Numerical simulation of ductile damage in pipeline steels across different constraint conditions using a combined void growth and coalescence model
Bibliographic record
Abstract
• Novel implementation of a two-surface Gurson-like ductile damage model. • Model incorporates various anisotropic aspects of ductile fracture. • Model used to simulate ductile fracture in pipeline steels. • Fracture test specimens under a wide range of constraint conditions simulated. • Better performance compared to existing models like GTN model. Finite element method (FEM) simulations using a two-surface Gurson-like ductile damage model were used to investigate the ductile crack growth behaviors on X80 and X100 pipeline steels, under a wide range of constraint conditions. The implemented approach combines models in the spirit of the Gologanu-Leblond-Devaux (GLD) and Thomason’s models to create a combined void growth and coalescence model. The implemented model can account for several ductile damage anisotropies which cannot be accommodated by the widely used standard Gurson-Tvergaard-Needleman (GTN) model, which is limited to constraint conditions similar to the data used to calibrate the model. It is demonstrated in the study that the implemented combined model significantly improves upon the GTN model and can accurately predict the ductile fracture behavior over a wide range of constraint conditions based on the same 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. Three different pipeline steels were studied. A wide range of SENT crack geometries were analyzed. These specimens represented a wide range of constraint conditions. The numerically calculated crack growth resistance curves were compared to experimental J - Δ a curves and curves developed using the GTN model.
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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 | 0.000 | 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 | 0.000 |
| 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".