Simulation of Fatigue Crack Growth Using XFEM
Bibliographic record
Abstract
Abstract Pipelines carrying oil and gas are susceptible to fatigue failure (i.e., unstable fatigue crack propagation) due to fluctuating loading such as varying internal pressure and other external loadings. Fatigue crack growth (FCG) prediction through full-scale pipe tests can be expensive and time consuming, and experimental data is limited particularly in the face of large uncertainty involved. In contrast, numerical simulation techniques (e.g., XFEM) can be alternative to study the FCG, given that numerical models can be theoretically and/or experimentally validated with reasonable accuracy. In this study, capabilities and limitations of existing fatigue analysis code (e.g., direct cyclic approach with XFEM) in Abaqus for low cycle fatigue simulation are explored for compact-tension (CT) specimens and pipelines assuming linear elastic material behavior. The simulated FCG curve for a CT specimen is compared with that obtained from the analytical method using the stress intensity factor prescribed in ASTM E647. However, for real pipelines with elastic-plastic behavior, direct cyclic approach is not suitable, and an indirect cyclic approach is used based on the fracture energy parameters (e.g., J integral) calculated using XFEM in Abaqus. FCG law (e.g., power law relationship like Paris law) is used to generate the fatigue crack growth curve. For comparison, the FCG curve obtained through direct cyclic approach for pipelines assuming linear elastic material is also presented. The comparative studies here indicate that XFEM-based FCG simulation using appropriate techniques can be applied to pipelines for fatigue life prediction.
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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) | 0.007 | 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".