A Strain Rate-Dependent Finite Element Model of Drop-Weight Tear Tests for Pipeline Steels
Bibliographic record
Abstract
The influence of crack speed on dynamic fracture toughness of pipeline steel has been observed in some recent tests, although it is still a challenge to obtain a specific relationship between dynamic fracture toughness and crack speed due to the expensive costs of experiments. Meanwhile, the understanding of the dependence of fracture toughness on crack speed is critical for material selection and crack-arrest design in high-strength steel pipelines. The present work develops a strain rate-dependent cohesive zone model and related finite element model to analyze speed-dependent dynamic fracture of pipeline steels observed in recent drop-weight tear tests. Different than most of existing cohesive zone models, the traction-separation law of the present model considers the role of rate of separation, and a strain rate-dependent elastic-viscoplastic constitutive model is employed for the bulk material. The speed-dependences of crack-tip-opening angle (CTOA) and energy dissipation observed in experiments are reproduced in our simulations for crack speed up to 150 m/s. A remarkable feature of the present work is that the present rate-dependent model can predict speed-dependent fracture as a consequence of the strain rate effect even when all fixed material parameters are speed-independent. These results suggest that the strain rate effect in the bulk material could be largely responsible for the speed-dependent dynamic fracture of pipeline steels, and the present rate-dependent model could be used to simulate dynamic fracture of pipeline steels especially when experiments are difficult or too expensive.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".