Fatigue Crack Driving Force for Axial Surface Cracks in Pipes
Bibliographic record
Abstract
Fatigue life assessment procedures require knowledge of the fatigue crack driving force, such as stress intensity factor range (ΔK) and cyclic J-integral (ΔJ), for the flaw geometry detected during inspection. Because three-dimensional closed-form crack driving force solutions are not available for typical flaws in pipelines, it is common practice to obtain these solutions from finite element analysis (FEA) or to adopt a closed-form crack driving force solution for the equivalent flawed plate and include a correction factor to take account of the pipe bulging effect. In the present study, pipes and plates with an axial rectangular crack with filleted corners under fatigue loading are simulated by FEA. The initial results show that the stress intensity factor range (ΔK) for a thin-walled pipe with a shallow crack (a/t < 0.5) is given reasonably well by the bulging factors given in BS 7910 combined with the stress intensity factor equation given by Newman and Raju for a plate with a semi-elliptical crack. However, the stress intensity factor is significantly over-estimated for a long and deep crack using this procedure. Different parameters for elastic-plastic fatigue are calculated and are proposed to be correlated with the rate of crack growth for thin-walled pipes with an axial rectangular crack with filleted corners. It is intended to use the results presented here in combination with full scale experimental fatigue data to obtain pipeline fatigue crack growth formulations, to accurately predict the rate of crack growth within a pipeline due to fluctuating internal pressure.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".