An Integrated Welding and Damage Model for Practical Prediction of Creep Life in Welded P91 Alloy
Bibliographic record
Abstract
Loss of creep resistance in post-weld P91 alloy occurs mainly due to the change in microstructure particularly in the heat-affected zone under actual service operating conditions as well as residual stress from the welding process that is not often properly addressed in many damage models. In this paper, a validated deformation mechanisms map (DMM) using low temperature creep strain accommodation processes i.e. GBS, is used for the P91 alloy that predicts the creep rates over a wide range of temperature and stress including those arising under in the actual service conditions. These creep rates are further utilized into a microstructure-based creep damage model for accurate life prediction. A 3D transient computational welding mechanics (CWM) modeling of a pipe in a super-critical water loop, predicts the thermal, microstructure and stress state from welding. It also determines the coarse and fine grain heat affected zone (CGHAZ & FGHAZ). The CWM results are coupled with physics-based creep damage modeling to practically predict the creep life under the actual service conditions considering the welding residual stress and microstructure states.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".