Z-Factor for Ferritic Pipe Axial Flaw Using J-T Analysis
Bibliographic record
Abstract
Tabulation solution for ductile tearing failure of piping axial flaws was first implemented in the A90 Addendum to the 1989 Edition of the ASME B&PV, Section XI, Appendix H. The solution was based on J-Tearing analyses of pipe axial semi-elliptical part-through wall flaws in a generic ferritic material that has J1C > = 600 in-lbs/in2. Analytical solution for pipe axial flaw has not been implemented in the Code. In this paper a general load multiflier Z-factor is developed for use in an analytical solution for pipe axial part-through wall flaw, which is compatible with the current Elastic-Plastic Fracture Mechanics (EPFM) tabular solution in the Section XI, Appendix C. The Z-curve is developed using the same J-Tearing analysis technique and the same generic material properties for ferritic materials that were used in the development of the Code tabulation solutions. The predicted failure stresses using the Z curve are fairly corelated and conservatively the actual failure stress in the available test specimens from the PIFRAC database.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".