Application of Enhanced Gurson-Like Ductile Damage Models to Simulate Crack Growth in Pipeline Steels Under Wide Range of Constraint Conditions
Bibliographic record
Abstract
Abstract For the safe design and operation of high-performance pipelines, it is important to characterize the crack growth behaviors (using J-R curves) for pipeline steels under a variety of constraint conditions, so that reliable pipeline integrity assessments can be performed. The finite element method (FEM) based on an enhanced Gurson-like ductile damage model was used to investigate the ductile crack growth behaviors on pipeline steels. The standard Gurson-Tvergaard-Needleman (GTN) model cannot account for several ductile damage anisotropies. This limitation narrows the applicability of the GTN model to constraint conditions similar to the data used to calibrate the model. It is demonstrated in the present study that the implemented enhanced model significantly improves upon the GTN model and can accurately predict the ductile fracture behavior over a wide range of constraint conditions based on limited calibration data. The ductile damage model was used to analyze ductile crack growth behaviors in single-edge notched bending (SENB) and single-edge notched tension (SENT) specimens. Several SENT crack geometries were analyzed, representing a wide range of constraint conditions. The numerically calculated crack growth resistance curves were compared to the experimental J-R curves and curves developed using the original GTN model.
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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) | 0.001 | 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".