Creep behavior and life prediction of a reactor pressure vessel steel above phase‐transformation temperature via a deformation mechanism‐based creep model
Bibliographic record
Abstract
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 (t3% and t5%) and rupture (tr), 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".