Closed-Form Stress Intensity Factor Solutions for Circumferential Through-Wall Cracks in Cylinder
Bibliographic record
Abstract
The stress intensity factor (SIF) solutions for circumferential through-wall cracks (TWCs) in cylinders are used for various fracture mechanics analyses. For example, it can be used to calculate the crack growth rate for stress corrosion cracking and to calculate the elastic J value which is needed to obtain the total J value for crack stability calculations. Thus, numerous SIF solutions have been published for circumferential TWCs in cylinders under axial tension and global bending. However, recently, it has been indicated that there is a need (e.g., for xLPR software code and ASME BPV Code Case N-513) to expand the solutions to wider ranges of crack lengths and cylinder geometries. In this paper, solutions from Lacire et al., API 579-1/ASME FFS-1 and Zang (SINTAP) were compared against results from independent finite element (FE) analyses performed by the authors. From these comparisons, it was demonstrated that the Zang (SINTAP) solution provided the most accurate results. Hence, additional FE calculations were performed to expand the Zang (SINTAP) solution to cover Ri/t between 2 and 100 and crack length between 1% and 85% of the cylinder circumference. Furthermore, for practical applications, closed-formed solutions were developed for both axial tension and global bending loads. These new solutions were planned for use in the xLPR software code and ASME BPV Code.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".