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Record W4404370128 · doi:10.1115/pvp2024-122817

Assessing Bulge-Induced Damage in Coke Drums to Plan Structural Weld Overlay Repairs

2024· article· en· W4404370128 on OpenAlexaff
Egler Dubin Araque Vivas, Stephen Park

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMetal Alloys Wear and Properties
Canadian institutionsCanadians Living with HIV
Fundersnot available
KeywordsOverlayCokeComputer scienceWeldingPlan (archaeology)Forensic engineeringReliability engineeringEngineeringGeologyMaterials scienceMetallurgyOperating system

Abstract

fetched live from OpenAlex

Abstract Depending on the cycle time, design, fabrication, and operations, coke drum repairs could be required in as little as five years of operation. In the last fifteen years, use of Structural Weld Overlay (SWOL) as a repair strategy to extend the remaining life of coke drums has increased. Bulge-induced damage in the cylindrical and conical sections is one of the main reasons for SWOL repairs to be executed in vessels. The Local Plastic Strain Indicator, abbreviated as LPSI, is used as a methodology to show two cases of how bulge-induced damage evolves and at what point SWOL repairs should be applied in coke drums. LPSI identifies and ranks bulges prone to local failures using an estimation of plastic strain calibrated against bulges that developed internal or external bulge-induced damage. The LPSI is the ratio of the plastic strain found at the analyzed point to the calibrated strain limit found at points where through-wall cracks occurred due to bulging, in a percentage form. As the cases show, there have been many premature repairs executed because of the lack of assessment of surface damage and problems with damage have then developed at the edges of those weld overlay repairs. At the end, guidance about how strain levels and surface damage should be combined to better assess SWOL repairs areas is provided.

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 categoriesInsufficient payload (model declined to judge)
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 score0.043
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.054
GPT teacher head0.320
Teacher spread0.266 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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