Numerical investigation of constraint-dependent crack growth in X70 pipeline steel using XFEM-based cohesive segments approach with surface strain-based J-integral evaluation
Bibliographic record
Abstract
This study employs the Extended Finite Element Method-based cohesive segments approach to investigate the constraint-dependent fracture behavior of API X70 pipeline steel. The Single Edge Notched Tension (SENT) geometry is used for numerical analysis within the Abaqus software since it closely replicates the tip constraint of the surface flaws typically observed on steel pipelines. The primary focus is to derive J-integral and Crack Tip Opening Displacement (CTOD) resistance curves for plain-sided and side-grooved specimens with varying initial crack depths. Before post-processing the XFEM models, validation occurred against experimental data. Then, three J-integral calculation methods were systematically compared, including incremental unloading compliance, CTOD conversion, and the surface strain-based XFEM method. Accordingly, a path-independency analysis of the surface strain-based XFEM method, which overcomes limitations for J-integral evaluation during crack growth, was conducted to assess the influence of the plastic zone. Results indicate that side-grooved specimens yield 3%–10% lower CTOD values than plain-sided counterparts due to enhanced crack-tip constraint while reducing variability in resistance curves. Among the J-integral calculation methods, the surface strain-based XFEM approach yielded results similar to standardized methods for crack extensions lower than 1 mm. However, the incremental unloading compliance-based method underestimates J-integral relative to the other two methods beyond 1 mm. It is also found that shallow cracks may exhibit up to 13% higher J-integral values than deep cracks, highlighting the constraint dependence of fracture toughness. This study’s outcomes show the efficacy of XFEM in simulating low-constraint fracture conditions and provide invaluable insights for pipeline integrity assessments.
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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 | 0.001 | 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 | 0.001 |
| 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".