MétaCan
Menu
Back to cohort
Record W4381511533 · doi:10.1111/ffe.14081

Creep behavior and life prediction of a reactor pressure vessel steel above phase‐transformation temperature via a deformation mechanism‐based creep model

2023· article· en· W4381511533 on OpenAlexaff
Chuanyang Lu, Peng Wang, Silu Zheng, Xijia Wu, Yanming He, Jianguo Yang, Zengliang Gao, S.T. Tu

Bibliographic record

VenueFatigue & Fracture of Engineering Materials & Structures · 2023
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsCarleton UniversityNational Research Council Canada
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsCreepMaterials scienceReactor pressure vesselPressure vesselDeformation (meteorology)Nuclear reactorStress (linguistics)Work (physics)Structural engineeringNuclear engineeringComposite materialMechanicsMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract For nuclear power generation as a carbon‐neutral energy source, in‐vessel retention (IVR) must be implemented to maintain the structural integrity of nuclear reactor pressure vessel (RPV) for more than 72 h under severe accidental conditions. This technology requires accurate prediction of creep deformation and life of RPV material being operated under pressure and extremely high‐temperature gradient. The current work develops a simplified deformation‐mechanism‐based true‐stress (DMTS) model for creep behavior/life‐prediction of SA508 Gr.3 steel, a typical RPV material, above the phase transformation temperatures (800–1000°C). This model is used to evaluate the time to specific creep strain ( t 3% and t 5% ) and rupture ( t r ), in comparison with popular empirical methods such as Orr–Sherby–Dorn (OSD) and Larson–Miller (LM). The simplified DMTS model achieves an excellent agreement with the experimental observations. The controlling deformation mechanisms are also discussed by metallurgical examinations, which provide the physical premise for the model development and application.

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 categoriesMeta-epidemiology (narrow)
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 score0.104
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.010
GPT teacher head0.222
Teacher spread0.212 · 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

Citations12
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueFatigue & Fracture of Engineering Materials & StructuresSame topicHigh Temperature Alloys and CreepFrench-language works237,207