problem. However, the present pressure vessel design codes do not address the effects of temperature on the integrity of the bolted joint, other than material properties. A research project currently being conducted at Ecole Polytechnique is intended to provide designers with an analytic approach for establishing the effects of thermal loading on the joint sealing ability. This paper is the fourth to be published as part of this research project. The presented analysis method enables the determination of the temperature response of the joint components to a transition in internal fluid temperature. Using this data, the worst case operating scenario may be selected and calculations performed to determine the impact of the temperature transition on the gasket stress levels. The presented analytical method is verified by comparison to finite element analysis and experimental measurement.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.232
Teacher spread0.215 · 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

Citations16
Published2002
Admission routes1
Has abstractyes

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