Metallurgical and fatigue assessments of welds in cast welded hydraulic turbine runners
Bibliographic record
Abstract
Decades of hydraulic turbine operation around the world have shown one undeniable fact; welded turbine runners can be prone to fatigue cracking, especially in the vicinity of welds. In this regard, three factors are essential to consider in runner fatigue assessments: (1) the runner's design, which can induce stress concentrations in the fillets, (2) the casting process, which inherently creates defects such as shrinkage cavities and (3) the welding process, which induces significant residual stresses as well as a heat affected zone in the cast pieces near the interface with the filler metal. This study focuses on the latter, the welding process, with emphasis on the influence of the heat affected zone on the runner's fatigue behavior. In a recently concluded study by a large research consortium in Montreal, the microstructure and fatigue crack propagation properties of a CA6NM runner weld heat affected zone were thoroughly investigated to find if this zone deteriorates the runner's resistance to fatigue cracking. The main results showed that this zone's intrinsic fatigue crack propagation resistance is only slightly lower than the unaffected base metal because of its somewhat finer martensitic microstructure leading to a less tortuous crack path. However, it was also confirmed that weld-induced residual stresses represent the dominant influencing factor regarding fatigue crack propagation, though post-weld heat treatments are usually very effective in reducing such residual stresses. This paper aims to further confirm, through a case study, that the weld-induced heat affected zone does not compromise the reliability of welded turbine runners when its fatigue crack propagation properties are considered in fatigue damage models.
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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 | 0.001 | 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) | 0.002 | 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".