Effect of Weld Residual Stress Fitting on Stress Intensity Factor for Circumferential Surface Cracks in Pipe
Bibliographic record
Abstract
Recent studies have shown that the crack growth of PWSCC is mainly driven by the weld residual stress (WRS) within the dissimilar metal weld. The existing stress intensity factor (K) solutions for surface cracks in pipe typically require a 4th order polynomial stress distribution through the pipe wall thickness. However, it is not always possible to accurately represent the through thickness WRS with a 4th order polynomial fit and it is necessary to investigate the effect of the WRS fitting on the calculated stress intensity factors. In this paper, two different methods were used to calculate the stress intensity factor for a semi-elliptical circumferential surface crack in a pipe under a given set of simulated WRS. The first method is the Universal Weight Function Method (UWFM) where the through thickness WRS distribution can be represented as a piece-wise cubic fit. In the second method, the through thickness WRS profiles are represented as a 4th order polynomial curve fit (both using the entire wall thickness data and only using data up to the crack-tip). In addition, three-dimensional finite element (FE) analyses (using the simulated weld residual stress) were conducted to serve as a reference solution. The results of this study demonstrate the potential sensitivity of stress intensity factors to 4th order polynomial fitting artifacts. The piece-wise WRS representations used in the UWFM was not sensitive to these fitting artifacts and the UWFM solutions were in good agreement with the FE results.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".