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Record W2318649366 · doi:10.1115/pvp2015-45028

A Rational Methodology for Determination of Service Life for a Delayed Coker Drum

2015· article· en· W2318649366 on OpenAlexafffund
John J. Aumuller, Jie Chen, Vincent A. Carucci

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Failure Analysis and Simulation
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsMaterials scienceService lifeShell (structure)CokeAlloyDelayed cokerMolybdenumWeldingDrumMetallurgyLow-cycle fatigueWork (physics)Structural engineeringComposite materialMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Delayed unit coker drums operate in a severe service environment that precludes long term reliability due to excessive shell bulging and cracking of shell joint and shell to skirt welds. Thermal fatigue is recognized as the leading damage mechanism and past work has provided an idealized description of the thermo-mechanical mechanism via local hot and cold spot formation to quantify a lower bound life estimate for shell weld failure. The present work extends this idealized thermo-mechanical damage model by evaluating actual field data to determine a potential upper bound life estimate. This assessment also provides insight into practical techniques for equipment operators to identify design and operational opportunities to extend the service life of coke drums for their specific service environments. A modern trend of specifying higher chromium and molybdenum alloy content for drum shell material in order to improve low cycle fatigue strength is seen to be problematic; rather, the use of lower alloy materials that are generally described as fatigue tough materials are better suited for the high strain-low cycle fatigue service environment of coke drums. Materials such as SA 204 C (C – ½ Mo) and SA 302 B (C – Mn – ½ Mo) or SA 302 C (C – Mn – ½ Mo – ½ Ni) are shown to be better candidates for construction in lieu of low chromium alloy steel materials such as SA 387 grades P11 (1¼ Cr – ½ Mo), P12 (1 Cr – ½ Mo), P22 (2¼ Cr – 1 Mo) and P21 (3 Cr – 1 Mo).

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.632
Threshold uncertainty score0.180

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 communication0.0000.000
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 head0.137
GPT teacher head0.335
Teacher spread0.197 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2015
Admission routes2
Has abstractyes

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