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Record W4389139943 · doi:10.1115/pvp2023-106173

Reliability and Life Assessment of Coke Drums Through Boat Sample-Based Testing

2023· article· en· W4389139943 on OpenAlexaff
Nitin Saini, Zhe Lyu, Yasin Suzuk, Travis Skinner, Ju Feng, Millar Iverson, Sudeep Bohra, Leijun Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMetal Alloys Wear and Properties
Canadian institutionsSuncor Energy (Canada)University of Alberta
Fundersnot available
KeywordsMaterials scienceCharpy impact testUltimate tensile strengthComposite materialCrackingToughnessMicrostructureDuctility (Earth science)Tensile testingMetallurgyCreep

Abstract

fetched live from OpenAlex

Abstract Life assessment of coke drums is vital to prevent circumferential through-wall cracking and to ensure it’s safe and serviceable. Cracking in the shell/skirt-shell junction/welds, permanent distortion, and bulging (due to the ratcheting effect) are common damage mechanisms due to low cycle thermal fatigue. The difficulties in performing an insightful inspection and life assessment are limited by the wall thickness, location heterogeneity due to service conditions, and test methods. In this study, a boat sample-based testing procedure is developed for the life assessment of coke drums. The boat samples are extracted from four different locations named crack, bulge, notch, and benchmark from a 40-year serviced coke drum. The boat samples are machined for sub-size smooth tensile, notched tensile, low-cycle fatigue, and Charpy toughness specimens. Smooth tensile, notched tensile and low-cycle fatigue tests are performed at three test temperatures (room temperature, 250°C, and 480°C). The results of sub-size specimens are calibrated to full-size and compared with the knuckle plate (least temperature in the coke drum). The fracture toughness of boat samples is predicted by the Haggag method using the smooth tensile and metallographic results. The boat samples have gained strength but have a reduction in ductility and toughness compared with knuckle plate material. The boat samples have shown a change in the microstructure and hardness. Carbide precipitation is observed on the grain boundaries and within the ferrite grains. This precipitation might have reduced the ductility and toughness.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.328
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

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

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 head0.099
GPT teacher head0.321
Teacher spread0.222 · 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 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
Published2023
Admission routes1
Has abstractyes

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