Simulating Natural Axial Crack Growth in Dissimilar Metal Welds due to Primary Water Stress Corrosion Cracking
Bibliographic record
Abstract
For axial subcritical crack growth in dissimilar metal (DM) welds due to Primary Water Stress Corrosion Cracking (PWSCC), the crack growth in the length direction is limited to the weld width since the base materials are not susceptible to this type of cracking mechanism. However, the crack may continue to grow in the depth direction until it penetrates the wall thickness. Since the weld width can be much less than the pipe wall thickness, axial cracks have the potential of growing much deeper than they are long. Published stress intensity factor influence functions for semi-elliptical axial cracks in pipe suggest that as the half crack length (c) becomes smaller than the crack depth (a), the stress intensity factor at the deepest point of the crack begins to decrease. These solutions suggest that in many cases, these types of cracks may arrest before penetrating the wall thickness. However, natural flaw growth using the Advanced Finite Element Method (AFEA) suggests that these cracks will not arrest and the stress intensity factor does not decrease in a manner suggested by idealized flaw growth analyses using semi-elliptical crack influence functions. In this paper, modifications to idealized flaw growth analyses are proposed to predict the natural PWSCC axial crack growth within DM welds. A series of modified flaw growth predictions are presented and compared to published AFEA results. The simplistic rules developed in the paper allow the use of standard influence functions in predicting the time to leakage for axial cracks in DM welds without having to conduct the more complex AFEA analyses.
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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 | 0.001 | 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".