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Record W2095820480 · doi:10.1115/pvp2003-1876

On the Use of Dual Kriging Interpolation for the Evaluation of the Gasket Stress Distribution in Bolted Joints

2003· article· en· W2095820480 on OpenAlexaff
Abdel‐Hakim Bouzid, Henri Champliaud

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Structural Analysis Methods
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsGasketFlangeStructural engineeringFinite element methodEngineeringKrigingMaterials scienceMechanical engineeringComputer science

Abstract

fetched live from OpenAlex

The leakage behavior of bolted joint is very much dictated by the gasket contact stress. In particular, the non-uniform distribution of this stress in the radial direction caused by the flange rotational flexibility has a major influence on the leak tightness of some gasket types. The current ASME flange design rules and the new ASME proposed design rules addresses this effect by introducing the concept of gasket effective width for which the validity of the suggested values has not been verified. This paper presents a simple comprehensive analytical approach based on the dual kriging interpolation technique to predict the gasket contact stress distribution in floating type bolted joints. The kriging methodology is shown to be very efficient when nonlinear modeling such as gasket material mechanical behavior is involved. Together with the flange rotational flexibility, this technique implemented in the “SuperFlange” program is supported and validated by numerical FEA conducted on different flange sizes and gasket materials combinations.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

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

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 head0.077
GPT teacher head0.311
Teacher spread0.234 · 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

Citations5
Published2003
Admission routes1
Has abstractyes

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