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Record W4414942966 · doi:10.1115/pvp2025-153320

Testing of Shot Peening for Coke Drum Application

2025· article· en· W4414942966 on OpenAlexaff
Haixia Guo, Simon Yuen, Enzo Falo, Mark E. Odegard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMetal Alloys Wear and Properties
Canadian institutionsSuncor Energy (Canada)
Fundersnot available
KeywordsShot peeningCokeDrumPeeningResidual stressShot (pellet)Quenching (fluorescence)Stress (linguistics)

Abstract

fetched live from OpenAlex

Abstract Due to batch cyclic operation and water quenching process, coke drums in Delayed Coking process units are exposed to severe damage caused by low cycle fatigue. Improving resistance to this damage mechanism is one of the major considerations when designing new coke drums. In a major project replacing totally eight coke drums, shot peening was considered and tested on specimens made of C-0.5Mo backing plate with alloy 625 cladding. This was based on the proven effect of this technique for enhancing fatigue performance of welds in other industries, such as bridges and aviation. After shot peening, surface stress profile of the specimen was measured. Cantilever bending fatigue testing was conducted to determine if fatigue resistance had been improved. Coke drum operation involves thermal cycles which has the potential to negate the effect of shot peening. To quantify the influence of such thermal cycles and detect whether there is a practical residual benefit, simulating heat treatment cycles were designed and performed on the fatigue coupons after the shot peening. This paper summarizes the shot peening process, surface stress measurement and fatigue testing results. It can be used for evaluating the application of shot peening on coke drums for both new drum fabrication and repair/maintenance of operating drums.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.101

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 head0.062
GPT teacher head0.299
Teacher spread0.237 · 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
Published2025
Admission routes1
Has abstractyes

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