Investigation of Fracture Prediction Capability of XFEM and Finite Element Method Using SENT Specimens
Bibliographic record
Abstract
Abstract This study investigates the fracture behavior of single-edge notched tension (SENT) specimens made from API X52 vintage pipeline steel by comparing the extended finite element method (XFEM) and the traditional finite element method (FEM). Both methods are implemented in abaqus finite element software to simulate specimens with varying notch length-to-specimen-width ratios, whose fracture properties have been experimentally determined. The analysis focuses on plotting force versus global displacement, crack tip opening displacement (CTOD), and crack mouth opening displacement (CMOD) for each method. These simulation results are then compared with experimental data. A mean absolute percentage error (MAPE) calculation quantifies the level of agreement between the model and test results. The findings demonstrate that both methods can replicate the experimental force–crack opening displacement (COD) and force–displacement curves. However, XFEM offers distinct advantages, including the elimination of the need for mesh refinement, easier numerical convergence, and accurate visualization of the crack propagation path.
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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 | 0.002 |
| 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) | 0.002 | 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".