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Record W2990124006 · doi:10.1115/pvp2019-93661

Measuring the Effectiveness of Metal Weld Overlay Repairs Through Bulge Depth and Bulge Sharpness Analysis

2019· article· en· W2990124006 on OpenAlexaff
Egler D. Araque, Darren Love, Stephen Park, Daryl Rutt, Rick Clark

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsCanadian Food Inspection Agency
Fundersnot available
KeywordsOverlayBulgeWeldingDistortion (music)Materials scienceStructural engineeringGeologyComputer scienceComposite materialEngineeringOptoelectronics

Abstract

fetched live from OpenAlex

Abstract Weld overlay is a repair method that has been used over the last 10 years to limit the growth of bulges and to extend the remaining life of delayed coking drums. Different refinery operators have used varied approaches, ranging from localized patches on specific regions of concern, to bands along circumferential welds, to large sections of structural repair that completely cover a bulged area. The authors have observed the evolution of regions repaired with internal weld overlay on 18 drums over periods of 5 and 8 years. A comparison of bulge sharpness and bulge depth on a year-over-year basis is presented to measure the effectiveness of metal weld overlay and how the overlay impacts continued distortion of the vessel. Furthermore, factors such as the taper ratio in the transition zones, and the distance between the peak of a bulged area and the edges of the weld overlay, are presented as key parameters that affect the likelihood of cracks developing along the transition zones at the upper and lower edges of the repair.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.216
Teacher spread0.201 · 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 designObservational
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

Citations1
Published2019
Admission routes1
Has abstractyes

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