Fatigue life predictions for welded boiler water walls
Bibliographic record
Abstract
Boiler water walls experience in-phase thermo-mechanical loading during start-ups and shut-downs, leading to low cycle fatigue (LCF) failure. This study aims at establishing an FEA-based failure prediction method for estimating the fatigue performance and service life of the welded water walls. The developed model is validated for predicting failure in the defect-free uniaxial fatigue specimens. Stress-mechanical strain hysteresis loops and accumulated inelastic strain energy density per cycle parameters are extracted from fatigue tests at 0.4%, 0.6%, and 0.7% strains. A combination of cyclic plasticity and continuous damage mechanics (CDM) theory is utilized to predict fatigue crack initiation sites and estimate the specimen fatigue life. Accumulated damage has been calculated for the life cycle of each specimen. FEA model predicted failure and service life agrees well with the experimental results. The established failure analysis parameters are then transferred from the specimen level to the water wall component level, thereby estimating the service life of defect-free water walls at 750 cycles. • Cyclic plasticity, damage, and failure of defect-free water wall uniaxial fatigue specimens subjected to in-phase thermo-mechanical loading are anlyzed. • Plasticity and continous damage mechanics theories have been used to predict fatigue initiation and crack propagation. • The model is successfully applied to water wall assemblies.
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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 | 0.001 |
| 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 | 0.000 | 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".