Application of the Extended Finite Element Method (XFEM) to Simulate Crack Propagation in Pressurized Steel Pipes
Bibliographic record
Abstract
The extended finite element method (XFEM) has recently become a very effective method to investigate the propagation of cracks in various structures under complex loading conditions. However, its use in the pipeline industry has been limited. This paper aims to apply XFEM to model our previous experimental results on NPS 12 grade of X52 steel pipes in which circumferential cracks with different sizes were pre-machined near the middle length and then eccentric tension was applied to the pressurized pipe specimens. Our previous experiments showed that the propagation of a surface crack was affected by the original crack configuration, the internal pressure level, and the external loading applied. The crack depth showed greater influence than the crack length on the burst load and the tensile strain capacity of the pipe. In this paper, the fracture criterion for modelling crack propagation using XFEM was defined by two damage parameters, the maximum principle stress and the fracture energy. The values of the damage parameters were varied until excellent agreement was obtained between one of our previous experiments and its numerical model. Then, this set of damage parameters was used to model another one of the experiments for verification. This paper describes our methodology for validation of XFEM and the adequate values of the damage parameters required to model crack propagation in X52 pipes.
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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 | 0.001 |
| 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) | 0.001 | 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".